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Journal of Plant Development Sciences (An International Monthly Refereed Research Journal)

Volume 9 Number 8 August 2017

Contents

REVIEW ARTICLE

— Nanotechnology: Application in the field of agriculture

Hem. C. Joshi, Alok Sukla, S.K. Guru, K.P. Singh, Prashant Singh ---------------------------------------- 731-737

— Molecular methods for plant taxonomy

Vinay M. Raole and Nagesh Chirumamilla ---------------------------------------------------------------------- 739-744

— Drought and salinity stress in crop plants

Ravi Kumar and Lakshyadeep ------------------------------------------------------------------------------------- 745-755

— Application of nanotechnology to plant biotechnology

Nayan Tara and Meena ---------------------------------------------------------------------------------------------- 757-760

RESEARCH ARTICLES

— Bioactive polysaccharides from wild Mushroom, Coprinosis sp.

Hemanta Kumar Datta and Sujata Chaudhuri ---------------------------------------------------------------- 761-769

— Mycotoxin research and mycoflora in some dried edible morels marketed in Jammu and Kashmir, India

Pinky Bala, Dimple Gupta and Y.P. Sharma -------------------------------------------------------------------- 771-778

— Effect of different concentration of iba on rooting of plum (Prunus domestica L.) cuttings cv. santa rosa

under valley condition of Garhwal Himalaya

Kranti Kumar Lalhal, Tanuja, D.K. Rana and Dinesh Chandra Naithani -------------------------------- 779-783

— Studies on combining ability in forage sorghum for yield and quality parameters

Akash Singh and S.K. Singh ---------------------------------------------------------------------------------------- 785-792

— Traditional agroforestry systems in Garhwal Himalaya Ram Gopal, R.S. Bali, D.R. Prajapati and D.U. Rathod ------------------------------------------------------ 793-797

— Growth performance of rubber (Heveabrasiliensis muell. Arg.) plantation in hilly zone of Karnataka

Shahbaz Noori and S.S. Inamati ----------------------------------------------------------------------------------- 799-804

— Yield maximization of hyv and scented varieties of rice

D.K. Gupta and V.K. Singh ----------------------------------------------------------------------------------------- 805-809

— Plant growth promoting rhizobacteria improves growth in Aloe vera

Meena, Nayantara and Baljeet Singh Saharan ------------------------------------------------------------------ 811-815

— Influence of thermal environment on phenology, growth, yield and development of mustard (Brassica juncea L.) varieties

R.K. Kashyap, S.K. Chandrawanshi, Pandhurang Bobade, S.R. Patel, J.L. Chourdhary and Deepak K.

Kaushik ----------------------------------------------------------------------------------------------------------------- 817-821

— Studies on the foraging activity of Indian honey bee, Apis cerana indica Fabr. and other honey bee spp. on

buckwheat flowers

Jogindar Singh Manhare, G.P. Painkra, K.L. Painkra and P.K. Bhagat ---------------------------------- 823-828

— Thermal requirement of mustard in late sown condition after rice crop at Raipur under Chhattisgarh plain

R.K. Kashyap, Pandhurang Bobade, S.K. Chandrawanshi, Deepak K. Kaushik and R. Singh ------- 829-833

ii

— Gene pyramiding of four BLB resistant genes (xa4, xa7, xa13 and xa21) from irbb65 into mahamaya using

mas.

Mekala Mallikarjun and Anil S. Kotasthane -------------------------------------------------------------------- 835-838

SHORT COMMUNICATION

— Field screening of different varieties of tomato against fruit borer, Helicoverpa armigera (Hubner)

Laxman Singh, P.K. Bhagat, G.P. Painkra and K.L. Painkra ----------------------------------------------- 839-841

Impact of system of rice intensification (sri) through front line demonstrations

B.K.Tiwari, K.S. Baghel, Akhilesh Kumar Patel, Akhilesh Kumar, Sanjay Singh and A.K.

Pandey ------------------------------------------------------------------------------------------------------------------- 843-845

— Study on the seasonal incidence of mustard aphid (Lipaphis erysimi Kalt.) in relation to weather parameters

N.C. Mandawi, Y.K. Yadu, S.S. Rao and V.K. Dubey --------------------------------------------------------- 847-850

— Effect of tillage practices and integrated nutrient management on growth, analysis parameters of sorghum

(Sorghum bicolor L.)

Samrat Dhingra and N.S. Thakur --------------------------------------------------------------------------------- 851-853

— Effect of date of sowing and weed management techniques on growth attributes and yield of Blackgram

(Vigna mungo L.)

Nishant Kumar Sai, R. Tigga and V.K. Singh ------------------------------------------------------------------- 855-857

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 9 (8) : 731-737. 2017

NANOTECHNOLOGY: APPLICATION IN THE FIELD OF AGRICULTURE

Hem. C. Joshi1*, Alok Sukla

1, S.K. Guru

1 ,K.P. Singh

2, Prashant Singh

3

1Department of Plant Physiology, College of Basic Sciences and Humanities, G.B.P.U.A &

T Pantnagar, U.S.Nagar (263145), Uttrakhand, India 2Membrane Biophysics and Nanobiosensor Research Laboratory, College of Basic Sciences and

Humanities, G.B. Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar -263145, India.

3Department of Biotechnology, Bhimtal Campus, Kumaun University,

Nainital, Uttarakhand, India

Email: [email protected]

Received-21.06.2017, Revised-02.08.2017 Abstract: Indian agriculture, passing through various revolutions ,has made appreciable achievement in terms of production

& productivity ,availability of food grains, horticultural produce,milk,meat & fish which has been possible through

technological interventions and critical role played by Indian council of agriculture research (ICAR) .although are continue

to be the same to 40million hectares for the last 40 years, production has increased apparently .the production of food crop

has increased 4.5 times ,many of the crops which were not known before, have emerged as important, and we have become

leader. Despite numerous challenges and short comings the horticulture has exhibited impressive growth. If Indian

agriculture has to attain its board national goal of sustainable growth, it is important that the nanotechnology research is

extended to the total agricultural production consumption system that is across the entire agricultural value chain.

Nanotechnology in agriculture could be used for enhancing the efficiency of the technologies; this includes nanoparticle

based disease diagnostics, nano-insecticides for insect pest control, nano-formulation for nutritional studies & various other

aspects.Nanomanufacturing makes Nanoscale building blocks including nanoparticles, nanotubes

&nanostructures.Nanoparticle can be formed by either milling of large particle or by directly chemical synthesis.however

carbon nano tubes and most nanoparticle are synthesized directly from liquid or vapor phases.Chemical & physical vapor

phase synthesis is well-established technologies for large scale production of metal, metal oxide and ceramic

nanoparticles.The recent development in plant science that focused on the role of nanoparticle in plant growth &

development and also on plant mechanism.

Keywords: NPs (nanoparticles), QDs (quantum dots), CNTs (carbon nanotubes), MWCNTs (multi-walled-CNTs)

INTRODUCTION

anotechnology, a new emerging field of science,

nanotechnological approaches could open up

novel application in the field of agriculture &rapidly

expanding global industry. Nanotechnology a fastest

growing technical field, involves the

design,characterization, production& application of

structure, devices and system by controlled

manipulation of size and shape at nanometer scale

that gives novel or superior characteristics property.

Nanotechnology in agriculture could be used for

enhancing the efficiency of the technologies; this

includes nanoparticle based disease diagnostics,

nano-insecticides for insect pest control, nano-

formulation for nutritional studies & various other

aspects. Projected application of nanotechnology in

agriculture and food system are pathogen and

contamination detection, identify presentation and

tracking, smart treatment delivery system.

Nanotechnology has been identified all essential in

solving many of the problems faced by humens;

specifically it is the key to address the foresight

nanotech challenges:

(i) Maximizing the productivity of agriculture

(ii) Providing abundant clean water globally

(iii) Making powerful information technology

available everywhere.

The word “nano” arises from Greek word “Nanos”

meaning dwarf. It denotes a factor of 10-9

or one

billionth of meter, the term nanotechnology has two

meaning i.e. Nanoscale operation technology and

molecular nano-technology .photography & catalysis

are example of nanotechnology. The major evolution

of nanotechnology is system of Nanosystem and

molecular Nanosystem(Roco 2006).

The initial concept of investigating material

&biological system at Nanoscale dates to more than

40 yearsago (Subramanian et.al.2010)two major

approaches are used while working with

nanotechnology:

(i) Bottom up approach

(ii) Top –down approach

In bottom up approach-different material and devices

are constructed from molecular component of their

own which are not required to assemble by an

external source. Two or more component can be

combined to form a new one or to be used as a

whole. Some of the example of molecular self

assembly are Watson and crick base pairing and

enzyme substrate.

In top-down approach, nano objects & material are

created by larger entities without bouncing its atomic

N

REVIEW ARTICLE

mailto:[email protected]

732 HEM. C. JOSHI, ALOK SUKLA, S.K. GURU, K.P. SINGH, PRASHANT SINGH

reaction, usually top down approach is practised less

as compared to the bottom up approach.

Nanotechnology, mainly consist of the processing of

separation, consolidation& deformation of material

by one atom or by one molecule (Tani guchi

1974)Nanotechnology will require the integration of

chemistry and material science with biology and

engineering capabilities to produce unique

performance resulting in customer

benefits.Nanomanufacturing makes Nanoscale

building blocks including nanoparticles,

nanotubes&nanostructures.Nanoparticle can be

formed by either milling of large particle or by

directly chemical synthesis.however carbon nano

tubes and most nanoparticles are synthesized directly

from liquid or vapor phases.Chemical & physical

vapor phase synthesis is well-established

technologies for large scale production of metal,

metal oxide and ceramic nanoparticles.Carbon black,

colourpigments& fumed silica are perhaps the oldest

commodity nanoparticles that have been widely used

for decades.

The engineered carbon nanotubes also boost seed

germination, growth& development of plants

(Lahianiet al.2013; Siddqui& Al-whaibi 2014)

however the majorities of studies on nanoparticles to

date concern toxicity, comparatively few studies

have been conducted on nanoparticles are beneficiary

to plants. Research in the field of nanotechnology is

required to discover the novel application to target

specific delivery of chemicals,proteins,nucleotides

for genetic transformation of crops(Torney et

al.2007;Scrinis and Lyons 2007)Nanotechnology has

large potential to provide an opportunity for

researchers of plant science and other fields , to

develop new tools for incorporation of nanoparticle

into plants that could augment existing functions &

add new ones (Cossins 2014)In present review, we

discuss the recent development in plant science that

focused on the role of nanoparticle in plant growth

& development and also on plant mechanism.

Effects of nanoparticles on plant growth

&development

When we read many references related to effects of

nanoparticle it gives information about how the

nanoparticle interact with plants and how they causes

changes on their morphology and physiology.

Depending on the properties of nanoparticle

efficiency of nanoparticle is determined by their

chemical composition, size, surface covering

,reactivity and most important the dose at which they

are effective (Khodakovskaya et al.2012 )Researcher

from their findingssuggested both positive and

negative effects on plant growth and development&

the impact of engineered nanoparticle on plants

depends on the composition,concentration,size,and

physical & chemical properties of engineered

nanoparticle as well as plant species (Ma et al.2010 )

Effect of silicon di-oxide nanoparticle

Plant growth and development starts from the

germination of seeds followed by root elongation and

shoot emergence as the earliest signs of growth and

development.therefore, itis important to understand

the course of plant growth and development in

relation to NPs. The reported data from various

studies suggested that effect of NPs on seed

germination was concentration dependent.The lower

concentration of nano-SiO2 improved seed

germination of tomamto(siddiqui and Al-Whaibi

2014).according to suryaprabha et al.(2012) nano-

SiO2 increased seed germination by providing better

nutrient availability to maize seed.Haghighi et al.

(2012), in tomato and Siddiqui et al. (2014) in squash

reported that nano-SiO2enhanced seed germination

and stimulated the antioxidant system under NaCl

stress.

Wang et al. (2014) performed an experiment on rice

plant treated with QDs, without QDs and with silica

coated with QDs, and found silica coated with QDs

promoted markedly rice root growth. Nano-

SiO2enhances the plant growth and development by

increasing gas exchange and chlorophyll

fluorescence parameters, such as net photosynthetic

rate, transpiration rate, stomatal conductance, PSII

potential activity, effective photochemical efficiency,

actual photochemical efficiency, electron transport

rate and photochemical quench.

Effect of Zinc Oxide Nanoparticles

In many studies, increasing evidence suggests that

zinc oxide nanoparticles (ZnONPs) increase plant

growth and development. Prasad et al. (2012) in

peanut; Sedghi et al. (2013) in soybean; Ramesh et

al. (2014) in wheat and Raskar and Laware (2014) in

onion reported that lower concentration of ZnONPs

exhibited beneficial effect on seed germination.

However, higher dose of ZnONPs impaired seed

germination. The effect of NPs on germination

depends on concentrations of NPs and varies from

plants to plants. de la Rosa et al. (2013) applied

different concentrations of ZnONPs on cucumber,

alfalfa and tomato, and found that only cucumber

seed germination was enhanced.Nano ZnO

supplemented with MS media promoted somatic

embryogenesis, shooting, regeneration of plantlets,

and also induced proline synthesis, activity of

superoxide dismutase, catalase, and peroxidase

thereby improving tolerance to biotic stress (Helaly

et al. 2014).

Effect of Carbon Nanotubes

Among the NPs, CNTs have acquired an important

position due to their unique mechanical, electrical,

thermal and chemical properties. The available data

reveal that studies on CNTs have mainly focused on

animals and humans (Ke et al. 2011; Tiwari et al.

2014). Comparatively, there has been scant

information available on CNTs and their relation

with plants cells and plant metabolism. Due to the

unique properties of CNTs, they have the ability to

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 9 (8) 733

penetrate the cell wall and membrane of cells and

also provide a suitable delivery system of chemicals

to cells. The single-walled-CNTs (SWCNTs) act as

nanotransporters for delivery of DNA and dye

molecules into plants cells (Srinivasan and

Saraswathi 2010). However, in various studies

researchers have reported that MWCNTs have a

magic ability to influence the seed germination and

plant growth, and work as a delivery system of DNA

and chemicals to plants cells. MWCTs induce the

water and essential Ca and Fe nutrients uptake

efficiency that could enhance the seed germination

and plant growth and development (Villagarcia et al.

2012; Tiwari et al. 2014). MWCNTs added to sterile

agar medium stimulated seed germination of three

important crops (barley, soybean, corn) due to the

ability of MWCNTs to penetrate the seed coats as the

nanotube agglomerates were detected inside the seed

coats using Raman Spectroscopy and Transmission

Electron Microscopy (Lahiani et al. 2013).

MWCNTs improve the root and stem growth and

peroxidase and dehydrogenase activity may be due to

primary uptake and accumulation of MWCNTs by

roots followed by the translocation from roots to

leaves (Smirnova et al. 2012) that could induce genes

expression (Khodakovskaya et al. 2012; Lahiani et

al. 2013; Wang et al. 2012). Tripathi and Sarkar

(2014) confirmed the presence of water soluble

CNTs inside the wheat plants using Scanning

Electron and Fluorescence Microscope, and they

reported that CNTs induced the root and shoot

growth in light and dark conditions. Also, MWCNTs

improve water retention capacity andbiomass,

flowering and fruit yield and increase medicinal

properties of plants (Khodakovskaya et al. 2013;

Husen and Siddiqi 2014). However, inhibitory effect

of MWCNTS on plants growth has been reported by

many researchers (Tiwari et al. 2014; Ikhtiar et al.

2013; Begum and Fugetsu 2012; Begum et al. 2014).

Thus, the effect of NPs on plants varies from plant to

plant, their growth stages, and the nature of

nanoparticles.

Effect of Gold Nanoparticles Few studies have been done on the interaction of

gold nanoparticle (AuNPs) with plants. Some

researchers found AuNPs induce toxicity in plants by

inhibiting Aquaphorins function, a group of proteins

that help in the transportation of wide range of

molecules including water (Shah and Belozerova

2009). However, Barrena et al. (2009) in lettuce and

cucumber, Arora et al. (2012) in Brassica juncea;

Savithramma et al. (2012) in Boswellia

ovalifoliolataand Gopinath et al. (2014) in Gloriosa

superb reported that AuNPs improve seed

germination. AuNPs improve the number of leaves,

leaf area, plant height, chlorophyll content, and sugar

content that lead to the better crop yield (Arora et al.

2012; Gopinath et al. 2014). Christou et al. (1988)

introduced neomycin phosphotransferase II gene into

soybean genome through DNA-coated gold particles.

The positive effect of AuNPs therefore needs further

study to explore the physiological and molecular

mechanism. Kumar et al. (2013) reported AuNPs

have a significant role on seed germination and

antioxidant system in Arabidopsis thalianaand

altered levels of microRNAs expression that

regulates various morphological, physiological, and

metabolic processes in plants.

Effect of Silver Nanoparticle

According to available data a large number of studies

on silver nanoparticles (AgNPs) have been

documented on microbial and animal cells; however,

only a few studies were done on plants (Krishnaraj et

al. 2012; Monica and Cremonini 2009). As we know,

NPs have both positive and negative effects on plant

growth and development. Recently, Krishnaraj et al.

(2012) studied the effect of biologically synthesized

AgNPs on hydroponically grown Bacopa

monnierigrowth metabolism, and found that

biosynthesized AgNPs showed a significant effect on

seed germination and induced the synthesis of

protein and carbohydrate and decreased the total

phenol contents and catalase and peroxidase

activities. Also, biologically synthesized AgNPs

enhanced seed germination and seedling growth of

trees Boswellia ovaliofoliolata(Savithramma et al.

2012). AgNPs increased plants growth profile (shoot

and root length, leaf area) and biochemical attributes

(chlorophyll, carbohydrate and protein contents,

antioxidant enzymes) ofBrassica juncea, common

bean and corn (Salama 2012; Sharma et al. 2012).

However, Gruyer et al. (2013) reported AgNPs have

both positive and negative effect on root elongation

depending on the plants species. They reported that

root length was increased in barley, but was inhibited

in lettuce. Also, Yin et al. (2012) studied the effects

of AgNPs on germination of eleven wetland plants

species (Lolium multiflorum, Panicum virgatum,

Carex lurida, C. scoparia, C. vulpinoidea, C. crinita,

Eupatorium fistulosum, Phytolaca americana,

Scirpus cyperinus, Lobelia cardinalis, Juncus

effusus) and found AgNPs enhanced the germination

rate of one species (E. fistulosum). AgNP induces

root growth by blocking ethylene signaling in Crocus

sativus(Rezvani et al. 2012). The impact of AgNPs

on morphology and physiology of plants depends on

the size and shape of NPs. Syu et al. (2014) studied

the effect of 3 different morphologies of AgNPs on

physiological and molecular response of

Arabidopsisand suggested that decahedral AgNPs

showed the highest degree of root growth promotion

(RGP); however, the spherical AgNPs had no effect

on RGP and triggered the highest levels of

anthocyanin accumulation in Arabidopsisseedlings.

The decahedral and spherical AgNPs gave the lowest

and highest values for Cu/Zn superoxide dismutase,

respectively. The three different size and shape of

AgNPs regulated protein accumulations such as, cell-

division-cycle kinase 2, protochlorophyllide

oxidoreductase, and fructose-1,6 bisphosphate


aldolase and also induced genes expression involved

in cellular events; for example AgNPs induced the

gene expression of indoleacetic acid protein 8

(IAA8), 9-cis-epoxycarotenoid dioxygenase

(NCED3), and dehydration-responsive RD22. Also,

AgNPs activated the aminocyclopropane-1-

carboxylic acid (ACC)-derived inhibition of root

elongation in Arabidopsisseedlings, as well as

reduced the expression of ACC synthase 7 and ACC

oxidase 2, suggesting that AgNPs acted as inhibitors

of ethylene perception and could interfere with

ethylene biosynthesis.

Role of Nanoparticles in Photosynthesis

We know that photosynthesis is a key process for

plants on earth that changes light energy to chemical

energy. Plants convert only 2–4 % of the available

energy in radiation into new plant growth

(Kirschbaum 2011). Nowadays, scientists are trying

to improve this low efficiency of vascular plants by

manipulating techniques and gene manipulations. For

speed-up of plant photosynthesis and turbocharged

crops, scientists are working with Rubisco, an

important enzyme for photosynthesis process to

catalyze the incorporation of carbon dioxide into

biological compounds. Recently, Lin et al. (2014)

developed new tobacco plants by replacing the

Rubisco gene for carbon-fixing in tobacco plant, with

two genes of cyanobacteriumn synechococcus

elongates; these new engineered plants have more

photosynthetic efficiency than native plants. Also, in

the field of nanobiotechnology, researchers want to

develop bionic plants that could have better

photosynthesis efficiency and biochemical sensing.

Giraldo et al. (2014) reported that embedded

SWCNTs in the isolated chloroplast augmented three

times higher photosynthetic activity than that of

controls, and enhanced maximum electron transport

rates, and SWCNTs enabled the plants to sense nitric

oxide, a signaling molecule. They suggested that

nanobionics approach to engineered plants would

enable new and advanced functional properties in

photosynthetic organelles. Also, they said that still

extensive research would be needed to see the impact

CNTs on the ultimate products of photosynthesis

such as sugars and glucose. Also, Noji et al. (2011)

reported that a nano mesoporous silica compound

(SBA) bound with photosystem II (PSII) and induced

stable activity of a photosynthetic oxygen-evolving

reaction, indicating the light-driven electron transport

from water to the quinone molecules, and they

suggested that PSII-SBA conjugate might have

properties to develop for photosensors and artificial

photosynthetic system. SiO2NPs improves

photosynthetic rate by improving activity of carbonic

anhydrase and synthesis of photosynthetic pigments

(Siddiqui et al. 2014; Xie et al. 2012). Carbonic

anhydrase supplies CO2to the Rubisco, which may

improve photosynthesis (Siddiqui et al. 2012)

CONCLUSION AND FUTURE PROSPECTS

No doubt, nanotechnology is an evolutionary science

and has introduced many novel applications in the

field of electronics, energy, medicine, and life

science. However, due to their unique properties, a

number of researches have been done on the

toxicological effect of NPs on plants, yet research

focusing on the realization of the beneficial effects of

NPs on plant remains incomplete. Few studies have

shown positive effect of NPs on plant growth and

development. It is evident from compiled

information that effect of NPs varies from plant to

plant and depends on their mode of application, size,

and concentrations. This chapter reveals that the

research on NPs, essentiality for plants, is in the

beginning; more rigorous works are needed to

understand physiological, biochemical, and

molecular mechanisms of plants in relation to NPs.

Also, more studies are needed to explore the mode of

action of NPs, their interaction with biomolecules,

and their impact on the regulation of gene

expressions in plants.

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MOLECULAR METHODS FOR PLANT TAXONOMY

Vinay M. Raole* and Nagesh Chirumamilla

Department of Botany, Faculty of Science

The Maharaja Sayajirao University of Baroda, Vadodara-390002


Received-02.08.2017, Revised-26.08.2017

Abstract: Molecular systematics is the use of molecular genetics to study the evolution of relationships among individuals

and species. The goal of systematic studies is to provide insight into the history of groups of organisms and the evolutionary

processes that create diversity among species. There are two separate tasks to which DNA specificity is currently being

applied. First one DNA data used to distinguish between species which is equivalent to species identification and the second

one to discover new species. The aim of this review is to present the techniques that are available to a taxonomist to

complement the conventional field methods of identification and delineation of plant species.

Keywords: Genetic diversity, PCR, AFLP, DNA Barcode

INTRODUCTION

axonomy is a synthetic science which deals with

identification, classification and nomenclature.

So, taxonomy provides names, but it is not only a

―biodiversity-naming‖ service: it is also a scientific

discipline requiring theoretical, empirical, and

epistemological rigor (Dayrat 2005). In the beginning

of the molecular era of life sciences the

biosystematics was the buzz word and researchers

were trying to understand the interrelationship and

phylogenetic considerations for explaining the

evolutionary concept in plant sciences. The focus of

this review is on the use of various nucleic acid

techniques i.e. molecular characters for a reliable and

efficient taxonomy.

Molecular taxonomy can be grouped into three

general approaches referred to as DNA taxonomy,

DNA barcoding and molecular operational

taxonomic units (MOTU). The terms themselves

sometimes lack a clear definition in the literature,

and some confusion has arisen from their

inconsistent application. A major distinction should

be made between species identification, generally

associated with the idea of molecular barcodes and

species circumscription and delineation, broadly

referred to as DNA taxonomy.

Most nuclear sequences targeted in molecular

taxonomy experiments belong to the category of

highly repetitive DNA. Nuclear ribosomal RNA

genes (nrDNA) are tandemly (side by side) repeated

and located at a few loci in plant genomes (Hamby

and Zimmer 1992, Hayashi 1992, Hillis and Dixon

1992) . These, and particularly the ITS (internal

transcribed spacers) (Alvarez and Wendel 2003,

Poczoi and Hyvonen 2010), have long been widely

used for resolving plant taxonomic issues, initially

using restriction analysis and then sequencing.

Restriction fragment length polymorphism (RFLP)

analysis was the first technology developed which

enabled the detection of polymorphisms at the

sequence level. The approach involves digesting

DNA with restriction enzymes, separating the

resultant DNA fragments by gel electrophoresis,

blotting the fragments to a filter and hybridizing

probes to the separated fragments. A probe is a short

sequence of oligonucleotides which share homology

and are thus able to hybridize, with a corresponding

sequence or sequences in the genomic DNA. The

sequence may be known (e.g. a cloned gene) or

unknown (e.g. from random cDNA or genomic DNA

clone). Specific probe/enzyme combinations give

highly reproducible patterns for a given individual

but variation in the restriction patterns between

individuals can arise when mutations in the DNA

sequence result in changed restriction sites. RFLP

analysis is used extensively in the construction of

genetic maps and has been successfully applied to

genetic diversity assessments, particularly in

cultivated plants (e.g. Deu et al. 1994; Jack et al.

1995) but also in populations and wild accessions

(e.g. Besse et al. 1994; Bark and Harvey 1995). As a

technique for diversity studies, there are three

important advantages which should be considered.

The first is that RFLPs are highly reproducible

between laboratories and the diversity profiles

generated can be reliably transferred. The second is

that RFLPs are co-dominant markers, enabling

heterozygotes to be distinguished from homozygotes.

The third advantage is that no sequence-specific

information is required and, provided suitable probes

are available, the approach can be applied

immediately for diversity screening in any system.

There are serious limitations, however, with the

RFLP strategy with respect to wide-scale usage at the

population level and particularly with regard to its

potential for immediate application to any system.

Firstly, a good supply of probes is needed that can

reliably detect variation at the below species level. It

may be possible to utilize (heterologous) probes from

other related species, a possibility very much

strengthened by syntenic relationships between

T

REVIEW ARTICLE


740 VINAY M. RAOLE AND NAGESH CHIRUMAMILLA

related genera. RFLPs are time-consuming and they

are not amenable to automation without considerable

capital investment. Once probe/enzyme combinations

have been selected, throughput will depend on the

number of gels that can be run each day in the

laboratory in question. To this must be added the

factor of DNA extraction. RFLP analysis requires

relatively large quantities of good quality DNA (e.g.

10µg per digestion). For some plant systems, where

extraction is problematic because of the presence of

polyphenols or polysaccharides which complex with

the DNA, or where only very limited amounts of

source material are available, this feature alone may

preclude the choice of RFLP analysis for diversity

screening.

Even in those systems where all the above

considerations are optimal, the main problem faced

may simply be that insufficient polymorphisms are

detectable at the below species level by RFLP

analysis. Taking all aspects of non PCR-based

screening approaches into consideration, it is difficult

to envisage that this would be the preferred choice

today, given that alternative strategies are now

available. When combined with PCR amplification

of a specific locus, however, both VNTRs and

standard RFLP probes have much to offer.

The development of the polymerase chain reaction

(PCR) for amplifying DNA led to a revolution in the

applicability of molecular methods and a range of

new technologies were developed which can

overcome many of the technical limitations of

RFLPs. A subset of the latter involve the use of a

single `arbitrary' primer, and result in the

amplification of several discrete DNA products. Each

product will be derived from a region of the genome

containing two short segments with sequence

similarity to the primer, on opposite strands and

sufficiently close for the amplification to work. AP-

PCR (arbitrary primed PCR) (Welsh and

McClelland, 1990) and DAF (DNA amplification

fingerprinting) (Caetano-Anolles, Bassam and

Gresshoff, 1991) differ from RAPDs principally in

primer length, primer to template ratio, the gel matrix

used and in the visualization procedure. The

enormous attractions of these arbitrary priming

techniques are: (a) there is no requirement for DNA

probes or sequence information for the design of

specific primers; (b) since the procedure involves no

blotting or hybridizing steps, it is quick, simple and

automatable and; (c) very small amounts of DNA (10

ng per reaction) are required. The data derived from

RAPDs (or AP-PCR and DAF) have their strength in

distinguishing individuals, cultivars or accessions,

although the difficulty of achieving robust profiles,

particularly in RAPDs, makes their reliability for

`typing' questionable

One important conclusion is that to achieve the same

degree of statistical power using RAPDs (or any

other dominant marker system), compared with co-

dominant markers, two to ten times more individuals

need to be sampled per locus and further, to avoid

bias in parameter estimation, the marker alleles for

most of these loci should be in relatively low

frequency.

Keygene have developed a method which is equally

applicable universally, which reveals very high levels

of polymorphism and which is highly reproducible.

This procedure, termed Amplified Fragment Length

Polymorphism (AFLP) is essentially intermediate

between RFLPs and RAPDs, in that the first step is

restriction digestion of the genomic DNA but this is

then followed by selective rounds of PCR

amplification of the restricted fragments. The

fragments are amplified by P33

- labelled primers

designed to the sequence of the restriction site, plus

one to three additional selected nucleotides. Only

fragments containing the restriction site sequence

plus the additional nucleotides will be amplified and

the more selected nucleotides added on to the primer

sequence (up to a maximum of three can be added at

either site) the fewer the number of fragments

amplified by PCR. This selection is necessary to

achieve a total number of fragments within the range

that can be resolved on a gel (approximately 150 to

200 fragments). The amplified products are normally

separated on a sequencing gel and visualized after

exposure to X-ray film. Recently, the technique has

been automated, using fluorescent labelled primers

and, therefore, high throughput can be achieved. Two

different types of polymorphisms are detected: (1)

point mutation in the restriction sites, or in the

selective nucleotides of the primers which result in a

signal in one case and absence of a band in the other;

and (2) small insertions/deletions within the

restriction fragment which results in different size

bands.

AFLPs have proven to be extremely proficient in

revealing diversity at below the species level and

provide an effective means of covering large areas of

the genome in a single assay. AFLPs, however, do

run into the same problem as RAPDs regarding the

type of data generated and the concomitant problems

of data analysis for population genetic parameters.

Plants possess three different genomes and,

therefore, three potential sources of sequences for a

PCR-targeted approach. The chloroplast genome

(cpDNA) is uniparentally (often maternally)

inherited in plants. It is highly abundant in leaves and

therefore amenable to isolation in large quantities.

Primers are available that will work either directly, or

with small alterations, across broad taxa e.g. across

all green plants (Demesure et al. 1995). The majority

of studies using sequence data from cpDNA have

been phylogenetic ones and at fairly high taxonomic

levels (intergeneric and above), although, recently,

primer pairs for cpDNA have been used for

population studies. In contrast, the mitochondrial

genome (mtDNA) of plants is less abundant in leaves

and more difficult to extract, there is less background

knowledge, fewer primers are available and these


have been less well characterized. The high rates of

structural rearrangements mean that mtDNA analysis

using restriction site assays is of limited use at the

interfamilial and interspecific taxonomic levels but it

can be very useful at detecting variation at the

intraspecific and population levels. Primer pairs for

conserved regions of mtDNA sequences are available

(Demesure et al., 1995) For assays of the nuclear

genome, only the ribosomal RNA (rDNA) gene

family has been widely used for diversity studies.

rDNA genes are located at specific chromosomal loci

(NOR, nucleolar organizing regions) where they are

arranged in tandem repeats which can be reiterated

up to thousands of times. Each repeat unit comprises

a transcribed region separated from the next repeat

by an intergenic spacer (IGS). The transcribed region

comprises: an external transcribed spacer (ETS), the

18S gene, an internal transcribed spacer (ITS1), the

5±8S gene, a second internal transcribed spacer

(ITS2) and the 26S gene. Primers pairs have been

designed which will enable amplification of the

different regions in a wide range of organisms. These

regions evolve at different rates and can thus, in

principle, be used at all taxonomic levels (Hillis and

Dixon, 1991). ITS has proven to be a valuable tool

for intergeneric studies in many organisms.

Botanists, however, may experience difficulties in

detecting sequence variations below the species

level.

The advantages of PCR-targeted approaches are in

the quality of the data and the information they

provide. The fragment in which polymorphisms are

studied is of known identity, therefore avoiding the

ambiguities of analyzing RAPD and AFLP bands, or

random RFLP probes. For population studies, the use

of an organellar sequence in complementation with a

nuclear sequence can provide particularly

illuminating data with respect to mechanisms of

differentiation, gene flow and dispersal. In contrast,

the origin of RAPD (and AFLP) fragments with

respect to the three genomes is generally unknown,

although where the origin of the fragments has been

studied, there is clear evidence that at least a

proportion of RAPD fragments are of cpDNA or

mtDNA origin. RFLPs, RAPDs and AFLPs provide

indirect data that is only useful when converted into

distance measures. This enables frequency data and

distance measures to be determined for each

genotype class but does not enable the classes to be

ordered or grouped in any way. Data based on DNA

sequences or restriction site mapping, on the other

hand, provide the means of both classifying

individuals into different classes and also of

assessing relationships among the classes

(Braslavsky et al. 2003).

There are clear disadvantages of the PCR-targeting

approach, however. The first is that, unless the

frequency of variants is high enough to be easily

detected by PCR-RFLP, or other sensitive gel assays,

sequencing will be required which, in turn,

necessitates investment of adequate resources and

experienced researchers. Another problem is that,

although the quality of the data is high, because the

approach is often resource-intensive the coverage of

the genome is highly restricted, usually to only one

sequence and, therefore, to one point of comparison.

For the PCR-targeted strategy to be widely

applicable, target sequences need to fit two specific

criteria. They must contain regions where the

sequence is sufficiently conserved such that primers

designed for one organism will amplify the same

region in a broad range of taxa. At the same time,

they must contain regions where the sequence varies

at a rate that is high enough for polymorphisms to

occur at the population level. Ideally, this should be

at a rate such that PCR-RFLP, or rapid assays such as

SSCP and TGGE, would uncover sufficient

polymorphism, although complete sequencing is the

only method that will detect all the variation present.

Fortunately, new investigators selecting this strategy

do not have to start entirely from scratch, because

regions of cpDNA and mtDNA that fit these criteria

have been identified, as described above. At the

present time, however, there is a dearth of nuclear

genes that fit the bill. Furthermore, the rate at which

sequences vary (and, therefore, the success of this

strategy) appears to differ between genomes and, at

present, the limited number of suitable sequences and

the worry that those available may not be variable

enough in the system under study, are the main

reasons why this approach may not be the choice

selected.

Most markers generated using RAPD or AFLP

technology have been shown by genome mapping

experiments to cluster around the centromeres of

chromosomes (Saliba-Colombani et al. 2000, Qi et

al. 1998, Saal and Wricke 2002, Young et al. 1999),

a heterochromatin region with mainly noncoding

sequences. Consequently, these markers often reveal

an important amount of variation. The evolutionary

rate of a molecule is also driven by its evolutionary

mechanisms. Microsatellite markers are the most

variable molecules known to date. They are mostly

noncoding molecules and vary in length (due to the

variation in the number of tandem repetitions or

VNTR) due to replication slippage (SMM model

(Shriver etal 1993), which occurs at a high frequency

(10 −6 to 10 −2 ) in plants (Bhargava and Fuentes

2010).

Microsatellites or simple sequence repeats (SSRs)

are highly mutable loci and, as mentioned earlier,

when used as RFLP probes are variable at the

population level and can even distinguish individuals

and assign parentage. The problems of using them as

multi-locus probes, outlined earlier, arise because the

repeat sequence may be present in many different

regions of the genome. However, since the flanking

sequences at each of these loci may be unique, if

SSR loci are cloned and sequenced, primers to the

flanking regions can be designed and used to amplify


only that single region containing the SSR, which is

then referred to as a sequence-tagged microsatellite

(or a sequence tagged SSR) (Morgante and Olivieri

1993).

There are several important advantages of choosing

sequence-tagged SSRs for population genetic studies.

They are usually single loci which, because of their

high mutation rate, are often multi-allelic (Saghai-

Maroof et al. 1994), they are co-dominant markers

and they can be detected by a PCR (non-

hybridization based) assay. They are very robust

tools that can be exchanged between laboratories and

their data is highly informative. As with conventional

VNTRs, the variation at the SSR locus is caused by

changes in the repeat length. Although many such

changes can be resolved on agarose gels, it is

common to run SSRs on sequencing gels where

single repeat differences can be resolved and all

possible alleles detected. The assay is relatively

quick, but throughput can be increased by selecting a

small number of different SSRs with alleles that have

different non-overlapping size ranges and

multiplexing either the PCR reactions, or the

products of the separate reactions, so that all the

alleles of the different loci can be run in a single lane

on the gel. Multiplexed SSRs have been automated,

in which case throughput can be increased further.

There are, nonetheless, some negative aspects of

using sequence tagged SSRs. Although they are co-

dominant markers, their mode of evolution is

different from normal coding loci. Different SSR

alleles are thought to arise by slippage or unequal

crossing-over and their rate of mutation, and the

possibility of deriving the same length alleles by

multiple events, mean that it is difficult to use them

to estimate relatedness beyond a few generations

(Setogouchi et al 2009). This in turn means that the

phylogenetic information cannot be derived from

SSRs. Furthermore, the potentially infinite number of

alleles possible at SSR loci make computation of

allelic frequencies problematic. Both these features

have been addressed by statisticians so that for

important population genetic parameters such as FST

estimators for SSR loci (RST) have been derived, but

phylogenetic inferences are still limited. Another

major problem with choosing this strategy is that

unless the investigator is extremely fortunate,

sequence-tagged loci will not be available for the

system that they wish to study. Although they are

ubiquitous, retrieval of SSRs has not been easy in

plants because of their relative low abundance

compared with animal genomes (Varshney et al.

2007). Where they have been isolated, it has often

been found that they show limited cross

transferability to other genera and even to other

species within the same genus.

DNA barcoding was proposed by Hebert et al.

(2003) as a method for identifying unknown

specimens. Short mitochondrial DNA (mtDNA)

sequences of cox1 gene, chloroplast genes rbcL,

matK and nuclear internal transcribed spacer ITS2

are used to group unknown individuals with a priori

defined taxonomic entities based on sequence

similarity, deriving a species identification from

DNA rather than morphological characters. As such,

DNA barcoding is not predictive, i.e. it fails when an

identical sequence is not available and a limit for

admissible divergence has not been established.

Hence, DNA barcoding is limited in its potential, as

it requires a near complete database of vouchers

against which individuals can be placed (Moritz and

Cicero 2004, Will and Rubinoff 2004).

However, the extent of genome coverage by

molecular markers is partly dependent on the

molecular technique that is used, and there is often a

trade-off between the possibility due to time and cost

limitations of surveying numerous markers and the

information content provided by each polymorphic

marker such as RAPD or AFLP. An important

parameter that is shared by ―traditional‖ and

molecular taxonomy studies is sampling strategy and

sampling effort. Taxonomy is based on a

comparative approach that requires the investigation

of as many specimens/samples as possible in order to

catch all the extent of natural variation.

It is also clear that the end users of taxonomy such as

conservation planners need an operational, character-

based, and cheap way to discriminate species

(Savolainen et al. 2005, Dunn 2003, Alves and

Machado 2007). This could tend to diminish the

perceived potential of molecular taxonomy, but in

this perspective and in spite of the shortcomings that

we have just underlined, molecular taxonomy

obviously has a great role to play. Current

technological capacities do not allow the routine

inclusion of the whole genome in taxonomic

analyses, choices must be made on the genomic

compartment(s) to survey nuclear, mitochondrial, or

chloroplast the molecular technique(s) to use and the

precise, individual marker(s) to be considered.

Another limitation of molecular taxonomy is the

possible lack of genetic divergence when sister

species have very recent origins because they will

share alleles due to recent ancestry and, if

reproductive isolation is not complete, to ongoing

gene flow—i.e., hybridization. Molecular markers

can also suffer from homoplasy, i.e., markers can

show similar character states that, however, do not

derive from a common ancestor. In this case, they do

not inform on the genealogy of taxa and, because

they do not reflect a shared evolutionary history, they

may be misleading on evolutionary and, as a

consequence, on taxonomic relationships.

ACKNOWLEDGEMENT

The authors gratefully acknowledge the support

from DST FIST to the Department of Botany, The

Maharaja Sayajirao University of Baroda, Vadodara-

390002, Gujarat, India.


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DROUGHT AND SALINITY STRESS IN CROP PLANTS

Ravi Kumar1* and Lakshyadeep

2

1Research scholar, Department of Plant Breeding and Genetics, SKNCOA, Jobner.

2 Department of Plant Breeding and Genetics, MPUAT, Udaipur.


Received-02.07.2017, Revised-18.08.2017 Abstract: Abiotic stress is a condition deviated from normal conditions which is mainly produced from the abiotic

environmental factors or non living components. These factors affect the crop plants adversely via reducing growth and

production. These non living components of environment are drought (water stress), water logging, extremes of temperature

(high and low), high salinity/alkalinity, high acidity nutrient toxicity etc. Temperature (high and low), salinity stress and

drought are major abiotic factor which affect much as compare to others non living factors. Abiotic stress severely limits

plant growth and development, due to that final yield is reduced.

Keywords: Drought, Crop plants, Abiotic factor, Production

INTRODUCTION

ccording to world estimates (Wang et al., 2007),

an average of 50% yield losses in agricultural

crops are caused by abiotic factors. These comprise

mostly of high temperature (40%), salinity (20%),

drought (17%), low temperature (15%) and other

forms of stresses (Ashraf et al., 2008). Only 9% of

the world area is conducive for crop production,

while 91% is afflicted by various stressors. As per

the current estimates (ICAR, 2010), 120.8 million ha

constituting 36.5 per cent of geographical area in

India is degraded due to soil erosion,

salinity/alkalinity, soil acidity, water logging, and

other edaphic problems (Anonymous, 2015). In

India, on an average of 50% yield losses in

agricultural crops are caused by abiotic factors

mostly shared by high temperature (20%), low

temperature (7%), salinity (10%), drought (9%), and

other forms of stresses (4%) (Anonymous, 2015). In

this review we are considering only drought and

salinity stress.

Drought: - Drought means the deficiency of water in

soil or an imbalance in the plant water regime

resulting in an excessive evapotranspiration from

shoot over water uptake by root. Agricultural

drought means that the soil moisture and rainfall are

less or not sufficient during the growing season.

Response of plants to drought (Chaves et al. 2003,

Larcher, 2003 and Kosova et al. 2014).

Drought escape: - It is based on the minimizing

adverse effect of drought condition on a plant. In this

mechanism plant completed its life cycle before

drought. Flowering time is an important trait related

to drought adaptation, where a short life cycle can

lead to drought escape (Araus et al., 2002). Crop

duration is interactively determined by genotype and

the environment. It is also determines the ability of

the crop to escape from climatic stresses including

drought. Matching growth duration of plants to soil

moisture availability is critical to realize high seed

yield (Siddique et al., 2003). Drought escape occurs

when phenological development is successfully

matched with periods of soil moisture availability,

where the growing season is shorter and terminal

drought stress predominates (Araus et al., 2002).

Time of flowering is a major trait of a crop

adaptation to the environment, particularly when the

growing season is restricted by terminal drought and

high temperatures. Developing short-duration

varieties has been an effective strategy for

minimizing yield loss from terminal drought, as early

maturity helps the crop to avoid the period of stress

(Kumar and Abbo, 2001). However, yield is

generally correlated with the length of crop duration

under favorable growing conditions, and any decline

in crop duration below the optimum would tax yield

(Turner et al., 2001).

Drought avoidance:- It is based on minimizing the

tissue dehydration by maintaining the high water

potential in plant cells under limited water supply.

Plant maximize water uptake by roots and minimize

water loss by leaves. Drought avoidance consists of

mechanisms that reduce water loss from plants, due

to stomatal control of transpiration, and also maintain

water uptake through an extensive and prolific root

system (Turner et al., 2001; Kavar et al., 2007). The

root characters such as biomass, length, density and

depth are the main drought avoidance traits that

contribute to final yield under terminal drought

environments (Subbarao et al., 1995; Turner et al.,

2001). A deep and thick root system is helpful for

extracting water from considerable depths (Kavar et

al., 2007).

Drought tolerance:-It represents an adaptation of

plant physiological functions under a limited water

supply and decrease plant cell water potential in

order to reach a sustainable balance between water

uptake by roots and water release by shoots.

Glaucousness or waxy bloom on leaves helps with

maintenance of high tissue water potential, and is

therefore considered as a desirable trait for drought

A

REVIEW ARTICLE

746 RAVI KUMAR AND LAKSHYADEEP

tolerance (Richards et al., 1986; Ludlow and

Muchow, 1990). Varying degrees of glaucousness in

wheat led to increased water-use efficiency, but did

not affect total water use or harvest index.

Determination of leaf temperature indicated that,

compared with non-glaucous leaves, glaucous leaves

were 0.7 0C cooler and had a lower rate of leaf

senescence (Richards et al., 1986). These authors

suggested that a 0.5 0C reduction in leaf temperature

for six hours per day was sufficient to extend the

grain-filling period by more than three days.

However, yield advantages are likely to be small as

many varieties already show some degree of

glaucousness.

Plant can resist drought conditions through

(Choudhary et al. 2014)

Reduced water loss from aerial portion.

Increased water uptake from deep layers of the soil.

Giving more yield at low water potentials.

Effects of drought stress on plants

Drought stress results in stomatal closure and

reduced transpiration rates, a decrease in the water

potential of plant tissues, decrease in photosynthesis

and growth inhibition, accumulation of abscisic acid

(ABA), proline, mannitol, sorbitol, formation of

radical scavenging compounds (ascorbate,

glutathione, α-tocopherol etc.), and synthesis of new

proteins and mRJNAs.

Mostly crops are affected by the drought stress due to

the reduction in growth and development. Water

loving crop like rice is probably more susceptible to

drought stress than most other plant species. In

pulses, the stem length was decreased under water

deficit conditions like soybean (Specht et al., 2001)

and Vigna unguiculata (Manivannan et al., 2007a).

The plant height was reduced up to 25% in water

stressed citrus seedlings (Wu et al., 2008). Stem

length was significantly affected under water stress

vegetables like potato (Heuer & Nadler, 1995) and

Abelmoschus esculentus (Sankar et al., 2007 & 08).

Water stress greatly suppresses cell expansion and

cell growth due to the low turgor pressure. Osmotic

regulation can enable the maintenance of cell turgor

for survival or to assist plant growth under severe

drought conditions in pearl millet (Shao et al., 2008).

The reduction in plant height was associated with a

decline in the cell enlargement and more leaf

senescence in A. esculentus under water stress (Bhatt

& Rao, 2005). Development of optimal leaf area is

important to photosynthesis and dry matter yield.

Water deficit stress mostly reduced leaf growth in

many species of plant like Populus (Wullschleger et

al., 2005) and soybean (Zhang et al., 2004).

The importance of root systems is also observed

during the drought stress. A prolific root system can

confer the advantage to support accelerated plant

growth during the early crop growth stage and

extract water from shallow soil layers that is

otherwise easily lost by evaporation in legumes

(Johansen et al., 1992). The development of root

system increases the water uptake and maintains

requisite osmotic pressure through higher proline

levels in Phoenix dactylifera (Djibril et al., 2005).

An increased root growth due to water stress was

reported in sunflower (Tahir et al., 2002). The root

dry weight was decreased under mild and severe

water stress in Populus species (Wullschleger et al.,

2005). An increase in root to shoot ratio under

drought conditions was related to ABA content of

roots and shoots (Sharp and LeNoble, 2002). The

root growth was not significantly reduced under

water deficits in maize and wheat (Sacks et al.,

1997).

Higher plant fresh as well as dry weights under

drought conditions are desirable characters. A

common adverse effect of drought stress on crop

plants is the reduction in fresh and dry biomass

production (Farooq et al., 2009). Plant productivity

under drought stress is strongly related to the

processes of dry matter partitioning and temporal

biomass distribution (Kage et al., 2004). Reduced

biomass due to drought stress was observed in almost

all genotypes of sunflower (Tahir and Mehid, 2001).

However, some genotypes showed better stress

tolerance than the others. Mild water stress affected

the shoot dry weight, while shoot dry weight was

greater than root dry weight loss under severe stress

in sugar beet genotypes (Mohammadian et al., 2005).

Reduced biomass was seen in drought stressed

soybean (Specht et al., 2001), Poncirus trifoliatae

seedlings (Wu et al., 2008), common bean and green

gram (Webber et al., 2006) and Petroselinum

crispum (Petropoulos et al., 2008). A moderate stress

tolerance in terms of shoot dry mass plants was

noticed in rice (Lafitte et al., 2007).

The yield components like grain number and grain

size were decreased under pre-anthesis drought stress

treatment in wheat (Edward & Wright, 2008). In

some other studies on maize, drought stress greatly

reduced the grain yield, which was dependent on the

level of defoliation due to water stress during early

reproductive growth (Kamara et al., 2003;

Monneveux et al., 2006). Water stress reduces seed

yield in soybean usually as a result of fewer pods and

seeds per unit area. In water stressed soybean the

seed yield was far below when compared to well-

watered control plants (Specht et al., 2001).

Water stress for longer than 12 days at grain filling

and flowering stage of sunflower (grown in sandy

loam soil) was the most damaging in reducing the

achene yield in sunflower (Mozaffari et al., 1996;

Reddy et al., 2004), seed yield in common bean and

green gram (Webber et al., 2006), maize

(Monneveux et al., 2006) and Petroselinum crispum

(Petropoulos et al., 2008).

Drought stress produced changes in the ratio of

chlorophyll ‘a’ and ‘b’ and carotenoids (Anjum et

al., 2003b; Farooq et al., 2009). A reduction in

chlorophyll content was reported in drought stressed

cotton (Massacci et al., 2008). The chlorophyll


content decreased to a significant level at higher

water deficits in sunflower plants (Kiani et al., 2008)

and in Vaccinium myrtillus (Tahkokorpi et al., 2007).

The foliar photosynthetic rate of higher plants is

known to decrease as the relative water content and

leaf water potential decreases (Lawlor and Cornic,

2002).

Drought stress affects the growth, dry mater and

harvestable yield in a number of plant species, but

the tolerance of any species to this menace varies

remarkably. A ramified root system has been

implicated in the drought tolerance and high biomass

production primarily due to its ability to extract more

water from soil and its transport to aboveground

parts for photosynthesis.

Wheat yield under drought stress suffer serious

moisture deficit throughout its growth period from

seedling to full maturity (Bilal et al. 2015). Under

drought condition decreasing pattern was

experienced in morphologically yield contributing

characters like plant height (PH), grains per spike,

spikes per plant, 1000- grain weight (TGW) in wheat

(Kilic and Yagbasanlar 2010). Blum and Pnuel

(1990) reported that yield and yield contributing

traits of wheat crop were drastically decreased under

least annual precipitation. Drought stress lead to

reduction in number of fertile tillers per plant, grains

per spike and 1000-grain weight (TGW) which

ultimately cause noticeably low grain productivity.

Relationship between plant height (PH), leaf area and

wheat grain yield has been noticed at booting and

anthesis phase which cause improvement in grain

yield under water deficit condition (Gupta et al.

2001). The decreasing graph in grain number was

linked with reduced leaf area and lower

photosynthesis as outcome of drought stress (Fischer

et al. 1980).

According to the study of Dencic et al. (2000), wheat

is paid special attention due to its morphological

traits during drought stress including leaf (shape,

expansion, area, size, senescence, pubescence,

waxiness, and cuticle tolerance) and root (dry weight,

density, and length). Lonbani and Arzani (2011),

claimed that the length and area of flag leaf in wheat

increased while the width of the flag leaf did not

significantly change under drought stress. Leaf

extension can also be limited under water stress in

order to get a balance between the water absorbed by

roots and the water status of plant tissues (Passioura,

1996). According to the study of Rucker et al.

(1995), drought can reduce leaf area which can

consequently lessen photosynthesis. Moreover, the

number of leaves per plant, leaf size, and leaf

longevity can be shrunk by water stress (Shao et al.,

2008). Singh et al. (1973) observed that leaf

development was more susceptible to water stress in

wheat. Root is an important organ as it has the

capability to move in order to find water (Hawes et

al., 2000). It is the first organ to be induced by

drought stress (Shimazaki et al., 2005). In drought

stress condition, roots continue to grow to find water,

but the airy organs are limited to develop. This

different growth response of shoots and roots to

drought is an adaptation to arid conditions (Sharp

and Davis, 1989; Spollen et al., 1993). To facilitate

water absorption, root-to-shoot ratio rises under

drought conditions (Morgan, 1984; Nicholas, 1998)

which are linked to the ABA content of roots and

shoots (Rane and Maheshwari, 2001). The growth

rate of wheat roots was diminished under moderate

and high drought conditions (Noctor and Foyer,

1998). In wheat, the root growth was not markedly

decreased under drought (Rao et al., 1993). Plant

biomass is a crucial parameter which was decreased

under drought stress in spring wheat (Wang et al.,

2005). The same outcomes were observed in

previous studies in wheat and other crops (Watson,

1952; Sudhakar et al., 1993). In winter wheat, the

yield was decreased or changed under drought and,

in contrast, the water use efficiency was boosted

(Xue et al., 2006; Kahlown et al., 2007).

For legumes, drought stress has adverse effects on

total biomass, pod number, seed number, seed weight

and quality, and seed yield per plant (Toker et al.,

2007b; Charlson et al., 2009; Khan et al., 2010;

Toker and Mutlu, 2011; Impa et al., 2012;

Hasanuzzaman et al., 2013; Pagano, 2014). Drought

alone resulted in about a 40% reduction in soybean

yield (Valentine et al., 2011). Faba bean and pea are

known to be drought-sensitive, whereas lentil and

chickpea are known as drought-resistant genera

(Toker and Yadav, 2010). Singh et al. (1999)

arranged warm season food legumes in increasing

order of drought tolerance: soybean < black gram <

green gram < groundnut < Bambara nut < lablab <

cowpea. Sinclair and Serraj (1995) reported that

legumes such as faba (broad) bean, pea and chickpea

export amides (principally asparagine and glutamine)

in the nodule xylem are generally more tolerant to

drought stress than cowpea, soybean and pigeon pea,

which export ureides (allantoin and allantoic acid).

The symbiotic nitrogen fixation (SNF) rate in legume

plants rapidly decreased under drought stress due to

(i) the accumulation of ureides in both nodules and

shoots (Vadez et al., 2000; Charlson et al., 2009), (ii)

decline in shoot N demand, (iii) lower xylem

translocation rate due to a decreased transpiration

rate, and (iv) decline of metabolic enzyme activity

(Valentine et al., 2011). Several reports have

indicated that drought stress led to inhibition in

nodule initiation, nodule growth and development as

well as nodule functions (Vadez et al., 2000;

Streeter, 2003; Valentine et al., 2011). The decrease

in SNF under drought conditions was associated with

the reduction of photosynthesis rate in legumes

(Ladrera et al., 2007; Valentine et al. 2011). In many

nodules of legumes, water stress resulted in

stimulation of sucrose and total sugars (Gonzalez et

al., 1995, 1998; Ramos et al., 1999; Streeter, 2003;

Galvez et al., 2005; Valentine et al,. 2011). This was


consistent with a study on pea mutants, which

showed that sucrose synthase (SS) is essential for

normal nodule development and function (Craig et

al., 1999; Gordon et al., 1999). Drought stress

induces oxidative damage in legumes and this has a

harmful effect on nodule performance and BNF

(Arrese-Igor et al., 2011). Some reports suggest that

nodules having an increment in enzymatic

antioxidant defence can display a higher tolerance to

drought/ salt stress in common bean (Sassi et al.,

2008) and chickpea (Kaur et al., 2009). In addition to

this, Verdoy et al. (2006) reported improved

resistance to drought stress in Medicago truncatula

by overexpression of Δ-pyrroline- 5-carbolyate

synthetase resulting in accumulation of high proline

levels.

Salinity stress

Salinity means a condition of soil with high

concentration of soluble salts. Salinity stress means

that this condition disturbs the normal growth and

development of plants which ultimately reduce final

yield. Salinity is one of the most serious factors

limiting the productivity of agricultural crops, with

adverse effects on germination, plant vigour and crop

yield. Salinization affects many irrigated areas

mainly due to the use of brackish water. Worldwide,

more than 45 million hectares of irrigated land have

been damaged by salt, and 1.5 million hectares are

taken out of production each year as a result of high

salinity levels in the soil (Munns & Tester, 2008).

High concentration of salts effect plants mainly by

creating two conditions

High concentration of salts in soil solution: It creates

difficulty to plant roots to extract water from soil

solution. It also affect the cell growth and

development due to that plant suffer from the salt

stress. It causes osmotic stress which affects the rate

of shoot growth.

High concentration of salts with in plant: This high

salt condition toxic for plant. It takes time to

accumulate salts inside the plants and after that it will

affect plant functions adversely.

Response of plants to salinity stress: - Plant species

vary in how well they tolerate salt-affected soils.

Some plants will tolerate high levels of salinity while

others can tolerate little or no salinity. In cereals rice

is more sensitive to salinity and barley is the most

tolerant crop.

Osmotic adjustment: - Osmotic adjustment in

plants subjected to salt stress can occur by the

accumulation of high concentrations of either

inorganic ions or low molecular weight organic

solutes. The compatible osmolytes generally found in

higher plants are low molecular weight sugars,

organic acids and nitrogen containing compounds

such as amino acids, amides, amino acids, proteins

and quaternary ammonium compounds. The growth

of salt-stressed plants is mostly limited by the

osmotic effect of salinity, irrespective of their

capacity to exclude salt that results in reduced

growth rates and stomatal conductance (Fricke et al.,

2004 & James et al., 2008). In fact, osmotic tolerance

involves the plant’s ability to tolerate the drought

aspect of salinity stress and to maintain leaf

expansion and stomatal conductance (Rajendran et

al., 2009). At the end, while the mechanisms

involved in osmotic tolerance related to stomatal

conductance, water availability and therefore to

photosynthetic capacity to sustain carbon skeletons

production to meet the cell's energy demands for

growth have not been completely unraveled, it has

been demonstrated that the plant’s response to the

osmotic stress is independent of nutrient levels in the

growth medium (Hu et al., 2007).

Salt secretion: - In many halophytes, another

important salt resistance mechanism is salt

secretion, which regulates salt tolerance by

secreting salt (especially NaCl) through salt glands

in the leaves and by modulating the internal ion

concentrations to a lower level. Na+ exclusion by

leaves ensures that Na does not accumulate to toxic

concentrations within leaves. In the majority of plant

species grown under salinity, Na+ appears to reach a

toxic concentration before Cl− does, and so most

studies have concentrated on Na+ exclusion and the

control of Na+ transport within the plant (Munns &

Tester, 2008). Therefore, another essential

mechanism of tolerance involves the ability to reduce

the ionic stress on the plant by minimizing the

amount of Na+ that accumulates in the cytosol of

cells, particularly those in the transpiring leaves. This

process, as well as tissue tolerance, involves up- and

down regulation of the expression of specific ion

channels and transporters, allowing the control of

Na+ transport throughout the plant (Munns & Tester,

2008 & Rajendran et al. 2009). Na+ exclusion from

leaves is associated with salt tolerance in cereal crops

including rice, durum wheat, bread wheat and barley

(James et al., 2011). Exclusion of Na+

from the

leaves is due to low net Na+ uptake by cells in the

root cortex and the tight control of net loading of the

xylem by parenchyma cells in the stele (Davenport et

al., 2005). Na+

exclusion by roots ensures that Na+

does not accumulate to toxic concentrations within

leaf blades. A failure in Na+ exclusion manifests its

toxic effect after days or weeks, depending on the

species, and causes premature death of older leaves

(Munns & Tester, 2008).

Salt compartmentalization: - The capacity for ion

compartmentalization among different tissues and

cells is the key mechanism regulating salt tolerance

in plants. Tolerance requires compartmentalization

of Na+ and Cl

− at the cellular and intracellular level

to avoid toxic concentrations within the cytoplasm,

especially in mesophyll cells in the leaf. Many

processes operate to enable plants to balance Na+

concentrations in their different organs, cell types

and subcellular compartments to optimize growth

and development under the given environmental

conditions. Generally, the primary tissue in which

https://www.intechopen.com/books/abiotic-stress-in-plants-mechanisms-and-adaptations/salinity-stress-and-salt-tolerance#B53


Na+ toxicity is manifested is the mature leaf (Munns,

2002). The toxicity of Na+ at agronomically relevant

Na+ concentrations has often been associated with

the extent of Na+

accumulation in leaves (Munns,

1993).

Effects of salinity stress on plants: In cereals,

salinity reduces the number of tillers due to that total

leaf area is reduced and ultimately final yield is

reduced. In pulses, size of leaves and number of

branches reduced due to salinity. The decreased rate

of leaf growth after an increase in soil salinity is

primarily due to the osmotic effect of the salt around

the roots. A sudden increase in soil salinity causes

leaf cells to lose water, but this loss of cell volume

and turgor is transient. Within hours, cells regain

their original volume and turgor owing to osmotic

adjustment, but despite this, cell elongation rates are

reduced (Passioura and Munns, 2000). Over days,

reductions in cell elongation and also cell division

lead to slower leaf appearance and smaller final size.

Cell dimensions change, with more reduction in area

than depth, so leaves are smaller and thicker. The

most dramatic and readily measurable whole plant

response to salinity is a decrease in stomatal aperture.

Stomatal responses are undoubtedly induced by the

osmotic effect of the salt outside the roots. Salinity

affects stomatal conductance immediately, firstly and

transiently owing to perturbed water relations and

shortly afterward owing to the local synthesis of

ABA (Fricke et al., 2004).

Seed germination and seedling growth of crops under

saline conditions is generally affected due to high

osmotic pressure of the solution. Salinity affects time

and rate of germination in crops (Mudgal, 2004).

Salinity is a major abiotic stress limiting

germination, plant vigour and yield of agricultural

crops especially in arid and semi-arid regions

(Munns and Tester, 2008; Abdel Latef and Chaoxing,

2011; Aggarwal et al., 2012; Ahmad and Prasad

2012a, 2012b; Porcel et al., 2012; Kapoor et al.,

2013). Approximately 20% of irrigated land

worldwide currently is affected by salinity,

particularly in arid and desert lands, which comprise

25% of the total land area of our planet (Yeo, 1999;

Rasool et al., 2013). High salinity affects plants in

several ways: water stress, ion toxicity, nutritional

disorders, oxidative stress, alteration of metabolic

processes, membrane disorganization, reduction of

cell division and expansion, and genotoxicity

(Hasegawa et al., 2000, Munns, 2002; Zhu, 2007;

Shanker and Venkateswarlu, 2011; Gursoy et al.,

2012; Djanaguiraman and Prasad, 2013). Together,

these effects reduce plant growth, development and

survival (Rasool et al., 2013; Hameed et al., 2014).

Food legumes are relatively salt sensitive compared

with cereal crops, thus farmers do not consider

growing food legumes in salinized soils (Saxena et

al., 1993; Toker and Mutlu, 2011; Egamberdieva and

Lugtenberg, 2014). The sensitivity in legumes may

be due to salt affecting bacterial activity and nitrogen

fixation (Materne et al., 2007; Toker et al., 2007a;

Toker and Mutlu, 2011; Egamberdieva and

Lugtenberg, 2014). Salt stress led to reduction in

shoot growth of soybean, chickpea, pea, faba bean

and mung bean plants (Elsheikh and Wood, 1990,

1995; Delgado et al., 1994; Hussain et al., 2011;

Saha et al., 2010; Rasool et al., 2013). The response

of BNF in contrasting tolerance lines of Medicago

ciliaris to salt stress did not show a clear trend in

relation to nodule carbohydrate metabolism (Ben-

Sala et al., 2009). Nodules of common bean (Sassi et

al., 2008) and chickpea (Kaur et al., 2009) display a

higher tolerance to osmotic/salt stress due to

increased enzymatic antioxidant defence (Arrese-

Igor et al., 2011). Salinity stress significantly

decreased the activities of nitrogenase and phosphate

enzymes (acid and alkaline) in faba bean (Rabie et

al., 2005; Hussain et al., 2011). The effect of salinity

stress on growth and some metabolic activities of

mung bean were investigated by Saha et al. (2010).

They concluded that salinity stress suppressed the

early growth of mung bean seedlings. Salinity also

damaged the photosynthetic machinery by causing

reduced chlorophyll content, and also induced the

accumulation of proline, malondialdehyde (MDA)

and H2O2 in roots and leaves of mung bean plants.

Furthermore, salinity stress caused increments in the

activity of superoxide dismutase (SOD), catechol

peroxidase (CPX) and catalase (CAT) in root and

leaves of mung bean plants. Recently, Rasool et al.

(2013) reported that tolerance of chickpea genotypes

(SKUA-06 and SKUA-07) to salinity seems to be

related to the efficiency of the enzymatic

antioxidants SOD, CAT, ascorbate peroxidase (APX)

and glutathione reductase (GR) against accumulation

of reactive oxygen species (ROS), which would

maintain the redox homeostasis and integrity of

cellular components.

Nutrient disturbances under salinity reduce plant

growth by affecting the availability, transport, and

partitioning of nutrients. However, salinity can

differentially affect the mineral nutrition of plants.

Salinity may cause nutrient deficiencies or

imbalances, due to the competition of Na+ and Cl

–

with nutrients such as K+, Ca2

+, and NO3

–. Under

saline conditions, a reduced plant growth due to

specific ion toxicities (e.g. Na+ and Cl

–) and ionic

imbalances acting on biophysical and/or metabolic

components of plant growth occurs (Grattan and

Grieves, 1999). Increased NaCl concentration has

been reported to induce increases in Na and Cl as

well as decreases in N, P, Ca, K and Mg level in

fennel (Abd El-Wahab, 2006); Trachyspermum ammi

(Ashraf and Orooj, 2006); peppermint and lemon

verbena(Tabatabaie and Nazari, 2007), Matricaria

recutita (Baghalian et al., 2008), Achillea

fragratissima (Abd EL-Azim and Ahmed, 2009).


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APPLICATION OF NANOTECHNOLOGY TO PLANT BIOTECHNOLOGY

Nayan Tara1 and Meena

2*

1Department of Bio and Nanotechnology, Guru Jambeshwar University of Science and Technology,

Hisar (Haryana)125004 2Microbial Resource Technology Laboratory, Department of Microbiology Kurukshetra University,

Kurukshetra (Haryana) 136119



Abstract: Nanotechnology is one of the most fascinating and promising science field having ability to transform research in

different disciplines of science such as agriculture, medicines, diagnostics and even plant tissue culture. Plant tissue culture

is one of the fundamental techniques of plant biotechnology. It not only involved in the micropropagation of endangered

plant species but also provide aseptic explants for transformation. But plant tissue culture technique have plethora of

methodological obstacles which prevent its full exploitation, such as contamination of explants. This paper mainly presents

a review on uses of nanomaterials in plant tissue culture such as decontamination of plant tissue culture and role of NPs in

intracellular delivery of biomolecules such as enzymes, proteins and DNA in plant cells.

Keywords: Nanoparticles, Decontamination, Intracellular delivery, Plant transformation

INTRODUCTION

Abbreviations: NPs-Nanoparticles

Nanoparticles for sterilization of explants for in

vitro culture

Sterilization of explants and the maintenance of

aseptic conditions is an essential part of plant in vitro

culture. Different microorganisms such as bacteria

and fungi grow faster in comparison to plant cells in

an in vitro culture medium and eventually inhibit the

growth of plant cells. Conventionally, plant organs

and tissues are sterilized with different antibiotics

and antifungal solutions to minimize the

contamination; but the microbial resistance to

common bactericides and fungicides and their

phytotoxic effect, like inhibitory effect on explants’

growth rate and survival, somatic embryo induction,

multiplication or genetic mutations (Leifert et al.,

1992., Teixeirada Silva et al., 2003) are limiting

factors in sterilization of plant and media materials.

Therefore, they are not recommended for use in plant

tissue culture techniques (Dabai et al., 2007). So use

of nanoparticles proved to be safe and cost effective

antimicrobial substitutions. So far different

nanoparticles have been used for decontamination of

plant tissue culture media in different laboratories.

Silver-based compounds such as silver nitrate have

long been recognized as highly toxic to

microorganisms, and show strong antibacterial,

antifungal and antiviral activity due to its capability

to release tiny particles of silver ions slowly and

silver ions can destroy the cell structure of

microorganisms (Sondi and Salopek-Sondi, 2004;

Lubick 2008). Adding nano silver particles to culture

media at concentration 4mg/l found to be effective in

elimination of in vitro contaminations of Olive by

Rostami and Shahsavar (2009). Nanosilver particles

have been successfully used for decontamination of

Araucaria excelsa explants in tissue culture by

Sarmast et al (2011). Al-Ani et al., 2011 reported

that the use silver nano particles helps in decreasing

bacterial infections in ornamental Blood leaf plant

i.e. Iresine herbstii. Safavi K. (2014) reported that

Titanium dioxide (TiO2) nanoparticles (NPs) act as

potential agent to remove bacterial contaminants in

potato plant tissue culture.

Mahna et al., 2013 also suggested NS in sterilizing

plant seeds and fragile tissues, such as leaf and

cotyledon, two important model plants, Arabidopsis

and tomato, as well as potato. It has been reported

that the nano Zn and nano ZnO particles can

effectively eliminate the bacterial and fungal

contaminants in banana in vitro culture at a dose of

100 mg/L dose because it showed the best effects on

the regeneration of plantlets with well-developed

root systems (Helaly et al., 2014). In case of Moringa

oleifera in vitro cultures, Silver & Copper

nanoparticles have been found beneficial to get clean

cultures, even with high concentrations (Al-Ani et

al., 2015). In case of grapevines in vitro nanosilver

treatments, low rates of burned explants and

moderate to high effect on bacterial contamination

control and moderate effect on fungi contamination

reported (Gouron et al., 2014). Application of 100

and 150 ppm NS both as an immersion and as

directly to the culture medium considerably reduces

internal as well as external contaminations on in

vitro establishment of G × N15 (hybrid of

almond × peach) rootstock (Arab M. et al., 2014).

Abdi et al., 2008 and Abdi., 2012 reported that the

application of 100 ppm Nano Silver for 60 min as the

efficient decontamination procedure for Valeriana

officinalis explants. The antimicrobial effect of

nanosilver particles has been shown around six

hundred microorganisms (Abdi et al., 2008).

However, in case of H. brasiliensis explants,

REVIEW ARTICLE


758 NAYAN TARA AND MEENA

Moradpour et al. (2016) reported low concentration

of 10 ppm NS at an immersion time of 20 min highly

effective in reducing microbial contamination,

increasing the survival and also act as an effective

antioxidant and polyphenol adsorbent compared to

silver nitrate and activated charcoal.

Table 1. Summary of different NPs that have been used in variouss Plant in vitro decontamination Sr.

No.

Type of NPs Used Type of Plant in vitro Culture Explant Used References

1. Silver Valeriana officinalis L. _ Abdi et al., 2008

2. Silver Olive Single nodes and shoot apices

Rostami and Shahsavar., 2009

3. Silver Iresine herbstii Stem cutting Al-Ani et al., 2011

4. Silver Araucaria excelsa Sarmast et al., 2011

5. Silver Valeriana officinalis L. Abdi, 2012

6. Silver Arabidopsis, Tomato and Potato Seeds, leaf and cotyledon Mahna et al., 2013

7. Titanium dioxide

(TiO2)

Potato Bud Safavi K., 2014

8. Silver Grapevines Leaf Gouron et al., 2014

9. Silver G × N15 (hybrid of almond × peach) Root Stock Arab M. et al., 2014

10. Zn and ZnO Banana Shoot tip Helaly et al., 2014

11. Silver and Copper Moringa oleifera Stem cutting Al-Ani et al., 2015

12. Silver H. brasiliensis Shoot tips and Axillary

buds

Moradpour et al., 2016

Nanoparticles in plant intracellular delivery of

biomolecules

The transgenic plants production is considered as an

important tool in plant genetic research. There are

different methods of Gene transfer in plants, namely,

Agrobacterium mediated, chemicals based methods,

and physical techniques such as electroporation,

microprojectile, etc. Now-a-days, new methods,

namely, the nanoparticles-based delivery systems,

with better efficacy and stability is presenting an

attractive alternative for plant transformation, since

nanoparticles can be specifically tailored to deliver a

different biomolecules such as DNA, proteins and

enzymes to the cell, tissue, or organism of interest

when needed (Du et al., 2012). However,

Nanoparticles as gene carriers have been mostly used

in the mammalian cultured cells (Rojas Chapana et

al., 2005., Mattos et al., 2011., Raffa et al., 2012),

whereas its application in plant cells is still very

limited (Nunes et al., 2011., Serag et al., 2012).

Nanoparticles gene carriers possess significant

advantages compared with traditional carriers.

Firstly, nanoparticles can be used for transformation

of both monocotyledons and dicotyledonous plants

and any types of organs. Secondly, nanoparticles can

efficiently overcome transgenic silencing via

controlling the DNA copies that combined to

nanoparticles. Thirdly, nanoparticles can be

functionalized easily that will further enhance

transformation efficiency. Finally, multigene

transformation can be achieved easily via

nanoparticles without undergoing traditional building

method of complex carrier. The key properties of

nanoparticles endow this gene carrier with the

potential application in plant transformation, and it is

promising to solve the existing problem of plant gene

engineering such as safety, multigene transformation

and so on.

Yu-qin et al., 2012, observed that ZnS nanoparticles

modified with positively charged poly-L-lysine

(PLL) successfully delivered GUS-encoding plasmid

DNA into tobacco cells by means of ultrasound-

assisted method. Torney et al., 2007 reported that

honeycomb mesoporous silica nanoparticle (MSN)

system with 3-nm pores that can transport DNA and

chemicals into isolated plant cells and intact leaves.

The MSN loaded with the GFP (Green Flourescent

Protein) gene and its chemical inducer and capped

the ends with gold nanoparticles to keep the

molecules from leaching out. Uncapping the gold

nanoparticles triggered gene expression in the plants

under controlled-release conditions. Martin-Ortigosa

et al., 2014 observed direct Cre recombinase protein

delivery to plant cells using mesoporous silica

nanoparticles (MSNs) as carriers into maize (Zea

mays) cells, the delivery of DNA modifying proteins

instead of DNA cassettes into plant cells allows for a

genome modifications and editing applications.

Hussain et al., 2013 reported the uptake of

mesoporous silica nanoparticles functionalised with

amine cross-linked fluorescein isothiocyanate (MSN-

APTES-FITC) by wheat, lupin and Arabidopsis

which could be used as a novel delivery system for

small molecules in plants. Carbon Nano

Tubes(CNTs) with immobilized cellulase can serve

as an efficient DNA delivery system for plant cells

(Foud et al., 2008). Burlaka et al., 2015

demonstrated the applicability of Single Wallled

CNTs-based nanocarriers for the transformation of

Nicotiana tabacum’s protoplasts and walled plant

cells and the Multi Walled CNTs-based nanocarriers

only for the transformation of protoplasts, because of

a limiting role of the cellulose wall against their

penetration into the cells. An increase in the genetic

transformation frequency for E. coli (Rojas Chapana

et al., 2005., Raffa et al., 2012) and Neisseria

meningitides (Mattos et al., 2011) in the presence of

MWCNTs has been reported.


Table 2. Summary of different NPs that have been used in intracellular delivery of biomolecules in plant cells Sr.

No.

Type of NPs Used Method used for NPs

Delivery

Plant Reference

1. ZnS Ultrasound-assisted Tobacco

Yu-qin et al., 2012

2. Mesoporous silica nanoparticle

(MSN)

Biolistic Tobacco

Torney F. et al., 2007


(MSN)

Biolistic Maize Martin-Ortigosa, S. et al., 2014


(MSN)

_ Wheat,

Lupin and Arabidopsis

Hussain, H. et al., 2013

5. Carbon Nano Tubes (CNTs) with

immobilized cellulase

Cell uptake Arabidopsis thaliana,

Glycyrrhiza glabra

Foud et al., 2008

6. SWCNTs and MWCNTs Cell uptake Nicotiana tabacum L. Burlaka et al., 2015

CONCLUSION

Current applications of nanobiotechnology to plant

biotechnology research can serve as a new and

potential tool for plant in vitro decontamination and

plant genome transfection and editing. However,

there are many unresolved challenges and issues

regarding the biological effects of nanoparticles.

Attentions must be given to suitable experimental

designs to provide a defensible use of nanoparticles

to avoid phytotoxicity due to NPs (Murachov, 2006).

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https://www.sciencedirect.com/science/article/pii/S0304423802002194






BIOACTIVE POLYSACCHARIDES FROM WILD MUSHROOM, COPRINOSIS SP.

Hemanta Kumar Datta and Sujata Chaudhuri*

Mycology & Plant Pathogy Laboratory, Department of Botany, University of Kalyani,

Kalyani, PIN:741235


Received-11.08.2017, Revised-24.08.2017 Abstract: The wild mushroom Coprinosis sp., was collected from local forest and water soluble fraction of the

polysaccharide was extracted from the fruiting body. The polysaccharide (CPSS) was partially purified by dialysis.

Biochemical estimation of crude extract shows that it contained 90.7% polysaccharides, 8.92% phenolics and 0.28% protein.

FTIR spectroscopy further confirmed the presence of polysaccharides. HPLC analysis of CPSS indicated that glucose,

galactose and xylose comprised monosaccharide units. Total antioxidant, total reducing power, nitric oxide scavenging and

DPPH assay confirmed antioxidant property of CPSS and it was found to work in a dose dependent manner. DCFDA stained

images of lung cancer line A549 showed that CPSS was able to reduce intracellular ROS in the cells. Cytotoxicity of CPSS

was observed on A549 and L132 cell. It showed that CPSS had potential antitumor activity and it was specific to cancer cells

only. Hochest and PI staining indicated the induction of apoptosis in cancer cell by PI positive cells after CPSS treatment.

Keywords: Wild mushroom, Polysaccharides, Antitumour, Antioxidant

INTRODUCTION

ushrooms are widely used as a food in various

countries throughout the world. They are well

known for their nutritional value and medicinal

effects. They exhibit antioxidant, antitumor and

immunomodulation properties. The lead molecule or

active compound responsible for these properties are

mainly polysaccharide. The cell wall of mushrooms

contain different types of polysaccharides. glucans,

α glucans and heteroglycans have been frequently

reported by researchers (Mizuno et al. 1990, Ukawa

et al. 2000, Zhuang et al. 1993). It has been observed

that the activity of these polysaccharides depends

upon their structure and branching activity (Mizuno

1996, 1999). The mushroom polysaccharides induces

apoptosis in cancer cells and modulates the immune

system by activating macrophages and other

monocytes (Naohito et al. 2002). It also has

promising antioxidant property and it reduces the

cellular ROS production in different cells (Daoyuan

Ren et al. 2015).

However major works on mushroom polysaccharides

have been done with edible mushrooms, because of

the availability (María Elenaet al. 2014). There are

very few reports on the polysaccharides of wild

tropical mushroom (Qin Liu et al. 2014). Besides, the

major area of interest has been the β glucans and

their potential activity. Little work has been done on

the other polysaccharides such as α glucans,

heteroglycans, Glucomannan etc., which also have

potential activity (Kiho et al. 1992).

In the present communication our study focusses on

the isolation of the polysaccharides (CPSS) from

wild mushroom Coprinosis sp. and their

characterization. We have also investigated the

antioxidant and antitumor property using lung

epithelial cell and have examined the free radical

scavenging property of the polysaccharides by

biochemical methods.

MATERIAL AND METHOD

Coprinosis sp. was collected from Ranaghat forest

(Lat: 23010’27” N, Long: 88

034’08” E), West

Bengal, India. DMEM media, Trypsin EDTA 1X

solution (0.25% Trypsin and 0.02% EDTA), FBS,

DMSO and MTT reagents were purchased from

Sigma-Aldrich. Cell lines were obtained from

National Center for Cell Sciences, Pune, India.

Standard monosaccharides for HPLC (D-glucose, D-

Xylose, D-Glactose, D- mannose, D- Ribose) PMP,

Trifluroacetic acid, DPPH, Dialysis membrane,

florescent dyes (DCFDA, Hechest3342 and PI) and

other chemicals were purchased from Sigma-Aldrich.

Taq DNA polymerase, Taq Buffer, MgCl2 (25mM),

dNTP mix (25mM each) and Primers were obtained

from Merck India.

HPLC carried out on HITACHI Chromstar system

using HITACHI LaChrom C18 column

(250x4.6mm), UV-VIS detector 5420, Auto sampler

5210, and pump 5110. FTIR data was obtained from

Perkin Elmer L120-000A FT-IR Spectrophotometer.

Cytotoxicity was measured in BioTek ELx 800

ELISA reader and Carl Zeiss Axioscope A1 was

used for florescence images. Eppendorf Thermal

cycler used for the DNA amplification. Cecil

CE7200 UV-VIS spectrophotometer was used for all

biochemical estimation and antioxidant assay.

Extraction of polysaccharides ( CPSS)

Polysaccharides were extracted by protocol described

by Mandal et.al (2010) with few modification. 1kg of

the fruiting body was washed with sterile double

distilled water and crushed and incubated at 900C for

6h in 4% NaOH. The extract was then filtered

through Whatman No.1 paper to remove the cell

M

RESEARCH ARTICLE

762 HEMANTA KUMAR DATTA AND SUJATA CHAUDHURI

mass and the soup was mixed with 80% ethanol in

the ratio 1:5 and kept at 40C overnight to precipitate

the polysaccharides. The precipitated crude

polysaccharides were collected by centrifugation at

8000 rpm for 40 min at 40C. The polysaccharides

were washed with 80% ethanol and acetone three

times each and dissolved in sterile water and

dialyzed (retaining >MW 10,000) against double

distilled water for 3 days at 40C to remove low

molecular weight compounds. After dialysis, the

alkali soluble and water soluble fractions were

collected. Water soluble fraction (CPSS) was further

centrifuged to reduce the contamination of alkali

soluble fraction and lyophilized for further

experiments.

Identification of mushroom

Mushroom was primarily identified by its

morphological characters. Molecular identification

was done by 28S rDNA sequencing. For molecular

identification, genomic DNA was isolated by CTAB

method. LSU (LROR-LR5) (Frw: 5′-

ACCCGCTGAACTTAAGC-3′ & Reverse: 5′-

TCCTGAGGGAAACTTCG-3′) primer (Schoch

et.al. 2012) was used for rDNA amplification. PCR

was performed in a total reaction volume of 50µl

containing 1.5 mM Mgcl2, 0.25 mM of each dNTP,

0.2 µM of each primer, 1x Taq buffer, 1.5 U of Taq

DNA Polymerase and 200 ng of template DNA. The

PCR cycling condition for amplification was 950C

for 10 min for initial denaturation; 35 cycles of 950C

for 15 s, 480C for 15s, 72

0C for 1.5 min; 72

0C for 7

min for final extension and hold at 40C.

FT-IR spectroscopy

To 2mg of CPSS, 200mg of KBr was added and the

mixture was ground gently. The FT-IR scan ranged

between 400-4000 cm-1

.

Biochemical estimation

Total polysaccharide was measured by phenol-

sulphuric acid method ((Dubois et. al., 1956) and

total reducing sugar was calculated by the 3, 4

dinitrosalicylic acid method ((Miller et al., 1959)

using D-glucose as standard in both experiment.

Total protein and total phenol were also estimated by

Bradford method (Bradford et.al., 1976) and Folin-

Ciocalteu’s reagent (Kaur et.al., 2002) respectively.

Bovine serum albumin was used as standard protein

and galic acid used for phenol estimation.

Monosaccharide composition analysis

For monosaccharide composition analysis (Honda

et.al., 1989), 2 mg of each standard monosaccharides

including the CPSS, was dissolved in 1 ml of 4 M

trifluoroacetic acid in a hydrolysis tube and

incubated at 1210C for 2h. After being cooled to

room temperature, all hydrolyzed products were

transferred to microcentrifuge tube and TFA

removed by evaporating it under reduced pressure till

dryness. The dried sample and all standards were

further dissolved in 2-propanol to remove the

remaining TFA residues and evaporated under

reduced pressure. All hydrolyzed products were then

labeled with PMP by incubating with 20 µl of 0.5 M

PMP dissolved in methanol and 20 µl of 0.3 M

sodium hydroxide at 700

C for 2h. The mixture was

neutralized by adding 20 µl of 0.3 M hydrochloric

acid to each tube. 0.5 ml chloroform was added to

the mixture and vortexed. To enhance the phase

separation, centrifugation was done and upper

aqueous layer was collected. Thin layer

chromatography on silica plate (solvent-

Choloroform:Methanol: Water=40:10:1) confirmed

the derivatization of monosaccharides by detection of

dark spot at 254 nm light. 20 µl of sample was

injected in HPLC separated by using 80% 0.1 M

phosphate buffer (PH-7.0) and 20% acetonitrile at

1ml/min flow rate and the wavelength for detection

was 245 nm.

Total antioxidant capacity

Total antioxidant capacity was measured by using

phosphomolybdenum reagent (28 mM sodium

phosphate and 4 mM ammonium molybdate in 0.6 M

sulphuric acid) (Prieto et. al., 1999) and ascorbic

acid as standard reference. 1 ml of CPSS was taken

to which was added the reference standard, in

different concentration (25 µg/ml, 50 µg/ml, 75

µg/ml, 100 µg/ml, 200 µg/ml, 300 µg/ml) and 3 ml

of phosphomolybdenum reagent. The mixture was

incubated at 950

C for 90 min and after cooling to

room temperature, the absorbance was measured at

695 nm against blank (1 ml water with 3 ml

phosphomolybdenum reagent). The antioxidant

activity of polysaccharide was calculated as the

number of gram equivalent of ascorbic acid.

Reducing power assay

Reducing power assay was done by the

spectrophotometric method described by Ferreira et

al, (2007). 1 ml of CPSS and standard i.e, ascorbic

acid in different concentration (25 µg/ml, 50 µg/ml,

75 µg/ml, 100 µg/ml, 200 µg/ml, 300 µg/ml) was

mixed with 2.5 ml phosphate buffer (0.2 M, PH-6.6)

and 2.5 ml potassium ferricyanide. The mixture was

incubated at 500C for 20 min. After cooling to room

temperature, 2.5 ml of 10% trichloroacetic acid was

added to it and centrifuged at 6500 rpm for 10 min.

2.5 ml of the supernatant was diluted with 2.5 ml of

distilled water and 0.5 ml of 0.1% ferric chloride

solution was added to it. After 10 min of incubation

at room temperature, absorbance was measured at

700 nm. Reducing power of the sample was

calculated as ascorbic acid equivalent per 100 gm of

dry polysaccharide.

Nitric oxide scavenging capacity

Nitric oxide scavenging activity was measured by

sodium nitropusside (SNP) as described by Marcocci

et al (1994). The 5ml of reaction mixture containing

SNP (5 mM) in phosphate buffered saline (pH7.3)

with or without the CPSS and standard (galic acid) at

different concentration. The mixture was incubated at

250C for 3h in front of 25W tungsten bulb. 1ml

aliquot of the reaction mixture was mixed with 1ml

of Griess reagent (1% sulfanilamide in 5%


phosphoric acid and 1% napthylethyl enediamine

dihydrochloride). Absorbance was measured at 546

nm. The amount of NO radical scavenging was

calculated from the sodium nitrite standard curve and

percentage radical scavenging of sample determined

as follows:

% NO radical scavenging activity= (Control OD -

sample OD/Control OD) x 100

Where control contained all reagents except

polysaccharide and galic acid.

DPPH scavenging assay

The free radical scavenging activity was confirmed

by 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay as

described by Brand-Williams et al (1995). 1 ml of

different concentrations (6, 12.5, 25, 50, 100, 250

µg/ml) of CPSS and standard (ascorbic acid) each

separately, was mixed with 3 ml of DPPH in

methanol (20 µg/ml) and incubated at room

temperature for 30 min in dark. After incubation, the

absorbance was measured at 517 nm against

methanol as blank. Inhibition of free radical

formation was calculated as follows:

% of free radical scavenging= (1- sample

OD/Control OD)x 100

Where control was the mixture of all reagents except

the polysaccharides or ascorbic acid.

Cell lines and Culture

Human lung carcinoma A549 and human lung

epithelial cell L132 were cultured in DMEM media

supplemented with 10% FBS, 1% penicillin and 1%

streptomycin. All cells were maintained at 370C

humified with 5% CO2.

Cytotoxicity assay

For cytotoxicity test, 1x104 cells were distributed in

each well of 96 well plates and incubated with CPSS

and cisplatin, separately, at different concentrations,

for 24h, 48h and 72h. After incubation, 10µl of MTT

(3-[4,5-methylthiazol-2-yl]-2,5-diphenyl-tetrazolium

bromide) (5 mg/ml) was added to each well and

incubated for another 4h. The media on the top was

discarded and the Formazan crystals formed at the

base were dissolved in DMSO and read on a

multiwell plate reader at 570 nm. The MTT assay

was done with both A549 and L132 cell lines to

observe the effect on cancer and noncancerous cells.

Detection of cellular ROS by florescence

microscopy

For detection of the effect on cellular ROS

production ((Wojtala et.al., 2014), 5x105 cells

(A549) were seeded in 35 mm tissue culture plate.

Cells were incubated with of 100 µg/ml CPSS and 50

µg/ml ascorbic acid for 2h. Cells were stanied with

DCFDA (5 µM) and Hoechest 33342 (10µg/ml) in

D-PBS for 30 min. After that, cells were washed with

D-PBS three times and observed under the

florescence microscope.

Detection of apoptosis by fluorescence microscopy

Apoptosis and necrosis studies (Eguchi et al., 1997;

Liu et al., 1999; Shimizu et al., 1996) were done with

A549. 5x105 cells were seeded in a 35 mm tissue

culture plate and incubated separately with CPSS ( @

50, 100 µg/ml) and cisplatin (25 µg/ml) and all the

treatments were incubated for 48h at 370C with 5%

CO2. After incubation, the cells were stained with

Hoechest 33342 (10 µg/ml) and PI (20 µg/ml) in D-

PBS and incubated for 30 min. The cells were then

washed with D-PBS three times and observed under

florescence microscope.

All experiments were repeated thrice and the values

expressed as mean of the replicates with standard

deviation.

RESULT

Based on morphological characters (Fig.1) and 28S

rDNA sequencing, the mushroom was identified as

Coprinosis sp. The 28S rDNA fragment was

approximetly 800 bp in length and was submitted to

the NCBI gene bank and the accession number given

was KY594042.

Fig 1: Carpophores of Coprinosis sp.

Phenol sulphuric acid estimation of CPSS confirmed

90.72% polysaccharide. Very low amount of

phenolics (8.92%) and negligable protein (0.28%)

contamintion was also found in CPSS. So it was

confirmed that CPSS was a polysaccharide-rich

fraction (Table.1).

Table 1. Components of CPSS

Component Content

(mg/1 mg extract)

Polysaccharides 0.907 ±4.3

Phenolics 0.0892 ±2.36

Protein 0.0028 ±0.002

The FTIR spectrum (Fig.2) of CPSS also supports

the results of the phenol sulphuric acid estimation. In

the FTIR spectrum, the band at 3400.49 cm-1

denotes

the pesence of O-H streching vibration; 2921.43 cm-1

band confirms the presence of C-H stretching

vibration and these two absorption are characteristic

of polysaccharides. The band at 1622.89 cm-1

is for

bound water. A weak signal at 1377.48 cm-1

indicates

C-H bending vibration. The bands at 1156.02 cm-1

,

1075.10 cm-1

, and 1049.89 cm-1

indicated the


presence of pyranose ring of polysaccharide. The

band at 771.81cm-1

corresponds to the skeleton

bending of pyranose ring.

Fig. 2: FTIR spectrum of CPSS

The HPLC chromatogram (Fig.3) confirms the presence of glucose in major amount, galactose in low amount

and negligible amount of xylose as monosaccharide units present in CPSS.

Fig. 3: HPLC chromatogram of CPSS. Top (red) was standards (*= Solvent front and PMP, 1=Mannose, 2=

Ribose, 3=Glucose, 4=Xylose, 5=Galactose) and below (violet) was for CPSS.

Total antioxidant assay (Fig.4A) revealed that CPSS

had significant antioxidant property and 1 mg/ml of

CPSS was equivalent to 353 µg/ml ±148.85 of

ascorbic acid. The reducing power assay (Fig.4B)

showed an EC50 value of 306.6 µg/ml in case of

CPSS while for ascorbic acid it was 78.44 µg/ml.

The nitric oxide scavenging capacity (Fig.4C) of

CPSS was also studied and the IC50 for NO

scavenging was 219.15 µg/ml for CPSS and 45.38

µg/ml in galic acid. DPPH free radical scavenging

experiment (Fig.4D) also supported and confirmed

the antioxidant activity. The IC50 value was 259.95

µg/ml for CPSS and 28.55 µg/ml for ascorbic acid.

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 600 450.0

49.2

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

67.9

cm-1

%T

3400.49

2921.43

1622.89

1377.48

1156.02

1075.10

1049.98

795.26

771.81

745.03

627.02


Fig. 4: Total antioxidant (A), reducing power (B), nitric oxide scavenging (C) and DPPH free radical

scavenging activity (D) of CPSS

The cytotoxicity assay was performed on A549 and

L132. From the Fig.5 (A, B, C) it was evident that

CPSS have cytotoxic activity on cancer cells and it

worked in a dose dependent and time dependent

manner. Table.2 shows the IC50 of CPSS with respect

of cisplatin after different periods of incubation. No

notable cell death occurred in L132 cell line even

after 72h of incubation with CPSS (Fig.5D, E, and

F). Nuclear fragmentation and cell membrane

permeability was observed in Hoechst/PI stained

A549 cells. In the control, no PI stained nuclei were

observed while in CPSS and cisplatin treated cells,

PI stained cells were found. The phase contrast view

also indicated some morphological changes in the

CPSS and cisplatin treated A549 cells ( Fig. 7).

Table 2. IC50 value (µg/ml) of A549 cells treated

with CPSS and cisplatin at different incubation

periods.

Incubation

period

CPSS Cisplatin

24h 324.02 184.21

48h 200.07 71.56

72h 72.23 7.93

Fig. 5: Cytotoxicity assay of CPSS and Cisplatin on A549 and L132 cell lines

Concentration (g/ml) Concentration (g/ml)

Concentration (g/ml) Concentration (g/ml)


For the investigation of antioxidant property at the

cellular level, A549 cells were treated with CPSS and

ascorbic acid separately and stained with DCFDA

and Hoechst 33342 (Fig.6). The fluorescent images

clearly show significant reduction of green

fluorescence in CPSS treated cells reduction than the

control. Reduction in green fluorescence was also

observed in the ascorbic acid treated cells. Hoechst

3342 was used to stain the nucleus and the blue

fluorescence showed no nuclear fragmentation after

2h of incubation with CPSS and ascorbic acid.

Fig 6: Cellular ROS production by A549 cell lines with different treatment after 2h of incubation.

Fig. 7: Detection of apoptosis by nuclear fragmentation and membrane permeability. PI positive cells (red)

indicate cellular apoptosis and membrane likage.

DISCUSSION

Fungal polysaccharides, especially those of

mushrooms have been studied for a long time for

their antioxidant, antitumor and immunomodulation

properties (Kozarski M. et al. 2011, Wang S. Y. et al.

1997, Leung M. Y. K et al. 1996). In the present

work, we focused on the antioxidant and antitumor


properties of the polysaccharide isolated from a wild

mushroom, Coprinosis sp., which is of common

occurrence in the region. A major aspect in such

studies is the identity of the source fungus and the

lead compound i.e. polysaccharides. Mushrooms

have traditionally been identified by their

morphological features but after the development of

molecular tools, such techniques are being

increasingly used for their identification. The rDNA

is most conserved region in fungi so, 28S rDNA and

ITS are the prime choice of researchers for the

identification (Menolli Junior N. et al. 2010, Lee J. S.

et al., 2006). Thus on the basis of 28S rDNA

sequencing, this mushroom was identified as

Coprinosis sp., and this was corroborated further by

the morphological features of the carpophore.

Polysaccharides from mushrooms have been

characterized using various techniques such as FTIR

spectroscopy, GC-MS, HPLC and NMR by several

workers (Andriy Synytsya & Miroslav Novak, 2014).

The initial identification is done by biochemical

estimations such as phenol sulphuric acid method

and further analyzed by FTIR. FTIR is a very strong

tool for the identification of the functional group

present in polysaccharides (Gómez-Ordóñez, E., &

Rupérez, P. 2011). The phenol sulphuric acid

estimation and FTIR both confirmed it was

polysaccharides. The marker bands for

polysaccharides such as 3600-3200 cm-1

and 2900–

2800 cm-1

for OH stretching vibration and C-H

stretching vibration respectively (Qin Liu et al.2017),

were present in CPSS.

HPLC (Dai, J et al. 2010) and GC-MS was widely

used to identify the monosaccharides present in a

polysaccharide. The hydrolyzed and PMP derivatized

monosaccharides confirmed the presence of glucose,

xylose and galactose. So, it is possible that CPSS

may glucan or heteroglycan. There was no evidence

for glucomannan or arabinoxylan, which are

commonly found in fungi.

The biochemical analysis for antioxidant property

and in vitro application of it for intracellular ROS

determination are combined approaches to

understand the oxidative stress response in cancer

cells. DCFDA is a fluorogenic dye that is used to

measure intracellular free radicals (Danli Wu &

Patricia Yotnda, 2011). Within the cell, cellular

esterases deacetylated DCFDA to a non-fluorescent

compound, and later it was oxidized by ROS into 2’,

7’ –dichlorofluorescein (DCF), which have strong

green fluorescent property. Increased intracellular

ROS gives high fluorescence while low ROS content

gives low fluorescence in the cell. In the present

study, the cell were stained with DCFDA and to

confirm whether the cells were alive, Hoechst 33342

was used. In general, in apoptotic cells, nuclear

fragmentation is observed when stained with Hoechst

33342. But in this case while intracellular ROS was

observed with DCFDA staining, no nuclear

fragmentation in A549 cells could be seen on

Hoechst staining, indicating that the cells were living

but significant decrease in green fluorescence clearly

suggested that CPSS had antioxidant property. It also

indicated that 2h of incubation with CPSS did not

trigger apoptosis in A549 but reduced cellular ROS.

The cytotoxicity assay showed that CPSS have

anticancer activity but had no effect on the non-

cancerous cells (L132). The cytotoxicity was

compared with cisplatin, a well known

chemotherapeutic drug and the results showed that

the activity of CPSS was promising with respect to

cisplatin. Hoechst and PI stained cells are widely

used to determine apoptosis (Thuret G et al., 2003,

Fenglei Chen et al. 2015). Hoechst 33342 is a cell

permeant nuclear counterstain while PI is not

permeant to live cells and stains the nucleus when the

membrane became permeable or disintegrated due to

apoptosis. In our study it was found that CPSS

treated A549 cells were PI positive clearly revealing

that the membrane of the cells disintegrated and

made it permeable to the PI dye, which was a

positive indication of cell apoptosis. Apoptosis may

have been triggered by decreasing the intracellular

ROS level of the cancer cells.

CONCLUSION

The water soluble polysaccharide-rich fraction

(CPSS) from the mushroom Coprinosis sp. exhibited

significant antitumor and antioxidant activities. It

worked in a dose and time dependent manner in the

cancer cells. CPSS triggered apoptosis in cancer cells

via reduction in intracellular ROS. The immense

potential of mushroom polysaccharides must

therefore be tapped and in future they may be a

valuable source for anticancer therapy.

ACKNOWLEDGEMENT

The senior author is grateful to the University of

Kalyani for granting UR Fellowship and the DST-

PURSE for the facilities available.

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MYCOTOXIN RESEARCH AND MYCOFLORA IN SOME DRIED EDIBLE

MORELS MARKETED IN JAMMU AND KASHMIR, INDIA

Pinky Bala, Dimple Gupta and Y.P. Sharma*

Department of Botany, University of Jammu, Jammu- 180006, India



Abstract: Morchella species are major wild edible mushrooms of Jammu and Kashmir, which is both exported as well as

largely consumed domestically. The aim of the present study was to characterize the toxigenic moulds and to screen different

mycotoxins in dried morels. The most commonly isolated fungi were species of Aspergillus, Fusarium and Penicillium and

the important mycotoxins detected were aflatoxins, citrinin, ochratoxin and zearalenol. The mean level of aflatoxin B1

(125.44± 78.14) was found to be highest among all other mycotoxins. This is the first report on mycoflora and mycotoxin

contamination in dried morels from Jammu and Kashmir.

Keywords: Aspergillus, Morchella, Mycotoxin, Mycoflora

INTRODUCTION

f the world’s 5.1 million estimated species

within kingdom Fungi (Blackwell, 2011), true

morels (Morchella, phylum Ascomycota) are

arguably the most charismatic and widely recognized

wild edible fungi intensively collected by mycophilic

people (Kuo, 2005). Morels are highly prized edible

mushrooms and have attracted human attention since

time immemorial. Worldwide morels (Morchella

spp.) are highly cherished and easily recogonizable

forest resources of edible fungi (Sher and Shah,

2014). These mushrooms have wide distribution in

India and is very common in the temperate forests in

Uttarakhand, Himachal Pradesh and Jammu and

Kashmir. Total world production of morels is

estimated to be about 150 tonnes dry weight,

equivalent to 1.5 million tonnes of fresh morels.

India and Pakistan are the major morel producing

countries, each producing about 50 tonnes of dry

morels, almost all of which are exported (FAO,

2002) The state of Jammu and Kashmir ranks second

in the morel trade after Himachal Pradesh with

Uttarakhand as the third largest producer of morels in

India (Shad and Lakhanpal, 1991).

The local people cook ascocarps (the fruiting body)

mixed with rice and vegetables, and consider it as

nutritious as meat or fish. The local inhabitants in

remote areas depend upon folk medicines and

household remedies to a great extent. The prevalent

practices of indigenous herbal medicines including

fungi have descended from generation to generation

and include cure of both simple and complicated

diseases. Of all the edible mushrooms of the North-

West Himalayas, morels have been traditionally used

for the cure of ailments such as pneumonia, fever,

cough and cold, dehydration, stomach pains and for

pregnant and lactating mothers. It is also known to

cure all the respiratory ailments and generally they

prefer to use black coloured fruiting bodies (Nautiyal

et al., 2001; Lakhanpal et al., 2010). Moreover, the

morels have shown noticeably significant

pharmacological activities viz. antimicrobial, anti-

inflammatory, immunostimulatory and anti-tumour

properties (Kumar et al., 2000; Nitha et al., 2006;

Halliwell, 2011; Badshal et al., 2012; Carocho and

Ferreira, 2013).

In Jammu and Kashmir, Morchella species are

collected systematically during the growing seasons

(spring and sometimes after rainy season) and sold to

established markets both as fresh and as dried

mushrooms. A variety of morels such as Morchella

conica, M. esculenta and M. deliciosa grow in the

wild. One of the major limitation, Morchella species

are seasonal and due to high moisture (90%) content

they have short post-harvest shelf life making them

prone to microbial spoilage and exhibit enzymatic

browning which is the major cause of quality loss

that accounts for reduction in their marketing as fresh

produce (Mohapatra et al., 2008). In order to make

their availability through the year around, drying has

been considered as the best method to minimize

biochemical and microbial activities (Kumari et al.,

2011).

After dehydration, dried morels are packed in various

storage containers, such as nylon bags, jute bags,

polythene bags, glass jars and tins and are

transported to various city markets and main

marketing centers of northern India. Like any other

agricultural product, morels are also exposed to a

wide range of fungal and mycotoxin contamination

during pre- and post-harvest period, especially during

processing, storage, distribution, sale or use. These

mycotoxins are considered to be among the most

significant food contaminants in view of their

negative impact on public health, food security and

the national economy of many countries, particularly

the developing ones (FAO, 2001). The

mycotoxigenic potential depends on species and

strains of fungus, composition of substrate and

environmental factors (temperature and moisture)

(Fernandez -Cruz et al., 2010). Among the thousands

O

RESEARCH ARTICLE


772 PINKY BALA, DIMPLE GUPTA AND Y.P. SHARMA

of fungal secondary metabolites currently known,

only a few groups of mycotoxins are important from

the safety and economic points of view; namely

aflatoxins (AFs), ochratoxin A (OTA), citrinin (CIT)

and zearalenol (ZOL) (Streit et al., 2013). The

majority of mycotoxins are produced by the genera

Aspergillus, Penicillium, and Fusarium, the so-called

field fungi, that frequently infects various food

commodities (Reddy et al., 2010).

Among the various mycotoxins, aflatoxins have

attracted the attention of many scientists, probably

because of their toxicological characteristics and

ubiquitous presence as unavoidable contaminants in

a variety of foods and feeds (Wood, 1992). These are

produced mainly by Aspergillus flavus and A.

parasiticus besides their known production by

several other species such as A. nomius, A.

pseudotamarii, A. ochraceoroseus, A. bombycis etc.

The four major aflatoxins, B1, B2, G1 and G2 are the

major toxic contaminants in dried commodities

because of their high prevalence in nature and

toxicity (Ayar et al., 2007). Ochratoxin A (OTA) is

mainly produced by Penicillium verrucosum,

Aspergillus ochraceus, and A. carbonarius while

citrinin (CIT) is produced by Penicillium and

Aspergillus species and is supposed to possess both

nephrotoxic and genotoxic potential (Ammar et al.,

2000).

Numerous reports are available on incidence of

mycoflora and mycotoxins in dried commodities

across the world (Mandeel, 2005; Lutfullah and

Hussain, 2011; Rodrigues et al., 2012; Ibrahim et al.,

2013; Gupta et al., 2013, 2017, Sharma et al., 2013,

2014; Bala et al., 2014, 2016). Few records are

available on the spoilage of cultivated edible

mushrooms (Kamal et al., 2009; Jonathan and Esho,

2010; Ezekiel et al., 2013) but no attention has been

paid towards the deterioration of dried edible morels.

Therefore the present study was designed to

investigate the incidence of mycoflora and

determination of natural occurrence of mycotoxins

(aflatoxins, citrinin, ochratoxin and zearalenol).

MATERIAL AND METHOD

Sample collection Seventy eight dried samples of morels were

randomly selected from different wholesale and

retailers of dry fruits, households from various

regions of Jammu and Kashmir during March, 2015-

February, 2016. The samples were collected in

sterilized polythene bags to avoid further

contamination and stored in refrigerator at 5ºC till

further studies.

Moisture content

Prior to mycological analysis, the moisture content of

dried samples was determined following Singh et al.

(2008).

Mycological analysis

Agar plate method as recommended by the

International Seed Testing Association (ISTA, 1966),

as well as serial dilution method following Harrigan

(1998) was employed to record the mycoflora

associated with dried Morchella species. Experiment

was performed in replicates for both the methods.

Table 1. Frequency percent and total colony count of fungal species recovered from dried morels. S.No. Fungal species recovered %

Frequency

Jammu Division Cfu/g×103 %

Frequency

Kashmir Division Cfu/g×103

CDA DRBC MEA CDA DRBC MEA

Zygomycota

1 Mucor mucedo 45.8 8.0 3.9 9.3 8.0 - 1.6 3.6

2 Rhizopus oryzae 14.5 3.2 - 2.1 16.0 - 1.8 -

3 Syncephalastrum

racemosum

- - - - 12.0 - 2.0 -

Ascomycota

4 Epicocum nigrum 27.0 1.2 - - - - - -

5 Eurotium amstelodami 20.8 2.7 - 8.0 - - - -

6 Eurotium echinulatum 18.1 5.0 1.4 6.0 - - - -

Mitosporic fungi

7 Acremonium roseolum 20.8 2.0 - 5.2 - - - -

8 Alternaria alternata 25.0 3.3 1.0 7.9 6.0 6.0 - 1.0

9 Aspergillus flavus 75.0 1.1 2.0 - 74.0 1.0 - 1.2

10 A. niger 54.1 1.8 - - 12.0 - 8.0 -

11 A. oryzae - - - 8.0 1.4 - -

12 A. parasiticus 70.8 9.1 - 1.0 66.0 7.0 - 8.2

13 A. sydowii - - - - 12.0 0.8 - -

14 A. terreus - - - - 14.0 - 4.0 -

15 A. viridinutans 4.1 1.2 - 1.4 - - - -

16 A. wentii 10.4 1.2 - - - - - -

17 Chaetomium globosum 20.8 1.6 - - - - - -

18 Cladosporium

cladosporoides

14.5 - 1.2 8.0 26.0 - 1.2 1.8

19 C. oxysporum - - - - 12.0 - 8.0 1.4

20 C. sphaerospermum 22.9 1.2 - 1.2 4.0 4.0 - -

21 C.tenuissimum 18.7 - 1.6 - - - - -

22 Curvularia lunata - - - - 18.0 1.6 - -


23 Dreschlera australiensis 37.5 - 0.5 - - - - -

24 Fusarium oxysporum 12.5 - 2.3 - 6.0 - - 2.8

25 F. graminearium 4.1 1.4 - 1.0 4.0 - 8.5 -

26 F. verticillioides - - - - 12.0 3.8 - -

27 F. sporotrichioides 32.1 - 8.0 - - - - -

28 Paecilomyces variotii - - - - 30.0 4.2 - 3.2

29 Penicillium citrinum 22.9 1.2 - - - - - -

30 P. expansum 27.0 1.4 2.7 - 26.0 2.2 - -

31 P. islandicum 10.4 - 1.1 - - - - -

32 P. puberulum - - - - 10.0 - 3.0 -

33 Phoma eupyrena 40.9 1.8 - 3.7 - - - -

34 Scopulariopsis brumptii 8.3 1.2 1.2 - - - - -

35 S. brevicaulis 6.2 - 2.7 - 6.0 1.6 - -

36 Trichoderma harzianum 4.1 - 2.2 1.0 - - - -

37 T. pseudokoningii 8.3 0.5 - - - - - -

Total number of fungal species 28 20 14 13 21 11 9 8

Not detected


Table 2. Mycotoxin contamination in dried morels. Mycotoxins Jammu Division Kashmir Division

Number of

samples

screened

Number of positive

samples

Range of toxin Number of

samples

screened

Number of

positive samples

Range of toxin

Aflatoxin B1 42 22(52.38%) 95.33±156.08 36 16 (44.4%) 125.44±78.14*

Aflatoxin B2 42 17(40.4%) 42.93±48.33 36 10(27.7%) 12.57±16.64

Aflatoxin G1 42 5(11.9%) 33.62±36.66 36 9(25.0%) 10.36±13.84

Aflatoxin G2 42 9(21.4%) 35.71±39.83 36 3(8.3%) 10.92±13.51

Citrinin 42 17(40.4%) 53.87±9.71 36 8(22.2%) 22.59±17.87

Ochratoxin A 42 8(19.0%) 49.03±55.81 36 13(36.1%) 26.08±4.52

Zearalenol 42 7(16.6%) 3.31±0.87 36 7(19.4%) 1.47±1.53

*Mean±SD

Agar plate method

Dried Morchella samples were surface sterilized with

1% sodium hypochlorite and rinsed three to five

times with sterilized distilled water. Ascocarps of

Morchella were placed equidistantly in Petri Plates

containing PDA medium and incubated for 7 days

(28±2ºC).

Serial Dilution Method

For determining the mycobiota of dried Morchella

species, 1g of the powdered sample was added to

9ml of sterlised distilled water in 100ml flask and

homogenized thoroughly on an horizontal shaker for

15 minutes. Ten fold serial dilutions were prepared

and 1ml portion of suitable dilution was poured in

Petri Plates by using a sterilised pipette. For the

recovery of maximum number of fungal propagules

from each sample, three different media-Czapek dox

agar medium (CDA), Dichloran glycerol agar

medium (DRBC) and Malt extract agar medium

(MEA) were used and for each medium five

replicates were maintained. The medium was poured

by making rotational movement of Petri Plates so as

to ensure uniform spreading of the samples. Petri

Plates thus prepared were incubated at 28±2ºC for 7

days. The developing fungal colonies by both the

above mentioned techniques were identified with the

help of relevant literature and recommended keys.

Percentage frequency of each fungal species was

calculated by using the formula given

Frequency (%) =

Number of samples from

which an organism was recovered x 100

Total number of samples tested

Average colony forming units per gram of sample

(cfu/g)

N= ƩC/ Vxnxd

N = Number of colony forming units per gram of

sample (cfu/g)

∑C = Sum of all colonies of the count

V = Volume of the dilution pipette in the count plates

in ml

n = Number of count plates that can be evaluated

d = Dilution factor

Quantitative estimation of mycotoxins by high

performance liquid chromatography (HPLC)

Aflatoxins (AFB1, AFB2, AFG1 and AFG2)

Quantitative estimation of aflatoxins was done by

modifying the method of Rohman and Triwahyudi

(2008). Isocratic elution was done with water:

acetonitrile: methanol (54:34:12v/v/v) at a flow rate

of 1.0ml/min. Injection volume of extract solution

was 30µl. A variable wavelength UV-VIS detector

set at 365nm was used. The retention times of AFB1,

AFB2, AFG1 and AFG2 was 6.484 min, 5.740 min,

5.453 min and 4.884 min respectively (Fig. 1a-b).

Ochratoxin A (OTA) and Citrinin (CIT)

For OTA and CIT analysis, the mobile phase

comprised acetonitrile and water, acidified with

phosphoric acid until pH 3.0 (50:50v/v) and flow rate

of 1.0ml/min with 15 min of run time. Injection

volume of extract solution was 40µl and analysis was

performed at room temperature (25°C-30°C). The

UV/VIS light detector wavelength was 220nm and

peaks of OTA and CIT were observed at retention

times of 8.431 min and 11.033 min respectively

(Hackbart et al., 2012) (Fig. 1c-d).

Zearalenol

The UV/VIS detector set at 236nm was used for the

analysis. An isocratic mobile phase of methanol:

water (75:24v/v) was used with a flow rate of 1.0

ml/min and an injection volume of 30µl. Retention

time of ZOL was 6.165 min (James et al., 1982) (Fig.

1e-f).

Statistical Analysis

All the experiments were replicated thrice and data

sets were statistically analyzed using IBM SPSS 20

software. The statistical level of significance was

fixed at P<0.05.

RESULT AND DISCUSSION

Mycoflora spectrum

The data presented in table 1 showed the prevalence

of population of altogether 37 fungal species

representing 18 genera. Among the recovered fungal

species, mitosporic fungi contributed highest count

of 31 fungal species with varying magnitude of

incidence whereas Zygomycota and Ascomycota

comprised each of three species of Mucor mucedo,

Rhizopus oryzae, Syncephalastrum racemosum and

Epicoccum nigrum, Eurotium amstelodami, E.

echinulatum respectively.

The report of the present study revealed that the

highest frequency was of Aspergillus flavus (75.0%)

followed by A. parasiticus (70.8%) and Aspergillus

niger (54.1%). Aspergilli grow on a large number of

substrates and their ability to thrive at high

temperatures (30-40ºC) and relatively low available


water (xerophillic nature) make them well suited to

colonize a number of dried commodities. Mycoflora

spectrum of the samples procured from the markets

of Jammu division revealed the presence of 28 fungal

species belonging to 15 genera. The results revealed

that Aspergillus flavus dominated over other fungal

species with the heavy mycobial load of 9.1x 103

cfu/g and lowest in case of Dreschlera australiensis

and Trichoderma pseudokoningii (0.5x 10

3 cfu/g).

Likewise, 36 samples procured from the markets of

Kashmir division revealed the lesser incidence of

mycoflora as compared to those from Jammu

Division. Data in table 1 show that 21 fungal species

representing 11 genera were recovered from dried

Morchella species. Mitosporic fungi inflicted the

maximum contamination (18) followed by

Zygomycota (3) while no sample was found

contaminated with any member of Ascomycota. The

highest colony forming unit was found in Fusarium

graminearum with 8.5x103. Our results are in

agreement with Jonathan and Esho (2010) who

evaluated the presence of fungi and aflatoxin in

stored dried oyster mushrooms from Nigeria.

The percent moisture content, one of the main abiotic

factor had a positive correlation with the extent of

contamination and species diversity in dried

Morchella samples. High moisture content of most of

the samples may be one of the factors for their

biodeterioration. In the present study, the average

moisture level of 12.6% was recorded from Jammu

division where as in Kashmir division, the moisture

level was found to be 10.2%.

The traditional methods of drying in various

environments and on exposed surfaces provide a

chance for opportunistic saprophytic moulds to

invade these dried Morchella species. Moreover, the

warm and tropical conditions prevailing in the region

are also expected to enhance the colonization and

proliferation of various storage fungi. In addition to

this, rich nutritional and biochemical composition of

Morchella species could be another important aspect

favouring the prolific growth of various microfungi

on it. Both, the fruiting bodies and mycelia of

Morchella species contain an uncommon aminoacid

cis-3-amino-L-proline that has been proved to be a

good source of fungal nutrition (Thind and

Randhawa, 1957; Hatanaka, 1969). M. esculenta has

been reported to produce high range of calcium

(Dursun et al., 2006) and therefore, the occurrence of

Chaetomium globosum on this edible mushroom

could be supported by the fact that Ca has been

found to stimulate perithecial production in

Chaetomium globosum (Basu, 1952). Aspergillus

flavus, one of the predominant species isolated in the

highest frequency from dried Morchella species,

possesses the capacity to produce numerous

extracellular hydrolases including serine, proteases,

pectinase, amylase and cellulases (Cleveland and

Cotty, 1991; Mellon et al., 2007) due to which it has

greater capacity for growth on complex protein

substrates (St. Leger et al., 1997).

Literature is sparse on the diversity of microfungi

contaminating macrofungi in India (Kotwal, 2010).

Moreover, the presence of a wide range of fungal

secondary metabolites contaminating mushrooms in

India is also not known. Hence, there is a need to

assess the mycotoxicological safety of Morchella

species presented for sale in local markets and

potential risk they may pose to consumers.

Mycotoxin analysis

During the present investigation, Aspergillus flavus,

the potent producer of aflatoxins, was detected as

prime fungal species from almost all the investigated

samples. However, the magnitude of different

aflatoxin contamination varied with the time and type

of the place of sampling.

Out of the 78 samples, 42 samples were found

contaminated with total aflatoxins. The range of total

aflatoxins in the present study was found to be

10.36±13.84 – 125.44±78.14µg/kg. Among the four

types of aflatoxins detected in present investigation

AFB1 appeared as a major contaminant with a mean

range of 95.33±156.08 – 125.44±78.14 µg/kg. From

Jammu division, 85.71% samples were found to be

positive for total aflatoxins. The mean level of

aflatoxin B1 and B2 ranged from 95.33±156.08µg/kg

and 42.93±48.33 µg/kg respectively and that of G1

and G2 ranged from 33.62±36.66 µg/kg and

35.71±39.83 µg/kg respectively. Ochratoxin was

found in 27.78% samples where as citrinin was found

in 20.37% with the level of contamination

49.03±55.81 and 53.87±9.71µg/kg respectively.

The level of Zearelenol was found to be low as

compared to other toxins (3.13±0.87µg/kg). On the

other hand, from Kashmir division, 59.18% of the

samples were found to be contaminated with

aflatoxins. The concentration range of aflatoxin B1

and B2 was found to be 125.44±78.14 and

12.57±16.64 µg/kg respectively where as G1 and G2

ranged from 10.36 ±13.84 and 10.92 ±13.51 µg/kg

respectively. Ochratoxin A was found 25.0%

samples with the mean value of 26.08±4.52 µg/kg.

In addition to this, 20.45% of samples were found

contaminated with citrinin and zearalenol having

values i.e. 22.59±17.87 and 1.47±1.53µg/kg (Table

2). These results are in accordance with the results of

Jonathan et al. (2011a) who recently detected low

level of aflatoxins B1 and B2 i.e. 0.005 and

0.002µg/kg in Lentinus squarrosulus Berk, from

Nigeria. Several maximum tolerable limits of

aflatoxins have been established by authorities like

FAO and EC but no legal limits have been stated for

edible mushrooms probably with the fact that

mushrooms have not been part of several reports

given on contamination of agricultural products by

moulds and mycotoxins but the results of the present

study showed that samples contained aflatoxins

beyond the maximum tolerable limit (MTL) of

4µg/kg set by European Union Commission for


ready to eat dry fruits for human consumption. (EC,

2010). At present there are no specific regulations in

the European Union concerning OTA, CIT, ZOL in

any kind of mushrooms. Review of literature reveals

that this is the first inclusive report on occurrence of

mycoflora and mycotoxins from Indian state of

Jammu and Kashmir where in several locally edible

dried Morchella species are consumed.

CONCLUSION

Fungal and mycotoxin contamination is currently

regarded as a public health concern and there is a

global trend to reduce the resulting health problems.

Moreover, considering the fact that dried Morchella

species consumption is high in Jammu and Kashmir

and elsewhere, a strict hygiene mycological

measured should be promulgated during harvest,

storage and drying to minimize mycobial

contamination.

ACKNOWLEDGEMENT

The authors are thankful to Special Assistance

Programme (SAP), Department of Botany,

University of Jammu, Jammu for providing

necessary laboratory facilities and UGC for

providing financial assistance in the form of Rajiv

Gandhi National Fellowship.

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EFFECT OF DIFFERENT CONCENTRATION OF IBA ON ROOTING OF PLUM

(PRUNUS DOMESTICA L.) CUTTINGS CV. SANTA ROSA UNDER VALLEY

CONDITION OF GARHWAL HIMALAYA

Kranti Kumar Lalhal, Tanuja, D.K. Rana and Dinesh Chandra Naithani*

Orchard Section, Horticultural Research Centre and Department of Horticulture, School of

Agriculture and Allied Science, H.N.B. Garhwal University (A Central University), Srinagar

(Garhwal)-246174, Uttarakhand, India


Abstract: A field investigation entitled "Effect of Different Concentration of IBA on Rooting of Plum (Prunus domestica

L.) Cuttings cv. Santa Rosa under Valley Condition of Garhwal Himalayas" was conducted during winter season 2015-16 at

orchard Section, Horticultural Research Centre and Department of Horticulture, H.N.B. Garhwal University (A Central

University), Srinagar Garhwal, Uttarakhand, India. The cuttings treated with 2500 ppm IBA showed the maximum number

of sprouted cuttings (6.67), minimum number of un-sprouted cuttings (1.67), minimum number of dead cuttings (1.66),

maximum number of sprout (10.53), length of sprout (20.66 cm), diameter of sprout (0.41 cm) number of leaves on new

shoots (81.90 cm), maximum percentage of rooting (73.33 %), number of primary roots (42.80), number of secondary roots

(91.40), length of longest root (28.12 cm), diameter of thickest root (0.21 cm), fresh weight of roots (1.88 gm) and dry

weight of roots (1.02 gm). On the basis of result achieved in the present study, it can be concluded that among the different

concentration of IBA, IBA @ 2500 ppm may be suggested for best shoot and root growth of plum cv. Santa Rosa under

valley condition of Gharwal Himalaya.

Keywords: Cutting, Diameter, Rooting, Percentage, Investigation, Prunus domestica

INTRODUCTION

lum (Prunus domestica L.) is an important

temperate fruit. It belongs to genus Prunus of

subfamily Prunoideae, family Rosaceae and order

Rosales and ranks next to peach in economic

importance. It is a delicious fruit prized both for its

exquisite fresh fruit flavour, aroma and in fruit

preservation industry. The fruits are fairly attractive

but usually are soft, clingstone, round and heart

shaped (Teskey and Shoemaker, 1978).

In India, plum is predominantly grown in Jammu and

Kashmir, Himachal Pradesh, and hills of Uttarakhand

and also to some extent in Nilgiri hills. In Jammu and

Kashmir state plum occupies an area of 4543

hectares with an annual production of 8218 metric

tonnes (NHB, 2015). Santa Rosa is a leading

commercial cultivar of Plum (Prunus domestica L.).

It is the result of cross between Prunus salicina as a

female parent with Prunus simoni and Prunus

americana as a pollen parent (Salaria, 2009). It is

known for its fair quality, characteristic flavour and

is widely grown in Kashmir valley. It is used as a

pollinizer for other cultivars but has got tendency

towards overbearing which leads to limb breakage

that invariably makes way to silver leaf disease. Due

to its perishable nature, it cannot be stored for longer

duration. Plum fruit contain copious amounts of

natural phenolic phytochemicals, such as flavonoids,

phenolic acids, anthocyanins, and other phenolics,

which may function as effective natural antioxidants

in our daily diet (Weinert et al., 1990; Gil et al.,

2002; Cevallos-Casals et al., 2006; Vizzotto et al.,

2007; Kristl et al., 2011). It is an established fact that

plum fruit have several times higher total antioxidant

capacity than apples (Wang et al., 1996). Plum can

be propagated through sexual method (by seeds) as

well as vegetative methods (hard wood, semi hard

wood cuttings) seeds of plum is hardy and cannot be

germinate easily and it required specific temperature

(chilling temp.) for germination so it is a big problem

in plum .To sort out this problem, vegetative method

specially cuttings may be an alternative method for

plant multiplication in plum. Asexual means of

propagation preserve the original characters of a

plant species . In plum propagation by cutting as

such give lower percentage of success. Plant growth

regulators have been well tested in promoting the

roots in cutting. To achive high percentage of success

in asexual propagation of plum through cutting, there

is a need to standardized the concentration of IBA

under valley conditions of Garhwal regions. In

vegetative propagation of fruits, bioregulators play a

very important role. Since the discovery of indole -3

acetic acid (IAA) as an active plant growth regulator,

indole -3 butyric acid (IBA) and NAA have been

freely used to boost vegetative propagation of plants

specially rooting of cutting . Among all the

bioregulators IBA is more effective, cause no

damage to cutting.

MATERIAL AND METHOD

Detail of experiments

The experiment was conducted under the open

condition at Horticultural Research Centre, Chauras

Campus, HNB Garhwal University (A Central

P

RESEARCH ARTICLE

780 KRANTI KUMAR LALHAL, TANUJA, D.K. RANA AND DINESH CHANDRA NAITHANI

University) Srinagar Garhwal, Uttarakahnd, India. The experimental detail is given below.

Concentration Notation

IBA @500 ppm T1

IBA @1000 ppm T2

IBA @1500 ppm T3

IBA @2000 ppm T4

IBA @2500 ppm T5

Control T0

Cultivar Santa rosa

Experimental design Randomized Complete Block Design

Number of replications per treatment 3

Number of cuttings per treatments 10

Number of treatment 6

Total number of cuttings 3x10x6 = 180

Preparation of rooting media

For preparing the rooting media, the sandy soil and

farm yard manure (FYM) in 1:1 were mixed

thoroughly. The mixture was filled in polythene bags

(1 kg capacity) tightly leaving one inch space at the

top.

Preparation of IBA solution

For the preparation of IBA solution of 500 ppm,

1000 ppm, 1500 ppm , 2000 ppm and 2500 ppm,

the required the amount of IBA (125 mg , 250 mg,

375 mg, 500 mg 625 mg. respectively) was weighted.

These amounts of IBA, then dissolved in small

amount of alcohol containing few drops of

ammonium hydroxide and finally diluted with

distilled water. The final volume of each solution

was maintained 250 ml.

Treatment of cuttings

Quick dip method was adopted for treatment of the

cuttings with IBA solutions. The basal portions of

cuttings up to 2.5 to 3.0 cm were soaked with

solution for 2 minutes.

Planting of cutting

The treated cuttings with different IBA concentration

were planted carefully in the polythene bags with the

help of dibbler without any injury to the buds. One

third basal portion of the cuttings was inserted into

rooting media. Each polythene bag was planted with

two cuttings were maintained and thus 5 bags with

10 cuttings were maintained under each treatment of

each replication. The soil around the cuttings was

tightly pressed and watered immediately.

Observations recorded

Shoot Observations

1. Number of sprouted cuttings

2. Number of un-sprouted cuttings

3. Number of dead cuttings

4. Number of sprouts per cuttings

5. Length of sprouts(cm)

6. Diameter of sprouts (cm)

7. Number of leaves on new shoots

Root observations

1. Percentage of rooted cuttings

2. Number of primary roots

3. Number of secondary roots

4. Length of longest roots (cm)

5. Diameter of thickest root per cutting (cm)

6. Fresh weight of roots per cutting (g)

7. Dry weight of roots per cutting (g)

Statistical analysis Data recorded during the course of investigations

were subjected to statistical analysis under

randomized block design as described by Snedecor

and Cochram (1968).Valid conciliations were drawn

after the determination, of significance of difference

between the treatments, at 5 per cent level of

probability. Critical difference was calculated in

order to compare the treatment means.


In this chapter an attempt has been made to discuss

the experimental findings obtained during the course

of present investigation with possible explanations

and evidences which were necessary in order to find

out the cause and effect relationship among different

treatments with respect to various character studies

and to sort out information of practical value.

Commercial propagators have developed

technologies that successfully manipulate

environmental conditions to maximize rooting and

survival percentage of cutting. Among various

factors, climatic factors, adequate plant bio regulator

application and right planting time for a particular

agro climatic condition are of paramount important

for rooting and sprouting of semi hardwood cutting

of plum.

Shoot Observations

A perusal of Table 1 indicated that the number of

sprouted cutting was increased significant in respect

of IBA concentrations. The maximum number of

sprouted cuttings (6.67) were recorded under T5

(2500 ppm) treatment. The minimum number of

sprouted cuttings (4.33) were recorded under T0

(control) treatment. Panwar et al., (2001) and Singh

et al., (2014) reported the same result in

pomegranate. Tahir et al., (1998) also observed best


performance of IBA treatments in hard wood cuttings

of guava with respect the survival percentage.

Data of present study revealed that in respect of un-

sprouted cuttings treatment T1 (500 ppm), T2

(1000ppm) and T5 (2500 ppm) showed the lowest

number of (1.67) un-sprouted cuttings. The

maximum number of un-sprouted cutting (3.00) were

recorded under T0 (control) treatment. Finding of

present investigation are more or less match with the

result of Panwar et al., (2001) and Singh et al.,

(2014) who observed the best survival of

pomegranate cuttings under IBA treatment. Tahir et

al., (1998) in guava also observed better survival of

hardwood cuttings treated with IBA.

It is evident from Table 1 that except the treatment T5

(2500 ppm), the number of dead cuttings were not

found significant in respect of IBA concentrations.

T5 (2500 ppm) treatment with an average of (1.66)

dead cuttings showed the best survival percentage of

cuttings. While, the maximum number of dead

cuttings (3.34) were recorded under T2 (1000 ppm)

treatment. The present findings are similar to the

findings of Tahir et al., (1998) in guava cuttings and

Panwar et al., (2001) in pomegranate. Patil et al.,

(2000) also recorded 80% survival of hard wood

cuttings of grape treated with IBA.

A perusal of Table 1 shows that the numbers of

sprouts per cutting were found significant in respect

of IBA concentrations. Except T1 (500 ppm)

treatment almost all the treatments increased number

of sprouts per cutting against the T0 (control)

treatment. The maximum number of sprouts per

cutting (10.53) were recorded under the treatment T5

(2500 ppm), while, the minimum number of sprouts

per cutting (5.76) were recorded under the treatment

T1 (500 ppm). Similar results have been obtained by

Singh et al., (2014) in hard wood cutting of

pomegranate.

The data presented in Table1 indicated that the

average length of sprouts per cutting was found to

increase significantly. The maximum average length

of sprouts per cutting (20.66cm) was recorded under

T5 (2500 ppm) treatment, while the minimum

average length of sprout per cutting (15.83 cm) was

recorded under the T0 (control) treatment. These

finding are very much on the line with the results of

Panwar et al., (2001) and Singh et al., (2014).

Treatment T5 (2500 ppm) obtained first rank in

diameter of sprouts/cutting with an average (0.41).

The minimum average diameter of sprout per cutting

(0.20cm) was recorded under the treatment of T0

(control) treatment during investigations. Finding of

Singh et al., (2014) and Panwar et al., (2001) in

pomegranate, Patil et al., (2000) in grape and Tahir

et al., (1998) in guava justified the result of present

investigation.

All the IBA concentrations affect the number of

leaves per cutting significantly (Table 1). It is evident

from the data the maximum number of leaves per

cutting (81.90) was recorded under the treatment T5

(2500 ppm). While the minimum average number of

leaves per cutting (22.23) was recorded under the

treatment T0 (control), during the present

investigations. Similar results of Singh et al., (2014)

in pomegranate and Jan et al., (2014) in olive

cuttings.

Root Observations

A perusal of Table 2 indicates that the percentage of

rooting was found to increase significantly in respect

of IBA concentrations. Among all the treatments, T5

(2500 ppm) treatment showed the highest percentage

of rooted cutting (73.33). The minimum percentage

of rooted cutting (23.33) was recorded under the T0

(control) treatment. Singh et al., (2014), Panwar et

al., (2001) and Bose and Mandal (1972) reported

that the hard wood cuttings of pomegranate rooted

well when treated with the IBA. Findings of these

scientists very much match with the result of present

investigation.

It is evident from the data the number of primary

roots were found significant in respect of IBA

concentrations. The highest number of primary roots

per cutting (42.80) was recorded under the treatment

T5 (2500 ppm). While, the minimum number of

primary roots per cutting (28.76) were recorded

under the treatment T1 (500 ppm). The present

findings are similar to the findings of Singh et al.,

(2014) reported maximum number of primary roots

in the cuttings of pomegranate treated with IBA.

During the present investigation it was observed that

all the treatment of IBA were found to increase

number of secondary roots in the cuttings

significantly (Table 2).The maximum number of

secondary roots per cutting (91.40) were recorded

under T5 (2500 ppm) treatment, while the minimum

number of secondary roots per cutting (74.18) were

recorded under the treatment T2 (1000 ppm). The

present findings are similar to the findings of Singh

et al., (2014) in pomegranate.

The data on average length of roots per cutting have

been presented in Table 2, revealed that length of the

longest root was not found significant in respect of

IBA concentration. The T5 (2500 ppm) treatment was

found to give longest root with an average length of

28.12 cm. The minimum length of roots per cutting

22.16cm was recorded under the treatment T0

(control) during the present investigations. This is

similar to the findings by Jan et al., (2014) which

was observed while working in olive cuttings.

The data presented in Table 2 indicated that all the

treatments of IBA were found significant to increase

the diameter of roots. With an average diameter of

root (0.21cm) to obtained highest rank the treatment

T5 (2500 ppm), while, the minimum value (0.17) was

obtained under the treatment T0 (control). Rahman et

al., (2002) in olive observed highest root length

thickness in the cuttings treated with IBA. The result

of present study in respect to length and thickness of

roots are more or less match with finding of above


scientist with a little variation due to the in

concentration and species.

Among all the treatments fresh weight of root per

cutting was recorded maximum (1.88g) under the

treatment T5 (2500 ppm). The minimum fresh weight

of roots per cutting (0.71g) was recorded under the

treatment T2 (1000 ppm). Similar and Satisfactory

result were also found by Ahmed et al., (2010) in

mulberry cuttings.

The data (Table 2) obtained during the course of

present study showed that all the treatments of IBA

gave significantly higher dry weight against the T0

(control) treatment. Average dry weight of roots per

cutting (1.02g) was recorded maximum under

treatment T5 (2500 ppm. The minimum average dry

weight of roots per cutting (0.36g) was observed

under the treatment T2 (1000 ppm) and T0 (control).

Which is also resemble with the findings of Ahmed

et al., (2010) in mulberry cuttings.

CONCLUSION

Among various concentrations of IBA, treatment T5

(2500 ppm) showed the best performance in terms of

number of sprouted cuttings, minimum number of

dead cuttings, un-sprouted cuttings, number of

sprouts/cutting, length and diameter of sprouts,

percentage of rooted cuttings, number of primary

roots, secondary roots, fresh weight and dry weight

of roots under Valley condition of Garhwal

Himalayas.

Table 1. Effect of Different Concentration of IBA on the Shoot Parameters of Plum (Prunas domestica L.)

Cuttings cv. Santa Rosa

Table 2. Effect of Different Concentration of IBA on the Root Parameters of Plum (Prunas domestica L.)

Cuttings cv. Santa Rosa Treatments % of

rooted

cuttings

Number of

Primary

roots/cutting

Number of

Secondary

roots/cutting

Length of

longest

root/cutting

(cm.)

Diameter of

thickest

root/cutting

(cm.)

Fresh weight

of

root/cutting

(g)

Dry weight of

root/cutting

(g)

T1 36.66 28.76 76.21 24.54 0.17 0.88 0.46

T2 40.00 37.02 74.18 24.67 0.18 0.71 0.36

T3 50.00 37.63 76.35 25.47 0.19 1.22 0.68

T4 56.66 38.15 76.24 26.28 0.20 086 0.55

T5 73.33 42.80 91.40 28.12 0.21 1.88 1.02

T0 23.33 29.62 79.00 22.16 0.17 0.74 0.36

S.Em± 3.80 1.60 3.96 1.48 0.73 0.31 0.16

C.D.at 5% 11.97 5.05 12.48 4.68 0.23 0.99 0.50

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T.M. (2010). Root growth of Morus alba as affected

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Treatments Number of

sprouted

cuttings

Number of

un-

sprouted

cutting

Number of

dead

cutting

Number of

sprouts/cutting

Length of

sprouts

/cutting (cm)

Diameter of

sprouts/cutt

ing (cm)

Average

number of

leaves on new

sprout

T1 5.34 1.67 2.99 5.76 15.95 0.21 33.06

T2 5.00 1.67 3.33 6.40 16.84 0.22 36.26

T3 5.66 2.34 2.00 6.83 17.66 0.25 59.33

T4 6.00 2.00 2.00 9.03 19.70 0.27 66.10

T5 6.67 1.67 1.66 10.53 20.66 0.41 81.90

T0 4.33 3.00 2.67 6.30 15.83 0.20 22.23

S.Em± 0.38 0.44 0.53 0.73 0.78 0.13 2.20

C.D.at 5% 1.19 1.39 1.68 2.30 2.47 0.41 6.95


Patil, V.N., Chauhan, P.S., Panchabhai, D.M.,

Shivankar, R.S. and Tannirawar, A.V. (2000).

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cuttings of some commercial grape varieties. J. Soils

and Crops., 10: 295-297.

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cultivar coratina using different concentration of

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on the rooting of pomegranate cv. Ganesh hardwood

cuttings. Plant Archives., 14(2): 1111-1114.

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(2014). Effect of various concentrations of IBA and

NAA on the rooting of stem cuttings of mulberry.

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Statistical Method. Oxford and IBH Publishing

company, New Delhi.pp.593.

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Effect of growth regulators on rooting performance

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D.H. (2007). Large variation found in the

phytochemical and antioxidant activity of peach and

plum germplasm. Journal of American Society for

Horticultural Science, 132: 334-340.

Wang, H., Cao, G .and Prior, R.L. (1996). Total

antioxidant capacity of fruits. Journal of Agriculture

and Food Chemistry, 44: 701–705.

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Diffusion of anthocyanins during processing and

storage of canned plums. Food Science and

Technology 23: 396-399.



STUDIES ON COMBINING ABILITY IN FORAGE SORGHUM FOR YIELD AND

QUALITY PARAMETERS

Akash Singh and S.K. Singh*

Department of Genetics and Plant Breeding

Sardar Vallabhbhai Patel University of Agriculture & Technology, Meerut - 250 110



Abstract: Estimates of variance among line with respect to gca was found highly significant for all the attributes and

variance among testers with respect to gca was recorded highly significant for all the traits except stem girth. The variances

among crosses due to interaction between lines x testers genotypes with respect to sca were expressed highly significant for

all the characters except stem girth and number of leaves per plant. Average degree of dominance (δ2s/δ

2g)

0.5 exhibited

partial dominance for plant height, leaf length, leaf breadth, internode length, leaf area, leaf stem ratio and green fodder yield

and over dominance was observed for days to 50% flowering, number of leaves per plant, stem girth, total soluble solids and

protein content. GCA effects and per se performance among the parents HC260, Pusa Chari23, SPV815, Pusa Chari6,

HC260 and HC171 were found to be as good general combiner and the F1’s hybrids i.e. HC260 x HC308, HC260 x G48,

SSG-59-3 x G48, HJ513 x HC308, HJ513 x HC171, ICSV700 x HC308, UP Chari2 x G48, UP Chari1 x Pant Chari6, Pusa

Chari9 x HC171 and Rajasthan Chari1 x G48 were identified with significant and positive SCA for fodder yield which may

be utilized for obtaining transgressive segment in the next generation and also could be exploited for development of

hybrids.

Keywords: Sorghum bicolor, Gene action, Combining ability, Quality parameters

INTRODUCTION

orghum is known as poor man’s crop and has the

potentialities of being used solely either as food

or feed or fodder. The major sorghum growing

countries in the world are United States, India,

Nigeria and Mexico. More than half of the world’s

sorghum is grown in semi-arid tropics (India and

Africa), where it is staple food as chapatti or roti in

India, injera in Ethiopia, tortillas in Latin America,

etc., for millions of population. It is grown for four

main purposes i.e. for grain, forage production,

sugar/alcohol production and for fiber (including

broomcorn, where stiff stems and panicles are used

for brooms). The lower milk production in India is

mainly attributed to production and feeding of poor

quality forage and that too in inadequate amount

(46.6 percent of requirement). To increase the milk

production in the country, emphasis has to be given

on bridging the gap between supply and demand of

fodder. The area under forage crops in India is also

very meager i.e. 4.4 percent of the total cultivated

land. It is, therefore, essential to maximize forage

production per unit area. The milk production mainly

depends upon the breed of animals and health

management apart from feeding practices which

costs 70-75% of the total cost of milk production.

Livestock play a crucial role in the development and

progress of mankind. They contribute about 30

percent of the value of output in agriculture. India is

the largest livestock holding country in the world. Its

present livestock inventories exceed 453 million with

growth rate of more than 1.5 percent per annum,

maximum (5.43 crore) of which population is found

in Rajasthan. White revaluation resulted in the fast

development of dairy industry but on the other hand

the production of forage did not raise, as a

consequence there is a chronic shortage of green and

dry fodder. If the requirement of green and dry

fodder yield is estimated as per global standards for

optimum productivity, the presently available

quantity of green and dry fodder falls short by 33.5

and 23.0 percent, respectively. This has lowered the

production capacity and fertility of Indian livestock

(Kumar and Singh, 2012). The cultivated area under

different forage crops is 4.4 per cent of the total area

under cultivation, of which about 2.3 m ha is under

forage sorghum. Improvement of sorghum is much

emphasized owing to its importance as food and

fodder crop. It is necessary to improve the fodder

sorghum yield with nutritionally superior qualities in

order to obtain better animal performance. The

fodder yield is the primary trait targeted for

improvement of fodder sorghum productivity

through exploitation of heterosis. Hence it is

necessary to understand the genetic nature of the

parents. Combining ability analysis helps in

identifying the parents, which could be used for

hybridization programme to produce superior

hybrids. In the present study, an attempt has been

made to estimate the general and specific combining

ability effects of the parents and crosses in forage

sorghum.

MATERIAL AND METHOD

In the present study, F1’s material was obtained from

crossing fifteen females (HC260, Pusa Chari23,

SPV815, Pusa Chari6, CSV15, Pant Chari3, SSG-59-

3, HJ513, UP Chari4, ICSV700, UP Chari3, UP

S

RESEARCH ARTICLE


786 AKASH SINGH AND S.K. SINGH

Chari2, UP Chari1, Pusa Chari9 and Rajasthan Chari

1) and four males (HC308, Pant Chari6, G48 and

HC171) as per line x tester mating design in Kharif

season 2016. The experiment was laid out with 60

F1’s progenies and their respective parents in

randomized block design with three replications at

Crop Research Center, Sardar Vallabhbhai Patel

University of Agriculture and Technology, Meerut

during Kharif 2016. Each F1’s and parents were sown

in two rows of 5.0 m length with inter row spacing of

30 cm and intra row spacing of 10 cm. All the

recommended practices were followed to raise good

crop of kharif sorghum. The observations on twelve

yield and yield attributing characters viz., days to

50% flowering, plant height, number of leaves per

plant, stem girth, leaf length, leaf breadth, internode

length, total soluable solids, leaf area, leaf stem ratio,

protein content and green fodder yield per plant were

recorded on five randomly selected plants per

replication. Mean of five plants for each entry for

each character was calculated and used for statistical

analysis. Data was analyzed by the methods outlined

by Panse and Sukhatme (1967) using mean values of

five random plants in each replication from all

treatments to find out the significance of treatment

effect. The variation among the hybrids was further

partitioned into genetic components attributable to

general combining ability (GCA) and specific

combining ability (SCA) following the method

suggested by Kempthorne (1957). The total soluble

sugars (%) i.e., brix was directly scored with the help

of hand refractometer. Crude protein content of

selected genotypes was estimated by using

Microkjeldhal method. Total nitrogen was estimated

by using Kel-plus (digestion and distillation unit).

Crude protein value was obtained by multiplying the

total nitrogen by the conversion factor.

Table 1. Analysis of variance for twelve characters in parents and F1 generation of forage sorghum Source of

variations

d.

f.

Days to

50%

floweri

ng

Plant

height

(cm)

No. of

leaves

per

plant

Stem

girth

(cm)

Leaf

length

(cm)

Leaf

breadt

h

(cm)

Interno

de

length

(cm)

Total

solubl

e

solids

(%)

Leaf

area

(cm2)

Leaf

stem

ratio

(w/w)

Protei

n

conte

nt

(%)

Green

fodder

yield per

plant (g)

Replicatio

n

2 0.67 2.71 0.02 0.03 0.35 0.04 0.13 0.87 60.62 0.06 0.15 58.07

Treatmen

t

78 51.12** 6162.37*

*

7.66** 0.16*

*

80.45

**

3.33** 30.80** 5.20*

*

8863.80** 0.28*

*

2.08*

*

8789.45**

Parents 18 79.32** 4059.94*

*

15.01** 0.12*

*

42.66

*

1.24** 29.35** 6.25*

*

3363.22** 0.36*

*

1.86*

*

2819.88**

Line 14 82.58** 4740.64*

*

13.77** 0.11*

*

50.09

**

1.04** 32.78** 6.88*

*

3256.03** 0.22*

*

2.19*

*

2684.71**

Tester 3 39.11** 1903.35*

*

25.38** 0.20*

*

19.03

**

1.25** 22.28** 2.40*

*

1911.45** 0.31*

*

0.99*

*

4384.02**

Parents

(L vs T)

1 154.32*

*

999.95** 1.27 0.01 9.55*

*

4.00** 2.64** 9.04*

*

9219.14** 0.42*

*

1.48*

*

199.85**

Parents

vs crosses

1 0.70 22023.53

**

1.85** 0.14* 6.58*

*

7.57**

*

88.83** 3.05*

*

23275.96*

*

0.32*

*

4.89*

*

42572.09*

*

Crosses 59 43.37** 6534.96*

*

5.52** 0.17*

*

93.23

**

3.89** 30.26** 4.91*

*

10305.60*

*

0.29*

*

2.10*

*

10038.09*

*

Error 15

6

4.94 22.88 0.36 0.04 1.44 0.14 0.43 0.31 411.45 0.02 0.29 23.62

*Significant at 5% probability level and ** Significant at 1% probability level

Table 2. Analysis of variance for combining ability12 characters in forage sorghum Source of

variation

d.f. Days

to 50%

floweri

ng

Plant

height

(cm)

No. of

leaves

per

plant

Stem

girth

(cm)

Leaf

length

(cm)

leaf

breadt

h

(cm)

Interno

de

length

(cm)

Total

solubl

e

solids

(%)

Leaf

area

(cm2)

Leaf

stem

ratio

(w/w

)

Protei

n

conten

t (%)

Green

fodder

yield per

plant (g)

Line (GCA) 14 100.52*

*

25929.34

**

3.02*

*

0.40*

*

158.03*

*

10.61*

*

90.10** 6.92*

*

24478.47

**

0.43*

*

3.58** 30895.97

**

Tester

(GCA)

3 135.62*

*

938.53** 28.60

**

0.05 158.84*

*

3.52** 53.19** 4.89*

*

16388.96

*

0.32*

*

1.95** 9089.19*

*

Line x

Tester

(SCA)

42 17.72** 469.90** 4.71*

*

0.10 66.95** 1.68** 8.68** 4.24*

*

5146.79*

*

0.02 1.62** 3153.24*

*

Error 11

8

5.60 24.78 0.42 0.02 1.48 0.17 0.36 0.37 471.13 0.06 0.37 24.34

Estimates of genetic components, its ratio (σ2g/σ

2s) average degree of dominance (σ

2s/σ

2g)

0.5


Source of

variation

Days

to

50%

floweri

ng

Plant

height

(cm)

No. of

leaves

per

plant

Stem

girth

(cm)

Leaf

length

(cm)

leaf

bread

th

(cm)

Interno

de

length

(cm)

Total

solub

le

solid

s (%)

Leaf

area

(cm2)

Leaf

stem

ratio

(w/w

)

Prote

in

conte

nt

(%)

Green

fodder

yield per

plant (g)

σ2 A 12.94 929.81 3.99 0.06 97.59 4.83 4.02 2.08 903.00

1.99 0.34 1943.14

σ2 D 26.76 360.47 4.77 0.08 37.27 1.03 3.01 2.82 546.7

5

1.09 0.58 1505.84

σ2 g/ σ2 s 0.52 4.95 0.79 0.93 3.74 3.77 1.56 0.72 4.09 0.06 0.68 1.69

(σ2s/ σ2g)0.5 2.95 0.29 1.88 1.29 0.57 0.66 0.80 6.75 0.39 0.07 2.71 0.97

δ2g (males) 5.11 3.02 0.57 1.95 2.02 1.03 0.08 0.19 1.05 5.02 6.03 3.01

δ2g (females) 4.96 2.99 0.47 1.81 2.31 1.56 0.07 0.18 0.99 4.32 5.00 2.89

δ2g (pooled) 0.62 0.57 0.69 0.49 0.35 0.22 0.32 0.59 0.41 0.72 0.50 0.82

δ2s (females x

males)

9.21 7.42 1.56 2.31 12.14 7.77 6.52 3.33 6.53 8.02 2.82 18.54

* Significant at 5 % probability level, and ** Significant at 1 % probability level.

Table 3. Estimates of general combining ability effect of line and testers with respect to twelve characters of

forage sorghum Line/Tester Days to

50% flowering

Plant height (cm) No. of

leaves per plant

Stem girth

(cm)

Leaf

length (cm)

Leaf

breadth (cm)

Line Mean GCA Mean GCA Mean GCA Mean GCA Mean GCA Mean GCA

HC 260 75.12 2.25** 319.56 94.06** 18.00 0.59** 2.11 0.19** 69.86 2.43** 7.17 2.38**

Pusa Chari 23 80.33 2.84** 302.53 81.89** 16.66 0.56** 1.99 0.04 71.33 1.55* 6.52 0.04

SPV 815 78.31 4.59** 213.00 67.08** 12.46 -0.12 1.8 0.24** 71.60 3.30** 5.91 2.10**

Pusa Chari 6 87.00 2.46** 298.60 42.42** 14.40 0.39** 1.62 0.39** 76.16 4.42** 6.92 2.05**

CSV 15 84.02 0.06 198.63 -17.86** 10.90 -0.21 2.06 -0.29** 70.96 1.44** 6.24 -0.82**

Pant Chari 3 72.66 -6.52** 295.66 3.63 15.30 -0.48** 1.65 -0.47** 63.70 8.91** 7.35 -1.70**

SSG -59-3 85.33 2.99** 289.20 -25.77** 14.77 -0.27 1.64 -0.06 75.86 -6.72** 7.25 0.77**

HJ 513 90.66 4.29** 285.80 40.66** 14.06 0.80** 2.00 -0.07 71.93 3.31 8.12 0.92**

UP Chari 4 82.64 0.05 297.50 34.42** 13.30 0.16 2.17 0.18** 75.76 1.79** 6.64 0.29**

ICSV 700 83.65 1.04 190.46 -20.89** 9.96 -0.48** 1.75 -0.08 65.86 -1.06** 6.33 0.74**

UP Chari 3 79.66 -1.64* 266.30 23.53** 11.80 -1.01** 1.62 0.01 73.66 2.12** 7.05 -0.17

UP Chari 2 88.33 0.98 289.13 33.65** 15.73 0.42* 1.70 0.10 71.43 -2.22** 7.29 0.03

UP Chari 1 85.00 -0.29 287.79 23.89** 15.00 0.24 1.78 0.03 72.00 -0.32 7.33 0.32**

Pusa Chari 9 86.66 -0.43 292.70 31.17** 14.20 0.55** 1.77 1.13** 64.66 3.91** 7.44 1.18**

Rajasthan

Chari 1

77.00 1.43* 268.30 29.99** 12.06 0.34* 1.72 0.23** 76.73 0.79 6.30 0.93**

Tester

HC 308 85.33 -0.45 306.40 4.65** 17.83 0.63** 2.07 0.40** 74.03 2.49** 7.49 2.02**

Pant Chari 6 92.00 2.55** 297.03 3.16** 13.50 0.43** 1.52 -0.04 74.30 0.29 7.93 0.30**

G 48 85.33 -0.79* 249.03 -3.50** 10.83 -1.15** 2.06 0.01 72.60 -1.87** 6.70 -0.38**

HC 171 84.00 1.71** 280.66 4.32** 14.80 1.08** 1.80 0.34** 68.83 0.91 8.17 2.05**

Line/Tester Internode

length (cm)

Total soluble

solids (%)

Leaf

area (cm2)

Leaf

stem ratio

(w/w)

Protein

content (%)

Green fodder

yield per plant

(g)

Line Mean GCA Mean GCA Mea

n

GCA Me

an

GCA Mean GCA Mean GCA

HC 260 24.27 0.72** 10.93 0.21 354.1

1

17.27** 0.43 0.08** 6.54 0.77** 406.65 70.71*

*

Pusa Chari 23

20.66 6.74** 7.43 0.92** 330.75

44.94** 0.39 0.05** 6.91 0.73** 390.40 61.10**

SPV 815 21.54 5.20** 6.66 0.74** 301.4

3

31.65** 0.34 -0.06** 5.81 0.01 350.76 43.88*

*

Pusa Chari 6 17.21 9.03** 7.83 1.01** 374.23

17.65** 0.37 -0.06** 7.06 0.56** 354.20 80.07**

CSV 15 23.72 2.98** 7.00 0.43** 314.1

6

-

34.72**

0.59 -0.01 6.05 -0.01 300.70 14.89*

*

Pant Chari 3 18.06 0.48* 7.43 -1.33** 332.3 - 0.42 -0.06** 7.44 0.04 369.66 -


0 53.60** 19.58*

*

SSG -59-3 25.77 1.49** 9.06 -0.95** 390.66

5.52 0.42 0.05** 6.69 0.16 375.53 22.73**

HJ 513 17.58 -0.01 10.56 0.76** 415.0

9

62.13** 0.34 -0.06** 8.76 -0.07 363.03 -

19.45**

UP Chari 4 18.69 -

1.42**

9.93 -0.39* 357.3

8

23.84** 0.41 0.03** 7.22 -

1.54**

342.03 -

19.40*

*

ICSV 700 24.46 1.84** 6.96 0.03 296.2

8

31.74** 0.49 0.02 7.37 0.26 289.20 29.60*

*

UP Chari 3 18.67 0.54** 10.73 1.35** 368.7

3

1.33 0.34 0.05** 6.53 -0.07 336.30 -

20.76**

UP Chari 2 16.27 0.25 6.86 -0.33* 370.1

1

-7.79 0.46 -0.02* 7.35 0.40* 351.40 -

13.42**

UP Chari 1 21.74 -0.40* 8.53 1.17** 374.7

6

14.44* 0.56 0.02** 8.77 -0.15 356.80 -3.05

Pusa Chari 9 18.60 1.56** 8.06 1.36** 359.57

43.70** 0.43 0.10** 6.33 0.16 349.46 56.16**

Rajasthan

Chari 1

14.95 0.51 7.36 0.55** 343.2

4

51.62** 0.39 0.01 6.54 0.39* 340.93 -

12.12*

*

Tester

HC 308 16.13 1.12** 7.76 0.43** 393.9

0

12.96** 0.50 0.35** 7.41 0.29** 388.33 24.17*

*

Pant Chari 6 18.96 -0.68**

8.46 -0.37** 394.29

15.42** 0.55 0.01 7.83 -0.15**

318.10 -8.93**

G 48 20.96 0.58** 6.63 0.09 345.7

2

-

26.10**

0.3 -0.05** 7.52 -0.15 316.80 15.11*

*

HC 171 22.43 1.22** 6.66 -0.06 399.61

22.28** 0.47 0.44** 6.92 0.31** 378.20 19.86**

Table 4. Estimates of specific combining ability effect with respect 12 characters in forage sorghum Crosses Days to

50% flowering

Plant height (cm) No. of

leaves per plant

Stem girth

(cm)

Leaf

length (cm)

Leaf

breadth (cm)

Mean SCA Mean SCA Mean SCA Mea

n

SCA Mean SCA Mean SCA

HC 260 X HC 308 91.16 5.76** 163.32 20.11** 11.80 2.99** 1.80 -0.17 75.89 2.57** 6.38 1.09**

HC 260 X Pant Chari 6 85.16 -

3.24**

148.61 -

13.12**

13.45 -0.14 1.93 0.01 81.38 10.27** 7.14 0.58**

HC 260 X G 48 85.93 2.87** 152.21 16.85** 13.63 1.62** 1.93 0.64** 65.48 3.48** 5.72 -0.16

HC 260 X HC 171 81.15 -

3.39**

170.10 15.86** 13.74 0.50 2.20 0.20 60.56 -9.36** 5.80 -0.51

Pusa Chari 23 X HC

308

88.86 2.86** 170.23 -5.14 14.81 -0.13 2.04 0.22** 69.87 -2.32** 6.80 0.10

Pusa Chari 23 X Pant

Chari 6

89.40 0.41 171.06 -2.88 16.00 1.26** 1.66 -0.12 70.08 0.10 6.22 -

0.75**

Pusa Chari 23 X G 48 85.15 -0.50 171.16 3.93 11.98 -1.18** 1.88 0.05 69.62 1.79 6.39 0.09

Pusa Chari 23 X HC 171 82.38 -

2.76**

170.49 4.09 14.44 0.05 1.71 -0.15 69.23 0.44 7.30 0.56**

SPV 815 X HC 308 78.36 -0.21 188.69 -1.50 14.62 0.37 1.95 0.12 74.10 -0.97 6.90 0.33

SPV 815 X Pant Chari 6 78.86 -

2.70**

179.49 -9.21** 12.49 -1.56** 1.52 -

0.25**

72.63 -0.21 5.77 -

1.08**

SPV 815 X G 48 78.13 -0.09 187.65 5.61 12.39 -0.09 1.89 0.07 71.09 0.41 7.87 1.70**

SPV 815 X HC 171 80.70 2.99** 186.31 5.10 14.99 1.28** 1.92 0.06 72.38 0.74 5.65 -

0.95**

Pusa Chari 6 x HC 308 80.97 0.28 201.28 -

13.57**

13.82 -0.95** 1.85 0.06 67.72 -1.60 4.34 -0.27

Pusa Chari 6 x Pant

Chari 6

82.30 -1.39 219.65 6.29 13.25 -1.31** 1.56 -0.18 67.54 0.42 5.19 0.30

Pusa Chari 6 x G 48 80.01 -0.34 219.36 12.66** 14.50 1.51** 1.90 0.10 68.62 3.65** 4.10 -0.11

Pusa Chari 6 x HC 171 81.28 1.45 200.49 -5.38 14.95 0.74 1.85 0.02 63.45 -2.47** 4.74 0.09

CSV 15 x HC 308 82.56 -0.65 277.43 2.30 13.86 -0.30 1.25 -

0.24**

78.70 3.51** 5.92 0.07

CSV 15 x Pant Chari 6 87.43 1.22 269.34 -4.30 14.60 0.63 2.01 0.56** 69.44 -3.55** 7.11 0.99**

CSV 15 x G 48 81.75 -1.12 267.78 0.81 12.93 0.54 1.413 -0.09 67.14 -3.69** 8.87 -

0.58**


CSV 15 x HC 171 82.90 0.55 267.34 1.19 12.75 -0.87 1.29 -

0.24**

5.52 3.73** 5.39 -0.49

Pant Chari 3 x HC 308 71.83 4.80** 247.51 -

13.38**

12.83 -1.06** 1.11 -0.20 83.82 1.16 4.96 0.09

Pant Chari 3 x Pant

Chari 6

80.86 1.23 245.08 -

14.33**

12.82 -0.87 1.30 0.03 86.22 5.77** 5.01 -0.23

Pant Chari 3 x G 48 76.62 5.33** 283.60 30.85** 13.65 1.54** 1.34 0.03 70.38 7.92** 4.84 1.27**

Pant Chari 3 x HC 171 79.16 3.24** 248.78 -3.14 13.74 0.40 1.49 0.14 80.25 1.00 4.96 -0.04

SSG-59-3 x HC 308 88.08 1.93 257.76 -

25.28**

14.95 0.85 1.71 -0.01 61.14 -5.89** 7.59 0.15

SSG-59-3 x Pant Chari 6 97.23 2.68** 289.07 7.53 14.62 0.71 1.56 0.22** 64.32 -0.50 7.55 -0.17

SSG-59-3 x G 48 83.46 2.85** 299.03 24.12** 11.03 1.29** 1.57 0.75** 61.69 8.98** 6.53 1.51**

SSG-59-3 x HC 171 83.02 -

2.27**

267.69 -6.37 13.28 -0.27 1.70 -0.06 71.06 7.38** 8.03 0.53**

HJ 513 x HC 308 89.48 5.93** 299.58 11.65** 15.37 1.79** 1.70 0.81** 84.21 7.16** 7.53 1.06**

HJ 513 x Pant Chari 6 94.03 3.58** 290.26 -6.18 15.51 0.53 1.99 0.32** 64.32 -

10.52**

8.98 1.12**

HJ 513 x G 48 84.61 -

2.49**

301.61 11.83** 14.11 0.71 1.57 -0.14 71.70 -1.00 6.97 -0.22

HJ 513 x HC 171 83.46 3.12** 281.64 17.31** 13.20 1.43** 1.52 0.67** 78.02 4.37** 6.77 1.84**

UP Chari 4 x HC 308 82.21 -0.99 299.59 7.90 14.60 0.07 2.13 0.18 75.44 -0.09 6.87 -0.08

UP Chari 4 x Pant Chari

6

85.02 -1.18 296.79 6.59 13.88 -0.45 1.70 -0.21 74.08 0.75 7.10 -0.13

UP Chari 4 x G 48 83.85 0.99 259.38 -

24.16**

11.94 -0.82 1.93 -0.03 71.82 0.65 6.52 -0.03

UP Chari 4 x HC 171 83.52 1.18 292.38 9.67** 15.19 1.20** 2.06 0.07 70.82 -1.31 7.22 0.24

ICSV 700 x HC 308 83.58 4.61** 291.07 12.91** 15.89 1.99** 1.74 0.04 74.95 2.27** 7.75 1.35**

ICSV 700 x Pant Chari 6 89.31 2.12 280.83 4.16 13.65 -0.04 1.51 -0.14 65.02 -5.46** 8.07 0.39

ICSV 700 x G 48 83.45 -0.40 255.78 -

14.23**

10.58 -1.54** 1.77 0.07 72.63 4.30** 6.54 -0.46

ICSV 700 x HC 171 82.23 -1.10 266.34 -2.84 12.94 -0.41 1.76 0.03 68.17 -1.11 7.16 -0.27

UP Chari 3 x HC 308 81.06 -0.46 281.31 0.51 12.94 -0.42 1.74 0.05 74.24 -1.63 5.98 -0.51


6

82.49 -

2.03**

287.33 8.02 12.93 -0.23 1.67 -0.08 75.39 1.73 7.04 0.26

UP Chari 3 x G 48 82.24 1.06 257.60 -

15.05**

11.11 -0.48 1.96 0.17 74.42 2.91** 5.83 -0.27

UP Chari 3 x HC 171 82.08 1.42 278.33 6.51 13.94 1.13 1.79 -0.04 69.45 -3.02** 7.04 0.51

UP Chari 2 x HC 308 83.54 -0.60 294.63 3.71 15.76 0.96 1.72 -0.16 73.12 1.60 8.46 1.77**


6

89.31 0.17 295.35 5.92 15.12 0.53 1.96 0.12 65.20 -4.12** 6.43 -0.54*

UP Chari 2 x G 48 82.99 5.81** 278.81 15.96** 13.15 1.84** 2.02 0.93** 62.46 6.70** 6.39 1.09**

UP Chari 2 x HC 171 82.51 -0.79 276.28 -5.66* 12.61 -1.64** 1.82 -0.09 75.35 7.23** 5.41 -

1.32**

UP Chari 1 x HC 308 82.62 -0.25 286.08 4.92 16.60 1.98** 1.94 0.13 72.07 -1.35 6.06 -

0.93**


6

85.53 -0.34 284.57 17.90** 14.44 1.03** 1.52 0.25** 73.09 5.87** 7.71 1.45**

UP Chari 1 x G 48 84.32 1.79 263.82 -9.19** 13.64 0.81 1.74 -0.08 71.58 2.52** 6.09 -0.49

UP Chari 1 x HC 171 80.80 -1.21 271.56 -0.63 11.25 -2.82** 2.04 0.19 66.99 -3.03** 7.99 0.98**

Pusa Chari 9 x HC 308 81.74 -0.98 292.51 4.07 14.20 -0.42 2.05 0.09 65.55 -5.28** 7.20 -

0.65**

Pusa Chari 9 x Pant

Chari 6

85.15 -0.57 293.87 6.92 13.90 -0.53 1.98 0.12 72.89 4.26** 7.92 -0.21

Pusa Chari 9 x G 48 83.55 1.17 268.28 -

12.01**

12.29 -0.56 1.69 -0.23* 69.97 3.49** 7.23 -0.21

Pusa Chari 9 x HC 171 82.25 3.38** 280.47 13.13** 15.58 1.59** 1.97 0.52** 64.96 2.47** 8.94 1.07**

Rajasthan Chari 1 x HC

308

81.28 -

3.31**

308.03 20.78** 13.5 -1.15** 2.00 0.05 75.36 0.83 7.23 -0.36

Rajasthan

Chari1xPantChari6

85.62 -1.97 285.45 -0.32 15.96 1.44** 1.81 -0.15 71.53 -0.80 6.89 -

0.98**

Rajasthan Chari 1 x G

48

86.12 3.87** 270.75 18.36** 12.02 1.92** 2.15 0.64** 72.23 2.06** 8.10 0.90**


171

87.14 3.41** 266.19 -

12.10**

14.80 0.63 2.05 0.01 69.04 -2.09** 8.07 0.44

Crosses Internode

length (cm)

Total soluble

solids (%)

Leaf

area (cm2)

Leaf

stem ratio

(w/w)

Protein

content (%)

Green fodder

yield per plant

(g)


Mean SCA Mean SCA Mean SCA Mea

n

SCA Mean SCA Mean SCA

HC 260 X HC 308 20.55 3.79** 10.97 2.44** 343.89 72.63** 0.69 0.83** 7.88 1.59** 434.67 98.35**

HC 260 X Pant Chari 6 17.50 0.29 7.36 -0.36 413.06 79.34** 0.45 -0.07 5.88 0.04 282.26 -

20.96**

HC 260 X G 48 16.38 2.09** 7.06 1.04** 266.57 65.62** 0.31 0.90** 4.93 1.89** 213.90 83.14**

HC 260 X HC 171 17.12 -

1.99**

7.00 -1.04** 249.67 -

66.35**

0.54 0.04 5.27 -0.74 317.76 -4.24

Pusa Chari 23 X HC 308 16.37 -

2.85**

7.63 -0.26 337.71 -5.88 0.39 -0.01 7.74 -0.05 298.96 -

36.96**

Pusa Chari 23 X Pant

Chari 6

20.02 0.35 8.00 0.91 309.72 -

36.33**

0.39 -0.05 6.44 -0.90 299.29 -3.54

Pusa Chari 23 X G 48 22.23 1.31** 7.13 -0.34 316.02 1.50 0.48 0.04 7.07 -0.28 310.17 3.52

Pusa Chari 23 X HC 171 22.75 1.19** 7.10 -0.30 359.05 30.71** 0.45 -

0.52**

8.75 -

1.23**

378.60 46.98**

SPV 815 X HC 308 18.71 1.03** 7.30 -0.27 363.15 2.97 0.27 -0.09 7.140 0.06 358.99 5.84

SPV 815 X Pant Chari 6 17.80 -0.33 6.94 0.16 297.75 -

54.89**

0.41 0.02 7.106 0.47 355.84 25.79**

SPV 815 X G 48 19.03 -0.36 7.65 0.50 397.32 86.22** 0.47 0.07 6.350 -0.29 321.39 -2.48

SPV 815 X HC 171 19.68 -0.34 6.69 -0.39 290.63 -

44.30**

0.42 0.02 6.563 -0.24 379.69 -

29.15**

Pusa Chari 6 x HC 308 9.340 0.89 7.72 -0.59 209.01 -

21.86**

0.44 0.07 7.656 0.14 509.23 -7.87

Pusa Chari 6 x Pant

Chari 6

11.33 2.44** 6.72 -0.79 249.03 5.70 0.43 0.01 7.586 0.51 500.82 -3.19

Pusa Chari 6 x G 48 9.21 -0.95 6.05 -1.84** 199.97 8.16 0.39 -0.02 6.633 -0.45 515.41 7.58

Pusa Chari 6 x HC 171 8.41 -

2.38**

11.05 3.23** 213.63 -2.00 0.34 -0.06 7.050 -0.20 526.27 3.48

CSV 15 x HC 308 18.37 -

2.09**

8.11 -0.64 330.81 7.00 0.37 -0.05 6.736 -0.32 434.17 -7.75

CSV 15 x Pant Chari 6 20.31 -0.59 7.90 -0.05 350.97 34.71** 0.59 0.12 6.773 0.16 443.34 24.52**

CSV 15 x G 48 22.20 0.04 8.70 0.38 232.34 -

42.40**

0.42 -0.04 7.693 1.07** 425.30 2.66

CSV 15 x HC 171 25.45 2.65** 8.56 0.31 289.25 -9.31 0.42 -0.03 5.86 -0.91 408.17 -2.43

Pant Chari 3 x HC 308 21.16 3.06** 6.87 -0.12 295.87 0.95 0.34 -0.03 7.03 -0.07 349.53 -7.92

Pant Chari 3 x Pant Chari

6

19.51 1.11** 7.44 1.25** 307.22 9.84 0.41 -0.01 6.80 0.14 324.13 -

27.23**

Pant Chari 3 x G 48 18.02 1.64** 6.25 1.31** 242.05 43.80** 0.49 0.19 6.913 0.95 400.66 72.49**

Pant Chari 3 x HC 171 17.78 -

2.52**

5.68 -0.82 282.70 3.02 0.34 -0.05 6.51 -0.32 375.80 2.66

SSG-59-3 x HC 308 16.40 -

2.56**

7.81 0.44 329.79 -

24.26**

0.46 -0.01 6.88 -0.34 399.61 -

20.15**

SSG-59-3 x Pant Chari 6 19.50 0.09 6.93 0.36 344.79 -1.72 0.63 0.11 6.01 -0.68 414.01 27.33**

SSG-59-3 x G 48 22.11 1.44** 5.94 1.00** 284.98 29.01** 0.46 0.55** 6.77 -0.01 441.35 80.87**

SSG-59-3 x HC 171 22.34 1.04** 7.08 0.20 403.79 64.98** 0.47 -0.04 7.98 1.03** 367.39 -

48.05**

HJ 513 x HC 308 17.46 2.01** 8.01 1.07** 450.24 39.59** 0.37 0.65** 7.27 1.28** 398.38 90.80**

HJ 513 x Pant Chari 6 17.07 -0.84 8.22 -0.25 410.50 -2.61 0.25 -0.16 6.05 -0.50 341.18 -3.30

HJ 513 x G 48 18.13 -

1.04**

10.26 1.57** 354.88 -6.70 0.59 0.18 6.51 -0.04 319.50 -

28.80**

HJ 513 x HC 171 21.70 1.89** 8.14 1.45** 375.14 20.27** 0.38 -0.02 6.99 0.27 394.56 91.30**

UP Chari 4 x HC 308 15.39 -0.67 6.82 -1.11** 368.05 -4.32 0.40 -0.06 6.49 0.97 394.43 16.80**

UP Chari 4 x Pant Chari 6 17.43 0.93 7.76 0.63 373.70 -1.12 0.51 0.07 5.55 0.47 359.36 4.83

UP Chari 4 x G 48 18.78 1.02** 8.72 1.22** 332.73 -0.57 0.58 0.09 4.44 -0.65 324.52 -3.84

UP Chari 4 x HC 171 17.11 -

1.29**

6.70 -0.79 363.13 6.01 0.47 -0.02 4.45 -0.79 375.53 2.21

ICSV 700 x HC 308 20.78 1.47** 7.98 1.36** 412.56 32.29** 0.43 -0.01 7.87 1.55** 415.78 90.84**

ICSV 700 x Pant Chari 6 18.75 -

1.02**

7.39 -0.15 372.59 -0.14 0.50 0.03 7.40 0.52 395.85 -7.68

ICSV 700 x G 48 20.67 -0.36 8.86 0.94 337.39 -3.82 0.42 -0.06 6.25 -0.63 413.51 6.16

ICSV 700 x HC 171 21.57 -0.09 7.42 -0.43 346.69 -8.33 0.55 0.07 6.61 -0.44 424.67 2.36

UP Chari 3 x HC 308 18.85 0.83 9.29 -0.37 315.40 -

34.46**

0.42 -0.06 6.90 -0.10 379.84 3.56


6

16.74 -

1.73**

10.16 1.30** 376.84 24.52** 0.56 0.03 6.83 0.28 356.97 3.80

UP Chari 3 x G 48 21.24 1.51** 9.33 0.09 308.15 -2.64 0.51 -0.01 6.50 -0.06 330.23 -6.76

UP Chari 3 x HC 171 19.76 -0.61 8.16 -1.01** 347.18 1.57 0.55 0.04 6.60 -0.12 381.36 9.40


UP Chari 2 x HC 308 16.64 -

1.10**

8.63 0.64 439.53 98.79** 0.38 -0.02 7.26 -0.020 389.37 5.76


6

18.46 0.28 6.60 -0.59 298.01 -

45.20**

0.44 -0.02 6.84 -0.18 335.16 -

25.35**

UP Chari 2 x G 48 20.19 2.75** 7.11 1.46** 283.84 77.83** 0.44 0.89** 6.65 1.37** 375.80 81.47**

UP Chari 2 x HC 171 20.10 0.07 7.90 0.41 289.72 -

35.77**

0.49 0.05 7.94 0.75 377.42 -1.87

UP Chari 1 x HC 308 18.08 1.00** 10.74 1.25** 309.94 -

53.02**

0.50 0.74** 5.34 -

1.57**

380.51 -3.46


6

15.99 1.53** 7.05 1.63** 400.25 34.83** 0.52 0.01 6.08 -0.39 379.81 88.93**

UP Chari 1 x G 48 18.27 -0.52 10.03 0.97** 309.75 -4.15 0.41 -0.09 8.32 1.95** 370.24 5.54

UP Chari 1 x HC 171 20.47 1.05** 8.40 -0.59 380.06 32.34** 0.51 0.03 6.74 0.10 388.65 -1.01

Pusa Chari 9 x HC 308 18.08 -0.97 6.08 -1.88** 334.90 -

57.33**

0.57 0.05 6.66 -0.56 423.33 -

29.86**

Pusa Chari 9 x Pant Chari

6

19.47 -0.02 7.12 -0.03 410.06 5.37 0.58 0.00 6.91 0.14 441.71 1.62

Pusa Chari 9 x G 48 20.94 0.19 7.53 0.06 359.33 6.17 0.56 -0.01 6.84 0.06 412.93 -0.98

Pusa Chari 9 x HC 171 22.18 1.80** 9.38 1.91** 412.77 35.79** 0.51 -0.04 7.30 1.36** 478.11 99.23**


308

16.17 -

1.82**

10.77 1.90** 387.05 -3.09 0.38 -0.05 7.08 -0.37 388.63 3.72

RajasthanChari1xPantCha

ri 6

19.06 0.57 7.12 -0.95** 350.32 -

52.29**

0.5 0.02 6.92 -0.08 376.24 4.43

Rajasthan Chari 1 x G 48 20.40 1.71** 7.77 1.67** 415.57 54.49** 0.49 0.68** 7.46 1.44** 341.35 94.29**


171

20.86 0.53 8.090 -0.28 395.79 1.89 0.47 0.01 7.19 0.01 376.74 -3.86


The results of analysis of variance revealed that

significant variability existed among the parents with

regard to all the characters under investigation. Such

divergences of parents lead to development of F1s

that differed significantly among themselves for all

characters. The estimates of combining ability

variances are translated in to genetic variance to

understand the nature and magnitude of gene action

and develop guidelines for selecting parents for

hybridization. It is an established fact that additive

genetic variance results mostly from additive gene

action while non additive genetic variance is

comprised of dominant and epistasis. Estimates of

variance due to line x tester showed highly

significant for all the characters except number of

leaves per plant and stem girth. Further partitioning

of treatment variance into parents and crosses were

revealed highly significant differences for all the

characters and variance due to parent vs crosses was

observed significant for all the traits except days to

50% flowering (Table-1). These results are in general

agreement with the findings Aaref et al. (2016), Rini

et al. (2016) and Chikuta et al. (2017). The variance

among line with respect to gca was found highly

significant for all the attributes. The variance among

testers with respect to gca was recorded highly

significant for all the characters except stem girth

while variances among crosses due to interaction

between lines x testers genotypes with respect to sca

were expressed highly significant for all the traits

except stem girth and leaf stem ratio which indicate

that both additive and dominance genetic variance

were involved in the determination of these attributes

and the parents and their progenies differed for their

combining ability effects (Table 2). Aaref et al.

(2016), Jain and Patel (2016) and Chikuta et al.

(2017) reported that same of the morphological and

quality traits were determined by additive and other

by non additive effects for yield. The ratio δ2g/δ

2s

being more than unity for the traits viz., plant height,

leaf breadth, internode length, leaf area and green

fodder yield per plant which indicated that the

involvement of additive gene action. To exploit the

additive genetic variance in the improvement of such

attributes, pedigree method of breeding can be used.

Similar findings were also observed by Jain and Patel

(2016) and Chikuta et al. (2017). Average degree of

dominance (δ2s/δ

2g)

0.5 exhibited partial dominance

for plant height, leaf length, leaf breadth, internode

length, leaf area, leaf stem ratio and green fodder

yield, suggesting there by the preponderance of

additive type of gene action with partial dominance

in the expression of these characters in this crop.

Over dominance was observed for days to 50%

flowering, number of leaves per plant, stem girth,

total soluble solids and protein content which

indicated that gene action is fixable and these

attributes played an important role for population

improvement in this crop. Magnitude of δ2s was

higher than δ2

g for all the traits except stem girth and

protein content, indicating role of dominance gene

action and interaction were involved in the

expression of these traits (Table 2). These findings

are in close conformity with Aaref et al. (2016), Jain

and Patel (2016) and Chikuta et al. (2017). Parents

among lines, HC260 were identified as a good

general combiner for all the attributes except total

soluable solids and protein content. Parent Pusa

Chari23 appeared as a good general combiner for all

the characters except stem girth and leaf breadth.

Genotype SPV815 was found to be good general

combiner for all the traits except number of leaves


per plant, leaf stem ratio and protein content. Line

Pusa Chari6 emerged as a good general combiner for

all the characters except leaf stem ratio. Parent

CSV15 expressed as a good general combiner for

leaf length, internode length total soluble solids and

green fodder yield per plant. Genotype SSG-59-3

was proved to be good general combiner for days to

50% flowering, leaf breadth, internode length, leaf

stem ratio and green fodder yield per plant and line

ICSV700 was considered as desirable good general

combiner for leaf breadth, internode length, leaf area

and green fodder yield per plant and parent Pusa

Chari9 was identified as good general combiner for

all the attributes except days to 50% flowering and

protein content. Among the testers HC308 was

appeared good general combiner for all the traits

except days to 50% flowering. Male G48 possessed

as a good general combiner for internode length and

green fodder yield per plant and tester HC171 proved

as a good general combiner for all the characters

except total soluble solids (Table 3). Similar results

have been reported by Kumar and Shrotria (2016),

Rini et al. (2016) and Chikuta et al. (2017). These

parents may be handled in suitable breeding

programme visa-vis selection breeding for

improvement productivity of green fodder yield and

per unit area in this crop. Thus, the study reveals that

there is lot of scope for the use of these lines in

future breeding programmes in the development of

either base populations or hybrids. The lines with

increased brix and protein contents can be exploited

for the improvement of quality of fodder sorghum

thereby enhancing the nutritive value of the crop. Out

of the sixty F1’s hybrids, only sixteen cross

combinations viz., HC260 x HC308, HC260 x G48,

Pusa Chari23 x HC171, SPV815 x Pant Chari6,

CSV15 x Pant Chari6, Pant Chari3 x G48, SSG-59-3

x Pant Chari6, SSG-59-3 x G48, HJ513 x HC308,

HJ513 x HC171, UP Chari4 x HC308, ICSV700 x

HC308, UP Chari2 x G48, UP Chari1 x Pant Chari6,

Pusa Chari9 x HC171 and Rajasthan Chari1 x G48

noted significant and positive specific combining

ability effects for maximum attributes including

green fodder yield per plant for 8 to 11 other

contributing traits, which proved as best specific

combiners for per se performance and also may be

utilized for obtaining transgressive segment in the

next generation (Table 4). These findings were

supported by Aaref et al. (2016), Jain and Patel

(2016), Kumar and Shrotria (2016), Rini et al.

(2016), Chikuta et al. (2017). These identified

specific cross combinations should be exploited

through heterosis breeding may be used in

recombination programme for tapping desirable

transgrassive segregants in segregating generations.

The inter mating between selected segregants in

advanced generations would help to accumulate

favorable, desirable alleles for further improvement

in yield and its component traits. The crosses which

exhibited high specific combining ability, involving

good combiner and moderate combiner, such a

combination may throw up desirable transgressive

segregates, if the additive genetic system is present in

the good combiner and complementary effect in

present in the crosses, act in the same direction so as

to maximize the desirable plant attributes. Breeding

for homozygous line by routine pedigree method

could mean only partial exploitation of additive

genetic variance in order to exploit different type of

gene action in a population. It is suggested that a

breeding procedure which may accumulate the

fixable type of gene effect and at the same time

maintains considerable heterozygosity for exploiting

the dominance gene effect, might prove most

beneficial in improving the population under study.

REFERENCES

Aaref, K. A. O.; Ahmad, M. S. H.; Hovny, M. R.

A. and Youns, O.A. (2016). Combining Ability and

Heterosis for some Agronomic Characters in Grain

Sorghum (Sorghum bicolor. (L.) moench). Middle

East Journal of Agriculture Research ISSN 2077-

4605 Volume: 05 | Issue : 02 Pages: 258-271.

Chikuta, S., Odong, T., Kabi, F. and Rubaihayo,

P. (2017). Combining Ability and Heterosis of

Selected Grain and Forage Dual Purpose Sorghum

Genotypes. Journal of Agricultural Science; Vol. 9,

No. 2, ISSN 1916-9752.

Jain, S.K. and Patel, P.R. (2016). An assessment of

combining ability and heterosis for yield and yield

attributes in sorghum [Sorghum bicolor (L.)

Moench]. Green Farming Vol. 7 (4): 791-794.

Kempthorne, O. (1957). An introduction to genetics

statistics. John Wiley and Sons. New York.; 458.

Kumar, A. and Singh, U. (2012). Fertility status of

Hariana cow. Indian Veterinary Journal 86:807-809.

Kumar, P. and Shrotria, P.K. (2016). Combining

ability & heterosis studies for yield & component

traits in forage sorghum (Sorghum bicolor (L.)

Moench).Green Farming Vol. 7 (1): 1-7.

Panse, V. G. and Sukhatme, P. V. (1967).

Statistical methods for agricultural workers. Indian

Council of Agricultural Research, New Delhi.

Rini, E.P.; Trikoesoemaningtyas; Wirnas D.;

Sopandieal, D. and Tesso, T.T. (2016). Heterosis of

sorghum hybrid developed from local and introduced

lines. International Journal of Agronomy and

Agricultural Research (IJAAR) Vol. 8, No. 3, 1-9.



TRADITIONAL AGROFORESTRY SYSTEMS IN

GARHWAL HIMALAYA

Ram Gopal1*, R.S. Bali

2, D.R. Prajapati

3 and D.U. Rathod

4

1Botany Division, Forest Research Institute, Dehradun,Uttrakhand.

2College of Forestry Ranichauri, Uttarakhand

3Navsari Agricultural University, Gujarat

4Silviculture Division, Forest Research Institute, Dehradun, Uttarakhand



Abstract: The study was carried out for documentation of agrobiodiversity in traditional agroforestry systems in Garhwal

Himalaya, Uttarakhand. A total of nine villages were taken for the study from the different geographical regions and were

categorized into three different elevation ranges.The predominant agroforestry systems were found viz. agrisilviculture,

agrisilvopastural and silvipastural system. In agrisilviculture system total 30 species were documented. In agrisilvopastural

system (home garden) total53 species were documented among them Trichosanthes dioica, Mangifera indica, Vitis vinifera,

Emblica officinalis, Carica papaya, Prunus amygdalus, Annona squamosa, Annona reticulate and Artocarpus heterophyllus

were newly documented species. In silvipastural system about 27 species of tree, shrub and grass species are documented

with livestock unit. In three agroforestry systems some new species were documented due to adaption of changing climate

and different traditional farming practices.

Keywords: Agroforestry, Agrisilviculture, Homegarden, Silvipasture

INTRODUCTION

rom the ancient times agroforestry has been

known as a traditional land-use system in India.

Since ancient times across the world it is practices

with integration of tree species with agricultural

crops and/or animals. Conventionally, people

resorted to agroforestry practices for the inter-

dependent benefits of the three components, viz.

trees, crops, and livestock with 6Fs, i.e. food, fruit,

fodder, fuel, fertilizer, and fiber. The prominent

traditional systems viz. shifting cultivation, taungya

and homegardens have evolved long ago. The

shifting cultivation, i.e. sequential agroforestry

system, believed that it has been originated in the

Neolithic period around 7000 BC, is still broadly

practiced in the North Eastern Hill (NEH) region and

other humid and hilly parts of the Indian

subcontinent (Sharma et al., 2007). The investigated

area of agroforestry is 11.54 m ha, which is 3.39% of

the geographical area of the country (Anon, 2013).

In present time agroforestry meets almost half of the

demand of fuel wood, two thirds of the small

timber, 70–80% wood for plywood, 60% of raw

material for paper pulp and 9-11% of the green

fodder requirement of livestock, additionally meeting

the subsistence needs of households for food, fruit,

fibre and medicine (NRCAF, Vision 2050). Garhwal

Himalaya of Uttarakhand forms major part of the

central Himalaya that comprises diverse agroforestry

systems. In this system small settlements and

terraced agricultural fields carved out of the hill

slopes for raising crops with numerous multipurpose

tree species growing, especially on the boundaries of

rainfed

terraces (Maikhuri, et al., 2000; Pratap, 2001). In

Garhwal Himalaya out of 0.46 million ha of gross

cropped area agrisilviculture (AS), agrihorticulture

(AH) and agrihortisilviculture (AHS) systems

occupy, respectively 0.102, 0.03 and 0.026 million

ha. It shows that AS, AH and AHS systems

represent, respectively, 22.2, 6.5 and 5.7% area of

the total cultivated land (Sachan, 2004). In hilly part

of Uttarakhand agriculture land is scattered and

fragmented.About 36% of rural families live below

the poverty linein Uttarakhandand per capita land

holding of farmers is 0.2 ha. In state gross domestic

product agriculture contributes around 37%

(Maikhuriet. al. 2009). The District TehriGarhwal

lies in the hilly part of the state and agriculture is the

major occupation of its inhabitants. This district

subsist of 182 villages with 61,569 ha area under

cultivation, of which irrigated land is only 7.4%

(Srivastava, 2007). Therefore, the present study was

conducted for documentations of agro-biodiversity in

traditional agroforestry systems of Garhwal

Himalaya.

MATERIAL AND METHOD

The study was carried out in Tehri Garhwal district

(Uttarakhand) between 1000 a.m.s.l to 2000 a.m.s.l

for documentation of agro-biodiversity in different

traditional agroforestry systems. During field work

25 percent house hold in a village were choosen

randomly on the basis of land holding capacity i.e.

Small (1-5 nali), Marginal (6-10 nali), Large (>10

nali). The questionnaire based survey was conducted

by interviewing with household head, Pradhan of

village and field observation for documentation of

F

RESEARCH ARTICLE


794 RAM GOPAL, R.S. BALI, D.R. PRAJAPATI AND D.U. RATHOD

agro-biodiversity. After documenting all agro-

biodiversity species are categories in all agroforestry

systems according to system property.

Table 1. Details of study site

Altitude

Range Villages Altitude (above msl) Latitude Longitude

A1-Higher

(>1600m)

S2- Maune 1898 N 300 18.460’ E 78

0 24.175’

S1- Dargi 1645 N 300 19.070’ E 78

0 19.069’

A2-Middle

(1300-1600m)

V2-Khatiyar gaun 1520 N 300 18.285’ E 78

0 23.219’

V1- Dandasali 1435 N 300 21.525’ E 78

0 23.740’

A3-Lower

(1000-1300m)

V2 - Chopadiyali 1287 N 300 19.667’ E 78

0 22.847’

V1- Nagni 1066 N 300 19.418’ E 78

0 21.165’

A semi-structure questionnaire was prepared based

on the following heads: (1) Name of village (2)

Elevation (3) Block (4) District. Type of system (a)

Agri-silviculture (b) Agri-silvo-pastoral system

(Homegarden) (c) Silvipasture system. The

parameters studied are based on following questions

i.e. name of agriculture crop whether it is a Kharib

and Rabi crop; name of legume crop whether it is a

Kharib and Rabi crop; name of oil crop whether it is

a Kharib and Rabi crop; name of vegetables whether

it is a Kharib and Rabi crop; name of fruit trees and

shrubs; name of fodder trees, shrubs and grasses;

name of new species of herb, shrubs and trees and

name of livestock units.


Agrisilviculture system

In this system mainly comprised of agriculture crops

along with forest trees (mainly on the bunds of

agriculture fields). The tree species were mostly on

the bunds, but occasionally the trees were also found

in between and middle of the agricultural fields. In

this system, total 31 species were documented

during present investigation. Out of 32 species,

10 were woody and 22 species were herbaceous food

crops raised by the farmers in the studied villages.

All multipurpose woody species viz. Grewia

opposetifolia, Celtis australis, Ficus roxburghii,

Quercus leucotrichophora, Bauhinia variegata,

Morus serrata, Ficus palmata, Melia azedarach,

Rhododendron arboretum, Myrica esculenta are used

for fodder, fuel and timber purpose. In all woody

species Rhododendron arboretum and Myrica

esculenta were recorded in middle and higher

altitude villages. In lower and middle altitude (A3

and A2 altitude) Grewia oppositifolia and in higher

altitude (A1alitude) Quercus leucotrichophora are

most dominate species. All herbaceous food crops

were growing in two prominent cropping seasons. In

Kharib and Rabi season cereals, pulses, oil crop,

oilseed crop are cultivate with all documented tree

species. Herbaceous crops Perilla frutescens, Vigna

sinensis and Sesamum indicum only documented in

lower altitude (A3).

Table 2. Agro-biodiversity in agrisilviculture system

Tree species Kharib season Intermediate Rabi season

Cereal crop Pulses Oilseed crop Cereal Pulses

Grewia

oppositifolia

Celtis australis

Ficus roxburghii

Quercus

leucotrichophora

Bauhinia

Zea mays

Eleusine

coracana

Echinochloa

frumentacea

Amaranthus

caudatus

Phaseolus

vulgaris

Vigna umbellata

Psophocarpus

tetragonolabus

Dolichos lablab

Vigna mungo

Glycine max

Sesamum

indicum

Brassica

campestris

Triticum

aestivum

Hordeum

vulgare

Lens culinaris


variegata

Morus serrata

Ficus palmata

Melia azedarach

Rhododendron

arboretum,

Myrica esculenta

Oryza sativa

Perilla

frutescens

Dolichos

uniflorus

Cajanus cajan

Vigna sinensis

Agrisilvopastoral system (Home garden)

Agrisilvopastoral system is stratified into different

strata like <1m height (1st strata), 1-3m height (2

st

strata), 3-10m height (3rd

strata) and 10-20m height

(4th

strata). In first strata, all annuals like weed grass

and vegetables are grown. In all the study sites, 26

species of vegetables was noted in two seasons. In

first strata elite species like Trichosanthes dioica are

grown in lower altitude only. In second layer fruit

plants (height 1-3m) are grown among them Malus

domesticais a temperate crop that is grown in higher

altitude only. Musa paradisiaca, Vitisinifera, Punica

granatum and Carica papaya are tropical and

subtropical fruit crop grown in middle and lower

altitude only. In third layer fruit plants and

multipurpose trees (3-10m height) are grown among

them Prunus persica, Prunus domestica, Prunus

armeniaca, Pyrus communis, Citrus sinensis and

Citrus aurantifolia fruit trees are grownin higher

altitude only. In lower and middle altitude Citrus

reticulate, Annona squamosal, Annona reticulate,

Psidium guajava, Emblica officinalis, Morus serrata

and Grewia oppositifolia are recorded.In fourth strata

multipurpose fruit and fodder trees (10-20m height)

are grown among them Celtis australis and Juglans

regia in higher altitude and Artocarpus heterophyllus

in lower altitude only. In this system Trichosanthes

dioica, Mangifera indica, Vitis vinifera, Emblica

officinalis, Caricapapaya, Prunus amygdalus,

Annona squamosa, Annona reticulate and

Artocarpus heterophyllus were newly documented

species

Table 3. Agro-biodiversity agrisilvopastoral system (Home garden)

1st Strata(<1m) 2

nd Strata (1-3m) 3

rd Strata (3-10m) 4

th strata (10-20m)

Kharib season vegetables Fruit Plants Fruit and MPT’s trees Fruit and MPT’s trees

Pisum sativum

Lycopersicon esculentum

Abelmoschus esculentus

Lagenaria siceraria

Luffa acutangula

Momordica charantia

Cucumis sativus

Cucurbita maxima

Trichosanthes dioica

Brassica oleracea var. capitata

Brassica oleracea var. botrytis

Spinacea oleracea

Brassica juncea

Colocasia antiquorum

Coriandrum sativum

Capsicum frutescens

Zingiber officinale

Curcuma longa

Allium cepa

Musa paradisiaca

Vitisvinifera

Punica granatum

Carica papaya

Malus domestica

Psidium guajava

Prunus persica

Prunus domestica

Prunus armeniaca

Prunus amygdalus

Pyrus communis

Emblica officinalis

Citrus aurantifolia

Citrus sinensis

Citrus reticulata

Annona squamosa

Annona reticulate

Mangifera indica

Morus serrata

Morus alba

Grewia oppositifolia

Celtis australis

Juglans regia

Artrocarpus heterophyllus

Rabi season vegetables

Capsicum annuum

Solanum tuberosum

Raphanus sativus

Daucus carota

Coriandrum sativum

Allium cepa

Allium sativum

Grasses

Andropogon munroi

Cynodon dactylon

Apluda mutica


Silvipastural system

In this system, trees with pasture and/or animals are

managed on same piece of lands. Total 11 species of

trees, 11 species of shrubs and 5 species of grasses

were documented silvipastural system and livestock

unit is grazed and browsed on vegetation of that

system. Cedrus deodara was recorded only in higher

altitude (> 1800m) only.

Table 4. Agro-biodiversity in silvipastural system

Tree species Shrub species Grass species Livestock

Ficus roxburghii

Quercus

leucotrichophora

Grewia oppositifolia

Melia azedarach

Pinus roxburghii

Ficus palmata

Myrica esculenta

Cedrus deodara

Rhododendron arboreum

Toona ciliata

Pyrus pashia

Rhus parviflora

Rumex hastatus

Artemisia vulgaris

Eupatorium

adenophorum

Murraya koenigii

Berberis asiatica

Rubus ellipticus

Rubus niveus

Desmodium elegans

Rosa brunonii

Indigofera gerardiana

Andropogon munroi

Cynodon dactylon

Aplud amutica

Euphorbia hirta

Avena fatua

Bos indicus

Bubalus bubalis

Capra hircus

Ovis aries

DISCUSSION

In traditional agrisilviculture system mainly

comprised of agriculture crops along with forest trees

(mainly on the bunds of agriculture fields.). The tree

species were mostly on the bunds, but occasionally

the trees were also found in between and middle of

the agricultural fields. Bijalwan et al. (2009) also

studied traditional agrisilviculture system and

documented total 19 tree species 12 agricultural

crops in Garhwal Himalaya between 1000m to

2000m a.s.l. The another study done by the Kala et

al. (2010) in an indigenous agroforestry systems in

changing climate of the middle Himalaya region of

Tehri Garhwal proclaimed 21 woody species and 26

herbaceous food crop species out of them 12 crop

species, 5 cereals and 6 pulses. Agri-silvo-pastoral

system has shown to provide a diverse and stable

supply of products and benefits to the families in the

villages that maintained them and have improved

their livelihood. Home gardening has been a way of

life for centuries in the hills for fulfills the household

needs.In Bangladesh by Alam and

Masum (2005) identified 142 plant species in home

garden out of which 34 species were fruit producing,

24 timbers, 21 fuel wood, 15 medicinal plants, 11

ornamental and 32 vegetable species. Most of the

farmers (76%) preferred to plant fruit tree for future

plantation followed by timber species (62%).

Conformably to present study, Sahoo (2009)

documented a total of 231 species with 105 trees, 50

shrubs and 76 herbs species from 45 indigenous

agroforestry home gardens. These tree, shrub and

herb species were distributed in 84 and 49; 31 and 22

and 59 and 39 genera and families respectively.

Overall, there were 88 families, out of which 24 tree

species (23%) were common to all the home gardens.

In silvipasture system, trees with pasture and/or

animals are managed on same piece of lands. Samant

(1998) also documented 279 fodder species from

west Himalaya belonging to 185 genera. In all

documented species, 112 are trees, 67 shrubs and

grasses. Among the genera the species richness

showed that the maximum number of species (13) of

the genus Ficus are used for fodder, followed by

Quercus (6 spp.), Berberis (6 spp.), Acer (5 spp.),

Bauhinia (5 spp.), Rosa (5 spp.), Pyrus (4 spp.),

Indigofera (4 spp.), Rubus (4 spp.) and Smilax (4

spp.) respectively.In remaining genera < 4 species

are used as fodder.

CONCLUSION

In agrisilviculture system all multipurpose woody

species are used for fodder, fuel and timber purpose.

In all woody species Rhododendron arboretum and

Myrica esculenta were recorded in middle and higher

altitude villages only. In lower and middle altitude

(A3 and A2 altitude) Grewia oppositifolia and in

higher altitude (A1alitude) Quercus leucotrichophora

are most dominate species. Herbaceous crops Perilla

frutescens, Vigna sinensis and Sesamum indicum

only documented in lower altitude (A3).

A homestead garden is stratified into different

strata according to their height. Maximum species

are common in all the villages. In first strata elite

species like pointed gourd are grown in lower

altitude only. Malus domestica is a temperate crop

that is grown in higher altitude. Musa paradisiaca,

Vitis inifera, Punica granatum and Carica papaya

are tropical and subtropical fruit crop grown in

middle and lower altitude. In higher altitude Prunu

spersica, Prunus domestica, Prunus armeniaca,

Pyrus communis, Citrus sinensis and Citrus

aurantifolia fruit trees are grown. In lower and

middle altitude Citrus reticulata, Annona squamosa,

Annona reticulate, Psidium guajava, Emblica

officinalis, Morus serrata and Grewia oppositifolia

are recorded. In fourth strata multipurpose fruit and

fodder trees (10-20m height) are grown. In higher


altitude Celtis australis, Juglans regia and in lower

altitude Artocarpus heterophyllus are documented

only. In agrisilvopastoral systemsystem

Trichosanthes dioica, Mangifera indica, Vitis

vinifera, Emblica officinalis, Carica papaya, Prunus

amygdalus, Annona squamosa, Annona reticulate

and Artocarpus heterophyllus were newly

documented species. In silvipastural system system

Cedrus deodara is recorded in highest altitude >

1800m only.

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homestead biodiversity in the offshore Island of

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(Ministry of Environment & Forests), Dehradun,

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Bijalwan, A.; Sharma, C. M. and Sah, V. K. (2009). Productivity status of traditional agri

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Kala, C. P. (2010). Status of an indigenous agro-

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Maikhuri, R. K.; Semwal, R. L.; Rao, K. S.;

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India [Ph.D. thesis]. USA: The University of

Michigan: NRCAF, Vision 2050. National Research

Centre for Agroforestry, Jhansi, 2013, p.30.

Makino, Y. (2009). Oak forests, lopping, and the

transformation of rural society in central Himalaya,

India [Ph.D. thesis]. USA: The University of

Michigan; NRCAF, Vision 2050. National Research

Centre for Agroforestry, Jhansi, 2013, p.30.

Partap, T. (2001). Mountain agriculture marginal

land and sustainable Livelihoods: Challenge and

opportunities. Paper Presented in International

Symposium on mountain agriculture in the

Hindukush Himalaya region. Organized by ICMOD.

Kathmandu, Nepal. Priorities for sustainability. The

India Economy Review, 6: 116–123.

Sachan, M.S. (2004). Stracture and functioning of

traditional agroforestry systems along an altitudinal

gradient in Garhwal Himalaya, India. PhD Thesis,

H.N.B. Garhwal University, Srinagar

(Garhwal) Uttranchal, India. 157.

Sahoo, U. K. (2009). Traditional home gardens and

livelihood security in North-East India. J. of food,

Agri. & Env., 7 (2): 665-670.

Samant, S. S. (1998). Diversity, distribution and

conservation of fodder resources of West Himalaya,

India. In B. Misri (Ed.), Proceeding of the third

temperate pasture and fodder network (TAPAFON),

Pokhr, Nepal, 9-13 March, 1998, sponsored by FAO,

Rome. pp. 109-128.

Sharma, R.; Xu, J. and Sharma, G. (2007).

Traditional agroforestry in the eastern Himalayan

region: Landmanagement system supporting

ecosystem services. Trop. Ecol., 48:1-12.

Srivastava, A.K. (2007). Enhancement of livelihood

security through sustainable farming systems and

related farm enterprises in North West Himalaya.

[Project Report.] Almora Vivekananda Parvatiya

Krishi Anusandhan Sansthan: 16.



GROWTH PERFORMANCE OF RUBBER (HEVEABRASILIENSIS MUELL. ARG.)

PLANTATION IN HILLY ZONE OF KARNATAKA

Shahbaz Noori* and S.S. Inamati

Department of Silviculture and Agroforestry

College of Forestry, Sirsi (UASD) - 581401



Abstract: The objective of this experiment was to assess the influence of site factors on growth and productivity of Hevea

brasiliensis clone RRII 105in different aged rubber plantation in Hilly zone of Karnataka. Hilly zone was classified into two

ecological zones based on annual rainfall distribution viz., Mundgod (798 mm) and Sagara (1918 mm).Seasonal diameter

increments during monsoon (June-September) and winter (October-December) was higher and declined subsequently in

summer (January-March) in all age gradation. The volume production and productivity was observed to be double in Sagara

for all the age gradation in comparison with Mundgod due to maximum DBH, tree height, favourable climatic conditions,

moderate soil fertility status and lesser temperature extremes prevailing in zone.

Keywords: Age,girth increment, Productivity, Site factor, Hilly zone

INTRODUCTION

ubber (Hevea brasiliensis Muell. Arg.) is

indigenous to Amazon basin and is one of the

major industrial crop grown mainly in tropical

climate between 12º latitude on either side of

equator. In India, it is largely cultivated in the humid

tropical zone between 8º 15′ N and 12º 52′ N

latitudes. Mature Hevea trees in native habitat are

about 25-30 metre tall with an average diameter at

breast height (DBH) greater than 1 metre. Potential

use of rubberwood was initially identified at Forest

Research Institute in 1953 and now rubberwood is

widely planted for the production of timber.

The deviating climate, consisting of sets of weather

attributes along with gaseous distribution (ozone,

CO2 etc.,) and biotic potential over years, brings

about erratic/unpredictable environment that imposes

stress on growth and productivity of plants. The fact

that acclimatization of Hevea to non-indigenous

climate, especially stress prone areas like low

temperature, high altitude, high windspeed etc., of

China resulted in profitable returns (Guohua and

Haiping, 2005) but areas having high temperature

and low rainfall might bring adverse social and

economic impact where rubber cultivation would be

non-recommendable.

Studies on growth are of fundamental importance in

understanding the interaction between plant and their

environment conditions. Data relating to changes in

plant morphology to plant age can be useful in

studies dealing with modelling long term growth.

The aim of this work was to present an analysis of

growth and productivity during immature, juvenile

and early mature phase of Hevea trees in two

contrasting ecological zone for understanding the

scope of rubber cultivation in Hilly zone of

Karnataka.

MATERIAL AND METHOD

The present study was conducted in established

plantation of Hevea brasiliensis in Mundgod (140 12’

390” N Latitude and 750 11” 580” E Longitude) and

Sagara (140 13’ 355” N Latitude and 75

0 11’ 635” E

Longitude) of Uttara Kannada and Shivammoga

district, respectively. The site was divided in relation

to rainfall viz., Low rainfall (Mundgod) and high

rainfall (Sagara) of 798 mm and 1915 mm,

respectively during the study period. Plantation of

three age gradationi.e. 4 year (immature), 7 year

(Juvenile) and 10 year (early mature) with uniform

spacing of 3.5 x 4.2 m were located in each site. Due

care was taken to choose sub plot under similar

situation of respective main plots.

Enumeration of Hevea stands was carried out in

randomly selected plantation. Four quadrates of size

20 x 20 m were laid out randomly in each plantation

to measure diameter at breast height, total height,

form factor for estimation of volume production and

productivity in each quadrat. The data on growth and

productivity were subjected to statistical analysis by

ANOVA (analysis of variance) using DMRT

(Duncan’s Multiple Range Test) to ascertain

significance of various growth parameters using

SPSS version 22 at five per cent significance level (p

= 0.05).


A comparison made with similar age plantation

across different ecological zones exhibited

statistically significant difference with respect to all

the growth attributes and productivity. Though the

rainfall is rather high in Sagara (1915 mm with 132

rainy days) its distribution is highly skewed with

rains mostly concentrated during months from June

to September. North east monsoon showers are

R

RESEARCH ARTICLE


800 SHAHBAZ NOORI AND S.S. INAMATI

limited and summers were relatively dry.

Temperature and relative humidity were also high

but adequate to this region.On the contrary,

Mundgod region received an annual rainfall of 798

mm with low number of rainy days. The distribution

of rainfall is far from satisfactory which results in

long dry spells extending from November to March,

due to which region experiences severe seasonal

drought (Table 1). High atmospheric heat load and

vapour pressure deficit with soil moisture conditions

near wilting point create adverse conditions for the

growth of Hevea.The minimum rainfall required for

rubber cultivation is 1500 mm but the preferred

average is 2500-4000 mm with a total of 100-150

rainy days per year (Krishnan, 2015).

Growth attributes

Tree height and diameter varies between plantations

of particular age and is strongly influenced by site

factors. In Sagara region, tree height and diameter

showed statistically higher growth varying from 5.52

m at 4 year to 12.08 m at 10 year and 10.66 cm at 4

year to 18.47 cm at 10 year, respectively. Whereas,

tree height and diameter in Mundgod region showed

variation from 4.26 m at 4 year to 8.92 m at age of 10

year and 8.67 cm at 4 year to 12.13 cm at 10 year.

These variations recorded between ecological zone

may be due to difference in climatic and edaphic

factors prevailing in respective location (Table 2 and

3).

Seasonal growth pattern of Hevea brasiliensis

Good girth is an important attribute for sustained

yield and girth increment is widely used in

Heveacultivation as a parameter of growth,

particularly during the immaturity period (Shorrock

et al., 1965). Hevea clone, RRII 105 showed very

limited difference in seasonal diameter increment

between the two ecological zone. Seasonal diameter

increment during monsoon (June-September) and

winter (October-December) was higher and declined

subsequently in summer (January-March) in all age

gradation.

Monsoon coupled with lifesaving irrigation in

immature phase (4 year) during monsoon recorded

highest seasonal diameter increment of (1.03 cm),

(0.82 cm) in Sagara and Mundgod region

respectively. While, in juvenile phase (7 year) and

early mature phase (10 year), reduction in seasonal

diameter increment was observed which may be due

to inappropriate practice of tapping observed in sixth

year in non-traditional belt of Karnataka (Fig 1).

However, between two ecological zones, Sagara

recorded highest annual diameter increment in age

gradation of 4 year (1.99 cm), 7 year (1.49 cm) and

10 year (1.15 cm) which may be due to adequate

monsoon showers, lesser temperature variations,

optimum relative humidity and compound interest

effect of the previous growth. While, Mundgod

recorded comparatively lower annual diameter

increment in age gradation of 4 year (1.48 cm), 7

year (1.06 cm) and 10 year (0.8 cm) which may be

due to low rainfall and high temperature variation

prevailing in Mundgod (Fig 2 and 3).This trend was

in agreement with the reported literature by Krishnan

(2015), Chandrashekhar (2003) in Hevea

brasiliensis.

Volume production and productivity

Hevea brasiliensis plantation of Sagara region at 4, 7

and 10 year recorded maximum volume production

of 18.44 m3/ha, 57.65 m

3/ha and 119.05 m

3/ha,

respectively due to favourable climatic and edaphic

conditions coupled with soil nutrients that enhanced

better growth, resulting in higher volume production.

However, significantly lower volume production of

9.39 m3/ha, 30.82 m

3/ha and 66.70 m

3/ha was

recorded at 4, 7 and 10 year plantation, respectively

in Mundgod which may be due to climatic variation

condition prevailing in Mundgod, resulting in long

dry spell (Table 5).

The productivity of Hevea brasiliensis plantation in

Sagara was almost double the productivity recorded

in Mundgod zone. Higher productivity in Sagara for

age gradation of 10 year followed by 7 and 4 year

may be attributed to maximum DBH, tree height,

favourable climatic conditions, moderate soil fertility

status and lesser temperature extremes. On the

contrary, comparatively lower performance of Hevea

brasiliensis plantation in Mundgod could be

attributed to climatic variation resulting in lower

productivity (Table 6).

Table 1. Average meteorological data of Mundgod and Sagara region.

Sl. No Particulars Mundgod Sagara

1. Annual rainfall (mm) 798 1915

2. Number of rainy days 77 132

3. Maximum temperature (0C) 32.0 ºC 29.5 ºC

4. Minimum temperature (0C) 19.5 ºC 18.0 ºC

5. Relative humidity (%) 69 72


Table 2. Mean tree height (m) of rubber plantation as influenced by age gradation and site condition

Main plot (M)/

Sub plot (S)

Mean tree height (m)

4 YAP 7 YAP 10 YAP

Mundgod 4.26cB

7.23bB

8.92aB

Sagara 5.52cA

9.18bA

12.08aA

F value (M) 5375.36* p (M) <0.05

F value (S) 12672.74* p (S) <0.05

F value (M x S) 372.76* p (M x S) <0.05

*Significant at 5 % level.

Means having same letter as superscript indicate they are homogenous (on par) row wise

Means having same letter as superscript indicate they are homogenous (on par) column wise

Table 3. Mean diameter at breast height (cm) of rubber plantation as influenced by age gradation and site

condition

Main plot (M)/

Sub plot (S)

Mean DBH (cm)

4 YAP 7 YAP 10 YAP

Mundgod 8.67cB

12.13bB

16.10aB

Sagara 10.66cA

14.72bA

18.47aA

F value(M) 13955.31* p (M) <0.05

F value (S) 50386.06* p (S) <0.05

F value (M x S) 78.45* p (M x S) <0.05




Table 4. Mean basal area (m2/ha) of rubber plantation as influenced by age gradation and site condition

Main plot (M)/ Sub plot (S)

Mean basal area (m2/ha)

4 YAP 7 YAP 10 YAP

Mundgod 4.03cB

7.87bB

13.85aB

Sagara 6.10cA

11.59bA

18.22aA

F value(M) 10232.04* p (M) <0.05

F value(S) 35982.24* p (S) <0.05

F value (M x S) 418.44* p (M x S) <0.05




Table 5. Mean volume production (m3/ha) of rubber plantation as influenced by age gradation and site condition


Mean volume production (m3/ha)

4 YAP 7 YAP 10 YAP

Mundgod 9.39cB

30.82bB

66.7aB

Sagara 18.44cA

57.65bA

119.05aA

F value (M) 14002.44* p (M) <0.05

F value (S) 34246.90* p (S) <0.05

F value (M x S) 2556.78* p (M x S) <0.05





Table 6. Mean volume productivity (m3/ha/yr) of rubber plantation as influenced by age gradation and site

condition


Mean volume productivity (m3/ha/yr)

4 YAP 7 YAP 10 YAP

Mundgod 1.86cB

3.85bB

6.14aB

Sagara 3.68cA

7.20bA

10.82aA

F value (M) 6459.77* p (M) <0.05

F value (S) 6498.80* p (S) <0.05

F value (M x S) 408.98* p (M x S) <0.05




Fig. 1. Mean seasonal diameter increment (cm) of rubber plantation in immature phase (4 year)

0

0.2

0.4

0.6

0.8

1

1.2

Summer Monsoon Winter

0.32

1.03

0.64

0.26

0.82

0.4

Sea

son

al

incr

emen

t (c

m)

Sagara Mundgod


Fig. 2. Mean seasonal diameter increment (cm) of rubber plantation in juvenile phase (7 year)

Fig. 3. Mean seasonal diameter increment (cm) of rubber plantation in early mature phase (10 year)

CONCLUSION

It can be concluded from present study, temperature

and water availability are most important constraint

for growth and productivity of Hevea and it can be

conveniently raised in Sagara zone. RRII 105 was

proved to be unsuitable for growing in drought prone

conditions in this era of climate change. Genotype

environment interactions (GEI) studies could pave

way in identifying best suited clones for dry

conditions. Also, a rubber based agroforestry system

as opposed to rubber monoculture would be an

excellent option and is recommended, as it could

improve the productivity and economic feasibility for

livelihood of farmers.

REFERENCES

Chandrashekhar, T. R.;Gireesh, T.; Raj, S.;

Mydin, K. K. and Mercykutyy, V. C. (2003). Girth

growth of rubber (Hevea brasiliensis) trees during

the immature phase. Journal of Tropical Forest

Science, 7(3): 399-415.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


0.27

0.82

0.4

0.13

0.62

0.31

Sea

son

al

in

crem

ent

(cm

)Sagara Mundgod

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


0.17

0.63

0.35

0.1

0.45

0.25

Sea

son

al

incr

emen

t (c

m)

Sagara Mundgod


Guohua, F. and Haiping, X. (2005). Emperical

analysis on supply and demand of NR in China.

International Natural Rubber Conference, 6-8

November, Cochin. 4: 7-12, Kerala, India.

Krishnan. B. (2015). Growth assessment of popular

clones of natural rubber under warm dry climatic

conditions of Chhattisgarh, Central India, Journal of

Experimental Biology and Agricultural Sciences,

3(2): 157-161.

Shorrock, V. M.; Templetons, J. K. and Lyer, G.

C. (1965). Mineral nutrition, growth and nutrient

cycle of Hevea brasiliensis III. The relationship

between girth and dry shoot weight. Journal of

Rubber Research, 19: 85-92.



YIELD MAXIMIZATION OF HYV AND SCENTED VARIETIES OF RICE

D.K. Gupta* and V.K. Singh

RMD College of Agriculture & Research Station, Ajirma, Ambikapur, Surguja (Chhattisgarh) –

497001 email id.: [email protected]

Department of Agronomy,

Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), 492 012 India


Abstract: The field experiment on “Yield maximization of HYV and scented varieties of rice” was conducted during kharif

seasons of 2015 at the Research Farm, IGKV, RMD College of Agriculture & Research Station Ambikapur, Surguja

(Chhattisgarh). In experiment first the main plot consisted two treatment of varieties viz. Chandrahasni (V1) and

Bamleshwari (C2). While the sub- plot consisted of seven treatments of nutrient management viz. N1- 20×10 cm with

RDF(120:60:40 NPK kg/ha), N2- 20×10 cm with 125% RDF(10% N at flowering)+5t FYM, N3- 15×10 cm with 125%

RDF(10% N at flowering)+5t FYM, N4- 20×10 cm with 150% RDF(K in two splits + 10% N at flowering)+5t FYM/ha, N5-

15×10 cm with 150% RDF(K in two splits +10% N at flowering)+5t FYM/ha, N6- 20×10 cm with 150% RDF(K in two splits

+ 10% N at flowering)+10t FYM/ha and N7- 15×10 cm with 150% RDF(K in two splits + 10% N at flowering)+10t

FYM/ha. In experiment second the main plot consisted two treatment of varieties viz. Jeerafool (V1) and Pusa Basmati (C2).

While the sub- plot consisted of seven treatments of nutrient management viz. N1- 20×10 cm with RDF(60:50:50 NPK

kg/ha), N2- 20×15 cm RDF+5t FYM, N3- 20×15 cm with RDF + 5t FYM + 5 t GM, N4- 20×15 cm with 75% RDF+10t

FYM/ha, N5- 20×15 cm with 50% RDF+10t FYM/ha+ 10 t GM + mechanical weeding, N6- 20×15 cm with 50% RDF+10t

FYM/ha+ 10 t GM + mechanical weeding + silicon spray + ZnSo4 and N7- 20×15 cm with 150% N + 10 t FYM +Staking.

The both experiment was laid out in split plot design with three replication. In experiment first the rice variety Bamleshwari

recorded significantly higher grain (7.93t/ha) and biological yield (16.82 t/ha) over Chandrahasni (6.97t/ha) and (14.74t/ha)

which was 13.7 and 14.1% higher. In case of nutrient management practices the higher grain and biological yield was

obtained with closer spacing and 150% RDF +10 t FYM (7.83 and 16.67 t/ha) followed by same geometry and dose of NPK

+ 5t FYM/ha (7.70 and 16.55 t/ha) the yields with these two treatments were at par, however wider spacing (20×10cm)

150% RDF+10t FYM gave marginally lower grain 2.2 and total yield 1.4% over closer spacing 15×10 cm. 150% RDF + 5t

FYM/ha may be on account of higher plant population (33% higher hills/m2) per unit area and the difference of only organic

manure FYM 5t/ha. In experiment second the Local scented fine rice variety Jeerafool had significantly tallest plants while

Pusa Basmati-1 had the shortest plants, number of total tillers, effective tillers (panicle/m2) and 1000-grain weight were

significantly higher under Pusa Basmati -1 but panicle length number of grains/ panicle and panicle weight were

significantly higher under jeerafool over Pusa Basmati -1. The data grain yield showed significant differences in the both

rice cultivars. Jeerafool produced 11.4 % higher grain yield over Pusa Basmati-1. Application of 50% NPK of RDF with 10 t

FYM + 10 t GLM+, mechanical weeding + silicon 3% spray+ 20 kg/ha ZnSo4 produced grain and biological yield of 4.3

and 9.16 t/ha, respectively while both the yields were almost equal in application of 150% N only with 10t FYM + staking

and the yield were also at par with the treatment i.e, 50% NPK + 10t FYM + 10t GLM + mechanical weeding.

Keywords: Yield maximization, Rice, Fertilizer, Production

INTRODUCTION

ice is the most consumed cereal grain in the

world, constituting the dietary staple food for

more than half of the planet’s human population. In

world, rice has occupied an area of 156.7 million

hectares, with a total production of 650.2 million

tonnes in 2007 (FAO, 2008). In Asian countries, rice

is the main major staple crop covering about ninety

per cent of rice grown in the world, with two

countries, China and India, growing more than half

of the total crop. Rice provides about two-third of

the calorie intake for more than two billion people in

Asia and a third of the calorie intake of nearly one

billion people in Africa and Latin America.

Chhattisgarh popularly known as “Rice bowl of

India” occupies an area around 3610.47 thousand

hectares with the production of 5.48 million tonnes

and productivity of 1517 kg per hectare (Anonymous

2008-09). The prime causes of low productivity of

rice in Chhattisgarh are limited irrigation (28.0 %),

lack of improved varieties suitable to different

ecosystems low imbalance use of fertilizer and

insufficient weed management.

The rice yields are stagnating or declining in post

green revolution era mainly due to imbalance in

fertilizer, soil degradation, type of cropping system

practiced, lack of suitable rice genotypes and other

agro-techniques. Partial substitution of chemical

fertilizer with organic sources of nutrients is useful in

different rice-based cropping systems. The use of

excessive chemical fertilizer & pesticide are causing

environmental hazard. It is therefore necessary to

develop a suitable production system in this context

proper selection of varieties, optimum density

(spacing) per unit area and appropriate nutrient

management are important for achieving higher

yields. Hence, there is a need to identify suitable

verity, planting geometry & standardize the

conjunctive use of nutrients. A judicious combination

R

RESEARCH ARTICLE

806 D.K. GUPTA AND V.K. SINGH

of organic and inorganic fertilizer can maintain long

term soil fertility and sustain higher productivity of

crops. Urea with organic materials minimizes N loss

and increase N-use efficiency (Mishra, 1992).

Farmyard manure (FYM) is being used as a major

source of organic matter in field crops. Limited

availability of this manure is, however, an important

constraint in its use as a source of nutrients.

MATERIAL AND METHOD

The present investigation was carried out at College

Research Farm of Indira Gandhi Krishi

Vishwavidyalaya, RMD College of Agriculture and

Research Station, Ambikapur (C.G.) India during the

kharif season (July-November) 2015. The soil of

experimental field was sandy loam in texture, acidic

in reaction (pH 6.2), low in available nitrogen,

phosphorus and medium in potassium. The

experiment was laid out in split plot design with

three replication.

EXPT-A: The main plot consisted two treatment of

varieties viz. Chandrahasni (V1) and Bamleshwari

(C2). While the sub- plot consisted of seven

treatments of nutrient management viz. N1- 20×10

cm with RDF(120:60:40 NPK kg/ha), N2- 20×10 cm

with 125% RDF(10% N at flowering)+5t FYM, N3-

15×10 cm with 125% RDF(10% N at flowering)+5t

FYM, N4- 20×10 cm with 150% RDF(K in two splits

+ 10% N at flowering)+5t FYM/ha, N5- 15×10 cm

with 150% RDF(K in two splits +10% N at

flowering)+5t FYM/ha, N6- 20×10 cm with 150%

RDF(K in two splits + 10% N at flowering)+10t

FYM/ha and N7- 15×10 cm with 150% RDF(K in

two splits + 10% N at flowering)+10t FYM/ha.

EXPT-B: The main plot consisted two treatment of

varieties viz. Jeerafool (V1) and Pusa Basmati (C2).

While the sub- plot consisted of seven treatments of

nutrient management viz. N1- 20×10 cm with

RDF(60:50:50 NPK kg/ha), N2- 20×15 cm RDF+5t

FYM, N3- 20×15 cm with RDF + 5t FYM + 5 t GM,

N4- 20×15 cm with 75% RDF+10t FYM/ha, N5-

20×15 cm with 50% RDF+10t FYM/ha+ 10 t GM +

mechanical weeding, N6- 20×15 cm with 50%

RDF+10t FYM/ha+ 10 t GM + mechanical weeding

+ silicon spray + ZnSo4 and N7- 20×15 cm with

150% N + 10 t FYM +Staking.


EXPT: A - The two varieties, Chandrahasni and

Bamleshwari exhibited differences in growth, yield

attribution and finally grain & total dry matter

productions. Rice variety Bamleshwari recorded

significantly higher grain (7.93t/ha) and biological

yield (16.82 t/ha) over Chandrahasni (6.97t/ha) and

(14.74t/ha) which was 13.7 and 14.1% higher.

In case of nutrient management practices the higher

grain and biological yield was obtained with closer

spacing and 150% RDF +10 t FYM (7.83 and 16.67

t/ha) followed by same geometry and dose of NPK +

5t FYM/ha (7.70 and 16.55 t/ha) the yields with these

two treatments were at par, however wider spacing

(20×10cm) 150% RDF+10t FYM gave marginally

lower grain 2.2 and total yield 1.4% over closer

spacing 15×10 cm. 150% RDF + 5t FYM/ha may be

on account of higher plant population (33% higher

hills/m2) per unit area and the difference of only

organic manure FYM 5t/ha. The lowest yields (grain

and total dry matter) was recorded under 20×10 cm

with RDF (120: 60:40 kg NPK/ha) and this was

mainly due lower values of growth and yield

attributes. The higher yields were achieved in plots

where inorganic and organic sources were added

with either 125 or 150% higher RDF +5 or 10 t

FYM/ha. The findings are agreement with Lakpale et

al.,(1999) reported that 150% RDF +10 t FYM or

150% RDF + 5t FYM/ha could produce comparable

yield with latter owing to slow and steady release of

N, resulting in efficient utilization. The superior

performance of these 2 treatments may also be owing

to improvement in physical, chemical and

microbiological environment of soil favouring

increased availability of macro and micro-nutrients

(Sengar et al., 2000).

EXPT: B - Local scented fine rice variety Jeerafool

had significantly tallest plants while Pusa Basmati-1

had the shortest plants, number of total tillers,

effective tillers (panicle/m2) and 1000-grain weight

were significantly higher under Pusa Basmati -1 but

panicle length number of grains/ panicle and panicle

weight were significantly higher under jeerafool over

Pusa Basmati -1. The data on biological and grain

yield showed significant differences in the both rice

cultivars. Jeerafool produced 11.4 and 14.2 % higher

grain and biological yield, respectively over Pusa

Basmati-1.The findings are agreement with Sarawgi

et al., (2006).

Application of 50% NPK of RDF with 10 t FYM +

10 t GLM+, mechanical weeding + silicon 3%

spray+ 20 kg/ha ZnSo4 produced grain and

biological yield of 4.3 and 9.16 t/ha, respectively

while both the yields were almost equal in

application of 150% N only with 10t FYM + staking

and the yield were also at par with the treatment i.e,

50% NPK + 10t FYM + 10t GLM + mechanical

weeding. The treatments received 100% NPK + 5t

FYM or 100% NPK + 5t FYM + 5t/ha GLM or 75 %

NPK + 10t FYM/ha gave increased grain and

biological yield performance over 100% NPK alone

(RDF). Enhanced plant height and dry matter

accumulation at 60 DAT with the treatments of

100% NPK, 50% NPK, 75%NPK or 100% N only

combined with 5t or 10t of FYM and GLM might

have increased the values of yield – attributing

characters, viz, total tillers, effective tillers/m2,

grains/panicle and panicle weight though the panicle

length and seed weight remained unaffected. The

increase in plant height may be due to the greater

availability and steady supply of essential plant


nutrients during the entire period of crop growth.

However, when organic source of nutrients applied

and supplemented with inorganic sources of nutrients

enhanced the nutrient availability and helped in

increasing plant growth. The results are in agreement

with the finding of Jha et al. (2004) and Sarawgi et

al., (2006). Similar results had also reported by

Mhaskar et al.(2005).Therefore, it may concluded

that integrated nutrient management improves the

growth and yield of scanted rice a well as impairs the

soil health with adequate nutrient balance after

harvest of rice.

Table 1. Growth and yield attributes as influenced by HYV varieties of rice and nutrient management Treatment Plant

height

(cm)

Dry matter

accumulation (g/m2)

Tillers

(m2)

Panicles

(m2)

Panicle

length

(cm)

Grains/p

anicle

(No)

Panicle

weight

(g)

1000-

grain

wt. (g)

60 DAT 90DAT

Varieties

V1-Chandrahasni 98.6 655 1063 391 352 24.7 172.9 3.55 23.30

V2-Bamleshwari 100.3 713 1175 430 360 20.8 114.8 3.56 31.75

SEm± 1.80 6.0 44 4 8 0.6 16.8 0.08 0.34

CD at 5% NS 18.5 NS 12 NS 2.1 51.1 NS 1.04

Nutrient Management

N1- 20×10 cm with

RDF(120:60:40 NPK

kg/ha)

93.7 586 945 370 321 23.0 134.3 3.09 27.90

N2- 20×10 cm with 125% RDF(10% N at

flowering)+5t FYM

95.0 607 993 379 318 21.8 135.2 3.47 27.62

N3- 15×10 cm with 125%

RDF(10% N at flowering)+5t FYM

98.8 662 1075 401 360 22.7 141.5 3.64 27.32

N4- 20×10 cm with 150% RDF(K in two splits + 10%

N at flowering)+5t FYM/ha

102.3 708 1182 403 345 23.2 160.5 3.78 27.70

N5- 15×10 cm with 150%

RDF(K in two splits +10% N at flowering)+5t FYM/ha

100.2 702 1140 444 392 22.5 136.3 3.67 27.25

N6- 20×10 cm with 150% RDF(K in two splits + 10%

N at flowering)+10t

FYM/ha

103.2 767 1273 415 359 23.4 157.0 3.76 27.52

N7- 15×10 cm with 150%

RDF(K in two splits +

10% N at flowering)+10t FYM/ha

103.0 756 1221 462 397 22.8 142.2 3.86 27.38

SEm± 1.7 4 21 7 9 0.4 9.2 0.16 0.25

CD 5.0 13 60 21 27 NS NS 0.49 NS

Table 2. Yield and economics as influenced by HYV varieties of rice and nutrient management

Treatment Yield (ton/ha) Harvest

index (%)

Cost of

cultivation

(Rs./ha)

Net

Income

(Rs./ha)

B:C ratio

Grain Biological Straw

Varieties

V1-Chandrahasni 6.97 14.74 7.78 47 37837 69717 1.84

V2-Bamleshwari 7.93 16.82 8.89 47 37837 84740 2.24

SEm± 0.28 0.36 - - - - -

CD 0.89 1.13 - - - - -

Nutrient Management

N1- 20×10 cm with

RDF(120:60:40 NPK

kg/ha)

6.97 14.57 6.96 48 33720 73613 2.18


N2- 20×10 cm with

125% RDF(10% N at

flowering)+5t FYM

7.27 15.00 7.27 48 37002 74827 2.02

N3- 15×10 cm with

125% RDF(10% N at

flowering)+5t FYM

7.40 15.65 7.40 47 37002 77323 2.09

N4- 20×10 cm with

150% RDF(K in two

splits + 10% N at

flowering)+5t

FYM/ha

7.47 15.72 7.46 48 38284 76874 2.01

N5- 15×10 cm with

150% RDF(K in two

splits +10% N at

flowering)+5t

FYM/ha

7.70 16.55 7.70 47 38284 81021 2.11

N6- 20×10 cm with

150% RDF(K in two

splits + 10% N at

flowering)+10t

FYM/ha

7.53 16.32 7.50 46 40284 76182 1.89

N7- 15×10 cm with

150% RDF(K in two

splits + 10% N at

flowering)+10t

FYM/ha

7.83 16.67 7.83 47 40284 80828 2.01

SEm± 0.07 0.13 - - - - -

CD 0.22 0.39 - - - - -

Sale price of produce: Grain@ 14000 and straw @ 1300/t

Table 3. Growth and yield attributes as influenced by scented varieties of rice and Nutrient Management Treatment Plant

height

(cm)

Dry matter

accumulation

at 60 DAT

(g/m2)

Tillers

(m2)

Panicle

(m2)

Panicle

length

(cm)

Grains/p

anicle

(No)

Panicle

weight

(g)

1000-Seed

weight

Varieties

V1-Jeerafool 137.4 630 371.9 296.8 26.71 276.14 2.31 12.17

V2-Pusa Basmati-1 99.7 612 489.4 396.3 24.55 103.95 1.72 25.31

SEm± 2.2 14 13.2 19.0 0.36 15.14 0.12 0.28

CD 6.6 NS 40.0 58.0 1.08 46.12 0.35 0.85

Nutrient Management

N1-RDF (60:50:50)kg 112.7 584 369.7 308.8 24.95 151.67 1.61 18.77

N2-RDF+5t FYM 111.0 600 402.5 335.8 26.00 167.50 1.83 18.92

N3-RDF+5t FYM 5t GM 117.7 627 414.0 320.3 25.70 193.50 2.06 18.65

N4-75%RDF +10t FYM 117.5 645 433.3 345.8 25.62 192.83 2.03 18.28

N5- 50% RDF+10t FYM +

10t GM+ Mechanical

weeding

125.0 619 455.8 350.7 25.73 213.33 2.09 18.60

N6- 50% RDF+10t

FYM+10t GM+ Mechanical

weeding+ Silicon Spray +ZnSo4

122.8 625 469.7 375.0 25.58 201.67 2.21 18.65

N7- 150% N +10t FYM+

Staking

123.2 616 469.7 383.3 25.82 209.83 2.32 19.32

SEm± 2.4 7 8.5 13.9 0.49 9.08 0.10 0.28

CD 7.0 20 24.8 40.5 NS 26.51 0.30 NS

N1: 20x10 cm and N2-N7: 20x15


Table 4. Yield and economics as influenced by scented varieties of rice and nutrient management Treatment Yield (ton/ha) Cost of

cultivation

(Rs./ha)

Net Income

(Rs./ha)

B:C ratio

Grain Biological Straw

Varieties

V1-Jeerafool 3.94 8.82 4.88 35079 57945 1.65

V2-Pusa Basmati-1 3.52 7.72 4.20 35079 47821 1.36

SEm± 0.07 0.11 - - - -

CD 0.20 0.33 - - - -

Nutrient Management

N1-RDF (60:50:50)kg 2.9 6.90 3.99 32822 36385 1.11

N2-RDF+5t FYM 3.2 7.45 4.21 34822 41931 1.20

N3-RDF+5t FYM 5t GM 3.6 8.10 4.46 36322 49556 1.36

N4-75%RDF +10t FYM 3.7 8.10 4.38 34765 52769 1.52

N5- 50% RDF+10t FYM +

10t GM+ Mechanical

weeding

4.0 8.78 4.77 33207 61214 1.84

N6- 50% RDF+10t FYM+10t

GM+ Mechanical weeding+

Silicon Spray +ZnSo4

4.3 9.16 4.86 34181 66776 1.95

N7- 150% N +10t FYM+

Staking

4.3 9.10 4.80 33761 67079 1.98

SEm± 0.1 0.23 - - - -

CD 0.4 0.66 - - - -

N1: 20x10 cm and N2-N7: 20x15 cm

Sale price of produce: Grain@ 22000 and straw @ 1300/t

REFERENCES

Jha, S.K., Tripathi, R.S. and Malaiya, S. (2004).

Influence of integrated nutrient management

practices on growth and yield of scented rice (Oryza

sativa L.). Annals Agricultural Research 25 (1):

159-161.

Lakpale, R., Pandery, N. and Tripathi, R.S.

(1999). Effect of levels of nitrogen and

preconditioned urea on grain yield and N status in

plant and soil in rainfed rice. Indian journal of

Agronomy 44 (1): 89-93

Mhaskar, N.V., Thorat, S.T. and Bhagat, S.B.

(2005). Effect of nitrogen levels on leaf area index

and grain yield of scented rice vaieties. Journal Soils

and Crops. 15 (1): 218-220.

Mishra, M.M. (1992). Enrichment of organic

manures with fertilizers. (In) Non-traditional sectors

for Fertiliser Use, pp. 48-60. Tendon, H.L.S. (Ed.)

fertilizer Development and Consultant Organization,

New Delhi.

Sarawgi, S.K, Sarawgi, A.K., Purohit, K.K. and

Khajanji, S.N. (2006). Effect of nutrient

management on tall and short to medium slender

scented rice verities in alfisol of Chhattisgarh plain.

Journal of Agricultureal Issues 11 (1): 91-93.

Sengar, S.S., Wade, L.J., Baghel, S.S., Singh, R.K.

and Singh, G.(2000). Effect of nutrient management

on rice in rainfed low land of southeast M.P. Indian

Journal of Agronomy 45 (2): 315-322.



PLANT GROWTH PROMOTING RHIZOBACTERIA IMPROVES GROWTH IN

ALOE VERA

Meena1, Nayantara

2* and Baljeet Singh Saharan

1

1Microbial Resource Technology Laboratory, Department of Microbiology

Kurukshetra University, Kurukshetra 2Department of Bio and Nanotechnology, Guru Jambeshwar University of Science

and Technology, Hisar, Haryana


Received-08.08.2017, Revised-22.08.2017 Abstract: Sustainableagriculture involves the use of biofertilizers and biopesticides to reduce the application of chemical

fertilizers. Microbial consortium-based sustainable and economicbionutrient package for Aloe vera has been developed to

reduce reliance on chemical fertilizers. Consortium includes Acinetobacter radioresistens SMA4, Bacillusthuringiensis

SMA5, Brevibacteriumfrigoritolerans SMA23 and Pseudomonas fulva SMA24. In the earlier studies all these four bacterial

strains have been found to possess multiple plant growth promoting attributes. Consortium used in this study increased all

biometric parameters in Aloe verasuch as plant biomass, root weight, shoot weight and gel content. Increase in aloinA

content was also observed in this study in plants treated with PGPR.

Keywords: Aloe vera, Consortium, PGPR, Aloin, Biofertilizer

INTRODUCTION

loe belongs to familyLiliaceae, is well known

for its marvelous medicinal properties. Aloe

verais a unique plant which is a rich source of many

chemical compounds and plays an important role in

the international market. Aloe veraplant is traded in

the medicinal drug market for flavoring liquid

formulations (Rajendranet al., 2007). Chemistry of

this plant revealed the presence of more than 200

different biologically active substances including

vitamins, minerals, enzymes, sugars, anthraquinones

or phenolic compounds, lignin, saponins, sterols,

amino acids and salicylic acid (Vogler and Ernst,

1999; Durejaet al., 2005; Park and Jo, 2006;

Chauhan et al., 2007).The gel present inside the

leaves of Aloe plant contains phenolic compounds

like aloin-A (barbaloin), aloesin, isoaloeresin D and

aloeresin E used in the treatment of tumors, diabetes,

ulcers and cancer (Ishii et al., 1990; Okamura et al.,

1996; Park et al., 1998). Aloin-A (barbaloin,

C21H22O9) is the major phenolic compound reported

from the plant (Groom and Reynolds, 1987).

Demand for this medicinal plant is increasing

worldwide. So, there is need to find associated

microbes which can promote plant growth without

having any negative impact on soil and environment.

Earlier reports have shown the effect of arbuscular

mycorrizal fungi and Azotobacter on the growth and

barbaloin content ofthe plant (Tawarayaet al., 2007;

Pandey and Banik, 2009). In the present study, the

effect of PGPR is reported for plant growth

parameters and the aloin-content in A. barbadensis.

The use of beneficial soil plant growth promoting

rhizobacteria (PGPR) for improving crop production

requires the selection of rhizosphere-competent

bacterial strains with multiple plant growth

promoting attributes (Nautiyalet al., 2008; Hynes et

al., 2008).PGPR promote plant growth directly by

either facilitating resource acquisition (nitrogen,

phosphorus and essential minerals) or modulating

plant hormone levels, or indirectly by decreasing the

inhibitory effects of various pathogens on plant

growth and development inthe forms of biocontrol

agents (Glick, 2012).

MATERIAL AND METHOD

Plant growth-promoting rhizobacteria used in this

study has been isolated from Aloe vera rhizosphere.

The four PGPR strains, Acinetobacter radioresistens

SMA4, Bacillusthuringiensis SMA5,

Brevibacteriumfrigoritolerans SMA23 and

Pseudomonas fulva SMA24 were found to possess

various plant growth promoting attributes such as

phosphate solubilisation, IAA production,

Siderophore production and Antifungal assay. The

identification of the isolates was done on the basis of

morphological, Biochemical and 16S rRNA studies

(Meenaet al., 2014; Meenaet al., 2017). The 16S

rDNA sequences of the isolates were deposited in

NCBI GenBank under the accession

numbersJQ618289, KC663437, KC986860 and

JQ618289 respectively. The cultures were

maintained on nutrient agar slants at 6 °C in a

refrigerator with regular subculturing. For inocula

preparation, the cultures were grown separately in

nutrient broth at 28 ± 2 °C. To obtain bacterial

cultures in mid log phase, flasks were incubated for

24h up to a cell density of 8 ×109CFU ml

-1on a rotary

shaker at 30 °C. Bacterial cells were harvested by

centrifugation (7000 rpm for 20 min). After removal

of the culture medium, the bacterial pellet was

washed in sterile water and centrifuged again (7000

A

RESEARCH ARTICLE


812 MEENA, NAYANTARA AND BALJEET SINGH SAHARAN

rpm for 20 min). Bacterial cells were then

resuspended in sterile saline solution and cell density

was adjusted to get approximately8 ×109 CFU ml

-

1(Van et al., 2000).

Experimental design and green house treatments

The two efficient plant growth promoting bacteria

from the in vitro experiments were analysed for

studying the efficacy on plant growth promotion in

vivo under greenhouse condition using pot culture

experiments. The experiment was arranged in a

complete randomized design (CRD) with three

replications per treatment.

Pot preparation

All experiments reported in this paper were

conducted in the greenhouse at the Department of

botany, Kurukshetra University Kurukshetra. Each

PGPR isolate was grown in nutrient broth at 30 °C in

an orbital shaker (150 rev min−1

) for 24 h. Cultures

were centrifuged in 50 ml sterile plastic tubes at 6000

× g for 15 min. The pellets were re-suspended in

nutrient broth to obtain a final concentration of 108

ml−1

colony forming units (CFU). The liquid cultures

of each isolate were used for individual inoculations.

About 10 treatments were designed for experiment

which includes individual inoculant and in

combination. Consortium was prepared by mixing all

individual cultures of isolates of equal cell density

108 CFU ml

−1 into 250 ml sterilized flask and was

used as a mixed inoculum (Mamtaet al., 2011).

Experiments were conducted in the greenhouse

(uncontrolled conditions) during March-August,

2014. Unsterile loamy soil (pH, 7.6; total organic C,

0.12%; available N, 42.8 mg kg−1

; available P, 6 mg

kg−1

; available K, 15.8 mg kg−1

; total Ca, 0.6 m Eq

100 g−1

; total Mg, 0.10 m Eq 100 g−1

) was

thoroughly mixed, passed through a 2 mm sieve and

dried at sunlight for 7 days. Ethanol disinfected

earthen pots (20 cm diameter × 25 cm height) were

filled with 5.0 kg of soil. Roots of tissue cultured

plantlets were sterilized by dipping in 2% NaOCl

solution for 10 min and then washed three times with

sterile distilled water. Roots were dipped into the

selected bacterial suspension (108 CFU ml

−1).

Experiments were performed in a completely

randomized block design. Plantlets were planted

under two different soil conditions and ten different

treatments; (i) Acinetobacter radioresistensSMA4(ii)

Bacillus thuringiensis. SMA5 (iii)

Brevibacteriumfrigoritolerans SMA23 (iv)

Pseudomonas fulva SMA24 (v) SMA4+SMA5 (vi)

SMA4+SMA23 (vii) SMA5+SMA24 (viii)

SMA5+SMA23 (ix) SMA4 + SMA5 + SMA23 (x)

SMA5 + SMA23 + SMA24 (xi) SMA4 + SMA5 +

SMA24 (xii) Mixture of all PSB.

Quantitative high-performance liquid

chromatography (HPLC) analysis of aloin-A

Leaves of harvested A. barbadensis were washed

with running tap water and then twice with distilled

water. The inner gel was mechanically separated

from the outer cortex of the leaf with the help of a

knife. Total gel was extracted from all the leaves of a

plant, then freeze dried (Heto dry winner, Model DW

1-0-110, Allerd, Denmark). The 0.5 g of each freeze-

dried gel sample was extracted with 10.0 ml

methanol for 24 h at 4 °C (Okamura et al.,1996). The

suspension was centrifuged at 4000 × g for 10 min at

4 °C. The supernatant was subjected to HPLC

(WATER Corporation, USA, Lichrocart® C- 18

coloumn, flow rate of 0.8 ml min−1

, 800 psi, run time

35.0 min). The mobile phase was 0.1% acetic

acid/acetonitrile (60:40). Analytes were detected at

wavelength 290 nm with a photodiode array detector

and identified by their retention time and by spiking

the sample with standard aloin-A (Sigma-Chemical,

USA). Quantification was done by reference to the

peak area percentage obtained for the standard

compound.


Plant Growth Promoting Rhizobacteria (PGPRs) are

able to exert a beneficial effect upon plant growth. In

rhizosphere, beneficial interactions between plant

and microbes are main determinant for plant growth

and development (Jeffries et al., 2003).

Effect of PGPR on plant growth

The PGPR treatments (applied individually or as a

consortium) increased all parameters of A.

barbadensisin un-amended soil with the consortium

treatment yielding better results than each individual

treatment. In plants treated with single PGPR strains,

maximum stimulatory effects on various biometric

parameters were obtained by Pseudomonas fulva

SMA24 followed byBacillus thuringiensis SMA5,

Brevibacteriumfrigoritolerans SMA23 and

Acinetobacter radioresistens SMA4.

SMA5significantly (P≤ 0.05) increased Plant

biomassby 105.72%, root weight by 111.67%, shoot

weight by 137.23%, total number of leaves weight by

63.23%, total gel volume by 193% as compared to

the control plants (Table 1).The individual PGPR

treatment also showed significant stimulatory effects

on plant growth. The application of PGPR as a

consortium increased all biometric parameters of A.

barbadensisplants grown in pots more than

individual plant growth promoting

rhizospherictreatments (Table 1). Compared to

control plants, an increase of over 213% in total gel

volume was observed in plants treated with the

consortium. In individual treatments, the degree of

stimulation varied with respect to the type of growth

parameter. Maximum increase in plant biomass

(130.03%) and root weight (111.49%) was shown by

T9treated plants after consortium(Table 1). The

plants treated with SMA4 and SMA23 showed

smaller increases in growth parameters than SMA24

and SMA5 treated plants. The increase in growth

parameter may be due to increase in availability of

nutrients such as nitrogen and phosphorus, and

increase in Indole acetic acid production by PGPR.


Similarly, promotion in plant height, number of

tillers, plant dry weight and grain yields of various

crop plants in response to inoculation with PGPR

were reported by other workers (Chen et al., 2008;

Khalid et al., 2004; Biswas et al., 2000; Hilaliet al.,

2000).

Table 1. Growth Enhancement in Aloe vera plant due to PGPR inoculation

Treatments Plant Biomass

(g pot-1

)

Root wt.

(g pot-1

)

Shoot wt.

(g pot-1

)

Leave wt.

(g pot-1

)

Gel wt.

(g pot-1

)

Control 180.40 40.24 120.78 26.60 8.52

T1 SMA4* 270.99 60.55 210.06 46.37 19.11

19,11

T2 SMA5* 352.00 55.83 300.57 41.61 24.95

T3 SMA23* 301.87 62.78 239.43 39.65 23.06

T4 SMA24* 371.13 85.18 286.53 43.42 25.02

T5 SMA4+SMA5 372.00 65.52 305.04 74.44 22.63

T6 SMA4+SMA23 350.64 64.75 286.59 52.30 23.94

T7 SMA5+SMA24 402.26 86.49 310.68 54.09 24.45

T8 SMA5+SMA23 310.81 60.15 250.82 41.84 13.89

T9 SMA4+SMA5+SMA

23

415.60 72.20 343.98 74.42 18.96

T10 SMA5+SMA23+SM

A24

330.18 51.38 279.51 52.28 25.42

T11 SMA4+SMA5+SMA

24

382.24 60.56 240.48 56.29 16.61

T12 SMA4+SMA5+SMA

23+SMA24

435.54 90.39 345.55 74.59 26.71

SEm 1.11 0.28 0.53 1.00 0.90

CD 3.08 0.78 1.47 2.76 2.50

Effect of PSB on aloin-A content

Aloeplants grown in soil treated with PGPR strains

showed higher aloin-A content than plants grown in

soil treated with control (Fig. 1). The increase in the

aloin-A content was due to both the increase in

biomass of Aloeplants and the increased biosynthesis

of aloin-A as compared to control plants. The PGPR

consortium treated plants grown in pots showed

maximum yield of aloin-A and also the highest

increase in thealoin-A content per plant. Amongst

individual PGPR treatments, a maximum increase

184% was observed with P. fulvatreated plants

followed by Bacillusthuringiensis SMA5

(152.6%)(Fig. 1). Lower values were obtained with

plants treated with SMA4 and SMA23. A significant

(P ≤ 0.01) positive correlation was found between

PGPR attributes and aloin-A biosynthesis (mg g−1)

in all plants grown in pots.Similar reports are

observed by Gupta et al. (2014) that use of

PGPRconsortium in Aloe vera greatly influences the

aloin-A production due to higher plant biomass.


Fig. 1. Influence of PGPR treatment on the aloin-A content. Error bars represent standard deviation. Bars with

same letter are not significantly different according to LSD at P ≤0.05.

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0

2

4

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8

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16A

loin

co

nte

nt

(mg

)

Treatment(s)

aloin (mg/plant)

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J. Gen. Pract., 49: 823-828.



INFLUENCE OF THERMAL ENVIRONMENT ON PHENOLOGY, GROWTH,

YIELD AND DEVELOPMENT OF MUSTARD (BRASSICA JUNCEA L.) VARIETIES

R.K. Kashyap1, S.K. Chandrawanshi

3*, Pandhurang Bobade

2, S.R. Patel

1, J.L. Chourdhary

1

and Deepak K. Kaushik1

1 Department of Agrometeorology, College of Agriculture, Indira Gandhi

Krishi Vishwavidhyalaya Raipur (C. G.) 2 RMD College of Agriculture & Research Station, Ambikapur (IGKV) (C.G.)

3Agricultural Meteorological Cell, Department of Agricultural Engineering,

N.M College of Agriculture, Navsari Agricultural University, Navsari (GJ)



Abstract: Among various important growth characters of these Mustard varieties, plant height was greatly influenced under

different thermal environments. Maximum plant height was observed in variety Varuna E1 (29th November) and minimum

height was recorded in E3 (19th December). First date of sowing had more duration from sowing to maturity as compared to

delayed sowing. This shortening of duration was due to thermal stress at later sowing dates. From phenological development

point of view, the thermal insensitivity of all the varieties was assessed based on the TSI and it was found that Vardan,

Kranti and Varuna Mustard varieties were tolerant to thermal stress. Different Mustard varieties show non significant

results under different thermal environments but the seed yield (kg/ha) showed significant results under different thermal

regimes.

Keywords: Thermal environment, Phenology, Development, Brassica juncea

INTRODUCTION

egetable oils have become an indispensable part

of present day civilized life being next to food

grains in importance. Oilseeds are the second largest

agricultural commodity after cereals. Oilseed

constitutes a foremost group of crops next only to

cereals. Oilseeds are vital source of energy, essential

fatty acids and fat soluble vitamins. Besides being a

plentiful source of edible oil and cooking media,

these serves as an important raw material for various

industrial products

Among all oilseed crops rapeseed and mustard is a

major oilseed crop of the world being grown in fifty

three (53) countries across the six (6) continents. It is

yet used not only as media in cooking but also as

condiment, medicine, in preparation of hair oil, soap

manufacturing of mineral oil and grease for

lubrication for softening of leather etc. Due to its

varied uses it is a principal oilseed crop of world

with edible as well as industrial importance.

The rapeseed and mustard crop had worldwide area

of 6.33 million hectare and production of 6.69

million tons an average yield of 1057* kg ha-1

.

(*Advance estimates as on 04.04.2007) In world

China and Canada leads in total area and production,

however UK ranks first in productivity of crops

rapeseed and mustard. India is one of the major

country in vegetable oil scenario of the world. At

present time nearly four (4) million farmers are

involved in oilseed production in the country. There

was five time increased in oilseed production during

the period 1950 to 2004 under predominantly rainfed

agro-ecological conditions. Which is higher than

corresponding production increase in total food

grains. During 2003-2004 total production of

rapeseed and Mustard was 5.83 million tones from an

area of 5.06 million hectare with an average

productivity of 935 kg/ha (Anonymous, 2005)

currently. India is witnessing crop shifts in favour of

oilseeds especially in non-traditional areas, owing to

growing great demand in oil development of high

yielding varieties suitable for an array of agro-

ecological situations and favorable price structure in

comparison with the traditional crops. Major oilseed

crops grown in India are groundnut, rapeseed

mustard, sesamum, soybean, safflower, sunflower

and linseed. As much as ninety per cent (90%) of the

total edible oil production in our country comes from

two oilseed crop viz. groundnut and mustard.

MATERIAL AND METHOD

Experimental details

Different thermal environment and mustard varieties

with different plant population were used. The

treatment combinations of three dates of sowing and

three varieties and two plant density of Mustard were

laid out in Factorial Randomized Block Design with

three replications.

Thermal sensitivity

The thermal stress is mainly expressed through the

duration for maturity by any crop. For evaluation of

thermal sensitivity of the genotypes the following

index was used as developed by Sastri et al. (2001).

Range of duration to maturity

with different sowing dates

TSI = x 100

Average duration

V

RESEARCH ARTICLE


818 R.K. KASHYAP, S.K. CHANDRAWANSHI, PANDHURANG BOBADE, S.R. PATEL, J.L. CHOURDHARY

AND DEEPAK K. KAUSHIK

On the basis of TSI (thermal sensitivity index) thermal sensitivity is categorized as follows TSI Thermal sensitivity Genotypes

< 5 Tolerant -

5.1 – 10 Moderately tolerant -

10.1 – 15 Moderately susceptible -

> 15 Susceptible -


In Chhattisgarh, due to shorter winter span and

temperature fluctuations the productivity of rapeseed

and mustard fluctuates considerably. It is therefore,

necessary to identify suitable high yielding varieties

of mustard for late sown conditions under rice based

cropping system which can tolerate the high

temperature during reproductive and seed filling

stages. Thermal units are used for describing the

temperature responses to growth and different

phenophases of the crop life cycle.

Phenological studies

The duration (days) taken for commencement of

different phenological events viz., emergence (P1),

first flower appearance (P2), 50 % flowering (P3),

start of seed filling (P4), end of seed filling (P5) and

physiological maturity (P6) by different mustard

varieties under different thermal environments. Also

given values in parenthesis are the numbers of days

taken to each particular phenophase (P1, P2, P3, P4,

P5 and P6).

Data presented in Table 5 reveals that the duration

for emergence enhanced by 1 day early sown varuna

in (E2) under both the spacing S1 (30x10cm) and S2

(40x10cm). In general, 6 days were taken for

emergence (P1) by 29th

November (E1S1 and E1S2)

and 19th

December sown crop, while it was 5 days

under 09th

December sown crop (E2S1 and S2) in

different varieties. On the other hand, the duration

for first flower appearance (P2), 50 % flowering

(P3), start of seed filling (P4), end of seed filling (P5)

and physiological maturity (P6) were shortened in

case of environment in E2S1 and S2 there after

increased in E3S1 and S2 as the sowing was delayed

from 29th

November to 09th

December and 19th

December. The decreases in duration for different

phenological events were different for different

varieties. The decrease in first flower appearance

(P2) stage was 1 day in case of variety Varuna and it

was also 1 day in case of Kranti from 29th

and 09th

December sown crop. Similar results were also

obtained by Saran & Giri (1987).

Similarly the duration for 50 % flowering (P3), start

of seed filling (P4) and end of seed filling (P5) varies

in different varieties when the sowing was delayed.

All the varieties behaved differently in different

phenological stages. The duration for 50 % flowering

(P3) was reduced by 6 to 7 days thus the difference

increased as the crop growth progressed towards start

of seed filling (P4), end of seed filling (P5) and

maturity (P6) stages. The decrease in duration for

start of seed filling (P4) was only 1 day in case of

variety Kranti. The decrease in duration (days) in 09th

December and 19th

December as compared to 29th

November in different Mustard varieties is shown in

Table 6.

It was recorded from the data given in Table 6 that

the duration for maturity decreased by 3 days in

E2S1 (30x10cm) and E2S2 (40x10cm) spacing

varieties Vardan, 6 days in E3S1 (30x10cm) and

E3S2 (40x10cm) spacing varieties Vardan as

compared to E1 in different varieties which Varuna

variety mature 5 days earlier in E2S1 and E2S2 and

also mature 7 days earlier under E3S1 and S2 as

compared to E1S1 and E1S2.

Where as in case of variety Kranti the duration from

maturity decrease by 4 days from E1S1, E1S2 to

E2S1, E2S2 and 6 days in case of E1 to E3 in both

the spacing S1 and S2.Among the variety maturity of

Varuna decreases in days from E1S1, E1S2 to E2S1,

E2S2 and E1 to E2 and E1 to E3 are 5 and 7 days

respectively. Decrease in days from E1 to E2 and E3

is mainly due to delayed sowing which result in early

attainment of heat unit that enhance early maturity.

The above finding is in conformity with the finding

of Kar and Charkravarty (1999) in case of variety

B.O.-54, Pusa Bold and T-9 under Delhi climatic

(semi arid) condition.

Thermal sensitive

The thermal sensitive index (TSI) of different

mustard varieties is shown in Table 1 and is assessed

as per criteria as shown in Table 2. As mentioned

earlier, Vardan, Kranti and Varuna was found

insensitive thermal stress.

Table 1. Thermal Sensitive Index (TSI) values of different Mustard Varieties

Varieties TSI values

Vardan 3.1

Kranti 2.3

Varuna 2.5


Table 2. Categorization of the effect of thermal stress based on TSI

TSI Value Thermal Sensitive Varieties

< 5 Insensitive Vardan, Kranti and Varuna

5.1 to 10.0 Moderately insensitive Nil

10.1 to 15.0 Moderately sensitive Nil

> 15 Sensitive Nil

Among different varieties i.e.Vardan, Kranti and

Varuna TSI value is in between < 5 (3.1), (2.3) and

(2.5), which is to be categorized as thermal

insensitive. Similar result was also found by Kar and

Chakravarty (1999) in case of Brassica species under

semi arid environment.

Plant population

Variations in plant population in the three thermal

environments studied are presented in Table 7 plant

population showed statistically significant difference

for different thermal environments. Maximum plant

population of 36.0 /m2 was recorded in 29

th

November sowing in S1 spacing (30x10cm) followed

by 35.8 /m2

for 09th

December, 34.0 /m2

19th

December sowing. Highest plant population was

recorded for variety Varuna(30.6 /m2), statistically at

par with Kranti (30.6 /m2)

and was lowest for cultivar

Vardan (30.1 /m2). It is clear that, only spacing (S)

treatment thermal environments (E) showed

significant variation for plant population.

As we compare the spacing, it is observed that the

plant population is always higher in S1 (30x10cm)

than S2 (40x10cm) spacing under all dates of

sowing. The above findings is in close conformity

with findings of Kumar et al., (1996)

Plant height

The plant height of the different mustard varieties

were recorded at 10 day intervals from sowing to

harvesting is given in Table 8.

It was observed that plant height increased with

advancement in crop growth and reached to

maximum at harvesting. The increase in plant height

took place slowly up to 25 DAS. Maximum rate of

increase in plant height was observed between 45- 55

DAS. Differences in plant height were significant

due to dates of sowing (E) (viz., at 25 DAS to

harvest.). The Interaction between (VxS) and (V)

differed significantly except at 25 DAS, 65 DAS.

Whereas the (ExV), (ExS), (ExVxS) and (S) are the

non-significant observed in 25 DAS to at harvest. In

general, higher plant height was observed in E1 as

compared to E2 and E3. Lowest plant height was

recorded in E3 in all the varieties. At maturity stage,

Varuna attained maximum height (161.9 cm)

followed by Vardan (160.6 cm) Kranti attained the

lowest height (159.5 cm). The minimum decrease

in plant height was noticed in Kranti in E2 and E3

under both the spacing. Whereas, the Varuna

attained maximum height in case of E2S2 (4.4)

and E3S2 (5.7) in both the sowing dates i.e. (E2

and E3) are given in Table 8.

The decrease in plant height from E1 to E2 and E3

is lowest in case of cv. Kranti, which is mainly due

to the effect of thermal stress. Similar result was

also found by Thakur and Singh (1998). In order to

quantify the thermal stress on plant height a criterion

was development as shown in Table 8.

Table 3. Thermal stress status

Percentage decrease in plant height at maturity Thermal stress status

< 5 per cent

5.1-10.0 per cent

10.1-1.5 per cent

>15 per cent

Tolerant (T)

Moderately tolerant (MT)

Moderately susceptible (MS)

Susceptible (S)

The decrease in height from E1 to E2 and E3 is

lowest in case of Kranti, which is mainly due to the

effect of thermal stress. Similar result was also

indicated by Thakur and Singh (1998).

As per the above criterion, the thermal stress statuses of

different varieties at E2 and E3 have been analyzed and

the results are shown in Table 8.

Table 4. Categorization of thermal stress tolerance of Mustard varieties in different thermal environments

Variety E2 E3

Vardan 1.86 (T) 3.05 (T)

Kranti 0.37 (T) 2.13 (T)

Varuna 2.18 (T) 2.49 (T)

From the above analysis it can be seen that in percentage decrease in height Vardan, Kranti and Varuna can be

ranked as number one as it showed tolerance to thermal stress in E2 and E3.



Table 5. Effect of different thermal environments on different phenophases in Brassica species.

Varieties P1 P2 P3 P4 P5 P6

S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2

Vardan

E1 6 6 41 41 48 48 58 58 90 90 109 109

E2 6 6 42 42 50 50 56 56 85 85 106 106

E3 6 6 40 40 49 49 58 58 86 86 103 103

Kranti

E1 6 6 41 41 48 48 56 56 89 89 109 109

E2 6 6 44 44 52 52 58 58 86 86 105 105

E3 6 6 42 42 50 50 58 58 87 87 103 103

Varuna

E1 6 6 42 42 48 48 60 60 90 90 111 111

E2 5 5 42 42 50 50 57 57 88 88 106 106

E3 6 6 43 43 51 51 57 57 86 86 104 104

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007

P1 –Emergence, P2 –First flower appearance, P3 -50%Flowering

P4 –Start of seed filling, P5 –End filling and, P6 –Physiological maturity.

Table 6. Effect of different thermal environments on maturity duration of Mustard varieties (days)

S. No. Decrease in days from E1 to E2 and E3

Variety E1 E2 E3

S1 S2 S1 S2 S1 S2

1 Vardan 109 109 106 (3) 106 (3) 103 (6) 103 (6)

2 Kranti 109 109 105 (4) 105 (4) 103 (6) 103 (6)

3 Varuna 111 111 106 (5) 106 (5) 104 (7) 104 (7)

The values in parenthesis are difference in duration from E1.

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007

Table 7. Plant population / m2 of Mustard varieties as influenced by different thermal environments

Varieties

Plant population /m2

Mean E1 E2 E3

S1 S2 S1 S2 S1 S2

Vardan 35.0 25.3 34.5 25.5 34.6 25.6 30.1

Kranti 36.4 26.0 36.5 25.0 34.3 25.1 30.6

Varuna 36.7 26.1 36.5 26.0 33.3 25.1 30.6

Mean 36.0 25.8 35.8 25.5 34.0 25.2

E V S ExV ExS VxS ExVxS

SEm± 0.36 0.36 0.29 0.62 0.51 0.51 0.88

CD (5%) 1.03 NS 0.84 NS NS NS NS

CV (%) 4.99

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007, NS – (Non significant).

Table 8. Decrease in plant height (cm) from E1 to E2 and E3 in different mustard varieties at maturity

Varieties

Decrease in plant height (cm) from E1 to E2 and E3

Mean E1 E2 E3

S1 S2 S1 S2 S1 S2

Vardan 160.1 160.6 157.6 (2.5) 157.9 (2.7) 155.7 (4.4) 156.3 (4.3) 158.0

Kranti 159.1 159.5 158.5 (0.6) 157.2 (2.3) 155.5 (3.6) 156.8 (2.7) 157.8

Varuna 160.4 161.9 156.9 (3.5) 157.5 (4.4) 156.4 (4.0) 156.2 (5.7) 158.2

Mean 159.7 160.7 157.7 157.5 155.9 156.4

The values in parenthesis are difference in height (cm) from E1

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007

Table 9. Grain yield (kg ha-1

) of Mustard varieties as influenced by different thermal environments Varieties Grain yield (kg/ha-1) Mean

E1 E2 E3

Vardan S1 1425.5 1199.8 1232.6 1286.0

S2 1427.5 1317.4 1219.1 1321.3

Kranti S1 1521.3 1296.3 1159.3 1325.6

S2 1549.0 1039.7 1178.6 1255.8

Varuna S1 1477.6 1213.3 1257.7 1316.2

S2 1442.9 1246.1 1153.5 1280.8

Mean 1474.0 1218.8 1200.5

E V S ExV ExS VxS ExVxS

SEm± 39.70 39.70 68.76 32.42 58.15 56.15 97.25

CD (5%) 114.10 NS NS NS NS NS NS

CV (5%) 12.99

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007, NS – (Non significant).


Grain yield (kg ha-1

) Grain yield of mustard as affected by various

treatments are presented in Table 9. Data revealed

that grain yield affected significantly due to different

thermal environment. The higher grain yield was

obtained 1474.0 kgha-1

in first thermal environment

on 29th

November under (E1) and lowest of 1200.5

kgha-1

in 19th

December sown crop. Among the

varieties Kranti (S1) production higher grain yield

(1325.6 kgha-1

) closely followed by Vardan S1

(1321.3 kgha-1)

and Varuna S2 (1280.8 kgha-1

).

The interaction between different thermal

environments, varieties and spacing found to be non-

significant. As we compare the spacing, it is

observed that the grain yield is always higher in S1

(30x10cm) than S2 (40x10cm) under all the date of

sowing. In case of varieties, they also produced

higher seed yield in S1 spacing than S2 spacing.

The higher yield was obtained in early sown crop

than the late sown crop, which is due to the favorable

effect of temperature and other weather parameters.

Sharma et. al. (1992) also found that early sown crop

produce higher seed yield kg ha-1

than the late sown

crop which is in close agreement of the present

findings.

CONCLUSION

The plant population showed statistically significant

difference for deferent thermal environments.

Maximum plant population of 36.0 /m2 was recorded

in 29th November sowing in S1 spacing (30x10cm)

followed by 35.8 /m2 for 09th December, 34.0 /m

2

19th December sowing. Differences in plant height

were significant due to dates of sowing (E) (viz., at

25 DAS to harvest.). The Interaction between (VxS)

and (V) differed significantly except at 25 DAS, 45

DAS. Whereas the (ExV), (ExS), (ExVxS) and (S)

are the non-significant observed in 25 DAS to at

harvest. In general, higher plant height was observed

in E1 as compared to E2 and E3. Lowest plant

height was recorded in E3 in all the varieties.

However, Varuna attained maximum height (161.9

cm) followed by Vardan (160.6 cm) Kranti attained

the lowest height of (159.5 cm). The phenological

studied all Mustard varieties under different thermal

environments. It was found that the first date of

sowing took more number of days (111 days) from

sowing to maturity as compared to delayed sowings.

Maximum duration to attain maturity was found in

Varuna (111 days) followed by Kranti (109 days)

and Vardan both (109 days). In general, the duration

of all varieties decreased due to delayed sowing.

Thermal insensitivity of all the varieties was assessed

based on the TSI (thermal sensitivity index) and it

was found that all varieties of Mustard were tolerant.

Different thermal environments significantly

influenced Grain yield (Kgha-1

) in different varieties.

Data revealed that grain yield affected significantly

due to different thermal environment. The higher

grain yield was obtained 1473.97 kgha-1

in first

thermal environment on 29th

November (E1S2) and

lower of 1200.16 kgha-1

in 19th

December sown crop

as compare to S2 spacing. Among the varieties

Kranti produce higher grain yield (1325.65 kg ha -1

)

closely followed by Vardan (1321.32 kgha-1)

and

Varuna (1280.85 kgha-1

).

REFERENCES

Anonymous (2005). Status Report XVIII Meeting of

ICAR regional Committee No.V, Venue : CIFA

Bhubaneswar (Orissa). 21-22 July 2005.

Kar, G. and Chakravarty, N.V.K. (1999). Thermal

growth rate, heat and radiation utilization efficiency of

Brassica under semi-arid environment. Journal of

Agrometeorology, 1(1): 41-49.

Kumar, R., Negi, P.S., Singh, C.M. and Mankotia,

B.S. (1996). Performance of gobhi sarson (Brassica

napus sub sp. Oleifera var. napus) under various

planting dates and row spacing in Himachal Pradesh.

Indian Journal of Agronomy, 41(1): 98-100.

Saran, G. and Giri, Gajendra (1987). Influence of

dates of sowing on Brassica species under semi-arid

rainfed conditions of north-west India. Journal of

Agricultural Science, Cambridge, 108: 561-566.

Sastri (2001). Thermal sensitivity of analysis, Annual

Progress Report 2001 pp. 11.

Sharma, M.L., Bharadwaj, G.S. and Chauhan, Y.S. (1992). Response of mustard (Brassica juncea)

cultivars to sowing dates under irrigated conditions.

Indian Journal of Agronomy, 37: 837-839.

Thakur, K.S. and Singh, C.M. (1998). Performance

of Brassica species under different dates of sowing in

mid-hills of Himachal Pradesh. Indian Journal of

Agronomy, 43(3): 464-468.



STUDIES ON THE FORAGING ACTIVITY OF INDIAN HONEY BEE, APIS

CERANA INDICA FABR. AND OTHER HONEY BEE SPP. ON BUCKWHEAT

FLOWERS

Jogindar Singh Manhare*, G.P. Painkra, K.L. Painkra and P.K. Bhagat

Department of Entomology

IGKV, RajMohini Devi College of Agriculture and Research Station, Ambikapur,

Surguja 497001 Chhattisgarh, India



Abstract: The foraging activity of Indian honey bee, Apis cerana indica Fabr. and other honey bee spp. on buckwheat

flowers was undertaken at Research cum Instructional Farm of RMD CARS, Ajirma, Ambikapur (C.G.) of Indira Gandhi

Krishi Vishwavidyalaya Raipur during year 2016-2017. The activity of Apis cerana indica was found higher in third week

of December 2016 (69.71 bees/5min/m2). Its maximum visitation was found at 1200 hrs (98.62 bees/5min/m2). The

maximum foraging activity of Apis dorsata was found at 1200 hrs (61.12 bees/5min/m2). Whereas, the lowest was observed

at 1700hrs (1.25 bees/5 min/m2) in Apis cerana indica and Apis dorsata the lowest was observed at 1700hrs (0.75 bees/5

min/m2). The foraging activity of Apis florea was noticed at 1400hrs (3.25 bees/5min/m2) and was found least at 0800hrs

(0.57 bees/5min/m2).

Keywords: Foraging behavior, Apis cerana indica, Apis dorsata, Apis florea, Buckwheat

INTRODUCTION

uckwheat is the most important crop of the

mountain regions both for grain and greens. It

occupies about 90% of cultivated lands in the higher

Himalayas with a solid stand. It is a short duration

crop (2-3 months) and fits well in the high Himalayas

where a crops growing season is of limited period

because of early winter and snow fall. In the higher

Himalayas, up to 4500m, this is the only crop grown

(Joshi and Paroda, 1991).

There are two species of buckwheat cultivated in the

Himalayas (F. esculentum and F. tataricum).

Buckwheat is one of the alternative crops which

obtain a repeated revival of growing these days. The

increased in this crop is caused by its nutritive value

but also by its short vegetation period, maximum

foliar feeding, pollination and low requirements on

growing condition. It is highly nutritive, unlike

cereals which are deficient in lysine, one of the

essential amino acids for human health.

Buckwheat, Fygopyrum esculentum L. is an

important pseudocereal crop grown extensively in

the hilly areas of Northern Hill Zone of Chhattisgarh

specially at Mainpath block in Surguja district in

approximately 10-15 ha. Area is by the” Tibbati”

refuge people in the past 7-8 year. It is herbaceous

plant, grows upon a height of 3-4 meter. The

buckwheat plant is complete its life cycle in 90-115

days. The white flower heads of 2-3 cm develop in

the leaf axil.

Buckwheat is cross pollinated and an entomophilic

plant. Honey bees are the major pollinators. The

cultivation of buckwheat along with bee keeping may

produce 40 to 60 kg of honey per hectare, due to its

extended flowering period for more than 30 days

(Rajbhandari, 2010).

METERIAL AND METHOD

The experiment was conducted at Research cum

Instructional Farm of RMD CARS, Ajirma,

Ambikapur of Indira Gandhi Krishi

Vishwavidyalaya, Raipur (C.G.) during rabi season

in year 2016-17. It was upland single plot keeping

plot size 10x10m, variety- Local spacing 20x10cm.

When the buckwheat crop started flowering different

honey bee spices were recorded starting from

0600hrs to 1800hrs at two hours intervals one square

meter area within five minutes early as well as peak

flowering period of crop.


The findings of the present study as well as relevant

discussion have been presented under the following

heads:

1. Foraging activity of Indian honey bee (Apis

erana indica) The foraging behavior of Indian honey bee (Apis

cerana indica Fab.) on niger flowers was observed

from 0600 to 1800hrs from starting to full blooming

period of the crop. The activity of Indian honey bee

was recorded during the year (206-17) 4th week of

Oct. 2016 to 2nd week of Jan. 2017 at weekly

interval at different dates and different hours of the

day, viz. 0600hrs, 0800hrs, 1000hrs, 1200hrs,

1400hrs, 1600hrs and 1800hrs. The mean number of

bees recorded on each date of observations i.e.

Number of bees/ 5min/ m2 is depicted in (Table.1).

B

RESEARCH ARTICLE


824 JOGINDAR SINGH MANHARE, G.P. PAINKRA, K.L. PAINKRA AND P.K. BHAGAT

During 1st week of observation the foraging activity

of Apis cerana indica, was started foraging on niger

flowers at 0800hrs (40 bees/ 5min/ m2) and

gradually reached its peak at 1200hrs

(85bees/5min/m2) thereafter it started decreasing at

1400hrs (44 bees/5min/m2) and at 1600hrs (35 bees/

5min/m2). The lowest activity was recorded at 180hrs

(0.00bees/5min/m2). The activity of bees was not

observed early in the morning i.e. at 0600hrs. The

mean activity of bees was 37.71bees/5min/m2.

During 2nd week, the maximum foraging activity of

bees was recorded at 1200hrs (119 bees/5min/m2). It

started declining at 1400hrs and 1600hrs accounting

62 and 20 bees/ 5min/m2, respectively. The activity

of bees was not observed in early morning 0600hrs.

Whereas started its activity at 0800hrs (42

bees/5min/m2). However, the least activity of bees

was recorded at 1600hrs (03 bees/5min/m2). The

mean activity of bees was 48.00bees/5min/m2.

On 3rd week of observation the activity of bees was

not seen at 0600hrs, while it started its activity at

0800hrs (49 bees/5min/m2) and reached its peak at

1200hrs (145 bees/5min/m2) with suddenly decreased

at 1400hrs and at 1600hrs with population of 81

bees/ 5min/m2 and 25 bees/5min/m

2, respectively.

The least activity of bees was noticed at 1800hrs (00

bee /5min/m2) and the mean activity of bees was

57.14bees/5min/m2.

During the 4th week, the least activity of bees was

noticed at 1800hrs (3 bees/5min/m2) and started its

foraging activity at 0800hrs (69 bees/5min/m2) with

maximum activity at 1200hrs (169 bees/5min/m2)

and decreased at 1400hrs (96 bees/5min/m2) and

1700hrs (03 bees/5min/m2). The activity of bees was

not seen at early morning at 0600hrs. During this

week the average bee activity was

69.71bees/5min/m2.

On 5th week, the maximum activity was noticed at

1000hrs (110 bees/5min/m2). It started foraging at


1200hrs (135 bees/5min/m2) and started decreasing

at 1400hrs (27 bees/5min/m2) and at 1600hrs

(03bees/5min/m2). There was no foraging activity

observed at 0600hrs and 1800hrs.

During 6th week, the foraging activity of bees was

started at 0800hrs (24 bees/5min/m2) thereafter

increased to its peak at 1200hrs (85.00

bees/5min/m2) and started declining at 1400hrs and

1600hrs with 36 bees/5min/m2 and 11 bees/5min/m

2

respectively. There was no foraging activity observed

at 0600hrs. The average activity of bees was 31.00

bees/5min/m2.

During 7th week, the maximum foraging activity was

observed at 1200hrs with (32 bees/5min/m2) and

decreased at 1400hrs (16 bees/5min/m2) whereas at

1600hrs the population was 09 bees/5min/m2. No bee

activity was recorded at 0700hrs and mean bee

activity was 14.57bees /5min/m2.

During last week, the activity of bees was not

observed at 0600hrs and at 1800hrs While its activity

was observed starting at 0800hrs (7 bees/5min/m2)

and increased to its peak at 1200hrs (19 bees/5min/

m2) and suddenly decreased at 1400hrs (11

bees/5min/ m2) and 1600hrs (5 bees/5min/m

2). The

average activity of bees was 8.14 bees/5min/m2.

The present findings are more or less conformity

with the earlier workers Chaudhary et al. (2002)

reported foraging activity of Apis cerana indica on

litchi flowers whereas. Chakrabarty and Sharma

(2007) observed the maximum activity of bees at

1000 and 1800hrs (1.24 bees/min/ capitulum) with

least number at 1400hrs (0.69 bees/ min/capitulum)

on sunflower. Gogoi et al. (2007) who observed Apis

cerana indica with maximum number of 9.42

foragers visited flowers of lemon during 1000- 1100

hrs. Shaw et al. (2008) recorded the Indian bee in 39

flora belonging to 23 families from October to March

whereas, the foraging behaviour of Italian bee was

recorded in 23 flora belonging to 18 families from

January to March.

2. Foraging behaviour of Rock bee (Apis dorsata)

on buckwheat flowers

During 1st week, the foraging activity of A. dorsata

started at 0800hrs (30 bees /5min/m2), further, it

increased and reached the peak at 1200hrs and it

declined at 1400hrs and 1600 hrs. The bee activity

was not seen at morning 0600hrs and 1800hrs. The

average foraging activity of bees was 26.00

bee/5min/m2 at different hours of the day (Table 2).

On 2nd

and 3rd

week, the least bee similar activity

was observed at 1600hrs (25bees /5min/m2) whereas

it started its activity at 0800hrs (25 and 38

bees/5min/m2) and reached its maximum activity at

1200hrs (80 and 96 bees/ 5min/m2), further, it

decreased at 1800hrs with 1 and 0 bees/5min/m2,

respectively. No bee activity was found at 0600hrs.

The average bee activity was 35.28 and 41.57 bees/

5min/m2.

During 4th week, A. dorsata started visiting at

0800hrs (45bees/5min/m2) and reached its peak at

1200hrs (117 bees/5min/m2) thereafter decreased at

1400hrs (75 bees /5min/m2).The least foraging

activity of A. dorsata was observed at 1600hrs (40

bees/5min/m2) whereas average activity during 4th

week was 52.00 bees/5min/m2.

On 5th week, the maximum foraging activity was

recorded at 1200hrs (92 bees/5min/m2) which

suddenly decreased at 1400hrs (49 bees/5min/m2),

further, it disappeared at 1800hrs. It was started

foraging at 0800hrs (40 bees/5min/m2). There was no

bee activity found at morning 0600hrs. The average

bee activity was 40.42 bees/5min/m2.

On 6th week, there was no foraging activity noticed

at 0600hrs and 1800hrs. It started its activity at


1200hrs (19bees/5min/m2) and decreased at 1400hrs

(14bees/5min/m2) and further it declined at 1600hrs.

The average activity of A. dorsata was recorded

(9.14 bees/5min/m2).


On 7th week, the maximum foraging activity of A.

dorsata was recorded at 1200hrs (19 bees/5min/ m2)

further it decreased at 1400hrs (8 bee/5min/m2) and

declined at 1600hrs and 1800hrs. The activity was

started at 0800hrs (7 bees/5min/m2). While, the bees

activity was not recorded at 0600hrs. The average

activity of A. dorsata was recorded (7.42

bees/5min/m2).

During 8th week, no foraging activities was observed

at 0600hrs and 1800hrs. However, it started its

activity at 800hrs (5 bees/5min/m2) and maximum

activity was recorded at 1200hrs (15 bees/5min/m2),

thereafter it declined at 1400 and 1600hrs. The mean

activity of bee was 5.57 bees/5min/m2). The present

results are in close agreement with the earlier

workers of Kumar and Singh (2008) reported peak

activity of Apis dorsata on safflower crop at 1100hrs

and the least at 1500hrs. Singh (2008) who recorded

the maximum foraging frequency of Apis species at

1200hrs, followed by 1000, 1400 and 1600hrs on

parental lines of Brassica napus. Dhurve (2008)

noticed maximum foraging activity of Apis dorsata

in between 1000 to 1600hrs of the day which ranged

from 36.90 to 45.56 bees/m2/5min. It was less at

0800hrs with 22.73 bees/m2/5min and least at

1800hrs which recorded 18.96 bees/m2/5min.

Selvakumar et al. (2001) who also recorded the

activity of Apis dorsata on cauliflower constituted

28.23 per cent and the pollen gatherers reached to its

peak at 1400hrs while nectar collectors remained

constant throughout the day.

3. Foraging behaviour of Little bee (Apis florea)

on buckwheat flowers

The mean foraging behaviour of Apis florea, is

depicted in (Table 3).

During 1st week of observation at the time flowering

of crop, the foraging activity of Apis florea was

started at 0800hrs (04 bees/5min/m2) and the activity

of bees was not recorded at 1000hrs, 1200hrs 1400

hrs, 1600hrs and 1800hrs. The mean activity of bees

was 0.57 bee/5min/m2.

During 2nd week, the bee activity was not observed

at 0600hrs. However, bees started its activity at

0800hrs (3bees/5min/m2), it reached maximum

activity at 1400hrs (3bees/5min/m2) and 1800hrs (3

bees/5min/m2) thereafter, it declined at 1600hrs. The

average activity of bee was 1.42 bees /5min/m2.

During 3rd week, the maximum foraging activity of

A. florea was recorded at 0800hrs (05 bees/5min/

m2), further it declined at 1000hrs and 1400hrs.

There was not foraging activity noticed at 0600hrs

and 1800hrs. The average activity of bees was

1.71bees/5min/m2.

During 4th week, the bees were not observed at

0600hrs. However, it started its activity at 0800hrs (6

bees/5min/m2) and declined it at 1200hrs

(3bees/5min/m2). The average activity of bee 1.85

bees/5min/m2 was recorded.

On 5th week, the foraging activity of A. florea was

not recorded at 0600hrs, later, reached its maximum

activity at 0800hrs (04 bees/5min/m2).. The activity

of bees was not observed at 1600hrs and 1800hrs.

The mean activity of bee was (1.00 bees/5min/m2).

On 6th week, the bees started its activity at 1000hrs

with 03 bees/5min/m2 and at 1200hrs with 03

bees/5min/m2 thus two maximum activity periods

were recorded. Further, it was not any activity during

1400hrs and 1800hrs. The mean activity of bee was

1.14 bees/5min/m2.

On 7th week, the maximum activity of bees was

observed at 1000hrs and 1200hrs (3& 6 bees

/5min/m2). The average activity of bee was

1.28bees/5min/m2.

During 8th week, the peak activity was similar

recorded at 1000hrs, and 1400hrs accounting to 02

bees/5min/m2, respectively. But it was not observed

at 0600hrs. The mean foraging activity of bee was

accounting to 0.57 bees/5min/m2. The present results

are in close agreements with the earlier workers. Lal

(2011) recorded Apis florea on different crops. The

maximum foraging activity was observed in between

1200hrs to 1400hrs time period. Painkra and Shaw

(2016) noticed at the higher foraging activity of Apis

florea on niger constituted 1300hrs (4.00

bees/5min/m2) and was found least at 0.56

bee/5min/m2.

Table 1. Foraging behavior of Apis cerana indica on buckwheat flowers S.No. Date of

observation

(Number of bees visit/5min/m2), Hours of the day (H) Total Mean

0600 0800 1000 1200 1400 1600 1800

1 25.11.2016 0 40 60 85 44 35 0 264 37.71

2 02.12.2016 0 42 90 119 62 20 03 336 48.00

3 09.12.2016 0 49 100 145 81 25 0 400 57.14

4 16.12.2016 0 69 125 169 96 29 03 491 70.14

5 23.12.2016 0 59 110 135 85 27 03 419 59.85

6 30.12.2016 0 24 60 85 36 11 01 217 31.00

7 06.01.2017 0 19 26 32 16 09 0 102 14.57

8 13.01.2017 0 07 15 19 11 05 0 57 8.14

Total 0 309 586 789 431 161 10 2286 326.15

Mean 0.00 38.62 73.25 98.62 53.87 20.12 1.25 285.75 40.82


Table 2. Foraging behavior of Apis dorsata on buckwheat flowers S.No. Date of

observation


0600 0800 1000 1200 1400 1600 1800

1 25.11.2016 0 30 49 51 37 15 0 182 26.00

2 02.12.2016 0 25 70 80 47 25 01 248 35.42

3 09.12.2016 0 38 79 96 53 25 0 291 41.57

4 16.12.2016 0 45 87 117 75 40 03 367 52.42

5 23.12.2016 0 40 77 92 49 25 0 283 40.42

6 30.12.2016 0 09 15 19 14 07 0 64 9.14

7 06.01.2017 0 07 13 19 08 05 02 54 7.71

8 13.01.2017 0 05 10 15 08 01 0 39 5.57

Total 0 199 400 489 291 168 06 1528 218.28

Mean 0.00 24.87 50.00 61.12 36.37 21.00 0.75 191.00 27.28

Table 3. Foraging behavior of Apis florea on buckwheat flowers S.No. Date of

observation


0600 0800 1000 1200 1400 1600 1800

1 25.11.2016 0 04 0 0 0 0 0 04 0.57

2 02.12.2016 0 03 0 0 03 01 03 10 1.42

3 09.12.2016 0 05 0 0 03 02 0 12 1.71

4 16.12.2016 0 06 0 03 01 03 0 13 1.85

5 23.12.2016 0 04 0 02 01 0 0 07 1.00

6 30.12.2016 0 0 03 03 0 02 0 08 1.14

7 06.01.2017 0 0 03 06 0 0 0 09 1.28

8 13.01.2017 0 0 02 0 02 0 0 04 0.57

Total 0 22 08 14 10 08 03 65 9.25

Mean 0.00 2.75 1.00 1.75 1.25 1.00 0.00 8.12 1.15

Fig. 1. Foraging behavior of Apis cerana indica on buckwheat flower

0

50

100

150

200 0600 hrs

0800 hrs

1000 hrs

1200 hrs

1400 hrs

1600 hrs

1800 hrs

Date of observation

Nu

mb

er o

fb

ees

viti

/5m

in/m


Fig. 2. Foraging behavior of Apis dorsata on buckwheat flower

Fig. 3. Foraging behavior of Apis florae on buckwheat flowers

CONCLUSION

It is concuded that Apis cerana indica L. foraged

more extra time per day on buckwheat flower as

compared to Apis dorsata and Apis florea. The peak

flowering hours for both bee species were recorded

around 1400 to 1600hrs. The Apis cerana indica F.

visited higher number of flowers and plants as the

compared to Apis dorsata and Apis florea has lowest

foraging against in other honey bee species.

REFERENCES

Chaudhary, D.K., Singh, B. and Singh, P.P.

(2002). Population dynamics of honey bees foraging

on litchi flowers. J. Entomological Res., 26(1) :71-

75.

Chakrabarty, S.K. and Sharma, S.P. (2007).

Foraging behaviour of honeybees in hybrid seed

production of sunflower (Helianthus annus). Indian

J. Agril. Sci., 77 (9): 629-631.

0

20

40

60

80

100

120

0600 hrs

0800 hrs

1000 hrs

1200 hrs

1400 hrs

1600 hrs

1800 hrs

Date of obswervation

Nu

mb

ero

f b

ees

visi

t/5

min

/m

0

1

2

3

4

5

60600 hrs

0800 hrs

1000 hrs

1200 hrs

1400 hrs

1600 hrs

1800 hrs

Date of observation

Nu

mb

er o

f b

ees

visi

ts/5

min

/m


Chandran, N. and Viraktamath, S. (2010).

Foraging behaviour of honeybees on three elite

genotypes of niger. Ecology Pollen & Fungal Spore,

28 : 33-37.

Dhurve, S.S. (2008). Impact of honey bee

pollination on seed producton of niger. M.Sc.(Ag.)

Thesis, Uniersity of Agricultural Sciences, Dharwad,

KARNATAKA (INDIA).

Gogoi, B., Rahman, A., Rahman, S., and Deka,

M.K. (2007). Foraging behaviour and effect of Apis

cerana pollination on fruit set and yield of Assam

lemon ( Citrus lemon). Indian J. Agril. Sci., 77(2)

:120-22.

Joshi, B. D. and R. S. Paroda, (1991). Buckwheat

in India. NBPGR, Shimla Sci. Monogr , No.2 pl17.

Kumar, N. and Singh, R. (2008). Relative

abundance of honey bee foragers visiting safflower

(Carthamus tinctorius L.) and nectar-sugar

concentration in bloom. Pest-Management & Econ.

Zool., 16(2) : 135-141

Lal, M. (2011). Irradiation impact of cell phone on

the behaviour, growth and development of honey bee

Apis cerana indica L. under Northern hills zone of

Chhattisgarh. M.Sc.(Ag.) Thesis, Indira Gandhi

Krishi Visvavidyala, Raipur (C.G.) INDIA

Painkra, G.P. and Shaw, S.S. (2016). Foraging

behaviour of honey bees in niger flowers, Guizotia

Abyssinica Cass. in North Zone of Chhattisgarh.

International J. P.Protection 9(1):100-106

Rajbhandari, B. P. (2010). Buckwheat in the land

of Everest. Himalayan College of Agricultural

Sciences and Technology (HICAST), Kathmandu,

Nepal: 314-316.

Shaw, S.S., Thakur, B.S., Ganguli, R.N. and

Nema, S. (2008). Status and prospects of beekeeping

in Chhattisgarh State. National Conference on Pest

management Strategies for Food security Raipur. 2-3

May. pp. 42-53.

Singh, J. (2008). Foraging frequency and pattern of

movement of different Apis spp. on parental lines of

Brassica napus L. Entomon., 33(2) : 91-99.

Selvakumar, P., Sinha, S.N. Pandita, V.K. and

Shrivastava, R.M. (2001). Foraging behavior of

honeybee on parental lines of hybrid cauliflower

Pusa hybrid-2. Standing Commission of Pollination

and bee Flora.www.apimondia.org. Apimondia

Journal .



THERMAL REQUIREMENT OF MUSTARD IN LATE SOWN CONDITION AFTER

RICE CROP AT RAIPUR UNDER CHHATTISGARH PLAIN

R.K. Kashyap1, Pandhurang Bobade

2, S.K. Chandrawanshi

3*, Deepak K. Kaushik

1

and R. Singh1

1 Department of Agrometeorology, College of Agriculture, Indira Gandhi Krishi Vishwadhyalaya

Raipur (C.G.) 2 RMD College of Agriculture & Research Station, Ambikapur (C.G.)

3Agricultural Meteorological Cell, Department of Agriculutral Engineering,

N.M College of Agriculture, Navsari Agricultural University, Navsari (GJ)



Abstract: The investigation of thermal requirement of mustard in late sown condition, after rice crop at Raipur under

Chhattisgarh plain. It was found that cumulative GDD for emergence increased by different varieties under E1 as compared

to E3. The PTU values were higher in 19th December sown crop as compared to 29th November and 9th December sowing.

Lower PTU values were observed under 9th December sowing (E2) in Kranti while, the PTU values were in increasing trend

for Vardan and varuna from29th November and 19th December. Different Mustard varieties show non significant results

under different thermal environments but the seed yield (kg/ha) showed significant results under different thermal regimes.

Highest seed yield was recorded in E1 (29th November) as compared to delayed sowings. Vardan was found out yielder in all

temperature regimes as compared to other varieties. The radiation use efficiency was more in E1 sowing under S1 spacing.

In early date of sowing (E1) both in case of S1and S2 the radiation use efficiency increase from 25 days to 75 days and then

decreases up to at harvest. RUE is maximum in case of Varuna (3.06gMj-1) under S1 spacing followed by Kranti (2.96gMj-1)

and lowest was recorded in variety Vardan (2.83gMj-1) under S1 spacing. Heat use efficiency was observed that variety

Kranti showed higher HUE for the entire thermal environment (different date of sowing) as compared to Varuna and Vardan

It may be attributed to higher biomass.

Keywords: GDD, PTU, HTU, Heat use efficiency (HUE), Radiation use efficiency

INTRODUCTION

n India lot of works have been done on date of

sowing. However, a very little work has been done

in Chhattisgarh on radiation effect on Brassica. In

Chhattisgarh, the shorter winter span and higher

temperature particularly during reproductive and

seed filling stages hasten the maturity which

decreases the yield. Under such condition, thermal

stress tolerant varieties play a vital role. Brassica

species are thermosensitive and therefore

phenological events of these crops are affected by

dates of sowing. An attempt has been made in this

section to review the effects of sowing dates on

performance of rapeseed and mustard (Brassica

spp.).

Kar and Chakarvarty (1999) reported reduction in

yield of Brassica species (viz. B.O. 54, Pusa Bold T-

9) in delayed sowing. RUE in the first crop season

was found to be varied from 3.01 g MJ-1 in first

sown crop of cultivar Pusa Bold to 2.13 g MJ-1 in

second and third sowing of Toria-T9. In the second

season, they found that the RUE range between 3.22

g Mj-1 in the first sowing of Pusa Bold and 2.28 g

Mj-1 in the third sown Toria-T9.

Khichar et al. (2000) noted that Brassica crop sown

on 20th

October showed higher growth parameters

(LAI, plant height and dry matter) and yield in

comparison with other time sown crop (i.e. 10th

November and 30th

November). The radiation use

efficiency (RUE) was highest from sowing to

harvesting in the 20th

October sowing and decreased

with successive sowings. Brassica cultivars also

differed significantly with respect to growth

parameters. While in case of yield the difference was

not significant. Also reported that plant spacing

influenced growth parameters and yield of mustard

crop, however, the influence on yield plant height

was not significant.

Hundal et al. (2004) found that cultivars and sowing

date effect on RUE and CGR in mustard (cv. Bio-

902 and Pusa Bold). The peak CGR was 33.7 and

30.4 gm-2

day-1

for Bio-902 and Pusa Bold,

respectively sown in first weak of November. The

highest RUE of 2.44 MJ-1 of dry matter

accumulation and 0.62 g MJ-1

for seed yield were

recorded when the crop was sown in the third week

of October. Significant linear regressions relationship

(R2 = 0.89) was observed between total dry matter

accumulation and cumulative.

India is one of the major producers of rapeseed and

mustard in the world. However, in terms of yield per

hectare, its position is one of the lowest when

compared to other oilseeds. Plants depend for growth

and development on their genetic constituents and

environmental conditions. The final yield of a crop is

highly dependent on the genetic constitution and the

weather condition, which its encounters during its

I

RESEARCH ARTICLE

830 R.K. KASHYAP, PANDHURANG BOBADE, S.K. CHANDRAWANSHI, DEEPAK K. KAUSHIK

AND R. SINGH

growth and development. Out of several approaches

through which the relationship between weather

parameters and crop production can be understood,

the crop phenology is an important aspect, since the

biomass production and seed yield are greatly

influenced by the prevailing environmental

conditions during various crop phenophases. In

Chhattisgarh, due to shorter winter span and

temperature fluctuations the productivity of rapeseed

and mustard fluctuates considerably. It is therefore,

necessary to identify suitable high yielding varieties

of mustard for late sown conditions under rice based

cropping system which can tolerate the high

temperature during reproductive and seed filling

stages.

MATERIAL AND METHOD

Location of Experimental site:

The field experiment was carried out at the Research

and Instructional farm of Indira Gandhi Krishi

Vishwavidyalaya; Raipur situated in Eastern Central

part of Chhattisgarh at latitudes of 210.16

’ N,

longitude 810.36

’ E and altitude 289.5 m above mean

sea level.

Experimental details Different thermal environment and mustard varieties

with different plant population were used. The

treatment combinations of three dates of sowing and

three varieties and two plant density of Mustard were

laid out in Factorial Randomized Block Design with

three replications.

Growing degree days

Growing Degree Days (GDD) concept assumes that

there is a direct and linear relationship between

growth and developments of plants and temperature

and the growth is dependent on the total amount of

heat to which it is subjected during its life time. The

growing degree days was computed by using

following formula:

GDD = [(Tx + Tn )/2 – Base temperature]

Where,

Tx = Daily maximum temperature

Tn = Daily minimum temperature

The base Temperature is defined as, “The

temperature below which no plant physiological

activity takes place” which is considered 5.0 for Rabi

crops.

Photothermal Unit (PTU) PTU is calculated by multiplying GDD with

maximum possible sunshine hours (N).

PTU = GDD X N

Where,

N = maximum possible sunshine hour.

Heliothermal Unit (HTU)

HTU is calculated by multiplying GDD with actual

sunshine hours (n) (Rajput, 1980).

HTU = GDD X n

Where,

n = actual sunshine hour.

Heat Use Efficiency (HUE)

Heat Use Efficiency (HUE) for total dry matter was

obtained as under:

Biomass (g / m2)

HUE (g/m2 /

0 day) = -------------------------

GDD (º days)

Radiation Use Efficiency (RUE)

RUE (gMJ-1

) = Biomass (g / m-2

) / IPAR (M J-2

)

Where,

IPAR is cumulative intercepted photo synthetically

active radiation.

The photo synthetic active radiation can be

calculated by using the following formula:

PAR = Rs X 0.5

Where,

Rs = incoming solar radiation

The incoming solar radiation can be calculated by the

formula

Rs = Rs0 (a + b x n/N)

Where,

Rs0 = Extra terrestrial radiation

n / N = Per cent sunshine hours

a = 0.2, b = 0.4


Growing degree-days

Growing degree-days is widely used for describing

the temperature responses to growth and

development of crops. Growing degree days (GDD)

requirement for completion of different phenophases

of mustard varieties were worked out and are

presented in Table 1

Table 1 revealed that accumulated growing degree

days for different varieties under different thermal

environments varied considerably from sowing to

maturity. The cumulative GDD required for first

flower appearance (P2), end of seed filling (P5) and

physiological maturity (P6) was maximum as

compared to Emergence (P1), 50% flowering (P3)

and start of seed filling(P4) in all thermal

environment as well as in all varieties.

Different mustard varieties responded differently in

terms of accumulated GDD at the time of maturity.

Lower GDD values were observed under 9th

December sowing (E2) in Kranti and varuna while,

the GDD values were in increasing trend for Vardan

from 29th

November to 9th

December and 19th

December. The GDD values were higher in 19th

December sown crop as compared to 29th

November

and 9th

December sowing. Mustard crop required

1753.9 to 1808.5°C day from emergence to

physiological maturity. Similar results were found by

Singh et. al. (2004) which showed that maximum

GDD was noticed in early sown crop which is similar

with the present findings.

Photo thermal units

Table 2 revealed that accumulated photo thermal unit

(PTU) for different varieties under different thermal



maturity. The cumulative PTU required for first

flower appearance (P2), end of seed filling (P5) and

physiological maturity (P6) was maximum as

compared to Emergence (P1), 50% flowering (P3)

and start of seed filling(P4) in all thermal

environment as well as in all varieties.

Lower PTU values were observed under 9th

December sowing (E2) in Kranti while, the PTU

values were in increasing trend for Vardan and

varuna from29th

November and 19th

December. The

PTU values were higher in 19th

December sown crop

as compared to 29th

November and 9th

December

sowing. Mustard crop required 19808.9 to 20785.7°C

day from emergence to physiological maturity.

Biomass production and heat use efficiency

Accumulated heat units to attain different crop

growth stages from sowing to maturity for three

cultivars and thermal environments are given in

Table 3. It is observed from the table that the variety

Vardan recorded higher GDD as compared to Varuna

and Kranti reflecting longer duration variety in the

different thermal environment. In all the thermal

environment and variety 1808.5 to 1753.9 GDD were

accumulated throughout the growing period.

Heat utilization efficiency (HUE) of three varieties to

maximum biomass accumulation as influenced by

sowing at different thermal environment are

computed and presented in Table 3. It is observed

that variety Kranti showed higher HUE for the entire

thermal environment (different date of sowing) as

compared to Varuna and Vardan It may be attributed

to higher biomass. It is also noted that unlike Varuna

and Vardan in the late sown condition. The

reduction of HUE was not significant probably the

biomass production was not affected due to E3.

Radiation Use Efficiency (RUE)

Radiation Use Efficiency of mustard varieties at 10

days intervals in different thermal environment (E1,

E2 and E3) and in case of variety (V1, V2 and V3)

has been presented in table 4. The radiation use

efficiency was more in E1 sowing under S1 spacing.

In early date of sowing (E1) both incase of S1and S2

the radiation use efficiency increase from 25 days to

75 days and then decreases up to at harvest. The

same trend of RUE was also noticed in case of E2

and E3 in both the spacing (S1 and S2). In all the

three dates of sowing i.e. E1, E2 and E3 RUE

efficiency is more in case of S1 spacing than the S2

spacing. The increase in RUE in is more than S2 due

to higher spacing in S1, which result less number of

total plant population. Less number of plant

populations under same unit area.

In case of different varieties (V1, V2 and V3) under

S1 and S2 spacing the RUE increase from 25 days to

75 and then decreases up to at harvest. RUE is

maximum in case of Varuna (3.06gMj-1

) under S1

spacing followed by Kranti (2.96gMj-1

) and lowest

was recorded in variety Vardan (2.83gMj-1

) under S1

spacing. In case of variety also RUE is more in S1

spacing than S2 spacing. Variety Varuna have better

RUE than the other two varieties i.e. Kranti and

Vardan. Which may be due to variation in spacing

and other environmental factor (temperature,

cloudiness) and plant characteristics (leaf structure,

plant anatomy and genotypes). The decrease in RUE

from 75 days after sowing onwards is mainly due to

the decrease in biomass production. Moreover at

maturity stage (75 DAS) the chlorophyll content of

leaf gets reduced to a lower level which in turns

decreases the radiation use efficiency. Findings of

Khichar et al. 2000 also conformity with findings of

the present result.

Table 1. Accumulated growing degree days (GDD) required to attain various phenophase of Mustard Varieties

under different thermal environments

Varieties

Stages

Accumulated growing degree days

Mean

(V) E1 E2 E3

Vardan P1 87.7 90.7 88.9 89.0

P2 625.5 657.3 629.0 637.3

P3 737.6 780.9 749.3 755.9

P4 900.0 855.2 883.0 879.4

P5 1376.1 1317.7 1393.8 1362.5

P6 1755.9 1777.0 1784.1 1772.3

Kranti P1 87.7 90.7 88.9 89.1

P2 625.5 692.4 651.1 656.3

P3 737.6 803.0 766.4 769.0

P4 868.4 884.9 883.0 878.8

P5 1356.9 1337.5 1417.2 1370.5


AND R. SINGH

P6 1755.9 1753.9 1784.1 1764.6

Varuna P1 87.7 75.1 88.9 83.9

P2 640.6 657.3 666.8 654.9

P3 737.6 780.9 781.7 766.7

P4 922.8 867.6 870.5 887.0

P5 1376.1 1375.6 1393.8 1381.8

P6 1801.1 1777.0 1808.5 1795.5

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007



Table 2. Photo Thermal Unit required toattain various phenophase of Mustard Varieties under different thermal

environments

Varieties

Stages

Accumulated Photo thermal unit

Mean

(V) E1 E2 E3

Vardan P1 958.4 984.1 964.6 969.0

P2 6807.1 7164.5 6871.0 6947.5

P3 8035.8 8519.1 8228.1 8261.0

P4 9815.7 9346.0 9771.6 9644.4

P5 15266.6 14706.3 15794.8 15255.9

P6 19808.9 20217.9 20477.8 20168.2

Kranti P1 958.4 984.1 964.6 969.0

P2 6807.1 7549.2 7113.2 7156.5

P3 8035.8 8761.3 8425.5 8407.5

P4 9469.3 9688.7 9771.6 9643.2

P5 15266.6 14944.5 16075.6 15428.9

P6 19808.9 19940.7 20477.8 20075.8

Varuna P1 958.4 814.3 964.6 912.4

P2 6972.6 7164.5 7285.3 7140.8

P3 8035.8 8519.1 8602.6 8385.8

P4 10066.1 9488.5 9626.8 9727.1

P5 15266.6 15401.7 15794.8 15487.7

P6 20351.9 20217.9 20785.7 20451.8

E1 -29.11.2007, E2 -09.12.2007, E3 -19.12.2007



Table 3. Heat utilization efficiency of three Mustard cultivars sown under different plant densities

Treatments Thermal

environment

Heat units at maximum

biomass level (0C)

Maximum biomass gram

/ m2

HUE (g/m2/D-

1)

Vardan E1 1755.9 835.5 0.48

E2 1777.0 825.3 0.46

E3 1784.1 797.3 0.45

Kranti E1 1755.9 863.6 0.49

E2 1753.9 859.0 0.49

E3 1784.1 839.5 0.47


Varuna E1 1801.1 874.0 0.49

E2 1777.0 857.7 0.48

E3 1808.5 840.6 0.47

Table 4. Radiation Use Efficiency (gMj-1

) of mustard varieties at 10 days interval

Varieties

Days after sowing (DAS)

0-25 25-35 35-45 45-55 55-65 65-75 75-at

harvest

S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2

Vardan

E1 0.32 0.21 0.59 0.42 1.35 1.47 2.25 2.09 2.78 2.35 2.83 2.26 1.92 1.48

E2 0.22 0.15 0.42 0.24 1.18 1.24 1.97 1.85 2.37 2.1 2.52 2.07 1.87 1.45

E3 0.15 0.12 0.24 0.15 1.2 1.13 2.05 1.93 2.4 2.12 2.48 2.08 1.86 1.47

Kranti

E1 0.28 0.22 0.52 0.43 1.77 1.3 2.69 2 2.95 2.3 2.96 2.28 2.03 1.5

E2 0.19 0.15 0.37 0.28 1.53 1.09 2.38 1.81 2.7 2.09 2.69 2.07 1.95 1.44

E3 0.17 0.13 0.18 0.18 1.52 1.18 2.47 1.86 2.7 2.11 2.74 2.08 1.96 1.46

Varuna

E1 0.25 0.25 0.58 0.45 1.95 1.22 2.85 1.88 2.99 2.29 3.06 2.28 2 1.51

E2 0.19 0.16 0.39 0.29 1.6 0.98 2.53 1.64 2.68 1.96 2.79 2.05 1.95 1.45

E3 0.16 0.14 0.18 0.15 1.45 0.96 2.63 1.7 2.8 1.97 2.73 2.05 1.96 1.46

CONCLUSION

The accumulated growing degree days (GDD) for

different Mustard varieties under different thermal


maturity. Different Mustard varieties responded

differently in terms of accumulated GDD at the time

of maturity. Higher GDD was observed under 29th

November sowing (E1) was observed in Vardan

varieties higher GDD was recorded 29th

November

(E1). The radiation use efficiency was more in E1

sowing under S1 spacing. In early date of sowing

(E1) both in case of S1and S2 the radiation use

efficiency increase from 25 days to 75 days and then

decreases up to at harvest. In case of different

varieties (V1, V2 and V3) under S1 and S2 spacing

the RUE increase from 25 days to 75 and then

decreases up to at harvest. RUE is maximum in case

of Varuna (3.06gMj-1

) under S1 spacing followed by

Kranti (2.96gMj-1

) and lowest was recorded in

variety Vardan (2.83gMj-1

) under S1 spacing.

REFERENCES

Hundal, S.S., Kaur, Prabhjyot and Malikpuri,

S.D.S. (2004). Radiation use efficiency of mustard

cultivars under different sowing dates. Journal of


Kar, G. and Chakravarty, N.V.K. (1999). Thermal

growth rate, heat and radiation utilization efficiency of

Brassica under semi-arid environment. Journal of


Khichar, M.L., Yadav, Yogesh. C., Bishnoi, O.P.

and Niwas, Ram (2000). Radiation use efficiency of

mustard as influenced by sowing dates, plant spacing

and cultivars. Journal of Agrometeorology, 2(1): 97-

99.

Singh, Raj., Rao, V.U.M. and Singh, Diwan (2004).

Effect of thermal regime on growth and development

of Indian Brassicas. Journal of Agrometeorology,

6(1): 55-61.


AND R. SINGH



GENE PYRAMIDING OF FOUR BLB RESISTANT GENES (XA4, XA7, XA13 AND

XA21) FROM IRBB65 INTO MAHAMAYA USING MAS.

Mekala Mallikarjun* and Anil S. Kotasthane

Department of Plant Pathology, College of Agriculture, Indira Gandhi Krishi Vishwavidyalaya

(IGKV), Raipur – 492012, Chhattisgarh, India



Abstract: Bacterial blight (BB) of rice (Oryza sativa) caused by Xanthomonas oryzae pv. oryzae (Xoo) is currently one of

the most important diseases limiting rice production and it has become widespread in India. This disease was first noticed by

the farmers of Japan in 1884 (Tagami and Mizukami 1962). Enhancing genetic resistance has proven to be the most effective

control method for controlling the disease. Four bacterial blight (BB) resistance genes, Xa4, Xa7, xa13 and Xa21, were

introgressed into an elite rice cultivar. Marker assisted selection was done using linked molecular markers for genes Xa4,

Xa7, xa13 and Xa21. The ability to quickly and reliably select desirable material and to eliminate individuals that contain

deleterious alleles is critical to the success of the plant breeding program (Dubcovsky 2004). We report here in two gene

pyramids Xa7+ Xa21 in 5 lines and Xa4+Xa7 in 1 line. Genes in combinations were found to provide high levels of

resistance. Besides pyramids a set of 8 genotypes with only Xa7, 2 genotypes with only Xa13 and 5 genotypes for Xa21 were

also found in this study. High resolution maps generated in-silico around Xa4, Xa7, xa13 and Xa21 can be useful for the

precise placement of a gene of interest,analysis of regional and sub regional rates of recombination and appropriate

combinations of markers for marker assisted selection.

Keywords: Gene Pyramiding, Bacterial blight, Resistance genes, Polymerase chain reaction, Marker Assisted Selection

Xanthomonas oryzae pv. oryzae, MAS, Pyramiding

INTRODUCTION

acterial blight (BB), caused by Xanthomonas

oryzae pv. oryzae (Xoo) is one of the most

destructive diseases active in the major rice growing

countries of Asia. In the northern plains of India,

including the State of Chhattisgarh, the disease is a

serious problem, as rice is grown under irrigated and

high fertilizer input conditions that are conducive to

disease development. In severe epidemics, yield

losses ranging from 20% to 40% have been reported

(Sonti 1998). Breeding for disease resistance is the

most effective and economical method for control of

BB that has a neutral impact on the environment. To

date, at least 39 BLB resistance genes conferring host

resistance against various strains of Xoo have been

identified and characterized (Verdier et al. 2012;

Zhang et al. 2014). Several of these genes have

already been incorporated into rice cultivars, which

are now widely cultivated in many countries.

Mahamaya is a long bold grain, high yielding,

medium duration, lowland variety is very popular

amongst the farmers and is well suited for cultivation

in rainfed condition. Expresses moderate resistance

to leaf blast and Brown spot. The disease, Bacterial

blight has become a major production constraint for

var. Mahamaya in particular. Marker assisted

selection (MAS) for pyramiding important genes

along with rapid background recovery of the

recurrent parent, while maintaining the exquisite

quality characteristics of rice, could be an effective

approach for rice improvement. Molecular markers

closely linked to each of the resistance genes make

the identification of plants with two and three genes

possible (Shanti et al. 2010; Singh et al. 2001;

Sundaram et al. 2008). The combination of genes

provided a wider spectrum of resistance to the

pathogen population prevalent in the region; gene

Xa4 are resistance to bacterial blight at all stages of

plant growth. Gene Xa7, which provides dominant

resistance against the pathogen with avirulence (Avr)

gene AvrXa7, has proved to be durably resistant to

BB. The xa13 gene is fully recessive, conferring

resistance only in the homozygous status. It interacts

strongly with other R genes such as xa5, xa4 and

xa21. The rice Xa21 gene confers resistance to

Xanthomonas oryzae pv oryzae in a race-specific

manner. Gene Xa21, encoding a receptor-like kinase,

is a member of a multigene family.

MATERIAL AND METHOD

Plant Material Experimental materials consisted of parental lines

and F2 segregating population derived from a cross

between Mahamaya (susceptible and high yielding

variety) x IRBB65 (resistance variety) was used for

genetic analysis and validation of selected markers

for Xa4 and Xa7 genes.

Preparation of inoculum

The isolate was derived from the stock maintained at

4c. Culture was revived and grown on Walkimoto’s

medium for 3 days at 30C. For inoculating the

plants bacterial suspension was prepared by mixing

bacterial culture in sterilized distilled water to a

concentration of 109 cells/ml. This suspension was

immediately used for inoculating the plants which

B

RESEARCH ARTICLE


836 MEKALA MALLIKARJUN AND ANIL S. KOTASTHANE

are at maximum tillering stage following the clip

inoculation technique (Kauffman et al. 1973)

Evaluation of F2 segregating population

Individual plants of 80 lines of Mahamaya X

IRBB65 derived F2 breeding segregating population

and two parents were evaluated for field infections to

bacterial blight. Observations were recorded by

physical measurement of lesion and percent leaf area

was worked out. Scoring for the disease incidence

was evaluated 21 days after inoculation. The lesion

length and total leaf length was recorded on five

leaves and were further categorized based on 0-9

(IRRI,1991) which is as follows: 0=No lesion length,

1=lesion restricted to 0.5 to 1.0mm, 3= lesion

elongated but less than ¼ of leaf blade, 5= lesion

extended to ½ of the leaf blade, 7= lesion extended to

more than ½ of the leaf blade, 9= lesion completely

destroyed the leaf blade and sheath. The disease

score were rated as HR, R, MR, S, and HS.

DNA extraction DNA extraction was carried out by using modified

CTAB protocol (Keb Llanes).The DNA samples

were quantified using Nanodrop Spectrophotometer

(ND1000). Further, concentration of DNA was

adjusted to 30 ng/μl with TE buffer and stored at

4°C. The diluted DNA was subsequently used in

PCR amplification.

Primers for DNA amplification

The marker RM224, Xa7M5, xa13pro and RM21

were used for Xa4, Xa7 xa13 and Xa21 respectively.

Table 1. SSR primers used for developing genotypic data for Xa4, Xa7 xa13 and Xa21.

PCR Amplification

It was performed in a thermocycler with 1.5 μl of

Genomic DNA. Autoclaved distilled water 5.25 μl,

1.0 μl of 10 X Buffer,1.0μl of dNTP mix, 1.0μl of

Forward and Reverse Primer , 0.25μl of Taq

polymerase. An initial denaturation was performed at

94oC for 2minutes prior to 30 cyles of denaturation at

94oC (1 minute), annealing at 55

oC (1 minute), and

extension at 72oC (2 minutes). A final extension for

5minutes at 72oC will be performed. Polymorphisms

in the PCR products will be detected after

electrophoresis on 40% Polyacrylamide gel for SSR

marker products for microsatellite before ethidium

bromide staining.


Thirteen hundred F2 individuals were artificially

inoculated and phenotyped for disease reactions. Out

of 1300 plant population, 763 plants were scored

resistant and 310 plants were susceptible and 187

plants were remained un-inoculated. Forty highly

resistant (HR reaction) and 40 highly susceptible

individuals were chosen from the F2 population and

were used for selective genotyping to track the

presence of genes (Xa4, Xa7) individually and/or in

combinations.

Table 2. Reaction of F2 segregating population ( MahamayaXIRBB65) against Xanthomonas oryvzae pv.oryzae

(Dhamtari isolate)

Cross Total

Population

Resistance

lines

Susceptible

Lines

Highly

Resistance Lines Uninoculated Lines

Mahamaya X

IRBB65 1300 763 310 40 187

Earlier reported PCR based markers for RM224,

Xa7M5, xa13pro and RM21 were screened for

parental polymorphism. Marker RM224 linked to

gene Xa4 resolved 160bp and 150bp band

representing resistant and susceptible parent. Marker

Xa7M5 linked to Xa7 gene has resolved amplicon of

310bp representing resistant parent and no band was

seen susceptible parent. xa13pro marker produced

530bp band and 280bp band representing resistant

and susceptible parents respectively. Marker RM21

linked to Xa21 gene resolved amplicon of 900bp

representing susceptible parent and 1100bp size of

band representing resistant parent.

S.No Primer Type of

marker

Forward/

Reverse

Sequence

5’→ 3’

1. RM224 SSR Forward ATCGATCGATCTTCACGAGG

SSR Reverse TCGTATAAAAGGCATTCGGG

2. Xa7M5 SSR Forward CGATCTTACTGGCTCTGCAACTCTGT

SSR Reverse GCATGTCTGTGTCGATTCGTCCGAGA

3. xa13pro SSR Forward GGCCATGGCTCAGTGTTTAT

SSR Reverse GAGCTCCAGCTCTCCAAATG

4. RM 21 SSR Forward ACAGTATTCCGTAGGCACGG

SSR Reverse GCTCCATGAGGGTGGTAGAG


RM224

P2:160bp

P1:140bp

Xa7M5

P2:310bp

P1:no band

xa13pro

P2:530bp

P1:280bp

RM21

P1:1100bp

P2: 900bp

Fig 1. Image showing the pattern of segregation of molecular marker with P1 (Susceptible parent) and P2

(Resistant Parent).

Table 3. Frequency distribution of phenotyped lines in which the markers Co-segregated as per the phenotype.

S.no.

Gene

Marker

Phenotype

Total Resistance

P2type band

Susceptible

P1 type band

1 Xa4 RM224 1 0 1

2 Xa7 Xa7M5 13 1 14

3 xa13 xa13pro 2 3 5

4 Xa21 RM21 10 11 21

P1 type band = Mahamaya (Susceptible); P2 type band = IRBB65 (Resistant); H = Presence of P1 and P2 type

band.

Fig 2. PCR amplification of population derived from cross Mahamaya X IRBB65 with primer RM21 (Xa21

gene specific primer).

Table 4. One gene lines identified through Linked markers.

Table 5. Two gene Pyramids identified through linked markers.

Introgressed lines with two genes

Xa7 + Xa21

Introgressed lines with two genes

Xa4 + Xa7

Line # 5,13,27,36,39 Line # 2

The IRBB 65 (a near isogenic line in the background

of IR24) was crossed with Mahamaya, former as the

male parent. A subset of selected 40 phenotypically

superior plants carrying genes for resistance and

susceptible F2 segregants were selected for

genotyping using RM224, Xa7M5, xa13pro and

RM21markers which are linked to Xa4, Xa7, xa13,

Xa21 genes respectively. Plants showed banding

Introgressed line(s) with one gene Line number

Co-segregating marker for Xa7 :Xa7M5 Lines #1, 8,11,12,14,17,19,37.

Co-segregating marker for Xa13:xa13pro Lines # 7,40.

Co-segregating marker for Xa21:RM21 Lines #15,16,25,28,29.

838 MEKALA MALLIKARJUN AND ANIL S. KOTASTHANE

pattern identical to that of its resistant parent IRBB65

were, therefore, assumed to carry the gene for

resistance in homozygous state. Eight lines in the

population which confirmed presence of Xa7 loci,

xa13pro confirmed the presence xa13 loci in two and

similarly RM21 identified the presence of Xa21 loci

in 5 lines (Table 4).Besides single gene carrying

lines, superior lines carrying two gene pyramids for

resistance were also identified. 5 lines

(#5,13,27,36,39 ) carrying two gene pyramids of

Xa7+Xa21 and one line (#2) carrying Xa4+Xa7 were

found in this investigation(Table 5).

CONCLUSION

Identified two gene pyramids Xa7 + Xa21 in 5 lines,

and Xa4 + Xa7 in line 1 line. Besides pyramid lines,

single gene containing lines with Xa4, xa13, Xa21

were also found in 8, 2, 5 number of lines

respectively. Physical map was developed by

identifying BAC or PAC clones that simultaneously

contained a hit from the marker. Such map will help

is to develop more markers for fine mapping of the

genes , foreground selection and a panel of selection

markers for MAS / MAB.

ACKNOWLEDGMENT

We are grateful to Dr.A.S.Kotasthane and Dr.Toshy

Agarwal for assistance and suggestions. This work

was supported by the Molecular Plant Pathology

Laboratory of Department of Plant Molecular

Biology and Biotechnology, CoA, IGKV Raipur.

REFERENCES

Dubcovsky, J. (2004). Marker-assisted selection in

public breeding programs: the wheat experience.

Crop Science, 44(6):1895–1898.

Kauffman, H. E., Reddy, A. P. K., Hsieh, S. P. Y.

and Merca, S. D. (1973). Improved technique for

evaluating resistance of rice varieties to

Xanthomonas oryzae. Plant Disease Reporter.

Kumar, P.N., Sujatha, K., Laha, G.S., Rao, K.S.,

Mishra, B., Viraktamath, B.C. and Madhav, M.S.

(2012). Identification and fine-mapping of Xa33, a

novel gene for resistance to Xanthomonas oryzae pv.

oryzae.Phytopathology, 102(2), 222-228.

Khush, G.S., Mackill, D. J. and Sidhu, G.S. (1989). Breeding rice for resistance to bacterial

blight. Bacterial blight of rice, 207-217.

Sanchez, A.C., Ilag, L.L., Yang, D., Brar, D.S.,

Ausubel, F., Khush, G.S., and Huang, N. (1999).

Genetic and physical mapping of xa13, a recessive

bacterial blight resistance gene in rice. Theoretical

and applied genetics,98(6-7), 1022-1028.

Shanti, M.L., Shenoy, V.V., Devi, G.L., Kumar,

V.M., Premalatha, P., Kumar, G.N., and

Freeman, W.H. (2010). Marker-assisted breeding

for resistance to bacterial leaf blight in popular

cultivar and parental lines of hybrid rice. Journal of

Plant Pathology, 495-501.

Singh, S., Sidhu, J.S., Huang, N., Vikal, N.Y., Li,

Z., Brar, D.S. and Khush, G.S. (2001). Pyramiding

three bacterial blight resistance genes (xa5, xa13 and

Xa21) using marker-assisted selection into indica

rice cultivar PR106.Theoretical and Applied

Genetics, 102(6-7), 1011-1015.

Sonti, R.V. (1998). Bacterial leaf blight of rice: new

insights from molecular genetics. Curr Sci 74:206–

212

Sundaram, R.M., Vishnupriya, M.R., Biradar,

S.K., Laha, G. S., Reddy, G.A., Rani, N.S. and

Sonti, R.V. (2008). Marker assisted introgression of

bacterial blight resistance in Samba Mahsuri, an elite

indica rice variety.Euphytica, 160(3), 411-422.

Tagami, Y. and Mizukami, T. (1962). Review of

rice bacterial leaf blight. Spec.Bull. Pest

Forecast, 10, 10-13.

Verdier, V., Cruz, C.V. and Leach, J.E. (2012).

Controlling rice bacterial blight in Africa: needs and

prospects. Journal of biotechnology, 159(4), 320-

328.

Zhang, F., Zhuo, D.L., Huang, L.Y., Wang, W.S.,

Xu, J.L., Vera Cruz, C. and Zhou, Y.L. (2015).

Xa39, a novel dominant gene conferring

broad‐spectrum resistance to Xanthomonas oryzae

pv. oryzae in rice. Plant Pathology, 64(3), 568-575.



FIELD SCREENING OF DIFFERENT VARIETIES OF TOMATO AGAINST FRUIT

BORER, HELICOVERPA ARMIGERA (HUBNER)

Laxman Singh, P.K. Bhagat, G.P. Painkra* and K.L. Painkra

Department of Entomology, IGKV, Raj Mohini Devi College of Agriculture and Research Station,

Ambikapur, Surguja 497001 (Chhattisgarh) India



Abstract: A field experiment was undertaken at research farm of Raj Mohini Devi College of Agriculture and Research

Station Ambikapur, Surguja of Indira Gandhi Krishi Vishwavidyalaya Raipur (Chhattisgarh) during 2016-17 on twelve

tomato varieties on fruit borer, Helicoverpa armigera (Hub.). Tomato varieties viz. JK Ratan, JK. 25, JK Nandni, prabhav,

Nirmal 2530, N.S. 962, NS 592, Siddharth, Amrita, Bhagya, Kapila and Pusa-Ruby were tested for resistance against

Helicoverpa armigera infestation under field conditions. The varieties JK 25 and Prabhav had minimum fruit weight loss

(1.57% and 3.26%) as well as minimum number of infested fruits (1.85% and 3.79%) respectively by the Helicoverpa

armigera. These variety also had minimum Halicoverpa armigera larval population, i.e. 0.14, and 0.22 larvae/plant,

respectively. The variety Pusa-Ruby and Amrita had maximum loss in fruit weight (30.41% and 21.67%) as well as

maximum number of infested fruit (30.85% and 23.28%) with larval population of 1.05 and 0.68 larvae/plant. Pusa-Ruby

was categorized as susceptible genotypes with fruit infestation (30.85%) and larval population per plant (1.05%). Variety

Bhagya, JK Ratan, Siddharth, NS 592, and Amrita (20.21%, 20.51%, 21.10%, 21.44% and23.28%) was categorized as

moderately susceptible. Variety JK Nandini, Kapila, NS 962 Nirmal 2530 (14.70%, 15.62%, 15.81%, and 19.51%,) was

categorized as moderately resistant. Variety JK 25 and Prabhav (1.85% and 3.79%) and declared as resistant variety to

tomato fruit borer.

Keywords: Screening, Tomato varieties, Fruit borer, Vegitable

INTRODUCTION

omato (Solanum Lycopersicum) is one of the

most popular and commercially important

vegetable crop in India which belongs to the family

Solanaceae. It ranks next to potato and sweet potato

in the world vegetable production

(Anonymous,1997) and tops the list of conned pests

are major ones that have been reported to attack

tomato at all stages of crop growth.

In India recent statistics show that tomato was grown

in 8.79 lakh hectare of land and the total production

was approximately 182.26 lakh tones and

productivity level of 20.7 tones/ha (Anonymous

2014).

Tomato, like other vegetables, is more prone to

insect pests and diseases mainly due to their

tenderness and softness as compared to other crops.

The damage caused by insect-pests is one of the main

constraints which limit the vegetables (Choudhary,

1979). Among many factors responsible for low

yields of tomato, insect production of tomato.

Among the insect pests, the key insect-pests of

tomato include jassids (Amrasca bigutulla Ishida),

aphid (Aphis gossypi Glover and Myzus persicae

Sulzer), white fly (Bemisia tabaci Gennadius),

cutworm (Agrotis sp.), tobacco caterpillar

(Spodoptera litura Fabr.) and tomato fruit borer

(Helicoverpa armigera Hubner), which infest and

hamper the growth of plants. Out of these insect-

pests, tomato fruit borer (Helicoverpa armigera) is

the major constraints in the higher production of

tomato fruits. Tomato fruit borer is highly destructive

pest causing serious damage and responsible for

significant yield loss up to 55 per cent (Talekar et al.

2006). However, tomato fruit borer causes 40-50 per

cent damage to the tomato crop (Pareek and

Bhargava 2003). Cultivation of Helicoverpa

resistant tomato cultivars is limited due to a lack of

data on potential genetic sources and plant

mechanisms (antixenosis) of resistance (Dhillon et

al. 2005). In USA alone, Helicoverpa spp. causes a

loss of more than one billion dollars to various crops,

despite insecticide applications, worth another $250

million per year (Anonymous 1976; Johnson et al.

1986). Control of the insect pests through the

application of insecticides cause ill effects like

development of insecticide resistance in the pests,

pest resurgence, environmental pollution and health

hazards. Now trend has been shifted towards an

integrated pest management (IPM). Host plant or

varietal resistance constitutes an important tool for

the integrated management of the pest insect. There

are many reported studies, where the populations of

Heliothis spp. were managed, using host plant

resistance, alone or in conjunction with other

methods (Lukefahr et al., 1971; Lukefahr, 1982).

MATERIAL AND METHOD

Varietal Screening: Seeds of twelve varieties, viz.,

J.K. Ratan, JK 25, J.K. Nandni, Prabhav, Nirmal

2530, NS 962, NS 592, Siddharth, Amrita, Bhagya,

Kapila and Pusa-ruby (susceptible check) were sown

in the field. The experiment was replicated three

times with plot size of 3X2 M2. 3-4 leaf stage

T

SHORT COMMUNICATION


840 LAXMAN SINGH, P.K. BHAGAT, G.P. PAINKRA AND K.L. PAINKRA

seedling were transplanted in the field. No plant

protection measures applied in the experimental

field. Number of larvae plant-1

from five randomly

selected plants and fruit infestation per plant from ten

randomly selected plants, in itch variety, was

recorded at weekly intervals for preliminary

screening experiment.

Data on the fruit infestation, by the pest and larval

population of Helicoverpa armigera, were recorded

by following procedure.

The average larval-population plant-1

for each variety

was calculated by the simple arithmetic means

(Wakil et al., 2009).

Damaged and undamaged fruits, from randomly

selected ten plants, in each variety, were counted, at

weekly intervals. Percent fruit-infestation was

calculated by the following formula (Wakil et al.,

2009).

Fruit Infestation Percentage = B/A ×100

Where

A= Total fruits (damaged + undamaged), and

B= Damaged fruits

RESULT

Fruit infestation and larval population per plant on

tested variety of tomato varied significantly (Table

1). The percentage fruit damage varied from 1.85 to

30.85 in different varieties. The least fruit damage

(1.85%) was recorded in variety J.K. 25, followed by

Prabhav (3.79%) with larval population (0.14% and

0.22%) and no significant difference among them

and scored as resistant categories. Whereas, the

percentage damaged fruit on weight basis in different

varieties ranged from 1.57 to 30.41. The tomato

variety J.K. 25 had significantly least weight loss

(1.57%) followed by Prabhav (1.94%).

The moderate fruit damage was recorded in J.K.

Nandini (14.70%) followed by Kapila (15.62%), N.S.

962 (15.81%) and Nirmal 2530 (19.51%) based on

the number of damage fruit percentage, with larval

population (0.41%, 0.48%, 0.48%, and 0.65%)

respectively whereas variety J.K. Nandini (13.67) as

well as N.S. 962 (14.76), had moderate loss on

weight basis. Significantly maximum fruit damage

on number basis was recorded on variety Pusa-Ruby

(30.85%), with larval population (1.05%) where as

the losses on weight basis was also maximum in

variety Pusa-Ruby (30.41 %).

The minimum per cent fruit damage by Helicoverpa

armigera was recorded in J.K. 25 and Prabhav on the

number basis and on the weight basis the minimum

fruit damage was recorded in J.K. 25 and Prabhav,

however it was moderate in case of J.K. Nandni,

Nirmal 2530, NS 962 and Kapila based on number

whereas, J.K. Nandni, NS 962, Bhagya and Kapila

had moderate level of infestation on weight basis.

Significantly maximum fruit damage on number

basis was recorded on variety Pusa Ruby, where as

the losses on weight basis was also maximum in

variety Pusa-Ruby.

Table 1. A Comparison of means for the data regarding the larval population of the fruit borer/plant and fruit

infestation/plant on different variety of tomato during 2016-17 Variety Fruit Infestation (%) Wt. of damaged fruit

%

Larval population (%) Remark

JK Ratan 20.51 18.88 0.64 MS

JK 25 1.85 1.57 0.14 R

JK Nandini 14.70 13.67 0.41 MR

Prabhav 3.79 3.26 0.22 R

Nirmal 2530 19.51 18.81 0.65 MR

NS 962 15.81 14.76 0.48 MR

NS 592 21.44 17.22 0.68 MS

Siddharth 21.10 21.43 0.66 MS

Amrita 23.28 21.67 0.68 MS

Bhagya 20.21 15.37 0.55 MS

Kapila 15.62 16.36 0.48 MR

Pusa-Ruby 30.85 30.41 1.05 S

DISCUSSION

A number of plant characteristics are known to

render the cultivars less suitable or unsuitable for the

feeding, oviposition and development of insect pests

(Rafiq et al., 2008). It may be due to plant trichomes

(Johnson, 1956), phenol contents (Banerjee and

Kalloo, 1989) and quality of host plant (Bazzaz et

al., 1987). In contrast, some characteristics like

nutrients (GoncalvesAlvin et al., 2004) improve the

quality of host plant which resultantly favors the

insects. Screening of tomato genotypes for

resistance/susceptibility against tomato fruit borer

was conducted to manage the fruit borer with

environmentally safe tactics. Similar kind of study

has been documented by Khanam et al. (2003) who

evaluated genotypic susceptibility of tomato

genotypes different from those in present study.

In present study we found JK 25, Prabhav as resistant

tomato variety against tomato fruit borer. It may be

due to less fleshy and smooth surface of fruits of

these genotypes. These genotypes may be resistant

due to tight mesocarp and hard pulp of fruits (Mishra

et al., 1988), high orthodihydroxy phenols and


trichome density in the foliage (Selvanarayanan and

Narayanasamy, 2006). The variety Pusa Ruby were

found susceptible, may be due to the reason of high

nitrogen content (Minkenberg and Ottenheim, 1990)

and high non reducing sugar in the foliage

(Selvanarayanan and Narayanasamy,2006). Kashyap

and Verma (1987) reported that Pusa Ruby was

found to susceptibility against H. armigera among

the various genotypes screened. Gc et al. (1997) have

also reported the susceptibility of Pusa Ruby against

H. armigera.

CONCLUSION

The present study revealed that none of the tested

genotypes were free from Helicoverpa armigera

infestation. However, based on the mean fruit weight

loss (%) by Helicoverpa armigera larvae (2016-

2017), the variety JK 25 and Prabhav were found to

be comparatively resistant, while variety Pusa-Ruby

were found to be most susceptible to Helicoverpa

armigera infestation. The larval population per plant

was positive correlated with fruit damage on weight

as well as on number basis. The fruit damage on

weight basis and on number basis showed positive

correlation with each other. The above genotypes

performed better in the field and need to be further

explored. In this context, investigating the physical

and biochemical plant characters of the studied

genotypes from a view point of host plant resistance

to Helicoverpa armigera, would be useful

contribution towards development of a resistant

variety that can be incorporated into an IPM strategy.

REFRENCECES

Anonymous (1997). FAO production year books,

basis Data Unit. Statistics Division, FAO, ROME

Italy, 51; 125-127.

Anonymous (1976). Agricultural Research Service.

ARS. Nalt. Heliothis Planning Conf. New Orleans.

La, US. Department of Agriculture. Washington D.C.

p. 36

Banerjee, M.K. and Kalloo (1989). Role of phenols

in resistant to tomato leaf curl virus, Fusarium wilt

and fruit borer in Lycopersicon. Current Sci. 58: 575-

576.

Bazzaz, F.A., Chiarello, N.R., Coley, P.D. and

Pitelka, L.F. (1987). Allocating resources to

reproduction and defense. Biosci. 37:58-67.

Choudhury, B. (1979). Vegetable (6th

Revised Edn.)

the Directors National BookTrust New Delhi, India,

pp;45.

Gc, Y.D., Pandey, R.R. and Bhoosal, D.L. (1997).

Management of tomato fruit worm Helicoverpa

armigera during 1996/97. Lumle Agric. Research

centre. No.97-46 10 pp.

Goncalves Alvin, S.J., Collevatti, R.G. and

Fernandes, G.W. (2004). Effects of genetic

variability and habitat of Qualea parviflora

(Vochysiacea) on herbivory by free feeding and gall

farming insects. Ann. Bot. 94:259-268.

Johnson, B. (1956). The influence on aphids of the

glandular hairs on tomato plants. Plant Pathol.

5:131-132.

Johnson, S.J., King, E.G. and Jr. Bradlay, J.R.

(1986). Theory and tactics of Heliothis population

management. I. Cultural and biological control.

South.Coop. Ser. Bull. 316:161.

Kashyap, R.K. and Verma, A.N. (1987). Factors

imparting resistance to fruit damage by Helicoverpa

armigera (Hubner) in some tomato phenotypes.

Insect Sci. Applic., 8: 111-114.

Khanam, U.K.S., Hossain, M., Ahmad, N., Uddin,

M.M. and Hussain, M.S. (2003). Varietal screening

of tomato to tomato fruit borer, Helicoverpa

armigera (Hub.) and associated tomato plant

characters. Pak. J. Biol. Sci. 6: 412-413.

Lukefahr, M.J. (1982). A review of the problems,

progress and prospects for host plant resistance to

Heliothis spp. p.223-231. In: W. Reed and V.

Kumble (eds.). International Workshop on Heliothis

Management. ICRISAT, India.

Lukefahr, M.J., Houghtaling, J.E. and Graham,

H.M. (1971). Supression of Heliothis population

with glabrous cotton stains. J. Econ. Entomol.

64:486-488.

Minkenberg, O.P.J.M. and Ottenheim, J.G.W.

(1990). Effects of leaf nitrogen content of tomato

plants on preference and performance of a leaf

mining fly. Oceologia 83: 291-298.

Mishra, P.N., Singh, Y.V. and Nautiyal, M.C.

(1988). Screening of brinjal varieties for resistance to

shoot and fruit borer, Leucinodes orbonalis Guen.

(Lepidoptera: Pyralidae). South Ind. Hort. 36:182-88.

Pareek, P.L. and Bhargava, M.C. (2003).

Estimation of avoidable losses in vegetables caused

by borers under semi arid condition of Rajasthan.

Insect Environ., 9: 59-60.

Rafiq, M., Ghaffar, A. and Arshad, M. (2008).

Population dynamics of whitefly (Bemisia tabaci) on

cultivated crop hosts and their role in regulating its

carryover to cotton. Int. J. Agric. Biol. 9:68-70.

Selvanarayanan, V. and Narayanasamy, P. (2006).

Factors of resistance in tomato accessions against the

fruit worm, Helicoverpa armigera (Hubner). Crop

Protec. 25:1075-1079.

Talekar, N.S., Open, R.T. and Hanson, P. (2006).

Helicoverpa armigera management: a review of

AVRDC’s research on host plant resistence in

tomato. Crop Protec., 5: 461-467.

Wakil, W., Ashfaq, M., Ghazanfar, M.U., Afzal,

M. and Riasat, T. (2009). Integrated management of

Helicoverpa armigera in chickpea in rainfed areas of

Punjab, Pakistan. Phytoparasitica 37:415-420.

842 LAXMAN SINGH, P.K. BHAGAT, G.P. PAINKRA AND K.L. PAINKRA



IMPACT OF SYSTEM OF RICE INTENSIFICATION (SRI) THROUGH FRONT

LINE DEMONSTRATIONS

B.K.Tiwari*, K.S. Baghel, Akhilesh Kumar Patel, Akhilesh Kumar, Sanjay Singh

and A.K. Pandey

JNKVV, Krishi Vigyan Kendra, Rewa, Madhya Pradesh-486001


Abstract: Front line demonstrations were conducted at farmer’s field in Umaria district during kharif seasons of 2009-10 to

2013-14 (five years) at seven different locations under real farming situations prevailing farmer’s practices were treated as

control for the comparison with recommended SRI practice. Result of front line demonstration showed a greater impact on

farmer’s economy due to significant increase in crop yield more than two fold over FP. Economics and benefit cost ratio of

both FP and RP plots were worked out of RS. 36942/ha was recorded net profit under RP while it was Rs. 16734/ha under

FP. Benefit cost ratio was 2.64 under RP, while 1.88 under FP. Demonstrating improved transplanting technique of rice open

new horizon of income of farming community of Umaria district as it is profitable in both sense i.e. input saving as well as

yield enhancing.

Keywords: Front line demonstration, SRI, Rice, BC ratio, Farmer, Productivity

INTRODUCTION

ice (Oryza sativa) occupies a position of

overwhelming importance in Indian agriculture

and it constitutes the bulk of the Indian diet. For

many people in the India, rice is the main source of

energy, and it plays an important role in providing

livelihood to the Indian population. It is largely

grown in India under diverse conditions of soil,

climate, hydrology and topography. Rice farming is

the most important source of employment and

income for the majority of rural people in this region.

The productivity of rice in the district can be increase

by following the appropriate agronomic practices

along with high yielding rice varieties/hybrids.

Thakur et al (2009) suggested that the system of rice

intensification (SRI) holds a great promise in

increasing the rice productivity. The basic principles

of SRI are; planting young seedlings (8-12 days),

singly in a square pattern (Stoop et al, 2002), the soil

is just kept saturated with water and flooding is not

allowed till reproductive stage, after which a thin

layer of water (1-2 cm) is kept in the field. Weeds are

primarily controlled by mechanical weeding (Cono

weeder) which also helps in incorporation of weed

biomass and maintains proper aeration in soil

(Satyanarayana et al; 2007).Various planting

densities have been evaluated for SRI with the

general recommendation being 25 cmx25 cm.

Rice is the staple food crop of the Umaria district of

Madhya Pradesh; occupies 45 % of total cropped

area of kharif season (45000 ha of total 100000 ha

cultivated area). The productivity of rice in the

district is only 2.26 t/ha, which is much below the

national productivity (3.37 t/ha). The reason of low

productivity may be attributed to non adoption of

improved production technology which includes the

agronomic practices and socioeconomic conditions

of the tribal people. An effort made by the KVK

scientists by introducing the SRI system of paddy

production through front line demonstration on

farmers field during kharif seasons of 2009-10 to

2013-14 (five consecutive years).

MATERIAL AND METHOD

Field demonstrations were conducted in Umaria

district of Madhya Pradesh under close supervision

of krishi vigyan Kendra. Total 48 front line

demonstrations under real farming situations were

conducted during kharif seasons of 2009-10 to 2013-

14 (five consecutive years) at seven different villages

namely; Lorha, Chandia, Chhotipali, Dogargawa,

Kohka, Patharikhurd and Taali, respectively under

krishi vigyan Kendra operational area. The area

under each demonstration was 0.4 ha. The soil was

sandy clay-loam in texture with moderate water

holding capacity, low to medium in organic carbon

(0.34-0.61%), low in available nitrogen (113.6-216.3

kg/ha), medium in available phosphorus (12.8-20.4

kg/ha), low to medium in available potassium (218.4-

317.1 kg/ha) and soil pH was neutral in reaction (6.8-

7.2). The treatment comprised of recommended

practice (SRI) vs farmers practice. The rice nursery

was grown on raised beds of 10mx1.5m with half

meter wide irrigation cum drainage channel around

the beds. Sprouted seeds of high yielding

hybrids/variety sown using 5 kg/ha seed rate. The

demonstration fields were well prepared by the

suitable implements; fields were puddled twice and

leveled properly. 12-14 days old seedlings were

transplanted singly (one seedling per hill) with the

25cmx25cm spacing using SRI line marker in muddy

field. Balance dose of fertilizers (100:60:40 kg

NPK/ha was supplied; 25% through organic sources

i.e. FYM and remaining 75% through chemical

fertilizers i.e. Urea, DAP and MOP) supplied. The

demonstration plots were kept moist throughout the

R

SHORT COMMUNICATION

844 B.K.TIWARI, K.S. BAGHEL, AKHILESH KUMAR PATEL, AKHILESH KUMAR, SANJAY SINGH

AND A.K. PANDEY

vegetative growth by applying light and frequent

irrigations, when required. During flowering to

milking stage about 2-3 cm standing water was

maintained continuously. Pyrazosulfuron @ 25 g

a.i./ha as pre emergence was applied at 3-4 days after

transplanting (DAT). Cono weeder operated at 30, 40

and 50 DAT for the mechanical weed control and

increases the soil aeration during 2009-10 to 2013-14

(five consecutive years).

Farmer’s practice constituted the application of high

seed rate (50 kg/ha), planting of old seedling (30-45

DAS), closer planting, not adopting the line sowing,

imbalance and insufficient supply of nutrients

(50:30:0 kg NPK/ha), submerged the paddy field

throughout the crop season, one hand weeding

between 30-40 days after transplanting (DAT) etc.

Harvesting and threshing operation done manually;

5mx3m plot harvested in 3 locations in each

demonstration and average grain weight taken at

14% moisture. Similar procedure adopted on FP

plots under each demonstration then grain weight

converted into quintal per hectare (q/ha).

Before conduct the demonstration training to farmers

of respective villages was imparted with respect to

envisaged technological interventions. All other steps

like site selection, farmers selection, layout of

demonstration, farmers participation etc were

followed as suggested by Choudhary (1999).Visits of

farmers and extension functionaries were organized

at demonstration plots to disseminate the technology

at large scale. Yield data was collected from farmers

practice and demonstration plots; cost of cultivation,

net income and benefit cost ratio were computed and

analyzed during 2009-10 to 2013-14 (five

consecutive years).


The yield performance and economic indicators are

presented in Table-1 and Table-2. The data revealed

that under demonstration plot, the performance of

rice yield was found to be double than that under FP

during 2009-10 to 2013-14 (five consecutive years).

The yield of rice under demonstration recorded was

62.9, 49.25, 57.0, 53.2, and 43.4 q/ha during 2009-

10, 2010-11, 2011-12, 2012-13 and 2013-14;

respectively. The yield enhancement due to

technological intervention was to the tune of 40% to

108% over FP. The cumulative effect of the

technological intervention over five years of

demonstrations, revealed on average yield of 53.19

q/ha, 74% higher over FP. The year to year

fluctuations in yield and cost of cultivation can be

explained on the basis of variations in prevailing

varieties/hybrids, social, economical and prevailing

microclimatic condition of that particular village.

Similar trends on straw yield and harvest index were

found. Mukhargee (2003) has also reported that

depending on identification and use of farming

situation, specific intervention may have greater

implications in enhancing systems productivity.

Yield enhancement in different crops in front line

demonstration has amply been documented by Haque

(2000), Sharma (2003), Gurumukhi and Mishra

(2003) and Kumar et al (2011).

Economic indicators i.e. gross expenditure, gross

returns, net returns and B:C ratio of front line

demonstration are presented in Table-2. The data

clearly revealed that the net return from the

recommended practice were substantially higher than

FP plot during all the years of demonstration.

Average net returns from recommended practice

were observed to be Rs. 36942/ha in comparison to

FP plot i.e. Rs 16734/ha. On an average Rs. 20208/ha

as additional income is attributed to the technological

intervention provided in demonstration plots i.e. SRI

system.

Economic analysis of the yield performance revealed

that benefit cost ratio of demonstration plots were

observed significantly higher than FP plots. The

benefit cost ratio of demonstration and FP plots were

2.62, 2.63, 2.96, 2.82, 2.07 and 1.93, 1.89, 2.21, 1.70,

1.70 during 2009-10, 2010-11, 2011-12, 2012-13 and

2013-14; respectively. Hence favorable benefit cost

ratios proved the economic viability of the

intervention made under demonstration and

convinced the farmers on the utility of intervention.

The data clearly revealed that the maximum increase

in yield observed was during 2009-10, while

maximum benefit cost ratio of 2.96 was observed

during 2011-12. The variation in benefit cost ratio

during all the years of demonstration may mainly on

account of yield performance and input output cost in

that particular years.

The result of front line demonstration convincingly

brought out that the yield of rice could be increased

almost double with the intervention on varietal

replacement i.e. JRH-4, JRH-8, MTU-1081 and

Sahbhagi in rice and SRI system of production in the

Umaria district. To safeguard and sustain the food

security in India, it is quite important to increase the

productivity of rice under limited resources,

especially water. Favorable benefit cost ratio is self

explanatory of economic viability of the

demonstration and convinced the farmers for

adoption of SRI system of rice production. The

technology suitable for enhancing the productivity of

rice and calls for conduct of such demonstration

under the transfer of technology programme by

KVKs.

Technology gap, Extension gap and Technology

index

The extension gap ranging between 12.35 to 32.7

q/ha during the period of study emphasized the need

to educate the farmers through various means for the

adoption of improved agricultural production to

reverse the trend of wide extension gap (Table-2).

The trend of technology gap ranging between 2.80 to

20.75 q/ha reflected the farmer’s cooperation in

carrying out such demonstration with encouraging


results in all the years. The technology gap observed

may be attributed to the dissimilarity in weather

conditions. The technology index showed the

feasibility of the evolved technology at the farmer’s

field. The lower the value of technology index, the

more is the feasibility of the technology. As such, the

reduction in technology index from 4.67% during

2011-12 to 29.64% during 2010-11 exhibited the

feasibility of the demonstrated technology in this

region.

Table 1. Productivity, Yield parameters, Grain yield, Straw yield and Harvest index of rice as affected by

recommended practices (SRI) as well as farmer’s practices: Year Variety Area

(ha)

No. of

farmers

No. of effective

tillers/hill

Grain yield (q/ha) %

increase

over FP

Straw yield

(q/ha)

Harvest index (%)

RP FP Potentia

l

RP FP RP FP RP FP

2009-10 JRH-4 6.4 16 18.25 9.28 70.0 62.9 30.2 108 80.5 48.6 44 38

2010-11 JRH-8 2.4 06 14.16 8.60 70.0 49.25 25.40 94 65.5 40.0 43 39

2011-12 MTU-

1081 3.2 08

18.75 11.25 60.0 57.20 41.0 40

74.8 62.0 43 40

2012-13 JRH-8 3.2 08 17.3 8.9 70.0 53.20 28.42 87 71.0 45.0 43 39

2013-14 Sahbhagi 4.0 10 15.3 10.2 50.0 43.40 31.05 40 60 51.0 42 38

Total/Me

an

--- 19.2 48

16.75 9.64 64 53.19 31.21 74

70.36 49.32 43 39

Table 2. Economics of Front Line Demonstration of rice as affected by recommended practices (SRI) as well as

farmer’s practices: Year Variety Gross

expenditure

(Rs/ha)

Gross returns

(Rs/ha)

Net returns

(Rs/ha)

Additio

nal net

return

(Rs/ha)

B:C ratio Technolo

gy gap

(q/ha)

Extension

gap (q/ha)

Technolo

gy index

(%)

RP FP RP FP RP FP RP FP

2009-10 JRH-4 21599 1408

0

5661

0

27180 3501

1

13100 21911 2.62 1.93 7.10 32.7 10.14

2010-11 JRH-8 19599 1408

0

5171

2

26670 3211

3

12590 19523 2.63 1.89 20.75 23.85 29.64

2011-12 MTU-1081 20562 1954

7

6100

0

43275 4043

8

23728 16710 2.96 2.21 2.80 16.20 4.67

2012-13 JRH-8 25500 2280

0

7210

0

38796 4660

0

15996 30604 2.82 1.70 16.80 24.78 24.00

2013-14 Sahbhagi 28400 2590

0

5895

0

44158 3055

0

18258 12292 2.07 1.70 6.60 12.35 13.2

Total/

Mean

-- 23132

1928

1

6007

4 36016

3694

2 16734

20208 2.64 1.88

10.81 21.97 16.33

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846 B.K.TIWARI, K.S. BAGHEL, AKHILESH KUMAR PATEL, AKHILESH KUMAR, SANJAY SINGH

AND A.K. PANDEY



STUDY ON THE SEASONAL INCIDENCE OF MUSTARD APHID (LIPAPHIS

ERYSIMI KALT.) IN RELATION TO WEATHER PARAMETERS

N.C. Mandawi*, Y.K. Yadu2, S.S. Rao

1 and V.K. Dubey

2

1College of Agriculture and Research Station, Raigarh (C.G.)

2Department of Entomology, IGKV,Raipur (C.G.)

Email:[email protected]


Abstract: The aphid incidence and its correlation with weather parameters were studied at college of Agriculture and

Research Station, Raigarh, Chhattisgarh during the Rabi 2013-14 and 2014-15 crop seasons. Mustard variety “Pusa bold”

was used as test crop. This study will provide an opportunity to fact the pest challenge by manipulating the manageable

ecological parameters in the form of planting to harvesting time adjustment, varietal election, correct time of pesticide

application, etc. The aphid appearance of mustard aphids were observed on January 2nd & 5th 2013-14 & 14-15 and

disappeared after mid March. The peak period of aphid population was found at 5th to 9th SMW 114.41 to 318.01 aphid/plant

during Rabi 2013-14 and 113.92 to 314.52 aphid /plant during 2014-15. The correlation coefficient (r) showed a non-

significant negative effect with maximum and minimum temperature whereas relative humidity showed non-significant

positive effect.

Keywords: Mustard, Aphid, Lipaphis erysimi, Population, Humidity, Temperature

INTRODUCTION

ustard is the second most important oil seed

crop in India after soybean. It accounts for

nearly 20- 22% of the total oilseeds produced in the

country. Mustard seed is grown with a different

consumption pattern in the country. Indian mustard is

mainly used for extraction of mustard oil while black

mustard is mainly used as a spice. White mustard is

used as fodder crop or as green manure.

Among different insect pests attacking mustard, the

mustard aphid (Lipaphis erysimi Kalt.) is the most

serious and destructive pest and major limiting factor

for mustard cultivation (Begum 1995 and Biswas and

Das 2000). An opportunity to fact the pest challenge

by manipulating the manageable ecological

parameters in the form of planting or harvesting time

adjustment, varietals selection, correct time of

pesticide application, etc. The natural appearance of

mustard aphid on variety and germplasm of mustard

was observed on January (50 days after sowing) and

disappeared after mid-February (92 days after

sowing). The peak aphid population was found at a

minimum, maximum and average temperature of

13.57 oC, 25.86

oC and 19.72

oC respectively and a

mean relative humidity of 88.86% on 24th January at

71 DAS. (Rashid et al., 2009)

The nymphs and adults of aphids suck saps from

leaves, stems, inflorescence and pods as the plant

shows stunted growth. Weather conditions play the

most important role for its rapid multiplication

(Sinha, et. al., 1989; Rana, et. al.1993; Singh and

Malik, 1998). Such study will provide an opportunity

to fact the pest challenge by manipulating the

manageable ecological parameters in the form of

planting or harvesting time adjustment, varietal

selection, timely pesticide application, etc. Therefore,

the present study was formulated to observe the

aphid population fluctuation in relation to the

weather parameters.

MATERIAL AND METHOD

The experiment was conducted at College of

Agriculture and Research Station, Raigarh (C.G.)

during the Rabi season 2013-14 and 2014-15.

Mustard was sown variety Pusa bold in total

experimental plot size was 25x20 m2

and row to row

distance was 30 cm and plant to plant 10 cm which

were maintained by thinning. The insecticides were

no sprayed in the plot. Experimental area was

divided into four replication. Aphid populations were

counted from 10 randomly selected plants in each

replicated plots on the appearance of the pest up to

harvesting of the crop at weekly interval. While at

flowering and podding stages of the crop, the simple

technique was used. In sampling method, tool a Petri

dish of 15 cm diameter and then divided it into eight

equal parts with the help of a white paper already

marked, affixing underneath the Petri plate. Now

dislodged the whole population of aphids (10 cm

length) on to Petri plate and spread evenly on it with

the help of a fine camel hair brush and then counted

the number of aphids in a part and multiplying by the

number of components in Petri plate, we could

become able to get the aphid population. The data so

obtained were then correlated with meteorological

parameters.


Aphid population during rabi 2013-14 ranged from

3.5 to 318.01 per plant. On the first date of

observation i.e. second January (1st SMW) the

M

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848 N.C. MANDAWI, Y.K. YADU, S.S. RAO AND V.K. DUBEY

population of pest was 3.35 per plant. In subsequent

observation the pest population increased and

attained a peak 318.01 aphid per plants (Table 1 ) on

27th

February (9th

SMW), afterwards the population

showed declined trend in up to the end of the crop

season. The minimum aphid population of 2.81 per

plant was recorded on 20th

March 2014 (12th

SMW).

During rabi 2014-15 the aphid population ranged

from 3.86 to 314.52 per plant. In the first date of

observation i.e. fifth January (1st SMW) 3. 86 aphids

per plants were recorded. There after the population

was found to be in increasing trend reaching to a

peak of 314.52 aphids per plants (Table 2 ) on 23rd

February (8th

SMW). Afterward aphid population

was sharply declined to a level of 2.98 aphids per

plants on 23 March 2015 (12th SMW).

The maximum and minimum temperatures during the

period of study were correlated with weekly average

of aphid population. For the year maximum and

minimum temperature were found to be statistically

non significant (NS) table 1 and 2. The maximum

and minimum temperature during the peak activity of

the pest are ranged from 26.14 0C to 28.43

0C and

12.85 0C to 15.71

0C on the 5

th to 9

th SMW in the

year 203-14. These figure ranged from 24.98 0C to

28.79 0C and 10.78

0C to 19.78

0C on 5

th to 8

th SMW

in the year 2014-15.

Relative humidity during the peak activity of the pest

5th

to 9th

SMW in 2013 - 14. The relative humidity of

maximum and minimum was noticed to be ranging

from 81.14 to 89.00 per cent and 36.28 to 56.71 per

cent respectively. Similarly, during the year 2014-15

it ranged from 81.67 to 89.82 per cent and 29.0 to

52.42 per cent 5th

to 9th

SMW in maximum and

minimum, respectively. However, during both the

years, significant relationship could not be

established between relative humidity and the pest

activity (Table 1 and 2). The similar reports have

been reported by Chandra and Kushwaha (1986) that

temperature had non-significant negative effect

whereas relative humidity is positively correlated

with the abundance of aphid. Devi et al. (1995)

suggested that due to increase in mean relative

humidity during third week of February favored the

multiplication on mustard aphid. Contrary results

have been reported by Rashid et al. (2009) that

significant positive effect with minimum, maximum

and average temperature whereas mean relative

humidity significant negative effect. The same

reports have been found by Singh and Singh (1994)

and Sinha et al. (1989).

During the peak activity of the pest 5th

to 9th

SMW in

2013-14 the rainfall was noticed to be ranging from

7.62 to 25.40 mm. While during 2014-15 these

figures were 0 mm (zero) no rainfall on 5th

to 9th

SMW. However during both the year non- significant

relationship with the pest activity (Table 1 and 2).

Table 1. Seasonal aphid population per plant and influence of weather parameters during Rabi 2013-14 on

mustard

SMW Observation

date

Aphid

Population

Per Plant

Temperature (oC) Humidity (%)

Rainfall

(mm) Max. Mini. Max. Mini.

1 02.1.14 3.35 25.43 13.85 89.42 54.57 0

2 09.1.14 13.10 25.29 14.86 90.71 61.14 0

3 16.1.14 13.82 25.29 14.71 90.14 61.42 0

4 23.1.14 66.28 25.71 15.42 89.57 57.28 0

5 30.1.14 114.41 26.14 14.57 84.28 41.57 0

6 6.2.14 195.97 28.43 13.00 84.42 36.28 7.62

7 13.2.14 208.80 28.43 14.00 81.14 41.71 25.40

8 20.2.14 308.32 26.28 12.85 89.00 48.71 0

9 27.2.14 318.01 27.28 15.71 88.85 56.71 0


10 6.3.14 135.46 26.14 13.43 81.57 55.14 0

11 13.1.14 19.96 29.85 14.71 78.14 53.85 5.08

12 20.3.14 2.81 34.42 16.86 71.85 33.71 0

Correlation Coefficient (r ) NS NS NS NS NS

-0.1096 -0.3852 0.1834 -0.1912 0.6437

Multiple Regression : R^2 = 0.5640 NS

Table 2. Seasonal aphid population per plant and influence of weather parameters during Rabi 2014-15 on

mustard

SMW Observation

date

Aphid

Population

Per Plant

Temperature (oC) Humidity (%)

Rainfall

(mm) Max. Mini. Max. Mini.

1 5.1.15 3.86 23.14 8.14 83.24 34.57 0

2 12.1.15 12.47 24.14 12.57 89.14 60.42 0

3 19.1.15 6.11 23.79 8.71 88.98 38.76 0

4 27.1.15 48.89 26.71 11 88.97 40.71 0

5 2.2.15 113.93 25.42 11.42 88.71 29.0 0

6 9.2.15 169.53 24.98 10.78 81.67 30.87 0

7 16.2.15 279.81 27.69 16.56 86.95 50.56 0

8 23.2.15 314.52 28.79 19.78 86.76 49.42

0

9 2.3.15 292.62 27.86 15.97 89.82 52.42

0

10 9.3.15 105.51 37.78 21.78 78.58 27.95

0

11 16.3.15 27.78 37.14 16.71 80.71 24.0 0

12 23.3.15 2.98 40.42 18.57 81.28 18.71

0

Correlation Coefficient ( r )

NS NS NS NS

-0.1089 0.4202 0.2367 0.456

Multiple Regression : R^2

= 0.8616 S

850 N.C. MANDAWI, Y.K. YADU, S.S. RAO AND V.K. DUBEY

REFERENCES

Begum, S. (1995). Insect pests of oilseed crops of

Bangladesh. Bangladesh J. Zool. 23(2): 153-158.

Biswas, G.C. and Das, G.P. (2000). Population

dynamic of the mustard aphid, Lipaphis erysimi

(Kalt.) (Hemiptera: Aphididae) in relation to weather

parameters. Bangladesh J.Entomol. 19(1 & 2): 15-22.

Chandra, S. and Kushwaha, K.S. (1986). Impact of

environmental resistance on aphid complex of

cruciferous crops under the agroclimatic conditions

of Udaipur.I. Abiotic Components. Ind. J. Ent., 48:

495-514.

Devi, N., Desh Raj and Chandel, Y.S. (1995).

Population dynamic of Lipaphis erysimi (Kalt.) on

rapeseed in mild hill zone of Himachal Pradesh

(India). J. Ent. Res., 49: 367-371.

Rana, J.S., Khokar, K.S., Singh, H. and Sucheta (1993). Influence of abiotic environment on the

populationdynamic of mustard aphid Lipaphis

erysimi (Kalt.). Crop. Res., 6: 116-119.

Singh, D. and Singh, H. (1994). Correlation

coefficient between abiotic biotic factors (Predator

and parasitoid) and mustard aphid Lipaphis erysimi

(Kalt.) population on rapeseed mustard. J. Aphidol.,

8: 102-104.

Singh, S.V. and Malik, Y.P. (1998). Population

dynamic and economic threshold of Lipaphis erysimi

(Kaltenbach) on mustard. Ind.J.Ent., 60:43-49.

Sinha, R P., Yazdani, S.S. and Varma, G.D.

(1989). Population dynamic of mustard aphid,

Lipaphis erysimi (Kalt.) in relation to ecological

parameters. Ind. J. Ent., 51: 334-339.



EFFECT OF TILLAGE PRACTICES AND INTEGRATED NUTRIENT

MANAGEMENT ON GROWTH, ANALYSIS PARAMETERS OF SORGHUM

(SORGHUM BICOLOR L.)

Samrat Dhingra* and N.S. Thakur

Department of Agronomy, RVS Krishi Vishwavidhyalaya,

College of Agriculture, Indore (M.P.)


Received-03.08.2017, Revised-19.08.2017 Abstract: An experiment was conducted during kharif 2009 & 2010 at the Rajmata Vijyaraje Scindia Krishi Vishwa

Vidyalaya College of Agriculture, Indore (M.P.) to study on the effect of tillage practiced and integrated nutrient

management on growth, analysis parameters of sorghum. Tillage practices influenced only leaf area significant; chlorophyll

content and leaf area index remained unchanged at all the growth stages. Reduced tillage encouraged all these parameters are

others. Amongst INM treatments, 100% RDF (N80 P40K40) recorded above parameters up to maximum. Crop growth rate,

relative growth rate and net assimilation rate remained unchanged due to tillage practices and INM treatments, reduced

tillage recorded maximum Dry matter, grain and stover yields. In case of integrated nutrient management, 100%

Recommended dose of fertilizer (80:40:40) and 75% Recommended dose of fertilizer (60:30:30) +5t FYM/ ha recorded

equally higher grain and stover yields, being significantly superior to other fertility levels.

Keywords: Tillage practices, Integrated nutrient management, Growth analysis parameters, Sorghum

INTRODUCTION

he optimum tillage system is one of the most

critical inputs required to make proper seed bed

for maximum germination, emergence and

establishment of sown seeds. Integrated nutrient

management is basically the complementary use of

chemical fertilizers and organic as well as biological

sources of plant nutrients. Both these crop production

technologies influence the growth and yield of

sorghum crop under a given set of agro climatic

conditions.

The leaves and leaf area index are the important

parameters for maximum photosynthetic efficiency

which is ultimately related to crop production. The

growth analysis is an important device which

provides an eco-physiological evaluation of the

applied crop production technologies for analyzing

the complex character of productivity.

Such information was lacking, hence the present

experiment was planned to study the photosynthetic

efficiency of leaves measured in terms of their area,

weight and distribution of assimilates to the sink for

net productivity per unit time.

MATERIAL AND METHOD

The experiment was conducted during kharif 2009 &

2010 at the RVS Krishi Vishwa Vidyalaya, College

of Agriculture, Indore (M.P.). The soil of the

experimental field was a typical black with

dominance of montmorillonite clay content. The soil

pH was 7.65, electrical conductivity 0.39 dS/m,

organic carbon 0.48 %, available N, P2O5 and K2O

212, 13.5 and 481 kg/ha, respectively. The total

rainfall received during June to October, 2010 was

896.6 mm with 40 rainy days. The treatments

comprised three tillage practices (conventional tillage

having 1 summer ploughing +2 harrowing +Atrazine

(preemergance+1 hoeing +1HW; reduced tillage

having all practices as in conventional tillage except

summer ploughing and minimum tillage having 1

harrowing + Atrazine+1 hoeing +1 HW) in the main

plots and four nutrient management treatments

(100% RDN i.e. N80 P40 K40 through inorganic, 75%

RDN +5t FYM/ha, 50% RDN+2.5t FYM/ha +

Azotobacter + PSM, and control i.e. native fertility)

in the sub-plots. The experiment was laid out in split

plot design keeping three replications. The Sorghum

was CSH-16 was sown on 1 July, 2010 keeping seed

rate 8 kg/ha, row distance 45 cm and plant distance

12 cm. Nitrogen, phosphorus and potash was given

through urea and mutriate of potash, respectively.

The crop was grown as per recommended package of

practices but without any irrigation. The crop was

harvested on 24 October, 2010. The percent

concentration of N, P and K in grain and stover was

determined through the standard procedures. The

nutrient uptake per hectare by grain and stover was

worked out by multiplying each of the percentage of

nutrient content with each of the grain or stover yield

of sorghum [1]. The chlorophyll content was

determined by acetone extraction method [2].

The growth analysis parameters were determined as

per standard procedures.

T

SHORT COMMUNICATION

852 SAMRAT DHINGRA AND N.S. THAKUR

Table 1. Chlorophyll content and growth analysis parameters of sorghum as influenced by tillage practices and

integrated nutrient management (Mean of two years) Treatments Chlorophyll content (SPAD) Leaf Area (cm2) Leaf area index

90 DAS 90 DAS 90 DAS

Tillage Practices

Conventional Tillage 47.63 3530 6.47

Reduced Tillage 53.25 3685 6.78

Minimum Tillage 47.4 3434 6.28

C.D.(P=0.05) NS 202.11 NS

Integrated Nutrient Management

100 % RDF 51.44 3733 6.78

75% RDF + 5t FYM/ha 50.66 3566 6.67

50 % RDF +2.5t FYM/ha +Aztobacter +

PSM

49.15 3474 6.42

Native fertility (Control) 46.45 3425 6.17

C.D.(P=0.05) NS 80.28 0.45

Table 2. Crop growth rate, relative growth rate and net assimilation rate of sorghum as influenced by tillage

practices and integrated nutrient management (Mean of two years) Treatments CGR (g/dm2

GA/day)

RGR (mg dry

matter)

NAR (g/dm2 LA/day)

90 DAS

90 DAS 90 DAS

Tillage Practices

Conventional Tillage 1.10 54.00 0.32

Reduced Tillage 1.20 83.00 0.40

Minimum Tillage 1.22 84.00 0.46

C.D. at 5% 1.10 0.65 0.09


100 % RDF 0.70 0.287 0.16

75% RDF + 5t FYM/ha 1.40 0.165 0.10

50 % RDF +2.5t FYM/ha +Aztobacter + PSM 1.10 0.212 0.12

Native fertility (Control) 1.60 0.178 0.09

C.D.(P=0.05) 0.10 0.10 NS

Table 3. Dry matter/plant grain and stover yields and economical grain from sorghum as influenced by tillage

practices and integrated nutrient management Treatments Dry matter/

plant (g)

Yield (q/ha) Net return(Rs./ha) B:C

ratio

90 DAS Grain Stover

Tillage Practices

Conventional Tillage 141.17 50.56 114.7 39942 3.78

Reduced Tillage 142.54 51.50 120.8 42250 4.13

Minimum Tillage 139.54 47.37 107.8 38040 3.95

C.D. at 5% 1.36 2.22 9.5 - -


100 % RDF 143.04 52.75 123.7 43640 4.23

75% RDF + 5t FYM/ha 142.38 53.77 123.6 42281 3.68

50 % RDF +2.5t FYM/ha +Aztobacter + PSM 140.41 47.07 110.4 37054 3.65

Native fertility (Control) 138.39 45.66 100.0 37334 4.26

C.D.(P=0.05) 1.28 4.87 9.7 - -


Growth and yield attributes

The periodical observations data (Table 1) indicate

that the chlorophyll content leaf area and leaf area

index were increased with the advancement of plant

growth up to 90 days stage. This was due to decrease

of functional leaves because of shedding of older

leaves beyond 90 days stage. SPAD-502, a hand held

chlorophyll meter was used for rapid and non

destructive estimation of extractable chlorophyll in

leaves [3].

Different tillage practices influenced only the leaf

area (LA) up to significant extent, whereas

chlorophyll content and leaf area index (LAI),

remained unchanged at all the growth stages.

However all these parameters were encouraged

considerably due to reduced tillage over conventional

and minimum tillage practices. Reported that the

high water holding capacity in no tillage system is

due to soil organic contents[4].

Among the INM treatments, 100% RDF resulted in

maximum chlorophyll content, LA and LAI at every

growth stage. This was followed by 75% RDF +5t

FYM/ha and them 50 % RDF + 2.5t FYM/ha+

Azotobacter +PSB. The maximum benefit from

100% RDF may be attributed to immediate increased

availability of major nutrients thereby increased

photosynthetic process for plant growth. Besides

low, variable and generally unbalanced nutrient


contents, it is difficult to provide the proper nutrient

balance to meet crop requirements with bulky

organic manure [5].

Crop growth rate, relative growth rate and net

assimilation rate tended to decrease rapidly beyond

30 days stage of growth. The different tillage

practices as well as INM treatments did not extent

any significant changes in these growth analysis

parameters almost at all the stages (Table 2).

Returning of crop residues to soil was important for

favourable soil structure, soil and water conservation

[6].

Among the tillage practices, reduced tillage

performed the best with respect to DM production /

plant at every stage. Accordingly the reduced tillage

resulted in significantly higher grain yield (51.50

q/ha) and stover yield (120.8 q/ha) as compared to

minimum tillage. The effect of conventional tillage

was at per with that of reduced tillage (Table 3). This

was owing to increased leaf area photosynthetic

surface as a result of better soil conditions created by

both these tillage practices. The results are in close

agreement with those of [7]&[8].

In case of INM, 100% RDF and 75% RDF +5t

FYM/ha recorded equally higher grain yields (52.75

to 53.77 q/ha) and stover yields (123.6 to 123.7

q/ha), being significantly superior to 50% RDF + 2.5t

FYM/ha + Azotobacter. + PSB and control

treatments. This was attributed to increased and

immediate supply of NPK there by increased

production and translocation of photosynthates for

the growing plants. These results conforms the

findings of [9]&[10].

REFERENCES

A.O.A.C. (1997). Official Methods of Analysis, 14th

Edn. Association of official Agricultural Chemists,

Washington, D.C.

Witham, F.H., Blaydes, D.F. and Devlin, R.M. (1971). Experiments in Plant Physiology.Van

Nostrand Rainheld Co., Newyork,p:245.

Earl, H. and Tollenarr, M. (1997). Maize leaf

absorptance of photosythetically active radiation and

its estimation using a chlorophyll meter. Crop

Science 37: 436-440.

Lal, R. (1989). Tillage effect on soil properties under

different crops in western Nigeria. Soil Sci Society

Am J. 40: 762-768.

Kumar, R. P., Singh, O. N, Singh, Y., Dwivedi, S.

and Singh, J. P. (2009). Effect of integrated nutrient

management on growth, yield, nutrient uptake and

economics of french bean (Phaseolus vulgaris).

Indian Journal of Agricultural Sciences, 79(2), 122-

128.

Badanur, V.P. and Malabasari, T. A. (1995).

Effect of recycling of organic residues on soil

characteristics and sorghum yield. Indian Journal of

Soil Conservation, 23: 236-238.

Subudhi, C.R. and Senapati, P.C. (2005). Low till

farming strategies for improving soil health in north-

eastern ghat of Orissa. Indian J. Dryland Agriculture

Research & Development, 20 (1) : 92-94.

Yeldedhalli et al. (2009). Effects of soil tillage

practices and in-situ vermiculture on productivity

and soil fertility in maize-groundnut cropping

sequence of semiarid tropics. Annals of Plant and

Soil Reaserh. 11(2) : 73-76.

Gawai and Pawar (2006). Integreted nutrient

management in sorghum (Sorghum bicolor) -

chickpea (Cicer arietinum) cropping sequence under

irrigated conditions. Indian Journal of Agronomy

51(1) : 17-18.

Jaya and Tripathi (2010). Integrated nutrient

management in sorghum mustard cropping system.

Annals of Plant and Soil Reaserh 12(2) : 105-107.

854 SAMRAT DHINGRA AND N.S. THAKUR



EFFECT OF DATE OF SOWING AND WEED MANAGEMENT TECHNIQUES ON

GROWTH ATTRIBUTES AND YIELD OF BLACKGRAM (VIGNA MUNGO L.)

Nishant Kumar Sai*, R. Tigga and V.K. Singh

IGKV, RMD College of Agriculture and Research station, Ambikapur-497001 (C.G.)India


Abstract: An experiment was carried out to evaluate the effect of date and weed management techniques on growth and

yield of blackgrama (Vigna mungo L.). Maximum seed yield was recorded when sowing was done on 15th July and weed

management practices mechanical weeding (15 and 30 DAS and removal of weeds within rows by hand) followed by

sowing on July 25th and weed management practices pendimethalin @ 0.75 a.i.ha-1 and mechanical weeding at 30 DAS. It

was due to higher plant height, higher number of branch plant-1, dry matter production.

Keywords: Blackgram, Weed management practices, Growth attributes

INTRODUCTION

lackgram (Vigna mungo L.) also known as

urdbean, belong to family leguminosae is one of

the important pulse crop grown in many Asian

countries including India, where the diet is mostly

cereal based. Blackgram is rich source of protein (17

to 25 %) as compared to cereals (6 to 10 %) and,

their ability to fix atmospheric nitrogen and improve

the soil fertility status. Among the pulses, blackgram

is extensively cultivated pulse crop. It has

originated from Indian sub-continent (De candoll,

1986). Its seed contain 55-60% carbohydrate, 22-

24% protein and 1.0-1.3% of fat besides, phosphoric

acid (H3PO4), being 5-10 times more than other

pulses. Black gram especially contains a higher

percentage of methionine compared to other food

legume. Its dry stalks along with the pod husk forms

a nutritive fodder especially for cattle. In India,

blackgarm is grown in 3.06 million ha area with total

production of 1.70 millions tones and productivity

5.55 qt.ha-1

whereas Chhattisgarh it occupies 0.10

million ha area with total production of 0.03 million

tones and productivity 3.04 q.ha-1

.

The weather parameters play an important role in

deciding the success or failure of the crop, because

they strongly influence strongly the physiological

expression and genetic potential of the crop. It is well

known that yield from any given crop or variety

depends on the availability of certain optimum

rainfall, solar radiation, temperature, soil moisture,

heat units etc. during different stages of crop growth.

Among different management factors, sowing time

plays a key role in obtaining higher yield. Time of

sowing is known to influence the yield and growth of

black gram. The optimum time is mainly dependent

on prevailing agro-climatic conditions of an area

besides the crop grown. Planting during the optimum

period, therefore, ensures better harmony between

the plant and weather which ultimately results in

higher crop yields (Venkateshwarulu and Sounda

Rajan, 1991).

Sowing date has the greatest effects on the grain

yield of mash bean. Delay in sowing beyond

optimum date results in a progressive reduction in

the potential yield of the crop. Sowing time is

considered as one of the important productivity

limiting factors that affect the plant growth and

ultimately crop yield. Time of sowing determines

time of flowering and it has great influence on dry

matter accumulation, seed set and seed yield. Sowing

time affects plant physiological and morphological

specifications like effect on vegetative and

reproductive periods, harvest time, yield and its

quality. To achieve good yield, crop must be sown at

appropriate time. Sowing times has makeable effects

on growth and yield of most crops in different parts

of the world as delay in sowing beyond the optimum

time usually results in yield reduction (Vange and

Obi, 2006).

The productivity of urdbean is very low in India as

well as in Chhattisgarh due to various agronomic

reasons, among them weed infestation is one of the

major limiting factors in production, especially

during rainy (kharif) season. Uncontrolled weeds at

critical period of crop-weed competition caused a

reduction of 80-90% in yield depending upon type

and intensity of weed infestation. Weed species

infesting urdbean vary according to the agro-

ecosystem of the growing region. Ageratum

conyzoids, Boreria hispida, Commelina banghalensis

and grasses like Echinochloa colona, Cynodon

dactylon, Paspalum scrobiculatum, Digiteria

sanguinalis and sedges like Cyperus rotundus are the

major weeds in mashbean. Most prominent weeds

species observed in blackgram fields are Panicum

colona L., Cynodon dactylon L., Cyperus rotundus

L., Digera arvensis Forsk, Euphorbia hirta L.,

Leucas aspera Spreng., Phyllanthus niruri L.,

Portulaca oleracea L., Indigoflora glandulosa L.,

Phyllanthus niruri L.

B

SHORT COMMUNICATION

856 NISHANT KUMAR SAI, R. TIGGA AND V.K. SINGH

MATERIAL AND METHOD

The experiment was conducted at research cum

Instructional farm of RMD College of Agriculture

and Research Station, Ambikapur (C.G.) during

kharif season 2016, which was located at latitude of

2308’ N, longitude of 83

015’E and an altitude of 623

m mean sea level. The treatment consist of three

date of sowing (15th

July 2016, 25th

July 2016 and 5th

August 2016) as main plot and four weed

management techniques (W1: Control (weedy check),

W2: Mechanical weeding at 15 and 30 DAS and

removal of weeds within rows by hand, W3:

Pendimethalin @ 0.75 lit. a. i. ha-1

at 0-2 DAS +

Mechanical weeding at 30 DAS, W4: Pendimethalin

@ 0.75 lit. a. i. ha-1

at pre-emergence + Sodium

acifluorfen (16.5%) + clodinafop - propargyl (8 % )

EC @ 0.245 lit. a. i. ha-1

at 20- 25 DAS) as sub plots

which were laid out in split plot design. Indira urd-1

variety was sown in 30cm X 10cm (row to row and

plant to plant). Data were recorded on plant height,

higher number of branch plant-1

, dry matter

production, days to 50% flowering, pod weight plant-

1, number of pods plant

-1, number of seed pod

-1, 1000

seed weight, seed yield, biological yield and harvest

index were recorded by following the standard

procedures.


Effect of date of sowing on growth attributes

The data on plant population was significantly

influenced by different date of sowing. Maximum

plant population was observed under sowing date on

July 15th

(23.02/m2) compared to other i.e. July 25

th

and August 5th

(20.94 and 17.68/m2) and minimum

plant population was recorded under sowing on

August 5. This might be due to favorable

environmental condition for proper germination of

crop.

The effect of different sowing times on plant height

was found to be significant and the higher plant

height was observed by the date of sowing i.e. 15th

July (52.75 cm) as compared to other sowing dates.

More plant height was recorded in S1 sowing time

i.e. 15th July this may be due to enjoying relatively

more time by the plants and more rainfall during

growing season. This result confirms the

observations of Malik et al., (2003). From the data

on mean number of branches, the sowing of

blackgram crop in i.e. 15th July (6.45) found to be

significantly superior over rest of all sowing times.

The higher mean number of leaves (17.68) and leaf

area index (806.61 cm2) recorded by i.e. 15th July it

was followed by the sowing at 25July and 5 August

respectively. Maximum leaf area (cm2) was produced

due to long vegetative period, bright sunshine and

high rainfall which favored more vegetative growth.

These results agree to those of Malik et al., (2003).

The sowing of i.e. 15th July recorded higher dry

matter accumulation plant-1

(58.33 g) followed by the

sowing at 25th July and August 5th

. Higher total dry

matter accumulation plant-1

was due to more cell

elongation and vigorous growth of the crop plant due

to the more rainfall and bright sunshine. The similar

trend in case of growth characters was reported by

Antony et al., (2006). Maximum days to 50%

flowering were observed with the date of sowing

July 15th

(38.58) as compare to other date of sowing.

Delay in sowing resulted early flowering and early

maturity.

Effect of weed management techniques on crop

growth attributes

The growth characters like plant height, number of

branch plant-1

, number of leaves plant-1

and dry

matter production plant -1

(g) were positively and

significantly increased by various weed management

treatments (Table 1). Mechanical weeding at 15 and

30 DAS and removal of weeds within rows by hand

recorded taller plants (36.69 cm) and more dry matter

production plant-1

(52.01 g plant-1

) followed by

pendimethalin @ 0.75 lit. a. i. ha-1

at 0-2 DAS +

Mechanical weeding at 30 DAS and pendimethalin

@ 0.75 lit. a. i. ha-1

at pre-emergence + Sodium

acifluorfen (16.5%) + clodinafop- propargyl (8 % )

EC @ 0.245 lit. a. i. ha-1

at 20- 25 DAS. The

increased in plant height, number of branch plant-1

,

number of leaves plant-1

and dry matter accumulation

plant-1

might be due to the less crop-weed

competition for different growth factor resulted

proper utilization of solar radiation, water, nutrients,

etc. where as in control plot because of more crop-

weed competition, plants unable to obtain water,

nutrients and solar radiation in sufficient quantity

resulted lowest height. Similar results have been

reported by Rao et al. (2010).

Table 1. Effect of date of sowing and weed management techniques on growth characters of blackgram Treatment Plant

population

Plant

height

(cm)

No. of

branch

plant-1

No. of leaves

plant-1

LAI (cm2) Dry matter

accumulation

Plant-1 (g)

Days to 50%

flowering

Seed yield (q

ha-1)

Date of sowing

S1: 15 July, 2016

23.02

52.75 6.45 17.68

806.61 58.33 38.58

10.68

S2:25 July, 2016

20.94

46.80 5.55 15.72

762.16 41.97 34.33

8.93

S3: 05 Aug., 2016

17.68

42.45 4.59 8.30

740.17 33.07 30.42

6.37

SEm± 0.59 1.07 0.12 0.46 9.14 0.84 0.25 0.33

CD(P=0.05) 2.33 4.20 0.48 1.80 35.92 3.33 1.00 1.31

Weed management

W1 20.20 45.13 5.33 11.93 767.23 32.43 34.22 6.23


W2

21.87

48.58 5.75 16.40

772.72 52.01 34.56

10.25

W3

20.10

47.84 5.60 13.76

770.04 46.76 34.55

9.27

W4

20.02

47.78 5.45 13.50

768.59 46.61 34.44

8.89

SEm± 0.37 0.78 0.08 0.74 0.80 0.88 0.20 0.33

CD(P=0.05) 1.11 2.32 0.26 2.19 2.38 2.62 0.60 0.97

CONCLUSION

The date of sowing of July 15th

and July 25th

were

found beneficial as compared to other date of

sowing. Among the different weed management

techniques mechanical weeding (15 and 30 DAS)

was found more profitable.

REFERENCES

Antony, E., Chowdhury, S. R. and Kar, G. (2006).

Variations in heat and radiation use efficiency of

green gram as influenced by sowing dates and

chemical sprays, J. Agromet, 2003, 5(2): 58-61.

Decandolle, A. P. (1986). Origin of cultivated

plants. Hafnor publishing, NEWYORK (USA).

Malik M. A., Saleem, M. F., Ali, A. and Rana, A.

F. (2003). Effect of sowing dates and planting

patterns on growth and yield of mung bean (Vigna

radiata L. Wilczek), J. Agri. Res. 44(2).

Rao, A.S., Subba Rao, G. and Ratnam, M. (2010).

Copies bio-efficacy of sand mix application of pre

mergence herbicides alone and in sequence with

imazethapyr on weed control in relay crop of

blackgram. Pak. J. Weed Sci. Res., 16(3): 279-285.

Vange, T., Obi, IU. (2006). Effect of planting date

on some agronomic traits and grain yield of upland

rice varieties at Makurdi, Benue State, Nigeria.

Journal of Sustainable Development and Agricultural

Environment 2, 1-9.

Venkateshwarulu, M. S. and Soundara Rajan, M.

S. (1991). Influence of season on growth and yield

attributes of blackgram. Indian journal of

Agronomy., 36: 199-123.

858 NISHANT KUMAR SAI, R. TIGGA AND V.K. SINGH

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