WEED CONTROL AND YIELD OF SWEET CORN ( ZEA ... NIGERIAN JOURNAL OF CROP SCIENCE Volume 1 No. 1...

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NIGERIAN JOURNAL OF CROP SCIENCE Volume 1 No. 1 September 2013 pp. 1 - 9

WEED CONTROL AND YIELD OF SWEET CORN (ZEA MAYS (L.) VAR.

SACCHARATA)/EGUSI MELON (COLOCYNTHIS CITRULLUS (L.) O. KITZ)

INTERCROP AS INFLUENCED BY PLANT POPULATION OF EGUSI MELON IN

THE HUMID TROPICS

Muoneke

1C. O. *,. Mbah

2 E .U, Orji

1 U.

1Department of Agronomy, College of Crop and Soil Sciences,

Michael Okpara University of Agriculture, Umudike, Abia State

2Department of Crop Production Technology, Federal College of Agriculture, Ishiagu,

Ebonyi State, Nigeria. *Corresponding Author: [email protected], [email protected]

ABSTRACT

Rainfed field experiment was conducted in 2006 and repeated in 2007 at the Department of Agronomy, Michael

Okpara University of Agriculture, Umudike, Abia State, Nigeria to determine the optimum plant population of

egusi melon for weed control, weeding frequency and yield of the component crops in sweet corn and egusi

melon intercrop. The treatments were cropping systems (sole and intercrop), egusi melon plant population (0;

10,000 and 20,000 plants/ha) and weeding frequency (zero weeding, one weeding at 3 weeks after planting

(WAP) and two weedings at 3 and 8 WAP). The treatments were fitted in a 2 x 3 x 3 factorial. There were three

replications. The results showed that intercropping had significant effect on growth, yield and yield components

of sweet corn and egusi melon in both cropping seasons. Higher egusi melon plant population had no significant

effect on sweet corn grain yield but gave better egusi melon seed yield. Weeding frequency significantly (P<0.05)

increased sweet corn grain and egusi melon seed yields as well as showed better biological advantage in the

intercrop relative to sole crops. The trend was the same in both cropping seasons. Intercropping sweet corn with

egusi melon at 20,000 plants/ha with one manual weeding at 3 WAP or 2 manual weeding regimes at 3 and 8

WAP was more effective in controlling weeds than the other treatments. The land equivalent ratios in the

intercropping were all above unity, indicating that intercropping had better yield advantage than sole cropping of

the component crops.

Keywords: Plant population, weeding frequency, Zea mays, Colocynthis citrullus.

INTRODUCTION Mixed intercropping is a common feature in farming

systems in southeast Nigeria. The system is

traditionally favoured by farmers because it gives total

crop yield per unit area (Muoneke et al., 2007; Cardoso

et al., 2007), enhances efficient use of limited growth

resources such as sunlight, moisture, nutrients and

space (Ano and Orkwor, 2006) and reduces insect pest

infestation (Anyim et al., 2003). Sweet corn and egusi

melon are basically planted by farmers in the region for

their yields and economic returns. However, egusi

melon, because of its spreading growth habit

(Planophile), may be used to suppress weeds, lower

soil temperatures and conserve soil moisture (Ikeorgu,

1987; Makinde and Bello, 2009).

Wahua (1985) reported that in cultivating egusi melon,

emphasis is normally on the plant population that will

rapidly cover the soil for effective weed control as live

mulch. According to Akobundu (1989), low growing

crops such as egusi melon and sweet potato have been

successfully used to replace hand weeding in mixed

intercropping systems involving maize, yam, and

cassava. In an intercrop involving sweet corn and egusi

melon, both are fast canopy forming crops with sweet

corn possessing an erectophile growth habit while

egusi melon has a planophile growth habit. Therefore,

the tendency for minimal or no weeding of the crop

mixture, depending on the weed prevailing in that area

and plant population of egusi melon seed used during

the entire growing period of the mixture, is relatively

high (Akobundu, 1993).

Studies have shown that crop species that have

planophile growth habit of leaf distribution are better at

smothering weeds and maintaining soil fertility than

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others (Ekeleme and Nwofia, 2005; Ghanbari et al.,

2007). Sweet corn is cultivated for human and

livestock consumption. Emuh (2007) surmised that

maize has strong industrial uses in the production of

germ oil, starch and alcohol, among other things.

Egusi melon is a popular crop in Nigeria; its seeds are

rich in oil and are used as soup thickener or flavoring

agent. According to Gorski (1985), the seeds of egusi

melon contain 4.60 g carbohydrates, 0.6 g proteins, 0.6

g crude fiber, 33 mg vitamin C, 17 g Ca, 16 mg P, 230

mg K per 100 g edible seeds. Also, the crop responds

well to organic and inorganic fertilizers, which

facilitate its growth performance as good live mulch

(Olaniyi and Fagbayide, 2008). However, there is

inadequate information on the optimal egusi melon

plant population and weeding frequency that would be

most appropriative for weed control and enhanced

productivity of the mixture. Therefore, the objectives

of the study were to determine the effects of weeding

frequency and egusi melon plant populations on the

growth and yield of sweet corn and egusi melon as well

as on weed control in the mixture.

MATERIALS AND METHODS Rain fed field trials were carried out in 2006 and 2007

at the Michael Okpara University of Agriculture,

Umudike (05°

29' N, 07°

23' E, 122 m altitude). The

study area was characterised by the tropical rainforest

vegetation with total annual rainfall, minimum monthly

air temperature and mean sunshine hours for the two

years as 2,351.4 mm (2006) and 2,416.7 mm (2007),

22.3 °C (2006) and 22.4

°C (2007) and 3.64 hours/day

(2006) and 3.53 hours/day (2007), respectively (Table

1). Air temperature and sunshine hours were relatively

high through the years and did not appear limiting at

any period of the cropping seasons.

The soil type was ultisol (Palustult) and its texture was

sandy loam (Enwezor, et al., 1990). Some of the soil

chemical characteristics at 0 – 20 cm depth were as

follows: soil pH 4.02 and 5.47 (1:2.5, soil:water);

organic matter content 2.50 and 1.14 %, total N 0.15 %

and 0.11 %, available P2O5 18.20 and 17.03 meq./100 g

soil and exchangeable K 0.16 and 0.14 meq./100 g soil

in 2006 and 2007 cropping seasons, respectively (Table

2).

The treatments were laid out in a 2 x 3 x 3 factorial

arrangement fitted into a randomized complete block

design (RCBD) with three replications. The size of

each plot was 6 m x 4 m. The experimental set up

consisted of two cropping systems (sole and intercrop),

three egusi melon planting densities (0; 10,000 and

20,000 plants/hectare) and three manual weeding

frequencies (zero weeding, one manual weeding at 3

weeks after planting (WAP) and two manual weedings

at 3 and 8 WAP). The test crops, sweet potato and

egusi melon were planted two seeds per stand on the

same day and later at 10 days after planting (DAP)

thinned to one plant/stand. The plant populations were

achieved by using 1 m x 1 m spacing for 10,000

plants/ha and 1 m x 0.50 m spacing for 20,000

plants/ha at one plant per stand for egusi melon sole

and intercropped. Sweet corn was planted at 20,000

plants/ha at 1 m x 0.50 m spacing.

A compound fertilizer, 12:12:17:2 (N:P:K:Mg) was

applied as a single dose at the rate of 400 kg/ha at 3

WAP. Egusi melon vines spread were confined within

each experimental plot as much as possible to prevent

mixing of the fruits at harvest. Plant height of sweet

corn and vine length of egusi melon were taken each

from a sample of three plants tagged within the two

central rows of each plot. Plant height of maize was

achieved by using a meter rule to measure height

starting from the base of the plant to the flag leaf and

the mean determined. Vine length of egusi melon was

obtained by using the ruler to measure from the base of

the longest vine to the tip of the vine. Days to 50 %

tarselling in maize and flowering in egusi melon were

obtained by assessing the percentage of plants that had

commenced tarseling or flowering relative to the total

number planted in each experimental plot.

In sweet corn, yield and yield components such as the

cob length was obtained by measuring the length of

three cobs from each treatment plot and their means

determined. Number of seeds per cob was obtained by

counting the number of seeds removed from each of

the three cobs. The seed weight was obtained by

weighing with a sensitive balance (Satorius Master

Series). The total grain yield at 14 % moisture content

was calculated for each plot, extrapolated and recorded

in tonnes per hectare. For egusi melon at maturity, the

number of pods/plant was obtained by counting the

number of pods of three plants in each plot; the values

recorded and the mean determined. The fruits were

beaten with strong, heavy sticks to break the hard

shells. They were left for about a week to allow the

pulp to soften. The seeds were extracted from the pulp,

washed in clean water and dried. The number of

seeds/pods was achieved by counting the number of

seeds from four pods in each plot and the mean

calculated. The dried seeds from the experimental plots

were weighed and yield converted into tons per

hectare.

The productivity of the inter crops was achieved using

the land equivalent ratio (LER) described below:

LER = {(Ia/Sa) + (Ib/Sb)}, where, Ia and Ib are intercrop

sweet corn and egusi melon yields, respectively, while,

Sa and Sb are their corresponding sole crop yields.

Data generated were subjected to analysis of variance

procedures using Genstat computer package (Genstat,

2003). Means were compared using the least

significant difference (LSD) at 5 % level of probability

according to Obi (2002). For each year, separate

Wed Control and Yield of Sweet Corn/Egusi melon

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statistical analysis were performed on maize and egusi

melon.

The treatment combinations are shown below:

-Sole sweet corn at 20,000 plants/ha + zero weeding

-Sole egusi melon at 10,000 plants/ha + zero weeding

-Sole egusi melon at 20,000 plants/ha + zero weeding

-Sole sweet corn at 20,000 plants/ha + one manual weeding at 3 WAP

-Sole egusi melon at 10,000 plants + one manual weeding at 3 WAP

-Sole egusi melon at 20,000 plants + one manual weeding at 3 WAP

-Sole sweet corn at 20,000 plants/ha + two manual weedings at 3 and 8 WAP

-Sole egusi melon at 10,000 plants/ha + two manual weedings at 3 and 8 WAP

-Sole egusi melon at 20,000 plants/ha + egusi melon at 10,000 plants/ha + zero weeding.

-Sweet corn at 20,000 plants/ha + egusi melon at 10,000 plants/ha + zero weeding

-Sweet corn at 20,000 plants/ha + egusi melon at 20,000 plants/ha + one manual weeding at 3 WAP

-Sweet corn at 20,000 plants/ha + egusi melon at 10,000 plants/ha + one manual weeding

-Sweet corn at 20,000 plants/ha + egusi melon at 10,000 plants/ha + 2 manual weedings at 3 and 8 WAP

-Sweet corn at 20,000 plants/ha + egusi melon at 10,000plants/ha + 2 manual weedings at 3 and 8 WAP.

-Sweet corn at 20,000 plants/ha + egusi melon at 20,000 plants/ha + 2 manual weedings at 3 and 8 WAP.

RESULTS Sweet corn growth Table 3 shows that in 2006 and 2007 cropping seasons,

cropping system and egusi melon population had no

significant (P>0.05) effect on plant height and days to

50 % tasselling contrary to weeding frequency, which

significantly induced plant height but had no effect on

days to 50 % tasselling of sweet corn.

Sweet corn yield and yield components Cropping system had significant (P < 0.05) effect on

cob length, number of grains/cob and grain yield in

2006 cropping season (Table 4). The trend was not

consistent in 2007. Cob length, number of grains/cob

and grain yield were higher in intercropping than in

sole crop in 2006. Egusi melon plant population had no

effect on yield and yield components of sweet corn in

both cropping seasons. The weeding frequency

significantly affected grain yield and yield parameters

of sweet corn in 2006 and 2007. Two weeding regimes

at 3 and 8 WAP had the highest cob length, number of

grains/cob, 100-grain weight/plant and grain yield/ha in

both years. Averaged over two cropping seasons, two

weeding regimes at 3 and 8 WAP gave the highest

grain yield of sweet corn by 61.58 % and 21.47 %

compared to zero weeding and one weeding regime at

3 WAP, respectively.

Muoneke1C. O. *,. Mbah

2 E .U, Orji

1 U.

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Table 1: Meteorological data of the study area

Table 2: Soil physical and chemical properties of the experimental sites in 2006

and 2007 cropping seasons

Physiochemical properties 2006 2007 Methodology

Sand (%) 83.50 77.50 Bouyoucos hydrometer method

Silt (%) 5.00 7.70 Bouyoucos hydrometer method

Clay (%) 11.50 14.80 Bouyoucos hydrometer method

Soil texture Sandy loam

Organic matter (%) 2.50 1.14 Wet oxidation method

Nitrogen (%) 0.15 0.11 Macro kjeldahl digestion and distillation

procedure.

Phosphorus (P2O5) 18.20 17.03 Bray-1 extraction procedure

Potassium (K2O) 0.16 0.14 Flame photometry

pH 4.02 5.47 Potentiameter in 1:2.5 soil:water suspension

using a pH meter.

Table 3: Plant height and days to 50 % tasselling of sweet corn as influenced by

cropping system, egusi population and weeding frequency in sweet

corn/egusi melon intercropping in 2006 and 2007 cropping seasons

Treatment Plant height (m)

10 WAP

Days to 50%

tasselling

2006 2007 2006 2007

Cropping system

Sole sweet corn 1.34 1.54 59.9 60.7

Intercrop sweet corn 1.29 1.26 60.1 60.9

LSD 0.05 Ns Ns ns ns

Egusi melon population (plants/ha)

0 1.31 1.29 60.0 60.8

10,000 1.31 1.29 60.2 61.0

20,000 1.32 1.28 59.9 60.6

LSD 0.05 Ns Ns ns ns

Weeding frequency

Zero weeding 1.44 1.08 60.8 61.1

One weeding (3 WAP) 1.36 1.36 60.3 60.8

Two weeding (3 and 8 WAP) 1.43 1.43 59.0 60.8

LSD 0.05 0.14 0.18 ns ns

Month Air temperature (0C) Total rainfall (mm) Rain days Sunshine

(hours/day) Minimum Maximum

2006 2007 2006 2007 2006 2007 2006 2007 2006 2007

January 24 21 33 34 3.1 0.0 1 0 4.2 4.0

February 24 24 33 35 107.1 62.9 3 2 4.3 4.6

March 24 24 34 35 68.5 35.5 7 5 4.4 4.9

April 24 23 33 32 259.0 78.4 11 9 4.6 3.4

May 23 22 31 32 436.3 444.9 16 17 4.0 4.7

June 22 22 31 30 240.1 354.0 16 18 4.0 2.2

July 23 22 30 35 187.6 23 18 3.2 1.2 -

August 22 22 29 29 333.7 464.8 21 26 3.0 1.9

September 22 22 29 20 238.5 319.9 18 18 2.0 3.2

October 22 22 31 30 247.5 335.6 18 19 3.2 4.0

November 23 23 31 31 37.8 112.1 3 8 3.0 4.0

December 20 22 32 32 0.0 25.0 0 2 3.1 4.3

Total - - - - 2351.4 2416.7 137 142 - -

Mean 22.3 22.4 31.4 31.7 - - - - - -

Source: Meteorological station, National Root Crops Research Institute, Umudike, Abia State, Nigeria.


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Table 4: Yield and yield components of sweet corn as influenced by cropping system, egusi melon

population and weeding frequency in sweet corn/egusi melon intercropping in 2006 and

2007 cropping seasons Treatment Cob length

(cm)

No of grains/cob 100 grain weight (g) Grain yield

(t/ha)

2006 2006 2006 2007 2006 2007 2006 2007

Cropping system

Sole 12.80 13.0 316.7 310.9 19.6 19.3 1.28 1.26

Intercropped 14.11 13.3 336.1 291.9 20.1 19.7 1.39 1.18

LSD 0.05 0.10 ns 10.3 ns ns ns 0.08 ns

Egusi melon population

(plants/ha)

0 13.7 13.24 329.6 298.2 19.9 19.6 1.35 1.21

10,000 13.9 13.33 325.2 298.1 19.9 19.9 1.33 1.22

20,000 13.4 13.11 334.1 298.3 20.0 19.3 1.37 1.20

LSD 0.05 ns ns Ns ns ns ns ns ns

Weeding Frequency

Zero weeding 12.1 11.2 226.7 178.9 16.9 16,9 0.77 0.59

One weeding (3 WAP) 13.6 13.2 335.0 335.2 20.5 20.5 1.42 1.36

Two weedings

(3 and 8 WAP)

15.3 15.2 420.0 380.6 22.3 22.3 1.86 1.67

LSD 0.05 0.10 0.71 11.8 34.5 1.40 1.40 0.09 0.15

Egusi melon growth Vine length and days to 50 % flowering, were not

influenced by cropping system in both years (Table 5).

Egusi melon population had significant (P<0.05) effect

on vine length of the crop days to 50 % flowering in

2006 and 2007 cropping seasons. Planting of egusi

melon at 20,000 plants/ha increased the vine length of

the crop compared to egusi melon at 10,000 plants/ha.

The number of weeding frequency significantly

influenced vine length and days to 50 % flowering. The

two weeding regimes (3 WAP and 8 WAP) produced

the longest vines while zero weeding significantly

(P<0.05) delayed flowering by 5 % (2006) and 9 %

(2007) and by 8 % (2006) and 10 % (2007) relative to

one weeding regime and two weeding regimes,

respectively.

Egusi melon yield and yield components Cropping system and egusi melon plant population had

no significant (P>0.05) effect on number of

pods/plant, number of seeds/pod, 100-seed

weight/plant and seed yield per hectare in both years

(Table 6). Number of pods/plant, seeds/pod,

seeds/plant, 100-seed weight/plant and seed yield per

hectare were significantly higher with one and two

weeding regimes relative to zero weeding. There was

no difference between one and two weeding regimes

on yield and yield components of egusi melon. The

trend was the same in both years

.


2 E .U, Orji

1 U.

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Table 5: Vine length and days to 50 % flowering as influenced by cropping system, egusi melon

population and weeding frequency in sweet corn/egusi melon intercropping in 2006 and

2007 cropping seasons Treatment Vine length

(m)

Days to 50 % flowering

2006 2007 2006 2007

Cropping system

Sole egusi melons 2.34 2.19 39.8 38.8

Intercropped egusi melon 2.33 2.13 40.5 38.6

LSD 0.05 ns Ns ns ns

Egusi melon population plants/ha

10,000 2.25 2.10 39.7 38.7

20,000 2.42 2.23 40.7 38.7

LSD 0.05 0.13 0.13 ns ns

Weeding frequency

Zero weeding 1.22 1.11 41.9 41.3

One weeding (3 WAP) 2.71 2.56 39.9 37.5

Two weedings (3 and 8 WAP) 3.08 2.82 38.6 37.3

LSD 0.05 0.16 0.16 1.18 1.34

Table 6: Yield and yield components of egusi melon as influenced by cropping systems, egusi melon

plant population per hectare and weeding frequency in 2006 and 2007cropping season Treatment No. of pods /plant No. of seeds/plant No seeds/pod 100-seed wt (g) Seed yield

(t/ha)

Two

year

mean

2006 2007 2006 2007 2006 2007 2006 2007 2006 2007

Cropping system

Sole egusi melon 4.3 3.7 635 598 126 129 11.3 11.1 1.11 1.06 1.07

Intercropped 3.9 3.4 575 540 123 120 11.2 11.7 1.05 0.97 1.02

LSD 0.05 ns ns ns ns ns Ns ns ns Ns ns ns

Egusi melon

population

(plants/ha)

10,000 4.1 3.7 618 571 128 127 11.2 10.9 0.74 0.68 0.71

20,000 4.1 3.5 592 567 121 122 11.3 10.9 1.42 1.15 1.39

LSD 0.05 ns ns ns ns ns Ns ns ns Ns ns ns

Weeding Frequency

Zero weeding 1.8 1.3 96 84 48 45 9.5 8.5 0.14 0.10 0.12

One weeding (3

WAP)

5.2 4.5 820 811 158 157 11.9 11.7 1.49 1.42 1.45

Two weeding (3 and

8 WAP)

5.3 4.9 898 812 167 172 12.3 12.6 1.62 1.52 1.57

LSD 0.05 0.6 0.6 94.0 7.4 6.00 6.00 0.54 0.73 0.20 0.16 0.14


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Table 7: Weed dry matter (g) as influenced by egusi melon plant population and weeding frequency

in sweet corn/egusi melon intercrops in 2006 and 2007 cropping seasons

Treatment Combination

Weed dry matter (g)

Weeding frequency (WAP)

8

2006 2007

Sole sweet corn at 20,000 plants/ha + zero weeding 93.30 94.00

Sole egusi melon at 10,000 plants/ha + zero weeding 44.60 46.00

Sole egusi melon at 20,000 plants/ha + zero weeding 35.33 36.00

Sole sweet corn at 20,000 plants/ha + 1 manual weeding at 3 WAP 38.00 39.33

Sole egusi melon at 10,000 plants/ha + 1 manual weeding at 3 WAP 18.33 18.35

Sole egusi melon at 20,000, plants/ha + 1 manual weeding at 3 WAP 10.03 11.00

Sole sweet corn at 20,000 plants/ha + 2 manual weeding at 3 and 8 WAP 38.40 39.00

Sole egusi melon at 10,000 plants/ha + 2 weeding at 3 and 8 WAP 17.20 17.33

Sole egusi melon at 20,000 plants/ha + 2 weeding at 3 and 8 WAP 9.00 9.33

Sweet corn at 20,000 plants/ha + egusi melon at 10,000 plants/ha + zero weeding 42.00 43.00

Sweet corn at 20.000 plants/ha + egusi melon at 20,000 plants +zero weeding 35.70 36.00

Sweet corn at 20,000 plants/ha + egusi melon at 10,000 plants/ha + I manual weeding at 3 WAP 17.33 18.00

Sweet corn at 20,000 plants/ha + egusi melon at 20,000 plants/ha + 1 manual weeding at 3 WAP 10.00 10.00

Sweet corn at 20,000 plants/ha + egusi melon at 10,000 plants/ha + 2 manual weeding at 3 and 8 WAP 16.33 17.00

Sweet corn at 20,000 plants/ha + egusi melon at 20,000 plants/ha + 2 manual weeding at 3 and 8 WAP 11.70 10.00

LSD0.05 3.85 3.42

Table 8: Land equivalent ratios for egusi melon plant population and weeding frequency in sweet

corn/egusi melon intercrops in 2006 and 2007 cropping seasons

Treatment Land equivalent ratio

2006 2007

Partial Total Partial Total

Egusi melon

plants/ha

Weeding frequency Sweet

corn

Egusi

melon

Sweet

corn

Egusi

melon

10,000 zero weeding 1.12 1.08 2.20 1.16 0.81 1.97

10,000 1 (3 WAP) 1.15 0.95 2.10 0.98 0,90 1.88

10,000 2 (3 and 8 WAP) 1.00 0.69 1.69 0.85 0.87 1.72

20,000 zero weeding 1.13 0.98 0.98 1.03 0.81 1.84

20,000 1 (3 WAP) 1.22 1.03 1.03 1.01 0.87 1.88

20,000 2 (3 and 8 WAP) 1.00 1.09 1.09 0.84 1.07 1.91

Weed dry matter At 8 WAP, sole sweet corn with zero weeding had the

highest weed dry matter of 93.30 g and 94.0 g in 2006

and 2007, respectively, while, sole egusi melon at

20,000 plants/ha followed by two manual weeding

regimes at 3 and 8 WAP had the lowest weed dry

matter of 9.0 g and 9.33 g in 2006 and 2007,

respectively (Table 7). There was no difference in

weed dry matter in intercropped sweet corn and egusi

melon at highest plant populations receiving one

weeding and those of 20,000 plants/ha sole egusi

melon that received two manual weedings.

Productivity The total LERs were all above unity, an indication that

there was yield advantage in intercropping sweet corn

and egusi melon than planting them separately (Table

8). The yield advantages ranged from 69-120% (2006)

and 72-97% (2007). The highest yield advantages were

in 2006 when sweet corn was intercropped with the

highest planting density of egusi melon that were not

weeded.


2 E .U, Orji

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DISCUSSION In the two years, weeding frequency significantly

increased plant height of sweet corn, especially under

zero weeding condition in 2007. Cob length, number of

grains per cob, 100-grain weight and grain yield of

sweet corn obtained from the mixture plots with two

manual weeding regimes in both copping seasons were

increased significantly probably due to less

competition for nutrients, water and solar radiation

from weeds at the critical period of crop growth.

Similarly, Takim and Fadayomi (2008) reported that

low growing cover crops such as egusi melon could

reduce weed pressure in intercropped small holder

farms, hence leading to increased crop yield and

monetary return. Further-more, Akobundu (1989)

reported that uncontrolled weed growth greatly depress

the productivity of maize by 34–55%, grain legumes by

40–67%, rice by 28–100% and root and tuber crops by

65–91% while timely cultural weed control at critical

periods such as the first thirty days of growth cycle for

maize help to reduce crop yield losses significantly.

Vine length of egusi melon was significantly increased

when 20,000 plants/ha of the crop was intercropped

with sweet corn with two manual weeding frequencies

while the delay in flowering exhibited in zero weeding

by egusi melon was perhaps due to competitive effects

arising mostly from weeds for light, moisture, soil

nutrients and space in both cropping seasons. The

findings are in consonance with field studies by Ekpo

et al. (2011) in which they determined the optimum

density and profitability of using cover crops for weed

control in cassava production in the humid tropics.

The marked decline in the number of pods/plant,

number of seeds/plant, number of seeds/pod, 100-seed

weight, and seed yield of egusi melon in 2006 and

2007, which were distinct in zero weeding compared to

one weeding frequency and two weeding frequencies

was strongly due to competition effects in the mixtures.

Similar results were obtained by Obiefuna (1989) as

well as Akinyemi and Tijani-Eniola (2001) in their

different studies on the efficiency of using cover crops

such as egusi melon plus two manual weeding

frequencies in weed suppression. Averaged over the

two cropping seasons, seed yield of egusi melon

obtained from one weeding frequency and two weeding

frequencies was higher by 91.7 and 92.4%,

respectively compared to zero weeding, an indication

that weeding frequency positively contributed to seed

yield of egusi melon. The results corroborated findings

by Ekpo et al. (2011) on the use of vegetable cowpea

(Vigna unguiculata Sub Spp. Sesquipedalis) and egusi

melon in the control of weeds in a cassava-based

intercropping system in the rainforest zone of Nigeria.

The weight of weed dry matter in both years was

influenced by egusi melon plant population as well as

weeding frequency. Similarly, Melifonwu (1994)

reported that the frequency and timing of weeding

depend on factors such as climate, cultural practices,

crop growth, soil fertility and weed species while Ekpo

et al. (2011) surmised that weed dry matter could be

significantly reduced when sweet corn at 20,000

plants/ha is intercropped with egusi melon at 20,000

plants/ha with one or two manual weeding regimes.

Total productivity per unit land was higher in the

mixture, although higher yields were obtained by

planting sweet corn and egusi melon in monoculture.

The benefits derivable in terms of shared labour costs

and other resources in intercropping could not make

sole cropping sustainable. Similar results have been

reported by other researchers such as Ayoola and

Makinde (2008) in their study on

cassava/maize/cowpea intercrop, Gustavo et al. (2008)

on shoot and root competition in potato/maize

intercrop, Dahmardeh et al. (2010) on maize/cowpea

intercrop as well as Ijoyah et al. (2012) in a three crop

intercropping system (cassava/maize/egusi melon)

across different locations within the Guinea savanna

agro-ecological zone of Nigeria. From the results

obtained, it is reasonable to recommend sweet corn at

20,000 plants/ha intercropped with 20,000 egusi melon

plants/ha followed by one standard manual weeding

regime at 3 WAP or two manual weedings at 3 and 8

WAP for the resource poor farmers in southeastern

Nigeria.

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Ekpo, T. U. U., Udosen, U. U., Ndaeyo, N. U. and

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with cowpea (Vigna unguiculata (L.) Walp)

and egusi melon (Citrullus lunatus (Thumb)

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Enwezor, W. O., Ohiri, A. C., Opuwaribo, E. E. and

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of intercropping maize and cucumber on

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(Colocynthis vulgaris) on soil moisture,

plant status and economic yield of

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Nigeria. Paper No. 23. In:

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Resources to Increase Food Production in

Africa. Oulttier, S. O. Fayen, J. (Eds.).

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March, 1987.

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Makinde, A. A. and Bello, N. J. (2009). Effects of soil

temperature pattern on the performance of

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cassava, African Crop Science Journal, 2

(4): 519 – 530.

Muoneke, C. O., Ogwuche, M. A. O and Kalu, B. A.

(2007). Effect of maize planting density on

the performance of maize/soybean

intercropping system in a guinea savannah

agroecosystem, African Journal of

Agricultural Research, 2 (12): 667 - 677.

Obi, I. U. (2002). Statistical Methods of Detecting

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issues in Laboratory and Field Experiments.

AP Express Publishing Company

Limited, Nsukka, Nigeria, 116 pp.

Obiefuna, J. C. (1989). Biological weed control in

plantations Musa AAb Egusi-melon,

Colocynthes citrullus, Biological Agriculture

and Horticulture 6: 221 - 227.

Olaniyi, J. O and Fagbayide, J. A. (2008). Growth and

seed yield response of egusi melon

to nitrogen and phosphorus fertilizers

application, American-Eurasian Journal of

Agriculture and Environmental

Science, 6: 707 – 712.

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tillage operations and cropping systems

on field emergency of weeds in a Southern

Guinea savanna zone. 36th Annual

Conference of Weed Science Society of

Nigeria. Held at the Federal University of

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Wahua, T. A. T. (1985). Effects of melon (Colocynthis

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intercropped maize (Zea mays) and


2 E .U, Orji

1 U.

10

melon, Experimental Agriculture, 21: 281 - 289 .

NIGERIAN JOURNAL OF CROP SCIENCE

Volume 1 No. 1 September 2013 pp 10 -18

AMELIORATING ALUMINIUM TOXICITY IN SOYBEAN (GLYCINE MAX (L.)

MERRIL) PRODUCTION WITH FERTILIZER MATERIALS ON AN ALFISOL IN

SOUTH-WESTERN NIGERIA

Adegoke¹ J. O., Akinrinde 1

E. A., and Ogunjinmi 2

S. O. 1Department of Agronomy, University of Ibadan, Nigeria

2Department of Agricultural Technology, Oyo State College of Agriculture, Igboora, Nigeria.

*Corresponding Author:[email protected]

ABSTRACT Aluminium toxicity is a major limitation to leguminous crop production in acidic soils but fertilizer treatment

could ameliorate the condition. In this investigation, direct and residual effects of different fertilizer materials on

the growth and yield of soybean grown with or without Al treatment were evaluated on an Alfisol. The

investigation involved a greenhouse (5kg soil/pot) experiment conducted at the Agronomy Department, University

of Ibadan, Nigeria, with two factors: fertilizer type and Al treatment. Fertilizer type consisted of organic fertilizer

(OF), inorganic fertilizer (IF), OF + IF mixture at ratio of 1:1 and the control which received no fertilizer. Al

was applied in four levels at 0, 50, 100 and 150µM AlCl3. A commercial formulation “Sunshine organic

fertilizer” and single superphosphate (SSP) were used as OF and IF, respectively. Treatment combinations were

replicated three times in a completely randomized design, giving a total of 48 experimental units. Data on growth

parameters (plant height, number of leaves and stem girth) as well as yield parameters (biomass and pod weights)

were analysed using ANOVA (p=0.05) while treatment means were separated by Duncan’s Multiple Range Test.

High Al rate (150µM) reduced the growth and yield of soybean while moderate Al application rates (50 and

100µM) enhanced the performance of the crop. Combination of OF+IF enhanced crop growth and yield even

when 100µM Al was applied. However, it was only the sole application of organic fertilizer that was able to

promote crop performance at 150µM Al concentration. Organic fertilizer had the highest residual effects among

the various fertilizer treatments confirming that organic based fertilizers could be used to minimize the

deleterious influence of aluminium toxicity on the production of soybean in acid soils.

Keywords: Soybean, fertilizer treatments, aluminium treatments, alfisols.

INTRODUCTION Aluminium (Al) is the most abundant metal in the

earth’s crust and occurs in a number of different forms

in the soil. It is generally accepted that Al toxicity is a

primary factor limiting plant growth on acid soils

(Kochian, 1995). Toxic effects of the element on plant

growth have been attributed to several physiological

pathways, but the precise mechanism has not yet been

understood. Proposed mechanisms include Al

interactions with the root cell wall, aluminum

disruption of plasma membrane and membrane

transport processes, and Al inhibition of mineral uptake

and metabolism, especially that of Ca and P (Rout et

al., 2001; Akinrinde, 2006).

Besides salinity, Al toxicity is among the widespread

problems of ion toxicity stress in plants. Deficiency of

P, Ca and Mo coupled with presence of phytotoxic

substances (soluble Al and Mn) are responsible for the

fertility limitation of acid soils as aggravated by

industrial pollution and nitrification. Poor growth in

acid soils could be related directly to Al saturation

(Akinrinde et al., 2004). In acid soils, Al toxicity limits

plants’ growth due to series of chemical interactions

including toxicities of H, Al and Mn. Estimates of soil

limitation to plant growth in developing countries show

that an average of 23% of the soil used is constrained

to Al toxicity (Oluwatoyinbo et al., 2005). The

restriction of plant growth by excess soluble Al in acid

soils may arise from either the direct inhibition of

nutrient uptake or disturbance of root cell functions

(Kochian, 1995).

Aluminium exists in soils in many mineral forms,

including hydrous oxide, aluminium silicates, sulphates

11

and phosphates. Accumulation and distribution of

many mineral elements are often strongly affected by

aluminium. A common symptom of aluminium toxicity

is P deficiency symptom (Haynes, 1984; Huang et al.,

1992). Acid soils result from leaching of basic cations

(in areas of high rainfall), leaving behind the more

resistant Al3+

which predominates. Inadequate farming

practice has further acidified agricultural soils in

developing countries. According to Hoekenge et al.

(2003), continuous use of ammonia fertilizer under

intensive agriculture is capable of further acidifying the

soil. Soil acidity is normally corrected by application

of calcitic and dolomitic lime. Liming has a beneficial

effect on plant growth under aluminium stress and

alleviating aluminium toxicity of plants (Bessho and

Bell, 1992). However, in many developing countries

where subsistence agriculture prevails, the lack or high

cost of lime effectively prevents its use. It

has been discovered that liming may also have some

negative effects on plant growth and soil properties

(Ahmad and Tan, 1986). Deficiencies, for example, of

some nutrients such as P, Sn, B, and Mn can be

induced by liming.

A number of workers have shown that the addition of

green manures and animal wastes to acid soils can

reduce aluminium toxicity and increase crop yields

(Berek et al., 1995., Hue, 1992). Additions of organic

residues have also been shown to increase nutrient

uptake and crop growth on P-deficient soils (Hue,

1990; et al., 1994).

The application of organic residue to acid soils in order

to minimize the need for lime and P fertilizer

application would be beneficial to resource poor semi-

subsistence farmers (Hayness and Mokolobate, 2001).

Soybean (Glycine max) is one of the most widely

cultivated grain legumes in the world

(Tukamuhabwa,2000). Soybean is a member of the

family Fabacea, subfamily Papilonidae (Kochobor,

1986). Soybean is a remarkable crop because of its

high protein and oil content both of which are adapted

to the nourishment of man and livestock. Thus, world

production of the crop has been stimulated by a strong

demand for edible oils and protein feed supplement. It

is also a source of vitamin B. Leguminous crops have

soil enriching ability as they are capable of fixing

atmospheric nitrogen (N), serving as mulching

materials thus protecting the soil against direct impact

of rain, conserving soil moisture and reducing soil

temperature. All over the world, poor growth of

soybean in acid soils has been attributed to a number of

factors that include: low pH, low levels of Ca, Mg, P,

K, and micronutrients like B and Zn (Fageria, 1994),

low population of beneficial micro-organisms like

rhizobia, vesicular arbuscular (VAM) fungi and

inhibition of root growth.

The present study sought to investigate the short term

and long term (residual) effects of different types of

fertilizer (organic, inorganic and mixture of organic

and inorganic at ratio 1 : 1) on the growth and yield of

soybean ( with or without Al toxicity inducement) on

an Alfisol.

MATERIALS AND METHODS The experiment was carried out in the greenhouse at

the Department of Agronomy, University of Ibadan,

Nigeria from February to June, 2012. The geographical

location is 7o 24’ N, 3

o 54’E, 234 m above sea level.

The experimental soil was an Alfisol collected from

Parry Road, University of Ibadan. A composite soil

sample of the experimental site was taken to the

laboratory for pre-cropping physicochemical analyses.

The soybean variety used was the early maturing type

TGX 1987- 62F and the treatments imposed were

“Sunshine organic fertilizer” a commercial

formulation, Single super phosphate and a mixture of

sunshine organic fertilizer and single super phosphate

at ratio (1:1), plus the control plants which had no

fertilizer treatment. The two fertilizers were obtained

from the Department of Agronomy, University of

Ibadan. The experimental treatments also involved the

application of four levels of Al (0, 50, 100 and 150µM)

corresponding to 0, 10.30, 20.60 and 30.90 mg/kg,

respectively using AlCl3.6H20. It was therefore a

factorial experiment with two factors; Aluminium at

four levels (0, 50, 100, and 150µM) and four fertilizer

types (organic, inorganic, mixture of organic and

inorganic at ratio 1:1, plus the control). There were

therefore 16 treatment combinations arranged in a

completely randomized design (CRD) with three

replicate. The fertilizer materials (both organic and

inorganic) were applied at the rate of 100 kg / ha before

planting while Aluminium treatment was applied along

with irrigation. Soybean variety TGX 1987-62F,

obtained from International Institute of Tropical

Agriculture (IITA), Ibadan was used as test crop.

There were 16 experimental pots (polybags) per

replicate, giving a total of 48 polythene bags. The soils

were air dried, sieved with 2-mm sieve and 5 kg

samples were filled into polythene bags. They were

randomly arranged on metal table within the

greenhouse complex and were watered to 60% field

capacity. The seeds were pre-germinated for three days

in moistened filter papers placed in germination boxes

in the laboratory to enhance uniform germination.

About 95% of seeds germinated. Three pre-germinated

seeds were later transplanted into each polythene bag

in the greenhouse. Thinning was carried out after 9

days of transplanting retaining two vigorous seedlings

per bag. The first hand weeding operation was carried

Ameliorating Aluminium Toxicity in Soybean

12

out at four weeks after transplanting and subsequent

operations done as necessitated by weed occurrence.

Cypermethrin was applied fortnightly at the rate of 2

ml per litre of water to control insect pest attack from

two weeks after transplanting and continued until

podding. The experiment was carried out twice. Al and

fertilizer treatments were applied only in the first

experiment. Hence, the second experiment was used to

evaluate the residual effects of the treatments on

soybean. Both experiments were terminated after eight

weeks. The parameters measured during morphological

growth included plant height, number of leaves, stem

girth, leave area and number of branches per plant. At

final harvest, the soils were moistened for easy removal

of roots and the plants were partitioned into shoots and

roots. All the pods and their grains produced per pot

were counted and weighed to obtain the respective

yields per treatment pot. The plant parts were dried in a

forced air tight oven at 650C until constant weight was

obtained.

Statistical Analysis: Data were subjected to analysis

of variance (ANOVA) using statistical analytical

system (SAS) with significant means separated using

Duncan multiple range tests (DMRT) at 5% probability

level.

RESULTS AND DISCUSSION Pre-cropping soil fertility status: The physical and

chemical properties of the soil used are given in Table

1.The particle size distribution of the soil indicated that

the soil was slightly acidic (pH 6.0) and the organic

matter content (17.21 g kg-1

) was below the critical

level of 26 g kg-1

given by Adeoye and Agboola

(1985). Exchangeable Ca (2.21 cmol kg-1

) was below

the critical level proposed by Agboola and Corey

(1973) while Mg (1.37 cmol kg-1

) was high compared

with 0.26 cmol kg-1

proposed as critical value. The

Aluminium content of the soil was 0.003 mg kg-1

,

while total N (0.73 g kg-1

) content and Available P (8

mg kg-1

) were marginal in the soil.

Table 1: Physical and chemical properties of soil used for the experiment

Parameters Values

pH (H2O) 6.0

Org C (g/kg) 9.98

Total N (g/kg) 0.73

Available P (mg/kg) 8.0

Exchangeable base (cmol kg-1

)

K 0.4

Ca 2.21

Mg 1.37

Na 1.51

Exchangeable acidity 0.2

Extractable micronutrients (mg kg-1

)

Mn 134

Fe 76.4

Zn 2.24

Cu 1.8

Al 0.003

Particle size distribution (g/kg)

Sand 789

Silt 91

Clay 120

Textural class Sandy loam

Adegoke J. O., Akinrinde

E. A., and Ogunjinmi S. O.

13

Fig. 1: Effect of (a) fertilizer type and (b) aluminum rate on height (cm) of soybean at sucessive weeks after planting (error bars on graph

represent standard error )

Fig.2: Effects of (a) fertilizer type and (b) aluminium treatments on number of leaves/plant of soybean at successive weeks after planting

(error bars on graph represent standard error).

Table 2: Effects of each fertilizer type (at different levels of aluminum application) on

number of leaves / plant of soybean plant Treatment combination No of leaves/plant at successive weeks after planting

AlCl3 Application (µM) levels Fertilizer type 3 4 5 6

0

Control

Organic (OF)

Inorganic (IF)

OF+IF

17

18

18

16

24ab

26ab

27ab

27ab

47bcd

59abc

58abc

57abcd

69abc

83a

80ab

79ab

50

Control

Organic (OF)

Inorganic (IF)

OF+IF

16

17

16

19

24ab

21b

21b

31ab

47bcd

52bcd

45cd

60ab

63abc

82a

56c

75abc

100

Control

Organic (OF)

Inorganic (IF)

OF+IF

17

17

18

17

25ab

30ab

33a

28ab

43d

56abcd

70a

59abc

60bc

82a

79ab

78ab

150 Control

Organic (OF)

Inorganic (IF)

OF+IF

17

18

18

16

22b

26ab

28ab

22b

47bcd

50bcd

49bcd

47bcd

70abc

79ab

66abc

73abc

Means with the same letters in the same column are not significantly different from each other, p=0.05 (DMRT)

First cropping: The immediate or short term

effects of fertilizer application (Fig. 1a & b)

reveal that soybean performed better with respect

to plant height under inorganic fertilizer

application. There was a general increase in

height from 4 to 7 weeks after planting and the

tallest height was obtained from plants treated

with inorganic fertilizer and this could be adduced


14

to the fast release of nutrients by inorganic fertilizer.

This is in consonance with the study of Mokuwunye

and Vlek (1986) which revealed that the judicious use

of N and P fertilizer can bring out substantial yield

increase in West Africa.

The effects of fertilizer application on the number of

leaves of soybean were not significant except at 7

WAP (Fig. 2a). However, the results showed that

soybean performed better with application of organic

fertilizer at 6 and 7 WAP when compared with other

fertilizer types and control. The effects of Al treatments

on number of leaves of soybean show that there were

no significant differences among the various treatments

except at 6 and 7 WAP (Fig. 2b). The highest number

of leaves was obtained from plants to which 50µM of

Al was applied.

Fig. 3: Effects of (a) fertilizer treatment and (b) aluminium rate on soybean shoot dry weight (bars on chart

represent standard error)

Table 3: Effects of each fertilizer type (at different levels of aluminium application) on soybean

dry shoot weight

Treatment combinations

AlCl3 Application (µM) level Fertilizer type Soybean dry shoot

(g pot-1)

0 Control 10.20ab

Organic (OF) 10.58ab

Inorganic (IF) 12.09ab

OF+IF 10.57ab

50 Control 8.50ab

Organic (OF) 9.72ab


OF+IF 11.79ab

100 Control 8.86b



OF+IF 13.53a

150 Control 9.20ab



OF+IF 9.69ab


Adegoke J. O., Akinrinde

E. A., and Ogunjinmi S. O.

15

Data summarizing the interaction between fertilizer

and Al application on the number of leaves produced

per soybean plant during the first cropping indicates

there were significant differences at 4 to 6 WAP

among the treatment means (Table 2). At 4 WAP, the

highest number of leaves (33) was obtained when

100µM AlCl3 was applied with inorganic fertilizer.

However, this trend was maintained throughout the

duration of the experiments. The result could be

attributed to fast release of nutrients by inorganic

fertilizer coupled with the(growth enhancement

ability of Al at low concentration). In addition, it was

observed that Al application reduced the performance

of crop when applied at the highest rate (150µM)

with the highest number of leaves (79) obtained from

organic fertilizer. This confirms the assertion that Al

limits plant growth at high concentration (Kochain,

1995). This observation shows that organic fertilizer

application had a beneficial effect on Al

detoxification.

The influence of fertilizer treatments on shoot dry

weight of soybean indicates that inorganic fertilizer

had the greatest dry matter yield when compared with

organic and OF + IF. Inorganic fertilizer therefore

proved to be the most effective fertilizer type for

soybean production (Fig. 3a). The results of the Al

application on soybean (Fig. 3b) indicate that the

application of 100µM AlCl3 enhanced the shoot yield

when compared with control, 50 and 150µM AlCl3,

though there were no significant differences between

the control, 50 and 150µM AlCl3. This confirms that

Al limits plant growth at high concentration. This is

in line with the submission of Kochain (1995) which

reported that Al toxicity is a primary factor limiting

plant growth on acid soil.

Fig. 4: Effects of (a) fertilizer type and (b) aluminium treatment on pod dry weight (bars on chart represents standard error)

Table 4: Effects of each fertilizer type (at different levels of aluminium application) on

soybean dry pod weight Treatment combinations

AlCl3 Application

µM level

Fertilizer

type

Dry pod Weight

0

Control Organic (OF)

Inorganic (IF)

OF+IF

(g pot-1)

3.86bc 5.67ab

4.92abc

5.67ab

50

Control

Organic (OF)

Inorganic (IF)

OF+IF

3.46c

5.57ab

5.00abc

5.00abc

100

Control

Organic (OF)

Inorganic (IF)

OF+IF

5.00abc

5.67ab

5.49ab

6.08a

150

Control

Organic (OF)

Inorganic (IF)

OF+IF

3.83bc

5.00abc

4.69abc

3.91bc



16

Table 5: Residual effects of each fertilizer type (at different levels of aluminium

application) on number of leaves/plant of soybean Treatment combination weeks after plating

AlCl3 Applicat ion

(µ M) level

Fert il izer

types

3

4 5 6

0 Control

Organic

Inorganic

0F + IF

21ab

22a

23a

23a

26

26

25

25

28ab

31a

26ab

30ab

32

38

32

34

50

Control

Organic

Inorganic

0F + IF

21ab

22ab

23a

23a

25

27

25

25

26ab

28ab

27ab

29ab

29

32

36

38

100

Control

Organic Inorganic

0F + IF

20b

22ab 21ab

23a

23

24 26

27

26b

27ab 30ab

28ab

33

30 42

34

150 Control Organic

Inorganic

OF + IF

20b 20b

22a

22a

25 26

24

27

29ab 26ab

26ab

28ab

32 40

29

37

Means with the same letter(s) in the same columns are not significantly different from each other, p = 0.05 (DMRT)

Table 6: Residual effects of each fertilizer type (at different levels of aluminium

application) on soybean shoot dry weight Treatment combination

AlCl3 Application

(µM) level

Fertilizer

Types

Soybean dry shoot weight

0

Control

Organic Inorganic

OF + IF

(g pot-1)

7.74b

8.05ab 5.72b

7.08b

50

Control

Organic Inorganic

OF + IF

5.00b

7.19b 7.03b

7.40b

100 Control Organic

Inorganic

OF + IF

5.88b 6.92b

6.09b

7.43b

150 Control

Organic

Inorganic

OF + IF

5.25b

10.62a

6.92b

7.62b

Means with the same letter(s) in the same columns are not significantly different from each other p = 0.05 (DMRT)

Adegoke J. O., Akinrinde E. A., and Ogunjinmi S. O.

17

Application of fertilizer significantly affected the

soybean pod dry weight (Fig. 4a).The control

plants had the least pod dry weight (4.2 g pot-1

)

while plants that received organic fertilizer

produced the highest pod weight (5.5 g pot-1)

followed by OF + IF (5.0 g pot-1

). This could be

adduced to the fact that organic fertilizer

improves moisture availability and nutrient

supply.

Application of Al on soybean at different rates

performed differently from each other in terms

of pod yield (Fig. 4b). Plants treated with

100µM AlCl3 gave the highest pod yield. The

least pod yield was obtained from plants treated

with 150µM AlCl3 confirming the fact that Al

limits plant growth at high concentrations

particularly on acid soils. This is in line with the

submission that Kochian (1995).

The interaction between fertilizer and Al

treatments on dry shoot weight of soybean

showed significant difference (Table 3).

Application of 100µM Al along with OF + IF

gave the highest shoot production (13.53g /pot).

Also, application of 50µM and 100µM AlCl3

enhanced the performance of soybean crop,

while high aluminium concentration reduced the

crop performance but organic fertilizer treatment

sustained shoot yield. This is in support of the

submission of some researchers who reported

that the addition of green manures and animal

wastes to acid soils can reduce aluminium

toxicity and increase crop yield (Hue, 1992,

Hue, 1990, and Hue et al., 1991)

There was significant interaction between AI

application and fertilizer treatments on soybean

pod dry yield (Table 4). The highest soybean

pod dry yield (6.08 g pot-1) was obtained with

100µM Al with OF + IF which could be adduced

to the fact that combined benefits of organic

manure such as nutrients supply and soil

structure improvement, and the high and fast

nutrient release of inorganic fertilizers enhanced

pod production. At high Al concentration, there

was reduction in the performance of soybean and

the organic fertilizer proved to be most effective

in ameliorating the effect. Carsky (2003) also

observed that organic additions substantially

increased soybean pod yield.

Second cropping The residual effect of the interaction between

fertilizer and Al treatments on number of leaves

(Table 5) indicates that there was a general

increase from 3 to 6 WAP with the highest

number of leaves of soybean (40.7) obtained

from plants treated with organic fertilizer at high

Aluminium concentration and this could be

adduced from the benefits of organic manure

such as slow rate of decomposition and

mineralization that enhanced vegetable

production (Olaniyan et al., 2006).

There was significant interaction between Al and

fertilizer treatment on the soybean dry shoot

weight (Table7). The highest soybean shoot

weight (10.62 g pot-1

) was obtained with the

high Al application at 150µM along with organic

fertilizer while the least shoot weight dry (5.00 g

pot-1

) was obtained with 50µM Al without

fertilizer. These results show that organic

fertilizer enhanced the yield of soybean in soil

with high Al concentration beyond 100µM.

CONCLUSION It is evident from this work that Al toxicity

(induced with 150µM) reduced the growth and

yield of soybean while low and moderate Al

application rates (50 and 100µM) enhanced the

performance of the crop. Combination of OF +

IF enhanced crop growth and yield even when

100µM Al was applied. However, it was only

the sole application of organic fertilizer that was

able to increase the growth and yield parameters

of soybean at 150µM Al concentration. Also,

organic fertilizer proved to have the highest

residual effects among the various fertilizer

treatments confirming that organic based

fertilizers could be used to minimize the

deleterious influence of Al toxicity on the

production of soybean in acid soils.

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Critical level for soil pH, P, K, Zn and

maize ear leaf contents of P, Cu and

Mn in sedimentary soil of south

western Nigeria Nutr. Cycle.

Agroecosys; 6: 65 – 71.

Agboola A.A and R.B. Corey (1973).The

relationship between soil pH, organic

matter, available phosphorus,

exchangeable potassium, calcium,

magnesium and nine elements in maize

tissue.Soil science 115, pp. 36-375.

Ahmad, F and Tan, K.H, (1986). Effects lime

and organic matter on soybean seeding

grown in aluminium toxic Soil. Soil Sci.

soc A.M.J. 50, pp 656-661.

Akinrinde, E. A. and Obigbesan, G.O. (1999).

Evaluation of the performance of

Nigerian Rock phosphates applied to

millet in selected benchmark soils.

Nigerian Journal of Science vol.12: 88

– 99.

Akinrinde, E.L. Iron; G. Obigbesan; T. Hilger;

V. Romheld and G. Neuman (2004).

Tolerance to soil acidity in cowpea

genotypes is differentially affected by

phosphorus nutritionals status. Plant

Anal. 12: 121-138.


18

Bessho T. and Bell L.C (1992). Soil solid and

solution phase changes and mung beam

responsible during amelioration toxicity

with organic matter. Plant 140:183-186.

Carsky, R.J. (2003). Response of cowpea and

soybean to P and K on terre de Barre

soils in southern Benin. Agric. Ecosyst.

Environ., 100: 241-249.

Fageria, N. K. (1994). Soil acidity affects

availability of Nitrogen, Phosphorus

and Potassium. Better crops

international 10: 8 – 9.

Haynes. R. J. and M.S. Mokolobate, (2001).

Amelioration of aluminium toxicity and

P deficiency in acids soils by addition

of organic residue: A critical review of

the phenomenon and the mechanism

involved. Nutrient Cycling in

Agroecosystems, 59: 47-63.

Hue N.V. and I. Amien (1989).Aluminium

detoxification with green manures.

Comm. Soil Sci. plant anal. 50, pp.

1499-1511.

Hue N.V. (1992). Correcting soil acidity if

lightly weathered ultisol with chicken

manure and sewage sludge. Commun

Soil Sci Plant Anal 23: 241-264.

Hue N.V., Ikawa H., and Silva J.A. (1994).

Increasing plant available phosphorous

in an utisol with a yard-waste compost.

Commun Soil Sci plant Anal 25: 3291-

3303.

IBSRAM, (1989). Soil Management and small

holder development in the pacific

islands. Thailand IBSRAM proceeding

No 8.

Kochian, L.V. (1995).Cellular mechanism of

aluminium toxicity and resistance on

plant. Ann. Rev. Plant Physiol Plant

Mol. Biol 46:237-260.

Mokwunye, A.U. and P.L.G. Vlek (1986).

Management of nitrogen and

phosphorus fertilizer in sub-Saharan

Africa soils. Martinus Nijhoff

Publishers, The Hague, The

Netherlands, pp. 117 – 172.

Olaniyan, A.B., A.H., Akintoye and O. Wunmi

(2006).Effect of different sources of

nitrogen in growth and yield of

solanumma crocarpon in derived

savannah of Nigeria. J. Agron, 5: 182 –

185.

Oluwatoyinbo, F.I., M.O. Akande and J.A.

Adediran (2005). Response of okra

(Abelmoschusesculentus) to lime and

phosphorus fertilization in acid soil.

World J. Agric. Sci., 1: 178 – 183.

Ostatek-Boczynski, Z., Kerven, G.L., and

Blamey, F.P.C., (1995).Aluminium

reactions with polygalacturonate and

organic ligands.Plants soils 171, pp.41-

45.

Peter, G., Francesco, G. and Montaque, Y.

(2000). Integrated nutrient

management; Soil fertilitynd

sustainable Agriculture: Current issues

and future Challenges. FAO and

environment discussion paper 327.

Petra S. Kidd and John Proctor (2000).Effects of

Al on the growth and mineral

composition of Betulapendula

Roth.Journal of experimental botany,

Vol. 51, No 347, pp.1057-1066.

Rout, G.R, S. Samantaray and P. Das (2001).

Aluminium toxicity in plants: A review,

Agronomie, 21: 3 -21.

Singh, C.M., P.K. Sharma, P. Kishor, P.K.

Mishra, A.P. Singh, R. Verma and P.

Raha, (2011). Impact of integrated

nutrient management on growth, yield

and nutrient uptake by wheat (Triticum

aestivum L.) Asian J. Agric. Res; 5:

76-82.

Tukamuhabwa, P (2000).Genesis of resistance to

pod shattering in soybean. Ph.D Thesis,

Makere University 93p.

Adegoke J. O., Akinrinde E. A., and Ogunjinmi S. O.

19


Volume 1 No. 1 September 2013 pp 19 - 26

ASSESSMENT OF GENETIC DIVERSITY IN GROUNDNUT

GERMPLASM USING SIMPLE SEQUENCE REPEAT MARKERS

Alhassan1*

U., Danquah2

E., Ado1

S.G, Yahaya1

A.I and Usman1

M. 1Department of Plant Science, Institute for Agricultural Research,

Ahmadu Bello University Zaria, 2West Africa Centre for Crop Improvement, University of Ghana, Leggon

*Corresponding Author: [email protected]

ABSTRACT

Cultivated groundnut (Arachis hypogaea L.) is an important source of edible oil and protein.

Considerable variation has been reported for morphological, physiological and agronomic traits,

whereas few molecular variations have been recorded for the crop. Understanding of the genetic

diversity of cultivated groundnut is essential in developing strategies of collection, conservation and

use of the germplasm in variety development. Simple sequence repeat (SSR) markers detected

significantly high degree of polymorphism in groundnut and are particularly suitable for evaluating

genetic diversity among closely related accessions. A total of 21 groundnut germplasm collections

were analysed for DNA profile using 28 SSR loci covering two loci per chromosome. A total of 105

alleles were detected. The number of alleles per locus ranged from 2 to 7, with an average of 3.75,

and the polymorphic information content (PIC) value ranged from 0.0907 for the marker detected by

IPAHM429 to 0.8210 for the marker detected by IPAHM171c with an average PIC of 0.5638. All the

loci were polymorphic and clearly distinguished the aphid and rosette resistant genotypes. At 10%

similarity, cluster analysis of the germplasm revealed seven cluster groups, distinguishing the aphid

resistant genotypes from rosette resistant lines, with aphid resistant lines in cluster I and rosette

resistant lines in clusters II and III. Many of the accessions included in this study were

morphologically similar and lack pedigree information. Hence, identification of genetic distances

among them should improve their use in breeding programs. From this study, genetically diverse

parents were identified, increasing the usefulness of germplasm collection by broadening the genetic

base of groundnut germplasm. The assessment of genetic diversity will help breeders to make crosses

from accessions with different genetic backgrounds and will assist in the development of populations

with greater marker polymorphism.

Keywords: Groundnut, genetic diversity, SSR markers.

INTRODUCTION Cultivated groundnut, Arachis hypogaea L., is

an important oil seed crop grown as a major

source of vegetable oil and protein for human

consumption and for animal feed. It is cultivated

in over 100 countries across Asia, Africa and the

Americas with around 25 million hectares

generating an annual production of nearly 35

million tons (FAO 2004). India, China, Nigeria

and Sudan are the top producers but more than

20 other countries, mainly in Asia and Africa,

each have 1–800,000 ha of groundnut

production. Although groundnut is an important

multipurpose crop for resource-poor farmers in

the semi-arid tropics (SAT), due to

environmental stresses and disease pressure,

average productivity is often below 1 ton per

hectare. The major disease constraints to

groundnut production in sub Saharan Africa is

groundnut rosette disease (causal agent Aphis

craccivora) causing sporadic yield loss of about

30% and with advent of epidermic will results to

total yield loss.

Despite the wide range of morphological

diversity observed in the cultivated groundnut

gene pool, molecular marker analyses have thus

far been unable to detect a parallel level of

genetic diversity. A variety of molecular markers

have been used to characterize the genetic

diversity in groundnut, e.g. RFLPs (Halward et

al., 1991), RAPDs (Dwivedi et al., 2001;

Subramanian et al. , 2000), and AFLPs (He and

Prakash, 1997, 2001; Gimenes et al. , 2002). All

20

these studies have reported low levels of

polymorphism within the cultivated gene pool

(Mace et al., 2006) However, the recent

development of simple sequence repeat (SSR)

markers presents new opportunities for

molecular diversity analysis of cultivate

groundnut. Simple sequence repeats (SSRs), also

known as microsatellites, are a class of

molecular markers based on tandem repeats of

short (2–6 bp) DNA sequences (Litt and Lutty

1989). These repeat sequences are often highly

polymorphic, even among closely related

cultivars, due to slippage mutations during DNA

replication causing variation in the number of

repeating units. Different alleles of a given locus

can be readily detected using primers designed

from the conserved DNA sequences flanking the

SSR and the polymerase chain reaction (PCR).

SSR markers are generally reported to detect

higher levels of polymorphism than RFLPs,

RAPDs and AFLPs (Powell et al. , 1996;

Milbourne et al., 1997; Russell et al. , 1997;

Crouch et al. , 1999), and have been widely

adopted for genetic analysis in plants (Panaud et

al., 1996). Thus, it is believed that SSR markers

will provide the molecular genetic differentiation

to facilitate routine diversity analysis and

molecular breeding applications (Dwivedi et al.,

2003). However, the first SSRs to be developed

in groundnut detected disappointing levels of

polymorphism in cultivated germplasm

(Hopkins et al., 1999). Nevertheless, additional

SSRs developed more recently through a

different approach appear to be much more

promising in cultivated groundnut genotypes

(He et al., 2003; Ferguson et al., 2003). In the

present study specific - groundnut SSRs markers

were used to analyze a diverse range of

cultivated groundnut germplasms. The objective

of this study was to investigate the level of

molecular polymorphism amongst groundnut

rosette resistant genotypes and to compare this

with the genetic diversity across the cultivated A.

hypogaea gene pool. This analysis is important

for the selection of genetically diverse parental

genotypes for mapping populations and

groundnut rosette resistance breeding

programmes aimed at the development of broad-

based cultivars with durable disease resistance.

MATERIAL AND METHODS A total of 21 groundnut accessions obtained

from International Crop Research Institute for

Semi-Arid Tropics (ICRISAT) Bamako – Mali,

Institute for Agricultural Research (IAR),

Ahmadu Bello University, Samaru, Nigeria and

some commercially grown varieties were used to

study molecular diversity following the SSR

assay. The description of the accessions is

presented in Table 1. Each accession was sown

in pots filled with sand in a greenhouse. Young

leaves of two-week-old plants were sampled and

dried using silica gel. Five gram (5 g) of each

accession was genotyped using 28 SSR markers.

RESULTS Results from 28 SSR groundnut markers

detected appreciable degree of polymorphism

within the set of germplasm. This high level of

polymorphism was consistent with the selection

of these SSR makers for their ability to detect

polymorphism among A. hypogea germplasm.

A total of 105 SSR alleles were detected in this

study and the total number of allele detected per

primer pair ranged from two for four primer

pairs (IPAHM73, IPAHM288, IPAHM373 and

IPAHM429) to seven for IPAHM103 with an

average of 3.75 alleles per primer pair (Table 2).

Value of each marker PICs ranged from 0.09 for

marker detected by IPAHM429 to 0.821 for the

marker detected by IPAHM171c. The PIC

values greater than 0.5 were considered highly

informative and markers with 0.5 > PIC > 0.25

were just considered to be informative (Botstein

et al., 1980). This variation was significantly

associated with the number of alleles detected at

each locus; hence the SSR marker revealed large

amount of variation in the sampled genome. An

allele observed in less than 5 % of the 21

groundnut germplasms was considered to be

rare. A total of 22 rare alleles were observed.

Fourteen of the 28 loci exhibited one or more

rare alleles. Most of the loci produced a

maximum of two rare alleles. Some of the rare

alleles may be useful as diagnostic markers for

some of the assayed groundnut germplasm.

Genetic Diversity In Groundnut Germplasm

21

Jaccard’s similarity coefficient

Fig. 1. Dendrogram constructed using Jaccard’s similarity coefficient and UPGMA clustering, for the 21 groundnut genotypes

Table 1: Groundnut genotypes employed in this study with levels of resistance and

susceptibility to groundnut rosette disease S/N Accession Type and level of resistance Source Biological status

1 ICGV-IS-07852 Rosette Resistant ICRISAT- Mali Interspecific derivative





6 ICGV-IS-07894 Rosette Resistant ICRISAT- Mali Interspecific derivative 7 ICGV-IS-07895 Rosette Resistant ICRISAT- Mali Interspecific derivative


9

ICGX – SM

00017/5/P10/P1

Aphid Resistant ICRISAT- Mali Breeding material

10 ICGX – SM 00020/5/9 Aphid Resistant ICRISAT- Mali Breeding material

11

ICGX-SM

00017/5/P1/P1


12

ICGX-SM

00017/5/P15/P2


13 ICGX-SM 00020/10/P9 Aphid Resistant ICRISAT- Mali Breeding material

14 ICGX-SM 00020/5/15/P2 Aphid Resistant ICRISAT- Mali Breeding material

15 KWANKWASO GRV susceptible IAR-ABU-Zaria Land race

16 SAMNUT10 GRV susceptible IAR-ABU-Zaria Cultivar

17 SAMNUT14 GRV Resistant IAR-ABU-Zaria Cultivar

18 SAMNUT21 GRV Resistant IAR-ABU-Zaria Cultivar 19 SAMNUT22 GRV Resistant IAR-ABU-Zaria Cultivar

20 SAMNUT23 GRV Resistant IAR-ABU-Zaria Cultivar

21 SAMNUT24 GRV susceptible IAR-ABU-Zaria Cultivar

I

II

III

IV

Alhassan U., Danquah E., Ado S.G, Yahaya A.I and Usman M.

22

SSR Analysis The 28 SSR markers were assayed for their ability to

detect polymorphism among the 50 cultivated

groundnut accessions selected (Table 2). These

markers were selected and used based on the level of

polymorphism and the reliability and quality of

amplicon detection. Detailed information about primer

sequences and allele sizes are shown in Table 2. The 28

SSRs PCR reactions were conducted in 20 ml volumes

using a GeneAmp PCR System ABI 9700 (Applied

Biosystems). The PCR reaction mixtures contained

between 5 and 25 ng of genomic DNA, 2.0 pmol/ of

each primer 2.5mM of dNTP, 5U/ul of Taq DNA

polymerase (Amersham), 10X PCR buffer (10mM

Tris–HCl pH 8.3, 50mM KCl) and 10mM MgCl2. The

fixed-temperature PCR programmes consisted of an

initial denaturation step for 3 min at 940C, followed by

40 cycles of denaturation for 30 s (940C), annealing for

1 min (590C; see Table 2) and extension for 2 min

(720C). The PCR products were then incubated at 72

0C

for another 30 min to ensure complete extension. A

total of 2ul of PCR product was loaded in a 0.8%

agarose gel at 100 voltages per hour and visualized by

silver staining. The reproducibility of the DNA profile

was tested by repeating PCR amplification.

Data Analysis Genetic diversity among the 21 groundnut germplasms

were evaluated using 28 SSR primers. Each fragment

size was treated as unique characteristics and scored 1

for presence or 0 for absence. Genetic similarity (GS)

index (UPGMA cluster analysis of the Jaccard’s

similarity coefficients (Shoba et al., 2010) was used to

construct a dendrogram which illustrated the genetic

relationship among the 21 genotypes of groundnut used

in the study. To assess the genetic relationships of the

groundnut genotypes, the dissimilarity matrix of

pairwise cultivars was calculated using simple

matching coefficient and clustered using TREECON

software (Van de Peer and De Wachter, 1994) with the

algorithm of unweighted pair-group methods using

NTSYSPC 2.01 (Rohlf, 2000) based on the similarity

matrix of 105 SSR bands, and plots of the first three

resulting principal components were made to assess the

germplasms associations and to identify genetically

distinct accessions. Monomorphic markers were

included in all the calculations. Intra-germplasm

variation (heterogeneity) was calculated for each

cultivar using molecular information on each genotype.

Variations among the genotypes were calculated for

each marker and across all markers. Standard statistics

for characterizing genetic variability were computed

for the entire set of genotypes including the total

number of alleles (allelic richness), the number of

unique alleles (those that appear only once),

heterozygosity (more than one allele within a single

cultivar), polymorphism (more than one allele

amplified by a marker), and polymorphism information

content (PIC) as described by Torres et al., 2008) as

follows:

∑=

−=

n

i

iPPIC1

21 Where iP is the frequency of the

thj allele for the th

i marker, and summed over n

alleles, it was used to represent the information value

of a marker for detecting polymorphism within a

population. It depends on the number of detectable

alleles and their frequency distribution. Genetic

similarities (SIMQUAL module) among all pairs of

cultivars were estimated using Jaccard’s coefficient of

similarity as follows )( cbacbd ij +++= where

ijd is the SSR similarity between the accession i

( 1=i to n) and the other accession j [ 1=j to (n −

1)], a = sum of all alleles (from all SSR loci) shared in

both i and j , b is the number of alleles present in i

but not shared in j , c is the number of alleles present

in j but not shared in i .


23

Table 2: Allelic variation of 28 simple sequence repeat (SSR) loci in the 21 groundnut

germplasm

Primer

Gene Bank

Accession

ID

Sequence Motif Number of

Alleles Size range

Number

of rare

allele

PIC

Base pair (bp)

IPAHM23 ER974415 (CA)17(TA)3 3 111 – 126 1 0.6531

IPAHM73 ER974423 (GA)13 2 159 – 160 0 0.4938

IPAHM82 IPAHM 82 ER974426 3 221 – 272 1 0.5588

IPAHM92 ER974430 (GT)11 5 165 – 174 2 0.5780

IPAHM103 ER974437 (CA)3(GA)17 7 130 – 153 2 0.5914

IPAHM105 ER974438 (CT)18 5 275 – 289 2 0.6961

IPAHM108 ER974439 (TC)18 4 239 – 246 1 0.6626

IPAHM123 ER974446 (GA)18 5 127 – 156 0 0.7074

IPAHM136 ER974452 (TC)2(CT)13 5 109 – 135 2 0.6179

IPAHM164 ER974461 (GA)20 5 89 – 97 1 0.7063

IPAHM165 ER974462 (GA)13 3 211 – 222 0 0.5738

IPAHM171a ER974466 (TC)7TGTT(TC)9 5 188 – 207 1 0.6391

IPAHM171c ER974468 (GA)16 3 117 – 136 0 0.8210

IPAHM176 ER974469 (GA)18 4 136 – 162 0 0.6080

IPAHM177 ER974470 (CA)11TA(CA)3 (TA)4 3 142 – 210 0 0.3330

IPAHM219 ER974474 (TG)15 4 112 – 125 0 0.7044

IPAHM229 ER974475 (CA)14TA(CA)3 5 121 – 133 0 0.7611

IPAHM282 ER974488 (CA)14(TA)5 4 135 – 154 2 0.6341

IPAHM283 ER974489 (TA)4(TG)26TTG (GT)2 3 121 – 147 1 0.4558

IPAHM287 ER974492 (TG)16(AG)22 5 182 – 194 0 0.7284

IPAHM288 ER974493 (GA)4AA(GA)2 2 174 – 177 0 0.4444

IPAHM290 ER974494 (TA)3(CA)3CC(CA)5(TA)8 3 274 – 278 0 0.4082

IPAHM354 3 2 0.1850

IPAHM356 ER974515 (GA)21G(GA)2 4 103 – 117 2 0.6260

IPAHM37 ER974519 (CA)10(TA)7 3 221 – 225 0 0.4050

IPAHM373 EE974518 (TTG)6CT(GTT)8 2 222 – 229 0 0.3750

IPAHM395 ER974522 (GA)14 3 179 – 185 1 0.2540

IPAHM429 ER974534 (GT)17 2 321 – 360 1 0.0907

105 22

Mean 3.75 0.78 0.5469

Alhassan U., Danquah

E., Ado

S.G, Yahaya

A.I and Usman

M.

24

Diversity among Accessions

Similarities among the germplasm reflected in 105

SSR alleles were estimated and grouped into four

major clusters (Fig 1) at similarity coefficient of 10%.

The first cluster consists of six aphid resistant

genotypes with two other rosette resistant genotypes

clustering at the similarity level of 0.10. The six

members of this group were developed and obtained

from ICRISAT, Mali and showed 100 % similarity.

Clusters II and III comprised six genotypes which are

rosette resistant with dormancy also originating from

ICRISAT, Mali. The fourth cluster comprising

SAMNUT10, SAMNUT14, AMNUT23, and

SAMNUT22 are genotypes developed from IAR

breeding and are tolerant to groundnut rosette disease.

KWAKWASO is a material susceptible to groundnut

rosette disease and was separately clustered in the

dendrogram. The cultivars developed from different

breeding programs over various breeding periods

were well distributed across the clusters

DISCUSSION Substantial diversity exists among cultivated

groundnut germplasm in morphological,

physiological, and agronomic traits. This is the base

for modern cultivar improvement by hybridization in

the long run. The present study detected a wide intra-

genetic diversity in cultivated groundnut at the

molecular level among the tested genotypes using the

SSR assay. To get an objective discrimination of

genetic diversity of cultivated groundnut and make a

correct classification of them, substantially more

DNA markers and more genotypes would be needed.

In the present study, only 28 polymorphic SSR

markers were screened, because of the limited number

of groundnut SSR markers available and only 21

genotypes of groundnut were utilized.

Microsatellites have become one of the most widely

used molecular markers in recent years. The results in

the present study show that there are abundant

polymorphic SSR molecular markers within the pool

of cultivated groundnut. The 21 groundnut genotypes

were distinguished from each other with the SSR

markers and further classified into different sub-

groups. As DNA markers, SSRs are more

advantageous over many other markers, as they are

highly polymorphic, highly abundant, genetically

codominant, and analytically simple (Ronghua et al.,

2007). The genotypes belonging to aphid resistant

group which are all from ICRISAT, Mali are difficult

to be identified with morphological or agronomic

traits because of their similar growing habits, plant

height, leaf shape, pod shape, and so on. However,

they are distinguished from one another by the SSR

markers. It is observed from this study that SSR

markers are very useful molecular markers in

groundnut genetic study and in large-scale breeding

programs.In the case of the codominant SSRs,

fragment polymorphism directly relates to specific

loci or alleles, except for known multi-locus SSRs. In

fact, most of the groundnut SSRs markers tested in

this study were multi-locus markers.

Genetic diversity studies in cultivated groundnut

germplasm using SSR markers were reported

previously by various authors. Twenty three SSRs

were screened across 22 groundnut genotypes with

differing levels of resistance to rust and late leaf spot

by Mace et al., (2006) and they reported that 12 of the

23 SSRs (52 per cent) showed high level of

polymorphism with PIC ≥ 0.5. In another

development, thirty four SSR markers were used to

assess the genetic variation of four sets of twenty –

four accessions each from four botanical varieties of

cultivated groundnut were reported by Tang et al.

(2007). In their report, among the tested accessions,

ten to sixteen pairs of SSR primers showed

polymorphisms and the dendrograms based on genetic

distance revealed the existence of different clusters.

The results of this study indicate that substantial

genetic variation exists within the groundnut

germplasm assayed. The relationship and

distinctiveness obtained for the materials used in this

study should facilitate the selection of less related

germplsm for intercrossing. Less genetic diverse

germplasms underline the need for continuous effort

to diversify groundnut gene pool to ensure sustainable

breeding programme in future. It has been

demonstrated that the set of microsatellite markers

previously described and used here provides a

powerful tool for germplasm characterization analysis

of Arachis species. Among the primer pairs presented

in this study, 18 are highly polymorphic and readily

available through web search. These primers could be

useful in all the steps from conservation to the use of

germplasm. The existence of duplicates, mislabeling

and loss of integrity due to physical contamination,

crosspollination or genetic drift are realities, so these

markers could be used as an aid in evaluating these

events in the germplasm collection. Furthermore, they

could also be used in identifying accessions and

cultivars and for selecting parents for hybridization.

In conclusion, assessment of molecular diversity

facilitated the identification of agronomically

valuable and diverse germplasm for use in genetic

enhancement of specific traits in groundnut.

Agronomically superior lines with relatively high

DNA marker polymorphism were been identified for

mapping rosette disease resistance traits through the

use of 28 SSRs. The SSR marker-based genetic

diversity analysis of 21 groundnut genotypes with

known resistance to rosette disease reported here,

indicated that morphological groupings are poor

indicators of genetic diversity. Moreover, sources of

disease resistance are available in Spanish, Virginia

and Valencia types that have narrow genetic

divergence. It is also clear that national groundnut

breeding program in Nigeria produced highly related

breeding material whereas ICRISAT disease resistant

breeding lines fall into three of the four genetic

groups, as identified through cluster analysis. Thus,


25

ICRISAT’s international groundnut breeding

programs appear to provide a broad genetic base for

groundnut improvement. This is a highly valuable

information both for the selection of genetically

diverse material for use in groundnut disease

resistance breeding programs and for the selection of

parental genotypes for generating recombinant inbred

line (RIL) mapping populations.

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27

NIGERIAN JOURNAL OF CROP SCIENCE Volume 1 No. 1 September 2013 pp 27 - 33

SELECTION OF AGRONOMIC TRAITS IN ADVANCED GENERATIONS OF THREE

INTER-SPECIFIC CROSSES OF SOLANUM SPECIES

Atugwu A I. and Uguru M.I.

Department of Crop Science, University of Nigeria, Nsukka, Nigeria.

*Correspondence Author: [email protected]

ABSTRACT The evaluation and selection of novel lines from advanced generations of interspecific hybrids of tomato were

carried out from 2004 to 2008 in the Department of Crop Science Research Farm, University of Nigeria Nsukka.

The hybrid populations were generated from interspecific hybridization between the cultivated tomato (Solanum

lycopersicum) and a wild relative of tomato (S .pimpinelifolium). They consist of Roma x Wild, Local x Wild and

Tropica x Wild. The results revealed consistent increase in the yield traits from one generation to another.

Selection differential showed consistent progress in the improvement of the traits in the progenies indicating that

selection was effective. The realised heritability estimate and genetic gains provide sufficient evidence of

exploitable additive gene effects. Trait improvement elasticity appear to have been exhausted at the F11

generation as there was discernable decline in performance of all the hybrids at the F12 generation.

Comparatively, the hybrids, Roma x Wild and Tropica x Wild out performed Local x Wild in all the traits from F7

to F12 generations thereby suggesting the two hybrids as potential candidates for selection and improvement of

yield traits in tomato.

Keywords: Tomato, Inter-specific hybrids, Genetic gain Solanum lycopersicum, S.

pimpinellifolium.

INTRODUCTION Tomato belongs to family, Solanaceae. It is one of the

most popular fruit vegetable that contribute

significantly to the dietary intake of vitamins A and C

and lycopene. Tomato is grown in almost every part of

the planet. Fruit yield in tomato is a cumulative effect

of the yield contributing traits (such as numbers of

flowers, fruit number, and fruit weight). It is highly

influenced by both genetic and environmental factors

and selection for yield is achieved with unalloyed

emphasis on the yield contributing traits. Therefore, a

successful selection would depend upon the

information on the genetic variability and association

of yield with its component traits. Poehlman (1991)

and Singh and Singh (1995) reported that selection

based on yield components is advantageous if

information on the different yield component traits are

available.

The rate of genetic improvement depends on

heritability of the trait and the difference between the

mean of the selected group (Xs) and the overall mean

of the population (Xo) usually referred to as the

selection differential (Hallular, 1980). Selection and

efficient use of selected genotype normally results in

an increased proportion of desirable genes (Allard,

1960) in the population and a subsequent genetic

improvement of the population means for the traits

under selection. Directional selection is the most

common type of selection that involved selecting

genotypes with highest value of the desired traits.

Selection response is directly proportion to the

accuracy of the genetic evaluation performed

(Stradberg and Malmfors, 2006). This research was

initiated to determine and compare the performance of

the novel tomato hybrids obtain from interspecific

crosses between Solanum lycopersicum and S.

pimpinellifollium using selection differential and

genetic gain as the yard stick for validating variation.

MATERIALS AND METHOD The materials for this study comprised three advanced

tomato hybrids (Roma x Wild, Local x Wild and

Tropica x Wild) obtained from interspecific crosses

between Solanum lycopersicum and Solanum

pimpinellifolium. The field evaluations were carried out

in the Department of Crop Science Research Farm,

University of Nigeria, Nsukka from 2004 to 2008. The

seeds were planted in nursery boxes filled with top soil,

well cured poultry manure and river sand mixed in a

ratio of 3: 2:1 by volume. Transplanting was done at

four weeks of planting.The seedlings were transplanted

28

in a single row spacing of 45cm on ridges spaced 1m

apart. Each ridge measured 1m x 27m. Weeding was

done manually using a hoe. Selection was imposed at

every generation from F7 to F12, with 5% selection

intensity. The following data were collected at each

generation: Fresh fruit weight, number of flowers,

fruits, trusses and branches per plant. Fresh fruit

weight was obtained by weighing all the harvested

fruits from the plant in an electronic weighing balance

and the number of flowers, fruits, trusses and branches

per plant were obtained by counting.

Statistical analyses In each generation, the number of flowers, fruits,

trusses and branches per plant and fresh fruit weight of

the mixed and selected populations were recorded from

every plant in the mixed population. From the data

collected, the mean of the base population ( xo ) and the

mean of the selected population( xs ) were calculated.

The selection differential (xs - xo) was obtained by

subtraction. Observed realised heritability (h2r) was

calculated from the observed genetic gain of each

generation divided by the selection differential from

the previous generation. The observed genetic gain of

each generation was calculated from the observed

population mean of the present generation minus the

observed population mean of the previous generation.

The average observed genetic gain of the five traits per

generation was calculated as the sum of the means of

the genetic gains in all the generation divided by the

number of generations. In addition to quantitative

estimates, trends in yield improvement were also

shown graphically for the different traits at the

different generations in the hybrids.

RESULTS The results of the selection differential, genetic gain

and observed heritability of the five traits in the three

populations, Roma x Wild, Local x Wild and Tropica x

Wild are presented in Tables 1 to 5.

Table 1 contains information on number of flowers per

plant. Number of flowers per plant increased

consistently from F7 - F12 in the three hybrids. Positive

selection differentials were obtained in the six

generations in the three hybrids. The values ranged

from 3.36 to 74.91 in Roma x Wild, 0.76 to 54.65 in

Local x Wild and 2.73 to 32.81in Tropica x Wild. The

lowest selection differential for number of flowers per

plant was observed in the 7th

filial generation in the

three hybrids. The difference in magnitude of selection

differential was expected because of the genetic

variation in the three hybrids. The highest genetic

gain (124.9) for number of flowers per plant was

obtained at F8 in Local x Wild followed by Roma x

Wild (70.82) at F8.

The results with respect to number of fruits per plant

are presented in Table 2. Selection differential at the

different generations ranged from 4.81 to 84.3 in Roma

x Wild, 0.88 to 117.97 in Local x Wild and 0.7 to 35.44

in Tropica x Wild. The highest selection differential

(117.97) in number of fruits per plant was recorded in

Local x Wild followed by 84.3 recorded in Roma x

Wild and 35.44 recorded in Tropica x Wild. An

increase in number of fruits per plant by 101.57 was

realized at F12 generation over 45.26 obtained at F7 in

Roma x Wild. The highest genetic gain for number of

fruits was obtained in Roma x Wild (94.53) at the 8th

filial generation. The realized heritability was positive

and largest (78.96) in Local x Wild at F8.

0

20

40

60

80

100

120

140

160

180

200

RxW LxW TxW

Number of flowers per plant

F7 F8 F9 F10 F11 F12

Figure 1 : Distribution of the F7, F8, F9, F10, F11and F12 population of Roma x Wild(R

X W), Local x Wild(L xW) and Tropica x Wild (Tx W)with respect to number of

Flowers.

Agronomic Traits In Advanced Generations Of Three Inter-Specific Crosses Of Solanum Species

29

Table 1: Number of flowers per plant at advanced generation of the three tomato genotypes.

Where: xo mean of the base population, xs selected Poppulation xs-- xo selection differential G Genetic Gain h2r Observed realized heritability

Table 2: Number of fruits per plant at advanced generation of the three tomato genotypes.

Where: xo mean of the base population, xs selected Poppulation xs-- xo selection differential G Genetic Gain h2r Observed realized heritability

0

20

40

60

80

100

120

140

160

180

200

RxW LxW TxW

Num

ber

of fr

uits p

er

pla

nt

F7 F8 F9 F10 F11 F12



Fruits per plant.

Roma x Wild Local x Wild Tropica X

wild

Gen X X s X s- X G h2r X X s X s- X G h2

r X Xs X s X G h2r

F7 61.04 64.4 3.36 - - 73.17 73.93 0.76 - - 82.57 85.3 2.73 - -

F8 131.86 154.98 23.12 70.82 21.08 198.1 144.83 6.73 124.9 164.3

8

106.2 116.2 10 23.63 2.77

F9 97.08 172 74.91 -34.78 -1.50 114.6 169.25 54.65 -23.5 -3.49 117.8 120.8 3 11.6 1.16

F10 163.23 184.51 21.28 66.15 0.88 165.6 171.63 6.03 51 0.93 148.6

3

169.5 20.9 30.83 10.28

F11 186.4 191.06 4.66 23.17 1.08 126.7 176.6 49.9 -38.9 -6.45 150.1

9

183 32.8 1.56 0.074

F12

Mean

135.50 171.17 35.65

-50.9

14.89

-10.9

2.13

120.8

167.66 46.86

-59

10.9

-1.18

30.84

137.9

3

143.8 5.87

-12.3

11.06

-

0.373

2.78

Gen Roma x Wild Local x wild Tropica x wild

X

X s

X s- X

G

h2r

X

X s

X s- X

G

h2r

X

Xs

X s - X

G

h2r

F7 40.45 45.26 4.81 - 45.60 46.73 1.13 - - 52.76 56.06 0.7 - -

F8 134.98 141.86 6.88 94.53 19.65 134.83 138.13 3.3 89.23 78.96 65.48 79.5 14.02 24.74 35.34

F9 72.7 157 84.3 -62.28 -9.05 160.37 161.25 0.88 25.54 7.74 94.02 103.3 9.28 -21.46 0.60

F10 124.6 165.91 41.31 51.9 0.61 94.08 167.63 73.55 -66.29 -75.32 111.72 123.53 11.81 17.7 1.90

F11 154.8 186.4 31.6 30.2 0.731 52.33 170.3 117.97 -41.75 -0.56 106.86 142.3 35.44 -4.86 -0.41

F12

Mean

101.57

161.13 59.6 -53.23

12.22

-1.68

2.05

58.73

150.8 92.07 6.4

2.62

-1.01

-2.78

96.90 132.32 35.42 -9.96

1.23

-0.28

7.43


30

.

Table 3 shows the results with respect to number of

trusses per plant. The number of trusses showed

positive selection differential in all the generations in

the three hybrids. The highest selection differential

(20.28) was obtained in Roma x Wild at the F9

generation. This was followed by Local x Wild with

13.02 at F10. Genetic gain of 17.79 was obtained in

Local x Wild at F8 and the value was higher than 12.57

in Roma x Wild and 3.95 in Tropica x Wild.

The results on number of branches per plant are

presented in Table 4. Positive selection differentials

were obtained in all the generations in the three

hybrids. The differentials ranged from 0.87 to10 in

Roma x Wild, 0.3 to 9.99 in Local x Wild and 0.86 to

7.73 in Tropica x Wild. The highest number of

branches (6) was obtained in Local x Wild in

comparison with 3.56 and 3.49 realized in Tropica x

Wild and Roma x Wild, respectively. The highest

realized heritability was obtained at F8 generation in

the three hybrids. The highest heritability value (20)

was obtained in Local x Wild followed by 4.01 in

Roma x Wild and 2.28 in Tropica x Wild.

The results with respect to fresh fruit weight per plant

are shown in Table 5. Positive selection differentials

were obtained in all the generations. There were

consistent increases in fresh fruit weight from

generation to generation in the three hybrids. The

highest selection differential (333.64) was obtained in

Tropica x Wild at the 11th

filial generation followed by

Roma x Wild (173.57) at the same F11 generation.

Genetic gain was highest (382.3) in Roma x Wild. This

was followed by Local x Wild with a value of 189.59.

The highest realized heritability was obtained in Roma

x Wild at F9 (82.71) followed by Local x Wild at F8

(58.87).

The population distributions with respect to number of

flowers, fruits, branches, trusses and fresh fruit weight

per plant are presented in Figures 1 to 5. The values

with respect to number of flowers in the six generations

are presented in Figure 1. Increases in the number of

flowers over the generations were observed in all the

hybrids. The highest number of flowers per plant was

produced at F11 with a slight drop at F12 in the three

hybrids.

Table 3: Number of trusses per plant at advanced generation of the three tomato genotypes.

Where: xo mean of the base population, xs selected Poppulation xs-- xo selection differential G Genetic Gain h2r Observed realized heritability.

Gen Roma x Wild Local x wild Tropica x wild

X

X s

X s- X

G

h2r

X

X s

X s- X

G

h2r

X

Xs

X s -

X

G

h2r

F7 13.09 14.53 1.44 11.13 11.96 0.83 - - 13.4 14.03 0.63

F8 25.66 27.86 2.2 12.57 8.72 28.22 29.75 1.53 17.79 21.43 14.72 16.14 1.42 1.32 2.09

F9 15.12 35.4 20.28 -10.54 -4.79 30.04 31.16 1.12 1.41 -3 18.66 25.6 6.94 3.94 2.77

F10 21.63 41.63 20 6.51 0.321 20.34 33.36 13.02 2.2 1.96 20.79 27.09 6.3 2.13 0.31

F11 30.22 44.6 14.38 8.59 0.43 18.4 38.4 20 5.04 0.387 20.65 27.26 6.61 -0.14 -0.02

F12

Mean

19.45 37.6 18.15

-10.8

1.27

-0.59

.082

12.77 32.77 20

11.13

-5.63

4.16

-0.28

4.50

21.11 21.26 0.49

0.46

1.54

0.069

1.04


31



Bronches per Plant.

Table 4: Number of branches per plant at advanced generation of the three tomato genotypesWhere: xo mean of the base population, xs selected Poppulation xs-- xo selection differential AG Genetic Gain h2r Observed realized

heritability.

Where: xo mean of the base population, xs selected Poppulation xs-- xo selection differential G Genetic Gain h2r Observed realized

heritability.

Gen Roma x wild Local x wild Tropica x wild

X

X s

X s- X

G h2

r X

X s

X s- X

G h2

r X

Xs

X s - X

G h2r

F7 6.63 7.5 0.87 - - 5.1 5.4 0.3 - - 6 6.86 0.86 - -

F8 10.12 11.32 1.2 3.49 4.01 11.1 12.05 0.95 6 20 7.9 14.78 6.88 1.9 2.20

F9 7.53 17.4 9.87 -2.59 -2.16 13.17 14.08 0.91 2.07 2.17 11.46 17.5 6.04 3.56 0.517

F10 9.08 19.08 10 1.55 0.157 10.38 18.2 7.4 -0.21 0.067 11.24 18.73 7.49 -0.22 -0.036 F11 10.58 19.03 8.5 1.5 0.15 10.17 20.16 9.99 1.96 0.26 11.47 19.2 7.73 0.23 0.030

F12

Mean

11.74 18.17 6.43

1.16

1.022

0.136

0.458

14.77 20.14 5.37 -0.02

1.96

-0.002

4.49

10.94 18.09 7.15

-0.53

0.988

-0.068

0.053



Trusses per plant.


32

Table 5: Fresh fruit weight per plant (g) at advanced generation of the three tomato

genotypes.

Where: xo mean of the base population, xs selected Poppulation xs-- xo selection differential G Genetic Gain h2r Observed realized

heritability.

Figure 5: Distribution of the F7, F8, F9, F10, F11 and F12 population of Roma x Wild(R X W), Local x Wild (L x W) and Tropica x Wild (T x W) with respect to fresh fruit weight per plant.

Number of fruits showed consistent increment from

F7 to F12. Roma x Wild produced more than 180

fruits, Local x Wild had 165 and Tropica x wild had

141 at the 11th

generation. The rate of increment

differed among the hybrids. A marked increase was

observed from F7 to F8 (43 to 140) in Roma x Wild,

but the rate decline after which the increment

thereafter. Similarly, the peak increase in the number

of fruits was recorded between F7 and F8 in Local x

Wild while Tropica x

Wild showed gradual increase in the number of fruits

up to F11 and dropped at F12.

Figure 4 shows the distributions of the plants with

respect to number of trusses per plant. There was a

steady increase in the number of trusses in all the

generations. Differences were observed in the rate at

which the hybrids increased over the generations.

Roma x Wild started with 15 trusses in F7 to 27 in F8,

35 in F9, and above 42 in F10 and F11, while Local x

Wild increased from below 15 at F7 to 29 at F8, 31 at

F9, 34 in F10 and 38 F11. Tropica x Wild increased

gradually from below 15 at F7 to slightly above 15

in F8 , 26 in F9, 27 in F10, and 27. 5 in F11.

The distributions with respect to number of branches

per plant indicated marked variations (Figure 5)

among the progenies. The population of Local x Wild

produced more branches (above 20) than Roma x

Wild and Tropica x Wild. Consistent increase in

branch number was observed after each generation

with a decline at F12 in the three hybrids.

Figure 6 shows the distribution of the plants with

respect to fresh fruit weight per plant. Increase in fruit

weight was evidenced in all the generations. The

highest fresh fruit weight was observed in Tropica x

Wild at the 11th

generation, followed by Roma x Wild

also at the 11th

generation. The increase in fresh fruit

weight of the Local x Wild hybrid was low compared

with the other two hybrids.

DISCUSSION Selection and efficient use of selected genotypes

normally result in an increased in the proportion of

desirable genes in the population and subsequent

genetic improvement of the population means for the

traits under consideration (Stradberg and Malmfors,

2006). The gene from the selected genotype will be

spread also to future generations, so that the genetic

gain obtained through selection will last. The

effectiveness of the original selection helps in the

improvement of the desired traits. In the present study

positive values obtained in the selection differentials

in all the traits studied indicated effective selection.

The results revealed that the observed realized

Generations Roma x Wild Local x wild Tropica x wild

X X s X s- X G h2

r X X s X s-

X G

h2r

X Xs X s - X

G h2r

F7 384.8

425.79 40.99 - - 361.31 364.53 3.22 - - 560.09 580.09 20 - -

F8 767.09 767.9 0.81 382.3 9.33 550.9 559.03 8.13 189.59 58.87 587.86 615.17 27.31 27.77 1.38

F9 834.09 848.84 14.75 67 82.71 529.38 569.03 39.65 -21.52 -2.65 509.07 571.4 62.33 -78.79 -2.88

F10 953.00 961 8 118.91 8.061 574.97 571.4 0.43 41.59 1.048 641.43 653.57 12.14 132.36 2.12

F11 806.05 979.62 173.57 -146.95 -18.36 515.70 605.69 89.99 -56.27 -130.8 712.86 1046.5 333.64 71.43 5.88

F12

Mean

950.50 954.6 4.1 144.45

113.14

0.832

16.51

503.39

39 90

-12.31

28.21

-13.67

-17.44

901.68 907.84 6.16

188.82

68.32

0.565

1.413


33

heritability was highest at F8 generation in the five

traits, suggesting high variability. A decline in the

observed realized heritability after F10 generation in

all the traits in the three hybrids is a suggestive of

reduction in genetic variability and onset of

homogeneity. A decline at F12 in all the characters

reinforces exhaustion of genetic variation in support

of the observed realized heritability. High genetic

gain observed in the F8, F10 and F11 in all the traits in

the three hybrids indicates additive gene effect and

selection based on these traits at these generations is

likely to produce the desired change. Roma x Wild

showed the highest positive values in the genetic

gain with respect to number of fruits per plant and

fresh fruit weight per plant while Local x Wild

exhibited the highest genetic gain in number of

flower, trusses and branches per plant . Tropica x

Wild had the least genetic gain in all the traits studied.

The high genetic gain couple with the moderate to

high heritability estimate for these traits suggested the

importance of additive genetic variance for these

traits. High genetic gain and high heritability on seed

cotton yield and number of balls per plant was

reported by Krishnarao and Mary (1996),

Sambamurty (1995), Kooner et al. (1981) and Pandey

et al. (1995). Based on generational performance,

Tropica x Wild produced the heaviest fruits. The

effective selection of the yield component traits and

the original selections of large fruit size could have

resulted in the improvement of fruit yield. This may

have caused changes in the gene frequencies that

culminated into new populations distinctly different

from the original unselected genotypes. Roma x Wild

out performed the other two hybrids in number of

flowers per plant, number of fruits per plant and

number of trusses per plant.

The distributions exhibited differences in the

incremental rate of the traits measured in the three

hybrids, an indication of high variability. The

consistent increase from F7 to F11 tends to suggest

additive gene effect that qualifies effective selections.

A marked increase observed between F7 and F8 in all

the traits in the three hybrids suggests accumulation

of additive genes, in support of the positive selection

differential observed in all the generations. The slight

drop at F12 in all the traits in the three hybrids is in

agreement with earlier report of exhaustion of genetic

variance and homogeneity. Therefore, selection at 11th

filial generation of each trait became very effective.

This study revealed increase in the yield potentials

over the generations due to additive genes. Roma x

Wild performed better than the other two hybrids in

the three traits: number of flowers, fruits and trusses

per plant out of five traits measured. Tropica x Wild

out performed the others in fruit production.

Therefore, the two hybrids, Roma x Wild and

Tropical x Wild are promising candidates for south

eastern Nigeria.

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Beeding. Principles of Plant Beeding. Jonh

Wiley and Sons Inc. New York pp. 115 -

116.

Hallular, A. R. (1980). Relation of Quantitative

Genetics to Applied maize Breeding. Rev.

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Kooner, A. S., Sandhu, B. S. and Nagi, H.S. (1981).

Genetic variability n some quantitative

character of cotton. (Gossypium arboreum

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Krishnarao, K. V. and Mary, T.N. (1996).

Variability, Correlation and Path analysis

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Madras Agric. J., 77 (3&4 ) :146-152.

Pandey, S. C., Saxens, M.K. and Julika, R. (1995).

Studies on variability in upland cotton.

Crop Res., 10(3):277-278.

Poehlman, J. M. (1991).The Mugbean, Oxford and

IBH Publishing Co. Pvt. Ltd., New Delhi,

375 p.

Sambamurty, J. S.V., Reddy, D. M. and Reddy,

K.H.G. (1995). Genetic variability and path

analysis in cotton. (Gossypium hirsutum L),

J. India Soc. Cotton Improv., 20:133-138.

Sing, K. P. and Sing, V. P. (1995). Comparative role

of seed yield components in mugbean

(Vigna radiate L. Wilczek) Legume

research.

Stradberg, E and Malmfors, B. (2006). Compendium

selection and genetic change, pp. 1-2 In

Department of animal breeding and

Genetics. Swedish University of

Agricultural Science Press, Uppsala,

Sweden.

Uguru, M.I. and Onwubiko, C. N. (2002).

Inheritance of fruit size in Lycopersicon

species, Agro-Science 3(1): 13-19.

Uguru, M. I. and Umukoro, O. E. (2005). Breeding

progress in tomato with pedigree selection

and generation hybrids. Discovery and

innovation 17(3/4) pp.


34


VARIABILITY IN CHARACTERS OF VITELLARIA PARADOXA GAERTN F. IN

NIGERIAN SAVANNAH AS REVEALED BY MULTIVARIATE ANALYSIS

Okolo E. C., Okwuagwu, C. O. Okoye, M. N. Enaberue L. O. and Yusuf A. O. *

Plant Breeding Division, Nigerian Institute for Oil Palm Research (NIFOR), P.M.B 1030 Benin City,

Edo State Nigeria.

*Corresponding author: [email protected]

ABSTRACT Twenty four accessions of Vitellaria paradoxa was analyzed to assess the existing variability with the aim of

identifying promising accessions for future breeding studies. Data were collected on quantitative and visually

assessed traits including trunk height (m), trunk girth (cm), crown diameter (cm), leaf length (cm), leaf width

(cm), petiole length (cm), number of main branches, sub branches and total number of branches per tree,

trunk/bark colour, crown shape, and leaf margin. All the traits were analyzed using simple statistical

estimates and cluster analysis. Results showed a wide range of diversity in the quantitative traits assessed

with variability in leaf length ranging from 5.80 -17.30cm, crown diameter from 5.10 – 12.90cm, and trunk

height from 1.30-5.38m. The accessions were classified into 7 distinct groups with the accessions in each

group having similar characteristics. Accessions within a cluster in the dendogram showed promise as

candidates for cultivar development. The distinct groups could serve as breeding lines for the genetic

improvement of V. paradoxa.

Keywords: Variability, Accessions, Vitellaria paradoxa, Genetic improvement.

INTRODUCTION The Shea tree (Vitellaria paradoxa Gaertn F.) is a

perennial and deciduous tree predominantly growing

naturally throughout the guinea and sahel savannah

regions (Yidana, 2004). Vitellaria paradoxa has

however, shown promise for its butter as a substitute

for cocoa butter in the production of chocolate both

locally and internationally. It has great economic

potentials as an important source of raw materials for

pharmaceutical and cosmetic industries (Wiesman et

al., 2003; Teklehaimanot, 2004).

Despite the increasing economic importance of this

tree, very little is known about the germplasm that

exists in Nigeria and the genetic relationships among

them. When the mandate of the Nigerian Institute for

Oil Palm Research (NIFOR) was extended to cover

research into the Shea tree, it became imperative to

establish a germplasm bank of the crop.

A systematic collection of Vitellaria paradoxa

genetic materials within the Nigerian grove ecology

has been undertaken for the domestication of this

economic tree through breeding and selection. The

preliminary results of the germplasm evaluation

revealed extensive variations among the accessions

with respect to trunk height, girth, crown diameter

and leaf characteristics (Okolo et al., 2008; Enaberue

and Okolo, 2010, Okwuagwu et al., 2010). Okolo et

al., (2008) reported that the variability observed in

some of the quantitative and qualitative traits could

aid in the evaluation of the genetic diversity of the

species in Nigeria. In an earlier study, Odebiyi et al.

(2000) reported significant differences between the

Vitellaria paradoxa leaves in the woodland and

northern guinea savannah. There is however, little or

no information on the extent and kind of diversity

present in Vitellaria paradoxa germplasm in

Nigeria.Several techniques have been successfully

employed to classify and measure patterns of

diversity in many tree crops. Multivariate analysis

based on phenotypic data has been used to assess the

genetic diversity of oil palm (Ataga and Fatokun,

1989), rubber (Omokhafe and Alika, 2003), coffee

(Fonseca et al., 2006), and coconut (Naziru et al.,

2009).

Consequently, this study was aimed at assessing the

variability within the 24 Vitellaria paradoxa

accessions in the Nigerian germplasm and their

relationships with a view to identifying promising

accessions for future breeding studies.

MATERIALS AND METHODS The data presented here were obtained during a

demographic survey undertaken for the distribution

and density of Vitellaria paradoxa in the savannah

ecological belt of Nigeria. Twenty four (24)

accessions were collected from 5 states (Niger,

Kebbi, Kaduna, Plateau and Nasarawa) representing

the derived/Southern Guinea and Northern Guinea

Savannah (Table1). In each state, 2-6 locations were

sampled according to Palmberg (1985) and the

collection protocol was in tandem with the Shea tree

descriptor of IPGRI/INIA (2006).

35

The following measurements and observations were

made: trunk height (m), trunk girth (cm), crown

diameter (cm), leaf length (cm), leaf width (cm),

petiole length (cm), number of main branches, sub

branches and total number of branches per tree.

Visually assessed characters include trunk/bark

colour, crown shape, and leaf margin.

The data recorded were subjected to analysis to

determine simple statistical estimates (means,

standard deviation and coefficient of variation).

Genetic diversity estimates were facilitated with

SPSS for Window 16. Pearson’s correlation

coefficient was used to calculate the degree of

association among the different quantitative traits.

The accessions were further classified into similar

groups by the non-hierarchical K-means technique of

cluster analysis with a dendogram (Hair et al., 1992).

RESULTS AND DISCUSSION All the quantitative traits measured exhibited broad

variability. Leaf length for example ranged from

5.80-17.30cm while trunk height ranged from 1.30-

5.40cm in all the accessions. Most of the accessions

had an entire leaf margin (90.48%), although some of

these accessions (66.67%) revealed a combination of

oblong, pyramidal, spherical, semi-circular and

elliptical crown shape (Table 2).

Of all the correlation coefficients presented in Table

3, five were positive and highly significantly

(P=0.01). Majority of the traits that were positively

correlated corresponded to traits of morphology. The

highest correlation was between number of sub

branches per tree and the total number of branches

(r=0.944) followed by crown diameter and trunk

girth (r=0.803). However, leaf width was negative

and significantly correlated with number of sub

branches, total number of branches and leaf length

(r= -0.795, -0.657, and -0.957).

Dendrogram using Single Linkage

Rescaled Distance Cluster Combine

+---------+---------+---------+---------+---------+

N. DAJI 15 ─┐ TAMAA 17 ─┤ D. TOFA1 20 ─┼───┐ KYAMOR 19 ─┘ ├─┐ N.RAWA 13 ─┬───┘ ├─┐ GAMA GIRA 16 ─┘ │ │ D. TOFA2 21 ─┬─────┘ ├───┐ UBBE 24 ─┘ │ │ ASSAKIO 23 ─────────┘ ├───────────────────────────────────┐ DM TANKO 12 ─────┬─┐ │ │ JING 18 ─────┘ ├─┐ │ │ KANGIMI 14 ───────┘ ├───┘ │ SHENDAM 22 ─────────┘ │ TENDAGARA 2 ───────┬─────┐ │ MATANI 8 ───────┘ │ │ T. TAKANGARI 3 ─────┬───────┤ │ T. ALURA 7 ─────┘ ├───┐ │ M. YAURI 4 ─────────────┤ ├─┐ │ YELWA 5 ─────────────┘ │ │ │ MAGAMA 6 ─────────────────┘ ├─────┐ │ T. ITCHE 10 ───────────────────┤ ├─┐ │ K. KONTANGORA 9 ───────────────────┘ │ ├─────────────────────┘ BADEGGI 11 ─────────────────────────┘ │ B. YAURI 1 ───────────────────────────┘ Fig.1: Cluster analysis dendogram of the 24 accessions of V. paradoxa.

Variability In Characters Of Vitellaria Paradoxa

36

Table 1: List of the accessions used in the study and their origin

ACCESSION NO. ACCESSION/ORIGIN

1 BIN YAURI 2 TENDAGARA

3 TUNGA TAKANGARI

4 MARARABA YAURI

5 YELWA

6 MAGAMA

7 TASHA ALURA

8 MATANI

9 K. KONTANGORA

10 TSOHON ITCHE

11 BADEGGI

12 DURAA MALLAM TANKO

13 NASSARAWA

14 KANGIMI

15 NGANU NDAJI 16 GAMA GIRA

17 TAMAA

18 JING 19 KYAMOR

20 DOKAN TOFA1

21 DOKAN TOFA2 22 SHENDAM

23 ASSAKIO

24 UBBE

Table 2: Statistical parameters for quantitative characters and status of qualitative

descriptors of the accessions evaluated.

CHARACTERS RANGE MEAN STANDARD

DEVIATION

COEFFICIENT OF

VARIATION (%)

TRUNK HEIGHT (m) 1.30 - 5.40 2.14 0.79 37.38

TRUNK GIRTH (cm) 0.60 -2.00 1.28 0.39 30.80

CROWN DIAMETER

(cm)

5.00 -12.90 9.22 2.07 22.41

MAIN BRANCH 1.80-3.00 2.25 0.28 12.61 SUB BRANCH 5.20-14.60 7.99 2.63 32.96

TOTAL NO. BRANCH 10.44-32.12 17.97 6.12 34.03

LEAF LENGTH (cm) 5.80 -17.30 10.02 4.78 47.71 LEAF WIDTH (cm) 5.20 -14.90 10.78 4.69 43.53

PETIOLE LENGTH

(cm)

16.60 -23.40 20.21 1.37 6.77

CHARACTERIZATION

CROWN SHAPE 66.67% of the accessions were a combination of oblong, pyramidal, spherical, semi circular and

elliptical while 8 of the accessions had broadly pyramidal crown shape.

TRUNK/BARK COLOUR 11 of the accessions were ash-grey in colour while 26.98% of the accessions were dark brown. Some of the accessions (23.79%) with white, dark grey and black.

LEAF MARGIN Entire leaf margin had the highest frequency of 90.48%, while2% of the accessions had

undulate leaf margin.

Okolo E. C., Okwuagwu, C. O. Okoye, M. N. Enaberue L. O. and Yusuf A. O.

37

Table 3: Correlations among selected quantitative traits in V. paradoxa

Table 4: Distribution of 24 V. paradoxa accessions in seven clusters. CLUSTERS NO. OF ACCESSIONS ACCESSION ORIGIN

1 1 B. YAURI

2 11 BADEGGI

3 3,4,7 T. TAKANGARI, M. YAURI, T. ALURA 4 13,15,16,17,19,20,21,24 N.RAWA ,N. DAJI, GAMA GIRA, TAMAA, KYAMOR, D.

TOFA1, D. TOFA2, UBBE

5 2,5,8 TENDAGARA, YELWA, K. KONTANGORA

6

12,14,18,22,23 DM TANKO,KANGIMI, JING, SHENDAM, ASSAKIO

7 6,9,10 MAGAMA, MATANI, T. ITCHE

The twenty four Vitellaria paradoxa accessions were

grouped into 7 clusters through the K-means non-

hierarchical clustering. Members of each cluster are

presented in Table 4 and the maximum number of

accessions (8) was observed in cluster 4 while

clusters 3, 5, and 7 had three accessions each.

Accessions grouped in cluster 4 had the least values

for all the morphological traits except for leaf width

(15.00cm) (Table 5). Cluster 7 accessions had the

highest trunk height (2.90m), leaf length (16.30cm)

and petiole length (22.50cm). Worthy of note is that

clusters 2, 3, 4, and 6 did not differ in number of

main branches per tree and petiole length. The result

of the dendogram (Fig.1) was consistent with the

results in Table 5.

The considerable range of variability inherent in the

trunk, crown and leaf morphology of the accessions

studied suggests that Vitellaria paradoxa is

characterized by a wide variability. This natural

diversity is vital for genetic improvement of the

species (Diarrassouba et al., 2002). The high

variation for trunk height could probably be linked to

the wide range of age-classes from which samples

were obtained. Similar results were reported by

Lovett and Haq, (2000b) in their diversity study of

Shea nut tree in Ghana. The proximity of other trees

and human interference could modify the crown

architecture of the trees. Diarrassouba (2000)

concluded that the variation in the crown parameters

(Crown diameter, number of main and sub branches,

crown shape) might pose some difficulties in the

discrimination of phenotypes. Zafar et al. (2006)

emphasized the importance of qualitative traits in

plant description. According to Ghafoor and Ahmad

(2003), qualitative traits are useful in separating

varieties especially when the range of quantitative

traits is limited. Most of the accessions in this study

were of ash-grey trunk/bark colour, broadly

pyramidal crown shape and entire leaf margin.

Correlation is a measure of the degree of association

between variables. The strong correlation between

the trunk morphology variables and the crown

parameters can be exploited directly or indirectly for

the genetic improvement of V. paradoxa. This will

be most pertinent in recommending the planting

density of the crop to the farmers.

The congruence between the dendogram and the

clusters grouping of the accessions is quite obvious

in this study. The most isolated accessions were in

clusters 1 and 2 which were equally represented in

the dendogram. Accessions in cluster 2 are

CHARACTER

TRUNK

HEIGHT

(m)

TRUNK

GIRTH

(cm

CROWN

DIAMETER

(cm)

MAIN

BRANCH

SUB

BRANCH

TOTAL

NO.

BRANCH

LEAF

LENGTH

(cm)

LEAF

WIDTH

(cm)

TRUNK

GIRTH (cm)

.309

CROWN

DIAMETER

(cm)

.143 .803**

MAIN

BRANCH

.076 .124 .302

SUB BRANCH .434* .497* .355 -.062

TOTAL NO.

BRANCH

.439* .522** .433* .264 .944**

LEAF

LENGTH (cm)

.426* .167 .021 -.304 .781** .643**

LEAF WIDTH

(cm)

-.328 -.184 .001 .313 -.795** -.657** -.957**

PETIOLE

LENGTH (cm)

.343 -.082 .100 -.071 -.002 -.048 .196 .063

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).


38

characterized by high trunk girth, crown diameter,

number of sub-branches per tree while the accession

in cluster 1 has moderate variability for all the traits.

From the results, accessions within each cluster are

closely related to each other than accessions in other

clusters; similarity in many quantitative traits of the

accession brought them in a particular group/cluster.

The characters contributing to the divergence are

given greater emphasis for deciding the type of

cluster for the purpose of further selection and the

choice of parents. The accessions in cluster 4 could

be referred to as trees with small architecture.

Grouping of the accessions from the locations by

cluster analysis would be of practical use as

representative trees from each cluster analysis may

be selected for future use in improvement

programme.

An understanding of the magnitude and pattern of

diversity in crop/forestry plants has important

implications in breeding programmes, and for

conservation of genetic resources. Multivariate

analysis provides an effective method of evaluating

germplasm materials in order to identify materials

that could be further evaluated and utilized at the

genetic level. In this preliminary study, means,

standard deviations, coefficient of variability,

correlations, cluster analysis and dendogram have

been applied to assess the accessions and group them

based on the quantitative and qualitative traits.

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Studies of the Nigerian Oil Palm germplasm

collection. In Proceedings of the

International Conference on Palms and

Palm Products, 1989. Ed. Rees, A.R. et al.

Vol. 1:55-62.

Diarrassouba, N. (2000). Caractérisation agro

morphologique de la population spontanée

de karité (Vitellaria paradoxa C. F.

Gaertn) du parc agro forestier a karité de

Tengrela, au Nord de la Côte d’Ivoire.

Mémoire de DEA, 43 P.

Diarrassouba, N., Bup, N. D., Kapseu, C., Kouame

,C., and Sangare, A. (2007). Phenotypic

Diversity of Shea (Vitellaria Paradoxa C.

F. Gaertn.) Populations across Four Agro-

Ecological Zones of Cameroon.Journal of

Crop Science Biotechnology 10 (4): 211

218.

Enaberue, L.O. and Okolo, E.C. (2010). Variation in

leafing characters of Shea tree (V.

paradoxa C.F.Gaertn.) populations.

Proceedings of the 44th

Annual

Conference of Agricultural Society of

Nigeria, 2010. Pp 1060-1064.

Fonseca, A.F.A., Sediyama, T., Cruz, C.D.,

Sakaiyama, N.S., Ferrão, M.A.G., Ferrão,

R.G. and Bragança, S.M. (2006).

Divergência genética em café conilon.

Pesq Agropec Bras 41:599-605.

Ghafoor, A. and Ahmad, Z. (2003). Exploitation of

Vigna munqo (L.). Hepper germplasm

using multivariate analysis based on

agronomic traits. Pak. J. Bot., 35: 187-

196.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black,

W.C. (1992). Multivariate Data Analysis,

3rd Edition. MacMillan Publishing

Company, New York, USA.

IPGRI, INIA. (2006). Descriptors for Shea tree

(Vitellaria paradoxa). International Plant

Genetic Resources Institute, Rome, Italy;

Instituto Nacional de Investigación y

Tecnología Agraria y Alimentaria,

Madrid, Spain. 54pp.

Lovett, P.N., and Haq, N. (2000a). Evidence of

anthropic domestication of the Shea nut

tree (Vitellaria paradoxa). Agroforestry

system 48: 273 – 288. Lovett, P.N. and Haq, N. (2000b). Diversity of the

Sheanut tree (Vitellaria paradoxa Gaertn

C. F.) in Ghana.Genetic Resources and

Crop Evolution 47 (3): 293-304.

Naziru, I., Amjad, H. and Sharaf, U. (2009).

Phenotypic Diversity of Coconut

Germplasm Conserved at Different

Stations of Bari. Bangladesh Journal of

Agric Research 34(1): 25-31.

Odebiyi, J.A., Bada, S. O., Awodoyin, R. O., Oni, P.

I. and Omoloye, A. A. (2004). Population

structure of Vitellaria paradoxa Gaertn F.

and Parkiabiglobosa (Jacq.) Benth. In the

Agroforestry parklands of Nigerian

HumidSavannah. West African Journal

ofApplied Ecology, 5:31 - 39.

Okolo, E.C., Enaberue, L. O., Okwuagwu, C.O.,

Okoye, M.N. and Yusuf, A. O. (2008).

Studies on Distribution, Density and

Variation of Shea Trees in Nigeria.

NIFOR In-House Research Review:

Progress Report 2001-2008.NIFOR, Benin

City. p. 209 – 212.

Okwuagwu, C. O., Okolo, E. C., Enaberue, L. O.,

Okoye, M. N. and Yusuf, O. A. (2010).

Biometric study of Shea tree (Vitellaria

paradoxa Gaertn) in Nigerian grove

ecology. Proceedings of the 25th

International Biometric Conference,

Florianapolis, SC, Brazil.

Omokhafe, K.O. and Alika, J.E. (2003). Phenetic

relationship of rubber tree clones. Biol

Plantarum 46:217-222.

Palmberg, G. (1985). L’echanfillonage dens la

re’colle des semences forestiers

amelioration genetique des arbres

forestieres. In cours de

formation/DANIDA – Me’rida

Venezualaa, FAO, Rome, pp 44 – 48.

Okolo E. C., Okwuagwu, C. O. Okoye, M. N. Enaberue L. O. and Yusuf A. O.

39

SPSS (2004). SPSS for Windows, Version 16. SPSS

Inc., Chicago, USA.

Teklehaimanot, Z. (2004). Exploiting the potentials

of indigenous agro forestry tree: Parkia

biglobosa and Vitellaria paradoxa in sub-

Saharan Africa. Agroforestry Systems

61:207- 220.

Yidana, J.A. (1991). Studies in the pollination of

shea trees. Cocoa Research Institute,

Ghana, Annual repport (1988 / 89): 99-

101.

Zafar, N., Aziz, S. and Masood, S. (2006).

Phenotypic divergence for agro-

morphological traits among landrace

genotypes of rice (Oryza sativa L.).

Pakistan International Journal of Agric

Biology 6: 335-339.


40


RESPONSE OF SWEET POTATO (IPOMOEA BATATAS (L.) LAM)

VARIETIES TO ORGANIC MANURE SOURCES AND RATES UNDER

RAINFED CONDITIONS IN AN ULTISOL

Muoneke

1*C.O., Mbah

2, E .U Udom

1. E. F.

1Department of Agronomy, College of Crops and Soil Sciences,

Michael Okpara University of Agriculture, Umudike, Abia State.

2Department of Crop Production Technology, Federal College of Agriculture, Ishiagu,

Ebonyi State, Nigeria. E*Corresponding Author: [email protected]; [email protected]

ABSTRACT Rainfed field experiments were conducted at Michael Okpara University of Agriculture, Umudike in

2006 and 2007 cropping seasons to investigate the response of two sweet potato varieties (Ex-

Igbariam and TIS-87/0087) to different sources (pig, poultry and goat) and rates (0, 10, 20 and 30

t/ha) of organic manure. The experiments were laid out in a 2 x 3 x 4 factorial in randomised

complete block design (RCBD) with three replications. In both years, poultry manure source

significantly (P<0.05) had highest number of leaves per plant, longest vine and number of branches

per plant than the other manure sources. Among the manure rates, growth parameters showed

higher values when compared with no manure application. The trend was the same in both cropping

seasons. Poultry manure source was significantly (P<0.05) highest for total number of tubers per

plant and total fresh tuber yield in the two cropping seasons than pig and goat manures. Shoot and

tuber yields of sweet potato were significantly (P<0.05) increased by manure source and rate in both

years. Interactions showed that it was more productive to grow TIS-87/0087 with poultry manure at

20 t/ha compared to Ex-Igbariam. Yield and yield components of the crop increased with increase in

rate of manure application but they declined after 20 t/ha of organic manure was applied. TIS-

87/0087 gave higher total fresh tuber yield than Ex-Igbariam in both cropping seasons. Therefore,

for optimum growth and yield of the crop, poultry manure applied at the rate of 20 t/ha was

recommended, especially for TIS-87/0078 sweet potato variety.

Keywords: Sweet potato variety, manure source, manure rate, growth, yield.

INTRODUCTION In humid tropical regions, soil fertility can be

maintained by applying organic materials

obtained through crop and animal production

systems. The benefits of organic manure in crop

production had been studied by many

researchers (Murray and Anderson, 2004;

Veeramani et al., 2012). However, crop

varieties, agro-climatic factors, manure

application rates and management practices

usually determine the magnitude of increased

crop production per unit nutrient applied.

According to Mittra et al. (2003), organic

materials with varying nutrient concentrations

and biochemical composition release nutrients at

different rates and in varying quantities.

Nedunchezhiyan et al. (2010) reported that

organic manure does not leach out easily from

the top soil, hence, it is readily available for

absorption by crops, improves the texture,

structure and water retaining property of the soil,

rehabilitates degraded soil, environmentally

friendly and crop yields obtained are safer for

human consumption.

Sweet potato, as an important root crop is fast

gaining prominence in the diet of people living

in Nigeria (Onunka et al. 2003). The leaves

which are rich in proteins are used as vegetable

in some parts of the country. They can also be

used as fodder for livestock. The root is a good

source of carbohydrate as well as raw materials

for the manufacture of industrial starch, glucose

and alcohol (Udo et al., 2005). Furthermore,

Onunka et al. (2004) in their study on the

performance of three sweet potato varieties to

poultry manure application, showed that

complimentary use of organic and inorganic

fertilizer in the production of root tuber crops

including sweet potato is sustainable. However,

there are differences in varietal responses to

different nutrient levels in the soil. This study

evaluated the growth and yield of two improved

41

sweet potato varieties and their responses to

different manure sources (pig, poultry and goat

manures) and rates (0. 10, 20, 30 t/ha)

considering the high cost and environmental

contamination associated with inorganic

fertilizers.

MATERIALS AND METHOD Rainfed field experiments were conducted at the

teaching and research farm of Michael Okpara

University of Agriculture, Umudike in 2006 and

2007 cropping seasons. Umudike (05º 29' N, 07º

33' E, 122 m altitude) is located in the tropical

rainforest agro-ecology of southeastern Nigeria.

The study area is characterized by the tropical

rainforest vegetation, bimodal rainfall, which

starts in April peaking in July and September

and ends in October, with a total of about

2,038.3 mm (2006) and 2,420.7 mm (2007).

Average sunshine and relative humidity ranged

from 2.4 - 6.7 hours/day and 31 – 80 %,

respectively.

Some of the physico-chemical characteristics of

the soil (0 - 20 cm) are shown in Table 1. The

soils of Umudike were formed on a coastal plain

sand deposit (Arenic hapludult) (Obasi et al.,

2005). The soil type is ultisol (USDA

Classification) and its texture is sandy loam.

Chemical analysis of the different types of

organic manure used for the experiment was as

shown in Table 2.

The experiment was designed as a 2 x 3 x 4

factorial arrangement in randomized complete

block design (RCBD) with treatments

comprising of sweet potato varieties (Ex-

Igbariam and TIS-87/0087), organic manure

sources (pig, poultry and goat manures) and

rates (0, 10, 20 and 30 t/ha) replicated three

times. The manures were applied one week

before planting.

Sweet potato varieties, TIS-87/0087

(characterized by pinkish roots with cream

coloured flesh, high yielding potential and rarely

flowering) and Ex-Igbariam (characterized by

cream coloured root with yellow flesh and

contains high levels of carotenoid), were

obtained from the National Root Crops Research

Institute (NRCRI), Umudike, whereas the

various manures were obtained from the

livestock unit of Michael Okpara University of

Agriculture, Umudike.

Each experimental plot measured 2.4 m x 3 m

(7.2 m2). Sweet potato vine cuttings 20 cm long

were planted at 1 m x 0.3 m to achieve a plant

population of 33,333 plants/ha. Supply of

missing stands was done 2 weeks after planting

(WAP). The plots were kept weed free by hoe

weeding. Data on number of leaves per plant,

vine length and number of branches per plant

were randomly taken in-situ on four plants taken

from the middle row at 8 WAP. Harvesting was

carried out when the leaves had senesced. Total

number of tubers per plant and fresh tuber yield

per hectare were determined from a net plot area

of 2.8 m2.

For each year, separate statistical analyses were

carried out on the data according to the

procedure for a factorial experiment in

randomized complete block design as outlined

by Steele and Torrie (1980) using the Genstat

Release Edition (2003) computer programme.

The significance of treatment effects was

estimated at P<0.05 as detected by Fisher’s least

significant differences (F-LSD) according to Obi

(2011).

RESULTS Sweet potato variety (Ex-Igbariam) at 8 WAP

significantly (P<0.05) produced fewer number of

leaves per plant, longer vines and fewer

branches per plant than TIS-87/0087,

irrespective of manure source and rate used in

the study (Table 3). The trend was the same in

both years, except that there were no varietal

differences in number of branches in 2006 and

vine length in 2007. Manure source and rate

significantly increased number of leaves per

plant, vine length and number of branches per

plant in the two cropping seasons, irrespective of

sweet potato variety compared to no manure

application. The evaluated growth parameters in

2006 and 2007 increased with increase in

manure rate to the highest amount studied.

In Table 4, irrespective of manure source and

rate, Ex-Igbariam had more number of

marketable tubers per plant, number of

unmarketable tubers per plant and total number

of tubers per plant in 2006. However, the trend

was not sustained in 2007. Manure source,

especially poultry manure significantly (P<0.05)

increased all the assessed yield parameters

relative to no manure application in both

cropping seasons. TIS-87/0087 variety produced

more marketable and total tuber yield,

irrespective of manure source and rate in 2006

and 2007 cropping seasons (Table 5). All

manure sources significantly (P<0.05)

influenced total tuber yield and yield

components in comparison with no application

but there were differences among the manure

sources in the two years. Application of manure

increased the marketable, unmarketable and total

tuber yields. Among the manure sources, poultry

manure performed better than pig and goat

manures in marketable and total yields. Tuber

yield per hectare and yield components

increased with increase in manure rate from zero

to 20 t/ha and thereafter declined with further

increase in manure application, an indication that

it is the optimum amount required by the crop.

Response of Sweet Potato Varieties to Organic Manure

42

Table 1: Some mechanical and chemical characteristics of the soils (0 - 20 cm) of the experimental sites Year Soil mechanical characteristics Soil chemical characteristics

Sand (%) Clay

(%)

Silt (%) pH

(H 0)

Org.M

(%

Org.C

(%)

Total N

(%)

Available P

(mg/kg)

Exchangeable bases CE

C Ca K Mg Na

2006 79.10 15.90 14.00 4.80 2.73 1.59 0.08 18.50 2.40 0.11 1.20 0.078 5.71

2007 74.60 15.70 9.70 4.64 2.75 1.59 0.13 26.35 2.80 0.27 1.60 0.33 7.08

Source: Soil Science Laboratory, Michael Okpara University of Agriculture, Umudike, Abia State.

Table 2: Chemical analysis of organic manure used in the study Mineral elements

(%)

Organic manure source

Pig Poultry Goat

2006 2007 2006 2007 2006 2007

Nitrogen 1.40 1.33 2.26 2.47 1.33 1.40

Phosphorus 2.01 2.13 2.11 2.32 0.084 0.60

Potassium 0.58 1.58 0.25 0.675 0.39 1.20 Calcium 1.303 3.112 6.81 15.46 1.403 2.71

Magnesium 1.76 0.91 2.07 3.95 0.49 1.16

Sodium 0.108 0.625 0.198 0.275 0.085 0.275

Table 3: Number of leaves per plant, vine length, and number of branches per plant of

sweet potato as influenced by variety, manure source and rate at 8 weeks after

planting (WAP) in 2006 and 2007 cropping seasons Sweet potato variety

Organic manure source

Number of leaves/plant Vine length (cm) Number of branches/plant

2006 2007 2006 2007 2006 2007

Ex-Igbariam 112.00 119.60 64.30 72.30 10.51 7.44

TIS-87/0087 137.30 156.90 30.00 70.20 12.47 10.57

LSD0.05 19.43 26.66 14. 64 ns ns 1.12 Manure source

No manure 92.80 75.20 31.80 51.80 7.10 4.55

Pig 142.90 129.00 49.70 71.20 13.65 8.89

Poultry 113.30 169.50 40.00 77.40 9.98 10.41

Goat 117.70 116.20 57.80 65.10 10.84 7.72

LSD0.05 23.68 50.20 18. 40 15.24 2.85 3.28 Manure rate (t/ha)

0 92.80 75.20 31.80 51.80 7.10 4.55

10 119.30 111.70 44.30 64.10 10.61 8.46

20 118.70 140.10 47.90 76.20 11.18 8.26

30 135.90 162.90 49.40 73.50 12.68 10.28 LSD0.05 24.48 50.89 ns 15.22 2.95 3.34

Table 4: Number of marketable, unmarketable and total number of tubers per plant of sweet

potato as influenced by variety, manure source and rate in 2006 and 2007 cropping

seasons. Treatment Number of marketable

tubers/plant Number of unmarketable

tubers/plant Total number of tubers/plant

2006 2007 2006 2007 2006 2007

Sweet potato variety Ex-Igbariam 2.28 2.02 1.22 1.23 3.50 3.25

TIS-87/0087 2.06 2.16 0.69 1.85 2.73 3.51

LSD0.05 0.20 Ns 0.31 Ns 0.34 Ns

Manure source

No manure 1.11 1.22 0.88 1.42 1.99 2.64

Pig 2.16 2.11 1.00. 1.15 3.16 3.26

Poultry 2.16 2.31 0.88 1.39 3.04 3.70

Goat 2.15 1.86 1.00 1.33 3.15 3.18

LSD0.05 0.31 0.54 0.35 0.58 0.46 0.73 Manure rate (t/ha)

0 1.11 1.22 0.88 1.42 1.99 2.64

10 1.90 1.91 0.97 1.01 2.87 2.92

20 2.14 2.29 1.05 1.26 3.18 3.55

30 2.44 2.08 0.85 1.59 3.29 3.67

LSD0.05 0.28 0.55 ns 0.55 0.45 0.70

Muoneke C.O., Mbah, E .U Udom. E. F.

43

Sweet potato variety x manure source x manure

rate interaction on total number of tubers per

plant showed that TIS-87/0087 variety produced

higher total number of tubers per plant,

especially at 20 t/ha rate than Ex-Igbariam

variety (Table 6). The application of pig manure

at 10 t/ha gave the lowest total number of tubers

per plant for Ex-Igbariam (2.53), while for TIS-

87/0087, goat manure gave the least total

number of tubers per plant (2.40). The

interaction of variety x manure source x manure

rate on marketable tuber yield showed that TIS-

87/0087 produced the highest marketable tuber

yield with the application of poultry manure at

20 t/ha (Table 7) in both varieties. Furthermore,

marketable tuber yield increased with increase in

manure rate, especially in TIS-87/0087 variety,

irrespective of manure source up to the 20 t/ha

and thereafter declined with further increment in

manure rate.

In Table 8, interaction of variety x manure rate

on total tuber yield showed that TIS-87/0087

produced higher tuber yield than Ex-Igbariam in

all the manure rates. However, total tuber yield

of the two varieties in the interaction declined

with increase in manure rate beyond 20 t/ha.

Interaction of sweet potato variety x manure

source on total fresh tuber yield showed that

TIS-87/0087 produced higher tuber yield

compared to Ex-Igbariam (Table 9). Among the

manure sources, poultry manure gave the highest

tuber yield with TIS-87/0087 variety whereas

goat manure applied to Ex-Igbariam gave the

lowest yield.

Table 5: Marketable, unmarketable and total tuber yield of sweet potato as influenced by variety,

manure source and rate in 2006 and 2007 cropping seasons

Treatment Marketable tuber yield (t/ha)

Unmarketable tuber yield (t/ha)

Total tuber yield (t/ha)

2006 2007 2006 2007 2006 2007

Sweet potato variety

Ex-Igbariam 16.20 16.02 2.19 2.08 18.39 18.83

TIS-87/0087 22.32 21.69 1.97 2.82 24.29 24.51

LSD0.05 0.93 1.13 ns ns 0.85 1.03

Manure source

No manure 8.84 7.79 1.61 1.47 10.45 9.25

Pig 18.07 15.32 2.28 2.50 20.34 17.81

Poultry 21.37 25.67 2.12 3.59 23.49 29.26 Goat 18.35 15.59 1.83 2.34 20.19 17.93

LSD0.05 2.84 5.37 0.54 0.86 2.74 5.55

Manure rate (t/ha)

0 8.84 7.79 1.61 1.47 10.45 9.25

10 15.93 17.59 2.13 2.54 18.05 20.12

20 22.11 24.17 2.27 3.23 24.37 27.40 30 19..76 14.82 1.84 2.66 21.60 17.48

LSD0.05 2.55 5.98 6.53 0.97 2.43 6.39

Table 6: Interaction of variety x manure source x manure rate on total

number of fresh tubers per plant of sweet potato in 2007

cropping season Sweet potato variety Manure

source

Manure rate (t/ha)

10 20 30

Ex-Igbariam Pig 2.53 3.10 2.92

Poultry 3.39 3.63 3.86

Goat 3.42 3.72 3.67 TIS-87/0087 Pig 3.10 3.25 4.66

Poultry 2.67 4.60 4.03

Goat 2.40 4.00 2.88

LSD0.05 0.93


44

DISCUSSION The variations shown among growth parameters

such as number of leaves per plant and vine

length between the two sweet potato varieties

had been reported in the work of Onunka et al.

(2003), who attributed such variations to varietal

differences. The production of more leaves per

plant in the pig and poultry manure treated plots

could be attributed to the nitrogen contents of

these two manures. Amujoyegbe et al. (2007)

surmised that nitrogen is an integral part of

chlorophyll and protein and that during the

vegetative growth phase of plants, the nitrogen

nutrients in the plants to a large extent control

the growth rate of the plants as meristmatic

tissues have very active protein metabolism.

Hence, high nitrogen promotes leaf development

in sweet potato, during the leaf forming phase

and less during tuberization. Also, Wijewardena

and Yapa (1999) in their study on sweet potato

response to manure application reported that

cultivars and climatic conditions could influence

the effect of nitrogen application by inducing

vine growth at the expense of tuber bulking.

Increase in number of leaves per plant and vine

length as a result of application of manure,

irrespective of the different rates was obtained in

this study in both years. The organic manures

contained and released considerable amount of

nutrient, especially nitrogen for plant use and it

is essential for chlorophyll and protoplasm

formation (Amujoyegbe et al., 2007).

The production of marketable, unmarketable and

total number of tubers per plant was higher in

TIS-87/0087 than Ex-Igbariam and this could be

due to the genetic variation between the two

varieties. The findings conformed to those of

Hay and Walker (1992) on crop yield and yield

components in which they reported that genetic

variation existed between and within species in

response to soil, nutrients and climate condition.

The plots treated with organic manure produced

more total number of tubers per plant compared

to the control (no manure). This could be

accounted for by the amount of nutrients

contained and released by the particular manure

applied to the plant. This observation

corroborated the reports of Van Lauwe (2000)

and Asaduzzaman et al. (2010) who concluded

that organic manure improved fertility and water

holding capacity of the soil, stored plant

nutrients, acted as a buffering agent against

undesirable nutrient fluctuations, served as a

major contributor of cation exchange capacity in

soils as well as stimulated microbial activities by

increasing temperature, which invariably

improves agro-physical properties of the soils.

In the two cropping seasons, the variations

indicated by the total number of tubers per plant

agreed with the findings of Onunka et al. (2003)

showing that sweet potato varieties respond to

different nutrient rates. Tuber yields and yield

Table 7: Interaction of variety x manure source x manure rate on marketable tuber yield

of sweet potato in 2006 cropping season Sweet potato variety Manure source Manure rate (t/ha)

10 20 30

Ex-Igbariam Pig 11.40 15.53 18.57

Poultry 14.51 22.87 17.44

Goat 13.92 14.83 16.73

TIS-87/0087

Pig

16.77

26.07

20.07

Poultry 20.67 29.33 23.40 Goat 18.29 24.00 22.33

LSD0.05 2.80

Table 8: Interaction of variety x manure rate on total tuber yield of sweet potato

in 2006 cropping season

Sweet potato variety Manure rate (t/ha)

10 20 30

Ex-Igbariam 15.66 20.16 19.36

TIS-87/0087 20.45 28.59 23.83

LSD0.05 = 0.73

Table 9: Interaction of variety x manure source on total tuber yield of sweet

potato in 2007 cropping season Sweet potato variety Manure source

Pig Poultry Goat

Ex-Igbariam 15.30 27.39 13.79

TIS-87/0087 20.32 31.13 22.08

LSD0.05 = 1.79


45

components of sweet potato were higher in TIS-

87/0087 than in Ex-Igbariam, an indicating

factor that TIS-87/0087 variety was a higher

yielding variety. The results obtained were

consistent with studies by Larbo, et al. (2007) on

different cultivars of sweet potato in which it

was that cultivars such as TIS-87/0087, TIS-

8470, TIS-8164 and TIS-82/0070, TIS-

82/0070.OP.120 had high potentials for fodder

and tuber production in the humid forest and

savannah zones of West Africa. Okorie and

Okpala (2000) reported that TIS-87/0087

showed superior growth and yield performance

compared with other cultivars under field

studies.

Poultry manure plots produced the highest total

tuber yield and yield components of sweet potato

in the two years of study confirming similar

results by Wijewardena and Yapa (1999) on the

effect of combined use of animal manure and

chemical fertilizer on potato and vegetable

production in Sri Lanka. Also, poultry manure

proved to be superior both in content and release

of its nutrients to plants relative to the other

types of animal manure, especially pig and cow

dung because of the type of feeds the animals

were exposed to (Agbede and Adekiya, 2011).

Sharplay and Meyer (2000) reported that most of

the phosphorus in different types of animal

manure was in inorganic form, which could be

readily available to crops. The observation that

total fresh tuber yield and yield components of

sweet potato increased with increase in manure

rate up to 20 t/ha, and declined thereafter with

further application in both cropping seasons

agreed with Cooper et al. (1984).

The interaction effects on growth and yield of

sweet potato in the study showed that organic

manure positively influenced the performance of

the crop. Similarly, Wijewardena (2000) and

Nedunchezhiyan, et al. (2010) concluded that

animal manure could boost the production of

crops as well as improve soil fertility. The

interaction of variety x manure source x manure

rate on yield and yield components of sweet

potato in both cropping seasons showed that

poultry manure at 20 t/ha applied to TIS-

87/0087 gave the highest amount of total

number of tubers per plant and fresh tuber yield

and yield components.

In the two cropping seasons, TIS-87/0087

showed better performance and gave higher

fresh tuber yield than Ex-Igbariam, which agreed

with the findings of Okorie and Okpala (2000).

Also, Onunka et al. (2004) stated that sweet

potato variety TIS-87/0087 could give high tuber

yield within the range 12 - 20 t/ha in different

ecological zones of Nigeria. The yield

superiority exhibited by TIS-87/0087 may not

only be due to its genetic constitution but also

agronomic influence from the treatments

studied.

CONCLUSION The results obtained showed that irrespective of

variety, sweet potato growth and yield

parameters were influenced by organic manure

source and rate. Poultry manure gave the highest

fresh tuber yield, especially at 20 t/ha compared

with the other sources and rates studied. TIS-

87/0087 variety gave superior tuber yield than

Ex-Igbariam variety. Therefore, for optimum

growth and yield of sweet potato, poultry

manure applied at the rate of 20 t/ha is

recommended.

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47


EFFECT OF SETT WEIGHT ON THE GROWTH AND YIELD OF SOME

COCOYAM SPECIES IN UYO, SOUTHEASTERN NIGERIA

Ndaeyo1 N. U.

*, Udeme K. Udoh

1, A.O. Ikeh

1, Edna A. Akpan

2, Eno I. Udoh

1 and O. R. Akata

3

1Department of Crop Science, University of Uyo, Uyo, Nigeria 2Department of Crop Science and Horticulture, Akwa Ibom State University, Obio Akpa Campus,

Nigeria. 3Department of Crop Science, University of Calabar, Calabar, Nigeria.

*Corresponding author’s email: [email protected]

ABSTRACT The experiment was conducted at the University of Uyo Teaching and Research Farm, Use-Offot during the

early cropping seasons of 2010 and 2011 to determine the effects of sett weight on the growth and yield of some

cocoyam species. A randomized complete block design with a split plot arrangement, replicated three times was

used. Cocoyam species viz: NXs 001 (known locally as Afia ikpong), NXs 003 (known locally as Asimeka) and

NCe 003 (known locally as Ikpong Nwa Ekpo) constituted the main treatments while sett weight (90, 180 and

270g) constituted the sub-treatments. Results showed no significant difference in establishment percentage

among the cocoyam species and sett weight. The NCe 003 species had the widest leaf area and the highest

number of leaves per plant, while NXs 003 produced the longest petiole in both years. The number of cormels

per plant (6.83 and 5.68 in 2010 and 2011, respectively) was significantly higher (P<0.05) in NCe 003 than in

other species. NXs 001 produced the highest corm yield (8.02 and 7. 92 t/ha) while NCe 003 had the highest

cormel yield (11.56 and 11.03 t/ha). Similarly, NXs 003 (Ikpong Nwa Ekpo) produced the highest total (corm +

cormel) yield which was 19.41 and 18. 56 t/ha in 2010 and 2011, respectively. The total yield from the NXs 003

was higher than those of other species by 6-36 and 5 -34% in 2010 and 2011, respectively. In both years, 270 g

sett weight was superior to other sett weights in all growth and yield parameters, irrespective of species, while

the least was from the 90g sett weight. Therefore, planting of NXs 003 and the use of 270g sett weight would be

more rewarding to cocoyam farmers in Uyo agro-ecology.

Keywords: Sett weight, growth, yield, cocoyam species, Uyo.

INTRODUCTION Cocoyam refers to two members of the Araceae

Family that are staple foods for many countries in

Africa, Asia and the Pacific (Agueguia et al., 1992).

Two genera Xanthosoma (tannia or new cocoyam)

and Colocasia (taro or old cocoyam) are important

and extensively cultivated in Nigeria. Species widely

grown in Nigeria are Xanthosoma sagttifolium (L.)

Schott and Colocasia esculenta (L.) Schott (Ibe and

Iwueke, 984). Cocoyam corms and cormels are

commonly boiled and consumed with palm oil or

pounded to obtain Usung Ikpong and eaten with a

native soup called Afia efre ikpong, stew or

vegetables. The corms and cormels can also be

roasted, fried and eaten, while the young leaves are

used in wrapping a native porridge (Otoh ebighe) that

is highly cherished in Akwa Ibom State (Ndaeyo et

al., 2003; Udoh et al.,2005). Colocasia leaves are

excellent sources of folic acid, vitamin C, riboflavin

and vitamin A and a good source of minerals

especially calcium and phosphorus (Akomas et

al.,1987). The corm and cormel can also be included

in a

number of manufactured foods such as noodles,

biscuits and bread as it has 20-25% starch, 1.5-3%

protein and significant amounts of vitamin C,

thiamin, riboflavin, niacin and carotene (Moi et al.,

1979). The starch grains of taro are small in size and

are more easily digested than those of yam, cassava

and sweet potato. The protein content of taro and

tannia are also higher than that of other tropical tuber

crops though it contains some irritating substances.

Despite its importance, cocoyam production is beset

with a lot of challenges some of which are inadequate

improved varieties, appropriate planting sett weight

(size) for the different species under different agro-

ecologies, pest and disease attack under field and

storage conditions, poor response to mini-sett

technique, and soil fertility problem. Currently, not

much work had been done to determine the

appropriate sett weight(s) for production of cocoyam

in different agro-ecologies in Nigeria. However,

Chukwu et al. (2009) utilized a seed rate of about

0.35 - 0.4t/ha compared to 1.0-2.0 t/ha currently in

use in cocoyam multiplication and found that corm +

cormel yield ranged from 7.34-15.5t/ha. Similarly,

sett multiplication ratio at harvest ranged from 19.0-

39.0, while available yield figures ranged from 89.5-

94.7% indicating beneficial effect of using larger sett

weight for planting compared to the small sett weight.

48

Ekpe et al. (2005) found that water yam establishment

and yield from sett size of 26-35g and 35-50g were

comparable to those of miniset (50g). Addai and

Scott (2011) found that as the size of the planted

bulbs increased, parameters measured also increased

in proportion to the size of the planted bulb up to 50g

weight. They attributed this to the extra carbohydrate

reserves possessed by the large bulbs (> 50 g bulb

sizes) which helped them grow faster to complete

their life cycle than those produced from medium and

small bulbs while senescence occurred earlier in the

former than the latter. In yam, Lyonga et al. (1973)

stated that yield was almost directly proportional to

the size of seed tuber or sett planted as it affected the

sprouting, vigour and yield. Onwueme (1978) stated

that sett size determines the rate of sprouting in edible

tubers while Lyonga et al. (1973) observed that large

sett produced more sprouting loci and more sprouts

per sett than small sett.

Demo et al. (2001) observed increase in tuber yield

with increase in seed-tuber size and concluded that

smaller tubers were more efficient in converting their

unit weight into tuber yield than larger seed-tubers. A

study by Udom et al. (2012) with potato as test crop

revealed that tuber size had significant effect on tuber

yield with large size category out-yielding small- and

medium-size categories by 11 and 14%, respectively.

Against this background, a study was conducted to

determine the effects of sett weight on growth and

yield performances of cocoyam in Uyo, a forest

agroecology in Nigeria.

MATERIALS AND METHODS Experimental site

The study was conducted at University of Uyo

Teaching and Research Farm, Use-Offot, in 2010 and

2011 (between the months of April and December)

planting seasons. Uyo lies between latitudes 40301 and

5027

1 and longitudes 7

050

1 E and 80

020

1E (UCCDA,

1988) at an altitude of 38.1m above sea level, and

receives about 2500 mm rainfall annually. The

rainfall pattern is bimodal, with long (March - July)

and short (September – November) rainy seasons

usually during the month of August that is

traditionally referred to as “August break” (Peters et

al., 1989). The mean relative humidity separated by a

short dry spell of uncertain length is 78%,

atmospheric temperature 30ºC and day length of 12

hrs. Soil analysis of the site revealed the following

physico-chemical characteristics: pH in water of 4.52,

1.66% organic matter, 0.08 % total nitrogen, 213.66

mg/kg available P while exchangeable bases values

were 0.12, 2.98 and 1.68 cmolkg-1 for K, Ca and Mg,

respectively. The soil particle distribution was: sand

90.4 %, silt 2.2% and clay 6.4%. .

Experimental design, treatment and cultural

details The experiment was laid out in a randomized

complete block design with a split plot arrangement

and replicated three times. The entire experimental

site measured 40m x 18m while each replicate was

40m x 3m. The main and sub-plots measured 9m x

3m and 3m x 3m, respcetively. Each replicate was

separated from the other by a 2m path while each

main and sub plots were separated from the other by

1m path. The main treatments were three cocoyam

varieties viz; NXs 001(known locally as Afia Ikpong)

and NXs 003 ( known locally as Asimeka), both are of

Xanthosoma sagittifolium species; and NCe 003

(known locally as Ikpong nwa ekpo) is of Colocasia

esculenta species, while the different sett weights (90,

180 and 270g) constituted the sub treatments. The

experimental site was manually cleared with machete,

raked, marked out and mounds constructed at 1m x

1m spacing. One sett was planted per mound for the

different weights at a spacing of 1m x 1 m. Weeding

was done manually at 1, 3, and 5 months after

planting (MAP) by hand hoeing.

Data Collection and analysis: Five plants were

randomly selected and tagged per plot for data

collection. Growth and yield parameters measured

were establishment percentage, number of leaves, leaf

area by Agueguia (1993) method, petiole length,

number of cormels,weights of corms and cormels and

total (corms + cormels) yield. The data collected were

subjected to analysis of variance while the least

significant difference was used to compare means

where there was significant difference at P<0.05.

RESULTS Cocoyam Establishment percentage and Number

of leaves per plant

There was no significant difference in establishment

percentage among the sett weights and species in both

years (Tables 1 and 2). The NCe 003 (Ikpong nwa

ekpo) cultivar had the highest number of leaves per

plant followed by NXe 001 (Afia Ikpong) while the

least was from NXs 003 (Asimeka) in both years.

NCe 003 produced 39-46, 37-38, 31 - 39 and 29-41%

more number of leaves than other cultivars in 2010 at

1, 2, 3 and 4 months after planting (MAP),

respectively. Similarly, NCe 003 produced 40- 47 ,36-

37,28- 42,and 31-38% more number of leaves than

other cultivars in 2011 . At 5 MAP, NXs 001

produced 15-35 and 13 -35 % more number of leaves

than other cultivars in 2010 and 2011, respectively.

Sett weight showed no significant difference at all the

sampling dates. Sett weight of 270g produced the

highest number of leaves in both years followed

by180g sett weight. The 90g sett weight produced

least number of leaves in all the cultivars. The

interaction effect between cultivars and varieties

indicated no significance.

Effect of Sett Weight on The Growth and Yield of Some Cocoyam Species

49

Cocoyam Petiole length and leaf area Among the cocoyam cultivars, NXs 003 produced the

longest petiole length of 8.73, 15.46, 18.03, 23.69 and

26.38cm in 2010 and 8.75, 15.60, 18.07, 29.79 and

26.72 in 2011 at 1, 2, 3, 4 and 5 MAP, respectively

followed by NCe 003 (Tables 3 and 4). Petioles from

NXs 003 were longer than those of the other cultivars

in both 2010 and 2011. The effect of sett weight on

petiole length was significant at 1, 2, 3, 4 and 5 MAP,

in both years with increase in sett weight resulting in

increase in petiole length in all the cultivars. The sett

weight of 270g produced the longest petiole, followed

by the 180g sett. Interaction effect between cocoyam

cultivars and sett weight was not significant.

Cocoyam leaf area was significantly influenced by

cultivars at all sampling intervals (Tables 3 and 4).

Among the cocoyam cultivars, NXs 003 produced the

widest leaf area in both 2010 and 2011, followed by

NCe 003. Leaf area from NXs 003 were wider than

those of other cultivars by 15-47, 15-43, 0-26, 23-29

and 9-30% in 2010 and 13- 50 ,12-44 ,3-26 23- 27

and 9-21% in 2011. Leaf area increased significantly

with increasing sett weight at 2, 3, 4 and 5 MAP in

both years and in all the cultivars. The 270g sett

weight produced the widest leaf area, followed by

180g. Interaction effect between cocoyam cultivars

and sett weight was significant at 4 and 5 MAP.

Cocoyam Yield and Yield Components

Cultivars had no significant effect on the number of

cormels per plant in both years (Tables 7 and 8).

However, NCe 003 cultivar produced the highest

number of cormels (6.83 and 7.11 in 2010 and 2011,

respectively) followed by NXs 001 (4.99 and 5.04).

The 270g sett weight produced the highest number of

cormels in both years, irrespective of cultivars,

followed by 180g while the least was obtained with

the 90g sett weight. Interaction effect between

cocoyam cultivar and sett weight was not significant

in the number of cormels per plant. Corm yield

differed significantly among the cultivars with NXs

003 having the highest corm yield (8.02 and 8.06

t/ha in 2010 and 2011, respectively), followed by NCe

003 (6.73 and 7.11 t/ha) while NXs 001 had the least

(4.71 and 4.74 t/ha). Cultivar NXs 003 produced 16-

41% more corms than other cultivars in both years.

Corm yield also differed significantly among sett

weights with increase in sett weight resulting in

increase in corm yield. The highest corm yield was

obtained from the 270g sett weight planting material

irrespective of cultivar, while the least yield was

obtained with 90g. The interaction effects between

cocoyam cultivars and sett weights were significant

for corm yield which increased as the sett weight

increased.

Table 1: Establishment Percentage And Number of Leaves Per Plant as Influenced by Sett

Weight and Cocoyam Species in 2010 Season Cocoyam species Sett weight (g) Establishment % No. of Leaves per plant

Months after planting

1 2 3 4 5

Nxs 001 (Afia ikpong) 90 95.00 2.88 4.78 6.87 7.36 5.07 180 100.00 3.28 4.78 7.13 8.40 6.20

270 100.00 3.67 6.53 8.80 8.47 9.27

Mean 98.33 3.28 5.36 7.60 8.08 6.85

Nxs 003 (Asimeka) 90 100.00 3.23 4.63 5.60 6.37 5.23

180 100.00 2.33 5.13 6.87 7.73 5.37

270 100.00 2.94 5.90 6.30 7.30 6.77

Mean 100.00 2.94 5.22 6.26 7.13 5.79 Nce 003 (Ikpong Nwa ekpo) 90 85.50 4.59 8.27 8.93 9.20 4.33

180 90.00 5.67 8.33 11.47 12.87 4.43

270 90.00 5.92 8.80 11.52 12.93 4.61 Mean 88.33 5.39 8.47 10.64 11.67 4.48

LSD (p<0.05)

Species (A) NS* NS 2.32 3.40 2.37 1.06 sett weight B) NS NS NS NS NS NS

Interaction (A x B) NS NS NS NS NS NS

*Ns = Not significant

Ndaeyo N. U. *, Udeme K. Udoh, A.O. Ikeh, Edna A. Akpan, Eno I. Udoh and O. R. Akata3

50

Table 2: Establishment percentage and Number of Leaves per plant as influenced by sett weight

and cocoyam species in 2011 season Cocoyam species Sett weight (g) Establishment % No of Leaves per plant

Months after planting

1 2 3 4 5

Nxs 001 (Afia ikpong) 90 98 .00 2.84 4.75 6.89 7.35 5.06 180 100.00 3.25 4.96 7.23 8.45 6.25

270 100.00 3.65 6.55 8.85 8.49 9.30

Mean 99.33 3.25 5.42 7.66 8.10 6.87

Nxs 003 (Asimeka) 90 100.00 3.25 4.69 5.62 6.39 5.62

180 100.00 2.38 5.43 6.48 7.72 5.40

270 100.00 2.98 5.94 6.40 7.80 6.97

Mean 100.00 2.87 5.35 6.17 7.30 6.00

Nce 003 (Ikpong Nwa ekpo)

90 90.50 4.63 8.25 8.98 9.25 4.30

180 95.21 5.69 8.38 11.52 12.88 4.45

270 95.00 5.97 8.85 11.60 12.95 4.70 Mean 93.57 5.43 8.49 10.70 11.69 4.48

LSD (p<0.05)

Species (A) NS* NS 1.51 2.38 NS 1.04 sett weight (B) NS NS NS NS NS NS

Interaction (A x B) NS NS NS NS NS NS

*NS = Not significant

Table 3: Cocoyam Petiole Length (cm) as influenced by sett weight and Species in 2010 season Months after Sprouting

Cocoyam species Sett weight(g) 1 2 3 4 5

Nxs 001(Afia ikpong) 90 4.01 8.17 9.77 9.82 12.20

180 4.33 8.96 10.17 18.87 24.20

270 5.61 11.40 20.30 22.04 25.33

Mean 4.65 8.84 13.41 16.91 20.58

Nxs 003 (Asimeka) 90 7.33 15.63 17.63 23.17 22.00

180 8.43 14.53 16.57 23.40 26.60 270 10.43 17.47 19.90 24.50 30.53

Mean 8.73 15.46 18.03 23.69 26.38

Nce 003 (Ikpong Nwa ekpo) 90 6.35 12.37 14.70 16.30 22.20 180 7.51 12.67 16.83 19.78 24.80

270 8.33 15.20 22.43 19.78 25.43

Mean 7.40 13.08 17.99 18.16 24.14

LSD (P<0.05)

Species (A) 1.33 2.16 2.04 3.87 NS

Sett weight (B) NS 1.47 3.40 2.47 3.04

Interaction (A x B) NS NS NS NS NS


Table 4: Cocoyam Petiole Length (cm) as influenced by sett weight and Species in 2011 season Months after Sprouting


Nxs 001(Afia ikpong) 90 3.91 8.15 9.76 9.84 12.24

180 4.30 8.97 10.26 18.85 24.26

270 5.98 9.43 20.34 23.01 25.38 Mean 4.73 8.85 13.45 17.23 20.62

Nxs 003(Asimeka) 90 7.36 15.44 17.60 23.27 22.60

180 8.45 14.90 16.61 23.43 26.71

270 10.43 16.44 20.00 24.85 30.85

Mean 8.75 18.07 18.07 23.79 26.72 Nce 003 (Ikpong Ewa ekpo) 90 6.35 12.40 14.72 16.27 22.24

180 7.51 12.70 16.98 18.51 24.89

270 8.33 14.22 22.55 18.71 25.69 Mean 7.40 13.08 18.08 17..83 24.27

LSD (P<0.05)

Species (A) 1.09 2.14 3.62 2.36 NS

Sett weight (B) NS 1.47 3.40 NS 2. 44

Interaction (A x B) NS NS NS NS NS



51

Table 5: Cocoyam leaf area (cm2) as influenced by sett weight and Species in 2010 season

Months after Sprouting


Nxs 001(Afia ikpong) 90 4.01 8.17 9.77 9.82 12.20

180 4.33 8.96 10.17 18.87 24.20

270 5.61 9.40 20.30 22.04 25.33

Mean 4.65 8.84 13.41 16.91 20.58

Nxs 003 (Asimeka) 90 7.33 15.63 17.63 23.17 22.00

180 8.43 14.53 16.57 23.40 26.60

270 10.43 16.47 19.90 24.50 30.53 Mean 8.73 15.46 18.03 23.69 26.38

Nce 003 (Ikpong Nwa ekpo) 90 6.35 12.37 14.70 16.30 22.20

180 7.51 12.67 16.83 18.40 24.80 270 8.33 14.20 22.43 19.78 25.43

Mean 7.40 13.08 17.99 18.16 24.14

LSD (P<0.05)

Species (A) 1.12 2.49 4.01 3.87 2.13

Sett weight (B) NS 1.47 3.40 3.32 3.04

Interaction (A x B) NS NS NS 0.61 2.11


Table 6: Cocoyam leaf area (cm2) as influenced by sett weight and Species in 2011 season

Months after Sprouting


Nxs 001 (Afia ikpong) 90 4.12 8.10 9.67 9.84 12.26 180 4.38 8.94 10.22 18.97 24.19

270 5.71 9.44 20.50 22.03 25.80

Mean 4.74 8.83 13.46 17.28 20.75

Nxs 003(Asimeka) 90 7.37 15.71 17.58 23.47 22.69

180 8.53 14.74 16.71 23.50 26.08

270 10.93 16.67 19.96 24.11 30.17

Mean 8.94 15.71 18.08 23.69 26.31

Nce 003 (Ikpong Nwa ekpo) 90 6.95 12.87 14.73 16.36 22.01

180 7.61 13.98 16.89 18.47 24.19 270 8.83 14.50 22.48 19.82 25.73

Mean 7.80 13.78 18.03 18.22 23.98

LSD (P<0.05)

Species (A) 1.25 3.19 3.01 3.87 2.32

Sett weight (B) NS* 1.47 3.23 2.14 3.04

Interaction (A x B) NS NS NS 0.57 .0.62


Cormel yield indicated significant difference among

cultivars and sett weights. Cultivar NCe 003 produced

the highest cormel yield of 11.56 and 11.60 t/ha in

2010 and 2011, respectively followed by NXs 003

(11.39 and 11.44 t/ha). The least cormel yield was

obtained with NXs 001 (7.80 and 7.87 t/ha). The NCe

003 produced 2-33% and 1- 32 % more cormel yield

than other cultivars. Cormel yield also differed

significantly among sett weights. The sett weight of

270g produced the highest cormel yield, irrespective

of cultivar, followed by 180g. Cultivar NXs 003

produced the highest total yield of19.41 and 19.50

t/ha in 2010 and 2011, respectively, followed by NCe

003 (18.29 and 18. 43 t/ha). The total cocoyam yield

from NXs 003 (Asimeka) was 6-36% and 5 – 35 %

more than those of other cultivars in 2010 and 2011,

respectively. Interaction effect between cocoyam

cultivars and sett weight for total yield was

significant. Total yield increased with increasing sett

weight across the cultivars.

Ndaeyo N. U.

*, Udeme K. Udoh, A.O. Ikeh, Edna A. Akpan, Eno I. Udoh and O. R. Akata

3

52

Table 7: Yield and Yield Components of Cocoyam as influenced sett weight and species in

2010 season Cocoyam

species

Sett weight

(g)

Number of

cormels

Corm

Yield (t/ha)

Cormel

Yield (t/ha)

Total yield

(t/ha)

Nxs 001(Afia Ikpong) 90 2.80 3.13 4.31 7.44

180 5.80 5.17 8.75 13.92 270 6.37 5.83 10.41 16.24

Mean 4.99 4.71 7.80 12.51

Nxs 003(Asimeka) 90 2.50 4.31 5.31 9.64

180 4.60 9.33 13.33 22.66

270 6.63 10.41 15.52 25.93

Mean 4.58 8.02 11.39 19.41

Nce 003(Ikpong Nwa ekpo) 90 4.30 4.22 6.31 10.53

180 8.07 7.78 12.11 19.89

270 8.13 8.20 16.25 24.45

Mean 6.83 6.73 11.56 18.29

LSD (p≤ 0.05)

Species (A) NS 1.75 2.25 2.62

Sett weight (B) 1.86 2.31 2.11 4.07

Interaction (A x B) NS 0.93 NS 1.13


Table 8: Yield and Yield Components of Cocoyam as influenced sett weight and species in

2011 season Cocoyam species Sett weight (g)

Number of cormels Corm Yield (t/ha) Cormel Yield (t/ha) Total yield (t/ha)

Nxs 001 (Afia Ikpong) 90 2.84 3.15 4.34 7.49

180 5.89 5.20 8.79 13.99

270 6.40 5.87 10.47 16.34 Mean 5.04 4.74 7.87 12.61

Nxs 003 (Asimeka) 90 2.55 4.34 5.35 9.69

180 4.69 9.35 13.38 22.73

270 6.69 10.49 15.59 28.08

Mean 4.64 8.06 11.44 19.50

Nce 003 (Ikpong Nwaekpo) 90 4.35 4.26 6.37 10.75

180 8.35 7.80 12.15 19.99

270 8.40 8.26 16.29 24.56 Mean 7.11 6.77 11.60 18.43

LSD (p≤ 0.05)

Species (A) NS 1.72 2.23 2.62 Sett weight (B) 1.78 2.28 2.04 3.86

Interaction (A x B) NS 0.96 NS 1.23


DISCUSSION The observed differences in growth, yield and yield

components among the cocoyam cultivars could be

attributed to the inherent varietal characteristics and

variation in sett size. Ugwu (1990) reported that

considerable variations exist both within and between

varieties for most characters, and that the coefficients

of variation for phenotypes and genotypes were

largest for root yield, especially large for roots per

plant and root size, and moderate for harvest index

and plant height at harvest. In yam, vine length,

number of leaves, and leaf area were found to be

more and wider in bulbils of 26-35 and 36-50g weight

than in bulbils of 10-25g (Ekpe et al.,2005; Ikeorgu et

al., 2003). These authors concluded that large setts

gave more vigorous plants than small setts, even

where the large and small setts sprouted and emerged

at the same time. The water yam raised from bulbils

of 26-50g produced bigger tubers than those raised

from bulbils of 10-25, though bulbils of 10-25g gave

a higher multiplication rate (Ekpe et al., 2005).

Nweke et al. (1991) also showed that varietal

differences in conjunction with other factors such as

location and cultural practices affected yield of root

and tuber crops. Similarly, IITA (1990) reported that

the number of roots which eventually form tuber

depends on several factors including genotypes,

assimilate supply, photoperiod and temperature.

Demo et al. (2001) attributed the low tuber yield of

plants raised from smaller seed tubers to the lower

initial food reserve which would subsequently slow

down developmental and photosynthetic capacity.

Results of the present study agree with that of Okoli

et al.(1999) that larger seed yam sprouted earlier and

had higher percentage sprouting and survival till

harvest making for higher total yield; whereas smaller

seeds, had higher multiplication ratios and lower

intra-plot variability. Findings by Ikeorgu and

Igbokwe (1999) was corroborated by this study as

they reported that growing sett weight below 25g

produced mostly minitubers and seed yams not


53

exceeding 200g, whereas 26-50g sett weight could

produce about 45% seed yams of 200-500g, while

using sett weight of 51-75g, produced over 86% seed

yams of 200-1000g.

CONCLUSION The use of appropriate sett weights for planting has

been demonstrated to be of good benefit in cocoyam

production. This study has shown that NXs 003

(Asimeka) cultivar produced the highest total yield of

corm and cormel. The use of 270 sett weight as

planting material outperformed the use of other sett

weights and as such was recommended for adoption

by the cocoyam farmers in the area.

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E. U. (1987). Food Forms of Cocoyam for

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T.U., Ogbonye.and Nwoko, S.U. (2009).

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MPRA Paper No. 17441, posted 25.

September 2009 17:49 UTC

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various sizes of minitubers on seed yam

size and yield – Annual Report: National

Root Crops Research Institute 36-40

Ikerogu, J. E. (2003). Effects of size and spacing on

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U. G. (2003). Growth and yield process of

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Ndaeyo N. U.

*, Udeme K. Udoh, A.O. Ikeh, Edna A. Akpan, Eno I. Udoh and O. R. Akata

3

54


EFFECTS OF COMPLEMENTARY USE OF ORGANIC AND INORGANIC

FERTILIZERS ON YIELD OF UPLAND RICE (ORYZA SATIVA L.) ON AN ULTISOL

Aderi

1 O. S. Ndaeyo,

1N. U. Idem,

1 N. U. A. Iwo

2G. A. and Ikeh

1 A. O.

1Department of Crop Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria.

2Department of Crop Science, University of Calabar, Calabar, Cross River State, Nigeria.

ABSTRACT Field experiments were conducted in 2009 and 2010 at the University of Uyo Teaching and Research

Farm, Use Offot, Uyo, Akwa Ibom State to investigate the effect of complementary use of organic

and inorganic fertilizers on the components of yield of upland rice. Treatments consisted of six

fertilizer complements and five cultivars of rice in a factorial combination to obtain thirty treatment

combinations laid on a randomized complete block design (RCBD) and replicated three times. The

results showed that 3.0t/ha organic + 200 kg/ha inorganic complement produced the highest number

of effective panicles per m2 and the highest number of spikelets per panicle in both years, while the

control produced the least. However, the percentage of filled spikelets and 1,000 seed weight

increased significantly (p≤≤≤≤0.05) with increase in inorganic fertilization, while grain yield increased

significantly with the application of 3.0 t/ha organic + 200kg/ha inorganic complement. Otokongtian

produced the highest significant number of effective panicles per m2 while FAROs 43 and 56

produced the highest number of spikelets per panicle and percentage of filled spikelets per panicle.

FARO 46 produced the highest 1,000 seed weight while FARO 43 produced the highest grain yield.

The interaction effects showed that the performance of the cultivars on most parameters improved

significantly (p≤≤≤≤0.05) with combined application of organic and inorganic fertilizers while inorganic

fertilization improved 1,000 rice seed weight. It was concluded that complementary application of

organic and inorganic fertilizers increased the productivity of upland rice on an ultisol.

Keywords: Organic and inorganic fertilizers, upland rice, grain yield, Oryza sativa.

INTRODUCTION Rice, Oryza sativa L. and Oryza glaberrima

Steud are important cereal food crops. They are

staples for more than half of the world’s

population, and about 1 billion households in

Asia, Africa, and the Americas depend on rice

cultivation for employment and main source of

livelihood (Shimamura, 2005). The Central

Bank of Nigeria (CBN, 2003) classified rice as a

food staple for over 60% of Nigerian homes.

According to WARDA (2003), Nigeria has

continued to experience rapid growth in per

capita rice consumption ranging from 5kg in the

1960s, 11kg in the 1980s to 25kg in the 1990s.

Unfortunately, the output of rice in the country

is low and declining by 3.4 percent (CBN,

1998). Odii and Nwosu attributed the decline to

inefficient use of farm resources, labour

shortages and severe scarcity of resources, poor

management practices and poor capital base.

Upland rice ecosystem in Nigeria accounts for

32% of the current cultivated rice hectarage and

30 to 35% of the total national rice production

(Ezedinma, 2005). Africa Rice Center (2008)

noted that the potential yield of rainfed upland

rice depended on several factors, including but

not limited to the variety, the fertility status of

the soil, rainfall and management practices of

the farmer. According to National Cereals

Research Institute (NCRI, 2008), long term use

of inorganic fertilizer is detrimental to the soil.

Garba et al. (2007) reported that the low yield of

rice grown in the upland ecology was due to low

plant nutrient and organic matter in most upland

soils. Eilitta (2006) recommended

complementary use of inorganic and organic

fertilizers as an effective practice in sustainable

soil nutrient management. This study was

conducted to evaluate the effect of

complementary use of organic and inorganic

fertilizers on the yield of upland rice (Oryza

sativa L.) on an ultisol.

MATERIALS AND METHODS Field experiments were conducted in two years

at the Teaching and Research Farm of the

University of Uyo, located in Use Offot, Uyo,

55

Akwa Ibom State, during the 2009 and 2010

cropping seasons. The experimental site was

located at Latitude 05°, 01' 56.2"N and

Longitude 07, 58' 20.3"E and 57m above mean

sea level. In 2010, the location was Latitude 05°,

0.1' 56.6"N and Longitude 07°, 58' 20.6"E and

55m above mean sea level (measured with the

Global Positioning System (GPS), Model

Garmin Etrex). Peters et al. (1989) reported that

this humid rain forest zone receives an annual

rainfall of about 2,500mm and a mean relative

humidity of 78%. The mean annual temperature

varies between 22 and 32°C and day length

(sunshine hours) of 3 – 8 hours. The soil is

acidic and belongs to broad soil classification

group, ultisol, formed from acid plain sand

(Enwezor et al., 1990).

Treatments consisted of six organic and

inorganic fertilizer combinations fitted in a

factorial arrangement with five cultivars of

upland rice. The fertilizer combinations were:

i) 6.0t/ha of poultry manure (PM) = 100%

organic

ii) 400 kg/ha of NPK (15-15-15) = 100%

inorganic

iii) 4.5 t/ha PM + 100kg/ha NPK = 75%

organic + 25% inorganic

iv) 3.0 t/ha PM + 200 kg/ha NPK = 50%


v) 1.5 t/ha PM + 300 kg/ha NPK = 25%


vi) Control

The rice cultivars were:

i) FARO 43

ii) FARO 46

iii) FARO 55

iv) FARO 56

v) A local check – Otokongtian

The treatment combinations were laid out on a

randomized complete block design and

replicated three times.

In each year, the experimental area measured

119m x 20m and was divided into three blocks

with each measuring 119m x 4m. A block was

then subdivided into 30 plots, each measuring

4m x 3m and separated from each other by 1.0m

while the space between two adjacent blocks

was 2.0m. A space of 2.0m was maintained

around the experimental area for farm sanitation.

Poultry manure, which had been collected and

stored under shade for three weeks was mixed

thoroughly, weighed out and uniformly applied

and incorporated into the soil in plots receiving

organic manure application two weeks before

rice sowing. Seeds were sown by dibbling, using

six seeds per hill. At two weeks after sowing

(WAS), seedlings were thinned to four per hill.

All the plots were sown with a spacing of 25cm

x 25cm to obtain 16 hills or 64 seedlings per

square metre. Within 24 hours after sowing, pre-

emergent herbicide, Paraquat was applied at the

recommended rate of 1.0 kg active ingredient

per hectare (NCRI, 2008) to control weed seeds

on the surface. NPK fertilizer was applied in

split doses at two, six and nine weeks after

sowing by side placement. Human bird scarers

were employed to scare birds away. Data were

collected on the number of effective tillers/m2,

number of spikelets per panicle, percentage of

filled spikelets per panicle, 1,000 seed weight,

and grain yield (t/ha). Data were analysed using

Genstat Discovery Edition 4 and means that

were significant were separated using Fisher’s

Protected Least Significant Difference at 5%

level of probability.

RESULTS The number of effective rice tillers m

−2 was

statistically similar for 1.5 t/ha organic + 300

kg/ha inorganic, 3.0 t/ha organic + 200 kg/ha

inorganic, 4.5 t/ha organic + 100 kgha−1

inorganic combinations and 6.0 tha−1

organic

fertilizer sole applied in 2009 (Table 1). These

were significantly (p<0.05) higher than the

control and 400 kgha−1 inorganic fertilization.

The 400 kgha−1 inorganic fertilizer produced

more effective tillers m−2

than the control and

the difference was significant. In 2010, 3.0 tha−1

organic + 200 kgha−1

inorganic combination

produced the highest number of effective tillers

m−2

compared to the other fertilizer

combinations, followed by 6.0 tha−1

organic

manure and 4.5 tha−1


inorganic combination. The lowest number of

effective tillers was obtained from the control,

followed by 400 kgha−1

inorganic fertilizer

application. Cultivar effects showed that in

2009, Otokongtian produced the highest number

of effective tillers, followed by FARO 43.

FAROs 46 and 55 produced similar number of

effective tillers m−2

. In 2010, Otokongtian also

produced the highest significant number of

effective tillers compared with other cultivars,

followed by FARO 43. FAROs 46 and 55

produced similar number of effective tillers m−2

in 2010. The interaction effects showed that

cultivars generally produced more tillers with

combined application of organic and inorganic

fertilizers when compared with the control and

400 kgha−1 inorganic fertilization.

In 2009, 3.0 tha−1 organic + 200 kg ha−1

inorganic combination produced the highest

number of spikelets panicle−1 compared with the

other fertilizer combinations (Table 2). Similar

Complementary Use of Organic and Inorganic Fertilizers on Yield of Upland Rice

56

Table 1: Effects of Organic and Inorganic Fertilizer Combinations and Rice Cultivars

on the Number of Effective Panicles m−−−−2

in 2009 and 2010 in Uyo, Nigeria Rice Cultivars

Fertilizer

combinations

FARO

43

FARO

46

FARO

55

FARO

56

Otokongtian Mean

6.0 t/ha organic manure 134.67 101.00 2009

102.67

116.67 121.67 115.33

400 kg/ha NPK 111.00 94.67 94.67 92.00 121.00 103.67

4.5 t/ha org. manure + 100 kg/ha NPK 121.00 98.67 102.67 107.00 133.00 112.47

1.5 t/ha org. manure + 300 kg/ha NPK 120.33 100.67 103.00 110.00 132.00 113.33 3.0 t/ha org. manure + 200 kg/ha NPK 138.33 98.33 105.00 104.67 141.00 117.47

Control 73.00 65.00 59.67 62.00 80.00 67.93

Mean 116.39 93.06 94.61 99.67 121.44 105.03

2010

6.0 t/ha organic manure 130.67 102.67 102.67 107.00 124.00 113.40 400 kg/ha NPK 109.67 92.33 93.67 97.00 124.67 103.47




Control 68.33 60.67 66.67 68.33 80.67 68.93

Mean 117.39 92.76 94.78 100.78 121.67 105.47

2009 2010

LSD (P<0.05) for fertilizer complement means (F) 5.35 2.74

LSD (P<0.05) for cultivar means (C) 4.89 2.50

LSD (P<0.05) for F x C means 11.97 6.12

Table 2: Effects of Organic and Inorganic Fertilizer Combinations and Rice Cultivars on the

Number of Spikelets Panicle−−−−1 in 2009 and 2010 in Uyo, Nigeria Rice Cultivars

Fertilizer

combinations

FARO

43

FARO

46

FARO

55

FARO

56

Otokongtian Mean

6.0 t/ha organic manure

169.67

96.67

2009

167.00

170.00

158.33

152.33

400 kg/ha NPK

167.33

98.00

164.00

190.67

164.67

153.87



Control 93.33 74.67 87.00 86.67 88.67 86.07

Mean 170.78 95.28 155.06 169.89 153.28 148.86

2010

6.0 t/ha organic manure 183.00 101.07 173.67 175.38 164.67 159.56

400 kg/ha NPK 167.00 97.33 164.33 175.33 166.67 154.13



3.0 t/ha org. manure + 200 kg/ha NPK 206.82 114.13 185..67 226.88 184.67 183.63

Control 105.17 74.67 101.99 103.67 102.39 97.58

Mean 179.71 98.20 161.25 180.36 159.23 155.75

2009 2010




Aderi O. S. Ndaeyo, N. U. Idem, N. U. A. Iwo G. A. and Ikeh A. O.

57


on the Percentage of Filled Spikelets Panicle−−−−1

in 2009 and 2010 in Uyo, Nigeria


on 1,000 Seed Weight (g) in 2009 and 2010 in Uyo, Nigeria

Rice Cultivars

Fertilizer

combinations

FARO

43

FARO

46

FARO

55

FARO

56

Otokongtian Mean


81.26

80.84 2009

80.77

81.87

80.39

81.02

400 kg/ha NPK 90.67 87.87 90.28 90.29 88.41 89.51



Control 73.87 73.99 75.15 73.49 71.77 73.74

Mean 82.56 81.90 82.24 82.63 80.71 82.01

2010


400 kg/ha NPK 90.36 88.85 90.14 90.33 88.36 89.61



Control 73.43 73.13 77.00 76.33 72.97 74.57

Mean 82.61 82.26 82.95 83.65 81.62 82.62

2009 2010




Rice Cultivars

Fertilizer

combinations

FARO

43

FARO

46

FARO

55

FARO

56

Otokongtia

n

Mean


25.87

29.67 2009 27.57

25.20

22.54

25.97

400 kg/ha NPK 28.50 31.00 27.27 25.87 23.12 27.15

4.5 t/ha org. manure + 100 kg/ha

NPK

26.17 30.33 26.97 24.91 22.06 26.09


NPK

26.57 30.70 27.23 25.47 22.66 26.53


NPK

25.80 30.13 26.57 25.43 22.73 26.13

Control 25.01 30.57 26.10 25.60 22.24 25.90

Mean 26.32 30.40 26.78 25.41 22.56 26.30

2010


400 kg/ha NPK 28.00 31.03 27.00 25.93 23.00 26.99


NPK

26.48 30.89 26.83 25.95 22.80 26.59


NPK

26.35 31.11 26.67 26.33 24.00 26.89


NPK

26.02 30.93 27.47 25.33 23.20 26.59

Control 25.67 29.88 26.17 24.09 22.00 25.56

Mean 26.44 30.81 26.70 25.57 23.10 26.52

2009 2010


LSD (P<0.05) for cultivar means (C) 0.47 0.30 LSD (P<0.05) for F x C means 1.16 0.73


58

Table 5: Effects of Organic and Inorganic Fertilizer Combinations and Rice

Cultivars on Grain Yield of Rice (tha−−−−1

) in 2009 and 2010 in Uyo, Nigeria

number of spikelets panicle−1

was produced by

4.5 tha−1

organic + 100kgha−1

inorganic and

1.5tha−1

organic + 300 kg/ha inorganic fertilizer

combinations. The 6.0tha−1

organic fertilizer and

400kgha−1

inorganic fertilizer produced similar

number of spikelets. The control treatment

produced the least spikelets. In 2010, 3.0tha−1


inorganic combination also

produced the highest number of spikelets

panicle−1

compared with the other fertilizer

combinations, followed by 4.5tha−1

organic +

100 kgha−1 inorganic combination. The 6.0 tha−1

organic and 400 kgha−1 inorganic fertilizer

produced similar number of spikelets panicle−1

while the control produced the least spikelets.

FAROs 43 and 56 produced the highest

significant number of spikelets compared to

FAROs 46, 55 and Otokongtian in 2009. FARO

55 was similar to Otokongtian while FARO 46

produced the least number of spikelets. In 2010,

similar number of spikelets panicle−1

was

produced by FAROs 43 and 56. They were

significantly higher than spikelets produced by

FAROs 46, 55 and Otokongtian. FARO 55

produced similar number of spikelets with

Otokongtian that was significantly higher than

FARO 46. The interaction effects showed that

cultivars produced more spikelets with

combined application of organic and inorganic

fertilizers compared with the sole application of

either the organic or inorganic fertilizer.

The percentage of filled spikelets panicle−1

increased with increase in the rate of inorganic

fertilizer (Table 3). In 2009 and 2010, 400

kgha−1

inorganic fertilizer increased the

percentage of filled spikelets significantly higher

than those of the other fertilizer combinations. It

was followed by 1.5 tha−1


inorganic combination. Similar percentage of

filled spikelets was produced by 3.0 tha−1

organic + 200kgha−1

inorganic and 4.5 tha−1

organic + 100 kgha−1 inorganic combinations.

Also, 4.5 tha−1 organic + 100 kgha−1 inorganic

and 6.0 tha−1

organic fertilizer produced similar

percentages of filled spikelets in both years. The

least percentage of filled spikelet panicle−1 was

obtained from the control. In 2009, FAROs 43,

46, 55 and 56 had similar percentages of filled

spikelets which differed significantly from that

of Otokongtian. In 2010, the percentages of

filled spikelets panicle−1

for FAROs 55 and 56

were similar but both were significantly higher

than the percentages of filled spikelets for

FAROs 43, 46 and Otokongtian. FARO 46 and

Otokongtian had similar but the least

percentages of filled spikelets panicle−1

in 2010.

The interaction effects showed that in both

years, higher percentage of filled spikelets was

obtained by cultivars at higher rates of inorganic

fertilization. In 2009, 400 kgha−1

inorganic

fertilizer produced the highest 1,000 seed weight

compared with the other fertilizer combinations

(Table 4). This was followed by 1.5 tha−1


inorganic combination

compared to 6.0 tha−1

organic fertilizer and the

Rice Cultivars

Fertilizer

combinations

FARO

43

FARO

46

FARO

55

FARO

56

Otokongtian Mean


4.80

2.34 2009

3.68

4.09

3.50

3.68

400 kg/ha NPK 4.80 2.53 3.82 3.97 4.07 3.84



Control 1.26 1.10 1.02 1.01 1.13 1.10

Mean 4.54 2.25 3.38 3.73 3.55 3.49

2010


400 kg/ha NPK 4.62 2.48 3.73 3.97 4.22 3.81



Control 1.36 0.99 1.36 1.30 1.32 1.27

Mean 4.83 2.38 3.49 4.06 3.80 3.71

2009 2010


LSD (P<0.05) for cultivar means (C) 0.21 0.11 LSD (P<0.05) for F x C means 1.51 0.27

Aderi O. S. Ndaeyo,

N. U. Idem,

N. U. A. Iwo

G. A. and Ikeh

A. O.

59

control. In 2010, 400 kgha−1

inorganic fertilizer

and 1.5 tha−1


inorganic

combination produced the highest significant

1,000 seed weight compared to the other

combinations. The lowest 1,000 seed weight was

obtained from the control. The 1,000 seed

weight for cultivars followed similar trend in

both years. FARO 46 produced the highest

significant seed weight compared with the other

cultivars. This was followed by FARO 55 which

had similar seed weight with FARO 43. They

were significantly higher than the seed weight

for FARO 56 and Otokongtian. Otokongtian had

the least seed weight. The interaction effects

showed that in both years, the highest 1,000 rice

seed weight was obtained from FARO 46 at 400

kgha−1 inorganic fertilizer. In both years, 3.0

tha−1 organic + 200 kgha−1 inorganic fertilizer

combination produced the highest significant

grain yield compared to the other fertilizer

combinations (Table 5). However, in 2009, 1.5

tha−1


inorganic

combination produced similar grain yields with

4.5 tha−1


inorganic and 400

kgha−1

inorganic combinations. The 6.0 t ha−1

organic manure produced similar grain yield

with 400 kgha−1

inorganic fertilizer. The lowest

grain yield was obtained from the control. In

2010, 1.5tha−1


inorganic

and 4.5 tha−1

organic + 100 kg ha−1

inorganic

combinations produced similar yields which

were significantly higher than the control, and

400 kgha−1 inorganic and 6.0 tha−1 organic

fertilizers treatments. Nevertheless, 6.0 tha−1

organic and 400 kgha−1

inorganic treatments

produced similar grain yields. The lowest grain

yield was produced by the control. Cultivar

effects showed that in 2009, FARO 43 produced

the highest significant grain yield, followed by

FARO 56 and Otokongtian compared to FARO

46. Otokongtian and FARO 55 were similar on

their grain yield in 2009 while FARO 46

produced the lowest grain yield. In 2010, all the

cultivars produced significantly different grain

yield in the following order: FARO 43 > FARO

56 > Otokongtian > FARO 55 > FARO 46. In

both years, cultivars produced higher grain

yields with combined application of organic and

inorganic fertilizers compared with single

application of fertilizer source and the control.

DISCUSSION The number of effective tillers m

−2 increased

significantly with the application of organic

fertilizer compared with the application of

inorganic fertilizer alone and the control. This

might have been due to the general improvement

of the soil environment with sufficient supply of

plant nutrients. Li et al. (2011) reported increase

in the yield attributes of rice as a result of

application of poultry litter and attributed the

effect to improvement in soil quality among

other factors.

The number of spikelets panicle−1

increased

significantly with combined application of

organic and inorganic fertilizers compared with

the single application of either of the nutrient

sources and the control. This observation was

supported by the findings of Gebrekidan and

Seyoum (2006) that increase in yield

determining traits was associated with better

nutrition and increase in nutrient uptake –

especially NPK which resulted in better and

healthier plant growth and development.

The percentage of filled spikelets panicle−1

increased with increase in the rate of inorganic

fertilization. High availability of K in inorganic

fertilizer – which is associated with increase in

the percentage of filled grains and 1,000 grain

weight (Slaton et al., 2001) enhanced spikelet

filling and weight. There was a general increase

in the grain yield of rice with combined

application of organic and inorganic fertilizer

compared with the application of either of the

two nutrient sources and the control. It was most

probable that mineralization and uptake of soil

nutrients improved with combined application of

nutrient of organic and inorganic fertilizer

resulting in enhanced growth and grain filling.

Majumdar et al. (2007) reported that the grain

yield, N, P and K uptake by paddy and various

forms of N in the soil increased significantly

with combined application of N, farmyard

manure and N-fixing bacteria.

Cultivar differences were observed on all the

parameters measured. It was evident that

cultivars different in their responses to the

edaphic and weather factors of the environment

due to variations in their genetic constitution.

Thus, while Otokongtian produced the highest

number of effective tillers m−2

, FAROs 43 and

56 produced the highest number of spikelets

panicle−1

. FAROs 55 and 56 generally produced

the highest percentage of filled spikelets,

whereas the highest 1,000 seed weight was

obtained from FARO 46, while FARO 43

produced the highest grain yield. Lack et al.

(2011) reported cultivar differences in all the

measured attributes and attributed those

differences to the inherent ability of the cultivars

to adapt, utilize and respond to the applied input

and local stresses. This view was supported by

Sarvestani and Pirdashti (2001) that dry matter

remobilization of shoot (stem + flagleaf and

other leaves) had an important effect on grain

dry matter accumulation and differed with

cultivars and that grain yield had significant

positive correlation with the number of grains


60

panicle−1

, the number of filled grains panicle−1

and the 1,000 grain mass.

CONCLUSION Combined application of organic and inorganic

fertilizer has been observed to improve the grain

yield of upland rice. The application of 3.0 tha−1

poultry manure and 200 kgha−1 NPK fertilizer to

FARO 43 could be used to improve the

productivity of upland rice on an ultisol.

REFERENCES Africa Rice Center (2008). Africa Rice Trends

2007. 5th

Edition. Contonou, Africa

Rice Center (WARDA) pp. 27-42.

CBN (Central Bank of Nigeria) (1998). Annual

Reports and Statement of Accounts.

Lagos: CBN, pp. 79-79.

CBN (Central Bank of Nigeria) (2003).

Statistical Bulletin. Lagos: CBN. 14:

260.

Eilitta, M. (2006). Achieving an African Green

Revolution: A Vision for Sustainable

Agricultural Growth in Africa. In:

International Center for Soil Fertility and

Agricultural Development (IFDC). Paper

Presented at the Africa Fertilizer

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Enwezor, W. O., Ohiri, A. C., Opuwaribo, E. E.

and Udo, E. J. (1990). A Review of

Fertilizer Use on Crops in the South

Eastern Zone of Nigeria. In: Enwezor,

W. O. (Ed.) Literature Review on Soil

Fertility Investigations in Nigeria.

Federal Ministry of Agriculture and

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Ezedinma, C. (2005). Impact of Trade on

Domestic Rice Production and the

Challenge of Self-Sufficiency in Nigeria.

Ibadan: International Institute of

Tropical Agriculture. pp. 1-14.

Garba, A., Hamidu, B. M. and Edmond, E.

(2007). Economics of using high

analysis fertilizer over low analysis

fertilizer for optimum rice production in

Nigeria. Short communication. Emir.

Journ. Food Agric. 19(1): 39-47.

Gebrekidan, H. and Seyoum, M. (2006). Effect

of Minieral N. and P. Fertilizers on

Yield and Yield Components of Flooded

Lowland Rice on Vertisols of Fogera

Plain, Ethiopia. Journ. Agric. Rural Dev.

in the Tropics and Subtropics, 107(2):

161-176.

Lack, S., Bayemni, M. and Mombeni, M.

(2011). The Study of Dry Matter

Remobilization in Rice Cultivars due to

Planting Density Variation. Adv.

Environ. Biol. 5(10): 3338-3344.

Li, J. T., Zhong, X. L., Wang, F. and Zhao, Q.

G. (2011). Effect of Poultry Litter and

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(2001). Rice Planting Geometry

Facilitates Relay Cropping at Zero

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435-437.

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and Returns of Rice Production under

Alternative Production Systems. Journ.

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Australian Agronomy Conference.

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life: Scientific Perspective for the 21st

Century. Toriyama, K., Heong, K. L.

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2004. p. 4.

Slaton, N. A., Wilson (Jr.) C. E., Norman, R. J.,

Ntamatungiro, S. and Frizzell, D. L.

(2001). Rice Response to Phosphorus

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Aderi O. S. Ndaeyo,

N. U. Idem,

N. U. A. Iwo

G. A. and Ikeh

A. O.

61


SEASONAL EVALUATION OF ADVANCED GENERATIONS OF INTERSPECIFIC

HYBRIDS OF TWO SOLANUM SPECIES IN THE DERIVED SAVANNAH AGRO-

ECOLOGY OF SOUTHEASTERN NIGERIA

Amuji, C.F

*, Ogbonna, P.E. and Uguru, M.I.

Department of Crop Science, University of Nigeria, Nsukka, Nigeria.


ABSTRACT Four experiments were carried out in the early and late planting seasons of 2011 and 2012 to assess

the growth, yield and disease tolerance of advanced generations of interspecific hybrids of tomatoes

and their parents. The hybrids comprised S2S, S3S, S4S and S1E, and the parents were Roma vf,

Tropica and the wild tomato relative, Solanum pimpinellifolium. These were evaluated in a

randomized complete block design (RCBD) with four replications. The hybrids flowered and fruited

earlier than the cultivated parents in both the early and late planting seasons. The hybrids had

higher percentage survival (54% to 80%) than the cultivated parents (14% to 28%). The wild parent

produced the highest number of branches/plant, trusses/plant, flowers/plant and fruits/truss and it

differed significantly (P < 0.05) from the other genotypes in those traits. The cultivated parents had

the highest disease severity and percentage fruit rot in the two seasons. The hybrids had higher

number of branches/plant, trusses/plant, flowers/plant and fruits/truss than the cultivated parents.

The fruit yield/hectare correlated positively with percentage survival of the plants at 110 days after

planting, number of flowers/plant, number of branches/plant, number of trusses/plant and number

of fruits/truss at maturity in the two seasons. The hybrid, S1E had the most stable fruit yield in the

four seasons of evaluation. Both the wild and the selected hybrids had comparable fruit yield values

in the early planting seasons of 2011 and 2012. But in the late planting season, the selected hybrids

produced higher fruit yield than all the parents in the two years.

Key words: Interspecific hybrids, tomato, Solanum lycopersicum, Solanum pimpinellifolium

INTRODUCTION Most farming activities in south eastern Nigeria

are rain fed, with basically little or no irrigation

facilities available. Tomato production has been

hampered by excessive precipitation. The bulk

of the tomatoes consumed in southern Nigeria

are transported from northern Nigeria where

fresh fruit yield is higher (Quinn, 1980) than the

yield obtained in the south (NIHORT, 1980).

The low yield has been attributed to several

factors including high temperature, high

humidity, excessive rainfall (Opena et al., 1989),

diseases and insect pests (Tee et. al., 1979; Ma,

1985), lack of improved varieties (Villareal,

1979) and poor cultural practices (Zindarcic et

al., 2003).

Tomato improvement to obtain cultivars that can

adapt to the region south of 10 0N latitude will

reduce the dependence on savannah region for

the supply of fruits. This may be achieved either

through introduction or hybridization

programmes aimed at generating cultivars

adaptable to the conditions prevalent in south

eastern agro-ecological zone. The wild tomato

species, S. pimpinellifolium appears to be a very

promising cultivar for improvement of the less

adapted varieties that are currently in cultivation.

There are reports that it does well under high

humid conditions and at relatively high

temperature, and it is also resistant to many

tomato diseases (Uguru and Igili, 2002; Chen

and Foolad, 1998). It has also been reported that

both S. pimpinellifolium and S. lycopersicum are

cross compatible (Rick, 1982; Walnock, 1988,

Uguru and Atugwu, 2001). Tomato breeding has

been successful in many developed countries,

but countries in Africa are still struggling to

develop varieties that are well adapted to the

local environment. This is the key consideration

in the present research set up to evaluate the

cultivated S. lycopersicum, and S.

pimpinellifollium species and the advanced

generations of the inter specific hybrids for their

level of adaptation to the humid south-eastern

Nigeria. The project is an on-going research in

the Department of Crop Science, University of

Nigeria, aimed at producing tomato cultivars

endowed with prolific fruiting in the humid

tropics.

62

MATERIALS AND METHOD Four field experiments were carried out in the

early and late planting seasons of 2011 and 2012

at the Faculty of Agriculture Research farm,

University of Nigeria Nsukka. Nsukka is located

in the derived savannah zone (06o 52 iN, 070 24 iE and 447m a.s.l.). The soil is a well-drained

sandy clay loam classified as ultisol belonging to

the Nkpologu series (Nwadialo, 1989). Tomato

seeds were obtained from the Department of

Crop Science, University of Nigeria Nsukka

tomato improvement programme. The planting

materials for the experiment comprised two

tomato species Solanum lycopersicum (varieties

i.e. Roma vf and Tropica) and Solanum

pimpinellifolium (wild) with the advanced

generations of the interspecific hybrids (S2S,

S3S, S4S and S1E).

Experimental Design and Field Plot Layout The experimental design was the randomized

complete block design (RCBD) with four

replications. Blocking was made against slope

and total land area with a dimension of 26m x

28m. The blocks were separated 1 m apart. Each

block was divided into seven plots with each

treatment assigned to a plot. Each of the plots

measured 6m x 3m. The plots were separated

from each by a pathway of 0.5m. Plant spacing

of 1m x 0.5m was used giving 30 stands per plot.

Weeding was done manually as and when

necessary. Harvesting was done by hand-picking

the fruits at full maturity.

Data collection: Data were collected on number

of days to flowering, number of days to fruiting,

number of trusses per plant, number of branches

per plant, number of fruits per plant and fruit

yield per plant.

Data analysis: Analysis of Variance (ANOVA)

was performed on the generated data following

the procedures outlined for randomized

complete block design (RCBD) using Genstat

10.3 version 2011 software. Significant

treatment means were separated with F-LSD at 5

% probability level. Combined analysis was also

carried out to determine the effect of season and

year on the tomato genotypes. Growth traits and

percentage survival were graphically presented

using excel chart wizard. GGE biplot analysis

was performed on the generated data to show the

mean stability and yield performance in the

different seasons. Pearson’s correlation

coefficients were also performed.

RESULTS On the number of days to flowering and fruiting,

the results showed that there was no significant

(P <0.05) difference between the seasons in

2011 planting. Similarly, the hybrids and the S.

pimpinellifolium parent did not differ

significantly with respect to number of days to

flowering and fruiting. The S. lycopersicum

parents (Roma vf and Tropica) had higher

number of days to flowering and fruiting than

the other tomato genotypes in both seasons for

the two years (Figures 1 and 2).

The early season plants had higher number of

branches and trusses per plant (Table 2) than the

late season plants. Also the S. pimpinellifolium

tomato genotype had the highest number of

branches and trusses per plant than the others

and these differed significantly (P <0.05) for

both seasons in 2011. The hybrids equally

possessed significantly (P <0.05) higher number

of branches and trusses per plant than the S.

lycopersicum parents for both seasons in 2011.

The early season plants had higher number of

fruits and total weight of fruits per plant than

those planted in the late season of 2011.

The late season plants had significantly (P

<0.05) higher number of days to flowering,

fruiting and fruit ripening than early season

plants. The S. lycopersicum parents took

significantly (P <0.05) higher number of days to

attain flowering, fruiting and fruit ripening than

the other genotypes (Figure 2). The late season

plants produced significantly (p<0.05) higher

number of branches and trusses than the early

season plants. The S. pimpinellifolium genotypes

produced significantly (P <0.05) higher number

of branches and trusses per plant for both

seasons in 2012 than the other tomato genotypes.

The S. lycopersicum parents had the least

number of branches and trusses per plant and

were favoured more by the early planting season

in 2012. The number of branches and trusses per

plant of the hybrids in the late season plants

were higher than those of the early season

plants. Higher percentage fruit rot was recorded

in the early season plants when compared to the

late season plants. The same trend was observed

in disease severity (Table 4). The hybrids

competed favourably with the S.

pimpinellifolium parent hence showing high

adaptive abilities to the high humidity

environment (Figure 3). The percentage fruit rot

result showed that the cultivated parents had

remarkably higher number of rotted fruits, which

differed significantly (P <0.05) from all the other

genotypes. Similar trend was observed on the

disease severity of the tomato genotypes, where

the cultivated parents were significantly (P

<0.05) more susceptible to the disease.

Seasonal Evaluation of Advanced Generations of Interspecific Hybrids of Two Solanum Species

63

Figure 1: Number of days to flowering and fruiting of the tomato genotypes in 2011 cropping

seasons

Table1: Total monthly rainfall, daily relative humidity and atmospheric temperatures

for 2011 and 201 Jan. Feb. March Apr. May June July Aug. Sep. Oct. Nov. Dec.

2011 weather record

Total rainfall

(mm)

0.00 54.86 14.46 87.12 140.46 127.25 192.96 149.1 253.95 183.93 27.94 0.00

Rainfall days 0 3 2 8 12 12 13 14 15 12 2 0

Temperature

maximum

32.06 32.29 33.94 30.83 30.39 28.63 27.71 26.87 29.07 28.26 30.33 31.58

Temperature

minimum

18.25 22.07 22.94 22.03 21.87 21.47 21.03 20.71 20.67 20.84 20.77 16.68

Relative humidity

%/day maximum

57.06 73.79 72.23 74.27 74.52 75.73 75.77 76.52 76.53 75.58 69.3 56.48

Relative humidity %/day minimum

44.65 60.53 57.26 65.1 70.06 71.3 72.45 73.94 73.73 72.10 59.47 47.26

2012 weather record

Total rainfall

(mm)

0.00 23.1 0.00 103.89 282.13 193.58 276.09 240.48 307.45 291.59 60.96 0.00

Rainfall days 0 3 0 4 13 13 20 21 16 18 4 0

Temperature

maximum

31.65 31.76 33.19 33.43 30.19 28.33 27.81 26.55 27.77 28.42 30.07 30.90

Temperature

minimum

19.77 22.0 22.97 22.43 21.19 20.3 20.32 19.45 20.37 19.55 21.6 18.71

Relative humidity

%/day maximum

52.23 73.59 71.23 73.47 74.13 75.8 75.39 74.61 75.8 73.19 73.83 75.0

Relative humidity %/day minimum

48.74 61.24 53.35 62.83 67.81 71.5 72.35 74.29 75.35 73.0 63.83 45.0

Source: Department of Crop Science, University of Nigeria Nsukka, Meterological Station.

Amuji, C.F*, Ogbonna, P.E. and Uguru, M.I.

64

Figure 2: Number of days to flowering and fruiting of the tomato genotypes in 2012 cropping seasons

Table 2: Effects of planting season and genotypes on the number of branches and

trusses per plant of the tomatoes in 2011 and 2012 Planting Genotypes

Season Roma S2S S3S S4S S1E Tropica Wild Mean

number of branches/plant

Early 7.00 23.75 21.50 28.50 21.00 8.25 43.00 23.00

2011 Late 6.00 24.25 25.50 26.25 22.50 9.25 47.25 21.86

Mean 6.50 24.00 23.50 27.38 21.75 8.75 45.12

number of trusses/plant Early 11.25 47.00 39.75 51.50 32.50 11.50 227.25 60.11

Late 7.00 37.75 47.50 45.00 34.50 15.50 90.50 39.68

Mean 9.12 42.38 43.62 48.25 33.50 13.50 158.88

number of branches/plant Early 7.2 24.2 23.2 25.2 15.7 8.2 80.0 26.3

2012 Late 9.7 36.5 45.2 53.0 18.5 7.2 139.8 44.3

Mean 8.5 30.4 34.2 39.1 17.1 7.8 109.9

number of trusses/plant Early 15.5 49.5 38.8 73.5 30.0 15.3 234.0 65.2

Late 8.2 79.2 71.8 56.5 34.5 10.8 248.2 72.8

Mean 11.9 64.4 55.2 65.0 32.3 13.0 241.0

branches/plant

2011

branches/plant

2012

trusses/plant 2011 trusses/plant 2012

LSD0.05 for season N.S. 6.46 3.191 11.39

LSD0.05 for genotype 3.086 12.09 5.970 21.32

LSD0.05 for season x genotype 4.365 17.09 8.443 30.15


65

Figure 3: Percentage survival of the tomato genotypes

Table 3: Effects of planting season and genotypes on the number of fruits and total weight of

fruits (g) per tomato plant in 2011 and 2012

Planting Genotypes


number of fruits/plant

Early 9.80 68.50 62.80 136.80 49.00 10.80 669.50 143.90

2011 Late 5.50 61.20 89.00 158.80 36.50 6.80 325.50 97.60

Mean 7.60 64.90 75.90 147.80 42.80 8.80 497.50

total weight fruits (g)/plant Early 338.0 1123.0 861.0 821.0 1019.0 360.0 1339.0 837.0

Late 147.0 639.0 985.0 1148.0 661.0 214.0 473.0 610.0 Mean 243.0 881.0 923.0 985.0 840 287.0 906.0

number of fruits/plant Early 7.2 64.5 83.0 122.8 44.8 6.5 122.8 137.1

2012 Late 11.3 149.5 181.2 192.8 72.5 8.2 424.8 148.6

Mean 9.2 107.0 132.1 157.8 58.6 7.4 528.0

total weight of fruits /plant Early 254.0 1001.0 1120.0 815.0 843.0 217.0 1263.0 787.0

Late 263.0 1319.0 1230.0 877.0 1317.0 220.0 809.0 862.0

Mean 258.0 1160.0 1175.0 846.0 1080.0 218.0 1036.0

number of

fruits/plant 2011

number of

fruits/plant 2012

weight fruits

(g)/plant 2011

weight fruits

(g)/plant 2012

LSD0.05 for season 25.22 15.08 112.8 98.3

LSD0.05 for genotype 47.19 28.22 211.0 183.0

LSD0.05 for season x genotype 66.74 39.91 298.4 260.1

The total weight of fruits produced per plant was

positively correlated with percentage survival of the

plants at 110 days after planting (DAP), number of

branches per plant and number of trusses per plant

measured at maturity in the early planting season.

Similarly, number of fruits per plant had significant

positive correlation with number of branches per

plant, number of trusses per plant, number of flowers

per plant and number of fruits per truss (Table 5).

Planting in the late season followed the same trend.

The number of fruits per plant had significant

positive correlation with number of branches per

plant, number of flowers per plant, number trusses

per plant, number of fruits per truss and percentage

survival of the plants at 110 days after planting in the

late season (Table 6).

Figure 4 is the average-environment coordination

(AEC) view of average fruit weight (AFW) biplot.

The single arrowed line is the AEC abscissa; it points

to higher AFW mean across environments (Yan and

Tinker, 2006). Thus, Tropica had the highest AFW

mean, followed by Roma, S1E and the wild had the

lowest AFW mean. The double-arrowed line is the

AEC ordinate; it points to greater variability (poorer

stability) in either direction. Thus, Roma, wild and

S4S were the most unstable whereas S3S, S2S and

Tropica were highly stable. Roma was highly

unstable because it had lower than expected AFW in

environments L11 and L12, but higher than expected

AFW in E11 and E12. Wild and S4S were also

unstable because they had lower than expected AFW

in E11 and E12 environments, but higher than

expected AFW in L11 and L12.


66

Figure 4: The average-environment coordination (AEC) view to show the average fruit weight mean performance and stability of

the genotypes. L11= Late planting season of 2011

L12= Late planting season of 2012

E11=Early planting season 2011 E12=Early planting season 2012

Table 4: Effects of planting season and genotypes on the percentage fruit rot, disease incidence

severity and score of the tomatoes Planting Genotypes


Percentage fruit rot Early 65.5 9.0 4.0 0.7 29.5 76.2 0.3 26.5

Late 14.7 1.5 0.5 0.2 7.0 17.2 0.0 5.9

Mean 40.1 5.2 2.2 0.5 18.2 46.8 0.1

Disease severity Early 83.51 36.90 37.28 39.61 45.07 87.76 30.36 51.50

Late 93.69 38.92 34.38 33.13 44.61 6.92 28.68 50.05

Mean 88.60 37.91 35.83 36.37 44.84 82.34 29.52

percent fruit rot disease severity LSD0.05 for season 5.63 N.S.

LSD0.05 for genotype 10.52 7.134

LSD0.05 for season x genotype 14.88 10.089


67

Figure 5: The Mean vs Stability View of the GGE biplot to show the evaluation of genotypes based on

their yield mean performance and stability across environments

Table 5: Correlation matrix of some morphological traits, percentage survival and yield

characteristics of the tomato genotypes evaluated during the early planting season brpl flpl trpl Frtr S110 Frpl wepl

Brpl 1 0.970* 0.990** 0.851* 0.755* 0.989** 0.723 Flpl 1 0.979** 0.751 0.585 0.992** 0.558

Trpl 1 0.857* 0.727 0.994** 0.643

Frtr 1 0.903** 0.824* 0.726

S110 1 0.673 0.877**

Frpl 1 0.626

Wepl 1

brpl = number of branches per plant, flpl = number of flowers per plant, trpl = number of trusses per plant, frtr =

number of fruits per truss, S110 = percentage survival at 110 days after planting, frpl = number of fruits per plant,

wepl = weight of fruits per plant.

**. Correlation is significant at 0.01 probability level

*. Correlation is significant at 0.05 probability level

Table 6: Correlation matrix of some morphological traits, percentage survival

and yield characteristics of the tomato genotypes evaluated during the

late planting season brpl flpl trpl Frtr S110 Frpl wepl

Brpl 1 0.983** 0.994** 0.970** 0.774* 0.984** 0.191 Flpl 1 0.981** 0.917** 0.711 0.940** 0.097

Trpl 1 0.970** 0.807* 0.984** 0.235 Frtr 1 0.862* 0.991** 0.344

S110 1 0.851* 0.748

Frpl 1 0.336 Wepl 1

brpl = number of branches per plant, flpl = number of flowers per plant, trpl = number of trusses per plant, frtr =

number of fruits per truss, S110 = percentage survival at 110 days after planting, frpl = number of fruits per plant,

wepl = weight of fruits per plant.

**. Correlation is significant at 0.01 probability level

*. Correlation is significant at 0.05 probability level


68

A superior genotype should have both high mean

performance and high stability across a mega-

environment. The Mean vs Stability view of the GGE

biplot (Figure 5) is an effective tool for visual

evaluation of genotypes on both aspects. The

genotypes are ranked according to their mean yield

asfollows: S3S>S2S>S1E>Wild>S4S> Roma>

Tropica. The genotype wild was the least stable

because it yielded extremely poorly in late planting

season, while it performed greater than the others in

the early planting season. Genotype S4S was not

stable either; it yielded well in late planting season

but poorly in early planting season. Genotype S1E

was the most stable across the four seasons of

evaluation with respect to fruit yield.

DISCUSSION The results of this research revealed that some of the

observable growth features were similar for all the

tomato genotypes studied. This supports the earlier

reports by Uguru and Atugwu (2001) that the two

species Solanum pimpinellifolium and Solanum

lycopercicum are cytologically related (2n=24) and

are bi-directionally cross compatible. With respect to

number of flowers, trusses and branches per plant at

maturity, the significant differences among the

genotypes (parents and hybrids) were indications that

a significant amount of genetic variation existed

among the genotypes in these characters due to the

presence of the hybrids (Achigan-Dako, 2008). The

higher morphological growth habit of the wild

tomato genotype and that of the hybrids appear to

have synchronized with their fruit production

potentials. The wild parent was superior in number of

flowers, trusses and branches per plant at maturity

when compared with the other genotypes in both

seasons. With respect to fruit rot, the cultivated

parents had the greatest percentage of fruit rot. Also,

the percentage disease severity of the different

tomato genotypes followed a similar trend. The

cultivated parents had a high percentage disease

severity of 76% to 93%. The hybrids had what could

be termed moderate percentage disease severity of

33% to 45%, while the wild had the lowest

percentage disease severity of 28% to 30%. These

were indications, as reported by Yang (1978) that

both varieties of the cultivated parents were

unsuitable for humid regions like Nsukka agro-

ecology. This also agrees with the findings of

Adelora and Oyedokun (1978) who recorded poor

adaptation of some cultivated tomato varieties to

high rainfall conditions of western Nigeria.

Therefore, the high yield obtained from the hybrids

could be attributed to significant percentage survival

of the hybrids and the increased fruiting potentials.

As it had been reported that hybrids perform better

than self-pollinated cultivars in respect of uniformity

of product, yield, quality, resistance to pest and

diseases and adaptation to different environmental

conditions and long storage life (Kumar, 2007). The

wild parent had the highest percentage survival and

produced fruits profusely. The cultivated parents i.e.

Roma and Tropica had the least yield signifying that

they were not tolerant to the prevalent humid

conditions of south eastern Nigeria. According to

Aderi (1994) the growth of crop and its final yield

are influenced by a complex of environmental,

meteorological, physical, chemical and biological

factors acting over the whole growing season.

The early planting season experiment had continuous

rainfall from May to July with a total of 460.67 mm

for 37 days and, 751.8 mm for 46 days in 2011 and

2012, respectively. The continuous rainfall during

the growing and fruiting stages shortened the

production span of tomatoes as most of the fruits

aborted and the few surviving ones rotted. Generally,

due to the similarities in the meteorological data of

the early planting season in the two years, the growth

features and yield of the tomatoes were similar.

The late planting season had excessive rainfall from

planting stage to growing stage which almost ceased

at the maturity stage. Heavy rainfall and high relative

humidity which characterized the experimental

period had been reported to result in greater

vegetative growth and increased disease incidence

(Pangey et al., 2006; Xu et al., 2006). However,

other environmental variables which were not

monitored in the experimental period could also

have, in part, exerted influences on the growth and

yield of the tomato genotypes.

Information on performance stability is important

when a significant genotype by environment (GE)

interaction is detected in yield trials (Bhan et al.,

2005). GGE biplot analysis showed that S1E-hybrid

was the most stable of all the genotypes in fruit yield.

The significant positive correlation between fruit

yield, number of branches per plant, number of

flowers per plant, number of trusses per plant and

percentage survival of the tomato plants at 110 days

after planting showed that there was a close

relationship between fruit yield, morphological

growth parameters and percentage survival ability of

the tomato genotypes.

CONCLUSION The ability to tolerate this high rainfall conditions

and the associated diseases is an interesting genetic

characteristic of the Solanum pimpinellifolium that

had been exploited in the breeding programme that

produced the hybrids evaluated. The hybrids with the

discernable adaptive features of the Solanum

pimpinellifolium as evidenced in the present study

are symbolic. The S1E-hybrid distinguished itself as

the genotype with the highest average fruit weight

mean performance among the hybrids, and also as

the most stable genotype across the four seasons of

evaluation. This should be further assessed for

eventual release to farmers in the humid tropics


69

typified by the rainforest and derived savannah

ecologies of Nigeria.

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EVALUATION OF DIPLOID HYBRID BANANAS OF DIFFERENT PEDIGREE ON BLACK

SIGATOKA RESISTANCE AND AGRONOMIC PERFORMANCE

*Igili1 D. N., Uguru

2 M. I. and Baiyeri

2 K. P.

1Department of Crop Science and Horticulture, Anambra State University, Uli, Nigeria.

2Department of Crop Science, University of Nigeria, Nsukka, Enugu State, Nigeria.


ABSTRACT Selected genetically related diploid Musa materials of the base, first, and second generations of the breeding

programme in the International Institute of Tropical Agriculture (IITA) high rainfall station Onne were

evaluated for black Sigatoka resistance and agronomic performance. This was done to assess the progress

made over time in the breeding programme. The base generation used in the crosses were, Tjau lagada (TL),

Pisang lilin (PL), Calcutta 4 (C4), Wh-O-Gu (WG), Bobby tannap (BT), and Obino l’ewai (OL). The results

obtained showed that in the primary hybrids, the crosses between Tjau lagada and Pisang lilin (TL x PL)

recorded highest values for both plant height and plant girth, while the crosses between Calcutta 4 and Wh-

O-Gu (C4 x WG) had least values for both traits. For disease response traits, C4 x WG recorded the youngest

spotted leaf at flowering and exceptionally high leaf retention index. For yield parameters, TL x PL and BT x

C4 had the highest and lowest values, respectively for both bunch weight and total biomass. TL x PL

recorded highest values for number of hands and number of fingers, while C4 x WG had the lowest values

for both traits. C4 x WG had highest values for both fruit length and fruit circumference. In the secondary

hybrids, (BT x C4) x (TL x PL) and (BT x C4) x (OL x C4) recorded the highest and the lowest values,

respectively for both plant height and plant girth. For disease response traits, (BT x C4) x (OL x C4) had the

best performance for both index of non-spotted leaves and the youngest spotted leaf at flowering. The yield

parameters showed that (BT x C4) x (TL x PL) had highest values for bunch weight, total biomass, number

of hands, number of fingers, and fruit length.

Keywords: Banana hybrids, pedigree, black Sigatoka resistance, Musa spp.

INTRODUCTION

Bananas constitute a major staple food for millions

of people. They rank fourth in world food production

after rice, wheat and animal products (Tribe, 1994).

The production however, is seriously challenged by a

virulent fungal leaf spot called black sigatoka. This

disease also known as black leaf streak causes

serious leaf defoliation resulting in yield losses of

30-50% (Stover, 1983; IITA, 1992).

IITA, in 1987, initiated a genetic improvement

programme targeting the incorporation of durable

host plant resistance to black sigatoka into plantain

(Vuylsteke et al., 1993b). A number of landraces

were used in making crosses resulting in the

development of a relatively large number of diploid

hybrids. The assessment of the performances of the

different hybrids to black sigatoka resistance and

various agronomic characters, taking into cognizance

their pedigree record would give insight into the

usefulness of the different landraces and their

progenies in the improvement programme. This is

the purpose of the present research.

The research was conducted at the International

Institute of Tropical Agriculture (IITA) High

Rainfall Station, Onne in 2006 and 2007. Onne is

located in south-southern Nigeria at Latitude 40 43l

N, Longitude 7

0 01

l E and altitude of 10m above sea

level. The materials for the research were primary

banana hybrids generated from crosses among the

landraces; Calcutta 4, Pisang lilin, Obino l’ewai,

Bobby tannap, Tjau lagada and Wh-O-Gu; and

secondary hybrids generated from crosses among the

primary hybrids. These materials were established

and maintained in the field from September 2004 to

June 2007 at the spacing of 3m between rows and 2m

within row, giving a theoretical population density of

1667 plants ha-1

. The phenological, disease and yield

data were collected on single row plots of three

plants per clone. Data were collected at flowering

and at maturity on days to fruit filling (DFF), plant

height at flowering (PHT), plant girth (PGT), number

of standing leaves at flowering (NSLF), and number

of youngest spotted leaves at flowering (YLSF).

71

YLSF was determined according to Vakili (1968)

and Meredith and Lawrence (1969) as the number of

leaves showing black spots due to black Sigatoka

disease from the youngest fully expanded leaf

downwards to the youngest leaf.

Other traits measured include; number of standing

leaves at harvest (NSLH); weight of standing leaves

at harvest (LWT); pseudostem weight at harvest

(PSWT); height of tallest sucker at harvest (HTS);

bunch weight (BWT), number of hands per bunch

(HND), number of fingers per bunch (FNG), fruit

length (FLT), and fruit circumference (FCR). Both

FLT and FCR were determined from the middle fruit

of the second hand. From the data collected, the

resistance to black Sigatoka termed index of non-

spotted leaves (INSL), crop cycling index (CI), leaf

retention index (LRI), harvest index (HI), and total

biomass (TB), were calculated.

INSL was calculated as the proportion of

standing leaves that were free from infection

expressed in percent.

INSL = [(YSLF-1/NSLF) x 100]

CI was calculated as the ratio of height of

the tallest sucker (HTS) of a mat to the plant height

(PHT) of the mother plant, using the formula below:

CI = HTS/PHT

LRI was calculated as the ratio of NSLH to

NSLF (NSLH/NSLF) x 100 expressed in percent.

TB was calculated as the sum total of the

above ground biomass comprising the weights of the

bunch, the pseudostem and the standing leaves at

harvest (TB = BWT + PSWT + LWT).

HI was calculated as the ratio of the bunch

weight to the total biomass expressed in percent (HI

= (BWT/TB) x 100).

Data analyses were done using the

GENSTAT statistical package (Genstat, 2003).

RESULTS The performances of the four primary hybrids

(generation 1) for various traits are shown in Figures

1-4. Figure 1 showed a similar trend in the

performances of the different hybrids for plant height

and plant girth. The crosses between tjau lagada and

pisang lilin (TL x PL) recorded the highest values for

both traits while the crosses between Calcutta 4 and

Wh-O-Gu (C4 x WG) had the lowest values. TL x PL

and BT x C4 showed good performance for crop

cycling index while C4 x WG recorded shortest

number of days to fruit filling. The disease response

traits (Figure 2) showed that OL x C4, BT x C4 and

C4 x WG had almost equal performances for index

of non-spotted leaves. However, C4 x WG recorded

the highest youngest leaf spotted at flowering with

exceptionally high leaf retention index. The yield

parameters are shown in Figures 3 and 4. A similar

trend was observed in the performances of the

different hybrids for bunch weight and total biomass,

with the highest and lowest values recorded by TL x

PL and BT x C4, respectively (Figure 3). In Figure 4,

a similar trend was observed in the performances of

the hybrids for number of hands and number of

fingers. TL x PL recorded the highest values while

C4 x WG recorded lowest values for both traits. The

hybrids equally showed a similar trend in their

performances for both fruit length and fruit

circumference, with the highest values recorded by

C4 x WG for both traits.

Figure 1: Bar charts showing the performances of four different genotypes of the primary hybrids for plant height, plant girth,

cycling index, and days to fruit filling.

050

100150200250300350400

OL x C4 BT x C4 C4 x WG TL x PL

Pla

nt h

eig

ht

(cm

)

Pedigrees

0

10

20

30

40

50

60

70


Pedigrees

Pla

nt

gir

th (

cm

)

0

20

40

60

80


Pedigrees

Cro

p c

ycli

ng

in

dex (

%)

0

20

40

60

80

100

120


Pedigrees

Days t

o f

ruit

fil

lin

g

Evaluation Of Diploid Hybrid Bananas Of Different Pedigree On Black Sigatoka Resistance

72

Figure 2: Bar charts showing the performances of four different genotypes of the primary

hybrids for index of non-spotted leaves, youngest leaf spotted at flowering and leaf

etention index.

Figures 5-8 show the performances of the six

secondary hybrids (generation 2) for various traits.

Plant height and plant girth appear to showed similar

trend in the hybrids (Figure 5). (BT x C4)(TL x PL)

and (BT x C4)(OL x C4) recorded the highest and

lowest values, respectively for both traits. The

highest and lowest performances were shown by (BT

x C4)(OL x C4) and (BT x C4)(PL), respectively for

crop cycling index, while (BT x C4)(BT x C4)

showed highest performance for days to fruit filling.

The disease response traits (Figure 6) showed that

(BT x C4)(OL x C4) had highest performance for

both index of non-spotted leaves and youngest leaf

spotted at flowering. No leaf was retained by (OL x

C4)(BT x C4) and (BT x C4)(OL x C4) at harvest.

The yield parameters shown in Figure 7 and 8

revealed that (BT x C4)(TL x PL) had highest

performance for bunch weight, total biomass,

number of hands, number of fingers, and fruit length.

(BT x C4)(PL) recorded lowest values for bunch

weight, harvest index, number of hands, and number

of fingers. (BT x C4)(OL x C4) had highest harvest

index, while there were not much variations in the

fruit circumference of the hybrids.

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

O L x C 4 B T x C 4 C 4 x W G T L x P L

P e d i g r e e s

Ind

ex

of

no

n-s

po

tte

d

lea

ve

s (

%)

0

2

4

6

8


P e d i g r e e s

Yo

un

ge

st

lea

f s

po

tte

d a

t

flo

we

rin

g

0

2 0

4 0

6 0

8 0

1 0 0


P e d i g r e e s

Leaf

rete

nti

on

in

dex (

%)

Igili D. N., Uguru M. I. and Baiyeri K. P.

73

Figure 3: Bar charts showing the performances of four different genotypes of the primary hybrids for bunch weight,

harvest index and total biomass.

0

2

4

6

8

1 0

1 2


P e d i g r e e s

Bu

nc

h w

eig

ht

(kg

)

0

5

1 0

1 5

2 0


P e d i g r e e s

Harv

est

ind

ex (

%)

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0


P e d i g r e e s

To

tal b

iom

ass (

kg

)

0

5

10

15

20


Pedigrees

Nu

mb

er

of

ha

nd

s

0

50

100

150

200

250

300

350


Pedigrees

Num

ber

of

fingers

0

5

10

15

20


Pedigrees

Fru

it le

ng

th (

cm

)

0

2

4

6

8

10

12

14


Pedigrees

Fru

it c

irc

um

fere

nc

e (

cm

)


74

Figure 4: Bar charts showing the performances of four different genotypes of the primary hybrids for number of hands, number

of fingers, fruit length, and fruit circumference.

Figure 5: Bar charts showing the performances of six different genotypes of the secondary hybrids for plant height, plant girth

cycling ndex, and days to fruit filling.

050

100150200250300350

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

Pedigrees

Pla

nt

he

igh

t (c

m)

05

101520253035404550

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

Pedigrees

Pla

nt

gir

th (

cm

)

0204060

80100120

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

Pedigrees

Cro

p c

yc

lin

g i

nd

ex

(%

)

020406080

100120140160

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

Pedigrees

Da

ys

to

fru

it f

illin

g

Igili D. N., Uguru M. I. and Baiyeri

K. P.

75

Figure 6: Bar charts showing the performances of six different genotypes of the secondary hybrids for index of non-spotted

leaves, youngest leaf spotted at flowering and leaf retention index.

01 02 03 04 05 06 07 08 0

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d i g r e e s

Ind

ex

of

no

n-s

po

tte

d l

ea

ve

s (

%)

012345678

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d i g r e e s

Yo

un

gest

leaf

sp

ott

ed

at

flo

weri

ng

0

5

1 0

1 5

2 0

2 5

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d i g r e e s

Leaf

rete

nti

on

in

dex (

%)


76

Figure 7: Bar charts showing the performances of six genotypes of the secondary hybrids for bunch weight, harvest

index and otal biomass.

0123456789

1 0

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d i g r e e s

Bu

nch

weig

ht

(kg

)

05

1 01 52 02 53 03 54 0

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d i g r e e s

Ha

rve

st

ind

ex

(%

)

05

1 01 52 02 53 03 54 0

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d i g r e e s

To

tal

bio

mass (

kg

)


K. P.

77

1

2

3

4

5

6

7

8

9

Figure 8: Bar charts showing the performances of six different genotypes of the secondary hybrids for number of hands, number of fingers,

fruit length, and fruit circumference.

0

2

4

6

8

10

12

(BT x

C4)(TL

x PL )

(BT x

C4)(BT

x C4)

(OL x

C4)(BT

x C4)

(BT x

C4)(OL

x C4)

(OL x

C4)(OL

x C4)

(BT x

C4)(PL )

Pe digre e s

Nu

mb

er

of

ha

nd

s

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

P e d ig re e s

Nu

mb

er

of

fin

ge

rs

02468

10121416

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

Pedigrees

Fru

it l

en

gth

(c

m)

0123456789

10

(BT x

C4)

(TL

x PL)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(BT x

C4)

(BT x

C4)

(OL

x C4)

(OL

x C4)

(OL

x C4)

(BT x

C4)

(PL)

Pedigrees

Fru

it c

ircu

mfe

ren

ce (

cm

)


78

DISCUSSION The four categories of the primary hybrids were the

crosses between obino l’ewai (OL) and Calcutta 4

(C4), bobby tannap (BT) and Calcutta 4, Calcutta 4

and Wh-O-Gu (WG), and tjau lagada (TL) and pisang

lilin (PL).

The performances of the four primary hybrids show

similar trends for plant height and plant girth, bunch

weight and total biomass, number of hands and

number of fingers, fruit length and fruit

circumference. These results suggest that the modes

of inheritance of these pairs of traits are the same in

generation 1. The cultivars most widely used at IITA

as female parents differ in their breeding value

(Vuylsteke et al. 1993c). The general good

performances of the crosses between tjau lagada and

pisang lilin for the yield and growth/phenological

traits suggest that clones with this parentage can be

used for the improvement of these traits. The crosses

between Calcutta 4 and Wh-O-Gu having general

good performance for the disease response traits,

suggests that clones from such crosses can be used for

black Sigatoka resistance improvement. The

performances of OL x C4, BT x C4 and C4 x WG for

index of non-spotted leaves were almost the same.

This is contrary to the report of Vuylsteke et al.

(1997) which stated that obino l’ewai had the highest

rate of transmission of black Sigatoka resistance to its

offspring.

The double-cross, (BT x C4) x (TL x PL) had general

good performance for the yield parameters in

generation 2. The cross, BT x C4 in generation 1

recorded lowest values for bunch weight, harvest

index, total biomass, fruit length and fruit

circumference. These results suggest that the good

performance of the double cross, (BT x C4) x (TL x

PL) for yield attributes must have been inherited from

the cross, TL x PL. This further substantiates the fact

that TL x PL can be useful in yield improvement. The

observation that the crosses (BT x C4) x PL had least

values for bunch weight, harvest index, number of

hands, and number of fingers, gives an indication that

the yield attributes’ improvement suggested by TL x

PL is likely to have come from tjau lagada (TL). OL

x C4 clones performed better than BT x C4 clones for

the yield attributes. This is in agreement with the

report of Vuylsteke et al. (1997), that the mean yields

of obino l’ewai progeny were higher than those of

bobby tannap-derived progeny.

On the other hand, the clones that came from the

double-crosses, (BT x C4) x (OL x C4) were observed

to have highest values for index of non-spotted leaves

and youngest leaf spotted at flowering in generation

2. This suggests that selection of these clones can be

of help in black Sigatoka resistance improvement.

REFERENCES GENSTAT. (2003). GENSTAT 5.0 Release 4.23DE,

Discovery Edition 1, Lawes Agricultural

Trust, Rothamsted Experimental Station.

IITA. (1992). Crop Improvement Division, Plantain

and Banana Improvement Programme: 1991

Annual Report and 1992 Work Plan. Ibadan,

Nigeria: IITA.

Meredith, D. S. and J. S. Larence. (1969). Black leaf

streak disease of bananas (Mycosphaerella

fijiensis): symptoms of disease in Hawaii,

and notes on the conidial state of the causal

fungus. Transactions of the British

Mycological Society 52:459-476.

Stover, R. H. (1983). Effect du Cercospora noire Sur

les Plantains en Amerique Centrale. Fruits

38-329.

Tribe, D. E. (1994). Feeding and Greening for World.

The Role of International Agricultural

Research. CABI, Wallingford Oxford, U.K.

274p.

Vakili, N. G. (1968). Response of Musa acuminata

species and edible cultivars to infection by

Mycosphaerella musicola. Tropical

Agriculture. 45:13-22.

Vuylsteke, D., R. Ortiz and R. Swennen. (1993a).

Genetic improvement of plantains at IITA.

Pages 267-282. In Breeding Banana and

Plantain for Resistance to Diseases and Pests

(Ganry J. ed). Montpellia, France: CIRAD

and INIBAP.

Vuylsteke, D., R. Ortiz, C. Pasberg-Gauhl, F. Gauhl,

C. Gold, S. Ferris, and P. Speijer. (1993b).

Plantain and Banana research at the

International Institute of Tropical

Agriculture. HortScience 28 (9): 873-874,

970-971.

Vuylsteke, D., R. Ortiz, R. Shaun, B. Ferris and J. H.

Crouch. (1997). Plantain Improvement.

Plant Breeding Reviews, Vol. 14. John Wiley

& Sons, Inc. p 267-320.


K. P.

79


COMPARATIVE EFFECTS OF NEEM (AZADIRACHTA INDICA)

EXTRACTS AND KARATE (LAMBDACYHALOTHRIN) IN THE CONTROL

OF THE ROOT KNOT NEMATODE (MELOIDOGYNE INCOGNITA) ON

CELOSIA ARGENTEA

*Okafor, S. A.

and Fadina, O.O.

Department of Crop Protection and Environmental Biology, University of Ibadan, Oyo State. *Corresponding author: [email protected]

ABSTRACT In order to compare the effects of lambdacyhalothrin, a synthetic pyrethriods and neem

(Azadirachta indica) extracts in the control of the root knot nematode of Celosia argentea, two

weeks old plants of C. argentea were inoculated with 7,000 eggs of Meloidogyne incognita. Seven

days after inoculation, the plants were treated with two concentrations of lambdacyhalothrin at

6,000ppm and 3,000ppm and neem oil extract at 2.14 ml/kg and 4.28ml/kg of soil. Untreated

nematode-inoculated C. argentea plants served as negative control while uninoculated C. argentea

plants served as positive control. Three weeks after inoculation and subsequently till the 8th

week,

data were collected on growth parameters such as stem height, number of leaves and yield

parameters such as fresh shoot weight, fresh root weight, and root galling indices. There were

significant differences between the untreated-nematode inoculated plants and all the other

treatments for both the growth and yield parameters except the mean galling indices. Although neem

oil at 2.14 ml/kg of soil consistently gave the highest means for leaf number, there were no

significant differences between the means for stem height even though untreated-nematode

inoculated plants had the lowest values. There was also no significant difference between

Lambdacyhalothrin-treated plants at both 6000ppm and 3000ppm for all the parameters considered.

Thus, neem oil at 2.14 ml/kg of soil is recommended as an alternative for Lambdacyhalothrin at

6000ppm and 3000ppm concentrations in the control of root knot nematode of C. argentea since the

leaf is the most important part and in view of its environmental friendliness.

Keywords: Neem extracts, lambdacyhalothrin, Meloidogyne incognita, Celosia argentea

INTRODUCTION Celosia argentea is an annual herbaceous

vegetable of the family Amaranthaceae. It has

its origin in West Africa, where it can be found

as a weed in open disturbed places (Omueti,

1980). In south-western Nigeria, it is known as

sokoyokoto (Yoruba). The leaves when boiled,

along with the young shoots can be used in

soups and stews. The seeds are used as a remedy

against diarrhea. The crop is highly susceptible

to nematodes, which is the main limiting factor

to its production (Schippers, 2000; Chweya and

Eyzaguirre, 1999). Intensive production of the

vegetable is therefore often accompanied with

frequent spraying of pesticides to improve its

cosmetic value thereby increasing profits

(Deang, 1991).

Nematodes are the most important pests of

vegetables in the tropics and of all the nematode

species, the southern root knot nematode

(Meloidogyne incognita) is the most common.

Studies have shown that because of the root knot

nematodes, it is very difficult and sometimes

impossible to grow important vegetables such as

okra, Celosia, tomato etc in the tropics

(Caveness and Wilson, 1975). Hussey (1985)

also reported that the root knot nematodes are

the most destructive group of nematodes known

at present in Nigeria. Attack by the root knot

nematode renders the plant vulnerable to

secondary infections caused by bacteria and

fungi. The symptoms shown by plants to attack

by Meloidogyne sp. are characterized by

numerous swellings or galls on roots of host

plants, disruption of water and nutrient uptake,

stunted growth, patchiness and chlorosis

(Hussey (1985). The different control measures

often adopted to control these root knot

nematodes on vegetables are; biological control,

use of resistant varieties, crop rotation, improved

cultural practices and the use of chemical

80

pesticides (nematicides). Chemical control

method has been found to be one of the most

effective ways of controlling the root knot

nematodes. One of such synthetic pyrethroids

that have been used in the control of root knot

nematodes is Lamdacyhalothrin, which was

introduced into the Nigerian market in 1984

(ICI, 1984). However, they have received series

of criticisms due to their roles in many

environmental hazards and their high toxicity,

pollution (abuse), actions on target and non-

target organisms and the problem of persistence

in the environment. Thus, the naturally occurring

pesticides have assumed importance because

they are easily bio-degradable, less toxic to

mammals and generally less persistent unlike the

chemical pesticides (Areekul, 1985).

Azadirachta indica (neem) is a native of India

but now found throughout West Africa where in

treating malaria in man. The tree is a fast

growing, sclerophyllous tree; it grows well in

climates from semi-arid to semi- humid and will

even thrive in places with less than 500mm of

rain per year. The effective ingredients are

present in all parts of the tree but are mostly

concentrated in the seeds. The oil content of the

seed is 35-45%. The Neem has broad range

action and is used as insecticides, repellants,

antifeedants, growth-inhibitor, fungicides and as

a nematicide (van Latum, 1985). Egunjobi and

Afolami (1975) reported over 80% increase in

yield of maize using water extracts from neem

leaves as a soil drench on maize field infested

with Pratylenchus brachyurus. They suggested

that the extracts were probably systemic in

function. This study was therefore set up to

compare the nematicidal potentials of

lambdacyhalothrin (synthetic pyrethroid) and

Neem extracts on the growth and performance of

C. argentea.

MATERIALS AND METHODS Planting: Top soil was collected at a fallow

bush behind the Department of Crop Protection

and Environmental Biology, University of

Ibadan, Ibadan, Nigeria. The soil was steam

sterilized at 160ºC for four hours, allowed to

cool and stored. Seeds of C. argentea were

collected from the National Institute for

Horticultural Research and Training (NIHORT),

Ibadan and were planted in 7kg pots and later

thinned to one healthy plant per pot. The design

used was a randomized complete block design of

six treatments replicated eight times. The

treatments are the uninoculated Celosia plant,

nematode-inoculated Celosia plants, nematode-

inoculated Celosia plant treated with

lambdacyhalothrin at 6,000ppm, nematode-

inoculated Celosia plant treated with

lambdacyhalothrin at 3,000ppm, nematode-

inoculated Celosia plant treated with neem oil at

4.28ml/kg of soil, and nematode-inoculated

Celosia plant treated with neem oil at 2.14ml/kg

of soil.

Preparation of plant extracts and inocula:

Neem seeds were collected from a tree in the

department of Chemistry, University of Ibadan

and its oil was extracted using soxhlet extraction

with N-Hexane. Solution concentrations of

6,000ppm and 3,000ppm were prepared by

diluting 6ml and 3ml of karate in 1,000mls of

water respectively. Extraction method used was

the Hussey and Barker (1973) method of

collecting inocula for Meloidogyne species. Eggs

of M. incognita were sourced from roots of C.

argentea which were washed in running tap

water and chopped into smaller pieces of about

1-2cm length before transferred into a conical

flask. After this, 0.35% sodium hypochlorite

(NaOCl) was added to the nematode solution

and shaken for 5 minutes and then rinsed over

two 200 mesh sieves to collect the large plant

debris. The filtrate was then passed through a

500 mesh sieve to collect the eggs. Water was

added continuously to the sieve containing the

egg. The resultant solution was then diluted with

distilled water to 1litre mark in a 1litre beaker.

Nematode egg population was estimated by

counting four samples of 2ml each from the

nematode solution using a Doncaster counting

dish and a tally counter under a light

microscope.

Nematode inoculation: Two weeks after

planting, Celosia plants were inoculated with

7,000 nematode eggs with the exception of the

controls. These eggs were added to the soil at the

base of each plant root with aid of a 5ml

hypodermic syringe after properly homogenizing

the inoculums in the beaker to uniformly expose

the nematode eggs. A week after inoculation,

the various treatments were applied to the plants.

Data collected on growth and yield parameters

were pooled before subjecting to analysis of

variance (ANOVA) using GENSTAT and means

were separated using the least significant

difference at 5% level of probability (LSD).

Effect Of Neem And Rarate On Root Kont Nematode

81

RESULTS Three weeks after planting (WAP), plants treated

with neem oil at 2.14mg/kg of soil gave the

highest mean number of leaves while the

nematode-inoculated plants gave the lowest.

This trend was also observed till eight weeks

after planting. There were no significant

differences at three and four weeks after

planting. At five to eight weeks after planting,

mean number of leaves varied significantly

among the treatments with the nematode-

inoculated plant being significantly

(p<o.o5)lower than others. However, at six

weeks after planting, nematode-inoculated plants

were not significantly (p<o.o5) different from

the nematode-inoculated Celosia plant treated

with neem oil at 4.28ml/kg soil while at seven

weeks after planting, it was not also different

from the nematode-inoculated Celosia plant

treated with lambdacyhalothrin at 3000ppm and

the nematode-inoculated Celosia plant treated

with neem oil at 4.28ml/kg soil (Table1). The

mean stem height of Celosia plants for the

duration of this experiment did not show any

significant (p<o.05) differences among the

treatments (Table 2).

Plate 1: Severely galled root of nematode-inoculated Celosia argentea plant

Table 1. Effect of lambdacyhalothrin and neem oil on mean number of Celosia argentea leaves

Treatment Weeks after planting

3 4 5 6 7 8

T0 17.75 34.60 61.80 81.40 100.00 120.0

T1 15.13 32.20 43.00 55.00 71.50 86.40 T2 18.38 35.50 62.90 74.60 96.10 130.60

T3 19.62 38.10 63.20 79.40 89.80 130.10

T4 16.50 34.50 61.60 73.00 86.80 109.40

T5 20.12 39.00 71.60 86.80 103.90 130.80

LSD0.05 NS NS 14.48 18.24 19.87 17.92

T0: Uninoculated Celosia plant; T1: Nematode-Inoculated Celosia plants; T2: Nematode-inoculated Celosia plant treated with

Lambdacyhalothrin at 6000ppm; T3: Nematode-inoculated Celosia plant treated with Lambdacyhalothrin at 3000ppm; T4: Nematode-inoculated Celosia plant treated with Neem oil at 4.28ml/kg application rate; T5: Nematode-inoculated Celosia plant

treated with Neem oil at 2.14ml/kg application rate.and NS: Non-Significant

Table 2. Effect of lambdacyhalothrin and neem oil on mean stem height of Celosia

argentea plants Treatment Weeks after planting

3 4 5 6 7 8

T0 7.88 12.50 20.55 33.40 50.90 59.60

T1 7.81 12.44 16.88 25.00 34.90 54.70

T2 8.44 12.31 20.00 30.70 46.20 56.30 T3 7.81 13.56 21.53 33.00 48.70 56.40

T4 7.56 12.65 20.12 31.10 46.90 60.30

T5 7.62 12.94 20.45 31.70 50.70 60.90

LSD0.05 NS NS NS NS NS NS


Lambdacyhalothrin at 6000ppm; T3: Nematode-inoculated Celosia plant treated with Lambdacyhalothrin at 3000ppm; T4:

Nematode-inoculated Celosia plant treated with Neem oil at 4.28ml/kg application rate; T5: Nematode-inoculated Celosia plant treated with Neem oil at 2.14ml/kg application rate.NS: non-Significant

Okafor, S. A. and Fadina, O.O.

82

Table 3: Effect of lambdacyhalothrin and neem oil on mean galling indices of Celosia

argentea

Treatment Mean Galling Indices

T0 1

T1 5

T2 3

T3 3

T4 2 T5 2



Nematode-inoculated Celosia plant treated with Neem oil at 4.28ml/kg application rate; T5: Nematode-inoculated Celosia plant

treated with Neem oil at 2.14ml/kg application rate.

Table 4: Effect of lambdacyhalothrin and neem oil on fresh shoot and fresh root weight of

Celosia argentea

Treatment Fresh Root Weight (g) Fresh Shoot Weight (g)

T0 18.29 44.84

T1 25.26 36.13

T2 21.98 40.58

T3 20.19 45.44

T4 23.16 41.50

T5 18.40 44.97

LSD0.05 2.498 4.351



Nematode-inoculated Celosia plant treated with Neem oil at 4.28ml/kg application rate; T5: Nematode-inoculated Celosia plant

treated with Neem oil at 2.14ml/kg application rate.

Mean galling indices on Celosia plants after

harvesting are shown in Table 3. The nematode-

inoculated plants had the highest mean galling

indices of 5 (severe galling) which was followed

by the lambdacyhalothrin treated plants at

6,000ppm and 3,000ppm that had mean galling

indices of 3 (mild galling). Nematode-inoculated

Celosia plant treated with neem oil at 4.28ml/kg

soil and 2.14mg/kg soil both had mean galling

indices of 2 (slight galling). The uninoculated

Celosia plants had mean galling indices of 1 (no

galling damage).

Yield components shows that the nematode-

inoculated Celosia plant treated with neem oil at

2.14mg/kg soil gave the highest fresh shoot

weight (44.97g) followed by the uninoculated

plants (44.84g). The nematode inoculated plants

had the least fresh shoot weight (36.13g) which

was significantly different from the remaining

treatments. The nematode-inoculated Celosia

plants treated with lambdacyhalothrin at

6,000ppm and 3,000ppm showed significant

difference between each other (Table 4).

Nematode-inoculated plants had the highest

fresh root weight of 25.26g which was

significantly different from the remaining

treatments while the uninoculated plants

(18.29g) had the lowest fresh weight (Table 4).

DISCUSSION The effectiveness of lambdacyhalothrin was

compared with neem oil for the suppression of

root knot nematode disease caused by M.

incognita on C. argentea. In this study,

significant reduction in growth and yield of

Celosia was observed but was more pronounced

in the uninoculated control. Odeyemi (2004) and

Odeyemi and Afolami (2008) have reported

stunted growth, root galling, fewer pods and

significant reduction in yield as main symptoms

of M. incognita infection on host crop.

Lambdacyhalothrin applied at the recommended

rates of 6,000 and 3,000 ppm showed

satisfactory control of root knot nematode on C.

argentea, thus affirming Fadina and Adesiyan

(1997) who had reported, with discernable

evidence some of the pyrethroids’ effects on the

various life stages of M. incognita. However

Celosia plants treated with lambdacyhalothrin at

6,000ppm showed a characteristic phytotoxic

symptom. Fadina and Adesiyan (1997) had that

lambdacyhalothrin used as soil drench on

soyabean was phytotoxic. This however

contradicts reports by Imperial Chemical

Industries (1984) that lambdacyhalothrin is not

toxic to vegetables at the recommended dose of


83

6,000ppm. In addition, neem oil may be

effective in the control of root knot nematode

disease of Celosia plants and this agrees with the

work of Egunjobi and Larinde (1978) who

reported that extracts from neem leaves contain

substances toxic to root knot nematodes.

Adegbite and Adesiyan (2005) also reported that

100% concentration of neem root extracts

caused larval mortality of root knot nematodes in

vitro. They concluded that it could be due to the

inhibitory effect of the extract and that the

inhibitory effect might be due to the chemicals

present in the extract that possess ovicival and

larvicidal properties. Neem extracts contained

alkaloids, flavonoids, saponives, amides

including benzamide and ketones that singly and

in combination inhibited hatching or caused

arrested development of Meloidogyne species

(Chitwood, 2002).

From this study, lambdacyhalothrin, 6,000ppm

and 3,000ppm and neem oil at 2.14ml/kg soil

and 4.28ml/kg soil are effective in the control of

root knot nematode disease of C. argentea due to

their inhibitory effects on the growth and

development of the root knot nematodes on the

crop. However, neem oil at 2.14ml/kg of soil is

recommended to vegetable growers as it

increased the yield (leaf number) of C. argentea

significantly. If lambdacyhalothrin is to be used

at all, the 3,000ppm application rate is advisable,

albeit considering the environmental implication.

The future looks bright for identifying new

classes of pesticides from natural plants to

replace the dangerous synthetic and expensive

chemicals used at present (Adegbite and

Adesiyan, 2005).

ACKNOWLEDGEMENTS The author are grateful to Dr. (Mrs.) A.H. Alabi

and Mr. O.O. Abaire of the Chemistry dept., U.I

for helping with the extraction of neem and also

Dr. Abiodun Claudius-Cole, Nematology unit,

Dept. of CPEB, U.I for providing the needed

assistance and laboratory for this experiment.

We also appreciate Mr A.O. Adediji and Mr O.

D. Ojelabi for critical review of this document.

REFERENCES Adegbite, A.A. and Adesiyan, S.O. (2005). Root

extracts of plants to control root knot

nematode on edible soybean. World

journal of Agricultural Sciences

1(1):18-21.

Areekul, S. (1985). Ecology environmental

consideration of pesticides. Dept of

Entomology, Kasetsart University,

Bangkok 10900, unpublished

manuscript.

Caveness, F.E. and Wilson, G.F. (1975). The

effects of root-knot nematodes on

growth and development of Celosia

argentea L. In Proc.ISHS 4th

African

symposium on horticultural crops,

Kumasi, Ghana. pp71-73,

Chitwood, D.J. (2002). Phytochemicals based

strategies for nematode control.

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249.

Chweya, J.A. and Eyzaguirre, P.B. (1999). The

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protection for resource poor

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resource poor farmers. CTA-NRI,

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– 8th

Nov 1991. pp 131-135.

Egunjobi, A.O. and Afolami, S.O. (1975) Effects

of water soluble extracts of neem

(Azadirachta indica) on

Pratylenchus brachyurus and on

maize. Journal of Nematology

7(4): 321.

Egunjobi A.O and Larinde M.A (1978)

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Nigeria: effects of some

ammendments on population of

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the growth and population of

maize in Ibadan. Nematologica

Mediterranea 3: 65-73.

Fadina, O.O. and Adesiyan, S.O. (1997). The

efficiency of Karate

(Lambdacyhalothin) in controlling

Meloidogyne incognita (Kofoid

and White) on soybeans (Glycine

max.L. Merril). Agrosearch journa

3:(1-20. 50-51.

Hussey R. S. and Barker, K.R. (1973). A

comparison of methods of

collecting inocula for

Meloidogyne sp. including a new

technique. Plant disease. Rep. 57:

1025-1028.

Hussey, R.S., 1985. Host–parasite relationships

and associated physiological

changes. In: Barker, K.R., Carter,

C.C., Sasser, J.N. (Eds.), An

dvanced treatise on Meloidogyne,

vol. 1. Biology and Control,

Raleigh, North Carolina State

University Graphics, pp. 143–153.

Imperial Chemical Industries (1984). Karate

Insecticide. ICI Technical data,

ICI, U.K. 21pp

Van Latum, E.B.J. (1985) Neem Tree in

Agriculture: its uses in low-input-

pest management; Ecoscript 31,


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stichting mondiaal Alternatief,

Zandvoort.

Odeyemi, I.S. and Afolami, S.O. (2008). Host

status of three Vigna unguiculata

varieties to Meloidogyne incognita

and its consequence on yield.

Nigerian Journal of Plant

Protection. 25: 1-7

Odeyemi, I.S. (2004). Effect of Glomus mosseae

(Nicol and Gerd.) and Trappe on

the pathogenicity of Meloidogyne

incognita on cowpea (Vigna

unguiculata (L) Walp.) M. Agric

dissertation, University of

Agriculture, Abeokuta, Nigeria.

204 pp.

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argentea Cultivars. Expl.

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vegetables. An overview of the

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Cooperation. pp 16-22.

85


EFFECTS OF PHOSPHORUS, MICRONUTRIENTS AND RHIZOBIA

INOCULATION ON GROWTH AND YIELD OF SOYBEAN (GLYCINE MAX

L.) ON A FALLOWED LOAMY SOIL

Umar, F.G.1*

and Yusuf, A.A2

1 Department of Soil Science, Faculty of Agriculture, Bayero University, Kano, P.M.B 3011,

Kano, Nigeria. 2 Department of Soil Science, Faculty of Agriculture / Institte for Agricultural Research,,

Ahmadu Bello University, P.M.B 1044, Samaru-Zaria, Nigeria.

*Corresponding author e-mail: [email protected]

ABSTRACT Greenhouse and field trials were conducted in 2010 and 2011 to evaluate the effects of phosphorus,

micronutrients and rhizobia inoculation on growth and yield of soybean (Glycine max L.) on a

fallowed soil. The treatments consisted of three nutrient levels: all nutrients (Macronutrients-P, K, S,

Ca, and Mg, and Micronutrients-Mn, Zn, Cu, Co, B, and Mo); all nutrients minus phosphorus; and

all nutrients minus micronutrients; and eight inoculants: 1495 MAR, TSBF mixture, Legumefix,

Histick, IRJ 2180A, RACA 6, TSBF 560, Biofix (soybean) and two controls: positive (mineral N)

and negative (without mineral N) were arranged in randomized complete block design and replicated

three times. The growth characters such as plant height, root length, chlorophyll content, number of

leaves per plant and leaf area, were assessed at eight weeks after planting (WAP). The result showed

a highly significant (p<0.001) difference among the above characters in terms of nutrient levels.

However, there was no significant difference among inoculant treatments except in chlorophyll

content and plant height. Furthermore, the interaction between nutrients and inoculation was only

significant (p<0.001) in chlorophyll content. In the field, only three inoculants were used;

Legumefix, RACA 6 and TSBF mixture while molybdenum was the test micronutrient and were

arranged in split-split plot design. The effect of the nutrients on soybean productivity followed

similar trend to that of the greenhouse study. The application of phosphorus gave higher yield (1.61

t/ha) than minus P (0.47 t/ha). However, there was no significant difference in terms of

molybdenum. Moreover, inoculation had no significant effect on grain yield. This shows that bush

fallowing is not enough to supply the phosphorus requirement of soybean when re-open for

cultivation while micronutrient and rhizobia inoculation seem not be necessary.

Key words: Fallow soil, Inoculant, Micronutrients, Phosphorus, and Soybean.

INTRODUCTION

Nigeria has N and P deficient soils but going

from the arid to the humid regions there are

absolute increases in N and P contents of these

soils. With increasing rainfall, the overall

biomass production and turnover increase and

the soil organic matter content and the relative

availability of N improve, especially under

fallow conditions. However, the soils are

increasingly leached and become more acidic,

with variable charged minerals rich in Fe and Al

oxyhydroxides which have high affinity for P

(Breman and van Reuler, 2002; Agbenin, 2003

and Abdu, 2009), thereby making P availability

progressively limited. The deficiency of N is

also a common phenomenon in such soils.

Legumes could be useful in tackling such N

deficiency and also for the low level of

available P and sometimes Mo in the soil

(Breman and van Reuler, 2002 and Unkovich et

al., 2008).

Several works have reported response of

soybean to phosphorus application in soils that

are low in P as the process of N2 fixation which

is energy intensive in the form of ATP (Havlin et

al., 2005). Also, inoculation of soybean with

effective rhizobium will improve its productivity

86

especially where indigenous soil population is

not adequate in strain number, quality or

effectiveness to carry out biological nitrogen

fixation (Olufajo and Adu, 1991; Sanginga et al.,

1995; Osunde et al., 2003; Jalaluddin, 2005;

Fatima et al., 2007; Tahir et al., 2009 and Yusuf

et al., 2011). The effectiveness of such

inoculation is often apparent in fallowed soils

not recently cultivated to soybean. In addition,

positive responses to Mo have been reported,

though not consistent (Adu and Olufajo, 1986;

Pal et al.,1989; Olufajo and Adu, 1991 and

Yakubu et al., 2010).

Therefore, this reseach work was conducted with

the aim of assessing the effects of phosphorus,

micronutrients and inoculation on growth and

yield of soybean (Glycine max L.) on a fallowed

soil. The main objectives of this study were as

follows:

1. to assess the effect of laboratory and

commercial rhizobia inoculants on the

growth and yield of soybean;

2. to assess the effect of phosphorus on

the growth and yield of soybean on a

fallow soil; and

3. to assess the effect of micronutrients

on the growth and yield of soybean on a

fallow soil.

MATERIALS AND METHODS

Description of the experimental site

This experiment was conducted in two stages: a

greenhouse study in 2010 at the Department of

Soil Science, Ahmadu Bello University, Zaria

(110 9

’ N, 7

0 37

’ E) and a field trial in 2011 on a

fallowed field at the Institute for Agriculture

Research (IAR) Samaru, Ahmadu Bello

University, Zaria (110 10

’ N, 7

0 36

’ E). This

location has a monomodal rainfall pattern

concentrated almost entirely in five months

(May/June to September/October ) of the

cropping season and is on a well-drained leached

soil. The experimental site has been under fallow

for over 20 years.

Greenhouse study

Soil sample preparation Bulk soil sample was collected from the fallow

field at 0-30 cm depth, air-dried, crushed, sieved

through 4-mm mesh and used for the

greenhouse experiment. A sub-sample was

collected and sieved through 2-mm mesh for

physico-chemical analyses. The bulk soil was

measured in plastic pots (10 kg each), mixed

with nutrient solution which contains all

nutrients, all nutrients minus P source, all

nutrients minus micronutrients and allowed to

stabilize for 24 hours before planting.

Treatments and experimental design Soybean variety (TGx 1448-2E) was inoculated

with eight different inoculants [1495 MAR;

TSBF mixture; Legumefix; Histick; IRJ 2180A;

RACA 6; TSBF 560 and Biofix (soybean)] and

two controls [positive control (with mineral N)

and negative control (without mineral N)] at

three fertility levels ( All nutrients

(Macronutrients-P, K, S, Ca, and Mg, and

Micronutrients-Mn, Zn, Cu, Co, B, and Mo), No

phosphorus, No micronutrients (Mn, Zn, Cu, Co,

B, and Mo)) based on the amount of nutrient that

will give optimum growth. The experiment was

laid out in a Radomised Complete Block Design

(RCBD), replicated three times.

Seeds sterilization, inoculation and planting The seeds were surface sterilized to disinfect

them from any contamination, especially from

rhizobia. This was done by immersing the seeds

in 95% ethanol for 10 seconds and thereafter

decanted. The seeds were then put into solution

of 0.5% sodium hypochlorite for 3 minutes and

rinsed six times with distilled water. After

surface sterilization, the seeds were inoculated

with eight different peat-based inoculants. And

then planting was done using four seeds per pot

and thinned down to two a week after

germination. After planting, the plants were

irrigated once every day depending on the

moisture content, which was determined by feel

method.

Data collection Growth characters, such as chlorophyll content,

plant height, root length, number of leaves and

leaf area, were assessed at eight weeks after

planting (WAP). The chlorophyll content was

measured using Minolta SPAD-502 chlorophyll

meter while plant height, root length and leaf

area were measured using a meter rule. Number

of leaves per plant was also recorded.

Laboratory analyses The following soil physico-chemical analyses

were conducted: particle size analysis; soil pH;

soil organic carbon; total N as described by van

Reeuwijk (1992); available P as described by

Mehlich (1984); exchangeable acidity and

exchangeable bases as described by Anderson

and Ingram (1993).

Field study

Land preparation, planting and weeding. The field was stumped, ploughed, harrowed and

ridged with a ridger set at 75cm apart and

planted with soybean by drilling at inter and

intra spacing of 75cm × 5cm. All plots were

manually weeded using hoe at two and six

WAP.

Effects Of Phosphorus, Micronutrients And Rhizobia Inoculation On Growth And Yield Of Soybean 87

Treatments and experimental design The treatments consisted of two levels of Mo ( 0,

250 g ha-1

), two levels of P ( 0, 30 kg ha-1

) and

three inoculants (Legumefix, TSBF mixture and

RACA 6) with two controls (positive and

negative) arranged in a Split-Split Plot Design

and replicated three times. The positive control

contained mineral N at 30 kg N ha-1

while the

negative control had no mineral N. The P and N

were applied basally as triple super phosphate

(46% P2O5), and Urea (46% N), respectively

whereas Mo was folially applied by dissolving

4.6 g Na2MoO4.4H2O in 15L of water for a plot

and sprayed using CP3 knapsack sprayer.

Potassium as muriate of potash (K2O) was

applied to all plots at 30 kg ha-1

.

Harvesting and data collection Harvesting for grains and haulms was done at

maturity, when the leaves had turned yellow and

started shedding. The grain yield and haulms

weight were determined by weighing for each

plot and later extrapolated on hectare basis.

Statistical analysis

The data collected were subjected to Analysis of

Variance (ANOVA) and differences between

means were separated using Duncan’s Multiple

Range Test (DMRT). The SAS software was

used for all statistical analyses (SAS, 2008).

RESULTS AND DISCUSSION Physico-chemical properties of the

experimental soils Detailed physico-chemical properties of the soils

used for the experiment are presented in Table 1.

Result showed that the soil loamy in texture.

The pH in water was slightly acidic (6.1-6.5).

The organic C (OC) and available P were low, a

typical feature of the savanna soil (Abdu, 2009).

The CEC was medium while the total N was

high. This might be attributed to the fallow

condition of the site. The exchangeable Ca and

Na were medium while K and Mg were high

(NSPFS, 2005). Moreover, available

micronutrients analysed were above critical

levels. These results showed the need to apply P

on this soil, as the value of P (1.78 mg/kg) was

below critical level (10-15 mg/kg) for soybean

production (Kamara et al., 2007).

Key: AN (All nutrient) NM (No micronutrient) NP (No phosphorus)

Fig. 1: Interaction between nutrients and rhizobia inoculants on chlorophyll content

Umar, F.G.* and Yusuf, A.A

88

Table 1: Physico-chemical properties of the soil used for the experiment (0-15cm)

Treatment effects on selected agronomic

features –greenhouse study

Plant height: The results presented in Table 2

showed a very highly significant (p<0.001)

difference among nutrient treatments in plant

height with All Nutrient (AN) being higher than

No Phosphorus (NP). However, No

Micronutrient (NM) was at par with All Nutrient

(AN). The percentage difference between AN

and NP was about 37.8%. Moreover, there was

highly significant (p<0.01) difference in terms of

inoculant, with inoculants and negative control

being higher than positive control. However, the

interaction between nutrient and inoculant (N×I)

was not significant (p>0.05). Several works

(Adu and Olufajo, 1986; Pal et al., 1989; Shahid

et al., 2009 and Bekere and Hailemariam, 2012)

have obtained similar results. For instance, Pal et

al. (1989) observed that plant without fertilizer P

were smaller and apppeared to be stunted

compared with those supplied with fertilizer P

while Molybdenum had no such visible effects

on crop growth. Tahir et al. (2009) reported 41%

difference between inoculated soybean and

uninoculated control.

Root length: The results (Table 2) revealed a

very highly significant (p<0.001) difference in

root length among the nutrient treatments with

AN having longest root while NP had the

shortest with about 30% percentage difference.

However, there was no significant difference

(p>0.05) in root length between inoculants.

Likewise, the interaction between nutrient and

inoculant (N×I) was not significant (p>0.05).

These results

Property Test value

Sand (g/kg) 420

Silt (g/kg) 400

Clay (g/kg) 180

Textural class Loam

pH (H2O) 1:1 6.1

Organic C (g/kg) 8.3

Total N (g/kg) 2.5

Available P (mg/kg) 1.78

Exchangeable cations (cmol(+)/kg)

Ca 2.17

Mg 1.16

K 0.33

Na 0.14

CEC (cmol(+)/kg) 6.8 Available micronutrient (mg/kg)

Cu 0.78

Mn 37.29

Fe 80.46

Zn 1.6

Mo 1.5

Effects Of Phosphorus, Micronutrients And Rhizobia Inoculation On Growth And Yield Of Soybean

89

agree with the findings of Tahir et al. (2009)

who observed 33% difference in root length of

soybean crop that received P fertilizer compared

to the control.

Number of leaves per plant: The results of this

study showed that the soybean crop exhibited a

very highly significant (p<0.001) difference in

number of leaves per plant (with the highest

number produced by AN and the lowest by NP)

due to nutrient treatments (Table 2). The

percentage differences between AN and NP was

about 59%. However, the effects of the

inoculants were not significant (p>0.05).

Likewise, interaction between nutrient and

inoculant (N×I) was not significant (p>0.05).

Similar results were reported by Pal et al. (1989)

that plants supplied with fertilizer P retained

their leaves longer, and hence had more number

of leaves.

Leaf Area: This study showed a very highly

significant (p<0.001) difference in leaf area in

terms of nutrients with AN having widest leaf

area while NP was smallest (Table 2). The

percentage difference between AN and NP was

about 52%, implying that AN was more than

double the area of NP. Meanwhile, there was no

significant difference (p>0.05) between AN and

NM. However, inoculant had no significant

(p>0.05) effect on the leaf area. Likewise,

interaction between nutrient and inoculant

followed the same trend with inoculant. A

similar result was found by Chiezey and Odunze

(2009) that application of P increased leaf area

index of soybean crop.

Cholophyll content: Chlorophyll content result

(Table 2) showsed a very highly significant

(p<0.001) difference in all treatments (both

nutrients and inoculants). The interaction

between nutrients and inoculants (N×I) was also

significant (p<0.001). The result further showed

that, AN had higher chlorophyll content while

NM had the least in nutrient treatment. This

means micronutrient affect N synthesis. In this

trial, plants grown with no micronutrient (NM)

showed chlorotic colour characteristic of N

deficiency. Where N was applied even without

micronutrients, such symptom disappeared

(Havlin et al., 2005; Johnson, 2006). For the

inoculants, positive control had highest

chlorophyll content (36.46 mg plant-1

), followed

by the MAR 1495 inoculant (34.96 mg plant-1

),

with the least being the negative control (28.20

mg plant-1

).

The interaction between nutrient and inoculant

(Fig. 1) showed that applying all nutrient (AN)

plus inoculation gave highest chrorophyll

content in all inoculants while the least was with

applying nutrient with no micronutrient (NM).

The results implied that N application could

reduced the effect of micronutrient deficiency,

thereby making P the most limiting nutrient.

This was similar to what was observed by

Kamara et al. (2007) that when there was

deficiecy of other nutrients, the response to P

application might not be well pronounced.


90

Table 2: Effects of nutrient and rhizobial inoculation on plant height, root length, number of

leaves per plant, leaf area and chlorophyll content of soybean at eight weeks after

planting in 2010 greenhouse study at Samaru

Treatments

Plant

Height

(cm)

Root

Length

(cm)

Number

of leaves

(plant-1)

Leaf

area

(cm2)

Chlorophyll

content

(mg plant-1)

Nutrients (N)

AN 54.20a 48.38a 46a 69a 38.65a

NM 55.08a 42.10b 39b 65a 26.48c

NP 33.72b 33.67c 19c 33b 33.84b

Mean 47.67 41.38 35 56 32.99

SED 1.09 1.87 2.11 2.68 0.83

Inoculants (I) Biofix 47.57a 44.39 33 59 31.44c

HiStick 49.01a 45.17 37 57 32.98bc

IRJ 2180A 49.96a 45 32 56 32.28bc

Legumefix 49.62a 36.28 31 58 32.29bc

MAR 1495 47.81a 41.11 34 56 34.96ab

RACA6 48.54a 42.33 36 57 34.07abc TSBF mixture 46.61a 41.44 35 55 34.46abc

TSBF 560 46.19a 40.44 34 52 32.77bc Negative control 49.26a 40.78 38 57 28.20d

Positive control 42.11b 36.89 37 49 36.46a

Mean 47.67 41.38 35 56 32.99

SED 1.99 3.41 3.86 4.89 1.51

Interaction

N×S NS NS NS NS ***

Means followed by the same letter(s) within a treatment are not significant (p<0.05) using DMRT (Duncan’s Multiple

Range Test), NS = Not significant, *** very highly significant (p<0.001), AN = All Nutrient, NP = No Phosphorus

and NM = No Micronutrients

Table 3 Effects of phosphorus, molybdenum and rhizobial inoculation on grain yield, 100-grain

weight and haulms dry weight of soybean at Samaru in 2011 wet season.

Treatments

Grain yield

(t/ha)

100-grain weight

(g)

Haulms dry weight

(t/ha)

Phosphorus (P) rate (kg/ha)

0 0.47b 10.93b 1.30b

30 1.61a 11.70a 4.52a

Mean 1.04 11.32 2.91

SED 0.07 0.14 0.22

Molybdenum (Mo) rate (g/ha)

0 1.087 11.4 2.96

250 0.999 11.23 2.86

Mean 1.04 11.32 2.91

SED 0.07 0.14 0.22

Inoculant (I)

Legumefix 1.04 11.67a 2.81

RACA6 0.98 11.08b 2.71

TSBF mixture 1.15 11.50ab 3.06

Negative control 1.00 11.25ab 2.99

Positive control 1.05 11.08b 2.98

Mean 1.04 11.32 2.91

SED 0.11 0.21 0.35

Interactions

P×Mo NS NS NS

P×I NS NS NS

Mo×I NS NS NS

P×Mo×I NS NS NS

Means followed by the same letter(s) within a treatment are not significant (p<0.05), using DMRT = Duncan’s Multiple Range Test, NS = Not significant


91

Effects of phosphorus, molybdenum and

rhizobial inoculation on grain yield, 100-

grain weight and haulms dry weight of

soybean at Samaru in 2011 wet season The results of grain yield, 100-grain weight and

haulms dry weight of soybean were significantly

(p<0.001) influenced by P application (Table 3).

The difference between the two P rates was

about 71% for both grain yield and haulms dry

weight. However, Mo rates had no significant

(p>0.05) effect on both grain yield, 100-grain

weight and haulms dry weight while inoculant

was only significant (p<0.05) on 100-grain

weight, with Legumefix being highest and

RACA 6 and positive control the lowest.

Moreover, the interactions were not significant

(p>0.05).

These results corroborated the findings of Pal et

al. (1989) who recorded significant yield

increase due to the application of P in ten out of

eleven experimental trials with 13.2 and 26.4 kg

P ha-1

increasing grain yield by 29 and 210%

over the contol, respectively. Furthermore, the

effect of Mo was not significant in eight out of

nine experiments, whereas P×Mo interaction

was only significant in three experiments out of

nine. Similar results were also obtained in other

works (Mokwunye and Bationo, 2002; Kamara

et al., 2007; Tahir et al., 2009 and Bekere and

Hailemariam, 2012) with respect to P rates.

Moreover, a similar result was observed with

respect to 100-grain weight response to P rates

by various researchers (Kamara et al., 2007;

Bekere and Hailemariam, 2012) that application

of 40 kg P ha-1

significantly increased 100-seed

weight compared to 0 and 20 kg P ha-1

rates.

The final yield of a crop is a function of

cummulative contribution of its various growth

and yield parameters which were influenced by

various agronomic practices and environmental

conditions (Shahid et al., 2009). Therefore, it

was not suprising that grain yield and haulms

dry weight were not significantly affected by Mo

and inoculation as well as various interactions,

as most of the growth characters assessed, such

as number of leaves per plant and leaf area, were

not significantly influenced by such treatments.

CONCLUSION The results of this research showed that bush

fallowing is not enough to supply the

phosphorus requirement of soybean when re-

open for cultivation while the application of

micronutrient and rhizobia inoculation seems not

be necessary.

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93


PHENOTYPIC AND GENETIC VARIATION OF S2 MAIZE (ZEA MAYS L.)

SELECTIONS FROM HIGH AND LOW INPUT CONDITIONS IN

CONTRASTING NITROGEN ENVIRONMENTS

Emede

*T. O. and Alika J. E.

Department of Crop Science, University of Benin, Benin City, Nigeria *Correspondence Author: [email protected]

ABSTRACT Nitrogen stress is a major constraint to maize production in sub-saharan Africa (SSA). Thus, improved

maize germplasm tolerant to low N with the capacity to utilize N more efficiently together with

improved agronomic practices appear to be the primary remedy for low input management. As part of

the breeding strategy, effort is currently being made to use second generation selfed (S2) selection to

improve maize for low N tolerance. This study assessed genotypic variation, heritability, G x E, and

genetic gain among selected high and low N S2 lines. Top 10 high N and 10 low N S2 progenies

selected from each of the high and low N environments, respectively, were used for the study. The

combined population of the selected S2 progenies, from both high and low N were planted during the

early and late seasons in separate High N and Low N environments at NIFOR, near Benin City, Edo

State and Ozoro, Delta State, respectively. Fertilizer was not applied in the low N environments, while

the high N environment was fertilized at the rate of 150 kg N, 75 kg P2O5 and 75 kg k2O per hectare. A

randomized complete block design with two replications was used for the study. The average grain

yield of the 10 low N progenies in high and low N environments was 2.5 t/ha and 1.0 t/ha, respectively,

while the average grain yields of the 10 high N progenies were 2.3 t/ha and 0.9 t/ha in high and low N

environments, respectively. The greater grain yield of low N progenies in both high and low N

environments, although not significant, implied that they were more efficient in the use of nitrogen.

Heritability estimates among the selected 10 high N and 10 low N S2 progenies were generally lower

under low N environments than high N. However, low N progenies expressed higher heritability

estimates for grain yield and other secondary agronomic characters than high N progenies under high

and low N environments. The magnitude of genotypic variance, heritability and gains from selection is

affected by N level and type of genetic material. The results also confirmed the effectiveness of S2

progeny selection as a population improvement procedure capable of improving the performance of

maize population for low N tolerance.

Keywords: S2 maize, High N environment, heritability, low N environment.

INTRODUCTION Maize was introduced to Europe from Central

and South America in 1492 by Columbus from

where it spread to Africa (Van Eijaanaten,

1964). Later, Carribean flint was introduced to

East Africa, by explorers from Portugal and

Persian Gulf and became the most predominant

type of maize in Africa (Obilana and Asnani,

1980). Although it is an introduced crop in

tropical Africa, maize has become well

integrated into the farming system and dietary

habits (Erfron, 1985). Since its introduction to

Africa, maize has spread and is cultivated

extensively all over the continent with wide

adaptation to many ecological regions.

Maize is grown virtually in all parts of Nigeria

from the mangrove swamps in the coastal areas

of the south to the Sudan/Sahel ecologies of the

north. It is a staple food crop grown throughout

the year, and eaten in various forms and used

extensively as raw-material in the

confectionery, brewery and livestock industries.

Improved varieties are being developed on a

regular basis by the National Agricultural

Research System and the International

Agricultural Centres (Ogunbodede et al., 2003).

However, one of the factors limiting maize

production in sub-saharan Africa is the wide

occurrence of low soil nitrogen (Low N) in

farmers’ fields. In seasons with adequate

rainfall, soil nitrogen can be leached below the

root zone (Bennet et al., 1989) and the crop

suffers nitrogen deficiency, Soil nitrogen

deficiency is further worsened by the wide

spread removal of crop residues for use as

animal feed and fuel (Zambezi and Nwambula,

1997). Moreover, the high cost of nitrogen

fertilizer and poor weed management increase

the incidence of nitrogen stress in many

94

instances (Lafitte and Edmeades, 1994).

Fertilizer can correct soil limitations but their

high cost and the uncertain economic return in

tropical regions are a factor of high risk for

producers. The situation is further aggravated by

the poverty prevailing in many countries in sub-

saharan Africa where many small holder farmers

cannot afford fertilizer in the absence of

government subsidy. The estimated annual loss

of maize grain yield due to low nitrogen stress

alone varies from 10 to 50% (Wolfe et al, 1988;

Longrono and Lothrop, 1997). It is therefore

necessary to develop maize genotypes tolerant to

low N stress in order to increase the productivity

of maize. One effective strategy to sustain maize

production and reduce fertilizer cost is to

develop maize genotypes with high nitrogen use

efficiency and high yield potential.

Nitrogen use efficiency is defined as the ability

of a genotype to produce superior grain yields

under low soil N conditions in comparison with

other genotypes (Graham, 1984; Sattelmacher et

al., 1994). It would be desirable to combine the

breeding goals of yield improvement for

conditions with high input of N fertilizers and

yield improvement for low N input conditions.

In principle, the following two breeding

strategies are possible (Atlin et al., 2000;

Falconer, 1952): (i) Indirect improvement

involving the selection at only one N level,

whereby performance at the the other N level is

improved by correlated response; (ii) Combined

improvement which involves selection based on

an index of the weighted performance means at

high and low input of N. Alternatively, it would

be necessary to breed for high nitrogen and low

nitrogen environments separately. To decide

which of the strategies would be the most

appropriate, knowledge of quantitative genetic

parameters such as genotypic and phenotypic

variance components, heritability estimates,

genotypic and phenotypic coefficient of

variation, as well as predicted gains from

selection, and selection response under high and

low N conditions is necessary. So far, previous

studies were conducted using European and US

source materials and further studies were,

therefore, needed to evaluate genotypic variation

for grain yield and other traits in high and low N

environments using tropical maize S2 progenies.

The objective of this present study was therefore

to evaluate genotypic variation, heritability,

genotype x environment (GxE) interaction, and

genetic gain of S2 maize lines selected under

high and low Nitrogen environments.

MATERIALS AND METHOD The selected second generation selfed (S2) maize

lines used in this study were developed from an

open pollinated (OP) elite cultivar; TZBR

ELD.3C2 with considerable genetic diversity.

Selfing was carried out in the breeding nursery

of the Department of Crop Science, University

of Benin, Benin City in two environments, high

N and low N. The top 10 high N and 10 low N

S2 progenies were selected from a total of 100

high N and 64 low N S2 progenies, respectively

(Emede and Alika, 2012). The evaluation of the

selected 10 high N and 10 low N S2 progenies

was carried out in the research farms of the

Nigerian Institute for Oil Palm Research

(NIFOR), near Benin City (Latitude 6o 33’ N,

Longitude 5o

33’ E), Edo State, and Delta State

College of Agriculture, Ozoro (latitude 6° 13’ E,

longitude: 5° 33’ N). Both locations are situated

in the rainforest ecological zone of Nigeria with

average rainfall of 2500 mm. The selected 10

high N and 10 low N S2 progenies were

combined in a single experiment to evaluate

their performance under high N and low N

environments. The combined population of the

selected high N and low N S2 progenies were

planted during the early and late seasons of the

year in separate Low N and High N experiments

at the two locations. In NIFOR, early season

planting was done in April 30 and May 1 for

High N and Low N experiments, while at Ozoro

early planting was done in June 2nd and 3rd for

High and Low N, respectively. Late season

planting was done in NIFOR in August 20 and

21 for High N and Low N, respectively, while in

Ozoro it was August 24 for both High and Low

N experiments. In all experiments, the 10 high N

and 10 low N S2 progenies were planted in a

randomized complete block design with two

replications. All maize entries were planted in a

two row-plot of 5m long with 75cm between

rows and 50cm within rows at two plants per

stand (53,333 plants per hectare). They were

over sown and thinned to the desired plant

density, and they were kept free of weeds.

The plot for low nitrogen experiments received

no fertilizer application, while the plot for high

Nitrogen experiments received 75kg N, 75kg

P2O5 and 75kg K2O/ha at planting, and at four

weeks after planting, 75kg N/ha was applied as

top dressing. Low and high N fields were

adjacent to each other and except for N, P and K

fertilization; management was same for both N

levels.

Phenotypic and Genetic Variation of s2 Maize SELECTIONS 95

Data Collection Days from sowing to 50% pollen shed (anthesis

date) and 50% silk extrusion (silking date) were

determined using all plants in a plot and anthesis

silking interval (ASI) was calculated as (silking

date – anthesis date). Leaf area of two plants per

plot was measured two weeks after the silking

stage. Area of individual blades was calculated

as LxWx 0.75 (Hassan et al., 2010), where L

and W are maximum blade length and width,

respectively. Plant height was taken at maturity

from ground level to collar of the most upper

leaf of the plant. Ear height was also taken at

maturity from ground level to the uppermost ear.

Ears were harvested from a bordered area at

physiological maturity, ear number was

determined, and ears per plant calculated. Ears

were weighed on the field and seed samples

were collected for each maize line for

determination of moisture content percentage at

harvest. Ears were dried, shelled and shelling

percentage was determined for each maize line.

Grain yield, expressed in t/ha, was obtained by

multiplying the field weight with the shelling

percentage and was adjusted to 15% moisture.

Data were also collected on Starch yield of

individual progeny and was measured by the

laboratory wet milling process as described by

Akingbala et al., (1981).

Statistical Analysis Analysis of variance was calculated for each

level of N and progeny type separately with SAS

Software Computer Package (SAS, 2002). All

factors were considered as random effects.

Estimates of genotypic variances (σ2G), genotype

by environment interaction (σ2

GE) and error

variances (σ2E) were estimated from the

ANOVA table calculated for each experiment

(Snedecor and Cochran, 1980; Alika, 2006) as

follows:

: σ2G = (MS S2 progenies – MSGE) / re

σ2GE = (MSGE - MSE) / r

σ2

E = MSerror

Where MS S2 progenies, MSGE and MSerror are

mean squares for S2 progenies, genotype x

environment interactions and error, respectively

from the ANOVA Table, while r is number of

replications, and e is the number of

environments.

Broad -sense heritability (h2) was calculated on

an entry mean basis (Fountain and Hallauer

(1996) as:

h2 = σ2G / (σ

2E/re + σ2

GE /e + σ2G),

Where r is the number of replications and e is

the number of environments. Variance

components such as genotypic coefficient of

variation (GCV), phenotypic coefficient of

variation (PCV) were assessed according to the

methods of Singh and Chaudhaury (1977) as

follows:

GCV = √ σ2G

/ 100 / 1,

Where σ2G is the genotypic variance and

is

the population mean of the S2 progenies.

PCV= √ σ2P / 100 / 1,

Where σ2P is the phenotypic variance and

is the population mean of the S2 progenies.

Predicted genetic gain from selection (Gs) was

calculated using the formular adapted from

Falconer (1981) as Gs = kσGh.

where σG is the square root of the genotypic

variance among the S2 progenies, h is the square

root of the estimate of heritability on progeny

mean basis, and k is the standardized selection

differential.

RESULTS The soil physical and chemical properties of the

experimental sites before planting and after

harvest are presented in Table 1. The textural

class of the soils of NIFOR and Ozoro before

planting and after harvest was loamy. The soil

pH was higher in NIFOR experimental site than

that of Ozoro. The exchangeable acidity was

higher in Ozoro experimental sites than that of

NIFOR. The soils of NIFOR and Ozoro were

low in N, P, K and organic carbon, and were

acidic. Mean square estimates for the four high

nitrogen environments were highly significant (P

≤ 0.01) for flowering, morphological characters,

starch yield, and grain yield among the high N

S2 progenies (Table 2). Among the low N S2

progenies, mean square for the four high N

environments were significant for starch yield (P

≤ 0.05), and highly significant for all other

observed agronomic characters (P ≤ 0.01). The

high N S2 progenies were highly significant

under high N environments in all flowering

characters, 1000 seed weight, and grain yield (P

≤ 0.01). There were no high N progenies x

environment interaction for all observed

agronomic characters. Among low nitrogen S2

progenies, significant differences were observed

in all flowering characters, leaf area, plant

height, 1000 seed weight, and grain yield under

high N environments. There was low N x

environment interaction for grain yield across

the four high N environments. The mean square

estimates for the four low N environments were

significant for starch yield (P ≤ 0.05) and highly

significant for all other characters (P ≤ 0.01)

among high N progenies. Among low N

progenies, the four low N environments were

highly significant for all characters (P ≤ 0.01)

with the exception of starch yield.

Emede*T. O. and Alika J. E.

96

Table 1. Soil Physical and Chemical Properties of the experimental sites for 20 maize genotypes

grown in high and Low N environments

at NIFOR and Ozoro

Sample Mechanical Analysis

pH (1 : 1)

Exchangeable Organic Total Available Exchangeable Cations (meq./100g)

Sand Silt Clay Textural

H2O

Acidity(EA) Carbon N P Ca Mg Na K ECEC

gkg-1

gkg-1 gkg-1 Class

Cmol

Kg -1 Gkg-1 gkg-1 gkg-1

cmol

Kg -1

NIFOR Early High –

N (BP)

870.0

10.0 120.0 Loamy 5.49 0.14 9.2 0.6 16.9 1.32 0.46 0.13 0.08 2.13

NIFOR Early High –

N (AH) 870.0 10.0 120.0 Loamy 6.03 0.20 8.0 0.5 87.5 2.04 0.73 0.19 0.21 3.37

NIFOR Early Low –

N (BP) 860.0 10.0 130.0 Loamy 5.27 0.10 7.4 0.5 32.7 1.16 0.51 0.07 0.10 1.94 NIFOR Early Low –

N (AH) 860.0 20.0 120.0 Loamy 5.92 0.16 8.1 0.6 33.1 1.44 0.67 0.20 0.10 2.57

NIFOR Late High – N (BP) 850.0 10.0 140.0 Loamy 5.02 0.18 9.2 0.6 17.0 0.80 0.40 0.07 0.22 1.67

NIFOR Late High –

N (AH) 850.0 20.0 130.0 Loamy 5.06 0.16 9.1 0.7 43.3 0.89 0.44 0.07 0.10 1.66

NIFOR Late Low - N

(BP) 850.0 20.0 130.0 Loamy 5.93 0.14 7.8 0.6 15.7 1.08 0.69 0.09 0.10 2.10

NIFOR Late Low - N

(AH) 840.0 30.0 130.0 Loamy 5.01 0.22 8.4 0.6 7.1 0.76 0.41 0.42 0.08 1.89

Ozoro Early High –

N (BP) 820.0 40.0 140.0 Loamy 4.16 1.84 12.6 0.9 15.9 0.38 0.20 0.09 0.18 2.69

Ozoro Early High –

N (AH) 830.0 50.0 120.0 Loamy 4.05 2.20 11.9 1.0 36.2 0.36 0.15 0.11 0.15 2.97

Ozoro Early Low - N (BP) 840.0 40.0 120.0 Loamy 4.36 1.88 8.9 0.6 30.6 0.38 0.20 0.06 0.09 2.61

Ozoro Early Low - N

(AH) 840.0 40.0 120.0 Loamy 4.10 1.80 9.9 0.7 32.1 0.42 0.16 0.05 0.06 2.49 Ozoro Late High - N

(BP) 830.0 40.0 130.0 Loamy 4.27 1.20 13.4 0.9 20.8 0.36 0.15 0.09 0.10 1.90

Ozoro Late High - N

(AH) 810.0 60.0 130.0 Loamy 3.95 3.72 11.9 0.8 28.5 0.25 0.15 0.06 0.10 4.28

Ozoro Late Low - N

(BP) 820.0 30.0 150.0 Loamy 4.18 1.06 14.5 1.0 18.8 0.44 0.10 0.15 0.23 1.98

Ozoro Late Low - N

(Ah) 810.0 50.0 140.0 Loamy 4.22 1.04 13.1 0.8 13.4 0.36 0.10 0.04 0.09 1.63

BP = Before Planting AH = At Harvest

Phenotypic and Genetic Variation of s2 Maize Selections

97

Table 2. Variation in agronomic characters among 10 high and 10 low N S2 progenies in

four high N and four low N environments at NIFOR and Ozoro

* Significant at P = 0.05; ** significant at P = 0.01; ns non-significant.

The high N progenies under low N environments

were significant for all characters except leaf

area, and 1000 seed weight. High N progenies x

environment interaction under low N was

observed in only anthesis silking interval (ASI).

Among low N progenies, significant differences

were observed in all the observed agronomic

characters under low N environments. Under

low N environments, low N x environment

interaction was highly significant (P ≤ 0.01) in

leaf area, plant height and ear height.

The mean values across the four high N and four

low N environments for the observed agronomic

characters are presented in Tables 3 and 4.

Among high N progenies, the minimum and

maximum mean for ASI, days to 50% tasselling,

silking and pollen shedding under high N

environments varied from -0.3 - 1.9, 57.5 - 62.4,

60.6 - 64.4, and 59.6 - 64.6 respectively. The

minimum and maximum means for ASI, days to

50% tasselling, silking, and pollen shedding

among low N progenies under high N

environments varied from 0.5 - 2.8, 56.4 - 61.1,

58.9 - 67.1, and 58.3 - 64.5, respectively. The

mean 1000 seed weight for high N and low N

progenies under high N environments varied

from 186.2 – 251.7g and 174.8 – 253.0g,

respectively. The grain yield of high and low N

S2 progenies under high N varied from 0.9 - 3.2

t/ha and 1.1 - 3.4 t/ha, respectively. Among high

N progenies the minimum and maximum mean

for ASI, days to 50% pollen shedding, silking

and tasselling under low N environments varied

from 0.3 - 3.4, 62.5 - 65.3, 64.6 - 67.5 and 59.4 -

63.4, respectively. On the contrary, under low N

environments, the minimum and maximum

means for ASI, days to 50% pollen shedding,

silking and tasselling among low N progenies

varied from 1.1 - 4.8, 59.3 - 66.0, 60.8 - 68.4,

and 57.1 - 62.8, respectively. The mean 1000

seed weight for high and low N progenies under

low N environments varied from 164.5 – 235.7g,

and 157.2 – 232.8g, respectively. Moreover, the

grain yield of high and low N S2 progenies under

low N varied from 0.5 - 1.4 t/ha and 0.5 - 1.5

t/ha, respectively. The mean, heritability,

genotypic and phonotypic CVS, and expected

genetic gain from selection (Gs) for flowering

and morphological characters, starch yield, and

grain yield among high N and low N progenies

under high and low N environments are

presented in Tables 5 and 6, respectively.

Traits N

Level

High N

Progenies

Low N

Progenies

Analysis of Variance

Min – Max

Mean

Min – Max

Mean

Environment High N

Progenies

(HN_P)

Low N

Progenies

(HN_P)

High N

Progenies x

Environment

Low N

Progenies x

Environment HN_

P

LN_P

Days to 50%

Pollen Shedding

High N 59.6 – 64.6 58.3 – 64.5 ** ** ** * ns ns

Low N 62.5 – 65.9 59.2 – 66.0 ** ** ** ** ns ns

Days to 50% Silking

High N 60.6 – 64.4 58.9 – 67.1 ** ** ** ** ns ns Low N 64.6 – 67.5 60.8 – 68.4 ** ** * ** ns ns

Anthesis

Silking

Interval (ASI)

High N -0.3 – 1.9 0.5 – 2.7 ** ** ** ** ns ns

Low N

0.3 – 3.6 1.1 – 4.8 ** ** ** ** * *

Days to 50%

Tasselling

High N 57.5 – 62.4 56.4 – 61.1 ** ** ** ** ns ns

Low N 59.4 – 63.4 57.1 – 62.8 ** ** ** ** ns ns

Leaf Area(cm2)

High N 464.4 – 630.8 371.1 – 582.4 ** ** ns ** ns ns

Low N 334.2 – 407.9 267.3 – 410.8 ** ** ns ** ns **

Plant

Height(cm)

High N 157.4 – 188.0 162.0 – 197.6 ** ** ns ** ns ns

Low N 117.7 – 149.4 121.7 – 150.5 ** ** * ** ns **

1000 Seed

Weight(g)

High N 186.2 – 251.7 174.8 – 253.0 ** ** ** ** ns ns

Low N 164.5 – 235..7 157.2 – 232.8 ** ** ns * ns ns

Grain

Yield(t/ha)

High N 0.9 – 3.2 1.1 – 3.4 ** ** ** ** ns **

Low N 0.5 - 1.4 0.5 – 1.5 ** ** * ** ns ns

Ogi Yield(g)

High N 34.2 – 37.7 32.8 – 37.4 ** * ns Ns ns ns

Low N 33.8 – 41.0 33.2 – 39.0 * ns * * ns ns


98

Table 3. Mean Values for Flowering Characters Among 10 High n and 10 Low n S2 Maize Progenies

Grown in Four High and Four Low Nitrogen Environments at Nifor, Near Benin City, Edo State

And Ozoro, Delta State

Genotype Days to 50% Pollen shedding

Days to 50% Silking

Anthesis Silking Interval (ASI)

Days to 50% Tasselling

High N Progenies High N Low N High N Low N High N Low N High N Low N

FS2-11 62.1b 63.5ab 63.9a 66.9 1.8a 3.4a 59.4bc 60.9bc FS2-16 61.3bc 63.4ab 61.5bc 65.9 0.3a 2.5a 57.5c 60.8bc

FS2-18 62.5b 65.0ab 63.4ab 67 0.9ab 2.0a 60.6bc 61.8bc

FS2-28 61.8bc 63.8ab 62.3abc 65.9 0.5ab 2.1a 59.3bc 61.6abc FS2-29 64.6a 65.3ab 64.4a 67.5 -0.3b 2.3a 62.4a 63.4a

FS2-39 60.9bc 63.5ab 61.1bc 65.5 0.3b 2.0a 57.5c 60.6bc

FS2-7 64.1a 65.9a 64.3a 66.1 0.1b 0.3a 61.4ab 62.9ab

FS2-77 60.8bc 62.0b 61.4bc 64.6 0.6ab 2.0a 57.5c 59.4c

FS2-9 61.4bc 63.8ab 63.3ab 67.4 1.9a 3.4a 57.5c 59.9c

FS2-96 59.6c 62.5b 60.6c 64.8 1.0ab 2.3a 57.6c 60.4bc

Low N Progenies

F0S2-16 61.6bc 62.9bc 62.1cd 66.3ab 0.5c 3.4abc 58.4bc 60.4ab

F0S2-24 62.0bc 63.3bc 64.3bc 66.4ab 2.3abc 3.1abc 59.8ab 60.9ab

F0S2-33 64.5a 66.0a 65.3b 67.1ab 0.9bc 1.1c 60.9a 62.8a

F0S2-35 59.9cd 61.0cd 60.8d 63.9b 0.9bc 2.9abc 58.4bc 59.3bc F0S2-41 61.6bc 62.6bc 62.6cd 64.4ab 1.0bc 1.8bc 59.0ab 60.0ab

F0S2-43 64.5a 64.6ab 67.1a 68.4a 2.6ab 3.8ab 61.1a 61.4ab

F0S2-47 58.3d 59.3d 58.9e 60.8c 0.6c 1.5bc 56.4c 57.1c F0S2-48 60.1cd 60.6cd 62.9cd 65.4ab 2.8a 4.8a 58.3bc 58.5bc

F0S2-5 62.9ab 65.1ab 64.3bc 67.0ab 1.4abc 1.9bc 61.1a 62.8a

F0S2-50 61.8bc 64.9ab 62.6cd 66.4ab 0.9bc 1.5bc 58.0bc 61.0ab

Means with the same letter are not significantly different according to SNKT

Means with the same letter are not significantly different according to SNKT

Heritability estimates under high and low N

environments were greater in low N progenies

than high N progenies in grain yield and most of

the secondary agronomic characters. Heritability

estimates for grain yield and other secondary

agronomic characters under high N environment

among low and high N progenies ranged from

37.5 - 95.1% and -14.8 - 87.2%, respectively. In

low N environments, heritability estimates for

grain yield and other secondary agronomic

characters among low and high N progenies

ranged from -17.5 - 89.2% and -14.8 - 87.2%,

respectively. Consequently, the estimates for

genetic gains from selection (Gs) among low N

progenies were greater in grain yield and most

of the other secondary agronomic characters

than high N progenies. Moreover, estimates of

genotypic and phenotypic CVS for grain yield

and most of the secondary agronomic characters

were greater among low N progenies than high

N progenies in high and low N environments.

Table 4. Means, heritability estimates (h2), genotypic coefficient of variation (GCV), phenotypic coefficient

of variation (PCV) and gains from selection (Gs) among 10 high N S2 and 10 low N S2 maize

progenies grown in four high nitrogen environments at NIFOR and Ozoro Character Progeny Mean h2 GCV PCV Gs

High N Low N High N Low N High N Low N High N Low N High N Low N Days to 50% Pollen

Shedding 61.9 61.7 87.6 95.1 2.3 3.1 2.5 3.2 2.4 2.9

Days to 50% Silking 62.6 63.1 80.8 93.0 2.0 3.5 2.2 3.7 2.0 3.3

ASI 0.7 1.4 79.5 86.0 87.9 57.0 98.6 61.5 1.0 1.1

Days to 50%

Tasselling 59.1 59.1 87.3 89.9 2.9 2.5 3.1 2.7 2.8 2.2

Leaf Area(cm2) 528.3 517.6 87.6 87.3 9.2 13.1 9.8 14.0 79.5 97.0 Plant Height(cm) 172.1 181.7 44.3 76.0 3.2 5.0 4.8 5.7 6.4 12.1

Ear height(cm) 86.7 90.4 -5.0 37.5 3.8 5.2 6.2 3.2

1000 Seed Weight(g) 217 218 71.6 90.3 8.0 9.9 9.4 10.4 2.6 3.1 Grain Yield(t/ha) 2.3 2.5 81.2 89.7 26.0 30.1 28.9 31.8 0.9 1.1

Ogi Yield(g) 35.7 35.2 -3.7 49.0 3.1 2.9 4.5 1.2


DISCUSSION The results of the soil chemical analysis in the

maize trials in NIFOR, near Benin City, Edo

State and Ozoro, Delta State showed that the

soils were rather poor in essential plant nutrients

and fairly acidic. The results of the soil chemical

analysis is in agreement with literature that the

Ultisols which constitute the main agricultural

lands of southern Nigeria cover more than 70%

of the total land area (Mbagwu, 1992) and are

characterized with low reserves of essential plant

nutrients and high soil acidity (Unamba-Oparah,

1985; Mbagwu, 1989).

The low exchangeable cation exchange capacity

(ECEC) exhibited by soils of NIFOR and Ozoro

indicated low activity clays which is a

characteristic of most southern Nigerian soils.

Most of the inorganic fertilizers applied to these

soils are lost to leaching and the expected yield

increases are usually not achieved (Obasi et al.,

2005). Therefore, in order to sustain maize

production in such soils, it is necessary to

develop maize cultivars with improved nutrient

uptake efficiency. The development and

subsequent adoption of nitrogen-use efficient

cultivars could reduce the use of input nitrogen

to levels below the recommended rate of 120 to

150 kg N ha-1

(Enwezor et al., 1989)

The results of the separate combined analysis for

high and low N maize trials across the two

locations and two seasons showed significant

mean squares for environment in most of the

observed secondary agronomic characters and

grain yield. It implies that there was variation in

response of the high N and low N S2 maize

progenies to both high and low N environments

across the two locations and two seasons. In

high N environments, the mean grain yield for

high N and low N S2 maize progenies were 2.3

t/ha and 2.5 t/ha, respectively. On the contrary,

in low N environments the mean grain yield for

high N and low N S2 maize progenies were 0.9

t/ha and 1.0 t/ha, respectively. Thus, maize grain

yield reduction in low nitrogen environments

amounted to 61% and 60% among high and low

N environments, respectively. These results

agree with previous studies which reported more

than 43% relative grain yield reduction in

nitrogen stressed farmers’ fields in tropical

regions (McCown et al., 1992; Van Reuler and

Prins, 1993).

The effects of nitrogen stress in low N

environments were also evident in delayed

flowering and reduction in the mean values of

other secondary agronomic characters. Hefny

and Aly (2008) reported that growing maize

plants under N stress resulted in the reduction in

grain yield and other related traits, but raised the

N use efficiently for dry matter and grain yield

production.

Among the high N and low N S2 progenies, no

significant variation was observed in starch yield

under high N environments. The high N and low

N S2 progenies under low N environments

recorded significant variation for starch yield

under low N environments. Alika (1993)

reported significant variation among 13 maize

cultivars with a local cultivar with floury

endosperm expressing the highest yield

(51g/100g of whole grain). Commercially

cultivated hybrid 8321-18 was the second lowest

yielder with 33.2g/100g weight of whole grains.

The results of this present study have shown that

variation in starch yield is dependent on N level,

and greater variability was observed under low

N environments.

Observed differences between high and low N S2

progenies were more under low N environments

than high N. However, there was no significant

difference in grain yield between high and low N

S2 progenies under high and low N

environments. Nevertheless, the yield of low N

progenies on the average was greater than high

N progenies in both high and low N

environments. The superior grain yield of low N

progenies under low soil N conditions in

comparison with high N progenies implies that

Table 5. Means, heritability estimates (h2), genotypic coefficient of variation (GCV), phenotypic coefficient of

variation (PCV) and gains from selection (Gs) among 10 High N, and 10 low N S2 maize progenies

grown in four low nitrogen environments at NIFOR and Ozoro Character Progeny Mean h2 GCV PCV Gs

High N Low N High N Low N High N Low N High N Low N High N Low N

Days to 50%

Pollen shedding 63.9 63.0 74.8 86.5 1.5 3.2 1.7 3.5 1.3 2.9

Days to 50% Silking 66.2 65.6 70.1 72.7 1.3 2.8 1.5 3.3 1.1 2.4

ASI 2.2 2.6 61.8 69.9 31.9 38.8 40.6 46.4 0.9 1.3

Days to 50%

Tasselling 61.2 60.4 87.2 89.2 1.9 2.8 2.1 2.9 1.7 2.4

Leaf Area (cm2) 373.6 368.6 -14.8 70.3 11.5 6.8 13.7 54.3

Plant Height (cm) 130.5 138.5 66.2 49.2 6.3 4.9 7.7 7.0 10.2 7.3

Ear height (cm) 61.2 64.4 80.0 -17.5 10.3 11.5 7.9 8.7

1000 Seed Weight (g) 199.0 202.0 30.1 67.4 6.3 7.7 11.4 9.4 1.0 2.0

Grain Yield (t/ha) 0.9 1.0 50.2 74.2 17.9 28.5 25.3 33.1 0.2 0.4 Ogi (Starch) Yield (g) 38.0 36.6 42.8 52.9 3.7 3.6 5.6 5.0 1.4 1.5


100

they were more efficient in the use of N

(Graham, 1984; Sattelmacher et al., 1994). The

results obtained have proved that direct selection

under low N conditions was more efficient to

improve N – use efficiency for grain yield than

indirect selection at High N (Banziger et al.,

1997; Presterl et al., 2003).

The significant mean squares in maize grain

yield and other secondary agronomic characters

among high N and low N progenies in both high

and low N environments implies significant

genetic variability (Santos et al., 1998). This

variability can be exploited for another cycle of

population improvement by selection under high

and low N environments. However the low N S2

progenies exhibited greater phenotypic

variability than high N S2 progenies in maize

grain yield and other secondary agronomic

characteristics. These results have further

demonstrated that yield improvement under

stress conditions cannot be achieved by selecting

high yielding genotypes in optimal conditions. It

is evident that the highest yielding genotypes in

a high yielding environment were generally very

poor in a severe stressed environment, and the

highest yielding genotypes under severe stress

cannot be identified if the breeding materials are

grown only in a high yielding environment

(Ceccarelli, 1989).

In high N environments, low N S2 progenies

exhibited highly significant G x E interaction for

grain yield, while no G x E interaction was

observed among high N S2 progenies. In low N

environments, low N S2 progenies also exhibited

highly significant G x E interaction for leaf area,

plant height, and ear height. Among high N S2

progenies, significant G x E interaction was

observed in ASI under low N environments.

This implies that the low N S2 progenies were

more specifically adapted to both high and low

nitrogen environments than high N S2 progenies.

The presence of G x E interaction is almost

unanimously considered to be among the major

factors limiting response to selection and, in

general, the efficiency of breeding programmes.

G x E interaction becomes important when the

rank of genotypes changes in different

environments. The change in rank has been

defined as a crossover G x E interaction (Baker,

1988).

In the present study the ranking of high and low

N S2 progenies, on the basis of grain yield and

other agronomic characters, changed under high

and low N environments in both locations. G x E

interactions in general, and G x E interactions of

a crossover type in particular, are considered to

have impact on the success of breeding

programmes, because breeders tend to search for

a few widely adapted cultivars. While this is

probably the best strategy in the case of breeding

programmes targeted at favourable

environments, it has been suggested (Ceccarelli,

1989; Hilderbrand, 1990; Simmonds, 1991;

Stroup et al., 1993, Ceccarelli, 1994) that, in the

case of less favourable environments, breeders

may need to look at G x E interactions in a

different way.

The Low N environments resulted in lower

broadsense heritability estimates than high N,

and this was due to decreased genotypic

variances and increased error variances

(Banziger et al., 1997; Hefny, 2007). However,

the heritability estimates under low nitrogen

environments were fairly high as a result of

genetic variability present in high and low N

progenies under low N. The magnitude with

which heritability estimate for grain yield under

combined four low N environments were

reduced among the high and low N S2 progenies

are 31% and 15.5%, respectively. Lower

genotypic variances and lower heritability

estimates for grain yield under stressed

conditions have been reported in many other

studies (Atlin and Frey, 1990; Frey, 1964;

Quinsenberry et al., 1980; Ud-Din et al., 1992).

Several investigations additionally found that

error variances decreased under stressed

conditions (Atlin and Frey, 1989 and 1990;

Pederson and Rathjen, 1981; Ud-Din et al.,

1992) leading in a few instances to increased

heritability estimates under stress conditions

(Atlin and Frey, 1989a; Pederson and Rathjen,

1981; Zhong Zhe et al., 2004). Presterl et al.,

(2003) found that in European flint and dent

maize breeding materials, heritability estimates

were similar at both low and high N

environments.

The results have shown however that low N

progenies had greater heritability for grain yield

and other secondary agronomy characters than

high N progenies under high and low N

environments. The greater heritability estimates

among low N progenies under high and low N

environments was due more to increased

genotypic variances than decreased error

variances. In the combined four high N

environments at NIFOR and Ozoro, heritability

for grain yield among low N S2 progenies was

greater than high N progenies by 8.5%.

The greater heritability estimates for grain yield

and other secondary agronomic characters

among low N S2 progenies also resulted in

greater selection gains for grain yield and other

secondary agronomic characters in both high and

low N environments. The study has further

confirmed lower heritability under low N

environments compared to high N environments.

However, heritability estimates for grain yield

and other secondary agronomic characters

among low N progenies were consistently higher


than high N progenies under high and low N

environments. Thus, the magnitude of genetic

variance, heritability and gains from selection is

affected by the type of genetic material.

CONCLUSION The study has revealed that S2 progeny selection

enhances sustainability of genetic variability for

grain yield even under nutrient stress in low N

environments. The results also confirmed the

effectiveness of S2 progeny selection as a

population improvement procedure capable of

improving the performance of maize population

for low N tolerance. The positive response for

grain yield obtained in this first selection circle

indicate that further yield increase will result

from subsequent selection cycles. The results

indicated that selection for low N tolerance in

early generation using S2 progeny selection is

feasible and would probably require additional

cycle of selection to significantly shift genetic

frequency. It is envisaged that this rate of

progress will be maintained in subsequent

selection cycles provided that the same S2

progeny selection method is practiced using an

effective population size in order to avoid

genetic drift.

ACKNOWLEDGEMENTS

The authors thank Dr. S.O. Ajala at the

International Institute of Tropical Agriculture,

Ibadan for providing the maize populations used

in this study. Financial support was provided by

the University of Benin Research and

Publication Committee (URPC).

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104


Volume 1 No. 1 September 2013 pp 105 - 115

EVALUATION OF THE GROWTH AND YIELD POTENTIALS OF

CULTIVARS OF COCOYAM (Colocasia esculenta) IN LOCATIONS IN

SOUTH EASTERN NIGERIA.

Ogbonna, P.E and Orji, K.O.

Department of Crop Science, University of Nigeria, Nsukka

410001, Nigeria.

ABSTRACT Agronomic evaluations were made in Nsukka and Umudike in south eastern Nigeria in 2008 and

2009 to assess the growth and yield potentials of cocoyam cultivars. This was aimed at identifying

stable and high yielding cocoyam cultivars. Five cocoyam cultivars; Ugwuta, Nworoko, Odogolo,

Nkpong and Nadu were evaluated in a randomized complete block design (RCBD) experiment in

Nsukka in 2008 and repeated at Umudike in 2009. Ugwuta produced the highest tuber yield/ha of

30015kg/ha among the cultivars in Nsukka while the Odogolo cultivar produced the highest tuber

yield of 15459kg/ha among the cultivars in Umudike. There was however no significant difference

between the yields recorded from Ugwuta and Odogolo in the two locations. It was also noted that all

the five cultivars produced higher yields at Nsukka than in Umudike. Ugwuta and Odogolo were

recommended to farmers in these locations.

Keywords: Cocoyam, cultivars, yield and locations.

INTRODUCTION Cocoyam is the common name for two tuber

crops Colocasisa esculenta and Xanthosomona

sagitifolum. Cocoyam is found throughout the

tropics and is of economic interest in these areas.

Together with yam and cassava cocoyam form

the major source of carbohydrates in Nigeria.

Cocoyam is widely cultivated and consumed in

various forms in the south eastern Nigeria

agricultural zone for decades (Ndon et al, 2003).

It is placed third after yam and cassava among

root and tuber crops in this zone (FAO, 2007).

Nigeria is presently, the world highest producer

of cocoyam producing about 1,800,000 tons of

cocoyam per annum, accounting for about 40%

of world total and 48% of Africa total

production. This is produced in an estimated

land area of 350,000 hectares (Onwueme and

Sinha, 1991, and Eze and Okorji, 2003). Global

average yield is only about 6000kg/ha. Yield is

still very low in Nigeria and may be attributed to

poor production practices. Due to low yield,

cocoyam cultivation has suffered neglect,

notwithstanding the fact that it has a higher

nutritional value than yam and cassava (Chukwu

and Nwosu, 2008; Okoye et al, 2008). However,

yield increases from 0.73 million ton in 1990 to

3.89 million tons in 2000 and to 5.068 million

tons in 2007 had been reported (Ojiako et al,

2007; FAO, 2007). Breeding genotypes that are

adapted to wide geographical area and that show

some degree of stability from year to year is one

of the major challenges facing plant breeders.

Dixon and Nukenine (2000) have suggested

testing at 3-5 locations for 2-3 years with 3-4

replications per location in cassava yield trials.

Xing-Ming Fan, et al, (2008) have also noted

that multi environment trials is important in

selection for stable performance in maize

hybrids. A stable genotype is one that is capable

of utilizing the resources available in higher

yielding environments and has a mean

performance that is above average in all

environments. The objective of this is to identify

high yielding taro (Colocasia esculenta)

cultivars for recommendation to farmers.

MATERIALS AND METHODS. To address the objectives of the study, field

practical experiments were conducted in two

locations in southeastern Nigeria namely;

University of Nigeria, Nsukka in Enugu state

(latitude 06052N longitude 07

024’E and at

altitude 442m above sea level) and National

Root Crop Research Institute (NRCRI),

Umudike in Abia State (latitude 05033E and at

altitude 122m above sea level. The experiments

were carried out in the growing season of two

years; Nsukka location (2008) and Umudike

(2009). Three local cultivars of cocoyam

(Colocasia esculenta) ; Odogolo, Nworoko and

105

Nadu were sourced from the study area while

two varieties; Ugwuta and Nkpong were also

obtained from NRCRI, Umudike, bringing the

number of cultivars to five. These cultivars were

evaluated in Randomised Complete Block

Design (RCBD) experiment in three replications

in the Nsukka location in 2008 and repeated in

the Umudike location in 2009. Planting was

made in June on ridges at the spacing of 100cm

x 40cm. The plots were kept weed free manually

using hoe. NPK 15:15:15 fertilizer was applied

at the rate of 200kg/ha at eight weeks after

planting. At maturity expert women harvesters

were engaged to harvest the cocoyam.

Data collection and analysis: Records were

taken on number of leaves at 8weeks after

planting, number of suckers/stand at 8weeks

after planting, number of cormels/stand, weight

of cormels/stand in kg, average cormel weight in

kg, weight of corm/stand in kg, total tuber

yield/stand in kg, cormel yield/ha in kg and total

tuber yield/ha in kg. These data were subjected

to analysis of variance (ANOVA). This was

carried out using the method outlined by Steel

and Torrie (1980) for factorial experiments.

Separation of means for statistical significance

was by the F-LSD procedure described by Obi

(1990)

Meteorological Data: Meteorological records

were collected at the nearest meteorological

station to the experiment sites.

Soil data: At the time of planting soil samples

were taken at different representative locations

in each experimental site at the depth of 0 to

20cm and were analysed to determine the

physical and chemical roperties of the soil.

RESULTS The records of temperature, rainfall and relative

humidity at the two locations were presented in

Table 1. The record indicated higher levels of

these weather factors in Umudike than in

Nsukka. Table 2 presents the result of the

physical and chemical properties of soils of the

two locations.

\Nsukka Location

Significant cultivar differences were identified

among the cultivars in the plant attributes

studied (Table 3). Nworoko had the highest

value in all the growth attributes among the

cultivars while Nkpong gave the least in these

attributes. On the yield attributes, Odogolo

produced the highest number of cormels/stand.

This was however statistically the same with

what were obtained from the other cultivars

except Nkpong. Similarly weights of

cormels/stand, average cormel weight recorded

were statistically the same in all the cultivars

except Nkpong which had the lowest value.

There was no significant difference in weight of

corm/stand among the cultivars. Ugwuta had the

highest tuber yield/stand, cormel yield/ha and

total tuber yield/ha. It however did not differ

significantly from the other cultivars except

Nkpong in these attributes.

Figure i: Cultivar x location interaction effect on number of leaves/plant.

Evaluation of The Growth and Yield Potentials of Cultivars of Cocoyam 106

Table 1: Weather records of the areas during the periods of the Experiment.

Figure ii: Cultivar x location interaction effect on number of suckers/stand.

Location NSUKKA

Year 2008

Months Jan Feb Mar April May June July Aug Sept Oct Nov Dec.

Rainfall amo unt (mm)

0.00 0.00 61.23 143.3 254.01 186.43 246.10 203.20 326.02 198.63 8.38

Number of

Rain days

0.00 0.0 4 11 12 15 14 19 22 11 2

Maximum

Temp (0C)

31.39 34.14 33.77 31.73 31.16 29.83 28.94 27.81 27.60 29.48 31.10

Minimum Temp (oC)

20.32 21.97 22.87 22.00 20.81 31.43 20.84 20.68 20.80 20.87 22.00

Relative Humidity

(0900)

56.03 56.17 74.13 74.83 75.00 76.93 78.16 79.55 78.67 76.35 74.80

Relative Humidity

(1500)

Location UMUDIKE

Year 2009

Months Jan Feb Mar April May June July Aug Sept Oct Nov Dec

Rainfall amount (mm)

62.80 62.80 47.80 100.50 416.20 236.70 306.30 287.40 203.50 311.10 23.70 0.00

Number of Rain

days

2 4 4 12 15 14 18 19 18 14 7 0

Maximum Temp

(0C)

33.00 350.0 34. 33 33 31 30 29 30 31 32 34

Minimum Temp (oC)

23 24 24 23 23 23 22 23 22 23 22 23

Relative

Humidity (0900)

75 79 78 78 81 83 87 88 86 82 74 78

Relative

Humidity (1500)

50 56 57 63 70 72 78 78 72 72 58 43


107

Table 2: Physical and Chemical Properties of the Soil of the Experimental Sites before

Planting

Umudike LocationCultivar effects on the plant

attributes presented on Table 4 showed

significant differences among the cultivars.

Odogolo produced the highest number of

leaves/plant and differed significantly from the

other cultivars. Nadu had the highest number of

suckers/plant. Odogolo was the highest in terms

of tuber yield attributes. It has the highest total

tuber yield/ha which also differed significantly

from the other cultivars.

The effect of location on the growth and

yield of cocoyam is presented in Table 5. The

result indicated that number of leaves/plant was

highest at Nsukka. On number of suckers/stand

Umudike leads. The yield attributes were best at

Nsukka location than in Umudike. The

performances at Nsukka were statistically

different from what were recorded in Umudike.

The result of cultivar by location

interaction effect showed that all the cultivars

produced higher number of leaves/stand at the

Nsukka location than at the Umudike location

(Figure i). The highest number of leaves/stand

was recorded in Nworoko planted in Nsukka

location while Nkpong at Umudike had the least

number of leaves among the different

combinations of cultivar and locations. The

cultivars reacted differently to the interaction

effect on number of suckers/stand. At Nsukka

location Nadu produced the highest value while

at Umudike, Odogolo was at the peak. Nkpong

however produced the lowest number of suckers

in both locations (Figure ii). Most of the

cultivars showed higher tuber yield at the

Nsukka location than at the Umudike. Ugwuta

planted at Nsukka produced the highest number

of cormels/stand followed by Odogolo grown in

the same location. The least number of

cormels/stand was obtained from Nadu grown in

Nsukka (Figure iii). The highest weight of

cormels/stand was obtained from Ugwuta grown

in Nsukka and was followed by Odogolo at

Nsukka. Nkpong grown in Umudike registered

the lowest weight of cormels/stand (Figure iv).

Nkong planted in Nsukka produced the highest

average corm weight among the combinations. It

however produced the lowest value when grown

in Umudike. Nadu had the highest average corm

weight in Umudike (Figure v). The highest

average cormel weight was obtained from

Nworoko planted in Nsukka while the lowest

value was recorded from Nadu grown in

Umudike (Figure vi). Ugwuta planted in Nsukka

location gave the highest total tuber yield/stand

while Npong and Nadu produced the same value

which happens to be the lowest value among the

combinations (Figure vii). Similar trends were

observed in cormel yield/ha and total tuber

yield/ha (Figures viii & ix).

Physical Properties (%) Nsukka Umudike

Course sand(%) 10.0 44.00

Fine sand (%) 60 40.00

Silt (%) 20.0 9.00

Clay (%) 64.0 7.00

Textural Class Clay soil Loam soil

Chemical Properties pH in Water 5.0 5.2

pH in KCL 4.6 4.1

Organic matter (%) 1.03 1.38

Total Nitrogen (%) 0.053 0.112

Total Carbon (%) 0.60 0.79

Available P (ppm) 2.60 10.30

Exchangeable Na (Meq/100g) 0.10 1.73

Exchangeable K (Meq/100g) 0.09 2.72

Exchangeable Ca (Meq/100g) 1.0 3.80 Exchangeable Mg (Meq/100g) 0.8 1.80

Exchangeable Al (Meq/100g) 1.0 Nil

Exchangeable H (Meq/100g) 0.4 1.40

Cation exchange capacity (Meq/100g) 6.0 6.0


Figure iii: Cultivar x location interaction effect on number of cormels/stand.

Table 3 : Mean effect of cultivar on growth and yield attributes of cocoyam in Nsukka

location in 2008. Cultivars No. of

Leaves/

plant

No. of

Suckers/

plant

No. of

Cormel

s/stand

Weight of

Cormels/

stand

(kg)

Average

corm

weight(kg)

Average

cormel

weight(kg)

Total

tuber

yield/stand

(kg)

Cormel

yield/ha

(kg)

Totaltuber

yield

(kg/ha)

NKPONG 6.1 3.97 13.11 0.706 0.299 0.0521 1.004 17274 25557

UGWUTA 6.8 4.08 15.53 0.876 0.312 0.0579 1.188 22180 30015

NWOROKO 9.5 6.61 14.08 0.792 0.347 0.0566 1.150 20204 29468

ODOGOLO 9.4 5.50 15.75 0.851 0.317 0.0549 1.168 21833 29937

NADU 6.2 6.39 13.75 0.829 0.288 0.0608 1.117 21211 28498

LSD(P<0.05) 1.69 1.127 2.423 0.1423 0.0739 0.00692 0.1872 3960.9 4977.3

Estimates of Correlation between the Growth and

Yield Attributes The result of the correlation analysis

presented in Table 6 indicated significant correlation

between most of the growth and yield attributes of

cocoyam. Number of leaves/plant correlated positively

with all other attributes at significant levels. Number of

suckers/stand also maintained significant positive

correlation with all the attributes.

Number of cormels/stand had positive

relationship with all the plant attributes studied with

the exception of average cormel weight where it

maintained non-significant negative relationship. The

result also indicated significant positive correlation

between weight of cormels/stand and other attributes at

significant level. A similar relationship was noted in

the other yield attributes studied


109

Figure iv: Cultivar x location interaction effect on weight of cormels/stand.

Table 4: Mean effect of cultivars on growth and yield attributes of cocoyam in Umudike location

Cultivars

No. of

Leaves/

plant

No. of

Suckers/pl

ant

No. of

Cormel

s/stand

Weight of

Cormels/stand

(kg)

Average

corm

weight(kg)

Average

cormel

weight(kg)

Total tuber

yield/stand

(kg)

Cormel

yield/ha

(kg)

Total

tuber

yield

(kg/ha)

NKPONG 4.778 4.33 6.64 0.246 0.125 0.0393 0.285 8066 9098

UGWUTA 4.778 6.00 9.34 0.323 0.198 0.0657 0.389 11832 13197

NWOROKO 4.667 6.33 10.11 0.312 0.135 0.0650 0.377 10443 11869

ODOGOLO 6.444 6.22 11.53 0.415 0.174 0.0671 0.482 14080 15459

NADU 4.778 6.44 6.42 0.223 0.135 0.0463 0.269 8195 8740

LSD(

P<0.05)

0.606 0.663 2.415 0.1002 0.1320 0.0192 0.083 2167.3 2541


Figure v: Cultivar x location interaction effect on average corm weight.

Figure vi: Cultivar x location interaction effect on average cormel weight.

Table 5: Mean effect of location on growth and yield of cocoyam Locations No. of

Leaves/

plant

No. of

Suckers/

plant

No. of

Cormel

s/stand

Weight of

Cormels/stan

d

(kg)

Average

corm

weight(kg)

Average

cormel

weight(kg

)

Total tuber

yield/stand

(kg)

Cormel

yield/ha

(kg)

Total tuber

yield(kg/ha)

Nsukka 9.18 5.619 14.35 0.842 0.2641 0.0640 1.100 20657 27533

Umudike 5.49 5.727 13.35 0.489 0.1376 0.0418 0.590 12307 15149

F-LSD(P<0.05) 0.756 0.4335 1.135 0.0683 0.0365 0.01168 0.0763 1644.0 1916.9


111

Figure vii: Cultivar x location interaction effect on total tuber yield/stand.

Figure viii: Cultivar x location interaction effect on cormel yield/ha.


Figure ix: Cultivar x location interaction effect on total tuber yield/ha.

Table 6: Estimates of correlation coefficients between the different growth and yield

attributes of cocoyam Plant Attributes

No. of

Leaves/

plant

No. of

Suckers/stand

No. of

Cormels/

stand

Weight of

Cormels/stand

Average

corm

weight

Average

cormel

weight

Total

tuber

yield/

stand

Cormel

yield/ha

Total

tuber

yield/ha

No. of leaves/plant - .133* .213* .443* .280* .308* .491* .472* .487*

No. of suckers/stand .440* .284* .154* .065 .284* .337* .311*

No. of cormels/stand .657* .353* -.020* .607* .674* .661*

Weight of cormels/stand .512* .219* .857* .857* .906*

Average corm weight .140* .598* .530* .619*

Average cormel weight .250* .229* .252*

Total tuber yield/stand .887* .952*

Cormel yield/ha .921

Total tuber yield/ha -

* = Correlation is significant at the 0.05 significant level.


113

DISCUSSION The five cocoyam cultivars showed remarkable

growth and yield performances in the two locations.

Nworoko had higher number of leaves/stand in

Nsukka location. At the Umudike location, Odogolo

cultivar produced the highest number of leaves/stand.

The difference observed among the locations may be

attributed to variations in environmental conditions in

these areas. High number of leaves is likely to result

to high yield, because assimilates are produced in the

leaves. The higher the number of leaves/unit area, the

greater the extent of solar energy interception.

Agueguia (1992) had reported that number of leaves

has high genotypic correlation with cocoyam yield,

and that leaf area has high positive correlation to

cocoyam yield. The observation made in this study

supported the above report as number of leaves had

significant positive correlation with yield attributes.

Considering the yield attributes Ugwuta

maintained the lead in Nsukka location and was

followed closely by Odogolo and Nworoko. Their

yields were higher in Nsukka than in Umudike.

Differences in environmental factors may also be

implicated as the cause of the variation in yield

between the locations. For instance Umudike location

recorded higher temperature, rainfall and relative

humidity than the Nsukka location. There was also an

outbreak of a taro disease suspected to be Taro leaf

blight caused by Phytophthora colocasiae, during the

2009 season in the whole of south eastern Nigeria.

That was the year the experiment at Umudike was

conducted and could have negative influence on the

cocoyam yield. Notwithstanding the disease

incidence, yield recorded at the Umudike was similar

to yields recorded earlier by other researchers in

NRCRI in Umudike (Chukwu, et al, 2009, Obasi et al,

2008 and Igbokwe and Ogbonnaya, 1991). Average

yield obtained in the present study was higher than

the global average of 6000kg/ha reported by

Onwueme (1991) in the early 1990’s. Edet and

Nsikak (2005) later reported higher yields in the late

1990’s and 2000’s and attributed the yield increases

to development of disease resistant and high yielding

varieties. The cultivars tested in this study should be

considered as part of the high yielding varieties.

Ugwuta, Odogolo and Nworoko maintained high

yield/ha in the two locations and this implies that they

are stable in performance and are recommended to

farmers. The study also revealed that cocoyam

production was best at the Nsukka location an

indication that the Nsukka environment provided

conditions most adequate for taro production. This

may be responsible for the extensive cocoyam

production in the Nsukka zone.

ACKNOWLEDGMENT. I acknowledge the financial and material aids from

the CODESRIA – IFS Sustainable Agriculture

Initiative, Grant No.T/4434-1 under which this study

was carried out. I also acknowledge the support of the

University of Nigeria, Nsukka and National Root

Crop Research Institute (NRCRI), Umudike, Nigeria

for granting me the use of their experimental facilities

and technical assistance.

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