Optimal Allocation Of Resources

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Optimal Allocation of Land and Water Resources for Crop

Planning in Barak Valley

Thesis submitted in partial fulfillment

for the award of the degree

of

BACHELOR OF TECHNOLOGY

in

AGRICULTURAL ENGINEERING

by

PRAVEEN KUMAR SHARMA

and

SUSHIL KUMAR

under the guidance of

Dr. Laxmi Narayan Sethi

DEPARTMENT OF AGRICULTURAL ENGINEERING

TRIGUNA SEN SCHOOL OF TECHNOLOGY

ASSAM UNIVERSITY (CENTRAL UNIVERSITY)

SILCHAR788 011, ASSAM

December 2011


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CERTIFICATE OF APPROVAL

/ /

Certified that the thesis entitled Optimal Allocation of Land and Water Resources

for Crop Planning in Barak Valley submitted by Praveen Kumar Sharma and

Sushil Kumar, students of Department of Agricultural Engineering, during the year

2010-2011 to Assam University, Silchar, for the award of the Degree of Bachelor of

Technology have been accepted by the external examiner and that the student have

successfully defended the thesis in the viva-voce examination held today.

(Supervisor) (External Examiner) (Head of the Department)


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CERTIFICATE

This is to certify that the thesis entitled Optimal Allocation of Land and Water

Resources for Crop Planning in Barak Valley submitted by Praveen Kumar

Sharma and Sushil Kumar to Assam University, Silchar, is a record of bona fide

research work under my supervision and is worthy of consideration for the award of

the degree of Bachelor of Technology of the Institute.

Date: (L. N. Sethi)


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ACKNOWLEDGEMENTS

We express our deep sense of gratitude to our supervisor Dr. L. N. Sethi, Reader and

head of the Department of Agricultural Engineering, Assam University, for his

constant encouragement, invaluable guidance and all possible help that have enabled

us to bring this research work to a successful completion.

We express our sincere gratitude and indebtedness to the Hon'ble Vice Chancellor,

Assam University, Silchar- Prof. T. Bhattacharjee, Pro- Vice Chancellor (Silchar

HQ)- Prof G. D. Sharma (Science, Technology & Management) and Dean -Prof. R.

K. Pal, , Triguna Sen School of Technology, Assam University, Silchar for providing

necessary facilities during the course of the study.

It gives us immense pleasure to express our gratitude to Dr. K. C. Swain, Mr. S.

Sarkar, Mr. M. Padhiary, Mr. A. Nath, Mr. M. S. Pawar, Mr. N. Kumar, Mr. A. Gora

and Mr. Prasanna K. G.V. for their help and encouragement in successful completion

of the thesis.

Sincere thanks to the Assam Lift Irrigation Corporation, Central Water Commission,

Cachar District Agriculture Office at Silchar, Assam, India for providing all the

necessary information.

During our study period in Assam University campus, we had been greatly supported

and helped by a number of friends whom we are very grateful.

We owe a lot to our parents for their blessings who have shouldered innumerable

difficulties enabling us to achieve this goal. We think simple dedication of this tiny

piece of research work would not be sufficient to repay their indebtedness.

Date: (Praveen Kumar Sharma) (Sushil Kumar)


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DECLARATION

We hereby declare that the project entitled Optimal Allocation of Land and Water

Resources for Crop Planning in Barak Valley submitted for the B. Tech. Degree

is our original work and the dissertation has not formed the basis for the award of any

degree, associate ship, fellowship or any other similar titles.

Place: (Praveen Kumar Sharma) (Sushil Kumar)

Date:


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CONTENTS

Chapter Title Page

Title Page i

Certificate of Approval ii

Certificate by the Supervisor iii

Acknowledgements iv

Declaration v

Contents vi

List of Tables viii

List of Figures ixList of Symbols and Abbreviations x

Abstract xi

Chapter 1 Introduction 1

1.1 Background 1

1.2 Objectives 3

Chapter 2 Review of Literature 4

2.1 General 42.1.1 Assessment of the land and water Resources 4

2.1.2 Optimization model for optimal land and water

resources allocation

7

2.2 Critiques on Literature Review 9

Chapter 3 Materials and Methods 11

3.1 Study Area 11

3.1.1 Location 11

3.1.2 Geomorphology 11

3.1.3 Soil 13

3.1.4 Climate 13

3.1.5 Agriculture 14

3.1.6 Land-use pattern 15

3.1.7 Water resources availability 17

3.1.7.1 Surface water availability 17

3.1.7.2 Groundwater availability 18


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Chapter Title Page

3.2 Model Formulation 18

3.2.1 Optimization model 18

3.2.1.1 Objective function 19

3.2.1.2 Constraints 20

3.3 Methods for Finding Optimal Solution 22

Chapter 4 Results and Discussions 24

4.1 Assessment of Existing Land and Water Resources 24

4.2 Net Return 27

4.3 Water Requirement 27

4.4 Optimal Allocation of Land and Water Resources 29

4.5 Alternative Scenarios for Land and Water

Resources Management

31

Chapter 5 Summary and Conclusions 38

5.1 Summary 38

5.2 Conclusions 39

REFERENCES 41


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LIST OF TABLES

TABLE NO NAME OF THE TABLE PAGE NO

Table 3.1 Land use pattern in Barak Valley 16

Table:3.2 The details of land covered by wetlands in the

Barak Valley

17

Table:3.3 Surface water availability in Barak Valley 18

Table 3.4 Surface of the groundwater resources in Barak

Valley

19

Table 4.1 Season-wise existing cropping pattern and water

resources available in different district of Barak

Valley

25

Table 4.2 Existing structures used for water supply(draft)

from different sources in the Barak Valley

27

Table 4.3 Existing cropping pattern and net return excluding

irrigation cost

28

Table 4.4 Crop coefficient at different development stages 28

Table 4.5 Evapotranspiration and net irrigation required at

different development stages of crops grown in the

Barak Valley

29

Table 4.6 Optimum cropping pattern (ha), water resources

allocation and net annual return for different

scenarios of cropping situation

30

Table 4.7 District wise water resources allocation (Mm ) for

different scenarios of cropping situation in Barak

Valley

30

Table 4.8 District wise feasibility of conjunctive use of

available water resources for existing cropping

pattern in Barak Valley

36


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LIST OF FIGURES

FIGURE

NONAME OF THE FIGURE PAGE NO

Fig. 3.1 Location of study area 12

Fig. 4.1

District wise total and seasonal river and groundwater

allocation for existing cropping pattern in Barak Valley32

Fig. 4.2

Optimal seasonal river and groundwater allocation and

net return at different ranges of cropping pattern in Barak

valley

34

Fig. 4.3

Variation of maximized annual return of alternative

scenario against the existing cropping pattern in Barak

Valley

35


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List of Symbols and Abbreviations

A Area allocated

A(R+G)k Total amount of available river and groundwater

bgl Below Ground Levelc Crop

Cgwg Unit Cost of groundwater for government tubewell

Cgwp Unit Cost of groundwater for Private tubewell

Crw Unit cost of river water

DSS Decision Support System

Er Effective rainfall

ETc Crop evapotranspiration

ETo Potential crop evapotranspiration

ha Hectare

i District

j Type of agriculture

k Crop growing season

Kc Crop coefficient

kg/ha Kilogram per hectare

LP Linear Programming

m Meter

m Square meter

m Cubic meter

m /hr Cubic meter per hour

MCM Millian cubic meter

Mm Millian square meter

Mm Millian cubic meter

MSL Mean sea level

NIR Net Irrigation Requirement

Qgwg Amount of groundwater from government tubewell

Qgwp Amount of groundwater from private tubewell

Qrw Amount of river water

QSB Quantitative System for Business

R Net return

Rs Incident solar radiation

RH Relative Humidity

Rs/ha Rupees per hectare

TA Total area allocated

TAk Total available land area for cultivation in a season

Tmean Mean temperature

Field application efficiency of water

Conveyance efficiency of river water


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To combat the complex problem of availability of land and water resources for

optimal crop planning in the Barak Valley of Assam it is imperative to go for

integrated land and water resources planning and management for agriculture. This

approach was adopted to allocate available surface (river) and ground water resources

to cropped area to maximize the net annual return using deterministic linear

programming model. Using the QSB (Quantitative Software Business) package the

linear programming model was solved to find out the optimal seasonal allocation of

surface and ground water and optimum cropping pattern to maximize the net annual

return for the study area.

The optimization model was solved for various strategies of land allocation

and water resources allocation levels. The land allocation strategies consider the four

ranges of deviation (20, 30, 40 and 50%) from the existing crop acreage to satisfy the

basic food targets of the region. However, the water resources allocation strategies

consider the variation of groundwater and/ surface water allocation (0 -100% of

available water resources) to maintain the system balance with conjunctive use

planning of water resources. Optimal cropping pattern found for the Valley was the

50% deviation from the existing high water requirement crop with the 31% higher

annual return than the existing return (17.8 Million rupees).

The model when imposed with a situation of varying the water resources

allocation pattern, net benefit remains constant for Cachar from 30% to 90%, for

Karimganj from 60% to 90% and for Hailakandi from 50% to 90% of the

groundwater availability. So, with optimal allocation of river water at 20, 30 and 40%

of available surface water (48000 Mm3) in conjunction with optimal allocation of

groundwater at 50, 50 and 30% of available groundwater could be made for

Hailakandi, Karimganj and Cachar districts, respectively for maximizing annual netreturn (15.60 Million Rupees) with optimal cropping pattern. Thus the optimal

cropping pattern with conjunctive scenarios of water resources allocation could be

followed for socio-economic development in the Barak Valley.

Keywords: Barak Valley, Land and water resources allocation, Optimum Cropping

Pattern, Optimization model, QSB package.


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CHAPTER-I

INTRODUCTION

1.1 Background

India is an agricultural based country and its economy is evaluated on the

performance of agricultural output. In recent years agricultural production has

decreased due to various factors like socio-economic, environmental and poor

management practices. Above all the agricultural production is determined by two

factor of land and water resources utilization. The soil type and the form of water

utilized in the agricultural greatly influences the yield and thereby the economy of the

farmers. Therefore, a detailed knowledge of these resources is an important

prerequisite for agricultural planning.

Currently, Barak Valley is faced with several fundamental problems: an ever-

increasing population, a limited available of cultivable land and utilization of

available water resources. These problems intensify the importance of developing

efficient natural resources use strategies. The future of the region depends on the

water stored in the Barak River and precipitation of rainfall on the land surface for all

purposes.

In recent years there has been increasing emphasis on helping decision-makers

with good information. The need is much greater in the field of water resources where

some of the components cannot be measured or modeled. As a result, knowledge-

based optimization model are found to be effective and popular in the field of water

resources management (Simonov, 1996 a, b). Labadie and Sullivan (1986) discussed

the need to develop procedures to incorporate experience and subjective judgment of

system managers and decision-makers.


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One of the recent trends of solution of water resources management problems is to

aggregate several models into an integrated software that focuses on interaction

between the user and the data, models and computers (Andreu et al., 1996). The

optimization model has been widely used for drought management (Raman et al.,

1992), river basin planning (Jamieson and Fedra, 1996 a, b), surface water planning in

river basin (Ito et al., 2001), flood prevention (Ford and Killen, 1995), flood warning

(Ford, 2001) and for conjunctive management of surface water and groundwater

(Emch and Yeh, 1998).

Several researchers have applied a number of simulation and optimization

models to derive planning and operating strategies for irrigation reservoir systems

(Vedula and Nagesh Kumar 1996; Divakar et al., 2011). In irrigated agriculture,

where various crops are competing for a limited quantity of land and water resources,

linear programming is one of the best tools for optimal allocation of land and water

resources (Kaushal, et al., 1985; Sethi et al., 2002, Sethi et al., 2006). The integrated

land and water resources planning and management for salinity control (Panda et al.,

1996 and Cai et al., 2003) has been studied using various optimization techniques.

Proper application of decision-making tools increases productivity, efficiency,

and effectiveness and gives many businesses a comparative advantage over their

competitors, allowing them to make optimal choices for technological processes and

their parameters, planning business operations, logistics, or investments. The project

focuses primarily on the optimal allocation of land and water resources, the part that

directly supports modeling decision problems and identifies best alternatives in the

Barak Valley.


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1.2 Objectives

Keeping the aforementioned issues in view, the present study has addressed the

following objectives:

1. Assessment of the land and water resources of the Barak Valley.2. Formulation of Optimization model and maximizing the net economic return

with optimal land and water resources allocation in Barak valley.

3. Compilation of model results for alternative scenarios of land and waterresources management.

The above objectives are considered to execute the Optimal Allocation of Land and

Water Resources for Crop Planning in Barak Valley and find out the scenarios for

sustainable crop production at different risk levels.


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CHAPTER-II

REVIEW OF LITERATURE

2.1General

This chapter deals with the review of research work carried out by various pioneers on

Optimal Allocation of Land and Water Resources for Crop planning of various

regions of the country as well as various parts of world.

2.1.1 Assessment of land and water resources

Assessment of land and water resources involves the analyses of the data related to it

and to interpret what are the resources and in what form being utilized in different

sections. It is possible because of collaborative effort involving several units of the

Bureau of Meteorology, various units of government and private organization and

also the individual research work.

Bhowmick, et al. (1999), prepared a report on Farming System in Assam. The

report provides some basic information on the prevailing farming systems in the state

of Assam, North-East India. According to them the prevailing farming system in

different agro-climatic zones is by far a mixed type. It is basically a diversified system

with dominance of crop activities. But in charareas especially in the Lower Barak

Valley Zone, farming system is either pure crop based or mixed (crops and animals).

In all other situations usually homestead component bears immense importance

because with a meager working capital investment this component can not only

contribute sizeable net returns but also generate employment to farm family labour.

But in char areas the scope of expansion of homestead farming is rather limited

because of very smallness of homestead area. At best farmers in the charareas can

rear few livestock. Usually during Rabi season the farmers in charareas grow a good

number of high valued crops.


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Central Water Commission (2007), Guidelines for Preparation of River Basin

Master Plan. According to Commission the need for integrated river basin planning,

development and management arises from the relationship between the availability of

water resources and its possible uses in various sectors.

Gupta (2003 and 2004) worked on water availability, poverty and socio-

economic crisis in the floodplains of Barak Valley, Assam, North East India and

suggested that an integrated and sustainable water management is crucial for

improving the situation. Participatory rural appraisals made in the study area indicate

that if road-cum-embankments that would remain above the flood level could be

cleverly designed and laid across the landscape, these could serve to augment the

retention of water in the floodplain, thereby enhancing fishery prospects and

providing water for agriculture, besides improving communication. Land regulations

should be strictly enforced to prevent loss of good agricultural land to brick kilns.

Another important strategy would be to change the cropping patterns in the

floodplains. This could include introduction of deepwater paddy varieties having

higher yields during flood period, rapid-growing yet high yielding summer rice

varieties that could be harvested before pre-monsoon inundation of the floodplain, and

cultivation of pulses and vegetables in the flood-free period (Borthakur, 1981).

World Bank (2007) prepared a report on Development and Growth in

Northeast India; the Natural Resources, Water and Environment Nexus and has shown

the significant potential that exists in the Northeastern Region for its renewable

natural resources to generate benefits at the regional and local levels. It has also been

shown that these resources alone, without enabling institutional frameworks and an

integrated vision, have not brought and will not bring development to the Northeast.

The report has made an initial effort to develop such an integrated view and to show


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how the different sectors are linked to each other, and also how the macro and micro

levels are connected. With a targeted thrust that comprises central government and

state stakeholders and, importantly, local communities and stakeholders, the natural

resource curse does not need to become a reality in the Northeast. The natural wealth

of the Northeastern Region is well acknowledged. However, in the available

documentation and literature, there are few suggestions on what to do in order to

develop this wealth to the benefit of the regions citizens. This report has shown that

for all of the topics covered in the study, institutional change is the necessary first

step. Incentives need to be changed at central levels as well as at state and local levels

in order to direct work towards the regions developmental goals.

Planning Commission (2001), Government of India, they prepared a report on

Agricultural Development in Eastern & North Eastern India for the formulation of the

tenth Five year plans. Agriculture and Horticulture Departments, besides those

dealing with Animal Husbandry and Fisheries, in the State Governments are relatively

ill-equipped to meet the challenges of today. Apart from being outdated in their

knowledge and perceptions, their procedures and systems are not designed to meet

modern day needs.

Economic survey, Assam (2004), prepared report with the help of irrigation

department on the area under different irrigation project and the cost of irrigation.

Economic survey, Assam (2004), also prepared a report on yield of different

agricultural commodities indifferent prevailing season and cost of agricultural

commodity.

Mahanta (2006), Water resources of the North-east state of the knowledge

base prepared a draft for discussion and assessed the water resources available in both

ground and surface.


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2.1.2 Optimization model for optimal land and water resources allocation

Linear programming (LP) is a mathematical modeling technique useful for the

allocation of scarce or limited resources, such as labour, material, machine, time

capital etc., to several competing activities, such as products, services, projects, etc.,

on the basis of the given criterion of optimality.

Mathematically a model consists of variables and a specification of

interactions among them, from the point of view decision making a model and its

variables represent the following three components: a measure of preferences over

decision objectives, available decision options, and a measure of uncertainty over

variables influencing the decision and the outcomes.

Kan, et al. (2009), evaluated the effectiveness of changing land allocation

among crops as a mechanism for increasing net-social benefits, where production

profit and amenity values are augmented. A positive mathematical programming

model is calibrated and applied in Israel, using a crop-discriminating amenity-value

function. Change in land allocation increase net-social benefits by 2.4% nationwide

and by up to 15% on the regional level. According to them a policy encouraging

amenity-enhancement of agricultural land use is warranted, provided that it is

implemented on a regional scale.

Divakar, et al. (2011), developed a model for optimal bulk allocations of

limited available water based on an economic criterion to competing use sectors such

as agriculture, domestic, industry and hydropower. The model comprises a reservoir

operation module and a water allocation module. Reservoir operation module

determines the amount of water available for allocation, which is used as an input to

water allocation module with an objective function to maximize the net economic

benefits of bulk allocations to different use sectors and the marginal net benefit from


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domestic and industrial sectors are established and are categorically taken as fixed in

the present study. The developed model was applied to the Chao Phraya basin and

result indicates that the water allocation module can improve net economic returns

compared to the current water allocation practices.

Darwish, et al.(1992), determined optimal decision rules of cropping mix and

irrigation water in Egypt under different policy settings and water inflow levels and

concluded that the net present value of revenue were always higher in the

international models as compared to the governmental ones. The enforcement of

certain cropping activities coupled with price distortions in both input and output

markets becomes the main factors responsible for reducing the net return to water in

the regulated compared to the unregulated scenarios.

Pryazchinskaya, et al. (1986), concluded that with minor adjustments to the

model input data, environmental concerns had been incorporated into linear

optimization model of resources use in agriculture. The model output provides the

optimal structure and allocation of crop and livestock production, irrigation water

withdrawal and consumption on a monthly basis, irrigation return runoff and

agricultural non-point pollution estimates for each allocation alternative. The model

results proved the significance of irrigation water.

Sethi, et al. (2006), developed Decision Support System for evolving optimal

cropping pattern and water resources allocation for various levels of conjunctive use

of river water and groundwater in a costal river basin. The developed DSS was used

for the alternate cropping pattern and water resources allocation so as to maximize the

net annual return.

Weng, et al. (2010), developed decision support system and incorporated

techniques of scenario analysis, multi-objective programming, and multi-criteria


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decision analysis with a decision support system framework which can solve complex

water resources management planning problem which may involve multiple conflict

objectives and need to concern uncertainty in a long-term period.

2.2. Critiques on Literature Review

There is a substantial amount of empirical evidence that human intuitive judgment

and decision making can be far from optimal, and it deteriorates even further with

complexity and stress. Because in many situations the quality of decisions is

important, aiding the deficiencies of human judgment and decision making has been a

major focus of science throughout history. Disciplines such as statistics, economics,

and operations research developed various methods for making rational choices. More

recently, these methods, often enhanced by a variety of techniques originating from

information science, cognitive psychology, and artificial intelligence, have been

implemented. The concept of optimal allocation is extremely broad, and its definitions

vary, depending on the author's point of view. Linear Programming Technique is

gaining an increased popularity in various domains, including business, engineering,

the military, medicine and agriculture. They are especially valuable in situations in

which the amount of available information is prohibitive for the intuition of an

unaided human decision maker and in which precision and optimality are of

importance.

Linear Programming can aid human cognitive deficiencies by integrating

various sources of information, providing intelligent access to relevant knowledge,

and aiding the process of structuring decisions. They can also support choice among

well-defined alternatives and build on formal approaches, such as the methods of

engineering economics, operations research, statistics, and decision theory. They can


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also employ artificial intelligence methods to address heuristically problems that are

intractable by formal techniques. Proper application of decision-making tools

increases productivity, efficiency, and effectiveness and gives many businesses a

comparative advantage over their competitors, allowing them to make optimal choices

for technological processes and their parameters, planning business operations,

logistics, or investments.


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CHAPTER-III

MATERIALS AND METHODS

In this chapter brief about the study area (location, geomorphology, soil, climate,

agriculture, land use pattern and water resources availability), and optimization model

formulation and methods for finding optimal solution are discussed.

3.1 Study Area

3.1.1 Location

The study area (Barak Valley) is situated in the southern part of the Indian state of

Assam. The geographical position of Barak valley is latitude 24o8 and 25o8 N and

longitude 92o15 and 93

o15 E and with altitude of 31.40 m from the mean sea level

(Figure 3.1). Barak Valley is one of the six agro-climatic zone of Assam (Bhowmic et

al., 1999). Other zones are Central Brahmaputra Valley, Lower Brahmaputra Valley,

North Bank Plain, Upper Brahmaputra Valley and Hills zone of Assam. The zone has

a geographical area of 6,922 km2 (8.84% of state) with three districts, viz. Cachar,

Hailakandi and Karimganj.

3.1.2 Geomorphology

Depending upon the factors like physiographic, soil types, flood proneness, water

retention/stagnation, cropping pattern etc., the Barak Valley Zone is delineated in to

five agro-ecological situations, viz., (i) Alluvial Flood Free Situation (ii) Alluvial

Flood Prone situation (iii) Beel and Hoars (iv) Piedmont and Plantation Crop situation

and (v) Hills and Forest.


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It has eight physiographic divisions, viz. (i) High hill region (ii) Dissected foot

hill region (iii) Low hill region, (iv) Undulating plain, (v) Detraital Valley, (vi) Broad

meander plain, (vii) Flood plain and (viii) Low lying area.

Figure 3.1 Location of study area.


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With human population of 2995769 which is distributed in three districts namely

Cachar, Hailakandi and Karimganj. The population of Barak Valley is predominantly

a rural one and 70.73% of the total work force engaged in agriculture and allied

activities. The region has an undulating topography characterized by hills, hillocks,

wide plains and low-lying waterlogged areas.

3.1.3 Soil

The type of soil of the zone varying from sandy to clay mostly suitable for field crops

including horticultural crops. The soil pH ranges from 4.6 to 5.7. The soils can be

classified into (i) Old riverine alluvial (ii) Old mountain alluvium (iii) non-laterised

red (iv) laterised red and (v) peat soils. Among all types of soil, the largest area of the

zone is covered by non-laterised red soils surrounding the entire alluvial region on all

sides. Generally peat soils are found in scattered patches of low-lying beel areas of

different agro ecological situations of the zone.

3.1.4 Climate

The climate of the Barak Valley region is subtropical, warm and humid. The average

rainfall of the region is 3180 mm with average rainy days of 146 days per annum. The

region owes this high rainfall mainly due to south-west monsoon, which usually

operates for a longer spell in the North-Eastern region compared to other part of India.

The monsoon rains mainly starts from early June and continued to October. Even pre-

monsoon heavy showers in late March and April are not uncommon. During the

summer months the temperature generally varies from 25 to 40oC, while during the

winter season the temperature ranges between 10 to 25

o

C. The humidity remains high


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throughout the year with minor recess during the months of March and April. The

relative humidity varies from 58 to 91 percent and the sunshine hour varies from 10 to

12 hours.

3.1.5 Agriculture

Barak Valley zone has 38.58 per cent of total geographical area under forest, 33.08

percent as net sown area and 8.48 per cent as area sown more than once. About 5.47

percent of land is used for plantation crops, miscellaneous trees and other. The forest

coverage is the highest in Hailakandi district (55.83%) with the highest cropping

intensity. Intensity of land use was low in Karimganj and Cachar district with high

proportion of cultivable wasteland. In the recent years, the cropping intensity has been

found to increase. Besides, shifting cultivation is also practiced in the southern hill

areas of the zone adjoining Mizoram. Mixed cropping of Colocasia, Turmeric, Ginger

and other spices are practiced in those areas.

Agriculture is the main occupation of the people. Rice is the staple diet of the

people and cultivation of rice is the main occupation of those engaged in agriculture.

Different pulses, jute, tea and fruit cultivation are the other agricultural crops.

Sugarcane, potatoes, cotton, oil seeds, coconut and arecanut cultivation are also

practiced on a substantial scale apart from the horticulture. Apart from crops, the zone

is suitable for fish and animals rearing throughout the year.

It is congenial to grow most of the crops during the year. Barak Valley Zone is

delineated in to five agro-ecological situations viz., (i) Alluvial Flood Free Situation

(ii) Alluvial Flood Prone situation (iii) Beel and Hoars (iv) Piedmont and Plantation

Crop situation and (v) Hills and Forest.


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In situation (i) rice is cultivated in high, medium and up land situation as

mono or double crop. In high and medium land rice is also grown in sequence with

vegetables/potato/mustard/pulses while Sali rice is a mono crop in low-lying areas in

sequence with ahu rice.

Situation (ii) is featured with medium to low lands, which are submerged

during monsoon. Ahu rice followed by Sali rice/vegetables/potato/oil seeds and

vegetables as mono crop is commonly practiced under this situation.

Situation (iii) is featured with low-lying water bodies having perennial water

bodies. Oro rice is cultivated in the traditional areas as mono crop. Natural fisheries

are common in this situation.

Situation (iv) is characterized by the presence of dissected foothills, low hills,

and undulating topography with tilla and narrow valleys. It is featured with high

lands, hillocks, detraital valleys, and tillas. The common crops are tea, pineapple, fruit

trees, sugarcane and vegetables.

Situation (v) has mostly non-laterised red-soils, laterite red soils, sandy and

loamy with fine silt. It is covered by reserved forests, and mixed rain forest. The tribal

people commonly practice shifting cultivation and mixed cropping in forest villages.

The situation has potentialities for growing horticultural crops and rearing animals.

3.1.6 Land-use pattern

The pattern of land-use of a country is determined by the physical, economical, and

the institutional framework taken together. In other words, the existing land-use

pattern in different region in India has been evolved as the result of the action and

interaction of various factors, such as physical characteristics of land, the institutional

framework, the infrastructural facilities, the structure of other resources (capital,


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labour etc.) and the location of the in relation to other aspects of economic

development e.g. those relating to transport as well as industries and trade (ICAR,

1991-92). A brief picture of the land use pattern in Barak Valley is presented in the

Table 3.1.

Table 3.1 Land use pattern in Barak Valley

Classification Area

(ha)

% of the total

geographical Area

Total Geographical Area 692200 100

1. Forest 253767 36.66

2. Not available for cultivation 141999 20.51

(a)Non-agricultural uses 71916 10.39

(b) Barren and unculturable 70083 10.12

3. Other uncultivated land (excluding fallow land) 43715 6.32

(a) Culturable waste land 6097 0.88

4. Fallow land 28744 4.15

5. Net area sown 222872 32.20

6. Gross cropped area 307107 44.37

7. Area sown more than once 84235 12.17

Source: Economic Survey, Assam 1996-97, Directorate of Economics and Statistics,

Govt. of Assam.

The total area under different types of wetlands in the Barak Valley districts of

Cachar, Hailakandi and Karimganj is 13737.5 ha, which in turn, represents about 14

% of the total natural wetland area in the state of Assam (Table 3.2). Chatla in Cachar

and Shon beel in Karimganj district are the two major floodplain wetlands of Barak

Valley. While Chatla has an area of around 10 km2, Shon beel is the largest floodplain

wetland in Assam, having an area of 15 km2

(Choudhury, 2000). Besides these two,


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xxviii

the other important floodplains include the Jabda and Lucca Haors in Cachar, Bakri

Haor in Hailakandi, and Anair Haor in Karimganj district. The floodplains are locally

calledHaors orBeels.

Table 3.2 The details of land covered by wetlands in the Barak Valley

District Area (ha)

Lake/Pond Seasonally

inundated

floodplain

Ox-

Bow

Lake

Swamp/Marsh Total

Cachar 1151.5 4869.5 592.5 564.5 7178

Hailakandi 322.5 480 37.5 0 840

Karimganj 95 4667 87.5 870 5719.5

Total 1569 10016.5 717.5 1434.5 13737.5

Source: Gupta, A. (2003)

3.1.7 Water resources availability

3.1.7.1 Surfacewater availability

The Barak basin is spread over India, Mayanmar and Bangladesh and drains and area

of 41723 sq. km in India, the basin lies in the state of Meghalaya, Manipur, Mizoram,

Assam, Tripura and Nagaland. Before entering Bangladesh, the river bifurcates into

two streams called Surma and Kushiara. Further lower down, the river is called

Meghna and joins the combined flow of the Ganga and the Brahmaputra. The

principal tributaries of the Barak in India are jiri, the Dhaleshwari, the single, the

Longai, the Sonai and the Katakhal. An average annual surface water potential of

585.6 km3 has been assessed in this basin out of this, 24.0 km3 is utilizable water. The

average annual yield of Barak in monsoon and non-monsoon are 12073 and 2004

Mcum, respectively (Barak Master Plan, 1988). The basins house a multitude of

species and are also characterized by water bodies such as beels (seasonally flooded


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xxix

wetlands), and by abundant groundwater resources. The information of the surface

water availability in the Barak valley is given in Table 3.3.

Table 3.3 Status of the surface water availability in Barak Valley

Description Quantity

1. Catchment area 41723 Mm

2. Mean flow 483.6 Mm

3. Utilizable 24000.0 Mm

4. Water availability per hectare 11600 m

5. Runoff per hectare 56680 m

6. Annual Surface water potential 585600 Mm

7. Annual surface water availability 48000 Mm

9. Annual availability of water per hectare ofcultivable area

43447 m

10. Average water potential 484 Mm

Source: World Bank, Strategy Report (2007).

3.1.7.2 Ground water availability

The entire Barak Valley is represented by unconsolidated, semi-consolidated and

consolidated formations. The semi-consolidated Tipam sandstones form good

repository in the Valley. The depth of water level varies from a few meters to 4 m bgl

in alluvial sediments. The static water level in shallow aquifer is within 1.3 to 4.0 m

bgl in the north of the Barak River and it varies from 1.8 - 2.22 m bgl in southern

parts. Discharge from tube well varies from 5.5 - 8.0 m3/hr with drawdown of 6.0 m.

The estimated replenish able and utilizable ground water in Barak Valley is

852 Mm3

and 780 Mm3, respectively. The ground water utilization for irrigation

purpose in the valley is very less. The status of the groundwater resources in Barak

valley is given in Table 3.4.

3.2 Model Formulation

3.2.1 Optimization model


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There are different methods of optimization like analytical, search, case study, linear

programming etc., but except linear programming, all the methods gives only one

solution with no any direction or path to reach it. Therefore we have used linear

programming technique to develop optimization model.

Table 3.4 Status of the groundwater resource in Barak Valley

District GW

resources

Dynamic

(Mm3)

Utilizable GW

resources for

irrigation

(Mm3)

Utilizable GW

resources for

drinking and

allied (Mm3)

Gross

Draft

(Mm3)

Balance

available

(Mm3)

State of GW

development

(%)

Cachar 817 694 123 1 693 0.15

Hailakandi 98 83 15 3 80 3.16

Karimganj 133 113 20 4 109 3.54

Source: Central Ground Water Board

The linear-programming model is used to develop objective function for three

district of agriculture (rainfed and irrigated), and seasons (monsoon and winter). The

objective of the optimization model is to maximize the net annual return (annual

return per hectare from crop yield excluding the cost of cultivation and other

equipment).

3.2.1.1 Objective function

The objective function consists of maximizing the net annual return (z) subject to the

constraints on the availability of land and water resources along with other input

parameters.

(3.1)

ijkcijkc

ijkcijkc

ijkcijkc

ijkcijkc

i j k

n

c

QgwgCgwg

QgwpCgwp

QrwCrw

ARZMax3

1

2

1

2

1 1


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xxxi

Where i = districts, i = 1 for Cachar, i = 2 for Hailakandi, i = 3 for Karimganj district;

j = type of agricultural, j = 1 for rainfed and j = 2 for irrigated agricultural; k= crop

growing season, k= 1 for monsoon and k= 2 for winter season; c= crop 1, 2, 3,..n;

n = number of crops grown in a season;Rijkc= net return per unit area, (excluding the

cost of irrigation water) for crop c grown in season k ofjth

type of agriculture in

district i, (Rsha-1

); Aijkc = area allocated to crop c grown in season k ofjth

type of

agriculture in district i, (ha) (decision variable); Crwijkc = unit cost of river water

utilization in season k forjth

type of agriculture in district i for crop c, (Rsm-3

); Qrwijkc

= amount of river water allocated in season kforjth type of agriculture in district i for

crop c, (m3) (decision variable); Cgwpijkc = unit cost of utilization of groundwater

from private tube well in season k for jth

type of agriculture in district i for crop c,

(Rsm-3

); Qgwpijkc = amount of groundwater utilized from private tube well in season k

for jth type of agriculture in district i for crop c, (m3) (decision variable); Cgwgijkc =

unit cost of utilization of ground water from government tube well in season kforjth

type of agriculture in district i for crop c, (Rsm-3

); and Qgwgijkc = amount of ground

water utilized from government owned tube well in season kforjth

type of agriculture

in district i for crop c, (m3) (decision variable).

3.2.2.1 Constraints

Maximization of objective function is subject to the following constraints.

1. Water requirementsThe calculated net water requirement for the three districts of all crops must be

satisfied by the water available from surface water and ground water for all the

season.


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xxxii

0)(2

1

2

1

3

1

2

1 1

i j

ijkijkijkijkc

i j

n

c

ijkc QggwQpgwQrwANIR (3.2)

For all value ofK,WhereijkcNIR is the net water requirement for the crop c in the season kof the

jth

type of agriculture in the ith

district andare the field application and conveyance

efficiencies respectively.

Various methods are available to estimate the reference crop

evapotranspiration (). It is used to calculate the net irrigation requirement for

irrigation (Allen et al., 1998).

occ ETKET (3.3)

rc EETNIR (3.4)

Where Crop evapotranspiration = ETc; crop coefficient = Kc; reference crop

evapotranspiration =ETo; net irrigation requirement =NIR and effective rainfall =Er

Among the evapotranspiration (ET) models, the Hargreaves model is the simplest one

for practical use, since it requires only two easily accessible parameters, temperature

and solar energy. The Hargreaves model is expressed as follows:

smeano RTET )78.17(0135.0 (3.5)

Where ETo = potential daily evapotranspiration, mm/day; Tmean = mean temperature,

oC; andRS = incident solar radiation converted to depth of water, mm/day.

2. Land area constraintsThe total area allocated to various crops must not be greater than the total cultivable

area.

3

1

2

1 1i j

n

c

kijkc TAA For all value ofk (3.6)


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xxxiii

Where kTA denotes the total available land area for cultivation in the season k, (ha)

3. Water availability constraintsThe water available from both surface and ground water must satisfy the water

requirement during the season.

k

i j

ijk GRAQgwpQgwgQrw )()(3

1

2

1

For all value of k

(3.7)

Where (Qrw + Qgwg + Qgwp)ijk = total amount of river and ground water utilization

in the season kforjth type of agriculture in the district i, (m3); andA(R + G)k = total

amount of available water in the both source river and groundwater in season k, (m3)

4. Allocation of areaThe maximum or minimum area allocated to particular crop according to the market

requirement

For Maximum Area

ijkcijkc TAAijkc

max (3.8)

For Minimum Area

ijkcijkc TAA ijkcmin

(3.9)

Where ijkcA is the area up to which may be allocatedmax

ijkc and min

ijkc

are the

factor for which allocated land area increased and decreased, ijkcTA is the area

allocated as per existing cropping pattern in ith

district ofjth

type of agriculture in the

season k for crop c.

5. Non-negativity of constraints

0ijkcA ; 0ijkQrw ; 0ijkQgwg ; and 0gijkQgwp (3.10)


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xxxiv

For all value ofi, j, k, and c.

3.3Method of Finding Optimal SolutionThe computer software package QSB (Quantitative Systems for Business) which is

capable of handling 100 constraint 100 numbers of decision variables, is used in

computer to solve the linear programming.

The following assumptions guided by Panda (1996), and Sethi et al. (2006)

are considered for the present study:

1. Soil of the districts is considered as homogeneous.2. Farm resources such as land, labour and capital are divisible3. Each unit of land receives the same management practices for a

particular crop

4. Time and period of the crop is same in every year.5. The rainfall is considered as uniformly distributed throughout the area

under considered

6. All the relationships in the objective function and in the constraints arelinear.

7. Uniform horse power electric motors are used for lifting water from aspecific water resources.

8.

Uniform pumping durations are considered for each type of water lifting

structures.

9. The following irrigation efficiencies are considered over the area(a)Conveyance efficiency of river lift system ( ) , fraction : 0.75(b)Field water application efficiency ( ),fraction : 0.65(c) Irrigation system efficiencies( ),fraction : 0.48


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CHAPTER-IV

RESULTS AND DISCUSSION

The results and discussion have been presented in the following sequences.

4.1 Assessment of Existing Land and Water Resources

4.2 Net return

4.3 Water Requirement

4.4 Optimal allocation of Land and Water Resources

4.5 Alternative Scenarios for Land and Water Resources Management

4.1 Assessment of Existing Land and Water Resources

Before actual evaluation of the optimization model, some of the facts revealed from

the assessment of the study area are discussed here. The study area, Barak Valley is

situated between the north latitude of 24o8 and 25o8 and east longitude 92o15 and

93o15 covering an area of 6922 km2 out of which 3071.07 km2 is cultivable area. The

Barak River and its tributaries cover the whole Barak Valley. The whole area is

politically divided into three districts viz. Cachar, Hailakandi and Karimganj.

A study of the existing cultivation practices reveals that the farmers usually

grow crops in two seasons viz. monsoon (June-September) and winter (January-May)

in both rainfed and irrigated areas. It is also seen that farmers left the field barren in

the month of October to December. The data of the study area were collected from

different Central and State government agencies such as the Central Water

Commission, the State Agriculture, and Statistics & Economics, Assam, India. Based

on the land and water resources information received from the different sources were

assessed and presented in Table 4.1.


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Table 4.1 Season-wise existing cropping pattern and water resources available in

different districts of Barak Valley

The Table 4.1 shows the existing cropping pattern in monsoon and winter

season and also the available the water resources in the three districts of the valley. A

study of the existing cultivation practices reveals that the farmers usually grow crops in

two seasons (monsoon and winter) in both rainfed and irrigated areas. Crops grown in

the study area during monsoon season are paddy (Oryzasativa L.), and pigeon pea

(Cajanus cajan Milsp.) and in winter season crops are rice, wheat (Triticum aestivum

District i Agriculture j Season K Crop c Aijkc(ha)

Qrwijkc(Mm3)

Qgwgijkc Qgwpijkc A(R+G)k(Mm3)

(Mm3) (Mm3)

Cachar

Rainfed

Monsoon Paddy 8035 48000 415.8 166 1177Vegetables 4450 48000 415.8 166 1177

Winter

Paddy 3960 48000 415.8 166 1177

Arahar 75 48000 415.8 166 1177

Potato 78 48000 415.8 166 1177

Pea 36 48000 415.8 166 1177

Irrigated

MonsoonPaddy 4836 48000 415.8 166 1177

Wheat 394 48000 415.8 166 1177

WinterPaddy 2768 48000 415.8 166 1177

Oilseed 1420 48000 415.8 166 1177

Karimganj

Rainfed

MonsoonPaddy 4367 48000 67.8 27.12 597

Vegetables 2225 48000 67.8 27.12 597

Winter

Paddy 198 48000 67.8 27.12 597

Arahar 38 48000 67.8 27.12 597

Wheat 100 48000 67.8 27.12 597

IrrigatedMonsoon

Paddy 2418 48000 67.8 27.12 597

Wheat 97 48000 67.8 27.12 597

Winter Paddy 1384 48000 67.8 27.12 597

Hailakandi

Rainfed

Monsoon

Paddy 2791 48000 49.8 29.4 567

Vegetables1483 48000 49.8 29.4 567

Blackgram 52 48000 49.8 29.4 567

Winter

Paddy 1320 48000 49.8 29.4 567

Mustard 40 48000 49.8 29.4 567

Vegetables 494 48000 49.8 29.4 567

IrrigatedMonsoon

Paddy 1612 48000 49.8 29.4 567

Potato 26 48000 49.8 29.4 567

Winter Paddy 922 48000 49.8 29.4 567

Note: Qrwijkc = Amount of river water in the district (i) of agriculture (j) in the season (k) to the crop (c), Qgwgijkc =Amount of

water from government owned irrigation system in the district (i) of agriculture (j) in the season (k) to the crop (c), Qgwpijkc =Amount of water from private owned irrigation system in the district (i) of agriculture (j) in the season (k) to the crop (c),

A(R+G) = Amount of available water in the district (i) of agriculture (j) in the season (k) to the crop (c)


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L.), groundnut (Arachis hypogaea L.), mustard (Brassica junceaCoss.), black gram

(Phaseolus mungo Linn.), and garlic (Allium sativum L.), oilseed, arahar and

vegetables. As a principal crop, the farmers normally grow paddy in most of the

cultivable areas (either irrigated or rainfed) in monsoon season and winter season. In

summer season, there is no cultivation in the study area due to unavailability of water

or flooding of fields. In various seasons, the cropping pattern varies due to uncertainty

of meteorological parameters, scanty available water resources, food habits of local

inhabitants, possibilities of theft of high valued crops and other socio-economic

constraints.

Rainfall and river water flowing in Barak River and its tributaries is the major

sources of water available for irrigation in addition groundwater is also used for

irrigation but comparatively vary less proportion. The groundwater is lifted for

irrigation by the government and private owned shallow tubewells (depth less than 20

m) and there is no deep tubewells (depth more than 60 m) available for irrigation

purpose. The cost of irrigation from government owned river lift and shallow

tubewells is Rs. 750 per hectare and from privately owned shallow tubewells is Rs.

1350 per hectare (based on rates of Government of Assam).

The structures used for irrigating the study area are river lift points, private

and government owned shallow tubewells. As canal water supply is not available, the

river lifts are considered as the surface water to compute the available irrigation water

supply. The surface water (river lifts) and ground water supply (pumping units) were

computed using the data of water resources of the study area. It is observed that there

is surplus river water of 482.86Mm3(after withdrawing 1.4 Mm

3) and surplus

groundwater of 869.28Mm3(after withdrawing 20.72Mm

3). Existing seasonal water

supply through river lift and different pumping units is shown in Table 4.2.


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Table 4.2Existing structures used for water supply (draft) from different sources in the Barak valley

Name of the

sources

Pumping

units

No. of pumping

units

Average

discharge

Operating hours

(h/day)

Total draft (Mm3)

Monsoon

season

Winter

season

(m3/s) Monsoon

season

Winter

season

Monsoon

season

Winter

season

Surface water River lift 5 12 0.019 6 9 0.3 1.1

Groundwater Government 45 78 0.03 8 10 5.8 12.63

Private 23 39 0.024 8 10 2.38 5.1

Source: District Agriculture Office and Lift Irrigation Department of Assam

4.2 Net Return

The existing cropping pattern of the study area in both monsoon and winter seasons

and the corresponding net returns (excluding irrigation water cost) considering the

corresponding yield, market price, and cost of cultivation has been evaluated a and

presented in Table 4.3. It is observed that the variation of return excluding irrigation

water cost depends upon the soil, agriculture, the crops with their corresponding yield,

cost of cultivation and market price. Though the paddy crop is grown in maximum

area but the highest return is earned from the vegetables cultivation.

4.3Water Requirement

Net irrigation requirement at various crop developmental stages for different crops

grown in the study area is computed from the evapotranspiration model based on

Food and Agriculture Organization (FAO) recommendations. The FAO recommended

crop coefficient (kc) and number of days for various stage of crop growth (for about

80% RH) are presented in Table 4.4.Daily rainfall and temperature of the study

areafor 10 years were collected and aggregated to get monthly values. These values

were used to predict the expected monthly rainfall, evapotranspiration and net

irrigation requirement (NIR) at different stages of crops grown in the valley and

presented in Table 4.5.


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Table 4.3 Existing cropping pattern and net return excluding irrigation cost

Districts

I

Agriculture

j

Seasons

K

Crops

C

Area

(ha)

Aijkc

Yield

(kg/ha)

Cost of

cultivation

(Rs./ha)

Net return

(Excluding

cost of

irrigation )(Rs./ha)Rijk

c

Cachar

Rainfed

MonsoonPaddy 8035 1794 10400 1745

Vegetables 4450 2972 8100 11825

Winter

Paddy 3960 1651 11000 1252

Arahar 75 500 5290 1629

Potato 78 2532 5570 1404

Pea 36 600 6322 1280

Irrigated

MonsoonPaddy 4836 1191 12000 1977

Wheat 394 1146 13000 2617

WinterPaddy 2768 1651 12000 1959Oilseed 1420 533 5550 10500

Karimganj

Rainfed

MonsoonPaddy 4367 991 10500 1475

Vegetables 2225 2972 8100 12800

Winter

Paddy 198 1551 13500 1075

Arahar 38 500 5920 1180

Wheat 100 1046 14000 2190

IrrigatedMonsoon

Paddy 2418 1794 13000 1663

Wheat 97 1046 14000 2517

Winter Paddy 1384 1551 13300 1865

Hailakandi

Rainfed

MonsoonPaddy 2791 1651 11050 1700Vegetables 1483 2872 7700 10825

Blackgram 52 500 5000 1320

Winter

Paddy 1320 1551 12850 1180

Mustard 40 524 5750 9500

Vegetables 494 2872 6500 14156

IrrigatedMonsoon

Paddy 1612 1794 12000 1794

Potato 26 2972 5770 1504

Winter Paddy 922 1651 12500 1688

Table 4.4 Crop coefficient at different developmental stages

Crops Initial stage Crop dev. stage Mid season stage Late seasonstage

Paddy kc days kc days kc Days kc Days

0.45 30 0.75 30 1.05 60 0.85 30

Vegetable 0.35 25 0.7 35 1 25 0.85 10

Arahar 0.25 20 0.7 30 1.1 60 0.8 40

Potato 0.35 25 0.7 30 1.1 45 0.8 30

Pea 0.35 15 o.70 25 1.1 35 0.8 15

Wheat 0.25 20 0.7 60 1.1 70 0.4 30

Oilseed 0.25 20 0.7 35 1.05 45 0.55 25

Blackgram 0.35 20 0.75 30 1.05 60 0.65 40

Mustard 0.3 25 0.7 35 1.1 60 0.5 15

Source:http://www.fao.org/docrep/S2022E/s2022e07.htm
http://www.fao.org/docrep/S2022E/s2022e07.htmhttp://www.fao.org/docrep/S2022E/s2022e07.htmhttp://www.fao.org/docrep/S2022E/s2022e07.htmhttp://www.fao.org/docrep/S2022E/s2022e07.htm


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Table 4.5Evapotranspiration and net irrigation required at different developmental

stages of crops grown in the Barak Valley.Crops Initial stage Crop dev. Stage Mid season stage Late season stage

ETo ETc NIR ETo ETc NIR ETo ETc NIR ETo ETc NIR

Paddy 7.59 102.52 68.92 7.59 170.77 137.17 7.59 478.17 410.97 7.59 193.54 159.94

Vegetable 7.59 66.41 46.41 7.59 185.95 157.95 7.59 455.41 407.41 7.59 64.52 56.52

Arhar 7.59 37.95 21.95 7.59 159.31 135.31 7.59 500.94 452.94 7.59 242.88 210.88

Potato 7.59 66.41 46.41 7.59 159.31 135.31 7.59 375.71 339.71 7.59 182.16 158.16

Pea 7.59 39.84 27.84 7.59 132.82 112.81 7.59 292.21 264.21 7.59 91.08 79.08

Wheat 7.59 37.95 21.95 7.59 318.78 270.78 7.59 584.43 528.43 7.59 91.08 67.08

Oilseed 7.59 37.95 21.95 7.59 185.95 157.9 7.59 358.62 322.62 7.59 104.36 84.36

Blackgram 7.59 55.65 39.65 7.59 170.77 146.77 7.59 478.17 430.17 7.59 197.34 165.34

Mustard 7.59 56.92 36.92 7.59 185.95 157.95 7.59 500.94 452.94 7.59 56.92 44.92

Source:http://www.fao.org/docrep/S2022E/s2022e07.htm#and Allen et al. (1998)

4.4 Optimal Allocation of Land and Water Resources

The data required to solve the objective function are: (1) the geographical information

of the study area; (2) the physical and chemical properties of soil; (3) the

meteorological data (4) the existing seasonal cropping pattern; (5) the cost of

cultivation and market price of the agricultural produce of the region; (6) water

availability for agriculture in aquifer of the study area; (7) information about the river

catchment, water availability; (8) sources of water supply for irrigation; and (9)

pumping units (tubewells) used with their efficiencies, duration of operation and cost

of irrigation.

Optimization model, developed for obtaining optimum cropping pattern and

water resources management, consists of 37 decision variables out of which 27

variables corresponds to crop variables and 10 corresponds to water resources, The

optimization model was executed for the existing cropping pattern in three districts of

the valley using Quantitative System Business (QSB) package (Chang, 1993).Initially

as Case-1, the model was solved without providing the ranges of constraints (no

minimum and maximum crop area and water allocation)and optimal allocation of land

and water resources are presented in Table 4.6 and 4.7, respectively.
http://www.fao.org/docrep/S2022E/s2022e07.htmhttp://www.fao.org/docrep/S2022E/s2022e07.htmhttp://www.fao.org/docrep/S2022E/s2022e07.htmhttp://www.fao.org/docrep/S2022E/s2022e07.htm


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Table 4.6 Optimum cropping pattern (ha), water resources allocation and net annual return for different

scenarios of cropping situation

District Agriculture Season Crop Optimal Area Allocation (ha)

Case1 Case2 Case3 Range1 Range2 Range3 Range4

Cachar

Rainfed

Monsoon

Paddy 0 8035 8035 6428 5624.5 4821 4017.5

vegetable 26052 0 4450 5340 5785 6230 6675

Winter

Paddy 0 3960 3960 3168 2772 2376 1980

Arahar 0 0 75 60 52.5 45 37.5

Potato 0 0 78 62.4 54.6 46.8 39

Pea 0 0 36 28.8 25.2 21.6 18

Irrigated

MonsoonPaddy 0 4836 4836 5803.2 6286.8 6770.4 7254

Wheat 0 0 394 472.8 512.2 551.6 591

WinterPaddy 0 2768 2768 2984.8 3093.2 3201.6 3310

Oilseed 0 0 1420 1704 1846 1988 2130

Karimganj

Rainfed

MonsoonPaddy 0 4367 4367 3493.6 3056.9 2620.2 2183.5

vegetable 10827 0 2225 2670 2892.5 3115 3337.5

Winter

Paddy 0 198 198 158.4 138.6 118.8 99

Arahar 0 0 38 30.4 26.6 22.8 19

Wheat 0 0 100 120 130 140 150

IrrigatedMonsoon

Paddy 0 2418 2418 2577.4 2657.1 2736.8 2816.5

Wheat 0 0 97 116.4 126.1 135.8 145.5

Winter Paddy 0 1384 1384 1660.8 1799.2 1937.6 2076

Hailakandi

Rainfed

Monsoon

Paddy 0 2791 2791 2529.2 3365.5 2267.4 2136.5

vegetable 0 0 1483 1779.6 1927.9 2076.2 2224.5

Blackgram 0 0 52 41.6 36.4 31.2 26

Winter

Paddy 0 1320 1320 1056 924 792 660

Mustard 0 0 40 48 52 56 60

vegetable 8740 0 494 592.8 642.2 691.6 741

IrrigatedMonsoon

Paddy 0 1612 1612 1934.4 1128.4 2256.8 2418

Potato 0 0 26 20.8 18.2 15.6 13

Winter Paddy 0 922 922 737.6 645.4 553.2 461

Water allocation (Mm3) 427.88 163.34 492.32 488.61 487.3 484.86 483.57

Net annual return (Million Rs.) 55.9 33.6 17.8 20.0 21.1 22.2 23.3

Table 4.7 District-wise water resources allocation (Mm ) for different scenarios of

cropping situation in Barak Valley

Districts Scenarios of cropping (Cases) and water resources (Ranges)situations(Mm3)

Case-1 Case 2 Case 3 Range-1 Range-2 Range-3 Range-4

Cachar 232.73 54.62 262.19 260.29 259.26 258.43 256.5

Karimganj 106.24 52.7 126.41 125.48 125.02 124.13 125.08

Hailakandi 88.91 51.82 104.28 102.83 103.02 102.31 101.99

Total 427.88 163.34 492.88 488.60 487.30 484.86 483.57

The maximum annual net return obtained is55.9Million rupees but the entire

cultivable area of three districts has been allocated to vegetables only. The results

computed are not realistic in nature as far as food habit and season concerned. Thus it

is required to incorporate certain constraints to solve the real study area problems for


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getting the alternative scenarios for crop planning in the Barak Valley. The detailed

alternative scenarios and their results are explained in the next section.

4.5Alternative Scenarios for Land and Water Resources Management

In the present study, it is found that the available water resources are much more than

the crop water requirements. Hence, the analysis for alternate scenarioto solve the real

study area problemswas carried out in two ways: (1) varying land allocation to

different crops, agricultures and growing seasons (Case 1 to 3, and Range 1 to 4),

details mentioned in the following paragraphs), keeping available water resources as

constant; (2) varying water resources allocation (different combinations of surface

water and groundwater), keeping the cropping pattern as constant. The system was

executed for three different cropping situations based on local requirements viz., Case

1: Without Area Constraints i.e. there are no lower and upper bounds on the area

cultivated for each crop. The analysis is required to ascertain the model response in

the absence of any restriction on maximum/minimum cropped area for any specific

crop; Case 2: With paddy area constraints i.e. paddy cultivation is restricted to

existing paddy area while there are no bounds for all other crops. This alternative was

examined since paddy cultivation is essential for the farmers of the area for their food

habits even if it is not economically viable; Case 3: Existing cropping pattern.

The district wise optimal annual surface water and groundwater allocations for

all the three cases are also shown in Table 4.7 and Figure 4.1. The optimal water

allocation for three different cropping situations attains the maximum level at case 1

and decreases in case 2 and case 3 after which increases for range 1, 2, 3 and 4. The

optimal annual returns at for three different cropping situations (Case 1, Case 2 and

Case 3) are 55.9, 33.6 and 17.8 million rupees, respectively.


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0

50

100

150

200

250

300

350

400

River Water Groundwater

Allocationofwater(M

m3)

Cachar

Total Available

Monsoon

Winter

0

20

40

60

80

100

120

140

River water Groundwater

Alloc

ationofwater(Mm3)

Karimganj

Total available

Monsoon

Winter

0

20

40

60

80

100

120

River water Groundwater

Allocationofwater

(Mm3)

Figure 4.1 District-wise total and seasonal river and groundwater allocation for

existing cropping pattern in Barak Valley

Hailakandi

Total available

Monsoon

Winter


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The optimum-cropping pattern, obtained from the model for the aforesaid

three different cropping situations (Case 1, 2 and 3) is presented in Table 4.6. For the

case 1, the crops with most economic benefit are preferred for cultivation in the entire

area. The cropping pattern with vegetables only was found as optimal in monsoon

season in Cachar and Karimganj and in winter season in Hailakandi district. Although

the crops may yield maximum annual return it may not be feasible due to the local

food requirements.

No data is available regarding the minimum requirements of crops for the

farmers as well as the food targets of the region. So, the model is executed at four

different ranges of the cultivable area considering the existing cropping patternto

satisfy the basic food targets of the region. These ranges with fixed deviations from

the existing area for each crop for all i, j, kand c are (1) Allowing 20% deviation i.e.

min

ijkc

= 0.8 andmax

ijkc = 1.2; (2) Allowing 30% deviation i.e.

min

ijkc

= 0.7 andmax

ijkc =

1.3; (3) Allowing 40% deviation i.e.min

ijkc

= 0.6 andmax

ijkc = 1.4; (4) Allowing 50%

deviation i.e.min

ijkc

= 0.5 andmax

ijkc = 1.5. It may be noted that the total cropped area

is restricted to the total cultivable area in the model. Fifty percent of the existing

cropped area is considered as the minimum required area to be cultivated for each

crop to satisfy the food habit and farmer can get more return from other crops with the

less water resources. Therefore, deviations more than 50% are not considered. District

wise optimal cropping pattern with optimalwater resources allocation, and maximal

annual return obtained for the said ranges of cropping pattern (Range 1, 2, 3 and 4)

are also shown in Table 4.6 and Figure 4.2. However, district wise optimal allocation

of water resources for different seasons are presented in Table 4.7. It is observed that


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with the increase in allowable deviation from the existing cropping pattern, more

economic benefit is obtained. The farmers depending on their prevalent situation may

choose a suitable cropping pattern from the above alternatives.

The possible scenarios obtained from the various ranges of land allocation

strategies shows the increase in net annual return with the increase in ranges of

deviation due to reduction of acreage for high water requirement crops with the low

water requirement crops (Table 4.6 and Figure 4.3). Considering the minimum

required crop acreage (50% of existing cropping pattern), the 50% deviation from the

existing crop acreage for each crop was found as optimal in the region (Table 4.6 and

Figure 4.3). It is observed that the annual return obtained from the optimal allocation

of cropping pattern for the range 4 is 31% higher than the existing return (17.8

Million rupees). So, the optimal cropping scenarios of range 4 allocation could be

considered as an alternative for the farmers and district planners to shift from the

existing rice-based cropping pattern, which will maintain a system balance with the

allocation of available water resources.

290.4

72.6

143.4

76.905

20.09

290.4

72.6

144.357

74.328421.16

290.94

72.6

145.312

71.7322.25

290.4

72.6

146.271

69.14723.33

050

100150200250300

Monsoon

Winter

Monsoon

Winter

Netreturn(MRs.)

Monsoon

Winter

Monsoon

Winter

Netreturn(MRs.)

Monsoon

Winter

Monsoon

Winter

Netreturn(MRs.)

Monsoon

Winter

Monsoon

Winter

Netreturn(MRs.)

River

water

Groundwater River

water

Groundwater River

water

groundwater River

water

Groundwater

Range 1 Range 2 Range 3 Range 4

Allocationofwater(Mm3)

Figure 4.2 . Optimal seasonal river and groundwater allocation and net return at different

ranges of cropping pattern in Barak Valley


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Figure 4.3 Variation of maximized annual return of alternative scenario against the

existing cropping pattern in Barak Valley

In the study area, the farmers only irrigate that much land area, which can be

irrigated by the individual river lift points depending upon its coverage capacity. So,

the system was executed for two situations by varying the river water and

groundwater availability at different levels(0 -100% of available water resources) for

predicting the seasonal optimum water resources (river water and ground water)

allocation for maximizing the net annual benefit from the existing cropping pattern

and presented in Table 4.8. The scenarios considered for further water resources

variation analysis are (1) fixing the availability of groundwater supply at 5%, but

varying the river water supply from 5% and 10% to 100% of the available potential;

(2) considering only 5% of the river water availability constraint and varying

groundwater availability from 5% and 10% to 100% of available potential. The

district-wise feasibility of existing cropping pattern with the variation of water

resources are also presented in Table 4.8. It is observed that the water resource

allocation pattern and net benefit remains constant in first situation for Cachar from

17.8 17.8 17.8 17.8

12.3418.54

24.7230.89

5

10

15

20

25

3035

40

45

50

55

Range 1 Range 2 Range 3 Range 4

Maximizedannua

lnetreturn

(MRs.)

Alternative scenario of existing cropping pattern

% Net return

Existing


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Table 4.8 District-wise feasibility of conjunctive use of available water resources for existing

cropping pattern in Barak Valley

Variation in river (RW) and groundwater

(GW) supply

River water (Mm ) Groundwater (Mm )

Cachar District

Keeping availability of groundwater at

5% fixed (GW=0.05)

Monsoon Winter Monsoon Winter

RW=full 170.13 21.03 69.4 34.7

RW=0.05 to 0.1 infeasible Infeasible infeasible Infeasible

RW=0.2 infeasible 75.26 infeasible 34.7


RW=0.4 to RW=1 224.36 75.26 34.7 34.7

Keeping availability of river water at 5%fixed (RW=0.05) GW=0.05 to 0.1

infeasible Infeasible infeasible Infeasible

GW=0.2 infeasible 21.03 infeasible 69.4

GW=0.3 0 21.03 170.3 69.4GW=0.4 to GW=0.9 0 21.03 170.3 69.4

Karimganj District

Keeping availability of groundwater at5% fixed (GW=0.05)


RW=full 132.72 48.4 6.65 0


RW=0.1 infeasible 48.4 infeasible 0

RW=0.2 infeasible 48.4 infeasible 0

RW=0.3 to RW=0.9 132.72 48.4 6.65 0

Keeping availability of river water at 5%fixed (RW=0.05) GW=0.05

infeasible 17.8 infeasible 6.65






GW=0.6 18.42 17.8 79.8 6.64

GW=0.7 to GW=0.9 0 17.8 91.59 6.64

Hilakandi District

Keeping availability of groundwater at5% fixed (GW=0.05)


RW=full 85.82 36.23 4.9 4.9

RW=0.05 to 0.1 infeasible Infeasible infeasible Infeasible

RW=0.2 to 0.9 85.82 36.23 4.9 4.9

Keeping availability of river water at 5%

fixed (RW=0.05) GW=0.05 to 0.1

infeasible Infeasible infeasible Infeasible


GW=0.3 infeasible 0 infeasible 28.09

GW=0.4 infeasible 0 infeasible 28.09

GW=0.5 16.91 0 49 28.09

GW=0.6 1.6 0 58.8 28.09

GW=0.7 to 0.9 0 0 68.6 28.09


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40 to 90%, for Karimganj from 30 to 90% and for Hailakandi from 20 to 90% 0f the

river water availability. In the second situation the resources allocation pattern and net

benefit remains constant for Cachar from 30% to 90%, for Karimganj from 60% to

90% and for Hailakandi from 50% to 90% of the groundwater availability. So, with

optimal allocation of river water at 20, 30 and 40% of available river water (48000

Mm3) in conjunction with optimal allocation of groundwater at 50, 50 and 30% of

available groundwater could be made for Hailakandi, Karimganj and Cachar districts,

respectively for maximizing annual net return ( Million Rs. 15.60 ) with optimal

cropping pattern. Thus it can be concluded the optimal conjunctive scenarios of water

resources allocation could be followed with the optimal cropping pattern for socio-

economic development of the region.


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CHAPTER-VSUMMARY AND CONCLUSIONS

5.1 Summary

The present studies are focused on assessment of land and water resources in the

different districts of Barak Valley viz. Cachar, Karimganj and Hailakandi and

optimization modeling for optimal allocation of land and water resources for crop

planning and maximizing the annual net return. In the study deterministic linear

programming model has been formulated and using the QSB (Quantitative Software

Business) package the linear programming model was solved to find out the optimal

seasonal allocation of surface and ground water and alternative scenario for

sustainable cropping pattern to maximize the net annual return for the study area.

Following specific summaries could be made from the present study:

The study area, Barak Valley is situated between the north latitude of 24 o8 and 25o8and east longitude 92

o15 and 93

o15 covering an area of 6922 km

2out of which 3071.07

km2

is cultivable area. The Barak River and its tributaries cover the whole Barak Valley.

It is observed that in the Barak Valley there is surplus river water of 482.86 Mm 3 (afterwithdrawing 1.4 Mm

3) and surplus groundwater of 869.28 Mm

3(after withdrawing 20.72

Mm3) than the present demand due to high contribution of rainfall which can be

effectively utilized for further use.

The optimum cropping and groundwater management linear programming modelyielded the cropping pattern for seven situations including three cases and four ranges

of deviation from the existing cropping pattern. The optimal annual returns at for three

different cropping situations (Case 1, Case 2 and Case 3) are 55.9, 33.6 and 17.8 million

rupees, respectively.


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Thee optimal annual returns for four ranges of deviation from the existing croppingpattern Range1, 2, 3 and 4 are found as 20, 21.10, 22.20 and 23.30 Million rupees

respectively . The optimal value increases with the increasing the deviation because of

reduction acreage for high water requirement crops (Paddy).

The increase of annual return with the increase of deviation of existing cropping patterRange 1, 2, 3 and 4 are 12.34, 18.54, 24.72 and 30.89 % against the existing cropping

pattern in Barak Valley

The reduction of water resources allocation with the increase of deviation of existingcropping patter Range 1, 2, 3 and 4 are 4.28, 5.58, 8.01, 9.31 Mm

3

against the allocation

of 492.88 Mm3

for existing cropping pattern in Barak Valley .

The model when imposed with a situation of varying the water resources allocationpattern, net benefit remains constant for Cachar from 30% to 90%, for Karimganj from

60% to 90% and for Hailakandi from 50% to 90% of the groundwater availability. So, with

optimal allocation of river water at 20, 30 and 40% of available surface water (48000

Mm3) in conjunction with optimal allocation of groundwater at 50, 50 and 30% of

available groundwater could be made for Hailakandi, Karimganj and Cachar districts,

respectively for maximizing annual net return ( 15.60 Million Rupees) with optimal

cropping pattern.

5.2 Conclusions

Following specific conclusions can be drawn for the study area based on the results obtained

from the models.

1. Barak valley of Assam state (North-Eastern India) shows that there is a surplussurplus river water of 482.86Mm

3(after withdrawing 1.4 Mm

3) and surplus

groundwater of 869.28Mm3(after withdrawing 20.72Mm

3for irrigation) available for

further use, which is very high due to occurrence of high rainfall.


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2. The linear programming model formulated for maximization of annual net returnwith optimal water and cropping pattern allocation for the monsoon and winter

seasons in the Barak Valley was found to be an effective tool for land and water

resources allocation.

3. The cropping pattern obtained for seven different ranges of deviation from theexisting area for the cropping pattern also gives significant output in the form of

alternative cropping pattern. The results of alternative cropping patterns and water

resources allocations for maximizing net annual return are found significant as per

the expectation under changing scenario.

4. Optimal cropping pattern found for the Barak Valley is the 50% deviationfrom the existing high water requirement crop (paddy) with the 31% higher

annual return than the existing return (17.8 Million rupees). So, the optimal

cropping pattern could be considered as an alternative for the farmers and

district planners to shift from the existing rice-based cropping pattern, which

will maintain a system balance with the allocation of available water

resources.

State agencies and farmers involved in the actual agricultural

production processes are advised to practise conjunctive use of river water

and groundwater so as to restrict further depletion of groundwater level.


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