AN ECONOMIC ANALYSIS OF INTEGRATED …...Sagar, Gayathri, Guruprasad, , Gourav, sahana,...

AN ECONOMIC ANALYSIS OF INTEGRATED

FARMING SYSTEM IN SIDLAGHATTA TALUK OF

CHIKKABALLAPURA DISTRICT, KARNATAKA

NATARAJA, H. M.

PALB 4091

DEPARTMENT OF AGRICULTURAL ECONOMICS

UNIVERSITY OF AGRICULTURAL SCIENCES

BENGALURU-560 065

AN ECONOMIC ANALYSIS OF INTEGRATED

FARMING SYSTEM IN SIDLAGHATTA TALUK OF

NATARAJA, H. M.

PALB 4091

Thesis submitted to the

UNIVERSITY OF AGRICULTURAL SCIENCES, BENGALURU

in partial fulfillment of the requirements

for the award of the degree of

MASTER OF SCIENCE MASTER OF SCIENCE MASTER OF SCIENCE MASTER OF SCIENCE (Agriculture)(Agriculture)(Agriculture)(Agriculture)

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ACKNOWLEDGEMENT

It is my pleasure to glance back and recall the path one travelled during the day

of hard work and perseverance. Interdependence is definitely more valuable than

independence. This thesis is the result of two years of work whereby I have been

accompanied, supported and guided by many people. I would thus like to thank everyone

who, knowingly or otherwise, has provided support, encouragement and assistance along

the way. I humbly place before my parents, my most sincere gratitude. Their blessings

have renewed me every day all the way on the journey through out my life.

I wish to express my deepest sense of gratitude and profound indebtedness to my

guide and chairman of the Advisory Committee, Dr.Murtuza Khan Professor,

Department of Agricultural Economics, UAS, Bengaluru. I deem it as my good fortune

for my opportunity to work under him and I owe to him for his valuable suggestions,

versatile guidance, unceasing support, sustained encouragement, untiring patience and

courteous words have finally shaped my present endeavour. I feel really proud for the

privilege of being his student and for all that he has done as a major advisor.

With immense pleasure and deep respect, I express my heartfelt gratitude to

members of Advisory Committee, Dr. K. B. Umesh, Professor and head, Department of

Agricultural Economics, GKVK, Bengaluru, Dr. B.V. Chinnappareddy, Professor,

Department of Agricultural Economics, GKVK, Bengaluru and Sri. H. S. Surendra,

Associate Professor, Department of Agricultural Statistics, Applied Mathematics and

Computer Science, GKVK, Bengaluru for their excellent guidance, encouragement,

valuable suggestions and critical evaluation of the manuscript.

I indebted to all my teachers, Dr. M. G. Chandrakanth, Dr. M.

N.Venkataramana, Dr. G. S. Mahadevaiah, Sri. Mallikarjuna Swamy, Sri. Honnaiah, Mr. Jagannath Olekar and Dr. P.S. Srikantamurthy for being the lighthouses in this

hard journey.

The love and patience of my family have been instrumental for completion of

study. Mere words cannot express my indebtedness to my mother Smt. Lakshmamma.,

my father Sri. Muniyappa H.N, my brother Mr. Manjunatha H.M., Mr.Narasimharaju

H.M and my sister Ms. Roopa H. M., and my brother-in-law Mr. Rajesh M, for their

support and encouragement in my life.

I wish to convey my thanks to the wonderful batch mates who were always ready

to offer unconditional help when needed. I thank Amaregouda, Arathi, Deekshith,

Kantesh, Manohar, Nishipriya, Raviteja, Shivashankar,venkaraddi and Venupraveen

for their support during Master Science journey in the department.

I also have been highly fortunate in having many friends whose hands were

evident at every moment of tension, anxiety and achievements. I am ever grateful to

Amaresh, Raghu, Mani, Mahesh, Banakar, Madhu,Vinay,Jeevan, Ravichandran,

Sandeep, Babu,Shivamurthy, Chandru, Shan, Soma, Mahesh, Chinni, Sushil, Shrikanth, and others. I thank u for being close to me and making my life a memory to be

cherished.

I was privileged to have a great group of seniors in our department who were

always ready to offer unconditional help when needed. I thank, , Harish,Ravi, Rohith,

Mohan, Vasanth, Negi, Nagesh, Rashmi, Hamsa, Amrutha, Afrin, Pavithra Divya,

Veerabhadrappa, Sathish, Range Gowda, Ranjith, Chikkathimme Gowda, Veun,

Basavaraj Jamhakandi, Mahadev Reddy, Roopa, Shripad Bhatt, Naveen, Sheikh, Sagar, Gayathri, Guruprasad, , Gourav, sahana, Chandrakanth, Mallik, and for your

guidance and support during my college days.

I was privileged to have a group of juniors who were always ready to offer help

when needed. I thank Uday, Malathi, Deepa, Suhas, Akash, Adarsh, Swamy, Krishna,

Shalini, Shaziya Sulthana, Akshatha, for their support during the degree programme.

I also thank all the farmers who responded calmly and helped me during data

collection. I would also acknowledge DES, Government of Karnataka and, Bengaluru

for providing the required secondary data.

I wish to express thanks to all the supporting staff of the economics department

Smt. Sujatha Devi, Sri. Devaraj and Sri. Murthy for their support and help during my

course of work.

Above all, I thank Almighty lord Shree Krishna and Shree

Lakshminarasimhaswamy for the blessings showered on me and helped to complete this

thesis work at required time.

Any omission in this brief acknowledgement does not mean lack of gratitude.

Bengaluru

December, 2016 (NATARAJA H M)

AN ECONOMIC ANALYSIS OF INTEGRATED FARMING

SYSTEM IN SIDLAGHATTA TALUK OF

NATARAJA, H. M.

ABSTRACT

The study was undertaken to assess the economics of integrated farming system in Sidlaghatta taluk of Chikkaballapura district, Karnataka. The primary data was collected from 160 farmers. The most profitable farming systems for small, medium and large farmers were, crop + diary + sericulture, crop + diary + sericulture, crop + small ruminants + sericulture respectively. The annual net income from the most profitable farming systems were Rs.276682/- per farm, Rs.323793/- per farm and Rs.489450/- per farm for small, medium and large farmers respectively. Annual employment generation in profitable farming systems were 695 man-days, 916 man-days, 1130 man-days for small, medium and large farmers respectively. Linear programming technique was used to obtain optimum farm plans and results indicated that, if the farmers use these plans their net income increases by Rs.75045/- for small farmers, Rs.117479/- for medium farmers and Rs.195451/- for large farmers. Farming systems like crop + diary, dairy + sericulture (own mulberry), crop + diary + sericulture, crop + small ruminants + sericulture shows complementary relationship among enterprises, whereas crop + sericulture sub system shows competitive relationship among enterprises in crop + dairy + sericulture and crop + small ruminants + sericulture systems. Further, diary + sericulture (purchased mulberry) system shows supplementary relationship among enterprises.

December, 2016

Dept. of Agricultural Economics (MURTUZA KHAN)

U.A.S, G.K.V.K. BENGALURU. Major Advisor

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CONTENTS

CHAPTER TITLE PAGE NO.

I INTRODUCTION 1-3

II REVIEW OF LITERATURE 4-10

III METHODOLOGY 11-20

IV RESULTS 21-37

V DISCUSSION 38-43

VI SUMMARY AND CONCLUSION 44-46

VII REFERENCES 47-50

APPENDICES 51-59

LIST OF TABLES

No. Title Page No.

3.1 Demographic features(2013-14) and livestock profile (2012 census) of the Study Area

3.3.1 Farmers categories based on the size of the land holdings 14

4.1.1 Age-wise classification of the respondents into different categories

4.1.2 Average family size in different categories of farm households 22

4.1.3 Classification of the farmers under different categories based on their Education level

4.1.4 Average size of the land holdings in different categories of farm households

4.1.5 Average livestock ownership possession of the sample farmers 23

4.2 Existing farming systems followed by sample house holds 24

4.3.1 Annual net income and employment generation from different farming systems of Small farmers

4.3.2 Annual net income and employment generation from different farming systems of medium farmers

4.3.3 Annual net income and employment generation from different farming systems of large farmer

4.4.1 Optimum farm plan for Small farmers 28

4.4.1.1 Annual Resource saving due to optimal plan in case of small farmers

4.4.2 Optimum farm planfor small farmers 29

4.4.2.1 Annual Resource saving due to optimal plan in case of medium farmers

4.4.3 Optimum farm plan for large farmers 30

4.4.3.1 Annual Resource saving due to optimal plan in case of large farmers

No. Title Page No.

4.5 Nature of economic relationship exist in the farming systems among enterprises.

4.5.1 Synergy(Complementary) among enterprises in(Crop + Dairy) system

4.5.2 Synergy (Complementary) among enterprises in (Dairy + Sericulture) system (Own Mulberry)

4.5.3 Synergy (Supplementary) among enterprises in (Dairy + Sericulture) system (Purchased Mulberry)

4.5.4 Synergy (Complementary) among enterprises in (Crop + Dairy + Sericulture) Farming system

4.5.5 Synergy (Complementary) among enterprises in (Crop +Small ruminants + Sericulture) Farming system

4.5.6 Synergy (Competitive) among enterprises in (Crop + Small ruminants + Sericulture) and (Crop + Dairy + Sericulture) Farming system

LIST OF FIGURES

Figure No. Title Between

3.1 Map showing location of the study area 12 - 13

LIST OF PLATES

Plate No. Title Between

4.1 Components of integrated farming system in the study area 24 -25

An Economic Analysis of Integrated Farming System in Sidlaghatta Taluk …………, Karnataka 1

I INTRODUCTION

Indian agriculture majored by marginal and small cultivators and their life is under struggle due to inefficiency in marketing system and irregular monsoon. And recent years parts of our country observed farmers suicides and this could be attributed to failure in monsoon, usurious informal lending rates, market failure and of course specialization of a particular area in particular crop creating situation of market glut during times of harvest. A great saying “Putting eggs in different baskets is better than putting all of them in single basket” is simple logic behind the integrated farming system. Thus diversifying among different enterprises helps farmers to come out of risk arising from market and monsoon. And developing the location specific integrated farming systems and popularizing among the farm households is the optimum possible solution for rising farm income.

Integrated farming system is a mix of farm enterprises to which farm families allocate its resources in order to efficiently utilize the existing enterprises for increasing the productivity and profitability of the farm. These farm enterprises are crop, livestock, aquaculture, agro forestry and Agri-horticulture. It is a multidisciplinary whole-farm approach and can be effectively employed in solving the problems of small and marginal farmers. The approach aims at increasing employment and income from small-holdings by integrating various farm enterprises and recycling crop residues and by-products within the farm itself through the concept of synergism.

Why to go for integration of enterprises? Population in Indian sub-continent is increasing by leaps and bounds which require production of more food from the limited available lands. The farmers particularly those belonging to small and marginal category are unable to meet both the ends with the income from cropping alone. With gradual decline in farm size, it has become increasingly difficult to produce enough food and other farm produces for the family. The situation is further weakened due to repeated failure of monsoons on one side and on the other side, due to ever increasing population and decline in per capita availability of land. Further, there is hardly any scope for horizontal expansion of land and only vertical expansion is possible by integrating various farm enterprises (Behera et al 2001).

Importance of IFS

The Integrated Farming Systems (IFS) assumes greater importance for sound management of farm resources to enhance the farm productivity and reduce the environmental degradation, improve the quality of life of resource poor farmers and maintain sustainability. In order to sustain a positive growth rate in agriculture, a holistic approach is the need of the hour (manjunath et al 2014). One of the measure to strengthen the repayment capacity of the farmer. It is also a risk aversion measure. With a view to mitigate the risk and uncertainty in agriculture, IFS serves as an informal insurance.

2 Nataraja, H. M., M.Sc. (Agri.) 2016

Scope of the study

The findings of the study would throw light on the possibility of synergies among different enterprises in agriculture. This study attempts to develop an optimum farming system plan for the farm households of Siddlagatta taluk of Chikkaballapura across different categories of farmers. This will help the planners and policy makers in formulating policy package and plan of action for implementation and replication of similar model based IFS system in other parts of the country. Thus the call given by Prime Minister of India for doubling the farm income by 2022 can be achieved and also risk associated with farming can also be brought down.

The objectives of the study along with hypothesis are listed below.

Objectives

The specific objectives of the study are

1. To assess the economics of integrated farming system and its impact on income and employment.

2. To determine the optimum integrated farming plan based on size of farm holdings.

3. To assess the magnitude of complementary, supplementary and competitive economic relationship among enterprises.

Hypotheses

1. The integrated farming systems are economically viable across different size groups.

2. As the degree of integration increases, the profitability and employment generation increases.

3. Optimum farm plan is identical for small, medium and large farmers.

4. Complementary and supplementary enterprises enhances the profitability while competitive enterprises reduces the profitability.

Limitations of the study

The present study mainly relied on the data collected through interview using a pre-tested schedule. Therefore, some amount of recall bias is bound to be associated with the collected data since the farmers did not maintain any record about the cultivation expenses, application of inputs, returns especially for subsidiary enterprises and the pattern of food consumption, expenditure on education and health. However, efforts were made to

minimize them through cross checks at the time of data collection. However, the degree of discrepancy if any would be negligible as the estimates presented are in averages. Since, the information was collected from farmers who practiced various farming systems in Chikkaballapura district, generalization of the results to other areas should be made carefully.

Presentation of the study

This study is undertaken in sidlaghatta taluk of chikkaballapura district. This thesis is organized into six chapters. The first chapter provides a brief introduction along with the specific objectives. In chapter second, some pertinent reviews are presented in consonance with the study objectives. Chapter three describes the main feature of the study area, sampling framework, database and analytical tools employed in the analysis of the data. The empirical results pertaining to study are presented in chapter four followed by critical discussion of results in chapter five. Finally, chapter six summarizes the major findings of the study with policy implications.

II REVIEW OF LITERATURE

CONCEPT OF INTEGRATED FARMING SYSTEM (IFS):

FAO (1977) Stated that “there is no waste”, and “waste is only a misplaced resource which can become a valuable material for another product” in IFS.

Agbonlabor et al. (2003) defined the IFS as a type of mixed farming system that combines crop and livestock enterprises in a supplementary and complementary manner.

Singh and Ratan (2009) defined the IFS is an integrated set of elements or components and activities that farmers perform in their own farms under their resources and circumstances to maximize the productivity and net farm income on a sustainable basis.

Economic analysis of Farming systems

Chandrashekaran et al. (1994) evaluated the feasibility of including dairy as an enterprise with rice. Additional income generated from IFS over the existing cropping system alone was Rs.11, 478 per year. Additional employment generated with cattle was 309 man days over existing cropping system.

Swaminathan (1996) lists the principal components of intensive integrated farming systems (IIFS) as seven pillars that include soil health care, water harvesting and management, crop and pest management, energy management, post-harvest management, choice of crops, farm animals and other components of the farming system and information, skill, organization and management empowerment.

Kandasamy (1998) stated that dairy based farming system gave the highest annual income (Rs.6090 per ha) with a per day income of Rs.16.16 and provided additional employment of 217 man days per year as against Rs. 1,902 and Rs.5.21 net annual income and per day income, respectively, with farmer’s method of sole cropping.

Rangaswamy (1999) stated that the concept of Integrated farming system has got more relevance in the present day farming to reap better harvest in the long range by maintaining a productive resource base on a holistic approach. The farm wastes are better recycled for productive purposes. A judicious mix of agriculture enterprises like dairying, poultry, mushroom, piggery, fishery etc. suited to the local agro-climatic situations and socio-economic status of farmer would bring in prosperity in the farming.

Elumalai and Pandey (2004) estimated value share of output from the livestock sector in Haryana. Among various constituents of output values, the share of milk group had ranked highest followed by meat group, draught power, dung and others during different periods of time. The share of milk group in total value of output, increased during the period 1970-71 to 1998-99. The highest contribution of milk group into the total

livestock output seems to be due to rearing of crossbred milch cattle with high milk production potentials along with murrah buffaloes yielding fat rich milk, in conjunction with adoption of scientific methods of rearing these animals. The share of meat group into total output growth increased from 28.53 per cent in 1970-71 to 29.79 per cent in 1998-99. Despite relative decline in the share of meat group into the value of output during 1980-81 to 1990-91, it constituted the second highest dominant product into total value of livestock output. In fact, the decline in share of milk group in total livestock output is offset by increase in the value share of meat.

Prasad (2004) assessed income and employment potential of dairy farming in the existing and alternative farm situations in Ranga Reddy district of Andhra Pradesh. The income realized from dairy farming under the existing situation were Rs.14.50, Rs.33.82, Rs.47.46, Rs.12.28 and Rs.11.09 per day for the landless, marginal, small, medium and large size group respectively under the existing situation, while it was Rs.49.43, Rs.72.77, Rs.63.83, Rs.19.37 and Rs.28.68 under the optimal situation for the corresponding size groups of farms in the same order as indicated above. When the income under the optimal situation was considered in terms of percentage increase over the existing situation, it was found that the increase in income was 240.89 per cent, 115.17 per cent, 34.49 per cent, 57.74 per cent and 158.61 per cent for the landless, marginal, small, medium and large size-groups, the optimal situation shows maximum gains to the landless size-group and the minimum gain to the small size group. The employment per day per animal under existing situation in terms of the number of man-days was 0.28, 0.38, 0.29, 0.30 and for the landless, marginal, small, medium and large size-groups respectively, while the optimum situation shows that they were 0.59, 0.51, 0.46, 0.41 and 0.49 in the order indicated. It could be observed that the employment was more than double in the landless size-group, nearly one and half times in the other size-groups compared to the existing situation.

Ramrao et al. (2005) who developed a crop livestock mixed farming model of 1.5 acre for small scale holders and results revealed that the employment generation of 571 man days, net income of Rs. 58,456 per year against crop farming alone with employment generation of 385 man days and net returns of Rs. 18,300 per year only.

Veerabhadraiah (2007) noticed that the crop livestock integrated farmers were getting higher returns i.e. a farmer with 2.5 acres of irrigated land, HF and Buffaloes were earning Rs. 1, 04,321 and a farmer with 3.5 acres of irrigated land with 2 cows and 4 sheep earning Rs. 78,867 and a farmer with one acre of irrigated land with 4 HF cows were getting Rs. 1, 32,000.

Singh and Joshi (2008) studied the economics of crop production and dairy farming in small and marginal farmers of Punjab. It has been found that dairy farming has emerged as major allied enterprises for supplementing the income of small and marginal farmers in Punjab. Income from off-farm sources has been identified another important factor contributing significantly to the disposable income of these farm households. Where expenditure on concentrates and labour accounted more in total cost structure of dairy farming across different size group of households.

Suresh et al. (2008) worked out the economics of Sheep farming and its economic efficiency has been reported in semi-arid regions of Rajasthan. Where expenditure on feed and fodder followed by veterinary care accounted more in total cost structure of sheep rearing across different size group of households. The net return per average flock of 54 has been found Rs. 25,000 per year.

Channabasavanna et al. (2009) seen the benefit cost ratio of 1.97 in IFS than conventional system which is of 1.64. Among the various components of Palladam district of goat recorded the highest benefit cost ratio (2.75) followed by fish (2.23), vegetables (2.00) whereas poultry showed the lowest benefit cost ratio (1.13) as a result of high cost of maintenance.

Mohanty et al. (2010) reported a successful tribal integrated farmer in Orissa who was getting enhanced the productivity as well as the profitability and sustainability after adopting the IFS as compared to the conventional farming system and earned seven times higher Net Monetary Return (NMR) as compared to traditional method of farming.

Jagadeeshwara et al. (2011) reported that the productivity of IFS was 26.3 per cent higher than the conventional system. Among the various components, the productivity was maximum in crop yield (46.32 %), closely followed by horticulture (16.77 %), dairy (42.26 %) and piggery (8.07 %) in the southern Karnataka state.

Manjunatha et al. (2014) studied Economic viability of Integrated Farming System Research models developed in different states of the India and found that that IFS enables the agricultural production system sustainable, profitable and productive. About 95 per cent of nutritional requirement of the system is self-sustained through resource recycling. As the number of enterprises are increased, the profit margin increases but simultaneously coupled with increase in cost of production and employment generation though the profit increase was marginal. Further, it is evident that profit margin varied with the ecosystem (rain fed/irrigated), management skill, and socio-economic conditions. On an average profit margin on account of IFS varied from Rs 15,000 to Rs 1,50,000/ha/annum. Simultaneously it takes care of the food and nutritional security of the farming family. The study further revealed improvement in the net profit margin varying from 30-50 per cent. The resource characterization study revealed that/ha improvement in profitability varied from Rs 20,000 to 25,000 under irrigated condition, resource recycling improve fertility led to 5 to 10 q/ha crop yield increase, generate 50-75 man-days/ family/ year and reduce the cost of production by Rs.500-1,000/ha. Therefore, there is an urgent need to promote the IFS concept under all agro-climatic conditions of the country.

Walia et al. (2016) studied integrated farming system in Punjab and found that net returns of Rs 380308/ha with BC ratio of 1.08 can be obtained from integrated farming system (IFS) which were about three times more than the prevailing rice- wheat cropping system. There is increase in the value for labour absorption in IFS farm due to additional components brought into integration within the IFS farm. The IFS is feasible with respect to socio- economic imperatives but actual adoption of integrated farming are limited and unevenly spread among farmers. Thus, in order to develop a nation, farmers should be

properly made aware about the use and management of IFS and Government should emphasize on this alternative to rice- wheat cropping system to give a ray of hope to the farmers in Punjab who are the real victims of agrarian crisis in Punjab agriculture.

The above studies indicated that IFS provides higher income compared to the conventional sole cropping system and it also generates better employment opportunities to the farmers. Integration of dairy enterprise with a crop is suggested by various researches as it helps in improving nutritional standards and socio-economic conditions of the farmers.

Optimization of Farming Systems

Sirohi et al. (1980) used linear programming to examine the possibilities of increasing income and employment through introduction of dairy and poultry into crop farming system in union territory of Delhi. Results revealed that on optimization with liberal credit facilities, the new enterprise system (crop + dairy + poultry) was found to increase labour employment besides augmenting income on small and marginal farms.

Gracy (1987) obtained optimum cropping plans of Bangalore north taluk of Karnataka using parametric linear programming (MOTAD- Minimization of Total Absolute Deviations). The results indicated that existing use of resources was less than optimum. The normative plans derived by MOTAD- Minimization of Total Absolute Deviations suggested that judicious use of resource would increase farm income, facilitate prompt repayment of loans, and create additional employment on small and large farms.

Ganesan et al. (1991) found that introduction of duck-cum-fish culture into rice farming system resulted in an increase of net profit from Rs. 13,790 to Rs. 24,117. The income per men per day increased from Rs. 37.78 in arable farming to Rs. 66.07 in mixed farming and an additional employment of 144 man-days was generated. Thus by following an integrated farming, the farmers could engage themselves productively and augment farm income even in the off-season.

Bhowmick et al. (1992) identified the different types of farming systems in Sonitpur district of Assam and optimized the resource use among different size groups of farms using deterministic linear programming technique. The net return increased from Rs. 11,516 to Rs. 18,480, Rs. 17,180 to Rs. 23,900 and Rs. 24,805 to Rs. 40,806 in small, medium and large farms respectively in the optimum farming system (crop + goat, pigeon + duck).

Chaudry and Chaudry (1992) investigated the possibilities of increasing net farm income by including labour-intensive dairy enterprise and vegetables along with crops under existing levels of technology in Pakistan. Linear programming was used to determine the optimum allocation of resources and combination of activities on farms. Activities were those of producing crops and livestock; augmenting resources, namely hiring additional labour, consuming wheat and paddy produced on the farm; and selling surplus quantities of produce. Results were obtained on the optimum and feasible number of buffaloes, and

optimum cropping patterns. Dairy animals (buffaloes) were found to be an essential part of farm plans. Besides providing milk and milk products, helped to secure net cash returns which could not be achieved through crops alone; provided employment for some of the family's excess labour; and served as a useful outlet for crop by-products which would otherwise go waste. Basically, the price of milk determined the profitability of buffaloes. It is concluded that increased net cash returns can be achieved by mixed farming even with subsistence food restrictions, through efficient resource allocation and improved marketing practices.

Nagaraja (1995) assessed the potential for increasing farm employment through an efficient farming system. The study was conducted in Bangalore rural district of Karnataka. An efficient system is one with the minimum income variability commensurate with high incomes. The data was analyzed using linear programming and its complements MOTAD - Minimization of Total Absolute Deviations, multiple objective and compromise programming techniques. An efficient farm plan has the potential to increase farm income by 124 per cent for crop + poultry system of marginal farms, 53 per cent for crop + sericulture system of small farms and 85 per cent for crop + dairy + sericulture system of medium farms. The efficient farm plan generated the highest employment for crop + sericulture system in all the categories of farms.

Thai (2006) used linear programming to analyze of the productivity and sustainability of crop-livestock-compost manure integrated farming systems in midlands of Vietnam. The study revealed that the farmers in the small, medium and large farm categories should raise livestock up to four pigs and three buffaloes, seven pig and five buffaloes, and 20 pigs and 13 buffaloes, respectively to match with the available resources. The optimized livestock herd holding and sugarcane cultivation area resulted in 88.7 per cent increase of the return from livestock and 62.5 per cent from crops in the small farm size, 25 per cent from livestock and nine per cent from crops in the medium farm size and 171 per cent from livestock while unchanged from crops in the large farm size.

Noorain (2010) used linear programming and MOTAD - Minimization of Total Absolute Deviations considering a sample of 90 farm households in central dry zone of Karnataka and examined that in the case of small farmers, efficient farm plan yielded 178 per cent of higher income than the existing plan. Similarly in the case of medium farmers, efficient farm plan yielded 189 per cent of higher income than the existing plan whereas in the case of large farmers, though the farmers are already working near to efficiency, efficient plan yielded 5 per cent of higher income than the existing plan.

Varalakshmi et al. (2011) used linear programming for drawing optimum plans for Kurnool district of Andhra Pradesh and results indicated the possibilities of increasing income even under existing technology with limited available own funds. The income was increased further through relaxation of borrowing credit and adoption of recommended technology. Credit played an important role in augmenting income. The effect of credit on income was inversely related with the size of the farm whereas the credit needs were directly related to the farm size. Adoption of recommended technology in conjugation with

relaxed borrowing of credit enhanced the income prospects on both small and large farms of the study area.

Igwe and Onyenweaku (2013) were used linear programming technique for sample of 30 farmers to identify optimum plan for Aba Agricultural Zone of Abia State, Nigeria and found Optimization and reallocation of available resources were found to bring significant changes in the existing plan. 20 enterprises were observed in the existing plan made up of one sole crop, 14 crop mixtures and five livestock enterprises across poultry, fish and piggery which an average farmer would make a gross margin of N(naira)232, 317.12. However the LP maximization model recommended that for optimum gross margin of N(naira)374, 850.00 which is about 61.3 percent of the existing gross margin, an average farmer should devote 0.31 hectare to yam + maize + melon, 0.33 hectare to cassava + maize + cocoyam and 1.30 hectares to Cassava + Maize + Yam + Mucuna Floanei while 0.14 of 500 birds of broiler one (1) raised usually between January – May and 0.11 of 1000 fish of fish two (2) done between July – December and 0.07 of 15 pigs be produced. Given the mean farm size of 0.45 hectares, the farming orientation is still subsistence. It is recommended that crop mixtures be undertaken by farmers in combination with poultry and fish enterprises for improved gross margin.

Felix et al. (2013) used linear programming model for small farmers considering a sample of 60 farm house holds in Bindura district, Zimbabwe and found that the LP model yield a gross income of $12,295.10 as compared to $8,500.00 that we obtain by using traditional methods. The difference in the gross incomes is 44.65 per cent. The “what if” land allocation plan obtained by using LP, yields more income than from traditional methods.

Otoo et al. (2015) Optimal Selection of Crops: A Case study Of Small Scale Farms in Fanteakwa District, Ghana. Comparison of results obtained by using existing farming plan and the LP Model indicate that results obtained from the LP Model were significant improvements of the existing farming plan. The LP Model saved 0.2 per cent and 0.6 per cent of available capital and labor requirement respectively. A 16.25 per cent significant increment of the net returns was obtained by the LP Model. This was as result of net returns increasing from Ghanaian cedi (GH¢) 77,848.00 to Ghanaian cedi (GH¢) 88,177.00. These results suggest the essence of application of formulated mathematical models like the LP Model to planning and management of limited resources.

Gameiro et al. (2016) used linear programming in the economic estimate of livestock-crop integration: application to a Brazilian dairy farm and the results showed that optimization models are relevant tools to assist in the planning and management of agricultural production, as well as to assist in estimating potential gains from the use of integrated systems. Diversification was a necessary condition for economic viability. A total cost reduction potential of about 30 per cent was revealed when a scenario of lower levels of diversification was contrasted to one of higher levels. Technical complementarities proved to be important sources of economies. The possibility of reusing nitrogen, phosphorus, and potassium present in animal waste could be increased to 167 per cent, while water reuse could be increased up to 150 per cent. In addition to economic

gains, integrated systems bring benefits to the environment, especially with reference to the reuse of resources. The cost dilution of fixed production factors can help economies of scope to be achieved. However, this does not seem to have been the main source of these benefits. Still, the percentage of land use could increase up to 30.7 per cent when the lowest and the highest diversification scenarios were compared. The labor coefficient could have a 4.3 percent increase. Diversification also leads to drastic transaction cost reductions.

Synergies to optimize the farm income

Bogahawatte (1984) analyzed the crop-livestock integrated farming system in three villages in Moneragala district of Srilanka. Linear programming was used to derive the optimal plan. The plan suggested the use of crop residues as a substitute for compost for farm crops. The result also showed that an increase in supplementary irrigation would result in the expansion of the cropped area, replacing hired labour, and increase in farm income.

Singh et al. (1997) inferred that many agricultural production systems which were based on mixed farming with crop and livestock integration had been found profitable over arable farming. The diversification of enterprises was likely to result in a suitable agricultural production and may help in encountering various challenges now being faced by small farmer for increasing production, income, employment, environmental conservation by preventing migration to towns and cities in search of employment. Further, the gainful exploitation of the potential synergy among different aspects of diversification especially in terms of employment and income linkages made it relevant even for those without land.

Valbuena et al. (2012) studied mixed crop–livestock systems in Sub-Saharan Africa and South Asia and concluded that the Complementary interaction exist between mixed crop-livestock systems apart from employment generation and additional income.

Remf et al. (2015) studied dairy horticulture integrated farming system in Tutur Nongkojajar, District of Pasuruan, East Java, Indonesia and results showed that The largest contribution subsystem in dairy horticultural farming system revenue is dairy cattle farming about 46.54 per cent, while contribution both of biogas and bioslurry are 2.17 per cent and 3.46 per cent. Contribution of biogas is still a bit, because the habits of households using biogas is still unfamiliar or still not accustomed than using kerosene or LPG (liquid petroleum gas) fuel. In the other aspects, forage farm revenue greater than horticulture farm revenue, because most farmer’s land is planted forage for support the main farming activities of dairy farming.

Sheeba et al. (2015) studied integrated farming system in Southern Kerala and concluded that IFS approach is better than traditional system in its contribution to productivity, profitability, economics and employment generation for small and marginal farmers and would also create confidence among farmers through higher profitability.

III METHODOLOGY

This chapter deals with the brief description of the study area, sampling design, data base and the analytical tools and techniques adopted to analyze the objectives of the research study. It is organized under the following sub-heads.

3.1 Description of the study area

3.2 Sampling design

3.3 Database

3.4 Analytical tools and techniques

3.1 Description of the study area

The geographical area of the taluk is 63811 hectares, of which the net cultivated area is 29795 hectares with a gross cropped area of 31240 hectares. The area under forest, land put to non-agricultural activities, barren land, permanent pasture and fallow land is 1417 hectares, 1722 hectares, 3236 hectares, 20294 hectares, and 3033 hectares respectively.

The Sidlaghatta taluk is bound by Chikkaballapur, Gudibande, Bagepalli, Chintamani & Kolar Taluks of Kolar district and Hoskote & Doddaballapur taluks of Bangalore Rural District. The taluk geographically lies between 77o 47’ 30’’ & 78o 0’ 30’’ longitude and 13o 12’ 57’ & 13o 40’ 5’’ latitude. The geographical area of the taluk is 670 square kilometers.

Sidlaghatta taluk comes under Chikkaballapura sub-division. This taluk is further divided into four hobalies, viz. Sidlaghatta, Jangamkote, Basettihalli and Sadali. There are 291 villages with one town. Sidlaghatta town is the Taluk Headquarters. All the taluk administration lies with the taluk panchayat for the implementation of developmental schemes and their progress.

The total population of the taluk is 2,14,169 of which the rural population is 163010 and urban population is 51,159. The population of cattle, buffalo, sheep, goat, and poultry birds in the taluk is 41240, 9254, 78610, 22799, and 100574 respectively.

Table 3.1: Demographic features (2013-14) and livestock profile (2012 Census) of the

Study Area

Source: http://Chikkaballapur.nic.in/index.html

I Demographic features (2013-14)

Sl.no. Particulars Sidlaghatta Taluk

1 Geographical area (Sq. Km) 670

2 Net cultivated area (hectares) 29795

3 No. of villages 291

4 Total population 214169(100)

5 Rural population 163010(76.11)

6 Urban population 51159(23.88)

7 Male population 108937(50.86)

8 Female population 105232(49.13)

9 Population density (2014) (people /Sq. Km)

10 Average rainfall (mm/year) 782

11 Actual rainfall (mm/year)(2014) 553

II Livestock profile (2012 Census)

(In numbers)

1 Cattle population 41240

2 Buffalo population 9254

3 Sheep 78610

4 Goat 22799

5 Poultry birds 100574

*Figures in parentheses represents percentage to total

CHIKKABALLAPURA DISTRICT

Fig. 3.1: Location of the study area

SIDLAGHATTA

KARNATAKA

3.1.1 Soil type and climate

The taluk has clayey-loam soil. This type of soil has moisture- retention capacity and allows deep furrowing which is suitable for cultivating paddy, Ragi, groundnut, maize and pulses. The soil is also suitable for cultivation of varieties of vegetables. Some parts of the taluk is also having red loam soil which is very easy for cultivation purposes and responds to good manure and other treatments. This soil is particularly suited for growing vegetable crops like potatoes, onion, cabbage and radish. The water-table in this type of soil is between 40 to 50 feet deep.

Climate is dry and hot during summer in Sidlaghatta taluk. Its average rainfall is 782 mm which is slightly above the district’s average rainfall (677mm). On an average the taluk receives rains on 45 days in a year.

3.1.2 Cropping pattern

Grapes and ragi are the major crops in the taluk along with mulberry. The area under ragi, mulberry, maize, tur, avare (field bean), cowpea, fruits, and vegetables was 11033 hectares, 5963 hectares, 3422 hectares, 311 hectares, 529 hectares, 153 hectares, 3996 hectares, and 2120 hectares respectively.

3.1.3 Livestock population

Sidlaghatta taluk as per 2012-13 livestock census has a sizable livestock of 41,240 cattle, 9,254 buffaloes, 78,610 sheep, 22,799 goats and about 1,00,574 poultry birds. Though the main occupation of the people is agriculture and sericulture, the livestock rearing comprising cows, buffaloes, sheep, goat, and poultry are the complementary sources of income.

3.2 Sampling procedure

3.2.1 Selection of the study area

The present study confined to Sidlaghatta taluk, In Sidlaghatta taluk, the pre-dominantly existing enterprises were sericulture, livestock and high value crops like grapes, capsicum, pole bean etc. so in order to analyze the synergism the taluk was purposively selected.

3.2.2 Selection of the villages

The three stage stratified random sampling procedure was adopted in order to select the sample villages. In Taluk, a cluster of 10 villages were selected for the study. From each village, 16 farmers were randomly chosen for eliciting information from the farmers. The selected villages in each cluster were as follows, Hujuguru, Dibburahalli, Anur,

Abloodu, Dyavapanagudi, Gudihalli, Sorakayalahalli, Kothanuru, Byraganahalli, and Handiganahalla.

3.3 Nature and source of data

The primary data required for the study was collected from the 160 sample farmers of which 20 farmers practising crop alone, 20 farmers practising dairy alone, 30 farmers practising crop + dairy system, 30 farmers practising dairy + sericulture system,30 farmers practising small ruminants + crop + sericulture system, 30 farmers practising dairy + crop + sericulture using structured schedule questionnaire through personal interview.

3.3.1 Farmers categories based on the size of the land holdings

For the purpose of analysis, the sample farmers were post-stratified as small, medium and large farm categories based on size of land holding. The range of land holdings for different categories of farmers are given in Table 3.3.1.

Table-3.3.1: Farmers categories based on the size of the land holdings

SI. No. Type of farmers Land holdings(hectares) Number of

farmers

1 Small farmers <2 109

2 Medium farmers ≥2 and≤ 4 36

3 Large farmers ≥4 15

Total 160

3.4 Analytical tools and techniques

3.4.1 Descriptive statistics

In order to analyze the economics of different farming systems and their synergies, economic measures like rate of return, averages, coefficient of variation and percentages were computed on per farm basis. The average livestock per household was considered to analyze economics of different farming systems and synergies involved in them.

3.4.2 Optimization of farming

Linear programming technique has been employed in order to determine the optimum utilization of available resources within the farm and to examine whether the

farmer under various combination of enterprises, the utilization of various available resources are optimum.

Optimum plans are those farm plans, which satisfy all the resource constraints at the farm level and yield the maximum value of the objective function. The deterministic linear programming was employed to work out the maximum attainable returns by large, medium and small farmers through optimum combination of various crops and livestock enterprises, using available resources. Optimum allocation is defined as one, which gives physical, technical and resource conditions shows the activities to undertake and how much of each resource should be allocated to each activity so that the net farm returns are maximized in a year. Linear programming technique was chosen because among the various analytical tools available for allocation of available limited farm resources among alternative enterprises, it is the most appropriate tool. The traditional tool of budgeting becomes less efficient when the number of constraints and real large and unique solutions are desired.

3.4.2.1 Mathematical formulation of the model

In linear programming analysis, a linear function of a number of variables (activities) is to be maximized subject to a number of constraints in the form of linear equalities and in-equalities. In mathematical form of one year, the linear programming model can be formulated in the following way.

For Small farmers

10 Maximize Z = ∑ Pi Xi i=1

Subject to the constraints,

1. aX1≥0.3 (minimum family food requirements)

1. ∑ biXi ≤2.67 i=1

2. ∑ ci Xi≤472 i=1

3. ∑ di Xi ≤ 168971 i=1

Xi ≥ 0

Where,

Z = Net returns from all crop and allied activities like livestock included in the model.

Pi = Net returns from ith activity or enterprise measured in rupees.

Xi = Level of ith activity or enterprise in acre.

a = minimum family food requirement.

bi = land available of the ith resource.

ci= labour available of the ith resource.

di= capital available of the ith resource.

For medium farmers

1. ∑ biXi ≤5.72 i=1

2. ∑ ci Xi≤622 i=1

3. ∑ di Xi ≤ 249654 I=1

Xj ≥ 0

Where,

For large farmers

1. ∑ biXi ≤10.88 i=1

2. ∑ ci Xi≤810 i=1

3. ∑ di Xi ≤379455 i=1

Xi ≥ 0

Where,

3.4.2.2 Objective function

The objective function of the model is maximization of the annual net returns to owned resources. The gross returns per acre of crop and per unit allied activities were calculated by using the data of sample farmers. Paid out costs such as hired human labour and FYM, fertilizer etc. was directly subtracted from the gross returns. For dairying, sheep and poultry activity, gross profits were calculated by deducting variable expenses such as of fodder, concentrate and labour from gross income of dairy, sheep and poultry activity. The maximization of net returns (profit) is subject to the assumption and resource constraints imposed in the model. It is assumed that products and factors markets are perfectly competitive.

3.4.2.3 Basic assumptions

Besides the general assumptions of linearity, divisibility, additivity and finiteness the following assumptions were made in developing the model.

In this study, the problem of resource allocation is dealt with the average farm level. Each farm is assumed to be an economic decision making unit. The farm operator is free to make decisions regarding business limited only by legal and contractual arrangements. The concept of time in production process is short-run nature. The model has an operational period of 12 months. All activities or processes such as crop marketing, transfer of cash terminated at the end of the year which is one planning period.

It is also assumed that each farm is operated with the objective of maximizing net farm returns, subject to the constraints listed already. Closely related to the above assumption, the study to start with, is in the static frame work. It is assumed that the yield and price expectation of the farmers are single valued.

3.4.2.4 Constraints and requirements:

Some of the important constraints considered in the study are land, labour, capital, ragi area for consumption purpose.

(i) Land: Land available for cultivation is considered. Cropping activities are being considered and the crops that occupied negligible area was not included in the model. The land available for cultivation includes owned land.

(ii) Labour: The labour available for cultivation is considered. It includes both hired and family labour

(iii)Capital: The available capital is considered as a constraint.

(iv)Land under ragi: The land required to produce ragi for minimum subsistence family food requirement is considered as a constraint in case of small, medium and large farmers.

(v) Activities in the Model:

Activities specify the resources, which could be put into various alternative uses. The activities included in the model are-

1. Crop, dairy, poultry, small ruminants rearing activities.

2. Labour hiring activities.

3. Product and labour sale activities.

4. Plantation crops cultivation.

5. Working Capital.

3.4.2.5 Input-output co-efficient:

The input co-efficient in this study pertained to land, labour, capital, land under ragi, fodder availability, fertilizer and fodder requirement. Land includes owned land. Labour referred is included in the total labour. Working capital outlay refers to funds required to meet the cost of seeds, fertilizers, FYM, purchase of feed, fodder, concentrates, plant protection chemicals and wages to labour.

The input-output co-efficient were derived for the average farms, based on the sample data. On this basis, the optimization exercise was carried out, with the same set of constraints. The linear programming problem was solved in computers “EXCEL” package using the solver option.

3.4.3 Partial budgeting: Partial budgeting is a statement of anticipated changes in costs, returns and profitability for a minor modification. It consists of four important elements viz., added costs, added returns, reduced returns and reduced costs.

1. Added costs: Additional costs are incurred, if the proposed modification is the introduction of a new enterprise or increase in the size of the existing enterprise.

2. Additional returns: could be received when the proposed modification is the addition of a new enterprise, or increase in the size of the existing enterprise or adoption of technology that results in higher productivity.

3. Reduced returns: decrease in the returns is observed when the proposed modification involves the elimination of an existing enterprise or reduction in the size of the existing enterprise.

4. Reduced costs: decrease in the costs is found when the proposed modification involves the elimination of existing enterprise or reduction in the size of the enterprise or adoption of technology that uses fewer amounts of resources. The partial budgeting technique is used in the study to analyze complementary and supplementary economic relations among enterprises.

3.4.3.1 Marginal rate of product Substitution (MRPS): Marginal rate of product substitution is used to analyze competitive economic relationship among enterprises. If MRPS < zero then the enterprises are competitive.

IV RESULTS

The results of the study, presented here under the following sub heads:

4.1: Socio-economic characteristics of the sample farmers.

4.2: Existing integrated farming systems followed by sample households.

4.3: Annual net income and employment generation from different farming system.

4.4: Optimum farm plan based on size of farm holdings.

4.5: Nature of relationship exist in the farming systems among enterprises.

4.1: Socio-economic characteristics of the sample farmers

Table 4.1.1: Age-wise classification of the respondents into different categories (In numbers)

group Small

farmers Medium

farmers Large

farmers Total Test value

18-35 years

(49.54)

(58.33)

(60.00)

(52.50)

X2=1.70 NS

36-50 years

(44.00)

(33.33)

(40.62)

>50 years 7

(6.42)

(8.33)

(6.66)

(6.87)

Total 109

Note: 1. Figures in the parentheses indicates the percentage to total.

2. NS- Non-significant both at one per cent and five per cent level.

The sample respondents were classified into young, middle and old age based on the age groups young (18-35years), middle-aged (36-50 years) and old (> 50years), respectively. Majority of the sample farmers came in the age group of young, (about 52.50 %) aged between 18and 35 years followed by middle age (40.62 %) and old age (about 6.87 %) (Table 4.1.1).

Table 4.1.2: Average family size in different categories of farm households

(In numbers)

Particulars Small farmers Medium

farmer

farmers

Test value

Male 2 3 3

X2=0.24 NS

Female 1 2 2

Children 2 2 3

Total 5 7 8

Note: 1. NS- Non-significant both at one per cent and five per cent level.

The average family size in case of small farmers is five members of which two members are male, one member is female and two members are children. In case of medium farmers the average family size is seven members of which three members are male, two members are female, and two members are children. In case of large farmers, the average family size is eight members of which three members are male, two members are female, and three members are children (Table 4.1.2).

Table 4.1.3: Classification of the farmers under different categories based on their

education level (In numbers)

Note: Figures in parentheses indicates the percentage to the total

Level of

education

farmers

Medium farmers Large farmers Total

Illiterates 13

(11.92)

(11.11)

(13.33)

(11.80)

Primary school 74

(67.88)

(44.44)

(40.00)

(60.00)

Secondary education

(11.00)

(25.00)

(13.33)

(14.37)

(5.50)

(11.11)

(20.00)

(8.15)

Graduation 4

(3.66)

(8.33)

(13.33)

(5.00)

Total 109(100) 36(100) 15(100) 160

The Table 4.1.3 depicts the educational status of the sample farmers. The educational level of the sample respondents reveals that 60 per cent of the sample farmers had primary school education, 14.37 per cent had secondary education, 8.15 per cent had PUC education, five per cent had graduation, and 11.80 per cent of sample farmers were illiterates.

Table 4.1.4: Average size of the land holdings in different categories of farm

households

Particulars Small farmers Medium farmers Large farmers

Land holdings (acres)

2.67 5.72 10.88

Average size of the land holdings of the small, medium and large farmers were 2.67acre, 5.72 acre and 10.88 acres respectively (Table 4.1.4).

Table 4.1.5: Average livestock possession of the sample farmers (In numbers)

Particulars Small farmers Medium farmers Large farmers

Milch animals 3 4 2

Small ruminants 3 2 9

Poultry birds 20 12 17

In the study area, on an average, small farmers has three milch animals, medium farmers has four milch animals whereas, large farmers had two milch animals. Small ruminants maintained by small, medium and large farmers were three, two, and nine respectively. The poultry birds maintained by small, medium and large farmers was 20, 12 and 17 respectively (Table 4.1.5).

Table 4.2: Existing farming systems followed by sample households

(In numbers)

SI.NO Farming

systems

Farmers (<2 ha)

Medium

farmers(2-4 ha)

farmers(>4ha)

1 D 20(18) - - 20

2 C 12(11) 6(16) 2(13) 20

3 C+D 19(17) 5(14) 6(38) 30

4 D+S 23(21) 6(19) 1(13) 30

5 C+D+S 21(19) 8(22) 2(13) 30

6 C+R+S 14(13) 11(30) 4(25) 30

109(68.13) 36(23.13) 15(10) 160

*C=Crop (Ragi, grapes, onion, pole bean, capsicum) D=Dairy, S=Sericulture,

R=Small ruminants

Figures in parentheses are percentage to total

The details of the different farming systems followed by sample farmers are furnished in the Table 4.2. Among 160 farmers the 68.13 per cent of farmers are small farmers in the study area of which 18 per cent of farmers practicing dairy alone, 11 per cent of farmers practicing crop alone, 17 per cent of farmers practicing crop + dairy system, 21 per cent of farmers practicing dairy + sericulture system, 19 per cent of farmers practicing crop + dairy + sericulture system, 13 per cent of farmers practicing crop + small ruminants + sericulture system. The 23.13 per cent of farmers are medium farmers of which 16 per cent of farmers practicing crop alone, 14 per cent of practicing crop + dairy system, 19 per cent of farmers practicing dairy + sericulture system, 22 per cent of farmers practicing crop + dairy + sericulture system, 30 per cent of farmers practicing crop + small ruminants + sericulture system. The 10 per cent of sample farmers are medium farmers of which 13 per cent of farmers practicing crop alone, 38 per cent of practicing crop + dairy system, 13 per cent of farmers practicing dairy + sericulture system, 13 per cent of farmers practicing crop + dairy + sericulture system, 25 per cent of farmers practicing crop + small ruminants + sericulture system (Table 4.2).

Integrated farming system

Sericulture Small ruminants

Diary Crops

Plate 4.1: Components of integrated farming system in the study area

4.3: Annual net income and employment generation from existing farming systems

Table 4.3.1: Annual net income and employment generation from existing farming

systems of small farmers

NO Enterprise

Total cost

returns

income

Rate of

return

Employment

generation

(Man days

per year)

1 D (3 cows) 95000 136020 41020 1.43 192

2 C (1.89 acre) 180120 270000 89880 1.49 286

3 C (1.5 acre) + D (2 cows)

205653 323792 118139 1.57 381

4 D (3 cows) +

S (150 DFL) 168120 317206 149086 1.88 486

5 C (1.2 acre) +

R (3 No) +

S (200 DFL)

172321 375250 202929 2.17 615

6 C (1.9 acre) +D (4 cows)

+ S (100 DFL) 264320 541002 276682 2.04 695

7 Mean 164242.8 327211.66 146289.33 1.76 442.5

8 Standard deviation

41357.42 145882.1 87932.83 0.30 193.09

9 C V (%) 25.18 44.58 60.10 17.54 43.63

*DFL=Disease free laying's

Note: To rear 100 DFL’s Silkworm area required under mulberry was 0.6 acre.

Net farm income (per year)

Farming system involves diversification of enterprises aiming at efficient use of resources in order to maximize the income. It also minimizes the production risk by spreading the risk across various enterprises instead of one activity. The details of net farm income derived from the existing farming system are furnished in the Table 4.3.1. The small farmers expended Rs.95,000, Rs.180120 , Rs.205653, Rs.168120, Rs.241021 and Rs.79,613 generating a net income of Rs.41020, Rs.89880, Rs.118139, Rs.149086, Rs.234229 and Rs.272682 respectively in D (3 cows), C (1.89 acres),C (1.5 acres) + D

(2 cows), D (3 cows) + S(150 DFL), C (1.2 acres) + R (3 no) + S (200 DFL) and C (1.9 acres) + D (4 cows) + S(100 DFL) systems. As the integration of enterprise increased the return to cost ratio also increased (Table 4.3.1).

Labour employment (Man days per year)

The employment generated on the farm varied with the type of farming system followed in the study area. The employment generated by various farming systems followed by small farmers was worked out and the details are given in the Table 4.3.1. As evident from the table, the farming systems of D (3 cows), C (1.89 acres), C (1.5 acres) + D (2 cows), D (3 cows) + S (150 DFL), C (1.2 acres) + R (3 no) + S (200 DFL) and C (1.9 acres) + D (4 cows) + S (100 DFL) creates an employment generation of 192, 286, 381,486, 615 and 695 respectively (Table 4.3.1).

systems of medium farmers

SI. No. Enterprise Total cost

returns

income

Rate of

return

Employment

generation

(Man days

per year)

1 C (3.36 acre) 200120 314000 113880 1.56 389

2 C (2.55 acre) +D (4 cows)

241125 392500 151375 1.62 492

3 D (3 cows) + S (300 DFL)

268120 438125 170005 1.64 521

4 C (3.21 acre) + R (2 No) +

S (250 DFL)

322540 554251 302710 1.71 650

5 C (2.95 acre) +D (3 cows)

+ S (200 DFL)

361452 685245 323793 1.89 916

6 Mean 278671.40 476824.20 212352.6 1.68 593.60

7 Standard deviation 64164.77 145390.91 94593.69 0.13 202.84

8 C V (%) 23.02 30.49 44.54 7.54 34.17

The details of net farm income derived from the existing farming system are furnished in the Table 4.3.2. The medium farmers expended Rs.200120, Rs. 241125, Rs.268120, Rs.322540 and Rs.361452 generating a net income of Rs.113880, Rs.151375, Rs.170005, Rs.302710, and Rs.323793 respectively in C (3.36 acres),C (2.55 acres) + D (4 cows), D (3 cows) + S (300 DFL), C (3.21 acres) + R (2 no) + S (250 DFL) and C (2.95 acres) + D (3 cows) + S (200 DFL) systems (Table 4.3.2).

The employment generated on the farm varied with the type of farming system followed in the study area. The employment generated by various farming systems followed by medium farmers was worked out and the details are given in the Table 4.3.2. As evident from the table, the farming systems of C (3.36 acres), C (2.55 acres) + D (4 cows), D (3 cows) + S (300DFL), C (3.21 acres) + R (2no) + S (250DFL) and C (2.95 acres) + D (3 cows) + S (200 DFL) creates an employment generation of 389, 492, 521, 650 and 916 respectively. As the integration of enterprise increased the return to cost ratio also increased (Table 4.3.2).

Systems of large farmers

Enterprise Total

cost(Rs.)

returns

Net income

Rate of

return

Employment

generation

(Man days per

2 C (9.33 acre) 451254 671245 217991 1.48 724

3 C (8.76 acre) + D(2cows)

498572 759396 260824 1.51 856

4 D (3 cows) + S (200 DFL)

168120 287200 119080 1.71 351

5 C (6.18 acre) + D (2cows) + S (300 DFL)

512482 895692 363210 1.74 928

6 C (7.39 acre) + R (9 No) + S (180 DFL)

592000 1081450 489450 1.82 1130

7 Mean 444485.60 738996.60 290111 1.65 797.80

8 Standard deviation 162583.11 296207 141765.80 0.15 289.72

9 C V (%) 36.57 40.08 48.86 9.03 36.31

The details of net farm income derived from the existing farming system are furnished in the Table 4.3.3. The large farmers expended Rs.451254, Rs.498572, Rs.168120, Rs.512482 and Rs.592000 generating a net income of Rs.217991, Rs.260824, Rs.119080, Rs.363210 and Rs.489450 respectively in C (9.33 acres),C (8.76 acres) + D (2 cows), D (3 cows) + S (200 DFL), C (6.18 acres) + D (2 cows) + S (300 DFL) and C (7.39 acres) + R (9 No) + S (180 DFL) system s(Table 4.3.3).

The employment generated on the farm varied with the type of farming system followed in the study area. The employment generated by various farming systems followed by large farmers was worked out and the details are given in the Table 4.3.3. As evident from the table, the farming systems of C (9.33 acres), C (8.76 acres) + D (2 cows), D (3 cows) + S (200 DFL), C (6.18 acres) + D (2 cows) + S (300 DFL) and C (7.39 acres) + R (9 No) + S (180 DFL) systems creates an employment generation of 724, 856, 351, 928 and 1130 respectively. As the integration of enterprise increased the return to cost ratio also increased (Table 4.3.3).

4.4: Optimum farm plan based on size of farm holdings.

Table 4.4.1: Optimum farm plan for Small farmers: (Per annum)

Sl. No. Small farmers (<2 ha) n=109

Enterprise Existing plan Optimum plan

1 Ragi (acre) 0.50 0.30

2 Onion (acre) 0.19 0.00

3 Tomato (acre) 0.17 0.00

4 Grapes (acre) 0.11 0.70

5 Capsicum (acre) 0.12 0.00

6 Moriculture (acre) 0.78 1.12

7 Dairy (No) 3.18 0.00

8 Small ruminants (No) 2.70 3.00

9 Mango (acre) 0.30 0.00

10 Pole bean (acre) 0.50 0.00

A Profit (Rs.) 162067.91 237112

B Increase in profit Rs.75045

In order to optimize the objective of maximization of net income, the farmers need to adopt the combination of the enterprises as indicated in the optimum plan presented above.

The optimum plans developed shows an increase in the net farm income from Rs.162067 to Rs.237112. The farming systems suggested that it requires cultivation of 0.30 acre, 0.70 acre, and 1.12 acre of land under ragi, grapes, and mulberry respectively. The livestock population in the optimum plan is 3 number of small ruminants (Table 4.4.1).

Table 4.4.1.1: Annual resource saving due to optimal plan in case of small farmers

Resource Existing plan Optimum plan Saving

Land(acre) 2.67 2.12 0.55

Labour(man-days) 472 359 113

Capital(Rs.) 1,68,971 1,33,441 35,530

Annual resource saving due to optimal plan was 0.55 acre of land, 113 labour (man-days) and capital of Rs. 35,530 respectively. The farmers can use saved resources for other operations.

Table 4.4.2: Optimum farm plan for Medium farmers

(Per annum) Sl. No. Medium farmers (2-4 ha) n=36

1 Ragi (acre) 1.17 0.60

2 Onion (acre) 0.07 0.00

6 Dairy (No) 3.89 1.00

8 Mango (acre) 0.76 1.09

A Profit (Rs.) 273551.6 391031

B Increase in profit Rs.117479.4

In order to optimize the objective of maximization of net income, the farmers need to adopt the combination of the enterprises as indicated in the optimum plan presented above. The land usage in case of existing situation was 5.72 acres and in case of optimum situation the land usage was 4.8 acres, due to adoption of optimum plan the land usage saved was 0.92 acres and farmers can use this land for further production.

The optimum plans developed shows an increase in the net farm income from

Rs.273551 to Rs.391031. The farming systems suggested that it requires cultivation of 0.60 acre, 2.71 acre, 0.5 acre and 1.09 acre of land under ragi, mulberry, mango and pole bean respectively. The livestock population in the optimum plan is 1 number of dairy animal (Table 4.4.2).

Table 4.4.2.1: Annual resource saving due to optimal plan in case of medium farmers

Resource Existing plan Optimum plan Left over

Land(acre) 5.72 4.8 0.92

Capital(Rs.) 2,49,654 1,98,452 51,202

Annual resource saving due to optimal plan was 0.92 acre of land, 60 labour (man-days) and Capital of Rs. 51,202 respectively. The farmers can use saved resources for other operations.

Table 4.4.3: Optimum farm plan for large farmers

(Per annum)

Sl. No. Large farmers (>4 ha) n=15

1 Ragi (acre) 2.00 0.80

2 Onion (acre) 0.40 0.00

5 Capsicum(acre) 0.50 1.20

7 Dairy (No) 2.00 2.00

9 Mango (acre) 2.59 2.80

A Profit (Rs.) 423215 618666

B Increase in profit Rs.195451

In order to optimize the objective of maximization of net income, the farmers need to adopt the combination of the enterprises as indicated in the optimum plan presented above.

The optimum plans developed shows an increase in the net farm income from Rs.423215 to Rs.618666. The farming systems suggested that it requires cultivation of 0.80 acre, 1.20 acre, 1.65 acre, 1.20 acre, 0.50 acre, and 2.80 acre of land under ragi, tomato, grapes, capsicum, mulberry and mango and pole bean respectively. The livestock population in the optimum plan is 2 number of dairy animal and small ruminants 6 number (Table 4.4.3).

Table 4.4.3.1: Annual resource saving due to optimal plan in case of large farmers

Resource Existing plan Optimum plan Saving

Land(acre) 10.88 8.1 2.78

Capital(Rs.) 3,79,455 3,02,564 76,891

Annual resource saving due to optimal plan was 2.78 acre of land, 74 labour (man-days) and Capital of Rs.76, 891 respectively. The farmers can use saved resources for other operations.

4.5: Nature of relationship exist in the farming systems among enterprises.

Sl. No. Farming system Sub system Synergy

1 C + D - Complementary

2 D + S Own Mulberry Complementary

Purchased Mulberry Supplementary

3 C + D + S C + D + S Complementary

C + S Competitive

4 C + R + S C + S Competitive

C + R + S Complementary

Complimentary relationship: In the study area C + D, D + S (Own mulberry), C + D + S, C + R + S systems shows complementary relationship among enterprises.

Supplementary relationship: In the study area D + S (purchased mulberry) system shows supplementary relationship among enterprises.

Competitive relationship: In the study area C + S in C + D + S system and C + S in C + R + S System shows competitive relationship among enterprises (Table 4.5).

Table 4.5.1: Synergy (Complementary) among enterprises in Crop + Dairy system

Crop (2.17 acre) + Dairy (4 cows)

(Per annum)

Additional cost: By existence of dairy a 10 gunta of land is brought under Napier perennial where every year a cost of Rs.5000 is incurred for fertilizer and intercultural ploughing and an imputed labour cost of Rs.22500 for 150 family labour @ Rs.150 per family labour.

Reduced income: If green fodder and dry fodder were sold to others instead of using for own dairy it would have generated a return of Rs 18000 for 3 tractor loads of dry fodder @Rs.6000 per tractor load & a return of Rs.4000 for 2 tractor loads of green fodder@Rs.2000 per tractor load to that extent returns reduced.

Reduced cost: annually dairy generates on an average 3 tractor loads of FYM which is used in own field, if it is purchased from others @ Rs.3500 per tractor load of FYM it will add to cost of cultivation there by an amount of Rs 10500 worth of FYM is reduced cost

Additional income: in dairy the milk yield obtained is attributed to concentrates and fodder, from 4 cows on an average 5670 liters of milk is obtained in a lactation where in

Debit Credit

Particular Amount

(Rs) Particular

Amount

Additional cost 27500 Reduced cost 10500

Reduced income 22000 Additional income

A. Total of additional costs and reduced income

B. Total of additional income and reduced Costs

Net change in profit (B -A) Rs. 34710/-

half of the milch yield is attributed to fodder. So it generates an additional income of Rs.73710 (Table 4.5.1).

Table 4.5.2: Synergy (Complementary) among enterprises in Dairy + Sericulture

system (Own Mulberry)

Dairy (3) + Sericulture (6 crops per year (@ 280 DFL’s per crop) (Per annum)

Additional cost: By existence of dairy an imputed labour cost of Rs.15000 for 100 family labour @ Rs.150 per imputed family labour.

Reduced income: If mulberry leaf sticks used in sericulture (rearing of silkworm), were sold to others instead of using for own dairy it would have generated a return of Rs 9000 @ Rs.1500 per tractor load mulberry leaf sticks used in sericulture (rearing of silkworm) for six tractor loads to that extent returns reduced.

Reduced cost: annually dairy generates on an average 2.5 tractor loads of FYM which is used in own field, if it is purchased from others it will add to cost of cultivation there by an amount of Rs 8750 worth of FYM is reduced cost

Additional income: in dairy the milk yield obtained is attributed to concentrates and fodder, from cows on an average 4252 liters of milk is obtained in a lactation where in half of the milch yield is attributed to fodder. So it generates an additional income of Rs.59535 (Table 4.5.2).

Debit Credit

Particular Amount

(Rs) Particular

Amount

A.Total of additional costs and reduced income

Table 4.5.3: Synergy (Supplementary) among enterprises in Dairy + Sericulture

system (Purchased Mulberry)

Dairy (2) + Sericulture (3 crops per year (@ 100 DFL’s per crop)

(Per annum)

Additional cost: By existence of dairy an imputed labour cost of Rs.13500 for 90 family labour @ Rs.150 per imputed family labour.

Reduced income: If 2 tractor loads of mulberry leaf sticks used in sericulture were sold to others @Rs. 1500 per tractor load of mulberry leaf sticks instead of using for own dairy it would have generated a return of Rs 3000 to that extent returns reduced.

Reduced cost: here the cost was zero.

Additional income: in dairy the milk yield obtained is attributed to concentrates and fodder, from 2 cows on an average 3500 liters of milk is obtained in a lactation where in half of the milch yield is attributed to fodder so it generates an additional income of Rs.49000 from milk @ Rs.28 per litre of milk and Rs.5250 from 1.5 tractor load of fym@ 3500 per tractor load of fym (Table 4.5.3).

Debit Credit

Particular Amount

(Rs) Particular

Amount

Additional cost 13500 Reduced cost -

Table 4.5.4: Synergy (Complementary) among enterprises in Crop + Dairy +

Sericulture Farming system

Crop (3.25 acre) + Dairy (4 cows) + Sericulture (6 crops per year (@ 200 DFL’s per crop)

(Per annum)

Additional cost: By existence of dairy a 10 gunta of land is brought under Napier perennial where every year a cost of 5000 is incurred for fertilizer and intercultural ploughing. And imputed family labour cost is Rs.19500@Rs.150 per labour for 130 imputed family labour.

Reduced income: If we sell mulberry leaf to that it generates an annual income of Rs. 75,600 for 420 bags of mulberry leaf @ Rs.180 per bag of mulberry leaf to that extent returns reduced.

Reduced cost: annually dairy generates on an average three tractor loads of FYM which costs about Rs.10500 @ 3500 per tractor load of FYM and sericulture generates on an average of 3 tractor loads of sericulture waste which costs about Rs.6000 @ Rs.2000 is used in own field, if it is purchased from others it will add to cost of cultivation there by an amount of Rs 16500 worth of FYM (farm yard manure) and three tractor loads of Sericulture waste is reduced cost.

Additional income: in dairy the milk yield obtained is attributed to concentrates and fodder, from 4 cows on an average 4120 liters of milk is obtained in a lactation where in half of the milch yield is attributed to fodder. So it generates an additional income of Rs.57, 680 from milk yield and Rs.1, 50,000 from Sericulture (Table 4.5.4).

Debit Credit

Particular Amount

(Rs) Particular

Amount

2,07,680

1,00,100

2,25,680

Net change in profit (B -A) Rs.125580/-

Table 4.5.5: Synergy (Complementary) among enterprises in Crop + Small ruminants

+ Sericulture Farming system

Crop (2.2 acre) + Small ruminants (23) + Sericulture (6 crops per year (@ 180 DFL’s per crop)

(Per annum)

Additional cost: By existence of Small ruminants and Sericulture it requires 200 labours @ Rs.150 it costs about Rs.30000.

Reduced income: If green fodder and dry fodder were sold to others instead of using for Small ruminants it would have generated a return of Rs. 18000 for three tractor loads of dry fodder @Rs.6000 per tractor load to that extent returns reduced.

Reduced cost: Annually Sheep and goat generates on an average three tractor loads of FYM which costs about Rs.12000 @ 40000 per tractor load of FYM and Sericulture generates on an average of 3 tractor loads of Sericulture waste which costs about Rs.6000 @ Rs.2000 is used in own field, if it is purchased from others it will add to cost of cultivation there by an amount of Rs 18000 is reduced cost.

Additional income: Annually the Sericulture generates a return of Rs. 150000 @Rs.25000 per crop and Small ruminants generates a return of Rs.25000. So it generates an additional income of Rs.193000 (Table 4.5.5).

Debit Credit

Particular Amount

(Rs) Particular

Amount

175000

193000

Table 4.5.6: Synergy (Competitive) among enterprises in Crop + Small ruminants +

Sericulture and Crop + Dairy + Sericulture Farming system

SI.NO Farming

system

Enterprise Synergy

condition

1 C + D + S C + S Competitive -0.693

2 C + R + S C + S Competitive -0.521

Note: MRPS=marginal rate of product substitution

C + D + S: In this System the competitiveness exists between C + S indicates for every one rupee increase in return in crop reduces 0.69 rupee in case of sericulture.

C + R + S: In this system the competitiveness exists between C + S indicates for every one rupee increase in return in crop reduces 0.521 rupee in case of sericulture.

V DISCUSSION

The results of the study, presented in chapter IV are discussed here under the following sub heads:

5.1. Socio-economic characteristics of the sample farmers.

5.2. Livestock position of sample farmers.

5.3. Farming systems followed by sample farmers.

5.4. Net income and employment of labour per farm from the Existing farming systems.

5.5. Cost incurred and income generated from different farming systems.

5.6. Optimum farm plan for different categories of farmers.

5.7. Synergies in farming systems.

5.1 Socio-economic characteristics of sample farmers

5.1.1 Age distribution of the sample respondents

The age of the respondents indicates that majority of them were in young age having positive attitude towards improving income through IFS. Hence, it would be easy to introduce innovative enterprises which improve their income.

5.1.2 Family size

There was no significant difference with respect to family size across different size groups of farmers. Since family size was almost same across small, medium and large farmers, medium and large farmers need to depend on hired labour for performing agricultural operations in view of their large size of holdings. The extent of male and female work force available for farming was almost same in all categories of farmers indicating that the involvement of the female workers was in equal proportion to that of the male workers.

5.1.3 Educational level of the sample respondents

Education plays an important role in converting subsistence agriculture into a commercial venture. It is observed from the Table 4.3.1 that primary school education was more among sample farmers followed by secondary school education, illiterates, pre-university education, and graduation. This phenomenon could be due to the fact that most of the family members of the small farmers were engaged in agriculture. In addition, poor

financial condition of small farmers discourages higher education. On the other hand, large farmers could afford to educate some of their children up to college and continue agriculture with some of their family members. Overall 11.80 per cent of the farmers were illiterate, reflecting that a large proportion of sample respondents were literates.

5.1.4 Average farm size of farm respondents

The small farmers operate on an average of 2.67 acres, medium farmers operate on an average of 5.72 acres, large farmers operate an average of 10.88 acres (Table 4.1.4). The farmland owned consists of a number of small fragments with different mix of enterprises including crops with dairy, sericulture and small ruminants.

5.2 Average livestock possession of sample farmers.

In the study area, most of the farmers own livestock especially dairy and small ruminants. Thus the livestock forms an integral part of farming system for majority of the farmers. This enabled converting low valued bulky by-products of crops into high valued milk and meat. Majority of the sample farmers own cows and buffaloes for dairy purpose. The number of livestock maintained by various categories of farmers was partly dependent on the quantity of fodder availability and labour force on these farms. In the study area, on an average, small farmers has three milch animals, medium farmers has four milch animals whereas, large farmers had two milch animals. Due to scarcity of fodder in the area, the fodder has become expensive hence small and medium farmers found difficulty in maintaining milch cows. (Table 4.1.5). It was observed that the farmers recycled farmyard manure from cattle shed and small ruminants waste into farmyard manure and applied to the soil. Buffaloes were also maintained even though they are less profitable as compared to cows mainly because of management ease and they were efficient converters of farm waste to milk and easy to maintain, less susceptible to diseases and their adoption to local environment was better. Small ruminants maintained by small, medium and large farmers were three, two, nine respectively. The poultry birds maintained by small, medium and large farmers were 20, 12 and 17 respectively.

5.3 Farming systems followed by sample farmers.

5.3.1 Type of farming system

The existing farming system indicated that sample farmers were following different integrated farming systems. Their enterprise combination depends on factors inter

alia include type of land, location, topography, fertility, irrigation, family labour availability, hired labour availability, preferences of the farm families, capital and other resources availability within and outside the farm family and relative prices of inputs and output. The study reveals that apart from practicing Crop alone, dairy alone system of farming, the sample farmers had C + D, D + S, C + R+ S, C+ D+ S farming systems. As the study area had more of dry land, sericulture, dairy and ragi along with other enterprises was practiced to a larger extent. In dry land ragi, mango, fodder, onion and maize were

taken up for meeting family consumption requirements. The study area is one of the small ruminants and dairy dominated area and they formed the major components in the combination of different farming systems.

5.4 Annual net income and employment from the existing farming systems.

5.4.1 Net income

The sample farmers in the study area followed several integrated farming systems. It was evident that the small farmers derived higher net income from C (1.9 acre) + D (4 cows) + S (100 DFL’s) system (Table 4.3.1), medium farmers derived higher net income from C (2.95 acre) + D (3 cows) + S (200 DFL’s) (Table 4.3.2) system and the large farmers derived higher net income from C (7.39 acre) +R (9 No) +S (180DFL’s) (Table 4.3.3) indicating that as the integration of enterprises increased the returns also increased. Returns to cost ratio indicates that the large farmers under existing farming system generated relatively higher income than small and medium farmers due to access to irrigation and efficient utilization of resource which contributes to increase in income. Thus large farmers are more efficient than medium and small farmers.

5.4.2 Labour employment

Adequate employment generation is crucial in the rural areas in order to check the migration of rural people to the urban frontiers. In this regard, integrated farming systems have potential to generate gainful employment throughout the year. The study results reveals that generation of employment was higher in C (1.9 acre) + D (4 cows) + S(100 DFL’s) system(Table 4.3.1) in small farmers , C (2.95 acre) + D (3 cows) + S (200 DFL’s) system(Table 4.3.2) in case of medium farmers, While large farmers generated more employment in C (7.39 acre) + R (9 No.)+ S (180 DFL’s) system (Table 4.3.3). The labour employed was relatively higher in large farmers compared to small and medium farmers as the large farmers owned more livestock than the other categories and used labour to maintain the livestock along with crop farming activity. The study also indicated that a combination of crop + dairy, dairy + Sericulture, crop + dairy + Sericulture and crop + Small ruminant’s + Sericulture enterprises was more promising and beneficial in that order. Thus the hypothesis that as the degree of integration increases, the profitability and employment generation also increases is tenable.

5.5 Annual Cost incurred and income generated by different enterprises in different

farming systems.

The income generated and cost incurred in different enterprises varies with the farming systems. income and cost was higher in C (1.9 acre) + D (4 cows) + S (100 DFL’s) system (Table 4.3.1) in small farmers, C (2.95 acre) + D (3 cows) + S (200 DFL’s) system (Table 4.3.2) in case of medium farmers, and C (7.39 acre) + R (9 No.) + S (180 DFL’s) system in case of large farmers (Table 4.3.3) Thus, the integrated farming system with dairy is land saving, labour absorbing and relatively profitable compare to non-integration.

Thus, it shows that integration of crop with dairy, Sericulture and small ruminants is highly remunerative and economical. The results are in consonance with findings of Ramrao et al. (2005) who developed a crop livestock mixed farming model of 1.5 acre for small scale holders and results revealed that the employment generation of 571 man days, net income of Rs. 58,456 per year against crop farming alone with employment generation of 385 man days and net returns of Rs. 18,300 per year only.

5.6 Optimum farm plan for different categories of farmers.

Farming system aims at the development of optimum enterprise combination on farms requiring an integration of different farming activities. In order to check the migration of rural people and to provide adequate employment and income IFS is one of the appropriate strategies to be followed by the farmer. In order to maximize the farmer’s income using IFS, linear programming was employed.

For achieving short-term farming objectives, models were developed for each category of farmers. The details of the combination of enterprises, cropping system, net farm income for respective categories are discussed below:

Large farmers had relatively better resources vis-à-vis other categories of farmers. Sericulture, Dairy and small ruminants rearing has emerged as important subsidiary activities in Sidlaghatta taluk as it has provided assured returns.

5.6.1 Small farmers

In order to optimize the objective of maximization of net income, the farmers need to adopt the combination of the enterprises as indicated in the optimum plan presented below.

The optimum plans developed shows an increase in the net farm income from Rs.162067 to Rs.237067. The farming systems suggested that it requires cultivation of 0.30 acre, 0.70 acre, and 1.12 acre of land under ragi, grapes, and mulberry respectively. The livestock population in the optimum plan is 3 number of small ruminants (Table 4.4.1).

The crops proposed in the optimum plan would require a complete replacement of onion, tomato, capsicum, mango and pole bean.

5.6.2 Medium Farmers

The optimum plans developed shows an increase in the net farm income from Rs.273551 to Rs.391031. The farming systems suggested that it requires cultivation of 0.60

acre, 2.71 acre, 0.5 acre and 1.09 acre of land under ragi, mulberry, mango and pole bean respectively. The livestock population in the optimum plan is one number of dairy animal (Table 4.4.2).

The enterprises completely replaced are onion, tomato, grapes and small ruminants.

5.6.3 Large Farmers

The optimum plans developed shows an increase in the net farm income from Rs.423215 to Rs.618215. The farming systems suggested that it requires cultivation of 0.80 acre, 1.20 acre, 1.65 acre, 1.20 acre, 0.50 acre, and 2.80 acre of land under ragi, tomato, grapes, capsicum, mulberry and mango and pole bean respectively. The livestock population in the optimum plan is two number of dairy animal and small ruminants six number (Table 4.4.3).

The results are in consonance with findings of Gracy (1987), who derived optimum cropping plans for Bangalore north taluk of Karnataka using parametric linear programming (MOTAD - Minimization of Total Absolute Deviations). The results indicated that existing use of resources was less than optimum these results support the hypothesis that optimum farming system models enable in generating higher farm income and employment opportunities.

5.7 Synergies in farming systems

Farming systems approach envisages the integration of different enterprises with efficient utilization of byproducts of crops. The advantage of IFS includes pooling and sharing of resources, efficient use of family labour, better utilization of farm bio-mass including non-consumptional feed and fodder, effective use of FYM along with improved employment and income.

Farming system models could enhance the productivity of the farm, improve the profitability of the farmer and sustain the productivity of the soil through recycling of organic sources of nutrient from the enterprises involved.

The integration is made in such a way that product of one component should be the input for other enterprise with high degree of complementarity effect on each other. The fodder fed to the cattle produces milk and the dung and urine produces FYM for crops.

The farmers predominantly cultivated ragi and sericulture, which produced fodder for the dairy enterprise, in turn the dairy animals provided cow dung, which was used as a manure towards crop production as a substitute for chemical fertilizers.

In addition to the tangible benefits provided by the IFS. Thus integrated farming system provided benefits in terms of enriching soil fertility and saving cost on external inputs. While in crop alone system without integration, the farmers had to depend on market to procure FYM towards manuring which is not cost effective. In addition, they also depend on external source for milk to meet the family requirement. But, the additional income realized was by selling the fodder as they did not have livestock to use it it is also similar in case of dairy alone practicing farmers. In the crop alone, dairy alone system there was too much dependency on the external inputs and market. Thus, the results support the hypothesis that, Complementary and supplementary enterprises enhances the profitability while competitive enterprises reduces the profitability.

The synergy contributed by different enterprise mix revealed that as the integration increases the synergy contributed to the farming systems also increases. The results are similar to the study by Singh and Singh (1993), who stated that the marginal and small farmers have very limited land which is getting further fragmented with each generation and therefore farm enterprises requiring less land but higher productivity and employment opportunities, needed to be integrated with crop production. The by-products of one crop/enterprise may become the input or raw material of the other. Further, as the crop and animal husbandry systems were developed and intensified, per capita land, requirement decreased dramatically. Thus, IFS systems are more profitable than without IFS. The results are in consonance with findings of Valbuena et al. (2012) who studied mixed crop–livestock systems in Sub-Saharan Africa and South Asia and concluded that the complementary interaction exist between mixed crop-livestock systems apart from employment generation and additional income.

VI SUMMARY AND CONCLUSION

A brief summary of the research results along with the salient findings is presented in this chapter. Also based on the conclusions drawn from this study, policy options are suggested for planners and administrators.

The present study was undertaken in Sidlaghatta taluk of Chikkaballapur district of Karnataka, with an objective of analyzing economics of integrated farming system and to develop optimum farming systems for increasing farm income and employment.

The specific objectives of the study are

1. To assess the economics of integrated farming system and its impact on income and employment.

2. To determine the optimum integrated farming plan based on size of farm holdings.

3. To assess the magnitude of complementary, supplementary, and competitive economic relationship among enterprises.

Sampling frame work

Purposive stratified random sampling was adopted in the study area. 10 villages are selected in the study area i.e., Sidlaghatta taluk, from each village, 16 farmers were randomly chosen for the study. The primary data required for the study will be collected from the 160 sample farmers of which 20 farmers practising crop alone, 20 farmers practising dairy alone, 30 farmers practising crop + dairy system, 30 farmers practising dairy + sericulture system,30 farmers practising small ruminants + crop + sericulture system, 30 farmers practising dairy + crop + sericulture using structured schedule questionnaire through personal interview. The primary data were collected on socio-economic condition, cropping pattern, size of operational holding, existing farming systems, cost of cultivation, prices of output, expenses and income from livestock and other enterprises. Secondary data on land utilization pattern, rainfall, population, workforce and irrigation were collected from State Development Departments, Directorate of Economics and Statistics and Directorate of Census. The data collected were tabulated and analyzed to draw inferences for the set objectives.

Salient findings of the Study

• 93 per cent of the sample respondents belonged to young and middle age group.

• There was no significant difference with respect to age across different groups of farmers.

• It was found that nearly 87 per cent of the sample farmers were literates and 75 per cent of the farmers had primary school education and middle school education.

• About 68.13 per cent of farmers are small farmers, 23.13 of farmers are medium farmers and 10 per cent of farmers are large farmers in the study area.

• It was found that among several farming systems, the most prominent farming system followed by all the farm groups was crops with livestock. Apart from crop, dairy (in case of Small farmers) alone system, the key farming systems followed by farmers were C + D, D + S, C + R + S, and C + D + S Systems.

• The economic analysis of different farming systems indicated that C (1.9 acre) + D (4 cows) + S (100 DFL) system is relatively profitable (1:2.04) than other systems in case of small farmers, and C (2.95 acre) + D (3 cows) + S (200 DFL) is reasonably profitable (1:1.89) than other systems in case of medium farmers. Similarly, C (7.39 acre) + R (9 NO) + S (180 DFL) are more profitable (1:82) than other farming systems followed by large farmers.

• It was also apparent that as the integration of enterprises increased, the returns to cost ratio also increased.

• In the case of small farmers, C (1.9 acre) + D (4 cows) + S (100 DFL) system generated highest employment of 695 man days vis-à-vis other farming systems and in case of medium farmer’s C (2.95 acre) + D (3 cows) + S (200 DFL) system generated highest employment of 916 man days vis-à-vis other farming systems. Similarly, large farmers generated highest employment of 1130 man days in C (7.39 acre) + R (9 NO) + S (180 DFL) vis-a-vis other farming systems.

• The existing farming systems regardless of the farm size are not optimum ones. The optimum farming systems developed through linear programming model exhibited potential for realizing higher income of Rs. 75045, Rs117479. and Rs. 195451 respectively in small, medium and large farmers.

• In the case of small farmers, optimum farm plan yielded 46.30 per cent of higher income than the existing plan. Similarly, in the case of medium farmers, optimum farm plan yielded 42 per cent of higher income than the existing plan whereas in the case of large farmer’s optimum plan yielded 46.18 per cent of higher income than the existing plan.

• The optimum farm plan saves 0.55 acre of land, 113 labour (man-days per year) and Rs.35530 capital in case of small farmers.

• The optimum farm plan saves 0.92 acre of land, 60 labour (man-days per year) and Rs.51202 capital in case of medium farmers.

• The optimum farm plan saves 2.78 acre of land, 74 labour (man-days per year) and Rs.76891 capital in case of large farmers.

• The C + D, D + S (own mulberry), C + D + S, C + R + S systems shows complementary relationship, while D + S (purchased mulberry) shows supplementary relationship and C + S enterprises shows competitive relationship both in C + D + S and C + R + S systems.

• In addition to the tangible benefits provided by the IFS, intangible benefits were also derived by the farmers by practicing crop rotation.

Policy recommendations

From the findings of the study the following recommendations are drawn:

• The farming system with crops and livestock turns out to be remunerative across all categories of farmers. In order to sustain and improve the income levels of farmers, linkage of production system with marketing, agro-processing and value added activities are crucial. Thus, agricultural extension programmes need to be developed with market led extension towards system efficiency.

• Optimum farming system models have indicated the potential for increased income and employment. The designed models should be demonstrated in the farmers’ fields to convince the farmers. At the same time, appropriate market led extension strategies should be identified to popularize the developed models.

• Diversification of farming systems also need greater emphasis on livestock, as they are land saving and stabilize the income and increase the employment opportunities on the one side, and reduce the risk of lower returns on the other.

• IFS itself serves as an insurance compare to non IFS, still it is desirable to link weather based crop insurance on a wider scale covering most crops which contributes to income security of farmers. The IFS concept has to be strengthened and expanded across different holding sizes to enable farmers to capture synergies. This needs Farm Management extension effort.

• There is a need to develop region specific integrated farming system models.

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APPENDICES

APPENDIX-I

Definitions of the terminologies used in the study

Marginal Rate of Product Substitution: The absolute amount, by which one product is decreased in order to gain another product by a unit is called marginal rate of product substitution.

Complementary products: The products are complementary, if an increase in one product causes an increase in the other product, when the total quantity of inputs used on the two products held constant.

Supplementary products: Two products become supplementary, if the quantity of one product can be increased without increasing or decreasing the quantity of the other product.

Competitive products: Two products are said to be competitive, when increase or decrease in the level of production of one results in decrease or increase in the level of production of another, given the fixed amount of resources.

Partial budgeting: Partial budgeting is a statement of anticipated changes in costs, returns and profitability for a minor modification.

Farming system: is an approach, which involves the allocation of available resources of a farm to the production enterprises in the manner that helps the attainment of the goals of maximization of farm income and employment.

APPENDIX - II

Department of Agricultural Economics

University of Agricultural Sciences, GKVK,

Bengaluru - 560 065

“AN ECONOMIC ANALYSIS OF INTEGRATED FARMING SYSTEM IN

SIDLAGHATTA TALUK OF CHICKABALLAPUR DISTRICT, KARNATAKA”

Schedule for primary data collection

A. Location Identification:

Date: / /2015

Name of the farmer : Village: Schedule no: Area:

B. General Family information

1.Age and Educational level of the respondent: Age: Education:

1.1 Family Size (no.)

SI.no. Gender No’s

Engaged in the

farm activities Off farm income

Own Others No’s Occupation Income

1 Adult Male

2 Adult female

3 Children

4 Total

2. Land holdings

Type Dry land Irrigated Garden/Perennial Others total

Area(acres)

2.1 Inventory of farm buildings

Sl.no. Item Year of

construction

Cost of

construction

Present

value(Rs)

1 Farm house 2 Cattle shed 3 Poultry shed 4 Pump shed 5 Storage shed 6 Any Others(Specify)

2.2 Source of water of irrigation and expenditure made on it.

Sl.no. Source of

irrigation

No’s and Life

of source

Irrigated

area (Acres)

Investment

cost (Rs)

Maintenance

Cost (Rs)

1 2 3 4 5 6

3.0 cropping system:

3.1 crops grown

SI.no. Crop Area

(Acre)

Production

Family

consumption

Quantity

marketed

(Rs/qtl)

Dry land 1 2 Irrigated 1 2 Garden/

perennial

3.2 By-products, if any

SI.no. Crop By-products Production (qtl) Price (Rs/qtl)

conomic A

nalysis of Integrated Farming System

in Sidlaghatta Taluk …

……

…, K

arnataka 54

3.3 Enterprise budget (per year)

Particulars Cereals oilseed pulses vegetables others

Quantity Value(Rs) quantity Value(Rs) quantity Value(Rs) quantity Value(Rs) quantity Value(Rs)

Manures(tonnes) Fertilizers(kgs) Seeds(kgs) Tractor(hrs) PPC(liters/kgs) LABOUR(mandays) a.preparation

b.planting F H

c.weeding F H

d. intercultivation F H

e.harvesting F H

Total cost Main products By-product Total returns

NOTE: F-family labour H-hired labour, if bullock labour is used could be indicated in others/machine labour is used for spray indicating in others

5.0 Post Harvest Activities

5.1 a) Any cleaning/grading/other value addition, processing of the produce before selling?

If yes, manually or by using machine

b) what percentage of produce is processed……………….. ?

5.2 Do you take your produce to the agro-processing unit like mills/oil distillation plants?

If yes, 6.0 Backward linkages (on farm activities)

6.1 Material input source, quantity and cost for entire farm

Particulars Crop

Dry land Irrigated Garden

source Distance & place

Cost (Rs)

Seed F

Fertilizers F

Machine hire charges

Note: F-farm produced, P-purchased

SI. no Produce

processing

unit name

Distance from

the village

Unit price for

processing

Income

generated

6.2 Labour Input Cost

Crop Family

labour

Hired labour

animal

labour

Hired animal

labour

Mechanized

charges

Labour

price(Rs)

Machine

price(Rs)

Dry land

1 2 Irrigated

1 2 Garden/ perennial crops

7. Livestock Inventory

7.1 Details of Livestock Inventory

Sl.no Animals No’s Purchase value

Current

value(Rs)

1 Milch animals

a. Cows

b. Buffaloes

2 Young stock

a. Cows

b. Buffaloes

3 Draught animals

a. Bullocks

b. Cows

c. Buffaloes

4 Poultry

5 Sheep

6 Goat

7 Pigs

7.2 Inputs for Livestock and Their source

Sl.no Item Quantity/month Price(Rs)

produced

purchased

1 Ground nut cake 2 Rice bran 3 Concentrate 4 Dry fodder 5 Green fodder 6 Medicines 7 Labour(Man days) Sheep/Goat

1 Mineral mixtures 2 Green fodder 3 Medicines 4 Labour(Man days) Poultry

1 Starter feed 2 Finisher feed 3 Medicines 4 Labour(Man days)

Note: F-farm produced, P-purchased

7.3 Income Earnings from Livestock

particulars

Production

Quantity/No.

(liters)

Manure

(tonnes)

others Milk manure others qt value qt value qt value

1 Milch cows

Milch buffaloes

2 Sheep 3 Goat 4 Poultry 5 Pigs 6 Other 7 Total

7.4 Purchase and Sale of Livestock

Particulars Purchase sale

1 Milch animals Nos. Price

Place Nos. Price

a. cows b. Buffaloes

2 Calf 3 Draught animals

a. Bullocks b. cows c. Buffaloes

4 Poultry 5 Sheep 6 Goat 7 Pigs

8.a) Different complementary enterprises practiced in the farm

Particulars

Farm produced

Purchased

Purpose of

use with

specific

quantity Quantity

Place, price

& qt. sold (if

Place &

qty. (kg)

purchased

Cow dung Dry fodder Green fodder Perennial crop leaves Vermicompost

8.b) Different supplementary enterprises practiced in the farm

Particulars

Farm produced purchased Purpose of

use with

specific

quantity

Quantity

Place,

price & qt.

sold (if

Place & qty.

purchased

Cow dung Dry fodder Green fodder Perennial crop leaves

Vermicompost

8.c) Different competitive enterprises practiced in the farm

Particulars

owned Borrowed Purpose

of use

specific

quantity

Quantity

Value(Rs) Quantity

Value(Rs)

Land(acres) Labour(mandays) Capital(Rs) Perennial crop(acres)

management

8.1 have constructed farm pond? Y/N

If yes, mention quantity of water harvested …………………………,purpose of use…………………,

Is fishery practiced in farm pond, if yes revenue from it………………………….

8.2 Do you practice crop rotation? Y/N If yes,

Sl.no Cropping pattern Cost saved in Total

savings Weeding Manures

fertilizers

Labour

charges

9.0 Economics of farming systems followed

Crops Livestock

(cow/goat/sheep)

Dairy Total

expenditure

income

AN ECONOMIC ANALYSIS OF INTEGRATED …...Sagar, Gayathri, Guruprasad, , Gourav, sahana,...

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