PERFORMANCE OF COTTON CROP IN NON-TRADITIONAL AREAS OF KARNATAKA AN ECONOMIC ANALYSIS. Doctor of Philosophy. Agricultural Economics PAVITHRA B. S.

Transcription

1 PERFORMANCE OF COTTON CROP IN NON-TRADITIONAL AREAS OF KARNATAKA AN ECONOMIC ANALYSIS Thesis submitted to the University of Agricultural Sciences, Dharwad in partial fulfillment of the requirements for the Degree of Doctor of Philosophy In Agricultural Economics By PAVITHRA B. S. DEPARTMENT OF AGRICULTURAL ECONOMICS COLLEGE OF AGRICULTURE, DHARWAD UNIVERSITY OF AGRICULTURAL SCIENCES, DHARWAD MARCH, 2013 DEPARTMENT OF AGRICULTURAL ECONOMICS

2 ADVISORY COMMITTEE DHARWAD MARCH, 2013 Approved by : Chairman : (L. B. KUNNAL) CHAIRMAN (L. B. KUNNAL) Members : 1. (H. BASAVARAJA) 2. (S. M. MUNDINAMANI) 3. (SHAILAJA NAIK) 4. (L. MANJUNATH)

3 CONTENTS Sl. No. CERTIFICATE ACKNOWLEDGEMENT LIST OF TABLES LIST OF FIGURES LIST OF APPENDICES 1. INTRODUCTION 2. REVIEW OF LITERATURE Chapter Particulars 2.1 Growth performance of cotton 2.2 Price and non-price variables influencing production 2.3 Profitability of cotton 2.4 Pace and pattern of arrivals and prices 2.5 Marketing costs, margins and marketing efficiency 2.6 Constraints in production and marketing of cotton. 3. METHODOLOGY 3.1 Description of the study area 3.2 Sampling procedure 3.3 Nature and sources of data 3.4 Statistical techniques employed 3.5 Definition of terms and concepts used 4. RESULTS 4.1 Growth performance of cotton 4.2 Identification of price and non-price factors influencing production of cotton 4.3 Profitability of cotton on different farm size 4.4 Pace and pattern of market arrivals and prices of cotton 4.5 Marketing pattern of cotton 4.6 Problems faced by the farmers in production and marketing of cotton in different markets 5. DISCUSSION 5.1 Growth performance of cotton in non-traditional areas of Karnataka 5.2. Identification of price and non-price factors influencing production of cotton 5.3 Profitability of cotton on different farm sizes Contd.

4 Sl. No. Chapter Particulars 5.4. pace and pattern of market arrivals and prices of cotton 5.5 Marketing channels for cotton and their efficiency 5.6 Problems faced by the farmers in production and marketing of cotton. 6. SUMMARY AND POLICY IMPLICATIONS 6.1 Growth analysis 6.2 Price and non-price factors influencing production of cotton 6.3 Costs and returns in Cotton production 6.4 Pace and pattern of market arrivals and prices of cotton 6.5 Marketing pattern of cotton 6.6 Constraints in production and marketing of cotton 6.7 Policy measures REFERENCES APPENDICES

5 Table No. LIST OF TABLES Title 3.1 Profile of the study area ( ) 3.2 land utilization pattern of the study area ( ) 3.3 Area under crops in study districts during Selected sample respondents from the study area 4.1 Compound growth rates of area, yield and production of cotton in non- traditional areas of Karnataka ( to ) 4.2 Estimated trend functions for cotton in non traditional areas of Karnataka ( to ) 4.3 Price and non-price factors influencing the cotton production in Mysore district 4.4 Price and non-price factors influencing the cotton production in Chamarajanagar district 4.5 Price and non-price factors influencing the cotton production in Shivamogga district 4.6 Price and non price factors influencing the production of cotton in Davanagere district 4.7 Social profile of sample farmers 4.8 Operated area under Cotton 4.9 Cropping pattern of sample farmers in study area 4.10 Labour use pattern in Cotton cultivation 4.11 Input use pattern and output obtained in Cotton cultivation (Bt Cotton) 4.12 Costs in cultivation of cotton (Bt Cotton) 4.13 Cost and returns profile of cotton production 4.14 Seasonal indices of arrivals and prices of cotton in the study markets 4.15 Relationship between annual market arrivals and average prices of cotton in selected markets 4.16 Correlation coefficients between annual arrivals and average prices of cotton in selected markets 4.17 Coefficients of variation in annual arrivals and average prices of cotton in study markets 4.18 Preference of sample respondents to different Channels of marketing of cotton in selected markets 4.18a Marketing cost incurred by the farmers in different Channels Table No. Title 4.19 Cost incurred by wholesale traders in cotton transaction under Channel II 4.20 Cost incurred by village merchants in cotton transaction 4.21 Marketing margins under different Channels of cotton in selected markets 4.22 Marketing efficiency of different marketing Channels for cotton in selected markets 4.23 Problems faced by the farmers in production of cotton in the study area (Bt Cotton) 4.24 Problems faced by the farmers in marketing of cotton in the study area (Bt cotton)

6 LIST OF FIGURES Figure No. Title 1. Map of Karnataka showing the districts selected for the study 2. Cost of cultivation of Bt cotton on small and large farms 3. Components of variable cost in cultivation of Bt cotton on small and large farms 4. Cost and returns profile of cotton production on small and large farms 5. Seasonal indices of arrivals of cotton in selected markets 6. Seasonal indices of prices of cotton in selected markets 7. Cost incurred by wholesales trader in cotton transaction under Channel II 8. Cost incurred by village merchants in cotton transaction LIST OF APPENDIX Appendix No. I. Interview schedule Title

7 INTRODUCTION Cotton often referred to as "White gold, has been in cultivation in India for more than Five thousand years. It is one of the oldest fibres and the time when it was first utilized is not known accurately. It is a soft fibre that grows around the seeds of the cotton plant (Gossypium spp.), a shrub native to the tropical and subtropical regions of both the Old World and the New World. The fibre is most often spun into thread and used to make a soft, breathable textile, which is the most widely used natural fibrer cloth in clothing today. Wild species of cotton generally occur in frost-free areas of subtropical & tropical regions. Freezing temperature kills the protoplast of all cultivated and most wild species. Cotton is sun loving plant but not a water loving plant. Water requirement of plant depends on weather conditions, but a successful cotton harvest requires at least 75 cm of rain or irrigation on an average. Although cotton is cultivated in both the hemispheres, most of it, is cultivated in the northern hemisphere. It is primarily grown between 370 N and 320S; however, its cultivation has been extended to 450N in china. Origin The earliest known reference to cotton is in India. The Arabic word Qutun or Kutun has given rise to the English word Cotton. Similarly, it is Katoen in Dutch, coton in French, cottone in Italian and algodon in spanish. The botanical term Gossypium seems to have risen from the word Gossypines in Tylos for cotton (Watt, 1907). The sanskrit words kurpasa or kurpusum denoting cotton and cotton cloth are mentioned in the sacred writing of Manu (3000 B.C.) and the word kapas had been derived from them. Cotton has been grown in India/Pakistan for more than 6,000 years since the pre- Harappan period and it is later referred to in the Rig- Veda, composed in 3000 BC. The earliest civilization to spin and weave cotton was perhaps that of the Indus valley. For many centuries, the cotton plant was known outside India through travellers tales. Two thousand years later, the famous Greek historian Herodotus wrote about Indian cotton: "There are trees which grow wild there, the fruit of which is a wool exceeding in beauty and goodness that of sheep. The Indians make their clothes of this tree wool". Importance The first literature on cotton trade is found in the beginning of Christian Era; but, they refer to the perennial cottons. The marvellously woven handloom fabrics and apparels of India were not only used in India, but also in Egypt, Greece and Rome. In Rome, Indian Muslins and Chintzes were the rage of fashionable women. From India, cotton spread in the 13th Century A.D. to China, where silk was the normal wear. Then, the cultivation of cotton spread to Persia, Arabia, Egypt, Africa and South Europe. Later on, cotton cultivation was discovered by Columbus in West Indies of the New World and still later, by others, in Mexico, Yucatan and Brazil. Cotton is one of the finest natural fibres available for man for his clothing. Cotton trade has developed into a big industry, since the use of cotton for manufacture of cloth. India's cotton-processing sector gradually declined during British expansion in India and the establishment of colonial rule during the late eighteenth and early nineteenth centuries. This was largely due to the East India Company's de-industrialization of India, which forced the closing of cotton processing and manufacturing workshops in India, to ensure that Indian markets supplied only raw materials and were obliged to purchase manufactured textiles from Britain. One of the unique features of the Indian Cotton is its diversity. Systematic efforts in cotton breeding commenced in India during the first decade of the last century with the establishment of Department of Agriculture in some states. The vigour and excellence of cotton research in India exemplified by the development of hybrid cotton like long and extra long staple cotton, some of which rivals the best in the world. At the time of independence India had only short and medium staple cotton, while the industry consumed a large quantity of long staple cotton which had to be imported, but with the scientific approach of purification, selection and hybridization processes, outstanding fibre properties and some of them are best in the world today. In spite of the advances in cotton production, it has to propagate the cause at the national & international lvel by joining the forces and harmonizing of the interest of the producers, users & other concerned. Encouraged by the results obtained in the field of oilseeds by adopting a mission mode approach during the 1990s and with a view to improve the quality of cotton, increase per hectare productivity, increase the income of cotton growers by reducing the cost of cultivation, to improve the processing facilities etc., the Government of India has launched Technology Mission On Cotton in February 2000 with Four Mini Missions for achieving the above objects.

8 Mini mission I With the Indian Council of Agricultural Research (ICAR) as the Nodal Agency, this Mini Mission has the following objectives: Development of short duration, high yielding, disease and pest resistant varieties/hybrids with appropriate fibre parameters to meet the need of the textile industry. Development of integrated water and nutrient management practices for cotton and cotton based cropping systems. Development and validation of Integrated Pest Management Technology for different cotton growing areas of India to improve yield and reduce the cost of cultivation to ensure better net return to the cotton growers. Mini mission II Technology transfer through demonstration and training. Supply of delinted certified seed by setting up of delinting units. Accelerating Integrated Pest Management activities. Providing adequate and timely information input to the farmers periodically. Mini mission III Improvement of marketing infrastructure through setting up new market yards and activation/improvement of existing market yards. Mini mission IV Modernization and technological upgradation of existing ginning and pressing factories so as to improve the processing of cotton. World cotton scenario World cotton production is estimated at million tons in (USDA, March, 2012) which is 5.73 per cent higher than the previous year India continued to maintain its position the second largest producer of cotton next to china with 22 per cent of world production. Area under cotton across the world has been sluggish for the past few years; however, production has been increased due to sharp rise in yield. China, India, USA and Pakistan are the major cotton producing countries in the world with share of 70 per cent of the world cotton production and area, respectively. India is the largest cotton growing country in the world with area under cotton around 34 per cent (12.20 million ha) followed by China (5.5 million ha). China and India are the major cotton consuming countries in the world (around 58 per cent of world cotton consumption). As regards export, USA and India export around 28 per cent and 20 per cent of the world cotton exports and China is the major importer in the world. Around 48 per cent of the total import will be from China and expected to import million bales of 480 kg each. Among the major cotton growing countries, Australia tops the productivity level of 1804 kg per ha followed by Brazil (1446 kg/ha) and China (1326 kg/ha). With worldwide annual production of some 22 million tonnes (2009/10) of cotton fibre grown in some 35 countries, cotton is among the most important commodities in international trade. The FAO estimates that nearly 100 million rural families directly depend on cotton production. For some countries in West Africa, cotton is the main driving force behind economic development, with cotton earnings accounting for per cent of GDP in countries like Burkina Faso, Benin, Mali, Chad and Senegal. Ninety per cent of all cotton worldwide is of the Gossypium hirsutum cotton species. In 2009, overall world production of cotton amounts to some 100 million bales (1 bale = 500 lbs or kg). The biggest producers are China, India and the USA, followed by Pakistan and Uzbekistan. The combined production of all West African countries currently accounts for only 4.7 per cent of the world market. The United States and Africa are the largest exporters of seed cotton. The total value of international trade is some billion. Since 1980, Africa's share of the cotton trade has doubled. Neither the US nor most African countries have a significant domestic textile industry. Indian cotton scenario

9 India is the second largest producer of cotton in the world after China accounting for about 18 per cent of the world cotton production. It has the distinction of having the largest area under cotton cultivation in the world ranging between 12 to 12.2 million hectares and constituting about 25 per cent of the world area under cotton cultivation. The yield per hectare is however, the lowest against the world average, but over the last two years have shown a promising potential to reach near the world average production level in near future. Cotton production which was just 103 lakh bales during , increased to about 260 lakh bales by the year , an increase of more than 150 per cent. There is scope to raise the export as the local consumption of cotton hovering around 250 lakh bales for past 4-5 years. No doubt that significant enhancement of area under cotton this year, but the productivity hovering around 500 kg per ha for the past six to seven years which is need to be enhanced. With good market price in the last season and augmentation of non-traditional cotton areas, overall increase of area under cotton (8.60%) has been reported in the country from (last year) to lakh ha in (CAB estimate). Gujarat, Rajasthan and Haryana reported significant area enhancement in The production more than doubled in compared to , from 168 to 345 lakh bales. Since India is having a large domestic textile industry, the mill consumption of cotton in the country especially, textile mills and small scale spinning units had been continuously on the rise from the beginning of 1990s. Thus, the consumption of Cotton, the white gold or the King of Fibres, enjoys a predominant position amongst all cash crops in India. In India, cotton occupies an area of nearly 7.39 million hectares, with a production of 2.38 million metric tones ( ), ranking third in the world. The lint productivity of cotton is 322 kg per ha, which is the lowest and far below that of the world average of 627 kg per ha. During the last fifty years, production of cotton rose from lakh bales (1 bale = 170 kg) in to 136 lakh bales in Significant increase in the area under cultivation of cotton was observed over a period of fifty years. Major cotton producing states in India are Andhra Pradesh, Gujarat, Haryana, Karnataka, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu and Uttar Pradesh. The cotton production in the country has touched an all time high during with a record production of lakh bales (1 bale = 170 kg). The productivity of cotton has also shown significant growth with 517 kg per ha during compared to 136 kg per ha during when the production was 179 lakh bales. The area under cotton has also increased from hectare in to hectare by Gujarat (103 lakh bales), Maharashtra (82 lakh bales) and Andhra Pradesh (53 lakh bales) are the leading cotton producing states in the country. In terms of productivity Tamil Nadu (697 kgs/ha) and Karnataka (312 kgs/ha) were leading for the period (Source: Cotton advisory board) Karnataka s cotton scenario Cotton is an important commercial crop which can be grown in all parts of Karnataka and it is one of the nine major cotton-growing states in the country. Area under cotton in the state in was around 5.6 lakh hectares, with the production and productivity of 7.75 lakh bales and 235 kg per ha respectively and for the year was around 5.45 lakh hectares, with the production and productivity of 10 lakh bales and 312 kg per ha respectively. The main cotton growing districts in Karnataka are Dharwad, Haveri, Gadag, Bellary, Belgaum, Raichur and Gulbarga. The major markets for cotton in Karnataka are Bailhongal, Bellary, Bijapur, Gadag, Haliyal, Hubli, Jamkhandi, Raichur, Ranebennur, Savadatti, Shivamogga and Yellapur. However, there is a spectacular shift in cotton growing areas in Karnataka. From traditional areas, it has spread to many non-traditional districts like Mysore, Shimoga, Chamarajnagar, Davanagere. Hence, the present study has made an attempt to analyse the performance of cotton crop in these non-traditional areas. The present study was undertaken with the following specific objectives. 1.1 Specific objectives of the study 1. To estimate the growth in area, production and productivity of cotton in non-traditional areas of Karnataka. 2. To identify the price and non-price factors influencing the production of cotton. 3. To analyze the profitability of cotton on different farm sizes (small and large farms). 4. To analyze the behaviour of arrivals and prices of cotton in the selected markets. 5. To study the marketing channels for cotton and their efficiency in the study area. 6. To identify the constraints in cotton production and marketing and to suggest the ways and means to overcome them.

10 1.2 Hypotheses The hypotheses formed for the study were as fallows: 1. There is an increasing trend in area, production and productivity of cotton in non-traditional areas of Karnataka. 2. Production of cotton in non-traditional areas of Karnataka is influenced by price and non-price factors. 3. Production of cotton is more profitable to large farmers compared to small farmers. 4. Heavy arrivals of cotton are seen in markets during picking season. 5. There exist different marketing channels for cotton in the study area. 6. Cotton producers are facing some problems in production and marketing of cotton. 1.3 Presentation of thesis The thesis is presented in six chapters. Chapter-I Introduction: In this chapter, the nature, importance of the present study and the specific objectives of the study have been indicated. Chapter-II Review of literature: It presents a comprehensive review of the relevant research work done on related topics by different economists. Chapter-III Methodology: It outlines the features of the study area, sampling design followed, relevant data and analytical tools used in the study. Chapter-IV Results: It is devoted to present the main findings of the study through tables and graphs Chapter-V Discussion: It presents meaningful interpretation and discussion of the results of the study. Chapter- VI Summary and policy implications: This chapter provides summary of the entire research work and suggests the policy implications emerged from the findings. Significance of the study The present study is an attempt to study the performance of cotton in selected non-traditional areas of Karnataka by estimating the growth in area, production and productivity of cotton. It also aims to identify the price and non-price factors that are influencing the production, to analyse the profitability, behaviour of arrivals and prices, marketing channels and their efficiency, constraints that exist during the cotton production and marketing and to suggest the ways and means to overcome them. Limitations of the study Due to the limitations of time and other resources, the study was confined to four districts of Karnataka which are considered as non-traditional cotton growing areas of Karnataka. The primary data was collected for the year Further, the expressed opinions of respondents with regard to the various issues of the study may not be totally free from personal bias and prejudice. Hence, the results of the study cannot be generalized beyond the limits of the study area.

11 REVIEW OF LITERATURE In this chapter, with a view to evaluate the objectives of the study, the findings of some of the earlier research studies and the methodology adopted there in have been reviewed. It was hoped that such a review of literature connected with the production and marketing performance of cotton in India and abroad as well as the related crops would provide a basis either for confirming the earlier results or for contradicting them and thereby suggesting the points for further improvement. heads. Looking to the objectives of the study, the review of literature is presented under the following 2.1 Growth performance of cotton 2.2 Price and non-price variables influencing production 2.3 Profitability of cotton 2.4 Pace and pattern of arrivals and prices 2.5 Marketing costs, margins and marketing efficiency. 2.6 Constraints in production and marketing of cotton. 2.1 Growth performance of cotton Reddy (1986) studied trend in area, production and productivity of cotton in Raichur district, the results showed that there was increase in production and productivity of hybrid cotton despite decreasing trend in area, the impact of increase in productivity counteracted the impact of decline in area whereas the growth rates for non-hybrid cotton were found to be negative for all variables. On the whole only productivity of cotton showed positive growth. The substantial fall in the area under this crop caused a decrease in the production. Choudhari et al. (1990) in their study on growth rates in area, production and productivity of gram in Bihar indicated that the compound growth rate in area (-4.62%) and production (-3.32%) were negative while in productivity it was positive (1.67%) and significant. The growth performance of banana was studied in state of Kerala for the period of 1970 to 1987 using time series data on area, production and productivity pertaining to the study period (Devi et al., 1990). The yield showed a declined trend in while there was an increasing trend during At aggregate level its trend was found to be slightly decreasing mainly due to the effect of the first period. Increasing trend was noticed in the second period in case of production. The area under this crop during 1970s showed a rising trend whereas sharp decline was noticed during 1980s. Though the area showed an increasing trend in the first period, the decline in production was noticed due to decline in yield. The investigators further indicated that banana production in the state showed a better performance in the 1980s mainly because of the increase in productivity. Mander and Sharma (1992) examined the growth performance of crop output in India for the period to It was evident from the result that cotton production registered a significant growth rate of 2.39 per cent per annum during the Green Revolution period. They also reported that increase in production of cotton occurred as a result of increase in yield (2.74%) while the area under this crop decreased significantly. Mundinamani and Mahajanashetti (1993) employed the orthogonal polynomial regression analysis to study trends and growth performance of oilseeds in Karnataka during the period to The results of the study indicated that in Bijapur district there has been a continuous decline in groundnut area till and a mild increase thereafter, but a continuous significant increase was seen in Dharwad district till and was stabilized thereafter. In Raichur district and at state level, an increase in area was noticed in recent years. However, the area under safflower and sesame over the years showed stable trend. Moreover, an impressive growth in area was observed under sunflower since its introduction. The yield trend showed mixed results but at state level an increasing trend in yield was observed and the production trend was more or less similar to the area trend. Investigation of growth rates in production of oilseeds in India (Rao et al., 1993) confirmed lack of uniformity across crops over years. The aggregate oilseed production was stagnant during the 1970s and recorded a growth rate of 5.25 per cent in the 1980s resulting in an overall growth rate of

12 3.04 per cent for the period to The authors concluded from the study that acceleration in production was due to significant growth in both area and productivity during the 1980s. Singh et al. (1993), in their study, Cotton Development and Export Potential in India, reported that cotton area and production increased by 49 and 31 per cent during the last four decades. They also suggested that this increase in cotton production was more on account of increased yield rather than an increase in cotton acreage. Patel et al. (1996) studied the compound growth rates of pulses in selected districts of Gujarat for the period The production growth rate for the study period worked out to be 3.05 per cent, the growth rate in area and yield was 0.70 per cent and 2.19 per cent, respectively for Gujarat state as a whole. Surrendranagar district registered highest growth rate in area (4.78%) and production (6.82%) the figure of co-efficient of variation was found to be per cent for production, per cent for area and per cent for yield. In his study on the performance of Indian agriculture, Sawant (1997) used time series data for the period from to The data was analyzed by compound growth rate after fitting loglinear function. It was found that, of the two cash crops, namely, cotton and sugarcane, further moved to high growth range compound growth rates of its output exceeding 4 per cent during to , mainly due to significant advances in its seed technology and resultant high growth in the yield per hectare. Sudhakar and Mishra (1997) analysed the growth of ragi production during the green revolution period ( to ). The study period was divided into two sub-periods i.e., period I ( to ) and period II ( to ). It was observed that over the period of twenty years the area had increased significantly at the rate of 2.90 per cent per year. The out put growth was seen to grow at an annual rate of 3.63 per cent which was significant at one per cent level. The results showed that at aggregate growth functions of area, yield and production of two subperiods were significantly different. Gaddi et al. (1998) studied growth rates in area, production and productivity of cotton for the major cotton producing countries and the state of Karnataka, in India for the period from to in the former case and from to in the later using exponential function. The results showed that World cotton area declined at 0.33 per cent per annum due to the improvement in productivity. Similar results were reported at all India level, Karnataka state and some of the traditional cotton growing districts. Production of cotton registered significant growth in all the cases mainly due to the substantial growth in productivity. This study considered only one period growth analysis that made it incredible compared to the present study, which is comprehensive in its dimensional objectives and the periods accounted. Sharanesh Handiganur (1998) conducted a study on Production and marketing performance of pulses in Karnataka-An economic analysis, to study the factors influencing the price and non-price factors for pulses was analysed. The principal component analysis was worked out, the lagged price and yield of the crop under consideration and competing crops, gross cropped area irrigated, rainfall and area under the crop were the variables which influenced the most the production of pulses. Gaddi et al. (1999) conducted study on growth performance of oilseed crops in India. They used co-efficient of variation technique to measure the contribution of area and productivity towards increase in production instabilities were higher when compared to yield and area instabilities. The findings of the study revealed that area was the major contributor of output in case of linseed (129.45%), sunflower (97.9%), soybean (82.56%), while it was productivity in sunflower (58.16%), sensamam (112.26%), Niger seed (106.79%), castor seed (63.44%), ground nut (57.2%) and rape seed (34.09%). They pointed that provision of good marketing facility as the most promising approach to achieve continued growth in oil seeds production. The foregoing studies revealed that the contribution of area, yield and their interaction to the total production varied from region to region and from time to time. Most of the studies revealed that the increased output was due to positive contribution of area and yield. However, the area over-shadowed the yield and emerged as main contribution for increased output. Ravikumar and Raju (1999) worked out compound growth rates for both production and market arrivals of important agricultural commodities in the regulated market of Andhra Pradesh. The results showed that rice, groundnut and chilies showed a positive and significant growth rate in production for the past 22 years. The growth in production and arrivals of chilies were positive and

13 significant while groundnut had recorded non-significant growth rate in its market arrivals (0.15%). In case of bajra, the growth rate for market arrivals was positive and significant (5.35%) when compared with its production trend which was negatively significant (-3.92%), while jowar an important cereal crop showed negative trend both in production and market arrivals. Girma (2002) studied the district wise growth of cotton production in Karnataka states for the period from to The compound growth rate analysis was carried out and the results revealed that area under cotton experienced a declining trend at the rate of 1.32 per cent per annum. Except districts of Mysore (46%) and Shimoga (3.63%) all other districts experienced negative growth of area in the initial years. During the second period from to growth rates of most districts turned out to be positive. A continuous rise in growth had been observed in Bijapur district. The study also found that the significant positive growth was evident for rainfed conditions in Dharwad, Gulbarga, Mysore and Raichur. The productivity of cotton in the state was found to increase substantially at the rate of 4.6 per cent per annum. All districts in the state experienced positive growth rate except Bijapur and Gulbarga during the initial period that during the second period declining trend in productivity was noticed for Belguam, Bellary, Mysore, Raichur and Shimoga districts while Dharwad and Chitradurga experienced a continual rise in productivity growth during the study period. Samui et al. (2002) studied the trends in Area, production and productivity of sugarcane in Maharashtra and Uttar Pradesh. The study revealed that the growth in area (1.85%) production (1.79%) and productivity (1.88%) were positively and significantly correlated in all districts of Maharashtra at one per cent level. In Uttar Pradesh the growth in area (1.75%), production (1.78%) and productivity (1.78%) were positively and significantly correlated at all the districts of Uttar Pradesh at one per cent level. Goswami et al. (2003) made an attempt to analyze the growth rate of area, yield and production of cotton in the cotton growing states of the country. The results of the compound growth rate analysis of area, yield and production of cotton in major cotton producing states of country and also of the country as a whole showed significant with positive growth ate of area (4.52%) and yield (4.63%) resulted in significant positive growth in production at 9.38 per cent in Andhra Pradesh. In Gujarat, a significant positive growth rate of 2.19 per cent in yield compensated the negative growth rate of 1.41 per cent in area and enchanced the production at significant positive compound annual growth rate of 0.85 per cent. In Karnataka and Madhya Pradesh significant positive growth rate in yield marginalized the impact of significant negative growth rate in area and resulted in significant positive growth in production. The empirical findings of the study showed that growth performance of cotton varied from state to state which in turn affected its performance at national level. Yield was the main source of growth in output of cotton, area being of secondary importance. There has been significantly high growth in yield and production of cotton in states like Andhra Pradesh, Karnataka, Madhya Pradesh and Maharashtra. However, significant high growth in area contributed for higher growth in production of cotton in states like Andhra Pradesh, Haryana, Punjab and Rajasthan. Dalwai (2004) studied the production of cotton in Karnataka. The pattern of growth in area, production and productivity of cotton was studied. The compound growth rate in area and production for the state as a whole showed a declining trend over the period ( to ). The rate of decline was per cent in second phase and per cent in the first phase. Jahangirdar et al. (2004) studied the growth of cotton in Maharashtra. The study was based on 35 years data dividing the period into pre-green revolution, green revolution and post green revolution. The districts covering more than 80 per cent area of the state were included in the study. The study revealed that with respect to growth in yield during overall period only Wardha district recorded significant and positive growth of 9.6 per cent. It was found that the phase prior to introduction of new innovation in five districts, there was positive area growth in cotton. While there was no significant growth of cotton production during any of the three phases Reddy (2005) studied the growth of chickpea production in India by assessing the state level performance. The study period was divided into two sub-periods. The first period ( ) representing post- green revolution period with pre-liberalisation of food sectors. Where as the second period ( ) represented post liberalization period in India. The growth rates showed that states like Andhra Pradesh, Maharashtra, Karnataka, Gujarat, Madhya Pradesh and Orissa showed positive growth rate, grouping them into high growth states. The states like Punjab, Bengal, Haryana, Uttar Pradesh, Bihar and Rajasthan showed negative growth rates of area forming the low growth states.

14 Thumar et al. (2006) examined the growth rates of area, production of Garlic in Gujarat and worked out the rate of growth of garlic exports from India. The CGR was worked out for three districts and the state as a whole. The results revealed positive rates for Junagadh and Rajkot districts and the states as a whole Junagadh district showed significant growth of 13.21per cent while Jamnagar district showed negative growth (-2.99%) for area. The exports of garlic revealed that in first ( to ),second ( to ) and fourth ( to ) decades showed positive growth where as in the third decade ( to ) negative growth rates for export quantity as well as for value were observed. The reason for the reduction was given as the three drought years causing reduction in domestic supply Nithya et al. (2008) studied cotton production in Karnataka, India a growth rate analysis reveal that growth in area (-1.73 and per cent simple and compound growth rates respectively) and production (-0.30 and per cent simple and compound growth rates respectively) of negative, while productivity had a positive growth (1.79 and 1.03 per cent simple and compound growth rates respectively) during pre-introduction of Bt cotton. However, during the post Bt introduction period, all the 3 i.e., area (1.33 and 6.73)ha, production (17.15 and 26.45)b and productivity (23.84 and 31.03)c of cotton showed a positive growth rate. The growth rates area (-2.55 and -3.18)1 and production ( and -3.97)2 was negative during the overall period, while that of productivity (1.84 and 1.04)3 exhibited a positive growth. This positive growth rate of productivity may be at least partly attributed to introduction of Bt cotton. Mohamed Elamin Abd Ellatif Mahir et al. (2010) conducted a study on Estimation of Growth Rates and Analysis of its Components in the Gezira Scheme. The study estimated the growth rate in area, productivity and production before and after the adoption of the liberalization policy for the four crops. Further, the study conducted a decomposition analysis to determine the contribution of different components to the growth rate. The results of the study showed that there were variations in growth rates of area and productivity for the crops during the two periods. The growth rate was positive and increasing during the two periods for sorghum, positive and decreasing for cotton and negative and decreasing for groundnut. The decomposition analysis revealed that the main components contributing to growth rate were area, productivity and cropping pattern. The growth rate under cotton was negative during period I (-4.28%) and it turned to be positive during period II (1.78%). Area under groundnut showed a higher decline in period I (-11.16%) compared to period II which was (- 1.65%).The productivity growth rate of cotton showed a decreasing trend, i.e. (3.73%) (1.01%) during period I and period II. Growth in cotton production was positive in period I and period II, it was (3.69%) and (1.37%), respectively. 2.2 Price and non-price variables influencing the production Price is certainly an effective factor in increasing agricultural production. Apart from price there are also some non-price factors which do influence the production. Reviews pertaining to these aspects are presented as they have a distinct relationship with this study. Being the first investigator to study farmers response to price, Bean (1929) employed simple graphical method by plotting the lagged price and the acreage under the crop. The results indicated that the price has a significant impact on acreage under the crop. Walsh (1944) estimated the area response of cotton with the time series data for the period using simple liner regression. Results revealed that the price in the preceding period influenced the area allocation decision of the farmers. Clark (1957) employed multiple regression analysis in logarithmic form to explain the response of jute area and output in Bengal for the period from to The results showed that the expectation of the level of jute and rice prices lagged by one year were the most important factors influencing the decision to plant jute. Change in jute area was directly associated with the logarithm of the lagged jute prices and inversely associated with the logarithm of the lagged rice prices. Basing result, he asserted that the regulation of both jute and rice prices was necessary to obtain the desired level of jute output. Brennan (1958) was of the opinion that the acreage under cotton in the US was influenced not only by its own price but also by the prices of its competing crops. He expressed the acreage under cotton as a function of its own price, the prices of its competing crops. viz., corn, tobacco, peanuts and wheat and the time trend variable.

15 Dharam Narain (1965) did an impressive work related to the price response of agricultural commodities using graphical analysis covering various crops, viz., cotton, jute, groundnut, sugarcane, rice and wheat in all the states over the period 1900 to His study touched a broad spectrum of acreage behavior in India, with the characteristics features of simplicity of approach and judicious use of simple analytical tools. It was concluded that price was a decisive factor in determining area under cash crops while non-price factors like rainfall was the case in food crops rather than price. Jaikrishna and Rao (1967) employed Nerlovian and other models to study the acreage allocation for wheat in Uttar Pradesh. The results revealed that wheat acreage was fairly elastic to change in relative prices of wheat and substitute crops. The three years average of pre-sowing prices of wheat deflated by the corresponding prices of competing crops, the yield and the rainfall emerged as important factors influencing acreage allocation. Jha (1970) estimated acreage response of sugarcane in factory areas of North Bihar for the period from to using adjustment lag model. The study revealed that farmers respond to changes in relative prices. The non-price variables like lagged yield and pre-sowing rainfall also emerged significant. Ahammed (1981) used Nerlove s traditional adaptive expectations model to estimate the impact of risk aversion on acreage planted to the crop. The results evidenced that jute farmers were responsive to acreage and have got attitude of risk aversion. Basavaraj (1982) conducted a comprehensive study on supply response of cotton in Karnataka with a view to evaluating the impact of price and non-price variables on area, yield and production of cotton. All three response functions were estimated by employing Nerlovian price expectation-cum-area Adjustment model in both linear and log-linear forms. The price expectation coefficient was assigned the values in the range of 0.5 to 1.5 at an interval of 0.1. The best of the resulting sets of the estimates were then chosen on the criterion of minimum error sum of squares. Dixit (1982) estimated area, yield and production response of groundnut in Karnataka adopting Nerlovian Price Expectation-Cum-Area Adjustment model. The method of assigning the value to the coefficient of expectation and selecting the best set of estimates was similar to that used by Basavaraj (1982). He also employed simultaneous equation model to analyze acreage response groundnut. Prasad and Krishna (1982) studied acreage response of groundnut using log-linear form of the Nerlove s type model for the period from to It was found that groundnut producers in Andhra Pradesh were responsive to the lagged acreage in allocating their land, but price, lagged yield and seasonal rainfall had no significant bearing on the area under the crop. Shapiro (1983) measured technical efficiency among Tanzanian cotton farmers in Geita district, which relied on an outer-bound Cobb-Douglas production function derived with a liner programming methodology. It was found that average level of technical efficiency in the sample was and thus if all farmers were to modify their operations so as to operate on the outer-bound production function, output would increase by 51 per cent without the use of new inputs and introduction of new technologies. The hypothesis that peasant agriculture is highly efficient and hence, important gains in production must rely solely on the infusion of new inputs and technology was finally not supported by the data analysed. Bhagat (1985) employed Nerlovian lagged adjustment model to ascertain the determinants of wheat acreage function in Bihar. The impact of price/gross return, risk arising from price/gross return variation, irrigation and rainfall was studied. The Bihar farmers appeared to be risk averters. The Supply Response of Sesamum in Andhra Pradesh was studied for the period to using model developed on the basis of Nerlovian lagged adjustment model (Prasad et al., 1989). It was evident from the result that regression coefficient for none of the factors (lagged area, lagged price, price risk, sowing period rainfall) was significant indicating that no factor could stand individually to affect the area allocation, though the combined effect of all factors put together was significant as confirmed by the value of R 2. Shantisarup and Pandey (1990) analysed the price and non-price factors influencing tur production. The study is related to nine important tur growing states of India for the period to The study revealed that the price factor has been almost dominated by the other non-price factors in some of the states. The impact of relative prices on tur acreage is observed to be positive and significant in Gujarat and Uttar Pradesh and negative in Madhya Pradesh. Regression co-efficient

16 of relative irrigated area are found to be positive and significant in Bihar and Tamil Nadu. The coefficient relating to relative yield was observed to be significant and positive in the state of Tamil Nadu and Uttar Pradesh. Grover (1994) used multiple regression technique to estimate the acreage response function of sugarcane in Punjab during to Results of the analysis implied that the area under sugarcane crop was positively and significantly influenced by lagged area under sugarcane, lagged relative profitability of sugarcane with respect to competing crops, price of gur, while it was negatively and significantly influenced by the lagged year disposal problems faced by the growers. In an attempt to estimate district wise area and yield response of Bajra, Patil and Singh (1994) used Nerlovian type lagged adjustment model. It was apparent from the results that the high coefficient of adjustment in Banskantha districts indicated the closeness of the short run and long run elasticities implying quick area adjustment. On the other hand, the relatively low value of coefficient of area adjustment in Kaira district implied the presence of certain constraints lowering the adjustment process. Ram Singh (1996) analysed the price and non-price factors influencing production of pulses in 4 different regions of Uttar Pradesh. The merlove s (1958) seminal model has been used for the analysis. The study hypothesized that the price factor doesnot play a significant role in influencing the supply of pulses over non-price factors like yield, irrigation, risks, technological changes in competing crops. The increased area under assured irrigation is expected to substitute crops like wheat, rice and corn for pulse crops. Dhundsa and Anjusharma (1997) studied the price and non-price factors influencing production of pulses. The study indicated that the non-price variables rather than price variables are significant in determining the area response of various pulse crops (except moong) in the state and its various sub-regions. The factors considered were lagged relative price, lagged relative yield and lagged relative profitability, irrigation, rainfall and risk factors. The response with respect to relative price has been found to be low and also further increase in prices of pulses will hit the poorer section of the society and will create serious imbalances in the dietary mix of the majority. Mundinamani (2000) reported from the study on cost of cultivation of cotton in Gadag district that average cost of processing of one quintal of kapas to lint was ` 1, and the gross returns obtained by ginning process were ` 1, The net value addition to cotton by ginning was ` per quintal of kapas ginned. The total cost incurred in the processing of one quintal of lint to yarn was ` 8, and gross returns obtained were ` 9, The net value added in spinning process was ` 1, per quintal of lint spinned. The total cost incurred in the processing of one quintal of yarn to cloth was ` 17, and the gross returns per quintal of yarn woven were ` 19, The net value added per quintal of yarn processed was ` 2, Damte et al. (2003) conducted their study on costs and returns of wheat, paddy and cotton in Punjab state on changes in variable, fixed and total costs and also changes in gross returns, returns over variable cost and net returns in the cultivation of wheat, paddy and cotton crops over different periods of time both at constant and current prices. The analysis indicated that the total cost of cultivation and returns of these crops have been increasing over the years. The total cost of cultivation of cotton per hectare at current prices has increased by per cent from ` 1, in to ` 3, in The total cost at current price increased further by per cent in over and fixed and variable costs by per cent and per cent, respectively during the same period. Gross returns per hectare at current price from cotton crop increased by per cent in over and returns over variable costs by percent. But net returns from cotton have declined by per cent over , which was due to drastic decline in the yield of cotton crops in the early eighties. Maharjan et al. (2003) carried out a study to measure the profitability of growing various crops in the Northern dry zone of Karnataka. The breakeven yield was computed to measure the profitability. The decision criteria are if the actual yield is beyond the breakeven yield, the farmer will start earning the profits and if the actual yield is below the breakeven yield the farmer incurs loss. Breakeven yields were relatively quite stable in HYV paddy, sunflower and cotton in kharif season, bengal gram and sunflower in rabi season, groundnut in summer season and sugarcane. The crops viz., HYV paddy, hybrid maize, sunflower and cotton in kharif season, hybrid wheat, Bengal gram, sunflower in rabi season, HYV paddy and groundnut in summer and sugarcane were profitable as the actual yield was greater than the breakeven yield for these crops over the years.

17 Goyal and Berg (2004) analyzed the marketed surplus response of cereals in Haryana State. A model that considers the effect of both factor and output prices on marketed surplus was used for this purpose. To derive input demand and output supply elasticities, the normalized quadratic profit function and demand equations were estimated jointly with the seemingly unrelated regressions (SUR) estimation technique using farm level panel data. The data confirmed the theoretical framework. The derived price elasticities of input demand, output supply, and marketed surplus have been simulated to examine alternative price policies for securing different levels of marketed surplus. Study revealed that at the observed price structure marketed surplus of wheat will increase almost equal to population growth, but in case of paddy it will grow at a very low rate. The study further revealed that besides price adjustment, technological improvement and non-price factors are also of critical importance for increasing output supply and, hence, marketed surplus. Murthy (2005) studied quality and non quality characteristic influencing price for vegetables in north Karnataka during the year A large number of variables found to be not only insignificant statistically but were also associated sign contrary to the expectation which suggest that price were not paid according to the scientific evaluation of quality characters. It was hypothesized that, the price depends on both qualitative and non-qualitative characters. The non-quality characters were area under crop, variety, soil type, time of harvest, time of sale and quality of information. Stepwise multiple regression functions were separately run for each selected crop. In order to overcome the shortcomings of eye-sight grading, the instrumental measurement of all the qualitative variables were used in this study. Eye-sight grades deviated from scientific grades quite significantly and that they do not reflect actual quality of vegetables. Testing of this hypothesis was accomplished on the basis of the instrument measurement of all quality variables used in this study. The coefficients of stepwise multiple regression analysis were compared with eye-sight grade and scientific grade. Scientific grading should be based on objective evaluation of quality aspects. Mohammad et al. (2007) analyzed the price responsiveness of wheat farmers in different agro-ecological in Punjab. Employing the multivariate cointegration procedure developed by Johansen (1988), the long-run relationship between wheat acreage and price incentives was examined. The impact of certain non-price factors such as irrigation and rainfall on wheat acreage were also studied.the Johansen multivariate cointegration approach indicates the presence of a cointegrating relationship in the supply response model. Inter-zonal comparison of supply response indicates different elasticities for each zone. Wheat acreage is significantly influenced by price of wheat, and other competing crops such as cotton and sugarcane. Among the non-price factors irrigation and rainfall has a positive effect on wheat acreage in the short-run. The wheat supply elasticities are found to be inelastic both in the short-and long-run. The long-run own-price acreage elasticities were 0.53, 0.46 and 0.49 in cotton, rice and mixed zones respectively. Vasisht et al. (2008) studied the price behaviour in fruits and vegetable markets using cointegration and error correction analysis techniques. The empirical results on the price behaviour provided evidence of high volatility in the prices of fruits and vegetables in major markets. There was a presence of long-run relationship across some of the state level markets for less perishable commodity like apple. The findings clearly indicated that the horticulture sector in India could thrive for greater benefit of both producers and consumers only if better infrastructural facilities like storage, modern marketing infrastructure, as well as timely availability of market information and better market intelligence are developed fast across all states. Rahman and Shankar (2009) studied on Profits, supply and HYV adoption in Bangladesh have investigated the manner in which price and non-price factor affect these three criteria, based upon a model of rational variety choice. The model is empirically implemented using translog profit functions and a switching regression framework, and applied to a cross-sectional farm-level dataset of Bangladeshi farms for the 1996 crop year. Results indicate that rice prices, land availability, irrigation, rural infrastructure, labour wages and prices of animal power services are important factors, while fertilizer price plays a marginal role. Given these results, the policy of liberalization of agricultural inputs (particularly fertilizers) and reforms to maintain high rice prices during harvest seasons appear sound since these allow producers to receive rice prices close to world levels without burdening the government with input subsidies. Result also shows that educated Bangladeshi farmers substitute their time inputs away from agriculture, resulting in lower HYV adoption, farm profitability and rice supply. Mohammad Aslam (2010) studied on acreage models for Basmati rice, both lagged sets of prices and lagged acreage variables are positively related to acreage of the crop while third variable of rainfall is negatively related to acreage of the crop. This may be due to the fact that acreage under

18 rice is largely canal irrigated and rain factor rather spoils the both due to excessive or untimely rains. The predictive power of the variables considered is quite strong as R 2 ranges between 86% t0 95%. In the case yield models, yield was found positively responsive to prices expect for the export price which is quite understandable. The factor of lagged yield is also positively related to current yield of the crop. The factor of fertilizer use is positively related to yield in the first and third model while it shows negative relation in the second model but its significance level is quite weak. Similarly, rainfall factor is also positively related to yield in the first and third equations while it shows negative response in the second model but its significance level is quite weak. The predictive power of the variables considered is quite strong as minimum value of R 2 is 93.5%. As is evident from above acreage equations for IRRI, only lagged acreage is found positively related to current acreage. Other two variables namely the three set of prices and rainfall are negatively related to acreage. Earlier rainfall was found negatively related to acreage of Basmati too. It looks that rainfall does not play a positive role in canal irrigated areas. But negative relation of IRRI rice acreage with three sets of prices particularly the procurement price does place a question mark on the efficacy of the Agricultural. 2.3 Profitability of cotton on different farm sizes Marothia (1974) studied the comparative economics of cotton with competing Kharif crops like groundnut maize and jowar with respect to both local and high yielding varieties in Khajuraho district of Madhya Pradesh. He noticed that larger quantities of strategic farm resources were used for cotton crop compared to the competing crops. High yielding variety of cotton was found to be the most profitable. Hiremath et al. (1984) estimated the gross returns, costs and profits per acre of major crops in Malaprabha command area and reported that the total cost of cultivation of Varalakshmi cotton worked out to ` 1, with a net profit of ` 2, and gross profit of ` 3, respectively. The net profit (` / acre) in hybrid maize was higher with a gross return of ` /acre and total cost of ` In the cultivation of hybrid Jowar, farmers obtained net profit of ` by incurring a total cost of ` /acre. Net profit from cultivations of local cotton was ` Cadre and Mahale (1988) worked out the per hectare returns from cotton comparing with non commercial competing crops in vidarbha region and reported that the cost of cultivation per hectare was ` 3, for hybrid cotton followed by Mung-safflower (` 1,984) and as sole crop (` 1,845). The same for desi improved cotton, mungram and mung wheat worked out to ` 1,541, ` 1,495 and ` 1,404 respectively. Net returns for hybrid cotton, desi improved cotton and mung wheat sequence worked out to ` 1,062, ` 888 and ` 948. Basavaraj et al. (1989) studied the Economics of cotton production in Karnataka. The actual productivity of cotton on sample farms was far below the productivity of cotton on demonstration plots. The actual yield of cotton ranged from kg per hectare on small farms of Dharwad district to kg per hectare. The cost of cultivation of cotton was ` per hectare. For Dharwad and Raichur districts, the estimated yield gap from attainable productivity was found to be 42.4 per cent and per cent, respectively. Ali and Choudhry (1990) attempted to measure technical, allocative and economic efficiencies in four irrigated cropping regions of the Punjab province of Pakistan by estimating deterministic and probabilistic frontier production function from whole farm survey data for the year The average technical efficiency ranged from 0.80 in the rice region to 0.87 in the sugarcane region, implying the existence of a per cent potential for increasing farmers income at the existing level of their resources. No significant difference in technical efficiency was found across the regions. The result further revealed that economic efficiency was similar across all cropping region except in the cotton region, which had significantly lower economic efficiency due to higher allocative inefficiency. The analysis indicated that the output due to allocative inefficiency ranged from 30 per cent to 47 per cent, while the profit loss amounted to about two per cent. Pandurangadu and Raju (1990) conducted a study on economics of pesticide use on cotton farms in Guntur district of Andhra Pradesh. They concluded that, an alarming rise in the cost of cultivation of cotton was largely attributed to the increased use of quite expensive insecticides. The pesticide alone accounted for nearly 20 per cent of the total cost of cultivation. Govindan and Ranganthan (1993) studied the cost of cultivation, gross and net returns and benefit cost ratio of cotton, under different density of plants and different fertilizer level. They concluded that fertilizer and labour alone accounted a lion share in the cost of cultivation. They

19 observed that it increased seed cotton yield of 3.4 quintal per acre and net returns of ` 2,680 was obtained under high plant density compared to the normal. Reddy et al. (1997) carried out a research on the comparative economics of cotton cultivation in Guntur district of district of Andhra Pradesh. Results indicated that the total cost of cultivation for cotton is positively associated with farm size, implying more adoption and accessibility of modern technologies for large farmers. Pesticides, labour and fertilizers were major cost components accounting for per cent of total costs. Similarly, productivity of cotton was directly related to farm size. Gross returns, net returns and input-output ratios were directly associated with farm size and were the highest on research farms by demonstration and sample farms. Mundinamani (2000) studied the economics of cotton production under rainfed conditions in Karnataka. His study showed that the variable cost (` ) alone accounted 81 per cent of the total cost of production. The fixed cost of ` 1784 accounted for only 18 per cent to the total cost of production. In variable cost labour cost accounted per cent and bullock labour cost accounted per cent. The total cost incurred by the farmers in marketing of cotton was ` The total cost of cultivation estimated in the study was ` 10, Mundinamani and Kunnal (2004) found that the average total cost of cultivation per hectare of cotton was ` 9, of which the variable cost was ` 7,674.89, accounting for per cent of the total cost of cultivation. The share of the fixed cost in total cost of cultivation was ` 1, accounting for per cent. The gross returns per hectare of cotton cultivation were ` 12, and gross returns per quintal of cotton were ` 1, The net returns obtained over total costs were ` per hectare and ` per quintal of cotton. Radha and Choudhary (2005) conducted study on costs and returns in cotton seed production vis-à-vis commercial production of cotton in Andhra Pradesh and revealed that the per acre total cost of production of cotton seed (` 74,412) was higher than that of commercial cotton production (` 26,461), of which human labour occupied the major share in both cotton seed production (53.86%) as well as commercial cotton production (19.03%). The operational costs of all the items were comparatively higher in seed production (` 68,101/acre) over commercial production (` 16,166/ acre). This was due to the additional operations like gap filling, rouging, emasculation, pollination, etc., involved in cotton seed production. Thus, the operational costs took the major share of 91 per cent in seed production as compared to 61 per cent in commercial production. Study revealed that seed production gives positive returns with the cost-benefit ratio of 0.29:1.00 when compared to commercial production (1.00:0.35). Ramasundaram et al. (2005) carried out a research on cost of cultivation of hybrid cotton under rainfed and irrigated conditions of central India and hybrid under rainfed conditions of south India and varieties under irrigated condition of north India. Study revealed that the per hectare total cost of cultivation of cotton varieties (` 25,358) was relatively highest in irrigated north India fallowed by rainfed hybrid cotton of south India (` 22, 637), irrigated (` 18,958) and rainfed (` 15, 640) hybrids of central India. The variable cost accounted for 83 per cent in south irrespective of ecosystem, while it was only 65 per cent for irrigated cotton variety cultivation under northern conditions. The fixed costs for central and south zones varied between 122 per cent, while for north it was 35 per cent. The main reason for high fixed cost was the exorbitant land values in the fertile indo-gangetic plans. Plant protection accounted for the major share (19% and 26%) followed by intercultural operation (15.45%) in southern rainfed farm samples and fertilizers and manures (14%) in case of others. The cost of cultivation per quintal ranged from ` 1,541 in irrigated cotton of north to ` 2, 148 in rainfed central. Though the cost of cultivation increased with irrigation availability, the associated high yields reduced the cost of production. Returns over total cost were the highest in irrigated hybrid (` 10,810) of central India followed by varieties (` 9,025) irrigated of north, rainfed hybrid (` 8,873) of south India and rainfed hybrid (` 4,448) of central India. Channakeshava and Patil (2006) studied performance and economics of Bt cotton hybrid compared to conventional cotton hybrid against major insecticides under irrigated ecosystem. The study was conducted at two locations namely Regional Agricultural Research Station (RARS), Raichur and at NeIahal village of Raichur. At both the locations, one acre of each hybrid viz., MECH- 184 Bt. and local popular hybrid NCSl45 (Bunny) were cultivated following recommended package of practices. Bt cotton hybrid recorded a gross total income of ` 32,994 and ` 52,434 in RARS and Nelahal village, respectively, while NCS-145 recorded a gross income of ` 22,968 and ` 37,710 in the above locations. The total cost including agronomic and plant protection inputs was ` 15,650 and ` 20,700 in MECH-184 Bt. and NCS-145 hybrids, respectively in RARS, Raichur whereas, it was `

20 16,550 and ` 21,150 in Nelahal village. Net profit was maximum in Bt cotton i.e. ` 17,344/ha and ` 35,884/ha in RARS and Nelahal village, respectively as compared to ` 2,268 and ` 16,560 in NCS-l45 hybrid cotton which works out and per cent more than popular hybrid under irrigated conditions. Gandhi and Namboodiri (2006) studied the Adoption and Economics of Bt Cotton in India the comparative information on the cost of production and returns of Bt and Non-Bt cotton across the different states. The total cost is higher for Bt cotton as compared to Non-Bt cotton. Even though the pesticide cost is substantially lower, the seed cost is substantially higher in all cases, often 2 to 3 times higher. Besides this several other cost, such as labour, tractor and irrigation are also higher. However, the value of output is also greater under Bt cotton in all states. As a result the net profit is substantially higher with Bt cotton in all statesthe human labour and fertilizers constitute a large share in the cost across the states. However, in Andhra Pradesh, the share of pesticides is very high. There is a significant reduction in the share of pesticide cost with the adoption of Bt cotton and the most dramatic reduction is seen in Tamil Nadu, where it drops from per cent to 8.29 percent. However, the seed cost in the same state increases from 5.82 per cent to percent. The share of pesticide cost remains very high at per cent even with Bt in Andhra Pradesh. Since with the impact on yield, the value of output, under Bt cotton is substantially higher, the profit as a per cent of the revenue is also substantially higher in Bt as compared to Non-Bt. In Gujarat, this increased from per cent to per cent and in the case of Andhra Pradesh, it increased from per cent to percent. Narayanamoorthy and Kalamkar (2006) conducted a study on is Bt cotton cultivation economically viable for Indian farmers Using the data collected from 150 sample farmers from two districts in Maharashtra, an attempt has been made to study the economics of Bt cotton cultivation. Study provides an in-depth analysis on costs of cultivation and profit. The result revealed that profit realised from Bt cotton crop is substantially higher than that of the non-bt cotton crop. While the average profit of the two districts comes to about ` 31,880/ha for Bt cotton, it is only about ` 17,790/ha for non-bt cotton crop, indicating a difference of about ` 14,090/ha. The profit realised by Bt cotton growers is nearly 80 per cent higher than that of non-bt cotton cultivators. Though the same trend is observed in both the varieties of Bt cotton, the profit is found to be higher with MECH 162 variety (` 34,560/ha) as compared to MECH 184 (` 30,173/ha). This is mainly because of the relatively higher productivity realised by the farmers cultivating MECH 162. Mahendra and Chandrasekhara (2007) studied socio-economic impact of Bt cotton. The cost of production in an acre of Bt cotton was 17 per cent higher in Bt cotton at ` compared to ` for non-bt cotton in the state. The cost A2, which mainly shows the paid out costs, was ` and was higher by 11.8 per cent over non-bt cotton.the expenditure on insecticides decreased by 18.2 per cent in Bt cotton over non-bt cotton. This decrease in cost on insecticides by ` 594 is more than matched by increased costs on seed (` 804), labour costs (` 801), fertilizers (` 86) and irrigation charges (` 45). The cost of seed increased by per cent from ` 598 in non-bt cotton to ` 1402 in Bt cotton, whereas labour costs increased by per cent in Bt cotton. Out of the ` 801 increase on labour, human labour accounted for the major portion viz., ` 676. The Bt. farmers on an average hired human labour more than non-bt. farmer by an amount of ` 303 and utilized family labour more intensively. Anonymous (2008) studied returns to Bt cotton vis-à-vis traditional cotton varieties in Gujarat state. Two districts were selected for study namely Rajkot and Varodara. Total of about 180 samples were collected including both Bt cotton and non Bt cotton. The cost of cultivation per quintal for Bt cotton and non Bt cotton were ` 875 and ` 1077 respectively. Thus the total cost of production of Bt cotton was lowered by ` 154/qtls. (14.24%). This shows that Bt cotton is more cost effective. Nithya et al. (2008) studied on cotton production in Karnataka growth rate analysis. The results reveal that growth in area & Production was negative, while productivity had a positive growth during pre introduction of BT cotton, during the post BT introduction period, all the 3 i.e. area, production and productivity of cotton showed a positive growth rate. Growth rates in area, production and productivity of cotton was computed for a period of 16 years from to The study Period was divided into two pre-introduction. ( to ) and Post-introduction of Bt cotton period ( to ). Growth rates were computed using simple and exponential growth function of the firms. Both the varieties, the large farmer group earned greater profits than the small farmer group.

21 Hugar et al. (2009) analysed productivity difference between Bt. and non Bt cotton farms in Karnataka. The study was carried out to assess the effect of Bt cotton technology on output and efficiency of inputs used in cotton cultivation in Karnataka state of South India during The modified Cobb-Douglas production function was used to estimate the influence of factors. The production function estimates have clearly indicated that the chosen factors of production have significantly influenced the production of cotton both in Bt cotton and non Bt cotton farms by 64 per cent and 70 per cent, respectively. The sum of output elasticities in the case of Bt cotton production (1.03) was more than one, indicating an increasing return to scale which was mainly due to significant influence of plant protection chemicals, organic manure and inorganic fertilizers. The increasing returns to scale clearly revealed that there is scope to increase the Bt cotton production by increasing the above inputs. In case of non Bt cotton farms, the sum of output elasticities (0.52) was less than one, indicating a decreasing return to scale. However, plant protection chemicals had significant positive influence in Bt cotton production while it was negative and non-significant on non-bt Cotton farms. Further, the ratio of MVP to MFC being 0.98 clearly indicated that the plant protection chemicals and other inputs were almost optimally utilized by Bt cotton farmers. In the case of non Bt cotton farmers, not only the regression coefficient of plant protection chemicals was negative (-0.15) but also the ratio of MVP to MFC was negative (-0.47) and less than one and clearly indicated that plant protection chemicals were used excessively by non Bt cotton farmers, resulting in lower returns. A study on the yield gap analysis was carried out in cotton in ankola district (Anonymous 2010). Results on correlation coefficient between the yield gap and input use gaps revealed that the total effect of nitrogen fertiliser was found to be the highest (0.664) on small farms. However, indirect effect of this 'via other inputs' (0.328) brought down its direct effect (0.228) marginally. In the medium type of farms, the potassic fertiliser input was found to be the most important variable conditioning yield gap as indicated by its higher correlation coefficient (0.385). Similarly, human labour influenced the yield gap more through its indirect effect (0.483) than its direct effect (-0.110). In the large category of farmers, machine labour with correlation coefficient (0.484) was found to be the most important variable conditioning yield gap followed by bullock labour (0.439). Machine labour influenced the yield gap more through its direct effect (0.501) than its indirect effect (-0.01). The total effect of bullock labour was the highest with a correlation coefficient of Thus, by reducing the observed input use gap between the farmers' fields and demonstration plots, the yield gap can be minimised to a greater extent. Daniel et al. (2010) carried out net income analysis and efficiency of resource use among cotton farmers in the Southern part of Adamawa State, Nigeria. For study purpose One hundred and twenty farmers were sampled using multiple stages, purposive and simple random sampling techniques. Result revealed that the average cost and returns per hectare of the cotton farmers was N46, and N56, respectively. This showed a profit of ` 10, per hectare. The R 2 of 0.86 of the regression model shows that 86 per cent of the farmers income is being explained by the variables included in the model. Land, labour and seed have positive influence on farmers income and the first two variables being significant at 1 per cent and 10 per cent levels, respectively. Fertiliser, chemical and transportation had negative influence on farmers income probably due to their escalating prices. The marginal physical product analysis revealed that an extra hectare of land acquired for cotton will result to an increase of over one tonne of cotton ceteris paribus. None of the resources used by the farmers was however efficiently, utilized. Manjunatha et al. (2010) carried out a study on Yield and yield components, uptake of nutrients, quality parameters and economics of Bt cotton (Gossypium hirsutum L.) genotypes as influenced by different plant densities. A field experiment was conducted during kharif, 2008 at Agricultural College Farm, Raichur to study the performance of Bt cotton genotypes to different plant densities under rainfed condition. The results revealed that among genotypes, Bunny Bt. BG-II recorded significantly higher uptake of nutrients (105.5, and kg ha-1; N, P and K, respectively) and also exhibited superior quality parameters with higher net returns (` 39,152 ha-1), lower cost of cultivation (` 20431) and BC ratio (2.91). Puran et al. (2010) studied economic profitability and adoption of Bt cotton and non-bt cotton in North India. The study was based on primary data collected from farmers through personal interviews in Haryana and Punjab states of North India. For collection of data, multi-stage sampling technique was used. From each state 100 farmers were interviewed, thus total 200 farmers were selected. For profitability analysis, partial budgeting tool is used which is a method of making a comparative study of costs and returns which results from a change in a part of the farm business. With Bt cotton, additional cost was mainly from increased use of seed, fertilizer, irrigation and picking

22 to the extent of ` per acre. On the other hand, there was reduced insecticide cost by nearly about 26 per cent cost of total increased cost with Bt cotton. But Bt. farmers had higher yield, thus they realised about 50 per cent more returns than non-bt cotton which compensate all increased cost. Thus with Bt cotton farmers got higher net return i.e. ` per acre. Kiresur and Ichangi (2011) studied on Socio-Economic Impact of Bt Cotton - A Case Study of Karnataka to analyse the input utilization pattern, productivity and cost-return profiles of Bt and non-bt cotton production. Results revealed that the average expenditure on seeds was higher (` 3718/ha) in Bt cotton than in non-bt cotton (` 2550/ha) farms, largely due to higher cost of Bt cotton seeds. It may be mentioned that Bt cotton (hybrid) seeds were initially sold at a price (` 1,650/450 gram), which was five-times that of the local hybrid variety DCH-32 (` 300/450 gram).the quantity of organic manure (tonnes) used in Bt (6.5/ha) and non-bt (6.7/ha) farms was almost same. But, the cost incurred on chemical fertilizers and organic manure was higher in non-bt (` 2605/ha) than Bt (` 2502/ha) farms. Similarly, the use of organic manure and chemical fertilizers was higher for non-bt cotton than Btcotton by about 0.2 t/ha and 27 kg/ha, respectively. There was a significant difference in expenditure on plant protection chemicals (PPC) between Bt (` 6369/ha) and non-bt (` 4394/ha) farmers. However, the expenditure on PPC to 11.6 per cent of the total cost as compared to 16.2 per cent by non-bt farmers. The net returns over cost-d were much higher from Bt-cotton production (` 30618/ha) than from non- Bt cotton (` 12189/ha), accounting for an increase of 151 per cent. Across farm-size categories, the net returns per ha varied between ` and ` for Bt cotton and ` to ` for non-bt cotton. The higher profitability of Bt cotton was also reflected in terms of benefit-cost ratio (1.83 in Bt cotton versus 1.31 in non-bt cotton). Thus, the additional return to Bt over non-bt was estimated at ` 15791/ha. The corresponding additional cost being negative (-` 2631), the net additional benefit from cultivating Bt cotton worked out to be ` Pavan kumar (2011) studied economics of Bt Cotton cultivation a comparative analysis across different farm sizes in northern transitional zone, Karnataka Cotton growing farmers. The results found were that the total variable cost of Bt cotton was ` where large farmers incurred high cost i.e. ` The total cost of Bt cotton was ` and it was high in large farmers ` The net return in Bt cotton was ` The yield per ha was qtls and medium farmers got high yields i.e qtls. The Cobb Douglas production function, revealed that the small farmers were underutilized all the inputs (land, seed, FYM and human labour, bullock labour, PPC, fertilizer), where as medium and large farmers were over utilized the resources bullock labour and PPC 2.4 Pace and pattern of arrivals and prices The seaonlaity of arrivals was found to have an impact on commodity prices in the short period and further it could be expected that the larger arrivals would depress prices in certain months of the year, while in other months, the low arrivals would boost up the prices. This effect might be offset in the longrun by some other factors depending on their strength. This section reveals the results of earlier studies showing in relationship between arrivals and prices in long run and short run periods. Singh et al. (1995) studied the seasonal variation in arrivals and prices of wheat based on the secondary data obtained from four agricultural produce regulated markets of Bihar. The study indicated that the farmers in the wheat growing areas of the state bring a substantial part of their produce for sale in the market during post-harvest months (April to July) and get a comparatively lower price for their produce. The wheat arrivals are more sensitive to their ruling prices in secondary markets than that of primary markets. Upender and Manohara Chary (1996) while analyzing market arrivals and prices of paddy in regulated agricultural markets pointed out that, in the three markets selected for the study, the maximum quantity of market arrivals of paddy were observed during the peak period probably due to distress sale by farmers having no post-harvest withholding capacity. The trend values of arrivals and paddy exhibited significant increase over the years in the three agricultural markets i.e.,karimnagar, Jammikunta and Vemulawada in Andrapradeash over time as a result of increase in productivity and production of paddy. The extent of variability in the market arrivals was found to be higher than in the prices of paddy in all markets selected for the study in Jammikunta and Vemulawada agricultural markets, in particular, the price elasticities of market arrivals of paddy were not only positive but also more than unity indicating that price response was very high. On the contrary, in Karimnagar market the price elasticity of market arrivals was positive but less than unity showing that price response was poor. The positive price elasticity of market arrivals reflected the price consciousness of farmers. With

23 a rise in the prices of agricultural products, farmers were tempted to dispose off more and retain less resulting in higher quantity of arrivals in regulated markets over a period of time. Nawadkar et al. (1999) reported that co-efficient of variation of arrivals (22 to 79%) and prices (30% to 55%) of cabbage in Pune regulated market from to were found to be higher. Similar trend in arrivals (31% to 69%) and prices (24% to 54%) was observed in cauliflower. The compound growth rates of arrivals and prices (2.20%) of the cole crops were significant in the same period the seasonal indices of prices and arrivals of both the vegetables were inversely related and prices of both the vegetables showed an increasing trend indicating good market integration for these vegetables. Mehta (2000) analyzed the seasonality in prices of groundnut and maize for the period of to The results showed linear trend in maize prices. The oscillatory movements affecting the prices are regular in period and amplitude. There exists a crop production periodicity of 12 months seasonality. Seasonality index ranged between implying that its supply and consumption were nearly equi- spread throughout the year. Steep price fall after September synchronized with crop attaining maturity in three months after sowing. In case of groundnut, the results showed moderately inceasing trend, the periodic variations were of non-uniform cycle and amplitude. The long-term price behaviour was approximately linear and the cyclical trend is less pronounced. Ravi Kumar et al. (2001) collected the data for the study for the period of to and the study concluded that, in general, arrivals showed mixed trend, whereas, prices showed an increasing trend for the selected commodities in Anakapalle regulated market of Andhra Pradesh. There existed an inverse relationship between seasonal indices of arrivals and prices of selected commodities. Therefore, the policy implication lies in encouraging the farmers to dispose off their produce at the opportune time to get good remunerative prices. Sanjaykumar (2003) studied relationship between arrivals and prices of onion in selected markets of India from The results revealed that the arrivals fluctuated to a great extent and prices had a tendency to rise in all the markets during the study period. The correlation coefficients between yearly arrivals and prices of onion were found to be negative and significant over the years in most of the markets. This indicated inverse relationship between market arrivals and prices. Wadhwani and Bhogal (2003) analyzed observed price behaviour of cauliflower and cabbage in Western Uttar Pradesh ( ). The results showed that, the prices of these two vegetables were found maximum in the month of September and started declining from October onwards. The prices were again found increasing from the month of May. The lowest prices were indicated in the month of March and also prices of cauliflower/cabbage responded negatively to the arrivals. Hiremath (2004) collected the data on monthly prices and arrivals of DCH-32 cotton for a period of to The study revealed that for DCH-32 cotton kapas in Hubli market, the seasonal indices was the lowest in the month of September and highest in the month of December. The seasonal index was below 100 during the months from April to August. Lavleen et al. (2005) analyzed the cyclical variation of arrivals and prices of tomato in Punjab from by employing Fourier analysis followed by periodo gram analysis to estimate the hidden periodicity along with amplitude in the cycles. The periodo gram analysis of time series of supply and prices of tomato showed that it followed regular cycles, seasonal within 12 months and cycles of longer duration viz., Kitchin cycles for arrivals with periodicity of 3 years and Jugar cycles for prices with a periodicity of 5 years. Navadkar et al. (2005) in their study on seasonal indices of monthly arrivals and prices of vegetables in Pune ( ) observed lowest coefficient of variation of arrivals for tomato and it was more than 50 per cent during remaining months. Whereas, the price was highest during the month of March and below 50 per cent during April to June. In case of bhendi, the coefficient of variation of arrivals was far below 50 per cent for the period from April to October, while it was more than 50 per cent in all the months except in November and May. It was noticed that the coefficient of variation ranged from per cent for arrivals and for prices these were in the range of per cent for cabbage. While for cauliflower the same were 31 to 69 per cent and 24 to 54 per cent respectively. Furthermore, it was indicated that when the arrivals of vegetables were at the higher side, the prices are at the lower side.

24 Khunt et al. (2006) analyzed the seasonal indices of potato in Ahmedabad during The results of the study indicated that the highest index of arrivals was observed in the month of March. The price index of potato was lowest in the month of March when the corresponding arrival was highest. The price index was below average (100) from January to May and above average from June to December. The season was the pattern of market arrivals. Yogish et al. (2007) in their study concluded there was a mixed trend in arrivals and prices of potato in all selected markets. The data pertaining to the study was collected for a period from to The monthly seasonal indices for arrivals of potato, onion, ragi and groundnut were found higher immediately after the harvest in all the study markets and the price indices found maximum during lean period and minimum during harvesting period. Hence, the dissemination of information on market arrivals, prices prevailing in the market, crops to grown to the season etc. will result in maintaining uniformity in supply and demand of the produce. Manasa (2009) analysed the long term and short term variations in prices and arrivals of pigeonpea in Bidar, Bellary, Gulbarga and Sedam markets in Karnataka. Monthly data on arrivals and prices were collected for the period of to All markets were showed fluctuations from year to year and showed an increasing trend for both arrivals and prices. Kerur et al. (2010) Physical and financial performance of selected regulated markets in Karnataka. The study was conducted in regulated markets of Karnataka. Jowar from cereals, groundnut from oilseeds and from fibre cotton was selected for the study. Study was extensively used the secondary data for the analysis. The averages and compound growth was computed to ascertain the growth performance of the regulated markets. The average number and percentage of market functionaries were worked out for Ranebennur, Bagalkot, Raichur and Gulbarga markets for five years. The average number of market functionaries in Ranebennur market for the period of five years was about in number. Among the different market functionaries, the commission agents accounted for per cent followed by traders (17.99%). The overall growth of market functionaries in Ranebennur market was decreasing trend, mainly due to decline in other market functionaries and arrivals of cotton. This means that drop in arrivals in cotton had adverse impact on growth of the market. The overall growth of market functionaries in Ranebennur market was decreasing trend, mainly due to decline in other market functionaries and arrivals of cotton. This means that drop in arrivals in cotton had adverse impact on growth of the market. Similarly in Bagalkot market for the period of five years was about 1031 in number. Here the traders accounted for per cent followed by hamals (18.23%). A rapid drop in processors was observed. The overall growth of market functionaries in both markets for a period of five years showed an increasing trend. Cotton in both market i.e., Ranebennur and Raichur. The estimated compound growth rate was per cent in Ranebennur market and per cent in Raichur market. 2.5 Marketing costs, margin, price spread and marketing efficiency Marketing efficiency is measured based on the information on marketing costs and margins. Many a time, the efficiency of a market over a period of time and pace as well, are compared and contrasted with the help of these indicators. Some of the useful studies are briefly reviewed with a view to have an idea about the nature and magnitude of marketing costs, margins and channels followed for disposing of the commodities. Katoria and Mehta (1969) conducted a study of cotton marketing at Katakapur market in Bhatinda district. The study revealed that the cotton production were able to get per cent of the retail price when they sold their output through the Co-operative Marketing Society contrasted with only per cent when the produce was marketed through aphasias. Pawaskar and Radhakrishnan (1970) evaluated the performance of the present marketing system for raw cotton on the basis of marketing costs in Dhullia and Jalagaon districts of Maharashtra. They found that the farmers share in the final price of raw cotton (Seed Cotton) was high as 90 per cent and the cost was accounted for marketing costs. The gross returns to the cotton merchants averaged only 3-4 per cent of the aggregate sale and the net returns mere very low. They concluded that the existing system of marketing of cotton was more efficient than what its cities considered. Ramaswamy (1970) undertook the study of profit margins in the marketing of cotton in Gadag market, in Karnataka. He found that the sale of cotton after its processing could increase the price made available to the produces to the extent of ` 25.7 per Candy (360 kg) as the outright minimum.

25 He therefore suggested that processing units should be established in the vicinity of markets to enable the farmers to connect the cotton into limit before its actual sale. Chidambaram et al. (2003) studied the marketing efficiency of the channels used for grey cotton fabrics manufactured in power loom units in selected areas of Coimbatore District. The manufacturers of grey cotton fabrics select the relevant distribution channels to make the product available at the right time. Besides selling directly to the buyers, the manufacturers use the following alternative channels to market their fabrics on a national level. Channel I Manufacturer-wholesalerretailer-consumer. Channel II Manufacturer-broker-retailer-consumer. Channel III Manufacturer-agentwholesaler-retailer-consumer. It is said that marketing is efficient, if the total marketing margin is reduced for a given marketing cost. It is observed from the study that the marketing efficiency indices in small units were 42.16, and for channels I, II and III respectively. Similarly the marketing efficiency indices in medium units in respect of channel I, II and III were 41.86, and Considering the indices in all the three sizes of units, marketing channel I is stated to be more efficient than the other two channels, namely channel II and III. In addition, the results of shepherd s method, Acharya and Agarwal s method and composite index method also confirm that channel I has more marketing efficiency than channel II and III. Verma et al. (2004) examined the price spread marketing efficiency and constraints in marketing of onion in Indore district of Madhya Pradesh. The results revealed that the producer received maximum share of consumer s rupee in channel-i (97.33%) followed by channel-ii (72%) and channel-iii (58.12%), the highest share in consumer s rupee was obtained by the farmers in channel-i as there was no intermediary between the producer to consumer. In II channel the producer received only 72 per cent of consumer s rupee and retailer received per cent of consumer s rupee. In channel-iii, the farmers revived still less i.e per cent of consumer s rupee. The share of wholesaler and retailer were 5.00 and per cent of consumer s rupee, respectively. The producer s share was less in channel-ii and III as producers were located at large distance from market place. The intervention of market intermediaries has reduced the producer s share in consumer s rupee. Chauhan and Chabra (2005) conducted a study on production, marketed surplus, disposal channels and price spread of maize cultivation in Hamirpur district of Himachal Pradesh. The results revealed that farm level marketable surplus of maize comprised of per cent of the total production. About two-thirds of the marketable surplus of maize was disposed of by about 72 per cent of the farmers in first quarter of harvesting period viz. October to December. Producer-local traderwholesaler/commission agent-processor/consumer had been the main marketing channel accounting for about 72 per cent of the marketed surplus. Singh (2005) examined the existing system of marketing of agricultural commodities in India; the extent of state intervention; and the factors impacting marketing efficiency. It also draws policy implications to improve marketing efficiency and reduce the need for a large-scale state intervention in different states. The crops covered in this study were: rice and wheat in Haryana, Punjab and Uttar Pradesh; rice and groundnut in Andhra Pradesh; rice and jute in Assam; and cotton, sugarcane and onions in Maharashtra. The study also examined the level of marketed surplus and the prices received by farmers by farm size; the share of public and private agencies in the marketed surplus; the price spread of individual commodities; and the spread of the marketing season. The study pertained to the agricultural year in all the states except in Punjab ( ). Among all the states, market intervention was very high in the marketing of rice and wheat in Haryana and Punjab and rice in Andhra Pradesh. The procurement of wheat and rice had been also going on for quite some time in Uttar Pradesh. The prevailing system of marketing and the extent of state intervention varied considerably in the case of the three study crops in Maharashtra. The monopoly procurement scheme for cotton in Maharashtra had accumulated a huge amount of losses. The marketing of rice in Assam threw light on how the system of marketing of rice in the state differed from that of other states and whether the farmers were able to receive minimum support prices. Naphade and Tingre (2008) studied the price spread in marketing of guava in Buldhana district of Maharashtra. They identified three channels of guava marketing. 1) Producer Wholesaler Retailer Consumer 2) Producer Retailer Consumer 3) Producer Consumer

26 The result of the study observed that the price spread in channel-i was ` 170, in channel-ii it was ` 113 and for channel-iii, it was ` 70. Marketing of guava was mostly done by channel-i. Anchal and Sharma (2009) conducted a study in the sub mountainous region of Gurdaspur district of Punjab and identified the following three channels for marketing of litchi. 1. Producer Pre-harvest contractor Retailer Consumer (Local market) 2. Producer Pre-harvest contractor Retailer (through commission agent) Consumer (Amritsar market) 3. Producer Pre-harvest contractor (wholesaler) Retailer (through commission agent) Consumer (Delhi market) Of the three channels, the first one was found to be the most efficient as the producer could get as high as per cent of the consumer s rupee. The price spread was ` for local market, ` 1,126 for Amritsar market and ` 1664 for Delhi market. Kerutagi (2009) conducted a study on sapota marketing in Belgaum and Dharwad districts of Northern Karnataka. He identified the following two channels of sapota marketing. i) Producer Commission agents Retailers Consumers ii) Producer Pre-harvest contractor cum wholesaler Retailers Consumers The producer s share in consumer s rupee in channel-l was higher (59.58 per cent) than in channel-ii (48.14 per cent). Similarly, the price spread in channel-i was ` 2500 (26.32%), in channel-ii it was ` 4,000 (42.11%) indicating higher efficiency of channel-i. Sindhu et al. (2011) the maximum quantity of green peas was sold by the growers in the wholesale market (about 89%) and the rest was sold at the farm, in the village and in Apni Mandi. The marketing of green peas has been studied by three supply chains, viz. I: Producer wholesaler (through commission agent) retailer consumer; II: Producer retailer (through commission agent) consumer; III: Producer consumer. The net price received by the producer was 67 per cent, 69 per cent and 94 per cent in supply chains I, II and III respectively in the Hoshiarpur market in January, The producer s share in supply chain III was the maximum because of direct sale by the producer to the consumer. The supply chain III has been found most efficient because its marketing efficiency was as compared to 2.70 in supply chain II and 2.38 in supply chain I. The low marketing efficiency in supply chain I was on account of a higher number of market intermediaries in this chain. The functional analysis of the factors affecting the marketing efficiency has revealed that with one per cent increase in marketing margins and costs, the marketing efficiency declined by 0.45 per cent and 0.44 per cent, respectively. The modern market infrastructure may be built up with the public-private partnership to bring efficiency in the marketing of green peas. Sindhu and Singh (2012) study was conducted on marketing efficiency of Brinjal under different supply chains in Punjab of Jalandhar district to analyse the price spread, marketing efficiency and farmer s share in consumer rupee in various supply chains. The price spread was worked out by using the mode method. The marketing efficiency was worked out by using Acharya s modified method. The price spread of brinjal in supply chain I in Jalandhar market brought out that the net price received by the producer was ` 586 per qtl which was about 59 per cent of the consumer s price. The expenses borne by the wholesaler and retailer were about ` 78 and ` 44 per qtl. The margin of the wholesaler was less. The marketing efficiency under different supply chains has been worked out. Supply chain III was the most efficient one because marketing efficiency was (producer to consumer) in this chain as compared to 1.78 in supply chain II (producer-retailer (through commission agent)-consumer) and 1.67 in supply chain I (producer to wholesaler-retailer-consumer).the low marketing efficiency in supply chain I was on account of more number of market intermediaries in this chain. The foregoing studies indicated that the channel selected for disposing of the produce plays a crucial role in influencing the magnitude of marketing costs and margins. The studies documented difference in producer s share in consumer s rupee crop to crop over time and pace. The producer s share in consumer s rupee was found to be maximum where the produce was sold directly to itinerant merchants/consumers.

27 2.6 Constraints in cotton production and marketing of cotton The farmers often face various problems at different stages of production as well as marketing. Specially in the recent past, many of the farmers faced the problem of credit who were having mounting debt as they could not recover even the cost of cultivation on account of the problem faced by them. Some of the studies pertaining to various constraints faced by the farmers are reviewed here under briefly. Umapathi et al. (1994) studied the quality characteristics of cotton in Karnataka and found that colour, staple length, staple strength and fineness have significant positive effects on the price of cotton kapas. The cotton with higher staple length had a higher demand and the study concluded that price and quality characteristics are closely related. Brithel et al. (2000) analysed economics of integrated pest management (IPM). A sample of 40 household was randomly drawn from Ashta village to collect information on cotton cultivation practice with an emphasis on plant protection. Partial budgets were prepared to assess the economic feasibility of IPM. The IPM appeared to be an effective alternative to chemical pest control. The IPM package implemented on the farmers field was bio-intensive in nature with bio-control agents and cultural control as major components. This would reduce the pesticide use to almost zero and without having any adverse effect on crop yield. Chitnis and Kothiklane (2000) reported the constraints of non-availability of inputs (19%), phenomena trap (14%), abiotic agents (12%) and non-availability of IPM lab at taluk level. Kadam et al. (2000) conducted a study on constraints in marketing management of oranges faced by farmers in 1998/99 in Amravati district, Vidarbha region, Maharashtra, India. Results indicated that a majority of farmers had a medium level of marketing management men constraints education, land holding, socioeconomic status, management orientation, achievement motivation, mass media exposure and knowledge emerged as the important factors which affected marketing management. Major constraints reported by farmers were absence of pre-cooling centres, absence of cold storage centres, high transportation cost and lack of processing units. Mahesh (2000) studied the constraints in the tea exports using the Garrett s ranking technique. The results revealed that the lack of superior quality fronts in international market (phytosanitary measures), lack of export promotional measures and existence of non- tariff barriers were the main constraints faced by the exporters in the export. Besides exim policy curbing teas imports, lack of infrastructural facilities, packaging and processing technologies were other constraints in the tea exports. Santakumar and Dandapani (2000) studied frequency, intensity and determinants of pesticide use in rainfed cotton, by using farm level cross sectional data from Nanded district of Maharashtra. Average pesticide use was 3.2 Kg active ingredient per hectare of cotton area. Farmers also used the number of cultural and physical methods directly or indirectly to limit the crop loss due to pest and diseases. The attitude of farmers towards insect pest risk varied and accordingly use of pesticides. Risk adverse farmers used pesticides excessively and indiscriminately. Findings suggested that improving existing stock of knowledge on pests and management practice could help reduce pesticide use. Chulaki (2001) identified the problems faced by seed growers in production and marketing of hybrid cotton seeds in Northern Karnataka. The problems faced by the farmers were classified into production and marketing problems. Among production problems, non-availability of skilled labour and non-availability of financial assistance were common. Jikun and Ruifa (2001) conducted a study on Small holders, transgenic varieties and production efficiency; the case of cotton farmers in China. They tried to measure the effect of genetically modified cotton varieties on the production efficiency of small holders in farming communities in China. Authors reported that the adoption of Bt cotton varieties lead to a significant decrease in the use of pesticides. Hence, Bt cotton appears to be an agricultural technology that improves both production efficiency and protects the environment. In terms of policies, findings of the study suggest that the government should investigate whether or not they should make additional investments to spread Bt. to other cotton regions and to other crops. Gaddi et al. (2002) in a study on yield gap and constraints in cotton production in Karnataka reported that Non-availability of labour during weeding and picking seasons was a major problem as expressed by three-fourths of the respondents. More than 70 per cent of the sample farmers opined

28 that, the incidence of pests and diseases like bollworm, whitefly and leaf reddening prevented them from achieving greater farm potential in cotton. The proportion of sample farmers expressing their difficulty in obtaining the operating fund was high (71.25%) more than 40 per cent of the sample farmers expressed their dissatisfaction towards the germination quality and cost of seed, quality and cost of fertilizers and plant protection chemicals. More than 40 per cent of the sample farmers were not aware of recommended spacing and seed rate, dose and schedule of application of chemical fertilizer and plant protection chemicals. Growing of cotton on the unsuitable soils as a factor hindering productivity was reported by one-third of the sample farmers. Kulkarni and Kunnal (2002) identified the constraints in production, marketing and processing of soybean in Belgaum district. It was observed that severe problem faced by growers was rust disease leading to heavy loss followed by high labour wages and non- availability of quality seeds. In marketing, farmers experienced problem of price fluctuation, low price for the produce, problem of transportation and delayed payment when produce was sold out to co-operative society. The other problems like inadequate power supply and non-availability of labour in times were faced by the processor. Nageswara Rao (2003) seeks to highlight the problems faced by cotton growers in marketing their produce during post-harvesting period in coastal Andhra Pradesh. The findings of the study revealed that out of the 400 respondents of the study, 176 or 44 per cent had 15 to 20 years experience in the cultivation of cotton. Three respondents were having more than 20 years experience. Many respondents sold their produce in their own village. A significant proportion of respondents sold their produce immediately after harvesting due to lack of withholding capacity. A few respondents expressed problems like inadequate storage, pressure from creditors, fear of slump in prices, deterioration in quality and loss of weight. It was found that storage is one of the major problems of the respondents. Due to lack of storage facility, about 80 per cent of the respondents stored their produce in their own residences. It is further, found that many of the respondents sustained a loss due to inadequate storage facilities. No single respondent felt that prices were very attractive. A majority of the respondents felt that prices were generally low. From the above findings, we can conclude that there are several problems being faced by cotton growers and as a result, they are unable to market their produce to their satisfaction. Qaim and Zilberman (2003) reported the results of data from three Mahyco Bt. hybrids along with their counterparts and a local check grown on 157 farms in 25 districts of Maharashtra, Madhya Pradesh and Tamil Nadu. On an average, Bt. hybrids required three times less sprays to control bollworm than non-bt. hybrids and local checks (Bt., 0.62; non-bt., 3.68; local check, 3.63). The number of sprays against the sucking pests was, however, same among the three. Insecticides sprayed on Bt cotton were lower by about 70 per cent both in terms of commercial products and active ingredients. More interestingly, the article reported higher average yield of Bt. hybrids exceeding those of non-bt. counterparts and popular checks by 80 per cent and 87 per cent, respectively. Analysis of the results showed that the general germplasm effect was negligible and the yield gain was largely due to Bt. gene itself. Bheemappa et al. (2004) studied the constraints faced by cotton farmers in Tungabhadra command area. Results of the study revealed that problems like non-availability of quality seeds, low yield potential, susceptibility of recommended hybrids to pests and diseases, poor quality of produce and high cost of seeds were the major reasons for partial adoption of recommended cotton production technology. The lack of knowledge, not sure of timely canal water availability, non-suitability of the recommended practice, costly to adopt, not convinced with the recommendations, larger holdings, labour problem, poor quality inputs were the other reasons for partial and non-adoption of recommended practices with respect to seed rate, chemical seed treatment, spacing, nutrient management, application of pesticides and weedicides and irrigation management. Shelke and Kalyankar (2004) studied the constraints in the transfer of technologies in cotton production and identified that lack of appropriate, low cost and locally suited agricultural technology, lack of awareness and sufficient knowledge about improved technology, lack of viable resource management technology like implements for seeding and fertilizer application and lack of specific package of practices were the major constraints. Udikeri et al. (2004) conducted a study in Dharwad and Raichur districts of North Karnataka to identify the implications and understand the constraints limiting the transfer of technology. The results confirmed that improved cotton production technologies (new hybrids/varieties, pest management and improved agro-technologies) have brought significant transformation in cotton

29 scenario of the country, Hence, transfer of technology played pivotal role, which ultimately help in enhancing the production and profitability and also impart stability and sustainability in the agroecosystem. Verma et al. (2004) analysed the constraints in production and marketing of onion. The selected farmers were contacted through opinion survey for analyzing the constraints in production as well as in the marketing of onion. They expressed number of constraints, that the high price of seed, fertilizers, pesticides and fungicides were the main problem expressed by per cent of the sampled onion farmers in production followed by non-availability of funds from institutional sources per cent, high wage rate of labour per cent, non-availability of good quality of seed 65 per cent and ignorance of severe infestation of insect pest disease control per cent non=availability of adequate storage facilities onion was the main problem expressed by per cent of sample farmers who could not store onion on their farm due to lack of storage structure followed by price fluctuations per cent. Bhavani and Thirtle (2005) studied the pesticide productivity and transgenic cotton technology on the South African smallholder farmes. The cross-sectional data used in this study related to the season and were obtained from a sample of 58 Bt. adopters and 33 non-bt. farmers. The study reported that farmers over-used pesticides. The transgenic technology benefited farmers by enabling large reductions in pesticide use, the econometric evidence indicated that non-bt. smallholders in South Africa under-used pesticide. Thus, the main potential contribution of the new technology was to enable them to realize lost productivity resulting from under-use. By providing a natural substitute for pesticide, the Bt. technology enabled the smallholders to circumvent credit and labour constraints associated with pesticide application. Thus the same technology that greatly reduced pesticide applications but only mildly affected yields, when used by large-scale farmers in China and elsewhere, benefited South-African smallholder farmers primarily via a yield-enhancing effect. Christain et al. (2005) reported that cotton growing farmers in Vadodara district of Gujarat had faced the major problems like non availability of timely training on IPM (100%) and lack of skilled labours (70%). Similarly the non-availability of plant production appliances, bioagents in time (47.5%) and high cost of plant protection inputs (38.33%) were the other constraints in the adoption of IPM practices. Janvry and Matin (2005) analysed the effects of insect-resistant Bt cotton on pesticide use and agricultural productivity in Argentina. Based on farm survey data, it was showed that the technology reduced application rates of toxic chemicals by 50 per cent, while significantly increasing yields. Using a damage control framework, the effectiveness of Bt. versus chemical pesticides was estimated and technological impacts were predicted for different farm types. Gross benefits could be the highest for smallholder farmers, who were not currently using the technology. The durability of the advantages was analyzed using biological models to simulate resistance development in pest populations. Rapid resistance build-up and associated pest outbreaks appeared to be unlikely if minimum non-bt. refuge areas were maintained. Thus, promoting a more widespread diffusion of Bt cotton could amplify enhance the efficiency, equity and environmental gains. They cautioned that conclusive statements about the technology s sustainability, however, required longer-term monitoring of possible secondary effects and farmers behavior in maintaining refuges. Fuat and Dilek (2006) Using a standardized questionnaire, farm management and pest management practices in cotton production were analyzed on the basis of information gathered from 100 farmers in two provinces, Adana and Kahramanmaras of East Mediterranean region of Turkey. Insecticide use varied considerably between two provinces. The average level of herbicides and fungicides use was slightly higher in Kahramanmaras than in Adana. The mean level of pesticide use, as a total active ingredient, in Adana (2.69 kg/ha) was more than twice of Kahramanmaras (1.20 kg/ha). The share of pesticides in the variable cost was per cent and 1.14 per cent in Adana and Kahramanmaras, respectively. Shifting from conventional cotton production to organic production was considered to increase the producers income and reduce the possible environmental and health hazards. Vasant and Namboodiri (2006) carried out a study on adoption and economics of Bt cotton in India. The results on the subjective assessment of Bt. versus Non-Bt cotton by Bt cotton growers indicated that by and large the farmers find no difference in the availability of seeds, fertilizer need, machine need, irrigation need or market preference. On the other hand, advantage or strong advantage is seen by a large majority of farmers in the pest incidence, pesticide need, cotton quality, staple length, yield and profitability. However disadvantage in the seed cost cannot be overruled as also expressed by many respondents. This may sum up the pros and cons of Bt cotton.

30 Muhammad et al. (2007) analysed factors influencing the Adoption of Bt Cotton in the Punjab, Pakistan. Based on farm-level-data collected in the main cotton growing district of the Punjab Pakistan, analysis showed that farmers were very eager to adopt Bt cotton, but its poor performance in some areas damaged the confidence of farmers. The main aim of study was to examine the factors influencing the farmers adoption of Bt cotton and results indicated that there were many reasons for the non-adoption of Bt cotton, of which higher irrigation and fertilizer requirements of the Bt cotton cultivars were main. Additionally, most of the farmers (69.2%) mentioned flower and fruit shedding in Bt cotton at high temperatures. A majority of the farmers (52.3%) cited the higher price of seed as a constraint to adopt Bt cotton technology. Most reasons given by the farmers related to agronomic and management practices, Among the other reasons reported by the farmers for the non-adoption of Bt cotton were: non availability of seed (44.6%), less height (41.5%), not sure of its effectiveness and low yield (36.9%), took more time to mature (35.4%), small boll size (21.5%), wilting (18.5%) and (15.4%) had no confidence to grow it. Which may have been due to a lack of knowledge and information on the genetically modified insect resistance of Bt cotton Thus findings of this study have important implications for the adoption and agronomic practices for insect-resistant Bt cotton. Padaria et al. (2009) using Logit model analysed Bt cotton adoption and assessment of farmers training need. The study was conducted with randomly selected 120 adopters and 60 nonadopters of Bt cotton from Punjab and Karnataka. Logit analysis revealed significant influence of size of holding, capital base, extension contact, innovativeness, achievement motivation and perception about Bt cotton on adoption decision of the farmers for Bt cotton, whereas in contrary to a prior expectation, information source pluralism, mass media exposure, social participation and education were not found to have a significant influence. Plant protection measures, identification of quality seed and use of refuge line were identified as the most important training needs of the farmers. Comparative analysis of training needs of farmers of Punjab and Karnataka with Mann-Whitney U test revealed significant difference in areas of identification of quality seed (P<.01), planting density (P<.01), use of fertilizers (P<.01), sowing (P<.01), use of micronutrients (P<.05), use of plant growth regulators (P<.05) and disease management (P<.01). Dodamani et al. (2010) conducted a study on antecedents of alienation of cotton production in Karnataka state. Results of the study revealed that twenty nine per cent of small farmers have received contingencies in cash and 65 per cent not received the contingencies in cash and whereas 55 per cent of the big farmers have not received the contingencies. Seventy per cent of small farmers benefited by the government policies, whereas in case of large farmers, only 58 per cent were benefited. Most of the government policies / schemes were not extensively utilized by the small farmers compared to large farmers. The results revealed that inadequate finance (47%) was the major problem of small farmers, followed by non-cooperation among family members (41%). Similar remarks were made by large farmers with regard to non cooperation of family member (35%) as family problem, followed by inadequate finance (33%). Dodamani et al. (2010) studied financial viability of cotton growers in northern Karnataka. Results revealed that major reason for non-repayment of loan by small farmers included crop failure (68%) drought, pest and disease problem, followed by low market price (45%). In the case of big farmers, the major reason was crop failure (52%) followed by low price (35%). Bt cotton is the major commercial crop in India. Bt cotton is more profitable comparing to non-bt cotton is because it is resistant to bollworms as these bollworms are the major insects for the cotton so it has reduced the cost on PPC. The major components of the cost of cultivation are human labour, bullock labour, machine labour, fertilizers, PPC and seeds. On an average these independent variables are the major contributor to change in the yield of Bt cotton. The financial infeasibility, price fluctuation and uncertainty of rainfall are the one of the problem in Bt cotton as it as have high cost of cultivation. Hosmath et al. (2012) did a survey analysis on advantages and constraints of Bt cotton cultivation in northern Karnataka. Survey on farmer s field was undertaken to know the advantages and the constraints of Bt cotton cultivation in Belgaum and Haveri districts. About 100 sample farmers were selected from the Bailhongal, Belgaum, Hukkeri and Sankeshwar taluks of Belgaum district and Savanoor, Haveri and Byadagi taluks of Haveri district. The collected data was analyzed by adopting Garrets ranking technique. Advantages and the constraints of Bt cotton cultivation as expressed by the farmers were listed based on Garret Scores. It was observed that higher yield followed by lower pest attack, pesticide cost and lower insecticidal sprays (2-3) compared to non Bt version cotton (8-10) were the major motivating factors for adoption of Bt cotton. The survey data on constraints of Bt cotton cultivation analysis in Belgaum and Haveri districts of Karnataka indicated that more than 93 per cent farmers had faced the problem of leaf reddening irrespective of Bt hybrids used from different firms and 92 per cent farmers expressed that the high seed cost, limiting its cultivation by the economically poor farmers.

31 METHODOLOGY The research methodology and design of the study is an important component of research. To analyze various objectives of the study, an appropriate methodology describing selection of area, sampling design, data collection and tools of analysis are important. This chapter discusses the methodology adopted in the present study. The chapter is presented under the fallowing heads: 3.1 Description of the study area 3.2 Sampling procedure 3.3 Nature and sources of data 3.4 Statistical techniques employed 3.5 Definition of terms and concepts used 3.1 Description of the study area Karnataka is purposively selected for the study as it is one of the major cotton producing states in the country. Karnataka ranks seventh in area and eighth in production. The Indian State of Karnataka is located within 11.5 and 18.5 North latitudes and 74 and 78.5 East longitude. It is situated on a tableland where the Western and Eastern Ghat ranges converge into the Nilgiri hill complex, in the western part of the Deccan Peninsular region of India. The State is bound by Maharashtra and Goa states in the North and Northwest, by the Arabian Sea in the West by Kerala and Tamil Nadu states in the South and by the state of Andhra Pradesh in the East. Karnataka extends to about 750 kms from North to South and about 400 kms from East to West. The highest point in Karnataka is the Mullayanagiri hill in Chikkamagaluru district which has an altitude of 1,929 metres (6,329 ft) above the sea level. Karnataka State has been divided into four revenue divisions, 49 sub-divisions, 30 districts, 176 taluks and 747 hoblies/ revenue circles and 5628 gram panchayats for administrative purposes. The state has 281 towns and 7 municipal corporations. Karnataka is situated in tropical zone and enjoys warm climate throughout the year. The mean temperature ranges from C to C, the maximum and minimum temperature being 42 0 C and 14 0 C respectively. The normal rainfall of the state ranges from as low as 569 mm to as high as 4,029 mm. Average annual rainfall of the state is 1,354 mm. The major part of the rainfall of the state is received from the southwest monsoon, which commences in the first week of June and continues till the end of September. Major part of the state has red soils. Laterite soils are found in the hilly and coastal regions of the western parts. The northern part of the state has black soils with high moisture holding capacity. Karnataka state presents varied topographical features which may be divided into four regions, viz., 1. The coastal region which is a narrow coastal plain between the Western Ghats edge and the Arabian Sea. 2. The Malnad hilly area lying east of the Western Ghats edge. 3. The Northern trappeanless undulating plateau. 4. The southern broad Archaean undulating plateau. Karnataka has a total land area of 1,91,791 km² (Table 3.1) and accounts for 5.83 per cent of the total area of the country (measured at 3,288,000 km²). The state is in eighth place in terms of size. With a population of 5,28,50562, it occupies ninth place in terms of population. The population density which stands at 275 persons per km² is considerably lower than the all-india average of 324 persons per km². Eleven groups of soil orders are found in Karnataka viz. Entisols, Inceptisols, Mollisols, Spodosols, Alfisols, Ultisols, Oxisols, Aridisols, Vertisols, Andisols and Histosols. Depending on the agricultural capability of the soil, the soil types are divided into six types viz., Red, lateritic, black, alluvio-colluvial, forest and coastal soils. The common types of soil groups found in Karnataka are; Red soils: Red gravelly loam soils, Red loam soils, Red gravelly clay soils, Red clay soils.

32 With a surface water potential of about 102 cubic kilometers, Karnataka accounts for about six per cent of the country's surface water resources. Around 60 per cent of this is provided by the west flowing rivers while the remaining comes from the east flowing rivers. The state has the following four seasons in the year: The winter season from January to February The summer season from March to May The monsoon season from June to September The post-monsoon season from October to December. The post-monsoon and winter seasons are generally pleasant over the entire state. The months of April and May are hot, very dry and generally uncomfortable. Weather tends to be oppressive during June due to high humidity and temperature. The next three months (July, August and September) are somewhat comfortable due to reduced day temperature although the humidity continue to be very high. The highest recorded temperature was 45.6 C (114 F) at Raichur on May 23, The lowest recorded temperature was 2.8 C (37 F) at Bidar on December 16, The Southwest monsoon accounts for almost 80 per cent of the rainfall that the state receives. The annual rainfall across the state ranges from low of 50 cm to copious 350 cm. The districts of Bijapur, Raichur, Bellary and southern half of Gulbarga experience the lowest rainfall ranging from 50 cm to 60 cm while the west coastal region and malnadu enjoy the highest rainfall. Agumbe in the Western Ghats experiences the heaviest rainfall in the country next only to Cherrapunji. About km² area (or 20 per cent of geographic area) is covered by forests. The forests are classified as reserved (28,611 km²), protected (3,932 km²), unclosed (5,748 km²), village (124 km²) and private (309 km²) forests. The percentage of forests area to geographical area in the State is less than the all-india average (23 %) and that prescribed in the National Forest Policy (33%). About 70 per cent of the people of the state live in villages and 71 per cent of the total population is agriculture dependent. The major crops grown in the state are, rice, ragi, jowar, maize and pulses besides oilseeds and number of cash crops. Cashew, coconut, areca nut, cardamom, chilies, cotton, sugarcane and tobacco are among the plantation and commercial crops grown in the state. Some of the objectives of the present study are analyzed using primary data obtained through survey in Mysore district, Chamarajanagar district, Shivamogga district and Davanagere district respectively. Therefore, an attempt is made in this section to provide a brief description about the selected districts as non traditional cotton growing areas of Karnataka (Fig. 1). All the four districts fall in the southern part of the Karnataka State. Table 3.1 shows the brief profile of the study area. Mysore district According to 2001 census, the population of the Mysore district was more compared to others with 26, 41,027. Constituted by 16,58,899 rural people and 9,82,128 urban people. The climate of the district is moderate and healthy. Soils of the district comprises of red soils, red sandy soils and sandy soils. The soils are moderately rich in plant nutrients. The district is ideally suited for cultivation of cotton in terms of their soil characteristics and agro climatic features. The percentage of net sown area to total area comes to about 65 per cent. The normal rainfall ranged mm and actual rainfall with 1029 mm. The main food crops of the district are ragi, paddy sugarcane, pulses etc., the important commercial crops in this area are cotton, sugarcane and tobacco. Cotton covers about hectares of land constituting 7.1 per cent of total gross cropped area. Hybrid cotton covered the major part in this area. The canal irrigation is the main source of irrigation accounting for 72 per cent for Mysore district. Mysore district covers 8 sub markets and 7 main markets. Chamarajanagar district The main food crops of the district are Jowar, Maize, Ragi, Paddy and all Pulses. The important commercial crops of the area constituted Cotton and Sugarcane. Cotton covers 754 hectares of land constituting 0.33 per cent of the gross cropped area of the district. In this area hybrid cotton covered the major area. The bore well irrigation is the main source of irrigation which covered about major portion with hectares. The number of people living in rural and urban areas of the district comprised about The normal rainfall received in this area was less compared to other selected districts which covered about mm. The literacy rate is 50.9 per cent. The main

33 markets as well as sub-markets have been operating as regulated markets for very long time and have specilised in handling of cotton. The total number of regulated markets in the area is 7. Shivamogga district Shivamogga district falls in southern part of the Karnataka state. It occupies the geographical area of hectares. According to 2001 population census, the number of people living in rural and urban areas comes to about 8, 30,559 and 8, 11,986 respectively. The normal rainfall of the district comes to 12742mm. The climate of the district is moderate, cold and healthy. Soils of the district comprises of red soils, red sandy soils and medium black soils. It is best suited for the production of cotton. The net sown area is hectares. Cotton covers about 1110 hectares. Gross cropped area is hectares. The percentage of net sown area to the total area for Shivamogga district was 26 per cent. The Shivamogga district covers major area with 72 per cent of paddy. The total number of regulated markets is 22 in Shivamogga district comprising of 7 main markets and 15 sub-markets. Davanagere district Davanagere district occupies an area of hectares. The normal rainfall of the district is mm. The percentage of net area sown to the total area comes about 61 per cent for Davanagere district as a whole. Cotton covers about hectare of land constituting 3.4 per cent of the total gross cropped area in Davanagere district. The hybrid cotton covers the major area. About 40 per cent of the arable land is estimated as potential area for irrigation in the district. Nearly 60 per cent of the area of the state falls under drought prone regions. The bore well irrigation is the main source of irrigation followed by cannal irrigation. The main food crops of the district are Paddy, Ragi, Bajra, Jowar, Wheat, Maize and pulses. Cotton, Groundnut and Sugarcane are the important commercial crops of the area. There are 14 regulated markets in this area. There are 452 regulated markets in the state of which 128 are main markets and 324 are submarkets. The market regulation in the state covers a wide range of crops like cotton, groundnut, paddy, ragi, maize, oilseeds, pulses, vegetables and fruits. All these regulated markets were brought under the preview of the Karnataka State Agricultural Produce Marketing (Regulation) Act, The markets selected for the study are provided with adequate infrastructure facilities and other facilities like water, light, cattle shed, shelter for ryots, dropping platforms etc. Most of the cotton received in the market belonged to hybrid varieties, which are produced in large quantities in the surrounding villages due to the availability of irrigation facilities. The main market as well as submarkets are working for a very long time and have specialized in the handling of cotton in large and also small amount in some districts. The cultivators who come to the market for sale of their produce approach the commission agents of their choice who would arrange to obtain the prices at which intending traders are prepared to take particular lots. The APMC authorities take out samples of each commission agent s lot and identify the grade to which the lot belongs. They issue tender forms to the traders who, after inspection of the lot indicate the price at which they are ready to purchase. The tenders duly filled up by the traders and those offering the highest price are declared to be eligible to take the lots at the tender price. A variety of functionaries operate in the market. They include commission agents, traders, village merchants and ginners etc. which ensure a fair competition in the market. 3.2 Sampling Procedure Karnataka is purposively selected for the study as it is one of the major cotton producing states in the country. Karnataka ranks seventh in area and eighth in production Selection of Cotton Crop There is shift in cotton growing areas in Karnataka. From traditional areas it has spread to many non traditional areas like districts of Mysore, Shivamogga, Davanagere and Chamarajanagar. Therefore, an attempt is made to know the performance of cotton crop in these non-traditional areas.

34 Fig. 1: Map of Karnataka showing the districts selected for the study N