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Acharya N G Ranga Agricultural University, Guntur

The Andhra Pradesh Agricultural University (APAU) was established on 12th June 1964 at Hyderabad. The University was formally inaugurated on 20th March 1965 by Late Shri. Lal Bahadur Shastri, the then Hon`ble Prime Minister of India. Another significant milestone was the inauguration of the building programme of the university by Late Smt. Indira Gandhi,the then Hon`ble Prime Minister of India on 23rd June 1966. The University was renamed as Acharya N. G. Ranga Agricultural University on 7th November 1996 in honour and memory of an outstanding parliamentarian Acharya Nayukulu Gogineni Ranga, who rendered remarkable selfless service for the cause of farmers and is regarded as an outstanding educationist, kisan leader and freedom fighter. HISTORICAL MILESTONE Acharya N. G. Ranga Agricultural University (ANGRAU) was established under the name of Andhra Pradesh Agricultural University (APAU) on the 12th of June 1964 through the APAU Act 1963. Later, it was renamed as Acharya N. G. Ranga Agricultural University on the 7th of November, 1996 in honour and memory of the noted Parliamentarian and Kisan Leader, Acharya N. G. Ranga. At the verge of completion of Golden Jubilee Year of the ANGRAU, it has given birth to a new State Agricultural University namely Prof. Jayashankar Telangana State Agricultural University with the bifurcation of the state of Andhra Pradesh as per the Andhra Pradesh Reorganization Act 2014. The ANGRAU at LAM, Guntur is serving the students and the farmers of 13 districts of new State of Andhra Pradesh with renewed interest and dedication. Genesis of ANGRAU in service of the farmers 1926: The Royal Commission emphasized the need for a strong research base for agricultural development in the country... 1949: The Radhakrishnan Commission (1949) on University Education led to the establishment of Rural Universities for the overall development of agriculture and rural life in the country... 1955: First Joint Indo-American Team studied the status and future needs of agricultural education in the country... 1960: Second Joint Indo-American Team (1960) headed by Dr. M. S. Randhawa, the then Vice-President of Indian Council of Agricultural Research recommended specifically the establishment of Farm Universities and spelt out the basic objectives of these Universities as Institutional Autonomy, inclusion of Agriculture, Veterinary / Animal Husbandry and Home Science, Integration of Teaching, Research and Extension... 1963: The Andhra Pradesh Agricultural University (APAU) Act enacted... June 12th 1964: Andhra Pradesh Agricultural University (APAU) was established at Hyderabad with Shri. O. Pulla Reddi, I.C.S. (Retired) was the first founder Vice-Chancellor of the University... June 1964: Re-affilitation of Colleges of Agriculture and Veterinary Science, Hyderabad (estt. in 1961, affiliated to Osmania University), Agricultural College, Bapatla (estt. in 1945, affiliated to Andhra University), Sri Venkateswara Agricultural College, Tirupati and Andhra Veterinary College, Tirupati (estt. in 1961, affiliated to Sri Venkateswara University)... 20th March 1965: Formal inauguration of APAU by Late Shri. Lal Bahadur Shastri, the then Hon`ble Prime Minister of India... 1964-66: The report of the Second National Education Commission headed by Dr. D.S. Kothari, Chairman of the University Grants Commission stressed the need for establishing at least one Agricultural University in each Indian State... 23, June 1966: Inauguration of the Administrative building of the university by Late Smt. Indira Gandhi, the then Hon`ble Prime Minister of India... July, 1966: Transfer of 41 Agricultural Research Stations, functioning under the Department of Agriculture... May, 1967: Transfer of Four Research Stations of the Animal Husbandry Department... 7th November 1996: Renaming of University as Acharya N. G. Ranga Agricultural University in honour and memory of an outstanding parliamentarian Acharya Nayukulu Gogineni Ranga... 15th July 2005: Establishment of Sri Venkateswara Veterinary University (SVVU) bifurcating ANGRAU by Act 18 of 2005... 26th June 2007: Establishment of Andhra Pradesh Horticultural University (APHU) bifurcating ANGRAU by the Act 30 of 2007... 2nd June 2014 As per the Andhra Pradesh Reorganization Act 2014, ANGRAU is now... serving the students and the farmers of 13 districts of new State of Andhra Pradesh with renewed interest and dedication...

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Subject: Agricultural Economics × End date: 2019 × Start date: 2017 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    AN ECONOMIC ANALYSIS OF ENERGY USE IN ZERO BUDGET NATURAL FARMING, ORGANIC FARMING AND CONVENTIONAL FARMING IN RICE PRODUCTION IN VISAKHAPATNAM DISTRICT OF ANDHRA PRADESH STATE
    (ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) SHRINE, SUTTI; UMADEVI, K
    Energy use in agriculture increased with the increase in population, limited supply of arable land and desire for better standard of living. Effective energy use in agriculture is one of the conditions for sustainable agricultural production, since it provides financial savings, resource preservation and reduction in air pollution. Application of integrated production methods are recently considered as a means to reduce production costs, efficiently use human labour and other inputs and to protect the environment. The research study entitled “An economic analysis of energy use in zero budget natural farming, organic farming and conventional farming in rice production in Visakhapatnam district of Andhra Pradesh state” was taken up with the following objectives. 1. to study the energy use pattern in ZBNF, organic farming and conventional farming in rice production 2. to work out the economics of energy use in ZBNF, organic farming and conventional farming in rice production 3. to analyse the energy use efficiency in ZBNF, organic farming and conventional farming in rice production 4. to identify the factors influencing energy use efficiency in ZBNF, organic farming and conventional farming in rice production. Multistage sampling technique was adopted for selection of sample at different levels in the present study. In Andhra Pradesh, Visakhapatnam district was selected as rice is cultivated in different farming methods like zero budget natural farming, organic farming and conventional farming. The criteria for selection of mandals and villages xii was the presence of maximum number of farmers belonging the three categories. Four mandals and from each mandal, two villages were selected. From each village, samples were selected proportionately from the three farming categories, making a total sample size to 136. The data pertains to the production year 2017-18. Primary and secondary data was collected and analysed through tabular analysis, costs and returns, data envelopment analysis (DEA) and regression analysis. The per hectare energy consumption was highest in conventional farming (23055.60 MJ/ha) followed by ZBNF (14260.50 MJ/ha) and organic farming (13978.84 MJ/ha). The fertilizer and manures energy consumed was highest amount of energy in all the three farming methods which accounts 33.01, 63.11 and 66.58 per cent of the total energy consumption in ZBNF, organic and conventional farming respectively. Under, three farming methods, human energy is the costliest source of energy which accounts to 81.87, 35.01 and 63.02 per cent of the total energy consumption in ZBNF, organic and conventional farming respectively. The energy use efficiency was highest in organic farming (5.42) followed by ZBNF (4.83) and conventional farming (2.80). The net energy was highest in organic farming (61818.87 MJ ha-1) followed by ZBNF (54645.75 MJ ha-1) and conventional farming (41716.27 MJ ha-1). The specific energy was highest in conventional farming (5.57 MJ kg-1) followed by ZBNF (3.22 MJ kg-1) and organic farming (2.63 MJ kg-1). The energy productivity was highest in organic farming (0.38 kg MJ-1) followed by ZBNF (0.31 kg MJ-1) and conventional farming (0.17 kg MJ-1). The technical efficiency was highest in organic farming (0.95%) followed by ZBNF (0.92%) and organic farming (0.89%). The total saving energy was highest in conventional farming (939.99 MJ ha-1) followed by ZBNF (498.87 MJ ha-1) and organic farming (389.24 MJ ha-1). Mechanical energy was positive and significantly contributed to energy use efficiency (EUE) in all the three farming methods. Seed energy was positive and significantly related to EUE in ZBNF and conventional farming. As the energy use efficiency, technical efficiency in energy terms and net energy were highest in organic farming when compared to ZBNF and conventional farming, organic farming is the best recommended farming method for rice production. To save human energy in the preparation of fertilizers and biocides in ZBNF, the inputs should be made available in the villages through cooperative societies. ZBNF and organic rice production may be certified to help the farmers to earn better prices in the domestic as well as in international markets.
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    ThesisItemOpen Access
    IMPACT OF RAIN WATER HARVESTING THROUGH FARM PONDS IN MIZORAM STATE: AN ECONOMIC ANALYSIS
    (Acharya N G Ranga Agricultural University, Guntur, 2019) LALHMINGHLUI; PAUL, K.S.R
    The research study entitled “Impact of rain water harvesting through farm ponds in Mizoram state: An economic analysis” was undertaken with the following objectives. 1. To study the economic feasibility of rain water harvesting structures through farm ponds 2. To analyse the impact of rain water harvesting through farm ponds on cropping intensity, crop diversification, yield, income and employment of farmers and water use efficiency 3. To determine the factors affecting adoption of rain water harvesting through farm ponds 4. To identify the constraints faced by farmers in practicing rainwater harvesting The present study was conducted purposively in Mizoram state of North eastern India during the year 2017-18. The state of Mizoram comprised of eight districts. Two stage random sampling technique was followed to draw a representative sample for the study. Out of eight districts, three districts viz., Aizawl, Kolasib and Serchhip were selected purposively based on highest number of rain water harvesting ponds and four villages from each selected district were selected based on highest number of rain water harvesting farm ponds. A sample of 5 with farm pond (WFP) respondents and 5 without farm pond (WOFP) respondents from each selected village were selected randomly, making a total of 10 farmers from each village. Hence 120 respondents were selected to carry out the present study. Primary data pertaining to the agricultural year 2017-2018 was collected from all selected sample of farmers through a pre tested schedule developed as per the objectives. Both conventional and functional analytical tools like simple mean, averages, percentages, cost concepts, farm income measures, financial feasibility tests, binary logistic regression etc., were used to arrive at valid conclusions. xiv The financial feasibility test indicated that the investment in farm-ponds was financially feasible and worthwhile with net present worth (NPW) of ₹90857, benefitcost ratio (BCR) of 1.92, IRR of 34.34 per cent and payback period of 4 years and 1 month. In WFP, the total cropped area was 46.93 ha in kharif and 22.85 ha in rabi while in WOFP, the total cropped area was 42.83 ha in kharif and 5.08 ha in rabi. There was no significant change in cropping pattern between the two groups of farmers during kharif whereas in rabi, conventional paddy (25.47%) and mustard (37.40%) occupied the majority of the cultivated area in with farm pond (WFP) and without farm pond (WOFP) respectively. The gross cropped area of WFP (69.78 ha) was considerably higher than gross cropped area of WOFP (47.91 ha). There was no significant difference as far as the net cultivated area was concerned. The cropping intensity was comparatively higher in WFP (148.68%) than WOFP (111.86%). The crop diversification was more in WFP (0.40) compared to WOFP (0.64). Yields of crops cultivated in WFP were found to be higher than WOFP. The net returns from crops cultivated by WFP were found to be significantly higher than WOFP in rabi while there was no significant difference in kharif. The average net income of household from farm was revealed to be significantly higher in WFP (₹79611) compared to WOFP area (₹46936). The average employment generated in a year was more in WFP (130 mandays) as compared to WOFP (94 mandays). The water use efficiency was higher in WFP compared to WOFP. WUE was highest in chayote with ₹63.02/m3 in WFP and ₹62.64/m3 in WOFP and lowest in chilli with ₹2.27/m3 in WFP and ₹1.76/m3 in WOFP. In rabi, WUE was lowest in rice with ₹3.14/m3 and ₹2.68/m3 in WFP and WOFP respectively while it was highest in leafy mustard with ₹223.39/m3 in WFP and ₹223.06/m3 in WOFP. The result of binary logistic regression revealed that among the explanatory variables, education was statistically significant at 1% level of significance whereas age, farming experience, extension agency contact and off farm income were statistically significant at 5% level of significance. Nagelkerke R square was 0.75 which implies that 75% of the variations in dependent variable (i.e. adoption of RWH) were explained by the explanatory variables. Garrett ranking revealed that evaporation and seepage loss, water extraction and application equipment and inadequacy of pond size were the most common constraints faced by the RWH adopters. Lack of required finance, lack of confidence about feasibility and labour constraint were the most common reasons for not adopting RWH through farm ponds.
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    ThesisItemOpen Access
    AN ECONOMIC ANALYSIS OF FARM MECHANISATION IN RICE CULTIVATION IN WEST GODAVARI DISTRICT OF ANDHRA PRADESH STATE
    (ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) GOUSIYA, SHAIK; SUSEELA, K
    Agricultural labour has become the most important resource in Indian agriculture and the cost of human labour constituted almost half of the cost of cultivation of major crops. Even though India has the second largest man-power in the world, all the sectors of the economy have been affected by the scarcity of labourers, but the impact is being felt more in the agricultural sector. Labourers are migrating to different parts of the country for higher income and growth opportunities in other sectors, when compared to agricultural sector. The ratio of agricultural labour to total workforce is expected to decline from 55 per cent in 2011 to 41 per cent by 2020, with a further decrease to 26 per cent by 2030 (FICCI, 2017). This trend of declining agricultural labour to total work force can become a serious threat to the overall sector‘s productivity, income level and standard of living of Indian farmers. Technology and machines will be the key solutions to the problem of growing shortage of labour as it saves time and money besides resulting in increased yield. The increased use of purchased inputs in agriculture necessitated to raise their use efficiencies through mechanisation. The overall level of farm mechanisation in India is less than 40-45 per cent, as compared to 90 per cent in most of the developed countries. Andhra Pradesh ranks third position in rice with 23.79 lakh hectares of area, 92.27 million tonnes of production and 3941 kg/ha productivity (2015-16, TE). In Andhra Pradesh at present the Farm power availability is below 2.00Kw/ha which is low and there is lot of scope for improvement. This can be improved by providing adequate subsidies to procure high cost machinery for adoption of technology. Rice is an important labour intensive crop, which requires 900-1000 man hours per ha and 100135 man days per ha (FICCI, 2017). In Andhra Pradesh, West Godavari district has 3.99 L ha area under paddy, which is the principal crop grown in the district. The district is facing a problem of labour shortage due to high economic growth, fast infrastructure development, higher wages in other jobs available locally, shifting to regular/permanent job from agricultural job, agriculture labour being presumed to be a low esteemed job, migration to nearby city for higher wages, migration due to improvement in educational status etc. This labour shortage necessitates promotion of mechanisation, which provides much needed support to farmers troubled by shortage of xiv labour (Hazarika, 2015). Keeping in view of above facts the present study entitled ―An Economic Analysis of Farm Mechanisation in Rice Cultivation in West Godavari District of Andhra Pradesh State‖, has been undertaken with the following objectives. 1. to study the status of implementation of farm mechanisation programmes in West Godavari district of Andhra Pradesh 2. to analyse the impact of farm mechanisation on income and employment in rice farming 3. to assess the energy use pattern and efficiency of mechanisation in rice farming 4. to analyse the constraints in adoption of farm mechanisation in rice cultivation. West Godavari district has been selected purposively as it stands first in area and production in rice crop. Among 48 mandals in the district, as per the information provided by the Govt. officials, four mandals following highest mechanisation were selected. From each mandal, two villages following highest mechanisation were selected, total eight villages were selected. The respondents from these eight villages were selected proportionately to the total paddy farmers in the selected mandals and making a final sample size 122. Out of which, 63 farmers were categorised as complete mechanised farmers and 59 farmers as partially mechanised farmers. Primary data were collected through personal interview method from farmers. The secondary data were collected from Chief planning office, Eluru, West Godavari district. Tools of analysis used were to study the status of implementation of farm mechanisation programmes tabular analysis was used. For cost of cultivation, costs concepts approach was used. To study the impact of farm mechanisation on income and employment, multiple linear regression analysis was used. To study the energy use pattern, energy input-output ratio analysis was used. To study the technical efficiency, stochastic frontier production function was used. To study the constraints of adoption of farm mechanisation, Garett ranking technique was used. In the analysis of status of implementation of farm mechanisation programmes there is no constant increase/decrease in the supply of machines. The costs and returns were analysed for both complete and partially mechanised farms. The results showed that in case of complete mechanised farms, the per hectare total cost of cultivation (C3) was Rs. 65,473.34 which was low as compared to partially mechanised farms with the per hectare total cost of cultivation (C3) was Rs. 75,927.91. The yield per hectare in complete mechanised farms was high with 65.625 q/ha as compared to partially mechanised farms with 56.25 q/ha. Thus the gross returns was high in complete mechanised farms with Rs.1,26,025/ha as compared to partially mechanised farms with Rs. 1,13,750/ha. The net income was high in complete mechanised farms with Rs.60,551.16/ha as compared to partially mechanised farms with Rs. 37,822.09/ha. Impact of farm mechanisation on income was estimated by using multiple linear regression and the results revealed that the coefficient of multiple determination R2 for complete mechanised farms, the coefficient of multiple determination R2 was 0.85 was indicated that the independent variables could explain about 85 per cent of variation in dependent variable. The coefficient of machine cost and fertilisers cost was found to be significant. An increase in machine cost and fertilisers cost by one unit would result in an increase of 2.31 units and 0.25 units in the net income respectively. In partially mechanised farms was 0.79 which indicated that 79 per cent of variation in dependent xv variable can be explained by independent variables. The coefficient of machine cost and fertilizer cost was found to be significant. An increase in machine cost and manures cost by one unit would result in an increase of 13.21 and 0.20 units in the net income respectively. Impact of farm mechanisation on employment was estimated by using multiple linear regression and the results found that the coefficient of determination R2 was 82 which indicate that 82 per cent of the variations in labour requirement are negatively explained by the independent variables. The coefficient of input costs and machine time were significant indicating that 1 unit increase in the input costs and machine used time, would decrease the labour requirement by 0.06 and 4.34 units respectively. The negative co-efficient of dummy variable indicate that with the increase of mechanisation the total labour used time would be decreased. In energy use pattern analysis, fertilizer energy was found to be the dominant source of energy contributing 19,784 MJ/ha and 28,425 MJ/ha under complete and partially mechanised farms respectively. In case of complete mechanised farms, the contribution of other major sources of energy were machine power energy with 15,002 MJ/ha followed by irrigation energy with 12,750 MJ/ha. The total energy input was found to be 54,719 MJ/ha. The output energy obtained was 1,06,006 MJ/ha. The energy productivity was found to be 0.15 kg/MJ. The specific energy was found to be 7.91 MJ/Kg. The net energy was found to be 51,287. The energy intensiveness was found to be 0.74. The Energy use efficiency was 1.93. In case of partially mechanised farms, the contribution of other major sources of energy were human energy was found to be 12,403 MJ/ha and irrigation energy was 12,750 MJ/ha. The total energy input was found to be 64,824 MJ/ha. The output energy obtained was 93,002 MJ/ha. The energy productivity was found to be 0.09 kg/MJ. The specific energy was found to be 11.04 MJ/Kg. The net energy was found to be 28,178. The energy intensiveness was found to be 0.88. The energy use efficiency was 1.45. In complete mechanised farms, the mean technical efficiency was found as 92.30 per cent and the reorganisation of existing inputs in an efficient manner and adoption of improved technology could increase the energy output by 7.70 per cent. In partially mechanised farms, the mean technical efficiency was found to be 80.25 per cent and the reorganisation of existing inputs in an efficient manner could increase the energy output by 19.75 per cent. Garett Ranking Technique was employed to identify the constraints for both complete and partially mechanised farmers. For complete mechanised farmers, the major constraints identified were insufficient number of machineries and high cost of machinery. For partially mechanised farmers, the major constraints identified were lack of awareness on Govt. subsidies for purchase of machines and Low amount of Govt. subsidy for tractors (>30 hp) with less than 50 per cent. Some policy recommendations, which may be useful for expanding the adoption of mechanisation in rice cultivation are the machinery/equipment under the Govt. schemes/programmes, which is intended to provide to small and marginal farmers should be provided on high subsidy for tractors (>30 hp) in order to encourage these categories of farmers to adopt mechanisation in rice. Farm mechanisation should be promoted among the partially mechanised farmers by providing cost effective and efficient machinery with high subsidy as mechanisation increases the net income increases by reducing the cost of cultivation in rice.
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    ThesisItemOpen Access
    IMPACT OF BACKWARD INTEGRATION OF AGRIBUSINESS FIRMS IN CHILLI FARMING IN PRAKASAM DISTRICT OF ANDHRA PRADESH STATE
    (ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) ASHA, RALLAPALLI; UMADEVI, K
    Market liberalization and growth of international trade have created export opportunities in agricultural sector for many developing countries. The traditional way for food production is replaced by practicing more similar to manufacturing processes, with greater co-ordination of farmers, processors, retailers and other stakeholders in value chain of agriculture. The agro-food sector can be conceptualized as a system of vertically intercorrelated stages. Vertical coordination is harmonizing of vertical inter dependence of the production and distribution of activities. Backward integration is a strategy under vertical integration where a firm gains control over ownership or increased control over its suppliers. Wide variation in yield levels leading to fluctuation in chilli prices and farmers are facing problems like high transportation cost, viral diseases, quality deterioration by contamination of pesticides, industrial chemicals and aflatoxins. It is vitally important to support the chilli farmers to produce high quality, sustainable food safe spices to compete in the international market. The major players like ITC, Synthite etc., are providing customised solutions to diverse challenges of chilli farmers through backward integration. The research study entitled “Impact of Backward Integration of Agribusiness Firms in Chilli Farming in Prakasam District of Andhra Pradesh State” was taken up with the following objectives. 1. to study the impact of backward integration on input use pattern, productivity and profitability in integrated and non-integrated chilli farmers 2. to estimate the resource use efficiency of integrated and non-integrated chilli farming 3. to analyse the impact of backward integration on adoption of technologies in chilli farming 4. to analyse the constraints in practising backward integration. xii Multistage sampling technique was adopted for selection of sample at different levels in the present study. In Andhra Pradesh, Prakasam district that ranks 2nd in area was selected, as chilli farmers integrated with agribusiness firms, ITC and Synthite are present in the district only. The criteria for selection of mandals and villages were the presence of highest number of integrated farmers. Four mandals and from each mandal two villages were selected. From each village, eight integrated farmers and eight non-integrated farmers were selected, making a total sample of 128. The data pertains to the year 2017-18. Primary and secondary data collected and analysed through Decomposition analysis, CobbDouglas production function, Technology adoption index, Poisson model with endogenous treatment and Garrett’s ranking technique were followed. The decomposition analysis showed that the per hectare returns of integrated farming was 13.28 per cent higher than that of non-integrated farming. The integrated technology component was contributing 3.7 per cent to the total increase in output. Input use to the outcome differences between the two groups was 9.58 per cent. Under integrated chilli farming, human labour (0.60), fertilizers (-0.15), plant protection chemicals (-0.16) and irrigation (0.15) showed a significant effect on output. Under non-integrated chilli farming, seed (0.04), human labour (-0.19), manures (0.07), plant protection chemicals (-0.26) and irrigation (0.24) showed significant effect on output. In the total sample of chilli farmers, 46.87 per cent of the integrated chilli farmers were adopting seven technologies and 73.43 per cent the non-integrated farmers were adopting less than four technologies. The marginal effect indicates that a farmer with own land, awareness on backward integration, less market distance and more farm size had a probability of adopting backward integration greater than others. Extension service and backward integration were positive and significant at 10 and 5 per cent levels respectively on adoption of technologies. Difficulty in meeting quality parameters was the first major problem faced by the sample integrated farmers. The major problem faced by firms was farmers negligence in maintaining quality. Frequent chilli price fluctuation in the market was the second major problem faced by the firm. The major problem faced by the non-integrated farmers was low production of produce. Backward integration technology increases output and quality of the produce so it should be expand by an assured alternative agency (Government or co-operative) for increase in quantity and value of export of chilli. Creating awareness on optimum use of inputs by Agricultural department. The problem of rejection rate and maintenance of quality of produce may overcome by provide technical support and create awareness through extension service among farmers from sowing to harvest in an integrated manner. Increase in extension service would create knowledge about technologies in chilli farming to farmers because most of the non-integrated farmers are adopting less technologies than integrated farmers.
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    ThesisItemOpen Access
    EFFICIENCY OF FUTURES TRADING AND DIRECTION OF TRADE IN SELECTED AGRICULTURAL COMMODITIES
    (Acharya N G Ranga Agricultural University, Guntur, 2019) NAIPUNYA, J; BHAVANI DEVI, I
    The present study entitled “Efficiency of futures trading and direction of trade in selected agricultural commodities” was undertaken to study the growth and instability of futures trading in terms of quantity and value of selected agricultural commodities i.e., maize, chilli and bengal gram which were traded in NCDEX, efficiency of futures trading in terms of price discovery and transmission, extent of volatility in spot market due to futures trading and analysis of direction of trade of maize, chilli and bengal gram. Among the three agricultural commodity exchanges, major share of agricultural commodities was in favour of National Commodity and Derivatives Exchange (NCDEX) i.e., 82.82 and 75.83 per cent in terms of quantity and value respectively. The share of Multi Commodity Exchange (MCX) and National Multi Commodities Exchange (NMCE) was 10.37 and 6.81 per cent in terms of quantity and 18.19 and 5.98 per cent in terms of value respectively. Analysis of growth and instability of maize traded in NCDEX revealed that maximum positive compound growth rates were observed in both quantity (38.91%) and value (43.67%) in 2006 and maximum negative compound growth rates were found both in quantity (-23.85%) and value xv (-23.46%) in 2008. Maximum positive compound growth rates of chilli in both quantity (18.45%) and value (20.78%) were observed in 2009 and maximum negative compound growth rates were observed in both quantity (-40.87%) and value of chilli traded in NCDEX (-41.19%) in 2014. Maximum positive compound growth rates of bengal gram were observed in both quantity (30.62%) and value (34.20%) in 2005 and maximum negative compound growth rates were observed in both quantity (-45.73%) and value (-39.31%) in 2016. Instability analysis of maize futures trade showed very high variation in 2005 in terms quantity (132.61%) and in 2017 in terms of value (112.59%). Futures trade showed least variation in quantity (25.84%) in 2010 and in value (27.10%) in 2011. Instability analysis of chilli futures trade showed very high variation in 2015 in terms quantity (130.47%) and in terms of value (123.32%) and least variation in quantity (11.13%) and in value (14.86%) during 2017. Instability analysis of futures trade of bengal gram showed very high variation in 2008 in terms of value (65.2%) and in quantity (61.62%) in 2016. The same analysis in futures trade showed least variation in quantity (16.38%) in 2007 and in value (16.83%) in 2007. “Seemingly unrelated regression” (SUR) model in maize and bengal gram showed an efficient price discovery where the futures market dominated in the price discovery i.e., Silbers and Garbage value of futures of maize and bengal gram were (0.00283), (-0.0367) and it was nonsignificant. Chilli spot market (Guntur) was efficient in price discovery. The Silbers and Garbage value of futures market was 0.0403 being significant at 1 per cent level (0.0037) indicating that futures market of chilli was inefficient in price discovery. The findings of the ADF test suggested that futures and spot prices of maize, chilli, bengal gram attained stationarity at first difference. The cointegration test revealed the presence of one co-integrating equation and confirmed the long-run equilibrium relationship among futures and spot prices of selected agricultural commodities. The causality test proved the bidirectional causality between futures and spot prices of maize implying that futures prices influenced the spot prices and spot prices influenced futures prices. Futures market showed uni-directional causality in chilli and bengal gram. Maize and bengal gram spot markets came to short-run equilibrium as indicated by level of significance at 1 per cent i.e., (0.001), (0.007) respectively and speed of equilibrium was rapid i.e., any disturbances in prices of maize and bengal gram would get corrected within 20 min and one hour in spot market prices respectively as indicated by co-efficient values. In chilli spot markets came to short-run equilibrium as indicated by level of significance xvi at 5 per cent i.e., (0.022), any disturbances in price would get corrected within 3 hours in spot markets as indicated by co-efficient values. The dynamics of changes in terms of quantity of exports of maize, chilli and bengal gram from India to different exports markets have been measured by employing Markov chain model. The results revealed that Nepal, Bangladesh and others were the most stable importers of the Indian maize with probability of retention of 88.52, 61.09 and 68.90 per cent respectively followed by Sri Lanka and Malaysia, Philippines and United Arab Emirates would be the unstable importers as it could not retain their original share. The changing pattern of chilli exports through transitional probability matrices indicated that Thailand, other countries and Vietnam were stable in importing Indian chilli with probability of retention of 80.52, 69.02 and 67.09 per cent respectively. Malaysia was the most unstable because they could not retain its original share. In bengal gram, Pakistan was one of the stable countries as revealed by the probability of retention of its share i.e., 61.35 per cent and Algeria was also another stable importer it retained its original share of 45.54 per cent fallowed by Turkey 41.13 per cent retained its original share. UAE and Saudi Arabia were the most unstable importers of Indian bengal gram because they could not retain their original share.
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    ThesisItemOpen Access
    STUDY ON SUSTAINABLE LIVELIHOOD SECURITY IN ANDHRA PRADESH
    (Acharya N G Ranga Agricultural University, Guntur, 2019) SUNITHA, G; VANI, N
    The present study entitled “Study on Sustainable Livelihood Security in Andhra Pradesh” was undertaken mainly to study Sustainable Livelihood Security Index of different districts. The study covered all the districts of Andhra Pradesh. The time series data was collected for 12 years from 2005 to 2017 on all the Ecological Security, Economic Efficiency and Social Equity indicators. The results pertaining to overall Sustainable Livelihood Security revealed that Srikakulam, Vizianagaram and Vishakapatanam remained as less sustainable districts throughout the study period. East Godavari, West Godavari, Krishna, Guntur and Prakasham districts maintained their status of high sustainability during the entire study period. S.P.S. Nellore, Y.S.R. Kadapa and Chittoor continued to be the moderate sustainable districts over the study period. Kurnool which remained in less sustainable district category in 2006 and 2016 improved its status in 2017 and became as moderately sustainable district. In contrary, Anantapur district which was in moderately sustainable category in 2006 and 2016 had fallen from its status and became as less sustainable district in 2017. In 2006, regarding, Ecological Security Index, the first five ranks were occupied by East Godavari, West Godavari, Srikakulam, Guntur and Vizianagaram districts. Krishna, Y.S.R. Kadapa, S.P.S. Nellore and Prakasham districts were placed in next four ranks. The remaining districts viz.,Vishakapatanam, Chittoor, Kurnool and Anantapur occupied last four ranks in Ecological Security Index. In terms of Economic Efficiency Index, the first five ranks were occupied by Guntur, Kurnool, Anantapur, Prakasham and Krishna districts. In EEI next four ranks were given to Chittoor, S.P.S. Nellore, East Godavari and Y.S.R. Kadapa districts. Last four ranks occupied by Srikakulam, Vizianagaram, West Godavari and Vishakapatanam districts. In case of Social Equity Index the districts Chittoor, West Godavari, S.P.S. Nellore, Y.S.R. Kadapa and Prakasham positioned in first five ranks. Krishna, East Godavari, Vishakapatanam and Guntur districts ranked as sixth, seventh, eighth and ninth respectively. The districts viz., Srikakulam, Anantapur, Kurnool and Vizianagaram occupied tenth, eleventh, twelfth and thirteenth ranks respectively. In 2016, in terms of Ecological Security Index, the first five ranks were occupied by East Godavari, West Godavari, Srikakulam, Vizianagaram and Guntur districts. Y.S.R. Kadapa, Krishna, Vishakapatanam and S.P.S. Nellore districts were ranked as sixth, seventh, eighth and ninth respectively. The districts viz., Chittoor, Prakasham, Anantapur and Kurnool ranked under tenth, eleventh, twelfth and thirteenth positions respectively. In terms of Economic Efficiency Index, the first five ranks were occupied by Guntur, Prakasham, Kurnool, Anantapur and Krishna districts. Next four ranks were given to East Godavari, Chittoor, S.P.S.Nellore and West Godavari districts. Y.S.R.Kadapa, Srikakulam, Vizianagaram and Vishakapatanam districts were given tenth, eleventh, twelfth and thirteenth ranks respectively under low EEI. In terms of Social Equity Index the districts West Godavari, Krishna, Chittoor, East Godavari and S.P.S. Nellore positioned in first five ranks. Sixth, seventh, eighth and ninth ranks were occupied by Y.S.R. Kadapa, Vishakapatanam, Prakasham and Guntur districts respectively. Anantapur, Srikakulam, Vizianagaram and Kurnool districts were given tenth, eleventh, twelfth and thirteenth ranks respectively. In 2017, regarding Ecological Security Index, the first five ranks were given to districts East Godavari, West Godavari, Srikakulam, Vizianagaram and Guntur. Sixth, seventh, eighth and ninth ranks occupied by S.P.S.Nellore, Chittoor, Y.S.R.Kadapa and Krishna districts respectively. Vishakapatanam, Anantapur, Prakasham and Kurnool districts occupied tenth, eleventh, twelfth and thirteenth ranks respectively. In case of Economic Efficiency Index, the first five ranks were occupied by Kurnool, Guntur, Prakasham, Krishna and Anantapur districts. Next four ranks given to districts East Godavari, Y.S.R.Kadapa, West Godavari and S.P.S. Nellore respectively. Tenth, eleventh, twelfth and thirteenth ranks occupied by Srikakulam, Vizianagaram, Chittoor and Vishakapatanam districts respectively. Regarding Social Equity Index the districts West Godavari, Krishna, Chittoor, East Godavari and S.P.S. Nellore positioned in first five ranks. Sixth, seventh, eighth and ninth ranks occupied by Y.S.R. Kadapa, Vishakapatanam, Prakasham and Guntur districts respectively. Anantapur, Srikakulam, Vizianagaram and Kurnool districts were given tenth, eleventh, twelfth and thirteenth ranks respectively. The results pertaining to Sustainable Livelihood Security Indices values of different districts of Andhra Pradesh before and after implementation of National Rural Livelihood Mission (NRLM). It was observed that there were no major differences in the indices values of SLS regarding NRLM implementation and functioning. But, observed slight changes in SLS index values of different districts.
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    ThesisItemOpen Access
    FINANCIAL INCLUSION AMONG RURAL HOUSEHOLDS IN CHITTOOR DISTRICT OF ANDHRA PRADESH
    (Acharya N G Ranga Agricultural University, Guntur, 2019) REVATHI, KATURU; RAJESWARI, S
    Financial inclusion denotes delivery of financial services at an affordable cost to the vast sections of the low-income groups. Access to financial services by the poor and vulnerable groups is a prerequisite for eradication of poverty. The present study entitled “Financial inclusion among rural households in Chittoor district of Andhra Pradesh” was under taken to study the socio-economic profile, income and consumption expenditure of rural households, savings, pension, insurance and also to assess the awareness of households towards financial products. The study was conducted in Chittoor district of Rayalaseema region. The data required for the study was collected using a well defined and pre-tested schedule by the personal interview method. The detailed information was collected and it pertained to the financial year 2018-2019. The results revealed that 84 per cent of the respondents are mainly depending on agriculture and rest were non-farm labour (5.00%), hotel (3.00%), LIC agent (2.00%), recharge (1.00%) and cooldrink shop (1.00%), kirana (1.00%), hardware (1.00%), ration dealer (1.00%), tailoring (1.00%). 9.53 and 56.25 per cent of the agricultural and non agricultural households were young aged. 17 per cent of respondents were educated up to high school level and 20.50 per cent were illiterates. Average land possessed by agricultural households was 1.57 ha higher than non-agricultural households (0.90 ha). The annual income of the agricultural households was Rs. 194810.71 and for the non-agricultural households it was Rs. 214950. The consumption expenditure for non-agricultural households was higher (Rs.1555637.50) as compared to that of agricultural households (Rs.109213.57). The study revealed that 17.85 per cent of the agricultural households had borrowed from institutional sources (15.47 per cent from public sector banks and 2.38 from self help groups). In the case of non-agricultural households 18.75 per cent of the households had borrowed from public sector banks and 6.25 per cent from self help groups. In agricultural households 16.67 per cent of agricultural households were covered under old age pension scheme, 14.29 per cent were receiving widow pension, 1.19 per cent each had weavers pension and disability pension. In the case of non agricultural households only 18.75 per cent of the households were receiving old age pension of Rs.2000 from state government. The study further revealed that 54.76 per cent of the agricultural households had crop insurance i.e., Pradhan Mantri Fasal Bhima Yojana. The study observed that agricultural households (41.66%) and nonagricultural households (43.75%) were assessed to be having sound financial knowledge. 92.85 per cent of the agricultural households and 100 per cent of the non-agricultural households were found to be having positive attitude. While behavioural assessment reflected that less percentage of agricultural households (52.38%) and non-agricultural households (56.25%) were exhibited good financial behaviour. Overall 27.38 per cent of agricultural households and about 25.00 per cent of non-agricultural households were found to have good financial literacy.
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    ThesisItemOpen Access
    AN ECONOMIC ANALYSIS OF CROP SHIFTS IN RAYALASEEMA REGION OF ANDHRA PRADESH
    (ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) RAGAMALIKA, V; RAJESWARI, S
    The present study entitled “An Economic Analysis of Crop shifts in Rayalaseema Region of Andhra Pradesh” was undertaken to study the growth rates of area, production and productivity of major crops, direction of cropping pattern changes, factors influencing the cropping pattern changes and affect of crop shifts on the economy in Rayalaseema region of Andhra Pradesh. The study was conducted in Rayalaseema region taking it as a primary unit with its four districts viz., Chittoor, Y.S.R Kadapa, Anantapuram and Kurnool as sub-units. Data required for the study were collected from various published and unpublished sources. The data covered a period of 25 years commencing from 1992-93 to 2016-17. In Rayalaseema region, groundnut, chillies, jowar, paddy, ragi, sugarcane and sunflower recorded significant negative area growth. On the other hand, crops like bengalgram, redgram, maize and cotton were found associated with positive significant growth of area. In Chittoor district, all the crops except redgram recorded significant and negative area growth. In Y.S.R Kadapa district area growth rate was the highest for bengalgram followed by cotton, sesamum and redgram. Productivity growth rate was the highest in chillies (7.21 per cent) followed by sesamun (4.43 per cent) and groundnut (1.86 per cent). In Anantapuram district, the important cropgroundnut recorded a negative and non-significant growth rate of 0.69 per cent in area, but the productivity and production were significant. In Kurnool district maize and castor were the crops with highest and next highest area growth rates of 24.40 and 23.51 per cent respectively. Highest xvii significant productivity growth rate of 4.72 per cent per annum was achieved by turmeric closely followed by maize. In Chittoor district other crops were the most stable as revealed by their retention share of 0.9520 followed by groundnut with retention probability of 0.6243.The most unstable crops of Y.S.R Kadapa district were redgram and sesamum while, cotton had highest retention probability of 0.8050 followed by groundnut with retention probability of 0.7624. Bengalgram besides retaining its share of 0.6985 gained from transfer of other crops with a probability of 0.4851. Groundnut was the most stable crop with high retention probability in Anantapuram district. The crops from which groundnut gained were bengalgram, redgram, jowar, chillies and sunflower but with varying transfer probabilities. Greengram, chillies and turmeric were the most unstable crops in Kurnool district, while sunflower and maize were the most stable crops followed by bengalgram, bajra, groundnut, redgram, cotton, paddy and jowar. Area under paddy, groundnut and sugarcane were significantly influenced by rainfall in Rayalaseema region. There was a positive influence of the lagged production of the same crop on the changes in the area of paddy, groundnut, bengalgram, cotton and sugarcane. Positive and significant influence of rainfall was observed on crops like paddy, groundnut and sugarcane. In Chittoor district, lagged price of competing crop significantly influenced the acreage changes in sugarcane. The area under bengalgram, and cotton was significantly influenced by their own lagged prices in Y.S.R Kadapa district. In Anantapuram district rainfall influenced significantly the area under crop paddy. Ragi, cotton and bengalgram were influenced by their own lagged prices. In Kurnool district the area under redgram, bengalgram and cotton was significantly influenced by their own lagged prices. The gross income from major food and non-food crops was increased in each district of Rayalaseema region. Kurnool registered highest growth rate in gross income from major food and non-food crops, which was 9.23 per cent per annum. Y.S.R Kadapa and Chittoor registered a growth rate of 5.92 and 5.68 per cent per annum respectively. A growth rate of 4.21 per cent per annum was observed in the case of Anantapuram district. Thus, crop shifts exhibited positive and significant influence in the economy of Rayalaseema region.
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    ThesisItemOpen Access
    CROP INSURANCE: PERCEPTION, ADOPTION AND EFFECTIVENESS IN ANANTAPUR DISTRICT OF A.P
    (ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) MARY SHARON B, M; APARNA, B
    In India, Andhra Pradesh is one of the leading states in implementing crop insurance schemes in the country. In 2016-17, claim amount and number of benefitted farmers were maximum under the crop insurance schemes in Anantapur district in Andhra Pradesh. In this context, an economic assessment of crop insurance schemes which is being implemented in the district for all crops assumes importance. Hence the present study has taken up to analyze the to study the status and progress of crop insurance schemes and extent of coverage of crops in Anantapur district of Andhra Pradesh, to examine the perception and awareness among farmers of existing crop insurance schemes in targeted crops, to assess the effect of crop insurance schemes on risk minimization at farm level in Anantapur district of A.P and to assess the determinants of farmer’s participation in crop insurance and constraints in implementation. Stratified purposive cum random sampling design was employed to select a representative sample of 120 farmers from twelve selected villages of six mandals in Anantapur district of Andhra Pradesh and primary data was collected from maize and groundnut farmers through well structured schedule. Secondary data was collected from Joint Director of Agricultural Office (JDA) of Anantapur and Agricultural insurance company of India Ltd. (AIC). Statistical tools used in data analysis were growth rate analysis, Loss ratio, xiv Break-even ratio, frequencies, percentages, cost concepts, Simpson diversification index, Garrett ranking technique and binary logistic regression analysis. In Anantapur, crop insurance started with the CCIS in 1996. From this scheme 155481 farmers were benefitted in 1999. Compound growth rates were calculated for coverage of NAIS and found that the scheme was progressive in all terms which showed positive growth rates. WBCIS also implemented in district in 2011 and beneficiary ratio was very high. PMFBY was started in 2016 but over the time growth was low and growth rates were found to be negative. The study concluded that the farmers do not have in-depth awareness about crop insurance schemes. Maximum number of insured farmers were in the age group of 36-50 years and non-insured were in the age group of more than 50 years. Insured farmers were more educated than non-insured farmers. Illiterates were more in non-insured category. Most of the farmers in the sample has agriculture alone as the occupation. Average family size of insured farmer was higher than the non-insured farmer. Average land holding of the insured farmer was higher than the non-insured farmer. Cropping pattern of insured and non-insured farmers was almost same. Most of the insured farmers were taking loans from PACS (46.67 per cent) and non-insured farmers mostly from neighbors (33.33 per cent). It was observed that most of farmers were not fully aware of existence of insurance scheme and only less percent of farmers said that they have full knowledge about scheme. Most of the farmers were getting information from implementing agencies (28.33 percent). Maximum insured farmers have taken insurance due to bank compulsion (68.33 per cent). Maximum number of noninsured farmers in high insurance coverage area were taking money from cooperative societies while in low insurance coverage area, money is mostly taken from money lenders.The proportion of variable cost to total cost was 80.2 per cent under the insured category and 78.72 percent under non-insured category. In the low coverage area it was almost same as insured farmers with 78.96 per cent and non-insured farmers with 77.63 per cent. The net returns realized from groundnut cultivation by high coverage insured beneficiaries was Rs.37481.71/ha, which was higher than non-insured farmers net returns which was Rs.29561.41/ha. In case of maize, the net returns from high coverage insured beneficiaries were Rs.19121.44/ha, which was higher than that for no-ninsured farmers Rs.11217.77/ha xv The factors like family size, accessibility to credit, farm income, access to information, awareness about insurance policy and extent of the irrigation are the significant factors influencing willingness to take insurance by the farmers. Diversification index was slightly higher for non-insured farmers than insured farmers as the farmers are practicing it as risk coping mechanism. Major constraint observed from the garrett ranking method was government was not paying compensation amount in time. So it creates difficulties to the famers in the sowing of next season. Main reason behind not taking insurance by farmers was lack of much awareness about the crop insurance schemes followed by lack of premium paying capacity. Awareness building programmes should be initiated in collaboration with local banks and local administration for creating in-depth awareness and benefit perceptions of the scheme among farmers in the state to encourage them to participate