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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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ThesisItem Open Access ECONOMIC ANALYSIS OF PRODUCTION AND MARKETING OF MAIZE IN KURNOOL DISTRICT OF ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, 2015) JAHNAVI KEERTHI PRIYA, P; Dr. K.N. RAVI KUMARThe present study entitled “Economic analysis of production and marketing of maize in Kurnool district of Andhra Pradesh” was undertaken mainly to study growth rates of area, production and productivity, costs and returns, resource use efficiency, marketing aspects and constraints in production and marketing of maize. Three stage sampling was adopted for the selection of district, mandals, villages and the sample farmers. A sample of 120 farmers was randomly selected from the selected two mandals and four villages. The farmers were stratified into Marginal (<1 ha), Small (1-2 ha) and Other (>2 ha) categories on the basis of their size of operational holding. The primary data for the year 2013-14 were collected through a pre-tested schedule by survey method. Conventional as well as functional analysis was used to analyze the data and arrived at valid conclusions. Compound growth rates of area and productivity of maize showed significant increase at All-India level, in Andhra Pradesh and in Kurnool district that were influential in boosting the production of maize during the overall reference period. The total cost of cultivation of maize per hectare was Rs. 54,610.82, Rs. 51654.7, Rs. 47,159.63 and Rs. 50,214.55 on marginal, small, other and pooled farms respectively. The per hectare cost of cultivation and cost of production (Rs/Qtls) are inversely related with the farm size. The gross returns were Rs. 62,260.75, Rs. 63,018.24, Rs. 65810.35 and Rs. 63,353.11 on marginal, small, other and pooled farms respectively indicated direct relationship with the farm size. The DEA analysis pertaining to the resource use efficiency in maize cultivation revealed that, 12.5 per cent of the farms are operating at CRS indicating efficient utilization of resources. Majority of farmers (62.50%) are operating at IRS and 25 per cent of the farmers are operating at DRS indicating that, more resources should be provided to the farms operating at IRS and the same should be decreased towards the farms operating at DRS. Price spread in transacting maize was studied with reference to two marketing channels: Channel-I (Producer → Commission agent → Wholesaler → Poultry feed unit → Retailer → Consumer (sale of poultry feed)), Channel-II (Producer → MARKFED → Co-operative dairy → Consumer (sale of animal feed)). Of the two channels identified in transacting maize, Channel-II was found more efficient than Channel-I, as indicated by the computed marketing efficiency indices. Majority of the farmers prioritized power cut as the major constraint for production of maize with a mean score of 73.47 followed by high cost of input and input services (67.72) and shortage of labour during production (62.63). Regarding marketing, they prioritized frequent price fluctuations as the major problem with a mean score of 71.84 followed by unorganized marketing (67.85) and lack of transportation facilities (64.38). In view of the production constraints, regulating quality power supply to the farmers, purchase of inputs on co-operative basis, encouraging farm mechanization, effective implementation of crop insurance scheme etc., deserve immediate attention to improve production scenario. The prospects of marketing of maize can be enhanced through improving transportation facilities, marketing news and information network and strengthening scientific storage facilities.ThesisItem Open Access AN ECONOMIC ANALYSIS OF REDGRAM AND REDGRAM BASED CROPPING SYSTEMS IN PRAKASAM DISTRICT OF ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, Guntur, 2015) VIJAYA PREETHI, A; Dr. K. UMA DEVIThe present research investigation entitled “An Economic analysis of Redgram and Redgram based cropping systems in Prakasam District of Andhra Pradesh” was undertaken with the following objectives. i) To estimate the costs and returns of redgram cultivation vis-à-vis redgram based cropping systems in Prakasam district. ii) To evaluate the resource use pattern and efficiency in redgram and redgram based cropping systems. iii) To study the yield gap analysis in redgram based cropping systems. iv) To identify constraints and their impact in the cultivation of redgram based cropping systems. A well structured and pretested interview schedule was used to collect the requisite information from sample farmers through survey method. All the mandals of prakasam district were listed out in the descending order of magnitude of the area under redgram cultivation and top three mandals were selected purposively. Similarly, top four villages from each of the selected mandals were selected based on area. From each village 10 farmers cultivating redgram and redgram based cropping systems were selected. Thus from the district, three mandals, twelve villages and 120 farmers were selected. Cost concepts for estimating cost of cultivation of redgram and redgram based cropping systems, farm income measures, Cobb-Douglas production function to estimate the resource use efficiencies and returns to scale were employed in the study. Multiple linear regression analysis was used to examine the contribution of each identified factor to the yield gap. Garret ranking technique was employed to prioritize/rank the problems faced by redgram farmers in practicing the intercropping. The analysis revealed that Cost A1 (₹.39692) was more in redgram sole cropping system. This is mainly due to increased application of plant protection chemicals and FYM & fertilizers, mainly because of the higher incidence of pests and diseases than other cropping systems. Cost B1 (₹.40571.19), Cost B2 (₹.47865.39), Cost C1 (₹.40822.63), Cost C2 (₹.48116.82) and Cost C3 (₹.52928.51) were more in redgram sole cropping system compared to other cropping systems which was mainly due to highest Cost A1 (₹.39692). In other words, the total cost of cultivation as per cost concepts was the highest in redgram sole cropping system (₹.52928.51) followed by redgram + bajra cropping system (₹.45637.28), redgram + castor cropping system (₹.40304.37), redgram + greengram cropping system (₹.37346.27) and redgram + sorghum cropping system (₹.35494.42) respectively. Redgram equivalent yield was found to be highest in redgram + greengram cropping system (8.27q/ha). Farm business analysis revealed that both the family labour income and farm investment income were more in redgram + greengram cropping system compared to other cropping systems. Gross income (₹.62860), net income (₹.25513) and Return per rupee of expenditure ratio (1.85) were more in redgram + greengram cropping system. The attributed reason was that the farmers obtained good yield associated with the two crops in the system, high sale price (₹.5500) of intercrop (greengram) and also less operational costs. The redgram + castor cropping system ranked second with respect to maximum net income (₹.17753) and least in redgram sole cropping system (₹.4475/ha) in the study area. Thus, including intercropping (mixed cropping) was found to be more beneficial than sole cropping in study area. The Production function analysis revealed that production elasticities of farm size, labour charges and seed were found to influence the productivity significantly in redgram sole crop. In case of redgram + bajra cropping system, the regression coefficient of farm size had a positive and significant influence on productivity. In pooled cropping systems regression coefficients of farm size and labour charges were found to be positive and significant influence on productivity. The cropping system dummy variables (redgram + greengram, redgram + castor, redgram + sorghum (fodder) cropping systems) were found to be positive and showing significant influence on productivity. This indicates that farmers in the redgram intercropping systems were better off in terms of obtaining redgram yield than redgram sole cropping system. Sum of elasticities of production function revealed that there was increasing returns to scale in redgram sole cropping system and redgram + bajra cropping system while it was decreasing returns to scale in pooled cropping systems. From multiple linear regression analysis, it is apparent that, the variables such as seed cost gap (Rs./ha), phosphorus gap (kg/ ha), labour charges gap (Rs/ha) and pesticide gap (Rs/ ha) were mainly responsible for the yield gap for redgram sole crop, redgram + bajra cropping system and pooled cropping systems. In pooled regression analysis, the regression coefficients of cropping system dummy variables (CS1, CS2 and CS4- redgram + bajra, redgram + greengram, redgram + sorghum (fodder) cropping systems) had positive and significant influence on yield gap. This indicates that more yield gap observed in redgram based cropping systems than sole redgram was because of relatively poor adoption concerning intercropping technologies by the sample redgram farmers. The average use of nitrogen and phosphorus fertilizers for all cropping systems were more than the recommended level. The actual use level of potash was below than recommended level in redgram sole crop, redgram + bajra cropping system and redgram + greengram cropping system whereas more than recommended level was observed in redgram + castor and redgram + sorghum cropping systems. The percent gap in potash was the highest in redgram + greengram cropping system at 70 percent followed by redgram + bajra cropping system at 62.84 percent. The actual use of manures was below recommended level in all cropping systems. The proportionate gap between recommended and actual use levels of manure was the maximum in redgram + greengram cropping system at 76.00 percent followed by redgram + sorghum cropping system at 66.40 percent and lowest in redgram + bajra cropping system at 12.26 percent. The Garrett ranking analysis revealed that inadequate credit, lack of knowledge of intercropping technology, low price of produce, non-availability of quality seeds, high cost of chemical fertilizers, non-availability of farmyard manure, diseases and pests, scarcity of owned funds, price fluctuations and lack of storage facilities were the major constraints faced by the farmers in study area. In Prakasam district, redgram + greengram cropping system was most profitable followed by redgram + castor cropping system, redgram + sorghum (fodder) cropping system and redgram + bajra cropping system respectively. There is a need to encourage farmers for practising these cropping systems to enhance productivity and profitability. There is a need for the farmers to continue with intercropping systems resulting in flexibility, profit maximization and risk minimization against total crop failure. It is essential to promote collaboration among various institutions engaged in agricultural productivity (Research station and KVK, Darsi) to develop appropriate technology with a view to minimize the yield gaps. There is a need to strengthen agriculture extension services to bridge all technological gaps in crop production on priority basis.ThesisItem Open Access AN IMPACT ASSESSMENT OF MGNREG ACT ON SUPPLY, DEMAND AND WAGES OF AGRICULTURAL LABOUR IN GUNTUR DISTRICT OF ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, Guntur, 2015) MAHAMMD, FARUKH; Dr. G. RAGHUNADHA REDDYThe present study was carried out to assess the impact of MGNREGA on supply, Demand and Wages of Agricultural Labour in Guntur district of Andhra Pradesh. The study was conducted in Guntur of Andhra Pradesh. Multi stage sampling procedure was adopted to select the sampling for the study. A total sample of 90 respondents were selected randomly i.e., 45 programme beneficiaries and 45 farmers. Data was collected with help of pre-tested interview schedule. To study the impact of socio-economic parameters like employment and income of the respondents before and after the implementation of MGNREGA, paired t-test was used. A multiple linear regression model was employed to estimate the determinants of total number of days worked under the Act. Garrett’s ranking technique was adopted to analyse the views of the beneficiary. Impact of MGNREGA on labour availability in the study area observed by supply demand gap analysis method. The month-wise supply of labour was assessed by considering the available agricultural labour force in the selected villages of mandal and average mandays of work delivered in a month by each labour. Impact of MGNREGA on direct and indirect changes like Wage rates and daily working hours was carried out by Z test. Forty nine per cent of the NREGA workers were literates, while the farmer’s literacy rate (57%) was higher than that of workers. Gender composition of the NREGA workers shows the higher participation of male (57%) as compared to women participation (42%). The number of days, the beneficiaries was employed in a year before working under NREGA was 182 days. While, after NREGA implementation they got manual work under the Act for 58 days. Over the last 3 years number of person days employed under MGNREGA during kharif and rabi season in Guntur district is increased. Employment generation scores of before and after NREGA differed significantly with ‘Z’ value (17.54) at 1 per cent level of probability implying that there was an improvement in the employment generation of beneficiaries due to introduction of NREGA. Easier works for women and aged workers was one of the best practices followed under MGNREGA which was expressed by 35 per cent of the programme beneficiaries. The profile of the farmer respondents shows 15 per cent of them were small farmers, 48 per cent medium farmers and 35 per cent large farmers. The nonparticipation of farmers under the wage employment programme was because of their busy working schedule on their own farm, revealed as is the main reason by 66% of them. It was reported that the scarcity of farm labourers due implementation of MGNGRES had increased, which was opined as the key problem (Garret score of 88.26), followed by its impact on labour wages which had increased substantially (Garret score 80.64). Increased Cost of Cultivation after implementation of MGNREGA was given 3rd rank. The labour availability before MGNREGA implementation was 6.2 of persondays with the implementation of the Program, the labour availability was 2.8 person days only. Thus, the absolute scarcity due to MGNREGA was of 40.4 per cent. Highest labour scarcity observed in Rabi season crops viz., cotton and chilli attributed to NREGA was 42.8 and 50.8 per cent respectively. In the study area estimated demand for agricultural labour exceeded labour supply during four months, viz. June, July, August, and September.ThesisItem Open Access AN ANALYSIS OF PRICE BEHAVIOUR OF TURMERIC IN GUNTUR DISTRICT OF ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, Guntur, 2015) MANJUNATH, ULLAGADDI; Dr. D. VISHNUSANKAR RAOThe present study entitled “An Analysis of Price Behaviour of Turmeric in Guntur District of Andhra Pradesh” was conducted with the specific objectives (1) To study the trends, seasonal, cyclical and irregular variations in market prices and arrivals of turmeric in Duggirala market in Andhra Pradesh. 2) To analyze the impact of arrivals on prices of turmeric in Duggirala market of Andhra Pradesh. 3) To study the export and domestic competitiveness of turmeric. 4) To forecast the future prices of turmeric in Duggirala market of Andhra Pradesh. Analytical tools employed in the study were: Multiplicative model has been used to estimate the trend, seasonal, cyclical and irregular movements in market prices and arrivals and 12 months moving average method was used to construct the seasonal indices. Regression analysis was used to study the impact of arrivals on prices of turmeric. Nominal Protection Coefficient (NPC) was used to estimate the export competitiveness of turmeric. Domestic resource cost (DRC) was used to analysis domestic competitiveness of turmeric. Box-Jenkins model (ARIMA) was used to forecast the future prices. The trends in arrivals were fitted by cubic model and market arrivals were recorded as highest in the year 2014 and lowest during the year 2002. The trends in prices were fitted by cubic model and prices were recorded as highest in the year 2011 and lowest during the year 2004. The month wise seasonal arrivals revealed that the highest arrivals were found in the month of June and May and lowest in the month of November and February respectively.The highest seasonal price index was found in December, January and lowest in the month of April and July respectively. Thus, the farmers can get benefited if they sell their produce in the month of January, December and August. The cyclical variations in market arrivals showed one cycle from 2002-2009 and prices of turmeric showed one cycle from 2004-2010. The irregular component in the market arrivals were highest in the year 2005 and were found lowest in the year 2013 and in the prices were recorded highest in the year 2010 and lowest during the year 2012.The impact of turmeric arrivals on prices in the Duggirala market revealed that arrivals were showing a negative impact on price of turmeric. The export competitiveness of turmeric showed highly export competitive. The Domestic Resource Cost(DRC) of turmeric indicated that the input is used efficiently and it is export competitive. The forecasts of turmeric prices were found to be fairly accurate when compared to real prices at market and observed less than five percent variation between the both the prices.ThesisItem Open Access INFLUENCE OF FUTURES MARKET ON PRICE BEHAVIOUR OF TURMERIC IN INDIA(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2015) SHIREESHA, M; BHAVANI DEVI, IThe present study entitled “Influence of Futures Market on Price Behaviour of Turmeric in India” was undertaken to study the marketing practices of turmeric farmers, price forecasts, the extent of market integration among turmeric markets, the relevance of futures on price behaviour of turmeric, price volatility and export competitiveness. The study was conducted in major turmeric markets in India viz., Kadapa, Duggirala and Nizamabad (Andhra Pradesh), Sangli (Maharashtra) and Erode (Tamil Nadu) which were purposively selected based on the maximum quantity of arrivals. The data pertained to the period from January 2001 to December 2014. The interdependence between futures and spot prices of turmeric was analysed using daily closing futures prices for the year 2014 collected from NCDEX and their respective spot prices from AGMARKNET. To study the marketing practices of turmeric farmers, Nizamabad district was purposively selected as it ranked first in area and production of turmeric in India. The study covered two mandals, four villages and 60 randomly selected farmers. To study the marketing costs and margins, a sample of 5 from each of the market intermediaries were selected randomly. The primary data for the year 2013-2014 were collected through a pre-tested schedule by survey method. The analysis of marketing costs and margins revealed that the producer received relatively higher share of consumer’s rupee in channel III over channel I and channel II in the case of dried turmeric, whereas in the case of turmeric converted into powder, the producer’s share in consumer’s rupee was higher in channel V over channel VI. The producers of turmeric xv realized 60.71 and 50.56 per cent of the consumer’s rupee in channel III and channel V respectively which appeared to be reasonable in the light of functions performed by various functionaries. The price spread was lower for dried turmeric when it was sold directly in the regulated market yard. When dried turmeric was exported, exporter incurred higher cost and also realized a higher margin. The retailer realized higher margin by incurring higher cost compared to other intermediaries involved in the disposal of dried turmeric and turmeric powder in the domestic market. The results of ARIMA model for turmeric indicated that the per quintal prices from January to March, 2015 would be ranging from ` 5,446 to ` 5,496 in Kadapa market, ` 5,350 and ` 5,399 in Duggiarala market, ` 5,916 to ` 5,972 in Nizamabad market, ` 7,253 to ` 7,330 in Sangli market and ` 6,532 to ` 6,581 in Erode market. When the forecasts were compared with the real time prices, it was observed that trend analysis and decomposition fit were relatively closer to real time prices of turmeric in Kadapa, Duggirala and Sangli markets, while double exponential smoothing and decomposition fit were better in Nizamabad market and ANN with regard to Erode market. Monthly price series in the selected turmeric markets became stationary after taking first difference as revealed from ADF test. Johansen’s Multiple Co-integration test revealed the presence of three co-integrating equations at five per cent level of significance and confirmed the long run equilibrium relationship among the markets. Except Kadapa market, all the remaining markets attained short run equilibrium. The prices of turmeric in Sangli and Erode markets were influenced by their own monthly lags for the long run equilibrium. The causality test revealed a bidirectional influence of turmeric prices between Duggirala and Kadapa, Nizamabad and Kadapa and Erode and Nizamabad markets. The findings of the ADF test suggested that daily futures and spot prices in all selected markets attained stationarity at their first difference. The co-integration test revealed the presence of three co-integrating equations at five per cent level of significance and indicated long run equilibrium relationship between futures and spot prices of turmeric in all the selected markets. The spot prices of Duggirala, Nizamabad and Erode attained short run equilibrium. The spot prices of all the markets were influenced by their own daily lags. Futures prices influenced the spot prices of Kadapa, Duggirala and Erode by one day lag and in turn were influenced by the spot prices of Kadapa. The causality test proved the unidirectional causality between futures and spot prices indicating that futures prices influenced the spot prices in all the selected markets but not vice-versa. xvi The price series of Duggirala and Nizamabad markets showed the presence of price fluctuations as indicated by the sum of Alpha and Beta coefficients which were nearer to one whereas in the remaining markets, the volatility shocks were not quite persistent. Volatility in futures prices was also observed from ARCH-GARCH analysis. Nominal Protection Co-efficients were found to be below one indicating high export competitiveness of turmeric.ThesisItem Open Access HORIZONTAL AND VERTICAL INTEGRATION OF MAJOR OILSEED MARKETS IN INDIA(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2015) PARVATHI DEVI, R; BHAVANI DEVI, IThe study entitled “Horizontal and Vertical Integration of Major Oilseed Markets in India” was undertaken to analyze the horizontal integration among major oilseed markets of India, to assess the vertical integration among oilseeds and their derivatives, to identify and evaluate various sources of risk in supply chain of oilseeds and to investigate the factors affecting the critical risks in supply chain of oilseeds in India. The study was conducted for three major oil seed crops viz., groundnut, sunflower and castor. The markets selected for the study were Kurnool and Yemmiganur (Andhra Pradesh) and Gondal (Gujarat) for groundnut, Kurnool and Yemmiganur (Andhra Pradesh) and Ranebennur (Karnataka) for sunflower and Kurnool and Yemmiganur (Andhra Pradesh), Patan and Dhanera (Gujarat) for castor. Bombay market was selected for collecting the daily price data related to oils and oilcakes of selected oilseed crops. To identify the sources of risks across the supply chain of oil seeds, primary data were collected from 120 farmers, 10 commission agents, 10 village merchants, 10 wholesalers and 10 oil millers. The data pertained to the period from April 2013 to March 2014. The horizontal and vertical integration in selected markets was analyzed through Augmented Dickey Fuller test, Johansen’s Multiple Cointegration analysis, Vector Error Correction Model and Granger Causality test. In order to identify and evaluate various sources of risk and to investigate the factors affecting the critical risks in supply chain of oilseeds, Failure Modes and Effect Analysis (FMEA), Risk Evaluation Matrix, Pareto Analysis and Ishikawa diagram were employed. xvii In the case of horizontal integration, all the selected markets showed integration for the three selected crops individually, which is evident by Johansen’s co-integration procedure. With respect to horizontal integration among groundnut markets, Kurnool and Yemmiganur markets came to short run equilibrium within 8 hours and 4 hours respectively, in the case of horizontal integration among sunflower markets, Kurnool and Ranebennur markets converged to short run equilibrium price within 5 hours and 4 hours respectively, whereas all the markets attained their short run equilibrium in castor. Granger causality test indicated that Gondal groundnut market, Ranebennur sunflower market and Patan castor market were the lead markets and the information had flown from these markets to other markets individually. The horizontal integration among the markets of selected oils viz., Malaysian palm oil, Bombay groundnut oil, sunflower oil and sunflower refined oil, showed that these oil markets were integrated in the long run. Further, the Granger causality test showed that the price information was communicated from Malaysian palm oil market to Bombay sunflower refined oil market. With respect to vertical integration among the daily price series of pod, oil and cake for the three selected crops (groundnut, sunflower and castor), the results revealed that in the case of groundnut, out of three selected pod markets, two pod markets (Kurnool and Yemmiganur) were integrated with the oils and cakes in Bombay market and the information has reached from terminal market (Bombay) to groundnut producing areas. With respect to sunflower, out of the three selected seed markets only one market (Yemmiganur) showed integration with sunflower oil of Bombay market. Out of three selected sunflower seed markets all the markets showed integration with the daily prices of sunflower refined oil and cake in Bombay market, which indicated that the price information was more from sunflower producing areas to terminal market (Bombay). In the case of castor, the daily prices of the selected seed markets showed the integration with the daily prices of both oils and oilcakes and the information flow was mutual or bi-directional from castor producing areas to terminal market (Bombay) with respect to oils, whereas, there was no transmission of price signals between the selected castor seed market and castor cake market. According to the results of FMEA, Pareto Analysis and Ishikawa Diagram for risk analysis in supply chain of oilseeds, out of 33 identified sources of risk across the supply chain of oilseeds, a total of 18 risks were identified as the high potential critical risks at the supply chain stages of input, production, post-harvest and marketing and the disruptive range of all these critical risks was between the RPN (Risk Priority Number) (Minimum 48 - Maximum 125). In the case of input risks, out of seven sources of input risks, four sources (high cost of inputs, inadequacy of capital, lack of availability and accessibility and timeliness of supply) were identified as the xviii critical risks and the disruptive range was between RPN (48-100). In respect of production risks, out of nine sources of critical production risks five sources (weather uncertainty and climate change, inadequate rainfall, pests and diseases, lack of access to farm technology and inadequate infrastructure) were identified as the critical risks and the disruptive range has fallen between RPN (60-125). Regarding post-harvest risks, out of seven sources of post-harvest risks, four sources (absence of quality control practices, absence of storage facilities, absence of grading and poor packaging) were identified as the critical risks and disruptive range occurred between RPN (48-100). With regard to marketing risks, out of ten sources of marketing risks, five sources (variability in output prices, lack of discriminatory pricing for quality produce, low bargaining power, exploitation by middlemen and lack of market information) were identified as the critical risks and the disruptive range was found between RPN (64-125).ThesisItem Open Access ECONOMICS OF RICE BASED CROPPING SYSTEMS IN NELLORE DISTRICT OF ANDHRA PRADESH(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2015) NAIPUNYA, J; RAJESWARI, SThe present study entitled “Economic of Rice Based Cropping Systems in Nellore District of Andhra Pradesh” was under taken to study the input use pattern, costs, returns and resource use efficiency in the different rice based cropping systems. The study covered three mandals and six villages with 90 farmers cultivated rice based cropping systems. The relevant data were collected from both primary and secondary sources. Data for the year 2013-14 were collected through a pretested schedule by survey method. The data were analyzed using tabular and functional analysis. Major Rice based cropping systems identified in the study area were cropping system-I (Paddy and Cotton), cropping system -II (Paddy-PaddyGreen gram) and cropping system-III (Paddy-Paddy-Groundnut). The results showed that total cost was highest in cropping system-I (66,368) followed by cropping system-III (64,741) and cropping system-II (48,826) respectively. Gross returns were maximum in cropping system-I (1,08,000) followed by cropping system-III (96,666) and cropping system-II (70,000). The net returns were highest in cropping system-I (42,179) followed by cropping system-III (31,925) and cropping system-II (21,174) in that order. It was observed that the returns per rupee of expenditure in all the cropping systems were ranged from 1.42 to 1.63. Higher returns per rupee of expenditure in cropping system-I i.e., 1.63 followed by cropping system- III (1.48) and cropping system- II (1.42). The functional analysis revealed that machine power and fertilizer in paddy, human labour and fertilizer in cotton in cropping system-I was significantly contributed to the increase in the crop yields and income. The MVP to MFC ratio was greater than unity for machine power and fertilizer in paddy, human labour and fertilizers in cotton in cropping system- I indicating greater potentiality for further use. In the case of cropping system-II, human labour and machine power seed in kharif paddy, human labour and machine power in rabi paddy significantly contributed to the increase in the crop yields and income. The MVP to MFC ratio was greater than unity for human labour and machine power in kharif paddy, human labour in rabi paddy in the cropping system-II indicating greater potentiality for further use. The same for machine power in rabi paddy, seed in kharif paddy were less than unity indicating lesser potentiality for further use. In the case of cropping system-III human labour and fertilizer were significant in both kharif paddy and rabi paddy. Seed and fertilizer in case of groundnut significantly contributed to the increase in the crop yields and income. The MVP to MFC ratio was greater than unity for human labour in kharif paddy, human labour and fertilizer in rabi paddy and for seed in groundnut indicating greater potentiality for further use. Fertilizer in kharif paddy and groundnut were less than unity indicating lesser potentiality for further use.ThesisItem Open Access SOCIO-ECONOMIC IMPACT ASSESSMENT OF TOBACCO CULTIVATION IN NELLORE AND PRAKASAM DISTRICTS OF ANDHRA PRADESH(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2015) KRISHNA TEJA, I; RAJESWARI, SThe present study entitled “Socio-Economic Impact Assessment of Tobacco Cultivation in Nellore and Prakasam Districts of Andhra Pradesh” was undertaken to study socio-economic and environmental impact of tobacco cultivation, impact created by ITC-ILTD among tobacco growers and trends in farmers economics between tobacco and non-tobacco growers. Prakasam and Nellore districts were purposively selected because of their production across 13 Tobacco Auction Platforms (TAPs). 12 tobacco farmers and 6 non-tobacco farmers from each TAP were selected randomly with a sample size of 234. The data related to crop season of 2013-14 were collected using pretested questionnaires through survey method. Conventional analysis was used to analyse the data and arrive at valid conclusions. Majority of farmers in both tobacco and non-tobacco were old aged and under nuclear family. The educational status of tobacco farmers was high when compared to that of non-tobacco farmers. The average size of landholding for tobacco farmers was 5.76 ha and for non-tobacco farmers it was 2.83 ha. Tobacco farmers were getting more credit facilities from commercial banks than that of non-tobacco farmers. Tobacco farming had a positive effect on the household’s income of tobacco farmers. There was a considerable difference between average family expenditure of tobacco and non-tobacco farmers. xiii The value of farm assets of tobacco farmers was greater than that of non-tobacco farmers. At a glance, 96.65 percent of the farmers were satisfied with all the services provided by the company in all aspects viz., required information, timely availability, full information, professionalism and staff attitude. At farm level, per hectare cost of reducing carbon dioxide emitted from cured leaves of FCV tobacco was ` 3,949.08 in Andhra Pradesh level, with a total production of 213.93 million kg of cured leaf, accounting for ` 56.26 crores. The results showed that only paddy crop registered a positive growth in productivity, all tobacco and non-tobacco crops showed a positive growth for the prices in the same period. Among all the crops redgram recorded the highest growth rate (11.05%) in cost of cultivation. The results further revealed that only paddy crop recorded positive growth in net returns and the remaining recorded a negative growth rate. FCV tobacco was more profitable as it earned more net returns and the next best alternate crop was bengalgram in un-irrigated conditions and paddy in irrigated conditions.ThesisItem Open Access A STUDY ON PRICE BEHAVIOUR OF IMPORTANT PULSES IN ANDHRA PRADESH(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2015) DIVYA, K; RAJESWARI, SThe Present study entitled “A Study on Price Behaviour of Important Pulses in Andhra Pradesh” was undertaken to study the price trends of bengalgram, blackgram, greengram and redgram, price volatility and to estimate price forecasts. The study was conducted in major pulse markets of Andhra Pradesh. Two markets were selected for each crop., bengalgram (Koilakuntla and Kurnool), blackgram (Tenali and Ponnur), greengram (Suryapeta and Thandur and redgram (Thandur and Kurnool) based on maximum arrivals. The data pertained to the period from 2000 to 2014. Apart from using simple linear trend method, twelve months ratio to moving average method, ARIMA, ARCH and GARCH models were also used. There was an increasing trend in the prices of bengalgram, blackgram, greengram and redgram in all the selected markets and were found to be highly significant at 1 per cent level of significance. The monthly increase in prices of bengalgram was found to be highest in Koilakuntla market (Rs.15.32/qtl)) whereas lowest in Kurnool market (Rs.12.28/qtl). In respect of blackgram monthly increase in prices was highest in Ponnur market (Rs.23.2/ qtl), whereas it was lowest in Tenali market (Rs.22.13/qtl). In the case of greengram the annual increase in prices was lowest in Suryapeta market (Rs.22.86/qtl), whereas it was highest in Thandur market (Rs.25.31/qtl) and in case of redgram annual increase in prices was highest in Thandur market (Rs.20.01/qtl), whereas it was lowest in Kurnool market (Rs.14.36/qtl). In all the selected markets, no seasonal variations in prices were observed. In Kurnool market the highest seasonal index for bengalgram was found in June (104.92) and lowest index was recorded in February (95.84). Blackgram recorded the highest seasonal index in Tenali market during the month of May (105.97) and lowest index was recorded in December (96.42). In the case of greengram, higher values were observed in Thandur market during the month of December (106.83) and lowest in May (93.19). In redgram the highest index was found in April (107.41) and lowest was in the month of October (94.4). The cyclical indices in the selected markets showed that there were cycles with definite period in prices of Tenali and Ponnur markets of blackgram and in Suryapeta and Thandur markets of Greengram. In the case of bengalgram and redgram prices there were no cycles observed. The irregular fluctuations in prices did not exhibit any definite periodicity in any of the selected markets. The results of ARIMA model for bengalgram indicated that the prices from January to March, 2015 would be ranging from Rs.2788 to Rs.2830 per quintal in Koilakuntla market and Rs.2565 to Rs.2583 per quintal in Kurnool market. For blackgram Rs.5966 to Rs.6009 per quintal in Tenali market and Rs.5986 to Rs.6037 per quintal in Ponnur market. For greengram Rs.6137 to Rs.3187 per quintal in Suryapeta market and Rs.6183 to Rs.6277 per quintal in Thandur market. In the case of redgram Rs.4933 to Rs.4969 per quintal in Thandur market and Rs.3680 to Rs.3813 per quintal in Kurnool market. When the forecasts were compared with the real time prices, it was observed that there was less deviation in the case of bengalgram, blackgram, greengram and redgram. With respect to price volatility, the results revealed that there was no volatility in prices of bengalgram, greengram, redgram in the selected markets and blackgram in Ponnur market. This indicated that the volatility shocks were not quite persistent in these markets. Blackgram prices in Tenali market were more volatile with a value equal to 1.02 as indicated by the sum of Alpha and Beta values.