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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 TOTAL FACTOR PRODUCTIVITY AND SUPPLY RESPONSE OF MAJOR CROPS IN ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, 2018) DIVYA, K; BHAVANI DEVI, IThe Present study entitled “Total factor productivity and supply response of major crops in Andhra Pradesh” was undertaken to study the productivity growth and acreage response of rice, maize, groundnut, cotton, sugarcane, bengalgram and redgram. Since these crops comprised of almost 50-60 per cent of the cultivated lands in the state. For the estimation of the cropping pattern and growth rates of area, production and yield of the selected crops, data was collected for the period from 1996-97 to 2015-16. The required data for this purpose was collected from various published documents of the Directorate of Economics and Statistics, Government of Andhra Pradesh. The selected period of study was from 1996-97 to 2014-15 for the estimation of total factor productivity (TFP) in the state. Data required for this purpose was collected from the published documents of the xv Cost of Cultivation scheme of Government of India and its website. The required data on the variables chosen as the determinants of TFP in the state were collected from various Statistical Year Books published by the Directorate of Economics and Statistics, Government of Andhra Pradesh. Data Envelopment Analysis (DEA) technique was employed to calculate and decompose the Malmquist TFP indices of the selected crops. DEA helped to decompose the TFP index into various efficiency measures. Multiple regression analysis was carried out by taking the TFP index of individual crop as dependent variable to determine the factors affecting the TFP growth in Andhra Pradesh state and ordinary least square adopted from nerlovian model was used to calculate the supply response for the major crops. The cropping pattern in the state was analysed in terms of the percentage of area under cultivation of the selected crops The gross cropped area in 1991-92 was 82.48 lakh ha while in 2014-15 it was 76.90 lakh ha. Rice was the main cultivated crop in the state in the year 1991-92 with a share of 26.07 per cent of total cultivated area, followed by groundnut (21.90 per cent), cotton (3.35 per cent), sugarcane (1.54 per cent), redgram (1.34 per cent), bengalgram (0.41 per cent) and maize (0.31 per cent). The growth rates of area, production and productivity are assessed in terms of annual compound growth rates (CAGR). Area under rice declined at an annual rate of 0.46 per cent, maize grew at a phenomenal annual rate of 16.39 per cent followed by bengalgram which too registered a very encouraging growth rate of 11.83 per cent. Cotton recorded a growth rate of 6.40 per cent annually. Redgram was another crop which recorded a production growth rate of above unity i.e., 1.85. Production of groundnut declined at an annual rate of 3.61 per cent. Sugarcane one of the important commercial crop grown in the state was observed to have a negative production growth rate of 0.96 per cent. Maize recorded the highest growth xvi rate of productivity with 4.57 per cent followed by cotton (3.73 per cent), bengalgram (2.08 per cent), rice (1.66 per cent) and redgram (1.58 per cent). The growth of all the inputs were in increasing trend except for the human labour and animal labour of all the selected crops from the base year to current year. The contribution of all the inputs are higher in all the selected crops except seed and animal labour inputs that was not impressive to contribute the huge percents to the total factor productivity growth in the state. The results for rice alone the MSP as percentage of cost A2 was 150 per cent during all the years of study, while it was 17 years of study each for maize and cotton, 14 years for sugarcane, 9 years out of 10 years in respect of bengalgram, 9 years in the case of redgram. MSP as percentage of cost C2 was maximum with 140.94 in one only year for rice and 145.82 per cent for cotton in year. Area effect was most responsible factor for an increase in production of sugarcane, groundnut, bengalgram and cotton. Yield effect was most responsible factor for increasing the production of rice and redgram. Increase in maize production was mainly due to interaction effect of area and yield. The decomposition of the TFPch for the corresponding years into EFFch and TECHch revealed that the increase in TFPch, which was due to the improvement in innovation (TECHch) for all the selected crops. The variables area under high yielding varieties and annual rainfall were significantly influencing the growth of TFP. The growth in total output index was higher than the growth in the total input index for rice, maize, groundnut, cotton and redgram. The total input index was highest for rice followed by groundnut, maize, bengalgram, cotton, sugarcane and redgram. xvii The estimates of instability in area, production and yield for major crops revealed that the production (44.29 %) and yield (35.92%) was highly unstable in the case of groundnut, area (32.63%) and production (41.37%) in cotton, production (34.39) in bengalgram and production (35.11%) in redgram was highly unstable. The area, production and yield of remaining crops i.e., rice, maize, sugarcane showed low instability. Acreage, production and yield response of crops were estimated and the results of the study period from 1996-97 to 2015-16 showed that the regression coefficients of the coefficient of lagged price and rainfall was in rice, lagged yield in the case of maize, lagged yield and rainfall in ground nut, rainfall and previous year’s area in cotton, lagged price and lagged area in sugarcane, lagged price, lagged yield and lagged area in bengalgram showed positive and significant influence on acreage. The regression coefficients of the previous year’s, lagged price and Irrigation in maize, rainfall in groundnut, price and rainfall in cotton, previous year’s production and irrigation in sugarcane, previous year’s production and rainfall in bengalgram showed positive and significant influence on production. The variables influencing yield were area under irrigation and lagged yield in rice, total rainfall in maize, total rainfall and lagged yield in groundnut, area under irrigation and lagged yield in cotton and total rainfall in bengalgram and redgram. The short run and long run elasticities of area response obtained from the regression coefficient of one year lagged prices was found to be less responsive to price changes of selected crops except for cotton and bengalgram. The short run and long run elasticities of production response obtained from the regression coefficient of one year lagged prices was found to be less responsive to price changes of selected crops except for maize and xviii cotton. The short run and long run elasticities of production response obtained from the regression coefficient of one year lagged prices was found to be less responsive to price changes of selected crops except for maize and cotton. The short run and long run elasticities of yield response obtained from the regression coefficient of one year lagged prices was found to be less responsive to price changes of all the selected crops. The coefficient of adjustment for rice, maize, groundnut, cotton and redgram was quicker for area response. The adjustment was quicker in the case of cotton and redgram for production response. The adjustment took less time in the case of sugarcane, bengalgram and redgram for yield response. The above mentioned crops indicated that the farmers took less number of years to realize 95 per cent of price effect.ThesisItem Open Access STUDY ON THE DYNAMICS OF AGRICULTURAL DEVELOPMENT IN ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, 2018) MAYURI, KORIPALLI; VISHNU SANKAR RAO, DThe present study is carried out to analyze the growth performance, presence of structural change and determinants of structural change in agricultural sector and finally the impact of structural transformation on agricultural development in Andhra Pradesh state. The study was carried out for 40 years period i.e., from 1970-71 to 2010-11, which was based on time series secondary data. The period was divided into two main sub-periods i.e., i) pre-reform period 1970-71 to 1989-90 and ii) reform period 1990- 91 to 2010-2011. The period was also studied under four sub-periods i.e., period I (1970-71 to 1979-80), period II (1980-81 to 1989-90), period III (1990-91 to 1999- 2000) and period IV (2000-01 to 2009-10). The data collected were analyzed using percentage change, index numbers, exponential growth model and Cuddy and Della instability index to study growth and instability. For studying presence of structural change and determinates of agricultural growth Chow test and regression analysis employing Cobb-Douglas production function by including dummy variables were used. The GSDP and NSDP exhibited significant positive growth rates during both pre-reform and reform period. The growth rate during reform period was more compared to pre-reform period. The overall growth rate during the study period was 5.3 per cent for both GSDP and NSDP. The agricultural GSDP and NSDP showed significant positive growth rate during both pre-reform and reform period. The growth rate during reform period was more compared to pre-reform period. The overall growth rate during the study period was 2.8 per cent for both agricultural GSDP and NSDP. x The gross cropped area, growth rate during pre-reform period (-0.1) was negative while the reform period exhibited positive growth of 0.005 per cent and 0.1 per cent during the overall study period. The gross irrigated area exhibited growth rate of 1.3 per cent during pre- reform period, 0.8 per cent during reform period and 1.1 per cent during the overall study period. The growth trend of area, production and productivity of food grains, pulses and oilseeds revealed that their growth in pre-reform period was better compared to reform period. The area of cereals and production, productivity of coarse cereals was better during reform period than pre-reform period. The area of coarse cereals and production, productivity of cereals was better during pre-reform period than that of reform period. There was a drastic increase in the area under the land put to non-agricultural use. During 1970’s the land put to non-agricultural use as a percentage of total geographical area was 7.70 per cent which gradually increased over decades. In 1980’s it increased to 8.10 per cent in 1990’s to 8.90 per cent and in 2000’s increased to 9.50 per cent. The total food crops area decreased from 78 per cent (99.58 lakh ha.) in 1970’s to 66.70 per cent (87.31 lakh ha.) of gross cropped area in 2000’s whereas the non-food crops increased from 22 per cent (28.01 lakh ha.) to 33.30 per cent (43.52 lakh ha.) of gross cropped area from 1970’s to 2000’s. The overall scenario of distribution of operational land holding in Andhra Pradesh revealed a gradual increase in area as well as number of marginal, small and semi-medium holdings whereas, the area and number of medium and large farmers declined over the study period. In total rural workforce, the share of cultivators reduced from 36 per cent in 1971 to 14.47 per cent in 2011, whereas the share of agricultural labourers increased from 43.35 per cent in 1971 to 47.89 per cent in 2011. The share of agricultural workers in total rural work force increased from 1971 (79.34%) till 1991 (81.37%) and then reduced in 2001 (75.15%) and 2011 (62.36%). The results of the Chow test revealed the presence of structural break in the agricultural sector by taking agricultural GSDP as dependent variable and land, labour and capital as independent variables. The tests with factor values as well as factor productivities indicated the presence of structural break. The results of regression analysis by including dummy variables technique for slope parameters - agricultural land, labour and capital regressed on agricultural GSDP, during pre-reform period, the land and agricultural labour factors are significant at one per cent level showing significant influence on agricultural GSDP. During the reform period all the three independent variables, gross cropped area, agricultural credit and agricultural labour had no significant influence on agricultural GSDP. The results of regression analysis by including dummy variables technique for slope parameters - agricultural land productivity, labour productivity and capital productivity regressed on agricultural GSDP, during pre-reform period, all of them showed significant effect on agricultural GSDP. During the reform period, only agricultural land productivity (-0.03) showed a significant but negative effect on agricultural GSDP.ThesisItem Open Access A STUDY ON THE IMPACT ASSESSMENT OF KRISHI VIGYAN KENDRAS IN ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, 2018) MALATHI, B; VISHNU SANKAR RAO, DThe present study attempts to evaluate the impact of Krishi Vigyan Kendras With the following objectives: 1. To study the impact of Krishi Vigyan Kendras in productivity enhancement by bridging the yield gaps. 2. To evaluate the impact of vocational training programmes conducted by Krishi Vigyan Kendras in the study area. 3. To assess the economic benefits from the adoption of improved technologies. 4. To assess the factors contributing to the adoption of improved technologies by the farmers. Andhra Pradesh state was purposively selected for the study. Three KVKs, one KVK from SAUs (KVK-Amadalavalasa, Srikakulam district), one from ICAR (KVK-Kalavacharla, East Godavari district) and one from NGOs (KVK-Yagantipalli, Kurnool district) were selected for the study which has completed minimum five years of functioning. A total of 360 farmers constituting 180 beneficiaries and 180 non-beneficiaries of the improved technology provided by the sampled KVKs, and 150 trainees were selected randomly making a total sample size of 510. Both primary and secondary data were collected for the present investigation. The analytical models used for analyzing the data were technology adoption index, Cobb-Douglas type of production function, regression, decomposition analysis, Lorenz curves and Gini concentration ratio. Impact of Krishi Vigyan Kendras on crop yields and returns revealed that, in KVK-Amadalavalasa increase in yield (12.80 q ha-1 ) and per cent increase in net returns (42.94 %) over farmers practice were highest in case of mechanized system of rice intensification. Zero tillage maize technology resulted in increased net returns of Rs. 18,106 per hectare over farmers practice. In KVKYagantipalli, the results of frontline demonstrations in rice revealed that the increase in yield was highest in case of management of problematic soils i.e. reclamation of sodic soils with gypsum application as per soil test results (20.88 %) followed by foliar application of zinc (14.98 %) and integrated weed management (9.59 %) over farmers’ practice. In redgram improved technology registered overall 25.34 per cent increase in yield over the farmers’ practice with increase in net income of 44.56 per cent. Under KVK - Kalavacharla, in rice the yield of demonstration plots exceeded that of farmer's plots in all FLDs. In case of banana, it was found that increase in yield was highest (38.03 percent) in nutrient management with direct feeding of banana bunches, which resulted healthy fingers in banana. Factors affecting productivity estimated by Cobb-Douglas production function for the improved technology/ variety revealed that seed in STCR paddy (0.162), HYV redgram (0.378) and planting material in direct feeding of nutrients and skirting of bunches in banana (0.944) were positively significant. Machine labour in MSRI rice (0.219) and human labour in STCR rice (1.945), HYV rice (0.26) and direct feeding and skirting of bunches in banana (0.104) were positively significant. Nitrogen was positively significant in zero tillage maize (0.138), HYV redgram (0.44) while it was negatively significant in STCR rice (-0.159). Phosphorous was positively significant in HYV rice (0.666) and MSRI rice (0.259). Potash was positively significant (0.162) in farmers’ practice of rice in KVK-Amadalavalasa, STCR rice (0.204), HYV redgram (0.138) and farmers’ practice (0.25) and direct feeding and skirting of banana bunches in banana (0.035). Irrigation was found to be positively significant in zero tillage maize (0.112), HYV rice (0.24) and direct feeding and skirting of bunches in banana (0.094). The decomposition analysis of yield gap between the improved technology/ variety and the farmers’ practice/ local variety indicated that the technology gap was the major contributing factor in the total difference in productivity in all the crops among all the three KVKs which was highest in case of soil test crop response (STCR) in paddy (392.98 %) in KVK-Yagantipalli of Kurnool district. All the vocational trainings have given a boost to trainees by giving supplementary income and employment. Lorenz curves and Gini concentration ratios depicted that there were comparatively lesser inequalities in distribution of income obtained from improved cultivation practices of crops when compared with the inequalities in distribution of income obtained from farmer’s practices. The technological adoption index (TAI) calculated for 180 technology adopting farmers indicated that all the farmers belonged to medium and high adoption category. Age of the farmer was a negative contributor in all the crops which revealed that with the increase in age of the farmer technology adoption level decreases. Educational level of the farmer had positive and significant contribution in the technology adoption of MSRI and STCR technology in rice. KVK training is the major contributor in the technology adoption whose impact was positive in all the three KVKs. The important policy implications from the study are: Forward linkages in terms of post-harvesting, transportation, packaging and marketing are necessary for new products or high yield products. KVK should focus on post harvest techniques to support farmers especially processing to encourage them to adopt new technologies. KVKs should be developed as resource centres which can provide/facilitate the access to inputs for farmers which are the crucial factor in the adoption of new technology. Krishi Vigyan Kendra in the district need to provide proper technical support to the farmers through different educational and extension methods to reduce the extension gap for better production in the district by creating awareness among the farmers about new technologies. Modernization of soil testing labs, keeping the farmers’ needs in focus while providing training, focus upon new emerging areas like climate change, pro-harvest management and non-farm activities are need to be considered by the KVKs, host organizations and ICAR (Indian Council of Agricultural Research). Measures to be adopted to increase the outreach of KVKs by adopting innovative techniques viz. forming farmers groups, train farmers-trainer, redefining cluster approach, continuous interaction at village level, need based training, use of ICT (Information and Communication Technology), etc. To cope with the future challenges of technical advancement, the existing vocational and technical training system needs to be improved and marked with the needs of the economy. To support and to mobilize entrepreneurial skills of the farmers, KVKs should coordinate to start agribusiness centres by the farmers in villages. Better feedback mechanism is needed and KVKs should follow up the trainee farmers, rural youth and women after the completion of training programmes to make sure that they will adopt the newly acquired skill in creation of employment which will eventually lead to increased income levels and livelihood security. The study leads to the observation that KVKs are playing a pro-active role in transferring new technology at field level and with beneficial impacts, but a lot is yet to be done to bridge the yield gaps in crops and entrepreneurial development of rural community.ThesisItem Open Access PRICE BEHAVIOUR, EXPORT COMPETITIVENESS AND DIRECTION OF TRADE OF SELECTED CEREALS IN ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, 2016) SRIKALA, MALLINENI; BHAVANI DEVI, IThe study entitled “Price behaviour, Export competitiveness and Direction of trade of selected Cereals in Andhra Pradesh” was undertaken to analyse the price trend of paddy and maize, price volatility, price forecasts and validating them with real time prices, to analyse the extent of market integration among major paddy and maize markets, direction of trade of rice and maize exports from India and to study export competitiveness of selected cereals in India. The study was conducted in selected paddy and maize markets viz., Nizamabad and Suryapet (Andhra Pradesh), Sindhanur (Karnataka) and Toofanganj (West Bengal) markets for paddy crop and Nizamabad (Andhra Pradesh), Udumalpet (Tamil Nadu), Davanagere (Karnataka) and Kota (Rajasthan) markets for maize crop, which were purposively selected based on the magnitude of arrivals. The data pertained to the period from January, 2000 to December, 2015 for Nizamabad, Suryapet and Sindhanur markets and from January, 2005 to December, 2015 for Toofanganj market. In the case of maize, the data were collected from January, 2003 to December, 2015 for all selected markets from the registers maintained by the respective market committees and from agmarknet website. xx There was an increasing trend in the prices of paddy and maize in all the selected markets and were highly significant at 1 per cent level of significance. The monthly increase in prices of paddy was found to be highest in Toofanganj market (Rs. 7.35/qtl), whereas it was the lowest in Nizamabad market (Rs. 5.56/qtl). In respect of monthly increase in prices of maize was highest in Udumalpet market (Rs. 6.78/qtl), whereas it was lowest in Nizamabad market (Rs. 6.14/qtl). In all the selected markets of paddy and maize, seasonal variations in prices were observed. The results of seasonal indices indicated that the highest seasonal indices of prices were observed in the months of July (112.32) and September (106.21) in Nizamabad market followed by Suryapet in the months of February (106.66) and September (105.34), and October (103.8) and February (103.04) in Sindhanur market and September (103.74) and October (103.52) in Toofanganj market for paddy. The lowest seasonal indices of prices were observed in the month of May (92.11 and 91.44) in Nizamabad and Suryapet markets followed by June (94.62 and 98.92) in Sindhanur and Toofanganj markets respectively. In respect of maize, in Nizamabad market the maximum prices prevailed in March, April, July, August and September and minimum prices were found in October (96.21). The prices started reviving from March and the prices were fairly high in July (105.17). In Udumalpet market, the month of August (110.75) showed maximum price for maize. The minimum price was observed in January (92.86). In Davanagere market of Karnataka, the maximum price was realised in July (107.36). The minimum price was found in October (90.97). In Kota market the maximum price was observed in the months of March (103.51) and July (104.32). The price of maize was at its minimum in October (93.69). No price cycles were discernible in the selected markets of paddy as well as maize. The irregular fluctuations in prices did not exhibit any definite periodicity in any of the selected markets. The results of ARIMA model for paddy indicated that the per quintal prices from November, 2015 to April, 2016 would be ranging from Rs. 1,800 to Rs. 1,847 in Nizamabad, Rs. 1,641 to Rs. 1,703 in Suryapet market, Rs. 1,835 to Rs. 1,872 in Sindhanur market and Rs. 1,503 to Rs. 1,555 in Toofanganj market. When forecasts were compared with real time prices, it was observed that ANN model was relatively closer to real time prices of paddy in Nizamabad market, while single exponential smoothing model was better in and Toofanganj market. Results obtained through ARIMA model were relatively close real time prices of paddy in Suryapet and Sindhanur markets. xxi For maize crop the results of ARIMA model indicated that the per quintal prices from November, 2015 to April, 2016 would be ranging from Rs. 1,300 to Rs. 1,333 in Nizamabad, Rs. 1,542 to Rs. 1,484 in Udumalpet market, Rs. 1,360 to Rs. 1,314 in Davanagere market and Rs.1,320 to Rs. 1,196 in Kota market. When forecasts were compared with real time prices, it was observed that single exponential smoothing model was relatively closer to real time prices of maize in Nizamabad market, Davanagere and Kota markets, while ARIMA model was better in Udumalpet market. Monthly prices of paddy corresponding to Nizamabad and Suryapet markets became stationary at level, whereas the prices of Sindhanur and Toofanganj markets became stationary only after first differencing. Johansen’s multiple co-integration tests revealed the presence of two co-integrating equations at 5 per cent level of significance and confirmed the long-run equilibrium relationship among the markets. The causality test revealed a bi-directional influence of paddy prices between Sindhanur and Nizamabad, Suryapet and Nizamabad and Sindhanur and Suryapet markets. The Toofanganj market prices have depicted uni-directional causality on the prices of Nizamabad and Sindhanur and Suryapet market prices have shown uni-directional causality with Toofanganj market prices. Nizamabad market came to short run equilibrium within 24 days as indicated by co-efficient values. The results obtained through Vector Error Correction Model (VECM) showed that most of the markets considered under this study were integrated to each other. The findings of the ADF test suggested that monthly modal prices of maize in Nizamabad, Udumalpet and Kota markets attained stationarity at their level whereas, Davanagere market became stationarity only after first difference.The co-integration test revealed the presence of three co-integrating equations at 5 per cent level of significance and confirmed the long-run equilibrium relationship among the selected maize markets. The causality test proved the bi-directional causality between Davanagere and Nizamabad and Kota and Davanagere markets. Nizamabad market showed uni-directional causality with Udumalpet and Kota market prices. Davanagere and Kota market prices showed uni-directional causality with Udumalpet market prices. Udumalpet market came to short-run equilibrium as indicated by level of significance and speed of adjustment was rapid ie., any disturbance in price would get corrected within 12 days in Udumalpet as indicated by co-efficient values. The results of Vector Error Correction Model (VECM) showed that most of the markets considered under this study were integrated to each other. The price series of Toofanganj market showed the presence of price fluctuations as indicated by the sum of Alpha and Beta co-efficients which were nearer to one (0.9736), whereas in the remaining markets, the volatility xxii shocks were not quite persistent. The volatility in maize prices was also observed from ARCH- GARCH analysis and it revealed that maize prices in all the selected markets were less volatile. The dynamics of changes in terms of quantity of exports of rice and maize from India to different export markets have been measured by employing Markov chain model. The results revealed that others (United Kingdom, USA, Malaysia, Iraq etc.,), Bangladesh, Benin and UAE were found to be stable destinations for Indian rice exports, whereas Sri Lanka, Nepal and Saudi Arabia were the most unstable importers as they could not retain their original share. Bangladesh, Indonesia, Malaysia and Vietnam and others (Mexico, Japan, Italy, Spain etc.,) were found to be major destinations for Indian maize exports from Markov chain results. The most unstable markets among the maize importing countries were Taiwan and UAE with the zero per cent retention. The analysis of export competitiveness revealed that the Indian rice and maize have moderate degree of competitiveness as NPCs during all the years studied were between 0.5 to 1.0. PAM analysis indicated that rice and maize cultivation enjoyed a total positive transfer of Rs. 434 and Rs. 152 per quintal respectively on its tradable input costs in overall period. The estimated DRC was less than unity indicating that rice and maize have long run comparative advantage in its cultivation as compared to other countries.ThesisItem Open Access IMPACT OF CLIMATE CHANGE AND ADAPTATION STRATEGIES IN RICE CULTIVATION UNDER KRISHNA RIVER BASIN OF ANDHRA PRADESH(Acharya N.G. Ranga Agricultural University, 2016) CHANDRA SEKHAR REDDY, M; VISHNU SANKAR RAO, DThe present study tries to bring out such relevant intervention options or water management technologies by estimating the impacts of climate change to the selected adaptation strategy. The objectives formulated for assessing the impact of climate change are as follows 1) to identify the parameters causing climate change 2) to study the socio-economic conditions of the farmers and their perceptions on climate change 3) to quantify the impact of climate change on production, income and resource use 4) to analyse the impact of adaptation strategies to the climate change scenarios and 5) to calculate the cost of uncertainty for not adopting the technological interventions. The study was conducted in Nagarjuna Sagar Project command area of Krishna river basin and pertains to the agricultural year 2013-14. Both primary and secondary data were used for the present study covering five districts (Nalgonda, Khammam, Krishna, Guntur and Prakasam) falling in the project area. Purposive multistage random sampling design was adopted to obtain a representative sample of 300 farmers and primary data were collected through a well-structured interview schedule. The Just and Pope production function was used to estimate the mean yield of crops and the variance associated with the mean yield and using the estimated yield, the multiple goal programming model was used to optimize the land and water use under mid and end century climate change scenarios. Tools of decision analysis falling under the gamut of decision theory was also used to analyze economics of different interventions. Four climate change scenarios and resource availability were considered in the study.viz. S1-Current status with current levels of yield, water availability and labour availability, S2-Near future status (Next 20 years) with current level of 10 per cent reduction in water availability and 5 per cent reduction in labour availability,S3-Mid century status with projected mid century yield levels and 10 per cent reduction in water availability and 5 per cent reduction in labour xviii availability and S4-End century status with projected and century yield levels and 10 per cent reduction in water availability and 5 per cent reduction in labour availability. Six management options were considered in rice production viz.M1Current management interventions; M2-System of Rice Intensification (SRI) with 20 per cent reduction in water use; M3-Modified SRI with 15 per cent reduction in labour use; M4-Alternate Wetting and Drying (AWD) with 10 per cent less water needed rice; M5-AWD+Machine transplanting with 15 per cent reduction in labour use and M6-Direct seeding of rice with 20per cent reduction in water use for rice,10 per cent reduction labour and 10 percent reduction in yield. Cost of adaptation and expected cost of uncertainty and adopted monetary values were estimated using probability distribution of irrigation strategies of farmers and prices of rice crop. The annual minimum and mean temperatures in five districts under NSP project area increased by 1.14 0C during 1984 to 2014. The 10 years moving average shows that minimum temperatures were increased by 0.8 0C. The annual maximum mean temperatures in the five districts were also increased by 0.610C. The 10 year moving average shows that average maximum mean temperature increased by 0.41 0C. There was a significant variation in rainfall distribution from 1984 to 2014, viz Nalgonda 34 to -38 per cent, Khammam 38 to -39 per cent, Krishna 42 to – 41 per cent, Guntur 34 to -43 per cent and Prakasam 41 to – 40 per cent. The results showed that there is an increasing trend in rice yields in both the seasons in all the five districts. In the mean yield function precipitation variable is non significant for both kharif and rabi season with negative coefficient in rabi. The coefficient of temperature variable is positively significant both in kharif and rabi. The dummy variables coefficients for the districts are negatively significant for kharif except in Guntur district. The dummy variable coefficient for districts in rabi season were positively significant. The yield variance function of climate change, precipitation variable is negatively significant both in kharif and rabi. The coefficients of dummy variables for rice are non significant within kharif and rabi seasons for kharif significant in Krishna district. The projected change in mean daily temperatures for mid-twenty-first- century period were 1.930C and 2.22 0C during kharif and rabi season and for end -twenty-first- century period were 4.030C and 4.260C during kharif and rabi seasons respectively. The projected percentage change in mean precipitation for mid century was 12.5 and 17.6 per cent in kharif and rabi seasons for end century period was 13 and 53.4 percentages respectively for kharif and rabi seasons. The impact on rice yield varied from 11.4 per cent loss to -1.5 per cent in kharif for mid century with a variability of 370 to 586 kgs/ha. In rabi for mid century the impact is to the tune of 20.3 percent to 40.2 percent loss in rice yield with standard deviation of 88 to 326 kgs/ha. The impact for end century scenarios in kharif rice yields ranged from 7.8 to 31.1 xix per cent loss with a variability of 558 to 715 kgs/ha. In the end century scenario in rabi the yield loss to the extent of 35.2 to 58.1 percent with variability of 117 to 434 kgs/ha. The overall five districts yield loss is more in rabi compared to kharif ranged from 35 to 45 per cent but with less variability (201 to 268 kgs/ha) compared to kharif (452 to 680kgs/ha). Most of the climate variable coefficients and their square terms are significant for both mean variance functions. The trend coefficient is also significant for both the seasons. The impact of climate change on rice production for the mid and end century scenarios were estimated and found that there will be yield loss in the districts. The predicted variability in yield in all the districts at end-century is higher than the mid-century. The findings conclude that climate change will induce both yield loss and greater variability in yield. The optimisation model indicated that the current rice production in kharif season will be reduced by 26 per cent in the next 20 years. By adopting water saving technologies SRI,MSRI,AWD and DSR the rice production can be increased by about 50,000 to 1,00,000 tons during kharif season. The SRI and AWD will improve the gross income by Rs.0.42 billion and Rs 0.18 billion respectively. The water requirement decrease by 15.7 per cent in MSRD, 7.8 per cent in AWD and 2.8 percent in DSR water use technologies of rice production. The cost of adaptation of MSRI was Rs.4395/ha; for AWD it was Rs.646/ha and for DSR it was Rs.-3520/ha. The expected cost of uncertainty for adoption of the above technologies was Rs.12423/ha for MSRI Rs.2744/ha for AWD and Rs.1904/ha for DSR. The cost of uncertainty is higher than the cost of adoption indicates that the farmers are incurring more losses due to non adoption of technologies. To meet the water demand in the basin or irrigation project area, it is important to adopt efficient water and land management practices, such as modified system of rice intensification (machine transplantation), alternate wetting and drying of rice and direct seeding of rice. As piloting the technologies on individual farms will not have a major impact, a cluster approach (covering a group of villages in a location for each technology) will be more useful in up-scaling these management technologies. This will help to form ‘climate smart’ villages for knowledge-sharing and better updating success stories. Given the projected future labour scarcity in rice production, machine transplanting packages should be organized in villages through the involvement of the local community. There are also possibilities for establishing public–private partnership that can provide necessary support to mechanization in rice farming. To speed up the technology spread, policy interventions in terms of supplying the quality seeds in time, machine transplanting (for MSRI), water regulation in the canals, capacity-building programs and monitoring the technology adoption in the fields through stakeholder participation are highly warranted.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.