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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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2018 - 201912020 - 20221
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Subject: Agricultural Economics × Author: AREEF, MULLA × Start date: 2018 ×
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    ThesisItemOpen Access
    A STUDY ON INCOME INEQUALITIES AMONG AGRICULTURAL HOUSEHOLDS IN ANDHRA PRADESH STATE
    (guntur, 2022-08-04) AREEF, MULLA; RADHA DEPARTMENT, Y.
    For this study, primary data on agricultural households were collected through personal interview method for the agricultural year 2018-19 from Andhra Pradesh state to analyse the agricultural household’s annual income from different sources, determinants of different income sources, construction & decomposition of the income inequality and the consumption expenditure pattern across landholding size of farmer households. Totally 300 agricultural households were selected with 100 farmers each from three selected districts (viz., Srikakulam, Guntur and Ananthapuramu) based on highest number of operational holding. One hundred farmers in each district were further distributed among top two mandals with highest operational holding by adopting the proportionate stratified random sampling method. To meet the objectives of present study, fifty farmers in each mandal were conveniently distributed among five categories of landholdings with ten farmers in each category. The descriptive statistics and various cost concepts (Cost A, B, C) were used to calculate the agricultural household’s income from different sources viz., cultivation, agricultural wages, livestock, business/ services, off-farm and other sources and analysed total annual net income of different landholding categories of farmers in the study area. Ogive Index (OI), Simpson Index (SI) and Herfindahl-Hirschman Index (HHI) were employed to capture the number of income generating activities. Seemingly Unrelated Regression Estimator (SURE) model was used to identify factors which help in distribution of income among various sources. Lorenz curve, Gini ratio, Atkinson’s coefficient, Mean Log Deviation (MLD) and Theil’s index were employed to measure the income inequality for agricultural households across landholding categories. Lerman and Yitzhaki (1985) methodology was followed to decompose the Gini coefficient of total income by income sources and XIII regression based inequality decomposition (Shorrocks, 1982) approach was employed to identify each factor contribution to income inequality. Average propensity to consumption (APC) was formulated to know the proportion of income consumed by agricultural households and Engel ratio was estimated to know the difference in expenditure on each of food and non-food items separately by the agricultural households. Lorenz curve, Gini ratio, Atkinson’s coefficient, MLD and Theil’s index were employed to measure the consumption expenditure inequality for agricultural households across landholding categories. For marginal and small farmer households, income from livestock and agricultural wages combinedly contributed to nearly 50 per cent of total income. Semi-medium, medium and large farmer households received nearly 50 per cent of income from cultivation only. Access to credit, access to extension services, access to irrigation, access to price information, age of household head, education, farming experience, non-farm income earning members, number of animals, family size of household, size of landholding, size of operational holding and value of farm assets were the major determinants to access various income sources among agricultural households. Across the landholding size wise categories, except large farmers higher unequal distribution of income was reported by other sources. Gini, Atkinson, MLD and Theil indices vary across the landholding size categories. Among different sources, the highest proportion of income inequality share was contributed by cultivation across the landholding size categories except marginal farmer households. Similarly, among the factors analysed, the highest proportion of income inequality share was contributed by access to credit followed by access to irrigation, age of household head, etc. Medium farmer households were observed with highest consumption expenditure share on high value commodities followed by large, small, marginal farmers and semi-medium farmer households. Marginal farmer households were recorded with lower monthly income and showed higher average propensity to consumption expenditure. However large farmers were with higher monthly income but lower average propensity to consumption expenditure. Diversification of income earning activities towards cultivation and animal husbandry will be useful to marginal and small farmer households to maintain minimum level of income per month. There is a need to emphasize on intensification as well as diversification of fragmented landholdings especially for marginal and small farmer households. Village level remunerative price realization (viz., FHP, MSP, etc.) may act as a push factor to enhance farmers income. Income received from non-farm activities, if reinvested in business activities through purchase of raw materials will improve the farmer’s income from non-farm activities. Government should focus on improving public provisioning of quality medical services and education, so as to reduce the share of expenditure on non-food items and enhance the consumption expenditure on high value commodities, among the food items.
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    ThesisItemOpen Access
    AN ECONOMIC ANALYSIS OF PRICE BEHAVIOUR OF ONION IN MAJOR MARKETS OF INDIA
    (Acharya N.G. Ranga Agricultural University, 2018) AREEF, MULLA; RAJESWARI, S
    The Present study entitled “An economic analysis of price behaviour of onion in major markets of India” was undertaken to study the price trends of onion, price forecasts and price volatility in selected markets of India. Three markets viz., Lasalgaon, Bangalore and Kurnool were selected based on maximum arrivals. The data pertained to the period from 2003 to 2017. Time series analysis was employed for studying the price behavior of onion for each market. ARIMA, trend analysis, moving average, single exponential smoothing, double exponential smoothing, winter’s multiplicative model, decomposition fit and Artificial Neural Network (ANN) were used for forecasting of prices. Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are used for analyzing price volatility. The annual increase in prices of onion was found to be the highest in Lasalgaon market (7.33 Rs/qtl) whereas it was the lowest in Kurnool market (6.22 Rs/qtl). Bangalore and Kurnool were found to be statistically significant at 1 per cent level of significance. In these three markets, the contribution of time to change in prices was to the tune of 19 per cent to 26 per cent as indicated by adj-R2. In Lasalgaon market the highest seasonal index was found in October, followed by November and August and the indices stood at 149.01, 139.30 and 122.76 respectively. Lowest seasonal index was recorded in May with 57.23. In Maharashtra harvesting of kharif season onion crop started from October to December. Hence, kharif crop starts arriving at the market in small quantities during the first fortnight of October. The prices in October reach their highest of all the months. It is mainly for the reason that stocks made from earlier rabi season dwindling and there are still some time for main kharif onion crop to enter the market. The limited arrivals fetch a good price and normally the prices during this month are rewarding. Even in November and December months also have highest prices, this is true because farmer respond to the higher prices and bring more produce to sale at market. In Bangalore market the highest seasonal index was found in November, followed by January and August as the indices stood at 130.94, 115.90 and 115.66 respectively. Lowest seasonal index was recorded in April with 70.91. In Kurnool market the highest seasonal index was found in August, followed by November and July as the indices stood at 140.80, 121.31 and 117.07 respectively. Lowest seasonal index was recorded in May with 62.72. In Bangalore and Kurnool markets the early kharif crop starts arriving the market in August month. And the late kharif crop hits the market in October and November months. These months where highest onion arrivals present also have the highest prices. From the results it was found that no price cycles were identified in the selected markets (Lasalgaon, Bangalore and Kurnool) of India for onion crop. Irregular fluctuations did not exhibit any definite periodicity in their occurrence in the Lasalgaon, Bangalore and Kurnool markets. The ARIMA model (0,1,1) (2,1,1) was found to fit the series suitably to forecast prices of onion in Lasalgaon market. According to the forecasts the price of onion would be ranging from Rs. 2879 to Rs. 2748 per quintal for the months from January to June 2018. The ARIMA model (1,1,0) (1,1,1) was found to fit for forecast prices in Bangalore market. According to the forecasts the price of onion would be ranging from Rs. 3495 to Rs. 3395 per quintal for the months from January to June 2018. The ARIMA model (1,1,1) (1,1,1) was found to fit the series suitably to forecast prices in Kurnool market. According to the forecasts the price of onion would be ranging from Rs. 2956 to Rs. 1651 per quintal for the months from January to June 2018. Price volatility results revealed that there was high volatility in onion prices in Lasalgaon market as the sum of alpha and beta values were 0.99 next followed by Bangalore (α+β = 0.94) and Kurnool (α+β = 0.93) during the period from 2004 to 2017. These values were very closer to one, indicated that the volatility shocks were quite persistent in these markets.