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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Open Access
CLUSTERING OF RICE GENOTYPES -A MULTIVARIATE APPROACH
(Acharya N.G. Ranga Agricultural University, 2018) DEVADASU CHINNI; NAFEEZ UMAR, SK
Secondary data on 17 yield and yield contributing characters was collected from Agricultural Research Station (ARS), Nellore, Andhra Pradesh, at which experiment was carried out on 60 rice genotypes, during early kharif, 2016 to evaluate, categorize and classify them and for computation of Principal Components to determine the relative importance of Principal Components and characters involved in them. Studies based on genetic divergence utilizing D2 analysis revealed that, the genotypes were grouped into 8 clusters of which clusters II was the largest cluster consisting of 21 genotypes while cluster III, IV, VII and VIII are the smallest clusters with only single genotype in each of them. The maximum intra cluster distance was found in cluster VI (D = 371.74) consisting of 8 genotypes. From the inter cluster D2 values of eight clusters, it can be seen that the highest divergence occurred between cluster V and cluster VI (1651.37) While the minimum inter cluster distance was noticed between cluster IV and cluster VII (94.06). It is observed that cluster III as well as cluster VIII had recorded highest means values for most of the characters. Out of 17 characters studied the maximum contribution (79.66 %) towards total divergence is by 5 characters only viz., days to maturity, test weight, flag leaf width, flag leaf length, days to 50% flowering. To know the relative importance and usefulness of variables and genotypes, principal component analysis was done which explained 76% variability through first six principal components. Data were further analyzed using principal factor analysis to offset the limitation of principal component analysis. All the variables exhibited high loading on different factors. Principal factor scores were obtained to know the performance of different genotypes in different factors that ascribed to a particular set of characters. Thus, the genotypes JGL 11118, WHITE PONNI, NLR 33671, NLR 33057 and TN 1 were having high principal factor score in PF I. Similarly, genotypes IR 109A235, IR 64, MTU1010, BG63672, NLR3217, NLR33359 and IR10C172 having high scores in PF II. Likewise, genotypes NLR 3042, NLR 40065, NLR 3296, ADT 37, NLR3350, NLR3407 and NLR30491 in PF III; NLR 3241, JGL 1798, NLR 40058, NLR40024 in PF IV; IR 11C208, IR 11C208, MDT 10, IR 11C228, ADT 43, IR64197, IR11C219 in PF V and IR 64197, IR11C186 in PF VI were found to be having high principal factor scores.
Open Access
FORECASTING OF ARRIVALS AND PRICES OF RED CHIILIES IN GUNTUR AND KHAMMAM MARKET YARDS
(Acharya N.G. Ranga Agricultural University, 2018) VENKATAVISWATEJA, B; SRINIVASA RAO, V
The present study entitled “Forecasting of Arrivals and Prices of Red Chillies in Guntur and Khammam Market Yards” has been undertaken to fit different time series models on arrivals and prices data of red chillies of selected market yards and to forecast the arrivals and prices by best fitted model up to 2019. The time series monthly secondary data was collected from April, 2002 to December, 2017 (189 months) for the study. Different time series models like Exponential Smoothing, ARIMA, ARCH, GARCH models were fitted to the arrivals and prices data and the best fitted model was selected based on highest R2, least MAPE, MAE, RMSE and BIC values for future projections. The study revealed that the arrivals of red chillies in Guntur market yard showed an increasing trend during the study period and the predicted values indicate that the highest arrivals observed in the month of March every year including the years 2018 and 2019 and followed same seasonality. As per the prices is concerned, the prices of red chillies in Guntur market yard showed an increasing trend during the study period and the predicted values indicate that the highest prices observed in the month of October every year including the years 2018 and 2019 and followed same seasonality. In Khammam market yard, the arrivals of red chillies showed an increasing trend during the study period and the predicted values indicate that the highest arrivals observed in the month of March every year including the years 2018 and 2019 and followed same seasonality. As per the prices is concerned, the prices of red chillies in Khammam market yard showed an increasing trend during the study period and the predicted values indicate that the highest prices observed in the month of January every year including the years 2018 and 2019 and followed same seasonality. It was observed that the arrivals and prices are positive non-significantly correlated in the both market yards. Hence to get good price for red chillies in both market yards the farmers are suggested to bring their produce to the market yards during the month showing highest prices.
Open Access
FOREWARNING MODELS FOR PESTS AND DISEASES OF GROUNDNUT
(Acharya N.G. Ranga Agricultural University, 2017) AMARNADH, V; RAVINDRA REDDY, B
The present study “Forewarning models for pests and diseases of groundnut” was mainly aimed at to study the behaviour of climate factors on major pests and diseases of groundnut, to establish association between climatic factors and pests and diseases of various groundnut varieties in different years in groundnut growing seasons, to generate forewarning statistical models for prediction of major pests and diseases based on climatic factors and also to study the influence of pests and diseases on various groundnut varieties with respect to climate factors. The selection of location (Regional Agricultural Research Station (RARS), Tirupati) for data collection was done on the basis of major groundnut grown area and also compatibility. The secondary data on major pests (%) and disease (%) incidence of various groundnut varieties along with climate factors were collected for the period from 2007 to 2016 (10 years) during crop seasons. The correlation studies were under taken to study the relationship between various pests and disease incidence subject to the climate factors. The Multiple Linear Regression (MLR) models were used for predication of groundnut pest and disease incidence. The logistic models were also used for prediction of the probabilities of occurrence /non-occurrence of pests and disease of groundnut in standard weeks of groundnut growing seasons. The descriptive statistics were used to know the behaviour of climate factors along with pests and disease incidence over years during the crop seasons. Analysis of Variance (ANOVA) techniques applied to test the significance between standard weeks/varieties/years with respect to pest and diseases of groundnut. Finally, markov chain models were used to identifying the presence/absence of pest in consequent days effectively. xvii The results revealed that climatic factors from 2007 to 2016 in groundnut growing seasons the rainfall distribution varied greatly within groundnut growing seasons over years (13.61 mm – 36.06 mm). The average minimum temperatures (21.52°C – 22.03°C), maximum temperatures 31.80°C – 34.75°C), morning relative humidity (73.11 - 83.58%) and evening relative humidity (43.81 - 58.36%) were observed. The results revealed that the days with RH > 78 per cent, temperature (13°C - 42°C) and weekly rainfall are most critical factors in the development of leafhopper incidence, the days with RH > 78 per cent, temperature (15°C - 42°C) are the most critical factors in the development of groundnut leaf miner incidence, the days with RH > 77 per cent, temperature (19°C - 37°C) are the most critical factors in the development of thrips incidence and the days with RH > 77 per cent, temperature (15°C - 43°C) are the most critical factors in the development of root grub incidence. The days with RH > 81 per cent, temperature (16°C - 35°C) and weekly rainfall are the most critical factors in the development of late leaf spot incidence and the days with RH > 82 per cent, temperature (21.2°C - 35°C) and weekly rainfall are the most critical factors in the development of rust incidence. Correlation coefficients were computed to ascertain the pattern of relationship between major pests/diseases and climate factors over years (2007-2016) and within year (groundnut growing seasons) under different groundnut varieties. Overall for the years 2007 to 2016 the results of correlation studies revealed that, there was a positive relationship between the leafhopper incidence and climate factors viz., rainfall, evening relative humidity and sunshine hours. There exist positive relationship between the groundnut leaf miner incidence and maximum temperature, minimum temperature, rainfall and evening relative humidity and negative relationship with morning relative humidity and sunshine hours. For thrips there exist positive relationship with temperatures and wind velocity and negative relationship with morning relative humidity, evening relative humidity, rainfall and sunshine hours. In case of root grub there exist positive relationship with temperatures, rainfall, evening relative humidity, wind velocity and negative relationship with morning relative humidity and sunshine hours. The results on late leaf spot revealed that the positive relationship with temperatures, sunshine hours and the negative relationship with morning relative humidity, evening relative humidity and wind velocity. For rust, among the climate factors evening relative humidity, wind velocity and rainfall exhibited negative association and rest of the climate factors were positively associated. xviii The results of ANOVA for major pests/diseases established that there was significant variation between the varieties, between the standard weeks and over years. The MLR models for within year and between years found to be useful in the prediction of various pests and diseases incidence. The logistic models were found to be useful in the prediction of probabilities for occurrence and non-occurrence of various pests and disease incidence of groundnut. The markov chain models revealed that there was significant change occurring of various pests except root grub in consecutive days for the latest period (2012-16). Further, with the help these models one can predict that the occurring of various pests/diseases of groundnut over the period of time.
Open Access
CROP-YIELD WEATHER RELATIONSHIP OF PRAKASAM DISTRICT IN ANDHRA PRADESH
(Acharya N.G. Ranga Agricultural University, 2016) HEROLD DEEPAK ROY, B; SHAIK NAFEEZ UMAR
Rainfall is one of the important climatic factor that influences crop production in particular and agriculture in general. Here, the distribution of rainfall can be studied by fitting suitable statistical distribution to the monthly rainfall data recorded over 15 years (2000-01 to 2014-15) for Prakasam district in Andhra Pradesh. It revealed that most of the months follow ‘Type I’ (or) Beta distribution while the months of July and September assumed Pearsonian “Type II” (or) “Type VII” i.e., Normal distribution and the month of November shown “Pearsonian Type IV” distribution. Statistically, estimation of a crop yield-weather relationship is fitting a multiple regression equation with yield as the dependent variable and weather parameters during the crop growth period as the independent variables. The analysis has been carried out on the basis of crop yields and weather variables for 15 years of monthly time series data (2000-01 to 2014-15). Weather impact on the crop yields was studied on the basis of rainfall, temperature (maximum and minimum) and relative humidity (AM and PM). In fitting the crop yield-weather relationships, the assumption of a continuous time trend was found to be inappropriate when the impact of new technology may exists in the form of quantum jumps over time which is termed as discrete time effect. For this situation, concept of control charts was applied using one sigma limits and sub-periods were identified. These sub-periods were formed with the year of quantum jump as the cut-off point. Nature of time trend in the sub-periods and overall yields was investigated by fitting time trend regression equation respectively. All the crops revealed ‘differential’ trend effect in the yields of the two sub-periods indicating that there could be a differential weather response of the crop. Hence, it is appropriate to fit the crop yield-weather relationships separately for each of the two sub-periods as well as for overall period. It was observed that an overall relationship may not be appropriate to explain the yield variations as it consisted of certain irrelevant regressors. Considering this behaviour, separate relationships were fitted for the different subperiods existing in the crop yield data and the analysis revealed the existence of a differential response of the yields to weather. The variables identified in these relations suitably explain the weather response with respect to the crop growth stages. Hence, it was concluded that yields under a given technology only could be forecasted (based on weather variables) on the basis of the corresponding subperiod relationship (equation).
Open Access
ANALYSIS OF SPATIAL AND TEMPORAL VARIATIONS IN AREA, PRODUCTION AND PRODUCTIVITY OF TOBACCO IN ANDHRA PRADESH
(Acharya N.G. Ranga Agricultural University, 2016) SRI RAMA JOGAMBA, J; SRINIVASA RAO, V
An attempt was made to study the spatial and temporal variations in area, production and productivity of tobacco in the major tobacco growing districts of Andhra Pradesh viz., Prakasam, Guntur, East Godavari, West Godavari and the Andhra Pradesh state as a whole. The study was based on 28 years of tobacco data from 1987 to 2014. The graphical analysis was used to study the variations in area, production and productivity of tobacco. An attempt was also made to measure the growth in area, production and productivity of tobacco with due consideration of discontinuity in the data. Generally, the time series data on production of crops often exhibits a discontinuity in the year to year variations. These disturbances are mainly due to the impact of technological innovations in the crop. Under this situation, the conventional time trend models fail to provide efficient forecasts, as these models are based on the assumption of uniformity in the year to year variations. To deal with this situation, the spline models were explored for forecasting the tobacco production, as discontinuity in the year to year variations is the fundamental assumption in these models. The graphical analysis indicated that the time series data on area, production and productivity of tobacco exhibited a discontinuous trend in all the districts as well as in the state as whole. The growth analysis revealed that the area of tobacco was increasing in the districts of Prakasam and West Godavari and there is a considerable decline in Guntur and East Godavari districts and in the whole state of Andhra Pradesh. Production of tobacco was increasing in all the districts, except in the district of East Godavari and there was a considerable increase in the average level of productivity over the years due to the technological innovations in the crop. The spline models were found to be relatively efficient than the conventional trend fitting models in forecasting of tobacco
Open Access
STATISTICAL ANALYSIS ON ARRIVALS AND PRICES OF COTTON IN SELECTED MARKETS OF ANDHRA PRADES
(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2015) CHAITHANYA KUMAR, G; MOHAN NAIDU, G
The present study “Statistical analysis on arrivals and prices of cotton in selected markets of Andhra Pradesh” was mainly aimed at to study the secular trend, seasonal, cyclic fluctuations, association and to forecast the arrivals and prices of cotton in the selected markets of Andhra Pradesh. Five markets were selected for the study viz., Adoni, Jammikunta, Karimnagar, Khammam and Warangal based on maximum quantity of arrivals. The secondary data on monthly total arrivals (Qtls) and modal prices (Rs/Qtls) were collected for the period from April 2000-01 to March 2013-14 (14years) for the selected markets. The method of least squares, twelve months ratio to centered moving average, correlation analysis and ARIMA model were used. xiv The results revealed that in the long run all the selected markets showed an increasing trend except in Warangal market with regard to cotton arrivals whereas the trend in prices of cotton were almost similar (increasing) in selected markets. In the case of seasonal indices of arrivals and prices of cotton in Adoni market revealed that the highest arrivals was noticed in month of January and the lowest arrivals was noticed in the month of July. The highest price was noticed in the month of September and the lowest price was noticed in the month of December. In Jammikunta market, the highest arrivals in month of November and lowest in the month of September while highest prices was observed in the month of July and lowest prices in October. In Karimnagr market, maximum arrivals in month of December and minimum in September whereas the highest prices was the month of March and the lowest prices in May. In Khammam market, the highest arrivals were noticed in month of November and the lowest in September while highest prices were observed during the month of July and the lowest prices in the month of October. In Warangal market, the peak arrivals were noticed in month of November and the lowest arrivals in the month of September whereas the highest prices were noticed in the month of September and lowest prices in the month of November. Well defined cycles could not be discerned in all the selected markets of cotton. The cyclical trend in selected markets showed that there were no constant period between the cycles in both arrivals and prices. The correlation coefficient was computed to ascertain the pattern of association between market arrivals and prices of cotton in selected markets. A positive and significant relationship was recorded in Adoni, Karimnagar and Khammam markets where as in Jammikunta market positive and non significant relationship was observed. The negative and non significant relationship was noticed in Warangal market. The arrivals and price were forecasted from April, 2014 to March, 2015 in the selected markets and the results indicated that the arrivals were ranging from 11627 to 284891 quintals of cotton whereas the prices ranged from 4236 to 5166 Rs/Quintals.
Open Access
TIME SERIES ANALYSIS OF AREA, PRODUCTION AND PRDOUCTIVITY OF MAJOR COARSE CEREALS IN ANDHRA PRADESH
(ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY, 2014) NIREESHA, VAKA; SRINIVASA RAO, V
The Present study entitled “Time Series Analysis of area, production and productivity of major coarse cereals in Andhra Pradesh” has been undertaken to fit different trend equations like linear, non-linear and time series models for major coarse cereals like Maize, Sorghum, Pearl millet and Finger milet and also made the future forecasts by 2020 AD. The study was carried out for Andhra Pradesh state using time series data from 1966 to 2012. For forecasting purpose ten linear and non-linear growth models viz., linear, logarithmic, inverse, quadratic, cubic, compound, power, s-curve, growth and exponential and time series models like ARIMA and Exponential Smoothing models were fitted to the area, production and productivity of selected crops and influence of weather parameters like Maximum temperature (0C), Minimum temperature (0C), Rainfall (mm), Morning Relative Humidity (RH1) (%), Evening Relative Humidity (RH2) (%) on productivity were calculated by using statistical analysis like Karl Pearson’s Correlation analysis and Multiple Linear Regression Analysis. the best-fitted model for future projection was chosen based upon highest Theil’s U-Statistic, coefficient of determination (R2) and significant Adjusted R2 with least MAPE values. The study revealed that the area, production and productivity of maize marked increasing trend during the study period 1966-2012 the same trend was continued for the forecasted period i.e up to 2020. In sorghum there was an increasing trend followed by decreasing trend in area and production whereas productivity showed an increasing trend; the forecasts also exhibited an increasing trend. The pearl millet crop revealed that area showed decreasing trend but production and productivity showed slightly increasing trend. Whereas finger millet crop area and production showed decreasing trend and productivity showed slightly increasing trend during the study period. Forecasts also exhibited the same trend. From correlation analysis where RH2 showed significant correlation with productivity of Maize crop. In sorghum crop, only RH1 showed significant correlation with productivity. In pearl millet, RH1 and RH2 showed significant correlation with productivity. In case of finger millet for productivity only RH1 showed significant correlation. The Multiple Linear Regression Analysis revealed that the predicted models for all the crops were significant in RH1. It was also identified that other than weather parameters many factors are influencing Productivity of these crops.

Acharya N G Ranga Agricultural University, Guntur

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