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.
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...
(guntur, 2022-08-17) GOVINDARAJU; SRINIVASA RAO, V
Coarse cereals are known for nutria-rich content and having characteristics like drought tolerance, photo-insensitivity and resilient to climate change etc. Hence, they form the backbone of dryland agriculture. Coarse cereals include jowar, pearl millet, barley, small millets, maize and ragi. At the turn of millennium, demand for coarse cereals was declining due to changes in food habits and uncertainty of prices in agricultural markets. Farmers are generally believed to be more aware of prices. The decisions of farmers concerning time and place of sale for their farm produce are motivated by price levels. Their decisions affect the pace of arrivals and prices in different markets. Hence, forecasting these prices has a significant role.
This study investigates the secular trend, seasonal indices, and forecasting the prices of coarse cereals. Three major markets for each crop, viz., Ballari, Bangalore and Koppal for bajra and jowar, and Arsikere, Bangalore and Hassan markets for maize and ragi, are considered for this study. The data for the analytical model was mainly from secondary sources. Polynomial curve fitting (least square method) and seasonal indices (ratio to moving average method) were used to analyze the trend and seasonal components of price series. The ANN and ARIMA models were used to forecast the prices of coarse cereals in selected markets of Karnataka.
The findings reveal that prices of coarse cereals have registered a significant increase in trend over the period for all the markets. All the selected markets for coarse cereals have shown a seasonal pattern except for jowar prices in Ballari and ragi prices in Arsikere and Hassan markets. The best fit ANN and ARIMA models were used to forecast the prices of coarse cereals in all the above markets for a duration of twelve
months through a lagged series. Based on the percentage of forecast error, an ideal model was proposed among the ANN and ARIMA models for each selected market. However, ANN models outperform ARIMA in all selected markets except for ragi prices in the Bangalore market where ARIMA outperforms.
The present study entitled “Behaviour of Prices and Arrivals of Major Vegetables
in Selected markets of Tamil Nadu” was mainly aimed at to study the secular trend,
seasonal indices, cyclic variation, irregular variation, association and to forecast the
arrivals and prices by best fitted model. Four major vegetable markets viz., Chennai
Koyambedu market, Oddanchatram Gandhi market, Madurai Paravai market and
Coimbatore wholesale market for three highly consumed vegetables (onion, tomato and
Green chillies) are considered for this study.
The secondary data on monthly modal prices (Rs/qtl) and total monthly arrivals
(qtls) were collected from respective market Committee for the period of 8 years (2011 to
2018). The trend in the data was studied by the principle of least square. For evaluation of
seasonality in arrivals and prices, Multiplicative time series analysis, twelve month
moving average have been calculated.
In Koyembedu market, tomato arrivals and prices were loftier in the month of
November; onion arrivals were maximum in the month of February whereas the prices
were on hike in the month of September; chillies arrivals were high in the month of
November whereas prices reached its peak in the month of June. In Oddanchatram
market, tomato arrivals were loftier in the month of March whereas peak prices were
observed in the month of July; onion arrivals were maximum in the month of March and
the prices were on hike in the month of October; chillies arrivals were high in the month
of March whereas prices reached its peak in the month of March.
In Paravai market, tomato arrivals and prices were loftier in the month of
November; onion arrivals were maximum in the month of May whereas prices were on
hike in the month of October; chillies arrivals were high in the month of February whereas
prices reached its peak in the month of July. In Coimbatore market, the tomato arrivals
were loftier in the month of December whereas peak prices were observed in the month
of June; onion arrivals were maximum in the month of January and the prices were on
hike in the month of November; chillies arrivals were high in the month of November
whereas prices reached its peak in the month of March.
To ascertain the relationship between arrivals and prices in respective market,
correlation coefficient was computed. There was a negative and significant correlation
between arrivals and prices of Chillies in Koyembedu market; Onion in Oddanchatram
market; Tomato in Paravai market and Coimbatore vegetable Market. It was inferred
infers that the negative correlation existed between arrivals and prices for all vegetables
in their respective markets except tomato in Koyembedu and Paravai markets.
Seasonal Auto Regressive Integrated Moving Average models were used for
forecasting the arrivals and prices. At the identification stage, one or more models are
tentatively chosen and the most suitable models were selected based on highest R2 and
least RMSE. The predicted values and actual values were similar to each other in most of
The Present study entitled “Statistical models for prediction of area, production
and productivity of selected oilseeds in Andhra Pradesh” has been undertaken to fit
different trend equations like linear, non-linear and time series models for selected oilseeds
like Groundnut, Niger, Sesame and Castor and also made the future forecast up to 2022-23
AD. The study was carried out for Andhra Pradesh state using time series data from
1965-66 to 2017-18.
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 were fitted to the area, production and productivity of
selected oilseed crops. The best-fitted model for future projection was chosen based upon
highest coefficient of determination (R2) with least RMSE and MAPE values.
The study revealed that the area, production and productivity of Groundnut marked
fluctuating increasing and decreasing trend during the study period 1965-66 to 2017-18;
for the forecasted period i.e. up to 2022-23 area and production showed decreasing trend,
productivity increasing trend . In Niger there was decreasing trend in area and production
whereas productivity showed an increasing trend; the forecasts exhibited an increasing
trend in area and production, slightly increasing trend in productivity. The Sesame crop
revealed that area and production marked decreasing trend but productivity slightly
fluctuating increasing and decreasing trend; forecasts of area, production and productivity
depict increasing trend. Whereas Castor crop area and productivity showed decreasing
trend and production showed fluctuating increasingand decreasing trend during the study
period; forecasts exhibited that area as decreasing trend but production and productivity
(ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) BINDUMADHAVI, N; NAFEEZ UMAR, SHAIK
The present study entitled “Time series analysis of area, production and productivity of major pulses in Andhra Pradesh” has been undertaken to fit different linear, non-linear growth models and Auto Regressive Integrated Moving Average (ARIMA) models for the area, production and productivity of major pulses such as Bengalgram, Redgram, Greengram, Blackgram and Horsegram as well as to provide forecasts up to the year 2022 AD. The study was carried out for the state of Andhra Pradesh using time series data from 1971 to 2017.
Different growth models such as linear, logarithmic, quadratic, cubic, power, exponential models and time series models such as ARIMA were applied for the data on area, production and productivity of respective pulses and the best fitted model was chosen on the basis of diagnostic criteria like highest R2 and lowest MSE, RMSE, MAPE and BIC. The best fitted models were used to obtain the future projections upto 2022 AD. In order to study the percentage contribution of area, productivity and their interaction effects towards the growth in the production of pulse crop, decomposition analysis has been carried out.
It was observed that the area, production and productivity of Bengalgram showed an increasing trend during the study period. Redgram as well as Blackgram area, production and productivity also showed an increasing trend during the study period. Greengram area and production exhibited a decreasing trend whereas, productivity showed an increasing trend during the study period. Area and production of Horsegram showed a declining trend whereas, productivity showed an increasing trend during the study period.
The study revealed that ARIMA (1, 1, 1) model was the best fitted model for area and productivity of Bengalgram, area and production of Redgram as well as production and productivity of Blackgram respectively. Cubic model was the best fitted model for productivity of Redgram, production and productivity of Greengram as well as production and productivity of Horsegram respectively. ARIMA (1, 2, 1) was the best fitted model for Bengalgram production and Blackgram area respectively. ARIMA (2, 2, 1) was the best fitted model for Greengram area and Horsegram area respectively.
The future projections of area, production and productivity of Bengalgram, Redgram and Blackgram showed an increasing trend up to the year 2022 AD. Area and production projections of Greengram showed a decreasing trend whereas, productivity forecast showed an increasing trend. Projections of Horsegram area showed an increasing trend whereas, production and productivity seem to be stable in the upcoming years. Overall decomposition analysis revealed that the percentage contribution of area was more dominant in all the crops.
(ACHARYA N G RANGA AGRICULTURAL UNIVERSITY, GUNTUR, 2019) AJITH, S; SRINIVASA RAO, V
The present study entitled “Effect of National Food Security Mission on Food Grain Production in India” has been undertaken to study the effect on National Food Security Mission (NFSM) on major food grain crops in India as well as in the leading producing states and to forecast the area, production and productivity major food grain crops in India. This present study is based on the time series data of area, production and productivity of major food grain crops such as rice, wheat, total pulses and total coarse cereals as well as the total food grain crops from the year 1951-51 to 2016-17.
To study the effect of NFSM the total study period was divided into four time periods viz., 1951-1965 (pre-green revolution period), 1966-1988 (green revolution period), 1988-2006 (post-green revolution period) and 2007-2017 (National Food Security Mission-NFSM period). The Compound Growth Rate (CGR) and Cuddy-Della instability index were calculated for each time period as well as for the total period. The growth rate and instability in the area, production and productivity of major food grain crops during National Food Security Mission period were compared with other periods.
Linear, non-linear and time series models such as Autoregressive Integrated Moving Average (ARIMA) and Holt‟s double exponential smoothing models were fitted to the area, production and productivity of rice, wheat, total pulses, total coarse cereals and total food grain crops in India The best fitted models have been selected based on the model selection criteria such as R2, RMSE, MAE, AIC and BIC values. Forecasting of area, production and productivity of major food grains crops in India was done up to the year 2021-22 by using the respective best fitted models.
This study revealed that there was positive growth rate in the production of major food grain crops during NFSM period in all the six leading states as well as in India. Noticeably the huge growth rate was obtained in the production of wheat (36.14 per cent), pulses (16.41 per cent) and coarse cereals (21.62 per cent) as well as in the
total food grain production (29.42 per cent) in Madhya Pradesh during NFSM period. Similarly there was high growth rate in the rice production (17.49 per cent) in Bihar during the NFSM period.
This study also revealed there will be an increasing trend in the area, production and productivity of rice, wheat, total pulses, total coarse cereals and total food grain crops in India in the next five years except the cultivated area of rice and coarse cereals. The forecasted area, production and productivity of food grain crops in India would be 1,28,009 thousand hectares, 2,90,744 thousand tonnes and 2,350 kg ha-1 respectively in the year 2021-22.
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.
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
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.
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.
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
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
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.
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
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.
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
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