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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar
After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960.
The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.
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ThesisItem Open Access COMPARATIVE STUDY OF APSIM-WHEAT AND CERES-WHEAT MODEL FOR PRECISION MANAGEMENT OF WHEAT CROP AND MITIGATION OF THE ADVERSE IMPACT OF CLIMATE CHANGE UNDER TARAI REGION OF UTTARAKHAND(G. B. Pant University of Agriculture & Technology, Pantnagar-263145, 2022-09-01) Pareek, Neha; Nain, A. S.Climate change impacts on wheat production has widespread and national ramifications for food and nutritional security. A field study was conducted during rabi2017-18 and 2018-19 at agricultural farm of G.B. Pant University forcomparative study of APSIM-Wheat and CERES-Wheat model for precision management of wheat crop and mitigation of adverse impact of climate change Tarai region of Uttarakhand. APSIM-Wheat and CERES-Wheat, which simulates yieldhas been calibrated for winter wheat using experimental dataof 2017-18 crop season.The calibration was performed against anthesis (DAS), physiological maturity (DAS), grain and biomass yield for the three treatments of sowing dates (viz.,15th November, 25th November and 05th December) , three levels of irrigation (number of irrigation five, four and three) for two wheat varieties (HD-2967 and PBW-502).The calibrated APSIM-Wheat and CERES-Wheat model were then applied to validate anthesis (DAS), physiological maturity (DAS), yield and biomass for rabi season of 2018-19 for the three treatments of sowing dates (viz., 15th November, 25th November and 05th December) , three levels of irrigation (number of irrigation five, four and three) for two wheat varieties (HD-2967 and PBW-502). The simulated output by the CERES-Wheat model found closer to the experimental field data therefore this model was applied for other two objectives. In this study sowing time and irrigation (number of irrigations) were optimised to get economical yield. A sowing window starting from last week of October (25th October) to the first week of December (06th December) was selected for optimization of sowing date. Crop sown in the first week of November produced highest grain yield followed by sowing in second and third week in the rabi season of 2017-18 and 2018-19.The optimum number of irrigations for wheat was also evaluated by CERES-Wheat model through projected irrigation scenarios. Present study revealed that in this region three irrigations are sufficient for optimum yield. This model also applied to predict the wheat biomass and yield in the year 2030, 2050 and 2080 and found that biomass and yield will be decreased in the future years due to increase in temperature and decrease in rainfall. In-situ moisture conservation, rainwater harvesting and recycling, and efficient irrigation water use are all important adaptation strategies to combat the adverse effect of climate change. Breeders will need to develop some short-duration varieties in the coming years by changing the genetic makeup of existing varieties. Early flowering (photo- and thermo-insensitivity), early maturity, and higher productivity should be characteristics of the new varieties. Identification of crops and varieties/ hybrids with high water use efficiency, adapted to temperature extremes and high concentration of CO2 is needed in this climate change scenario.ThesisItem Open Access Paddy characterization and yield estimation using optical and SAR data(G. B. Pant University of Agriculture and Technology, Pantnagar, 2023-02) Sonam; Nain, A. S.Reliable and accurate monitoring of rice crop is vital for food security and the global economy. This study aims to characterize rice types and provide an early yield estimation using optical and Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed for summer and kharif rice to discriminate between different rice types and identify their optimum stages to represent crop growth profiles. Critical stages of discrimination were determined statistically for all rice types. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during both the seasons. Savitzky-Golay fitted temporal profile of remotely sensed indices from Sentinel-1 and Sentinel-2 satellite sensors were used to extract seasonality parameters using TIMESAT. Correlation analysis was performed at different phenophases to identify sensitive biophysical and remote sensing parameters. Finally, machine learning models were used to predict yield of rice at 45, 60 and 90 days after transplanting using remote sensing indices and biophysical parameters as inputs. The performances of ML models for early yield estimation were evaluated using standardized ranking performance index. Results revealed significant differences between backscatter profiles of different rice types especially in early vegetative stages with transplanting being the most critical stage. Transplanting/seeding, tillering, panicle initiation, peak vegetative/flowering and maturity were identified to be the most important stages to represent the backscatter profile of entire rice growth. Decision tree algorithm discriminated rice types with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The extracted seasonality parameters revealed that start time derived from SAR indices was found to accurately distinguish different types of rice. The correlation strength generally increased with the progress of crop growth in both seasons. All significant correlations were positive except that with moisture content. NRPB was strongly correlated with VV and VV/VH ratio at all stages in both the seasons. LAI and dry biomass were found to be the most sensitive biophysical parameters towards remote sensing data. Area under rice cultivation in the study area was found to be 37.1%, using random forest classifier that yielded an accuracy above 90% and kappa coefficient of 0.89. The machine learning models were able to achieve R2 and d-index values above 0.9 and RMSE below 0.5 t/ha. XGB was the best model for predicting rice yield using remote sensing and biophysical parameters. The findings of this study have the potential to contribute to the greater goal of sustainable agriculture and food security.ThesisItem Open Access Study of Energy Balance of Sugarcane (Saccharum Officinarum L.) using Remote Sensing and Crop Simulation model in Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-09) Sharma, Neha; Nain, A.S.The present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar to Study the energy balance of sugarcane using surface energy balance algorithm and CANEGRO model during 2015 and 2016. The sugarcane variety selected for the study was Co-Pant 5224. The performance of the CANEGRO model was reasonably well when compared with the observed crop parameters like Leaf Area Index, fresh cane yield (t/ha), Dry weight Yield of Cane (t/ha) etc. during the period of study. Predicted values through CANEGRO model were very close to the observed values in the experimental year. The model performance was tested on statistical ground on the basis of Index of agreement (d) and RMSE (%). The d value for the analysis was 0.86 and RMSE (%) was 17.28 %, which shows that there was limited error in the predicted values as compared to the observed values. The model was found to be more sensitive to the effect of temperature either decreasing or increasing it than mean temperature, CO2 concentration and Irrigation amount (mm) and Radiation (MJ/m2/day). Calibrated CANEGRO simulation model was also used to analyze the impact of climate change on growth and development parameters of Sugarcane. Leaf Area Index, dry weight yield of cane (kg/ha) and time taken for emergence were found to decrease in the future climatic scenarios (2030-2090). SEBAL is a surface energy balance algorithm predicting evapotranspiration using remote sensing technique. It calculates ET through a series of procedures that generates residual energy flux as precursor of ET. In this study, LANDSAT-8 (OLI+TIRS) satellite images for the crop period (2015-16 and 2016-17) have been utilized for extraction of various components of SEBAL in sugarcane crop. The parameters required for SEBAL procedure includes surface albedo, emmissivity, land surface temperature (LST), NDVI, LAI, Vegetative Fraction, momentum roughness length, canopy height, and elevation represented by SRTM-1 arc sec (DEM). The Daily ET computed through SEBAL was later validated by DSSAT computed ET. The results revealed the mean bias error (MBE) of 0.62 mm/day for SEBAL, and R2 of 0.702, represents a higher similarity between the remotely sensed and model estimated evapotranspiration values.ThesisItem Open Access Prediction of regional mustard yield of Uttarakhand and western Uttar Pradesh by developing homogeneous zones and multivariate statistical models(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Rawat, Shraddha; Singh, R.K.Rapeseed- mustard being a cool season crop, the growth and yield of the crop in a particular agroclimatic condition is mainly influenced by temperature. It is highly sensitive to temperature and photoperiod, showing quite diverse patterns of growth and development under different sets of environmental conditions. In India, mustard is mostly grown in northern and north-western parts of the country as a rabi (winter season) crop after harvest of kharif (wet rainy season) crop primarily in marginal lands with limited irrigation or on residual soil moisture. However, due to interannual variability in weather conditions, the large amount of year-to-year variability in productivity and production of mustard is observed. Therefore, there is a need to develop a system for timely and accurate estimation/prediction of productivity and production of mustard for major mustard growing area of UP and Uttarakhand. Considering yield variability and importance of mustard for the farmers, an attempt has been made to develop an approach for large (regional scale) area yield estimation. The approach includes i) zonation of study area (districts in the different zones) on the basis of inter-annual variability in mustard yield arising due to varying weather conditions, ii) to study relationship between mustard yield and weather variables iii) development of multivariate statistical models for different zone yield prediction by using SPSS iv) prediction of mustard grown area by using different approaches at zone level, v) aggregation of zonal yield at regional scale using predicted area weightage method. The zone and region level mustard yields were also predicted and forecasted by developing multivariate statistical models for individual zones and aggregation of the mustard yield at regional scale. The regional yield prediction was carried over 33 districts of Uttar Pradesh and Uttarakhand for the period of 17 years (1997-98 to 2013-14) and yield forecast for two years (2014-15 to 2015-16). The zonation of 33 districts yielded 4 clusters of districts on the basis of similarity in inter-annual yield deviations, which were mapped with the help of GIS software and were further divided into 5 homogeneous zones (Zone 1, 2A, 2B, 2C, 3A and 3B) based on geographical discontinuity. The zone level mustard yield prediction for a period of 17 years (1997-98 to 2013-14) shows quite good agreement between observed mustard yield and predicted yield with RMSE ranging from 2.36% to 12.11 %.Similarly zone level mustard yields were also forecasted for two years (2014-15 to 2015-16) by adopting same approach. The zone level mustard yields were also predicted by developing multivariate statistical model in SPSS environment. Important weather parameters such as solar radiation, air temperature, RH, wind speed, and were considered for development of multivariate model for each zone. The zone level predicted mustard yields were aggregated at region level by applying area weightage method by different approaches of area prediction (trend model, moving average method. Econometric model and ARIMA model) and were compared with observed regional mustard yields. The RMSE value for predicted mustard yield at regional scale was found to be 2.78% to 2.86%, which is considerably low as compared to CV of observed yield and trend yields. For the Forecast year the RMSE at regional level yield prediction of mustard by different methods varies from 5.10% to 6.27%. Effect of climate change showed decrease in mustard yield for zone 3A, while rise in zone 1 and 2A. Therefore, it can be concluded that application of multivariate statistical model with weather parameters, based approach for zone level mustard yield prediction/forecasting and aggregation of mustard yield at regional scale is better approach than other existing approaches.ThesisItem Open Access Retrieval of crop biophysical parameters and monitoring of rice using SAR images(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Bhatt, Chetan Kumar; Nain, Ajeet SinghUdham Singh Nagar is one of major rice producing area of Uttarakhand state, and falls in Tarai region. Sentinel-1A satellite launched in 2014 as part of the European Union's Copernicus program provides Synthetic aperture radar (SAR) data. SAR images are independent of weather conditions and solar illumination and allow observations of different features of earth. The basic goal behind the present study was to apply new generation Sentinel-1A data with dual polarization (VH and VV) to rice cropping system mapping and monitoring with the short revisit period of Sentinel-1A satellite. SAR data were pre-processed by applying European Space Agency’s Sentinel Application Platform (SNAP). The SAR images classified with a Support Vector Machine (SVM) algorithm provided in ENVI- 4.8 produced the accurate LULC map, which shows that rice area in Udham Singh Nagar covers 108,095 ha area. The overall classification accuracy of 92.88% and a Kappa coefficient of 0.9 were obtained. The relationship between Sentinel-1A backscattering coefficients (𝜎0) or their ratio and rice biophysical parameters were analyzed. The regression models were developed between biophysical parameters and (𝜎0𝑉𝑉/𝜎0𝑉𝐻). The value of coefficient of determination for LAI, fPar, crop height, biomass and water content were found 0.53, 0.47, 0.50, 0.63, 0.34 respectively which exhibit that these biophysical parameters are significantly, consistently and positively correlated with the VV and VH 𝜎0 ratio (𝜎0𝑉𝑉/𝜎0𝑉𝐻) throughout all growth stages. Two approaches (crop simulation model and SAR coupled model and statistical model) have been used to predict the field level rice yield and district level rice yield. The biases (RMSE) of coupled model and statistical model were recorded as 7.61% and 9.12%, respectively. The average district yield generated from these two models were 3190 and 3344 kg/ha respectively which is quite close to five years average district yield of 3160 kg/ha. However, estimates provided by coupled model are more accurate than statistical models. Therefore, coupled model could be a good option to predict the plot level and regional yield of rice. On the basis of results obtained it can be concluded that Sentinel-1A SAR data has great potential for mapping of rice, estimation of biophysical parameters and timely rice growth monitoring with the ability to forecast the yield of rice crop. The prediction of rice crop is an important step that could be used to assist farmers and policy makers by providing in-season estimates of the rice yield and production.The information could be used for better planning of the resources.
