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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 Extreme weather events and contingency crop planning under changing climatic scenario in tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Dhek, Himanshu; Ravi KiranThe current study entitled “Extreme Weather Events and Contingency Crop Planning under Changing Climatic Scenario in Tarai region of Uttarakhand" was undertaken to analyze and detect trends in rainy days & rainfall and to study occurrence of drought, extreme rainfall events, heat & cold waves, probabilities of dry & wet weeks utilizing Markov chain model in order to to generate a contingency crop plan using daily weather data of Pantnagar from 1981-2021. The long term daily weather data was utilized to compute annual, seasonal, monthly & weekly rainy days & rainfall of tarai region as well as to calculate the heat & cold waves occurrence during the study period.The statistical parameters, drought, extreme events and probabilities of dry & wet week were estimated by Weather Cock software. XLSTAT was used with MS-excel for trend detection through Mann Kendall & Sen’s slope estimation test. The study revealed that the average annual rainfall in tarai region was 1552.31 mm distributed across 57 annual rainy days. Monsoon season accounted for 85% of annual rainfall & 77% of rainy days respectively. August received the maximum rainfall. The major concentration of rainfall was observed from the start of 23rd SMW to 39th MW. The findings disclosed the percentage of drought occurrence for no drought, moderate drought & severe drought in tarai region to be 71%, 29% and 0%, respectively. Monsoon season in contribution 92.89% of the total heavy rainfall events (235 recorded events) in the study period. The high probability of occurrence of moderate drought, heavy rainfall events, heat waves & cold waves in tarai region in an erratic & uncertain weather caused due to climate change confirmed need of a plan to ameliorate the damages caused by such events. Hence, a contingency crop plan was developed to tackle weather related contingencies in tarai region of UttarakhandThesisItem Open Access Use of crop simulation model and machine learning techniques to predict rice yield in Udham Singh Nagar district of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-09) Susheel Kumar; Rajeev RanjanThe present study was conducted at C6 Block in the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar to predict rice yield during kharif season 2021. Calibration and validation were carried out using the CERES-Rice (version 4.8) model. The experiment was laid out with two prominent cultivars (HKR-47 and Pusa Basmati 1509), two transplanting dates (07th July and 17th July), and two fertilizers doses (F1= 150:60:40 and F2= 180:90:60) to calibrate the CERES-Rice model, so that model could be used for district level rice yield prediction. SMLR and ANN techniques were used to predict rice yield at the district level. Using genetic coefficients, the model was calibrated with the experimental dataset from 2021. The effectiveness of the genetic coefficient was verified for variety HKR-47 using the experimental dataset of 2018 and for variety Pusa Basmati 1509 using the experimental dataset of 2020. Two different approaches i.e., statistical and machine learning were used to predict rice yield at the district level. Experimental analysis suggested that HKR-47 and Pusa Basmati 1509 both varieties performed better when transplanted on 17th July as compared to 07th July. The performance of the model CERES-Rice was satisfactory for two transplanting dates, two fertilizer doses, and two varieties during the period of study for almost all crop characters. In case of calibration the % RMSE for observed and simulated data for emergence days panicle initiation, anthesis, physiological maturity, grain yield, Leaf Area Index and harvest index was found to be 25, 9.59, 3.46, 7.56, 17.77, 9.67, and 8.69, respectively for HKR-47. While in the case of Pusa Basmati 1509 the % RMSE for emergence days, panicle initiation, anthesis, physiological maturity, grain yield, Leaf Area Index, and harvest index was found to be 20, 6, 16.77, 2.30, 5.74, 15.17, and 7.59, respectively. In the case of validating the data for HKR-47, the % RMSE for observed and simulated data for anthesis days, physiological maturity, and grain yield was found to be 12.32, 10.84, and 8.82, respectively. While in the case of Pusa Basmati 1509 % RMSE for grain yield and harvest index was found to be 8.09 and 8.02, respectively. Stepwise multiple regression equations were developed for yield prediction by using 19 years of weather and observed yield data. A total of four models were developed among them model 4 gave the best results (R2 = 0.91 and RMSE = 3.14%). Yield prediction models for rice crop have been developed using stepwise multiple linear regression (SMLR) and the ANN approach. For calibration of the model 15 years (2001-2015) of weather and yield data and for validation 4 years (2016-2019) of weather and yield data were used. The SMLR model performed well with an R2, nRMSE, and MBE value of 0.89, 0.177 %, and -0.149 t ha-1 respectively for calibration and 0.545, 0.66% and -0.22 t ha-1 for validation. In the ANN method, the value of R2, nRMSE and MBE is 1, 0.012 % and 6.136383e-05 t ha-1 respectively for calibration and 0.99, 0.0092 % and -0.0011 t ha-1, respectively for validation. In between statistical and machine learning approaches, the machine learning approach performs better for rice yield prediction with the coefficient of correlation reaching (R2) almost 1.ThesisItem Open Access Optimising fertilizer applications to wheat cultivars under different sowing dates using CERES-Wheat model(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Rawat, Shivali; Singh, R. K.Wheat, (Triticum aestivum L.) is the most significant among the crops grown for the grain purpose worldwide. A field experiment was conducted during rabi season of 2021-22 at Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar (Uttarakhand) for evaluation of two different wheat cultivars under three dates of sowing and varying levels of nitrogen fertilizer using CERES-Wheat model. The experiment was laid out in split-split plot design with 3 dates of sowing (D1= 9th November 2021, D2= 24th November 2021 and D3= 11th December 2021) as main plots, 2 wheat varieties (V1= UP-2855 and V2= DBW-187) and 3 levels of nitrogen fertilizer (N1=75 kg ha-1 with 60% K2O and 40% P2O5, N2=112.5 kg ha-1 with 60% K2O and 40% P2O5 and N3=150 kg ha-1 with 60% K2O and 40% P2O5) as sub plots. The experimental dataset of the field was used to calibrate and validate the CERES-Wheat (DSSAT v4.7) model for two cultivars of wheat. The findings showed that there was a fair amount of agreement between simulated and measured crop phenology, yield and its attributes using the genetic coefficients derived from calibration of the CERES-Wheat model under various treatment combinations as RMSE value lied within the confidence band limit (<20%). Different dates of sowing significantly influenced the crop growth attributing characters as well as yield and yield attributing characters. Due to the longer growing period, the crop sown on 9th November produced the highest crop growth characters, grain yield (4340.72 kg ha-1) and B:C ratio (2.15) while the crop sown on 11th December reported the lowest crop growth characters, grain yield (3036.44 kg ha-1) and B:C ratio (1.2). Among the varieties, DBW187 had higher crop growing characters, grain yield (3787.14 kg ha-1) and high B:C ratio (1.75) than UP-2855. It was mainly due to higher uptake of N, P and K by the variety DBW-187. Highest crop growing characters, grain yield (3967 kg ha-1) and B:C ratio (1.83) was noticed from the crop with 150 kg N ha-1with 60% K2O and 40% P2O5. The model's sensitivity was investigated for various irrigation amount and it was discovered that the model was sensitive to variations in irrigation amount whether increased or decreased from normal irrigation. The simulation for grain yield was better with reasonable error (Grain yield RMSE= 8.32%) according to the model's overall performance as measured by the test criterion to evaluate the CERESWheat model for phenology and yield attributes of three dates of sowing, two varieties and three levels of nitrogen fertilizer. The model performed well in terms of simulating the decrease in grain yield that was observed in the experiment with delayed sowing and decreasing nitrogen fertilizer levels. The model's highest simulated grain yield for the DBW-187 variety of wheat under 9th November sowing date and with 150 kg N ha-1 with 60% K2O and 40% P2O5 was comparable to the observed grain yield of the experiment. Thus, it can be inferred that the model can be used to precisely predict the wheat yield at various regional level.ThesisItem Open Access Crop classification and cropping intensity estimation using geospatial technology in Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Hegde, Arjun Shreepad; Ranjan, RajeevTimely and accurate crop mapping plays an important role in food security, political, economic and environmental proposition. Crop maps, in particular, provide baseline information for efficient resource management and monitoring of agricultural production. Crop maps are also utilized for agro-environmental assessments and crop water usage monitoring. As a result, accurate and timely crop classification is essential for agricultural management and monitoring. The number of crops grown by a farmer on the same field in an agricultural year constitutes the cropping intensity. It provides a measure of cropland usage with significant implications for agricultural intensification and bridging the food production gap. It estimates the intensification of production from the same piece of land. With these perspectives, a study has been conducted on crop classification and cropping intensity estimation using high-resolution multispectral satellite imageries in Udham Singh Nagar district of Uttarakhand. For this study high resolution, multispectral data of sentinel-2 satellite released by the European Space Agency (ESA) has been used. Cloud-free image of October 13, 2021, December 7, 2021 and March 6, 2022, has been acquired through the official website of the European Space Agency, Copernicus open access hub (https://scihub.copernicus.eu/) for more accurate differentiation of the feature classes. Ground truth points have been collected manually by using an android app named ‘Mapmarker’ and also by means of Google Earth. Further, pre-processing of satellite imageries like resampling, mosaicking and sub-setting are done using Sentinel Application Platform (SNAP) software. Then ENVI 4.7 software is used for crop classification and acreage estimation. The entire Udham Singh Nagar district has been classified based on crop seasons with the help of three different images for different major crop differentiation based on their respective maximum vegetative stage. Rice and Sugarcane are classified with help of the October 13, 2021 image with respective areas of 108884 ha and 11479 ha. The pea crop is classified from December 7, 2021 image and the pea crop area was estimated as 6227 ha. Using March 6, 2022, Sentinal-2 image, the other two major crops (wheat and mustard) are classified. Wheat crop area is estimated as 105334 ha whereas mustard crop occupied 2018 ha area according to estimation. A comparative study was done between three different classifiers namely, Artificial Neural Network (ANN), Maximum Likelihood (MXL) and Minimum distance to mean (Md) which yielded better results in the case of ANN with an R2 value of 0.999 and % RMSE of 3.20. The area occupied by major crops (rice, sugarcane, wheat, pea and mustard) is estimated in their respective seasons by taking their maximum vegetative stage into account. The estimated area of each major crop is further utilized to calculate three indices namely, Multiple Cropping Index (MCI), Area Diversity Index (ADI) and Cultivated Land Utilization Index (CLUI) which measures the cropping intensity as well as efficiency of the cropping system followed in the study area. High cropping intensity with an MCI value of 174.4%, Medium ADI of 2.4 and High CLUI of 0.7 is reported in the study area. Based on the calculated value of MCI, ADI and CLUI a recommendation was given to go for diversification with short-duration crops.ThesisItem Open Access Application of machine learning technique for diagnosis of powdery mildew disease in wheat(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-10) Negi, Archana; Nain, A.S.Powdery mildew is one of the most common fungal disease of wheat caused by an obligate biotrophic pathogen Blumeria graminis f. sp. tritici. Present investigation was conducted in rabi season 2020-21 at Norman E. Borlaug Crop Research Center, Pantnagar, Uttarakhand. The study was undertaken to create a disease diagnosis model for powdery mildew in wheat. Ten different deep learning approaches namely VGG16 (without augmentation), VGG16 (with under sampling), VGG16 (with over sampling), ResNet50 (without augmentation), ResNet50 (with under sampling), ResNet50 (with over sampling), ResNet50 (with under sampling and augmentation), EfficientNetB3 (with augmentation), EfficientNetB5 (with augmentation) and EfficientNetB7 (with augmentation) were used to check the best model for disease diagnosis. The accuracies attained by these algorithms were 61.7%, 59 %, 77 %, 58-63 %, 55-61 %, 74.7 %, 74%, 74.8 %, 73.6 % and 75.1 %, respectively. Automatic computer system for detecting and classifying of diseases is very important for efficient management. The present study will provide the opportunity for disease management by using advanced learning technologies with least interference of mankind. The study was also conducted to check the influence of weather parameters with disease progress of powdery mildew. Infection rate and PDI were used to analyze the effect of weather variables. PDI was positively correlated with both maximum (r=0.82) and minimum temperature (r=0.61) and positively for bright sunshine hours (r=0.81) while with morning (r=-0.73) and evening relative humidity (r=-0.77), it was negatively correlated. Maximum temperature (r=-0.52) and sunshine hours (r=-0.51) showed a negative correlation with the rate of infection while a positive correlation was seen with morning (r= 0.54) and evening relative humidity (r= 0.61). Step-wise multiple regression analysis was done and a prediction equation was developed (R2=0.45).ThesisItem Open Access Recognition of stripe and leaf rust disease in wheat using artificial intelligence technique(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-10) Vatsala Chand; Nain, A.S.Stripe and leaf rust of wheat are one of the common problems not only in India but also in other wheat-growing areas, which is caused by Puccinia striiformis and Puccinia triticina respectively. Present study was conducted at Norman E. Borlaug Crop Research Center, Pantnagar, Uttarakhand in rabi season 2020-21. The investigation was undertaken to create an auto-detection model for identification of rust disease in wheat using artificial intelligence technique. Seven different deep learning approaches namely ResNet50, VGG16 without augmentation, VGG-16 with augmentation, VGG-16 with augmentation and binary classification, EfficientNetB3 with augmentation, EfficientNetB5 with augmentation and EfficientNetB7 with augmentation were used to check the best algorithm for disease identification. The classification accuracy of 56%, 68%, 70.8%, 74.1%, 69.6%, 70.8%, 71.2% and 73.4% respectively, was attained by algorithm. Automated method for an early detection of a plant disease is vital for precision crop management. The present study provides a groundwork for auto-detection of disease through smartphone. This would be beneficial to the country's farmers, who otherwise faces multiple challenges in diagnosing the disease. The study was also conducted to analyze the relation between weather parameters and leaf rust disease progression. Rate of infection was calculated which show a positively correlated with both maximum and minimum temperature and negative with relative humidity. Regression analysis was done to develop a model for predicting rate of infection, which was found to be quite accurate with R2 value of 0.637.ThesisItem Open Access Studies on Crop Growing Environment Under Climate Change Scenario in Tarai Region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Goel, Shubhika; Singh, R.K.The study is conducted for Tarai region of Uttarakhand regarding trend analysis of weather parameters namely maximum temperature, minimum temperature, rainfall, sunshine hours and evaporation on annual and seasonal basis over the periods from 1981-2020. The moving averages for 5-year, 10-year interval and pentadal, decadal variation has been studied for the above stated parameters on annual basis. Results revealed that, an increasing trend in maximum and minimum temperature of about 0.0004˚C/year and 0.0180˚C/year respectively by graphical method. A decreasing trend in rainfall, sunshine hours and evaporation is observed of about 1.461 mm/year, 0.042 hr/year and 0.028 mm/year respectively by graphical method. Mann-Kendall test has been also performed for trend analysis of above stated parameters. The results revealed the similar trend in the weather parameters but some changes in the Sen’s slope can be observed i.e., in the magnitude of the trend. Similarly, trend analysis on seasonal basis has also been performed for these parameters and it can be concluded that there is a decreasing trend during monsoon and winter season while increasing trend in post monsoon and summer season for maximum temperature. Increasing trend for minimum temperature was found during all the seasons. Decreasing trend was also observed for rainfall, sunshine hours and evaporation during all the seasons over the periods from 1981-2020 for Tarai region of Uttarakhand. There is a decrease in rainfall of about 28.3 mm during monsoon season over the periods ranging from 1981-2020 by Mann Kendall method. This study also focuses on average weekly water balance and its components for this region based on Thornthwaite Mather model. Results revealed that, there is an increase in the water surplus during 1981-2020 when compared with IMD data for Pantnagar during 1971- 2005. Water surplus is found to be 670.0 mm and water deficit is found to be 440.2 mm. Total potential evapotranspiration is found to be 1339.4 mm, which is calculated by Penman Monteith equation during 1981-2020 and has decreased when compared with the PET calculated by IMD for the year 1971-2005 of about 1463.9 mm. The classification of climate has been done for Tarai region of Uttarakhand based on moisture index, results revealed that humid climate exists in this region and length of growing period is found to be about 225 days which indicated a good crop growth in this region. According to Subramanyam (1982), if MAI (Moisture Adequacy Index) lies below 40% in the region, then crop can only be grown by proving supplemental irrigation to fulfill crop water need.
