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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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  • ThesisItemOpen Access
    Evapotranspiration studies on chickpea (Cicer arietinum L.) and moong (Vigna radiata L. Wilczek) in a mollisol of tarai region of Uttaranchal
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2006-05) Ahmed, Gulrez Jahangeer; Suman Kumar
    This study was conducted at the Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar for quantifying evapotranspiration losses of chickpea and moong under Uttaranchal tarai conditions and to select some suitable mathematical methods based on meteorological parameters for estimating chickpea and moong ET of this type of agroclimatic conditions. Evapotranspiration of chickpea and moong were measured with weighing type lysimeters. Data on pan evaporation measured with USWB class A pan evaporimeter and various chickpea parameters for the corresponding period were collected from Meteorological Observatory. Evapotranspiration of chickpea and moong was also estimated by using mathematical methods of Thornthwaite, Turc, Stephens-Stewart, Jensen-Haise, Blaney-Criddle and Modified Penman. The relationship of measured ET with pan evaporation and ET estimated by different mathematical methods was studied by linear regression and simple correlation. It can be concluded that the evapotranspiration of chickpea and moong under tarai conditions is about 342.1 mm and 340.8 mm respectively. The average total rainfall during chickpea and moong season is 119.4 mm and 1028.4 mm respectively. Thus supplementary irrigation is required during chickpea season due to low rainfall but not for moong season due to sufficient rainfall. As the Pan evaporation did not give accurate estimate of chickpea and moong ET, both on seasonal and as well as weekly basis. So Pan evaporation does not seem to be good criteria for ET estimation in chickpea and moong in this region and Modified Penman and Jensen-Haise methods are very suitable for estimation of ET in this tarai region of Uttaranchal.
  • ThesisItemOpen Access
    Effect of microbial inhibitors on emission of nitrous oxide from paddy fields
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2005-08) Srivastava, Aditi; Bharadwaj, Venkatesh
    The hazards of increasing concentration of green house gases in the atmosphere are becoming visible day by day. Global warming of the earth is taking place at an unprecedented rate and climates all over the world are fast changing. This unusually higher rate of change has been attributed to nothing but human intervention into the processes of nature. N2O concentration in the atmosphere at present stands at 311 ppbv and is increasing at the rate of 0.25% per year. It has been reported to be contributing almost 5% to the total green house effect. Both unfertilized and fertilized soil have been found to be emitting N2O which is produced during denitrification and nitrification reactions taking place through soil microbes. In the present investigation, an attempt was made to develop techniques for mitigation of N2O evolved from soil by the use of microbial inhibitors without reducing yield of the crop under study (paddy). Effect of nine treatments were observed upon N2O emission as well as upon the plant growth parameters, yield attributes and yield of crop which was kept under submerged condition during the period of study. The treatments were control (no N, with crop), 100% NPK + Nitrapyrin, 100% NPK + Nitrapyrin + green manure (GM), 100% NPK + ECC, 100% NPK + ECC + GM, 100% NPK + Thiourea, 100% NPK + Thiourea + GM, 100% NPK + Dicyandiamide (DCD) and 100% NPK + DCD + GM which produced an average N2O flux rate of 0.21, 0.44, 0.39, 0.01, 0.43, 0.57, 0.38, 0.28 and 0.25 mg m-2 hr-1. Maximum inhibition of N2O emission was observed by 100% NPK+ECC treatment, but this treatment had a more or less adverse effect on plant growth as well as yield. The treatment, 100% NPK + Thiourea + GM had an extremely positive effect on plant growth as well as yield but its inhibitory effect on N2O emission was not found to be substantial in comparison to other treatments. However the treatment, 100% NPK + DCD + GM was found to fit both the roles perfectly. It was able to effectively control N2O emission rate from the soil while also maintaining the yield at a considerably higher level than other treatments.
  • ThesisItemOpen Access
    Evapotranspiration studies on wheat (Triticum aestivum) and soybean (Glycine max) in a mollisol of tarai region of Uttaranchal
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2004-07) Sunil Kumar; Suman Kumar
    This study was conducted at the Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar for quantifying evapotranspiration losses of wheat and soybean under Uttaranchal tarai conditions and to select some suitable mathematical methods based on meteorological parameters for estimating wheat and soybean ET of this type of agroclimatic conditions. Evapotranspiration of wheat and soybean was measured with weighing type lysimeters. Data on pan evaporation measured with USWB class A pan evaporimeter and various wheat parameters for the corresponding period were collected from Meteorological Observatory. Evapotranspiration of wheat and soybean was also estimated by using mathematical methods of Thornthwaite, Turc, Stephens-Stewart, Jensen-Haise, Blaney-Criddle and Modified Penman. The relationship of measured ET with pan evaporation and ET estimated by different mathematical methods was studied by linear regression and simple correlation. It can be concluded that the evapotranspiration of wheat and soybean under tarai conditions is about 445.2 mm and 513.1 mm respectively. The average total rainfall during wheat and soybean season is 256.8 mm and 1187.9 mm respectively. Thus supplementary irrigation is required during wheat season due to low rainfall and also for soybean season during the years of low rainfall. As the Pan evaporation did not give accurate estimate of wheat and soybean ET, both on seasonal and as well as weekly basis. So Pan evaporation does not seem to be good criteria for ET estimation in wheat and soybean in this region and Modified Penman and Jensen-Haise methods are very suitable for estimation of ET in this tarai region of Uttaranchal.
  • ThesisItemOpen Access
    Analysis of drought occurrence in Uttarakhand using remote sensing and meteorological data
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2014-08) Bhatt, Prakash Chandra; Nain, A.S.
    Drought is complex event which may affect social, economic, agricultural and other activities of society. It is a prolonged, abnormally dry period when there is shortage of water for normal needs.Drought is considered as extreme weather event.The present study was conducted at the Uttarakhand state of India to analysefrequency, spread and monitoring of the drought. The monthly weather data, SPOTVGT satellite data and rice data have been used for study in Kharif season. Seasonal SPI gives the drought frequency in every district of Uttarakhand with magnitude. NDVI deviation Maps were used to analyse the drought spread for Kharif season in every district of Uttarakhand with magnitude of mild to extreme condition. Thevegetation condition index was also calculated for analysing condition of vegetation in each district of Uttarakhand. On the basis of thesetwoindicesdrought prone region were identifiedineachdistrict. NDVI and VCI images are good indicator of spatial drought pattern. The multidated images can be used to analyse frequency and spread of drought in the state. The multivariate model was also used to analysing drought conditions in Dehradun district of Uttarakhand. The multivariate model involving remote sensing derived VCI and meteorological data based SPI was used to estimate the inter-annual rice yield variability shows a value of correlation coefficient as 0.424. When the value of the year 1998 is dropped from the analysis the model could estimate the yield deviation quite accurately the value of correlation coefficient as 0.589. It can be concluded that combination of VCI and SPI could analyse the drought conditions in state with reasonable accuracy.
  • ThesisItemOpen Access
    Wheat yield prediction of southern region of Uttarakhand by integrating remote sensing and WOFOST model
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Latwal, Asha; Nain, A.S.
    Accurate and real-time information on crop yield at national, international and regional scales is becoming gradually more important for food security in the world. Crop yield prediction may play a crucial role in advanced planning, strategy formulation and management of crop production. On the basis of previous research works, it has been broadly acknowledged that regional level crop yield estimates can be improved by assimilating the remote sensing data with crop simulation models. Considering the significance of yield prediction in food security, the present investigation was carried out to predict wheat yield using WOFOST model and remote sensing. The study was mainly designed in five parts: 1) application of multi-resolution data to calculate area-weighted mean NDVI, 2) development of spectralmeteorological models for yield prediction, 3) calibration and validation of WOFOST model, 4) yield gap analysis using WOFOST model and satellite data, and 5) regional level wheat yield prediction using WOFOST model and remote sensing. The calibration and validation of the model was conducted at Pantnagar region of US Nagar district, while regional yield prediction was carried over US Nagar and Haridwar districts of Uttarakhand for the period of 10 years (2005-06 to 2014-15). The LANDSAT image of each year was classified using supervised classification technique of ENVI-4.8 software and wheat crop was discriminated in both the districts. Wheat mask of each year was generated and overlaid on the SPOT derived NDVI images to develop the temporal growth profile of wheat crop and thereby calculating area-weighted mean NDVI. The spectral-meteorological models (SMM) were developed using combinations of NDVI and decadal weather variables at different crop growth stages using SPSS 16.0 software for regional wheat yield prediction. Yield gap analysis was accomplished after the estimation of potential yield through WOFOST model. Regional yield prediction of wheat was also performed using WOFOST model and trend yield. The normal growth profile showed that as the number of days increases from November/December onwards, wheat canopy increases linearly up to the first/second week of February when peak vegetation growth stage is attained. The NDVI values starts to decrease from March onwards and decreased sharply when the crop reached physiological maturity. During calibration the closed prediction was found between observed and simulated values with RMSE 5.56% and 11.67% for phenological stages and yield attributes, respectively. The validation results revealed that observed and simulated values were quite close with RMSE value 7.86% and 19.64% for phenological stages and different yield attributes, respectively. The yield gap analysis using model explained large variability between observed and potential yield in US Nagar (RMSE = 33.10%) and Haridwar district (RMSE = 43.66%). The average yield gap for US Nagar and Haridwar district was calculated as 1.47 t/ha and 0.35 t/ha, respectively using satellite data and WOFOST model. The results for US Nagar district demonstrated that RMSE of yield prediction was 2.43%, 2.82% and 2.27% using SMM, combination of WOFOST model & trend yield and assimilation of satellite data with WOFOST model, respectively. In Haridwar district RMSE was calculated as 2.44%, 7.22% and 8.36% between observed yield and predicted yield using SMM, combination of WOFOST model & trend yield and assimilation of satellite data with WOFOST model, respectively. Therefore, it can be concluded that these approaches can be used in the study region for regional level wheat yield prediction.
  • ThesisItemOpen Access
    Analyzing the accuracy and usability of medium range weather forecast in the Udham Singh Nagar district of Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-06) Kothiya, Shivani; Singh, R.K.
  • ThesisItemOpen Access
    Analyzing the effect of climate change on productivity of scented and bold seeded rice (Oryza sativa L.) using CERES-Rice simulation model under Tarai region of Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-06) Chaturvedi, Gaurav Kumar; Nain, A.S.
  • ThesisItemOpen Access
    Integration of remote sensing, crop simulation model and land based observations for predicting wheat (Triticum aestivum L.) yield in northern India
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-06) Mall, Pawan; Singh, R.K.
  • ThesisItemOpen Access
    Regional yield prediction of soybean (Glycine max L. merill) using CROPGRO simulation model
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-07) Rawat, Himanshu; Nain, A.S.
    Soybean, an important kharif crop of Madhya Pradesh is grown on 5.5 million hectares. The crop is heavily supporting the economic conditions of the farmers as well as the state. Many agrobased industries are using soybean as raw product. However, due to its cultivation in rainfed ecosystem there is large year-to-year variability in productivity and production. In view of large variability, there is greater need to develop a system for timely and accurate estimation/prediction of productivity and production of soybean. Therefore, an attempt has been made in the present study to devise 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 interannular variability in soybean yield arising due to varying weather conditions, ii) calibration and validation of crop simulation model CROPGRO on farmer’s field conditions, iii) use of CROPGRO simulation model on zone for simulating response of soybean crop to ambient environmental conditions, iv) computation of yearto-year deviations in observed yields and simulation yields, v) relating observed yield deviations with simulation yield deviations for prediction of yield deviations, vi) estimation of technological trend yields, vii) incorporation of predicted deviations into trend yields for predicting zone level soybean yields and, viii) aggregation of zonal yield at regional scale using area weightage method. The present study was conducted in the Ujjain district for the calibration and validation of CROPGRO simulation model on farmer’s fields, while 22 districts of Western Madhya Pradesh were selected for the regional yield prediction for the period of 13 years (2001-2013) and yield forecast for two years (2014-16). Cultivar JS 335, which is grown over large area was selected for the model calibration and regional yield prediction. The soil of Ujjain and other districts of the Western MP is clay. The soil is black in colour and is widely known as Black Cotton soil or Regur soil. The calibrated and validated CROPGRO simulation model was used to simulate the response of soybean crop at zone level by applying zone level average conditions. The zonation of 22 districts yielded 3 clusters of districts on the basis of similarity in interannual yield deviations, which were mapped with the help of GIS software and were further divided into four zones (zone 1A, 1B, 2 and 3) based on geographical discontinuity. The zone level soybean yield prediction for a period of 13 years (2001-13) shows quite good agreement between observed soybean yield and predicted yield with RMSE ranging from 11.3 % to 17.3% and R2 value from 0.64 to 0.73. Similarly zone level soybean yields were also forecasted for two years (2014-15) by adopting same approach. The zone level predicted soybean yields were aggregated at region level by applying area weightage method and were compared with observed regional soybean yields. The RMSE value for predicted soybean yield at regional scale was found to be 11%, which is considerable low as compared to CV of observed yield and trend yields. Therefore, it can be concluded that zone based approach together with CROPGRO-simulation model can be used for regional soybean yield prediction and forecasting with quite high accuracy.