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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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20164
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Subject: Agricultural Meteorology × End date: 2016 × Start date: 2016 × Type: Thesis ×
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Now showing 1 - 4 of 4
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    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.
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    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.
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    ThesisItemOpen Access
    Impact assessment of climate change on wheat and possible mitigation strategies using APSIM crop model in foot hills of Western Himalayas
    (G.B. Pant University of Agriculture and Technology, Pantnagar (Uttarakhand), 2016-12) Gupta, Smita; Singh, R.K.
    The present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar to analyze effect of climate change on wheat productivity using APSIM crop model in foot hills of Western Himalayas during rabi season of 2014-15 and 2015-16. The experiment was laid in randomized block design (RBD) using three dates of sowing i.e. 1st December, 20th December and 06th January and three replications using the variety UP2565. UP2565 was a new variety cultivated in the given soil and experimental material. The soil of the experimental material was of sandy clay loam. The observed parameters were compared and calibrated against the simulated parameters by using APSIM crop model. UP2565 was found to yield more when the crop was sown on 1st December than the crop which was sown on 20th December and 6th January. The performance of the APSIM crop model was well with the crop sown on 20th December, 30th December and 9th January during the period of study for almost all crop characters. Predicted values through APSIM crop model were very close to the observed values in the experimental year. All the crop characters in terms of Leaf Area Index, total dry matter, grain yield decreased as the temperatures were increased by 1, 2 and 3°C and vice versa across sowing dates. The model was found to be more sensitive to the effect of temperature either decreasing or increasing it than mean temperature .Leaf Area Index, total dry matter and grain yield were found to decrease at all projected levels of temperatures (1.3°C in 2020 and 3.9 °C in 2050, 5.2°C in 2080). Leaf area index, total dry matter, grain yield, biological yield and harvest index increased with all levels of projected CO2 concentration (i.e. 414, 522 an 682 ppm in 2020s, 2050s and 2080s respectively) among the dates of sowing. With optimized package of practices in climate change scenario during the year 2050, days to anthesis and physiological maturity shifted almost one week and dry matter and grain yield increased by 403 kg/ha and 1088kg/ha, respectively, over present package of practices. Enhancement of sowing date by almost one week i.e. 26th November resulted in higher yield under modified or changed climatic scenario.
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    ThesisItemOpen Access
    Development of a web‐based disease monitoring system for wheat crop
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-03) Jha, Ankita; Nain, Ajeet Singh
    Present study has been conducted in US Nagar district of Uttarakhand to monitor yellow rust disease in wheat using web based system. To monitor yellow rust disease in wheat, a web based system was developed using five step approach: a) discrimination of wheat and development of normal growth profile of the crop by employing remote sensing technique, b) identification of the incidence of yellow rust by constructing normal growth profile and identifying dips in the growth profile together with suitable weather conditions, c) computing inter annual deviation in mean NDVI from normal value to quantify the effect of yellow rust on growth of wheat d) developing a spectral/spectro-meteorological model for predicting and estimating wheat disease, and e) development of a web-based disease monitoring system. LULC map was generated using ENVI-4.8 image processing software for analyzing area under different entities with greater emphasis on crop area. This was achieved through the use of LANDSAT-TM/ETM+/OLI images of the years 2005-06 to 2014-15. Atmospheric correction of the images was carried out using Quick Atmospheric Correction (QuAC) technique in order to know the exact reflectivity of an entity in absence of atmosphere. Ground truth of wheat field for the month of February and March (2013-2015) was carried out for identification of the crops and wheat fields. QGIS software was used for digitization of district boundary of Udham Singh Nagar. Maximum likelihood classifier was used to generate LULC map and to discriminate wheat, sugarcane and mustard crop. SPSS software was used for the development of meteorological/spectro-meteorological model. The spectral reflectance decreases significantly with the increasing severity level in near-infrared and increases in blue and red bands. As a result the value of NDVI decreases with increasing severity of the disease. The normal growth profile of wheat crop was generated from 1st of November to 30th April by using mean NDVIs derived through SPOT data. The mean NDVI was calculated for each year and disease impact was analyzed using observed and predicted NDVImean. As observations on disease severity are not available, a disease severity index was developed for 2005-06 to 2014-15 by comparing the projected value of dip NDVI and the previous decadal NDVI. The disease severity index was used for the development of multiple regression meteorological models considering the weather parameters. Two meteorological models were developed and among them the performance of model 2 was found to be the best in wheat (R2=0.69). A spectro-meteorological model was also developed using remote sensing derived index of January (at 10 days interval) and the decadal meteorological parameter. The value of coefficient of determination for model 3 was 0.91, which suggest that remote sensing based model could prove to be an important tool foretelling yellow rust disease in wheat. Finally a web-based yellow rust disease monitoring system was developed using output of spectro-meteorological model 3 and converting them in vector format for all ten years. A simple one click on map displays a pop up to show disease severity level in per cent along with its management practices.