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Theses

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2014 - 20175
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Subject: Agricultural Meteorology × End date: 2017 × Start date: 2010 × Thesis Type: M.Sc ×
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Now showing 1 - 5 of 5
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    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.
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    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.
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    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.
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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.