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ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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.ThesisItem Open 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 SinghPresent 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.