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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 Study of Energy Balance of Sugarcane (Saccharum Officinarum L.) using Remote Sensing and Crop Simulation model in Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-09) Sharma, Neha; Nain, A.S.The present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar to Study the energy balance of sugarcane using surface energy balance algorithm and CANEGRO model during 2015 and 2016. The sugarcane variety selected for the study was Co-Pant 5224. The performance of the CANEGRO model was reasonably well when compared with the observed crop parameters like Leaf Area Index, fresh cane yield (t/ha), Dry weight Yield of Cane (t/ha) etc. during the period of study. Predicted values through CANEGRO model were very close to the observed values in the experimental year. The model performance was tested on statistical ground on the basis of Index of agreement (d) and RMSE (%). The d value for the analysis was 0.86 and RMSE (%) was 17.28 %, which shows that there was limited error in the predicted values as compared to the observed values. The model was found to be more sensitive to the effect of temperature either decreasing or increasing it than mean temperature, CO2 concentration and Irrigation amount (mm) and Radiation (MJ/m2/day). Calibrated CANEGRO simulation model was also used to analyze the impact of climate change on growth and development parameters of Sugarcane. Leaf Area Index, dry weight yield of cane (kg/ha) and time taken for emergence were found to decrease in the future climatic scenarios (2030-2090). SEBAL is a surface energy balance algorithm predicting evapotranspiration using remote sensing technique. It calculates ET through a series of procedures that generates residual energy flux as precursor of ET. In this study, LANDSAT-8 (OLI+TIRS) satellite images for the crop period (2015-16 and 2016-17) have been utilized for extraction of various components of SEBAL in sugarcane crop. The parameters required for SEBAL procedure includes surface albedo, emmissivity, land surface temperature (LST), NDVI, LAI, Vegetative Fraction, momentum roughness length, canopy height, and elevation represented by SRTM-1 arc sec (DEM). The Daily ET computed through SEBAL was later validated by DSSAT computed ET. The results revealed the mean bias error (MBE) of 0.62 mm/day for SEBAL, and R2 of 0.702, represents a higher similarity between the remotely sensed and model estimated evapotranspiration values.ThesisItem Open Access Application of ceres-rice model embedded in DSSAT 4.7 for district level rice yield forecast(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Chauhan, Pritam Singh; Ravi KiranThe present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar during the kharif season 2018. For district level rice yield forecast and Impact of climate change on rice yield under RCP 4.5 and RCP 6.0 by using CERES-Rice model in Tarai region of Uttarakhand. The experiment was laid out with two prominent cultivars (Pant Basmati-1 and HKR-47), three transplanting dates (29th June, 09th July and 19th July) and two levels of irrigation (100mm and 75mm) to calibrate the CERES-Rice model so that model could be used for district level rice yield forecast and to study the impact of climate change on rice yield under RCP 4.5 and RCP 6.0. Experimental analysis suggested that Pant Basmati-1 and HKR-47 both varieties performed better when transplanted on 29th June as compared to 09th July and 19th July. The performance of the model CERES-Rice was satisfactory for all transplanting dates, both irrigation levels and both varieties during the period of study for almost all crop characters. %RMSE for observed and simulated data for Panicle initiation, Anthesis, Physiological maturity and grain yield were found 4.09, 5.54, 3.99 and 4.92, respectively for Pant Basmati-1. While in case of HKR 47 %RMSE for Panicle initiation, Anthesis, Physiological maturity and Grain yield were found 5.4, 2.67, 3.94 and 5.48, respectively. The sensitivity analysis of crop simulation model suggests that the grain yield decreased with increasing temperatures by 1, 2, 3°C, increased with increasing CO2concentration by 25, 50, 75, 100 ppm, increased with increasing in Solar Radiation by 1, 2, 3 MJ/m2/d, increased with increasing Nitrogen by 25, 50, 75% and vice versa across all transplanting dates. The model was found to be highly sensitive to the change in temperature and Nitrogen. The district level rice yield prediction for a period of 11 years (2006 to 2016) shows quite good agreement between observed rice yield and predicted yield with %RMSE 5.24 %. Similarly district level rice yields were also forecasted for two years (2017 to 2018) by adopting same approach. The simulation result shows that increase in daily average temperature can slow down rice phonological development in Udham Singh Nagar under both RCPs. The yield of both varieties (Pant Basmati 1 and HKR 47) would decrease in the future and decreases were hiegher under RCP 6.0 then RCP 4.5. (Ravi Kiran) (ThesisItem Open Access Modelling and prediction of sweet corn (Zea mays L.) yield at district level under Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-07) Bijlwan, Amit; Ravi KiranThe 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 different type of mulching on productivity of maize yield and prediction of maize yield at district level in Tarai region of Uttarakhand during 2018. The experiment was laid out in 2 factor randomized block design with three date of sowing and four type of mulching for hybrid maize cultivar (Maize Sugar 75). During crop growth period all recommended cultural practices were followed. The various ancillary observations on the growth were periodically recorded along with post-harvest studies to evaluate the treatment effects. Experimental analysis suggests that maize sugar 75 perform better when sown on 11th July as compared to 23rd July and 21st August. Green cob yield and grain yield of Maize sugar 75 was better when sowing was done on 11th July. Crop growth parameter such as plant height, leaf area index, and dry matter accumulation was higher under plastic film mulch. Under the mulching treatment, there was no significant effect on green cob yield but grain yield under plastic film mulch treatment was lower than the dhaincha mulch. The performance of the model CERES-Maize was satisfactory for all sowing dates during the period of study for germination, anthesis, silking and grain yield. The prediction of model was found good when compared with actual observation in case of anthesis where value of R2 was 0.71. The model output was also good for silking and grain yield, there was good relationship between observed and crop simulation model value and R2 was 0.66 and 0.88 for silking and grain yield respectively. Maize has adopted diverse set of climatic conditions therefore, it is grown from the plains land of Uttar Pradesh to lower hills of Uttarakhand. However, due to interannual variability in weather conditions, the large amount of year-to-year variability in productivity and production of maize is observed. Therefore, there is a need to develop a system for timely and accurate estimation/prediction of productivity and production of maize for Udam Singh Nagar district. Considering yield variability and importance of maize for farmer, an attempt has been made to develop an approach for large (district level) area yield estimation. The approach included i) calibration of crop simulation model CERESMaize on experimental data set, ii) use of CERES-Maize simulation model on district level for simulating response of maize crop to ambient environment conditions, iii) computation of year-to-year deviations in observed yields and simulation yield, iv) estimation of technological trend yields at district level and, v) incorporation of predicted deviation into trend yields for predicting district level maize yield . The district level maize yield prediction for a period of 10 years (2006 -07 to 2015- 16) shows quit good agreement between observed maize yield and predicted yield with RMSE of 298.98 kg and R2 0.51.ThesisItem Open Access Crop management using CROPGRO simulation model in Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-07) Negi, Ankita; Rajeev RanjanThe present investigation was carried out at E4 plot of Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology Pantnagar, US Nagar district of Uttarakhand. The main aim of the study was to develop best management strategies under varied climatic conditions using CROPGRO simulation model for soybean variety PS 1347. The experiment was conducted in two factor randomized complete block design with two treatments and three replications under three dates of sowing i.e. June 29, July 9 and July 19 during kharif season of 2018. The plant growth and development parameters were noted throughout the season. The change in soil moisture percent and leaf area index was measured at every ten days interval starting from sowing to harvesting. The plant biomass was calculated at twenty days of interval after sowing of crop. The phonological stages of soybean were recorded for all the three dates of sowing. The calibration and validation of CROPGRO model was done after developing genotypic coefficient for soybean variety PS 1347. The different dates of sowing, tillage, irrigation and fertilizer treatments was simulated using CROPGRO simulation model and compared with observed dataset to select the best management practices for soybean variety PS 1347. The study of effect of weather parameters on soybean productivity revealed that bright sunshine hour of July first week was the most important parameter to affect soybean productivity followed by minimum temperature of October fourth week and maximum temperature of November first week. A total of three models were developed using 10 years (2007-2016) average weekly weather parameters with district level yield using SPSS software. The first model (CD=0.71) only used average bright sunshine hours of 1st week of July. The observed yield ranged between 0.92 q ha-1 and 1.93 q ha-1 and yield predicted by model-3 varied between 0.92 q ha-1 and 1.92 q ha-1 with RMSE value of 2.6%. It was found that bright sunshine hour of July 1st week shows significant positive relationship with soybean yield. Minimum temperature of 4th week of October has more significant effects (R2=0.66) than maximum temperature of 1st week of November (R2=0.14). Observed phenophases of soybean variety PS 1347 was compared with the model simulated value. The observed yield ranged from 5124 kg/ha (D3F2) to 6543 kg/ha (D1F1) and yield predicted by CROPGRO model ranged from 5035 kg/ha (D3F2) to 6564 kg/ha (D1F1), respectively for the year 2018. The observed grain yield was found close to the CROPGRO simulated yield for both the years 2018 (RMSE=7.2%) and 2017 (RMSE=10.9%). The effect of climate change on soybean yield was analyzed using the MarkSim weather generator for the year 2020, 2030, 2050 and 2080. Soybean yield showed decreasing yield trend from 2018 to 2080 with increase in temperature. The simulation results of model confirmed that soybean will produce highest yield when crop is sown on 20th June after two plowing followed by two harrowing under 90 mm irrigation and fertilizer dose (N:P:K:S) @ 25:60:40:20.ThesisItem Open Access Application of crop simulation model and agrometeorological observations for optimization of inputs in chickpea under Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-06) Sonam; Nain, A.S.The current focus of Indian agriculture is to maximizing the production by optimizing the limited resources so that production system could be sustained over a longer period of time. Considering this fact, the present study was conducted to optimize input resources in chickpea by calibrating the crop simulation model on experimental data set under Tarai region of Uttarakhand. The experiment was laid during rabi 2017-18 at Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar with three dates of sowing and two irrigation levels. CROPGRO-Chickpea model was used as a tool to achieve the objective and was calibrated using rabi 2017-18 experimental field data. The important finding of the study is that sowing on 29th November resulted into highest number of root nodules per plant as well as maximum dry weight of nodules per plant. The relative increase in number of root nodules per plant was found to have negative correlation with temperature (R²=0.36) and positive correlation with RH (R²=0.34). Similarly, relative gain in dry weight of root nodules per plant possessed negative correlation with temperature (R²=0.32) and positive correlation with relative humidity (R²=0.21). The model could capture all phenological stages reasonably. The growth and yield parameters of chickpea could also be simulated very well with RMSE less than10%. Under non-limiting condition of other resources, the model identified first fortnight of November sowing date to produce maximum yield. Nitrogen and irrigation were optimized by considering four rabi seasons. Varying number of doses of nitrogen (18kg/dose) from one to three caused an increase in yield of chickpea but the relative increase per dose followed a decreasing trend. Another important finding was that if sowing date is delayed, nutrient use efficiency is also declined, therefore excess amount of nitrogen application results into wastages of resources. In case of failure of winter rains, model suggested two irrigation (one at pre-flowering stage and other at pod development stage) for crops sown up to first fortnight of December and three irrigation (at early vegetative stage, pre-flowering stage and pod development stage) for crops sown during 2nd fortnight of December. However, if limited water is available, one irrigation during the initiation of pod development stage was simulated to be optimum by the model. If winter rain occurs, only one irrigation during sensitive stage of chickpea facing stress should be applied.ThesisItem Open Access Prediction of regional mustard yield of Uttarakhand and western Uttar Pradesh by developing homogeneous zones and multivariate statistical models(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Rawat, Shraddha; Singh, R.K.Rapeseed- mustard being a cool season crop, the growth and yield of the crop in a particular agroclimatic condition is mainly influenced by temperature. It is highly sensitive to temperature and photoperiod, showing quite diverse patterns of growth and development under different sets of environmental conditions. In India, mustard is mostly grown in northern and north-western parts of the country as a rabi (winter season) crop after harvest of kharif (wet rainy season) crop primarily in marginal lands with limited irrigation or on residual soil moisture. However, due to interannual variability in weather conditions, the large amount of year-to-year variability in productivity and production of mustard is observed. Therefore, there is a need to develop a system for timely and accurate estimation/prediction of productivity and production of mustard for major mustard growing area of UP and Uttarakhand. Considering yield variability and importance of mustard for the farmers, an attempt has been made to develop 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 inter-annual variability in mustard yield arising due to varying weather conditions, ii) to study relationship between mustard yield and weather variables iii) development of multivariate statistical models for different zone yield prediction by using SPSS iv) prediction of mustard grown area by using different approaches at zone level, v) aggregation of zonal yield at regional scale using predicted area weightage method. The zone and region level mustard yields were also predicted and forecasted by developing multivariate statistical models for individual zones and aggregation of the mustard yield at regional scale. The regional yield prediction was carried over 33 districts of Uttar Pradesh and Uttarakhand for the period of 17 years (1997-98 to 2013-14) and yield forecast for two years (2014-15 to 2015-16). The zonation of 33 districts yielded 4 clusters of districts on the basis of similarity in inter-annual yield deviations, which were mapped with the help of GIS software and were further divided into 5 homogeneous zones (Zone 1, 2A, 2B, 2C, 3A and 3B) based on geographical discontinuity. The zone level mustard yield prediction for a period of 17 years (1997-98 to 2013-14) shows quite good agreement between observed mustard yield and predicted yield with RMSE ranging from 2.36% to 12.11 %.Similarly zone level mustard yields were also forecasted for two years (2014-15 to 2015-16) by adopting same approach. The zone level mustard yields were also predicted by developing multivariate statistical model in SPSS environment. Important weather parameters such as solar radiation, air temperature, RH, wind speed, and were considered for development of multivariate model for each zone. The zone level predicted mustard yields were aggregated at region level by applying area weightage method by different approaches of area prediction (trend model, moving average method. Econometric model and ARIMA model) and were compared with observed regional mustard yields. The RMSE value for predicted mustard yield at regional scale was found to be 2.78% to 2.86%, which is considerably low as compared to CV of observed yield and trend yields. For the Forecast year the RMSE at regional level yield prediction of mustard by different methods varies from 5.10% to 6.27%. Effect of climate change showed decrease in mustard yield for zone 3A, while rise in zone 1 and 2A. Therefore, it can be concluded that application of multivariate statistical model with weather parameters, based approach for zone level mustard yield prediction/forecasting and aggregation of mustard yield at regional scale is better approach than other existing approaches.ThesisItem Open Access Retrieval of crop biophysical parameters and monitoring of rice using SAR images(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-07) Bhatt, Chetan Kumar; Nain, Ajeet SinghUdham Singh Nagar is one of major rice producing area of Uttarakhand state, and falls in Tarai region. Sentinel-1A satellite launched in 2014 as part of the European Union's Copernicus program provides Synthetic aperture radar (SAR) data. SAR images are independent of weather conditions and solar illumination and allow observations of different features of earth. The basic goal behind the present study was to apply new generation Sentinel-1A data with dual polarization (VH and VV) to rice cropping system mapping and monitoring with the short revisit period of Sentinel-1A satellite. SAR data were pre-processed by applying European Space Agency’s Sentinel Application Platform (SNAP). The SAR images classified with a Support Vector Machine (SVM) algorithm provided in ENVI- 4.8 produced the accurate LULC map, which shows that rice area in Udham Singh Nagar covers 108,095 ha area. The overall classification accuracy of 92.88% and a Kappa coefficient of 0.9 were obtained. The relationship between Sentinel-1A backscattering coefficients (𝜎0) or their ratio and rice biophysical parameters were analyzed. The regression models were developed between biophysical parameters and (𝜎0𝑉𝑉/𝜎0𝑉𝐻). The value of coefficient of determination for LAI, fPar, crop height, biomass and water content were found 0.53, 0.47, 0.50, 0.63, 0.34 respectively which exhibit that these biophysical parameters are significantly, consistently and positively correlated with the VV and VH 𝜎0 ratio (𝜎0𝑉𝑉/𝜎0𝑉𝐻) throughout all growth stages. Two approaches (crop simulation model and SAR coupled model and statistical model) have been used to predict the field level rice yield and district level rice yield. The biases (RMSE) of coupled model and statistical model were recorded as 7.61% and 9.12%, respectively. The average district yield generated from these two models were 3190 and 3344 kg/ha respectively which is quite close to five years average district yield of 3160 kg/ha. However, estimates provided by coupled model are more accurate than statistical models. Therefore, coupled model could be a good option to predict the plot level and regional yield of rice. On the basis of results obtained it can be concluded that Sentinel-1A SAR data has great potential for mapping of rice, estimation of biophysical parameters and timely rice growth monitoring with the ability to forecast the yield of rice crop. The prediction of rice crop is an important step that could be used to assist farmers and policy makers by providing in-season estimates of the rice yield and production.The information could be used for better planning of the resources.ThesisItem Open Access Agroclimatic characterization of Trinidad and Tobago(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Khudan, Surya; Murty, N.S.The present study was conducted to analyse the agroclimatic characteristics of Trinidad and Tobago based on 35 years of daily weather data from 1981 to 2015using the Weathercock 15 software developed by CRIDA, Hyderabad to analyse the climate of Trinidad and Tobago by determining climatic normals, rainfall trends, rainfall probabilities, drought conditions, temperature trends, potential evapotranspiration rates, length of growing period and normal water balance studies. The climate of the station experienced an increasing rainfall and temperature trend at Piarco, with Crown Point having increased rainfall but steady temperatures. The number of rainy days was more during the wet season (June to December) than the dry season (January to May) with Piarco receiving more than Crown Point as a result of increased day time convections over Piarco. Seasonally, higher probabilities of larger amounts of rainfall were found in the wet season for both stations coinciding with the peak of hurricane season in October and November allowing for crop cultivation. Initial and conditional probabilities for both stations proved that initial values of a wet week were over 75% for 10mm and 20mm of rainfall during 22nd to 1st SMW which is suitable for fulfil crop water requirement. While agricultural droughts were non-existent for both stations, mild meteorological droughts were observed while there was no presence of moderate or severe droughts. Heatwaves and cold waves are not experienced at these two stations as the temperatures do not exceed 400C and drop below 00C. The length of growing season spans throughout the year for both stations as rainfall is sufficient to keep soil moisture levels at suitable levels to allow for sustainable crop production with supplemental irrigation at Crown Point during the dry season. Thornthwaite’s classification indicates that the Piarco belongs to moist sub humid and Crown Point to dry subhumid climates. The normal water balance for Piarco and Crown Point indicated that good and varied agricultural crops can be grown throughout the year and can support two to three vegetable crops in a year with Crown Point utilizing protected irrigation during the dry season.ThesisItem Open Access Detection of abiotic stresses in wheat (Triticum aestivum L.) using crop simulation model and aerial photography in Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Pokhariyal, Shweta; Ravi KiranA field experiment was conducted at Norman E. Borlaug Crop Research Centre, Pantnagar (Uttarakhand) during Rabi season of 2017-18 to study the response of late sown (December) and very late sown wheat crop (January) to different irrigation and nitrogen management practices in order to understand the effect of stress condition on wheat yield and its parameters by using CSM-CROPSIM-CERES-wheat model, infrared thermometer, and aerial photography. Twenty seven treatments consisted of three dates of sowing (12th December, 22nd December and 02nd January), three levels of irrigation (100% irrigations, 75% irrigations and 50% irrigations) and three levels of nitrogen (100%, 75% and 50% of recommended nitrogen doses). The experiment was laid in a Factorial Randomized Block Design (R.B.D.) with three replications. The results revealed that the genetic coefficients derived from calibration of the CSM-CROPSIM-CERESWheat model under different treatment combinations showed reasonably good agreement between simulated and measured data of crop phenology, LAI and grain yield. For the above described treatment combinations, the calibrated model with wheat variety PBW-502, simulated wheat yield with root mean square error (RMSE) of 11.61%. Sensitivity of model was analysed for weather (temperature, solar radiation) and non –weather parameters (nitrogen and irrigation) under optimal condition. The results showed that grain yields as simulated by the model due to alteration of ambient temperature in incremental units showed a gradual decrease in yield, while decreasing (- 1 to -3°C) ambient temperature led to the increase in wheat yield by 9 to 26 %. Increase in daily solar radiation (1 to 3 MJm-2), resulted into nearly 2 to 12 % increase in yield over the base yield under optimal condition. This showed that the model was less sensitive to solar radiation than it was to temperature. Likewise, variation in irrigation and nitrogen showed variation in wheat yield. Canopy temperature was measured on 22nd Feb, 9th March and 24th March. Stress degree days (Tc-Ta) and subsequently accumulated stress degree days for whole growing season were calculated. These measurements were made to determine the occurrence and severity of water stress resulting from different water treatments and to evaluate the influence of nitrogen (N) fertilization on canopy temperature. With decrease in irrigation and nitrogen application, canopy temperature increased by 5.310C to 8.710C and 5.95 to 7.920C, respectively. Increase in irrigation and nitrogen levels has lowered the SDD, indicating a better canopy thermal environment under higher irrigation and nitrogen application. The significantly linear and negative relationship has obtained with canopy temperature and grain yield as well as SDD and grain yield. Low altitude aerial photography presents an exciting opportunity to monitor crop field with high spatial and temporal resolution in order to monitor water and nutrient stresses in the area of study. NDVI was estimated by image processing and regression analysis revealed a linear and positive one to one relationship with grain yield. Yield losses were calculated by evaluating the difference between simulated and observed yield which was positively related with accumulated stress degree days and negatively with NDVI. On the basis of experience gained in the present study, it can be concluded that an integration approach (crop simulation model, low altitude photography and canopy temperature) can be efficiently used with reasonably higher accuracy for assessing the yield losses in real time. The real-time yield loss assessment will prove to be crucial for disbursing agricultural insurance claims under Pradhan-Mantri Fasal Beema Yojna and such other schemes.
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