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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.
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 Singh
Udham 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.
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.
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 Kiran
A 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.
Open Access
Epidemiology of Rhizoctonia aerial blight in soybean and its autodetection through Machine Learning Technique (MLT)
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Nainwal, Mukta; Ravi Kiran
Rhizoctonia Aerial blight (RAB) caused by Rhizoctonia solani is one of the most important diseases of soybean in Uttarakhand causes heavy loss of crop yield every year. Present investigation on RAB of soybean was undertaken in relation to its occurrence, pathogenicity, epidemiology, autodetection and management of the disease. The study was conducted in Kharif, 2017 at Norman E. Borlaug Crop Research Centre, Pantnagar Uttarakhand. Pantnagar is considered as a hotspot for this disease. Out of total sixteen cultivars studied against R.solani, the cultivars exhibited moderate resistance were PK-472 (24.43%) and PK- 262 (25.60%), while cultivars JS-7105, JS-72-280, JS-93-05, Bragg, KHsb-2 and NRC-7 were found moderately susceptible. Infection rate was found to be maximum for cultivar and minimum for cultivar. Area under disease progressive curve was also calculated.It was maximum for cultivar VLS-58 (424.19) and minimum for cultivar PK-262 (160.99). Infection rate was calculated on the basis of disease index. Maximum infection rate was found to be cultivar JS-7244 (0.317 unit/day) and minimum for Shivalik (0.0008). Disease progression of RAB was started in the third week of September. In initial phase, the disease progression was quite high month of October, however it slowly declined later on. September and October are suitable months for initiation, development and progression of RAB disease. Disease progression was maximum for full seed to beginning of maturity (51-34 percent) phase. The PDI of all varieties exhibited negative correlated with temperature, relative humidity, rainfall and wind speed but significant, it was positive only for bright sunshine hours and evaporation but non significance for weather data on same day of disease incidence. Same trend was follow by weather variables on one week before and two week before from disease incidence. Infection rate was positively correlated with all weather variables.The data on disease progression in relation to corresponding weather variables with maximum temperature, maximum relative humidity, rainfall and bright sunshine hours were subjected to step-wise multiple regression analysis being a significant contribution in the prediction of disease index. By using these prediction equations for different cultivars, it is now possible to predict the disease index in advance and it provides sufficient time for contingency plan with plant protection input to restrict and manage RAB growth and its development. The second and important part of present investigation was to use machine learning technique for autodetection of disease considering these 10 different algorithms namely logistic regression. Support vector machine, VGG-16, VGG-16 (with data augumentation), RseNet-18, ResNet18 (with data augumentation), ResNet-18(with augmentation and large size increase, ResNet-18(with augmentation and a further increase in size), ResNet-34 and ResNet-34(with data augmentation ) were used. The classification accuracy 66.77%, 65.77%, 74.16%, 78.18%, 73.49%, 77.85%, 76.51%, 76.84%, 94.15% and 95.53%, respectively was provided by these algorithms. Automated method for an early detection of a plant disease is vital for precision crop management. Farmers can capture the images of the diseased plant with a simple Smartphone and the algorithm (classifier) classify and diagnosis different diseases, The modern communication and sensor technologies conjugate with robust pattern recognition algorithms for information extraction and classification allow the development and use of the integrated system to tackle disease problems. However the study will provide the opportunity for disease management by using advanced technologies of computer. I.T. and smart telephony with least interference of mankind.
Open Access
Evaluation of different soybean cultivars using CROPGRO simulation model embedded in DSSAT in Tarai region of Uttarakhand
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-06) Lavanya; Murty, N.S.
Soybean, an important kharif legume crop of India mostly cultivated in Madhya Pradesh region of the country . The crop is heavily supporting the economic conditions of the farmers as well as the state. Many agro-based industries are using soybean as raw product. The crop has high nutritional value as well. 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 make the Use of Crop simulation model i.e. CROPGRO to predict the yield of the crop and also to evaluate the performance of different varieties so that farmers can make wise decision in selecting the variety. The approach includes i) calibration and validation of crop simulation model CROPGRO on farmer’s field conditions, ii) use of CROPGRO simulation model for simulating response of eight different cultivars of soybean to ambient environmental conditions, iii) comparison of observed and simulated values of different parameters for different varieties, iv) Evaluation of the different cultivars of the crop using CROPGRO. The present study was conducted at Norman Borlaug Crop research centre, GBPUAT. Model was calibrated with eight different varieties of soybean in order to fine tune the results. The calibrated model was then used for validation and sensitivity analysis was completed. Soil of Norman Borlaug Crop Research centre is sandy loam. The soil is dark brown in colour. The calibrated and validated CROPGRO simulation model was used to simulate the response of eight different cultivars of soybean crop under the same climatic condition. The simulated yield of all the cultivars shows quite good agreement with that of the observed soybean yield. The RMSE of observed and simulated yield was found to be 9.64%. Similarly calibration also resulted in good match between simulated and observed phenophases and other yield attributing trait. Statistical analysis of different crop parameters was also satisfactory and can be correlated with the modelling results for evaluating varieties. On the basis of above variety PS 1572 was found to be most suitable for Udham Singh Nagar district. Therefore, it can be concluded that CROPGRO-simulation model can be used for varietal evaluation of different cultivars of soybean on the basis of comparison of simulated and observed parameters
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.
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.
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.

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