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Open Access
COMPARATIVE STUDY OF APSIM-WHEAT AND CERES-WHEAT MODEL FOR PRECISION MANAGEMENT OF WHEAT CROP AND MITIGATION OF THE ADVERSE IMPACT OF CLIMATE CHANGE UNDER TARAI REGION OF UTTARAKHAND
(G. B. Pant University of Agriculture & Technology, Pantnagar-263145, 2022-09-01) Pareek, Neha; Nain, A. S.
Climate change impacts on wheat production has widespread and national ramifications for food and nutritional security. A field study was conducted during rabi2017-18 and 2018-19 at agricultural farm of G.B. Pant University forcomparative study of APSIM-Wheat and CERES-Wheat model for precision management of wheat crop and mitigation of adverse impact of climate change Tarai region of Uttarakhand. APSIM-Wheat and CERES-Wheat, which simulates yieldhas been calibrated for winter wheat using experimental dataof 2017-18 crop season.The calibration was performed against anthesis (DAS), physiological maturity (DAS), grain and biomass yield for the three treatments of sowing dates (viz.,15th November, 25th November and 05th December) , three levels of irrigation (number of irrigation five, four and three) for two wheat varieties (HD-2967 and PBW-502).The calibrated APSIM-Wheat and CERES-Wheat model were then applied to validate anthesis (DAS), physiological maturity (DAS), yield and biomass for rabi season of 2018-19 for the three treatments of sowing dates (viz., 15th November, 25th November and 05th December) , three levels of irrigation (number of irrigation five, four and three) for two wheat varieties (HD-2967 and PBW-502). The simulated output by the CERES-Wheat model found closer to the experimental field data therefore this model was applied for other two objectives. In this study sowing time and irrigation (number of irrigations) were optimised to get economical yield. A sowing window starting from last week of October (25th October) to the first week of December (06th December) was selected for optimization of sowing date. Crop sown in the first week of November produced highest grain yield followed by sowing in second and third week in the rabi season of 2017-18 and 2018-19.The optimum number of irrigations for wheat was also evaluated by CERES-Wheat model through projected irrigation scenarios. Present study revealed that in this region three irrigations are sufficient for optimum yield. This model also applied to predict the wheat biomass and yield in the year 2030, 2050 and 2080 and found that biomass and yield will be decreased in the future years due to increase in temperature and decrease in rainfall. In-situ moisture conservation, rainwater harvesting and recycling, and efficient irrigation water use are all important adaptation strategies to combat the adverse effect of climate change. Breeders will need to develop some short-duration varieties in the coming years by changing the genetic makeup of existing varieties. Early flowering (photo- and thermo-insensitivity), early maturity, and higher productivity should be characteristics of the new varieties. Identification of crops and varieties/ hybrids with high water use efficiency, adapted to temperature extremes and high concentration of CO2 is needed in this climate change scenario.
Open Access
Paddy characterization and yield estimation using optical and SAR data
(G. B. Pant University of Agriculture and Technology, Pantnagar, 2023-02) Sonam; Nain, A. S.
Reliable and accurate monitoring of rice crop is vital for food security and the global economy. This study aims to characterize rice types and provide an early yield estimation using optical and Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed for summer and kharif rice to discriminate between different rice types and identify their optimum stages to represent crop growth profiles. Critical stages of discrimination were determined statistically for all rice types. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during both the seasons. Savitzky-Golay fitted temporal profile of remotely sensed indices from Sentinel-1 and Sentinel-2 satellite sensors were used to extract seasonality parameters using TIMESAT. Correlation analysis was performed at different phenophases to identify sensitive biophysical and remote sensing parameters. Finally, machine learning models were used to predict yield of rice at 45, 60 and 90 days after transplanting using remote sensing indices and biophysical parameters as inputs. The performances of ML models for early yield estimation were evaluated using standardized ranking performance index. Results revealed significant differences between backscatter profiles of different rice types especially in early vegetative stages with transplanting being the most critical stage. Transplanting/seeding, tillering, panicle initiation, peak vegetative/flowering and maturity were identified to be the most important stages to represent the backscatter profile of entire rice growth. Decision tree algorithm discriminated rice types with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The extracted seasonality parameters revealed that start time derived from SAR indices was found to accurately distinguish different types of rice. The correlation strength generally increased with the progress of crop growth in both seasons. All significant correlations were positive except that with moisture content. NRPB was strongly correlated with VV and VV/VH ratio at all stages in both the seasons. LAI and dry biomass were found to be the most sensitive biophysical parameters towards remote sensing data. Area under rice cultivation in the study area was found to be 37.1%, using random forest classifier that yielded an accuracy above 90% and kappa coefficient of 0.89. The machine learning models were able to achieve R2 and d-index values above 0.9 and RMSE below 0.5 t/ha. XGB was the best model for predicting rice yield using remote sensing and biophysical parameters. The findings of this study have the potential to contribute to the greater goal of sustainable agriculture and food security.
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.
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
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
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.
Open Access
Study of crop-weather relationship and its impact on growth and yield of chickpea (Cicer arietinum L.) in mollisol of Uttarakhand
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2008-07) Singh, Shweta; Suman Kumar
For the present study, the experiments were conducted at the Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar with the objectives for quantifying evapotranspiration losses and the effects of temperature and relative humidity on the canopy of chickpea under tarai conditions, and to select some suitable mathematical methods based on meteorological parameters for estimating ET from chickpea. Evapotranspiration of chickpea was measured with weighing type lysimeter. Data on pan evaporation measured with USWB class A pan evaporimeter and chickpea parameters for the corresponding period were collected from Meteorological observatory. Evapotranspiration from chickpea was also estimated by using mathematical methods of Thornthwaite, Turc, StephensStewar, Jensen-Haise, Blaney-Criddle and modified Penman. Higher growing degree days 1810.4 was observed during 2005-06. Air temperature profiles at different stages indicate that temperature inside the crop canopy was lower than above canopy. The yield per hectare was higher during 2005-06 in comparison to 2006-07 due to favourable weather condition in 2005-06. Evapotranspiration of chickpea during 2005-06 and 2006-07 are about 416.5 and 475.6 mm, respectively. The average total rainfall during 2005-06 and 2006-07 were 18.2 and 275 mm, respectively. Thus, supplementary irrigation was required during crop season due to low rainfall. As the pan evaporation did not give accurate estimate of ET, both on seasonal and as well as weekly basis. Thus, the pan evaporation does not seem to be good criterion for the estimation of ET. Modified Penman method was found to be most very suitable for estimation of ET in tarai region of Uttarakhand.
Open Access
Drought monitoring in Uttarakhand using meterological and space borne observations
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)) Dhami, Jaya; Nain, Ajeet Singh

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