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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 Kiran
The 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) (
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 Kiran
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 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.
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 Ranjan
The 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.
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.
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.

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