No abstract

Crop yield data are critical for managing agricultural sustainability and assessing national food security. This study aims at increasing peanut productivity from its current levels by analyzing the yield gap (difference) of potential production between theoretical yield and actual farmers’ yields. The spatial yield gap of peanut for the Tiruvannamalai district of Tamil Nadu is examined in this investigation by integrating the products of microwave remote sensing (SAR Sentinel-1A) with the DSSAT CROPGRO Peanut simulation model. The CROPGRO (crop growth) Peanut model was calibrated and validated by conducting a field experiment at Oilseeds Research Station, Tindivanam during Rabi (spring) 2019 for predominant cultivars, i.e., TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing crop cutting experiments (CCEs) with the help of the Department of Agriculture Economics and Statistics in the respective monitoring villages. The regression analysis between the maximum recorded DSSAT leaf area index (LAI) at the peak flowering stage of peanut and the yield recorded by CCEs for the spatial yield estimation of peanut in the Tiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The DSSAT CROPGRO simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (from 1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in peanut for Tiruvannamalai district were assessed as 1890, 2324 and 2134 kg/ha, respectively. In order to reduce the production difference of peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

Crop yield data is critical for managing sustainable agriculture and assessing national food security. Current study aims to increase Peanut productivity from current levels by analyzing the yield gap of production potential between theoretical yield and actual farmers’ yields. The spatial yield gap of Peanut for Thiruvannamalai district of Tamil Nadu is examined in this paper by integrating the products of microwave remote sensing (SAR Sentinel-1A) with DSSAT CROPGRO peanut simulation model. CROPGRO Peanut model was calibrated and validated by conducting field experiment at Oilseeds Research Station, Tindivanam during Rabi 2019 for predominant cultivars viz. TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing CCE with help of Department of Agriculture Economics and Statistics in the respective monitoring Villages. Regression analysis between maximum recorded DSSAT Leaf Area Index (LAI) at peak flowering stage of peanut and yield recorded by Crop Cutting Experiment (CCE) for spatial yield estimation of Peanut in Thiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The results showed that the simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in Peanut for Thiruvannamalai district was assessed as 1890, 2324 and 2134 kg/ha, respectively. To reduce the production difference (Yield gap) of Peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

No abstract

Aim: The research study was conducted to calibrate and validate the DSSAT CROPGRO peanut model for simulating the potential yield of groundnut to deciding the best possible management options at major growing areas of Northern Agro-Climatic zone of Tamil Nadu. Study Design:  The experiment was conducted in Split plot Design with four Sowing dates and cultivars. Methodology: The DSSAT model requires layer wise soil data (physical and chemical), including soil texture and other soil properties. Daily weather data, including maximum and minimum air temperature (°C), solar radiation (MJ m−2 day−1), Relative Humidity (%) and precipitation (mm) were used as inputs. Data describing management practices and information of cultivar-specific genetic coefficients were used to calibrate the model. Validation of model were carried out using observed growth and yield attributes of TMV13 and G7 varieties using RMSE (Root Mean Square Error), NRMSE (Normalized Root Mean Square Error) and agreement per cent as test criteria for the evaluation. Results:  The performance of DSSAT CROPGRO peanut model for simulated growth attributes were underestimated the growth attributes like days to anthesis, leaf area index, days to first pod and days to maturity than compared to observed growth attributes of TMV13 and G7 varieties. But the model performs better for G7 as compared to TMV13. Whereas, yield and yield attributes of CROPGRO peanut model were overestimated than the observed yield. Conclusion: The simulation model shows the low RMSE, NRMSE and high agreement per cent for growth and yield of groundnut which was more than 90 per cent, it shows the higher level of confidence on model simulation with observed characters.