Calibration and Validation of DSSAT CROPGRO Peanut Model for Yield and Yield Attributing Characters of Groundnut Varieties in Northern Agro-Climatic Zone of Tamil Nadu

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.

Combining deficit irrigation and nutrient amendment enhances the water productivity of tomato (Solanum lycopersicum L.) in the tropics

“…The validation employed one-year weather data from 2019. The genetic coefficient of the Pectomech tomato cultivar was derived using the trial-and-error method as described by Thirumeninathan et al (2021). After every trial-and-error simulation run, adjustment was made to match the observed crop phenology and yield with the simulated values.…”