Calibration and Evaluation of DSSAT-CERES Model for Kharif Sorghum Genotypes

Abstract

Background: Sorghum (Sorghum bicolor (L.) Moench) is one of the world’s most important nutritional cereal crops and also the major staple food and fodder crop of millions of people in semi-arid tropics. It is considered as the ‘King of millets’ and extensively grown in Africa, China, USA, Mexico and India, but sorghum productivity is highly influenced by chosen genotype, climatic factors of a given location and management practices followed, thus requires testing new genotypes as and when released for the yielding potential and response to management. Aims: The current generation of crop models requires calibration as and when new genotype (cultivar) was introduced into the model vis-à-vis cultivar specific coefficients. Therefore, present study calibrated and evaluated the DSSAT-CERES-Sorghum model for four new genotypes introduced. Study Design: The data from field experiment with four genotypes and three dates of sowing conducted during Kharif seasons of 2011 and 2012 under All India Coordinated Research Project (AICRP) on Sorghum at Main Agricultural Research Station, Dharwad, Karnataka, India was used for model calibration (2011 data) and evaluation (2012 data). The borrowed data included phenology, biomass and yield components. Results and Discussion: Calibration process showed anthesis, physiological maturity and yield were perfectly matched using 2011 data which achieved RMSE value of 0.0, 1.41 and 97.17 for anthesis, maturity and grain yield, respectively, and when 2012 data was used for evaluation the calibrated model could simulate with high accuracy as shown by minimum RMSE values of 2.94, 1.29 and 51.76 for anthesis, maturity and grain yield, respectively. Conclusion: This exercise of calibration of crop specific parameters of four kharif sorghum genotypes using DSSAT-CERES-Sorghum model followed by evaluation of model using another independent set of data showed that DSSAT-CERES-Sorghum performed well and the model could be used as decision support tool for all those optimized four genotypes for various applications viz., optimizing dates of sowing, population, spacing and inputs.