Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

Abstract

28Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide 29 the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect 30 of most modelling exercises because it requires expensive and time-consuming field experiments. 31GSPs could also be estimated using multi-year and multi locational data from breeder evaluation 32 experiments. This research was set up with the following objectives: i) to determine GSPs of 10 33 newly released maize varieties for the Nigerian Savannas using data from both calibration 34 experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the 35 accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-36 Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different 37 experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. 38 Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were 39 evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 40 120kg N ha -1 ). For the model calibration using experimental data, calculated model efficiency (EF) 41 values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. 42 Calibration of time-series data produced nRMSE below 7% while all prediction deviations were 43 below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges 44 were lower. Prediction deviations were below 17% of the means for all measured variables. Model 45 evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high 46 EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen 47 contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from 48 detailed experiments. If unavailable, data from breeder experimental trials collected from many 49 locations and planting dates can be used with lower but acceptable accuracy.

CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria

“…The soil data tool ( SBuild ) of DSSAT was used to create the soil database which was used for general simulation purposes. The parameterization of SBuild for calibration and simulation experiments were done as previously reported ( Adnan et al, 2019 ).…”

“…All the weather stations were less than 2 km away from the experimental sites. The Weatherman utility in DSSAT was used to input the weather data to create the weather file used by the CERES- Maize model ( Adnan et al, 2019 ).…”

“…The three locations selected for calibration were optimum sites and thereby deliberately selected for calibrating the model, while the last location was not optimal and thereby deliberately selected for model evaluation. The model was calibrated and evaluated following the procedures of Adnan et al (2019) . Genotype specific parameters (GSPs) for the 16 maize varieties were calibrated and evaluated previously by Adnan et al (2019) and were used in this study ( Table 2 ).…”

“…The model was calibrated and evaluated following the procedures of Adnan et al (2019) . Genotype specific parameters (GSPs) for the 16 maize varieties were calibrated and evaluated previously by Adnan et al (2019) and were used in this study ( Table 2 ). The cultivar coefficients were fixed in the calibration and validation exercise, while soil and weather of the different environments were inputted.…”