Calibration and Evaluation of CERES-Maize and CROPGRO-Dry Bean Crop Simulation Models of the DSSAT in the Great Rift Valley Region of Ethiopia

Abstract

Maize (Zea mays L.) is one of the most principal cereal crops ranking first in production in Ethiopia, predominantly produced and consumed directly by the smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Common bean (Phaseolus vulgaris) is also the most important legume crops as the source of protein and export commodity in the GRV. However, the average maize and common bean yields in Ethiopia are still low due to abiotic, biotic and socioeconomic constraints. In this regard, Crop simulation models (CSMs) are used in predicting growth and yield of crops and associated yield gaps under various management options and changing climatic parameters that are profitable with minimal unwanted impacts on the environment. Before using the CSMs, it is necessary to specify model parameters and understand the uncertainties associated with simulating variables that are needed for decision-making. Therefore, the research objective of this study was to calibrate and evaluate the performance of the CERES-Maize and CROPGRO-Dry bean CSMs of the Decision Support System for Agrotechnology Transfer (DSSAT) in the GRV of Ethiopia. The generalized likelihood uncertainty estimation (GLUE) method was used to estimate the genetic parameters of the CSM-CERES-Maize and CROPGRO-Dry bean models. Root mean squared error (RMSE) and Index of agreement (I) were used to evaluate the performance of the models. The DSSAT model reasonably reproduced observations for days to anthesis, days to physiological maturity, and grain yields, with values for the index of agreement of 0.97, 0.88 and 0.61 for CERES-Maize and 0.84, 0.75 and 0.51 for CROPGRO-Dry bean. Similarly, root mean square errors were moderate for days to anthesis (1.2 and 1.2 days), maturity (4.1 and 1.6 days), and yield (0.8 and 1.1 t/ha) for CERES-Maize and CROPGRO-Dry bean, respectively. The model has been successfully calibrated and evaluated for maize and common bean crop varieties and can now it can be taken for further applications in evaluating various crop and soil management options including climate smart agriculture technologies and climate change impact studies.