Evaluation of DSSAT-CROPGRO Model for Greenhouse Tomato in Northern Ghana

Abstract

Water and nutrient constraints challenge greenhouse adoption by farmers in Ghana, with resource optimization experiments proving costly. Predictive modeling, such as the Decision Support System for Agrotechnology Transfer (DSSAT), offers a practical alternative for simulating crop yield influenced by fertilizer, irrigation, genotype, and micro-climate interactions. This study calibrated and validated the DSSAT model to predict indeterminate tomato yields in Northern Ghana under varying fertigation regimes and greenhouse conditions. Treatments included fertilizer rates (100%, 80%, and 60%), irrigation levels (100%, 80%, and 60%), and two tomato genotypes (Jalila F1 and Yetty F1). The model accurately simulated key parameters, including maximum leaf area index (RRMSE: 44.97–140.99; D-Value: 0.31–0.77), aboveground dry biomass (RRMSE: 16.88–25.04; D-Value: 0.66–0.81), and yield (RRMSE: 17.03–22.43; D-Value: 0.67–0.90). Results demonstrated the model’s capacity to capture yield variations influenced by fertigation and genotype under dynamic greenhouse environments, closely aligning with observed data. The DSSAT model proves valuable as a decision-support tool, enabling farmers to optimize crop management strategies, conserve resources, and enhance sustainable food production in resource-limited settings.