RiceGrow: A rice growth and productivity model

Growth and yield formation in rice (Oryza sativa L.) depend on integrated impacts of genotype, environment and management. A rice growth simulation model can provide a systematic and quantitative tool for predicting growth, development and productivity of rice under changing environmental conditions. Existing rice models perform well but are somewhat difficult to use because of the large number of parameters that users must estimate. Experience in modelling wheat suggested that using physiological development time (PDT) as a scaler for phenology and a partitioning index for organ growth could result in fewer parameters while providing good predictability and applicability. RiceGrow was developed using PDT and a partitioning index to quantify relations among rice growth and environmental factors, genotypic parameters and management practices. RiceGrow includes seven sub-models for simulating phenology, morphology and organ formation, photosynthesis and biomass production, dry matter partitioning, yield and quality formation, water relations and nutrient balance. The model was calibrated with three datasets involving various cultivars, sowing dates and N rates at multiple sites. Validation with independent datasets showed the model had good predictability and applicability. The RiceGrow model was compared with the ORYZA2000 model, showing that both provided satisfactory estimates for phenology, shoot biomass and yield. Overall, RiceGrow can be used to predict rice growth and development with varied genotypes, environmental conditions and management practices for multiple uses including scientific understanding, policy formulation and optimizing crop management.