RiceGrow: A rice growth and productivity model

Abstract

a b s t r a c tGrowth 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.

Evaluation of Indigenous and High Yielding Rice Varieties for Growing in Tidal Floodplain Ecosystem of Southern Bangladesh

“…Rice grain yield is related with shoot biomass and partitioning of biomass that agronomic management practices can largely influence on [13][14]. Shoot dry weight or biomass is the product of photosynthesis.…”

Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment

“…Eight genotype-specific parameters relating to rice yield were used: temperature sensitivity, photoperiod sensitivity, optimum temperature, intrinsic earliness (for phonological development), basic filling factor, maximum CO 2 assimilation rate, potential partitioning index for panicle, and potential relative growth rate for LAI. The submodel equations of the RiceGrow model have been exhaustively described in a previous publication by Tang et al 32 The genetic and cultivar ecotype parameters of RiceGrow were adjusted by the trial and error method 33 according to characteristics of the rice cultivars and experimental treatments from the previous experiments, 32 which ensure the good predictability and applicability of RiceGrow at various ecosites. The RiceGrow model also could reliably simulate the dynamic processes of rice growth, development, and yield with data from experiments 1, 2, and 3 by validation analysis.…”

Assessing ways to combat eutrophication in a Chinese drinking water reservoir using SWAT

“…Data for estimating crop growth parameters for rice was limited and parameters (focusing on radiation use efficiency, harvest index, maximum leaf area index, light extinction coefficient and minimum and optimum growth temperature) were adjusted according to literature values alone (e.g. Liangzhi et al 1987;Tang et al 2009). Likewise, eucalyptus (EFST) is not a part of the parameterised forest types in the SWAT database (Neitsch et al 2009), so literature parameters were used (e.g.…”