Evaluation of WARM Model for Simulating Some Rice Varieties in Northern Delta

The present research study was carried out in Milan University, Italy during 2013 to simulate rice yield by using WARM simulation model. Research on rice cropping systems carried out in Egypt has to face the great climate changes, and the linked abundance of cultivated varieties, characteristic of the high latitudes-temperate areas where rice is traditionally grown. Therefore, dynamic simulation models can provide a useful tool for system analysis needed to improve the knowledge, the agronomic management and crop monitoring.  WARM (Water Accounting Rice Model) simulates yield of paddy rice (Oryza sativa L.), based on temperaturedriven development and radiation-driven crop growth. It also simulates; biomass partitioning, floodwater effect on temperature, spikelet sterility, floodwater and chemicals management, and soil hydrology. Biomass estimates from WARM were evaluated. The test-area was Sakah , Kaferelsheikh (Egypt). Data collected from 2003 to 2012 from rice crop grown under flooded and non-limiting conditions were split into a calibration (to estimate WARM model parameters) and an evaluation sets. Plants were sampled during the life cycle from rice plots of two rice cultivars Sakha 101 and Giza 177, maintained at potential production, to determine some important crop variables and parameters such as aboveground biomass (AGB), leaf area index (LAI), potential yield, specific leaf area, and the date of the main phonological stages. Results show that the model was able to simulate rice growth for both varieties. The assessment of model performances has shown average of relative root mean square error (RRMSE) calculated on AGB curves was above 50% for the calibration and 30% for evaluation sets. The modelling efficiency (EF) is always positive and the coefficient of determination (CD) is always very close to 1. Indeed, intercept and slope were always close to their optima and (R2) was always higher than 0.90. The indices of agreement calculated for the evaluation datasets were better than the corresponding ones computed at the end of the calibration, indirectly proving the robustness of the modeling approach. WARM’s robustness and accuracy, combined with the low requirements in terms of inputs and the implementation of modules for reproducing biophysical processes strongly influencing the year-to-year yield variation, make the model suitable for forecasting rice yields on regional, national and international scales.