Evaluation of Plant Models in Simulating Rice Yield under Crop Management in Rasht

Nowadays, food production systems need to be explored for supplying the needs of the world's growing population, as well as sustainable production in the face of global climate change. In this regard, Cereal's yield has played a significant role in supporting global food security. The unceasing growth in demand for water in the industrial sector, drinking water, and reduction in the amount of water available for the agricultural sector has led to a reduction of water usage in rice, which threatens its production. Crops simulation models can be used to carry out various studies such as the selection of suitable cultivar and plant, determining the best agricultural management and production capacity of the area. The purpose of this study was to investigate the ORYZA2000 and AquaCrop accuracy in simulating grain and biomass yields of rice affected by irrigation and planting dates.

To evaluate the ORYZA2000 and AquaCrop models for rice production under irrigation management and planting date, a split-plot experiment based on a complete randomized block design with three replications was carried out on a local (Hashemi) cultivar in the years of 2016 and 2017 in the Rice Research Institute of Iran, Rasht. Irrigation interval was considered as the main factor at four levels including full flooding, 5, 10, and 15 days irrigation intervals, and transplanting date was assigned to subplot at three levels (April 21st, May 11th, and May 31th). Simulated and observed values of grain yield and biomass yield were evaluated based on the coefficient of determination, T-test, root mean square error (RMSE), Model efficiency (EF), Structural deviation of the model (RMD), and normalized root mean square error (NRMSE).

The results showed that normalized root mean square error of the grain yield and biomass yield were 9% and 5%, in the Aquacrop model and were 7% and 6% in the ORYZA2000 model, respectively. Evaluation of the results showed that the efficiency of the model and the coefficient of the explanation were above 0.7 and structural deviations were less than 2% that showed good accuracy in simulating the grain yield and biomass yield during calibration and validation of models. Evaluation of the amplitude of actual grain yield (3000 to 4761 kg.ha-1) and simulated by AquaCrop model (1741 to 4231 kg.ha-1) and ORYZA2000 model (with 2215 to 4766 kg.ha-1 range) showed simulated values had between -15 and 20 percent of error. The results showed that with increasing irrigation intervals, the actual grain yield decreases. The planting date of April 21st and May 11th (with an average of 3795 and 3820 kg.ha-1, respectively) had the highest yield of grain in two years, and the models also had a predicting of changes in grain yield during calibration and validation. ORYZA2000 model, due to the high coefficient of explanation and efficiency (0.83 and 0.81, respectively), has higher accuracy in simulating the grain yield than the AquaCrop model. The results showed that based on the mentioned equations, the ORYZA2000 model is well able to simulate the effect of environmental conditions including water shortage, air temperature, time, and intensity of stress during the growth cycle under the influence of irrigation and planting date on yield production. It seems that due to the better description of plant and soil conditions in the model and better genetic evaluation, it increased the accuracy of yield estimation in the ORYZA2000 model. Also, according to the acceptable results obtained in the AquaCrop model, it can be concluded that the mentioned model has a good simulation of the changes in the plant's water relations under the influence of the interaction effect of planting date and irrigation intervals.

According to the present study, the ORYZA2000 and Aquacrop models can be used to support the results of experiments under irrigation management conditions and planting dates.