Testing and Improving the WOFOST Model for Sunflower Simulation on Saline Soils of Inner Mongolia, China

Jiangxu Zhu, Wenzhi Zeng, Tao Ma, Guoqing Lei, Yuanyuan Zha, Yuanhao Fang, Jingwei Wu, Jiesheng Huang
2018 Agronomy  
Monitoring and improving environmental stress in crops is vital for the sustainable development of agriculture and food security. Traditional experimental methods are costly and time-consuming, yet crop growth models focus mainly only on water and nutrient stresses. In this study, a new World Food Studies (WOFOST) model, WOFOST-ES, was developed by the addition of a general environmental stress factor (ES). To calibrate and validate WOFOST-ES, two-year micro-plot experiments and one-year field
more » ... xperiments with sunflower were conducted in the Hetao Irrigation District, China. The results of the micro-plot experiments indicated that the WOFOST model failed to simulate sunflower growth correctly but that the WOFOST-ES model was highly accurate in simulating both yield (R2 = 0.99, root mean square error (RMSE) = 56 kg/ha) and leaf area index (LAI) (R2 = 0.86, RMSE = 0.44). A statistical method for estimating ESs based on the dominant stress factor (salt at our study site) was also proposed as a supplemental tool for WOFOST-ES, and micro-plot and field experiments conducted in 2013 and 2017 both proved acceptable accuracy of the statistical method when using WOFOST-ES. Comparison between ESs and the water and salt stress factors of Feddes-type stress reduction functions indicated that ESs failed to reveal actual environmental stresses during the sunflower seeding stage but did reflect other environmental stresses in addition to water and salt during the bud, flowering, and maturity stages. Although the present WOFOST-ES model proved to be accurate, stable, and practical, future studies should be performed, focusing on the physical separation of ESs, their mechanistic quantification, and their evaluation at small time steps using more observations.
doi:10.3390/agronomy8090172 fatcat:2ygqxdvt5jeqrewc33xbhs4wxi