Abstract
We used the Decision Support System for Agro-technology Transfer-Cropping System Model (DSSAT) and data assimilation scheme (DSSAT-DA) to estimate maize (i.e., corn) yield and to evaluate the sensitivity of maize yield to hydroclimatic variables (i.e., precipitation, air temperatures, solar radiation, soil water). The remotely sensed soil moisture products, which includes Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity, were assimilated to DSSAT model by using the Ensemble Kalman Filtering approach. It was observed that both DSSAT and DSSAT-DA models can able to capture the annual trend of maize yield, although they overestimate the observed maize yield. The DSSAT-DA scheme assimilated with remotely sensed products slightly improves the model performance. The antecedent hydroclimatic information can influence the subsequent maize yield. The maize yield is sensitive to the soil water availability and precipitation amount, especially at the antecedent 1 month time to sowing and the subsequent second and third month’s growing period.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Stochastic Environmental Research and Risk Assessment
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
