Abstract
Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.
Highlights
Food security is always an important issue; high price and scarcity of food increased agriculture’s capacity to feed burgeoning global population [1,2]
From the prediction results on 30 April and 15 May, we have found that the error in predicting the anthesis dates is even higher than that of the maturity dates, which points out an advantage of the assimilation framework in this study
The parameters of World Food Studies (WOFOST) crop model was optimized by Shuffled Complex Evolution-University of Arizona (SCE-UA) to minimize the normalized cost function
Summary
Food security is always an important issue; high price and scarcity of food increased agriculture’s capacity to feed burgeoning global population [1,2]. Winter wheat is one of the staple crops around the world, for which the maturity dates significantly influence wheat production and grain quality due to their bearing on plant dry matter accumulation. Yield loss occurs when winter wheat is harvested at an immature stage, with prematurely harvested grain needing additional post-harvest drying to reduce moisture level to a maximum of 13% (the safe upper limit for storage) [3]. The above problems can be mitigated by harvesting at the suitable maturity dates [5]. Forecasting the optimal harvest time of winter wheat is conducive in guiding harvester scheduling and improving harvested yield
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