Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate status by simulating soil-crop interactions, such require extensive inputs that seldom available. Through model-data fusion (MDF), we combine climate LAI time-series with an intermediate-complexity model infer The DALEC-Crop was calibrated for wheat yields across field experiments covering applications ranging from 0 200 kg ha−1 in Scotland, UK. Requiring daily meteorological inputs, this simulates C cycle responses LAI, climate. model, which includes a N-dilution function, treatments based on observations, tested at validation plots. We showed single parameterization varying only could simulate development all treatments—the mean normalized root-mean-square-error (NRMSE) 10%. Leaf accurately retrieved the (NRMSE = 6%). Yield also reasonably estimated 14%) if data available assimilation during periods typical application (April May). Our MDF approach generated robust content estimates timely predictions complement existing agricultural technologies. Moreover, EO-derived products high spatial temporal resolutions provides means apply our regionally. Testing over fields is critical next step determine broader utility.