Combining process modelling and LAI observations to diagnose winter wheat nitrogen status and forecast yield

Andrew Revill, Vasileios Myrgiotis, A Florence, SP Hoad, RM Rees, Alasdair MacArthur, Mathew Williams

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
82 Downloads (Pure)


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 N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.
Original languageEnglish
Article number314
Issue number2
Early online date10 Feb 2021
Publication statusPrint publication - Feb 2021


  • crop modelling
  • experimental wheat trials
  • model-data fusion
  • wheat nitrogen estimation
  • yield prediction
  • Crop modelling
  • Experimental wheat trials
  • Wheat nitrogen estimation
  • Model-data fusion
  • Yield prediction


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