期刊
AGRONOMY-BASEL
卷 11, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/agronomy11020314
关键词
wheat nitrogen estimation; model-data fusion; crop modelling; yield prediction; experimental wheat trials
资金
- joint Biotechnology and Biological Sciences Research Council (BBSRC) [BB/P004628/1, BB/P004458/1]
- National Environment Research Council (NERC) Sustainable Agricultural Research and Innovation Club (SARIC) initiative [BB/P004628/1, BB/P004458/1]
- NERC [NE/P018920/1]
- Royal Society
- NCEO
- BBSRC [BB/P004458/1, BB/P004628/1] Funding Source: UKRI
- NERC [NE/P018920/1] Funding Source: UKRI
Climate, nitrogen, and leaf area index are key factors affecting crop yield. By combining climate and LAI data with a model, researchers successfully estimated leaf nitrogen content and crop yield. The model calibration showed accurate results, providing a valuable tool for crop studies.
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.
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