Dr. Andrew Revill Profile

Dr. Andrew Revill

at Univ of Edinburgh

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Proceedings Article | 12 October 2021 Presentation + Paper

Farming from space: model-data fusion approaches for simulating crop nitrogen and yield estimates

A. Revill, V. Myrgiotis, M. Williams

Proceedings Volume 11888, 118880D (2021) https://doi.org/10.1117/12.2600826

KEYWORDS: Data modeling, Calibration, Atmospheric modeling, Solar radiation models, Statistical modeling, Statistical analysis, Nitrogen, Photosynthesis, Environmental sensing, Climatology

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Farmers are under increasing pressure to manage agricultural resources in a more sustainable and efficient manner. Information on crop nitrogen (N) status can be used to support variable rate fertiliser applications. Furthermore, yield forecasts can aid the logistical planning of harvest operations and ameliorate any negative economic impacts on food supply chains. Complex crop models simulating crop N dynamics and yields often require extensive model inputs that are seldom available. By combining observations of leaf area index (LAI) with a process-based crop model, this research presents a novel and scalable analytical solution for generating robust daily estimate of wheat N and yield, which can be applied at the sub-field scale. The crop model, DALEC-Crop, is a carbon cycle model that simulates the key processes involved in crop growth and development in response to daily meteorological observations. The model was first calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha^-1 for two consecutive growing seasons. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 11%). Using these developments at the plot scale, the model yield estimates had a high agreement with observations (mean R² = 0.7 and NRMSE = 7%) when applied at the sub-field scale across field sites under the constraints of Sentinel-2 data. Although additional field sites and seasons are required for further testing, the modelling approach could be feasibly applied to estimate yields across large areas with only minimal inputs.

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