Decoding spatio-temporal drivers of yield variability with interpretive machine learning

S. Poole; T.F.A Bishop; D. Al-Shammari; P. Filippi

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Decoding spatio-temporal drivers of yield variability with interpretive machine learning

In: Precision agriculture '25

Authors:

S. Poole

S. Poole sally.poole@sydney.edu.au

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T.F.A Bishop

T.F.A Bishop
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D. Al-Shammari

D. Al-Shammari
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P. Filippi

P. Filippi
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Type:: Chapter

Pages:: 916–922

DOI:: https://doi.org/10.1163/9789004725232_120

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Dar Hadith al Hassania

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Interpretive machine learning (IML) techniques were utilised to determine if the spatio-temporal drivers of yield could be identified, quantified and map. For two case study farms, in different agroecological zones of Australia, digital soil maps and elevation data were used in XGBoost predictive models for multiple seasons of yield data. The Shapley Additive exPlanations (SHAP) values provided a localised explanation within the field of each year’s predictive yield model. Temporally, the most commonly occurring yield limiting variable was determined for different crop types and rainfall seasonality. This study shows, the IML provides an interpretable way of determining the spatio-temporal drivers of yield that can vary in different agronomic contexts.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Decoding spatio-temporal drivers of yield variability with interpretive machine learning

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Precision agriculture '25

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