Understanding the spatial drivers of grain protein content for optimal nitrogen management

M.J. Tilse; T.F.A. Bishop; S. Poole; P. Filippi

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Understanding the spatial drivers of grain protein content for optimal nitrogen management

In: Precision agriculture '25

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M.J. Tilse

M.J. Tilse mikaela.tilse@sydney.edu.au

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T.F.A. Bishop

T.F.A. Bishop
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S. Poole

S. Poole
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P. Filippi

P. Filippi
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Type:: Chapter

Pages:: 886–893

DOI:: https://doi.org/10.1163/9789004725232_116

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Dar Hadith al Hassania

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Variability in wheat grain protein content (GPC) is driven by complex genetic, environmental, and management interactions. This study applies an interpretive machine learning (IML) approach to a case-study dryland winter wheat field in Western Australia to understand site-specific GPC variability. Predictions of GPC were broken-down into components driven-by features within (e.g. nitrogen application) or outside the growers control (i.e. inherent features including elevation, soil water holding capacity, and seasonal conditions). A retrospective analysis revealed that nitrogen was overapplied by the grower in the previous season. Understanding these drivers can help growers better manage GPC variability to improve profitability and sustainability in production systems.

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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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Understanding the spatial drivers of grain protein content for optimal nitrogen management

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Precision agriculture '25

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