Integrating soil monitoring and machine learning to map historical tillage and stubble management across Australian cropping systems (2001–2023)

Chiara Pasut; Ming Li; Bonnie Armour; Christina Asanopoulos; Glenn Brown; Doug Crawford; Mark Farrell; Mary Garrard; Frances Hoyle; Malcolm McCaskill; Massimiliano De Antoni Migliorati; Robert Moreton; Tamara O’Keeffe; Katherine Polain; Steven Reeves; Amanda Schapel; Brian Wilson; Senani Karunaratne

doi:10.1016/j.compag.2026.111408

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Integrating soil monitoring and machine learning to map historical tillage and stubble management across Australian cropping systems (2001–2023)

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Integrating soil monitoring and machine learning to map historical tillage and stubble management across Australian cropping systems (2001–2023)

Chiara Pasut, Ming Li, Bonnie Armour, Christina Asanopoulos, Glenn Brown, Doug Crawford, Mark Farrell, Mary Garrard, Frances Hoyle, Malcolm McCaskill, …

Computers and electronics in agriculture, Vol.243, 111408

2026

DOI: https://doi.org/10.1016/j.compag.2026.111408

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Abstract

Random forest in agriculture

Spatial-temporal products

Stubble and tillage management

Tillage and stubble management are key drivers of soil health, crop productivity, and greenhouse gas (GHG) emissions, yet long-term spatially explicit data on these practices remain scarce. Using data from two Australian national soil monitoring programs Soil Carbon Research Project (SCaRP) and Soil Organic Carbon Monitoring (SOC-M) we analyzed over two decades (2001–2023) of tillage and stubble management records collated across 300 Australian farmer paddock scale datasets. We developed machine learning models based on random forest to predict spatial and temporal trends in these practices, incorporating crop type, soil classification, climate variables, and spatial coordinates as predictors. Our models were benchmarked against multinomial logistic regression and a nearest-neighbour baseline, demonstrating that random forest consistently achieved higher accuracy, particularly for dominant practices such as no-tillage and stubble grazing. Variable importance analyses identified “Year” as the most influential predictors, capturing widespread no-tillage adoption post-2010, while latitude and crop type further explained regional variations. Partial dependence plots revealed sharp inflection points in adoption trajectories around 2010, coinciding with major policy and technological shifts. We applied the models to generate gridded predictions (0.5° resolution) across the Australian cropping zone, providing insights into the evolving distribution of tillage and stubble practices. These spatial products not only improve GHG accounting frameworks and Earth system models but also offer actionable intelligence for soil health policy and targeted extension programs. Our approach demonstrates how combining ground-based monitoring with machine learning can fill critical data gaps and provide a scalable framework for agricultural management assessment and climate mitigation strategies.

Details

Title: Integrating soil monitoring and machine learning to map historical tillage and stubble management across Australian cropping systems (2001–2023)
Authors/Creators: Chiara Pasut - CSIRO Agriculture & Food, Kaurna Country, Gate 4 Waite Road, Urrbrae, SA 5064, Australia
Ming Li - Data61
Bonnie Armour - Department of Primary Industries and Regions South Australia
Christina Asanopoulos - Agriculture and Food
Glenn Brown - Department of Natural Resources and Environment Tasmania
Doug Crawford - Ellinbank Observatory
Mark Farrell - Agriculture and Food
Mary Garrard - University of New England
Frances Hoyle - The University of Western Australia
Malcolm McCaskill - Action Network
Massimiliano De Antoni Migliorati - Department of the Environment, Tourism, Science and Innovation, 41 Boggo Road, Dutton Park, QLD 4102, Australia
Robert Moreton - Department of Natural Resources and Environment Tasmania
Tamara O’Keeffe - Murdoch University
Katherine Polain - University of New England
Steven Reeves - Queensland Department of Environment and Science
Amanda Schapel - Department of Primary Industries and Regions South Australia, Research Rd, Nuriootpa, SA 5355 Australia
Brian Wilson - University of New England
Senani Karunaratne - Commonwealth Scientific and Industrial Research Organisation
Publication Details: Computers and electronics in agriculture, Vol.243, 111408
Publisher: Elsevier B.V.
Number of pages: 15
Identifiers: 991005855187607891
Murdoch Affiliation: SoilsWest; Centre for Sustainable Farming Systems
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration
Citation topics: No Topic Assigned; No Topic Assigned; No Topic Assigned
Web Of Science research areas: Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications
ESI research areas: Computer Science