Prediction of pasture condition in the Kimberley rangelands of Western Australia using simple classification tree models

Authors

• Ben Nestor, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Kath Ryan, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Charles Martin, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Philip Thomas, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Chris Hetherington, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Matthew Fletcher, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Robert Sudmeyer, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Karyn Reeves, Department of Primary Industries and Regional Development, Western AustraliaFollow

Document Type

Article

Publication Date

8-2026

Journal Title

The Rangeland Journal

ISSN

Online ISSN: 1834-7541; Print ISSN: 1036-9872

Keywords

classification trees, condition assessment, Kimberley, pastures, pasture condition, random forest, rangelands, WARMS

Disciplines

Natural Resources and Conservation | Natural Resources Management and Policy

Abstract

Pasture condition assessments assist pastoralists, regulators, and policy makers in making informed decisions around livestock production and the preservation of natural resources in the Kimberley rangelands of Western Australia (WA). Reliable qualitative assessments of pasture condition require assessors with extensive expertise, which means frequent and reproducible assessments can be difficult to achieve. To develop a quantitative approach that can complement existing qualitative assessment approaches in the Kimberley, we investigated the use of simple classification tree models to predict pasture condition using assessment data from the Western Australian Rangeland Monitoring System (WARMS). Quantitative traits were derived from WARMS observation data for three major pasture groups in the Kimberley region and assessed for variable importance using random forest models. The traits with highest variable importance were used to train classification tree models for each pasture group. Important variables identified from random forests and classification trees were the percentage of quadrats containing at least one of a set of desirable perennial species and the percentage of quadrats without perennials. Classification trees selected to represent each pasture group had prediction accuracies between 0.71 and 0.81 when applied on reserved WARMS testing data, and between 0.65 and 0.74 when applied on independently collected validation data, indicating they generally performed well when used on data from the Kimberley rangelands. The classification tree models generated here can be used to complement qualitative assessments of pasture condition and to provide paddock-scale assessments, helping to inform decision making in the management of important WA pastoral resources.

Recommended Citation

Nestor BJ, Ryan K, Martin C, Thomas P, Hetherington C, Fletcher M, Sudmeyer R, Reeves K. (2026) Prediction of pasture condition in the Kimberley rangelands of Western Australia using simple classification tree models. The Rangeland Journal 48, RJ25052. https://doi.org/10.1071/RJ25052