Management zones for climate-resilient precision agriculture: A critical review of temporal stability, machine learning and seasonal climate information

Context While machine learning has expanded options for management zone delineation, the process remains resource-intensive and often fails to account for the systemic influence of seasonal climate variability. Objective This review evaluates: (i) the agronomic variables associated with spatial and temporal management zone stability; (ii) the machine-learning approaches used for zone delineation, and (iii) the evidence for integrating climate risk and seasonal climate forecasts into management-zone decisions. It further proposes a framework linking stable management zones with forecast-informed tactical management. Methods A thematic synthesis approach was employed, integrating evidence from agronomy, data science, and agricultural economics. Database searches of Scopus and Web of Science were complemented by reference tracking and targeted searches, yielding 61 included records. Results The synthesis reveals that temporal stability is maximised when zones are anchored by yield-limiting factors such as soil texture and bulk density and when supported by multi-year productivity observations. However, the evidence base was heterogeneous, and independent external validation was rare. The reviewed methods showed trade-offs among predictive performance, temporal stability, interpretability, data requirements and operational feasibility and no universally superior method was identified. More complex approaches, including deep clustering and ensemble methods such as XGBoost, offered predictive flexibility but raised concerns about interpretability and validation. Among the forecast-related documents, none reported using forecast information to alter management-zone delineation or to generate a forecast-conditioned variable-rate prescription within established sub-field zones, identifying a specific gap between climate-informed decision support and spatially differentiated management. Conclusion We propose a four-component framework that: (i) integrates soil, yield, spectral, weather and experimental data; (ii) delineates interpretable baseline management zones using persistent yield-limiting factors; (iii) evaluates candidate zones using spatial, temporal, agronomic and operational criteria; and (iv) applies seasonal climate forecasts to tactical management decisions within established zones. Significance The proposed framework provides a pathway for translating seasonal climate forecasts into spatially differentiated input decisions while retaining stable, interpretable management zones. It offers a basis for more responsive management of both within-field variability and seasonal climate risk.