FARMER KNOWLEDGE AND VALIDATION IN DIGITAL SOIL MAPPING: A SYSTEMATIC REVIEW (2013–2025) WITH EMPHASIS ON SUB-SAHARAN AFRICA AND COMPARATIVE REGIONS
DOI:
https://doi.org/10/3303/jees.2026.0301/026Keywords:
Land suitability assessment, digital soil mapping, machine learning, farmer knowledge, participatory validation, co-productionAbstract
Land suitability assessment plays a central role in agricultural planning, sustainable land management, and climate adaptation. Recent decades have witnessed rapid methodological advancement driven by Geographic Information Systems (GIS), Digital Soil Mapping (DSM), and Machine Learning (ML). However, the extent to which these technological developments incorporate farmer knowledge remains unclear. This study conducts a systematic review of 60 peer-reviewed land suitability and DSM studies published between 2013 and 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were analysed across three dimensions: methodological approach, validation strategy, and degree of farmer knowledge integration. Results indicate that 81.67% of studies excluded farmer participation entirely, while only 6.67% implemented structured participatory validation. The lowest levels of engagement were observed in ML/DSM studies, despite their methodological sophistication. Validation practices were dominated by statistical and biophysical approaches, with limited attention to socio-ecological relevance. These findings reveal a structural imbalance in contemporary land suitability research: predictive accuracy has advanced faster than contextual validation. The paper proposes a hybrid validation framework integrating predictive modelling, independent field verification, structured farmer participation, and uncertainty communication. Incorporating experiential knowledge alongside computational methods is argued to be essential for producing land suitability assessments that are scientifically robust, socially credible, and operationally adoptable.
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