Integrating crop modelling with remote sensing: Application on data-limited small-scale farming systems

Climate change poses growing risks to agricultural productivity and food security in southern Africa, particularly for resource-constrained small-scale, rainfed farming systems. Although crop growth models (CGM) and remote sensing (RS) offer potential to improve yield estimation and climate adaptation planning, their use in data-scarce small-scale contexts remains limited. This thesis examines how CGM and RS can be integrated and applied in small-scale maize systems in South Africa’s Eastern Cape Province. Using a mixed-methods approach, the study combined a systematic literature review, crop modelling experiments, climate impact assessments, and institutional surveys. A global review revealed strong biases toward large-scale, high-input systems and limited evidence from African small-scale settings. Recalibration of the WOFOST model using Sentinel-2–derived Leaf Area Index reduced yield estimation errors by over 90%. Climate scenario analysis projected potential yield declines under increasing temperature and rainfall variability, although adaptive strategies could reduce impacts. Institutional findings showed high awareness but low adoption of digital tools due to capacity constraints. Overall, the study demonstrates that CGM–RS integration is feasible and valuable for small-scale farming, provided that user-friendly tools and capacity-building are strengthened.