Work

Estimating actual maize yield with WOFOST in data-scarce small-scale cropping systems of South Africa: Data assimilation approach

Year: 2025

Type: article

Abstract: Accurate farm-level yield estimation is crucial for informed agricultural decision-making and food security, particularly in data-scarce, small-scale rainfed systems such as those found in South Africa. This study explores the integration of remote sensing-derived leaf area index (LAI) into the WOFOST crop model using a recalibration-based data assimilation (DA) approach. Data from eight farms across two growing seasons (2020–2021) in the Eastern Cape were used. Key phenological parameters were calibrated, and a yield gap factor influencing daily gross assimilation was introduced and optimized alongside the specific leaf area using LAI observations from two farms in 2020. The optimized parameters were validated across additional farms and seasons. Results show that DA significantly improved yield predictions (RMSE = 472 kg.ha−1; NRMSE = 11%) compared to simulations without assimilation (RMSE = 4817 kg.ha−1; NRMSE = 112%). These findings highlight the method's potential to adapt crop models to data-limited contexts, quantify yield gaps, and support efficient resource management. The approach offers scalable benefits for decision-making in similarly constrained agricultural systems.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104848

Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam, Deborah V. Gaso, Allard de Wit

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $3,550

Funders: National Research Foundation, University of Cape Town, National Retail Federation