Abstract :
[en] The low fertility of plinthosols is a major constraint on agricultural production, largely due to the presence of plinthite, which restricts the availability of water and nutrients. This study aimed to simulate the growth and yield of grain maize on a loosened plinthosol amended with termite mound (from Macrotermes falciger) material in the Lubumbashi region. A 660-hectare perimeter was established, subdivided into ten maize blocks (B1–B10) and a control block (B0), which received the same management practices as the other blocks except for subsoiling and termite mound amendment. The APSIM model was used for simulations. The leaf area index (LAI) was estimated from Sentinel-2 imagery via Google Earth Engine, using the Simple Ratio (SR) spectral index, and integrated into APSIM alongside agro-environmental variables. Model performance was assessed using cross-validation (2/3 calibration, 1/3 validation) based on the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE). Results revealed a temporal LAI dynamic consistent with maize phenology. Simulated LAI matched observations closely (R2 = 0.85 − 0.93; NSE = 0.50 − 0.77; RMSE = 0.29 − 0.40 m2 m−2). Maize grain yield was also well predicted (R2 = 0.91; NSE > 0.80; RMSE < 0.50 t ha−1). Simulated yields reproduced the observed contrast between treated and control blocks: 10.4 t ha−1 (B4, 2023–2024) versus 4.1 t ha−1 (B0). These findings highlight the usefulness of combining remote sensing and biophysical modeling to optimize soil management and improve crop productivity under limiting conditions.
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