Simulation of the Growth and Yield of Maize ( Zea mays L.) on a Loosened Plinthosol Amended with Termite Mound Material in the Lubumbashi Region

John Banza Mukalay (), Joost Wellens, Jeroen Meersmans, Yannick Useni Sikuzani, Emery Kasongo Lenge Mukonzo and Gilles Colinet ()
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John Banza Mukalay: Water-Soil-Plant Exchange Research Unit, TERRA Gembloux Agro-Bio-Tech, University of Liège, 5030 Gembloux, Belgium
Joost Wellens: Water-Soil-Plant Exchange Research Unit, TERRA Gembloux Agro-Bio-Tech, University of Liège, 5030 Gembloux, Belgium
Jeroen Meersmans: Water-Soil-Plant Exchange Research Unit, TERRA Gembloux Agro-Bio-Tech, University of Liège, 5030 Gembloux, Belgium
Yannick Useni Sikuzani: Ecologie, Restauration Écologique et Paysage, Faculté des Sciences Agronomiques, University of Lubumbashi, Lubumbashi P.O. Box 1825, Democratic Republic of the Congo
Emery Kasongo Lenge Mukonzo: Land Assessment, Soil Conservation and Agro-Meteorology Research Unit, Faculty of Agronomy, University of Lubumbashi, Lubumbashi P.O. Box 1825, Democratic Republic of the Congo
Gilles Colinet: Water-Soil-Plant Exchange Research Unit, TERRA Gembloux Agro-Bio-Tech, University of Liège, 5030 Gembloux, Belgium

Agriculture, 2025, vol. 15, issue 21, 1-24

Abstract: 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 (R 2 ), 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 (R 2 = 0.85 − 0.93; NSE = 0.50 − 0.77; RMSE = 0.29 − 0.40 m 2 m −2 ). Maize grain yield was also well predicted (R 2 = 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.

Keywords: APSIM; maize ( Zea mays L.); google earth engine; SR (simple ratio); LAI; plinthosol; Lubumbashi (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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