Simulating Maize Productivity under Selected Climate Smart Agriculture Practices Using AquaCrop Model in a Sub-humid Environment

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Abstract:

Crop models are crucial in assessing the reliability and sustainability of soil water conservation practices. The AquaCrop model was tested and validated for maize productivity under the selected climate smart agriculture (CSA) practices in the rainfed production systems. The model was validated using final biomass (B) and grain yield (GY) data from field experiments involving seven CSA practices (halfmoon pits, 2 cm thick mulch, 4 cm thick mulch, 6 cm thick mulch, 20 cm deep permanent planting basins (PPB), and 30 cm deep) and the control (conventional practice) where no CSA was applied. Statistics for coefficient of determination (R2), Percent bias (Pbias), and Nash–Sutcliffe (E) for B and GY indicate that the AquaCrop model was robust to predict crop yield and biomass as illustrated by the value of R2 > 0.80, Pbias −1.52–1.25% and E > 0.68 for all the CSA practices studied. The relative changes between the actual and simulated water use efficiency (WUE) of grain yield was observed in most of the CSA practices. However, measured WUE was seemingly better in the 2 cm thick mulch, indicating a potential for water saving and yield improvement. Therefore, the AquaCrop model is recommended as a reliable tool for assessing the effectiveness of the selected CSA practices for sustainable and improved maize production; although, the limitations in severely low soil moisture conditions and water stressed environments should be further investigated considering variations in agroecological zones.

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Simulating Maize Productivity under Selected Climate Smart Agriculture Practices Using AquaCrop Model in a Sub-humid Environment

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