Abstract:
Process-based computer simulation models are often used as reasoning support tools to integrate the
complex processes involved in the soil-plant-atmosphere system. The objectives of this study were to
evaluate the performance of the SWB-Sci model as a reasoning support tool for sludge management in
agricultural lands, and use the validated model to assess the long-term agronomic and environmental
implications of water availability and crop intensity on sludge-amended land. The model was calibrated
for the test crops, maize (Zea mays Pan6966) and oats (Avena sativa L.), using data collected during the
2004/2005 growing season from irrigated plots at the East Rand Water Care Works, Gauteng, South
Africa. Model validation was performed using independent data sets collected during the 2004/2005 to
2007/2008 growing seasons. The model was successfully calibrated for maize and oats as allthe statistical
parameters were within the prescribed ranges [index of agreement (d) >0.8; relative mean absolute
error (MAE%) <20%; coefficient of determination (R2) >0.8]. The results indicate that SWB-Sci simulated
aboveground biomass (TDM) and grain yield (GY) of maize and oats with high accuracy (d > 0.85, MAE%
≤20%, and R2 > 0.91) but with a slight overestimation by 0.2–4 Mg ha−1. The model predicted nitrate
leaching and crop N uptake reasonably well(d > 0.85,MAE% ≤14%, and R2 > 0.8), withslight overestimation
of TDM and GY N uptake by 11–57 and 4–48 kg ha−1, respectively. Long-term model simulations indicate
that fixed sludge application rate recommendations generated from laboratory incubation studies may
in the long-term result in spontaneous excessive nitrate leaching below the active root zone during high
rainfall events, if recommendations do not consider N contribution from soil organic matter. Modelling
also showed that leaving room for rain during each irrigation event may minimize the risk of nitrate
leaching
