To come up with suitable recommendations across diverse agro-ecologies, multi-location studies are often necessary. Conversely, the advantages of intercropping across SSA can easily be confounded by heterogeneous agro-ecological characteristics within existing rainfed cropping systems (Cooper et al., 2008). Under limited water availability, intercropping has been observed to improve productivity per unit area through increased water use efficiency (Rezig et al., 2010, Tsubo et al., 2003, Yang et al., 2011). Intercropping is defined as the growing of two or more crops (species or varieties) within the same spatial and temporal resolution (Willey, 1979). Therefore, there is need to generate relevant information that can be used to enhance promotion of intercropping within rainfed cropping systems. However, due to past research emphasis on monocrop systems, information that can assist in formulation of policy for promotion of intercropping in rainfed cropping systems is scant. Intercropping has emerged as a suitable approach for sustainable intensification of agriculture, especially under water limited conditions. In addition, climate change predictions indicate an increase in the occurrence and severity of weather extremes such as drought and flooding within the region (Connolly-Boutin and Smit, 2015). Low levels of investment in infrastructure in the region also make farming challenging, especially for resource–poor farmers. In addition, rural farmers lack access to capital, technical know how and inputs (Nkonya et al., 2015). However, the region is characterized by low yields owing to low and variable rainfall, degraded soils and inherently infertile soils (Chikowo et al., 2009, Chikowo et al., 2014). In rural sub-Saharan Africa (SSA), rainfed agriculture is the most important sector for providing food security (Gowing and Palmer, 2008). APSIM can still be used to determine best management practices for intercropping under water scarce environments.
The APSIM model was able to simulate growth, yield and WU of an intercrop system under varying water regimes, However, it is still limited with regards to rainfed conditions since it overestimated biomass (6.25%), yield (14.93%) and WU (7.29%) and under estimated WUEb (−14.86%). The model underestimated LAI (36.98%) this was associated with defoliation of crop canopy due to hail damage. The model simulated phenology satisfactorily for sorghum (R 2 = 0.98, RMSE = 6.62 days) and cowpea (R 2 = 0.86, RMSE = 13.67 days) across different water regimes. Model performance was assessed using R 2, root mean squared error (RMSE) and its components (RMSEs and RMSEu) and the d-index. Model simulations were evaluated using observed data for phenology, leaf number, leaf area index (LAI), biomass, yield, ET and water use efficiency (WUE). Thereafter, the model was tested using data obtained from 2014/15 under various water management strategies. Data from optimum experiments (2013/14) was used for local adaptation of APSIM. Weather and soil data were observed in situ and input into APSIM.
Growth, yield and crop water use (ET) of a sorghum–cowpea intercrop system were evaluated using APSIM and data from field experiments conducted at Ukulinga Research Farm, South Africa over two seasons (2013//15).