Simulating soil organic carbon in maize-based systems under improved agronomic management in Western Kenya

Improved management practices should be implemented in croplands in sub-Saharan Africa to enhance soil organic carbon (SOC) storage and/or reduce losses associated with land-use change, thereby addressing the challenge of ongoing soil degradation. DayCent, a process-based biogeochemical model, provides a useful tool for evaluating which management practices are most effective for SOC sequestration. Here, we used the DayCent model to simulate SOC using experimental data from two long-term field sites in western Kenya comprising of two widely promoted sustainable agricultural management practices: integrated nutrient management (i.e. mineral fertilizer and crop residues/farmyard manure incorporation) and conservation agriculture (i.e. minimum tillage and crop residue retention). At both sites, correlations between measured and simulated SOC were low to moderate (R² of 0.25−0.55), and in most cases, the model produced fairly accurate prediction of the SOC trends with a low relative root mean squared error (RRMSE < 7%). Consistent with field measurements, simulated SOC declined under all improved management practices. The model projected annual SOC loss rates of between 0.32 to 0.35 Mg C ha^-1 yr^-1 in continuously tilled maize (Zea mays) systems without fertilizer or organic matter application over the period 2003–2050. The most effective practices in reducing the losses were the combined application of 4 Mg ha^-1 of farmyard manure and 2 Mg ha^-1 of maize residue retention (reducing losses up to 0.22 Mg C ha^-1 yr^-1), minimum tillage in combination with maize residue retention (0.21 Mg C ha^-1 yr^-1), and rotation of maize with soybean (Glycine max) under minimum tillage (0.17 Mg C ha^-1 yr^-1). Model results suggest that response of the passive SOC pool to the different management practices is a key driver of the long-term SOC trends at the two study sites. This study demonstrates the strength of the DayCent model in simulating SOC in maize systems under different agronomic management practices that are typical for western Kenya.