A Bayesian approach to understand controls on total and labile soil carbon in cultivated soils of Central and Southern Malawi

Abstract

Soil degradation on cultivated lands of Sub-Saharan Africa is a threat to food security. Even so, drivers of soil C total and labile pools are little understood for smallholder farms. A unique opportunity to evaluate how environment conditions management drivers was afforded by a systematically representative on-farm study of 1108 cultivated plots in marginal to mesic environments across Central and Southern Malawi. Soil sample collection and analysis and surveys of farmer practice were conducted, and linked to remote-sensed data on environmental and spectral factors. Soil properties included the following ranges (mean values per site), soil clay (6.41% to 17.36%), pH (6.09 to 6.54), soil organic carbon (SOC) (6.31 g C kg soil−1 to 16.17 g C kg soil−1), and two labile soil C assays: permanganate oxidizable carbon (POXC) (291.5 mg C kg soil−1 to 504.5 mg C kg soil−1) and 24-h mineralizable C (Cmin) (28.71 mg C kg soil−1 to 65.34 mg C kg soil−1). Incorporation of domain specialists’ expectation of uncertainty levels is key to carrying out a multiscale assessment of soil total and labile C status, thus a Bayesian linear regression approach was used for determining the influential drivers. Overall, the soil clay content is a strong predictor of SOC (0.479–0.517), POXC (0.139–0.266), and Cmin (0.125–0.223) at the 95% Bayesian credibility level from the Gibbs posterior samples. Vegetative cover, reflected by Normalized Difference Vegetation Index (NDVI), is also a dominant driver for SOC (0.234–0.329), POXC (0.163–0.285), and Cmin (0.249–0.38). Of the management practices studied, crop diversity, residue incorporation, and weed presence are all positive drivers for total soil C, whereas fertilizer N is not. At both regional and local scales, labile soil C pools (as reflected by POXC and Cmin) are not consistently responsive to management. The drivers of SOC are highly consistent, a strong indication of statistical robustness. This contributes to the understanding of patterns of carbon pools in intensively cultivated fields in Sub-Saharan Africa.