Evaluating indicators of land degradation in smallholder farming systems of western Kenya

Understanding the patterns of land degradation indicators can help to identify areas under threat as basis for designing and implementing site-specific management options. This study sort to identify and assess the patterns of land degradation indicators in selected districts of western Kenya. The study employed the use of Land Degradation Sampling Framework (LDSF) to characterize the sites. LDSF a spatially stratified, random sampling design framework consisting of 10 km × 10 km blocks and clusters of plots. The study broadly identified and classified the indicators and attributes of land degradation into soil and site stability, hydrologic function and biotic integrity. Assessment of general vegetation structure showed that over 70% of the land was under cropland with forests accounting for 8% of the area. Sheet erosion was the major form of soil loss. High variability was observed for the soil properties and this can be due to both inherent soil characteristics as well as land management practices. There was distinct variation in the soil properties between the topsoil (0–20 cm) and the subsoil (20–30 cm) with the topsoil having higher values for most of the parameters compared to the subsoil. Using coefficient of variation (CV) as criteria for expressing variability, Ca, TON, Mg, SOC and silt were most variable soil properties for the 0–20 cm depth. Moderate variability (CV 0.15–0.35) was observed for CEC, P, K and clay while Na, Sand and pH had the least variability (CV < 0.15). For the subsoil (20–30 cm), Ca, Mg and silt were the most variable. About 94% of the farms sampled were recorded to have very strongly acidic soil levels (pH 4.5–5.5) while 6% of the farms had moderately acidic soil levels (pH 5.6–6.0). Over 55% of the farms had low (< 2%) total organic carbon levels and this varied with land use. Soils with SOM below this ‘critical level’ are at a threat of degradation if not well managed. The principal component analysis (PCA) identified three main explanatory factors for soil variability: ‘soil fertility potential’, ‘soil physical properties’ and ‘available P’. Improving productivity of land therefore calls for the adoption of integrated soil fertility management (ISFM) options as a strategy to ensuring nutrient availability while at the same time building the natural nutrient reserve through soil organic matter build up.