Soil degradation assessment across tropical grassland of Western Kenya

Quinton, John and Yesuf, Gabriel and Baldi, German and Gong, Mengyi and Kinuthia, Kelvin and Fry, Ellen L. and Odongo, Yuda and Nyakundi, Barthelemew and Hitimana, Joseph and de Britto Costa, Patricia and Onyango, Alice Anyango and Leitner, Sonja M. and Bardgett, Richard and Rufino, Mariana C. (2026) Soil degradation assessment across tropical grassland of Western Kenya. SOIL, 12. pp. 451-469. ISSN 2199-3971

Abstract

Soils across sub-Saharan Africa are exposed to extensive degradation processes, which can reduce their ability to produce crops and support livestock. While there has been a significant research effort focussing on soil degradation in sub-Saharan croplands, less research effort had been directed towards grasslands. Here, we tested the effectiveness of remote sensing to classify the soil degradation status of smallholder grazing lands. Focussing on grasslands used by smallholders in the districts of Nyando and Kuresoi in Western Kenya, we first used remote sensing (RS) to classify grasslands as productive grazing lands, grazing lands that followed a variable trend in vegetation productivity (transition), and unstable and unproductive (degraded) grazing lands. We then tested how this classification related to measured soil parameters indicative of soil degradation. We then used this classification, which was based on a temporal analysis of Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalised Difference Water Index (NDWI) between 2013 and 2018, to identify 90 field sites across the two districts, which we then sampled and analysed for a range of physical, chemical and biological soil properties. Only soil microbial biomass carbon (C) showed consistent alignment with the RS classification, although there was some overlap with other soil parameters at one or other of the study areas. To group the sites using the soil variables, which we split by study area and into stable (those that are slow to change) and transient (those that change rapidly in response to a changing pedological environment), K-means clustering was undertaken. Two sets of clusters were produced for each district for the stable and transient variables. For the stable variables, at Kuresoi one of these clusters included sites with higher levels of C, nitrogen (N), phosphorus (P) and pH, that aligned well with the RS classification, with seven out of 10 productive sites being assigned to this cluster. At Nyando one of the stable variable clusters included sites with high soil C and N, but low pH and relatively low soil bulk density, and corresponded to 12 out of the 16 productive sites. For the transient variables, agreement between the clusters and the remote sensing classification was poor indicating a lack of utility for degradation assessment. Overall, our results suggest that while the use of RS methods for classifying degraded grasslands and the soils supporting them does have significant advantages in terms of time and costs over field survey, supplementing these methods with a limited set of soil parameters related to nutrient cycling, such as microbial biomass C, soil P, percent C and N, and soil pH, could enhance our ability to identify degraded soils and target restoration efforts.