Evaluating indicators of land degradation in smallholder farming systems of western Kenya Boaz S. Waswa a, ⁎, Paul L.G. Vlek a , Lulseged D. Tamene b , Peter Okoth c , David Mbakaya d , Shamie Zingore e a Center for Development Research (ZEF), University of Bonn, Germany b International Center for Tropical Agriculture (CIAT), Lilongwe, Malawi c International Center for Tropical Agriculture (CIAT), Nairobi, Kenya d Kenya Agricultural Research Institute (KARI), Kakamega, Kenya e International Plant Nutrition Institute (IPNI), Nairobi, Kenya abstract article info Article history: Received 9 May 2012 Received in revised form 14 November 2012 Accepted 21 November 2012 Available online 7 January 2013 Keywords: Integrated soil fertility management (ISFM) Land degradation Principal component analysis (PCA) Soil fertility indicators Soil variability Sustainable land management (SLM) 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 var- iability, Ca, TON, Mg, SOC and silt were most variable soil properties for the 0–20 cm depth. Moderate vari- ability (CV 0.15–0.35) was observed for CEC, P, K and clay while Na, Sand and pH had the least variability (CV b 0.15). For the subsoil (20–30 cm), Ca, Mg and silt were the most variable. About 94% of the farms sam- pled 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 (b 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. © 2012 Elsevier B.V. All rights reserved. 1. Introduction Information on variability of soil properties can aid to map out patterns of land degradation and to guide decisions on selection of appropriate restoration options. However, ecological processes are difficult to observe in the field due to the complexity of ecosystems (Pellant et al., 2005). To determine whether the land is degraded or not, direct assessment can be done based on different criteria such as soil stability, vegetation, nutrient cycling and many other aspects (NRC, 1994). This therefore calls for the defining and use of attributes and indicators that may take a qualitative or quantitative form. Classifying or rating the attribute indicators along ordinal or categorical scales is often used to capture the degree of departure from expected levels for each indicator and hence level of degrada- tion. Spatial variability in landscapes arises from a combination of intrinsic and extrinsic factors while temporal variability is caused mainly by changes in soil characteristics and rainfall patterns over time (Rao and Wagenet, 1985). Intrinsic spatial variability refers to natural variations in soil characteristics, often a result of soil formation processes, such as weathering, erosion, or deposi- tion processes, and variability in organic matter content due to the architecture of native plant communities (Zacharias, 1998). On the other hand, extrinsic spatial variability refers to the varia- tions caused by lack of uniformity in management practices such as chemical application, tillage, and irrigation (Vieira et al., 2002; Zacharias, 1998). Geoderma 195–196 (2013) 192–200 ⁎ Corresponding author at: Centre for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3 53113, Bonn, Germany. Fax: +49 228/73 18 89. E-mail addresses: bswaswa@yahoo.com, bwaswa@uni-bonn.de (B.S. Waswa). 0016-7061/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.geoderma.2012.11.007 Contents lists available at SciVerse ScienceDirect Geoderma journal homepage: www.elsevier.com/locate/geoderma