Assessment of soil physical degradation in Eastern Kenya by use of a sequential soil testing protocol

Soil physical degradation is a gradual process of many steps beginning from structural deterioration and ending in differential loss of finer particles through erosion. Control of the degradation remains a challenge to many scientists due to lack of proper assessment protocols. This study developed a sequential protocol with emphasis on definition of physical degradation and successive soil testing to determine the stages of degradation development. The protocol was tested in Cambisols, Arenosols, and Ferralsols in Eastern Kenya. Soil physical degradation due to 10 years land use change was defined as more than 25% drop in infiltration and water retention characteristics and aggregate stability and more than 30% increase in bulk density and silt content. Then a soil testing model was sequentially applied to identify physical degradation phases. Visual assessment of degradation symptoms, RUSLE model, and diffuse infrared spectral reflectance were used in the soil testing model as predictors of physical degradation. Visual assessment was found to be cheap and fast method for identifying final stages of physical degradation with 60% accuracy. Visual assessment combined with RUSLE model improved the assessment accuracy to 80%. Infrared spectral reflectance, which is sensitive to subtle changes in soil physical conditions, was also found as a potential surrogate predictor of early-warning signs of soil physical degradation. Inclusion of spectra into the assessment model improved the accuracy to 95%. This protocol is effective in identifying phases of soil physical degradation, which are useful for planning degradation control and monitoring schemes. Its further testing and worldwide application is recommended.

[1]  R. Morgan.  Soil Erosion and Conservation , 1988 .

[2]  Robert J. Hickey,et al.  Computing the LS factor for the Revised Universal Soil Loss Equation through array-based slope processing of digital elevation data using a C++ executable , 2004, Comput. Geosci..

[3]  Shuhui Dun,et al.  Using the Water Erosion Prediction Project (WEPP) model to simulate field-observed runoff and erosion in the Apennines mountain range, Italy , 2007 .

[4]  R. H. Brooks,et al.  Hydraulic properties of porous media , 1963 .

[5]  S. Prince,et al.  Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series , 2004 .

[6]  K. Lee,et al.  Scaling effect for estimating soil loss in the RUSLE model using remotely sensed geospatial data in Korea , 2006 .

[7]  Gerard Govers,et al.  A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units , 1996 .

[8]  G. R. Foster,et al.  Evaluating Irregular Slopes for Soil Loss Prediction , 1974 .

[9]  Kenneth G. Renard,et al.  Using monthly precipitation data to estimate the R-factor in the revised USLE , 1994 .

[10]  J. Diamond,et al.  Infiltration rate assessment of some major soils , 2014, Irish Geography.

[11]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[12]  Budiman Minasny,et al.  Integral energy as a measure of soil-water availability , 2003, Plant and Soil.

[13]  G. Eshel,et al.  Spectral Reflectance Properties of Crusted Soils under Solar Illumination , 2004 .

[14]  M. Isabirye,et al.  Prediction of soil erosion in a Lake Victoria basin catchment using a GIS-based Universal Soil Loss model , 2003 .

[15]  R. Hickey,et al.  Slope length calculations from a DEM within ARC/INFO grid , 1994 .

[16]  Luca Montanarella,et al.  Soil erosion risk assessment in Italy , 1999 .

[17]  Anthony R. Dexter,et al.  Soil physical quality: Part III: Unsaturated hydraulic conductivity and general conclusions about S-theory , 2004 .

[18]  J. Philip.  THE THEORY OF INFILTRATION: 1. THE INFILTRATION EQUATION AND ITS SOLUTION , 1957 .

[19]  R. Bro.  Multiway calibration. Multilinear PLS , 1996 .

[20]  D. Shrestha,et al.  Remote sensing techniques for rapid detection of soil physical degradation , 2007 .

[21]  G. R. Foster,et al.  Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) , 1997 .

[22]  Gerard B. M. Heuvelink,et al.  About regression-kriging: From equations to case studies , 2007, Comput. Geosci..

[23]  T. Børresen,et al.  Adaptation of RUSLE in the Eastern Part of the Mediterranean Region , 2004, Environmental management.

[24]  Johannes J. Feddema,et al.  Estimated impacts of soil degradation on the African water balance and climate , 1998 .

[25]  M. Stocking,et al.  A Handbook for the Field Assessment of Land Degradation , 2001 .

[26]  J. K. Henshall,et al.  Soil structural quality, compaction and land management , 1997 .

[27]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[28]  C. Valentin,et al.  Morphology, genesis and classification of surface crusts in loamy and sandy soils , 1992 .

[29]  E. Ben-Dor,et al.  The Spectral Reflectance Properties of Soil Structural Crusts in the 1.2‐ to 2.5‐μm Spectral Region , 2003 .

[30]  G. Spoor,et al.  Vulnerability of subsoils in Europe to compaction: a preliminary analysis , 2003 .

[31]  B. Minasny,et al.  Nonlinear mixed effect modelling for improved estimation of water retention and infiltration parameters , 2006 .

[32]  Keith D. Shepherd,et al.  Rapid characterization of Organic Resource Quality for Soil and Livestock Management in Tropical Agroecosystems Using Near Infrared Spectroscopy. , 2003 .

[33]  M. Isa,et al.  Development of an appropriate procedure for estimation of RUSLE EI30 index and preparation of erosivity maps for Pulau Penang in Peninsular Malaysia , 2008 .

[34]  D. E. Elrick,et al.  Ponded infiltration from a single ring : I. Analysis of steady flow , 1990 .

[35]  R. Lal.  Physical management of soils of the tropics: priorities for the 21st century. , 2000 .

[36]  G. Gee,et al.  Particle-size Analysis , 2018, SSSA Book Series.

[37]  A. Vrieling.  Satellite remote sensing for water erosion assessment: A review , 2006 .

[38]  Andrew A. Millward,et al.  Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed , 1999 .

[39]  W. Sombroek,et al.  Exploratory soil map and agro-climatic zone map of Kenya, 1980 , 1982 .

[40]  Francis N. Gichuki,et al.  More People, Less Erosion: Environmental Recovery in Kenya , 1994 .

[41]  A. Klute.  Methods of soil analysis. Part 1. Physical and mineralogical methods. , 1988 .

[42]  Mark A. Nearing,et al.  Estimation of rainfall erosivity using 5- to 60-minute fixed-interval rainfall data from China , 2007 .

[43]  Dino Torri,et al.  Predictability and uncertainty of the soil erodibility factor using a global dataset , 1997 .

[44]  Honglei Zhu,et al.  RUSLE applied in a GIS framework: Calculating the LS factor and deriving homogeneous patches for estimating soil loss , 2005, Int. J. Geogr. Inf. Sci..

[45]  C. Dirksen.  Soil physics measurements , 1999 .

[46]  D. Elrick,et al.  Measuring Water Transmission Parameters in Vadose Zone Using Ponded Infiltration Techniques , 2002 .

[47]  M. G. Hodnett,et al.  Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: a new water-retention pedo-transfer functions developed for tropical soils , 2002 .