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

Agriculture, Ecosystems and Environment 128 (2008) 199–211

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Assessment of soil physical degradation in Eastern Kenya by use of a sequential soil testing protocol C.T. Omuto * Department of Environmental and Biosystems Engineering, University of Nairobi, P.O. Box 30197-0100, Nairobi, Kenya

A R T I C L E I N F O

A B S T R A C T

Article history: Received 17 March 2008 Received in revised form 27 May 2008 Accepted 2 June 2008 Available online 26 July 2008

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. ß 2008 Elsevier B.V. All rights reserved.

Keywords: Physical degradation Physical properties Degradation symptoms RUSLE Spectral reflectance Sequential testing

1. Introduction Soil physical degradation entails the destruction of number and arrangement of soil pores and peds. It is a gradual process through a number of steps: beginning at first with structural deterioration and ending in differential loss of finer particles through erosion. It can therefore take some time before manifesting visual degradation symptoms in the field. During its progress, physical degradation destroys the soil matrix which carries air, moisture, and nutrients for supporting plant biomass production. It also affects soil surface characteristics with negative consequences in water infiltration, capacity of land to buffer atmospheric heat, and the general beauty of the landscape (Feddema, 1998). In spite of the recognized negative impacts and advancing rate, there is still a lack of adequate protocols to assess soil physical degradation (Lal,

* Tel.: +254 736 107383. E-mail address: [email protected]. 0167-8809/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2008.06.006

2000; Paglia and Jones, 2002). Knowledge of these characteristics is vital for effective control and monitoring of the degradation. Capturing soil physical degradation characteristics requires a clear protocol for identifying degraded from non-degraded soil. Once developed, the protocol can then be used to separate different stages of degradation development and their occurrence in the landscape. There are varied attempts in literature to develop such a protocol. According to Lal (2000), it may entail series of tests on a suite of soil physical properties over time. The tests are then used to allocate degradation to cases where negative changes have occurred in soil physical properties. Some researchers have successfully used this approach. Chong and Green (1983) used sorptivity while Ball et al. (1997) used bulk density to define relative compaction as index of soil physical degradation. Recently, Dexter (2004) developed a slope index from water retention characteristics for determining soil physical quality. Apart from soil physical properties, other researchers have also developed a protocol with emphasis on observable degradation features in the field. They argue that degraded plots have observable symptoms separating them from non-degraded plots.

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The use of expert opinion (Oldeman et al., 1991) and field assessment procedures by Stocking and Murnaghan (2001) are application examples of this protocol. The protocol has also received a technological boost from the recent advancements in space-borne remote sensing (Vrieling, 2006). Degradation symptoms can now be determined with increased spatial and temporal coverage using remote sensing. Furthermore, improvements in remote sensing such as hyperspectral infrared spectroscopy are currently under tests for their suitability in detecting subtle changes in soil physical conditions (Ben-Dor et al., 2003; Eshel et al., 2004). In some other applications, researchers are using erosion modeling to identify risk of degradation. Many models have been applied with varied focus on the risk factors for soil physical degradation (Paglia and Jones, 2002; Pieri et al., 2007). Some models such as the Revised Universal Soil Loss Equation (RUSLE) have been integrated with GIS to improve modeling and area-wide applications (Renard et al., 1997). Although these attempts seem successful, they tend to concentrate only on one or two stages of soil physical degradation. Hence, they have not been quite able to make a huge impact in controlling worldwide degradation progress rate. There is potential for integrating all degradation aspects to develop an exhaustive protocol for assessing the stages of soil physical degradation. This study used long-term changes in a suite of soil physical properties to define physical degradation and develop a protocol for assessing its stages of development. 2. Methods and measurements 2.1. Study area The study was carried out in the Upper Athi river watershed in Eastern Kenya. The watershed stretches from the latitude 18090 to 18590 South and from the longitude 368560 to 378450 East and covers 4513 km2 (Fig. 1). It is gently sloping to almost flat around the centre and south-eastern parts with altitudes of less than 1500 m a.s.l. Steep slopes (>20%) occur in the south and northwestern parts where the altitude is above 1500 m a.s.l. More than 50 years ago, most parts of the watershed were under either intact savannah shrublands or thickets (Tiffen et al., 1994). However, a

large proportion of these original land use types have been converted to croplands or developed for human settlement. The soil is predominantly loam to sandy Cambisols, Ferralsols, and Arenosols with low nutrient content (Sombroek et al., 1982). Cambisols are common in high altitudes while Arenosols dominate the footslopes and lower parts of the watershed. The remaining parts of the watershed have Ferralsols. The watershed receives rainfall in March through June (as period of long rains of about 1200 mm) and in September and October (as period of short rains of about 600 mm) (Tiffen et al., 1994). Mean annual temperature is 25 8C. During dry spell the temperatures may go above 30 8C thus creating high evaporative demand on the soil surface. 2.2. Data collection 2.2.1. Secondary data Secondary data for the sequential soil testing protocol were Landsat TM images for February 11, 2005, 30 m Digital Elevation Model (DEM), Land use maps for 1995 and 2005, and monthly rainfall amounts (from 1995 till 2005) for 15 stations in and around the study area. Landsat images were downloaded from http://glcf.umiacs.umd.edu/index.shtml on 20 June 2005. These images were corrected as indicated in Omuto and Shrestha (2007). Image correction involved conversion of image digital numbers to ground reflectance (i.e. radiometric correction) and coordinate transformation of image projection to correspond to coordinates on the ground (i.e. geometric correction). The land use maps and DEM were obtained from the Department of Environmental and Biosystems Engineering of the University of Nairobi while rainfall data were obtained from the Kenya Meteorology Department. 2.2.2. Ground sampling framework for primary data collection Ground sampling involved field measurements and observations of the indicators of soil physical degradation. The measurements were done on soil physical properties (infiltration and water retention characteristics, soil texture, bulk density, aggregate stability, and infrared spectral reflectance) while observations were made on signs of physical degradation and for the presence of any soil conservation practices.

Fig. 1. Study area.

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A hierarchical sampling framework was used to spatially sample the study area and exhaustively characterize potential soil variability at every sampling location. The framework consisted of sampling points placed within square plots and the plots grouped into clusters (Fig. 2). The plots in each cluster were arranged in a Yframe; as a convenient method to represent all parts of a cluster. Altogether, there were 45 clusters randomly placed to cover the major land use types, soil types, and climatic regimes of the study area. Each Y-cluster had four plots: one plot at the centre and the other three plots on the arms of Y at 480 m from the centre plot (Fig. 2). The plots were georeferenced at their centre with a GARMIN1 GPS (GARMIN International, 2002). Each plot had three sampling points: P1, P2, and P3, such that P2 was at the centre and the other two points (P1 and P3) placed 10 m away from the centre (P2) as shown in Fig. 2. 2.2.3. Collection of primary data Sampling and/or measurement of degradation indicators were made at each point in the plots. Measurement involved infiltration tests on the soil surface while soil samples were collected for topsoil only (0–20 cm from soil surface). This depth was chosen for convenience to test the sequential protocol. Infiltration tests were done using three single-ring infiltrometers (with an internal diameter of 30 and 25 cm deep) on prewetted soil surface. There was an infiltrometer for every sampling point in a plot; so that infiltration tests for sampling points P1, P2, and P3 were simultaneously done for the plot. Pre-wetting was done 24 h in advance by sprinkling about 5 l of water on a gunny sack placed on the soil surface. Pre-wetting prevents soil surface disturbance during installation of infiltrometers and also standardizes antecedent moisture content for early infiltration characteristics. Infiltrometers were carefully inserted into the pre-wetted

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soil surfaces up to 5–10 cm deep and infiltration rates determined as the rate of drop in water level inside the infiltrometers (Elrick and Reynolds, 2002; Diamond and Shanley, 2003). Fig. 3a shows a plot of measured infiltration characteristics from the study area. Undisturbed soil samples for water retention and bulk density were collected using 100 cm3 stainless steel core-rings. These samples were collected about 1 m away from the centre of the infiltrometers. The core-rings were placed inside a ring-holder and then inserted into the soil surface by hammering the ring-holder with an impact absorbing hammer (Dirksen, 1999). The samples were then carefully removed and transported to a laboratory for determination of water retention characteristics and bulk density. Water retention characteristics were determined by removing moisture from the soil samples at increasing values of suction pressure heads. A sandbox apparatus was used for pressure heads 1.0 m and a pressure chamber for pressure heads <1.0 m (Dirksen, 1999). After moisture removal, the samples were ovendried for 48 h at 105 8C. Altogether, 13 levels of tension heads were used (150, 100, 50, 25, 5, 3, 2, 1, 0.79, 0.63, 0.32, 0.1, 0.001 m). Fig. 3b shows a plot of measured water retention characteristics for the study area. Bulk density was determined from the ratio of volume and weight of dry soil after oven drying. Soil samples for aggregate stability, texture, and spectral reflectance were collected using Edelman soil auger. They were carefully packed in polythene bags and transported to the laboratory for further testing. In the laboratory, they were airdried and sieved to pass through 2 mm sieve. Samples for aggregate stability were then analyzed using a wet-sieve apparatus while samples for texture were analyzed using the hydrometer method after pre-treating them with hydrogen peroxide to remove organic matter (Gee and Bauder, 1986; Eijkelkamp Agrisearch Equipment, 2006).

Fig. 2. Ground sampling protocol.

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Fig. 3. Measured infiltration and water retention characteristics.

Soil samples for spectral reflectance were scanned with a FieldSpec FR spectroradiometer (Analytical Spectral Devices Inc., 1997). Scanning was performed in the soil spectral laboratory at World Agroforestry Centre (ICRAF). This spectrometer gives diffuse infrared spectral reflectance with wavelengths between 0.35 and 2.5 mm and spectral resolution between 0.003 and 0.01 mm (Analytical Spectral Devices Inc., 1997). The scanning process, spectral averaging, and resampling methods used were those reported in Shepherd et al. (2003). Fig. 4 shows examples of infrared spectral reflectance for soil samples from the study area. 2.3. Definition of physically degraded soil

Nielsen, 1994). Philip (1957) proposed a function combining these two soil properties as shown in Eq. (1). pffiffi iðtÞ ¼ f c þ 0:5S= t (1) where i(t) is the infiltration rate, fc is the steady infiltration rate, S is the sorptivity, and t is the time. Soil physical properties pertaining to water retention characteristics include saturated moisture content (us) and air-entry potential (ha) (Brooks and Corey, 1964). van Genuchten (1980) proposed Eq. (2) for combining these soil physical properties with soil moisture levels. 

uðhÞ ¼ ur þ ðus  ur Þ 1 þ 2.3.1. Estimation of soil physical properties Soil physical properties for definition of physical degradation were soil properties related to infiltration and water retention characteristics, bulk density, aggregate stability, and texture. The soil properties pertaining to infiltration characteristics are steady infiltration rate and sorptivity. Steady infiltration rate is the capacity of soil to transmit water in saturated conditions while sorptivity is a measure of the uptake of water by soil in unsaturated conditions. Both sorptivity and steady infiltration rate are influenced by the arrangement and number of soil pores; thus, they are a reflection of soil structural conditions (Kutilek and

Fig. 4. Examples of infrared spectral reflectance for soil samples from the study area.



h ha

n ð1n1 Þ

(2)

where u(h) is the soil moisture content at h suction potential and n and ur are the shape parameters for the water retention curve (van Genuchten, 1980). Soil physical properties in Eqs. (1) and (2) were determined from the measured infiltration and water retention characteristics using nonlinear mixed effects (NLME) approach (Omuto et al., 2006). This approach considers sampling design, global and individual variability of infiltration and water retention characteristics, and any possible parameter interdependence to determine the physical properties (Omuto et al., 2006). Consequently, it gives fairly more accurate and reliable results compared to other parameter estimation methods (Omuto et al., 2006). In this study, NLME approach was implemented using a HydroMe package (Omuto, 2007). The package is freely downloadable at http://cran.r-project.org/web/ packages/HydroMe/index.html and is executable in R (R Development Core Team, 2008, www.cran.r-project.org). A comparison of NLME fitted and measured data showed that Eqs. (1) and (2) were adequate in estimating the above soil physical properties (Fig. 5). Although the shape parameters (n and ur) in Eq. (2) are often referred to as non-physical parameters of water release characteristics (Hodnett and Tomasella, 2002), they have been widely used to derive other important indices related to soil physical conditions. Minasny and McBratney (2003) used them to obtaining integral energy as a measure of soil-water availability while Dexter (2004) used them to develop an index of soil physical quality. Hence, they may still be useful in predicting soil physical degradation. In this study, the Dexter (2004) index of soil physical quality was used to combine n, us, and ur for application alongside

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Fig. 5. NLME predicted infiltration and water retention characteristics. RSE is the standard error of estimate while r2 is the coefficient of determination.

other soil physical properties in defining physical degradation. Dexter (2004) index is given in Eq. (3). SI ¼ nðu s  ur Þ

  2n  1 ð1=nÞ2 n1

(3)

Bulk density represents relative compaction of soil and is a known index of compaction or crusting (Ball et al., 1997; Lal, 2000; Paglia and Jones, 2002). It was computed from the ratio of mass of dry soil to volume of the dry soil as shown in Eq. (4). bulk density ðrb Þ ¼

Mass of dry soil Volume of dry soil

(4)

Soil aggregate stability is the resistance of soil structure against mechanical, physical or chemical destructive forces (Marshall et al., 1996). Wet sieving apparatus is one of the commonly used methods for determining aggregate stability (Diaz-Zorita et al., 2002; Eijkelkamp Agrisearch Equipment, 2006). It uses the principle of aggregate breakdown on impact with low energy stress such as wetting by water to separate unstable from stable aggregates (Diaz-Zorita et al., 2002). Unstable aggregates easily breakdown leaving stable aggregates intact if they are both slowly immersed in water. The ratio of the amount of stable aggregates to total aggregates for a given soil sample is the index of aggregate stability (Eq. (5)). Stable aggregates ðAs Þ ¼

Amount of stable aggregates Total amount of aggregates

(5)

Eq. (5) was used in this study to determine the index of aggregate stability. The amount of stable aggregates and total aggregates were obtained by slow immersion of soil samples in water using the wet sieve apparatus (Eijkelkamp Agrisearch Equipment, 2006). Altogether, the soil physical properties used in defining physical degradation were: steady infiltration rate (fc), sorptivity (S), porosity (us), air-entry potential (ha), Dexter’s index of soil physical quality (SI), index of aggregate stability (As), sand, silt, and clay fractions, and bulk density (rb). There were three replicates of these soil properties for every plot in the Y-clusters. 2.3.2. Definition of degraded soils Soil physical degradation was defined using negative changes in the above soil physical properties. The negative changes were those associated with historic land use changes. Five main land use types considered were: thickets, open savannah shrublands,

croplands, built-up areas, and grasslands (Tiffen et al., 1994). Soil physical properties for plots where land use had remained intact between 1995 and 2005 were compared to where land use had changed by 2005. The comparison was paired for plots with similar soil types (Wessels et al., 2004). Plots where soil physical properties had changed for the worst during this period (1995– 2005) were considered degraded. Otherwise, they were considered non-degraded if the soil physical properties had remained within 95% confidence limit of the average in 1995 or had positively improved. Percent average differences for soil physical properties in degraded and non-degraded plots were explored with a hierarchical tree model to understand variable importance in the definition of soil physical degradation. Hierarchical tree models are suitable in understanding variables importance (and their interactions) in partitioning different classes in a dataset (Breiman et al., 1984). The most important variables appear at the top of the model while least important variables appear at the bottom (Breiman et al., 1984). 2.4. Data preparation for sequential testing of soil After defining soil physical degradation, some commonly used methods for assessing physical degradation were sequentially applied to identify soil physical degradation phases. The methods tested were visual assessment of degradation symptoms in the field, risk of soil loss using RUSLE model, and infrared spectral reflectance (Millward and Mersey, 1999; Omuto and Shrestha, 2007). 2.4.1. Observable symptoms of degradation Symptoms of soil physical degradation for visual assessment were obtained within the plots during the field survey. They included evidence of sheet, rill or gully erosion and signs of in situ structural deterioration such as crusting, hardsetting, and/or compaction. They were obtained using the guidelines in Table 1 (Omuto and Shrestha, 2007). 2.4.2. Risk of soil loss using RUSLE Risk of soil loss was estimated using the Revised Universal Soil Loss Equation (RUSLE) proposed by Renard et al. (1997). This model combines soil loss risk factors such as climate, soil type, land cover, topography, and land use practices to predict the risk of soil loss (Morgan, 1995). The model, which is shown in Eq. (6), has been widely used due to its low data demand (Lufafa et al., 2003; Lewis

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Table 1 Criteria for visual assessment of soil physical degradation in the field Form of degradation

Type

Degradation symptoms

In situ physical degradation

Crusting and sealing

Hard layers on soil surface Algae-strengthen pedestals, biological crusts in sheet eroded fields Hard and difficult-to-auger surfaces

Compaction

Compaction

Signs of water logging Uneven plant/grass growth Mottling of subsoil Hard and difficult-to-auger subsoil

Soil loss

Sheet

Small heaps of washed sand Fine soil particle in small depressions Soil deposits in high sides of small obstructions such as wood splinters or fences

Rill

Small depressions (<30 cm) Exposure of plant roots

Gully

Deep depression (>30 cm) Exposure of lower soil depths

where Dg is the mean soil particle diameter and which is estimated by Eq. (9) (Torri et al., 1997).

et al., 2005). E ¼ R  K  LSt  C  P

(6)

where E is the risk of soil loss in tonnes ha1 yr1, R is the climate factor known as rainfall erosivity (in MJ mm ha1 h1 yr1), K is the soil factor known as erodibility (in tonnes ha h ha1 MJ1 mm1), C is the land cover factor, and P is the support practice factor (Morgan, 1995). Input data for this model include soil physical properties, rainfall characteristics, DEM, Landsat image, and land management factors (van der Kniff et al., 1999; Lee and Lee, 2006). Within the RUSLE model, rainfall erosivity (R) is estimated as the average of EI30 (the energy of 30 min rainfall intensity) measurements over a period of 1 year (Morgan, 1995). Several modifications have been undertaken which include the change from RUSLE annual to seasonal format (Cook et al., 1985) and use of rainfall amounts in place of EI30 (Renard and Freidmund, 1994; Yin et al., 2007; Shamshad et al., 2008). A document by Moore (1979) provides a means of predicting R based on rainfall amounts in Eastern and Southern Africa. The models in this document have been successfully used to predict R in the greater Horn of Africa (Lufafa et al., 2003; Hammad et al., 2004). This study applied Eq. (7) from Moore (1979) to estimate erosivity. ! 12 X R ¼ 0:029 3:96ð Pi Þ þ 3122  26

(7)

i¼1

where Pi is the mean monthly rainfall amounts (in mm) for month i and R is the erosivity in MJ mm ha1 h1 yr1. The input Pi in Eq. (7) was obtained by spatial interpolation of mean monthly rainfall amounts from 15 weather stations in/and around the study area. The spatial interpolation of rainfall amounts was done using kriging method (Hengl et al., 2007). Soil factor (K) in the RUSLE model quantifies the cohesive character of a given soil type and its resistance to physical degradation by raindrop impact and runoff shear forces. It is a function of soil structure, texture, organic matter content, and permeability. It can also be obtained from textural compositions in the absence of all these data (Wischmeier and Smith, 1978). Eq. (8) was proposed by Morgan (1995) for deriving K from soil textural characteristics. "

2

ðlog Dg þ 1:519Þ Erodibility ¼ 0:0035 þ 0:00388 exp 0:5 0:57517

# (8)

" Dg ¼ exp

X

pffiffiffiffiffiffiffiffiffiffiffiffi 0:01 f i ln di1 di2

# (9)

i

where fi is the particle fraction in percent, d1 is the maximum diameter (mm) of the soil fraction, and d2 is the minimum diameter (mm) of the soil fraction. Eqs. (8) and (9) were used to estimate K for each plot. The inputs for this estimation were fi from measured soil textural fractions, d1 taken as 2 mm for sand, 0.05 mm for silt, and 0.002 mm for clay, and d2 taken as 0.05 mm for sand, 0.002 mm for silt, and 0.0005 mm for clay (Torri et al., 1997). Slope-length factor (LSt) in the RUSLE model defines whether the soil loss model is in two dimensions or three dimensions (Desmet and Govers, 1996). It is a dimensionless quantity from the product of length of slope L and slope angle St. The length of slope, L, is the horizontal distance from the origin of overland flow to either where the slope decreases to a point at the onset of deposition or where runoff becomes focused into a defined channel (Renard et al., 1997). It a sensitive factor in the RUSLE model and its calculation can lead to flow convergence and deposition especially in undulating terrain (Lee and Lee, 2006). In this study, it was derived from the length of upslope drainage contributing area within a three-dimensional space (Desmet and Govers, 1996). The calculation of L from upslope drainage area has the potential of accounting for the flow divergence and convergence patterns (van Remortel et al., 2004). Foster and Wischmeier (1974) have proposed Eq. (10) for deriving L from upslope drainage contributing area using DEM grids as input. 2 mþ1

L¼

ðAin þ pixel Þ mþ2

pixel

 Amþ1 in

ðjsin aj þ jcos ajÞm ð22:13Þm

(10)

where Ain is the drainage contributing area at the inlet of a grid for which L is being estimated, pixel is the DEM grid resolution (which is 30 m for the DEM used in this study), a is the flow direction within the grid, and m is the exponent that addresses the ratio of rill-to-interrill soil loss. The value of m is taken as 0.4 for slope angle St > 38, 0.3 for 28 < St  38, 0.2 for 18 < St  28, and 0.1 for St  18 (Morgan, 1995). Since long lengths of slope can concentrate overland flows into channels, care must be taken in calculating L for use in the RUSLE model (Lewis et al., 2005). Renard et al. (1997) recommended a maximum of 200 m for L for modest accuracy. In this study, Eq. (10) was solved using computer modules in SAGA

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GIS (http://saga-gis.org) by imposing an upper sealing of 120 m (four pixels of the DEM map) for the slope length. Slope St in the slope-length factor was derived from the DEM using maximum downhill slope algorithm by Hickey et al. (1994). This algorithm was implemented in ArcGIS software (ESRI, 2004). The DEM is the input for calculating both L and St. The slope St and L were combined to produce LSt factor using Eq. (11) (Wischmeier and Smith, 1978). LSt ¼



m   L 0:065 þ 4:56 sin St þ 65:41 sin2 St 22:13

(11)

where St is the slope in degrees and m is obtained as in Eq. (10). Eq. (10) was determined for the whole study area as LSt map. The LSt-factor for use in Eq. (6) for each surveyed plot was extracted from this map. Land cover factor (C) in the RUSLE model was determined using van der Kniff et al. (1999) model in Eq. (12).   NDVI C ¼ exp a (12) b  NDVI where a and b are the constants. van der Kniff et al. (1999) suggested values of b as 1 and a as 2. The input NDVI for Eq. (12) was obtained from the ratio of the difference and sum of the third and fourth bands of the corrected Landsat TM image for 2005 (Omuto and Shrestha, 2007). Eq. (12) was also determined for the whole study area as a map of C factor. C factor for use in Eq. (6) for each plot was extracted from this map. Land management practice factor (P) in the RUSLE model represents the effect of soil and water conservation practices for controlling soil loss. Wischmeier and Smith (1978) defined P as an index for comparing the amount of soil lost with a specific conservation practice to the corresponding soil lost with downslope cultivation. They developed a monograph for allocating P to different land use practices. In this study, soil conservation practices observed during field survey were assigned P indices using this monograph. The main soil conservation practices observed in the study area were contour ploughing and cut-off drains. Deep ripping and strips of close-growing vegetation were also observed in a few places (Tiffen et al., 1994). P-indices were assigned to plots where the conservation practices were found (or for plots adjacent to an immediate upslope area with such conservation practices). A P value of 1 was assumed for plots without conservation practises. Although RUSLE is a widely used model, it is important to note that it has uncertainties in predicting the risk of soil loss. There are two main uncertainties in RUSLE estimates: uncertainties in the input data and those due to model inadequacies. The uncertainties in input data can be estimated as linear propagation of standard errors of the input data while uncertainties due to model inadequacies should be assessed by comparing its output with actual measurements. In this study the model uncertainties were estimated using Eq. (13). X  @E  s2 ¼ s 2i u (13) @wi where s2 is the standard error of the predicted risk of soil loss, wi is the input data, and u is the variance–covariance matrix for the input data. The model was, however, not compared with actual field measurements due to lack of reliable data. 2.4.3. Infrared spectral reflectance Soil spectral reflectance consisted of reflectance for wavelengths between 0.35 and 2.5 mm in 216 contiguous groups of 0.01 mm wavebands (Fig. 4). The replicate measurements of

205

spectra for each plot were calibrated to corresponding measured soil physical properties. The calibration tested the relevance of spectra in relation to soil physical conditions. Partial Least Squares (PLS) regression method was used for the calibration process (Bro, 1996). After the calibration, an optimum number of principal components (PCs) accounting for over 90% of spectral variations were selected for use in the sequential soil testing protocol. 2.5. Sequential testing of soil for physical degradation The above methods for assessing soil physical degradation were sequentially applied as predictors of physical degradation to test their ability in identifying different stages of degradation development. The testing involved recursive partitioning of sampled plots into their correct degradation statuses using indices for assessment for the above methods as splitting variables. Recursive partitioning is the process of successive splitting of data into homogeneous groups. It begins with a heterogeneous data (known as parent) which is split into two less heterogeneous groups (known as children). The splitting continues until the children are too homogeneous to be split. The final group of the homogeneous data are then assigned group name corresponding to their common characteristics (e.g. degraded or non-degraded soil) (Breiman et al., 1984). The splitting rules and the class assignment to terminal children have been discussed in Breiman et al. (1984). The ratio of correct classification to the total number of partitioned classes in a terminal group determines the accuracy of the splitting variables in identifying the homogeneous groups. In this study, degradation indices for recursive partitioning were field-observable symptoms of degradation, risk of soil loss, and infrared spectral reflectance. A hierarchical tree model for the sequential testing with these indices is shown in Fig. 6. The model was implemented using CART1 V 6.0 (Salford Systems Inc., 2008). It was developed on a random selection of two-thirds of the data and validated on the remaining one-third of the data. The accuracy on validation dataset was used to assess the performance of each assessment method in identify stages of degradation development. 3. Results and discussions 3.1. The variation of soil physical properties with land use types and soil types Soil properties were grouped into the main soil types and land use types. Good soil physical qualities were found in land use types with vegetation irrespective of the soil types (Fig. 7). However, differences in soil physical conditions between soil types emerged when plots were compared on the basis of the presence of vegetation cover. Cambisols had the largest differences between plots with vegetation (thickets or grasslands) and without vegetation (tilled croplands or built-up areas). The largest difference was among soil properties related to structure such as porosity and aggregate stability. The average porosity or aggregate stability in croplands and built-up areas was around 50% less than in thickets or grasslands (Fig. 7). These results suggest that Cambisols are more sensitive to changes in land use/cover characteristics compared to Arenosols or Ferralsols. Ferralsols, which are deeply weathered soils, remained rather stable between different land use types (WRB, 2006). However, their porosity can reduce by 40% or more if soil is continuously modified (e.g. many years of tillage in croplands) (Fig. 7). Arenosols appeared to be unstable with poor soil physical qualities (Fig. 7). All plots in

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Arenosols had index of aggregate stability less than 0.50 and Dexter’s index of physical quality less than 0.03 (except for the plots in shrublands). 50% index of aggregate stability and 0.03 for Dexter’s index are the suggested lower limits for stable soils of good physical qualities (Marshall et al., 1996; Dexter, 2004). Given that most plots in Arenosols were below these limits, they may be referred to as unstable soils. WRB (2006) also classifies Arenosols as unstable soils. High variability and poor physical characteristics were observed in croplands, built-up areas, and grasslands than in thickets or shrublands (Table 2). These soil characteristics give the impression that land use changes from thickets/shrublands to croplands/grasslands/built-up areas have the potential of degrading the soil physical conditions. The poorest physical characteristics and highest variability in soil texture were in croplands and built-up areas (Table 2). Although texture is a stable soil property, high variation in texture can occur due to advanced physical degradation (Lal, 2000; Paglia and Jones, 2002). High variation in texture and poor soil physical characteristics in croplands and built-up areas is therefore an indication that these land use types are risk factors for soil physical degradation. 3.2. Definition of soil physical degradation in the study area

Fig. 6. Sequential soil testing model.

GPS locations for the surveyed plots were superimposed on the land use maps for 1995 and 2005. The major land use changes during this period were mainly from thickets or shrublands to grasslands, built-up, or croplands. Out of 180 surveyed plots, 113 had land use changes between 1995 and 2005. 54 plots in the thickets in 1995 were converted to built-up area (10 plots), grasslands (20 plots), and croplands (24 plots) by 2005. Similarly, 32 plots in the shrublands in 1995 were

Fig. 7. Variation of soil physical properties between soil types and land use types.

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Table 2 Summary of the soil physical properties according to land use types Soil property

Steady infiltration rate Sorptivity Saturated moisture content Air-entry potential Dexter’s index Aggregate stability Bulk density Sand Silt Clay a

Land use types Symbol

Thickets

Shrublands

Croplands

Grasslands

Built-up

fc (cm h1) S (cm h0.5) us (cm3 cm3) ha (m) SI As rb (g cm3) Sand (%) Silt (%) Clay (%)

30.9 (1.05a) 25 (2.28) 0.45 (0.01) 0.23 (0.02) 0.06 (0.002) 0.61 (0.008) 1.19 (0.017) 40.2 (1.2) 28.2 (0.8) 31.6 (1.2)

26.5 (1.45) 11.2 (2.21) 0.33 (0.02) 0.29 (0.03) 0.04 (0.002) 0.45 (0.006) 1.35 (0.023) 39 (1.6) 30.4 (0.7) 29.6 (3.9)

13.6 (1.93) 11 (3.76) 0.29 (0.04) 0.41 (0.12) 0.02 (0.009) 0.33 (0.069) 1.49 (0.072) 33.3 (4.6) 35.7 (4.3) 31 (2.02)

26.3 (2.37) 16.1 (3.44) 0.34 (0.02) 0.33 (0.04) 0.03 (0.005) 0.47 (0.045) 1.42 (0.029) 43 (3.1) 31.2 (1.7) 25.8 (3.1)

10.9 (2.6) 10.1 (1.56) 0.14 (0.03) 0.36 (0.15) 0.02 (0.01) 0.23 (0.018) 1.53 (0.09) 39 (0.8) 31.6 (4.1) 28.4 (3.7)

Values in the brackets are 95% confidence intervals of the means.

Table 3 Mean of soil physical properties and corresponding land use changes between 1995 and 2005 Soil property

Steady infiltration rate Sorptivity Saturated moisture content Air-entry potential Dexter’s index Aggregate stability Bulk density Sand Silt Clay Number of plots a

Intact land use types between 1995 and 2005

Land use changes by 2005

Symbol

Thickets

Croplands

Shrublands

Grasslands

Croplands

Built-up

fc (cm h1) S (cm h0.5) us (cm3 cm3) ha (m) SI As rb (g cm3) Sand (%) Silt (%) Clay (%) –

30.9 (1.05a) 25 (2.28) 0.45 (0.01) 0.23 (0.02) 0.06 (0.002) 0.61 (0.008) 1.19 (0.017) 40.2 (1.2) 28.2 (0.8) 31.6 (1.2) 15

13.8 (2.08) 18.1 (3.76) 0.36 (0.11) 0.37 (0.15) 0.02 (0.001) 0.32 (0.01) 1.51 (0.07) 36 (0.15) 37 (0.1) 27 (0.22) 25

26.5 (1.45) 11.2 (2.21) 0.33 (0.02) 0.29 (0.03) 0.04 (0.002) 0.45 (0.006) 1.35 (0.023) 39 (1.6) 30.4 (0.7) 29.6 (3.9) 27

26.3 16.1 0.34 0.33 0.03 0.81 1.33 43 31.2 25.8 31

13.4 3.51 0.24 0.50 0.02 0.34 1.48 30.4 36.6 33 38

10.9 10.1 0.14 0.36 0.02 0.23 1.53 39 31.6 28.4 44

Values in the brackets are the 95% confidence intervals of the means.

converted to croplands (10 plots), grasslands (16 plots), and built-up (6 plots) by 2005 and 16 plots in the croplands converted to grassland (4 plots) and built-up areas (12 plots) in the same period (Table 3). Almost all plots with land use changes by 2005 had more than one standard deviation drop (or increase in bulk density) from their average soil properties in 1995. The plots where the changes were outside 95% confidence limit of their averages in 1995 were considered degraded (Table 3). Percent average differences between soil properties in the degraded and nondegraded plots were explored with a tree model to enable understanding of variable importance in the definition of physical degradation (Breiman et al., 1984). A drop of more than 25% in Dexter’s index of soil physical quality (SI) was the most important in separating degraded from non-degraded soil (Fig. 8). Thus, it is a possible important indicator for assessing physical degradation. The next important variables included changes in aggregate stability (As) and air-entry potential (ha) (Fig. 8). As is directly related to soil structure and inversely related to ha (Reynolds and Elrick, 1990; Marshall et al., 1996). An increase in ha and a decrease in As correspond to the deterioration of soil structure. The relative importance of changes in As and ha in Fig. 8 is therefore an illustration of the high priority of deterioration of soil structure in the definition of soil physical degradation. The second last important group of variables involved changes in steady infiltration rate (fc), bulk density (rb), and saturated moisture content (us) (Fig. 8). These soil properties represent relative soil compaction (Valentine and Bresson, 1992; Ball et al., 1997; Jones et al., 2003). Their occurrence below soil properties related to structure in Fig. 8 suggest that soil

compaction follows deterioration of soil structure in the definition of physical degradation. Percent change in silt content was the least important variable for defining soil physical degradation (Fig. 8). A change in silt content, which represents a change in soil particle distribution (such as due to soil loss), implies that any alteration in soil texture comes after the deterioration of structure and compaction in the definition of soil physical degradation. The above sequence of variable importance in the definition of physical degradation also suggests a possible similar sequence of sensitivity of the processes in the degradation. Soil physical degradation may therefore be regarded as a process which begins with deterioration of soil structure and ends in deferential loss of particles through soil erosion. 3.3. Characteristics of the evidence of physical degradation Soil properties were compared for plots with observable symptoms of degradation and without any degradation symptoms. Majority of plots with symptoms of degradation had lower average soil properties (and high bulk density) than plots without degradation symptoms (Table 4). This correlation points to the potential of observable degradation symptoms in identifying degraded from non-degraded soil. The average risk of soil loss from all the plots was 22.3 tonnes ha1 yr1 with standard deviation of 2.5 tonnes ha1 yr1. There was low standard deviation (1.1 tonnes ha1 yr1) for plots with low risk of soil loss (<15 tonnes ha1 yr1) and high standard deviation (5.5 tonnes ha1 yr1) for plots with high risk of soil loss (>22 tonnes ha1 yr1). The increase in uncertainty with predicted risk of soil loss suggests that the input data need to be further assessed to improve accuracy of the RUSLE model. Alternatively, it

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Fig. 8. Hierarchical tree model for the definition of soil physical degradation in Cambisols, Arenosols, and Ferralsols of the Upper Athi River Basin in Eastern Kenya.

could also suggest that the model was only fairly accurate in estimating low risk of soil loss. Other studies have also shown that the model is unreliable in predicting high risk of soil loss (Lewis et al., 2005; Lee and Lee, 2006). In spite of RUSLE’s unreliability in predicting high risk of soil loss, its comparative accuracy in predicting low risk of soil loss could still support the objective of identifying non-degraded from degraded soil. Hence, its application was still valid in the sequential testing of soil for physical degradation. The average potential risk of soil loss from degraded sites was 27 tonnes ha1 yr1 with standard deviation of 2.8 tonnes ha1 yr1 while the average potential risk of soil loss from non-degraded sites was 17 tonnes ha1 yr1 with standard deviation of 1.5 tonnes ha1 yr1. These averages were significantly different at 95% confidence interval. Hence, there was a promise in using RUSLE outputs to separate degraded from nondegraded soil.

Infrared spectral reflectance was also found to have some potential in predicting soil physical conditions. This is illustrated in the summary of calibrations between infrared spectral reflectance and soil physical properties in Table 5. Relatively good validation statistics were found between spectra and bulk density, steady infiltration rate, index of aggregate stability, and clay content (r2 > 0.6). Some of these soil properties were among the important variables defining physical degradation (Fig. 8). Their relationship with spectra implies that spectral reflectance is a possible surrogate indicator of soil physical degradation. Apart from the calibration with soil physical properties, the spectra also differentiated between degraded and non-degraded soil. A plot of the scores of first principal component (explaining 57% of the spectral variation) and second principal component (explaining 19% of the spectral variation) attempted to separate degraded from non-degraded soil (Fig. 9).

Table 4 Characteristics of soil physical properties and degradation symptoms Soil property

Steady infiltration rate Sorptivity Saturated moisture content Air-entry potential Dexter’s index Aggregate stability Bulk density Sand Silt Clay Number of plots

Symbol

fc (cm h1) S (cm h0.5) us (cm3 cm3) ha (m) SI As rb (g cm3) Sand (%) Silt (%) Clay (%) –

Observable signs of degradation

Difference in mean

Without signs (mean)

Without signs (standard deviation)

With signs (mean)

With signs (standard deviation)

p-Value (5%)

50.8 31.9 0.43 0.19 0.03 0.68 1.36 41 24 35

3.05 1.51 0.05 0.05 0.002 0.05 0.09 0.35 1.37 1.28

18.1 13.0 0.27 0.43 0.02 0.37 1.46 40 35 25

3.95 2.87 0.07 0.05 0.003 0.05 0.05 0.34 4.33 2.24

0.0001 0.0001 0.0001 0.0001 0.001 0.0001 0.0001 0.15 0.0001 0.0001

71

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C.T. Omuto / Agriculture, Ecosystems and Environment 128 (2008) 199–211 Table 5 Summary statistics of calibration between spectra and soil physical properties Soil property

Steady infiltration rate Sorptivity Saturated moisture content Air-entry potential Dexter’s index Aggregate stability Bulk density Sand Silt Clay a b

Symbol

fc (cm h1) S (cm h0.5) us (cm3 cm3) ha (m) SI As rb (g cm3) Sand (%) Silt (%) Clay (%)

Calibration set (120 plots)

Validation set (60 plots)

r2a

RMSEb

r2

RMSE

0.72 0.57 0.72 0.64 0.63 0.7 0.81 0.77 0.61 0.71

0.08 0.70 0.02 0.36 0.17 0.01 0.05 0.11 0.27 0.05

0.68 0.54 0.67 0.58 0.61 0.66 0.76 0.71 0.57 0.67

0.10 0.80 0.03 0.39 0.21 0.01 0.02 0.23 0.30 0.12

r2 is the coefficient of determination. RMSE is the root mean square error.

3.4. Sequential testing of soil for physical degradation 3.4.1. Using observable symptoms of the degradation in the field Soil testing model using observable symptoms of degradation separated degraded from non-degraded soil with a validation accuracy of 64% and a confidence level of 57% (Table 6). There were eleven degraded and seven non-degraded plots misclassified by the model. Misclassification of degraded plots was largely due to lack of prominent symptoms of degradation. Nine of these plots belonged to cropland while the remaining two were in grassland. Surface modification by tillage operations in croplands and the presence of grass cover in grasslands could have masked the symptoms of degradation and therefore leading to their misclassification. Non-degraded plots were misclassified due to the presence of desert-like features, which could be easily mistaken for degradation symptoms. Four of these plots belonged to savannah shrublands while the remaining three were on rocky slopes. Majority of correctly identified degraded plots had high bulk density, high proportion of silt, and very low infiltration characteristics. These soil characteristics are associated with the late stages of progressing soil physical degradation (Fig. 8). Their correlation with observable symptoms of degradation means that the symptoms are also associated with final stages of physical degradation. It is important to note that although the model is quite uncertain in its prediction (low confidence interval of 57%), it

Fig. 9. Scores of the first two principal components for infrared spectral reflectance.

209

is more accurate in identifying degraded than non-degraded plots (Table 6). Having association with late stages of degradation development and relatively high model sensitivity imply that the observable symptoms of degradation are only fairly accurate in detecting advanced physical degradation. 3.4.2. Use of the risk of soil loss from RUSLE model When the risk of soil loss was added to the soil testing model with observable symptoms of degradation, the validation accuracy improved to 82% (Table 7). This model managed to detect the three degraded lots from croplands and three non-degraded plots from rocky slopes previously misclassified by observable degradation symptoms (Table 7). The plots from croplands had high silt content and were positively picked by RUSLE model as degraded. The presence of high silt fractions could not be easily detected by visual observation; thus, the plots were misclassified by observable symptoms of degradation. The plots from rocky slopes misclassified by observable symptoms had low risk of soil loss due to low LSfactors arising from short lengths of slope. The soil testing model with RUSLE outputs still did not correctly classify eight degraded and four non-degraded plots in the validation set (Table 7). The misclassified degraded plots were from a sedimentation plane with a mix of tall grass and sparse vegetation. Nevertheless, the model was able to correctly identify misjudgements by visual assessment and improved the soil testing accuracy by over 10%. It also improved the confidence interval to 80%; thus, indicating improved repeatability of combined application of visual assessment and soil loss modelling. These improvements reinforce the argument that soil degradation assessment by expert opinion should be augmented with erosion modelling to improve the assessment accuracy (FAO, 2003). 3.4.3. Use of infrared spectral reflectance Further improvement on the soil testing protocol was achieved by using infrared spectral reflectance. Seven principal components, which accounted for 97% of the spectral variation, were added to the soil testing model. The validation accuracy for the model improved to 95% (Table 8). Infrared spectral reflectance managed to resolve soil physical conditions of some plots previously misclassified by the combined application of visual assessment and risk of soil loss. The spectra correctly identified the three non-degraded plots in the shrublands which were previously misclassified by RUSLE and visual assessment. Although these plots had desert-like features, they were found to be spectrally similar to other non-degraded plots. Similarly, six plots from the sedimentation plane were also found to be spectrally similar to degraded plots. They had no degradation symptoms and were therefore misclassified as non-degraded by visual assessment. They were also misclassified by the RUSLE model due to the model’s poor accountability of sand deposits (Renard et al., 1997). The soil testing model with spectral reflectance was able to correctly identify degraded soil misclassified by the combined application of soil loss modelling and visual assessment. The improved accuracy with spectra shows that infrared spectral reflectance can detect subtle changes in soil physical conditions which are not yet apparent to visual assessment and soil loss modelling. In this regard, infrared spectral reflectance behaves as a sensitive indicator of soil physical degradation. Due to improved accuracy and repeatability (confidence interval of 95%), the spectral can be regarded as a reliable indicator for incipient degradation. This is particularly important for the assessment of early-warning signs of soil physical degradation.

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Table 6 Confusion matrix for the soil testing model using observed symptoms of degradation Calibration set

Validation set

Model predictions

Model predictions

Degradation classes

Non-degraded

Degraded

Total

Degradation classes

Non-degraded

Degraded

Total

Non-degraded Degraded

29 21

19 65

48 86

Non-degraded Degraded

10 11

7 18

17 29

a

Sensitivity = 76% and specificity = 60%

Sensitivity = 62% and specificity = 59%

Overall predicted accuracy is 64% on the validation set and confidence interval is 57% a

Sensitivity = correct identification of degraded soil and specificity = correct identification of non-degraded soil.

Table 7 Confusion matrix for the soil testing model using observed symptoms of degradation and risk of soil loss Calibration set

Validation set

Model predictions

Model predictions

Degradation classes

Non-degraded

Degraded

Total

Degradation classes

Non-degraded

Degraded

Total

Non-degraded Degraded

40 9

8 77

48 86

Non-degraded Degraded

13 8

4 21

17 29

a

Sensitivity = 90% and specificity = 83%

Sensitivity = 76% and specificity = 72%

Overall predicted accuracy is 80% on the validation set and confidence interval is 80% a

Sensitivity = correct identification of degraded soil and specificity = correct identification of non-degraded soil.

Table 8 Confusion matrix for the soil testing model using observed symptoms of degradation, risk of soil loss, and infrared spectral reflectance Calibration set

Validation set

Model predictions

Model predictions

Degradation classes

Non-degraded

Degraded

Total

Degradation classes

Non-degraded

Degraded

Total

Non-degraded Degraded

46 4

2 84

48 88

Non-degraded Degraded

16 2

1 27

17 29

a

Sensitivity = 96% and specificity = 95%

Sensitivity = 94% and specificity = 93%

Overall predicted accuracy is 95% on the validation set and confidence interval is 95% a

Sensitivity = correct identification of degraded soil and specificity = correct identification of non-degraded soil.

4. Conclusion This study shows a consistent evidence of the decline in soil structure which gives way to compaction and finally soil loss. This evidence supports the suggestion that soil physical degradation is a gradual process which begins with structural deterioration and ends into the differential loss of soil particles through erosion. A sequential soil testing protocol presented in the study has the potential of screening soil samples for evidence of any stage of soil physical degradation. The protocol takes into account changes in soil physical properties as the basic foundation for defining soil physical degradation. Although soil physical properties are difficult and expensive to measure, limited sampling over time of selected important soil properties can still facilitate the definition of physical degradation. For assessing soil physical degradation in Cambisols, Arenosols, and Ferralsols in the Upper Athi River watershed in Eastern Kenya, the selected soil properties include Dexter’s index of soil quality, index of aggregate stability, air-entry potential, steady infiltration rate, bulk density, porosity, and silt content. The sequential soil testing protocol showed that visual assessment of observable degradation symptoms is a less expensive and rapid method for assessing advancing physical degradation. However, the repeatability of the method is quite low

(with about 60% confidence interval). Improvements in this method can be achieved through augmented use with erosion modeling. About 80% repeatability and accuracy can be achieved with the combined application of erosion modeling and visual assessment of soil physical degradation. This study has also shown that diffuse infrared spectral reflectance is a potential surrogate predictor of soil physical conditions. Spectral reflectance is especially sensitive to subtle changes in soil physical conditions not readily amenable to visual assessment and erosion modeling. Hence, it can be used to test for early-warning signs of soil physical degradation. Inclusion of spectra in the sequential soil testing improved the accuracy and repeatability of testing to 95%. The sequential soil testing protocol presented in this study may be useful in designing a soil degradation monitoring framework for timely advice to control soil physical degradation. Further testing and worldwide application with different models in different soil types and landscapes is recommended. Acknowledgements This study was funded by International Foundation for Sciences (IFS, www.ifs.se) through the project number C/9353-1. Soil spectral reflectance was determined at ICRAF, through the generous support from Dr. Keith Shepherd and the entire ICRAF

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