Assessment of soil erosion indicators for maize-based agro-ecosystems in Kenya

Catena 59 (2005) 231 – 251 www.elsevier.com/locate/catena

Assessment of soil erosion indicators for maize-based agro-ecosystems in Kenya C.A.J.M. de Bie* Department of Natural Resources, International Institute for Geoinformation Science and Earth Observation (ITC), Hengelosestraat 99, P.O. Box 6, 7500 AA, Enschede, The Netherlands Received 27 January 2004; received in revised form 31 August 2004; accepted 22 September 2004

Abstract Measuring soil loss is costly, must cover a range of field situations, is not standardized, and is season dependent. In addition, use of sparse soil loss data (from other studies) compromises the integrity of many erosion models. Easily assessable soil erosion indicators to monitor the cumulative effect of erosion between tillage/weeding and harvesting called eroding clods, flow surfaces, pre-rills, and rills were surveyed directly after the 1995 rainy season in the Taita Taveta district of Kenya, to assessed the utility of each indicator. Their incidences were modeled using CPA. In the area, 70 maize plots in 11 map units, having considerable variation in altitude, land cover, rainfall, and geomorphology, were surveyed. Soil loss was considered variable between plots due to differences in surface soil, land cover, infrastructure (trash lines, grass strips, and Fanya-juu), crop management, slope, and map unit. The eroding clods indicator proved of little significance because the initial clods cover was unknown; the indicator probably relates better to soil erodibility then to soil loss. Flow surfaces, formed during erosive showers, were less present on fields with a higher ground and canopy cover, if the area of eroding clods was high, and if the topsoil had no loam which reduces chances of sealing; no impact of infrastructure, tillage, and weeding were detected. Fewer pre-rills were present where the fraction of groundcover was high, where Pigeon Peas were not grown (they cause micro-relief and concentrated flows), where weeding ended late (time effect), where more flow surfaces occurred, where Fanya-juu was constructed (less steep slopes), where the top-soil contained little sand (less sediment entrainment), and where maize was intercropped with vegetables (positive canopy cover effects). The model was

* Tel.: +31 53 4874362; fax: +31 53 4874388. E-mail address: [email protected]. 0341-8162/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.catena.2004.09.007

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not map unit specific and had an Adjusted R 2 of 67%. The log-linear relationship indicates that combined positive conditions exponentially reduce the occurrence of pre-rills. The bpre-rillQ indicator related best to management-affected site conditions and seems to reflect best the cumulative effects of soil loss over time. Rills were found at 18 sites located in drier areas on sandy–clay soils. The model suggested more rills if the topsoil contains no silt; this makes the soil susceptible to compaction, peptisation when wet, and rill formation. D 2004 Elsevier B.V. All rights reserved. Keywords: Erosion indicators; Soil loss; Land use; Sustainable agriculture; Land management; Maize

1. Background and objective Land cover, soil conservation practices, and the presence of soil erosion control infrastructure all influence actual soil loss. Land users can modify all these. In the Universal Soil Loss Equation (USLE) model (Wischmeier and Smith, 1978), the factor combination (C+P) reflects the effectiveness of soil erosion control measures (Bergsma, 1996). C and P represent dcover plus crop managementT and dconservation practicesT, respectively; they adjust dpotential soil lossT to dactual soil lossT. In situations where CP cannot be made sufficiently low, practices have to be considered that will modify the LS effect (slope length and slope gradient), e.g., by constructing bunds and furrows (Hurni, 1988). Almost all soil erosion models, including USLE, are empirical and require careful calibration between actual soil erosion measurements and predictors prior to each application. Measuring erosion is costly and time consuming whereas results may be conditioned by single events such as rain storms (Hudson, 1995); Lal (1994a) called it an art rather than a science. Calibration requires soil loss data from the full range of field situations for which the model will be applied. In practice, calibration is often based on data from few runoff plots with or without use of an artificial rainfall simulator (FAO, 1993), and/or on data from sites in other environments and/or measured according to nonstandard techniques (Lal, 1994b, 2001). All this compromises the predictive capacity of soil erosion models. To improve the models, addition of an intermediate step, i.e., use of observed, site-specific soil erosion indicators is studied; this could bypass the massive data requirements of soil erosion models. The two-phased logic is then: Soil Loss=f(Soil Erosion Indicators), and: Soil Erosion Indicators=f(Field Conditions). The second part of this logic is explored in this paper. It thus remains essential that proven indicators, at a limited scale, be calibrated against observed actual soil loss. Successful use of the logic, i.e., using indicators to directly assess soil loss, eventually saves cost and time while possibly: (i) improving the predictive capacity of the models, (ii) improving standardization of soil loss studies, and (iii) providing options to assess and monitor soil loss across areas for various soil, terrain, and management conditions. Foreseen is that, while still few runoff plots remain required to calibrate selected indicators, soil loss assessments can be made for a large number of sites

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across years. The minimum expectations are that soil loss indicators capture relative erosion severity in sufficient detail to support soil loss field surveys and monitoring. The objective is thus to identify and assess the dcause–effectT value of suitable indicators that are easy to work with and that capture the impact of measurable field conditions associated with actual soil loss. This study does not relate actual soil loss with indicator scores, but evaluates, through multiple regression analysis, if selected indicators correlate with applied technology and land conditions that are associated to actual soil loss. Variables selected to capture field conditions are borrowed from USLE literature; additional management aspects that impact on soil and land cover are added. Maize-based cropping systems with varying land and land use conditions are studied. Map units are (considered) homogeneous in terms of rainfall erosivity and soil erodibility since it is based on an dgeomorphological present day erosion processesT map by Runge and Spo¨nemann (1992) and an annual rainfall map (Jaetzold and Schmidt, 1983; Etzler, 1990). Soil loss between sites is considered in relation to differences in land cover, infrastructure, soil conservation practices, crop management, position in the terrain, and slope.

2. Study area A section of Taita Taveta district, Coast Province, Kenya, having a high variability in weather and soils, was selected (Fig. 1). It ranges in altitude from 600 to 1600 masl, and receives 800–1400 mm rainfall per annum in a bimodal rainfall regime (Fig. 2; Jaetzold and Schmidt, 1983; Etzler, 1990). Wundanyi has two humid periods because of orographic rainfall; rainfall exceeds 565 mm in 6 out of 10 years during the first rainy season (130 days) and 485 during the second one (110 days). For Voi, these data are 130 and 165 mm, respectively (Jaetzold and Schmidt, 1983; Etzler, 1990). Runge and Spo¨nemann (1992) identified four types of major geomorphological present-day erosion processes in the study area labeled dnoneT (waterlogged), dsheet and splashT, drill and gullyT, and dmass movementT (Fig. 1). The study focuses on two geological zones (Fig. 1): ! !

The upper zone (highlands): hills of strongly metamorphosed rocks (mainly gneiss with quartz, feldspar, and hornblende). The lower zone (lowlands): a non-dissected plain with erosion/sedimentation areas (with some residual hills), composed of recent and Pleistocene red brown sands and calcareous deposits over limestone with gneiss veins.

From low to high altitude, the following soil catenas occur (Jaetzold and Schmidt, 1983): !

On the non-dissected erosion plain: soils developed in basement material rich in ferromagnesian minerals (rhodic Ferralsols)—well-drained, deep to very deep, dusky red to dark red, friable sandy clay.

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Fig. 1. Map of Taita Taveta, Kenya, showing the units surveyed. Based on: Geomorphological Present Day Erosion Processes (1:100.000; Runge and Spo¨nemann, 1992) and Annual Rainfall and Rainfall Reliability (1:250.000; Jaetzold and Schmidt, 1983; Etzler, 1990).

!

!

On footslopes: a complex of soils developed in colluvium from undifferentiated basement deposits (rhodic Ferralsols with ferralitic Arenosols and ferralo-chromic Luvisols)—well-drained, very deep, dark red, friable, coarse, LS to SC. On mountains and major scarps: soils developed in undifferentiated basement deposits, predominantly gneiss (humic Cambisols with eutric Regosols and rock outcrops)—

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Fig. 2. P–Et pan diagram for Voi and Wundanyi.

well-drained, moderately deep, reddish brown to brown, friable, stony, sandy clay loam with humic topsoil.

3. Study method Data on soil/terrain, land cover, infrastructure, land management, and incidence of soil erosion were collected at 70 sites during Juyl–August 1995 (Fig. 1), i.e., six or seven sites per map unit. Only accessible sites where maize was grown during the preceding season and where no recent soil disturbance through tillage had occurred were surveyed. At each site (plot), a field sketch was made of a brepresentativeQ area (surveyor judgment) of 1020 m, delineating cover types and infrastructure Fig. 3). Next, six brepresentativeQ areas of 22 m, selected on the basis of terrain slope and cover type, were checked for (incidence of) soil erosion indicators. Operations carried out on the site were recorded by interviewing the land user. 3.1. Soil erosion indicators The indicators used to infer soil erosion were labeled beroding clodsQ, bflow surfacesQ, bpre-rillsQ, brillsQ, and bsoil accumulationQ (Bergsma, 1992, 1996; Bergsma and Kwaad, 1992). Indicators were expressed in terms of percentage incidence of bare soil area; the thickness of soil accumulation over solid subsoil was also assessed. All data were aggregated from sub-sample to cover type level and then to plot level. The soil erosion indicators are defined as: ! ! !

Eroding clods: located above flow surfaces, having predominantly convex forms, shaped by splash and disintegration. Flow surfaces: almost flat areas with a smoothed micro-relief with (often-parallel) linear flow marks or sediments, partly eroded by shallow non-concentrated flows. Pre-rills: shallow channels (3–5 cm deep) cut by concentrated flows, slightly concave and not part of the micro-drainage system of the area.

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Fig. 3. Sampling strategy for obtaining field data.

! !

Rills: deeper channels (5–30 cm) cut by concentrated surface flows, with clear lateral micro-scarps on the sides, usually part of the micro-drainage system of the area. Soil accumulation: areas with smoothed micro-relief, caused by deposition of eroded materials.

Ample erodable material becomes available upon tillage and weeding (e.g. soil clods). Clods erode through splash and detached soil material is deposited on flow surfaces. Flow surfaces and depressions may develop pre-rills and (under concentrated runoff) rills. In places, eroded material settles in depressions to form soil accumulations. Wainwright et al. (2000) discuss the basic micro-interactions between vegetation, water movement, and erosion for shrub and grass dominated landscapes in further detail. Mainam (1991) describes the development of these processes by monitoring changes in the incidence of soil erosion indicators. This study merely evaluates the status of soil erosion indicators at the end of a rainy season. This means that data on erosion features represent the cumulative effect of erosion over the period from tillage/weeding to observation. The canopy and ground cover during this period are assumed constant; hence, rainfall erosivity data (E i30) were not required to aggregate period specific C C and C G sub-factors (refers to dsoil loss ratiosT in USLE).

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3.2. The C factor The C factor accounts for ground and canopy cover effects on soil loss in USLE. The C factor is derived as [C C*C G*C Res*C Rec*C OM], where: !

!

!

!

!

C C=Canopy Cover factor. (Splash) erosion depends inter alia on the erosivity of rainfall and on the properties of the canopy that intercepts it. Through-fall has a bigger drop size than direct rain and thus a different kinetic energy. This energy increases with canopy height and matches the energy of free-fall raindrops if its fall-height is about 5 m (Kooiman, 1987 after Wischmeier and Smith, 1978). C G=Ground Cover factor. A high ground cover fraction (litter, basal cover, and stones) greatly reduces potential soil loss (Wischmeier and Smith, 1978). It provides a protection against eroding rainfall and reduces runoff velocity while increasing infiltration. C Res=Residual land use factor. Accounts for any residual effects in the first years after forest or virgin land are opened up for arable uses (Dissmeyer and Foster, 1981). In this study, C Res was set to 1 because all fields were under cultivation for more than 5 years. C Rec=Reconsolidation factor. Applies when a field is left fallow for a long time (Dissmeyer and Foster, 1981). This factor was also set to 1 because fields in the study area are never left fallow due to scarcity of land. C OM=Organic Matter factor. Accounts for the accumulation of soil organic material on the soil (also set to 1).

C C and C G values were computed by cover type and aggregated to field level, weighted according to relative cover type areas. Note that lower C values correspond with less soil loss. 3.3. The C C factor A canopy cover protects bare soil against soil loss. The protection offered by the foliage decreases in the direction of higher layers in the canopy. The canopy cover factor for a specific vegetation type must take this into account. Assuming a random distribution of basal cover and a non-random positioning of successive canopy layers, the deffective canopy coverT by layer is (all fractions): ! ! !

1st layer=(canopy cover basal cover)*bare soil. 2nd layer=2nd canopy cover*(1 1st layer)*bare soil. 3rd layer=3rd canopy cover*(1 2nd layer)*(1 1st layer)*bare soil.

The deffective canopy heightT of a specific layer is defined as the mean of the center of the canopy and the height of the lower layer. C C is computed layer by layer according to Fig. 4 (Wischmeier and Smith, 1978; Kooiman, 1987; Palmer, 1989). C C was computed by summing all (X) layer-specific C C factors as [(C C,1+ C C,2+. . .C C,X ) (X 1)]. C C values at field level were calculated by weighing the cover type specific C C values according to the relative areas under each cover type.

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Fig. 4. C C and C G factors assessment. The C C figure presents [1 ((1 (37.13H 4.02H 2+0.146H 3))/100)*C], where C is the deffective canopy coverT (%) and H the deffective canopy heightT (m). C G is defined by [e-(b*Ground Cover (%))], where b is the surface roughness. For suggested modifications of these equations, see USDA (1991) and Renard et al. (1994).

3.4. The C G factor Fig. 4 shows that soil loss decreases sharply as a sparse ground cover becomes denser. C G is computed by cover type using [e-(b*Ground Cover (%))], where b is the ground surface roughness. Ground cover (%) is computed as [100% (%litter+%basal cover+%stones)]. A value of 0.05 was substituted for roughness factor b for rough field surfaces and 0.04 for smooth field surfaces. The overall C G value was calculated by weighing the cover type specific C G values according to the relative areas occupied by each cover type. 3.5. Data analysis To identify and quantify the impact of land and management factors on the incidence of a specific erosion indicator, all land and management data were included in step-wise multiple regression analysis; the estimated functions are: Indicator=f(Land, Management). This does not require prior knowledge of the processes involved. However, the results give insight in aspects of the sustainability of maize-based land use systems and the role of erosion processes. Prior to model estimation, all data collected were statistically screened for normality and mutual correlations. This concerns erosion indicators and their relation with the bare soil area, the C C and C G factors, actual crop cover, soil and terrain conditions, infrastructure, and land management.

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4. Results 4.1. Soil erosion indicators; frequency of occurrence Eroding clods and flow surfaces were present at all 70 sites surveyed, pre-rills at 55 sites, rills at 18 sites, and soil accumulations at 26 sites. The occurrence of the indicators (in % of the bare soil area) is shown in Fig. 5. The dominant indicator was dflow surfacesT. One-third of all identified soil accumulations had a height of 0 cm, indicating that the indicator was difficult to differentiate from flow surfaces, and that its assessment based on surveyor’s recognition thus failed; it is not further used. Data on flow surface areas thus comprised also the areas with soil accumulation. Where rills were present (18x), all farmers perceived erosion as a problem. Where only pre-rills were present (37x), 28 farmers perceived erosion as a problem. Rills occurred mostly in drier areas. Sites with more bare soil had relatively more pre-rills and rills within their bare soil areas (Fig. 6). 4.2. The C C and C G factors The C C and C G factors for all 70 fields were plotted to assess their distribution (Fig. 7). According to the (two-tailed) Kolmogorov–Smirnov One Sample Test, the probability that C C is normally distributed is 63%; this is 96% for C G. C C values ranged from 0.45 to 0.96 and C G from 0.05 to 0.87. The correlation between C C and C G is low (20%). This makes it possible to study the impacts of ground cover and canopy cover on the incidence of soil erosion indicators independently. Fig. 8 suggests that using less maize in mixed cropping systems and/or including beans in the crop mix, both reduce erosion problems (lower C C). The highest estimates for basal and litter covers were 27% (average: 8%) and 40% (average: 11%). The value of C G falls sharply with a decrease of the bare soil area (Fig. 4); a 10-fold reduction in soil erosion is suggested if the bare soil area decreases to 55%. Stones were recorded in 49 plots; the average stone cover of these plots was 2.9%. Only four plots had a greater stone cover (5–25%).

Fig. 5. Percent bare soil on sites surveyed, and the incidence of soil erosion indicators in bare soil areas.

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Fig. 6. The bare soil area by site (%) vs. the occurrence of indicators (as % of that bare soil area).

4.3. Soil and terrain data Texture was determined by the btexture-by-feelQ method of Thien (1979). Most soils are formed in sandy–clay–loam (30x), sandy–loam (21x), and sandy–clay (12x); most are reddish or brownish in colour. Sandy–clay textured soils occur exclusively in the low rainfall zone; they are reddish and more compact than other soils. Sandy–loam soils, mainly found in the low to medium rainfall zone, have pH values around 7.0, whereas most other soils, especially in the high rainfall zone, have a field pH around 5.0. Sandy–loam and sandy–clay–loam soils are dominant in map units where mass movement, rill, and gully erosion occur. These tend to have steep slopes (up to 50%). Surveyed fields lie in areas with slopes from 8% to 54% (average: 25%). The slopes are normally convex if the field is located at a high position in the terrain, concave if located at a low position, and irregular in intermediate locations. Straight slopes occurred in both high and low fields.

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Fig. 7. Distribution of the C C and C G sub-factors.

4.4. Infrastructure Three types of soil erosion control measures occurred: !

Trash lines. Crop and weed residues are placed along contours. When properly placed and maintained, trash lines reduce runoff velocity and induce sediment deposition. Well-maintained trash lines develop over time into contour ridges. Unfortunately, most trash lines seen were poorly constructed, rarely wider than 0.5 m, and often damaged

Fig. 8. Mixed cropping reduces C C. C C=0.61 0.004*(%-Bean cover)+0.000028(%-Maize cover)2 (Adjusted -R 2=0.49).

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during land preparation. When present, they covered around 8% of the sampled sites (range of 2–15%). ! Grass strips. Strips planted to Napier grass (Elephant grass; Pennisetum purpureum Schumach.) have similar effects on erosion as trash lines. They are commonly used as fodder sources and are not destroyed during field preparation although grazing of fallow fields leaves the strips often almost bare. Where present, they covered some 10% of the sampled area (range of 2–18%). ! Fanya-juu. This is a method that is widely practised in Kenya. In Swahili, it means dmake it upT and refers to digging a ditch along a contour and throwing the spill uphill to form a shallow ridge that is later stabilized by Napier grass (Thomas and Biamah, 1991). Trash lines are more common in lowlands (25x) than on hills (14x) where grass strips outnumber them (10x and 20x, respectively). In lowlands, the annual rainfall is clearly insufficient to let grass strips survive. Fanya-juu is most common on hills with steep slopes (17x); cultivation of steep slopes is undertaken where alternative arable land is in short supply. 4.5. Land management Selected data on management operations were collected alongside data on crops. Soil tillage by hoe (jembe) is completed in February–March (Fig. 9), is entirely done by hand, and is conducive to clod formation. Planting is completed in March–April. Maize was planted in rows on 51 fields; five fields were ridged before planting (Table 1). Manure is the commonest fertilizer (27x); NPK was only used as a supplement to manure (8x). Weeding was done once (32x) or twice (35x) and was discontinued in April–June. All farmers shoved earth against the maize plants while weeding. Harvesting took place during the survey period (July–August). After harvesting, most farmers leave their field fallow; 3 farmers use it for fallow grazing. Maize residues are widely collected for cattle feed (31x); 26 farmers concentrated the crop residues in trash-lines; 13 farmers left the maize stalks in place. Half of the farmers judged the season’s rainfall

Fig. 9. Sequence and timing of operations that cause soil detachment.

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Table 1 Count of operations done and observations made Maize spacing

Method of planting Flat

On ridges

Random Rows

18 46

1 5

Fertilizer applied

Manure applied

No Yes

Total 19 51 Total

No

Yes

43 0

19 8

62 8

2 35

3 2

Frequency of weeding 0 1

1 32

Maize residue treatment after harvesting Left on the field

Put on trash lines

Given to livestock

13

26

31

P during growing season

P intensity after last weeding Low

High

Inadequate Sufficient

10 20

27 13

Total 37 33

as dsufficientT; 40 farmers classified the rainfall intensity after the last weeding as dhighT (Table 1).

5. Discussion 5.1. The incidence of eroding clods Eroding clods are created during hand tillage and weeding. The remaining cover of deroding clodsT was estimated after 3–5 months of exposure to the elements (expressed as % of the bare soil area). A high incidence of eroding clods indicates less erosion. The eroding clod data were tested for normality using the Kolmogorov–Smirnov test with a Normal (9.4,5.5) distribution. The probability (two-tailed) was low (10%). Log-transformed eroding clod data, tested for a Normal (2.1,0.6) distribution, had a probability of 68%. The logtransformed data were therefore used to infer causes of variability. Table 2 presents details on the variability of the occurrence of eroding clods. More clods were present if: ! !

the ground cover factor (C G) was low (high groundcover), maize was harvested late. Late harvesting (September) is normal in the high rainfall zone, whereas harvests are early (August) in the low and medium zones,

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Table 2 Multiple regression inference model of causes of variation in the rate of occurrence of eroding clods in maize fields (% of bare soil area; N=70; Adjusted R 2=48%) Ln [Eroding clods] (% of bare soil area) Independents

Coefficient

Constant 1.658 Ground cover sub-factor 2.386 Last month of harvesting 0.289 If SiCL, CL topsoil 0.600 If map unit 3 or 5 0.498 Basal cover (%) 0.060 If planted randomly 0.367 Tree cover (%) 0.043 If SL topsoil 0.339 If loose topsoil structure 1.080 If map unit 11 0.369 Estimation of Ln [Eroding clods]: Estimation of Eroding clods:

!

! ! !

Probability 0.3% 0.0% 0.0% 0.6% 1.9% 0.0% 0.9% 0.3% 1.1% 0.2% 8.6%

Measured values

Valuescoefficient

Best

Average

Average

Best

1 0.055 11 1 1 1.338 0 0 0 0 0

1 0.439 8.200 0.100 0.100 7.779 0.729 2.800 0.300 0.043 0.086

1.7 1.0 2.4 0.1 0.0 0.5 0.3 0.1 0.1 0.0 0.0 2.1 7.8

1.7 0.1 3.2 0.6 0.5 0.1 0.0 0.0 0.0 0.0 0.0 5.7 306.0

Gap

%

0.9 0.8 0.5 0.4 0.4 0.3 0.1 0.1 0.0 0.0 3.7 298.2

25.0 22.1 14.7 12.2 10.5 7.3 3.3 2.8 1.3 0.9

the topsoil had a fine texture (best are SiCL/CL, then SCL/SC, and lastly SL). This agrees with the general notion that soil erodibility changes with the mean geometric particle diameter (see the USLE soil erodibility nomograph), the site was in map unit 3 or 5 (with patches of low forests), basal cover was low (less weeds, etc.), and maize was planted randomly (not in rows).

Impacts of soil erosion control infrastructure, timing of tillage, and timing or frequency of weeding on the incidence of eroding clods could not be detected. The variability explained was a mere 48% of total variability and comparison of the actual dbestT value (23.6%) with the estimated dbestT value (30.6%; Table 2) shows that the model prepared has insufficient predictive qualities. The indicator hardly captures the impact of field conditions on anticipated (accelerated) soil erosion. This is perhaps caused by ignored site differences that affect creation and exposure of clods, e.g., the initial cover by clods. In addition, the indicator is likely to relate better to soil erodibility (degradation of soil clods) then to site-wise soil loss (transport of soil). 5.2. The incidence of flow surfaces The Kolmogorov–Smirnov test on the normality of the flow surface data had a (twotailed) probability of 11.5%. Use of logarithmic transformed data did not improve this result. After inspection of Z-scores and data distribution, it was decided to proceed with the development of a multiple regression model using the original data (Table 3). Less flow surfaces were present in fields if (from high to low importance): ! the ground cover sub-factor (C G) was low (high groundcover), ! cover by eroding clods is (still) high (mutually exclusive areas),

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Table 3 Multiple regression inference model of causes of variation in the rate of occurrence of flow surfaces in maize fields (% of bare soil area; N=70; Adjusted R 2=56%) Flow surfaces (% of bare soil area) Independents Constant CG sub-factor Cover by eroding clods (%) If topsoil texture is SC Cover by cereals (%) If site is in map unit 1 If site is in map units 7–10 Estimation of Flow surfaces:

!

! !

Coefficient 66.3 35.154 1.007 16.026 0.213 8.874 7.685

Probability

0.0% 0.0% 0.0% 2.5% 7.4% 0.5%

Measured values

Valuescoefficient

Worst

Best

Worst

1 0.868 1.70 0 34 1 1

1 0.055 23.6 1 100 0 0

66.3 30.51 1.72 0.00 7.24 8.87 7.69 104.5

Gap

%

28.58 22.06 16.03 14.06 8.87 7.69 97.28

29.4 22.7 16.5 14.5 9.1 7.9

Best 66.3 1.93 23.87 16.03 21.30 0.00 0.00 7.2

the topsoil is SC. This soil has no loam (silt). Low silt contents reduce a soil’s sensitivity to sealing by splash. Sealing reduces the initial infiltration rate and enhances overland flow (Bergsma, 1996), the cover by cereals is high (high C C factor), and the site is not in map unit 1 (dominantly sheet and splash erosion) or in units 7–10 (medium to high rainfall zones). More flow surfaces thus occur in high rainfall areas except if the area is prone to sheet and splash erosion.

No effects of infrastructure, timing of tillage, and timing or frequency of weeding could be detected. It is believed that the flow surfaces formed after a few showers. Some of the variability of the indicator could be explained (56%). Model estimates relate fairly well with the surveyed values. 5.3. The incidence of pre-rills The Kolmogorov–Smirnov test on the normality of Ln (pre-rill+1) had, mainly because of the many dzeroT values, only a (two-tailed) probability of 26%. After inspection of the Z-scores, it was decided to use the Ln data for model estimation (Table 4). Fewer pre-rills were present in fields if (from high to low importance): ! !

the ground cover sub-factor (C G) was low (high groundcover), no Pigeon Peas (Cajanus cajan) are grown. In the lowlands, this crop was planted on 26 of the 38 sites surveyed with a cover from 2% to 20%. The crop remained on fields as a pure stand after the maize was harvested. The dense plant cover protects the soil against splash erosion and its dense root system consolidates the soil and reduces the impact of runoff. Slightly higher areas form after some time; concentrated flows that create (pre-) rills are the result. Gyssels and Poesen (2003) confirm that the influence of roots play, besides the aboveground characteristics of the vegetation, a particular (exponential) role concerning soil loss by concentrated flows,

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Table 4 Multiple regression inference model of causes of variation in the rate of occurrence of pre-rills in maize fields (% of bare soil area; N=70; Adjusted R 2=67%) Ln [Pre-rills] (% of bare soil area) Independents

Coefficient

Constant Ground cover sub-factor Cover by Pigeon Peas (%) Last month of weeding Cover by flow surfaces (%) If Fanja-juu is present If topsoil is SiCL or CL Cover by vegetables (%) Estimation of Ln [Pre-rills]: Estimation of Pre-rills:

2.638 1.453 0.038 0.183 0.010 0.943 0.825 0.060

Probability

0.0% 0.5% 2.3% 3.9% 0.0% 0.1% 2.8%

Measured values

Valuescoefficient

Worst

Worst

Average

2.64 1.26 0.76 0.55 0.27 0.00 0.00 0.00 3.84 46.58

2.64 0.64 0.15 0.92 0.60 0.27 0.08 0.07 1.48 4.39

0.87 20.00 3.00 26.91 0.00 0.00 0.00

Average 0.44 3.90 5.03 59.87 0.29 0.10 1.21

Gap

%

0.62 0.61 0.37 0.33 0.27 0.08 0.07 2.36 42.19

26.4 25.9 15.7 14.0 11.4 3.5 3.1

weeding ended late (shorter period for pre-rill formation), cover by flow surfaces (%) is high (mutually exclusive areas and less likely occurrence of concentrated flows), ! Fanya-juu is practised (control of slope length and slope gradient), ! the topsoil is SiCL or CL (not sandy). Soil particle detachment is less in soils with clay having cohesive properties (Meijerink, 1995 after Hjulstro¨m, 1935; Bryan, 1977), and ! intercropping with vegetables is practised (positive C C effects as occur in 58% of the surveyed plots of the high rainfall zone; Fig. 8 illustrated already that a higher bean cover led to lower C C values, which relates to less soil loss). ! !

Impacts by infrastructure (Fanya-juu) and timing of weeding (month that it ended) were significant. The negative impact of growing pigeon peas is surprising, whereas the effects of C G, texture, and vegetables grown are as expected. None of the 11 map units had a significantly higher or lower pre-rill incidence. The explained indicator variability was good (67%) and estimates relate well with actual values. The log-linear relationship indicates that conditions that trigger the formation of pre-rills add up exponentially. Where flow surfaces serve transport and deposition of sediment within a field, pre-rills are more likely to be signs of actual soil loss from the field. 5.4. The incidence of rills Rills occurred only in the low rainfall zone and where pre-rills were present. Through logistic regression a b0,1Q Poisson distribution denoting dpresenceT (18x) vs. dabsenceT (62x) was estimated. All coefficients of the model have a probability below 5%; the McFadden’s Rho2 is 56%. The model is [e lp /(1+e lp )], with dlpT defined by: lp= 2.0+6.50*(C G)+4.92*(if texture is SC) 1.41*(month in which planting ended)+2.36*(if P intensity after last weeding was low). The model’s sensitivity (accuracy

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247

to predict rills) is 70% and specificity (accuracy to predict no-rills) 90%. The model suggests that rills would have been absent if: ! ! ! !

the ground cover sub-factor (C G) was low (high groundcover), the topsoil texture is not SC but contains loam (silt), planting, and hence tillage, was done late in the season (less time to form rills), and rainfall intensity after the last weeding was low.

The logistic model is prone to errors; a total of 40% of all cases would have been wrongly predicted. Hence, a linear multiple regression equation was estimated. The Adjusted R 2 of regression (56%) suggests a similar performance as the logistic model. The advantage of linear regression is that actual incidence rates of rills can be used (no information loss); the disadvantage is that the data are not normally distributed (too many dzerosT). Three out of four factors identified by logistic regression were retained in the model (not: time of planting) and three new ones were added (presence of trash lines, cover by cereals, and planting of maize on ridges). It was decided to use the regression model for further elaboration. The model suggests that fewer rills are formed if (Table 5): !

! !

the topsoil contains silt (not SC). Besides more rills, SC soils had less flow surfaces. This suggests that SC is less prone to sealing. De Ploey (1981) related sealing of loamy soils with more runoff and more rills. The surveyed SC soils showed a reverse pattern. They have a particle distribution that is conducive to compaction. When the soil becomes wet, the ionic strength between clay particles becomes less. This favours the peptisation of clay and increases vulnerability to soil detachment and rill formation. This erosion process is ignored by the USLE soil erodibility nomograph (Wischmeier and Smith, 1978; USDA, 1991; Renard et al., 1994), the cover by cereals is low (low C C sub-factor). A dense root system close to maize stalks can cause concentrated flows that create rills, the ground cover sub-factor (C G) is low (high groundcover),

Table 5 Multiple regression inference model of causes of variation in the occurrence of rills in maize fields (% of bare soil area; N=70; Adjusted R 2=56%) Rills (% of bare soil area) Independents Constant If texture is SC Cover by cereals (%) CG sub-factor If trash lines are present P intensity after last weeding If planted on ridges Estimation of Rills:

Coefficient 2.975 4.069 0.048 3.049 1.471 1.244 2.064

Probability

0.0% 4.9% 8.1% 3.2% 5.5% 6.9%

Measured values

Valuescoefficient

Worst

Average

Worst

1 1.00 100.00 0.87 0.00 1.00 0.00

1 0.17 62.79 0.44 0.56 0.57 0.09

2.98 4.07 4.80 2.65 0.00 1.24 0.00 9.43

Gap

%

3.37 1.79 1.31 0.82 0.53 0.18 8.00

44 23 17 11 7 2

Average 2.98 0.70 3.01 1.34 0.82 0.71 0.18 1.79

248

! ! !

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trash lines are present (soil erosion control measure), rainfall intensity after the last weeding is low (less potential soil loss), and maize is planted on low ridges (only six cases; ridges follow contours).

Almost all rills were seen in the low-rainfall zone (800–1000 mm). All SC soils occur in that zone (12 out of 38 sites). Adding drainfall zoneT as a dummy to the model proved not significant. This makes the reported model more robust. The presence of pre-rills had no impact on the model. This suggests that rills (encountered on 18 sites) mainly form on SC soils (11x); only one site with a SC topsoil had no rills. The explained variability of the indicator was moderate (56%); the estimates relate well with actual values (Table 5).

6. Conclusion Lal (2001) stated that results of field erosion measurements are often techniquedependent and that erosion models still require considerable field validation, which poses problems since runoff plot techniques are not standardized. He mentioned use of dquickT field techniques to assess soil loss like the buried nail or plate techniques, exposure of plant roots, and depth of deposition of sediments along fence lines. This paper reviews the merits of soil loss indicators and contributes to studies aimed at standardization of soil loss assessments for widespread and easy adoption. It is concluded that the deroding clodsT indicator relates better to soil erodibility than to soil loss. The effects of the remaining three indicators, flow surfaces, pre-rills and rills, are summarized in Table 6. The pre-rill indicator has the best general performance: its variability was not map-unit specific or related to specific soils prone to rill formation. It rather integrates effects over time, and it detects contrasting impacts of different cover types. The value of this particular indicator must be verified with actual soil loss measurements in the field taking into consideration that the indicator is expressed as % of the bare soil area. Both parameters must be linked, especially since they show strong interactions. Lastly, eroding clods relate more to erodibility and rills to very soil specific erosion processes. Once the utility of the pre-rill indicator is validated for assessing soil-loss in agricultural fields through runoff studies, existing indicator-based environmental impact models can be upgraded. Van der Werf and Petit (2002) evaluated 12 such models; only three included erosion indicators such as the amount of erosive rainfall, soil cover by crops, use of undersowing, direct drilling and mulching, and the presence of perennial crops. The prerill indicator integrates their cumulative effects, is easy to measure and requires no additional fieldwork costs. Chaplot and le Bissonnais (2000) also concluded that their results with runoff experiments call attention to the importance of an adapted experimental set-up to get reference data for interrill erosion model development and validation. Bouma and Imeson (2000) already investigated through rainfall simulation experiments the relationships between dsoil-basedT rill erosion indicators and erosion processes on badland surfaces in Spain. Indicators based on erosion features form the next promising step; they integrate the combined effect of earlier defined erosion dresistanceT and dprotectionT indicators as studied by Shakesby et al. (2002).

Table 6 Summary of factors explaining the variability in the incidence of three soil erosion indicators Independents

Flow surfaces Preferred

Environmental conditions Topsoil texture

Crop management Cover by cereals (%; relates with the C C sub-factor) Cover by Pigeon Peas (%) Last month of weeding Cover by vegetables (%) Planting on ridges Soil (surface) management Ground cover sub-factor Cover by eroding clods (%) Cover by flow surfaces (%)

No No

High (Nmicro-relief and dense roots)

%

16.0

16

8.9 7.7

9 8

14.1

No sand (clay has cohesive properties)

Low value (Nground cover) High (mutually exclusive)

Gap

%

0.08

3

28.6

29

22.1

23

0.61 0.37 0.07

Worst–Average Gap

%

With loam (compaction causes clay peptisation)

3.37

44

Low (berosivity)

0.53

7

Low (bmicro-relief and dense roots)

1.79

23

Done (control of slope)

0.18

2

Low value (Nground cover)

1.31

17

Present (control of runoff velocity)

0.82

11

26 16 3

Low value (Nground cover)

0.62

26

High (bconcentrated flows)

0.33

14

Present (control of slope)

0.27

11

24.2 5.4 0.0 67%

Preferred

14.2 1.8 0.0 56%

249

85.0 59.9 26.9 56%

Rills Worst–Average

15 Low (micro-relief) Late (time effect) High (C C effect)

Infrastructure management Fanja-juu Trash lines Actual worst: Actual average: Actual best: Variability explained:

Gap

Preferred

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Area with sheet and splash erosion High rainfall area P intensity after last weeding

No loam (bsealing)

Ln [Pre-rills] Worst–Best

250

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Acknowledgement Thanks to Rabby Mulangala who collected the basic survey data used for this study.

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