Catena 63 (2005) 85–108

www.elsevier.com/locate/catena

Spatial modeling of soil erosion potential in a

tropical watershed of the Colombian Andes

Natalia Hoyos *

Department of Geography, University of Florida, P.O. Box 117315, Gainesville, FL 32611-7315, USA

Received 8 July 2004; received in revised form 11 January 2005; accepted 30 May 2005

Abstract

Soil erosion potential of a 58 km2 watershed in the coffee growing region of the Colombian

Andes was assessed using the Revised Universal Soil Loss Equation (RUSLE) in a GIS

environment. The RUSLE factors were developed from local rainfall, topographic, soil and land

use data. Seasonal erosivity factors (R) were calculated for six pluviographic stations (1987–1997)

located within 22 km of the basin. Two regression models, one for the wet and one for the dry

seasons, were created and used to estimate seasonal erosivity for 10 additional stations with

pluviometric data. Erosivity was on average higher in the wet seasons (4686 MJ mm ha�1 h�1

season�1) than the dry ones (2599 MJ mm ha�1 h�1 season�1). Seasonal erosivity surfaces were

generated using the local polynomial interpolation method, and showed increases from west to east

in accordance with regional elevation. Soil erodibility was calculated from field measurements of

water stable aggregates (N2 mm) and infiltration, which were influenced by land use. Three

erodibility scenarios were considered (high, average and low) to represent the variability in

infiltration measurements within each land use. The topographic and land cover factors were

developed from existing contour and land use data. Model results indicated that in the dry seasons,

and under the average erodibility scenario, 534 ha (11%) of the basin’s rural area were within the

extreme erosion potential category (above 3.5 t ha�1 season�1). During the wet seasons, this area

increased to 1348 ha (28%). In general, areas under forest and shrub had low erosion potential

values, while those under coffee and pasture varied according to topography. Modeling of probable

0341-8162/$ -

doi:10.1016/j.

* Fax: +1 35

E-mail add

see front matter D 2005 Elsevier B.V. All rights reserved.

catena.2005.05.012

2 392 8855.

ress: nhoyos@ufl.edu.

N. Hoyos / Catena 63 (2005) 85–10886

land use change scenarios indicated that the erosion potential of the basin would decrease as a result

of coffee conversion to pasture.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Soil erosion potential; RUSLE; Andisol; Coffee; Andes; Colombia

1. Introduction

Erosion by water is a primary agent of soil degradation at the global scale, affecting 1094

million hectares, or roughly 56% of the land experiencing human induced degradation

(Oldeman et al., 1991). This process is considered the major form of soil degradation in the

Colombian Andes, and has been related to overgrazing and inadequate agricultural practices

such as frequent burning, tillage and lack of cover crops (Oldeman et al., 1991; Muller-

Samann, 1999). Undesirable effects include reduced soil productivity and deterioration of

water quality (Lal, 2003). Therefore, the study of soil erosion patterns in the landscape, and

interactions among the major factors that affect this process is essential, particularly in

humid tropical mountainous areas, due to their steep topography and frequently high rainfall

amounts and intensities. One of the most widely used models to study water soil erosion is

the Revised Universal Soil Loss Equation (RUSLE, Renard et al., 1997), an empirically

based model founded on the Universal Soil Loss Equation (USLE, Wischmeier and Smith,

1978). It is designed to predict long-term average annual soil loss from field slopes under a

specific land use and management system, based on the product of rainfall erosivity (R), soil

erodibility (K), slope length and steepness (LS), surface cover and management (C) and

support conservation practices (P). The R factor is calculated by adding individual storm

EI30 values (product of total storm energy and maximum 30-min intensity) over a year, and

averaging annual values over a period longer than 22 years to account for cyclical patterns

of rainfall (Renard et al., 1997). It is also recommended that this factor be calculated on a

seasonal basis to reflect intra-annual patterns of rainfall (Yu, 1998; Millward and Mersey,

1999; Santos and Azevedo, 2001; Dias and Silva, 2003). The K factor reflects the

susceptibility of soil particles to detachment by rainfall splash or surface flow, and is related

to the integrated effect of rainfall, runoff, and infiltration. It is measured from runoff plots or

through predictive relationships (nomographs, Renard et al., 1997). Specific properties of

Andisols that have been related to their stability include mean weighted aggregate diameter

(dominant aggregate size class), clay content, delta pH (pH in potassium chloride minus pH

in distilled water), organic matter, permeability, and presence of allophane (El-Swaify and

Dangler, 1977; Rivera et al., 1998). The LS factor accounts for the effect of slope length and

gradient. In a slope, the length factor (L) is defined as the horizontal distance from the origin

of overland flow to the point where deposition starts or runoff goes into a channel (Renard et

al., 1997). In a two- and three-dimensional situation however, it should be replaced by the

unit contributing area, i.e., upslope drainage area per unit of contour length (Moore and

Wilson, 1992; Desmet and Govers, 1996). The C factor reflects the effects of cover and

management variables, while the P factor represents the effects of support practices such as

contouring, strip cropping, terracing and subsurface drainage (Renard et al., 1997).

Although the USLE and RUSLE were developed to predict soil loss under temperate

N. Hoyos / Catena 63 (2005) 85–108 87

conditions, their use in other regions is possible by the determination of its factors from

local data (Millward and Mersey, 1999; Mati et al., 2000; Angima et al., 2003; Lufafa et al.,

2003). Additional advantages include attainable data requirements under the limitations

common in developing countries, its compatibility with Geographic Information Systems

(GIS) allowing prediction of erosion potential on a cell by cell basis, and the wealth of

information already available for its factors.

The objective of this study was to predict the spatial patterns of soil erosion potential

for a watershed of the Colombian Andes, by adapting each of the RUSLE factors to local

conditions.

2. Data and methods

2.1. Site description

The Dosquebradas basin is located in the coffee-growing region of the Central

Cordillera of Colombia and covers an area of 58 km2 (Fig. 1). Elevation ranges from 1350

to 2150 m, with a relief characterized by a valley floor surrounded by moderate (up to

25%) to steep (25% to more than 75%) slopes to the west, north and east (Instituto

Geografico Agustın Codazzi [IGAC], 1988). Annual precipitation ranges between 2600

mm in the south and 3200 mm at the northern watershed divide, and has a bimodal

distribution, with two wet (March–May, September–November) and two dry (December–

February, June–August) seasons (Guzman and Jaramillo, 1989). The predominant soil

mapping unit is Chinchina, made up mostly of Melanudands (80%) derived from volcanic

ash deposits (IGAC, 1988). About 16% (908 ha) of the basin has been urbanized, most of

it concentrated on the valley floor. Coffee and pasture are the major land uses in the rural

area (62% and 18% of the rural area, respectively), while forests (natural, planted,

bamboo), shrub and temporary crops make up the remaining 20% (Corporacion Autonoma

Regional de Risaralda [CARDER], 1997).

Modeling of soil erosion potential was limited to the rural area of the basin (4923 ha).

Digital topographic, land use and stream data were provided by the regional environmental

office (CARDER, 1997). The spatial distribution of coffee systems (sun and shade) was

derived from maps of the Colombian Coffee Federation (Federacion Nacional de Cafeteros

[FNC]), and was used for soil sampling and for the model’s land cover and management

factor surface. The Federation regards these data as private so both categories are

presented as one in all maps. All spatial data were processed within a Geographic

Information System GIS (ArcGIS, ArcINFO and IDRISI) with a spatial resolution of 25

m, equivalent to 1 mm in the topographic base maps (1:25,000). Fig. 2 summarizes the

methods used to derive each of the factors required by RUSLE.

2.2. Erosivity factor

Data for the calculation of the EI30 factor were available from 16 gauging stations

located within 27 km from the center of the basin, operated by the National Coffee

Research Center (CENICAFE) and the National Environmental Institute (IDEAM). From

Cen

tral

Cen

tral

Cor

dille

raC

ordi

llera

Mag

dale

na

Mag

dale

naR

iver

Riv

er

Cau

caC

auca

Rive

rRi

ver

11

1200

1200

12001200

1500

1500

30003000

36003600

1200

1200

0000

2100

2100

2400

2400

900

900

18001800

Cau

ca R

iver

Cau

ca R

iver

Ot˙ n R

iver

Otun R

iver

0 5 10km

75º45'0"W 75º30'0"W

4º45

'0"N

5º0'

0"N

2233

44

55

66

Legend

Rainfall stations

1. Catalina2. Jazmín3. Cenicafé4. Naranjal5. Pta. Tratamiento6. Cedral

Pluviographic

Pluviometric

`

Fig. 1. Location of the Dosquebradas basin on the western flank of the Central Andean Cordillera of Colombia.

Rainfall gauging stations also shown. Only stations with pluviographic data are labeled (modified from Digital

Chart of the World [DCW], 1992; Centro Internacional de Agricultura Tropical [CIAT] y Programa de las

Naciones Unidas para el Medio Ambiente [PNUMA], 1998; CARDER, 1997).

N. Hoyos / Catena 63 (2005) 85–10888

these, only 6 stations had the rainfall intensity (pluviographic) data required to calculate

EI30 values (Renard et al., 1997). In order to use the rainfall totals (pluviometric data)

from the other 10 stations, the following steps were taken:

! Individual storm EI30 values were calculated for the stations with pluviographic data

according to the RUSLE methodology (Renard et al., 1997). A period of 11 years (1987

Infiltration andaggregate stability

Land useRainfall data

Regional erosivity model

Erosivity surface

Digital contours

DEM

Slope length andsteepness surface Erodibility surface

Cover managementsurface

Soil erosion potential

Fig. 2. Summary of data and methods used to derive the RUSLE factors.

N. Hoyos / Catena 63 (2005) 85–108 89

to 1997) was used because 1987 was the earliest date with available data at all 6

stations, and because it represented a reasonable compromise between the number of

available stations and the amount of data to be analyzed.

! For each pluviographic station, EI30 values were added on a seasonal basis and

differences among seasons were tested through analysis of variance. Two regression

models (one for the wet and one for the dry seasons) of seasonal EI30 (dependent

variable) and seasonal rainfall (independent) were built and subsequently used to

estimate seasonal EI30 at the pluviometric stations.

! Seasonal EI30 surfaces for the wet and dry seasons were generated by interpolating the

seasonal values from all stations with the local polynomial interpolation technique

(ArcGIS Geostatistical Analyst).

2.3. Slope length and steepness factor

The LS factor for this study was calculated with the USLE2D (Desmet and Govers,

1996), a program readily available over the Internet that works within IDRISI. The LS

surface was derived by:

! Creating the basin’s DEM from 50 m interval contours (Arc/INFO Topogrid

command).

! Running the USLE2D using the multiple flow algorithm (Quinn et al., 1991) as it was

considered more appropriate than the single flow algorithm, given the complex relief of

the basin.

! Calculating the LS algorithm by using the USLE slope length function (Wischmeier

and Smith, 1978) and Nearing’s slope steepness function (Nearing, 1997). The original

USLE slope length function was selected as it has performed better on steep slopes (up

to 60%) than the RUSLE algorithm (Liu et al., 2001). Nearing’s slope steepness

function, developed with empirical data from slopes up to 55%, was also considered a

N. Hoyos / Catena 63 (2005) 85–10890

better fit for this basin than the RUSLE algorithm, developed with data from slopes up

to 25% (Wischmeier and Smith, 1978; McCool et al., 1987). As a reference, the

Dosquebradas basin has 43% of its rural area on slopes between 25% and 55%, and

13% on slopes above 55%.

! Modifying the generated LS surface by (a) removing a 25 m strip from the watershed

divide as unrealistically high LS values were generated at the edge of the basin, and (b)

removing the urban areas which were assigned a bno dataQ value so that they were

excluded from further analyses.

2.4. Soil erodibility factor

Erodibility is determined largely by the stability of soil aggregates and the hydrological

properties of the soil profile (Keersebilck, 1990; Barthes et al., 2000; Barthes and Roose,

2002). The former is an indication of how susceptible soil particles are to detachment,

while the latter indicates how easily water moves and is retained in the soil profile, thus the

probability of runoff generation. The soil erodibility (K) factor for the study area was

estimated as a qualitative index based on soil properties measured in the field (June–

August 2002), i.e., the percentage of water stable aggregates and infiltration. Soil sampling

units were defined based on land use and topography as follows (Table 1):

! The urban land use was discarded. Within the rural area (4923 ha), minor land uses

were also discarded as their combined area represented only 2.1% (108 ha). These

included annual crops (tomatoes, yuca, beans, maize) and bamboo forests.

! Slopes were classified into three major categories: 0–15% (ridges and footslopes), 15–

50% (shoulders and backslopes) and N50% (very steep slopes), based on the DEM.

! The selected land uses were subdivided by slope class and the area of each land use/

slope combination was determined. The number of sampling locations within each land

use/slope unit was calculated proportional to its areal extent.

! In the field, sampling locations were determined with a GPS (Magellan 315) and later

downloaded to the GIS. At each location, soil samples were collected for aggregate

stability (0–20 cm).

The percent of water stable aggregates was measured using a method modified from

Arshad et al. (1996). Soil samples were air-dried in the shade for 24 h and passed through

2 mm and 1 mm sieves to obtain three aggregate sizes (N2 mm, 1 to 2 mm and b1 mm).

Ten grams of each of these subsamples were oven dried at 105 8C to determine the dry

weight (W1). Another 10 g were spread evenly over (a) a 2 mm sieve for the aggregates N2

mm, (b) a 1 mm sieve for the 1 to 2 mm aggregates, and (c) a 0.05 mm sieve for aggregates

b1 mm. Sieves were placed on a saturated terry cloth sheet for 5 min to let aggregates

absorb water slowly. Sieves with aggregate samples were placed in a container filled with

distilled water, so that the water level was just above the aggregates. Then, sieves were

moved up and down at a rate of 30 oscillations per min (one oscillation being an up and

down stroke of 3.7 cm in length) for 3 min. After wet sieving, aggregates were placed in a

weighing can and oven dried at 105 8C to determine their weight (W2). The weight of the

sand fraction (W3) of each subsample was determined by (a) removing organic matter with

Table 1

Soil sampling units defined by land use and slope categories, and number of sampling locations within each unit

Land use Slope (%) Area (ha) Sampling locations

Each class Total for land use Each class Total for land use

Coffee-sun (COS) 0–15 724.3 2752.5 6 41

15–50 1588.9 21

N50 439.3 14

Coffee-shade (COSSH) 0–15 37.4 283.7 3 13

15–50 185.0 6

N50 61.3 4

Pasture (PA) 0–15 366.1 885.2 8 23

15–50 416.6 10

N50 102.5 5

Pasture-shrub (PASHR) 0–15 39.3 194.0 4

15–50 84.1 2

N50 70.6 2

Shrub (SHR) 0–15 66.6 260.7 2

15–50 133.3

N50 60.9 2

Planted forest (PF) 0–15 5.6 52.9 3

15–50 39.3 2

N50 8.0 1

Natural forest (NF) 0–15 16.6 386.8 3

15–50 220.3

N50 150.0 3

Total 4815.8a 4815.8a 89 89

a This area represents the total rural area (4923.3 ha) minus discarded rural land uses (107.5 ha).

N. Hoyos / Catena 63 (2005) 85–108 91

peroxide, and (b) chemical dispersion through the addition of 20 ml of sodium

hexametaphosphate followed by overnight shaking.

The percent of water stable aggregates for each aggregate size was calculated as

Water stable aggregates %ð Þ ¼ W2 �W3

W1 �W3

� 100

Infiltration was measured in the field with a single ring infiltrometer by pouring 500 ml

of water at a time (USDA, 1999). This process was repeated until infiltration was fairly

constant and these values were taken to approximate the saturated infiltration rate. The

probability that the 30-min rainfall intensity exceeded the saturated infiltration rate at each

location was calculated by:

! Assigning each sampling location to the closest rainfall intensity station (Thiessen

polygons, Spatial Analyst Distance Allocation function in ArcGIS).

! Calculating the cumulative probability distribution for each rainfall station’s maximum

30-min intensity (I30), with data from 1987 to 1997 transformed with natural logarithm

since they were log-normally distributed.

! Using the I30 distribution parameters (mean and standard deviation) and measured

saturated infiltration rate to calculate the probability of exceedance at each location.

N. Hoyos / Catena 63 (2005) 85–10892

The selection of properties to calculate the erodibility index was performed in the

following way. Correlation analyses were run to determine which properties were

redundant in order to exclude them from further analysis (a =0.05). Exploratory spatial

analysis on the surviving properties showed no spatial pattern suggesting that

interpolation of values between sampling locations would be meaningless. Therefore, it

was decided to study the effect of land use on selected soil properties to use the land use

grid as the base for the erodibility surface. The K value for each land use was then

calculated as the product of the selected soil properties (average value for that specific

land use) scaled to the range of K values reported in the literature for this soil unit

(Hincapie et al., 2000).

2.5. Cover management factor

Coffee and pasture cover 80% of the rural area (Table 1). Most of the coffee is grown as

sun or btechnifiedQ coffee with very little or no shade, while there are some patches of

shade coffee clustered on the west side of the basin. Major differences between the coffee

systems include (a) coffee varieties, with short varieties used for sun coffee and tall

varieties used for shade coffee, (b) planting densities, higher in sun coffee systems (up to

10,000 plants ha�1) than in shade coffee (around 2500 plants ha�1), (c) plot cycle, which

lasts between 7 and 12 years for sun coffee, and 20+ years for shade coffee before the plot

is renewed, and (d) input levels, higher in sun coffee in terms of fertilizers (Perfecto et al.,

1996; Moguel and Toledo, 1999; FNC, 2000). The following characteristics on growth and

management practices were used to estimate the C factor:

! Coffee: usually planted at the beginning of the rainy seasons (March–May,

September–November). Planting preparation includes clearing with machete and

burning, or herbicide spraying. Planting densities and plot cycle as mentioned before.

Cultural practices include: weeding with machete, hoe or brush cutter every 2.5–3

months (early stages of shade coffee, regularly for sun coffee during whole plot

cycle), and insecticide spraying as needed to control pests and diseases like coffee

borer, leaf rust and leaf cutting ants (FNC, 2000; Ariza, 2003, pers. commun.).

According to these characteristics, values of 0.035 and 0.030 were assigned to sun

and shade coffee, respectively (RUSLE program; Weesies, 2003, pers. commun.).

Both represented the average over an 8-year period (average plot cycle for sun

coffee).

! Pasture: although some plots were being managed for milk production, most of them

had little management and were used for occasional grazing. A value of 0.01 was

assumed, corresponding to pasture in good condition (Morgan, 1995), or to pasture

with rotational grazing with 50% growth removal in each grazing cycle (RUSLE

program for Puerto Rico humid uplands). This value was selected because although it

was common to see some signs of overgrazing (cattle trampling), the pasture cover was

usually dense. For pasture-shrub, a value of 0.0028 was used (RUSLE program for

Puerto Rico humid uplands), described as dense grass with low vigor and not harvested.

! Forest and shrub: a value of 0.001 was used for natural and planted forest, and shrub

(Roose, 1977).

N. Hoyos / Catena 63 (2005) 85–108 93

2.6. Support practice factor

A value of 1 (no support practice factor) was assumed for the entire basin, since the

only support practice observed on some sun coffee plots was contouring but was not

consistent throughout the area, and detailed information for each agricultural plot was not

available.

2.7. Erosion potential surfaces

The seasonal erosion potential surfaces were calculated as the product of the seasonal

erosivity, erodibility, slope length and steepness, and land cover and management surfaces,

on a cell by cell basis. In addition, an annual erosion potential surface was generated

following the same procedure but using an annual erosivity surface created by

interpolation of annual erosivity values (local polynomial interpolation, ArcGIS

Geostatistical Analyst). This annual surface allowed for comparison with published

erosion potential values under similar conditions, usually expressed on an annual basis.

2.8. Assumptions and limitations

Several assumptions were made in this analysis, such as the use of single erodibility

and land cover/management factors, which in reality vary over time. For example,

erodibility changes seasonally depending on temperature, moisture conditions, cultivation

and cultural practices (Roose and Sarrailh, 1989; Renard et al., 1997). On the other hand,

the land cover/management factor for land uses such as planted forest, coffee and pasture

varies throughout the plot cycle (higher C factor during early stages of the growing season,

when the soil is exposed). Another assumption was the consideration of a single C factor

for all plots within each land use, although they had different management levels. For

instance, at the time of field work (June–August 2002), most sun coffee plots were in a

semi-abandoned state due to low international coffee prices. Nevertheless, there were still

a few plots with fertilization, weeding and pest control practices. In terms of the GIS

processing, it was assumed that topographic, erosivity, erodibility and land use conditions

within each cell (25�25 m) were uniform. Finally, rill erosion, sediment delivery and the

effects of single rainfall events were not considered as they are not modeled by RUSLE.

3. Results

The layers used to predict soil erosion potential (seasonal EI30, LS, C and K) are

presented in Fig. 3, while model results are presented in Fig. 6 (only for average soil

erodibility conditions).

3.1. Erosivity surface

Seasonal erosivity at the six pluviographic stations showed a pattern similar to rainfall,

being generally higher during the wet seasons although differences were not statistically

4600

4400

4800

4200

5000

4000

2600

2400

2800

2200

3000 (b)

(c) (e)

C factor

Urban area

Excluded ruralland uses

0.0010 - NF,PF0.0028 - PASHR

0.0100 - PA

0.0300 - COSSH0.0350 - COS

Wet season EI30

(d)

K average factor

0.00090.00100.00110.0013

Excluded ruralland uses

LS factor

0 - 2525 - 5050 - 75>75 Urban area

Urban area

(a) Dry season EI30

(MJ mm ha-1 h-1 season-1) (MJ mm ha-1 h-1 season-1)

0 1 2 3 4km

Fig. 3. Surfaces used to calculate the soil erosion potential of the basin. (a, b) Dry and wet seasons EI30 (MJ mm ha�1 h�1 season�1), (c) slope length and steepness LS,

(d) average erodibility K (t ha h ha�1 MJ�1 mm�1), and (e) land cover and management C. Transverse Mercator Projection with origin at 4835V56W N and 77804V51W W,

International Spheroid 1924, Bogota Observatory Datum.

N.Hoyos/Caten

a63(2005)85–108

94

N. Hoyos / Catena 63 (2005) 85–108 95

significant for all stations (Table 2). Nevertheless, it was considered worthwhile to carry

out subsequent analyses at the seasonal scale to reflect the bimodal rainfall pattern

identified in long-term regional studies (Guzman and Jaramillo, 1989) and analyze its

effect on soil erosion potential. The dry seasons’ erosivity surface had an average value of

2599 MJ mm ha�1 h�1 season�1. Its spatial distribution followed the regional elevation

pattern, increasing from the southwest (2103 MJ mm ha�1 h�1 season�1) to the northeast

(3074 MJ mm ha�1 h�1 season�1). The wet seasons’ erosivity surface had an average

value of 4686 MJ mm ha�1 h�1 season�1. Its spatial pattern presented more local

influences, particularly evident in the high values towards the central and northern part of

the basin (up to 5147 MJ mm ha�1 h�1 season�1).

3.2. Slope length and steepness surface

The LS surface replicated the local drainage network as well as the slope gradient.

Lines of flow concentration (concave), where overland flow tends to accumulate, had the

highest LS values. On the other hand, areas of convex topography such as ridges, where

flow diverges, had low LS values. A comparison with the slope gradient map (not shown)

revealed a clear effect of steepness on the LS factor, with areas of greater slopes having

high LS values and usually corresponding to the backslopes between the summits and

drainage lines.

3.3. Erodibility surface

The correlation analysis of measured soil properties revealed that water aggregate

stability of all size classes had a significant positive correlation, but was not correlated

with infiltration (Table 3). When land uses with a large enough sample size were analyzed

separately (sun coffee, shade coffee and pasture), there was still a positive correlation

among aggregate sizes, but its strength varied among land uses (Table 3). In addition, the

water stability of 1–2 mm aggregates under pasture had a positive correlation with

infiltration. The general trend, however, was for water stable aggregates of all size classes

Table 2

Seasonal EI30 for pluviographic stations

Station n Seasonal EI30 (MJ mm ha�1 h�1 season�1)a

Dry season 1 Wet season 1 Dry season 2 Wet season 2

Catalina 43 1947 a 3220 b 1854 a 3345 b

Jazmın 43 3004 ab 4528 a 2242 b 4310 a

Cenicafe 43 3319 4952 3131 4670

Naranjal 43 3006 a 5183 b 3409 ab 4350 ab

Pta. Tratamiento 43 2895 a 4908 b 2566 a 4673 b

Cedral 42 2920 a 3384 a 819 b 4298 a

Values followed by different letters within the same line are significantly different from each other (a =0.05,Tukey–Kramer test). Location of stations shown in Fig. 1.a Dry season 1=December–February, Wet season 1=March–May, Dry season 2=June–August, Wet season

2=September–November.

Table 3

Significant correlation coefficients (Spearman’s r) among water stable aggregates and infiltration (*a =0.05,**a =0.10, n =89)

Variable Water stable aggregates (%)

N2 mm 1–2 mm b1 mm

Water stable aggregates (%)

N2 mm –

1–2 mm 0.63* (all) –

0.55* (COS)

0.89* (COSSH)

0.38** (PA)

b1 mm 0.51* (all) 0.51* (all) –

0.39* (COS) 0.40* (COS)

0.64* (COSSH) 0.71* (COSSH)

0.36** (PA) 0.37** (PA)

Infiltration (cm h�1) 0.19** (all) 0.19** (all)

0.55*(PA)

COS=sun coffee (n =41), COSSH=shade coffee (n =13) and PA=pasture (n =23).

N. Hoyos / Catena 63 (2005) 85–10896

to be positively correlated, while little or no correlation existed between water stable

aggregates and infiltration.

Land use influenced the water stability of aggregates N2 mm and infiltration, but did

not affect the stability of smaller aggregates. Average water stability of aggregates N2 mm

was above 80% for all land uses (Fig. 4), being greatest under planted forest (99.3%) and

lowest under shrub (82.5%). Both values may have been affected by the small number of

samples (3 and 2) and by a rocky substrate in one of the shrub sites. The same is true for

natural forest, with only 3 sampling locations and one of them on rocky substrate, with

particularly low values of water stable aggregates N2 mm (71.2% versus 100% at the other

two forest locations).

Infiltration was significantly different across two land use groups (Fig. 5a), being higher

under forests and coffee and lower under pasture and pasture-shrub. There was however,

great variability within each land use. For example, sun coffee, with 41 samples, had

infiltration values ranging from 1.2 cm h�1 to 148.5 cm h�1. Sixty-eight of the sampling

locations were within Pta. Tratamiento station’s area, with a mean maximum 30-min

intensity of 28.6 mm h�1. The remaining 21 locations were assigned to Jazmın, with a mean

maximum 30-min intensity of 27.8 mm h�1. The probability of exceeding these thresholds

was affected by land use, but there was also large variability within each category (Fig. 5b).

Two properties were selected to generate the erodibility surface, water stability of

aggregates N2 mm and infiltration. These were chosen because they did not provide

redundant information (not correlated), and were affected by land use. Since there was

large variability within each land use, particularly for infiltration, three surfaces were

generated (Table 4):

! Average erodibility: each land use had an erodibility value calculated as the product of

the average percent of water stable aggregates N2 mm and average probability of not

exceeding the maximum 30-min intensity.

0

25

50

75

100

Wat

er s

tabl

e ag

greg

ates

>2

mm

(%

)

a ab abc bcd cdd

COS COSSHNFPF PA PASHR SHR

abca c

Fig. 4. Effect of land use on water stable aggregates N2 mm. All samples taken at 0–20 cm depth. Error bars

represent standard error. Land uses under same letter are not significantly different from each other (a =0.10,Kruskal–Wallis Z-test).

N. Hoyos / Catena 63 (2005) 85–108 97

! Low erodibility: each land use was assigned an erodibility value calculated as the

product of the 75th percentile for water stable aggregates N2 mm and the 75th

percentile for probability of not exceeding the maximum 30-min intensity (high water

aggregate stability and low probability of runoff).

! High erodibility: each land use was assigned an erodibility value calculated as the

product of the 25th percentile for water stable aggregates N2 mm and the 25th

a

b b

NF

a

aa

ab

NF PF0

100

200

300

400

500

COS COSSHPF PA PASHRSHR

Land use

Itnf

iltr

atio

n (c

m h

-1)

(a) (b)

0.0

0.2

0.4

0.6

0.8

1.0

COS COSSHPAPASHR SHR

Pro

babi

lity

of

exce

edin

gth

e in

filtr

atio

n ra

te

a

ab

bcd

ccd

dc

Fig. 5. Effect of land use on (a) infiltration (cm h�1) and (b) probability of exceeding the maximum 30-min

intensity. Error bars represent standard deviation. Land uses under same letter are not significantly different from

each other (a =0.10, Tukey–Kramer and Kruskal–Wallis Z-test).

Table 4

Values for average, low and high erodibility (Kavg, Klow, Khigh in t ha h ha�1 MJ�1 mm�1)

Land use Average erodibility Low erodibility High erodibility

WSAN2 P Kavg WSAN2 P Klow WSAN2 P Khigh

Coffee-sun 95.6 0.8792 0.0010 100.0 0.9999 0.0009 93.1 0.9001 0.0010

Coffee-shade 90.7 0.9694 0.0010 99.2 0.9999 0.0009 83.9 0.9850 0.0010

Pasture 95.4 0.6637 0.0011 100.0 0.9852 0.0009 92.0 0.4204 0.0012

Pasture-shrub 92.1 0.3058 0.0013 97.0 0.7057 0.0011 84.5 0.0378 0.0014

Shrub 82.6 0.9672 0.0010 90.0 0.9999 0.0010 75.1 0.9345 0.0011

Natural forest 90.4 0.8947 0.0010 100.0 0.9999 0.0009 71.2 0.6842 0.0012

Planted forest 99.3 0.9999 0.0009 100.0 0.9999 0.0009 97.8 0.9999 0.0009

Numbers are based on water stable aggregates N2 mm (WSAN2) and the probability ( P) that the maximum 30-

min rainfall intensity does not exceed the saturated infiltration rate.

N. Hoyos / Catena 63 (2005) 85–10898

percentile for probability of not exceeding the maximum 30-min intensity (low water

aggregate stability and high probability of runoff).

Finally, these values were scaled to a range of 0.0009–0.0014 t ha h ha�1 MJ�1 mm�1

found by Hincapie et al. (2000) on field runoff plots on bare soil from this same soil unit.

Fig. 6. Soil erosion potential under average erodibility conditions for the (a) dry seasons (t ha�1 year�1), (b) wet

seasons (t ha�1 season�1), and (c) annual summary (t ha�1 year�1). Categories defined by quintile values as

indicated in the legend. Transverse Mercator Projection with origin at 4835V56W N and 77804V51WW, International

Spheroid 1924, Bogota Observatory Datum.

N. Hoyos / Catena 63 (2005) 85–108 99

3.4. Erosion potential surfaces

Seasonal erosion potential values were grouped into five ordinal classes defined by the

average quintile value (Fig. 6a and b):

! Minimal: 0–0.2 t ha�1 season�1

! Low: 0.2–0.7 t ha�1 season�1

! Medium: 0.7–1.7 t ha�1 season�1

! High: 1.7–3.5 t ha�1 season�1

! Extreme: 3.5–10.0 t ha�1 season�1

Maps showed particularly well the influence of land use, topography and rainfall

seasonality, on erosion potential. In general, minimal and low erosion potential occurred in

areas under forest, shrub, and pasture-shrub, regardless of the relief and erodibility. Areas

under pasture and coffee (sun and shade) showed the combined effects of the cover

management factor and the slope length and steepness factor. Minimal and low erosion

potential was present under these land uses when the slope length and steepness factor was

also low, but increased with higher LS values. There were few cells with extreme erosion

potential in the dry seasons, and these were usually restricted to corridors along stream

channels on coffee and pasture with very high LS values. In the wet seasons, extreme

erosion potential comprised larger areas on coffee and pasture along the drainage network

(high LS values).

The annual erosion potential surface had a spatial pattern similar to the seasonal ones

(Fig. 6c). Categories were also defined by quintiles as follows:

! Minimal: 0–0.7 t ha�1 year�1

! Low: 0.7–2.7 t ha�1 year�1

! Medium: 2.7–7.0 t ha�1 year�1

Table 5

Land area (ha) under each erosion potential category for all model scenarios

Erodibility scenario Erosion potential category Wet seasons (ha)a Dry seasons (ha)a

t ha�1 season�1 Category

Average 0–0.7 Minimal–low 1725.4 (36.4%) 2239.5 (47.2%)

0.7–3.5 Medium–high 1667.9 (35.2%) 1968.1 (41.5%)

N3.5 Extreme 1348.3 (28.4%) 534.1 (11.3%)

Low 0–0.7 Minimal–low 1834.1 (38.7%) 2371.6 (50.0%)

0.7–3.5 Medium–high 1709.8 (36.1%) 1945.6 (41.0%)

N3.5 Extreme 1197.8 (25.3%) 424.4 (8.9%)

High 0–0.7 Minimal–low 1693.4 (35.7%) 2211.4 (46.6%)

0.7–3.5 Medium–high 1692.4 (35.7%) 1994.6 (42.1%)

N3.5 Extreme 1355.9 (28.6%) 535.6 (11.3%)

Categories have been grouped for easiness of interpretation. Wet seasons: March–May and September–

November. Dry seasons: December–February and June–August.a In parenthesis, percent out of 4742 ha (basin’s rural area minus discarded rural land uses and 25 m strip along

watershed divide removed during LS surface generation).

N. Hoyos / Catena 63 (2005) 85–108100

! High: 7.0–14.4 t ha�1 year�1

! Extreme: N14.4 t ha�1 year�1

Erosion potential was clearly affected by seasonality, and to a lesser extent by

erodibility (Table 5). There was consistently more land below the high erosion potential

category during the dry seasons. By comparison, during the wet seasons there were

between 2 and 3 times more hectares with extreme erosion potential. The effect of

erodibility was less evident. Still, under the high erodibility scenario, there was between

1.1 (wet seasons) and 1.3 (dry seasons) more land under the extreme erosion potential

category than under the low erodibility scenario.

4. Discussion

4.1. Model results

Although many assumptions were made in this study, the erosion potential values

obtained were reasonable when compared to measured soil losses from erosion plots

under similar conditions. Studies in Colombia, Venezuela and Indonesia on runoff plots

measured soil losses ranging from 0.2 to 8.9 t ha�1 year�1 in established coffee

plantations (Suarez de Castro, 1953; Suarez de Castro and Rodrıguez, 1962; Ataroff and

Monasterio, 1997; Ijima et al., 2003; Table 6). These studies found that soil losses under

both coffee systems were low after the plantation became established, and that pasture

was very effective in preventing soil loss but generated greater runoff than coffee due to

lower infiltration. In addition, a large proportion of the soil loss took place during a few

Table 6

Soil losses from studies on runoff plots under similar conditions

Land use Location Plot characteristics Averagea Sourceb

Slope

(%)

Measurement

time (years)

Area

(m2)

Soil loss

(t ha�1 year�1)

Shade coffee Established Colombia 53 8 90 0.2–1.1 (a)

Established Colombia 10–60 2 6000 10.4 (b)

Established Venezuela 60 2 12 0.6 (c)

Recent Colombia 45 8 120 0.6–4.8 (a)

Sun coffee Established Venezuela 60 2 12 1.2 (c)

Recent Venezuela 60 1 12 3.2 (c)

Recent Indonesia 27 4 108 2.0–8.9c (d)

Pasture Colombia – 2 2500 0.5 (b)

Pasture-corn rotation Colombia 21 8 10–40 34.0–61.4 (a)

Bare soil Colombia 21 8 30 514.0–873.3 (a)

a Ranges correspond to the minimum and maximum values from different plots.b (a) Suarez de Castro and Rodrıguez (1962), (b) Suarez de Castro (1953), (c) Ataroff and Monasterio (1997);

(d) Ijima et al. (2003).c Under various treatments: tillage, no-tillage, alley cropping and no alley cropping.

N. Hoyos / Catena 63 (2005) 85–108 101

large and intense rainfall events, which should be kept in mind when interpreting the

average values predicted by RUSLE.

The erosion potential model for the Dosquebradas basin indicated that 50% of the area

planted in coffee had values below 9 t ha�1 year�1. The maximum value, however, was

very high, reaching 300 t ha�1 year�1. By comparison, 50% of the land on pasture was

below 2 t ha�1 year�1, and the maximum value was 134 t ha�1 year�1. Higher median

and maximum values under coffee are the product of higher C values, as well as higher

LS values related to the steeper slopes found under this land use relative to pasture (Fig.

7). Overall, a large percentage (70%) of the area analyzed was below the frequently cited

soil loss tolerance value (10–11 t ha�1 year�1), described as the maximum permissible

rate of erosion at which soil fertility can be maintained over 20 to 25 years (Morgan,

1995). This tolerance value, however, varies depending on factors such as the rate of soil

formation, subsoil characteristics, and effects of erosion on soil productivity and water

quality (Wischmeier and Smith, 1965; El-Swaify and Dangler, 1982; Morgan, 1995).

Specific aspects of tropical mountainous regions that need to be considered include

rainfall aggressiveness and steep topography. Soil formation may also be limited by the

supply of parent material which is the case in the study area where soils form through

weathering of volcanic ash. Studies have identified at least six paleosols, the upper three

being younger than 10,930F65 years BP (Toro et al., 2001). Unfortunately, these data

are not detailed enough to infer formation rates for the most recent soil layer. Restrepo

and Kjerfve (2000) estimated an average sediment yield of 5.6 t ha�1 year�1 for the

entire Magdalena Basin of Colombia (daily data 1975–1995), which contains the study

area. According to this value, they placed the Magdalena as the river with highest

sediment yield along the Caribbean and Atlantic coasts of South America, even

surpassing the Amazon (1.9 t ha�1 year�1, Milliman and Syvitski, 1992). Such results

Perc

ent o

f to

tal l

and

use

area

0

5

10

15

20

25

30

0-10

10-2

0

20-3

0

30-4

0

40-5

0

50-6

0

60-7

0

70-8

0

80-9

0

90-1

00

100-

110

110-

120

Slope (%)

Coffee (sun and shade)

Pasture

Land use

Fig. 7. Land (%) under coffee and pasture discriminated by slope categories. Percent calculated from total land

under each land use.

N. Hoyos / Catena 63 (2005) 85–108102

suggest that a threshold lower than 10 t ha�1 year�1 may better represent the conditions

of the study area.

4.2. Relative importance of erosion factors

The strength of this model relies on the relative spatial differences rather than on exact

values. Therefore, the resulting patterns of erosion potential should indicate which factors

are more or less influential and under what conditions. The spatial distribution of the

erosion potential categories showed the effects of:

! The cover management factor, which was particularly important in areas under forest,

shrub and pasture-shrub where it minimized the effect of topography. Agricultural land

uses (coffee and pasture) showed a more dynamic interaction with topography. The

similarity of C values for shade and sun coffee lead to similar, low erosion potential

values. This was explained by both being the average over a long period (8 years);

nevertheless, the sun coffee factor was 17% higher than the shade coffee factor. This

difference came from different agricultural practices, mainly weeding with soil

disturbance in sun coffee, versus no soil disturbance under shade coffee.

! The topographic factor was reflected by the similar spatial distribution of erosion

potential (in pasture and coffee) with the LS surface. Quantification of the relation

between topography and erosion potential was performed by generating 1000 random

points within the basin coordinates, 506 of which were located in the basin’s rural area.

LS and erosion potential values for these points yielded correlation coefficients

(Spearman r) ranging from 0.57 to 0.59 (a =0.01). The LS surface also showed the

effect of both subfactors, the slope steepness (S) and the contributing area (L). Slope

steepness was the major control on LS except in areas of flow concentration where L

values were very high (along stream channels).

! The effect of the erosivity factor was evident as there was more area in the extreme

erosion potential category during the wet seasons. This effect varied with land use,

affecting most areas in pasture and coffee regardless of their topography, but only areas

with high (N25) LS values under forests, shrub and pasture-shrub.

Although there was an erodibility effect as shown by the results (Table 5), it was

spatially difficult to differentiate because it had the same spatial distribution as the cover

factor, and a very narrow numerical range.

4.3. Sources of error and assessment

In addition to the intrinsic model limitations already mentioned, each of the layers had

errors associated with its data source and generation technique. Their combined effect on

the model results would be multiplicative, since erosion potential was calculated as the

product of all layers. On the other hand, the use of local data whenever possible, instead of

extrapolations, was intended to maintain relative differences in the model outcome as close

to reality as possible. Furthermore, model results were pooled into qualitative categories in

order to keep the focus on relative differences instead of numerical values. Although it was

N. Hoyos / Catena 63 (2005) 85–108 103

not possible to quantify the overall effect of each surface’s error on the model outcome, an

independent error assessment of each layer provided an idea of the uncertainty associated

with the model results.

The erosivity surfaces had a prediction error associated with the interpolation

technique, with maximum values of 43% in the wet seasons and 69% in the dry

seasons. Both of these values were returned from stations located at the edges of the

interpolation surfaces (16 km and 22 km from the watershed divide) and for this

reason were not expected to have a significant effect within the basin. The second

largest interpolation errors were in the order of 28% (wet seasons) and 24% (dry

seasons) at stations near the northern watershed divide (1.5 km and 4 km,

respectively). Over- and underprediction at these stations seemed to result from a

combination of interannual rainfall variability and local topographic effects. There was

also uncertainty associated with the low number of stations used to generate the

interpolation surface (16).

The final precision of the slope length and steepness factor surface was a function of

the accuracy of the DEM and the algorithms used to calculate flow, slope length and

slope steepness. To assess the precision of the digital contours, three random sections of

10 km2 each were printed on transparencies and overlaid on the original paper maps. A

distance of 1–2 mm between digitized and original contours was found in localized

sections that represented no more than 5% of the area evaluated. Finally, quantification of

the error associated with the algorithms used to generate the surface was not possible.

Nevertheless, a visual assessment of the topographic map and the LS surface indicated a

good correspondence between ridges and low LS values, and depressions and high LS

values.

The land cover and management factor surface was developed from the digital land

use map provided by CARDER (1997). Five categories were used, sun coffee, shade

coffee, pasture, pasture-shrub and shrub-forests. Error assessment of this map was carried

out in two ways. First, a visual assessment during the fieldwork of June–August 2002

confirmed by interviews with local residents was used to correct a few major

inconsistencies. Second, random points were generated within the coordinates of the

study area and exported as a point layer within the GIS. Then, approximately 30 points

within each category in the land use map were compared with the actual land use in the

aerial photos used to generate the map. These photos were taken in July 1997, with a

scale of 1:21,000 to 1:22,000 (Area Metropolitana Pereira-Dosquebradas, 1997). This

assessment showed accuracies ranging from 75% (sun coffee) to 91% (pasture). The

erodibility surface was affected by the same error of the land cover surface (as it was

based on it), and by the inaccuracy of the field infiltration measurements, which were a

rough estimate.

Finally, the chosen cell size (25 m) would also affect the accuracy of model predictions.

This cell size was selected based on the original topographic and land use data. Although

there is no hard rule to determine the appropriate cell size, a common procedure is to set it

at half the distance of the smallest feature to be represented. The smallest topographic

features (e.g. hilltops) and land use patches (shade coffee) had dimensions close to 50 m,

so the cell size was set to 25 m. A visual assessment of the elevation and land use grids

indicated that this resolution adequately represented contours and land use polygons.

N. Hoyos / Catena 63 (2005) 85–108104

Model predictions, however, would have the limitations inherent to the original data,

particularly topography with a fairly coarse scale (1:25,000) for a landscape where relief is

highly variable.

4.4. Implication for current land use change trends

During the fieldwork of the summer of 2002, two major trends on coffee plots were

observed. First, many were completely abandoned, or received few agricultural practices

(no fertilization or harvesting). Second, several pasture areas had been recently (past 5

years) established from previous coffee plots. Conversations with local environmental

officers confirmed that one of the major land use changes at the regional scale was the

conversion of coffee to pasture (Orozco, 2002, pers. commun.). Data from other coffee

producing municipalities throughout the Colombian Andes show this same trend starting

in the late 1990s (Guhl, 2003, pers. commun.).

To understand the effects of this land use change on erosion potential, two model

scenarios were run based on the current distribution of land use and topography. The

current land use distribution has pasture areas on lower slopes than coffee, with

approximately half of the pasture on slopes lower than 20% (Fig. 7). Accordingly, the

modeled scenarios included (a) conversion of sun and shade coffee plots on land with

slopes lower than 20% to pasture, and (b) conversion of sun and shade coffee plots on

slopes lower than 50% to pasture. The model was then run for the wet and dry seasons,

with average soil erodibility conditions. Results showed that under both scenarios and for

both seasons, the percent of land in the minimal–low (b0.7 t ha�1 season�1) erosion

0

10

20

30

40

50

60

70

Min

imal

-low

Med

ium

-hig

h

Ext

rem

e

Wet season

Min

imal

-low

Med

ium

-hig

h

Ext

rem

e

Dry season

current

coffee on slopes lowerthan 20% to pasture

Land use scenario

coffee on slopes lowerthan 50% to pasture

Erosion potential category

Per

cent

of

anal

yzed

are

a

Fig. 8. Effect of coffee conversion to pasture on seasonal soil erosion potential (t ha�1 season�1) under average

erodibility conditions.

N. Hoyos / Catena 63 (2005) 85–108 105

potential categories increased while that in the extreme category (N3.5 t ha�1 season�1)

decreased (Fig. 8). They also suggested that the protective effect of the pasture cover

(lower C value) would override its higher erodibility (higher K value due to lower

infiltration). Other studies at the runoff plot and basin level have shown minimal erosion

potential under well managed pasture (Suarez de Castro and Rodrıguez, 1962; Veloz and

Logan, 1988; Mati et al., 2000). On the other hand, if water movement, sediment delivery

and land use spatial distribution were considered, results may differ. Although increased

runoff generation was indirectly included in the erodibility factor, its effect on soil

detachment from neighboring cells and sediment delivery to streams was not analyzed.

5. Summary and conclusions

The modeling of soil erosion potential for this basin provided several insights into the

interactions among erosion factors in a tropical, mountainous environment. The cover and

management factor was particularly important in areas under forest, shrub and pasture-

shrub. Its numerical value for these covers was so low that the resulting erosion potential

was low regardless of the topography and erosivity. This point should be kept in mind

when considering areas for conservation. For example, two of the sampled forest areas

were located on very steep slopes, with thin soils underlaid by weathered, fragmented

bedrock. Under such conditions, forest would probably be the only appropriate cover to

maintain soil stability. The influence of topography was evident in areas with agricultural

land uses (coffee and pasture). Land use conversion to less protective covers such as

annual crops, would require use of soil conservation measures if the impact of topography

and climate on erosion potential is to remain minimized.

Modeling of land use change scenarios that are likely to happen given the current

trends, indicated that the erosion potential of the basin would decrease as a result of coffee

conversion to pasture. These results must be interpreted in the context of other issues such

as the gradual deterioration of soil structure under pasture, greater sediment production

due to higher runoff (associated with pasture), and socioeconomic effects. Major changes

on labor availability and land tenure may be expected as coffee usually requires intensive

labor and is planted on smaller plots (b3 ha for most farms at the state level; FNC, 1997)

compared to pasture.

Acknowledgements

I would like to thank A. Jaramillo from CENICAFE for the pluviographic data, R.

Ariza for the information on local cultivation practices, G. Weesies for the C factor values

for the coffee systems and J. Orozco from CARDER for the digital topographic and land

use data. Thanks also to P. Waylen and J. Southworth for their valuable comments on this

manuscript. Funds for this study were provided by the Tropical Conservation and

Development Program and College of Liberal Arts and Sciences of the University of

Florida, the Colombian Institute for Science and Technology COLCIENCIAS, the Tinker

Foundation and LASPAU.

N. Hoyos / Catena 63 (2005) 85–108106

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