Conservation strategies for effective land management of protected areas using an erosion prediction information system (EPIS)

Journal of Environmental Management (2001) 61, 329–343 doi:10.1006/jema.2000.0415, available online at http://www.idealibrary.com on

Conservation strategies for effective land management of protected areas using an erosion prediction information system (EPIS) A. A. Millward† and J. E. Mersey‡*

This research demonstrates the predictive modeling capabilities of a geographic information system (GIS)-based soil erosion potential model to assess the effects of implementing land use change within a tropical watershed. The Revised Universal Soil Loss Equation (RUSLE) was integrated with a GIS to produce an Erosion Prediction Information System (EPIS) and modified to reflect conditions found in the mountainous tropics. Research was conducted in the Zenzontla subcatchment of the RKıo AyuquKıla, located ´ Biosphere Reserve (SMBR), Mexico. ´ within the Sierra de Manantlan Expanding agricultural activities within this area will accentuate the already high rate of soil erosion and resultant sediment loading occurring in the RKıo AyuquKıla. Two land-use change scenarios are modeled with the EPIS: (1) implementation of soil conservation practices in erosion prone locations; and (2) selection of sites for agricultural expansion which minimize potential soil loss. Confronted with limited financial resources and the necessity for expedient action, managers of the SMBR can draw upon the predictive capacity of the EPIS to facilitate rapid and informed land-use planning decisions.  2001 Academic Press

Keywords: soil conservation, erosion prediction, land management, GIS modeling, Mexico.

Introduction In recent decades, land conversion has accelerated within many tropical countries, often in an unchecked fashion (Miller, 1993; Landa et al., 1997). Land transformation is most prevalent in rural areas where expansion of unsustainable agriculture practices are contributing to an unprecedented rate of land degradation (Guzman, 1991; Van der Zaag, 1992; Vazquez-Garcia, 1993; Aide and Cavelier, 1994; Aide et al., 1995; Landa et al., 1997). Conversion of land from forest to agriculture has created, and continues to produce, many on- and offsite problems for rural people living in tropical watersheds. One of the most destructive and insidious processes, occurring as a result of this anthropogenic activity, is soil erosion (Lal, 1977; Chonghuan and Lixian, 1992; Miller, 1993; Morgan, 1995; Landa et al., 1997). 0301–4797/01/040329C15 $35.00/0

In this study, a soil erosion potential model is integrated with a geographic information system (GIS) to yield a valuable watershedbased planning aid (EPIS) for land managers of protected areas. Land-management initiatives in protected areas, such as Biosphere Reserves, have as their goal the development of sustainable management plans which balance environmental conservation with the social and economic development of their inhabitants (Laboratorio Natural Las ´ 1989). With Joyas de la Sierra de Manantlan, limited financial resources for restoration or the implementation of best management practices (BMPs), it is imperative that each new land conversion activity be undertaken in an area that mitigates undesirable outcomes such as soil erosion. The soil erosion potential model used in this research is based upon the Revised Universal Soil Loss Equation (RUSLE), with several modifications made to accommodate

Ł Corresponding author † Department of Geography, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 ‡ Department of Geography, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Received 17 April 2000; accepted 26 December 2000  2001 Academic Press

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the mountainous terrain and wet/dry precipitation regime of the study site (Millward and Mersey, 1999). Other researchers (Mulengera and Payton, 1999; Kinnell, 2000; Van Oost et al., 2000) also recognize the importance of refining the components of the RUSLE model to reflect the biophysical characteristics present in different environments. The integration of the modified RUSLE with the IDRISI GIS constitutes an Erosion Prediction Information System (EPIS). With site specific data, the EPIS can evaluate the effects of conservation-oriented changes to the model variables (such as land-cover or agricultural practices) on soil erosion within the watershed. For example, the model can be queried to identify those land areas currently under agriculture, with a high or extreme soil erosion potential ranking. The effects of implementing BMPs (leaving crop residue on the fields after harvest, or employing contouring or terracing techniques) on the resulting soil erosion potential values can then be predicted, along with the optimum timing (wet or dry season) of these activities.

USA

Study site characteristics The study site for this research is the Zenzontla subcatchment of the R´ıo Ayuqu´ıla ´ watershed, within the Sierra de Manantlan Biosphere Reserve (SMBR) (Figure 1). The SMBR, located in southwestern M´exico, represents a partnership among the federal, state and municipal governments (Laboratorio Natural Las Joyas de la Sierra de ´ 1989). It was established in 1987 Manantlan, to honour a land-use-management plan based upon guidelines provided by the World Conservation Strategy and the UNESCO Man and Biosphere (MAB) program (Laboratorio ´ Natural Las Joyas de la Sierra de Manantlan, 1989; Guzman and Iltis, 1991; Martinez R., pers. comm., 1997). From the perspective of agricultural productivity, the Zenzontla subcatchment is not overly fertile, with the exception of the floodplain area along the R´ıo Ayuqu´ıla and the land abutting its many tributaries (Martinez R., pers. comm. 1997). Further agricultural expansion must encroach on areas where

' Sierra de Manantlan Biosphere Reserve

Jalisco

MEXICO

Colima

' Sierra de Manantlan Biosphere Reserve Study Site

Guadalajara

Mexico City

0

Figure 1.

1000 km

´ Biosphere Reserve. Location of the study site in the Sierra de Manantlan

Conservation strategies using an EPIS

steep slopes render the land marginal for cultivation. Land not used to grow crops or graze cattle is covered with a combination of plant aggregations ranging from tropical scrub to pine-oak forest (Jardel et al., 1992). Elevation in this rugged area ranges from 760–2540 m above sea level. The site’s tropical wet–dry climatic zoning (Aw-K¨oppen Climatic Classification) is characterized by a distinct wet period from mid-May to mid-October. Annual precipitation in the area ranges between 903 and 1169 mm. Agrarian activities typify the livelihood of many rural people within the SMBR. These activities occur mostly for subsistence; maize, sugar cane, some vegetables and a few specialty items are the main crops cultivated (Van der Zaag, 1992; Gerritsen, 1995; Landa et al., 1997).

Land-use-management issues ´ in the Sierra de Manantlan Biosphere Reserve M´exico has a long history of converting land from forest to agriculture and pasture (Lal, 1977; Laboratorio Natural Las Joyas de la ´ Sierra de Manantlan, 1989; Miller, 1993; Gerritsen, 1995; Landa et al., 1997). In addition to direct forest clearance, many treed areas are often unintentionally destroyed by unruly fires which are set by farmers clearing crop residue prior to planting (Martinez R., pers. comm., 1997). The inherent effects of a denuded watershed on soil erosion, soil fertility and sediment loading in the R´ıo Ayuqu´ıla, are generating increasing concern among residents and land managers in the SMBR (Laboratorio Natural Las Joyas de la Sierra ´ de Manantlan, 1989; Van der Zaag, 1992; Gerritsen, 1995; Martinez R., pers. comm., 1997). Poverty and lack of political influence characterize many rural residents of tropical areas (Gerritsen, 1995). Under such stressed conditions, land tenure becomes an important consideration when reckoning the potential success of soil conservation initiatives (Guzman, 1991). Contemporary M´exico has in excess of half its land area in what is termed an ejido jurisdiction (Guzman, 1991; Martinez R., pers. comm., 1997). An ejido represents a fixed area of land, defined by a

presidential decree, encompassing a communal area where a designated group of people undertake the majority of their productive activities (agrarian or otherwise) (Guzman, 1991). In M´exico most protected natural areas, including the SMBR, fall within the jurisdictional boundaries of ejidos (Guzman, 1991; Martinez R., pers. comm., 1997). This often presents a considerable challenge when attempting to plan and implement conservation initiatives. Since many internal boundaries are undefined (Guzman, 1991), conflicts over land use and rights to land frequently arise among residents (Gerritsen, 1995; Martinez R., pers. comm., 1997). The Laboratorio Natural Las Joyas de la Sierra de ´ (1989) indicates that undefined Manantlan property boundaries, poverty and political impotence have allowed for the growth and perpetuation of venturous land-use practices which have contributed markedly to resource exploitation and deterioration in the SMBR.

Modeling soil erosion potential: the evolution of EPIS The RUSLE model takes the following form (Renard et al., 1997): ADLSÐRÐKÐCÐP

.1/

where A is the estimation of average annual soil loss (t ha 1 yr 1 ) caused by sheet and rill erosion; LS is the combination of the slope steepness (S) and slope length (L) sub-factors (unitless); R is the rainfall erosivity factor (MJ mm ha 1 h 1 yr 1 ) which accounts for the energy and intensity of rainstorms; K is the soil erodibility factor (t ha h ha 1 MJ 1 mm 1 ) which is a measure of the susceptibility of soil to erode under a standard condition; C is the cover and management factor which estimates the soil loss ratio at seasonal intervals throughout the year, accounting for effects of prior land use, canopy (forest or crop), surface cover, surface roughness and soil moisture; P is the support practice factor, calculated as a soil loss ratio, which accounts for tillage techniques, stripcropping and terracing (land under cultivation), and understory burning, cattle grazing and road construction (land under forested canopy).

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A. A. Millward and J. E. Mersey Table 1. Derivation of the ordinal categories of soil erosion potential and the area and proportion of each category during the wet and dry season Numeric range (t ha 1 yr 1 )

Erosion potential

Dry season Wet season

0 to 1 >1 to <10 >10 to <30 >30 to <100 >100

No data 391 Minimal 2821 Low 1805 Moderate 251 High 64 Extreme 4

ha

%

ha

%

7 53 34 5 1 0

391 662 1629 1126 858 670

7 12 31 21 16 13

Extending the RUSLE to operate within a raster GIS allows values for A to be calculated for each 25 m2 pixel or cell within the Zenzontla catchment (Millward and Mersey, 1999). This modification gives rise to the EPIS which can be used to study spatial patterns of erosion potential within a catchment. RUSLE (Equation (1)) was run within the GIS on a dry and wet season basis (Millward and Mersey, 1999). The quantitative predictions of soil loss were collapsed into five ordinal classes ranging from minimal to extreme (Table 1) and mapped for the Zenzontla subcatchment (Figure 2). This classification was performed in part to eliminate the sense of precision conveyed by the specific soil loss values, when some data-based error is known to exist (Millward and Mersey, 1999). Also, the model is intended as a management tool, where relative comparisons among land areas are more critical than assessing absolute soil loss in a particular cell. Areas and proportions were tabulated for each of the soil erosion potential categories for both the wet and dry seasons (Table 1).

Modeling biophysical characteristics Primary modifications to the RUSLE model were undertaken through alterations to the R and L sub-factor (Renard et al., 1997; Millward and Mersey, 1999). Specifically, the R-factor was calculated on a seasonal (wet and dry) basis to reflect variations consistent with a tropical monsoon climatic regime and thus to accommodate temporal variability in erosivity. This seasonal calculation considered rainfall intensity and permitted the modeling of soil erosion potential based upon

Figure 2. Soil erosion potential maps for (a) dry season conditions; and (b) wet season conditions.

location-specific and seasonally appropriate delineations (Millward and Mersey, 1999). The precipitation surface was interpolated from 30 climate stations in the Zenzontla vicinity (Millward and Mersey, 1999). Slope length (L), the distance from the origin of overland flow to the point where either the slope gradient decreases causing

Conservation strategies using an EPIS

deposition, or runoff becomes focused into a defined channel, was calculated for the Zenzontla subcatchment using the EPIS (Millward and Mersey, 1999). Generation of the L-factor was performed using the upslope drainage area substitution method (Desmet and Govers, 1996). The ability of this GISbased method to account for complicated flow divergence and convergence patterns within the mountainous terrain of the Zenzontla subcatchment make it a more appropriate choice when compared with the conventional one-dimensional calculation of LS documented in the RUSLE (Renard et al., 1997). The K-factor derivation involved digitizing of 1:50 000 soil maps (Millward and Mersey, 1999) and assigning erodibility values according to soil type and characteristics (Renard et al., 1997).

Modeling the human ecological footprint The cover management factor (C) and the support practice factor (P) reflect various anthropogenic practices that may either act to mitigate, or permit the perpetuation of soil loss (Wischmeier and Smith, 1978; Renard et al., 1997). The cover management factor

(C) reflects the effect of cropping and management practices on the soil erosion rate (Renard et al., 1997). Knowledge of the type of crop, planting preparation, growth and harvest stages were determined for the SMBR by synthesizing information from the Secretaria de Agricultura y Recursos Hidraulicos (1991), Gerritsen (1995) and Martinez R. (pers. comm., 1997) as well as through site visits to the Zenzontla subcatchment during the summers of 1996 and 1997. Maize is the most prevalently grown crop within the Zenzontla subcatchment (Martinez R., pers. comm., 1997). Field visits, and Gerritsen (1995) suggest that meadowless, conventional tillage form the dominant agricultural management practice. The quantity of spring residue left on the fields is often minimal due to its alternative value as livestock forage during the dry season, with any remaining residue burned prior to planting at the end of the dry season (Gerritsen, 1995). A synthesis of this information is presented in Table 2. C-values were allocated according to the seasonal wet and dry distinction (Millward and Mersey, 1999) identified for the Zenzontla vicinity. This was accomplished by proportioning R for each of the wet and dry climatic phases and applying the soil loss ratio (SLR) data for the agricultural class in the manner presented in Table 3.

Table 2. Assignment of SLR values for varying agricultural stages of maize production within the Zenzontla subcatchment Time period Nov. 10–March 10 March 10–April 1 April 1–May 10 May 10–June 20 June 20–Nov. 10

Crop phase

SLR

Harvest to plowing or new seeding Seedbed preparation to 10% canopy cover Growth from 10% cover to 50% Growth from 50% cover to 75% Growth from 75% cover to harvest

0Ð74 0Ð77 0Ð68 0Ð49 0Ð35

Source: Secretaria de Agricultural y Recursos Hidraulicos (1991).

Table 3. Assignment of C-values on the basis of seasonal wet and dry distinctions for the Zenzontla subcatchment Season

Time period

SLR calculation

Proportion of R

C-value

Dry

Nov. 15–May 15

0Ð0639

0Ð0460

Wet

May 15–Nov. 15

.115/180/.0Ð74/C.20/180/.0Ð77/ C.40/180/.0Ð68/C.5/180/.0Ð49/D 0Ð72 .45/180/.0Ð49/C.130/180/.0Ð35/ C.5/180/.0Ð74/D 0Ð40

0Ð9361

0Ð3744

Annual value

0Ð4204

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Individual soil loss ratios were calculated for each time period over which contributing variables are assumed to remain homogeneous. Each of the SLR values was assigned a proportional weight based upon the fraction of the rainfall and runoff factor (R) that corresponded to the temporal period when cover and management remain constant. These proportionally weighted values were then combined to yield an annual average C-factor (Millward and Mersey, 1999). Generation of the land-cover factor for application in the EPIS was conducted with the use of a classified Landsat Thematic Mapper satellite image of the Zenzontla subcatchment and a priori site data (Figure 4(a)). River and shadow areas were removed from subsequent analysis. The effect of contouring and tillage practices on soil erosion is described by the support practice factor (P) within the RUSLE model (Renard et al., 1997). Wischmeier and Smith (1978) define the support practice factor (P) as the ratio of soil loss with a specific support practice or mechanical method of erosion control. The lower the P value the more effective the conservation practice is deemed to be at mitigating soil erosion. Up and down slope cultivation is used as the reference support practice since it reflects a style of agriculture that offers minimal soil conservation (Renard et al., 1997). Support practices affect soil erosion directly by reducing the amount and rate of runoff (Renard and Foster, 1983). P-factor values were introduced in the predictive modeling stage of the EPIS to focus on soil conservation and prevention of sedimentation in the R´ıo Ayuqu´ıla.

A diagnostic tool for criticality: identifying locations of high erosion potential The RUSLE was designed primarily as a long term annual predictor of soil loss and was modified in the EPIS to run predictive soil conservation scenarios (Millward and Mersey, 1999). The EPIS’s factor based design conveniently partitions the variables contributing to soil erosion potential into those that are of anthropogenic origin (C, P), and those that have a biophysical foundation (LS, R, K) (Renard et al., 1997; Millward

and Mersey, 1999). The EPIS permits the isolation of specific areas at a 25 m2 pixel resolution within the larger Zenzontla subcatchment. Under current conditions aggregations of these pixels can be associated with varying degrees of soil loss potential (Figures 2(a) and (b)). Verification of the EPIS by field observations and comparison with GLASOD maps is described in Millward and Mersey (1999). Sites that are potentially contributing large amounts of soil loss within an area can be evaluated easily with the EPIS to determine the major contributing factors. Where these factors relate to anthropogenic influence, the C and/or P-factors may be modified within the model to determine the success, reflected in soil loss mitigation, of implementing changes to human activities. For example, conventional till practices that are oriented in an up and down fashion (Figure 3) could be altered to subdue concentration and channelization of highly erosive overland flow in steeper terrain. An alternate capability of the EPIS is the ability to model hypothetical land use change scenarios. When considering the cover management factor (C) within the Zenzontla subcatchment, agriculturists have the ability to effect change in both cropped and grazed locations. For example, the vegetative canopy cover could be restored to reduce the energy associated with both raindrop impact and overland flow (Laflen et al., 1985; Renard et al., 1997), and therefore decrease erosion potential. The BMP approach to soil conservation ensues by way of mechanical or technological modification to the land (Morgan, 1995). The support practice factor (P) methods of contouring, stripcropping and terracing involve alteration of the field by the agriculturist (Sheng, 1981). Contouring and terracing attempt to modify, to varying degrees, the slope length and slope steepness of existing topography (Sheng, 1981; Renard et al., 1997). These methods are focused on here due to the overwhelming presence of monoculture within the Zenzontla subcatchment (Martinez R., pers. comm., 1997). A contour or contour bank is an earthen or stone ridge designed to channel the flow of runoff before sufficient volume is achieved that could potentially induce erosion. Within the EPIS, the RUSLE bases its mitigative SLR values for contouring on

Conservation strategies using an EPIS

Figure 3.

Field Preparation for maize cultivation in the Zenzontla subcatchment.

those present in its predecessor the USLE (Renard et al., 1997). Additional experimental work was conducted with the application of the physically based CREAMS model (Foster et al., 1981) to determine the success with which this erosion model could predict the reduction in soil loss with the introduction of various support practice factors (Renard et al., 1997). This work validated the SLR values employed by the RUSLE (Renard et al., 1997). Researchers have also found that long slope lengths and high slope steepness characteristics often nullify the positive soil conservation effects of contouring (Wischmeier and Smith, 1978; Sheng, 1981). As well, studies conducted to test the success of contouring under varying conditions of rainfall intensity revealed that this soil conservation technique is much less effective with long storms than with shorter duration events (Jasa et al., 1986). Terracing is an effective means by which to reduce sheet and rill erosion by partitioning a slope into shorter slope lengths, and thereby reducing the slope angle on the plateau areas (Sheng, 1981). Eroded sediment originating from the inter-terrace region (most vulnerable to erosion) may be deposited on the terrace itself. Terraces permit the redistribution of soil around the field, and are very successful at discouraging complete soil loss (Sheng, 1981; Renard et al., 1997). A properly designed terrace has the ability to channel

overland flow, which in the absence of a terrace has tremendous erosive potential, into outlet ditches that can dissipate its kinetic energy to non-erosive levels. Maximum benefits of this conservation technique are witnessed when terraces do not exceed 30 m in width (Renard et al., 1997). The ability of a terrace to promote deposition of sediments transported in overland flow is correlated with the slope angle of the plateau portion (Foster and Ferreira, 1981; Sheng, 1981); with gentler slopes in the inter-terrace zone exhibiting the most appreciable soil conservation success.

Applying the EPIS in the Zenzontla subcatchment The application of the EPIS in evaluating two land use change scenarios, both of which are relevant to future development within the Zenzontla subcatchment, are described. These scenarios are based upon the literature (Guzman,1991; Guzman and Iltis, 1991; Gerritsen, 1995; Jardel, 1995), extensive discussion with Martinez R. (pers. comm., 1997), and site visits to the SMBR during the summers of 1996 and 1997. The first scenario addresses current soil loss conditions within the Zenzontla subcatchment by assessing the success of different mitigative strategies. The

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second deals with considerations of landuse change and the pressures for future agricultural expansion mounting within the SMBR (Gerritsen, 1995; Martinez R., pers. comm., 1997). At present in Zenzontla, a variety of extension, technical assistance and farmer training initiatives have been undertaken, including soil conservation projects (Gerritsen, 1998). The EPIS was developed in collaboration ´ Institute with researchers at the Manantlan of Ecology and Biodiversity (IMECBIO) who recognize the potential of a GIS to assist in the planning and implementation of soil conservation initiatives.

Scenario one: improving agricultural practices in highly erosive locals Erosion prone locals were identified for Zenzontla by aggregating the high and extreme soil erosion potential categories (Figure 2(a)). These consequential locations were then overlaid with the agricultural category of the land cover map (Figure 4(a)). This yielded agricultural areas predicted by the EPIS to be experiencing damaging amounts of soil loss. Image differencing change detection procedures (Eastman, 1997) were employed using the GIS to confirm that all agricultural areas predicted to have damaging erosion rates during the dry season also had identical spatial locations in the wet season (Figures 2(a) and (b)). Therefore, the wet season soil erosion potential surface was adequate for selecting locations for the introduction of BMPs. Figure 5(a) illustrates wet season consequential locals that are currently under agricultural production. Implementation of BMPs was not deemed to be efficient, or financially viable, in areas of the Zenzontla subcatchment that were small and spatially isolated, despite categorization as a consequential local (Figure 5(a)) (Martinez R., pers. comm., 1997). To spatially isolate an area for implementation of a BMP, the GIS was employed to identify three fields of contiguous pixels displaying suitable size characteristics (greater than 25 hectares) (Figure 5(b)). Alteration of the C-factor by leaving crop residue on the field after the harvesting of maize was selected as a modification to

Figure 4. GIS data layers for the Zenzontla subcatchment. (a) land-cover map; (b) digital elevation model.

present conditions. This practice was implemented for each of the three BMP locations; however, given the frequent requirement of this residue to feed cattle in the dry season (Gerritsen, 1995; Martinez R., pers. comm., 1997), only the minimal amount of residue possible for modeling within the RUSLE (Renard et al., 1997) was selected. The

Conservation strategies using an EPIS

Figure 5. Scenario one: (a) agricultural areas with high and extreme soil erosion potential values; (b) locations for the implementation of BMPs.

C-factor was modified to reflect the practice of leaving 2000 kg/ha of crop residue on the field (Table 4). A new cover management layer was developed for the BMP locations based upon the C-factor values derived in Table 4. EPIS was then employed to calculate revised soil erosion potential values for the three areas

employing this cover-management practice (Table 6). As a result of the minimal changes observed with the application of the C-factor in isolation (Table 6), additional support practice options were introduced with the aim of further reducing the predicted erosion potential values. Because slope is an important consideration in the selection of an effective BMP for a particular site (Hudson, 1977; Sheng, 1981; Morgan, 1995), a digital elevation model (DEM) (Figure 4(b)) was converted to a slope map using the GIS (Millward and Mersey, 1999). This slope map was then overlaid with the three locations for implementation of BMPs. Slope statistics were generated for each of the BMP locations (Table 5). Based upon the mean slope and associated standard deviation for each of the BMP areas, suitable soil conservation strategies were selected. For location B (Figure 5(b)), the mean slope is measured to be 6Ð1% with a standard deviation of 5Ð3%. Under these topographic conditions implementation of contouring as a support practice factor was selected (Hudson, 1977; Morgan, 1995; Renard et al., 1997). Field data from a site visit (summer 1997), augmented by conversations with a local land manager (Martinez R., pers. comm., 1997) confirmed that locations near to the Zenzontla subcatchment employed contouring as a support practice factor (Figure 6). By implementing this support practice factor in conjunction with the aforementioned C-factor, the EPIS predicted a potential reduction in soil loss of 22% for location B (Table 6). BMP locations A and C require an alternate support practice factor since average slope angles approach or exceed 9% (Table 5) (Morgan, 1995). The literature (Hudson, 1977; Sheng, 1981; Morgan, 1995) suggests the application of bench terracing under the slope angles found within locations A and C. The drawback to this type of soil conservation initiative is the cost and time involved in implementing the structures (Sheng, 1981). However, the potential for soil loss mitigation is great. Similar to BMP location B, the EPIS was used to predict the P-factor value for implementation of bench terracing at locations A and C. Specifying a terrace spacing of 30 m with closed outlets (Renard et al., 1997), the

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A. A. Millward and J. E. Mersey Table 4. Assignment of C-values with 2000 kg/ha of crop residue left on the field Season

Time period

SLR calculation

Proportion of R

C-value

Dry

Nov. 15–May 15

0Ð0639

0Ð0333

Wet

May 15–Nov. 15

.115/180/.0Ð47/C.20/180/.0Ð68/C .40/180/.0Ð60/C.5/180/.0Ð45/D 0Ð52 .45/180/.0Ð45/C.130/180/.0Ð33/C .5/180/.0Ð47/D 0Ð36

0Ð9361

0Ð3406

Annual value

0Ð3739

Table 5. Scenario one: Slope characteristics of Locations A, B and C Slope characteristics

BMP Location A

B

C

Mean (%) Standard deviation (%) Minimum (%) Maximum (%)

9Ð9 4Ð5 3Ð6 24Ð5

6Ð1 5Ð3 2Ð9 28Ð3

8Ð0 4Ð9 3Ð2 24Ð5

Area (ha)

35

30

41

EPIS predicted a 40% overall reduction in soil loss. Based upon the results presented in Table 6, the identified soil conservation practices were successful at reducing soil loss in each of the three locations (Figure 5(b)). In fact, BMP locations A, B and C moved from 100% of their area contained in high and extreme categories to 58%, 81% and 63%, respectively contained in the minimal and low soil loss categories which is at or below the target soil loss tolerance (10 t ha 1 yr 1 ).

Scenario two: identifying land for conversion from forest to agriculture Rising population, coupled with deteriorating soil will eventually lead to future expansion of

maize-crop agriculture within the Zenzontla subcatchment (Gerritsen, 1995; Martinez R., pers. comm., 1997). Under such conditions, this expansion will occur in areas that are currently under vegetative cover, and not in less fertile pasture areas (Aide et al., 1995). This scenario demonstrates the use of the EPIS to locate new areas for agricultural expansion, that will have the least detrimental effect on the soil. To facilitate planning of agricultural expansion in a fashion sensitive to soil conservation, the EPIS was employed to identify areas presently under forest cover (mesophilic, montane, oak and pine oak), and possessing agriculturally fertile soil (fluvisol, rendzinas and lithosol-rendzinas) (FAO-UNESCO, 1975). These locations were then combined to determine locations within Zenzontla which would be most attractive to farmers for agricultural expansion. Suitable areas were overlaid in the EPIS with the wet season soil erosion potential map (Figure 2(a)) to eliminate all those areas presently exhibiting high or extreme soil loss (Figure 7(a)). To remain consistent with the first scenario, locations of contiguous pixels were sought with areas of at least 25 hectares. Three areas of suitable size emerged from this analysis (Figure 7(b)). The cover management factor (C) for each of

Table 6. Scenario one: Comparison of soil erosion potential for Locations A, B and C before and after the implementation of soil conservation practices Category

BMP Location A (% area)

Minimal Low Moderate High Extreme

B (% area)

C (% area)

Pre

C

C and P

Pre

C

C and P

Pre

C

C and P

0 0 0 25 75

0 0 0 27 73

25 33 14 12 16

0 0 0 42 58

0 0 0 42 58

37 44 11 7 1

0 0 0 24 76

0 0 0 29 71

27 36 12 10 15

Pre, pre soil conservation practice; C, cover management employed; P, support practice employed.

Conservation strategies using an EPIS

Figure 6.

´ Biosphere Reserve. Agricultural contours in the Sierra de Manantlan

these three locations was then changed following Renard et al. (1997) and Martinez R. (pers. comm., 1997) to reflect a shift from forest cover to agricultural production of maize without the use of any soil conservation techniques. The EPIS was re-run to yield new soil erosion potential values for the cells within these three sites. The proportion of the area within the three isolated locations of expansion (Figure 7(b)) covered by each erosion potential class is summarized in Table 7. The results in Table 7 support the selection of location A (Figure 7(b)) for future agricultural expansion. This scenario does not introduce any soil conservation practices in the form of modification to the cover management factor (C) or the support practice factor (P). Therefore, despite the selection of location A, the introduction of a soil conservation practice is highly recommended since soil loss potential for this area is still high during the wet season.

Discussion The seasonal precipitation regime present within the Zenzontla subcatchment has a definite influence on the spatial distribution of

soil erosion potential (Figures 2(a) and (b)). These distinctions are well known to agriculturists within the Zenzontla subcatchment. However, it is essential that their timing and magnitude be documented by land managers as they work with agriculturalists toward realizing the SMBR’s goal (Gerritsen, 1998) of developing a land-use planning model. With greater knowledge of the site-specific conditions, agriculturalists may work with land managers to convey the need for financial assistance from the government of Jalisco to support implementation of BMPs (Martinez R., pers. comm., 1997). When attempting to mitigate soil erosion, knowledge of this seasonal difference is essential for two reasons. Removal of forest for the purpose of agricultural or pastoral expansion could be timed to occur at the beginning of the dry season (mid-November). Removal of vegetative cover during this time period would permit some re-growth of ground cover that would serve as a barrier to rainfall and runoff when the rainy season commences in mid-May. More haphazard planning of forest clearance immediately prior to the rainy season could potentially cause dramatic amounts of soil loss

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Figure 7. Scenario two: (a) areas currently under forest cover, with agriculturally fertile soils and not in high or extreme soil erosion potential classes; (b) locations for potential agricultural expansion.

and corresponding sedimentation of the R´ıo Ayuqu´ıla and its tributaries. The second consideration is reflected in the most appropriate time of year with which to implement a BMP. The initial building of a soil conservation measure, such as contours or terrace structures, inevitably requires that the ground surface be moderately to heavily

impacted (Sheng, 1981). This impact is manifested through the removal of vegetative cover and mechanical alteration to the soil surface (Sheng, 1981; Morgan, 1995). These alterations to soil surface conditions can dramatically enhance soil loss. Furthermore, this effect can be augmented tremendously with increasing slope length and steepnesslocations where support management structures are most urgently needed. Furthermore, maize farmers are frequently either underemployed or unemployed during the dry season (Martinez R., pers. comm., 1997). Logically, the dry season emerges as the most suitable time to implement BMPs from both and environmental and a labor perspective. Mitigating soil loss represents the primary goal of both modifying the cover management (C) and support practice (P) factors within the EPIS. Although it was demonstrated that these measures can be very successful, they are costly (Sheng, 1981) and can require considerable disruption to an agricultural operation. However, since water quality within the R´ıo Ayuqu´ıla watershed is a serious concern for farmers and local communities, prohibiting soil removal and entrainment by runoff should be the foremost consideration of soil conservation initiatives (Lyons and NavarroPerez, 1990; Gerritsen, 1995; Martinez R., pers. comm., 1997). A further means of employing soil conservation and protection of water quality that can be modeled with the EPIS is through promoting the maintenance and enhancement of riparian strips along the R´ıo Ayuqu´ıla and its tributaries. Riparian buffers offer tremendous benefits with respect to reducing off-site deposition of sediment within bodies of water. A densely vegetated soil surface has a high surface roughness value in comparison to heavily grazed pasture or unprotected agricultural field (Tolloner et al., 1976; Marsh, 1987; Vought et al., 1995). It was observed during field visits to the Zenzontla subcatchment (summers 1996 and 1997), that riparian vegetation buffers have generally not been protected from clearance for pasture or agriculture. In fact, one can observe many areas along the R´ıo Ayuqu´ıla and its tributaries where pastoral and agricultural activities directly abut the watercourse. The effects of enhancing riparian vegetation would be reflected in a change to the cover factor in the EPIS model, which could be used to assess

Conservation strategies using an EPIS Table 7. Scenario two: percentage of area within each soil erosion potential category for the three locations for agricultural expansion Erosion potential class Location A (50 ha) B (17 ha) C (47 ha)

Minimal (%)

Low (%)

Moderate (%)

High (%)

Extreme (%)

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

44 19 15

0 0 0

45 52 57

7 12 8

7 20 22

9 0 0

0 9 5

24 4 6

0 0 1

61 84 86

the effectiveness of restoring these areas on mitigating erosion and hence inhibiting of sediment entry to the watercourses. Discussion of utilizing the EPIS to evaluate alternative erosion potential scenarios has focused on manipulating the anthropogenic factors (C and P); however, the model also allows for investigating the effects of modifications to the biophysical components (LS, K, R). For example, should a change in climatic conditions modify the seasonal distribution of rainfall events, corresponding adjustments in the R-values can be calculated. As the EPIS has been developed separately for the wet and dry seasons, timing of land-use activities can be adjusted as well as their spatial location.

Summary and Conclusions Land conversion not only impacts the natural environment, but also affects the lives of residents within the watershed. The process of resource polarization between rich and poor, occurring rapidly in rural Mexico, is accentuated with the deterioration of agricultural fertility in marginal locations (Santana et al., 1989; Gerritsen, 1995; Lubchenco, 1997). The economically disadvantaged must often resort to clearing territories of an even greater marginal nature. With modest funds to direct toward watershed planning and management in rural and remote locations, a method to rapidly appraise erosion potential associated with present and impending agricultural activities is useful. The EPIS was designed to be a location and context relevant model for use by land managers of protected areas in the rural tropics. It is based upon the modification of the RUSLE model, an erosion predictor designed primarily as a soil conservation tool. The EPIS constitutes an important tool to spatially isolate biophysically vulnerable

locations. It represents an accessible model to researchers in the developing world as data requirements are modest and processing is not computationally intensive. Further research into rapid and reliable means of effectively managing land in rural and remote locations may see the functionality of EPIS incorporated into a larger model employing multi-criteria analysis.

References Aide, T. M. and Cavelier, J. (1994). Barriers to lowland tropical forest restoration in the Sierra Nevada de Santa Marta, Columbia. Restoration Ecology 2, 219–229. Aide, T. M., Zimmerman, J. K., Herrera, L., Rosario, M. and Seran, M. (1995). Forest recovery in abandoned tropical pastures in Puerto Rico. Forest Ecology and Management 77, 77–86. Chonghuan, N. and Lixian, W. (1992). A preliminary study of soil erosion and land degradation. In Erosion, Debris Flows and Environment in Mountain Regions Proceedings of the Chengdu Symposium, pp. 439–445. IAHS Publ. no. 209. July 1992. Desmet, P. J. and Govers, G. (1996). A GISprocedure for the automated calculation of the USLE LS-factor on topograpically complex landscape units. Journal of Soil and Water Conservation 51, 427–433. Eastman, R. J. (1997). IDRISI for Windows: User’s Guide, Version 2Ð0. Chapters 4–17. Worcester: Clark Labs for Cartographic Technology and Geographical Analysis. FAO-UNESCO (1975). Soil Map of the World, Volume III: M´exico and Central America, 96 pp. Paris: UNESCO. Foster, G. R. and Ferreira, V. A. (1981). Deposition in uniform grade terrace channels. In Crop Production with Conservation in the 80’s, pp. 185–197. St. Joseph: American Society of Agricultural Engineers. Foster, G. R., McCool, D. K., Renard, K. G. and Moldenhauer, W. C. (1981). Conversion of the universal soil loss equation to SI metric units. Journal of Soil and Water Conservation 36, 355–359. Gerritsen, P. R. W. (1995). Styles of Farming and Forestry: The Case of the Mexican Community of

341

342

A. A. Millward and J. E. Mersey Cuzalapa, 99 pp. Wageningen: Circle of Rural European Studies. Gerritsen, P. R. W. (1998). Community development, natural resource management and biodiversity conservation in the Sierra de Manantl´an Biosphere, Mexico. Community Development Journal 33, 314–324. Guzman, M. R. (1991). Gauging Success in Protecting Mexican Biosphere Reserves, 114 pp. Unpublished MSc. Thesis. University of Wisconsin-Madison. Guzman, M. R. and Iltis, H. H. (1991). Biosphere reserve established in M´exico to protect rare maize relative. Diversity 7, 82–84. Hudson, N. (1977). Soil Conservation, 324 pp. Ithaca: Cornell University Press. Jardel, E. J. (1995). Las areas protegidas en la practica: una discusion sobre conservacion biologica y desarrollo sustentible. Revista de la Universidad de Guadalajara. March–April: 23–26. Jardel, E. J., Gutierrez, R. and Leon, P. (1992). Conservacion de la diversidad biologica y problematica agraria en la Reserva de la Biosfera ´ Sierra de Manantlan. In Las Areas Naturales Protegidas de M´exico (A. L. Anaya, ed.), pp. 129–151. M´exico, D.F.: UNAM-SEDUESEP. Jasa, P. J., Dickey, E. C. and Shelton, D. P. (1986). Soil erosion from tillage and planting systems used in soybean residue: part 2. Influences of row direction. Transactions of the American Society of Agricultural Engineers 29, 761–766. Kinnell, P. I. A. (2000). AGNPS-UM: applying the USLE-M within the agricultural non point source pollution model. Environment Modelling and Software 15, 331–341. Laboratorio Natural Las Joyas de la Sierra de ´ Manantlan. (1989). The Sierra de Manantlan ´ Biosphere Reserve and the Laboratorio Natural Las Joyas, 7 pp. Guadalajara. Laflen, J. M., Foster, G. R. and Onstad, C. A. (1985). Simulation of individual storm soil loss for modeling the impact of soil erosion on crop productivity. In Soil Erosion and Conservation (S. A. El-Swaify, W. C. Moldernhauer and A. Lo, eds), pp. 285–295. Ankeny, IA: Soil Conservation Society of America. Lal, R. (1977). Review of soil erosion research in Latin American. In Soil Conservation and Management in the Humid Tropics (D. J. Greenland and R. Lal, eds), pp. 331–340. New York: John Wiley and Sons. Landa, R., Meave, J. and Carabias, J. (1997). Environmental deterioration in rural Mexico: an examination of the concept. Ecological Applications 7, 316–329. Lubchenco, J. (1998). Entering the century of the environment: a new social contract for science. Science 279, 491–497. Lyons, J. and Navarro-Perez, S. (1990). Fishes ´ Biosphere Reserve, of the Sierra de Manantlan west-central M´exico. The Southwestern Naturalist 35, 32–46.

Marsh, W. M. (1987). Earthscape A Physical Geography, 510 pp. Toronto: John Wiley and Sons. Miller, G. T. (1993). Environmental Science: Sustaining the Earth, 478 pp. Belmont, CA: Wadsworth Publishing Company. Millward, A. A. and Mersey, J. M. (1999). Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38, 109–129. Morgan, R. P. C. (1995). Soil Erosion and Conservation, 198 pp. Edinburgh: Addison Wesley Longman Limited. Mulengera, M. K. and Payton, R. W. (1999). Estimating the USLE-soil erodibility factor in developing tropical countries. Tropical Agriculture 76, 17–22. Renard, K. G. and Foster, G. R. (1983). Soil conservation: Principles of erosion by water. In Dryland Agriculture (H. E. Dregne and W. O. Willis, eds), pp. 155–176. Madison: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K. and Yoder, D. C. (1997). Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). US Department of Agriculture, 404 pp. Handbook No. 703. Santana, C. E., Guzman, M. R. and Jardel, P. E. ´ Biosphere (1989). The Sierra de Manantlan Reserve: the difficult task of becoming a catalyst for regional sustained development. In Proceedings of the Symposium on Biosphere Reserves, Fourth World Wilderness Congress, pp. 212–222. September 14–17, YMCA of the Rockies, Estes Park, CO, USA. Secretaria de Agricultura y Recursos Hidraulicos. (1991). Manual de Prediccion de Peridas de Suelo por Erosion, 115 pp. Guadalajara: Colegio de Postgraduados. Sheng, T. C. (1981). The need for soil conservation structures for steep cultivates slopes in the humid tropics. In Tropical Agricultural Hydrology (R. Lal and E. W. Russell, eds), pp. 357–372. New York: John Wiley and Sons Ltd. Tollner, E. W., Barfield, B. J., Hann, C. T. and Kao, T. Y. (1976). Suspended sediment filtration capacity of simulated vegetation. Transactions of the American Society of Agricultural Engineers 19, 678–682. Van der Zaag, P. (1992). Chicanery at the Canal: Changing Practice in Irrigation Management in Western Mexico, 267 pp. Amsterdam: CEDLA Publication. Van Oost, K., Govers, G. and Desmet, P. (2000). Evaluating the effects of changes in landscape structure on soil erosion by water and tillage. Landscape Ecology 15, 577–589. Vazquez-Garcia, J. A. (1993). Cloud forest archipelagos: preservation of fragmented montane ecosystems in tropical America. In Tropical Montane Cloud Forests: Proceedings of an International Symposium, pp. 203–216. San Juan, Puerto Rico 31 May–5 June. Vought, L. B., Pinay, G., Fuglsang, A. and Ruffinoni, C. (1995). Structure and function of

Conservation strategies using an EPIS buffer strips from a water quality perspective in agricultural landscapes. Landscape and Urban Planning 31, 323–331. Wischmeier, W. H. and Smith, D. D. (1978). Predicting Rainfall Erosion Losses: A Guide

to Conservation, 58 pp. Agricultural Handbook 537. Washington DC: Planning, Science and Education Administration, United States Department of Agriculture.

343