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Modeling the potential spatial distribution of beef cattle grazing using a Geographic Information System
Journal of Arid Environments (1998) 38: 325–334
Modeling the potential spatial distribution of beef cattle grazing using a Geographic Information System
Timothy G. Wade*, Bradley W. Schultz*, J. D. Wickham† & D. F. Bradford‡ *Biological Sciences Center, Desert Research Institute, Reno, NV 89512, U.S.A. †Environmental Research Center, Tennessee Valley Authority, Norris, TN 37828, U.S.A. ‡National Exposure Laboratory, U.S. EPA, Las Vegas, NV 89193, U.S.A. (Received 14 January 1997, accepted 12 September 1997) Data regarding grazing utilization in the western United States are typically compiled within administrative boundaries (e.g. allotment, pasture). For large areas, an assumption of uniform distribution is seldom valid. Previous studies show that vegetation type, degree of slope, and distance from water largely determine the distribution of livestock. We demonstrate how range managers can use Boolean logic and a Geographic Information System (GIS) to develop models that predict which areas beef cattle are most and least likely to graze. The models show that approximately 60% of the state of Oregon has potential for rangeland grazing. The results compare favorably with the 1992 census of beef cattle. ©1998 Academic Press Limited Keywords: GIS; modeling; grazing; land management
Introduction Cattle (Bos taurus) on western rangelands generally graze in administratively-defined, but not necessarily physically-bounded allotments. Allotments may range from several hundred to well over 40,000 ha. The assumption that cattle (or any ungulate) uniformly distribute themselves across large allotments is seldom valid (Roath & Krueger, 1982; Senft et al., 1983, 1985; Bureau of Land Management, 1984; Kauffman and Krueger, 1984; Smith, 1988; Owens et al., 1991). Unequal distribution and utilization by livestock occur because the composition and production of the vegetation, topographic features, and availability of water are not uniformly distributed. Intuitively, as allotment size increases, landscape diversity increases, and distribution of grazing animals becomes an important management consideration. Several studies found that cattle distribution can be predicted using regression analysis (Senft et al., 1983, 1985; Smith, 1988; Owens et al., 1991). Vegetation type, percent slope, and distance from water generally explain most of the distribution 0140–1963/98/020325 + 10 $25.00/0/ae970333
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pattern (see Senft et al., 1983, 1985; Owens et al., 1991). Geographic Information Systems (GIS) are well suited for modeling the potential (probable) distribution of rangeland livestock because data for vegetation, slope, and distance from water are inherently spatial (Wickham & Meentemeyer, 1992; Wickham et al., 1995, 1997). The purpose of this paper is two-fold. First, to demonstrate to range management specialists how Boolean modeling with a GIS can predict the potential distribution of rangeland cattle over a large area. Second, to stimulate thought among rangeland managers about how GIS technology can be applied to their specific problems. Methods We used vegetation and topographic data to develop two Boolean, raster-based GIS models for the state of Oregon, U.S.A. Both models were developed with Arc/Info’s GRID software (Environmental Systems Research Institute, Redlands, CA), using a pixel size of approximately 107 m on a side (the resolution of the topographic data). Both models were developed using data for vegetation type, percent slope, and proximity to water. Digital vegetation data were furnished by the Oregon chapter of The Nature Conservancy, and consisted of 133 community types, and 206 mixed communities that were complexes of two or more community types. Classification was performed through photo-interpretation of Landsat prints, supported by extensive ground truthing (Kagan, 1995, pers. comm.). Each community or complex was classified into one of four grazing preference categories for cattle (Table 1). We used published information about livestock preferences when available (e.g. Pickford & Reid, 1948; Miller & Krueger, 1976; Skovlin et al., 1976; Bryant, 1982; Roath & Krueger, 1982; Campbell & Johnson, 1983; Gillen et al., 1984; Mitchell & Rodgers, 1985; Smith et al., 1992). When information was unavailable, vegetation types were classified based upon personal experience and knowledge relating to: (1) cattle behavior (grazing patterns); (2) average forage production and cattle selectivity among forage and cover types; (3) differences in habitat structure (e.g. accessibility); and (4) typical landform position among cover types. United States Geological Survey (USGS) 1:250,000 scale Digital Elevation Model (DEM) data were used to derive percent slope. Slope values were grouped into eight categories (Table 2). Data defining the locations of all flowing streams, water impoundments, and developed and undeveloped water sources in Oregon were unavailable; therefore, we Table 1. Preferences assigned to each vegetation type
Livestock preference
Description
Value
None
Vegetation types cattle do not graze, or that cattle select so infrequently the type is essentially ungrazed
0
Low
Vegetation types that cattle graze, but utilization is usually less than in adjacent communities
1
Moderate
Vegetation types that cattle graze roughly in proportion with their occurrence
2
High
Vegetation types cattle generally select more than the communities abundance across the landscape
3
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were forced to make some assumptions to define probable watering points. Since rivers and streams generally have gradients less than 3% and most water sources are typically on gentle terrain we assumed that all areas having slopes of 3% or less were potential water sites. Three percent was chosen because it is recognized in agriculture as the threshold after which steepness becomes limiting (United States Department of Agriculture, 1951). We recognize that useable water sources occur on slopes greater than 3% and that not all areas with slope less than 3% actually contain water, but could not define a set of criteria that could provide a more accurate assessment of probable water locations. A least accumulative cost distance to potential water sources was calculated for each cell. Cost of crossing a cell was determined by its percent slope (Table 2), because range cattle avoid steep slopes (Mueggler, 1965). For example, to travel 1000 m up a slope of less than 3% ‘costs’ 1000 units (distance (m) 3 cost or 1000 3 1), approximately the same as travelling only 285 m on a slope of 40 to 50% (285 3 3·5). Areas within 1000 cost units of a 3% slope were considered as having water readily available, and given a value of 2. Areas between 1000 and 2000 cost units were assigned a value of 1; and areas greater than 2000 cost units were considered not suitable for grazing, and given the value 0. Two thousand (corresponding to 2000 m on flat terrain) was selected as the limit of range use because cattle are not likely to travel further than that distance from water, regardless of vegetation type (Heady & Child, 1994). Our first model determined locations that beef cattle would probably graze. Areas with a value greater than zero (0) in each of the three input data sets (vegetation preference, slope, and cost distance to water) were assigned a value of 1. Remaining areas were assigned the value zero (0). The result is a binary map (Fig. 1) of areas with grazing potential (Class 1) and areas where grazing is unlikely (Class 0). The second model estimated the relative probability that areas with grazing potential (Class 1 from the first model) would be utilized. This was accomplished by multiplying the values of the input data. For example, a high preference vegetation type (value 3), on flat ground (value 7), within 1000 cost units of water (value 2) would have the highest possible value of 42 (3 3 7 3 2; vegetation 3 slope 3 distance to water). On the other extreme, any input with a value of zero would classify the location as unlikely to be grazed (identical to Class 0 in the first model). The second model produced 21 different grazing probability classes. Numeric values assigned to each class were relative to one another, not absolute. An area with a value of 28 is more likely, but not twice as likely to be grazed as an area with value of 14. Classes with high values are simply more likely to be selected by rangeland cattle. For analysis and display purposes, values were reclassified into four categories using an equal-area approach. This method attempts to divide observations into classes that represent a similar amount of area. An ideal breakdown in this case would Table 2. Slope categories with associated values and travel costs
Percent slope
Value
Travel cost
0–3 >3–10 >10–20 >20–30 >30–40 >40–50 >50–60 >60
7 6 5 4 3 2 1 0
1 1 1·45 1·93 2·6 3·5 7 10,000
Figure 1. Grazing potential of areas in Oregon for beef cattle.
328 T. G. WADE ET AL.
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Figure 2. Relative grazing probability for beef cattle in Oregon.
result in each class containing 25% of the area in the state. Class 0 contains values of zero and is identical to Class 0 in the first model. Class 1 contains values from 1–20, Class 2 has values from 24–30, and Class 3 has values from 36–42 (Fig. 2; Table 3). Higher class values represent a higher probability of being grazed.
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Results Both models predicted that 59·3% of Oregon had potential to be grazed, while 40·7% was unlikely to be grazed (Figs 1 and 2). The majority of land classified as ungrazed is located in the Coastal and Cascade Mountain Ranges in western Oregon (Fig. 1). Large ungrazed tracts are also present in the Blue and Wallowa Mountains in central and north-east Oregon. Most land that would probably be grazed occurred east of the Cascade Range. Figure 2 depicts the results of the second model, or the relative probability of a location being grazed. In this model, Class 0 (ungrazed) contains 40·7% of the area in the state, Class 1 (limited grazing potential) contains 14·1%, Class 2 (moderate potential) contains 19·8%, and Class 3 (high potential) contains 25·4% (Table 3). Large ungrazeable tracts (Class 0) primarily occur in mountainous regions. Several factors that limit livestock use in these areas are: (1) level but densely forested areas that have little or no herbaceous growth in the understory; (2) dense forested areas through which cattle cannot travel; (3) steep sideslopes that retard cattle movement; and (4) high elevation areas that are barren, or are snow covered much of the year. Areas with low grazing potential (Class 1) primarily occur east of the Cascade Range. Forested areas in the Blue and Wallowa Mountains encompass a large part of this category. Sparse herbaceous growth under dense forest canopies and moderately (but not excessively) steep topography limit forage availability and access. Large blocks in the arid south-eastern and south-central Oregon often coincide with areas dominated by juniper trees or salt desert shrub communities. Both cover types generally have low forage production. Many sagebrush and juniper areas (e.g. Steens Mountains, Owyee River Canyon) also have steep slopes and/or rocky soil surfaces that limit access. Locations with moderate (Class 2) and high (Class 3) grazing potential, in western and eastern Oregon, respectively, differ in their spatial distribution (Fig. 2). In the Willamette Valley, as well as other level river valleys, locations with high grazing preference often occur in large tracts. Landscape and plant communities in eastern Oregon are more variable than in western Oregon. The increased heterogeneity results in a complex mosaic of areas that have high, moderate, low and no preference by cattle. Eastern Oregon locations likely to be grazed typically occur in the Palouse Prairie grasslands in north-central and north-eastern Oregon, the widespread rolling sagebrush–grass rangelands in central and south-eastern Oregon, and mid-elevation Ponderosa pine forests. The pine forests typically have abundant herbaceous growth in the understory. Discussion Validation of the models was performed by comparing the proportion of each county classified as having a given grazing potential with the density of beef cattle in the county in December 1992 (US Department of Commerce, 1993) (Table 4; Figs 1 and Table 3. Summary of results for model 2
Class
Grazing potential
Percent of area in Oregon
Model values
0 1 2 3
Not Grazed Low Moderate High
40·67 14·10 19·82 25·41
0 1–20 24–30 36–42
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2). The percent area in Class 0 (i.e. unlikely grazing potential) was significantly negatively correlated with cattle density (Spearman rank correlation, rs = –0·61, p < 0·0001), whereas the percent area in each of the Classes 1, 2, and 3 (i.e. low, moderate, and high grazing potential, respectively) was significantly positively correlated with cattle density (rs = 0·535, 0·473, and 0·514, p = 0·0011, 0·0048, and 0·0019, for Class 1, 2, and 3, respectively). Thus, in general, counties that had large proportions of their areas classified as likely to be grazed had high cattle densities. For example, Gillian, Crook, and Malheur Counties were classified almost entirely as likely to be grazed (Classes 1, 2, or 3; Table 4) and had relatively high densities of beef cattle. Similarly, Lincoln and Tillamook Counties classified almost entirely as unlikely to be grazed (Class 0; Table 4), had relatively low densities of beef cattle. Nevertheless, the Table 4. Beef cattle density (December 1992) and percentage of associated land area by county (Hood River and Sherman not included due to lack of cattle data). Due to rounding error, sums may not equal 100
County
Beef cattle (per km2)
Percent area in Class 0
Percent area in Class 1
Percent area in Class 2
Percent area in Class 3
Tillamook Josephine Curry Deschutes Lane Lincoln Clatsop Douglas Jefferson Washington Linn Polk Benton Multnomah Clackamas Lake Harney Marion Jackson Yamhill Klamath Grant Coos Wasco Wheeler Malheur Columbia Crook Gilliam Wallowa Umatilla Union Morrow Baker
0·3589 0·4815 0·9002 1·1464 1·1524 1·4168 1·6325 1·6721 1·7400 1·7675 1·8385 1·9128 1·9900 1·9912 2·0584 2·1786 2·2712 2·3000 2·5744 2·6174 2·6470 2·6758 2·6962 2·7231 2·8278 2·9307 3·1296 3·4490 3·5341 3·7441 3·7504 3·8047 3·8302 5·6662
99·94 94·58 97·42 33·30 91·19 100·0 99·74 89·99 18·07 75·28 72·67 57·85 67·30 88·79 88·35 11·37 5·11 49·31 88·23 55·37 44·25 28·57 94·92 16·05 24·07 3·65 91·17 2·71 1·68 54·24 31·68 62·96 10·18 28·83
0·00 1·15 1·09 7·04 0·68 0·00 0·00 4·20 27·98 0·05 1·01 1·30 1·26 0·63 0·05 23·36 27·93 0·74 2·89 0·91 14·22 33·31 1·31 20·06 33·14 13·75 0·01 30·28 10·90 15·90 8·70 9·18 7·06 23·16
0·00 1·36 0·55 28·25 1·80 0·00 0·01 5·17 31·70 2·45 4·84 8·16 8·69 1·87 0·64 31·93 31·71 3·35 6·51 4·58 26·02 27·79 0·97 30·36 30·98 27·52 0·06 47·21 31·50 11·98 12·57 8·32 24·57 24·05
0·06 2·91 0·93 31·42 6·34 0·00 0·25 0·64 22·25 22·22 21·48 32·69 22·76 8·71 10·95 33·34 35·24 46·60 2·37 39·15 15·51 10·34 2·81 33·53 11·81 55·09 8·77 19·79 55·93 17·89 47·05 19·54 58·19 23·96
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density of cattle in these two counties was not zero. This may be due to cattle being located on improved pasture units (i.e. human maintained) that were too small to be detected at the scale of the remote sensing imagery used. These cattle represent fewer than 0·8% of the beef cattle in Oregon. Therefore, the effect of the model error in these counties appears to be small. The principle factor that limited potential grazing in both models was vegetation type. Fewer areas were classified as ungrazeable because of steep slopes or distance from water. This was partly due to the scale of the input data. Small scale/low resolution (1:250,000) elevation data tends to smooth terrain due to a larger sampling distance between elevation measurements, resulting in gentler slopes (Walsh et al., 1987). While large scale/high resolution (1:24,000) elevation data are available for much of the United States, the increase in data volume makes it unwieldy to work with over large areas and complete coverage for the state of Oregon was not available. In general, using large scale data results in slope being a limiting factor more often. However, using low resolution elevation data, long narrow valley bottoms that are bounded by steep mountains may be classified as areas too steep to support livestock, because the width of a valley bottom may be insufficient to separate it from adjacent mountain sideslopes. This may explain why the model predicts that Tillamook and Lincoln Counties, which have narrow river valleys, should not have beef cattle, although small numbers were present. Also, productive bottomland soil may support small improved pastures that were too small to classify as pastureland when the multi-spectral satellite imagery was analysed. This is probable since most farms have between one and nine cows (United State Department of Commerce, 1993). Additional errors may occur when data types do not match the objectives of the model. For example, agricultural cropland and improved pastureland were classified as one vegetation type in the Nature Conservancy data. The choice was made to categorize this cover type as preference Class 3 (likely to be grazed). This resulted in all cropland areas being incorrectly classified. Areas in Wasco, Sherman, Gilliam, Morrow, and Umatilla Counties produce wheat (i.e. improved cropland), consequently the area identified as likely to be grazed is probably overstated in these counties. Partitioning cropland and pastureland into separate cover types would better support grazing management goals and would have improved the accuracy of both models. Several counties (e.g. Coos, Columbia, Wallowa, Union) have relatively high cattle densities, and large percentages of land classified as ungrazeable. Other areas have relatively low cattle densities (e.g. Deschutes, Jefferson), but large areas with high grazing potential. These discrepancies reflect potential interpretation errors the input data were not designed to answer. First, the models were not developed to address qualitative differences due to variation in above-ground net primary production. The input data (slope, proximity to water, and cover type) determined the relative probability of an area being grazed. These variables did not address forage production which largely determines how many animals may graze an area. Intuitively, one would expect that wet fertile river valleys in Coos County, where soil is deep and the growing season long, are capable of stocking beef cattle at much higher densities than arid sagebrush rangelands in Jefferson County. Although Coos County has little grazeable land (95% ungrazeable), it can support higher stocking densities than Jefferson County (18% ungrazeable). Second, livestock density data are from one date (31 December, 1992). Counties, such as Union, that have low-elevation pastures can support high winter densities because of supplemental feeding programs. Additional grazing and vegetation data collected at a larger scale (e.g. 1:24,000) might enhance the model’s utility for managing rangeland ecosystems. For example, at the allotment level, data about above-ground annual production by species, forage selection (preference) by domestic and wild herbivores, annual forage utilization,
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nutrient composition by season, and animal nutritional requirements could be integrated to develop a forage allocation model based on the biological needs and population size of each species. Data about forage preferences, nutrient composition, and nutrient requirements exist for many species (e.g. Culley, 1938; Cook & Harris, 1950, 1968; Smith & Julander, 1953; Cook et al., 1954; Herbel & Nelson, 1966; National Research Council, 1970).
Conclusion Two grazing models were developed using readily accessible data and a GIS. The first model showed areas that were most and least likely to be grazed. The second model ranked areas based on factors that indicate greater grazing potential. Locations that the models identified as being suitable or not suitable for grazing (Figs 1 and 2) compared favorably with the density of beef cows in 1992 (Table 4). Virtually all data collected by range management professionals have a spatial component. Each data type or layer often describes several attributes (e.g. cover, density, biomass, nutrient quality). Integration of these data types and their multiple attributes can yield several outputs addressing management problems. The abilities of GIS to integrate these data and to help manage rangelands have gone largely untapped. The U.S. Environmental Protection Agency (EPA), through its Office of Research and Development, partially funded and collaborated in the research described here under Cooperative Agreement CR-819549-0105 to the Desert Research Institute. This manuscript has been reviewed by EPA and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
References Bryant, L.D. (1982). Response of livestock to riparian zone exclusion. Journal of Range Management, 35: 780–785. Bureau of Land Management (1984). Rangeland Monitoring: actual use studies. Technical reference 4400-2. Denver, CO: Bureau of Land Management: Denver Service Center. 8 pp. Campbell, E.G. & Johnson, R.L. (1983). Food habits of mountain goats, mule deer, and cattle on Chopaka Mountain, Washington. Journal of Range Management, 36: 488–491. Cook, C.W. & Harris, L.E. (1950). The nutritive value of range forage as affected by vegetation, type, site, and stage of maturity. Utah Agricultural Experiment Station Bulletin 344. Logan, UT: Utah Agricultural College. 45 pp. Cook, C.W. & Harris, L.E. (1968). Nutritive value of seasonal ranges. Utah State University Agricultural Experiment Station Bulletin 472. Logan, UT: Utah State University. 55 pp. Cook, C.W., Stoddart, L.A. & Harris, L.E. (1954). The nutritive value of winter range plants in the Great Basin. Utah Agricultural Experiment Station Bulletin 372. Logan, UT: Utah Agricultural College. 56 pp. Culley, M. (1938). Grazing habits of range cattle. American Cattle Producer, 19: 3–4, 16–17. Gillen, R.L., Krueger, W.C. & Miller, R.F. (1984). Cattle distribution on mountain rangeland in northeastern Oregon. Journal of Range Management, 37: 549–553. Heady, H.F. & Child, R.D. (1994). Rangeland Ecology and Management. Boulder, CO: Westview Press. 519 pp. Herbel, C.R. & Nelson, A.B. (1966). Species preferences of hereford and Santa Gertrudis cattle on a southern New Mexico range. Journal of Range Management, 19: 177–181. Kauffman, J.D. & Krueger, W.C. (1984). Livestock impacts on riparian ecosystems and streamside management implications — a review. Journal of Rangeland Management, 37: 430–438.
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Miller, R. & Krueger, W.C. (1976). Cattle use on summer foothill rangelands in northeast Oregon. Journal of Range Management, 29: 367–371. Mitchell, J.E. & Rodgers, P.T. (1985). Food habits and distribution of cattle on a forest and pasture range in Northern Idaho. Journal of Range Management, 37: 430–438. Mueggler, W.F. (1965). Cattle distribution on steep slopes. Journal of Range Management, 18: 255–257. National Research Council. (1970). Nutrient requirements of beef cattle No. 4 (4th revised Edn). Washington: National Academy of Science. 90 pp. Owens, M.K., Launchbaugh, K.L. & Hollaway, J.W. (1991). Pasture characteristics affecting spatial distribution of utilization by cattle in mixed brush communities. Journal of Range Management, 44: 118–123. Pickford, G.D. & Reid, E.H. (1948). Forage utilization on summer cattle ranges in Eastern Oregon. United States Department of Agriculture Circular 796. Washington, D.C: United States Department of Agriculture. 27 pp. Roath, L.R. & Krueger, W.C. (1982). Cattle grazing influence on a mountain riparian zone. Journal of Range Management, 35: 100–104. Senft, R.L., Rittenhouse, L.R. & Woodmansee, R.G. (1983). The use of regression models to predict spatial patterns of cattle behavior. Journal of Range Management, 36: 553–557. Senft, R.L., Rittenhouse, L.R. & Woodmansee, R.G. (1985). Factors influencing patterns of cattle grazing behavior on shortgrass steppe. Journal of Range Management, 38: 82–87. Skovlin, J.M., Harris, R.W., Strickler, G.S. & Garrison, G.A. (1976). Effects of cattle grazing methods on Ponderosa pine bunchgrass range in the Pacific northwest. United States Department of Agriculture Technical Bulletin No 1531. Washington, D.C: United States Department of Agriculture, Forest Service. 40 pp. Smith, M.S. (1988). Modeling: three approaches to predicting how herbivore impact is distributed in rangelands. Las Cruces, NM: New Mexico State University, Agricultural Experiment Station, Research Report 628. 56 pp. Smith, J.G. & Julander, O. (1953). Deer and sheep competition in Utah. Journal of Wildlife Management, 17: 101–112. Smith, M.A., Rodgers, D., Dodd, J.L. & Skinner, Q.D. (1992). Declining forage availability effects on utilization and community selection by cattle. Journal of Range Management, 45: 391–395. United States Department of Agriculture (1951). Soil Survey Manual. Agricultural Handbook 18. Washington, DC: U.S. Department of Agriculture. 503 pp. United States Department of Commerce (1993). Cattle and Calves — Inventory and Sales: 1992 and 1987. In: 1992 Census of Agriculture Oregon State and County Data, pp. 267–271. Washington, D.C: U.S. Department of the Census. Walsh, S.J., Lightfoot, D.R. & Butler, D.R. (1987). Recognition and assessment of error in geographic information systems. Photogrammetric Engineering and Remote Sensing, 53: 1423–1430. Wickham, J.D. & Meentemeyer, V. (1992). Landscape indicators. In: Franson, S.E. (Ed.), Environmental Monitoring and Assessment Program: EMAP-Arid Colorado Plateau Pilot Study — 1992: Implementation Plan, pp. 114–124. EPA 620/R-93/001. Washington, DC: U.S. Environmental Protection Agency. 161 pp. Wickham, J.D., Wu, J. & Bradford, D.F. (1995). Stressor data sets for studying species diversity at large spatial scales. USEPA 600/R-95/018. Washington, DC: Office of Research and Development, U.S. Environmental Protection Agency. Wickham, J.D., Wu, J. & Bradford, D.F. (1997). A conceptual framework for selecting and analyzing stressor data to study species richness at large spatial scales. Environmental Management, 21: 247–257.
Modeling the potential spatial distribution of beef cattle grazing using a Geographic Information System
Timothy G. Wade*, Bradley W. Schultz*, J. D. Wickham† & D. F. Bradford‡ *Biological Sciences Center, Desert Research Institute, Reno, NV 89512, U.S.A. †Environmental Research Center, Tennessee Valley Authority, Norris, TN 37828, U.S.A. ‡National Exposure Laboratory, U.S. EPA, Las Vegas, NV 89193, U.S.A. (Received 14 January 1997, accepted 12 September 1997) Data regarding grazing utilization in the western United States are typically compiled within administrative boundaries (e.g. allotment, pasture). For large areas, an assumption of uniform distribution is seldom valid. Previous studies show that vegetation type, degree of slope, and distance from water largely determine the distribution of livestock. We demonstrate how range managers can use Boolean logic and a Geographic Information System (GIS) to develop models that predict which areas beef cattle are most and least likely to graze. The models show that approximately 60% of the state of Oregon has potential for rangeland grazing. The results compare favorably with the 1992 census of beef cattle. ©1998 Academic Press Limited Keywords: GIS; modeling; grazing; land management
Introduction Cattle (Bos taurus) on western rangelands generally graze in administratively-defined, but not necessarily physically-bounded allotments. Allotments may range from several hundred to well over 40,000 ha. The assumption that cattle (or any ungulate) uniformly distribute themselves across large allotments is seldom valid (Roath & Krueger, 1982; Senft et al., 1983, 1985; Bureau of Land Management, 1984; Kauffman and Krueger, 1984; Smith, 1988; Owens et al., 1991). Unequal distribution and utilization by livestock occur because the composition and production of the vegetation, topographic features, and availability of water are not uniformly distributed. Intuitively, as allotment size increases, landscape diversity increases, and distribution of grazing animals becomes an important management consideration. Several studies found that cattle distribution can be predicted using regression analysis (Senft et al., 1983, 1985; Smith, 1988; Owens et al., 1991). Vegetation type, percent slope, and distance from water generally explain most of the distribution 0140–1963/98/020325 + 10 $25.00/0/ae970333
© 1998 Academic Press Limited
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pattern (see Senft et al., 1983, 1985; Owens et al., 1991). Geographic Information Systems (GIS) are well suited for modeling the potential (probable) distribution of rangeland livestock because data for vegetation, slope, and distance from water are inherently spatial (Wickham & Meentemeyer, 1992; Wickham et al., 1995, 1997). The purpose of this paper is two-fold. First, to demonstrate to range management specialists how Boolean modeling with a GIS can predict the potential distribution of rangeland cattle over a large area. Second, to stimulate thought among rangeland managers about how GIS technology can be applied to their specific problems. Methods We used vegetation and topographic data to develop two Boolean, raster-based GIS models for the state of Oregon, U.S.A. Both models were developed with Arc/Info’s GRID software (Environmental Systems Research Institute, Redlands, CA), using a pixel size of approximately 107 m on a side (the resolution of the topographic data). Both models were developed using data for vegetation type, percent slope, and proximity to water. Digital vegetation data were furnished by the Oregon chapter of The Nature Conservancy, and consisted of 133 community types, and 206 mixed communities that were complexes of two or more community types. Classification was performed through photo-interpretation of Landsat prints, supported by extensive ground truthing (Kagan, 1995, pers. comm.). Each community or complex was classified into one of four grazing preference categories for cattle (Table 1). We used published information about livestock preferences when available (e.g. Pickford & Reid, 1948; Miller & Krueger, 1976; Skovlin et al., 1976; Bryant, 1982; Roath & Krueger, 1982; Campbell & Johnson, 1983; Gillen et al., 1984; Mitchell & Rodgers, 1985; Smith et al., 1992). When information was unavailable, vegetation types were classified based upon personal experience and knowledge relating to: (1) cattle behavior (grazing patterns); (2) average forage production and cattle selectivity among forage and cover types; (3) differences in habitat structure (e.g. accessibility); and (4) typical landform position among cover types. United States Geological Survey (USGS) 1:250,000 scale Digital Elevation Model (DEM) data were used to derive percent slope. Slope values were grouped into eight categories (Table 2). Data defining the locations of all flowing streams, water impoundments, and developed and undeveloped water sources in Oregon were unavailable; therefore, we Table 1. Preferences assigned to each vegetation type
Livestock preference
Description
Value
None
Vegetation types cattle do not graze, or that cattle select so infrequently the type is essentially ungrazed
0
Low
Vegetation types that cattle graze, but utilization is usually less than in adjacent communities
1
Moderate
Vegetation types that cattle graze roughly in proportion with their occurrence
2
High
Vegetation types cattle generally select more than the communities abundance across the landscape
3
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were forced to make some assumptions to define probable watering points. Since rivers and streams generally have gradients less than 3% and most water sources are typically on gentle terrain we assumed that all areas having slopes of 3% or less were potential water sites. Three percent was chosen because it is recognized in agriculture as the threshold after which steepness becomes limiting (United States Department of Agriculture, 1951). We recognize that useable water sources occur on slopes greater than 3% and that not all areas with slope less than 3% actually contain water, but could not define a set of criteria that could provide a more accurate assessment of probable water locations. A least accumulative cost distance to potential water sources was calculated for each cell. Cost of crossing a cell was determined by its percent slope (Table 2), because range cattle avoid steep slopes (Mueggler, 1965). For example, to travel 1000 m up a slope of less than 3% ‘costs’ 1000 units (distance (m) 3 cost or 1000 3 1), approximately the same as travelling only 285 m on a slope of 40 to 50% (285 3 3·5). Areas within 1000 cost units of a 3% slope were considered as having water readily available, and given a value of 2. Areas between 1000 and 2000 cost units were assigned a value of 1; and areas greater than 2000 cost units were considered not suitable for grazing, and given the value 0. Two thousand (corresponding to 2000 m on flat terrain) was selected as the limit of range use because cattle are not likely to travel further than that distance from water, regardless of vegetation type (Heady & Child, 1994). Our first model determined locations that beef cattle would probably graze. Areas with a value greater than zero (0) in each of the three input data sets (vegetation preference, slope, and cost distance to water) were assigned a value of 1. Remaining areas were assigned the value zero (0). The result is a binary map (Fig. 1) of areas with grazing potential (Class 1) and areas where grazing is unlikely (Class 0). The second model estimated the relative probability that areas with grazing potential (Class 1 from the first model) would be utilized. This was accomplished by multiplying the values of the input data. For example, a high preference vegetation type (value 3), on flat ground (value 7), within 1000 cost units of water (value 2) would have the highest possible value of 42 (3 3 7 3 2; vegetation 3 slope 3 distance to water). On the other extreme, any input with a value of zero would classify the location as unlikely to be grazed (identical to Class 0 in the first model). The second model produced 21 different grazing probability classes. Numeric values assigned to each class were relative to one another, not absolute. An area with a value of 28 is more likely, but not twice as likely to be grazed as an area with value of 14. Classes with high values are simply more likely to be selected by rangeland cattle. For analysis and display purposes, values were reclassified into four categories using an equal-area approach. This method attempts to divide observations into classes that represent a similar amount of area. An ideal breakdown in this case would Table 2. Slope categories with associated values and travel costs
Percent slope
Value
Travel cost
0–3 >3–10 >10–20 >20–30 >30–40 >40–50 >50–60 >60
7 6 5 4 3 2 1 0
1 1 1·45 1·93 2·6 3·5 7 10,000
Figure 1. Grazing potential of areas in Oregon for beef cattle.
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Figure 2. Relative grazing probability for beef cattle in Oregon.
result in each class containing 25% of the area in the state. Class 0 contains values of zero and is identical to Class 0 in the first model. Class 1 contains values from 1–20, Class 2 has values from 24–30, and Class 3 has values from 36–42 (Fig. 2; Table 3). Higher class values represent a higher probability of being grazed.
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Results Both models predicted that 59·3% of Oregon had potential to be grazed, while 40·7% was unlikely to be grazed (Figs 1 and 2). The majority of land classified as ungrazed is located in the Coastal and Cascade Mountain Ranges in western Oregon (Fig. 1). Large ungrazed tracts are also present in the Blue and Wallowa Mountains in central and north-east Oregon. Most land that would probably be grazed occurred east of the Cascade Range. Figure 2 depicts the results of the second model, or the relative probability of a location being grazed. In this model, Class 0 (ungrazed) contains 40·7% of the area in the state, Class 1 (limited grazing potential) contains 14·1%, Class 2 (moderate potential) contains 19·8%, and Class 3 (high potential) contains 25·4% (Table 3). Large ungrazeable tracts (Class 0) primarily occur in mountainous regions. Several factors that limit livestock use in these areas are: (1) level but densely forested areas that have little or no herbaceous growth in the understory; (2) dense forested areas through which cattle cannot travel; (3) steep sideslopes that retard cattle movement; and (4) high elevation areas that are barren, or are snow covered much of the year. Areas with low grazing potential (Class 1) primarily occur east of the Cascade Range. Forested areas in the Blue and Wallowa Mountains encompass a large part of this category. Sparse herbaceous growth under dense forest canopies and moderately (but not excessively) steep topography limit forage availability and access. Large blocks in the arid south-eastern and south-central Oregon often coincide with areas dominated by juniper trees or salt desert shrub communities. Both cover types generally have low forage production. Many sagebrush and juniper areas (e.g. Steens Mountains, Owyee River Canyon) also have steep slopes and/or rocky soil surfaces that limit access. Locations with moderate (Class 2) and high (Class 3) grazing potential, in western and eastern Oregon, respectively, differ in their spatial distribution (Fig. 2). In the Willamette Valley, as well as other level river valleys, locations with high grazing preference often occur in large tracts. Landscape and plant communities in eastern Oregon are more variable than in western Oregon. The increased heterogeneity results in a complex mosaic of areas that have high, moderate, low and no preference by cattle. Eastern Oregon locations likely to be grazed typically occur in the Palouse Prairie grasslands in north-central and north-eastern Oregon, the widespread rolling sagebrush–grass rangelands in central and south-eastern Oregon, and mid-elevation Ponderosa pine forests. The pine forests typically have abundant herbaceous growth in the understory. Discussion Validation of the models was performed by comparing the proportion of each county classified as having a given grazing potential with the density of beef cattle in the county in December 1992 (US Department of Commerce, 1993) (Table 4; Figs 1 and Table 3. Summary of results for model 2
Class
Grazing potential
Percent of area in Oregon
Model values
0 1 2 3
Not Grazed Low Moderate High
40·67 14·10 19·82 25·41
0 1–20 24–30 36–42
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2). The percent area in Class 0 (i.e. unlikely grazing potential) was significantly negatively correlated with cattle density (Spearman rank correlation, rs = –0·61, p < 0·0001), whereas the percent area in each of the Classes 1, 2, and 3 (i.e. low, moderate, and high grazing potential, respectively) was significantly positively correlated with cattle density (rs = 0·535, 0·473, and 0·514, p = 0·0011, 0·0048, and 0·0019, for Class 1, 2, and 3, respectively). Thus, in general, counties that had large proportions of their areas classified as likely to be grazed had high cattle densities. For example, Gillian, Crook, and Malheur Counties were classified almost entirely as likely to be grazed (Classes 1, 2, or 3; Table 4) and had relatively high densities of beef cattle. Similarly, Lincoln and Tillamook Counties classified almost entirely as unlikely to be grazed (Class 0; Table 4), had relatively low densities of beef cattle. Nevertheless, the Table 4. Beef cattle density (December 1992) and percentage of associated land area by county (Hood River and Sherman not included due to lack of cattle data). Due to rounding error, sums may not equal 100
County
Beef cattle (per km2)
Percent area in Class 0
Percent area in Class 1
Percent area in Class 2
Percent area in Class 3
Tillamook Josephine Curry Deschutes Lane Lincoln Clatsop Douglas Jefferson Washington Linn Polk Benton Multnomah Clackamas Lake Harney Marion Jackson Yamhill Klamath Grant Coos Wasco Wheeler Malheur Columbia Crook Gilliam Wallowa Umatilla Union Morrow Baker
0·3589 0·4815 0·9002 1·1464 1·1524 1·4168 1·6325 1·6721 1·7400 1·7675 1·8385 1·9128 1·9900 1·9912 2·0584 2·1786 2·2712 2·3000 2·5744 2·6174 2·6470 2·6758 2·6962 2·7231 2·8278 2·9307 3·1296 3·4490 3·5341 3·7441 3·7504 3·8047 3·8302 5·6662
99·94 94·58 97·42 33·30 91·19 100·0 99·74 89·99 18·07 75·28 72·67 57·85 67·30 88·79 88·35 11·37 5·11 49·31 88·23 55·37 44·25 28·57 94·92 16·05 24·07 3·65 91·17 2·71 1·68 54·24 31·68 62·96 10·18 28·83
0·00 1·15 1·09 7·04 0·68 0·00 0·00 4·20 27·98 0·05 1·01 1·30 1·26 0·63 0·05 23·36 27·93 0·74 2·89 0·91 14·22 33·31 1·31 20·06 33·14 13·75 0·01 30·28 10·90 15·90 8·70 9·18 7·06 23·16
0·00 1·36 0·55 28·25 1·80 0·00 0·01 5·17 31·70 2·45 4·84 8·16 8·69 1·87 0·64 31·93 31·71 3·35 6·51 4·58 26·02 27·79 0·97 30·36 30·98 27·52 0·06 47·21 31·50 11·98 12·57 8·32 24·57 24·05
0·06 2·91 0·93 31·42 6·34 0·00 0·25 0·64 22·25 22·22 21·48 32·69 22·76 8·71 10·95 33·34 35·24 46·60 2·37 39·15 15·51 10·34 2·81 33·53 11·81 55·09 8·77 19·79 55·93 17·89 47·05 19·54 58·19 23·96
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density of cattle in these two counties was not zero. This may be due to cattle being located on improved pasture units (i.e. human maintained) that were too small to be detected at the scale of the remote sensing imagery used. These cattle represent fewer than 0·8% of the beef cattle in Oregon. Therefore, the effect of the model error in these counties appears to be small. The principle factor that limited potential grazing in both models was vegetation type. Fewer areas were classified as ungrazeable because of steep slopes or distance from water. This was partly due to the scale of the input data. Small scale/low resolution (1:250,000) elevation data tends to smooth terrain due to a larger sampling distance between elevation measurements, resulting in gentler slopes (Walsh et al., 1987). While large scale/high resolution (1:24,000) elevation data are available for much of the United States, the increase in data volume makes it unwieldy to work with over large areas and complete coverage for the state of Oregon was not available. In general, using large scale data results in slope being a limiting factor more often. However, using low resolution elevation data, long narrow valley bottoms that are bounded by steep mountains may be classified as areas too steep to support livestock, because the width of a valley bottom may be insufficient to separate it from adjacent mountain sideslopes. This may explain why the model predicts that Tillamook and Lincoln Counties, which have narrow river valleys, should not have beef cattle, although small numbers were present. Also, productive bottomland soil may support small improved pastures that were too small to classify as pastureland when the multi-spectral satellite imagery was analysed. This is probable since most farms have between one and nine cows (United State Department of Commerce, 1993). Additional errors may occur when data types do not match the objectives of the model. For example, agricultural cropland and improved pastureland were classified as one vegetation type in the Nature Conservancy data. The choice was made to categorize this cover type as preference Class 3 (likely to be grazed). This resulted in all cropland areas being incorrectly classified. Areas in Wasco, Sherman, Gilliam, Morrow, and Umatilla Counties produce wheat (i.e. improved cropland), consequently the area identified as likely to be grazed is probably overstated in these counties. Partitioning cropland and pastureland into separate cover types would better support grazing management goals and would have improved the accuracy of both models. Several counties (e.g. Coos, Columbia, Wallowa, Union) have relatively high cattle densities, and large percentages of land classified as ungrazeable. Other areas have relatively low cattle densities (e.g. Deschutes, Jefferson), but large areas with high grazing potential. These discrepancies reflect potential interpretation errors the input data were not designed to answer. First, the models were not developed to address qualitative differences due to variation in above-ground net primary production. The input data (slope, proximity to water, and cover type) determined the relative probability of an area being grazed. These variables did not address forage production which largely determines how many animals may graze an area. Intuitively, one would expect that wet fertile river valleys in Coos County, where soil is deep and the growing season long, are capable of stocking beef cattle at much higher densities than arid sagebrush rangelands in Jefferson County. Although Coos County has little grazeable land (95% ungrazeable), it can support higher stocking densities than Jefferson County (18% ungrazeable). Second, livestock density data are from one date (31 December, 1992). Counties, such as Union, that have low-elevation pastures can support high winter densities because of supplemental feeding programs. Additional grazing and vegetation data collected at a larger scale (e.g. 1:24,000) might enhance the model’s utility for managing rangeland ecosystems. For example, at the allotment level, data about above-ground annual production by species, forage selection (preference) by domestic and wild herbivores, annual forage utilization,
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nutrient composition by season, and animal nutritional requirements could be integrated to develop a forage allocation model based on the biological needs and population size of each species. Data about forage preferences, nutrient composition, and nutrient requirements exist for many species (e.g. Culley, 1938; Cook & Harris, 1950, 1968; Smith & Julander, 1953; Cook et al., 1954; Herbel & Nelson, 1966; National Research Council, 1970).
Conclusion Two grazing models were developed using readily accessible data and a GIS. The first model showed areas that were most and least likely to be grazed. The second model ranked areas based on factors that indicate greater grazing potential. Locations that the models identified as being suitable or not suitable for grazing (Figs 1 and 2) compared favorably with the density of beef cows in 1992 (Table 4). Virtually all data collected by range management professionals have a spatial component. Each data type or layer often describes several attributes (e.g. cover, density, biomass, nutrient quality). Integration of these data types and their multiple attributes can yield several outputs addressing management problems. The abilities of GIS to integrate these data and to help manage rangelands have gone largely untapped. The U.S. Environmental Protection Agency (EPA), through its Office of Research and Development, partially funded and collaborated in the research described here under Cooperative Agreement CR-819549-0105 to the Desert Research Institute. This manuscript has been reviewed by EPA and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
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