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The herder's environment: a GIS case study of resource use patterns amongpastoralists in Central TibetKenneth Bauera; Antoni Magrib

a 95 Stowell Road, Norwich, Vermont, USA b Magri Consultores Ltda., Las Condes, Santiago, Chile

First published on: 28 July 2010

To cite this Article Bauer, Kenneth and Magri, Antoni(2010) 'The herder's environment: a GIS case study of resource usepatterns among pastoralists in Central Tibet', Journal of Land Use Science,, First published on: 28 July 2010 (iFirst)To link to this Article: DOI: 10.1080/1747423X.2010.500682URL: http://dx.doi.org/10.1080/1747423X.2010.500682

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The herder’s environment: a GIS case study of resource use patternsamong pastoralists in Central Tibet

Kenneth Bauera* and Antoni Magrib

a95 Stowell Road, Norwich, Vermont, USA;bMagri Consultores Ltda., Las Condes, Santiago, Chile

(Received 17 October 2008; final version received 14 May 2010)

This article investigates how environmental features such as altitude, aspect, slope, andvegetation cover structure resource use patterns among pastoralists in the Porong regionof Central Tibet. Abundance, distribution (spatial and temporal), and use of forageresources in the study area were characterized by integrating field observations,participatory mapping, interviews, and remotely sensed images in a geographicinformation system (GIS). Georeferenced locations of livestock corrals enabled us tocharacterize seasonal grazing areas, resulting in a spatially explicit model of theenvironment in which Porong’s herders operate. The methodology, which included theuse of normalized difference vegetation index (NDVI) values to assess inter- and intra-annual rangeland productivity, availed this description of the environmental variabilitywith which these nomads cope. Using culturally nuanced and spatially explicitmethodologies we can enhance our understanding of pastoral systems in limitingenvironments, hopefully to gain clearer estimations of how economic and policychanges might affect the livelihoods of livestock-dependent communities in CentralTibet and beyond.

Keywords: rangelands; Tibet; pastoralists; vegetation index; environmental limits; live-stock development

1. Introduction

Pastoralists are people who are principally dependent on livestock for their subsistence andfor whom spatial mobility is regularly employed as a survival strategy (Dyson-Hudson andDyson-Hudson 1980) and as a mechanism for managing environmental risk (Paine 1972,1994). Pastoralism has been an economic mainstay of Tibetans for more than four millennia(Miller 1999) and huge swaths of the Tibet Autonomous Region (TAR) continue to be usedfor pastoral production, with rangelands constituting more than 50% of the total land area(Gustafsson 1993).

Tibetan nomads cope with an environment that is characterized by a patchy andseasonally variable resource base with low primary productivity except in wetlands and inthe more temperate areas of the Tibetan Plateau, the last of which occur principally outsidethe TAR. Strong seasonal and year-to-year variability in forage production and occasionallyheavy snow cover make livestock production unpredictable and prone to large, unforeseenlosses (Walker 1977; Li and Wiener 1995; Kreutzmann 1996; Jianlin 2000; Sheehy 2000;Xue, Zhao, and Zhang 2005). Climatic conditions and elevation constrain the upper reaches

Journal of Land Use ScienceiFirst, 2010, 1–12

*Corresponding author. Email: [email protected]

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of vegetation growth and the cover/composition of plants varies depending on factors suchas slope, moisture, aspect, exposure, soil erodibility, rainfall, and permafrost depth, amongothers (Walker 1977; Netting 1981). The interaction of altitude, climate, and soil fertility hasbeen shown to set limits on the types of livestock animals that can survive and be productivein high-altitude environments such as the Tibetan Plateau (Guillet 1983). Although vegeta-tion is sparse in Central Tibetan rangelands like Porong’s, high groundwater tables occurringin some places create productive pastures. In spite of these limitations, Tibet’s rangelands areproductive, having sustained for thousands of years some of the largest herds of wild anddomestic ungulates in the world (Schaller 1998). This article uses a combination of satelliteimagery, GPS survey locations, participatory mapping, and geographic information system(GIS) spatial analyses to investigate regional resource use patterns in Porong Township. Itprovides a better understanding of highland pastoral production systems and the physicallimitations within which the pastoralists of this region operate. Although no forecasts aremade in this article, a clearer understanding of these types of pastoral systems is essentialwhen evaluating how this nomadic way of life may be affected by climatic, political, andeconomic change in the future.

2. Methods

2.1. Study area

This study focuses on Porong Township (28�47¢N, 85�45¢E, shown in Figure 1), which islocated just north of the China–Nepal border, some 700 km west of Lhasa, the capital of theTibet Autonomous Region (TAR). Porong is situated at the very upper limit of habitableenvironments: the mean altitude of its historical and contemporary settlements (n = 33) is4690m above sea level. The area drains into Peiku Tso Lake and consists mainly of flat, gravel-covered surfaces, which were once shallow lakes. The township comprises nine administrativevillages containing some 380 households with a total population of just over 2000. Porong isthe largest animal husbandry production area in Nyelam County and the government reportedin 2003 that the total number of animals was 54,065 (6643 yak; 37,789 sheep; 9157 goats; 476horses); the average annual income was US $228 per capita (Nyelam AHB 2003).

2.2. Mapping in the field

Historical and contemporary pasture boundaries within Porong Township were georefer-enced using an eTrex� Garmin (Garmin, Olathe, KS, USA) GPS during visits to the regionin 2003 and 2004. Additional information was obtained through a participatory mappingprocess that involved individuals and groups of community members. Participants wereasked to draw features of the landscape they considered important and to illustrate (throughwords, lines, and symbols) their seasonal uses of rangelands and routes of migration. Themember of a group considered to be the most literate recorded the spelling of place names. Aspatial database was subsequently built to incorporate this information into the GIS.

2.3. GIS analysis

Elevation data for the region was obtained from the Shuttle Radar Topography Missionconducted in 2000 (NASA 2006). Spatial resolution of the pixels was 87 m. Separate fileswere merged to cover the study area, and missing values were excluded from furtheranalysis, as they were located beyond the upper limits of vegetation and pastoral activity.

2 K. Bauer and A. Magri


The digital elevation model was processed in ArcGIS 9.1 (ESRI, Redlands, CA, USA) toproduce slope and aspect (inclination with respect to North) grids for the study area.

To compile a time series of vegetation, MODIS Terra Vegetation Index compositeimages (MODIS Terra version 004, LP DAAC 2006) were obtained for the period between24 February 2000 and 9 June 2006. This time period was chosen based on the availability ofimages for the region and was deemed to be long enough to quantify and illustrate theseasonal and year-to-year variation in the study area’s vegetation. Each of these images is acomposite of normalized difference vegetation index (NDVI) values taken over consecutive16-day periods. The images were clipped and projected to UTM Zone 45N (to match theother spatial data) using the MODIS Reprojection Tool.

As our objective was to investigate seasonal resource use, the NDVI images weregrouped to match the Tibetan calendar as closely as possible. This calendar divides theyear into four main seasons, each of which is further divided into two subseasons ofapproximately 45 days each – denoted ‘upper’ (yar) and ‘lower’ (med) halves of eachseason (Table 1). Images were grouped by selecting those with start or end dates within �7days from each subseason’s start or end date. In the majority of cases, this translated to threeimages per subseason. Each image was used only once so that the data would not beduplicated from one season to the next.

Figure 1. Location of Porong Township study area in the Southern Tibet Autonomous Region,People’s Republic of China.

Journal of Land Use Science 3


Groups of images were condensed into one new NDVI image for each subseason bytaking the maximum value of each pixel within the group. Maximum pixel values were usedto reduce the influence of low-quality pixels in the final image. An analysis of bad pixels(which occur because of cloud cover, other atmospheric conditions, and low solar angle)indicated that images generally had few pixels below average quality. Special attention waspaid to subseasons showing little or patchy vegetation, which was determined to be primarilydue to snow cover.

NDVI values vary between -1 and +1, with positive values indicating the presence ofvegetation. Higher NDVI values indicate active photosynthesis (Lillesand and Keifer 2000).The MODIS images had a pixel size of 250 by 250 m on the ground (the smallest available),and as such pick up a mixture of vegetation and soil surface reflectance. High NDVI values(close to +1) are indicative of both dense and photosynthetically active vegetation, whereaslower positive values indicate sparser vegetation or less photosynthetic activity. Values closeto 0 result from bare soil and dead vegetation, whereas clouds, snow, and bright non-vegetated areas have negative NDVI values. To simplify the analyses, NDVI values wereclassified as shown in Table 2.

Satellite imagery and GPS point locations (500 in total) were incorporated directly intothe GIS. Information contained in the participatory maps was digitized using a Landsat 7ETM+ image of the study area taken in 2000 (Global Land Cover Facility 2006) as a basemap. Corrals that had not been georeferenced in field, but had been depicted in theparticipatory maps, were digitized based on their relative positions to mountains, rivers,

Table 1. Tibetan seasons.

Season Tibetan season Identifier Gregorian date

Winter dgun yar A1 Dec 1–Jan 15med A2 Jan 16–Mar 1

Spring dpyid yar B1 Mar 2–Apr 15med B2 Apr 16–May 31

Summer dbyar yar C1 Jun 1–Jul 15med C2 Jul 16–Aug 31

Fall ston yar D1 Sep 1–Oct 15med D2 Oct 16–Nov 30

Table 2. Vegetation classes.

NDVI values

Vegetation class InterpretationFrom To

-1.0 0.05 0 No vegetation0.05 0.2 1 Sparse/relatively inactive vegetation0.2 0.4 2 Medium-low vegetation0.4 0.6 3 Intermediate vegetation0.6 0.8 4 Medium-high vegetation0.8 1.0 5 Dense/active vegetation

Note: The classification of NDVI values is based on five equal interval classes for positive NDVI values, and acutoff value of 0.05 (used as an upper limit for bare soil reflectance).



and lakes. The corrals were then classified according to their subseasons of use (as identifiedby herders), resulting in a set of corral locations for each of the eight subseasons (19 corralsfor season A1, 26 for A2, 32 for B1, 44 for B2, 47 for C1, 27 for C2, 19 for D1, and 8 for D2).

To characterize the topography and vegetation used by pastoralists in the Porong area,buffers of 50 and 3000 mwere constructed around each of the corrals. The smaller value wasselected to represent the immediate environs of the corrals, whereas the larger value wasselected to characterize the vegetation resources available in the surrounding landscape.Drawing from interviews with herders and field observations, a buffer of 3000 mwas chosenbased on the assumption that dairy-producing animals and small livestock typically travelbetween 3 and 6 km in a day, for they must return to the corrals at night for milking andprotection from predators like wolves and snow leopards. The daily travel distance was set to3000 m for all seasons; although it is possible it may vary due to snow cover in winter andhigher altitudes in summer. However, in our analyses, the departure points (corrals) for dailytravel do vary from season to season, which is one mechanism that the pastoralists use toaccess and manage the vegetation within the study area.

The zonal statistics function in ArcGIS 9.1 was used to summarize topographic andvegetation characteristics within the buffers for each season and year. Mean elevation, slope,aspect, and vegetation class values for the entire study area (delimited by the pastureboundaries of the township) were obtained by statistically summarizing these attributesbetween 2000 and 2006.

2.4. Participatory mapping

Participatory mapping yielded valuable cartographic information about the study area aswell as insight into Porong’s human ecology. The most commonly depicted elements wereridges and mountains, rivers and bodies of water, open plains, and grasslands. Constructionssuch as corrals and settlements were also typical features in herders’ maps. Information fromthese hand-drawn maps and other sources were compiled into a database that contained:place names for various geographical points; environmental characteristics; and socialhistory and cultural rituals linked to locales. The compiled geographic index also includedassociated linguistic (e.g., definitions and alternate spellings), ecological (e.g., vegetationspecies present), and management (e.g., season of use) data. The cumulative result was agazetteer of the Porong region (4034 km2) that contained over 1400 features of interest.

3. Results and discussion

3.1. Seasonal and inter-annual variability in vegetative resources

NDVI values produced evidence that was spatially explicit and time-rich in terms of theseasonal and annual variations in vegetation growth on Porong rangelands. Average NDVIclasses were calculated for each season between 2000 and 2006 (illustrated in the video file‘NDVI and Corrals Video’ available at http://dx.doi.org/10.1080/1747423X.2010.500682).This provided a measure of the contemporary distribution of natural resources across thePorong study area as well as seasonal variations in plant productivity, as shown in Table 3.

First, these results confirm that, even with high elevation and harsh conditions, the studyarea is covered with vegetation. Only about 2% of the area was classified as bare soil/rock(see seasons C1–D2). In other seasons (A1–B2) the percentage of non-vegetated areaincreases, which is due to snow cover. Second, although most of the study area is coveredby Class 1 vegetation, which is not very photosynthetically active or dense, a significant shift



http://dx.doi.org/10.1080/1747423X.2010.500682

toward higher vegetation classes occurs in late summer and early fall (seasons C2 and D1),when plant life reaches peak activity. Jun, Zhongbo, and Yaoming (2004) and Immerzeel,Quiroz, and De Jong (2005) also found that monthly NDVI values for grasslands reachmaximum values between August and September for this region of the Tibetan plateau. Thisproductive period corresponds with peak nutritional demands and peak dairy production of thelivestock. Very little forage harvesting for storage as hay is currently possible in Porong,though certain zones of standing vegetation are set aside for winter. Very small areas (,0.01%of the study area), which are located in the main wetland of the study area, reach Class 5 (verydense and active vegetation) in seasons C2 and D1. By contrast, winter and early spring offermostly Class 1 and very small areas of Class 2 (,0.01% of the study area) vegetation forlivestock; this fact is observable in the field and verified by conversations with herders –vegetation is so sparse during this period that livestock lose up to a third of their body weight.

Livestock operations are driven by the distribution, amount, and variety of fodderavailable, which, in turn, are affected by inter-annual variations in snowpack, precipitation,and other climatic factors. Figure 2 illustrates the variation in vegetation class within thestudy area between seasons and years. Peak vegetation (Class 5) occurs predominantly in thesummer, although some isolated occurrences are observed in other seasons, but not on aconsistent basis. While the year 2000 produced vegetation of Class 5 in seasons B2–C2, theyear 2005 only achieved vegetation Class 4. Another example of inter-annual variation isthat the mean vegetation class for season C1 in 2000 was considerably higher (.30%) thanfor the same season in 2004.

Seasonal variations in vegetation available for grazing are illustrated by changes in photo-synthetic activity. The high range of variability in spring (B1, B2) is likely to be a function ofsnowpack and subsequent melt that provides soil moisture available for plant growth. Based oninterviews with herders and the patterns revealed in Figure 2, spring is a crucial bottleneck in theyearly production cycle. In years when vegetation growth is strong (as in 2000 and 2001), morelivestock offspring are likely to survive and pastoralists aim to expand the herd.

Although the time series of NDVI data is limited, it is clear that there is considerablevariation between consecutive seasons, indicating that the environment of Porong not only isharsh during most of the year, but also requires adaptation from year to year. Poor growth inthe summer and early fall is likely to have a significant effect on animal survival and herdsize in subsequent years. These conditions, coupled with the dependence of nomads on theiranimals (especially sheep in Porong) for their livelihood, make the system potentially

Table 3. Mean areal coverage by vegetation class.

Season

Winter Spring Summer Fall

A1 A2 B1 B2 C1 C2 D1 D2

Vegetation class (% of study area)

0 3.30 10.70 12.12 4.85 2.03 1.75 1.59 2.631 95.68 89.11 87.26 93.22 83.99 41.33 56.53 93.742 1.02 0.19 0.61 1.93 13.31 53.22 39.74 3.553 0.01 0.65 3.07 1.68 0.084 0.03 0.61 0.455 0.02 0.01



vulnerable to both climatic events and external economic shifts. The lack of available foragestored as hay in this pastoral system means that local pastoralists are entirely reliant on theclimate and careful herding of their animals in search of fodder on a range that is, by localaccounts, desiccating. One herder said,

There isn’t enough grass for the yaks any more. We sold off our herd several years ago. But thesheep still survive, even though the grasslands are drying up. Besides, we can still sell our sheepand goats to Nepal. (Bauer, unpublished notes, 2004)

An additional preoccupation is the change in vegetation:

More and more sand has slowly come. These days a lot of sand comes especially during droughtyears. Then during the following winter, the wind will blow the sand everywhere. The lengthsand kinds of grasses are decreasing. Poisonous grasses like sran rdzi ma [Astragalus sp.] areincreasing and spreading regardless of whether the rain is good or not. (Bauer, unpublishednotes, 2004)

These concerns illustrate the fragility of the pastoral system in Porong and highlight trendsthat may significantly affect the viability of the system.

3.2. General relationships between vegetation classes and elevation, slope, and aspect

Summaries of elevation, slope, and aspect for each vegetation class are shown in Figures 3aand b (for elevation), 4a and b (for slope), and 5 (for aspect). The figures indicate that Class 1and 2 vegetation are present throughout all seasons and occur at high elevations, withaverages over 5000 m and maximum values over 6000 m in some seasons. Vegetationclasses 3, 4, and 5 tend to occur at lower elevations and during the summer months. Class 5

0

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A2 B1 B2C1C2D1D2 A1A2 B1 B2C1C2D1D2 A1A2 B1 B2C1C2D1D2 A1A2 B1 B2C1C2D1D2 A1 A2 B1 B2C1C2D1D2 A1 A2 B1 B2C1C2D1D2 A1 A2 B1 B2

Veg

etat

ion

clas

sMax. veg. classAvg. veg. classMin. veg. classStudy area avg.

2001 20020002 2003 2004 2005 2006

Seasons and years

Figure 2. Time series of vegetation classes in grazing areas.Note: Avg. veg. class corresponds to the average vegetation class in the grazing areas (3000 mbuffers centered on corral locations). Study area avg. corresponds to the average vegetation classfor the entire township of Porong. Slight differences between these series occur because thegrazing areas do not cover 100% of the township in all seasons.



vegetation, in particular, occurs in the main wetland located in the center of the township, atan elevation of 4650 m.

An analysis of slope shows similar patterns: vegetation classes 1 and 2 occur on steeperslopes (mean values of up to 10%, and maximum slopes over 50%). Classes 4 and 5, on theother hand, occur on flat slopes (averages of 1%), corresponding to the center of the PorongTownship basin.

Average aspects for all vegetation classes are about 180�, which is a result of the PorongTownship boundaries following the outer limits of a basin which is surrounded by moun-tains. This indicates that vegetation classes occur on all aspects (0�–360�), without a cleardominance, as evidenced by the NDVI values and corral locations, which span the entirebasin (shown in the accompanying video). Aspect values for Class 5 vegetation are morevariable than for other classes (Figure 5) due to the small surface area covered by thisvegetation class.

4500

4600

4700

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4900

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5100(a)

A1 A2 B1 B2 C1 C2 D1 D2Vegetation class

Mea

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m)

Veg. Class 1Veg. Class 2Veg. Class 3Veg. Class 4Veg. Class 5

(b)


4500

4750

5000

5250

5500

5750

6000

6250

Max

imu

m e

leva

tio

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m)


Figure 3. (a) Relationship between mean elevation and vegetation. (b) Relationship betweenmaximum elevation and vegetation.



3.3. Environmental characteristics of corral locations and seasonal grazing areas

On the Tibetan Plateau the occurrence of pastoralism or agriculture (as well as hybridlivelihoods) can be predicted based largely on altitude (Ekvall 1974; Ryavec 2001).Between and within the seasons, elevation, topography, and other factors like wind/sunexposure, shelter, and access to water determine the movements of pastoralists. As shown inFigure 6, the elevation of corrals in Porong is low during late autumn and winter (D2, A1)and rises until the height of summer (C1, C2), as herd movement expands to higherelevations. Grazing areas expand quickly after winter, reaching areas with elevationsabove 5700 m during later spring and summer, when snowmelt provides moisture forvegetation of Class 2 to grow in these areas.

Use of forage in higher elevations during summer helps vegetation at lower elevationsrecover and preserves pastures near villages for colder seasons of the year. The mean corralaltitude dips during the late spring (B2) period, which is when animals are brought lower and

0.0

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ean

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(a)


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Max

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lop

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)



Figure 4. (a) Relationship between mean slope and vegetation. (b) Relationship between maximumslope and vegetation.



closer to settlements so that herders can assist with birthing, the most crucial period of theyear for a pastoral household.

Most herding activity was found to take place in flatter areas with slopes of less than 10�

(Table 4), even though the range of slope values was quite wide. For example, maximum valuesas steep as 44� were recorded for grazing zones, a slope where few herders would choose tospend much time. Yaks, however, thrive in these areas, and wild yaks are typically found onsuch slopes. High incline valuesmay also reflect the proximity of livestock corrals to vertiginousridges, beside which herders and their animals often take shelter, particularly in windy areas.

Aspect plays an important role in vegetation cover and type as it determines exposure towind and sun and contributes to environmental variation at the community and species

120.0

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spec

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egre

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rom

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Veg.Class 1Veg.Class 2Veg.Class 3Veg.Class 4Veg.Class 5


Figure 5. Relationship between mean aspect and vegetation.

4500

4750

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6000

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vati

on

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) Corral avg.

Corrals max.

Corrals min.

Grazing area avg.

Grazing area max.

Grazing area min.

A1 A2 B1 B2 C1 C2 D1 D2Season

Figure 6. Elevations of corrals and grazing areas.



scales. Variations in aspect may also translate into differing forage availability, whichinfluences the types of livestock that will typically graze a given area. The range of meanvalues for aspect in the seasonal grazing zones was found to be relatively narrow – between154� and 175� – meaning that grazing is generally concentrated on slopes facing fromsoutheast to south. This makes sense from the standpoint of favorable shelter, exposure to thesun, and overall warmth. However, aspect values for each season had large variations,indicating that vegetation is found on a variety of slopes in the study area, which is alarge, variegated basin with mountain ranges to the north and south.

4. Conclusions

The nomadic decision-making process is complex and must be responsive to significantenvironmental variation. As such, this production system is fruitfully examined usingqualitative and quantitative approaches. Indeed, the case study underscores the fact thatpatterns emergent from complex phenomena like resource use and land management comeinto better focus by observing both social and environmental phenomena.

This article has demonstrated a quantitative approach for characterizing the ecologicalfactors that affect the livelihood strategies and resource use decisions in a pastoral productionsystem like Porong’s. The spatial approach adopted here integrates data gathered throughon-the-ground interviews, field research, and remote sensing, and provides measures that arepotentially useful in development planning. Classifying vegetation from satellite imagesprovides a rapid and widely applicable method to monitor rangeland areas, particularly theproductivity of vegetation over time. In related research, Bauer (2004) has used field inter-views to identify the tenure status (household or collective), seasonal use, and other informa-tion regarding the management of natural resources over time.

The application of GIS to trans-disciplinary research may facilitate ongoing improve-ments in development praxis on the Tibetan Plateau. Supplied with more accurate informa-tion that is locally specific, planners and managers may be better positioned to develop moreappropriate land use policies. Development praxis may be augmented with enhancedcapacity to forecast how changes in policies – such as the promotion of fenced enclosuresor fixed carrying capacity policies that standardize stocking rates across broad scale areas –will affect the viability of pastoral production systems such as those of Central Tibet. Themethods adopted here provide a mechanism to integrate qualitative and quantitative datausing a spatially explicit methodology.

Table 4. Slope and aspect of the grazing areas.

Season Min slopea Max slope Mean slope Mean aspectb

A1 0 38 5 154A2 0 37 6 166B1 0 44 7 165B2 0 39 6 162C1 0 39 7 168C2 0 38 6 165D1 0 37 5 175D2 0 35 7 159

Note: aSlope is presented in degrees from horizontal (0 to 90).bAspect is presented in degrees from North (0 = North, 90 = East, 180 = South, 270 = West).



Acknowledgments

The authors acknowledge and appreciate the support of the National Science Foundation and theWenner Gren Foundation for this research.

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