Brief overview
More so than any other use today, people rely on drylands to provide forage for the production of domestic livestock. From cattle, sheep, and goat herds, to horses and camels, drylands support large numbers of domestic animals, which become the source of meat, milk, wool, and leather products for humans.
Map description (Map 5)
This map show the density of livestock, including cattle, buffalo, sheep, goats, horses, mules, donkeys, and pigs. Densities range from less than 5 tropical livestock units (TLUs) to more than 40 TLUs per square kilometer. A tropical livestock unit is the common unit for describing livestock numbers of different species; as a single value this expresses the total amount of livestock present regardless of the specific composition.
Some of the highest livestock densities in the world are in the drylands of Asia, Africa, the Middle East, and South America. Very high densities in drylands (greater than 40 TLUs per square kilometer) are found in India and Pakistan, eastern China, the Sahel, Turkey, and parts of southeastern South America. Livestock can help maintain soil fertility, increase nutrient retention and water-holding capacity, and create a better climate for micro-flora and fauna. If drylands are overgrazed, soil compaction and erosion may follow with a decrease in soil fertility, organic matter, and water-holding capacity.
Map description (Map 6)
The International Livestock Research Institute (ILRI) has prepared this global map of livestock production systems (for the developing world only). ILRI defined these 10 production systems based on whether the systems were livestock only, livestock mixed with irrigated cropland, or livestock mixed with rainfed cropland. Each system is further defined according to agro-ecological zone: arid/semi-arid; humid/subhumid; or temperate/tropical highland. The last category, “other,” includes areas where human and livestock populations are low and where native vegetation is widespread.
In comparing the livestock production systems map with the map of dryland extent, a fairly obvious pattern emerges. Livestock only, rangeland-based systems are most predominant in drylands – in Mexico, southern South America, the Sahel, southern Africa and parts of China. Some important areas of mixed irrigated and mixed rainfed production systems are found in drylands, but are much less extensive.
Soils in drylands
The capacity for drylands to produce forage for livestock is determined, in part, by soil condition. The Global Assessment of Human-Induced Soil Degradation (GLASOD) and, more recently, the Assessment of the Status of Human-Induced Soil Degradation in South and Southeast Asia (ASSOD) represent efforts to qualitatively assess soil degradation (See, “Measuring soil condition”).
GLASOD indicates substantial areas of soil degradation around the world. Globally, approximately 20 percent of the soils in drylands are degraded – 17 percent lightly to moderately degraded; over 2.5 percent strongly to extremely degraded (Table 6). Regionally, the soils in Asia and Africa are the most degraded, approximately 370 million hectares of degraded dryland in Asia; 319 in Africa. Although Asia has more total degradation in its drylands, Africa has more soils in the strong to extremely degraded classes (43.5 million hectares in Asia vs. 74.2 million hectares in Africa).
ASSOD focuses on South and Southeast Asia, including seven countries: China, India, Myanmar, Nepal, Pakistan, Sri Lanka, and Thailand. When analyzed according to aridity zone within these seven countries, more than half of the drylands (approximately 53 percent) have degraded soils, most predominantly in the arid zone. More so than the sub-humid zone, the arid and semi-arid zones include more dryland area in the strong and extremely degraded classes. (Table 7).
Vegetation in drylands
Several indicators based on satellite images and with long-term trends can be used to examine dryland vegetation. These indicators include the Normalized Difference Vegetation Index, Net Primary Productivity, and Rain- Use Efficiency.
Normalized Difference Vegetation Index (NDVI)
The Normalized Difference Vegetation Index is a remote sensing tool used to track global vegetation cover. It is derived from Advanced Very High Resolution Radiometer (AVHRR) data and related to the proportion of photosynthetically-absorbed radiation. This index describes the capacity of vegetation canopies to absorb solar radiation.Various institutions have used NDVI for an array of applications, including the USGS-IGBP Global Land Cover Characterization and the USGS-FAO Map of the World’s Forests. A long term analysis of NDVI by UNEP showed wide variation across the world’s drylands. In some semi-arid environments, positive trends in NDVI have corresponded to areas with irrigation systems, increased production, and cover of wetland plant species while negative trends have corresponded to areas with negligible rainfall.
One drawback of the NDVI is that while it provides values for total vegetation, it cannot distinguish species composition. For example, high NDVI values might represent relatively luxuriant vegetation, but in semiarid rangelands they also could represent disturbed vegetation communities with unpalatable forbs.
Net Primary Productivity (NPP)
Net Primary Productivity is the total vegetative production of an ecosystem minus losses due to respiration. As the amount of organic carbon that plants actually make available to other organisms in an ecosystem, NPP may be a more direct indicator of actual yield of vegetation than the NDVI, which is a measure of light absorption. Direct observations of NPP are not available globally, but computer models derived from local observations and NDVI have been developed to represent global NPP.
Map 7 shows the pattern of mean annual NPP for a twelve year period (1982-1993). Globally, NPP is highest in low latitudes and lowest at the poles. The tropics and eastern edges of the continents tend to have high mean annual NPP. Western and more poleward continental areas have lower productivity.
When drylands are clipped from the global map of NPP (Map 8) several additional patterns become apparent. Drylands exhibit a range in productivity around the globe, from low NPP values around the Sahara and Namib deserts and in portions of central Asia and western Australia to the highest values, most extensive in low latitudes, in the tropical areas of South America, Asia and Africa. Dry sub-humid areas tend to correspond to the highest NPP values while arid and semi-arid areas average lower mean annual NPP.
Map descriptions (Maps 9 and 10)
Researchers have used eight years of NDVI data (1982-1989) to analyze interannual variation of NPP and to determine the coefficient of variation (ratio of the standard deviation of annual totals to the long-term mean) from the Global Production Efficiency Model (GLO-PEM) developed by the University of Maryland, Department of Geography. Interannual variation in mean NPP can reveal the complexity of spatial variation in species composition and biomass that is caused by climate, topography, soil types, and human-induced change.
Map 9 illustrates that some regions have stable NPP values from year to year, whereas other regions have highly variable values. Generally, the regions of lower NPP correspond to areas with the largest percentage variation in productivity from one year to the next.
Map 10, focusing on the variation in NPP in drylands only, shows that many of the areas with high variability in NPP are found in drylands–on all continents–the Great Plains of North America, southern Patagonia, the Sahel, Southern Africa, and much of central Asia and Australia. This variation may affect human behavior and household decisions. It may influence whether people migrate on a seasonal or permanent basis or whether they abandon livestock herding for a more sedentary, agrarian existence.
Rain-Use Efficiency (RUE)
Rain use efficiency is the ratio of net primary productivity to rainfall. It normalizes vegetative production to rainfall and may be helpful in revealing trends in land degradation, by separating vegetation declines due to lack of rainfall from declines associated with longer-term degradation. This index can be calculated from satellite observations of NPP (modeled with annual integrals of NDVI) and rain gauge data. Some studies using local NPP observations have found strong correlations between declines in RUE and increases in livestock followed by reductions in rangeland condition. Further study is needed, however, to determine whether these local correlations hold on a regional scale.
Map descriptions (Maps 11 and 12)
Map 11 shows the rain-use efficiency index for countries of southern Africa. Differences in the water balance of various climatic regimes make drylands influenced by the same climate more meaningful than cross-continental comparisons of RUE. This map shows the RUE indexes for a 13-year period. Low indexes, most extensive along the west coast, may indicate low biomass production regardless of rainfall patterns and thus possible land degradation; high indexes, scattered across the region and in several countries, may indicate high biomass production and potentially drylands in good condition.
Accurate interpretations of RUE require information on topography, soil texture, soil fertility, vegetation type, human population, and management regimes. Low and decreasing RUE could be due to various factors, including degradation and run-off, soil evaporation, and infertile soils. Conversely, high and increasing RUE may be due to factors such as run-on, fertilizer use, and changes in species composition.
Map 12 shows the RUE map of southern Africa clipped for drylands. Generally, much of the low RUE areas are included within drylands – areas with a ratio below .25 along the west coast from Angola south to South Africa. A large portion of the drylands in the region has indexes of less than .5. The less extensive, scattered areas of high RUE (ratios greater then 1) may indicate “bright spots,” or drylands that are in good condition in terms of biomass production.
Sources
Cramer, W. and C.B. Field. 1999. Comparing global models of terrestrial net primary productivity (NPP): Introduction. Global Change Biology 5 (Supplement): iii–iv.
Delgado, C., Rosegrant, M., Steinfeld, H., Ehui, S., and Courbois, C. 1999. Livestock to 2020, The Next Food Revolution. Food, Agriculture, and the Environment Discussion Paper 28. International Food Policy Research Institute. 72pp.
ESRI. 1993. Environmental Systems Research Institute. Digital Chart of the World CD-ROM. Redlands, CA: ESRI.
Fuller, D.O. 1998. Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993. International Journal of Remote Sensing 19(10):2013-2018.
GLCCD. 1998. Global Land Cover Characteristics Database, Version 1.2. Data available online at: . See also Loveland, T.R., B.C. Reed, J.F. Brown, D.O. Ohlen, Z. Zhu, L. Yang, and J. Merchant. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1–kilometer AVHRR data. International Journal of Remote Sensing 21(6-7): 1303–1330.
Goetz, S.J., S.D. Prince, S.N. Goward, M.M. Thawley, and J. Small. 1999. Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecological Modeling 122:239–255.
Goetz, S.J., S.D. Prince, J. Small, A. Gleason, and M. Thawley. 2000. Interannual variability of global terrestrial primary production: results of a model driven with satellite observations. Journal of Geophysical Research Atmospheres 105(D15):20,077-20,091.
Le Houerou, H.N. 1984. Rain use efficiency: A unifying concept in arid land ecology. Journal of Arid Environments 7: 213-247.
Milchunas, D.G., and W.K. Lauenroth. 1993. Quantitative effects of grazing on vegetation and soils over a global range of environments. Ecological Monographs 63(4): 327-366.
Oldeman, L.R., and G.W.J. van Lynden. 1997. Revisiting the GLASOD methodology. In Methods for Assessment of Soil Degradation, ed. R. Lal, W.H. Blum, C. Valentine, and B.A. Steward, 423–439. New York: CRC Press.
Prince, S.D., and S.N. Goward. 1995. Global primary production: a remote sensing approach. Journal of Biogeography 22:815-835.
Prince, S.D., E. Brown de Colstoun, and L.L. Kravitz. 1998. Evidence from rain-use efficiencies does not indicate extensive Sahelian desertification. Global Change Biology 4: 359–374.
Thomas, D.S.G. 1993. Sandstorm in a teacup? Understanding desertification. The Geographical Journal 159(3):318–331.
Thornton, P.K., Kruska, R.L., Henninger, N., Kristjanson, P.M., Reid, R.S., Atieno, F., Odero, A.N., and Ndegwa, T. 2002. Mapping Poverty and Livestock in the Developing World. International Livestock Research Institute, Nairobi, Kenya. 118 pp.
Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127-150.
UNEP. 1997. United Nations Environment Programme. World Atlas of Desertification, 2nd edition. Edited by N. Middleton and D. Thomas. London: UNEP. 182pp.
UNEP/GRID. 1991. United Nations Environment Program/Global Resource Information atabase. Global Digital Datasets for Land Degradation Studies: a GIS Approach. Prepared by U. Diechmann and L. Eklundh. GRID Case Study Series No. 4. UNEP/GEMS and GRID. Nairobi, Kenya.
