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1.
Global livestock production
systems
Below, we point
out several caveats regarding our analysis and identify ways in
which the map of global livestock production systems could be
improved.
1.1 Our
classification depends on data of land cover/land use from the
United States Geological Survey (USGS), and is limited by that data
set. In the analysis, we classified all USGS categories that
represent pure or mixed cropland as mixed farming systems. We think
this results in an overestimation of the area of cropland in East
Africa and an underestimation of the cropland in West Africa. For
example, in West Africa, the map shows very little cultivation on
the border of the semi-arid/sub-humid zone (the Guinean savanna).
This is particularly apparent in central Nigeria. Our long-term
field observations of farming systems in Nigeria, Niger, Burkina
Faso and Côte d’Ivoire do not match this interpretation: for
example, in central Nigeria, mixed crop–livestock systems are common
and cover large areas. Using human population density, we were able
to correct for this problem in the current analysis. In eastern and
southern Africa, our field observations lead us to conclude that
there are large areas where cropping was overestimated. This was not
corrected in the current analysis, but the solution is more
complicated. Improvements in the future will come from consulting
detailed land-use maps for each of the countries of the region, and
substituting those for the USGS coverage.
1.2 Another
caveat and improvement concerns the amount of variation within all
of our production systems categories. For example, the mixed-farming
systems or rangelands in East Africa are predominantly on rich
volcanic soils associated with the highland areas. These differ
markedly in agricultural production potential from those systems
that are classified in the same way on our map in the rest of
Africa: there, soils were weathered from the nutrient-poor African
shield and thus their potential is much lower. In rangelands, this
results in the presence of a very different complement of
herbivores, different vegetation, and thus different livestock
production potential. In the nutrient-poor (dystrophic) savannas of
southern and western Africa, forages are poor in quality and
carrying capacity is lower than in the nutrient-rich (eutrophic)
savannas of East Africa. The impact of livestock on natural
resources also contrasts strongly in these regions: integrated
livestock–wildlife systems in East Africa appear to be compatible
(Reid
et al., 2001), whereas they often are not in southern Africa
(du Toit and Cumming, 1999). There may also be differences in the
level of poverty of livestock keepers in these areas that is
associated with livestock production potential, but this is unknown.
These illustrations demonstrate that it is important to recognise
the wide range in variation within each of the production system
categories when using this map.
1.3 Our
projection of human populations to the year 2050 contains a weakness
that affects the population projections and also the future
production systems map. The human population projection assumes that
any cell with zero population in the year 2000 will still have no
population in the year 2050. In order to overcome this problem, we
intend to create a model of migration rates and locations in the
future. It is logical to assume that there will be some migration
into these areas as the human population grows over the next 50
years, and this improvement will account for those anticipated
changes in the location of future human
populations.
1.4 Lastly,
there is a caveat associated with the category classified as ‘other’
on this map. This category contains ecosystems that range from
arctic tundra to tropical rainforest to desert. Thus, it is
important appreciate the wide range of systems represented by this
category across the world. For example, the ‘other’ category in
southern Tanzania is miombo woodland on nutrient-poor soils, whereas
the same category in northwestern Tanzania is the water body of Lake
Victoria. The map was created to show production systems alone and
puts all non-livestock production systems into this category. The
‘other’ category can be described as all areas where human
populations and livestock populations are low, and that support
widespread, intact native vegetation.
Also within the
‘other’ category are areas that are barren and sparsely vegetated.
In some cases, these include very arid rangeland systems where
livestock are kept by nomadic pastoralists on annual grasslands. One
such area that is classified in this way is northwestern Kenya,
where the Turkana pastoral people live and raise stock. Another is
along the border of the Sahel where Fulani graze their cattle in the
wet season and Tuareg people depend heavily on livestock. There are
other areas like these in northern Africa, Eritrea, Djibouti and
Namibia. These areas are small, so their exclusion does not
significantly affect the production systems map.
2.
Poverty
maps on a global scale
The poverty maps in Section 2 of this report represent a considerable
improvement over existing maps at national scale. In all such analyses,
however, some compromises had to be made because of the lack of data. For
example, for the global analysis, we had to assume that poverty rates for a
particular country applied to all the production systems within that
country. Data that would allow us to differentiate between different
production systems within countries simply do not exist except in specific
areas. Further, even if these data were available, there is little
information on the geographical distribution of poverty within each
production system within a country. It is reasonable to suppose that there
are more poor people in more marginal systems, but currently information on
these questions are very limited. Future improvements in global poverty maps
will require the collection of such information.
3.
Poverty maps of livestock keepers
General poverty maps may only poorly represent the distribution of poverty
among livestock keepers. This is because the proportional importance of
livestock to household income differs from one culture to another and within
production systems. For example, mixed crop–livestock farmers have
opportunities for obtaining income from a variety of sources; thus, income
from livestock probably contributes a smaller proportion to their household
food basket. By contrast, most pastoralists depend on livestock for a large
proportion of their income, although this is changing. Thus, any map of
poverty in livestock keepers needs to account for the importance of
livestock to income at the household level. Information on the importance of
livestock for rural livelihoods is difficult to find. As described earlier
in this report, one possibility is to use the density of tropical livestock
units per person as a proxy for this importance on the basis that higher
livestock numbers per person indicate that livestock are more im portant
to household incomes within a particular system. This assumption has some
obvious flaws; perhaps areas with more livestock per person are areas that
have more income opportunities of all kinds. Other options are outlined in
Section 2.5 of the main report.
References
du
Toit J.T. and Cumming D.H.M. 1999. Functional significance of ungulate
diversity in African savannas and the ecological implications of the spread
of pastoralism. Biodiversity
Conservation 8:
1643–1661.
Reid
R.S., Rainy M.E., Wilson C.J., Harris E., Kruska R.L., Waweru M.N.,
Macmillan S.A. and Worden J.S. 2001. Wildlife cluster around settlements
in Africa. People, Livestock and Environment Working Paper No. 2. ILRI
(International Livestock Research Institute), Nairobi, Kenya. 25 pp.
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