TARGETING ANIMAL PRODUCTION VALUE CHAINS FOR UGANDA
1. Livestock production systems
Seré and Steinfeld (1996) developed a global livestock production system classification
scheme. The system breakdown has four production categories: landless systems (typically
found in peri-urban settings), livestock/rangeland-based systems (areas with minimal
cropping, often corresponding to pastoral systems), mixed rainfed systems (mostly rainfed
cropping combined with livestock, i.e. agropastoral systems), and mixed irrigated systems
(significant proportion of cropping uses irrigation and is interspersed with livestock). All but
the landless systems are further disaggregated by agro-ecological potential as defined by
Length of Growing period (LGP): arid–semi-arid (with LGP <180 days), humid–subhumid
(LGP >180 days), and tropical highlands/temperate regions.
The first attempt to map livestock production systems, at least in the developing world, was
by Thornton et al. in 2002, based on this classification scheme. This method is revised,
including more accurate and higher spatial resolution (circa 1 km) input data (Robinson et al.,
2011). Figure 1 shows the spatial distribution of the livestock production systems in Uganda.
Figure 1: Distribution of production systems in Uganda (Robinson et al., 2011)
As the mixed irrigated systems cover not even 1% of the surface land area, we present the
results simplified to rangeland based and mixed systems. Table 1shows the relative area of
these different production systems.
Table 1: Surface area of production systems in Uganda Nicaragua (derived from Robinson et
al., 2011)
Production system
Rangeland based, (Hyper-) Arid/Semi-arid (LGA)
Rangeland based, Humid/Sub-humid (LGH)
Rangeland based, Temperate/Tropical highlands (LGT)
Mixed, (Hyper-) Arid/Semi-arid (MRA)
Mixed, Humid/Sub-humid (MRH)
Mixed, Temperate/Tropical highlands (MRT)
Urban
Other
Area
Percentage (%)
5,700
28,300
1,200
11,900
150,000
19,000
1,400
19,400
2
12
1
5
63
8
1
8
Only 14% of the area in Uganda is under grasslands supporting (agro-) pastoral livestock
production, the most common production system is mixed crop-livestock systems, covering
just over 75% of the land.
2. Socio-economic data
2.1 Human population & poverty
To show the spatial distribution of human population, we use the estimates of human
population of Global Rural-Urban Mapping Project (GRUMPv1) for the year 2000. The
population density grids measure population per square km (CIESIN, 2011). Figure 2 shows
the spatial distribution of human population densities for Uganda.
Figure 2: Distribution of human population density in Uganda (CIESIN, 2011)
Table 2 shows the distribution of the population densities over the different production
systems. As expected, in the rangeland areas, the lowest population densities prevail, while
densities increase in the mixed systems. The high standard deviation in the mixed systems
highlights the large regional variation within these systems.
Table 2: Average population densities by production system (derived from CIESIN, 2011)
Population density (head/km2)
Standard deviation
LGA
8.4
5.5
LGH
10.0
6.1
LGT
9.3
5.2
MRA
18.2
44.6
MRH
124.0
146.0
MRT
202.7
166.3
Urban
2040.9
3326.0
Other
109.0
180.5
Production system
Poverty is defined as an economic condition in which one lacks both the money and basic
necessities, such as food, water, education, healthcare, and shelter, necessary to thrive. The
common international poverty line has in the past been roughly $1 a day. In 2008, the World
Bank came out with a revised figure of $1.25 and at 2005 purchasing-power parity (PPP)
(Ravallion et al., 2009). Commonly measured by the average daily amount of money a person
lives on, poverty is currently set at less than US$2 (PPP) per day (also called the $ 2 poverty
line) for poverty and less than US$1.25 (PPP) per day (also called the $ 1.25 poverty line) for
extreme poverty. The most common poverty metric is head count ratio (HCR), the percentage
of the population living below the established poverty line (Wood et al, 2010).
Figure 3 shows the spatial distribution of the number of people living on less than $1.25 per
day. Figure 4 shows the spatial distribution of the number of people living on less than $2 per
day.
Figure 3: Distribution of the number of people living on less than $1.25 per day (Wood e al,
2010)
Figure 4: Distribution of the number of people living on less than $2 per day (Wood e al,
2010)
Table 3 show the total population of Uganda by region. The table shows as well the number
of people living under 1.25$ and 2$ a day, and the percentage of poor people per region.
Table 3: Total population by regions, and number of people living of less than 1.25 and
2$/day (derived from CIESIN, 2011 and Wood e al, 2010)
Region
Central
Eastern
Northern
Western
Total
population
(1000)
Poor people living <1.25$/day
Poor people living <2$/day
Total number
(1000)
% of poor
people in the
region
Total number
(1000)
% of poor
people
9,370
3,970
42.4
5,840
62.3
8,720
4,810
55.2
6,900
79.2
6,350
8,960
5,570
87.8
48.3
6,100
6,090
96.1
67.9
4,330
To obtain a better idea about the distribution of the human population, Table 4 and Table 5
present total population and number of poor over the different production systems.
Table 4: Total population and number of people living of less than 2$/day by production
system (derived from CIESIN, 2011 and Wood e al, 2010)
Production
system
Total
population
Poor <2$
% poor of total
poor population
LGA
58,000
53,100
0.2
% of poor persons
within farming
systems
91.6
Standard
deviation
LGH
340,000
311,100
1.3
91.5
18.3
LGT
13,000
13,000
0.1
100.0
23.5
MRA
261,000
247,500
1.0
94.9
16.2
MRH
22,332,000
17,373,900
72.2
77.8
23.6
MRT
4,640,000
2,994,100
12.4
64.5
25.7
Urban
3,379,000
1,395,700
5.8
41.3
25.5
Other
2,376,000
1,683,800
7.0
70.9
17.5
19.8
Table 5: Total population and number of people living of less than 1.25$/day by production
system (derived from CIESIN, 2011 and Wood e al, 2010)
Production
system
Total
population
Poor <2$
% poor of total
poor population
Standard
deviation
0.3
% of poor persons
within farming
systems
86.6
LGA
58,000
50,200
LGH
340,000
266,800
1.4
78.5
24.2
25.3
LGT
13,000
12,600
0.1
96.9
15.8
MRA
261,000
232,100
1.2
88.9
21.7
MRH
22,332,000
13,826,100
73.5
61.9
26.3
MRT
4,640,000
2,264,300
12.0
48.8
26.0
Urban
3,379,000
964,300
5.1
28.5
18.1
Other
2,376,000
1,194,800
6.4
50.3
17.2
Poverty levels are high in Uganda. The percentages of people who are poor according to the
$1.25 a day poverty line is and $2.00 a day poverty line, is 62.0 and 78.5% respectively. As
most people live in mixed production systems, the absolute number of poor people living in
these areas is highest as well.
2.2 Market access
Travel time to market centers is used as a proxy for market accessibility and shows the likely
extent to which farming households are physically integrated with or isolated from markets. It
is important to farming households and other producers to have access to markets in order to
trade/sell their goods. The more accessible markets are to the given population the greater the
population’s ability to remain economically self-sufficient and maintain food secure (Nelson,
2008).
The travel time maps indicate the degree of accessibility to a populated place. The patterns
shown here describe the physical accessibility between places in Uganda, whereby
accessibility is defined as the time in hours required travelling from a given single point to the
nearest market centre of 50,000 or more people (Figure 5). The travel time approach is
estimated based on the combination of different global spatial data layers which represent the
time required to cross each single point.
Figure 5: Travel time (hr) to the nearest town of 50,000 people (Nelson, 2008)
To obtain a better insight about the differences in travel time between production systems, the
spatial data layer of travel time was overlaid with the spatial data layer of production systems.
Table 6 shows the mean travel time for each production system.
Table 6: Mean travel time (hr) for each production system (derived from Nelson, 2008)
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
Mean travel time (hr)
7.5
7.5
10.0
6.8
3.8
4.9
0.7
5.6
Standard deviation
3.7
5.3
4.2
3.6
3.0
3.4
1.1
4.1
The table shows clearly that travel time in (peri-) urban areas is lowest, and that travel time
can increase quickly in the mixed systems, but with large regional variation (high standard
deviation).
2.3 Consumption
In this report, we use FAO’s livestock consumption data to estimate national surplus – deficit
areas, when it is combined with other data sets later on (section 5). The FAO data are
estimates of the capita amount of food available for human consumption, during the reference
period (3-year period). Per capita supplies represent only the average supply available for
each individual in the population as a whole and do not indicate what is actually consumed by
individuals.
Table 7 shows the average consumption of bovine meat, milk, pig and goat/mutton meat for
Uganda, based on FAOSTAT for several years. Figure 6 shows the spatial distribution of pig
meat, based on population density (CIESIN, 2011).
Table 7: Average consumption of livestock products in Uganda (FAOSTAT, 2012)
Average consumption (kg/capita/yr)
Bovine Meat
Milk, Whole
Pig meat
Mutton & Goat Meat
1999
2000
2001
2002
2003
2004
2005
2006
2007
4.05
20.5
3.18
1.22
3.96
19.89
3.17
1.23
4.02
19.27
3.21
1.24
4.07
25.62
3.24
1.2
4.09
24.86
3.24
1.37
3.82
24.02
3.44
1.33
3.69
24.39
3.44
1.21
3.57
23.63
3.45
1.17
3.46
22.94
3.44
1.14
Figure 6: Average pig meat consumption in Uganda
Table 8 shows the average pig meat consumption over the various production systems. The
table shows clearly that most pig meat consumption takes place in urban areas, and that in the
pastoral rangelands consumption is low.
Table 8: Average pig meat consumption by production systems
Production system
LGA
Average meat consumption
(kg/km2/year)
29.1
LGH
34.5
LGT
32.2
MRA
63.3
MRH
425.5
MRT
693.2
Urban
7214.1
Other
373.5
3. Livestock
Livestock sector planning, policy development and analysis depend on reliable and accessible
information on the distribution, abundance and use of livestock. The 'Gridded Livestock of
the World' database provides standardised global, sub-national resolution maps of the major
agricultural livestock species. The map values are animal densities per square kilometre, and
are derived from official census and survey data. Livestock distribution data give an
estimation of production; they evaluate impact (both of and on livestock) by applying a
variety of rates; and they provide the denominator in prevalence and incidence estimates for
epidemiological applications, and the host distributions for transmission models (Wint &
Robinson, 2007).
Table 9 shows the number of pigs per production system. Figure 7 shows the spatial
distribution of pigs.
Figure 7: Average pig densities in Uganda (Wint & Robinson, 2007)
Table 9: Average densities of bovine, goat, pigs and sheep by production system (head/km2)
(derived from Wint & Robinson, 2007)
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
Average (head/km2)
Bovine
Goat
Pigs
Sheep
8.3
0.3
0.0
0.2
11.3
3.4
0.3
2.0
7.1
0.9
0.0
0.5
12.7
0.5
0.0
0.2
27.4
15.3
3.2
5.8
47.6
41.8
4.4
15.3
29.9
18.5
6.9
11.7
22.1
22.8
5.9
9.4
Table 9 shows clearly the high densities of livestock in the mixed systems, however, it shows
as well high cattle densities in (peri-) urban systems. Especially the density of pigs in (peri-)
urban areas is rather high.
4. Feeds
Herrero et al. (2012) estimated the consumption of feed resources (biomass use), by:
1. Estimating diets for each livestock species, in each production system
2. Estimating intake of each feed and estimating animals productivity
3. Multiplying animal productivity by the number of animals in each system (and their spatial
distribution) to get production
4. And matching this production to match national production statistics for milk, meat, etc.
Figure 8 shows the spatial distribution of the biomass use of pig feed resources for meat
production in Uganda, Table 10 averages the feed consumption by production system.
Figure 8: Pig feed requirements for meat production in Uganda (Herrero et al, 2012)
Table 10: Pig feed requirements by production system (derived from Herrero et al, 2012)
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
Average feed requirements (kg/km2/year)
Mean
STD
0
0
21
156
0
0
21
224
338
4,587
394
2,251
12,089
15,232
1,446
6,857
Average (pig)
head/km²
0.0
0.3
0.0
0.0
3.2
4.4
6.9
5.9
5. Production
Figure 9 shows respectively the spatial distribution of pig meat production for Uganda, Table
11 summarizes this production by production system.
Figure 9: Pig meat production in Uganda (Herrero et al, 2012)
Table 11: Average pig production by production system (derived from Herrero et al, 2012)
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
Average production (kg/km2/year)
Mean
STD
0
0
11
43
0
0
5
48
126
469
187
792
1,165
1,322
322
1,252
Average consumption
(kg/km²/year)
29.1
34.5
32.2
63.3
425.5
693.2
7214.1
373.5
As we are interested in the surplus versus the deficit areas of meat production, we subtract the
consumption data layers (Figure 6) from the production layers (Figure 9). Surplus areas are
those areas where production exceeds the consumption; deficit areas are those areas where
local production cannot supply the consumption.
Figure 10: Surplus - deficit areas for pig meat in Uganda
6 Excretion
Figure 11 shows the spatial distribution of pig N excretion for Uganda,
Table 12 summarizes the average excretion by production system.
Figure 11: Pig excretion in Uganda (Herrero et al, 2012)
Table 12: Average pig N excretion by production system (derived from Herrero et al, 2012)
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
N excretions (kg/km2/year)
Mean
STD
0
0
4
14
0
0
1
8
42
88
62
310
138
134
64
202
Production
(kg/km2/year)
0
11
0
5
126
187
1,165
322
Density
(head/km2)
0.0
0.3
0.0
0.0
3.2
4.4
6.9
5.9
7. Emissions
Figure 12 shows the spatial distribution of pig emissions for Uganda, Table 13 averages these
emissions by production system.
Figure 12: Average pig emissions in Uganda (Herrero et al, 2012)
Table 13: Average emissions by production system (derived from Herrero et al, 2012)
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
Emissions (ton CO2 eq/km2/year)
Mean
STD
0
0
22
86
0
0
9
97
254
942
377
1,593
2,342
2,658
648
2,517
8. Climate
Figure 13 shows the spatial distribution of the length of growing period (LGP) of rainfed
agriculture for Uganda, Table 14 averages the LGP by production system.
Figure 13: Length of growing period (in days) for Uganda
Table 14: Average length of growing period (days) by production system
Production system
LGA
LGH
LGT
MRA
MRH
MRT
Urban
Other
Average LGP (days)
167
215
193
164
239
258
265
254
9. Trends
The population of Uganda is expected to increase in the coming decades. At the moment the
growth rate is around 3.8%, this is likely to decline to 2.1% the year 2050 (Figure 14)
according to UN projections.
Figure 14: Projected UN medium increase in human population to 2050 (in millions)
Figure 15 shows the countries of east Africa and their share of calorie intake by commodities;
Uganda has an average calorie intake by meat. In the year 2000, the share of calorie intake by
meat consisted out of 3.0% of the diet and is expected to increase to 5.1% of the total calorie
intake by the year 2030 (Figure 15).
Figure 15: Share of calorie intake by food commodities for East Africa
Although the share of calorie intake by meat is expected to increase, the share of pork is likely
to decline. In the year 2000, 60% of the calorie intake by meat consisted out of pork meat
(Figure 16), by the year 2030 this is expected to decline to 51%.
Figure 16: Composition of calorie intake by meat products for Viet Nam
Figure 17 shows the projected increase in pork production for Uganda, which is needed to
meet the increasing demand due to the rising population.
Figure 17: Projected increase in pork production for Viet Nam
Figure 18 shows the projected rates of change of livestock numbers versus change in
production of pork in developing countries, between 2000 and 2030. In many developing
countries the increasing demand will be met by an increase in animal numbers. In Uganda,
however, the increasing demand is projected to be met by an increase in productivity.
Uganda
Figure 18: Rates of change in livestock numbers versus production for developing countries,
between 2000 and 2030
10. Targeting
-
11. References
Center for International Earth Science Information Network (CIESIN), Columbia University;
International Food Policy Research Institute (IFPRI); the World Bank; and Centro
Internacional de Agricultura Tropical (CIAT). 2011. Global Rural-Urban Mapping Project,
Version 1 (GRUMPv1): Population Density Grid. Palisades, NY: Socioeconomic Data and
Applications Center (SEDAC), Columbia University.
FAOSTAT (2012)
Nelson, A. 2008. Travel time to major cities: A global map of Accessibility. Global
Environment Monitoring Unit – Joint Research Centre of the European Commission, Ispra
Italy. Available at http://gem.jrc.ec.europa.eu/
Ravallion, M., Chen, S., Sangraula, P. (2009). Dollar a Day Revisited. World Bank Econ Rev,
23(2): 163-184.
Robinson, T.P., Thornton P.K., Franceschini, G., Kruska, R.L., Chiozza, F., Notenbaert, A.,
Cecchi, G., Herrero, M., Epprecht, M., Fritz, S., You, L., Conchedda, G. & See, L. 2011.
Global livestock production systems. Rome, Food and Agriculture Organization of the United
Nations (FAO) and International Livestock Research Institute (ILRI), 152 pp.
Seré, C., Steinfeld, H. 1996. World livestock production systems: current status, issues and
trends. Rome: Food and Agricultural Organization of the United Nations.
Thornton, P.K., Kruska, R.L., Henninger, N., Kristjanson, P.M., Reid, R.S., Atieno, F.,
Odero, A. & Ndegwa, T. 2002. Mapping poverty and livestock in the developing world.
Nairobi, International Livestock Research Institute. 124 pp.
William Wint and Timothy Robinson, 2007. Gridded livestock of the world. Rome, Food and
Agriculture Organization of the United Nations (FAO).
Wood, S., G. Hyman, U. Deichmann, E. Barona, R. Tenorio, Z. Guo, S. Castano, O. Rivera,
E. Diaz, and J. Marin. 2010. Sub-national poverty maps for the developing world using
international poverty lines: Preliminary data release. Available from http://povertymap.info
(password protected).
Appendix
Census data 2012: Number of livestock per district
Census data 2012: Proportion of households owning livestock