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TARGETING ANIMAL PRODUCTION VALUE CHAINS FOR TANZANIA 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): aridsemi-arid (with LGP <180 days), humidsubhumid (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 Tanzania. Figure 1: Distribution of production systems in Tanzania. 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 1 shows the relative area of these different production systems.
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Page 1: Targeting tanzania 13_march

TARGETING ANIMAL PRODUCTION VALUE CHAINS

FOR TANZANIA

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 Tanzania.

Figure 1: Distribution of production systems in Tanzania.

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 1 shows the relative area of

these different production systems.

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Table 1: Surface area of production systems in Tanzania

Production system Surface area (km2) Percentage (%)

Rangeland based, (Hyper-) Arid/Semi-arid (LGA) 18,140 20

Rangeland based, Humid/Sub-humid (LGH) 7,760 9

Rangeland based, Temperate/Tropical highlands (LGT) 1,270 1

Mixed, (Hyper-) Arid/Semi-arid (MRA) 27,880 31

Mixed, Humid/Sub-humid (MRH) 14,910 17

Mixed, Temperate/Tropical highlands (MRT) 4,270 5

Urban 330 0

Other 14,010 16

Although about one third of the area in Tanzania is under grasslands supporting (agro-)

pastoral livestock production, the most common production system is mixed crop-livestock

systems, covering just over 50% 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 Tanzania.

Figure 2: Distribution of human population density in Tanzania

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.

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Table 2: Average population densities by production system

Production system Population density (head/km2) Standard deviation

LGA 10.1 4.9

LGH 11.1 4.8

LGT 11.3 4.2

MRA 32.7 31.8

MRH 52.0 44.1

MRT 46.3 37.5

Urban 1985.6 1072.0

Other 41.1 61.2

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.

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 e 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

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Figure 4: Distribution of the number of people living on less than $2 per day

Table 3 show the total population of Tanzania 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.

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Table 3: Total population by regions, and number of people living of less than 1.25 and

2$/day Region Total

population (1000)

Poor people living <1.25$/day Poor people living <2$/day

Total number (1000)

% of poor people

Total number (1000)

% of poor people

Arusha 1303 886 68.0 941 72.2

Dar es Salaam 2142 1,292 60.3 1,387 64.8

Dodoma 1755 1,399 79.7 1,453 82.8

Iringa 1798 1,253 69.7 1,371 76.3

Kagera 2116 1,626 76.8 1,715 81.0

Kigoma 1802 1,771 98.3 1,789 99.3

Kilimanjaro 1454 1,080 74.2 1,183 81.3

Lindi 858 819 95.5 844 98.4

Manyara 1031 775 75.2 821 79.6

Mara 1325 1,289 97.3 1,303 98.4

Mbeya 1924 1,137 59.1 1,239 64.4

Morogoro 1701 1,247 73.3 1,355 79.7

Mtwara 1133 1,079 95.3 1,128 99.6

Mwanza 2829 2,756 97.4 2,779 98.2

Pwani 931 853 91.6 898 96.5

Rukwa 1175 1,113 94.8 1,157 98.5

Ruvuma 1128 1,071 95.0 1,088 96.5

Shinyanga 2819 2,781 98.7 2,804 99.5

Singida 1100 1,092 99.3 1,094 99.4

Tabora 1738 1,704 98.0 1,719 98.9

Tanga 1629 1,280 78.6 1,373 84.3

To obtain a better idea about the distribution of the human population, Table 4 and 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 Production system

Total population

Poor <2$ % poor of total poor population

% of poor persons within farming

systems

Standard deviation

LGA 2,140,470 1,937,310 6.7 90.5 14.6

LGH 1,023,080 948,800 3.2 92.7 9.8

LGT 161,800 125,830 0.4 77.8 14.7

MRA 10,541,500 9,278,140 32.1 88.0 18.7

MRH 9,695,670 8,785,380 30.5 90.6 14.6

MRT 2,139,480 1,745,000 5.9 81.6 15.6

Urban 6,859,070 5,273,420 16.9 76.9 20.5

Other 1,439,050 1,267,200 4.3 88.1 16.9

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Table 5: Total population and number of people living of less than 1.25$/day by production

system Production system

Total population

Poor <2$ % poor of total poor population

% of poor persons within farming

systems

Standard deviation

LGA 2,140,470 1,879,250 6.9 87.8 16.6

LGH 1,023,080 904,350 3.4 88.4 12.1

LGT 161,800 116,830 0.5 72.2 16.2

MRA 10,541,500 8,970,800 33.2 85.1 20.4

MRH 9,695,670 8,511,740 31.4 87.8 16.4

MRT 2,139,480 1,643,710 6.2 76.8 16.4

Urban 6,859,070 4,717,270 18.9 68.8 21.8

Other 1,439,050 1,205,680 4.5 83.8 18.0

Poverty levels are high in Tanzania. 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 85.6 and 89.0% 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 Tanzania, whereby

accessibility is defined as the time in hours required to travel from a given single point to the

nearest market centre of 50,000 or more people. 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.

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Figure 4: Travel time (hr) to the nearest town of 50,000 people

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

Production system Mean travel time (hr) Standard deviation

LGA 16.2 9.1

LGH 13.3 7.4

LGT 21.3 10.5

MRA 12.7 9.1

MRH 10.8 7.2

MRT 11.7 7.9

Urban 0.8 0.7

Other 10.4 7.1

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).

To obtain a better idea about the possibilities of farmers to make use of local markets, we

combine data on population density with travel time. As a proxy for market access to local

markets, we selected all regions with a population density of more than 150 head/km2 and

those areas with a travel time of less than two hours. Figure 5 shows the spatial distribution of

access to local markets.

[In case we indeed want to use a proxy for local markets, we can play around with the cut-off

values used. In the appendix I added two figures, where used different cut-off values.]

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Figure 5: Travel time (hr) to local markets.

2.3 Consumption

Food supply data is some of the most important data in FAOSTAT. In this report, we use

livestock consumption data to estimate national surplus – deficit areas, when it is combined

with other data sets later on (section 5).

Table 7 shows the average consumption of bovine meat, milk, pig and goat/mutton meat for

Tanzania, based on FAOSTAT for several years. Figure 6 and 7 shows the spatial distribution

of bovine meat and milk consumption, based on population density (CIESIN, 2011).

Table 7: Average consumption of livestock products in Tanzania (FAOSTAT, 2012)

Food supply quantity (kg/capita/yr)

1999 2000 2001 2002 2003 2004 2005 2006 2007

Bovine Meat 7.8 6.7 7.3 7.4 7.3 7.0 6.9 6.7 6.0

Milk, Whole 19.2 19.5 21.8 21.3 21.0 20.4 19.8 19.4 19.1

Pig meat 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3

Mutton & Goat Meat 1.2 1.2 1.2 1.1 1.2 1.1 1.1 1.0 1.0

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Figure 6: Average bovine meat consumption in Tanzania

Figure 7: Average bovine milk consumption in Tanzania

Table 8 shows the average meat and milk consumption over the various production systems.

The table shows clearly that most consumption takes place in urban areas, and that in the

pastoral rangelands consumption is low.

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Table 8: Average meat and milk consumption by production systems Production system Average milk consumption

(kg/km2/year) Average meat consumption

(kg/km2/year)

LGA 203 73

LGH 225 81

LGT 222 80

MRA 663 239

MRH 1046 378

MRT 927 335

Urban 39381 14221

Other 815 294

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 livestock per production system. Figure 8 shows the spatial

distribution of bovine densities.

Figure 8: Average bovine densities in Tanzania

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Table 9: Average densities of bovine, goat, pigs and sheep by production system (head/km2) Production system Average (head/km2)

Bovine Goat Pigs Sheep

LGA 11.8 9.7 0.6 3.2

LGH 6.4 4.3 0.2 1.1

LGT 18.8 14.5 0.5 6.4

MRA 28.2 16.6 0.5 5.8

MRH 31.2 18.8 0.3 4.6

MRT 16.5 12.0 1.0 3.6

Urban 23.1 17.6 1.3 4.4

Other 8.8 9.7 0.6 2.0

Table 9 shows clearly the high densities of cattle in the mixed systems, however, it shows as

well high cattle densities in (peri-) urban systems.

The national bureau of statistics collected in the agricultural census of 2002-2003 data on the

total number of cattle by type by district (http://www.countrystat.org/tza). Based on this data

we mapped the total number of dairy cattle per district and the percentage of improved cattle.

Figure 9 and 10 show respectively the total number of improved dairy cattle and the

percentage of improved beef and dairy cattle.

Figure 9: The total number of dairy cattle

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Figure 10: Percentage of improved cattle (%)

[We can add an appendix with data on indigenous and exotic breeds. As the tables are rather

large, I add them in Excel file until decided what data to use (http://www.nbs.go.tz/).]

4. Feeds

Herrero et al (***) 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 11 and 12 show the spatial distribution of the biomass use of bovine feed resources for

meat and milk production in Tanzania, table 10 summarizes the feed consumption by

production system.

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Figure 11: Bovine feed requirements for meat production in Tanzania

Figure 12: Bovine feed requirements for milk production in Tanzania

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Table 10: Bovine feed requirements by production system

Production system

Average feed requirements (ton/km2/year)

Milk production Meat production

LGA 4.1 13.0

LGH 2.7 8.8

LGT 10.1 27.9

MRA 11.0 33.0

MRH 14.9 43.4

MRT 6.3 23.0

Urban 12.9 39.5

Other 4.3 13.0

5. Production

Figure 13 and 14 shows respectively the spatial distribution of the bovine milk and meat

production for Tanzania, table 11 summarizes this production by production system.

Figure 13: Bovine milk production in Tanzania.

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Figure 14: Bovine meat production in Tanzania.

Table 11: Bovine milk and meat production by production system

Production system

Average production (kg/km2/year)

Milk Meat

LGA 666 117

LGH 1,262 35

LGT 2,794 130

MRA 1,331 273

MRH 2,555 346

MRT 1,969 151

Urban 6,418 400

Other 1,965 126

As we are interested in the surplus versus the deficit areas of milk and meat production, we

subtract the consumption data layers (figure 6 and 7) from the production layers (figure 13

and 14). Surplus areas are those areas where production exceeds the consumption; deficit

areas are those areas where local production cannot supply the consumption.

Figure 15 and 16 shows respectively the surplus - deficit areas for bovine milk and meat for

Tanzania.

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Figure 15: Surplus - deficit areas for milk in Tanzania.

Figure 16: Surplus - deficit areas for bovine meat in Tanzania

[Several people remarked that Figure 15, doesn’t look like it is a comparison of Figures 13

and 6. I checked the data and it is the correct result of abstracting consumption data from

production data for milk. However, it was difficult to compare these figures as different

legends was used – I now changed that and the data is now presented with an identical

legend.]

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To obtain a better idea about surplus – and deficit of cattle meat and milk in Tanzania, it is as

well needed to look at trade balances. Table 12 shows the average export of cattle, meat and

milk for the period 2000-2004 and 2005-2009.

Table 12: Export versus import of cattle in Tanzania, for between 2000-2009 item Average export Average import

2000-2004 2005-2009 2000-2004 2005-2009

Cattle meat (Tonnes) 1.4 25 30.8 32.6

Cow milk, whole, fresh (tonnes) 0.2 5.8 1164.2 2213.8

Cattle (Head) 1327 2850 72 84

The table shows clearly that Tanzania imports milk and meat in this period, but that it exports

live animals.

6 Excretion

Figure 17 shows the spatial distribution of bovine N excretion for Tanzania, table 13

summarizes this excretion by production system.

Figure 17: Bovine excretion in Tanzania.

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Table 13: Bovine N excretion by production system

Production system

N excretions (kg/km2/year)

Milk production Meat production

LGA 45 142

LGH 29 94

LGT 107 274

MRA 122 359

MRH 141 464

MRT 59 227

Urban 131 377

Other 43 128

7. Emissions

Figure 18 shows the spatial distribution of bovine emissions for Tanzania, table 14

summarizes this emissions by production system.

Figure 18: Bovine emissions in Tanzania.

Table 14: Bovine emissions by production system Production system Emissions (ton CO2 eq/km2/year)

Milk production Meat production

LGA 3.5 163.5

LGH 2.5 17.4

LGT 9.4 47.4

MRA 8.3 363.7

MRH 11.3 26.9

MRT 5.0 44.7 Urban 11.4 14.4

Other 3.7 10.9

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8. Climate

Figure 19: Length of growing period (in days) for Tanzania

Table 15: Average length of growing period (days) by production system

Production system Average LGP (days)

LGA 158

LGH 202

LGT 187

MRA 154

MRH 204

MRT 192

Urban 196

Other 205

Current climate + foreseen changes in the regions under study (CCAFS)

9. Trends

Information from the scenarios of alternative futures (Herrero et al., 2010)

Projections of consumption of different animal products (demand)

Feed surpluses/deficits

Growth in animal numbers

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Figure 20. The number of live animals per species over time

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10. Targeting

Figure 1: Mixed production systems (arid systems – light green; humid and temperate systems

– dark green; others - grey) versus all others

The next step is to combine Figure 1 with population density, we use a cut-off value of 25

persons/km2.

Figure 2: Areas with high population densities (dark red) versus low population densities

(pink)

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Map A: Mixed production systems with high population densities versus others (arid systems

– light green; humid and temperate systems – dark green; others - grey)

The next step is combining Map A with market access, whereby we use a threshold of 0.5 and

5 hours.

Figure 4: Areas with good market access (dark red) versus low access (pink)

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Map B: Mixed production systems with high population densities, and low market access

versus others (arid systems – light green; humid and temperate systems – dark green; others -

grey)

Map B gives us areas for rural production for rural consumption.

Map C: Mixed production systems with high population densities, and high market access

versus others (arid systems – light green; humid and temperate systems – dark green; others -

grey)

Map C gives us areas for rural production for urban consumption

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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/

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.

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).

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Appendix

Alternative options for local market access indicators:

Figure A: Travel time (hr) to local markets;

travel time of less than 1 hour or with population density of 100 head/km2

Figure B: Travel time (hr) to local markets;

travel time of less than 1 hour or with population density of 150 head/km2