Targeting tanzania 13_march


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Tanzania dairy VC targeting report

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Targeting tanzania 13_march

  1. 1. TARGETING ANIMAL PRODUCTION VALUE CHAINS FOR TANZANIA1. Livestock production systemsSeré and Steinfeld (1996) developed a global livestock production system classificationscheme. The system breakdown has four production categories: landless systems (typicallyfound in peri-urban settings), livestock/rangeland-based systems (areas with minimalcropping, often corresponding to pastoral systems), mixed rainfed systems (mostly rainfedcropping combined with livestock, i.e. agropastoral systems), and mixed irrigated systems(significant proportion of cropping uses irrigation and is interspersed with livestock). All butthe landless systems are further disaggregated by agro-ecological potential as defined byLength 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, wasby 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 theresults simplified to rangeland based and mixed systems. Table 1 shows the relative area ofthese different production systems.
  2. 2. Table 1: Surface area of production systems in Tanzania 2Production system Surface area (km ) Percentage (%)Rangeland based, (Hyper-) Arid/Semi-arid (LGA) 18,140 20Rangeland based, Humid/Sub-humid (LGH) 7,760 9Rangeland based, Temperate/Tropical highlands (LGT) 1,270 1Mixed, (Hyper-) Arid/Semi-arid (MRA) 27,880 31Mixed, Humid/Sub-humid (MRH) 14,910 17Mixed, Temperate/Tropical highlands (MRT) 4,270 5Urban 330 0Other 14,010 16Although about one third of the area in Tanzania is under grasslands supporting (agro-)pastoral livestock production, the most common production system is mixed crop-livestocksystems, covering just over 50% of the land.2. Socio-economic data2.1 Human population & povertyTo show the spatial distribution of human population, we use the estimates of humanpopulation of Global Rural-Urban Mapping Project (GRUMPv1) for the year 2000. Thepopulation density grids measure population per square km (CIESIN, 2011). Figure 2 showsthe spatial distribution of human population densities for Tanzania.Figure 2: Distribution of human population density in TanzaniaTable 2 shows the distribution of the population densities over the different productionsystems. As expected, in the rangeland areas, the lowest population densities prevail, whiledensities increase in the mixed systems. The high standard deviation in the mixed systemshighlights the large regional variation within these systems.
  3. 3. Table 2: Average population densities by production systemProduction system Population density (head/km2) Standard deviationLGA 10.1 4.9LGH 11.1 4.8LGT 11.3 4.2MRA 32.7 31.8MRH 52.0 44.1MRT 46.3 37.5Urban 1985.6 1072.0Other 41.1 61.2Poverty is defined as an economic condition in which one lacks both the money and basicnecessities, 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 iscurrently set at less than US$2 (PPP) per day (also called the $ 2 poverty line) for poverty andless than US$1.25 (PPP) per day (also called the $ 1.25 poverty line) for extreme poverty. Themost common poverty metric is head count ratio (HCR), the percentage of the populationliving 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 perday. Figure 4 shows the spatial distribution of the number of people living on less than $2 perday.Figure 3: Distribution of the number of people living on less than $1.25 per day
  4. 4. Figure 4: Distribution of the number of people living on less than $2 per dayTable 3 show the total population of Tanzania by region. The table shows as well the numberof people living under 1.25$ and 2$ a day, and the percentage of poor people per region.
  5. 5. Table 3: Total population by regions, and number of people living of less than 1.25 and2$/dayRegion Total Poor people living <1.25$/day Poor people living <2$/day population Total number % of poor Total number % of poor (1000) (1000) people (1000) peopleArusha 1303 886 68.0 941 72.2Dar es Salaam 2142 1,292 60.3 1,387 64.8Dodoma 1755 1,399 79.7 1,453 82.8Iringa 1798 1,253 69.7 1,371 76.3Kagera 2116 1,626 76.8 1,715 81.0Kigoma 1802 1,771 98.3 1,789 99.3Kilimanjaro 1454 1,080 74.2 1,183 81.3Lindi 858 819 95.5 844 98.4Manyara 1031 775 75.2 821 79.6Mara 1325 1,289 97.3 1,303 98.4Mbeya 1924 1,137 59.1 1,239 64.4Morogoro 1701 1,247 73.3 1,355 79.7Mtwara 1133 1,079 95.3 1,128 99.6Mwanza 2829 2,756 97.4 2,779 98.2Pwani 931 853 91.6 898 96.5Rukwa 1175 1,113 94.8 1,157 98.5Ruvuma 1128 1,071 95.0 1,088 96.5Shinyanga 2819 2,781 98.7 2,804 99.5Singida 1100 1,092 99.3 1,094 99.4Tabora 1738 1,704 98.0 1,719 98.9Tanga 1629 1,280 78.6 1,373 84.3To obtain a better idea about the distribution of the human population, Table 4 and 5 presenttotal population and number of poor over the different production systems.Table 4: Total population and number of people living of less than 2$/day by productionsystem Production Total Poor <2$ % poor of total % of poor persons Standard system population poor population within farming deviation systems 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
  6. 6. Table 5: Total population and number of people living of less than 1.25$/day by productionsystem Production Total Poor <2$ % poor of total % of poor persons Standard system population poor population within farming deviation systems 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 18.0Poverty 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. Asmost people live in mixed production systems, the absolute number of poor people living inthese areas is highest as well.2.2 Market accessTravel time to market centers is used as a proxy for market accessibility and shows the likelyextent to which farming households are physically integrated with or isolated from markets. Itis important to farming households and other producers to have access to markets in order totrade/sell their goods. The more accessible markets are to the given population the greater thepopulation’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 patternsshown here describe the physical accessibility between places in Tanzania, wherebyaccessibility is defined as the time in hours required to travel from a given single point to thenearest market centre of 50,000 or more people. The travel time approach is estimated basedon the combination of different global spatial data layers which represent the time required tocross each single point.
  7. 7. Figure 4: Travel time (hr) to the nearest town of 50,000 peopleTo obtain a better insight about the differences in travel time between production systems, thespatial 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 systemProduction system Mean travel time (hr) Standard deviationLGA 16.2 9.1LGH 13.3 7.4LGT 21.3 10.5MRA 12.7 9.1MRH 10.8 7.2MRT 11.7 7.9Urban 0.8 0.7Other 10.4 7.1The table shows clearly that travel time in (peri-) urban areas is lowest, and that travel timecan increase quickly in the mixed systems, but with large regional variation (high standarddeviation).To obtain a better idea about the possibilities of farmers to make use of local markets, wecombine data on population density with travel time. As a proxy for market access to localmarkets, we selected all regions with a population density of more than 150 head/km2 andthose areas with a travel time of less than two hours. Figure 5 shows the spatial distribution ofaccess to local markets.[In case we indeed want to use a proxy for local markets, we can play around with the cut-offvalues used. In the appendix I added two figures, where used different cut-off values.]
  8. 8. Figure 5: Travel time (hr) to local markets.2.3 ConsumptionFood supply data is some of the most important data in FAOSTAT. In this report, we uselivestock consumption data to estimate national surplus – deficit areas, when it is combinedwith other data sets later on (section 5).Table 7 shows the average consumption of bovine meat, milk, pig and goat/mutton meat forTanzania, based on FAOSTAT for several years. Figure 6 and 7 shows the spatial distributionof 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 2007Bovine Meat 7.8 6.7 7.3 7.4 7.3 7.0 6.9 6.7 6.0Milk, Whole 19.2 19.5 21.8 21.3 21.0 20.4 19.8 19.4 19.1Pig meat 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3Mutton & Goat Meat 1.2 1.2 1.2 1.1 1.2 1.1 1.1 1.0 1.0
  9. 9. Figure 6: Average bovine meat consumption in TanzaniaFigure 7: Average bovine milk consumption in TanzaniaTable 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 thepastoral rangelands consumption is low.
  10. 10. Table 8: Average meat and milk consumption by production systemsProduction system Average milk consumption Average meat consumption (kg/km2/year) (kg/km2/year)LGA 203 73LGH 225 81LGT 222 80MRA 663 239MRH 1046 378MRT 927 335Urban 39381 14221Other 815 2943. LivestockLivestock sector planning, policy development and analysis depend on reliable and accessibleinformation on the distribution, abundance and use of livestock. The Gridded Livestock ofthe World database provides standardised global, sub-national resolution maps of the majoragricultural livestock species. The map values are animal densities per square kilometre, andare derived from official census and survey data. Livestock distribution data give anestimation of production; they evaluate impact (both of and on livestock) by applying avariety of rates; and they provide the denominator in prevalence and incidence estimates forepidemiological 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 spatialdistribution of bovine densities.Figure 8: Average bovine densities in Tanzania
  11. 11. Table 9: Average densities of bovine, goat, pigs and sheep by production system (head/km2)Production system Average (head/km2) Bovine Goat Pigs SheepLGA 11.8 9.7 0.6 3.2LGH 6.4 4.3 0.2 1.1LGT 18.8 14.5 0.5 6.4MRA 28.2 16.6 0.5 5.8MRH 31.2 18.8 0.3 4.6MRT 16.5 12.0 1.0 3.6Urban 23.1 17.6 1.3 4.4Other 8.8 9.7 0.6 2.0Table 9 shows clearly the high densities of cattle in the mixed systems, however, it shows aswell high cattle densities in (peri-) urban systems.The national bureau of statistics collected in the agricultural census of 2002-2003 data on thetotal number of cattle by type by district ( Based on this datawe 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 thepercentage of improved beef and dairy cattle.Figure 9: The total number of dairy cattle
  12. 12. Figure 10: Percentage of improved cattle (%)[We can add an appendix with data on indigenous and exotic breeds. As the tables are ratherlarge, I add them in Excel file until decided what data to use (]4. FeedsHerrero et al (***) estimated the consumption of feed resources (biomass use), by:1. Estimating diets for each livestock species, in each production system2. Estimating intake of each feed and estimating animals productivity3. Multiplying animal productivity by the number of animals in each system (and their spatialdistribution) to get production4. 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 formeat and milk production in Tanzania, table 10 summarizes the feed consumption byproduction system.
  13. 13. Figure 11: Bovine feed requirements for meat production in TanzaniaFigure 12: Bovine feed requirements for milk production in Tanzania
  14. 14. Table 10: Bovine feed requirements by production systemProduction Average feed requirements (ton/km2/year)system Milk production Meat productionLGA 4.1 13.0LGH 2.7 8.8LGT 10.1 27.9MRA 11.0 33.0MRH 14.9 43.4MRT 6.3 23.0Urban 12.9 39.5Other 4.3 13.05. ProductionFigure 13 and 14 shows respectively the spatial distribution of the bovine milk and meatproduction for Tanzania, table 11 summarizes this production by production system.Figure 13: Bovine milk production in Tanzania.
  15. 15. Figure 14: Bovine meat production in Tanzania.Table 11: Bovine milk and meat production by production systemProduction Average production (kg/km2/year)system Milk MeatLGA 666 117LGH 1,262 35LGT 2,794 130MRA 1,331 273MRH 2,555 346MRT 1,969 151Urban 6,418 400Other 1,965 126As we are interested in the surplus versus the deficit areas of milk and meat production, wesubtract the consumption data layers (figure 6 and 7) from the production layers (figure 13and 14). Surplus areas are those areas where production exceeds the consumption; deficitareas 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 forTanzania.
  16. 16. 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 13and 6. I checked the data and it is the correct result of abstracting consumption data fromproduction data for milk. However, it was difficult to compare these figures as differentlegends was used – I now changed that and the data is now presented with an identicallegend.]
  17. 17. To obtain a better idea about surplus – and deficit of cattle meat and milk in Tanzania, it is aswell needed to look at trade balances. Table 12 shows the average export of cattle, meat andmilk for the period 2000-2004 and 2005-2009.Table 12: Export versus import of cattle in Tanzania, for between 2000-2009item Average export Average import 2000-2004 2005-2009 2000-2004 2005-2009Cattle meat (Tonnes) 1.4 25 30.8 32.6Cow milk, whole, fresh (tonnes) 0.2 5.8 1164.2 2213.8Cattle (Head) 1327 2850 72 84The table shows clearly that Tanzania imports milk and meat in this period, but that it exportslive animals.6 ExcretionFigure 17 shows the spatial distribution of bovine N excretion for Tanzania, table 13summarizes this excretion by production system.Figure 17: Bovine excretion in Tanzania.
  18. 18. Table 13: Bovine N excretion by production systemProduction N excretions (kg/km2/year)system Milk production Meat productionLGA 45 142LGH 29 94LGT 107 274MRA 122 359MRH 141 464MRT 59 227Urban 131 377Other 43 1287. EmissionsFigure 18 shows the spatial distribution of bovine emissions for Tanzania, table 14summarizes this emissions by production system.Figure 18: Bovine emissions in Tanzania.Table 14: Bovine emissions by production systemProduction system Emissions (ton CO2 eq/km2/year) Milk production Meat productionLGA 3.5 163.5LGH 2.5 17.4LGT 9.4 47.4MRA 8.3 363.7MRH 11.3 26.9MRT 5.0 44.7Urban 11.4 14.4Other 3.7 10.9
  19. 19. 8. ClimateFigure 19: Length of growing period (in days) for TanzaniaTable 15: Average length of growing period (days) by production systemProduction system Average LGP (days)LGA 158LGH 202LGT 187MRA 154MRH 204MRT 192Urban 196Other 205 Current climate + foreseen changes in the regions under study (CCAFS)9. TrendsInformation 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
  20. 20. Figure 20. The number of live animals per species over time
  21. 21. 10. TargetingFigure 1: Mixed production systems (arid systems – light green; humid and temperate systems– dark green; others - grey) versus all othersThe next step is to combine Figure 1 with population density, we use a cut-off value of 25persons/km2.Figure 2: Areas with high population densities (dark red) versus low population densities(pink)
  22. 22. 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 and5 hours.Figure 4: Areas with good market access (dark red) versus low access (pink)
  23. 23. Map B: Mixed production systems with high population densities, and low market accessversus 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 accessversus others (arid systems – light green; humid and temperate systems – dark green; others -grey)Map C gives us areas for rural production for urban consumption
  24. 24. 11. ReferencesCenter for International Earth Science Information Network (CIESIN), Columbia University;International Food Policy Research Institute (IFPRI); the World Bank; and CentroInternacional de Agricultura Tropical (CIAT). 2011. Global Rural-Urban Mapping Project,Version 1 (GRUMPv1): Population Density Grid. Palisades, NY: Socioeconomic Data andApplications Center (SEDAC), Columbia University.FAOSTAT (2012)Nelson, A. 2008. Travel time to major cities: A global map of Accessibility. GlobalEnvironment Monitoring Unit – Joint Research Centre of the European Commission, IspraItaly. Available at, 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 UnitedNations (FAO) and International Livestock Research Institute (ILRI), 152 pp.William Wint and Timothy Robinson, 2007. Gridded livestock of the world. . Rome, Foodand 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 usinginternational poverty lines: Preliminary data release. Available from protected).
  25. 25. AppendixAlternative 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/km2Figure B: Travel time (hr) to local markets;travel time of less than 1 hour or with population density of 150 head/km2