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Animal production stratificationConsultant report and CD-Rom by Guy Hendrickx.The maps described in this document are part of an agro-veterinary GIS consultancyconducted for FAO (Rome, Italy). Final outputs where included in the Livestock AtlasSeries 1 available at: http://ergodd.zoo.ox.ac.uk/livatl2/index.htmOn the basis of FAO AGA GIS layers already available on livestock geography, agro-ecology, farming systems, disease occurrence, and the associated FAO statistics onanimal production, FAO productivity and FAO foreign trade data, the following GIS datalayers were produced:

1. Livestock density: Individual maps for sheep and goats, for Africa, Asia andEurope, using existing data on distribution, and percentage sheep out of totalruminants. Newly produced maps include:

o Sheep densityo Goat densityo Small ruminant densityo Ruminant densityo All livestock density

2. Livestock production: Aggregated dollar/km² production maps for: all protein,meat, milk equivalent and egg production. Newly produced maps include:

o Protein production: all livestock, ruminant, bovid, small ruminant, nonruminant, poultry.

o Meat production: all livestock, ruminant, bovid, small ruminant, nonruminant, chicken, pig.

o Milk production: ruminant, bovid, small ruminant.o Egg production: poultry.

3. Human population density: Remoteness and human population distributionpattern maps relevant to animal production. Newly produced maps include:

o Remotenesso Classified human population density

4. Individual maps depicting the relationship between livestock density and totalanimal protein production. Newly produced maps include:

o Total livestocko Total ruminanto Total poultryo Porko Total non ruminant

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1. Maps livestock density

1.1 Maps available (newly combined or improved maps have yellow background):

All livestock

Ruminants

Large ruminants

Small ruminants

Sheep

Goat

Non Ruminants

Poultry

Pigs

1.2 Map units: kg/km²1.3 Map formats: single ArcView raster for Europe, Asia and Africa1.4 All maps are available in standard Atlas publication TIFF format (blue background,FAO, title, legend and pie chart world distribution): e.g. all livestock distribution map.

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1.5 Improvements: New small ruminant raster for Africa replaces previous country-ecozone map.

Since no 5km raster predictions for Africa are yet available this map is based onthe existing cattle raster map and the known ratio small ruminants over largeruminants (FAOSTAT data). It assumes that small and large ruminants aredistributed in a similar way. This is obviously not always the case, smallruminants tend to be more evenly distributed than large ruminants. In Africasmall ruminants will mainly be underestimated in tsetse infested areas (e.g.humid coastal West Africa). Nevertheless the obtained increased spatialresolution for Africa is believed to be an improvement and is expected to triggercomments from the user community.Asia (5km raster prediction) and Europe (country-ecozone) data are unchanged.As an example highlighting differences between the Asia 5km SR prediction anda “large ruminant derived map” compiled in a similar way as described above is given below.

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New sheep and goat raster maps for Europe-Asia-Africa derived from the smallruminant map and the known proportion of sheep and goats per country(FAOSTAT data). Similar distribution patterns for sheep and goat are assumed.This is not always the case as was recently shown in The Gambia (see e.g.below1). Nevertheless as for the small ruminant map the increased spatialresolution is believed to be an improvement and to trigger user communitycomments.

1 Source : Livestock Development Study of the Gambia, 2001 (ADB)

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Compiled Ruminant and Non ruminant distribution maps using existing data:

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2. Maps production $ figures

2.1 Maps made available (all maps are new):

All Proteins Meat Milk Equivalent Eggs

All livestock Protall Mtall NA NA

Ruminants Protrum Mtrum Mqrum NA

Bovids Protbv Mtbv Mqbv NA

Smallruminants

Protsr Mtsr Mqsr NA

Non ruminants Protnr Mtnr NA NA

Poultry Protap Mtch NA Egap

Pigs NA Mtpg NA NA

2.2 Methodology: Dollar production maps :

o Step 1 country production maps based on FAOSTAT data :kg commodity / kg species / country

o Step 2 country $US value production maps :(output step 1) * $US value per commodity2

o Step 3 production/km² maps (5km grid): Grid map of output step 2 :

km grid of values per commodity per species per country (A) (A) * 5km grid livestock density maps (see 1.1) :

5 km grid of $ produced per commodity per km² Total production value charts (cf. bottom right corner of $ production livestock

atlas maps) : to allow comparison between maps the same class limits were usedfor all commodities (see legend maps). Per commodity the total produced valueper class was calculated for each continent. Since the scale of each chart isdifferent only relative values within one chart are comparable.

The maps allow to highlight production zones per commodity at a continental, regionalor sub-regional scale. Since map units are the same for all commodities aggregateproduction maps can be compiled (e.g. ruminant meat production). At this stageuniform distribution of production per commodity per country is assumed since moredetailed data were not available. This obviously is not the case, e.g. km² productionfigures will tend to be overestimated in marginal areas and underestimated in higherproduction peri-urban areas. Therefore as for the livestock density maps these should beconsidered as first generation maps open for discussion, feed-back and improvement.The charts allow to crudely compare how much is produced, where and in which kind ofenvironment (low to high production).

2 Bovine meat 1.8$/kg ; bovine milk 0.22$/kg ; small ruminant meat2.5 $/kg ; small ruminant milk 0.3$/kg ;poultry meat 1.2$/kg ; poultry egg 0.5$/kg ; pig meat 1.6$/kg

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3. Population related maps

3.1 RemotenessHuman distribution patterns can be depicted in different ways. Here a first attempt wasmade to define remoteness expressed as a measure of distance to populated places androads.

Step 1: a road raster layer using the Digital Chart of the World data base(ArcView).

Step 2: distance to roads grid data layer (Idrisi). Step 3: populated places raster data layer (ArcView Idrisi). Step 4: cost surface grid layer using distance to roads as friction layer (Idrisi

ArcView)The obtained map is a measure of remoteness from populated places and roads. Ratherthan the absolute value, the relative value of the calculated “index” is important. In bottom right map areas earmarked as “remote” are depicted in pink. They are of particular importance later when calculating High –Medium –Low production classlimits.

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3.2 Population distribution patternsA more common way of depicting population patterns is by simple classification ofpopulation density maps.Here an effort is made to define population class limits of particular importance whenreferring to mixed farming systems and potential cattle movement3 :

a. <5/km²: Low density population, marginal areas, pastoral activity andimportant seasonal migration patterns over large distances.

b. 5-20/km²: No mixed farming, in dry areas dominantly pastoral systems (highcattle movement rates and seasonal migration to more humid areas), inhumid areas crop driven systems.

c. 20-85/km²: Mixed farming is very likely, cattle movement becomes morerestricted with increasing human population numbers.

d. >85/km²: Population density too high for proper fallow management,intensification crucial factor, livestock movement for grazing very restricted.

e. Two urban / peri-urban classes :i. 250-750/km²ii. >750/km²

3 limits based on personal communications by AGAH staff and other sources (R. Reid et al.).

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4. Relationship between livestock density and production

4.1 Total livestock

4.1.1 Rationale:Highlight production patterns (strata) at a regional and sub regional level.4.1.2 Data processing:

a. Define low-medium-high class limits for total livestock density and totallivestock production. Per data layer quantiles were calculated based onvalues within “non-remote areas”. Class limits were defined as : Low = first quantile, Medium = second and third quantile, High = fourth quantile4.

b. Three class maps were produced and cross-tabulated.4.1.3 Map outputs are shown below.

a. Three class maps and percentage occurrence of cross tabulation results perclass combination.

b. Cross tabulated map highlighting areas where density and production match(75%), where production is lower than expected from density figures (17%)and where production is higher than expected from density figures (8%).Extreme over or under production does not occur.

4 Class limits total livestock density : 0-732 / 732-7653 / >7653Class limits total livestock production : 0-424 / 424-5086 / > 5086

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4.1.4 Results:This model assumes uniform production standards for Europe Asia and Africa. Thisobviously isn’t the case but nevertheless and in addition to the intrinsic limitations of theindividual data layers highlighted above, the obtained map shows production patternsfor the three continents such as: regional strongholds, areas with major growth potentialand areas with production discrepancies. On the following pages patterns obtained fordifferent livestock groups are also given.A next step is now to:a. Identify regional and sub regional production clusters. This may be improved by

adopting different class limits within different production systems and addingpopulation distribution strata such as shown above.

b. Analyze livestock movement within and between these clusters :i. At micro-level = herd movement patterns.ii. At macro-level = using FAOSTAT country trade data base (import –export)

recently developed by Marius Gilbert.c. Link obtained results with improved disease occurrence data layers.Such an approach should be the basis for improved stratified disease managementmodels where no uniform control measures are imposed but where measures areadapted to existing production levels, actual disease risk and trade relationships withinand between country clusters.

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4.2 Total ruminant

Data were processed as discussed above. Respective class limits were : Density : 0-579 / 579-6152 / >6152 Production : 0-199 / 199-2335 / >2335

The relative occurrence of cross-tabulation classes are given in table below.

Density% occurrence

Low Medium High

Low 35 3 0

Medium 11 30 5Production

High 0 5 11

To highlight the potential of further stratification possibilities maps are also given ofpopulation distribution patterns and bovine disease occurrence.

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4.3 Total poultry

Data were processed as discussed above. Respective class limits were : Density : 0-30 / 30-388 / >388 Production : 0-59 / 59-1120 / >1120

The relative occurrence of cross-tabulation classes are given in table below.

Density% occurrence

Low Medium High

Low 39.5 12 0

Medium 0.5 32 4Production

High 0 1 11

Since no 5km raster predictions are available for poultry remote areas are given as gray.

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4.4 Pork

Data were processed as discussed above. Respective class limits were : Density : 0-3 / 3-252 / >252 Production : 0-4 / 4-669 / >669

The relative occurrence of cross-tabulation classes are given in table below.

Density% occurrence

Low Medium High

Low 24 0.5 0

Medium 2.5 56 0.5Production

High 0 0.5 16

Since no 5km raster predictions are available for pigs remote areas are given as gray.

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4.5 Total non ruminants

Data were processed as discussed above. Respective class limits were : Density : 0-58 / 58-917 / >917 Production : 0-98 / 98-1282 / >1282

The relative occurrence of cross-tabulation classes are given in table below.

Density% occurrence

Low Medium High

Low 22 3 0

Medium 19.5 40.5 0.1Production

High 0 2 13

Since no 5km raster predictions are available for non ruminants remote areas are givenas gray.