1. PPLPI Mid-Term Review March 2005 FAO, Rome
PPLPI Output 3: Effective systems for livestock policy information, analysis, decision-support and monitoring and evaluation
PPLPI Mid-Term Review
March 2005
FAO, Rome

2. Overview Baseline Information Analysis Decision support tools
Information dissemination
Monitoring and evaluation

3. Overview IGAD Network of Data Providers and Users
Djibouti
Eritrea
Ethiopia
Kenya
Somalia
Sudan
Uganda
Policies
Trade
Biophysical
Socio-economic
Livestock
Decision Support Tools
Livestock Policy Information System
Policy Analysis
Policy Recommendations

4. 1. Baseline Information Quantitative Qualitative GIS data
Living Standards Measurement Surveys (LSMS) data
Global Livestock Information System
Qualitative
policy; legislation; institutions
networks of livestock policy stakeholders
documentation, reports etc.

5. Global livestock information system
Overview
Data collection
Livestock modelling
Information dissemination
Applications of global livestock distributions

6. Global livestock information system
SALB / UNCS
OGC - compatible
ISO metadata
FAOSTAT
GeoNetwork
(KIDS)
Agro-MAPS
EMPRES-I
Sub-national livestock data
Sub-national admin. boundaries
Livestock disease data
Livestock prod. coefficients
Oracle Database
GLiPHA
Livestock modelling
Livestock sector reports
Ad hoc queries and reports

7. Data collection Sub-national administrative boundaries
Standards and codes (UNCS, SALB, FAO-SDRN)
Sub-national livestock data
Global network of data providers
Statistical year books, project contacts, Internet
Data verification - link to FAOSTAT
Livestock disease data
OIE standards
Livestock production coefficients
Regional estimates

8. Livestock modelling Sub-national numbers from database
Area suitable for livestock (slope, land-use etc.)
Sub-national “Observed” densities
Environmental predictor variables (5 km rasters)
Ecological zonation (cluster analysis)
Multivariate models for each zone
“Predicted” density of livestock
Adjust to observed totals where available
Adjust to FAOSTAT 2000 national totals

9. Cattle in Africa - Observed

10. Cattle in Africa - Predicted

11. Information dissemination
Database query
Livestock sector reports
Ad hoc queries and reports
Web-based information dissemination
GLiPHA
GeoNetwork
Output to other FAO applications (planned?)
EMPRES-I
Agro-MAPS
FAOSTAT (2)

12. Applications Livestock projections
Livestock production system classification
Livestock production estimates
Environmental risk mapping
Livestock disease risk mapping
Public health risk mapping
Poverty analysis
Policy analysis

13. 2. Analysis Living Standards Measurement Surveys (LSMS) GIS analysis
Agricultural production systems mapping
Poverty mapping

14. GIS analysis Vietnam Population (population census 1999)
Poverty (VLSS 1997/98 & VHLSS 2002)
Environmental data
Accessibility
Local spatially weighted regression

15. Poverty incidence and density

16. Environmental data
Topography
Land Cover

17. Environmental data
Forest
Soil Type

18. Environmental data
Rainfall
Temperature

19. Infrastructure and accessibility

20. livestock holdings Regional variation in livestock holding
North-South decline in the share of livestock-derived income in total income
Dominance of pig-derived income in all regions, but less so in poorest regions
Cattle relatively important in CH and SCC

21. Spatial aspects of livestock holding

22. Local spatially weighted regression
Local regression coefficients
Local indicators of goodness of fit

23. Local spatially weighted regression
Based on traditional regression framework
Produces all standard regression output at local level (R2, b, t, P)
Reveal local variation in spatial pattern
Parameters can be quantified for sub-regions
Parameters and the reliability of estimates can be mapped
Divergence from global model can be tested

24. Local spatially weighted regression
Vietnam Household Living Standards Survey 2002
over 2200 locational data points
nearly 45,000 households
Dependant variables
Income, Expenditure
Poverty
Independent variables
Income composition
environmental variables
accessibility

25. Local spatially weighted regression
Income source PIG POULTRY
Per capita income
vs.
income composition

26. Local spatially weighted regression
Per capita income – pig and poultry
Clear relationship where the share of income from pig or poultry declines with rising per capita income
This relationship tends to exhibit the strongest negative coefficient in proximity to urban centers
There are two sub-urban areas near Hanoi and HCMC where the share of income derived from poultry tends to rise with rising per capita income

27. Local spatially weighted regression
Income source PIG POULTRY
Accessibility
vs.
income composition

28. Local spatially weighted regression
Road access – pig and poultry
Share of income derived from pig and poultry tends to increase with improved road access in areas where accessibility is generally low, whereas
an inverse relationship can be observed in areas where accessibility to roads is generally better
This pattern of spatial variation and change of sign in the relationship is stronger for poultry - easier to transport ?

29. Conclusions Locate poor livestock keepers
Visualize, communicate and disseminate spatial dimensions of patterns and trends in livestock holdings
Importance of different livestock types across space
Identify and quantify relationships among poverty, livestock, environment and infrastructure
Analyze spatial variation in these relationships
Generate information to analyze policy impacts and to inform policy development

30. 3. Decision support tools
EXTRAPOLATE
Integrated Poverty Assessment for Livestock Promotion (IPALP)
Dairy household model
Policy menus
Livestock policy analysis tables

31. EXTRAPOLATE Participatory decision support tool Uganda
Model for pro-poor stakeholder analysis
Stakeholder analysis
Field testing in five regions

32. Model for pro-poor policy analysis
Actual beneficiaries
Groups of potential beneficiaries
Constraints
Opportunity
Policy change
Key
Stakeholders
Trade-offs
Constraints

33. Stakeholder analysis – Uganda
Autonomous – DDA, COCTU
NAGRC&DB
Research – NARO,
Universities, Int. Organizations
NGOs – Panos, Oxfam, HPI,
Send-a-Cow, Land O’ Lakes
Private Sector – Services,
Traders ....
Government – Local Govt.,
Other Ministries, Bureaus
Farmers’ Organizations –
UNFU, Local Organizations
MTIP
Parliament
Legislation
Finance
PEAP
Policy
UPPAP
PMA
MAAIF
International –
NEPAD-CAADP
Donors (DfID, USAID, DANIDA, EC, etc...)
Banks (WB, ADB)

34. EXTRAPOLATE

35. EXTRAPOLATE Identify development opportunity / commodity
Identify beneficiary groups – determine livelihood status
Determine constraints faced by different groups
Determine outcomes – the measurable effects of relaxing constraints
Estimate the impact of each outcome on the livelihood status of each beneficiary group
Estimate the impact of policy changes with respect to each constraint

36. Ex-ante analysis of policy change

37. Ex-ante analysis of policy change

38. Field testing and development
Uganda – small holder dairy
Senegal – small ruminants (Tabaski feast)
Peru – camelid fibre
India (AP) – small ruminants
Viet Nam – small holder pork

39. IPALP Vietnam Northern Mountain Region Social Accounting Matrix
Calculable General Equilibrium (CGE) model
Poverty (VLSS 1997/98 & VHLSS 2002)
Two policy scenarios

40. CGE modeling
Analytical tool to estimate impacts of economic policies on production structure, household income, poverty rates
Aimed at policy makers and analysts
Improve visibility for policy makers by identifying resource constraints, tradeoffs and adjustment challenges
Elucidate complex direct and indirect linkages that determine the costs and benefits of policy implications

41. Schematic modeling facility
Social
Accounting
Matrix
Household
Survey
Data
Policy
Scenarios
(tariffs, taxes, etc.)
CGE model
Sectoral
Results
(production,
exports-imports)
Household
Results
(income, expenditures,
occupation)
Poverty Mapping
(incidence and
inequality)

42. Northern Vietnam: CGE model
Prototype dynamic CGE micro-macro model developed for Northern Mountain Region of Vietnam
14 rural provinces and aggregated urban households
Two scenarios for the period
WTO accession
Livestock productivity growth (livestock promotion policy) coupled with WTO accession

43. Northern Vietnam: preliminary results
Scenario 1 (WTO only)
Most benefits to urban populations where significant poverty reduction occurs (21%)
Some gains propagated to the rural poor but to a much lesser degree (1%)
in some provinces poverty even increases in the first two years.
Scenario 2 (WTO plus production increase)
Poverty reduction is less in urban areas (7%)
more significant gains in rural provinces where poverty is reduced more than 30 percent
Over 27% of population in the region escape poverty

44. Scenario 2 (WTO + production increase)90
100
110
120
130
140
150
160
170
2004
2005
2006
2007
2008
2009
2010
Index (2004 = 100)
Ha giang
Cao bang
Lao cai
Bac can
Lang son
Tuyen quang
Yen bai
Thai nguyen
Phu tho
Bac giang
Quang ninh
Lai chau
Son la
Hoa binh
Urban Households
Dynamic of household income growth

45. 4. Information dissemination
PPLPI web site
GLiPHA
GeoNetwork

46. PPLPI web site Current static pages updated monthly
Developing a news and events front page
Developing an interactive resource searching facility based on EIMS

47. PPLPI web site

48. GLiPHA

49. Data Display Tables Graphs Text Maps Bar Line Pie Metadata Links
Chloropleth
Vector
Point
Raster

50. GeoNetwork better slide Claudia ?
FAO’s portal for hosting and sharing georeferenced thematic information
Provides a common metadata structure and search facility through which geographic (map) outputs can be searched for and downloaded by users
Used to disseminate livestock population and production data

51. 5. Monitoring and Evaluation
Indicators - Framework
Time-frame:
Level
Short
1-2 yrs
Medium
5 yrs
Long
10+ yrs
PPLPI activities
OVIs
Livestock policy
Policy documents
Impact on PRSPs
Legislation
Investment
Projects
Programmes
Livestock development
Wealthier livestock keepers
Contribution of livestock to GDP
Rural development
Rural poverty
Contribution of agric. to GDP
National development
National poverty
Trade
GDP

52. Thank You