ASSESSMENT OF NATIONAL AGRICULTURAL STATISTICAL SYSTEMS IN AFRICA by Prof. Ben Kiregyera

1. UGANDA BUREAU OF STATISTICS ASSESSMENT OF NATIONAL AGRICULTURAL STATISTICAL SYSTEMS IN AFRICA by Prof. Ben Kiregyera PARIS21 CONSULTANT

2. 1 COVERAGE • I. Introduction • Review of Current National • Agricultural Statistical Systems (NASSs) • III. Way Forward - Paradigm shift • IV. Recommendations

3. 2 I. INTRODUCTION • Millennium Development Goals (MDGs) • 8 goals • Eradication of extreme poverty and hunger • Poverty Reduction Strategy Papers (PRSPs) • national planning frameworks and development strategies • instruments for relations with donors • basis for concessional lending/debt relief (HIPC) • PRSP and Agriculture Linkage • agriculture plays a central role in economy (see next slide) • agric. sector central to improved economic performance, increased incomes, raising standards of living and poverty reduction.

4. Contribution of agriculture to national economies Country Contribution of agriculture to: GDP Exports Employment Ethiopia 50 90 80 Kenya 30 50 75 Tanzania 49 85 80 Malawi 37 85 90 Rwanda 44 - 90 Uganda 43 90 80 3

5. 4 II. REVIEW OF NATIONAL AGRICULTURAL STATISTICAL SYSTEMS (NASSs) • A: Forty years on, no satisfactory NASSs • project and piecemeal ad hoc approach • Success of projects = success of NASSs • Quotation FAO (1997) • B: Audit/scan of NASS • Triple dilemma • agendas made elsewhere • weak capacity to deliver • seemingly intractable methodological problems • What has gone wrong?

6. 4B • C: Summary of what has gone wrong • NSSs are unstructured with no strategic direction • NSSs largely donor funded and driven with limited • government commitment • uncoordinated and prioritized • wide use of “quick fixor ad hoc” approach with • long-term planning taking a back seat • inadequate data – inaccurate, conflicting, • insufficiently processed and analyzed, • insufficiently disaggregated and not easily • accessible • no lasting benefits – capacity building and raising • the profile of statistics • methodological problems

7. 5 D: Paradox of data gaps • Data Demand outstrips Supply Demand for good data Supply of good data • Demand versus Resources demand resources Time

8. 6 • Paradox of data gaps Yawning gaps on some indicators and a plethora of data on other indicators which are not used. Quotation - Cisse (1990) • Critical data gaps • profile of rural populations • household food security • nutrition • on-farm stocks • disaggregated poverty levels • post-harvest losses • yields for staples such as cassava and bananas • horticultural production • environment and forestry • gender (especially role of women), etc.

9. 7 E: Lack of coordination – a serious problem • horizontal coordination to avoid working at • cross-purpose • generally poor • destructive rivalry between MOA and CSO • technical coordination to ensure mutual consistency • of data from different sources • generally poor • leads to conflicting data • Quotation – Blackwood (1997) • F: Main sources of data • Agricultural Reporting Services • reports by extension staff • administrative registers • Generally data suspect

10. 8 • Agricultural censuses • Countries participating in World Census Programme 1930 1950 1960 1970 1980 1990 1 3 16 22 17 14 • Few countries been able to repeat the census • Long period between censuses • lack of census data constrained long-term planning and investment decisions • unable to build expertise; dependence syndrome • Based on small samples; unable to provide small • area statistics

11. 8B

12. Agricultural sample surveys • timeliness • less cost • increased data quality • unable to provide small area statistics • lack of expertise and dependence on TA • Data collection methodologies • guess estimates • self-enumeration • farmer interviews • physical (objective) measurement • household budget surveys • special problems of data collection • cropping systems (mixed cropping, continuous planting and harvesting, etc) • production of root crops • In production environment that occurs in family smallholder sector in Africa, neither objective nor subjective methods have proved reliable 9

13. 10 • Data management • data processing • computer hardware & software no longer a problem • problem is with computer personnel (liveware) • data analysis • (see next slide)

14. 11 G: Data cycle Planning Stage 1 Stage 2 Implementation Dissemination Feedback Stage 3 Reporting Processing Analysis/Interpretation

15. 12 H: Data versus Information End Users Intermediate User (researchers) Data Producers Raw Data (low level information) Data Analysis Information Add value to data

16. 13 Policy-related information information End users Policy/ decision- maker Policy-related Analysis Basic Analysis Tables Raw Data

17. 14 I: Other issues • involvement of of subject-matter specialists and • experts (starting in some countries) • production of new analytical products e.g. poverty • and vulnerability maps using GIS functionality • (starting in a few countries) • Databases and data warehouses • (recognized but not enough done)

18. 15 J: Major problems and constraint • limited political commitment • organizational problems • insufficient coordination/collaboration/networking and • information sharing • limited coordination • user/producer, producer-produce, producer/research/ training institutions • Human resources • shortage of critical skills and expertise • Methodological • given above • Data quality problems • inconsistency, incompleteness (data gaps), inaccuracy, • lack of timeliness; insufficient small area statistics • Data management problems

19. 16 • knowledge management A way of promoting integrated approach to identifying, capturing, retrieving, sharing and evaluating organization’s information assets. Information assets: • databases • documents • policies and procedures • library services • tacit expertise & experience stored in peoples’ heads Experience in countries • poor or no documentation of methods/procedures • no institutional memory • experience in people’s heads • datasets and no databases

20. 17 • PARADIGM SHIFT: WINDOW I WINDOW II WINDOW I APPROACH Ad hoc Largely donor driven, limited government commitment INPUTS • data which are inadequate • no database • yawning gaps OUTPUTS

21. 18 WINDOW II • Identify Partners • Master Plan Coordinated System • user driven • ownership • long-term • partnerships • prioritized • Capacity building Main Feature • government • donor Inputs • adequate data • data base • sustainable system Outputs

22. 19 A: Develop an Integrated Framework • Process Analysis Planning Implementation monitoring evaluation • establish long- • tem objectives • generating • actionable • strategies • development of • statistical • programme • identify • activities • outputs • indicators • plan • budgets • external environment • users and • producers • coordination arrangements • current and future data needs • crate awareness • positioning the NSS • sticking to priorities and • implementation plan • track inputs, activities, • outputs • monitoring schedule • evaluation

23. 20 B: Address statistical governance Issues • All National Statistical Systems grappling with governance-related questions: • What is our mission? • How do we perform and can we do better? • How do we convince government that statistics • useful and adequate resources are needed? • Some governance issues: • Improving relevance • Improving coordination, networking, • partnerships • Benefiting from technical assistance • Knowledge management • Improving data quality • Improving data analysis, dissemination, access • Better data management (Databases)

24. 21 C: Improving relevance • Advocacy for statistics • raise awareness about and create demand • raise profile of statistics • resource mobilization • create partnerships for statistics • stakeholders to take ownership • increase relevance and funding for NSS • make national statistics demand-driven • User-producer Committees D: Improving coordination, partnerships & collaboration

25. 22 Improving Coordination, partnerships and collaboration National Statistical System DATA PRODUCERS Main data producers DATA USERS Other data producers • government (s) • public/private sector • NGOs • research/training orgs. • donors/international orgs. • press • wider public NSO Research/Training Organs Partnerships

26. 23 E: Improve benefits from technical assistance Follow UN guidelines • exchange expertise • development of skills & expertise • demand driven • not distort national priorities • not undermine national institutions and authority • F: Improve knowledge management Especially • documentation of methodologies and procedures • develop writing and reporting skills

27. 25 G: Improving data quality • Consistency- improved coordination • - system-wide adoption/standardization of • concepts, definitions, classifications • Completeness- Strategic Plan for the Statistical Institute - comprehensive programme (Master Plan) • Accuracy- use of “best methods” • - human resources/capacity development • - proper handling of data after collection • - need for adaptation/research • Timeliness- release calendar and sticking to it • Small area statistics - increase sample size - combine data from surveys and censuses

28. 26 H: Improve data management Enable • networking • sharing of information • data archiving • creation of user-friendly and accessible databases • creation of data warehouses/data mining

29. 27 I: Others • Role of NSO • set standards, promote “best practices” • need realignment of Statistics Act • Role of Technical Assistance • need f to follow UN Guidelines • many countries not following guidelines • Capacity is not built as it should • Opportunities for developing NASSs • great demand for statistics to track progress • increased international partnership • Quotation – Clare Short • PARIS21 • advances in information technology (IT)

30. IV: RECOMMENDATIONS International Community • Multi-country methodological research project • World Training and research Centre for Food and Agricultural Statistics • Statistical advocacy • Technical cooperation Countries • Role of National Statistical Office • Development and implementation of Integrated Framework • Staying ahead of demand • Role of technical assistance • Improve knowledge management • improve statistical products and services 28

31. 29 Thank You END