livestock data collection analysis and use in tanzania l.
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Livestock Data Collection analysis and Use in Tanzania
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  1. Livestock Data Collection analysis and Use in Tanzania Presenter L. Nsiima (MLFD). Workshop: New Perspectives on Livestock Data Organized by the Tanzania Ministry of Livestock and Fisheries Development in collaboration with the WB-FAO-ILRI Livestock Data Innovation Project Arusha, Tanzania, 10-11 August 2011

  2. Mandate of the Ministry of Livestock and Fisheries Development (MLFD). Mandate of MLFD is overall management and development of livestock and fisheries resources for sustainable achievement of the: • Millennium Development Goals; • National Strategy for Growth and Reduction of Poverty; • Improved Livelihood of Livestock and Fisheries Dependent Communities; and • Food Safety & Security without compromising Animal Welfare and Environmental Conservation.

  3. Overarching rationales for collections / using livestock related data: Overarching rationales for collections / using livestock related data is: • to have evidence-based policies and strategies and decision-making (which have increased demand for good statistical information); • to monitor the contribution of livestock industry in the implementation of the National Strategy for Growth and Reduction of Poverty; • essential for management and control of the livestock industry (which continuously requires decision-making in relation to production, reproductive management, marketing and trade and animal health).

  4. Livestock-related data at MLFD

  5. Livestock-related data at MLFD

  6. Livestock-related data at MLFD

  7. Livestock-related data at MLFD

  8. Methods of livestock data collection Livestock data and statistics are generated mostly by a secondary sources including administrative records, surveys and censuses. • Who decides which variables or indicators are collected? • Administrative records – indicators/variables decided by Local Governments • Surveys and Censuses - indicators/variables decided by the National Bureau of Statistics in collaboration with MLFD. • Other data – indicators/variables decided by institutions (e.g. Import/Export by Tanzania Revenue Authorities). • Who collects the data? • Administrative records – collected by Local Governments • Surveys and Censuses - collected in collaboration with the National Bureau of Statistics and Agriculture Line Ministries. • Other data – are collected from institutions (e.g. Import/Export by Tanzania Revenue Authorities, live animals prices by Ministry of Industry and Trade).

  9. Methods of livestock data collection Livestock data and statistics are generated by a secondary sources including administrative records, surveys and censuses. For administrative records; • Data are collected by LGAs in all districts using extension staff, • MLFD retrieve data as a mutual understanding with LGAs. • Currently, Agricultural routine data system being tested in four districts. For surveys and censuses. • NBS and ASLMs through collaboration collects the data at Household Level based on designed sample size. • Surveys are done annually while censuses are organized after every 5 years. • Interviewing methods are used using structured questionnaires. • The Government and donor funds pays for data collection?

  10. Methods of livestock data collection • Who first cleans / processes the data? • Administrative records – data is cleaned by Local Governments and no further processes except putting in required format. • Surveys and Censuses - data is cleaned by the National Bureau of Statistics in collaboration with MLFD. • Other data – data is cleaned by source institutions except putting in required format. • What data / indicators and in what formats arrive at your institutions? • Animal health • Water and pasture • Livestock infrastructure (dip tanks, slaughter slabs, water dams, charcos) • Hides and skins production • Artificial insemination • Livestock movements The data is retrieved from quarterly, annual reports, ad hoc requests. Normally data are in hard copies.

  11. Livestock data analyses Analyses done with the livestock data collected: • Do you organize the data? In what formats? • Data are always organized in Ms. Office excel formats. • Do you build your own indicators? On what basis? • Indicators are built on specific demand, with consideration of the MLFD indicators • What is their level of spatial and time aggregation • country, regions, districts, and on annual, quarterly, monthly.

  12. Livestock data analyses • Do you use livestock data collected by other organization? YES • Do you elaborate those data? YES • Do you make joint analyses of the data collected by your own institution and those collected by others? NO

  13. Livestock data uses Main three (or less) uses of livestock-related data, • 1st use of livestock-related data • Policy : formulate policies and implement sector projects and programmes. • 2nd use of livestock-related data • Planning: appraisal/monitoring and evaluation reporting. • 3rd use of livestock-related data • Management: general management of the sector issues (Environmental, drugs and vaccines, water for livestock, pastures).

  14. Livestock data storage • Do you store the livestock-related data you collect? Yes • How do you store the livestock data you collect? • Almost all collected data are stores in a computer readable form (MS Office – Excel, Word), in different file without a database system for their easy management.

  15. Livestock data dissemination Dissemination of the livestock related data MLFD collects: • Are all raw livestock-related data you collect available to the general public or only some of the data / indicators / summary statistics are available? • only some of the data / indicators / summary statistics are available. • Are data disseminated through databases / reports / other means? • Ministry’s website (http://www.mifugo.go.tz). • Annual Basic Data booklet. • Websites - LIMS, TSED, CountryStat • Budget speeches • How people can access your livestock data/ indicators? Internet by accessing website, reading reports. • Are your livestock-related data available for free / for a fee? Data are for free.

  16. Livestock data issues Main constraints related to: • Livestock data collection • Lack of coordination and cost-effectiveness • Inadequacy in designing a particular set of data as “official data” for the sector because of multiplicity of sources • Insufficient budgetary allocations • Weak field organization – missing links • Livestock data analyses • Lack of trained and skilled personnel in livestock data analysis • Livestock data uses (both those collected by your own institutions and by other institutions) • Adequacy of available data (inaccurate, inconsistent through time and between sources , not complete, data not put in optimal use by data users , lack timeliness, increasing demand) • Livestock data storage and dissemination • Data are scattered among different sources and there is no one stop center for livestock data

  17. Options to improve livestock data Main options to address identified constraints and improve quantity / quality of the livestock data needed to fulfill MLFD mandate • Options to improve livestock data collection • Data harmonization within livestock production (MLFD & stakeholders) • Conducting a census for livestock on complete enumeration basis – bench mark; • Conducting a dialogue between data producers and users to strengthen collaboration among stakeholders; • Review available indicators. • Options to improve livestock data analyses • Capacity building of staff – equip staff with adequate knowledge and skills • Provide incentives to staff in data production chain. • Building capacity of resource hardware and software availability. • Options to improve livestock data storage & dissemination • Review data collection, analysis, storage and dissemination system for livestock sector. • Establishing a comprehensive database management system.

  18. Joint options to improve livestock data Opportunities for collaboration with other institutions to improve the livestock data you need to fulfill MLFD mandate; • Harmonization of data system (collection, analysis and interpretation for policy formulation hence sector growth and poverty reduction). • Strengthen capacity in data and information sharing among major livestock data producers (databases and dissemination – one stop center and training of staff).

  19. Thank you