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Spatial Data and Analysis in Support of Improved Policy and Planning – An ACIAR example using Africa Christopher Auricht

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Presentation on theme: "Spatial Data and Analysis in Support of Improved Policy and Planning – An ACIAR example using Africa Christopher Auricht"— Presentation transcript:

1 Spatial Data and Analysis in Support of Improved Policy and Planning – An ACIAR example using Africa Christopher Auricht chris@auricht.com chris@auricht.comACFIDCanberra 21 August 2012 Australian Centre for International Agricultural Research ACIAR

2 Current status of spatial data and applications  Applications now matured to point where such systems:  Can and are being used in various capacities. For example -  Humanitarian scenarios (especially as they relate to malnutrition, morbidity and mortality)  Economic scenarios  with and without interventions at differing stages i.e. decision support systems e.g. pre- emptive, resilience building / risk management interventions v’s emergency response triggered by high mortality or threat i.e. once a crisis has eventuated  Have ability to look at multiple scales( local, national, regional) and longitudinally (forwards and backwards) 2 See for example – FAO FIVIMS http://www.fivims.org/ and World Bank sites http://data.worldbank.org/indicator?display=maphttp://www.fivims.org/http://data.worldbank.org/indicator?display=map

3 Percentage urban and urban agglomerations by size class 3 1960 1980 2011 2025 Source: UN Pop Division World Urbanisation Prospects, 2011 Revision http://esa.un.org/unpd/wup/Maps/maps_overview.htm http://esa.un.org/unpd/wup/Maps/maps_overview.htm

4 Urban agglomerations by size class and potential risk of drought 4 1970 2011 2025 Source: UN Pop Division World Urbanisation Prospects, 2011 Revision http://esa.un.org/unpd/wup/Maps/maps_overview.htm http://esa.un.org/unpd/wup/Maps/maps_overview.htm

5 Talk outline – Sub-Saharan Africa Example  Context and Background  Need for a strategic approach  Issues and status of spatial data  Methodology used in developing an updated farming systems dataset and analysis for Sub- Saharan Africa  Food Security and Nutrition AIFSC 5

6 Stitch in time saves nine  Spatial data and systems can help inform where the stitch is needed and the type of stitch required 6

7 Facts  According to CGIAR analysis  One billion of the worlds poor within Africa and Asia (those living on less than $1 per day) are fed primarily by:  hundreds of millions of small-holder farmers (often with less than 2 ha of land, several crops, and a cow or two), or  Herders (most with fewer than five large animals) 7 Solution ?  Develop sustainable farming systems that improve efficiency gains to produce increased food production

8 One Billion People Suffer Chronic Hunger and Poverty 8

9 Scale of Rural Hunger  Nearly one billion people experience debilitation, health-threatening hunger each year  4 out of 5 of these people are rural farmers 9 Trends in maize shortage in Zambia Percentage of farm households with maize shortage The Hunger Period

10 10 Hunger Hotspots and Farming Systems

11 Background  ‘Business-as-usual’ investments in agriculture unlikely to deliver sustainable solutions in many countries  Numerous obstacles to progress e.g. inefficiencies in program delivery, political uncertainty etc. These are not the only problem!  Existing systems (often under stress) have been / are expected to continue to accommodate large increases in population, increasing urbanisation, rising demand for animal products, plus competition for land and water  Forecasts suggest that current practices will not stay abreast with population growth, environmental change and increasing demand for animal products. 11

12 Population 2000 and 2040 Sub- Saharan Africa (Millions) Population2000200520102015202020302040 Total Pop 659746843 952 1,071 1,333 1,623 Rural Pop 447491537586635724795 Urban 212255306366436609828 Agric Pop 403437472508544 Females in Ag 788797109121 12 Source: UN Pop Division World Urbanisation Prospects, 2011 Revision and FAOStat http://esa.un.org/unpd/wup/Maps/maps_overview.htm and http://faostat.fao.org/site/550/DesktopDefault.aspx?PageID=550#ancor http://esa.un.org/unpd/wup/Maps/maps_overview.htm http://faostat.fao.org/site/550/DesktopDefault.aspx?PageID=550#ancor

13 Needs  Requires a strategic approach, an appreciation of scale, and an understanding of the interactions between and within systems 13

14 The current ACIAR SSA Farming Systems project  Builds on the work of Dixon et al 2001 14 www.fao.org/farmingsystems/

15 2001 Farming Systems and Poverty  Widely accepted as pioneering body of work – looked at things as a ‘surface’ across landscape not confined by country borders – often problems are regional  Largely driven by LGP/AEZ and market access, supplemented by expert opinion  Extensively used to guide investment at the program level and frame analysis in numerous global studies  Approach focused on high level farming systems within six developing regions  Involved use of various thematic data layers to underpin the delineation, characterisation / description and subsequent analysis of systems 15

16 16 Program Application

17 17 Hunger Hotspots and Farming Systems

18 Sub-Saharan Update  Is there a demand for this information? Farming systems website in FAO still one of the most visited sites within the organisation – up to 4,000 hits per month (Site > 10 years old!)  Consistent seamless datasets somewhat limited in original work  In need of updating as spatial extent of systems and frame conditions changed e.g. climate, population, urbanisation, market access, economics etc.  Many updated and new datasets available 18

19  Capture and use data and information in an manner that informs decisions in a simple fashion  Maintain rigour and transparency  Establishing an enduring infrastructure/framework to enable changes to be monitored over time  Ability to support numerous policy initiatives – Principle: collect it once – use it many 19 Challenge

20 Current Situation  2012 – Large quantity of potential datasets – approx. 300 alone in the Harvest Choice database  temporal and some predictive data now available  GAEZ 3.0 - 1,000’s of datasets representing 100’s of thematic layers  Question - which ones to use and how  Strategic approach  Access and collation  Assess (fit-for-purpose) and Prioritise (currency, coverage, scale etc)  Process  Products  Disseminate 20

21 Methodology  Work in collaborative fashion with authors and other large data providers e.g. IFPRI – Harvest Choice, UN-FAO, ILRI, ICRAF, IIASA, CGIAR others 21 Delineate new Farming System Boundaries – Iterative process based on concept of central tendency Statistics and Analysis Characterise and describe systems

22 Approach  Integration of new datasets –  LGP and Market access  Supporting Datasets  Population (rural, urban, total)  Livestock – cattle, sheep, goats, poultry, LU and TLU  Crop areas and production  Yield gaps  Protected areas  Poverty  $2.00 and $1.25 /day  Nutrition 22

23 Elevation Slope, aspect, drainage Settlements, ports, markets Road, rail, river, ICT networks Market travel times & costs Hunger, Poverty & Productivity Spatial Covariates/Proxies & Analytical Flow Port travel times & costs Terrain, Demography, Infrastructure, Admin Units Production Environment & Constraints Production Systems & Performance Interventions/ Responses Agroecological Zones Cropland extent & intensity Pests & Diseases (Maize Stem Borer) Drought Incidence & Severity Runoff Administrative Units Farming Systems Crop Suitability: Rainfed Wheat Crop Distribution & Yields Value of Production per Rural Person Yield Responses to Inputs, Management, CCProfitability of small scale irrigation Quantity of Nutrients Removed Fertilizer Profitability Distribution of Welfare Benefits Linkage to Macro Models Aggregate to FPUs Source: HarvestChoice 2010 23

24 24 Changes between 2001 and 2012

25 Yield Gap – Aggregate of Major Crops 25

26 Big questions for management and policy  What is it?  Where is it?  What are its characteristics and how does it operate ?  What are the risks/threats ?  What are the opportunities (Research / Extension) ?  How are these issues changing with time ?  Evaluation and Performance 26

27 Spatial data  Tool to support process  Understand  Analyse  Develop interventions  Monitor  Not the answer in itself   has limitations  Fit for purpose  Complement with expert knowledge 27

28 Spatial data and ACIAR Activities  Update of Farming Systems for Sub-Saharan Africa  Informing development of policy and program development as part of the ACIAR ‘ Australian International Food Security Centre (AIFSC)’  Announced by Prime Minister Gillard October 2011- International focus, recognising the significance of food security to developing countries.  Providing a bridge between agriculture (technologies, policies and practices) and their adoption by smallholder farmers (including livestock keepers). Increase adoption  increase productivity and diversity and generate additional income 28

29 AIFSC  Research gaps in terms of food security, agriculture and nutrition in line with the AIFSC strategy and African priorities  Support in determining how AIFSC could best complement work being undertaken by partners in target countries and where we should invest  Nutrition indicators – under-nutrition, child nutrition, maternal under-nutrition, micronutrient deficiencies  Nutrition interventions, regional analysis, country snapshots 29

30 Thanks  Acknowledgements  ACIAR  IFPRI – Harvest Choice  CGIAR  ILRI  ICRAF  FAO  IIASA  others  Questions & Discussion 30


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