1 / 33

Building capacity to assess the impact of climate change/variability and

Building capacity to assess the impact of climate change/variability and develop adapt ation responses for the mixed crop/livestock production systems in the Argentinean , Brazilian and Uruguayan Pampas. Principal Scientists  Graciela Magrin, INTA, Argentina

tabib
Download Presentation

Building capacity to assess the impact of climate change/variability and

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Building capacity to assess the impact of climate change/variability and develop adaptation responses for the mixed crop/livestock production systems in the Argentinean , Brazilian and Uruguayan Pampas • Principal Scientists  • Graciela Magrin, INTA, Argentina • María I. Travasso, INTA, Argentina • Osvaldo Canziani, Argentina • Gilberto Cunha, Brazil • Mauricio Fernandes, Brazil • Agustin Gimenez, GRAS- INIA, Uruguay • Walter E. Baethgen, IFDC, Uruguay • Holger Meinke, APSRU, DPI, Australia

  2. Establishing Applied Systems Analysis Networks for Building Regional Adaptation Capacity (AIACC LA 27)

  3. OBJECTIVE: Incorporate Climate Information for Improving Planning / Decision Making Agriculture / Natural Resources Planning Agencies (Public, International) Emergency Systems Credit / Insurance Programs Farmers (commercial, subsistence)

  4. Applied Systems Analysis WHY? • Planning, Decision Making are Complex Processes • Many Variables • Many Interactions • Different Priorities

  5. Improving Planning / Decision Making • Is Climate the main source of Variability? • Consider other sources of variability • Product Prices • Production Cost • Other Factors

  6. Price Trend for Finished Steer (1983-1999) (INAC, Uruguay) US$/kg of Finished Steer

  7. Price Trend for Finished Steer (1983-1999) (INAC, Uruguay) US$/kg of Finished Steer

  8. Price Trend for Finished Steer (1983-1999) (INAC, Uruguay) (100% Interannual Variability) US$/kg of Finished Steer

  9. Improving Planning / Decision Making • Is Climate the main source of Variability? • Consider other sources of variability • Product Prices • Production Cost • TECHNOLOGY ?

  10. Maize Rice Why lower variability? 100% rice is irrigated TECHNOLOGY Yield (detrended) variability (1960 – 2000) Detrended considering technology changes Therefore: Mostly Climate Variability

  11. Improving Planning / Decision Making Consider many sources of variability +Complex Interactions + Environmental Impacts + Socio-economic Impacts NEED TO INTEGRATE DATA AND TOOLS APPLIED SYSTEMS ANALYSIS

  12. But: No Priorization No Processing No Analysis NOT USED EFFECTIVELY INFORMATION Some Difficulties for Disseminating Often Information is Available (Especially Latin America) (even “in excess”)

  13. Tools for Processing and Anlyzing Information • Simulation Models • Expert Systems • Risk analysis • Remote Sensing (Satellites) • Geographic Information Systems (GIS) • Global Positioning Systems(GPS) But: Use is not generalized

  14. Answer: IDSS Approach Use Modern Tools for: Acquiring, Processing and Analyzing Information and generate results in simple formats, Understandable and therefore USABLE by stakeholders acting in the Agricultural Sector (e.g., map of Rio de la Plata with red and green areas)

  15. 1990’s Established and IDSS working group in SESA Information and DECISION SUPPORT SYSTEMS

  16. Uruguay Latin America Argentina NASA, USA Brazil NOAA, USA EPA, USA Australia Columbia University Spain European Commission Since 1990’s

  17. SIMULATION MODELS REMOTE SENSING CLIMATE CHANGE/VAR GIS IDSS Approach Monitoring, risk analyses, environmental impact, projections

  18. Impact Studies: Example of Climate Change • Technology Impact • New Alternatives (not only wheat) • Management (fertilizers, cultivars, irrigation) • Variation of Prices and Cost

  19. REMOTE SENSING: MONITORING NOVEMBER DECEMBER JANUARY Very high High FEBRUARY MARCH Low Very low La Niña 1999 / 2000

  20. Ing. Juan Notaro, Uruguayan Minister of Agriculture in 1999/2000 (Letter to our INIA-IFDC-NASA Project) "(...) The results of your work during the recent drought were useful for making both, operational and political decisions. From the operational standpoint, your work allowed us to concentrate our efforts in the regions highlighted as being the ones with the worst and longest water deficit. We prioritized those identified regionsfor concentrating the use of our resources, both financial aid and machines for dams, water reservoirs, etc. (...) From the strictly political standpoint, your work provided us withobjective information to defend our prioritization of regions, in a moment in which every governor, politician and farmer in the country was asking for aid. We received no complaints in this respect. In the same line, your work also allowed to mitigate pressures since we provided the press and the general public with transparent, technically sound and precise information”.

  21. Gravel Slope Rooting Depth Fertility Drainage Feasible Moderately feasible Unfeasible • GIS + Databases= • Agro-climatic • Zoning: • Land Use • Feasibility

  22. Feasible Moderately feasible Unfeasible • Agro-Climatic Zoning • WHEAT • Soil • Climate • Terrain Climate Change will have different effect on areas with Different Feasibility (Risk) (And: Feasibility is Dynamic (Technology)

  23. Farm Level: Expected Income (US$/ha) for Diferent Systems 30-year Mean Climate Change Scenario 1 System 2 System 1 System 3 System 4

  24. AIACC LA 27 Project Premise One of the most effective manners for assisting agricultural stakeholders to be prepared and adapt to possible climate change scenarios, is by helping them to better cope with current climate variability “Climate-proof Systems”

  25. 1999/2000?? 1988/89 Previous La Nina La Nina 1999/2000

  26. 1999/2000 Improved Pastures Supplementary feed MORE RESILIENT SYSTEM “CLIMATE PROOF”

  27. Uruguay: IFDC/INIA/NASA: Climate Forecast Applications in Agriculture Workshops (Quarterly) Regional Outlook Regional Outlook Meetings “TWG” Nat. Climate Res. Ctrs. Tech. Reps. Agri-Business MAF Planning Statistics NGOs Gov.Org. Growers Local Outlook ENSO and “Global” Climate Forecasts Local Outlook IAI Needs (Variables, Moments, Tools) Tools IFDC INIA NASA Un.Fla. QSLD IRI NOAA ECM Others Met. Service Media Internet

  28. Establishing Applied Systems Analysis Networks for Building Regional Adaptation Capacity Next steps (2003): Assist establishing IDSS Approach in other developing countries (Latin America and beyond) Train “operators” (as opposed to MSc, Ph.D.) How? Establish an IDSS “Center” for South-South Cooperation

  29. Objectives of the Proposed IDSS Center for South-South Cooperation To take advantage and build upon the capacity developed by the IDSS work group and the existing technical and scientific cooperation agreements established with specialized institutes (NASA, NOAA, IRI, APSRU, JRC, EPA, US Universities) and apply the concept of South-South Cooperation to: 1. Collaborate with developing countries to establish applications of the IDSS approach (including climate variability and climate change) to improve agricultural planning and decision-making 2. Utilize the Center to train personnel from developing countries in the application of the IDSS approach under conditions and with resources (hardware, software) that are typical of developing countries. Seed funds: IDB, UNDP, FAO Most Activities: Funded with Specific Projects

  30. Walter E. Baethgen International Soil Fertility and Agricultural Development Center IFDC Oficina Uruguay

  31. Regional Crop Yield Forecasts: ANNUAL (Planning, FEWS, etc.) • NDVI vs Yields • Field Identification • and area measurement 2. NDVI at anthesis 5. Crop Simulation Models 4. Seasonal Climate forecasts 6. Surveys, groundtruthing, etc

  32. Regional Crop Yield Forecasts LONG TERM (Planning) • NDVI vs Yields • Field Identification • and area measurement 2. NDVI at anthesis • Climate Change • Scenarios 5. Crop Simulation Models • Consulting with Planning • Agencies

More Related