Can Crop Models be Helpful for Understanding Climate Change Impact at the Landscape Level?

498 views

Published on

This presentation by Timothy Thomas, IFPRI, shows the lessons learned from considering the Kenya data from “East African Agriculture and Climate Change” in developing a crop model and integrating the landscapes approach.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
498
On SlideShare
0
From Embeds
0
Number of Embeds
15
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • In 2013, IFPRI published three books (monographs) on agricultural adaptation to climate change in Africa: one for West Africa, one for East Africa, and one for Southern Africa. These were done in partnership with regional institutes CORAF, ASARECA, and FANRPAN, and under the umbrella and support from CCAFS and funding for 2 from BMZ.The East Africa book has been launched yet. We give a very limited release here at the Global Landscapes Forum, but the full release happens December 9 in Bujumbura, Burundi, as part of the ASARECA General Assembly.
  • Not original – I borrowed this from the Global Landscapes Forum website
  • Note how Kenya does well in these models to not have predicted decreases in rainfall
  • This is hectares per cell, with a cell size being around 8,500 hectares
  • Need to site stat about the importance of maize for KenyaWanted to show results for 1 GCM, before looking at them allGreen yield gainOrange yield declineBlue area gain (unproductive in 2000, productive in 2050)Red area lost (productive in 2000, unproductive in 2050)Possibly the most important slide for talking about insights from the landscapes perspectiveFor just now, since we are talking about possibilities for this kind of data, let us assume that whatever we mention as a possible use, it is reflected in general agreement among all the crop models.Situation 1: Consider western part near Uganda border where area is lost and where yield reductions are steep. This area needs help. If not new varieties are developed, climate change will have devasting impact on the maize farmers
  • Top left CNRMNote obvious differences. ECHAM coastal yield decline, while CNRM has much gain there. Loss of cultivable area in the west in the CNRM.
  • Wanted to show results for 1 GCM, before looking at them allGreen yield gainOrange yield declineBlue area gain (unproductive in 2000, productive in 2050)Red area lost (productive in 2000, unproductive in 2050)
  • The orange inside the oval is “Shrub cover, closed / open, deciduous”. Most of the tan is “Herbaceous Cover, closed-open”
  • Lighter green, almost a blue (most of oval area at site 1) - is East African Acacia Savanaspurple is East African Montane forestsGreen bordering Ethiopia and the Kenya coast is Masai Xeric grasslands and shrublandsorange is Southern acacia-commiphorabushlands and thicketspink by Uganda is Victoria Basin forest savanna mosaic
  • The city seen in the oval is Nakuru. Diagonally up is Eldoret. By Lake is Kisumu
  • Can Crop Models be Helpful for Understanding Climate Change Impact at the Landscape Level?

    1. 1. Can Crop Models be Helpful for Understanding Climate Change Impact at the Landscape Level? Lessons Learned from Considering the Kenya Data from “East African Agriculture and Climate Change” Presentation for the Global Landscape Forum, Warsaw, Poland November 16, 2013 Timothy Thomas Research Fellow, International Food Policy Research Institute (IFPRI)
    2. 2. East African Agriculture and Climate Change Available at IFPRI.org after December 9. West Africa and Southern Africa available now.
    3. 3. Approach and Purpose of 3 Books • Use crop models together with climate models to discover the direct yield effect • Use IMPACT, a global model of food and agriculture to incorporate climate change effects, along with the effects of population growth, GDP growth, and technological change • Contextual results to work within the institutional setting of each country • Analysis that policymakers, researchers, and donors might use
    4. 4. Our Crop Model Work • Divided each country into 10 km by 10 km squares • Took soils and climate data in each square, and evaluated yields in 2000 and 2050 • Crops evaluated were maize, rice, wheat, soybeans, groundnuts, an d soybeans • Did this for both rainfed and irrigated • Limited analysis to in and near where already
    5. 5. National to Sub-national • Since we have geographically disaggregated results, could they be helpful in smaller areas, perhaps provinces or districts or some other natural way of defining an area? • That is what we hope to consider today
    6. 6. Defining a Landscapes Approach • A conceptual framework that provides a structured way of assessing geographical spaces of interest as well as the impacts of interventions into these spaces. • Offers solutions in areas where land uses compete with environmental and biodiversity goals. • Emphasizes processes instead of projects. • Focuses on adaptive management, stakeholder involvement, multifunctionality and resilience. • Sensitive to multiple scales – from fields on farms to forests to global markets – and the synergies, feedback processes, interactions and time lags between these scales.
    7. 7. Challenges of a Landscape Approach • Choosing and defining the geographic boundaries (administrative vs natural) • Choosing objectives and the desired balance between objectives • Finding experts that can manage multiple disciplines
    8. 8. How Might Our Analysis Help? • “Stakeholder involvement” requirement suggests that what we do is not a landscapes approach • Also, real landscapes approach would have more precise information on the region, not using global datasets, but surveys and experts • BUT, what if we wanted to select an area in which to intervene? • Our analysis might help narrow the choice AND it might help identify critical issues
    9. 9. Annual Rainfall, mm, 1950-2000
    10. 10. Climate Change 2000-2050: Rainfall Model predictions for A1B scenario and 4 AR4 GCMs: CNRM (top left); CSIRO (top right); ECHAM (bottom left; and MIROC (bottom right).
    11. 11. Temperature (0C), 1950-2000
    12. 12. Climate Change 2000-2050: Daily Maximum Temperature in Warm Month Model predictions for A1B scenario and 4 AR4 GCMs: CNRM (top left); CSIRO (top right); ECHAM (bottom left; and MIROC (bottom right).
    13. 13. Harvested Area of Main Crops
    14. 14. Cultivated areas (SPAM) Rainfed maize Rainfed sorghum Rainfed wheat Irrigated rice Rainfed potatoes Rainfed millet
    15. 15. Yield Change for Rainfed Maize under Climate Change, 2000-2050, CNRM A1B
    16. 16. Yield Change for Rainfed Maize under Multiple Climate Scenarios, 2000-2050
    17. 17. Area Selected for Landscapes Example
    18. 18. Elevation, GLOBE
    19. 19. Population per square kilometer
    20. 20. Protected Areas, 2009, WDPA
    21. 21. Recommendations for Kenya From our little example here: • Be prepared in legal framework and personnel to minimize encroachment of protected areas • Evaluate whether slopes are too steep for cultivation and consider policies encouraging agroforestry • Ensure sufficient laws for settling new line, and selling or subdividing existing land
    22. 22. Broader Conclusions for Landscapes • This pixel approach can be useful to guide and inform researchers, donor, policymakers, and NGOs in prioritizing landscapes • Also useful in identifying potential issues to be dealt with – both positive and negative • Pixels approach does not make for a landscape approach by itself

    ×