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.
Can Crop Models be Helpful for Understanding Climate Change Impact at the Landscape Level?
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. East African Agriculture and Climate Change
Available at IFPRI.org after December 9. West
Africa and Southern Africa available now.
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. 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. 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. 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. 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. 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
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).
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. 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
Editor's Notes
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