Adapting to Change: How to Accelerate Impact

Adapting to Change: How to Accelerate Impact

J. Rubiano1 S. Cook2, B. Douthwaite3, M.Rajasekaran2, V.Soto3

1 King’s College, University of London, [email protected] Basin Focal Project, CPWF

3 International Center for Tropical Agriculture, CIAT, Colombia.

2nd International Forum on Food and Water, Ethiopia 10th to 14th of November

2008

mailto:[email protected]

This analysis shows where local projects have regional or global potential to change the lives of millions.


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Extrapolation domains for the PN15 Quesungual Project

EDA2nd International Forum on Food and

Water, Ethiopia 10th to 14th of November 2008

What is this work useful for?

Assuming successful project interventions (technological/ institutional)

• To identify the potential for wide scale adoption (scaling out) and its impact while linked with Global (B&SEM)

• To link research with development (partnerships)

• Add value to individual PNs’ and the CPWF • Guide Investors (SDC, WB…etc…)


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Outline

• Overall concept• How the method works?

– A graphical example– An example from a CPWF project

• Where to next?– Assessment– Recomendations


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What’s a right bed for my seeds?


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Pilot sites of 11 CPWF projects (nursery)


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The extrapolation problem

Where else can we go with these

projects?


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A knowledge common sense game2nd International Forum on Food and

Water, Ethiopia 10th to 14th of November 2008

It is black


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It is hairy


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Has more than 2 legs


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It is not a pet


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Back to the CPWF

• How to realise about the potential of the CPWF projects?

• How to wisely use information?– End of phase 1, 40+ CPWF funded projects– Knowledge– Improving our ability to adapt


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Use evidence and knowledge to estimate the likelihood of an event

• Given an evidence…– Black (one in four) - Uncertain– Hairy (three in four) - Maybe– Has more than three legs (two in four) - Perhaps– It’s not a pet (one in one) - Certain

• The inference is a …


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The extrapolation problem: Where else can we go with these projects?

• Knowledge– Tell us factors are important

• Data– About the original and target location– About the distribution of important factors

• A method– To link these two components


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How would I know a particular place is a good site for a project?

• From the project sites we know that particular factors are important for the project (e.g):– Stony soils– Mid altitude ranges– Low to medium precipitation– Technical institutional support– Prevalence of poverty– Etc…

• From spatial analysis we also know how important these factors are for the project


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How to weight the contribution of each factor?


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Pantropical world

Important factors

Research sites


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1. Weights of EvidenceArea=173 Pilot sites=20 Prior Prob=0.115

Factor Area = 22 Pilot sites in Factor Area = 8


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2. Weights of EvidenceArea=173 Pilot sites=20 Prior Prob=0.115

Conditional ProbabilitiesA. Factor and sites

B. Factor but no sites

C. No Factor but sites

D. No Factor no sites

W- = ln C/D

W+ = ln A/B

Factor Area = 22 Pilot sites in Factor Area = 8


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4. Weights of Evidence

Test of Conditional Independence of the

evidential layers

Posterior Probability=0.14

Area=173 Pilot sites=20 Prior Prob=0.115

Conditional ProbabilitiesA. Pattern and sites

B. Pattern but no sites

C. No pattern but sites

D. No pattern no sites

W- = ln C/D

W+ = ln A/B

Odds Post = ln (Odds Prior + ∑(W+ +W-)

Post Prob = Odds Post /(1+ Odds Post )

Evidential Pattern Area = 22 Pilot sites in Pattern = 8

P(SjF1F2) = [P(F2jS) P(F1jS) P(S) ] / [P(F2) P(F1)]

How would I know a particular place is a good site for a project?

• Summarizing up to here….– We use the contribution of each factor (positive

and negative weights)– Associate this information with the basic

knowledge of the site (prior probability of pilot sites)

– Calculate a value per each potential new site (posterior probability)


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How to read results?

• Check for total conditional independence and uncertainty

• Organised results in maps for dialogue with project team members

• Iterate after adjustments• Review results to the light of complementary

information


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Quesungual seed sites


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AVAILABLE

• Poverty (2 USD/day) (Thornton et al., 2002)• Climate (length of dry season) (Jones et al., 2005)• Slope (From SRTM data, Jarvis et al., 2006)• Water availability (Proximity to water sources,

from Lehner et al., 2004)• Access to markets (Nelson, 2006)• Land Cover (Tree cover from GLCF, 2008))

NOT AVAILABLE OR NON INDEPENDENT

• Erosion• Agriculture and Livestock productivity• Agricultural subsistence systems• Land Tenure• Soils Type (Shallow soils)Community and Institutional Participation/support

“Important” factors


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Location of prospecting sites for the PN15 Quesungual Project


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Population

Area

Thanks


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Summary of Feedback

Assessment

• Mixed response about the approach (you love it or hate it)

• Data availability is a key constraint but solvable• Local knowledge will improve its validity• EDA, in general, is a useful starting point for the

promotion of innovative techniques to new locations• A recognition that suitable sites are not the unique

guarantee for successful scaling out of interventions• Does not work for all type of projects


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Recommendations

• Incorporate it in future projects’ development– More dialog at the beginning of projects to ensure the

validation of variables to be used– Capture local knowledge– Use it as an aid for partnership search

• Dynamic elements should be introduced– To allow the generation of different EDAs under different

scenarios• A Country level EDA

– Use of higher resolution data– To target more specific locations for intervention


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Thanks


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• There must be other ways of doing this, if better, use them, but please let me know I am always open to learn


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Key References

• Bonham-Carter, GF, Agterberg, FP., Wright, DF. Weights of evidence modeling: a new approach to mapping mineral potential. In: Agternerg, FP, Bonham-Carter, GF, editors. Statistical Applications in the Earth Sciences. Canada: Geological Survey of Canada; 1989, 171-183.

• Bonham-Carter, GF. Geographic Information Systems for geoscientists: modelling with GIS, First ed. Ottawa, Canada: Pergamon; 1994

• Bonham-Carter, GF. Geographic information systems for geoscientist: Modelling with GIS. In: Merriam DF, editors. Computer Methods in the Geosciences. New York: Pergamon/Elsevier; 2002, 302-334.


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The Holidays dilemma

• Who knows where are you going on Christmas?– Paris?, The Caribbean?, London?, Home?

• From previous experience you reckon that you enjoy being at…– Historical places ?– A sandy beach ?– Warm temperature ?– Being with relatives ?


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Adapting to Change: How to Accelerate Impact

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