Aagw2010 June 09 Andrew Farrow Spatial Analysis at Ciat

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Eco-Efficient Agriculture for the Poor

www.ciat.cgiar.org

Glenn Hyman (Colombia), Andy Jarvis (Colombia)

Andrew Farrow (Africa) , Peter Laderach (Central America)

Decision and Policy Analysis, CIAT

[email protected]

9th June 2010

Spatial Analysis at CIAT



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Read all about it

Terra-i

Global Poverty Maps

Downscaled Climate Change data ArcGIS Server applications


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What: An eye on habitat change

Who: The Nature Conservancy, CIAT,

University of applied Sciences of Western

Switzerland (HES-SO), Kings College London

Maps at www.terra-i.org

How
http://www.terra-i.org/http://www.terra-i.org/http://www.terra-i.org/http://www.terra-i.org/


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Inputs:

MODIS NDVI & Quality

TRMM Rainfall

WorldClim Temperature

Methods:

Cleaning HANTS algorithm

Clusters reduce computation

Bayesian Neural Network Training

rainfall/NDVI

Output:

Land Use change probability

Deforestation and Flood detection

Cleaning of

NDVI using

HANTS

K-means to distribute

in various components

Model

training

Range of accumulated probabilities


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Terra-i


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Direct usage for developing negotiation position

of Colombia in Copenhagen

September 2009 Colombia were going to COP15

with a figure of 100,000Ha/year deforestation

Terrai analysis identified MINIMUM

180,000Ha/year, most likely 250300,000Ha/year

Discussions underway for to become a 1st tier

monitoring tool for National Parks



Global Poverty Maps

Compile all the different poverty maps

Merge them together into a single developing

country data set

Make the adjustments from national poverty lines to$1.25 a day and two dollar a day poverty lines

Make the adjustment to a standard 2005 date

Use the headcount ratio (percent population that ispoor) with global population maps to calculate the

number of poor



Global Poverty Maps



Downscaled climate change data

http://gisweb.ciat.cgiar.org/gcmpage

Assume geographies of changes in climates don't vary too much atregional scales and relationships between different variables willremain the same in the future

Empirical downscaling approach instead of re-modelling the climate

patterns using an RCM (Regional Climate Model) Calculation of anomalies (if they're not provided directly by IPCC) by

simply subtracting each variable's future values from the baseline(both provided by IPCC)

Interpolation of anomalies to a 30 arc-seconds resolution (approx.1km)

Addition of the interpolated anomalies to the current distribution ofclimates in WorldClim, for temperature we make an absolute sum, butfor precipitation (as there are differences between the GCM baselineand our WorldClim baseline), we use the relative difference
http://gisweb.ciat.cgiar.org/gcmpagehttp://gisweb.ciat.cgiar.org/gcmpage



Downscaled climate change data



ArcGIS Server Apps



1. Atlas of spatial data relevant to

Generation Challenge Program

2. Seasonal Drought Index

animations of water stressthroughout the growing season

3. Drought timing for agronomic

screening tool for planning,

cultivar trials

4. Genebank accessions

in thecontext of constraints to

production



Land-use change analysis for REDD

Visual interpretation of land-use for 1990, 1997 and 2007

Digital photography and GPS for field validation

Google fusion tables, Google maps and ArcGIS server Input to opportunity costs analysis of avoided deforestation

Similar analysis for Cameroon (IITA), Indonesia (CIFOR)



GIS in the Mega-Programs

Potential Models (not mutually exclusive)

Original CSI model

DAPA model Realistic/opportunistic model

CSI tax




Original CSI model

IWMI data management and coordination

IFPRI impact assessment CIMMYT global dimension of crop varieties

ICARDA natural resource degradation

Poverty mapping CIAT




DAPA model

Strategic research (CIAT-HQ) big team,

infrastructure, well integrated with HQ crop

teams

Applied research (Africa, CAM) small team,

well integrated with regional crop teams and

reaching end users




Realistic/opportunistic model

MP 7.2

MP 3.2MP 3.3

MP 6.4

MP 7.1

MP 6.3

MP 1.3

MP 1.1

MP 1.1




Realistic/opportunistic model

Centres still responsible for fiscal policy

(indirect costs, admin, HR) Ad Hoc inclusion in mega-programs (based on

mandates)




CSI Tax

General tax to enable core functions of CSI

such as coordination, meeting expenses and

seed money for strategic proposal

development



Future Work - Future Labs


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Site specific agriculture

The hypothesis

If it were possible to compile the information on what the

farmer did and characterize the conditions of a largenumber of these experiments it would be possible to

deduce optimum practices for specific conditions


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The Concept

Production systems are highly heterogeneous

We need massive amounts of data to understand them, inorder to act and provide recommendations

Every farm is essentially an experimental station

Every crop cycle, management practice is an event (trial, n)

Farmers learn and adapt from an n+ 1 + 1 ..

Learning from collective knowledge far more powerful (n of1,000s)

But, all trials go uncompiled we learn, but not enough


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The project at last!

Mango, Avocado, Citrus and Plantain

Colombia ASOHOFROCOL, Corpoica, CIAT

Documents

Aagw2010 June 09 Andrew Farrow Spatial Analysis at Ciat