Geographical extrapolation domain analysis

8/8/2019 Geographical extrapolation domain analysis

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Gegraphical EtraplatiDmai Aalysis:

Scalig p Watershed Maagemet Research Prjects,

A Tlit t Gide Implemetati

Jrge Rubia, Assciate Researcerad Victr St, Lad Use Prject,

Iteratial Ceter r Trpical Agriculture, Cali, Clbia.

CPwf WoRkInG PAPER04

2009


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The CGIAR Challege Prgram Water ad Fd, Clmb, Sri Laa.

Impact Assessmet f Research i the CPWF prject

2009, by the Challege Prgram Water ad Fd.

All rights reserved. Pblished 2009.

This paper is a ctribti t the sythesis wr f the CGIAR Challege Prgram Water ad Fd.

It shld be cited as:

Rbia, Jrge, ad Victr St, 2009. Geographical Extrapolation Domain Analysis:

Scaling up Watershed Management Research Projects, a Toolkit to Guide Implementation.CPWF Wrig Paper 04. Clmb, Sri Laa: The CGIAR Challege Prgram Water adFd. 26pp.

keywrds: Etraplati, methdlgical gidelies, research impact, spatial aalysis,similarity aalysis, targetig.

You can nd the CPWF Working Paper series online at www.waterandfood.org.

The Challenge Program on Water and Food (CPWF), an initiative of the ConsultativeGroup on International Agricultural Research (CGIAR), is a multi-institutional research-for-development program that aims to increase water productivity for agriculturethatis, to change the way water is managed and used to improve food security and help meetinternational poverty eradication goals.

The CPWF encourages institutions and policy-makers to implement developments in water,food, and environmental management that are benecial to the rural poor. The program isfounded upon the conviction that practical innovations in research-for-development arisefrom people working together from a variety of disciplines and backgrounds.

The CPWFs Working Paper series contributes to sharing information from research inprogress, work generated before a projects completion. Working papers may containpreliminary material and research results - data sets, methodologies, observations, andndings - that have not been subject to formal external reviews, but have been reviewedby at least two experts in the topic. They are published to stimulate discussion and criticalcomment.

ISBn 978-92-990053-8-5

You can nd the CPWF Working Paper series online at

http://www.wateradfd.rg/pblicatis/prgram-pblicatis/wrig-papers.html.


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ABSTRACT

Fdig agecies, research prgrams, ad rgaizatis ivlved i the implemetati fresearch eed t w the ptetial wrldwide impact ad applicability f their effrts adivestmets. The etraplati dmai aalysis methd (EDA) was develped t prdce

information about the location, areas, and population potentially inuenced by researchtpts.

This wrig paper presets detailed steps hw t implemet a EDA. Fr a particlarresearch prject, it starts with establishig a baselie assessmet f the prject, ad pr-ceeds thrgh data cllecti, preparati, ad similarity mdelig ccldig with reprt-ig ad validati.

The gide is desiged fr sers with itermediate wledge f GIS ad Bayesia statisticsfr a smth ad easy implemetati f the methd. It als reqires the participati fthe members of the research project for proper identication of key variables to be used inthe prcess.


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ConTEnTS

List of Tables 5

List of Figures 5

Preface 6

1 Itrducti 7

2 Ratiale r acti 7

2.1 Prpse f this Gide 8

2.2 Adiece f this gide 8

2.3 Strctre f the gide 8

2.4 Case stdies 8

3 Teretical bacgrud t EDA etdlg 8

3.1 What is etraplati dmai aalysis? 8

3.2 Predictive mdelig 9

3.3 Weights f evidece mdelig 9

3.4 Hmlge mdelig 10

4 Etraplati dai etdlg 10

4.1 overview 10

4.2 Guide step 1: Baseline assessment and project denition 11

4.3 Gide step 2: Data gatherig ad preparati 14

4.4 Gide step 3: Similarity aalysis 15

4.5 Gide step 4: Reprtig ad validati 19

5 Issues ad calleges 20

6 Acledgeets 22

7 Reereces 22

8 Glssar e ccepts 23

Appendix A. Key variables 25

Appendix B. Parameters for Homologue layers Reprojection 26


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Table 1. Lcati f the pilt sites i case stdy Pn6 i rther Ghaa.................................... 13

Table 2. Areas ad peple livig i sch areas where a agrfrestry

prject is ptetially replicable............................................................................................. 21

Table A.1. List f ey variables fr a selecti f

projects dened by project specialists as being important....................................................... 25

Figre 1. A cceptal verview f etraplati dmai aalysis..................................... 9

Figre 2. EDA prcess framewr diagram..................................................................... 11

Figre 3. Map shwig prject pilt sites i case stdy Pn6 i rther Ghaa................... 13

Figre 4. Climatic Hmlge areas f prject Pn6 research sites.

Step 3.2: Weights f Evidece Mdellig ....................................................................... 17

Figure 5. Dialogue box for entry of parameter denition for WofE modeling........................ 18

Figre 6. Dialge b fr the etry f data fr weights calclati................................... 18

Figre 7. Graphical represetati f etraplati

dmai areas accrdig t prbability degrees............................................................... 21

Figure 8. Bivariate map showing inuential

critical grp f factrs acrss the pa-trpical wrld...................................................... 21

LIST oF TABLES

LIST oF FIGuRES


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CPwf WoRkInG PAPER6

The Challege Prgram Water ad Fd (CPWF) spprts research prjects itegrated water, pverty, ad prdctivity thrght the wrld. The CPWFs researchactivities have already shw hw ew practices have brght abt imprvemets bth i

evirmetal (imprved ecsystem fcti thrgh higher sil water reteti, lessersi, etc) ad scial aspects (imprved prdctivity, well beig, icmes).

Whilst these impacts are imprtat, they are restricted t areas where fdig wasavailable for the rst phase of the CPWF. However, one of the CPWFs goals is also toeted ad accelerate this impact ad certaily this is a ecessary step wrig twardsachievig the Milleim Develpmet Gals. A ey research qesti is therefre, hw isit pssible t accelerate impact?

The question of how is not easy to dene. Simply taking localized success stories andscalig p ad t is a misgided startig pit, becase t all places have the samecharacteristics at the start. T idetify accrately ther areas i the wrld where aactivity r a prject ca be reprdced reqires aalysis f hge amts f data tdetermie whether the cditis are favrable t the itrdcti f ew appraches.It als reqires derstadig f hw lesss ad wledge acqired previsly ca bemodied to t the new geographical context to which it will be introduced.

The CPWF sght t se gegraphical ifrmati appraches, which have bee well es-tablished i the literatre, ad develped the ccept f Etraplati Dmai Aalysis(EDA). EDA is a meas t idetify areas elsewhere where ew methds f ecsystem ma-agemet might be itrdced with a high prbability f sccess.

EDA combines a number of techniques of spatial analysis. It was rst investigated in 2006,whe it was applied t assess hw similarity aalysis cld be sed t scale t researchndings within seven Andean basins (Otero et al. 2006). The method was developed

frther by icrpratig sci-ecmic variables it the Hmlge aalysis (Jes et al,2005) sed t idetify sites elsewhere i the trpics that are similar t a site with wcharacteristics. It has sice bee sed t evalate impact pathways ad i glbal impactaalysis (Bma et al. 2006).

We develped this step-by-step gide t EDA t help decisi maers ad prject imple-meters idetify ptetial areas t which ew methds ad techlgies might be appliedwith condence so that investments may be more accurately targeted, and thereby ensurebetter sccess rates.

Jrge RbiaImpact Assessmet f Research i the CPWF prject

PREFACE


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1. InTRoDUCTIonEtraplati dmai aalysis (EDA) is a methdlgy fr idetifyig gegraphical areasthat are sitable fr adpti f ivative practices f ecsystem maagemet thebasis f selectig sites that have similar climate, lad-se ad sci-ecmic idicatrs.

Whilst it has been specically applied to 16 CPWF projects, in 9 river basins, the conceptis geeric ad ca be applied t ay prject where acceleratig chage is csidered asa cetral develpmet bjective. The CPWF, lie all research fr develpmet prgrams,eeds t esre that its research impact i lcal areas ctribtes cllectively by spreadigsccess t ther areas, thrgh the prcesses f p ad t scalig (Rbia et al. 2008).

S far, the tpts f EDA have bee sed t qatify the glbal ecmic impact f imple-menting specic innovations together with their effect on water resources (Bouman et al.2007). EDA research has stimlated members f several f the CPWF prjects t eplreptetial areas fr scalig t, sch the Qesgal agrfrestry system, which is beigadopted in new areas identied by EDA

EDA sees t idetify pprtities fr t-scalig i research fr develpmet prjectsad assist i their delivery. Plitical, cltral ad evirmetal barriers cstrai the sc-cessfl implemetati f techlgical r istittial ivatis t areas tside thegeographic context in which they where developed. By identifying these barriers in the rstplace, better decisis ca the be made as t whether they are sitable fr what is beigprpsed.

The qesti where t g et? the becmes less datig t aswer. At the very least,the riss assciated with the itrdcti f ivative watershed maagemet ca beclaried and quantied, making projects easier to manage and also contribute to their suc-cess. This gide gives a ratiale fr acti, gives a theretical bacgrd ad describesthe methdlgy ad the prvides a step-bystep recipe fr the implemetati f theEDA methdlgy.

2. RATIonALE foR ACTIon

This gide prvides a methd hw t determie where ivestmet might be fcsedet. EDA shld be sed whe prject maagemet wats t estimate systematically theptetial target areas ad idetify the pplatis where adpti ad impact ca easilybe achieved. Havig this isight is imprtat at all stages f a prject, frm prpsal devel-pmet, prject implemetati ad i scalig t cmpleted research. By dertaig theanalysis, there are many benets:

It identies more accurately areas where new research approaches will have a betterchace f scceedig t esre adpti ad implemetati;

It mitigates the riss assciated with wasted ivestmet, bth time ad mey, adavids the apathy created amgst lcal cmmities if (whe) a prject fails;It qatitatively determies the ptetial pplati that will be impacted by the prjectproposal, which is a fundamental criterion in inuencing decisions about project funding;Thrgh trasparet evidece-based aalysis ad dedcti, plaig ad ris maage-met are ehaced ad ctribte t accelerated impact; adIt lwers the barriers t sccessfl implemetati by esrig the barriers are well -derstd at the prjects tset.

The methdlgy is der ctis develpmet t imprve its accracy by esr-ig that the critical idicatrs, which are thse csidered imprtat t describe whetheradoption will succeed or not, are identied and incorporated into the analysis. We havefd that ifrmati sci-ecmic cditis ad istittis are mre impr-tat predictrs f sccessfl adpti tha biphysical cditis.


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2.1 Purpse tis GuideThis implemetati gide is prvided t mae EDA mre accessible t ewcmers t theeld, to project implementers, to donors, and to decision makers. We hope that EDAs po-tential can be realized through further application and testing, leading to renement of themethdlgy ad its cslidati it a wrig tlit directed t acceleratig impact ata brad scale. The verall strategy f the EDA apprach is describes elsewhere (Rbia etal. 2008).

2.2 Audiece tis guideIteded sers reqire the aid f GIS aalysts ad statistical data maagers sice ad-vaced wledge spatial aalysis ccepts, sch as the weights f evidece methd, isecessary t help t assess the lgic ad reliability f the mdelig tpts.

2.3 Structure te guideThis gide is divided it three mai sectis:

Itrdcti ad ratiale;A theretical discssi ad a descripti f the methdlgy; ad

The EDA implemetati gide itself, describig the implemetati i detail.

2.4 Case studiesThe gide ses the CPWF prjects as case stdies t illstrate the methdlgy ad theepected tpts: Pn6 Strategic innovations in dryland farming, Pn15 The Quesungualagroforestry system ad Pn16 Natural resource conservation and management for in-creased food availability and sustainable livelihoods: Empowering farming communitieswith strategic innovations and productive resources in dryland farming. The prcess is de-scribed geerically, which shld eable a GIS aalyst t perfrm the peratis sig ayGIS sftware. Hwever, the aalytical frmlae are already pacaged it ArcSDM (spatialdata mdeller), which is based the ESRI ArcGIS etesi. The case stdies give spe-cic detail of the use of ArcSDM.

3. ThEoRETICAL BACkGRoUnD To EDA mEThoDoLoGy

3.1 wat is etraplati dai aalsis?Extrapolation domain analysis is about nding target sites (called the response theme datalayer) with similar characteristics t a grp f prject pilt sites (called the training pointsdata layer). A sitable target site is called a extrapolation domain. A etraplati d-mai is a gegraphical area that is liely t behave i the same way as the pilt sites. Theetet t which a etraplati dmai crrespds t the pilt sites depeds the de-gree fsimilarityf the chse key variables, which are the factrs that cmprise the evi-

dential theme data layers acrss the ttal gegraphical area beig aalyzed, which i trare dened by the search area r unit cells.

The prpse is t scale t a ew ecsystem maagemet practice that scceeded at aseveral particlar pilt sites ad implemet it at ew target sites selected t have charac-teristics similar t the sccessfl pilt sites. The cceptal fdati f the methdlgy(Figre 1) is preseted i mre detail i Rbia et al. (sbmitted).


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Figure 1. A conceptual overview of extrapolation domain analysis.

3.2 Predictive deligT derive the etraplati dmais, we se Bayesia ad freqetist statistical mdeligtechiqes. Largely based the ccepts f Bayesia prbabilistic reasig (Bham-Carter et al. 1989; Bham-Carter 2002), we apply the weights-f-evidece (WfE) meth-dlgy. I essece, a statistical iferece is established that estimates the prbabilityf target sites adptig the chage demstrated i pilt areas. The assmpti is that acllecti f traiig pits that cmprise the pilt areas will, i aggregate, have cmmcharacteristics ad we the prceed t estimate the degree t which these characteristicsca be fd i sites elsewhere. The cmm characteristics are sed t create evidetialtheme data layers ad are shw t be csistet with sccessfl implemetati at piltsites. We the assme that target sites that ehibit similar sci-ecmic, climatic adladscape attribtes t the pilt sites are thse where t-scalig is highly liely t sc-ceed.

3.3 weigts evidece deligWfE mdelig was selected fr several reass. Firstly the methd is wledge-based,s it wrs t reifrce prject scietists derstadig, sig factrs deemed by them tbe f practical imprtace. Secdly it is able t icrprate certai ad sparse data ithe prcess f prbability pdatig ad is hece sefl fr prspectig ver very large ar-eas fr which data are fte f variable availability ad qality. Thirdly it cmbies severallies f evidece, bt i a maer that avids the sbjective chice f weights.

We sed ArcGIS sftware fr the mdelig cmptatis, ad the ArcSDM etesifr the WfEs calclatis. ArcSDM is a spatial data-mdelig pacage develped by the

Gelgical Srvey f Caada (GSC) ad the uited States Gelgical Srvey (kemp et al.2001). The detailed thery behid the methdlgy is tlied i Rbia et al (sbmitted).


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3.4 hlgue deligJes et al. (2005) develped the Hmlge prcedre t determie the etet t whichsites elsewhere i the trpics pssessed the same climate ad sils f a give pilt site. Iessece, it aswers the qesti, Where else i the trpics are climate ad sil cditissimilar to here?, where here is dened a source pixel or cloud of pixels on a digital map.Homologue uses the predened source pixels to search for other pixels that have similarclimate and soils. Similarity of climate, which is normally the dominant attribute, is denedby multivariate classication of a global climate database from over 21,000 stations in LatinAmerica, Africa ad Asia. The methd ses a etesi f the Flramap algrithm (Jesad Gladv 1999).

Hmlges mial piel size is 10 arcmites, r abt 18 m at the eqatr. Lcalizedvariation (e.g. mountainous country) can unduly inuence the attributes of the source pix-els, so to avoid spurious variation, Homologue allows users to dene the source area ac-crdig t a selecti f piels withi a lcality.

Homologue denes the geographical distribution of soils on the basis of the database of thewrld ivetry f sils emissi ptetials (WISE, Batjes ad Bridges 1994; Batjes 1995),

ad the FAo sils map f the wrld (FAo 1995). Give the certaities cased by thelimited spatial resolution of the FAO map, and the potential mis-denition of soil variationwithin map units, Homologue allows the user to modify the inuence of soils in dening thesimilarity to source pixels. In the EDA work, we reduced the inuence of soils to the mini-mm.

4. ExTRAPoLATIon DomAIn mEThoDoLoGy

We w detail the prcess step by step t gide thse wishig t replicate the apprach.Each step makes extensive use of graphics and gures. We assume an intermediate levelf wledge f GIS ad statistics.

4.1 overvieThere are fr ey steps ivlved t idetify the etraplati dmai areas (Figre 2).We assme iitially that wledge abt the ew practice t be etraplated cmes frmeperts i the tpic wrig the research prject. This wledge icldes eperiecef which idicatrs ctribted t the sccess f the prject i the pilt areas. net cmesdata cllecti ad preparati, perhaps the mst time-csmig stage. This is fllwedcarryig t the aalysis sig GIS tls. The reslts are the cslidated ad cmm-icated t prject implemeters sig graphs ad maps. Fially, the reslts are validatedthrgh csltati with prject eperts. Each step is eplaied i detail i the fllwigsectis.


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Figure 2. EDA process framework diagram.

4.2 Guidestep1:Baselineassessmentandprojectdefnition

Overview of Step 1Befre embarig the aalysis itself, it is imprtat t carry t a baselie assessmetat the tset f the prcess, t derstad the lcati ctet ad bjectives f the re-search prject. The baselie assessmet is crcial t grd the selecti f variables adderstad the ptetial cstraits. The bjectives i this step are t:

obtai a fll derstadig f the research prject ad what is the ivati it is at-temptig t scale t;Dene the pilot research sites; andSeek denition by the project experts of the key factors/variables that led to the successf the prject at pilt sites.

Information sources for the baseline assessmentA csiderable bdy f evidece is liely t already be available at the lcal level; this willeed t be reviewed ad csidered as part f applyig the methdlgy. The eact scpef what eeds t be reviewed will vary with lcati bt a cre set f dcmets may i-clde:

Eistig prpsal dcmets;Prject prgress reprts (these sally prvide the lcati f pilt research sites adchages applied t the rigial prpsal); adkey pblicatis abt the pilt sites, implemetati sites, ad the resrce maage-met apprach beig ivestigated

I additi, emphasis etractig lcal wledge ad wrig with prject implemet-ers is imprtat.

Who do you involve in step 1?The rst step in EDA requires project participants to select a range of factors that theythink will inuence the replication of an innovation beyond the pilot sites. This assumes that

replicati f the prject will be mst liely i places where cditis are eqal r similart the pilt sites. The set f factrs iclded i WfE are typically thse grped as sci-ecmic attribtes, hwever they ca iclde ther, biphysical attribtes (e.g. slpe fthe lad, r primity t a particlar pilt site). Participats shld select factrs that are


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idepedet (i.e. t at crrelated), becasethe pwer f csiderig mltiple factrs tgeth-er derives frm the assmpti that each piecef evidece ctribtes additial ifrmati.This is imprtat t btai a hlistic perspectiveof all the factors/variables that may inuence theadpti f the ivati.

Detailed stepsStep 1.1: Georeferencing the pilot sitesOnce the pilot sites are identied by the projectmembers, a GIS aalyst creates a data layer f

traiig pits (Figre 3). The data mdel i-cldes the latitde, lgitde ad lcati ame.T idetify lcati ames, lie gazetteers cabe sed (see Bp 4). If the ifrmati is tavailable, the prject implemeters ca be asedt prvide gegraphic crdiates ad place

ames at the reqired detail. I sme cases,gegraphic crdiates f lcatis ca be b-taied frm a map verlaid Ggle Earth.

Step 1.2: Size constraints of the dataset of train-ing sitesThe WfE apprach reqires a miimm f 100data traiig pits spread ver a reasablearea to give sufcient sample variation in the variables used. Smaller numbers of datapoints typically give unrealistically low probabilities of nding similar areas elsewhere. Inpractice, hwever, research prjects sally perate at ly a limited mber f sites. Ithe case stdies we have made s far, mre tha 30 lcatis i each were csidered

pilt research sites. I this sese, it is ecessary t icrease that mber by geeratigradm sites (withi 5-10 m) f thse spplied by the prjects eperts. With higher res-lti data it is pssible t se a smaller bffer radis. Cceptally this apprach is jsti-ed because project outputs developed in a single location are generally applicable to thearea srrdig it r earby.

Step 1.3: Dening the search extentThe search extent is the geographical extent within which one expects to nd extrapolationareas. The geeral ccepts f primity are assmed, i that the bigger the area ad thefurther away it is from the training/pilot sites, the lower the probability is of nding similarareas; the closer and smaller the search area, the higher the probability is of nding ex-traplati dmai areas.

Selecti f a search area depeds the prject ad chice f the prject eperts. Ap-plicatis t date have iclded the etets that ecmpass the pa trpical wrld, c-tiets ad sb-ctiets. The chse level will be cstraied by the cmptig eqip-met available, althgh i these days with cheap terabyte hard drives ad mlticreprcessrs this t s mch a isse as frmerly. If ecessary, hwever, partial searchesat ctietal r sb-ctietal level ca be a alterative. oce chse, a search etetGIS data layer is created, which acts as the aalysis mas t delieate the bdary e-tet f the aalysis widw.

Step 1.4: Identication of key variables/ success factorsThere is limit t the mber f variables that may be chse, hwever, care mst betaken to avoid autocorrelation between them. Identication and selection of critical vari-

ables should be undertaken in consultation with a number of specialists in the elds ofsci-ecmics ad biphysical scieces, a prject-by-prject basis. It is imprtat thave this rst-hand expert knowledge when selecting which variables are the appropriatees t chse. I cases where eperts are t available, they may be derived frm prj-

Box 1: Creating a training pintdata layer in ArcGIS.

Create a table with the latitude and lon-

gitude and name of sites and save astxt, xls or dbf format.

InArcGISusetoshapelefromtable

orEventThemecommandtoimportthe

table and create a data layer. If using

the latter, an extra step is then required

to convert the event theme into a shape-

ledatalayer.

Box 2: Creating randm pints

in ArcGIS.

The ArcView Extension Random points

generator V 1.1 can be used for this

purpose.


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ect prpsals ad impact pathway dcmets. Clearly, the mst imprtat variables arethse csidered as determiats fr the prjects sccess r are ey t addressig a spe-cic research problem. Typical variables for particular project types (agroforestry systems,water systems ad salie evirmets) are listed i Appedi A. These variables are themapped i step tw it evidetial themes fr the aalysis.

5.2.5 Key outcomes/productsThe key outcomes of this rststage are:

Bacgrd ctet;ad

Identication of keyvariables tgether with theirstats, which may eed t bechaged s that implemeta-ti f the prject i atherspecic location will be viable.

T drive the GIS aalysis, thefllwig data layers are re-qired:

A list f the variablesthat were critical t the sc-cess f the prject i the se-lected pilt sites; ad

The data layer depictigthe traiig pits, that is, thegegraphical lcati (latitde

ad lgitde crdiates) fprject pilt research sites,alteratively the apprimatelcati f pilt sites, theirames ad the type (rral rrba) f cmmities wherethe prject was implemeted.

Figure 3.Map showing project pilot sites in case study PN6 in northern Ghana.

PN6 began working in

300 focal households in

17 communities in eight

pilot districts in northern

Ghana. Table 1 showsthe location of the 17 re-

search pilot sites. These

were plotted within a GIS

to create a map, Figure 3.

To arrive at the necessary

100 points, the rest were

randomly assigned within

a 25km buffer distance of

the PN6 sites. The choice

of a buffer radius of 25km

took into account that the

pixel resolution of the so-

cio-economic data used is

5km and the data changes

little from pixel to pixel.

Box 3: Case study PN6: Identifying andmapping prject pilt lcatins.

Location Latitude Longitude

Gbung 909N 036W

Damongo 905N 048W

Nyankpala 924N 059W

Yendi 922N 041W

Demon-Naa-Yili 855N 000W

Chambuligu 912N 035WKagberishie 912N 034W

Mazeri 930N 001W

Walewale 1012N 049W

Kabingo 1057N 006W

Kuguri 1057N 011W

Gambaga 1031N 026W

Mirigu 1054N 059W

Kpabi 853N 001E

Nanoori 1029N 026W

Manga 1101N 016W

Salaga 0833N 031W

Table 1.Location of the pilot sites in case study

PN6 in northern Ghana.


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4.3 Guide step 2: Data gaterig ad preparati

Overview of step 2I this step we cllate the variables ad create GIS data layers ready fr the similarityaalysis i step 3. If climate layers were sggested fr the etraplati dmai search,the traiig sites are sed t geerate areas with similar climate sig Hmlge (seestep 3.1). The ey bjectives f this step are:

Acqire, dwlad ad cllate data relatig t the chse ey variables;Data preparation by geo-coding, aggregation or disaggregation and dening resolution ofall layers; adCreati f GIS data layers that cmprise the evidetial themes.

4.3.2 Sources of information for data gathering and preparationOnce the variables are dened, we undertake an extensive global search for data. Whereeact data d t eist, we se pries f the critical variables istead. This is the mstchallegig part f the wr, as data availability ad qality ca be a cstrait. We cm-mly se e r mre f several lie srces (see B 4 fr frther details).

4.3.3 Who do you involvein step 2?This step ivariably reqires GISaalysts ad specialists, becase thedata layers mst be created i a

apprpriate frm s that they cabe sed fr the similarity aalysis.It may als ivlve prject imple-meters, as they fte w f datasrces, r have ey etwrs adctacts fr the data that are re-qired. They ca als act as criticalreviewers f data reliability.

4.3.4 Detailed steps4.3.4.1 Step 2.1: Data preparationThere are several steps i data prep-

arati; these ivlve a) gecdigr gereferecig; b) settig the lay-er reslti; c) aggregati f thedata; ad d) disaggregati f data.

Box 4: Web links

Online global data sources

Scial and ecnmic

http://earthtrends.wri.org/

http://www.ciesin.org/download_data.html

http://www.map.ox.ac.uk/MAP_dissemination.html

Agriculture and livestck

http://www.fao.org/geonetwork/srv/en/main.home

http://ergodd.zoo.ox.ac.uk/livatl2/index.htm

Biphysical

http://www.fao.org/nr/water/aquastat/gis/index.stm

http://geodata.grid.unep.ch/

http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html

http://www.iwmidsp.org/iwmi/SearchData

http://srtm.csi.cgiar.org/

Box 4: Case study PN6: Selectin f key variables

ThevariablesidentiedforPN6sextrapolationdomainwere:

Existenceofshproduction.Source:Levelofshproduction(FAO2006);

Status of water and sanitation facilities. Source: Rural access to an im-

provedwatersourceinpercent(%)(WHOandUNICEF2006);(thesedata

were supplied at the country level and adjusted for population density for

2005(whichisavailableataresolutionof5km2);

Levelofpoverty:Source:ThepovertylineasdescribedbythebelowUS$2

per/dayindex(Thorntonetal.2002);and

ClimaticconditionsidentiedbyHomologue(Jonesetal.2005).


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We gather ad dwlad data frm typical srces. If ecessary, these have t be geref-erenced. Georeferencing is typically done using the country ISO code as a unique identierand using this attribute eld to perform spatial joins. The data layers are projected to thecrdiate system f the regi r sb-regi; fte sig the wrld crdiate system(WGS84 prjecti) if a larger search area is beig sed.

If ecessary, the data may als eed t be aggregated t match the bdaries f the ad-miistrative its beig stdied. Sme data are ly available at glbal scale, hwever, f-te the search eeds t be de at regial r sb-regial scale. T btai data at the ap-prpriate scale, the data therefre eed t be disaggregated t distribte them at a higherreslti. Typically, we d this sig pplati mbers as the demiatr. I this waythe idicatr reprted at a glbal level is divided by the mber f peple at a ctry(r ther admiistrative area) level t distribte the fracti f the atial statistics h-mogeneously across the population of the dened search area. Major renements can bemade by adjstig disaggregated vales depedig the ctet. Fr eample, whetherthe pplati is rral r rba, r by sig accessibility t weight the vales accrdig tprimity t pblic services. Spatial aalysis ffers may alteratives t rebild variables ata better reslti, always eepig i mid isses f data qality ad the errrs that these

prcedres ca itrdce.

Step 2.2: Dening the geographical extent and resolutionA data layer may be created t delieate the search area sig it as a mas layer t re-strict the traiig pits ad evidetial theme layers. This is de fr tw reass, t re-duce the le size, and to eliminate zones not considered part of the pan-tropics, that is,abve latitdes 45n ad 30S ad all lgitdes that fall i the pe cea. Thse c-tries witht data are assiged the label nD ( data).

All the data layers, icldig thse frm Hmlge, are cverted it grid frmat at thesame piel size as that f the data with the lwest reslti. We se EDA at the pa-trp-ical level sig gegraphical data i Grid frmat at the highest reslti available. never-

theless, the tpt f ay particlar aalysis will be restricted by the lwest reslti ithe ipt data. I the case stdies that we have carried t, the lwest reslti has beef the pplati data, which cmes at sqared grids f 2.43 arc mites (abt 4.5 m atthe eqatr).

Key outcomes/products of step 2The ey prdcts f this stage f the wr are:

Gerefereced data crrespdig t each ey variable (sci-ecmic ad biphysical;climate data are dened in next step);One evidential thematic data layer for each of the key factors dened by project special-ists; adA mask dening the extent of the study area.

4.4 Guide step 3: Siilarit aalsis

Overview of step 3This step ivlves sig the data cllated ad prepared i the previs steps ad rigthe aalysis. The bjectives are t:

Idetify similar climatic areas sig Hmlge;Weight the evidetial themes sig Weights f Evidece (WfE) mdellig; adDerive the Etraplati Dmai areas frm the itersecti f Hmlge climate layerswith the WfE tpt.

Sources of information for similarity analysisThis step ses tw mai aalytical tlits:ArcSDM, a ESRI ArcGIS, which icrprates the WfE algrithms; adHmlge, t derive the climate similarity areas.


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Who do you involvein step 3?This stage als reqires spe-cialist GIS aalytical sills adsme familiarity sig the GIS

tls described belw.

Detailed stepsStep 3.1: identication of cli-mate similar areasPtetial sers f this gideare ivited t read the Hm-lge maal, which describesthe bacgrd ad methd-lgical steps i mre detail. Traiig sites are etered it Hmlge sig their respec-tive gegraphical crdiates. The Hmlge tl allws e pit at the time, s ly thesites spplied by prject members are sed here. The radmly-geerated pits, if ay,are sed ly i the WfE search. The ser shld ispect all the layers t avid ptetial

errrs that are still embedded i the beta versi f the Hmlge tl. Accrdig t theathrs, the ser shld l fr sesible ad reasable similar areas. oce all the lay-ers are csidered apprpriate, a sigle Hmlge area created; we call this the cld fHmlge areas. This cld represets the maimm prbability i each piel fd frmthe set of pixels in the group of individual layers. The output shapele map gives probabil-ity f similarity vales that rage frm 0.1 t 1 (Figre 4).

The Hmlge tpt layer is the imprted it ArcGIS where the -data cells are as-siged a vale f 0 t allw averagig with the ther evidetial layers later i the prcess.This is done by combining the Homologue shapele output with a shapele of the mask ofthe stdy area with zer vales i each f their their plygs.

After the cmbiati, tw grids are geerated by reclassifyig the data layer, e it teclasses (deciles) fr statistical calclatis ad a secd it fr classes fr a bivariatemap to display later in the map representation step. To do so, the shapele must be re-projected into meters rst and then converted to grid specifying the parameters presentedi the Appedi B.

Step 3.2: Adjust the cell sizeAt present the cell size is adjusted by taking the shapele into ArcView 3.2 and using the

Analysis Properties function until the point at which the shapele squares match the gridpiels. The ser has t d this i ArcView becase ArcGIS des t allw sch adjstmet.Several tests are reqired t achieve this gemetric cicidece by idetifyig the appr-priate cell size fr the cversi. This step is reqired becase straight cversi frmshapele to grid implies a translation of the pixels values.

A guide to obtain this match is to set up the display for four squares of the shapele andi the Aalysis Prperties widw, chse Same as Display as the Aalysis Etet, whichwill adjust the cell size accordingly. Once the correct cell size has been identied, the grid isre-created bt fr the whle wrld ad adjst the vale f the cell size as described abve.Radm checs are a sefl measre t verify the matchig f prbability vales betweethe shapele and the nal grid. The grid conversion process for a global extent takes lesstha a hr, depedig the amt f ifrmati ad sig a 2.6 GHz prcessr with2.5 Gb i RAM. Figre 3 shws a eample Hmlge areas tpt fr Pn6

Box 5: Mre web Links

Hmlgue sftware dwnlad

http://gisweb.ciat.cgiar.org/homologue/index.htm

Hmlgue users manual

http://gisweb.ciat.cgiar.org/homologue/ing/user_manual.htm

ArcSDM etensin dwnlad

http://www.ige.unicamp.br/sdm/ArcSDM93/download.php


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Figure 4.Climatic Homologue areas of project PN6 research sites. Step 3.2: Weights of Evi-dence ModellingCase study PN6: Mapping Homologue climatic zones.

The prpse f this tas is t idetify similar areas fr sci-ecmic ad biphysical vari-ables (t climate). This part f the prcess reqires the se f the spatial data mdelertl, ArcSDM. The mdel is crretly writte as a ArcGIS (ESRI 2005) etesi sitablefr versis 8.3 ad 9.1/2/3 (Sawatzy et al. 2005). Details f the methd are preseted

in Bonham Carter (1994). The tool comes with different search and classication methods:lgistic regressi, weights f evidece, eral etwrs ad fzzy lgic. All fllw differ-et prcedres ad ly that relevat t the EDA methd preseted here will be described.This prcess ivlves:

Step 3.2.1: Data pre-processingIn the rst window (Figure 5), the denition of the following parameters are required in thedialge b:

Techiqe: Weights f Evidece;Stdy area: The grid represetig the dmai area r stdy ze;Area it: The size f the area which t base calclatis, this ca iclde frm e tmay piels; adTraining sites: The name of the shapele containing the point layer with the training sites.


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Figure 5.Dialogue box for entry of parameter denition for WofE modeling.

Step 3.2.2: Selection of evidential themesoce the parameters are etered, the system prmpts fr the list f layers sbject t the

calculation of weights. To do so, the appropriate variable must be selected, the eld withthe data identied, the data type stated and the format for weights calculation selected.The dialge bes fr data etry are preseted i Figre 5 ad Figre 6.

Figure 6.Dialogue box for the entry of data for weights calculation.


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Step 3.2.3: Generalization of variablesThis step reqires the cversi f ctis ad mlti-class variables it their bi-nary forms. To do so, the threshold must be identied using one of the following methodsthat the tool provides. This is a simple re-classication of the variable using the identiedweights.

Step 3.2.4: Posterior probability calculationoce the variables have bee geeralized, the ser ca r the prbability calclati. De-pedig the size f the grids, the mber f variables ad the prcessr hardware, thecalculation can take between 10 and 30 minutes. The output le is a table linked with theset f variables that are reqired fr frther later prcessig.

Step 3.2.5: Output map reclassicationThe probability map is re-classied into deciles for statistical treatment and into fourclasses fr a bivariate map as with the Hmlge layer. This ttal mber f classes is talways achievable e.g. i thse cases i which prbabilities are belw 75%. The deciles areused for the identication of in-country areas and populations.

Step 3.2.6: Test of conditional independenceThe ser shld als r a test f cditial idepedece t idetify ptetial prblemswith dplicati f ifrmati i selected variables. If all the variables are iitially impr-tat, the selecti f the mst apprpriate f them is facilitated with this test. It is cmmat this stage to nd problems of conditional dependence among the variables. If this oc-crs, several rs f the mdel are reqired with a redced mber f variables t ecldethse that case depedece prblems til a csistet reslt is achieved. The ser caals carry t a t-test t chec the relevace f each variable sed i the aalysis. ucer-taity de t missig data is als reprted fr each site at this stage.

Step 3.3: Intersection of climate and other WofE modelled variablesAs metied abve, the tw grids layers frm Hmlge with gegraphical prjecti

are cverted it meters (Mercatr, WGS84). This cversi allws the sci-ecmicad ther biphysical variables prdced by the WfE mdelig t be cmbied. usig theraster calclatr available i ArcGIS, the tpts frm Hmlge ad WfE are mltipliedby 100 t avid the lss f decimal vales i this perati. The average is the calclatedt prdce the prbability map f etraplati dmai areas. oly vales abve 0.10(10%) are mapped.

This tpt map is the sed t calclate areas ad pplati ptetially sbject t rep-licati f the prject. T d this, the ser cmbies grids f pplati grid with a layerf ctries fr the selected etet. The calclati is made by idetifyig the mber fpiels fr each f the te classes f the prbability map fallig withi each ctry. Thepplati is als calclated smmig the piel pplati fallig i each f the classes fthe prbability map (Table 3). The data frm the GIS tables ca the be trasferred t a

spreadsheet for graphic representation of areas and population gures.

Key outcomes/products of step 3Hmlge climate areas;Areas f sci-ecmic ad ladscape similarity;Extrapolation domain areas from the combination of the rst two outputs; andTables acctig areas ad pplati fr each it f aalysis (ctries) grped it10 classes f prbability similarity.

4.5 Guide step 4: Reprtig ad validati

Overview of step 4

I this step, the tpts f the aalysis are prdced i a frm that ca be easily iterpret-ed by prject implemeters. The bjectives are:


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Etract the tablar data ad graph them; adVisalise the respse theme i maps at the apprpriate scale

(glbal, regial, ctietal).

Sources of information for reporting and validationThe srce f ifrmati fr this step is the aalysis tpt itself. I additi, t aalyzethe tablar data, pivt table ad/r database tls (e.g. MS Access) are als reqired. Ithe cases on which records account for more that 32,000 elds, it is necessary to use da-tabase sftware r alterative spreadsheets that allw hadlig data sets f this size.

Who do you involve in step 4?This step ivlves a GIS aalyst wrig tgether with the prject implemeters ad prj-ect eperts t verify reslts.

Detailed stepsTables geerated by the itersecti mst be smmarized sig graphical r databasetls. Eamples f bth these are preseted i Figre 7, Figre 8 ad Table 2 belw. Thetpt maps, tables ad graphs shld be shared with prject eperts t idetify the valid-

ity f, r amalies i, the dmai areas. Feedbac frm this csltati eeds t be as-sessed ad where ecessary sed t adjst the iitial parameters ad rer the similarityaalysis til reasable reslts are geerated. This is a imprtat step i the aalysis asfte several simlatis rs are reqired. I the ed, the aalysis shld gide, t pr-scribe, the areas chse fr replicati.

Key outputs/products for step 4Fial etraplati dmai areas with pplati ad areas tables fr each ctry;Ctietal r mre detailed maps; adA recrd f the dialge ad descripti f the prcess.

5. ISSUES AnD ChALLEnGES

It is imprtat t emphasize that assessig the ptetial impact f research ivatis isa cmple ad certai isse. There are hard data that wld allw precise cst-be-et analysis to be carried out in these types of projects. International research generatespblic gds at a glbal level, bt they eed t be assessed ad evalated t help targetigivestmets ad itervetis.

The EDA methd is drive by epert wledge, i that the selected variables are based what experts know works, rather than relying solely on the ndings generated by a com-pter mdel. o the ther had, althgh fdametal data are crretly lacig, theseare epected t becme available with advaces i data gatherig ad the iteratialrmalizati f atial statistics.

There are tw challeges fr the et phase i develpmet f the methd: a) Prgram amre ser-friedly ser iterface t r the etraplati dmai search, ad b) Cmpilesocio economic and institutional data at higher resolution and detail to rene the identica-ti f dmais. Meawhile, it is imprtat that the ser derstads the limitatis f theprocess as well as its power as only then can the ndings help guide and foster debate dur-ig the decisi-maig prcess.

Fdig ad develpmet agecies are bliged t implemet a systematic mitrigprcess f research prjects as well as jstify ad be held acctable fr the selecticriteria they se fr chsig ew areas fr t-scalig. These shld be based bettercriteria tha a hch. EDA prvides a systematic ad trasparet apprach t evalatiad etraplati.


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Box 6: Case study PN15: Tabulatin and mapping f utputs

Figure 7. Graphical representation of extrapolation domain areas according to probability degrees.

Figure 8. Bivariate map showing inuential critical group of factors across the pan-tropical world

AFRICA Areas

Cuntry ( km2 * 1000) Ppulatin * 1000

Cameroon 5.5 5.5Democratic Republic Congo 7.5 7.5

Nigeria 5.1 5.1

Guinea 0.3 0.3

Malagasy Rep. 0.1 0.1

Table 2. Areas and people living in such areas where an agro


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6. ACknowLEDGEmEnTS

We tha the CPWF fr fdig. Develpers f CPWF prjects ctribted i may waysad especially with their respses ad cmmets t imprvig the EDA methdlgy. Weespecially tha kay Pallaris fr editig ad her cstat iterest t mae this gide der-

stadable.

7. REfEREnCES

Agterberg, F. P., Bham-Carter, G. F., Cheg, Q., ad Wright, D. F. 1993. Weights f evi-dece mdelig ad weighted lgistic regressi fr mieral ptetial mappig. I: J. C.Davis, ad u. C. Herzfeld, ed. Computers in Geology - 25 Years of Progress, 13-32. newYr: ofrd uiversity Press.Bham-Carter, G. F. 2002. Gegraphic ifrmati systems fr gescietist: Mdelligwith GIS. I: D. F. Merriam, ed. Computer Methods in the Geosciences, 302-334. new Yr:Pergam/Elsevier.Bham-Carter, G. F. 1994. Geographic Information Systems for Geoscientists: Modelingwith GIS. ofrd: Pergam.Bham-Carter, G. F., F. P. Agterberg, ad D. F. Wright 1989. Weights f evidece mdel-lig: A ew apprach t mappig mieral ptetial. I: F. P. Agtererg, ad G. F. Bham-Carter, ed. Statistical Applications in the Earth Sciences, 171-183. Gelgical Srvey fCaada. Paper 89-9.Bma, Bas, Sim C, Br Dthwaite, Cladia Rigler, Jrge Rbia, ad TigjZh 2007. Impact Potential of the Temperate ad Trpical Aerbic Rice (STAR) i Asia. I-teral dcmet prepared by the CPWF Impact Prject fr the Eteral Review team.Evirmetal Systems Research Istitte ESRI, 2005. ArcGIS Sftware. uRL: http://www.esri.cm.Fd ad Agricltre orgaizati f the uited natis (FAo) 2006. Fishery Ifrmati,Data ad Statistics uit. 2006. Captre prdcti: qatities 1950-2004. FISHSTAT Pls -Universal software for shery statistical time series [online or CD-ROM]. Rome: FAO.

uRL: http://www.fao.org//statist/FISOFT/FISHPLUS.aspJes, P. G., W. Diaz, ad J. H. Cc. 2005. Homologue: A computer System for Identify-ing Similar Environments throughout the Tropical World. Versi Beta a.0. Cali, Clmbia:CIAT.kemp, L. D., G. F. Bham-Carter, G. l. Raies, ad C. G. Ley 2001, Arc-SDM: Arcviewetesi fr spatial data mdellig sig weights f evidece, lgistic regressi, fzzylgic ad eral etwr aalysis. uRL: http://www.ige.icamp.br/sdm/.oter, M. F., J. Rbia, V. St, ad G. Lema 2006. usig similarity aalyses t scalig tresearch. Water International31,376386.Rbia, J, C, S. ad Dthwaite, B. 2008. Adaptig t chagehw t accelerate im-pact. Prceedigs f the CGIAR Challege Prgram Water ad Fd 2d IteratialFrm Water ad Fd, Addis Ababa, Ethipia, nvember 1014, 2008. The CGIAR

Challege Prgram Water ad Fd, Clmb. 183pp.Rbia, J., S. C, M. Rajasehara, V. St, ad B. Dthwaite 2009. Etraplati d-mai aalysis: A tl t aticipate research impact. Impact Assessment Review Journal(ipress).Sawatzy, D. L., G. L. Raies, G. F., Bham-Carter, ad C. G. Ley 2005, ARCSDM3:ArcMAP etesi fr spatial data mdellig sig weights f evidece, lgistic regressi,fzzy lgic ad eral etwr aalysis. uRL: http://tserv.gis.rca.gc.ca/sdm/ARCSDM3/.Thrt P. k., R. L. krsa, n. Heiger, P. M. kristjas, R. S. Reid, F. Atie, A. n.oder, ad T. ndegwa 2002. Mappig pverty ad livestc i the develpig wrld. nai-rbi, keya: Iteratial Livestc Research Istitte ILRI. 124 pp.Wrld Helath orgaizati (WHo) ad uited natis Childres Fd (unICEF) 2006.Meeting the MDG Drinking Water and Sanitation Target: The Urban and Rural Challengeof the Decade. Geeva: WHo ad new Yr: unICEF. uRL: http://www.wssif.rg/pdf/

JMP_06.pdf.


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8. GLoSSARy of kEy ConCEPTS

Baesia prbabilistic reasig: I shrt, accrdig t Jayes (2003, p86), t frma jdgmet abt the liely trth r falsity f ay prpsiti A, the crrect prcedre ist calclate the prbability that A is tre: P(A|E1E2 ) cditial all the evidece at

had. Spelled i ther wrds, prir ifrmati helps s evalate the degree f plasibilityin a new problem. New information is always expected that will help to rener ifereces.

Evidetial variables: This is a map r area layer (i either vectr r raster frmat, ashape or a grid le) used for prediction of point objects (such as mineral occurrences). Theplygs r grid cells f the evidetial themes have tw r mre vales (class vales). Freample, a gelgical map may have tw r mre vales represetig the classes (mapunits) present. Although weights of evidence was originally dened for binary eviden-tial themes (als amed biary patters i several pblicatis), it ca als be applied tthemes with mre tha tw classes. Freqetly, mlti-class evidetial themes will be ge-eralized (simplied) by combining classes to a small number of values,

facilitatig iterpretati.(http://www.ige.icamp.br/wfe/dcmetati/wfeitr.htm).

Etraplati dai: Areas outside the training sites dened by a probabilistic similar-ity t these give a particlar set f evidetial variables.

hlgue: Homologue (Jones et al., 2005) is software that identies the geographicalextent of environments that are similar to a pre-dened point or area. In essence, it an-swers the qesti: Where else i the trpics are climate ad sil cditissimilar t here?,

Pilt sites: Same as prject sites ad traiig sites.

Searc area: In Arc-WofE the study area is a binary theme that denes the region ofiterest. It acts as a mas ad areas f evidetial themes ad traiig pits tside thestdy area, which are igred i the calclatis f weights ad tpt maps.(http://www.ige.icamp.br/wfe/dcmetati/wfeitr.htm).

Siilarit: The degree f symmetry, aalgy r eqivalece betwee tw r mre bjectsr patters.

Target sites: Refers t thse etraplati dmais with the highest prbabilities f simi-larity t the traiig sites.

Traiig sites: This is a pit layer csistig f the lcatis at which the pit bjects

are w t ccr. Ths i mieral eplrati, fr eample, the pits are the mieraldepsits (shwigs, ccrreces, etc.) previsly discvered by prspectrs, mappers, adeplrati cmpaies. Bt i ther stdies, the pit bjects may csist f lcatis fseismic evets, itersectis f falts, lcatis f sprigs, ad ther pit types. The setf pit lcatis is sed t calclate the weights fr each evidetial theme, e weight perclass, sig the verlap relatiships betwee the pits ad the varis classes thetheme. The characteristics f the traiig pits are held i a attribte table. Pit sb-sets may be selected sig the vales f attribtes, sch as depsit size, r depsit type(i mieral eplrati). Hwever, pits are treated as beig either preset r abset ithe mdel, ad are t weighted by characteristics sch as depsit size. (http://www.ige.icamp.br/wfe/dcmetati/wfeitr.htm).

weigts evidece: Weights f evidece is a qatitative methd fr cmbiig evi-dece i spprt f a hypthesis. The methd was rigially develped fr a spatialapplicati i medical diagsis, i which the evidece csisted f a set f symptms adthe hypthesis was f the type this patiet has disease . Fr each symptm, a pair f


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weights was calclated, e fr presece f the symptm, e fr absece f the symp-tm. The magitde f the weights depeded the measred assciati betwee thesymptm ad the patter f disease i a large grp f patiets. The weights cld thebe sed t estimate the prbability that a ew patiet wld get the disease, based thepresece r absece f symptms. Weights f evidece was adapted i the late 1980s frmappig mieral ptetial with GIS. I this sitati, the evidece csists f a set f e-plrati datasets (maps), ad the hypthesis is this lcati is favrable fr ccrrecef depsit type . Weights are estimated frm the measred assciati betwee wmieral ccrreces ad the vales the maps t be sed as predictrs. The hypth-esis is the repeatedly evalated fr all pssible lcatis the map sig the calclatedweights, prdcig a map f mieral ptetial i which the evidece frm several map lay-ers is cmbied. The methd belgs t a grp f methds sitable fr mlti-criteria deci-si maig (http://www.ige.icamp.br/wfe/dcmetati/wfeitr.htm).


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APPEnDIx A. kEY VARIABLES

Table A.1. List of key variables for a selection of projects dened by project specialists as being important.

PROJECT TOPICPoliticalscientist

BiologistNatural Res.Management

Hydrologist

Agroforestry systemsCommunity and institutional participation/support x xClimate (length of dry season) x x xSlope x x xErosion x xWater availability xAgriculture and livestock productivity xAgricultural subsistence systems x xLand tenure xSoils type (shallow soils) x xPoverty level

PN20 ScalesCommunity and institutional participation/Support x x x xEducational level x x

Political stability xExistence of water related problems/constraints xPoverty level x x xLevel of participation xInstitutional legitimacy x xLevel of corruption x xLegislation on participation x

PN40 Integrating governance and modelingClimate (length of dry season) x xPopulation density xInstitutional legitimacy (enabling policy environment) x xAvailability of biophysical Information x xCommunity and institutional participation/support xEquity x

PN46 Small Multi-Purpose Reservoir Ensemble PlanningCommunity and institutional participation/support x x xClimate (length of dry season) x xSmall reservoir existence x xSatellite info availability xDisease dissemination data (malaria) x

PN42 Groundwater governance in IGB and YRBWater use and availability (current groundwater use) x xPrecipitation xGroundwater recharge rate xLand use patterns xIrrigation from groundwater xWater technologies xLand tenure x xGender x

Educational levelRegulations and legal issues x xFarming dependency on groundwater xPopulation density xInstitutional support x


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