Analyzing Water Poverty, 2nd

WorkshopChiang Mai Thailand; October 31-Chiang Mai, Thailand; October 31-

2 November 2007Jorge Rubiano

A d f l b lAssociated Professor, Colombian National UniversityEnvironmental Engineering Faculty, PalmiraJerubianome@unal.edu.co

ECUADOR-DATAECUADOR-DATA

Farrow, A., Larrea, C., Hyman, G. G., and Lema, G. (2005). Exploring the spatial variation of food poverty in Ecuador. Food Policy 30 510

What is the question

• Question (Targeting interventions)

• Knowledge acquisition (panel of food security experts inKnowledge acquisition (panel of food security experts in Ecuador)

• Data Acquisition/processing (1998 Living Standards q p g ( gMeasurement Study (LSMS) survey (INEC and World Bank, 1998) and the 2001 Ecuadorian national population census (INEC, 2001)).

A l i• Analysis (Geographically weighted regression (GWR))

• New knowledge/questions

R A U C it E i t OthResource Access Use Capacity Environment Other

-Mean Noofconsecutivedry months

-MeanAccess tolocalmarkets

-% of areawith crops

-%offarmers withSalary-%of

-Meanelevation-Mean Slope

-FoodPovertySeverity-Mean Fooddry months

-%ofirrigatedunits

markets(minutes)-Mean TimetoProvincial

%offarmerseconomically active-%of

Mean FoodConsumption

ov c aCapital

%oindigenouspopulationGINI

Table 1 Variables used in the Ecuadorian study case organisedaccordingly to the WPI components.

What is the current situation

What is the current situation

Some Conclusions

• Poor accessibility to markets and services and environmental constraints to agriculture have negative impacts on wealth and foodsecurity outcomes.

• Different problems in different locations• Land tenure , off-farm income ,

productivity, remittances among other variables.

Variables• FID Internal code for identification• PAR_CODIGO Parroquia code (Administrative code)• PARROQUIA Parroquia Name• INDNBI Basic Insatisfied Needs Index• INDNBI Basic Insatisfied Needs Index• AVG_ACC_20 Mean Access to local markets (minutes)• AVG_FGT2HP % County food poverty severity using the higher food poverty line• AVG_MN_DRY Mean No of consecutive dry months• AVG_MNAPHR Mean Time to Provincial Capital• AVG_MN_ELE Mean elevation• AVG_MN_SLP Mean Slope• AVG PR RIE Proportion of productive units with irrigation per county_ _ p p g p y• AVG_GINI GINI coefficient of land ownership per county• AVG_PORASA % of farmers with Salary• AVG_PORAGR % of area with crops• AVG PORIND % of indigenous population• AVG_PORIND % of indigenous population• AVG_COASTA Dummy variable for counties that have a coastline (counties that

benefit from fishing and tourism)

Figure 1 Bayesian Network of poverty related variables in the Ecuadorian case study.

Figure 2 Bayesian Network of the Ecuadorian case study after setting up evidence on the state 3 of Food Poverty Severity (encircled).

Figure 3 Bayesian Network of the Ecuadorian case study after setting up evidence on the driest parroquias and in those with less irrigated number of units (encircled).

VOLTA-DATAVOLTA-DATA

ANALYSIS OF WATER RELATED POVERTY IN THE VOLTA BASIN OF GHANA

ByF li A k h A tFelix Ankomah Asante

Institute of Statistical, Social and Economic Research (ISSER)University of Ghana

P. O. Box LG 74P. O. Box LG 74Legon, Accra Ghana.

What is the question

• Question (Not explicitly defined, Poverty is a fact)

• Knowledge acquisitionKnowledge acquisition • Data Acquisition/processing

A l i• Analysis • New knowledge/questions

SOURCES

• Core Welfare Indicators Questionnaire (CWIQ) (2003) Survey Report.

h d i i d• Ghana Census Based Poverty Map, District and Sub District Levels. 2005

• GLSS 4 Ghana Living Standards Survey 4th• GLSS 4 Ghana Living Standards Survey, 4th round(1998/99).

• GSS Housing and population census, 2000 GSS g p p ,ISSER

• INSD La pauvreté au Burkina Faso (INSD 2003)

Study Area

45

162162

Adm.Bnds.

45

BF

1.Quintile: Poverty Distr.

2.Poverty: Poor-NonPoor

3.Water-source: (Main source of water)

5. Access-time to water in minutes:

7. Food-Security:

9. Landless (Cropped area in Ha):

11. Population Distribution in %:

13. tetes gros bétail possédées (Cattle)

15. catégorie petit betail (Minor Cattle)

162

BF-var.

CHILDNUT_U % underweight children

EDUC_ADULT % Adult Literacy

EDUC_YOUTH % Youth Literacy

UNEMPLOYED % Unemployed

UNDEREMPLO % Underemployed

LANDLESS % Landless

LL1_2HA % with less than 2 Ha

LL2_3HA % with less than 3 Ha

LL3_4HA % with less than 4 Ha

LL4_5HA % with less than 5 Ha

LL5_8HA % with less than 8 Ha

LL_8_HA % with more than 8 Ha

FOODNEEDS Foodneeds

CER_AMOY92 Cereal Area

CER_PMOY92 Cereal Production

POP Population

MAISAMOY92 Maize Area

MAISPMOY92 Maize Production

MAISYMOY92 M i Yi ld

162

MAISYMOY92 Maize Yield

MAISWP9201 Water Productivity of Maize

NBDRY_MONT Number of consecutive dry months

GH-vav.

3O1

BFBF-Adm.Bnds

149149

V0LTA SET

141

V0LTA SET-P0V.

OTHER VARS.

• Soils texture, drainage, depth, fertility constraints.

• Roads • Climate data• Climate data

Figure 4 Relationship between the lowest poverty headcount level and three water related variables.

Figure 5 Volta bayesian network with an expert defined water-poverty node.

Figure 6 Volta Bayesian network with the new water-poverty node and the relationship with the lowest water productivity of maize.

Figure 7 Water-Poverty when setting evidence in the lowest maize water productivity level compared with standard measure of child nutrition (% underweight).