Post on 03-Jul-2015
description
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).