Analyzing Water Poverty, 2nd
           Workshop
Chiang Mai Thailand; October 31-
       Mai,
       2 November 2007
     ...
ECUADOR-DATA
Farrow, A., Larrea, C., Hyman, G. G., and Lema, G. (2005).
Exploring the spatial variation of food poverty in...
What is the question
• Question (Targeting interventions)
• Knowledge acquisition (panel of food security experts in
  Ecu...
Resource
R             Access
              A               Use
                              U            Capacity
      ...
What is the current situation
What is the current situation
Some Conclusions
• Poor accessibility to markets and services
  and environmental constraints to agriculture
  have negati...
Variables
•   FID                Internal code for identification
•   PAR_CODIGO Parroquia code (Administrative code)
•   ...
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 (...
Figure 3 Bayesian Network of the Ecuadorian case study after setting up evidence on
the driest parroquias and in those wit...
VOLTA-DATA
  ANALYSIS OF WATER RELATED POVERTY IN THE
                  VOLTA BASIN OF GHANA
                             ...
What is the question
•   Question (Not explicitly defined, Poverty is a fact)
•   Knowledge acquisition
•   Data Acquisiti...
SOURCES
• Core Welfare Indicators Questionnaire (CWIQ)
  (2003) Survey Report.
• Ghana Census Based Poverty Map, District ...
Study Area
45




      162




Adm.Bnds.
BF
1.Quintile: Poverty Distr.
2.Poverty: Poor-NonPoor                           45
3.Water-source: (Main source of water)
...
CHILDNUT_U   % underweight children
          EDUC_ADULT   % Adult Literacy
          EDUC_YOUTH   % Youth Literacy
      ...
3O1




  BF-
  BF
Adm.Bnds
149




V0LTA SET
141




V0LTA SET-P0V.
OTHER VARS.
• Soils texture, drainage, depth, fertility
  constraints.
• Roads
• 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...
Figure 7 Water-Poverty when setting evidence in the lowest maize water productivity level
compared with standard measure o...
Analysing Water Poverty
Analysing Water Poverty
Analysing Water Poverty
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Analysing Water Poverty

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Presented at the Basin Focal Project Poverty Mapping Workshop, November 2007, Chiang Mai, Thailand

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Analysing Water Poverty

  1. 1. Analyzing Water Poverty, 2nd Workshop Chiang Mai Thailand; October 31- Mai, 2 November 2007 Jorge Rubiano Associated Professor, Colombian National University A d f l b l Environmental Engineering Faculty, Palmira Jerubianome@unal.edu.co
  2. 2. ECUADOR-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
  3. 3. What is the question • Question (Targeting interventions) • Knowledge acquisition (panel of food security experts in Ecuador) • Data Acquisition/processing (1998 Living Standards q p g( g Measurement Study (LSMS) survey (INEC and World Bank, 1998) and the 2001 Ecuadorian national population census (INEC, 2001)). • A l i (Geographically weighted regression (GWR)) Analysis • New knowledge/questions
  4. 4. Resource R Access A Use U Capacity C it Environment E i t Other Oth -Mean No -Mean -% of area -%of -Mean -Food of Access to with crops farmers with elevation Poverty consecutive local Salary -Mean Slope Severity dry months markets -%of %of -Mean Food Mean -%of (minutes) farmers Consumptio irrigated -Mean Time economicall n units to y active Provincial ov c a -%of %o Capital indigenous population GINI Table 1 Variables used in the Ecuadorian study case organised accordingly to the WPI components.
  5. 5. What is the current situation
  6. 6. What is the current situation
  7. 7. Some Conclusions • Poor accessibility to markets and services and environmental constraints to agriculture have negative impacts on wealth and food security outcomes. • Different problems in different locations • Land tenure , off-farm income , productivity, remittances among other variables.
  8. 8. Variables • FID Internal code for identification • PAR_CODIGO Parroquia code (Administrative code) • PARROQUIA Parroquia Name • 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 p p productive units with irrigation p county g per y • AVG_GINI GINI coefficient of land ownership per county • AVG_PORASA % of farmers with Salary • AVG_PORAGR % of area with crops • AVG_PORIND AVG PORIND % of indigenous population • AVG_COASTA Dummy variable for counties that have a coastline (counties that benefit from fishing and tourism)
  9. 9. Figure 1 Bayesian Network of poverty related variables in the Ecuadorian case study.
  10. 10. Figure 2 Bayesian Network of the Ecuadorian case study after setting up evidence on the state 3 of Food Poverty Severity (encircled).
  11. 11. 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).
  12. 12. VOLTA-DATA ANALYSIS OF WATER RELATED POVERTY IN THE VOLTA BASIN OF GHANA By Felix A k F li Ankomah A t h Asante Institute of Statistical, Social and Economic Research (ISSER) University of Ghana P. O. Box LG 74 Legon, Accra Ghana.
  13. 13. What is the question • Question (Not explicitly defined, Poverty is a fact) • Knowledge acquisition • Data Acquisition/processing • Analysis A l i • New knowledge/questions
  14. 14. SOURCES • Core Welfare Indicators Questionnaire (CWIQ) (2003) Survey Report. • Ghana Census Based Poverty Map, District and h d i i d Sub District Levels. 2005 • GLSS 4 Ghana Living Standards Survey, 4th Survey round(1998/99). • GSS Housing and population census, 2000 GSS g p p , ISSER • INSD La pauvreté au Burkina Faso (INSD 2003)
  15. 15. Study Area
  16. 16. 45 162 Adm.Bnds.
  17. 17. BF 1.Quintile: Poverty Distr. 2.Poverty: Poor-NonPoor 45 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.
  18. 18. 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 162 MAISAMOY92 Maize Area MAISPMOY92 Maize Production MAISYMOY92 Maize Yi ld M i Yield MAISWP9201 Water Productivity of Maize GH-vav. NBDRY_MONT months Number of consecutive dry
  19. 19. 3O1 BF- BF Adm.Bnds
  20. 20. 149 V0LTA SET
  21. 21. 141 V0LTA SET-P0V.
  22. 22. OTHER VARS. • Soils texture, drainage, depth, fertility constraints. • Roads • Climate data
  23. 23. Figure 4 Relationship between the lowest poverty headcount level and three water related variables.
  24. 24. Figure 5 Volta bayesian network with an expert defined water-poverty node.
  25. 25. Figure 6 Volta Bayesian network with the new water-poverty node and the relationship with the lowest water productivity of maize.
  26. 26. Figure 7 Water-Poverty when setting evidence in the lowest maize water productivity level compared with standard measure of child nutrition (% underweight).

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