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Session: Adaptation Opportunities and Capacity

“Assessing adaptive capacity of farmers in Argentina and Mexico.” Mónica B. Wehbe and Hallie C. Eakin with Luis Boj órquez. Session: Adaptation Opportunities and Capacity

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Session: Adaptation Opportunities and Capacity

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  1. “Assessing adaptive capacity of farmers in Argentina and Mexico.” • Mónica B. Wehbe and Hallie C. Eakin • with Luis Bojórquez Session: Adaptation Opportunities and Capacity Second AIACC Regional Workshop for Latin America and the CaribbeanRegente Palace Hotel, Buenos Aires, Argentina, 24-27 August 2004 Integrated Assessment of Social Vulnerability and Adaptation to Climate Variability and Change Among Farmers in Mexico and Argentina AIACC LA-29

  2. Impacts Adaptations Adaptive Capacity Flexibility Stability Access to Resources Sensitivity of farm units Perceptions Multiple stressors • Sensitivity indicators: • Climate impacts on farmers, crops, • livestock and infrastructure • Other stressors on livelihoods security Capacity indicators: Weighted measure of resource endowments and access, management and actions SOCIAL VULNERABILITY Social Vulnerability

  3. Objectives and Challenges • OBJECTIVE: Understand the relationship between livelihoods and vulnerability in two regions (Cordoba, Argentina and Gonzalez, Mexico) To develop methods for integrating vulnerability attributes (e.g., sensitivity, adaptive capacity) To explore which are most important variables in determining differences in vulnerability within each region • CHALLENGES: The absence of the dependent variables “vulnerability” “sensitivity” “adaptation” • CHALLENGES: The multivariate nature of vulnerability and its attributes: How to integrate qualitative and quantitative data, rigorously? How to capture complexity and uncertainty? • CHALLENGES: The lack of temporal data in one-time surveys AIACC LA-29

  4. Approaches • Farm household surveys • Data collected on: production, climate risk and impacts, resource use and access • n = 240 Cordoba, Arg; • n = 234 Gonzalez, Mex (and n = 60 Veracruz, Mex) • Survey data used to: • Classify population according to production systems and size of landholding • Differentiate production systems by sensitivity and adaptive capacity indices • Integrate sensitivity and adaptive capacity scores • Compare vulnerability of production systems in each location and between locations, based on the above indices AIACC LA-29

  5. South-center of Cordoba Province • The Region: Survey: • Average worked area: 653 hs. • Average rented area: 44% • 91 % has finished primary school • 42 % has finished secondary school National Agriculture Census: • Number production units: 1988: 20,817 2002: 13,128 • Bovine cattle 1988: 4,876,752 2002: 3,819,795 • Farmer’s production strategies highly focused on soybeans mono cropping • Drought, Hail and Flooding greatest climate concerns (survey data) Oncativo Marcos Juarez Rio Cuarto Laboulaye AIACC LA-29

  6. Sensitivity Main climatic events affecting each main crop, frequency of adverse events, percentage of area affected, and type of damage. Each response has been given a value, representing (0) no impact; (1) low impact; (2) medium impact; (3) high impact. R1g= (freq * affa * typd) For each crop, these values were weighted by proportion of agriculture producers concerned with each particular event within their group and by area dedicated to that particular crop related to the total worked area by each producer, including crop lost (differences between planted and harvested area). R2g= (R1g * (ne/Ng) * (%aded) * ( %nhara) ) To get a measure of sensitivity for a whole location, each group has been weighted by the number of the group related to the number of producers of that location and summed. Wig =  [R2g* (Ng/NL)] AIACC LA-29

  7. Total sensitivity of crop producers by locality and climate event AIACC LA-29

  8. Total sensitivity of agriculture producers by group AIACC LA-29

  9. Adaptive Capacity Indicators defined for the three attributes have been classified into: Material Resources: Worked area; Soil quality; Machinery; Net income Human Resources: Experience; Schooling; Participation in organizations; Official technical assistance; Private technical assistance Management Capacity: Percentage of hired area; Crop diversity; Percentage of cattle income; Buying land; Selling land; Other important income Adaptations: Number of blocks; Hail insurance; Use of climate information; Change in cattle management; Change in crop management AIACC LA-29

  10. Adaptive CapacityLaboulaye Variablesweighted through consultation with farmers AIACC LA-29

  11. Indices display Vulnerabilidad AIACC LA-29

  12. AIACC LA-29

  13. Vulnerability worked area 300 sens infrast. gross margin 250 200 150 soil quality sens crop hail 100 50 0 technical assistance sens crop drought other sources of income sens crop flood High Moderate %hired land hail insurance Low AIACC LA-29

  14. Vulnerability Context: Gonzalez, Tamaulipas • The municipio: • 85% EAP earn less than 2 minimum salaries • 57% adults lack primary school • 70% farmers are communal, w/ only 34% land • Planted area primarily in sorghum/safflower • Farmers face declining grain prices, rising input costs • Credit, technical assistance, insurance very limited, farmers dependent on government intervention • Current policy: Crop conversion (sorghum to pasture), commercialization, specialization • Drought and high temperature greatest climate concerns AIACC LA-29

  15. w c ij ij Methodology (Mexico) • Define variables to be used in determining Sensitivity and Adaptive Capacity • Apply a multi-criteria model to develop a Sensitivity index and an Adaptive Capacity index • is obtained through Analytical Hierarchy Process (AHP), which determine weights (e.g., importance) of each variable • is obtained through value functions, which transform the natural scales of all variables or criteria into a scale of 0 - 1 • Aggregate the two indices through Fuzzy Logic AIACC LA-29

  16. Adaptive Capacity Human Resources Material Resources Financial Resources Information Diversity Total area Total animal units Irrigation Tractor Land rental Farm tenure type Age, Education (Hh-head) Adults w/primary Adults/ Hh Credit Insurance PROCAMPO Oportunidades Technical assistance Climate information Sources Types Income Land use Crops Sensitivity Agricultural Sensitivity Livelihood Sensitivity Principal Crop (Spr/Fall) Crop losses Past climate events Perception of climate change Pest sensitivity Change in income Migration of Hh members % of Income from crops Channel of commercialization AIACC LA-29

  17. Fuzzy Sets for Vulnerability 1.0 0.8 High Moderate Low 0.6 μ(x) 0.4 0.2 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Index Vulnerability is defined by Linguistic Variables: Low Vulnerability, Moderate Vulnerability, and High Vulnerability These Linguistic Variables are transformed to Fuzzy Sets, as follows: AIACC LA-29

  18. Fuzzy Sets for Sensitivity Fuzzy Sets for Adaptive Capacity Low High Low High Moderate Moderate 1.0 1.0 1.0 α=0.80 0.8 0.8 α=0.67 0.8 0.6 0.6 0.6 μ(x) μ(x) α'=0.33 0.4 0.4 0.4 α'=0.20 0.2 0.2 0.2 0.0 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 Capacity Index Sensitivity Index 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Index Fuzzy Sets for Sensitivity Fuzzy Sets for Adaptive Capacity Low High Low High Moderate Moderate 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 μ(x) μ(x) 0.4 0.4 0.4 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Index Sensitivity Index Capacity Index 1.0 0.8 0.6 Fuzzy solution space 0.4 Crispy Value 0.2 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Index Fuzzyfication Combination Defuzzyfication Fuzzy Addition

  19. Vulnerability Classes AIACC LA-29

  20. Sensitivity Adaptive Capacity AIACC LA-29

  21. Vulnerability and Farm Systems AIACC LA-29

  22. Validation Generic Adaptation: Made any important investment in production (e.g., irrigation infrastructure, changing crops, expanding area planted) • Those with moderate to high capacity (χ2 = 6.26, p < 0.05) • Those classified as moderately vulnerable (χ2 =5.96, p < 0.05) Specific Adaptation: Took action with respect to climate risk • Those with high sensitivity (χ2 = 19.53, p < .001) • Those classified as highly vulnerable (χ2 = 8.635, p = 0.07) • Those with moderate to high capacity (not significant, p = .567) AIACC LA-29

  23. Advantages of AHP/Fuzzy Logic Approach • Uncertainty in classification is made explicit • Enables direct participation of stakeholders/ experts in determining variable weights • Variable weights can be adjusted to reflect different future socio-economic scenarios • e.g., advantage of crop diversity vs. crop specialization • Enables simultaneous and transparent consideration of multiple attributes of vulnerability AIACC LA-29

  24. Conclusions Approach: • Successfully identified differences in vulnerability within each case study • Identified factors contributing to sensitivity and capacity • Illustrates complex interaction of attributes in defining vulnerability: No one variable is sufficient for explaining vulnerability, capacity or sensitivity Flexible methodology: • Variables change to suit circumstances, but indicators allow comparison within and between case studies AIACC LA-29

  25. Actions With Respect to Climate Risk

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