1. Session: Adaptation Opportunities and Capacity Second AIACC Regional Workshop for Latin America and the Caribbean Regente 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 “Assessing adaptive capacity of farmers in Argentina and Mexico.” Mónica B. Wehbe and Hallie C. Eakin with Luis Bojórquez

2. Sensitivity of farm units Perceptions Multiple stressors Adaptive Capacity Flexibility Stability Access to Resources SOCIAL VULNERABILITY 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 ImpactsAdaptations

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) AIACC LA-29 Laboulaye Oncativo Marcos Juarez Rio Cuarto

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. R1 g = (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). R2 g = (R1 g * (n e /N g ) * (%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.  Wi g =  [R2 g * (N g /N L )] 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 Capacity Laboulaye AIACC LA-29 Variables weighted through consultation with farmers

11. Indices display AIACC LA-29 Vulnerabilidad

12. AIACC LA-29

13. Vulnerability 0 50 100 150 200 250 300 worked area gross margin soil quality technical assistance other sources of income %hired land hail insurance sens crop flood sens crop drought sens crop hail sens infrast. High Moderate 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. 1.Define variables to be used in determining Sensitivity and Adaptive Capacity 2.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 3.Aggregate the two indices through Fuzzy Logic Methodology (Mexico) ij w c AIACC LA-29

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

17. Fuzzy Sets for Vulnerability 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Index μ(x) These Linguistic Variables are transformed to Fuzzy Sets, as follows: Vulnerability is defined by Linguistic Variables: Low Vulnerability, Moderate Vulnerability, and High Vulnerability Low Moderate High AIACC LA-29

18. 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Index α'=0.33 α=0.67 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Index Fuzzy Sets for Sensitivity 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Sensitivity Index Low Moderate High μ(x) Fuzzy Sets for Sensitivity 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Sensitivity Index Low Moderate High μ(x) α'=0.20 α=0.80 Fuzzy Sets for Adaptive Capacity 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Capacity Index Low Moderate High μ(x) Fuzzy Sets for Adaptive Capacity 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Capacity Index Low Moderate High μ(x) Fuzzyfication Combination Fuzzy AdditionDefuzzyfication 0.0 0.2 0.4 0.6 0.8 1.0 0.00.10.20.30.40.50.60.70.80.91.0 Index Crispy Value Fuzzy solution space

19. Vulnerability Classes AIACC LA-29

20. Adaptive CapacitySensitivity 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

26. Actions With Respect to Climate Risk