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

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“Assessing adaptive capacity of farmers in Argentina and Mexico.”

Mónica B. Wehbe and Hallie C. Eakin with Luis Bojórquez

Sensitivity of farm unitsPerceptions

Multiple stressors

Adaptive CapacityFlexibilityStability

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

Impacts Adaptations

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

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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

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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)

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Laboulaye

Oncativo

Marcos JuarezRio Cuarto

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)]

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Total sensitivity of crop producers by locality and climate event

     

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Marcos Juarez Oncativo Laboulaye Rio Cuarto Total Region

Flood 0,11 0 2,29 0 0,52

Drought 0,63 1,56 0,73 1,08 1,05

Hail 0,31 1,48 0,18 1,61 0,97

Total events 1,06 3,10 3,20 2,69

Total sensitivity of agriculture producers by group

 

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Locality Group Cropping Livestock Infrastructure Total

Crop small 1,68 1,68

Crop large 0,61 0,61

Marcos Juárez Mixed small 1,38 1,38

Mixed large 0,68 0,68

Livestock 0,75 0,75

All (weighted) 1,06 0,06 1,12Crop small 3,01 0,05 3,06

Oncativo Crop large 3,30 3,30

Mixed 3,00 3,00

All (weighted) 3,10 0,03 3,13Crop 1,27 1,27

Laboulaye Mixed small 4,36 0,85 5,21Mixed large 4,92 0,75 5,67Livestock 1,35 0,54 1,89

All (weighted) 3,20 0,37 0,64 4,21Crop small 1,08 1,08

Río Cuarto Crop large 3,05 3,05Mixed small 3,42 0,07 3,49Mixed large 2,08 0,18 2,26

All (weighted) 2,69 0,07 2,76

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

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Adaptive Capacity

Laboulaye

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Physical/material resources Total Cropping Mixed small Mixed large Livestock

Worked area 1,03 0,78 0,67 3,11 0,55

Soil quality 1,03 0,43 0,84 0,48 0,86

Machinery 0,81 0,70 0,66 0,92 0,31

Net income 1,47 1,03 0,33 4,00 -0,06

Total 4,34 2,94 2,50 8,52 1,66

Social/human resources Total Cropping Mixed small Mixed large Livestock

Experience 1,11 0,89 1,10 1,03 1,32

Schooling 0,98 1,16 0,89 1,22 0,70

Participation in organizations 1,17 1,30 1,56 1,47 1,30

Official tecnical assistance 0,90 0,00 1,30 0,81 1,08

Private tecnical assistance 1,06 0,78 0,86 1,57 1,31

Total 5,23 4,14 5,72 6,09 5,71

Management capacity Total Cropping Mixed small Mixed large Livestock

% hired area 0,96 1,70 0,74 0,99 0,85

Crop diversity 0,87 0,82 0,92 1,34 0,07

% cattle income 1,79 0,35 2,42 1,23 6,00

Buying land 0,84 0,00 0,61 1,15 0,00

Selling land 1,04 1,67 1,00 0,00 1,67

Other important income 0,93 0,92 1,38 0,69 3,22

Total 6,44 5,46 7,08 5,41 11,83

Adaptations Total Cropping Mixed small Mixed large Livestock

Number of blocks 1,04 0,94 1,04 1,12 0,87

Hail insurance 0,93 0,56 0,67 1,27 0,14

Use of climate information 0,92 1,01 1,21 0,60 1,01

Change in cattle management 1,20 0,88 2,63 3,95 0,44

Change in crop management 1,02 1,36 1,53 1,53 0,34

Total 5,10 4,75 7,09 8,47 2,79

Variables weightedthrough consultationwith farmers

Agriculture producers

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6Sensitivity

Adaptive capacity

Indices display

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Vulnerabilidad

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Current Vulnerability Group LocationMixed small LaboulayeMixed large Laboulaye

HIGH Crop small OncativoMixed OncativoMixed small Rio CuartoCrop large OncativoCrop large Rio CuartoLivestock LaboulayeMixed large Rio Cuarto

MEDIUM Crop small Marcos JuarezLivestock Marcos JuarezMixed small Marcos JuarezCrop LaboulayeCrop small Rio Cuarto

LOW Mixed large Marcos JuarezCrop large Marcos Juarez

Vulnerability

0

50

100

150

200

250

300

worked area

gross margin

soil quality

technical assistance

other sources of income

%hired landhail insurance

sens crop flood

sens crop drought

sens crop hail

sens infrast.

HighModerateLow

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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

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Area Planted in Principal Winter Crops, Gonzalez

0

2000

4000

6000

8000

10000

12000

14000

198519871989199119931995199719992001

hectares maize and sorghum

0

20000

40000

60000

80000

100000

120000

hectares safflower

Safflower

Sorghum

Maize

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)

??

?n

iijijj cwI

1

ijw

ijc

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Adaptive Capacity

Human Resources Material Resources Financial Resources Information Diversity

Age, Education (Hh-head)Adults w/primary Adults/ Hh

Total areaTotal animal unitsIrrigationTractorLand rentalFarm tenure type

CreditInsurancePROCAMPOOportunidades

Technical assistanceClimate information Sources Types

IncomeLand useCrops

Sensitivity

Principal Crop (Spr/Fall)Crop losses Past climate eventsPerception of climate changePest sensitivity

Agricultural Sensitivity Livelihood Sensitivity

Change in income Migration of Hh members % of Income from cropsChannel of commercialization

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Fuzzy Sets for Vulnerability

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

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

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0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Index

35.01

==∑=

n

iijijj cwsα'=0.33

α=0.67

0.0

0.2

0.4

0.6

0.8

1.0

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

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Sensitivity Index

Low Moderate High

μ(x)

Fuzzy Sets for Sensitivity

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Sensitivity Index

Low Moderate High

μ(x)

43.01

==∑=

n

iijijj cwa

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)

FuzzyficationCombinationFuzzy AdditionDefuzzyfication

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Index

Crispy Value

Fuzzy solution space

Vulnerability Classes

0

20

40

60

80

100

HIGH MOD LOW

Vulnerability Class

Cases (%)

High Sensitivity Moderate Sensitivity Low Sensitivity

High Capacity Moderate Capacity Low Capacity

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VULNERABILITY

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TOT_CULT

SUP_TOTA

IRRIG

TRACTOR

TENANCY

CREDIT

INSURE

FALLCRP

SPGCRP

CLIMCHG

TOTEVNT

DISEASE

AGINCOM

DIRECCOM

HIGH

MOD

LOW

Adaptive Capacity Sensitivity

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Vulnerability and Farm Systems

0%10%20%30%40%50%60%70%80%90%

100%

% of cases

livestock non-farm

crop/livestockcrop/non-farm

crop

livestock/non-farm

Farm Systems

Vulnerability of Farm Systems

Low

Moderate

High

Farm Systems*livestock 4.3%crop/livestock 6.8%livestock/non-farm 7.3%crop 17.5%non-farm 28.6%crop/non-farm 35.5%

100.0%* defined by >= 66% of income

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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)

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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

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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

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Actions With Respect to Climate Risk

Principal Actions of Those Farmers Undertaking Mulitple Strategies

0

10

20

30

40

50

60

70

Change cattle

breed

Change inputmanagementChange crops

Changeplanting dateChange crop

variety

% of respondants

Top Five Resposes to Climatic Risk

0

5

10

15

20

25

30

Changecattle breed

Change crop Changeplanting date

None Multiplestrategies

% of respondants