Modelling climate change adaptation:

logit and probit

Glwadys Aymone Gbetibouo

C4ECOSOLUTIONS

27 June 2012ACCRA

DEFINING ADAPTATION

1. Adaptation or “action of adapting” from the Latin word “adaptare” means modification of an entity/being to suit new conditions or needs.

2. Adaptation refers to both a process of adapting and a condition of being adapted.

3. Synonymous with words such as conversion, change, shift, variation, adjustment, transformation, modification, alteration.

There are two directions and purposes in adaptation research (Burton et al. 2002) : i) adaptation research for mitigation policy; and

ii) adaptation research for adaptation policy Since the IPCC’s AR4 presented the first evidence

that climate change is now occurring, interest in adaptation as a legitimate policy response has increased, led by developing country negotiators.

Adaptation research has a critical role to help us collectively understand and develop adaptation options to enhance the benefits and reduce the social and economic vulnerabilities induced by climate change and variability.

ADAPTATION RESEARCH

ADAPTATION RESEARCH (2)

Adaptation research, is driven by a broad range of multi-dimensional determinants characterised by four core questions (Preston and Stafford-Smith 2008):

i) “Who or what adapts?”;

ii) “What do they adapt to?”;

iii) “How do they adapt?”; and

iv) “What do they want to achieve?”. The adaptation cycle is iterative, dynamic,

interconnected, non-linear, and likely chaotic and any specific adaptation research can start at any point in the adaptation cycle (Wheaton and Maciver 1999).

ADAPTATION CYCLE

Adaptation

What are they adapting to?

ClimateConsequencesVulnerability

Scale

How do they adapt?

Capital/assetsEntitlements

Options

What do they want to achieve?

Who or what adapt?

ScopeBenefitStrategyForesight

Agent/privateDecision making

Stakeholders

Barriers Limits

Preston and Stafford-Smith (2008)

APPROACH TO STUDY ADAPTATION

Top-down ‘what are we adapting to?’

Scenario-based, hypothetical are invariably treated as primarily technical adjustments

what do they want to achieve?’ and aims to evaluate alternative adaptations: assess the overall merit, suitability, utility or appropriateness

Bottom up ‘who or what adapt?’

(agents) and their decision-making processes;

‘how do they adapt?’ (determinants of adaptation, such as capital and entitlements).

TOOLS

Top-down Agronomic-economic and

Integrated assessment models ( e.g.Adams et al. 1998; Rosenzweig and Parry 1994);

Future Agricultural Resources Model (FARM) (Darwin et al. 1995) and Ricardian models (Mendelsohn et al. 1996; Gbetibouo and Hassan 2005; Dinar et al. 2008).

Bottom up Qualitative way via survey data analysis with in-depth interviews, and focus group discussions with farmers and other farms experts (e.g. Belliveau et al. 2006;; Smit et al. 1996)

Quantitative discrete choice (probit, logit,) models (e.g. Deressa et al. 2009; Kurukulasuriya and Mendelsohn 2008; Gbetibouo et al, 2009).

DISCRETE CHOICES MODELS

Logit and probit are used to model a relationship between a dependent variable Y and one or more independent variables X.

Y is a discrete variable that represents a choice or category.

The independent variables are presumed to affect the choice or classification process.

Estimate the choice models

Set of choices or classification must be finite.

Set of choices or classifications must be mutually exclusive, that is a particular outcome can only represented by one choice or classification.

Set of choices must be collectively exhaustive, that all choices or classifications must be represented by the choice set.

Choice models are deriving from the random utility theory.

Example of farmers’ adaptation choice model

Research questions:1. Are farmers aware of the changing

climate?2. What are the different types of

adaptation strategies in rural areas in the face of climate variability and change?

3. What are the factors enhancing adaptation among farmers?

Farmers’ adaptation modelExogenous factors

PERCEPTION

Adaptation appraisal

Adaptation actions

ADAPTATION

Climate change risk appraisal

Climate change signal detection

Endogenous factors

Past Risk experiencesSocial

Economic

Cultural factors

Attitudes, beliefs, judgments: age, gender, educationProvision of

climate information

Farms characteristics:

Crop type, irrigation, soil conditions, etc…

Institutional support

Government programs (subsidies, regulations, etc.)

Awareness and education about adaptation options Infrastructure

Market forces (prices, costs, etc.)

Personal attribute of farmer, family and farms: age, education, gender, farm type,

Perceived self efficacy

Access to resources and entitlements

INTENTION TO ADAPT

Analytical model The decision of whether or not to use any adaptation

option could fall under the general framework of utility and profit maximization.

Consider a rational farmer who seeks to maximize the present value of expected benefits of production over a specified time horizon, and must choose among a set of J adaptation options.

The farmer i decides to use j adaptation option if the perceived benefit from option j is greater than the utility from other options.

Farmer practices an adaptation option that generates net benefits and does not practice an adaptation option otherwise.

Analytical model

Multinomial logit model (MNL)The probability that household i with characteristics X chooses adaptation option j is specified as follows:

Marginal effects :

)1( YprobPij

j

j

x

x

e

e

1

1

1

1

j

jjkjjkj

k

j PPx

P

PRATICAL TRAINING: LIMPOPO CASE STUDY

Data:• 794 farm households

• Agricultural season April/May 2004 to April/May 2005

• Four provinces of the Limpopo River Basin in South Africa.

• Large dataset but this study used principally the section of the survey on perceptions of climate change, adaptations made by farmers, and barriers to adaptation.

• Monthly precipitation and temperature data from the South African Weather Service (SAWS). The data covers the period from January 1960 to October 2003.

Farmers' perceptions of changes in temperature in the Limpopo River Basin South Africa

Farmers' perceptions of changes in rainfall in the Limpopo River Basin

Spatial clustering of climate change perceptions

Perception of temperature Moran I statistics Perception of rainfall Moran I statistics

Increased temperature 0.044** Increased rainfall -0.013

Decreased temperature 0.002 Decreased rainfall 0.125**

More or less extreme 0.001 Change in the timing 0.051**

No change -0.003 Change in frequency of droughts/floods

-0.007

** Significant at 1% level

* significant at 5% level

 

  No change 0.003

Moran’s I test for spatial correlation of climate change perception

Factors influencing farmers’ perceptions

 Perceive change in

temperaturePerceive change in rainfall

Education -0.0049 -0.0371***

Farming experience 0.0136* 0.0048

Farm size 0.2900 -0.3474

Crop farm 0.0822 -0.0219

Infertile soil -0.3838 0.0994

Highly fertile soil -0.3231** 0.6542**

Access to water for irrigation -0.5917** -0.7279**

Access to extension services 0.3361** 0.2271

Access to climate information -0.0101 0.2044

Gauteng dummy -0.6374*** 0.2454

Intercept 1.91923 *** 2.4828***

Log likelihood: -186.0339 Number of observations: 632

Athrho: 0.8027*** Rho: 0.6655

Clustering at district level Wald test of rho=0: chi2(1) = 28.5094 Prob > chi2 = 0.0000

.*** significant at 1% level; ** significant at 5% level; * significant at 10% level

Results of the seemingly unrelated biprobit of farmers’ perception of change in the climate, Limpopo River Basin

Adaptation choices in the study area

Variable Total Basin

Limpopo North West Gauteng Mpumalanga

Adaptation to long-term changes in temperature (% respondents)

Change crop variety 3.03 1.21 3.92 2.27 6.57

Increasing irrigation 3.96 3.38 1.96 6.82 5.56

Plant different crops

6.86

9.66 3.62 4.04

Change planting date 3.69 3.62 0.98 6.82 4.55

Change amount of land 3.43 4.11 1.96 2.27 3.03

Livestock feed supplements 3.69

3.62 5.88 4.55 2.53

Crop diversification/mixing 0.53 0.97

Other[1] 5.01 4.83 2.94 6.82 6.06

No adaptation 69.39 67.87 78.43 70.45 67.68

Adaptation choices in the study area (2)

Adaptation to long-term changes in rainfall (% respondents)

Variable

Total Basin

Limpopo North West Gauteng Mpumalanga

Change crop variety

0.66

0.72 1.01

Increasing irrigation 7.75 4.82 13.99 4.55 11.56

Plant different crops 4.99 6.75 2.91 2.27 3.02

Change planting date 4.73 3.13 3.88 9.09 7.54

Change amount of land 2.76 4.43 1.51

Livestock feed supplements 2.23 2.41 3.88 2.27 1.01

Water-harvesting scheme 3.81 3.61 1.94 4.55 5.03

Other3 5.12 4.34 4.85 4.55 7.04

No adaptation 67.94 69.88 68.06 72.73 62.31

Barriers to adaptation in the Limpopo River Basin (%)  Lack of

information about long-

term climate change

Lack of knowledge concerning appropriate adaptations

Lack of credit or savings / poverty

No access to water

Insecure property

rights

Lack of market access

poor transpor

t links

OtherNo

barriers to adaptation

Total Basin 6.03 1.95 53.9 20.75 9.57 6.21 10.99 0.78

Limpopo 4.32 2.65 24.24 32.58 14.27 10.3 7.97 8.31

North West 10.47 0.00 54.65 3.49 3.49 1.16 9.3 22.09

Gauteng 0.00 0.00 32 12 0.00 4 20 10

Mpumalanga 8.56 1.98 48.04 8.56 5.92 1.32 13.10 23.03

Empirical specification of the variables

The choice sets considered in the adaptation model include 7 variables: (1) Portfolio diversification; (2) Irrigation; (3) Change planting date; (4) Change amount of land; (5) Livestock feed supplements; (6) Other and (7) No adaptation.

Explanatory variables is based on data availability and the literature: Households characteristics: age, education level and gender of the

head of the household, family size, years of faming experience, and wealth

Farm characteristics: farm size (large-scale or small-scale) and soil fertility

Institutional factors: Extension, access to credit, off-farm employment, and land tenure

Other factors that describe local conditions are hypothesised to influence farmers’ decisions : climate variables (temperature and rainfall); . latitude and longitude references for each household; dummy variables for provinces

Variables Estimated coefficients

outcome equation: adaptation model

Estimated coefficients selection equation: perception model

Access to water for irrigation

  -0.621***

Gender 0.134 -0.088Education -0.011 -0.012Farming experience 0.01*** 0.006Wealth 0.114 0.051Farm size 0.649*** -0.036Soil fertility -0.142* -0.005Extension 0.179* 0.364***Climate information -0.1 -0.115Credit 0.232* -0.0650Off-farm employment 0.127 0.0472Land tenure 0.268*** 0.0359Mpumalanga -0.006 -0.031Gauteng -0.603*** -0.527**North West -0.445*** -0.029Intercept -0.6615*** 1.83***

Wald test (zero slopes):36.26***Wald test (independent equations): 12.7***

Total observations: 577Censored observations:43

Results of the Heckman probit model of adaptation behaviour, Limpopo River Basin

Estimate of the marginal effects of the MNL adaptation model, Limpopo River Basin

Portfolio diversification

IrrigationChanged

planting datesChanged the

amount of land

Livestock supplement

feedsOther

No Adaptation

Education-0.0023(0.39)

0.0019(0.50)

-0.0003(0.82)

0.0003(0.56)

-0.0003(0.62)

0.0009(0.49)

-0.0003(0.94)

Gender -0.0084(0.75)

0.0388(0.22)

0.0115(0.38)

-0.0034(0.54)

0.0046(0.41)

-0.0044(0.8)

-0.0387(0.37)

Household size-0.0021(0.60)

0.0058(0.25)

-0.0002(0.94)

0.0003(0.79)

-0.0010(0.25)

-0.0041(0.09)*

0.0013(0.85)

Farming experience

0.0020(0.01)***

0.0007(0.59)

0.0011(0.03)**

0.0005(0.09)*

-0.0002(0.47)

-0.0001(0.8)

-0.0039(0.03)**

Wealth-0.0083(0.29)

0.0128(0.23)

0.0231(0.00)***

0.0030(0.22)

0.0010(0.49)

0.0026(0.62)

-0.0343(0.01)***

Farm size0.0536(0.32)

0.1176(0.09)*

0.0034(0.91)

0.0077(0.58)

-0.0007(0.94)

0.0030(0.9)

-0.1846(0.05)**

Highly fertile soil

0.0342(0.21)

0.0314(0.39)

-0.0066(0.64)

0.0125(0.10)*

0.0080(0.32)

-0.0148(0.33)

-0.0648(0.17)

Infertile soil-0.0375(0.29)

-0.0168(0.73)

0.0091(0.70)

0.0176(0.30)

-0.0032(0.64)

0.0471(0.20)

-0.0162(0.81)

Extension0.0434(0.09)*

-0.0075(0.80)

0.0138(0.30)

0.0052(0.35)

0.0016(0.73)

-0.0027(0.84)

-0.0537(0.08)*

Climate information

-0.0257(0.32)

0.0018(0.95)

-0.0112(0.43)

0.0031(0.60)

-0.0011(0.82)

0.0172(0.26)

0.0161(0.69)

Credit0.0355(0.06)*

0.0289(0.42)

-0.0014(0.93)

-0.0093(0.19)

0.0149(0.09)*

0.0172(0.37)

-0.0858(0.08)*

Off farm 0.0302(0.27)

-0.0046(0.88)

0.0006(0.96)

-0.0077(0.09)*

0.0339(0.00)***

0.0074(0.63)

-0.0597(0.18)

CONCLUSIONS AND CONTRIBUTIONS

Conclusions

Perceptions are not only based on observed changes in climate conditions but are also influenced by other factors: improved farmer education and awareness about climate change

Factors that enhance adaptive capacity: Access to water, credit, extension services, off-farm income and employment opportunities, tenure security , farmers’ asset base and farming experience

Appropriate government interventions to improve farmers’ access and status of these factors are needed.

THANK YOU