Variable Variable definition Measures
dist~nmarket Distance to the urban market Km
distanceto~d Distance to the paved road Km
dist~lmarket Distance to the local market Km
dist~kmarket Distance to the livestock market Km
distanceto~r Distance to potable water Km
distanceto~y Distance to the nearest electricity Km
disttothen~r Distance to the nearest health centres Km
disttothen~e Distance to the nearest public telephone Km
dailyavail~r Daily potable water availability 1=yes and 0 =no
hhgenderd~0f Household gender 1=Male and 0 =Female
yrsspenton~n Years spent on education by HH head Years
hhage Age of the household head Years
farmsize Farm size (ha) Hectare (Ha)
incomefrom~k Proportion of income from livestock Percentage (%)
cropvaluep~a Crop value per hectare Mts (1US$ =27Mts)
numberofna~e Number of natural resources accessed Count
accesstora~r Access to rangeland 1=yes and 0 =no
accesstofo~s Access to forest 1=yes and 0 =no
accesstow~10 Access to water resources 1=yes and 0 =no
freepaidra~s Free/paid ratio to access resources Ratio
incomedive~s Income diversification indices Index
livestockd~s Livestock diversification indices Index
cropdivers~s Crop diversification indices Index
orgcommuni~e Membership to organization 1=yes and 0 =no
grouppatic~r Membership to a group 1=yes and 0 =no
numberofco~d Number of constrains listed Count
savingsdum~e Saving undertaken in the last 1 year 1=yes and 0 =no
emergencyd~e Emergency needed in the last 1 year 1=yes and 0 =no
hhsizeadul~t Herd size TLU
hhsizeadul~t Household size (AE) Adult equivalent (AE)
lengthofil~s Length of illness Months
hhdepenenc~o Household dependent Ratio Ratio
Maximum temperature 0C
Minimum temperature 0C
Annual rainfall Mm
Rainfall variability Index
NDVI Index
NB: The expected sign could not be assigned because of the large number of coping strategies in consideration
Seasonal climate variables: Seasonal temperature and precipitation influence households’
agriculture in Africa have shown that climate attributes (temperature and precipitation) significantly affect the net farm revenue and such impact can be significantly reduced through coping strategies (Seo and Nhemachena 2006; Benhin 2006). Kurukulasuriya and
Mendelsohn (2006b) have also shown that household choice of crop and livestock species is sensitive to seasonal climate variables. These studies show the importance of seasonal climate variables in influencing the choice of coping strategies adopted by households. It is our hypothesis that drier and warmer climates favour livestock production and irrigation and are a contributor to crop failure.
Socio-economic attributes: In adoption literature ‘household size’ has been shown to have
mixed influences on adoption of technologies related to agriculture (Birungi 2007). Large households might be forced to divert part of their labour force into non-farm activities, in order for example to generate more income (Tizale 2007). Nevertheless, opportunity cost of labour might be low amongst most households. For other households coping strategies such as irrigation might be labor intensive thereby not adopting them. Consequently, we
hypothesize household size to have a positive influence on the adoption of such coping strategies.
Also, the influence of ‘household head age’ has been mixed in literature. For example Nyangena (2007) and Anley et al. (2007), found that age is significantly and negatively related to a farmer’s decision to adopt coping strategies related to water. On the other hand, Bayard et al. (2007) found age to be positively related to coping strategies associated with conservation measures. The ‘gender of the household head’ has been found as an important variable affecting adoption decisions. For example, Bayard et al. (2007) found female households to be more likely to adopt coping strategies related to natural resources management and conservation practices. Accordingly, Clay et al. (1998) found that ‘education’ was a significant determinant of coping strategies adopted while Gould et al. (1989) found that education was negatively correlated with such decisions. We expect that households with more education have some information about climate change and coping strategies that they can use in response.
Farm asset and wealth factors: Empirical studies have found mixed effects of ‘farm size’ on
adoption of coping strategies. For example, a study in South Africa showed that farm size was not a significant adoption factor (Anim 1999) while in Nigeria it was farmers with large farms
that were found to allocate more land for constructing bunds (Anleyet al. 2007). In this study it is hypothesized that farmers with small farms would adopt coping strategies that require small areas of land, such as diversification.
Several studies have shown that ‘access to emergency loan/cash aid’ is an important factor influencing the adoption of various technologies (Kandlinkar and Risbey 2000; Tizale 2007). With more financial and other resources at their disposal, farmers are able to make use of all the available information to change their management practices in response to changing climatic and other conditions. For instance, with financial resources and access to markets, farmers are able to buy new crop varieties, new irrigation technologies and other important inputs they may need to change their practices to suit the forecasted climate changes.
Market access is another important factor influencing coping strategies (Feder et al. 1985).
Input markets allow farmers to acquire the inputs they need such as different seed varieties, fertilizers and irrigation equipment. At the other end, access to output markets provides farmers with positive incentives to produce cash crops that can help improve their resource base and hence their ability to respond to changes in climate (Mano et al. 2003).
Maddison (2006) observed that long distances to markets decreased the probability of farm coping ability in Africa and that markets provide an important platform for farmers to gather and share information. Access to ‘electricity’ was found to be an important factor explaining crop choice (Kurukulasuriya and Mendelsohn 2006b) and livestock choice (Seo and
Mendelsohn 2006a). Household access to electricity may reflect either higher levels of market access or both. Farmers with better access to public infrastructure therefore are expected to be able to take up coping strategies measures that enable them to cope better.
Econometric analysis with cross sectional data is normally associated with problems of heteroscedasticity and multicollinearity. Multicollinearity among explanatory variables can lead to imprecise parameter estimates. To explore the potential multicollinearity among explanatory variables, we calculated the correlation between continuous independent variables (see Appendix E).
The results of the correlation analysis indicated that distances to the nearest electricity, nearest public telephone and health centre were highly correlated and we had to combine the three distances to public facilities. For dummy variables we used the chi-square test for
independence to determine the dependencies between variables. The variance inflation factor of all included variables was less than 10, which indicate that multicollinearity is not a serious problem in the reduced model (see Appendix 5).
To address the possibilities of heteroscedasticity in the model, we estimated a robust model that computes variance estimator based on a variable list of equation-level scores and a covariance matrix (StataCorp 2005a). Table 10 presents the estimated effect of different variables to the coping strategies utilized by households. The results show that most of the explanatory variables are statistically significant at 10 or lower and the signs on most variables were as expected except for a few. The chi-square results show likelihood ratio statistics were highly significant (P<0001) suggesting the models had a strong explanatory power.