# **_All farms_**

We estimated three sets of equations in our first model. The first set of equations determined whether a farmer chooses a particular animal, the second estimated the number of animals a farmer chooses given that he has picked a particular type of animal, and the third estimated the net revenue per animal given that a farmer has chosen that animal. Because the choice of an animal and the net value of each type of animal are linked, we used a selection model (Heckman 1979). We followed McFadden’s model of multinomial choice to estimate the probability each animal is chosen. The choice of each type of animal was assumed to be independent of the choice of any other animal. The probability of choosing each animal was assumed to be a function of summer and winter temperature and summer and winter precipitation. Other explanatory variables included a dummy variable for West Africa, a dummy for large farms, and a dummy variable for electricity.

Table 1 shows the results of the multinomial logit regression of the probability of choosing each of the five animals. The base case is a household that chooses chickens. Most of the estimates are very significant. The test of global significance of the model verifies that the model is highly significant. The positive coefficients imply that the probability of choosing the animal increases as the corresponding variable increases. The amount of increase of the probability can be read from the odds ratio. For example, the odds ratio of the electricity dummy for beef cattle is 2.2, which implies that farms with electricity are 2.2 times (odds ratio) more likely to own beef cattle.

The climate variables are mostly significant. The linear term on summer temperature for sheep is negative but the quadratic term is positive. The same is true for winter temperature. The sheep temperature response function is U-shaped. In contrast, for beef cattle, the linear term is positive but the quadratic term is negative in summer temperature. The cattle temperature response function is hill-shaped. The negative coefficients for West Africa for beef cattle and dairy cattle indicate that West African farms are less likely to have cattle. The coefficients are positive for goats and sheep, indicating that West African farms are more likely to choose goats and sheep. One reason why this region is different from the rest of Africa may be livestock diseases such as nagana (trypanosomiasis), also known as sleeping sickness. Thirty percent of Africa’s 160 million cattle population are said to be at risk from these diseases.<sup>7</sup> Large farmers are more likely to choose large animals than chickens. Farms with electricity are more likely to choose beef cattle, dairy cattle, and sheep, but not goats.

Although this is not included in Table 1, we also tested a set of soil variables, a set of household characteristics, a set of labor use variables, and other social statistics such as religions. These variables were dropped since they were not significant.

Figure 2a graphs the relationship between the probability of choosing a species and annual temperature. Note that the mean temperature in sub-Saharan Africa is 22 ° C. The probability of choosing beef cattle and dairy cattle decreases rapidly as temperature rises. In contrast, the probability of choosing goats and sheep climbs as temperature rises. With chickens, the estimated probability is hill-shaped, with a maximum at the current mean temperature of Africa. The graph clearly reveals that the choice of animals in Africa today is very temperature sensitive.

> 7 International Livestock Research Institute website at <u>http://www.ilri.org.</u>

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