74 CHAPTER 3


expected total income, total cost, expected net income, marginal benefit, and benefit/cost ratio for each of the five scenarios. To introduce variability in the partial budgets, the study used @Risk soft-


ware (Palisade Corporation, www.palisade.com/risk/) to estimate candidate distributions for each input variable. Note that in @Risk language, there are two kinds of variables: input variables, which are predetermined, and output variables, which are estimated based on the input variables. @Risk selected a best-fit distribution for input variables feasible to be obtained from farmers using the survey instrument. For input variables with limited or no informa- tion, we used triangular distributions, defined by low, mode, and high values. The triangular distribution is the simplest distribution to elicit from farm- ers, it approximates the normal distribution, and it is especially useful in cases where no sample data are available (Hardaker et al. 2004). For generation of the variable parameters (low, mode, and high values), we assumed values gen- erated by expert consultation or literature review. Input variables generated using survey information were yield, output price,


pesticide use/cost, herbicide use/cost, and spraying cost (mainly labor). Input variables adjusted to triangular distributions were technology efficiency (trait expression), the technology fee, reduction rates in pesticide use, reduction rates in spraying costs in the case of Bt cotton, and increase rates in herbicide use for the case of HT cotton. Details on the minimum, mode, and maximum values adopted for these variables are reported in Table 3.2. The @Risk software used the input variables to predict the distribution


of the selected output variable (marginal benefit). In this way we not only compare marginal benefits across scenarios but also determine how sensitive the output variable is to changes in each input variable (within a scenario). A tornado graph is used to express the relative impact of a particular input parameter to the output from the simulations. The @Risk program regresses each output variable, in this case marginal benefits, to each of the param- eters included in the simulation with a probability distribution. The result- ing parameter gives an indication of the relative strength of the relationship between parameters and outcome.


Is Cotton Profitable?


Basic statistics of the household and production characteristics of interviewed farmers are presented in Table 3.3. In terms of cotton seed yield, aggregated values are higher than national averages for 2007 (FAO 2010), indicating some selection bias in the sampling.


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