There is a growing body of literature about the impacts of Bacillus thuringiensis (Bt) cotton in developing countries. While many studies show remarkable benefits for farmers, there are also reports that question these results. Most previous studies consider impacts in deterministic terms, neglecting existing variability. Here we explain the main factors influencing the agronomic and economic outcomes. Apart from differences in pest pressure and patterns of pesticide use, germplasm effects can play an important role. Theoretical arguments are supported by empirical evidence from India. Better understanding of impact variability can help explain some of the paradoxes in the recent controversy over genetically modified crops.B acillus thuringiensis (Bt) cotton, which provides resistance to several bollworm species, has been commercialized in a number of countries, including the United States, Australia, China, Mexico, Argentina, South Africa, and India. Over the last few years, the technology spread rapidly among large and small farms (Food and Agriculture Organization). Several studies carried out in different developing countries show that farmers who have adopted Bt cotton experience remarkable pesticide savings and higher effective yields (Pray et al.; Morse, Bennett, and Ismael; Bennett et al. 2004b; Thirtle et al.; Traxler et al.; Qaim and de Janvry; Qaim and Matuschke). Yet there are also reports claiming genetically modified (GM) crops in general and Bt cotton in particular, are unsuitable for developing Matin Qaim is a professor in the

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Traditionally, the 2 x 2 contingency table method has been used for qualitative evaluation of forecasts. However, the conclusions drawn based on this method could be misleading because it does not account for the direction of the turning or no turning points. A 4 x 4 contingency table which overcomes this weakness and which gives more information on the qualitative performance of the forecast is suggested.Several qualitative and quantitative measures have been used in the literature to evaluate the performance of alternative forecasting techniques. Though the usefulness of these measures depends on the purpose for which the forecast is made, both qualitative and quantitative measures can be important in guiding economic agents in making decisions. The most commonly used measures of quantitative evaluation of forecasts are mean forecast error, mean absolute error, mean squared error, root mean squared error, percentage root mean squared error and goodness of fit. As far as qualitative evaluation is concerned, researchers have used directional change, binomial probability functions, and the turning point method. It is the purpose of this note to demonstrate a limitation to the now popular turning point method of qualitative evaluation, which potentially can lead researchers to incorrect interpretation of their results. We will then offer an alternative qualitative measure which can provide more insight into the forecast evaluation. Qualitative MeasuresSeveral researchers (Hee; Kulshreshtha, Spriggs, and Akinfemiwa; Leuthold; Meyers, Havlicek, and Henderson) have used directional change as a measure of qualitative forecast evaluation. Usually, this is a matter of Gopal Naik and Raymond M. Leuthold are, respectively, a graduate student and a professor, Department of Agricultural Economics, University of Illinois.This study was a part of Hatch Project 05-381 of the University of Illinois Agricultural Experiment Station.Review was coordinated by Rulon Pope, associate editor.simply noting whether the forecasted variable is higher or lower than the current actual variate and if the subsequent move of the actual variable corresponded. The number or percentage of correct forecast directions is usually noted. Hee and Meyers, Havlicek, and Henderson used a binomial probability function in order to compare the number of correct directional changes the forecast has predicted with the number of correct directional changes that could be obtained purely by a chance factor. Kulshreshtha, Spriggs, and Akinfemiwa used the ratio of correct directional moves forecasted to total directional moves over the entire forecast period. The ratio is compared with a random correct directional move of .5. Theil suggested using the number of over or under predictions and the turning point method to evaluate the qualitative performance. This turning point method is now a popular qualitative measure frequently used by researchers Brandt 1979, 1981;Bourke;Bessler 1981, 1984;Harris and Leuthold;Hudson and Capps;Kost;Kulshreshtha and Rosaasen). This m...