Genetic modification has led to fierce debates around the world. Nevertheless, scientific evidence for its potential effects on the structure and performance of industries has hitherto remained rather meagre. In this article, we take some preliminary steps towards closing this gap by exploring the effects of the introduction of a genetically modified cassava variety on the structure and efficiency of the starch industry in Thailand. Currently, producers of cassava starch are confronted with a problem called post-harvest deterioration, which reduces the quantity and quality of starch in cassava roots within 24 to 48 hours after harvesting, leading to lower payments for farmers and lower starch recovery rates for factories. In addition, post-harvest deterioration prohibits factory owners to hold large stocks of fresh cassava. Combined with a strong seasonal fluctuation of supply, this leads to a low utilisation of installed processing capacities at the starch factories. In this article, we examine how the structure and performance of the Thai starch industry would change in case a genetically modified cassava variety would be introduced that no longer suffers from post-harvest deterioration. After having interviewed 19 stakeholders in this industry, we developed two simple linear programming models to examine the optimal locations, capacity classes and utilisation percentages for starch processing plants in the Northeast of Thailand. Our findings demonstrate that an extension of the storability of cassava to 45 days will not only diminish the number of factories needed, but it will also cause most large-sized factories to be replaced with medium-sized plants. Moreover, introducing a new cassava variety with such properties is estimated to render benefits of approximately US $ 35 million for Thai cassava farmers and factory owners.
Vlaar, P. W. L., van Beek, P., & Visser, R. G. F. (2007). Genetic modification and its impact on industry structure and performance: post-harvest deterioration of cassava in Thailand. Journal on Chain and Network Science, 7(2), 133-142. https://doi.org/10.3920/JCNS2007.x083