The Role of Economic Thresholds in Optimizing Insecticide Application in Soybean Production in the Major Producing Countries of the World

Global population growth has increased the demand for food production and, therefore, for higher crop yields, especially for soybean, which is one of the most cost-effective and affordable sources of high-quality protein for animal and human nutrition. This frequently leads to an overuse of traditional chemical insecticides to maximize yields, thereby triggering negative side effects. However, both consumers and governments around the world demand a reduction in chemical insecticides in agriculture. To address this challenge, pest control must be guided by proper adoption of economic thresholds (ETs), which indicate the most appropriate time to initiate control in the crop. Despite the well-documented science behind ETs, farmers have questioned the adoption and reliability in a search for higher production, highlighting the importance of reviewing this topic. Thus, based on the available literature, the role of ETs in optimizing insecticide application in soybean production around the world is herein discussed, highlighting the importance of their adoption to mitigate the overuse of chemicals by emphasizing examples from the major world soybean producers (Brazil, the United States of America, Argentina, and China). In conclusion, this review highlights the importance of changing farmers’ perception that using more insecticide is necessary to guarantee higher yields. On the contrary, only by the adoption of ETs within Integrated Pest Management is it possible to obtain better pest management and, consequently, reduce yield loss. Despite some limitations related to their simplicity and risks of adoption, the use of ETs improves soybean sustainability and farmers’ profits while benefiting the agroecosystem. For future directions, their complexity should increase to more realistically represent agroecosystems; in addition, tools should be developed (computer programs and smartphone apps using, for instance, artificial intelligence) to translate this complexity into easy-to-adopt ETs.