摘要

With increased scrutiny of the neonicotinoid class of chemistry and its negative impact on the pollinator community, ecological cost/benefit analyses of agronomic crops that use these insecticides are increasingly important. This study initially sought to address the question of yield benefit due to neonicotinoid seed treatment in maize (Zea mays L.), using North Carolina yield contest data from 2002 to 2006, the time period from initial neonicotinoid seed treatment adoption to nearly ubiquitous adoption. However, we recognized that several agronomic practices, including planting date, hybrid selection, and fertilization, could affect the yield of this crop; moreover, they could be collinear with one another and the analysis could be skewed by early adopters of new technology. Hence, we used all available data to compare among traditional approaches and a data-mining approach for analyzing the impact of neonicotinoid seed treatment on maize yield. At-planting insecticide treatment was not an important predictor of maize yield. When analyzed using the traditional approach (T-test), yields were significantly higher for fields planted with neonicotinoid treated seed compared to seed without neonicotinoid; however, data-mining approach (Decision tree analysis) that took into account other factors contributing to yield did not identify seed treatments as important. The contrast in these results highlights the need for future carefully designed studies that target to minimize inter- and intra-site variation; and include measurements of additional factors that may influence yield, such as seeding rate, tillage, and herbicide applications, as input variables that are largely lacking in current approaches on the subject.

  • 出版日期2018-8