Emma Kukielski & Daniel Book

GMOs and their Effect on Crop Yield

Our interest in GMOs (genetically modified organisms) began when examining the wide variety of opinions regarding their use and popularity. One of the most major issues revolving around them was how they affected crop yield. Theoretically, genetic modification of a plant or other organism is done to make it more efficient and more likely to survive all the way to harvest; however, many opponents argue that modification can disturb natural pathways in both the organism and its environment. If implemented improperly, GMOs can even unintentionally alter the genome of pests to the point where the organism is no longer immune to the pest and all means of preventing the pest from destroying crop populations are gone. Our goal for this assignment was to examine the rise in usage of GMOs and compare that data to general data about crops in the US to discover if GMOs help or hinder crop production.

Our first task was to consider what data would be necessary to measure any change in crop yields throughout the years and decipher whether or not this change was due to an increase in GMOs. To find a trend as a whole, we had to look at crops from different groups and and see if in rise in production resulted from rise of GMOs of the specific crop. We decided on corn, a grain, soybeans, a legume, and cotton, a gossypium; these three species are a huge portion of US crop production while also being distinctly different plants. The unfortunate side-effect to choosing three mainly unrelated crops was that any data on the crops would be grouped in with other similar crops making it necessary to pull and combine large amounts of data from different sources. The USDA (United States Department of Agriculture) compiles yearly lists of acreage planted and quantity produced dating back to the mid 1900s for most of the staple crops in the US. After finding what data groupings of crop families contained our chose crops, we moved both the acreage of production numbers to a separate spreadsheet. Next task was finding a reliable data set on the increase in use of GMOs which turned out to be quite tricky as most sites were funded by anti-GMO groups or large corporations like Monsanto; however, the USDA kept track of planted acreage of GMO crops by crop per state. Running a simple sum algorithm in Excel we were able to quickly create a useable data set using the same measurement as our comparison data. Using a mapping algorithm in C++, we calculated percent of GMO acreage over time, crop yield (production/acreage) over time, crop yield by percent of GMO acreage, pesticide usage by percent of GMO acreage, and crop yield by pesticide usage. Using this data, we graphed the trends, drew general trend lines, and calculated R^2 values to help determine significance of findings.

The first discovery we made was that GMO usage has, unsurprisingly, been increasing since 2000, when the technology became more reliable and more readily available. We also find that while crop yield has been increasing with GMO usage, it’s really part of a large trend in which crop yield has been increasing altogether, with GMOs and without. We coupled this with another question we had in the earlier part of our investigation: how does pesticide usage, another major concern of the American public, factor in? We discovered that pesticide use has been increasing over time, but not during the major period of GMO usage, but that pesticide use appears to increase crop yield. If pesticide usage hasn’t been increasing with GMO usage, that might explain the lack of increase in crop yield with increased GMO usage, and we may more readily attribute crop yield to pesticides than to GMO technology.