The acreage planted in corn and soybean crops is vast, and these crops contribute substantially to the world economy. The agricultural practices employed for farming these crops have major effects on ecosystem health at a worldwide scale. The microbial communities living in agricultural soils significantly contribute to nutrient uptake and cycling and can have both positive and negative impacts on the crops growing with them. In this study, we examined the impact of the crop planted and soil tillage on nutrient levels, microbial communities, and the biochemical pathways present in the soil. We found that farming practice, that is conventional tillage versus no‐till, had a much greater impact on nearly everything measured compared to the crop planted. No‐till fields tended to have higher nutrient levels and distinct microbial communities. Moreover, no‐till fields had more DNA sequences associated with key nitrogen cycle processes, suggesting that the microbial communities were more active in cycling nitrogen. Our results indicate that tilling of agricultural soil may magnify the degree of nutrient waste and runoff by altering nutrient cycles through changes to microbial communities. Currently, a minority of acreage is maintained without tillage despite clear benefits to soil nutrient levels, and a decrease in nutrient runoff—both of which have ecosystem‐level effects and both direct and indirect effects on humans and other organisms.

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Modern scientific research is often multidisciplinary, involving scientists from two or more backgrounds. A multidisciplinary approach is frequently necessary to advance our knowledge in a diverse range of fields, from genomics to climate change. Many of the projects undertaken in these areas involve a combination of field, lab, and computational analysis components. Our research initiatives demonstrate how the principles of active learning -performing tasks while engaging in analysis, synthesizing and evaluating the tasks being performed [10] -can be applied to undergraduate science education using 16S rRNA metagenomics as the basis. Beginning with development of the scientific questions, students work through the entire process of designing, testing and implementing physical and digital sampling protocols, hardware and software platforms for collecting geographically coded (geocoded) environmental metadata, and lab protocols; they work in the field taking samples and in the lab preparing them; they perform the computational analysis of the sequencer output and synthesis of metadata and metagenomic data; and finally they disseminate the results. The students come primarily from backgrounds in computer science, biology, geology, and physics. This broad range makes it possible to select teams that cover many of the traditionally underrepresented groups in science. Working together for a year or more, the students learn the science, vocabularies, skill sets, etc. of all the disciplines, as well as how their own discipline, in conjunction with others, contributes to addressing large, complex questions.