Student Spotlight: Ashley Camhi
The Center for Biology and Society recently caught up with Ashley Camhi, a Biology with a concentration in Biology and Society PhD candidate, about what she did this summer. Here is what she had to say:
This summer I spent a week in Iowa groundtruthing my research. I am looking at the impact of the USDA’s Conservation Reserve Program (CRP) on lake water quality in Iowa. My goal is to provide the USDA with constructive feedback on how they can utilize the targeting of land enrolled in CRP to be more efficient and effective when improving lake water quality. CRP is the oldest private conservation program in the United States, which provides monetary compensation to farmers to “retire” their land. Farmers sign a contract for between 15 and 20 years, during which they are required to leave the land in CRP fallow or possibly restore an area in return for an annual payment.
My time in Iowa was spent meeting with the Iowa Corn Association, Iowa Soybean Association, Iowa Association of Water and Agriculture, Iowa State University professors, farmers, and visits to lake watersheds. I learned a significant amount from the meetings that I had with agriculturalists. Interestingly, many farmers have a negative stigma against CRP because they see “retiring” land that could be put into production as a waste – so much so that even if they were to receive a higher compensation from CRP they would not enroll their land.
Visiting the lake watersheds lead to a critical realization – that most of the lakes are surrounded by trees, wetlands, or some type of land use that would likely mitigate the runoff of fertilizers and pesticides into the lakes. Also improvements are made to many of the lakes by the Iowa Department of Natural Resources on a continuous basis so that is an external factor that would need to be taken into account when understanding whether CRP was actually having an impact on lake water quality.
Moral of the story is that I left Iowa with more questions than answers, but with an appreciation for the need to understand what is actually occurring on the ground when utilizing large data sets.
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