Assessing correlates of farmer behavior to prevent Harmful Algal Blooms (HABs): A multi-level analysis

Harmful algal blooms (HABs) and eutrophication in Lake Erie pose a threat to public health. Nutrient runoff, particularly phosphorus, from agricultural practices is a primary contributor to HABs. Encouraging farmers to adopt best management practices (BMPs) is an important prevention strategy. As part of a mixed-methods team science initiative, this analysis assessed correlates of BMP adoption, including individual (farmer-level) and county-level factors. Data was collected from a survey of farmers in the Maumee Watershed (n=602), and a BMP index was derived by summing the number of BMPs reported by each farmer. County-level indicators reflecting the public health context were gathered from the County Health Rankings dataset. Multi-level log-Poisson models with random intercepts for county were used to assess associations between the number of BMPs adopted, farmer-level attributes, and county-level factors. Statistically significant correlates at the individual level included farmers’ level of concern about environmental issues (p=0.0352) and confidence in using BMPs such as cover crops on their farms (p=0.0275). At the county level, significant correlates included higher per-capita income (p<0.0001), access to opportunities for recreation/exercise (p=0.0058), higher population (<0.0001), and lower levels of income inequality. Total acres farmed, years of farming, farmer age, total farm income, and percentage of minority residents per county were not significant.

Findings suggest that higher resource areas where ecosystem services are valued may provide supportive contexts for BMP adoption. Farmers with higher levels of environmental concern and confidence reported more BMPs, reflecting opportunities to educate farmers about the public health co-benefits associated with agricultural practices, particularly in counties with greater income inequality.