Statistical Meta-Analysis of Agricultrual BMPs and BMP group performance

Eutrophication threatens freshwater quality and is oftentimes exacerbated by agricultural practices that produce nutrient rich runoff. This can lead to harmful algal blooms, like those observed in Lake Champlain’s Missisquoi Bay, which may grow more severe in a changing climate. Farmers can adopt agricultural best management practices (BMPs) to reduce the nutrient concentration in the runoff from their farms; however, the reported efficacy of these practices is highly variable, with a range of efficacies frequently reported for a single management practice. Furthermore, there is a lack of data specific to Vermont’s Missisquoi basin. Water quality BMPs often overlap with those used by farmers to mitigate the impacts of climate change to their farms as well. A database, consisting of eight BMPs, their observed reduction efficacies and 20 variables, was constructed; the meta-analysis of which provided weighted average reductions in total phosphorus (TP) and total nitrogen (TN) for each BMP. The Integrative Assessment Model (IAM) is an agent-based model used to predict algal blooms in the Missisquoi Bay under different climate change, economic, and LULCC (land use and land cover change) scenarios. The model also predicts probability of BMP adoption among farmers and assumes certain performances of these BMPs. This study obtains informed, weighted estimates of TP and TN reduction efficacies for BMPs and BMP groups and compares them to the assumptions used in the IAM to better inform the model, and thus provide a better understanding of the future state of the lake and the role BMP’s play in the lake system’s resilience.