Data inaccessibility at sub-county scale limits implementation of manuresheds.

The manureshed concept aims to rebalance surplus manure nutrients produced at animal feeding operations (sources) and the demands from nutrient-deficient croplands (sinks) to reduce negative environmental impacts and utilize nutrients more efficiently. Due to water quality implications, studies focused on this rebalancing have typically created domain boundaries that match a particular watershed. However, a majority of agricultural datasets that are used to inform these analyses-specifically, livestock populations-are only available at the county scale, which generally does not match watershed boundaries. The common method used to address this mismatch is to weight the county statistics based on the proportion of watershed area within the county. However, these straightforward assumptions imply that animal density is uniform across a county, which can be highly problematic, especially in an era of increasing concentration of livestock production on a smaller land area. We present a case study of the Lake Mendota watershed in south-central Wisconsin using both a typical county-based downscaled dataset as well as a more spatially explicit dataset of livestock counts from the Census of Agriculture that aggregates a set of zip codes that best matches the watershed boundary. This comparison reveals a substantial difference in estimated livestock numbers and their associated manure production that is due to a concentration of dairy operations within the watershed compared with the rest of the county. We argue that sub-county scale data need to become more available and integrated into nutrient and water quality management efforts so that manuresheds can be more effectively delineated and implemented.