Assessing the relationship between groundwater nitrate and animal feeding operations in Iowa (USA)

Abstract

Nitrate-nitrogen is a common contaminant of drinking water in many agricultural areas of the United States of America (USA). Ingested nitrate from contaminated drinking water has been linked to an increased risk of several cancers, specific birth defects, and other diseases. In this research, we assessed the relationship between animal feeding operations (AFOs) and groundwater nitrate in private wells in Iowa. We characterized AFOs by swine and total animal units and type (open, confined, or mixed), and we evaluated the number and spatial intensities of AFOs in proximity to private wells. The types of AFO indicate the extent to which a facility is enclosed by a roof. Using linear regression models, we found significant positive associations between the total number of AFOs within 2km of a well (p trend <0.001), number of open AFOs within 5km of a well (p trend <0.001), and number of mixed AFOs within 30km of a well (p trend <0.001) and the log nitrate concentration. Additionally, we found significant increases in log nitrate in the top quartiles for AFO spatial intensity, open AFO spatial intensity, and mixed AFO spatial intensity compared to the bottom quartile (0.171log(mg/L), 0.319log(mg/L), and 0.541log(mg/L), respectively; all p<0.001). We also explored the spatial distribution of nitrate-nitrogen in drinking wells and found significant spatial clustering of high-nitrate wells (>5mg/L) compared with low-nitrate (≤5mg/L) wells (p=0.001). A generalized additive model for high-nitrate status identified statistically significant areas of risk for high levels of nitrate. Adjustment for some AFO predictor variables explained a portion of the elevated nitrate risk. These results support a relationship between animal feeding operations and groundwater nitrate concentrations and differences in nitrate loss from confined AFOs vs. open or mixed types.

Keywords: Animal feeding operations; Drinking water; Generalized additive models; Nitrate; Private wells; Spatial cluster; Spatial clustering.