Assessment of nitrogen application limits in agro-livestock farming areas using quantile regression between nitrogen loadings and groundwater nitrate levels

Abstract

The excessive application of nitrogen in agro-livestock farming areas has led to serious groundwater contamination around the world; thus, the efficient control of N loads is crucial to manage nitrate contamination of groundwater. In this study, to suggest an optimal N application limit as a key guideline of N management, we examine the impact of anthropogenic N loading on nitrate levels of shallow groundwater, using a large dataset (n = 4,000) collected in 2012-2014 from 100 agro-livestock farming districts in South Korea. Not considering the time lag and legacy problem, quantile regression is performed to overall assess the relationship between nitrate contamination and anthropogenic N input across the full range of conditional distribution of groundwater nitrate concentrations because of heteroscedasticity. As a result, positive gradients (beta(tau)) meaning the rates of changes are found between groundwater nitrate concentrations and land-derived N loads at all quantiles; beta(tau) increases as the quantile is higher and is as large as 36.48 +/- 10.73 mg NO3-/L per land-derived N input (on a log scale) at the 90% quantile. The quantile map indicates that the high gradient (i.e., the large sensitivity to N loading) is related to agricultural land use, low elevation, and low topographic slopes. In fact, groundwater nitrate concentrations increase as the percentage of agricultural land increases but the percentage of forest, elevation and slope angle decreases, which suggests that land use, elevation, and slope should be considered when attempting to evaluate N application limits. This study suggests the N limits below 170 kg ha(-1) year(-1) based on the drinking water standard (44.3 mg/L as NO3-) at the quantiles higher than 70% which mostly occur in flat agricultural lowland areas. Our method used in this study can be applied elsewhere to establish efficient management practices for groundwater nitrate contamination, considering local environmental factors.