Assessment of nitrogen application limits in agro-livestock farming areas using quantile regression between nitrogen loadings and groundwater nitrate levels
Introduction
Nitrate (N) pollution in freshwater, including groundwater, is an ongoing problem across the globe. Global N inputs (dominantly as dissolved inorganic nitrogen) to fresh water environment have more than doubled over the last 4–5 decades because of anthropogenic pressure with the expanded production of N fertilizers and fossil fuels (Vitousek et al., 1997; Galloway et al., 2004, 2008; Seitzinger, 2009; Seitzinger et al., 2010). Of the total anthropogenic N input, agricultural N input for crops and livestock occupies approximately 85% of the global total N input and is expected to further increase toward 2050, which indicates that agriculture is the main cause of global N pollution (Galloway et al., 2003). Meanwhile, agricultural activities have been intensified (e.g., concentrated animal feeding operations) due to economic competitiveness and production efficiency, which causes unintended environmental consequences such as eutrophication and water pollution (Tilman, 1999; Tilman et al., 2002). Thus, N management practices, especially in intensive agro-farming areas, have been raised as an important issue for water sustainability. Therefore, better understanding of the connections between nutrient loads including N and water quality is required for water quality management (Johnes, 1996; Alexander et al., 2008; Hansen et al., 2017).
In South Korea, N-use efficiency (i.e., the ratio of N output to N input) in the agricultural sector was reported to be lowest (29.47%) among OECD countries in 2002–2004 (OECD, 2008), and eutrophication and water pollution with nutrients (esp. N) have been often observed. According to the Korea Ministry of Environment (KMOE, 2017), the total number of days when the water quality exceeded a minimum reporting level (i.e., cyanobacteria abundance of 1000 cell/mL) increased from 114 days in 2008 to 608 days in 2015 at 12 weirs of major rivers, and phytoplankton blooms increased during this period. In addition, a number of recent papers reported that the overuse of fertilizers and manure in agricultural areas resulted in acidification and nitrate contamination of shallow groundwater in South Korea (Chae et al., 2004; Choi et al., 2007; Kim et al., 2015a, 2019), and suggested the urgent need of reduced application of fertilizers and manure.
It is a challenge, however, to optimize the application amount of fertilizers and manure for human resources (e.g., food and energy production) while minimizing nitrate pollution (Tilman et al., 2002; Ju et al., 2009; Hansen et al., 2011, 2017). To solve this optimization problem, many researchers and environmental agencies have made an effort: the USDA Natural Resources Conservation Service provides guidelines for addressing nutrient imbalance concerns based on a nutrient management standard (code 590) (NRCM, 2011). In California, N fertilizer guidelines for major crops are available online (https://apps1.cdfa.ca.gov/go/FREPguide) suggesting an environmentally safe and sound N fertilizer application rate. The Nitrate Directive (91/676/EEC) (European Commission, 1991) imposed the maximum application amount of livestock manure for nitrate vulnerable zones (i.e., 170 kg manure-N ha−1 year-1). A few EU countries including Denmark, Germany, and the Netherlands revised the N application rate on the basis of scientific data (Oenema, 2004; Schröder and Neeteson, 2008; D’Haene et al., 2014).
However, most fertilizer response studies have been conducted in a single small watershed and tried to provide a site-specific N fertilizer application limit, while there were only a few regional- or global-scale studies about the relationship between anthropogenic N inputs and nitrogen fluxes (McIsaac et al., 2001; Seitzinger et al., 2010; Sinha et al., 2017). The N fertilizer application limits from small-scale studies may not reflect the response difference due to the spatial change of environmental factors such as soil variability (Kitchen et al., 1995; Rashid and Voroney, 2005). In particular, in many countries including South Korea, which are characterized by diverse geomorphological features, a large spatial variation makes it difficult to apply a single N fertilizer rate to a whole region. Consequently, the application of an N fertilizer rate to a whole region can result in over- or under-fertilization. Thus, a comprehensive guideline is needed to suggest sustainable N application rates, especially in areas with varying environmental conditions (e.g., elevation) like South Korea.
Based on a large dataset collected from a national-scale groundwater quality survey in South Korea, Kim et al. (2019) overall evaluated the status of nitrate contamination and associated hydrochemical changes (esp., acidification) of shallow groundwater. The present study using the same dataset aims (1) to find relationships between N loadings and groundwater nitrate levels in 100 agro-livestock farming districts (section 3),(2) to investigate the relative importance of environmental factors, i.e., land use, elevation, slope angle, soil depth (section 4.1.1.) and manure management (section 4.1.2) in nitrate contamination of groundwater, and (3) to suggest an optimal N limit (section 4.2). We use a regional-scale database for N loading and environmental factors that covers South Korea (Korea Ministry of Environment (KMOE, 2014a). In particular, quantile regression (QR) is adopted to provide a complete view of the relationship between median groundwater nitrate concentrations and land-derived N inputs (on a log scale) for 100 agro-livestock districts by estimating rates of changes, called gradients (βτ) (Koenker and Bassett, 1978; Yu et al., 2003). QR is useful for identifying heterogeneity across the quantiles of the response variable (i.e., groundwater nitrate concentrations in this study) when the response variable is affected by more than one factor or has a large variance, which is common in hydrochemical data. QR has been widely used in econometric (Koenker and Hallock, 2001), ecological (Sankaran et al., 2005), meteorological (Elsner et al., 2008; Hirschi et al., 2011), and hydrological studies (Wasko and Sharma, 2014), while there are only a few research studies related to hydrochemical dataset using QR, including Monteith et al. (2014) which used QR to assess the acidification of surface water.
Section snippets
Study area
For this study following Kim et al. (2019), 100 representative agro-livestock farming districts were evenly chosen in five provinces: Gyeonggi, Gangwon, Chungcheong, Gyeongsang, and Jeolla (n = 20 for each province in Fig. 1b). Each district consisted of two to 33 animal feeding operations (AFOs), and as a result, a total of 613 AFOs were included in the study area (Table 1). According to the KMOE (2014a), 80% of the 613 AFOs collected manure and used it directly on the farm as an
Results
A total of 3,928 groundwater samples are used for data interpretation in this study, with excluding 72 samples whose charge balance errors are larger than ± 10%.
Discussion
Because Fig. 5 indicates higher sensitivity of groundwater to land-derived N loading at higher quantiles, external factors that affect sensitivity (i.e., heteroscedastic data) are discussed in Section 4.1 and an optimal N limit is suggested for each quantile group in Section 4.2.
Conclusion
Groundwater in South Korea is highly contaminated by nitrate due to pervasive livestock agricultural activities. This study investigates the relationship between N loading and nitrate levels in groundwater using quantile regression to deal with the heteroscedastic data. The positive gradients βτ at all quantiles (0.1–0.9) gradually increase as the quantile is higher, which indicates that the groundwater is more sensitive to N loading at high quantiles. A quantile map shows that the high
Acknowledgments
This study was performed with support from a research project (11-1480523-002666-01) sponsored by the Korean Ministry of Environment (MOE) and Korea National Institute of Environmental Research (NIER). Partial support was also provided by Korea Environment Industry & Technology Institute (KEITI) through Subsurface Environmental Management (SEM) Project, funded by Korea Ministry of Environment (MOE) (2018002440002), Basic Research Project (GP2018-002; 19-3415) of the Korea Institute of
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