Groundwater nitrate contamination: Factors and indicators
Highlights
► We provide a statistical analysis of determinants of groundwater nitrate levels. ► We assess the capacity of the Gross Nitrogen Balance to predict nitrate levels. ► High average temperature and precipitation decrease groundwater nitrate levels. ► The Gross Nitrogen Balance is a stat. significant predictor for nitrate levels. ► Its capacity can be improved if precipitation is taken into account.
Introduction
Nitrogen fertilizers are applied extensively in agriculture to increase crop production, but excess nitrogen supplies can cause air, soil, and water pollution. Arguably one of the most widespread and damaging impacts of agricultural overapplication of nitrogen fertilizers is the degradation of groundwater quality and contamination of drinking water supplies, which can pose immediate risks to human health (Lord and Anthony, 2002; Schroeder et al., 2004). The EU Directive 91/676/EEC, which protects waters against pollution caused by nitrates from agricultural sources, sets the acceptable threshold of nitrate concentration in groundwater at 50 mg/l as nitrate. The EU Directive 2006/118/EC also attempts to protect groundwater against pollution and deterioration by suggesting that Member States establish quality standards, develop methodologies for assessing and monitoring groundwater quality, and implement measures supporting groundwater protection, including changes to farming and forestry practices.
An assessment of the impact and magnitude of nitrate leaching from agricultural activities is necessary in order to design and implement appropriate regulatory policies that support these goals. Consequently, a meaningful indicator is needed to allow governments to implement effective policy measures and monitor policy compliance (de Ruijter et al., 2007; Lord and Anthony, 2002; Watson and Atkinson, 1999). The most commonly used indicator to monitor and assess nitrogen use across countries is the Gross Nitrogen Balance (EEA, 2001; OECD, 2007; Parris, 1998).
These concerns about groundwater nitrate contamination have led to the following research questions: (1) What are the factors that determine nitrate contamination in groundwater? In particular, which agricultural practices tend to contaminate groundwater and what is the influence of external factors such as weather conditions and soil characteristics? (2) Is the Gross Nitrogen Balance a good predictor of groundwater contamination and is its frequent use in guiding policies justified?
Researchers have identified several factors affecting nitrate groundwater contamination, including fertilizer levels and build-up of soil organic matter, which can result in a large mineral nitrogen pool and thus in a higher risk of nitrate leaching (Korsaeth and Eltun, 2000; Sieling and Kage, 2006). Furthermore, manure management, crop cultivation practices (Lord and Anthony, 2002; Rankinen et al., 2007), soil texture (de Ruijter et al., 2007), and precipitation surpluses (Boumans et al., 2001; Elmi et al., 2002; Fraters et al., 1998; Salo and Turtola, 2006) have been found to influence the extent of agricultural nitrate leaching. Potential determinants of nitrate leaching have been investigated using several methods. For instance, farm and field experiments have been performed (Boumans et al., 2001; de Ruijter et al., 2007), controlling for alternative cropping systems (Korsaeth and Eltun, 2000), management practices (Rankinen et al., 2007; Sieling and Kage, 2006), and soil types (Salo and Turtola, 2006). Even though these experiments provide precise results and insights, they have the disadvantage of being limited in scale and time such that routine applications may not be feasible, or the data is not suited for generalization (Buczko et al., 2010). Devising measures of groundwater contamination on a national scale can be cumbersome and difficult. Groundwater sampling is costly and nitrate concentration often responds slowly to changes in agricultural management practices (Lord and Anthony, 2002; OECD, 2008), making it difficult to determine causality. Additionally, direct measurements and long-term monitoring are often influenced and distorted by weather events (Lord and Anthony, 2002). Bio-physically based process models are often used to simulate the nitrogen cycle at field to watershed scale (Buczko et al., 2010; Cannavo et al., 2008; van der Laan et al., 2010). However, data requirements, model parameterizations and initial assumptions may limit their use (Buczko et al., 2010; Cannavo et al., 2008).
Alternatively, risk indicators or agri-environmental indicators, such as the Nitrogen Balance, are often used to predict nitrate leaching in groundwater (OECD, 2007). Nitrogen Balances are appreciated for being objective, transparent, and readily verified agri-environmental indicators (Lord and Anthony, 2002). As such, they raise awareness about nutrient use issues and support the enforcement of regulatory nutrient management policies (Oenema et al., 2003). Nitrogen Balances can be assessed at different degrees of complexity and can be computed on a farm as well as national scale (Oenema et al., 2003; Watson and Atkinson, 1999). There are three types of Nitrogen Balances commonly in use: (i) the farm-gate balance accounts for nutrients in all kinds of products which enter and leave a farm. This method allows researchers to obtain precise results but is difficult to compute on the regional level (Lord and Anthony, 2002; OECD, 2007; Schroeder et al., 2004); (ii) the soil surface balance, such as the Gross Nitrogen Balance as suggested by the Organisation for Economic Co-operation and Development (OECD), lists inputs and outputs to the soil (Lord and Anthony, 2002; OECD, 2007; Schroeder et al., 2004); and (iii) the soil system balance accounts for inputs and outputs to the soil as well as for recycling of nutrients within the system and changes in the soil nutrient pool (Oenema et al., 2003). The OECD suggests the Gross Nitrogen Balance methodology as the appropriate indicator to calculate comparable nitrogen balances on a regional or national scale (OECD, 2007).
However, the degree to which the indicator is capable of reflecting actual nitrate leaching effects is unclear (de Ruijter et al., 2007; Lord and Anthony, 2002; Sieling and Kage, 2006), because the Gross Nitrogen Balance is a theoretical concept and as such only captures the potential for groundwater contamination. This is an important point when extrapolating from nutrient balance trends to actual environmental impacts (OECD, 2008). Much of the scientific literature agrees that Nitrogen Balances perform rather poorly when predicting observable nitrate concentration levels (Buczko et al., 2010; Korsaeth and Eltun, 2000; Oenema et al., 2003; Rankinen et al., 2007; Salo and Turtola, 2006; Schroeder et al., 2004; Sieling and Kage, 2006). For instance, de Ruijter et al. (2007) could not find clear results concerning the appropriateness of the Nitrogen Balance as an indicator of nitrate contamination. Research studies assess the concordance of actual nitrate leaching and predictions made by the Nitrogen Balance by correlation analysis (Buczko et al., 2010; Lord and Anthony, 2002). Rankinen et al. (2007) performed an analysis of covariance to investigate the potential of the Nitrogen Balance to predict nitrate leaching. Many researchers have also used regression analyses to explain nitrate leaching with a limited number of explanatory variables, such as the Nitrogen Balance and annual precipitation sums (Buczko et al., 2010; Korsaeth and Eltun, 2000; Rankinen et al., 2007; Salo and Turtola, 2006; Sieling and Kage, 2006). These statistical analyses have two important weaknesses. First, they are often limited geographically as well as temporally. Second, they usually do not take into account (i.e. do not “control for”) all relevant external factors that could potentially influence nitrate contamination. This can result in a so-called omitted variable bias, or the wrong attribution of effects to certain factors.
We employ a regression approach to systematically investigate the determinants of groundwater nitrate contamination as well as the effectiveness of the Gross Nitrogen Balance as a predictor of nitrate contamination in groundwater. We construct an extensive and detailed panel dataset on the Austrian situation that includes time series of nitrate concentration levels in groundwater, land cover types, land uses, as well as soil and weather data for around 1200 Austrian municipalities between 1992 and 2007. The temporal and geographical magnitude of our analysis is larger than in other studies. Additionally, our dataset allows us to account for many potential explanatory variables, which makes our analysis less prone to omitted variable bias than other statistical investigations. We are able to quantify the marginal effects of several potential explanatory variables and their relative and absolute magnitude. In addition, our framework allows us to forecast potential nitrate contamination in groundwater given agricultural practices as well as weather and soil conditions (i.e. counterfactual experiments).
Section snippets
Data sources and manipulation
Concentrations of groundwater nitrate in mg/l were obtained from the Federal Environment Agency Austria (Umweltbundesamt, 2010b). This data is available on a quarterly basis from 01/1992 to 04/2008 for 1238 Austrian municipalities. The number of observations in each time period varies (i.e. nitrate concentration is not available for every time period and municipality). In the course of this analysis we aggregate the quarterly values to annual average values for each municipality.
Data on daily
Site-specific characteristics
We investigate the relationship between nitrate concentration in groundwater and various site-specific characteristics such as land cover, weather conditions, and soil quality. The time dimension (t) is given by years and the cross-sectional dimension (i) represents municipalities. Year dummies are included to control for aggregate annual shocks. These are defined as follows:
The regression equation takes the form:
Results and discussion
The results of the introduced models are presented in Table 3. We firstly investigated the factors influencing nitrate concentration in groundwater. Table 3 (column 1) illustrates the results of Equation (1), through which the effect of land covers and site specific characteristics have been estimated. Our expectations of the effect of soil and land cover types on nitrate concentration in groundwater have been verified. Except for the effect of the share of area with buildings on nitrate
Conclusion
Nitrogen is a crucial input in agricultural production, but it puts environmental pressure on (ground)water, soil and air. We identify the likely factors influencing nitrate contamination in Austrian groundwater. We find that on average, increased agricultural activity (especially in conventionally cultivated crops) leads on average to higher nitrate contamination in groundwater. Additionally, environmental factors such as precipitation and temperature play an important role. Higher average
Acknowledgments
We thank Ulrich Morawetz, Michael Weichselbaumer, Franz Sinabell and Amy Johnson for their support. This research has been realized within the project CC – Tame (Climate Change – Terrestrial Adaptation & Mitigation in Europe; www.cctame.eu) funded by European Commission within the 7th Framework and the programme proVISION introduced by the Austrian Federal Ministry of Science and Research (BMWF; http://www.provision-research.at/).
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