Advances in NANI and NAPI accounting for the Baltic drainage basin: spatial and temporal trends and relationships to watershed TN and TP fluxes
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In order to assess the progress toward eutrophication management goals, it is important to understand trends in land-based nutrient use. Here we present net anthropogenic nitrogen and phosphorus inputs (NANI and NAPI, respectively) for 2000 and 2010 for the Baltic Sea watershed. Overall, across the entire Baltic, between the 5-year periods centered on 2000 and 2010, NANI and NAPI decreased modestly by −6 and −4%, respectively, but with substantial regional variation, including major increases in the Gulf of Riga drainage basin (+19 and +58%, respectively) and decreases in the Danish Straits drainage basin (−25 and −40% respectively). The changes were due primarily to changes in mineral fertilizer use. Mineral fertilizers dominated inputs, at 57% of both NANI and NAPI in 2000, increasing to 68 and 70%, respectively, by 2010. Net food and feed imports declined over that period, corresponding to increased crop production; either fewer imports of food and feedstocks were required to feed humans and livestock, or more of these commodities were exported. A strong linear relationship exists between regional net nutrient inputs and riverine nutrient fluxes for both periods. About 17% of NANI and 4.7% of NAPI were exported to the sea in 2000; these relationships did not significantly differ from those for 2010. Changes in NANI from 2000 to 2010 across basins were directly proportional rather than linearly related to changes in total N (TN) fluxes to the sea (i.e., no change in NANI suggests no change in TN flux). Similarly, for all basins except those draining to the Baltic Proper, changes in NAPI were proportional to changes in total P (TP) fluxes. The Danish Straits decreased most between 2000 and 2010, where NANI and NAPI declined by 25 and 40%, respectively, and corresponding fluxes of TN and TP declined 31 and 18%, respectively. For the Baltic Proper, NAPI was relatively unchanged between 2000 and 2010, while riverine TP fluxes decreased 25%, due possibly to lagged effects of fertilizer reduction resulting from socio-political changes in the early 1990s or improvements in sewage treatment capabilities. For most regions, further reductions in NANI and NAPI could be achieved by more efficient production and greater substitution of manure for imported mineral fertilizers.
KeywordsNANI NAPI Anthropogenic nutrient inputs Baltic watershed Total nitrogen Total phosphorus
In the modern, developed world, nitrogen (N) and phosphorus (P) fluxes from land to coastal waters are dominated by anthropogenic sources (Howarth et al. 1996, 2012; NRC 2000; Bennett et al. 2001; Galloway et al. 2004; Howarth 2008). Impacts of nutrient loading on coastal water quality and ecological function are significant and associated with eutrophication, hypoxic (“dead”) zones, harmful algal bloom development, and declines in the economic value and ecosystem services of coastal areas (Anderson et al. 2002; Smith 2003; Diaz and Rosenberg 2008). While magnitudes vary widely, both N and P loads can be largely attributed to agricultural sources and the waste streams from food consumption (Howarth et al. 1996, 2012; Han et al. 2011; Russell et al. 2008). In watersheds with substantial crop production, the regional exports of food and livestock feed that drive the economy are associated with inputs of fertilizer that can contribute to excess nutrient loads to regional waters. In watersheds with high population density or intensive livestock production whose food and feedstock demands exceed local production, imports needed to support these populations can also result in excess nutrient loading in the waste stream (Swaney et al. 2012a, b).
Eutrophication is a significant environmental pressure in the Baltic Sea, home to the world’s largest hypoxic “dead” zone and persistent cyanobacteria blooms (Carstensen et al. 2014). Nutrient loads from the drainage basin to the sea have been well-documented (Hong et al. 2012; HELCOM 2015); however, the international, transboundary nature of these nutrient loads complicate management efforts. A number of policies have sought to reduce land-based nutrient loads to the Baltic Sea, such as the European Union (EU) Water Framework Directive, the Nitrates Directive, and the Helsinki Commission (HELCOM) Baltic Sea Action Plan. While there has been progress in reducing TN and TP loads in the past few decades (HELCOM 2015), further actions are needed on land to fulfill the commitments of existing policy measures. To assess the progress toward eutrophication management goals, it is important to understand recent trends in nutrient use on land. Such understanding is especially needed given the social, political, and economic changes that have occurred in the past decades in the former Soviet-bloc countries located in the southern and eastern regions of the Baltic Sea basin.
Here we build on the work of Hong et al. (2012), who applied a well-established nutrient accounting method for net anthropogenic nitrogen inputs (NANI) and net anthropogenic phosphorus inputs (NAPI) to the Baltic Sea basin and found a strong linear relationship between the human-induced N and P inputs and riverine TN and TP fluxes for the year 2000. We apply the NANI and NAPI framework to more recent data for the periods around 2000 and 2010 with three objectives; to explore changes in land-based N and P inputs between 2000 and 2010; to investigate the relationship between changes in net anthropogenic nutrient inputs and changes in riverine fluxes; and to estimate nutrient use efficiency in agriculture. First, we compare the major components of NANI and NAPI between 2000 and 2010 at sub-basin and sub-national scales. Second we examine the relationship between NANI and NAPI and the corresponding TN and TP fluxes in order to gain insight into primary factors controlling regional variation and temporal variation (between 2000 and 2010) of nutrient loading to the Baltic Sea. Lastly, we use NANI and NAPI accounting to understand the magnitude of nutrient demand by the crops and the current sources of nutrient inputs meeting this demand, thereby exploring the potential for a more effective nutrient management strategy.
Materials and methods
The primary “data collection unit” used here is level two of the European Union’s data collection system, organized by its Nomenclature of Territorial Units for Statistics (referred to as NUTS, http://ec.europa.eu/eurostat/web/nuts/overview). Information compiled at the NUTS2 level from the EuroStat database together with additional regional information at the oblast level (Russia and Belarus; Belarus oblasts are also referred to as voblasts) are the basis of our estimates of regional variation of NANI and NAPI across the Baltic drainage basin.
Source of data used in this study
Use of inorganic fertilizers
International Plant Nutrition Institute
EuroStat (ef_lu_ovcropaa, ef_lu_ovcropesu, ef_oluaareg, ef_oluecsreg)
Statistical Yearbook of Russia (Russian Federation Federal State Statistical Service 1999–2015)
BelarusInfo database, Belstat (2001, 2010–2015)
EuroStat (agr_r_animal, ef_ls_ovaareg, ef_olsaareg)
Belstat (Belstat 2009–2015)
EuroStat (agr_r_crops, ef_lu_ovcropaa, ef_lu_ovcropesu, ef_oluaareg, ef_oluecsreg)
Statistical Yearbooks (Agriculture of the Republic of Belarus 2001, 2002, 2009–2015)
NANI can be calculated as the sum of oxidized N deposition, fertilizer N application, agricultural N fixation and N in net food and feed imports. The net food and feed imports are, in turn, calculated as the sum of human and livestock N consumption (positive fluxes adding N to the area of interest) minus the sum of livestock and crop N production (negative fluxes removing N from the area of interest). The NAPI calculation is performed in a very similar way, though it is generally simpler than the NANI calculation because it is assumed that there is no (or very little) atmospheric P deposition, and there is no analog in P for agricultural N fixation. However, NAPI has an additional term, human non-food use of P (e.g., detergent). Below we describe the calculation of each component of NANI and NAPI in our study area.
Atmospheric N deposition
Following previous work (Boyer et al. 2002; Howarth et al. 2006, 2012; Han and Allan 2008; Hong et al. 2011, 2012, 2013; Eriksson Hägg et al. 2012; Schaefer and Alber 2007), we considered the oxidized forms of atmospheric N deposition, since most reduced N deposition typically originates from nearby ammonia/ammonium emissions redeposited on the same area of interest. While it is recognized that some forms of reduced nitrogen can travel greater distances when scavenged by atmospheric aerosols or precipitation, Eriksson Hägg et al. (2012) found no advantage in considering NOy + NHx deposition over just NOy deposition in their study of Swedish catchments. It is important to distinguish between the composition of long distance (i.e., new sources) and local sources (i.e., recycled nitrogen). However, as a proxy of the reduced N that arises from non-local sources, for each watershed in the region we estimated the reduced N from external sources (i.e., long distance transport from sources outside the country containing the river basin) using EMEP country-grid source receptor estimates (http://www.emep.int/mscw/mscw_srdata.html#GridData), and summed these over all watersheds in each of the seven Baltic catchment regions. The resulting basin-scale ratio of NHx/NOy deposition was used to estimate the contribution of reduced N deposition for the seven Baltic regions (Tables S10–S11, supplemental materials). Comparing NANI without NHx to NANI with all NHx and to NANI with nonlocal NHx had no significant impact on our analysis (Fig. S2, supplemental materials), so we present NANI with the contribution of NOy deposition only in the main text.
Oxidized N deposition estimates were downloaded from the EMEP model simulation output available at http://www.emep.int/ with a grid resolution of 50 km × 50 km. EMEP deposition estimates are updated periodically; we used the most recent version available at the time of our study for each year (v2015 for the 2000–2012 estimates and v2006 for the 1998–1999 estimates). A GIS tool, the Geospatial Modelling Environment (freeware compatible with ArcGIS, available at http://www.spatialecology.com/gme/), was used to overlay the EMEP grid and NUTS2/oblasts maps and calculate the annual area-weighted averages of oxidized N deposition.
Fertilizer N and P application
For accounting units within the EU, data on inorganic fertilizer N and P use were obtained from the “aei_fm_usefert” table of the EuroStat database (Table 1). NUTS2 level data were often missing outside the 2008–2012 period, and only the national level fertilizer data were available for Denmark and Germany. When it was not possible to estimate the allocation of the national level fertilizer use among its NUTS2 areas from the available EuroStat datasets, the allocation was performed based on the relative use of inorganic fertilizer in 2000 as estimated by Joint Research Centre (EU-JRC) in Institute for Environment and Sustainability (ISPRA) (Grizzetti et al. 2007) and used in the previous NANI/NAPI analysis by Hong et al. (2012). For regions outside the EU (i.e., Russia and Belarus), we obtained oblast-level estimates from other sources. Data on N and P in mineral fertilizer for Russian oblasts (Leningrad, Kaliningrad, Karelia, Pskov and Novgorod) for the years 2008–2015 were made available by the International Plant Nutrition Institute (S. Ivanova, IPNI, personal communication). Estimates of N and P in mineral fertilizer in Belarus oblasts were obtained from official Belarus agricultural statistics for the years 2000–2001 and 2005–2014 (National Statistical Committee of the Republic of Belarus 2013, 2015). A factor of 0.4266 was used to convert from the reported P2O5 values to P equivalents.
Agricultural N fixation
Agricultural N fixation may be estimated by multiplying each N fixing crop area by its N fixation rate and summing over all crops. Alternatively, agricultural N fixation can be estimated from reported yields of N fixing crops and proportions of N production attributable to N fixation (Hong et al. 2013). In this study we used the area-based approach for consistency with the previous Hong et al. (2012) analysis and because yield information was not readily available for all the N fixing crops. The NUTS2 level N fixing crop areas for the EU countries were obtained from several tables (“ef_lu_ovcropaa”, “ef_lu_ovcropesu”, “ef_oluaareg” and “ef_oluecsreg”) of the EuroStat database. Oblast-level crop areas for N fixing crops in Russia were obtained from the Russian Federation Federal State Statistical Service (FSSS) (2015). For Belarus, areas of N fixing crops, including legumes and grasses, were obtained from official Belarus agricultural statistics for the years (National Statistical Committee of the Republic of Belarus 2013, 2015). The country-specific N fixation rates corresponding to each crop type were obtained from Hong et al. (2012) (Table S1, supplemental materials).
Net food and feed imports
In most NANI and NAPI calculations, net food and feed imports are estimated as the difference between the consumption of food and feed by human and livestock and the production from crops and livestock. If the consumption of food and feed is greater than the local agricultural production, the N and P in deficit is assumed to be met by imported food and feed from outside the area of interest and consumed by the human and livestock. If local agricultural production exceeds demand, it is assumed to be exported, and so there will be a net export of N and P from the region.
Human N and P consumption
Population data for the European countries were obtained from the “demo_r_d2jan” table of the EuroStat database, and were multiplied by the country-specific human N and P intake rates (Table S2, supplemental materials; Hong et al. 2012) to estimate human N and P consumption. Population data for Belarus are from the Statistical Yearbook Regions of the Republic of Belarus (Belstat 2001, 2010–2015) and the BelarusInfo database (http://www.belstat.gov.by/ofitsialnaya-statistika/bazy-dannyh/baza-dannyh-belarusinfo/). Population data for Russia are from the Russian State Statistical Service database (FSSS 2008, 2001–2013). Following Hong et al. (2012), the N:P ratio of human intake was assumed to be 5. This consumption estimate does not include non-food use of P, which was separately accounted for as described below.
Livestock N and P consumption and production
Uncertainty ranges of the N:P ratios for the livestock intake and excretion parameters and the best estimates used in this study
N:P excretion (mass)
Sheldrick et al. (2003)
Tybirk et al. (2013)
Crop N and P production
The N and P in crop production are calculated as the product of the mass of harvested crops and their N and P contents. Most of the crops in the EuroStat table “agr_r_crops” containing NUTS2-level crop harvest data (Table S5, supplemental materials) are found in the JRC crop list used by Hong et al. (2012). For those crops, nutrient contents were taken from Hong et al. (2012). The nutrient contents of some crops that were not included in the previous study (rice, cotton, tobacco and olives), mostly in minor proportion, were obtained from Lander et al. (1998) and Hong et al. (2013). Crops with the EuroStat code starting with “C” in Table S5 (supplemental materials) have NUTS2-level harvested crop production data available (in thousand tonnes). Productions of the crops without the harvest data, mostly different types of grasslands (permanent, temporary, fallow, etc.; Table S5) and minor crops, were estimated from the crop areas (obtained from the EuroStat database “ef_lu_ovcropaa”, “ef_lu_ovcropesu”, “ef_oluaareg” and “ef_oluecsreg”) and available yield information. Crop data for Russia are from FSSS (2015), and from the Statistical Yearbooks, Agriculture of the Republic of Belarus (2001, 2002, 2009–2015) for Belarus. Distribution of crop production to human (versus livestock) and processing losses are taken from Hong et al. (2012, 2013). Although some crops that are not consumed as food or feed (e.g., cotton and tobacco) are included in this calculation, they only comprise small portions of the overall budgets.
Non-food use of P by humans
The non-food use of P by human (e.g., in detergents) is a relatively small term in the overall nutrient budget (e.g., Han et al. 2011). It is often combined with the P in food consumption described above and becomes part of the “net (non-)food and feed imports.” A coefficient of 0.35 kg-P/capita/year was applied to estimate the human non-food use of P (Hong et al. 2012; Rybicki 1997; Han et al. 2011). There may be substantial spatial and temporal variability in this estimate, as the use of laundry and dish-washing products has evolved differently among different countries within the Baltic basin.
Manure N and P application
Manure is not an explicit term of the NANI/NAPI calculation, as manure N and P production from animals and their application to crops are considered as internal fluxes (as are the ammonia/ammonium emissions and their redeposition; see oxidized N deposition section above). However, we estimated manure N and P fluxes in this study to better understand the potential magnitude of livestock N and P excretion that can meet the crop nutrient demands. Three coefficients are applied to convert livestock N and P excretion (see livestock N and P consumption and production section above) to manure N and P application: (1) fraction of livestock excretion collected in-house for manure production, estimated from the livestock excretion allocated to non-pasturelands (National Inventory Submissions report of the UN Framework Convention on Climate Change, http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/8108.php), (2) fraction of livestock excretion (collected in-house) converted into manure, estimated from the country-specific volatilization and leaching losses during the in-house manure production (Oenema et al. 2007) (Table S6, supplemental materials), and (3) fraction of manure (produced in-house) applied to croplands of interest. Note that the last parameter was set to one in this whole-watershed based analysis, but it may be refined depending on how the accounting boundary is defined (e.g., utilized agricultural area/arable lands only).
Comparison with basin-wide riverine N and P exports
In the case of complete coverage, the sum of the contributing areas would equal the area the entire Baltic drainage basin; in fact, it is a little over 97% of the 1,729,500 km2 comprising the drainage areas of the nine major Baltic basins (Archipelago Sea, Baltic Proper, Bothnian Bay, Bothnian Sea, Gulf of Finland, Gulf of Riga, Kattegat, The Sounds, and Western Baltic) described in the latest HELCOM pollution compilation PLC5.5 (HELCOM 2015). The matrix of aij values can be found in the supplemental materials, Table S7.
For each of nine basins, total N (TN) and total P (TP) fluxes were obtained from HELCOM (2015; http://www.helcom.fi/baltic-sea-trends/indicators/inputs-of-nitrogen-and-phosphorus-to-the-basins/data-description-and-confidence/). Some of the neighboring basins in HELCOM (2015) were aggregated (e.g., Archipelago Sea + Bothnian Sea to Bothnian Sea; The Sounds + Western Baltic to Danish Straits) to facilitate comparison with Hong et al. (2012) and to avoid outlier behavior of small basins. The resulting categorization of 7 basins (Fig. 1) and their watersheds is consistent with some earlier studies. As noted in Hong et al. (2012), the data set incorporates nutrient fluxes from major monitored rivers as well as partially monitored or unmonitored coastal areas. The 217 rivers drain approximately 86% of the total catchment area and 24 partially monitored or unmonitored coastal areas, composed of many small streams, drain approximately 13%. These areas were aggregated together to include both the monitored and unmonitored areas, and nutrient flux estimation was made based on the data available in small monitored streams.
Results and discussion
Spatial variation of NANI, NAPI and their components in 2010
Spatio-temporal trends in NANI, NAPI and their components, 2000–2010
The relationship between NANI/NAPI and riverine TN/TP fluxes at the basin scale remained strong (Figs. S1a,b) as previously demonstrated in Hong et al. (2012). The slope of the linear NANI versus TN flux relationship for 2000 estimated from the NUTS2/oblast-level data (1998–2002 averages) (Fig. S1a, black line) increased slightly, but not significantly, from that of Hong et al. (2012; brown line) which was developed based on average fluxes for the period 1994–2006. Partly due to parameter refinements (e.g., updated N:P ratios for the livestock intake and excretion rates; Table 2; Tables S3, S4, supplemental materials) and partly due to changes in riverine TP flux estimates, the slope of the NAPI versus TP flux regression line has increased significantly (Fig. S1b, black line) compared to the previous estimation (brown line), but yielded similar R2 values. All regression lines are statistically significant. Intercepts of the regressions from Hong et al. (2012) are not significantly different from those of the present study, indicating that non-NANI/NAPI driven (background) nutrient flux estimates are the same in both cases. Slopes of the regressions suggest that about 17% of NANI and 4.7% of NAPI were exported to the sea in 2000. Previous studies reported similar percent export values: about 10–30% for nitrogen (Howarth et al. 2006, 2012; Eriksson Hägg et al. 2012; Han et al. 2009; Han and Allan 2008; Schaefer and Alber 2007; Boyer et al. 2002) and 2–10% for phosphorus (Russell et al. 2008; Han et al. 2011; Zhang et al. 2015). The nutrient accounting approach can be used to estimate nutrient retention, which has been reported to vary across individual watersheds, regions, and continents (Garnier et al. 2015). Its variation has been shown to be strongly correlated with climatic and hydrologic conditions; for example, in Europe the fraction of NANI exported as riverine fluxes is lowest in Mediterranean watersheds, highest in Nordic watersheds, and in-between in temperate watersheds (Romero et al. 2016; Billen et al. 2011).
For NAPI and TP, nearly identical linear relationships (slopes and intercepts are not significantly different) apply to each 5-year period average (Fig. 4c). While the decadal differences of NAPI correspond to the differences of TP fluxes for most watersheds (Fig. 4d), the relationship is not statistically significant (p > 0.05) due to the outlier behavior of the Baltic Proper, BP, which exhibits a relatively large decline in TP flux corresponding to an apparently small change in NAPI over the same period. This is in contrast to TN in BP, which declines slightly in accordance with the slight decline in NANI. The reason for this discrepancy is unclear at present, but some insights can be gained by looking at the relationships between individual components of NANI and NAPI and their corresponding waterborne TN and TP fluxes (Table S9, supplemental materials).
Fertilizer N and P is as strong a predictor as is NANI and NAPI for periods centered on 2000 and 2010, which is consistent with it being the dominant component of the overall nutrient budget (Table S9, supplemental materials). While the relationship between the change in N fertilizer and TN flux is highly significant, the relationship between P fertilizer and TP flux over the same period is not. Change in both N and P in net food and feed imports between 2000 and 2010 is significantly related to corresponding TN and TP fluxes across basins, respectively, but within a given period shows weaker relationships with these fluxes than does fertilizer (Table S9). This is attributable in part to the inherent spatial variability of net food and feed and its individual components.
Components of NANI and NAPI
Agricultural nutrient inputs versus crop demand
Although N deposition and agricultural N fixation provide additional sources of nitrogen to crops, their contribution to the overall N budget is comparatively small in agricultural regions of the Baltic, irrespective of the details of the assumptions about oxidized vs reduced N contributions. However, in the northernmost, sparsely populated Baltic catchments (Bothnian Bay, Bothnian Sea), potential contributions of long-distance atmospheric transport are important, because atmospheric N deposition is a significant component of NANI (Eriksson Hägg et al. 2012).
Our results suggest that more efficient use of manure could reduce the use of mineral fertilizer, thereby lowering nutrient surpluses. Inefficient use of manure N and P can result from mismatches between the locations of intensive livestock production and areas of crop production, and associated losses in transport and handling of manure (Oenema et al. 2007). Bringing livestock and crop production closer together, and applying manure and mineral fertilizer at levels closer to the apparent net crop demand should see improvements in both agricultural efficiency and environmental quality (Nesme et al. 2015; Garnier et al. 2016).
The NANI/NAPI framework of nutrient accounting is a robust framework for estimating nutrients to major basins of the Baltic Sea, and thus can serve as the basis for regional and interregional comparisons and analyses of meaningful environmental management strategies. Within the Baltic, significant variations in nutrient inputs exist, primarily along a north–south gradient. Direct relationships between NANI/NAPI and corresponding watershed exports of TN/TP to the Baltic have been documented (Hong et al. 2012) and this analysis, using refined data available from EuroStat and other sources, continues to support this conclusion. Further, decadal changes in TN and TP fluxes across major subbasins between 2000 and 2010 were directly related to corresponding changes in NANI and NAPI which were strongly affected by changes in fertilizer use and, to some degree, by changes in net food and feed imports. Finally, it appears that, in principle, much of the current nutrient demand by crops and forage lands could be satisfied by livestock excretion, if effectively managed. Switching to a manure-based system would require careful investigation at several management scales to ensure that organic nutrient export to waters does not become an unintended impact on the Baltic Sea ecosystem. This potential management option, combined with the observation of the direct response of TN/TP export to NANI/NAPI change, suggests that significant reductions in nutrient loading could occur by reducing mineral fertilizer application through effective manure production and application throughout the region. Strategies to do so should be developed on an international (EU and neighbors), national, regional and watershed basis, depending upon the locally predominant livestock and crop mix, and corresponding best management practices appropriate for local soils and climatic conditions.
We dedicate this work to the memory of our esteemed colleague and mentor, Fredrik Wulff, whose insights and humor informed much of this and previous work in the Baltic. We gratefully acknowledge Svetlana Ivanova, Vice President, Eastern Europe and Central Asia Division, International Plant Nutrition Institute, for her assistance in obtaining statistics of fertilizer nutrient consumption in western Russian oblasts. We also thank Alexander Sokolov for his insight and assistance in considering aspects of nitrogen deposition in the region, Bob Howarth for his continuing support, and three anonymous reviewers for their thoughtful comments which improved this manuscript. Funding was provided by Baltic Eye and the Baltic Sea 2020 Foundation.
Compliance with ethical standards
Conflict of interest
The authors are aware of no potential conflicts of interest.
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