Next Article in Journal
Influences of Power Structure Evolution on Coffee Commodity Markets: Insights from Price Discovery and Volatility Spillovers
Previous Article in Journal
Developing a Framework for Fake News Diffusion Control (FNDC) on Digital Media (DM): A Systematic Review 2010–2022
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 22
10.3390/su142215286
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of the Environmental Performance of Cropping Systems under Different Nitrogen Management Scenarios Considering Regional Nitrogen Resilience

by
Karoline Wowra
,
Vanessa Zeller
* and
Liselotte Schebek
Chair of Material Flow Management and Resource Economy, Institute IWAR, Technische Universität Darmstadt, Franziska-Braun-Strasse 7, 64287 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15286; https://doi.org/10.3390/su142215286
Submission received: 13 October 2022 / Revised: 9 November 2022 / Accepted: 14 November 2022 / Published: 17 November 2022
Browse Figures
Versions Notes

Abstract

:
The agricultural sector is a primary driver of nitrogen (N) pollution. Several European and German policy measures exist regulating N inputs and fostering mitigation measures in crop management. Life cycle assessment (LCA) is an established tool for assessing environmental impacts which are also broadly applied for crop production systems and evaluation of N management strategies. However, due to the multiple spatial and temporal pathways of N losses from crop production, assessing N-related impacts in LCA is not straightforward. Consequently, this study further developed and applied a novel distance-to-target approach including regional carrying capacity based normalization references for N assessment in LCA. The overall aim was to prove its applicability as regional decision support for the assessment of N management strategies in cropping systems considering environmental interventions with regional N resiliencies. Therefore, environmental interventions were evaluated within a case study for four different N management scenarios for rapeseed cropping systems in five German NUTS-3 regions. Regional carrying capacity based normalization references were derived for two N-related impact categories: terrestrial eutrophication and terrestrial acidification. The regional normalization references also included background interventions of non-crop producing sectors and were provided for all German NUTS-3 regions applicable as distance-to-target values in LCA. Overall results showed that environmental interventions and exceedance of N resilience were lowest in the N-management scenario applying catch crops for both impact categories. The case study demonstrated that considering absolute sustainability references as regional N resilience in LCA is a valuable tool for agricultural decision-makers to evaluate N management strategies for crop production systems.

1. Introduction

Nitrogen (N) is an essential nutrient for agricultural activities necessary for food and feed security. Growing demand for food is followed by agricultural intensification, increasing N fertilizer and land use coming at high environmental cost [1,2,3]. However, not all N is taken up by crops. About 80% of N, utilized for agricultural production in fertilizers and biological N fixation, is lost to the environment [4]. Consequently, higher N inputs result in losses of reactive N compounds such as nitrous oxides (N2O), ammonia (NH3) or nitrate (NO3−), leading to adverse environmental impacts such as climate change, eutrophication or acidification of ecosystems and threatening human health [5,6,7]. Furthermore, the extent to which N accumulation has already occurred is made visible by the exceedance of planetary boundaries in terms of biogeochemical N cycles [4,8,9,10,11,12].
Since N fertilizers are one of the largest N inputs resulting from agricultural activities, sustainable N management aiming to reduce N inputs and increase N use efficiencies is vital [4]. In practice, N management strategies such as improving N fertilizer timing and placement, matching N fertilizer application rate to crop requirements, or using catch crops and reduced tillage may reduce N losses [13,14]. Multiple policies and strategies have been introduced at the European and the German level to implement these measures. A central part of the European Green Deal is the Farm to Fork Strategy, which aims to reduce by 50% nutrient losses such as N and P, leading to fertilizer reductions of 20% by 2030. In Germany, as a key agricultural and environmental policy instrument, the Fertilizer Ordinance (in German Düngeverordnung—DüV) aims to reduce and regulate the use of mineral and organic fertilizers on farms, and thus the input of N emissions from agricultural production systems to terrestrial and aquatic ecosystems [15]. However, recommendations about suitable N management strategies to achieve necessary reduction targets depend on regional conditions such as different N mineralization (Nmin) rates, yield requirements or other biogeographical aspects [16]. Hence, identifying the impact of N management strategies of cropping systems requires a detailed assessment on the regional level to address the effects of the diverse environmental impacts.
A well-established method for determining the environmental impacts of crop production systems and decision support is Life Cycle Assessment (LCA). LCA serves as a support tool for implementing policy strategies such as the Farm to Fork strategy [17] and can provide an added value in evaluating and monitoring N reduction measures, as proposed by the German Fertilizer Ordinance for instance. However, assessing the impacts of N losses resulting from agricultural production systems and considering different management strategies is not straightforward due to the multiple spatial and temporal N pathways.
The relevance of regionalization in LCA to cover N-related impacts such as eutrophication and acidification of terrestrial and freshwater systems is widely acknowledged [18,19,20] and well established in standard Life Cycle Impact Assessment (LCIA) methods [18,21,22,23,24,25]. Hence, it also plays a crucial role in agricultural LCA. However, standard LCA allows for a comparative assessment of alternative crop management systems, for example, but it does not express sustainability in absolute terms [26]. Therefore, the environmental performance of cropping systems in relation to absolute boundaries, such as carrying capacities, is not covered by LCA [27].
Approaches studying absolute environmental sustainability (AES) in LCA exist with the planetary boundary concept, where a reference is integrated with, e.g., characterization factors (CF) [26,28,29,30] or applied as normalization reference (NR) [31]. Normalization is an optional part of the Life Cycle Impact Assessment (LCIA) and is defined as "calculating the magnitude of category indicator results relative to reference information" [32]. Therefore, it can play a valuable role in facilitating the interpretation and communication of results to decision-makers [33]. Furthermore, the integration of normalization references based on carrying capacities for ecological thresholds may improve the assessment of, for instance, management strategies of cropping systems by an interpretation in absolute terms.
In a recent study by Wowra et al. [34], the authors developed an approach to determine regional carrying capacity based normalization references for N assessment in LCA. The references are applied as distance-to-target values to assess the impacts of crop production systems on regional N resilience, contributing to terrestrial acidification and terrestrial eutrophication impacts. The regional N resilience herewith displays the carrying capacity of a region defined as the maximum persistent impact that the environmental compartments in a region, affected by these impacts, can sustain while maintaining their function and structure [35,36]. The study by Wowra et al. [34] revealed that the environmental performance of cropping systems in terms of N resilience might differ regionally, and concluded that it is necessary to also consider current N-related background interventions of non-crop producing sectors. However, the study did not evaluate the influence of different N management strategies based on policy measures and their impacts on regional N resilience.
Consequently, this study aims to evaluate different N management scenarios affecting N resilience by further developing and applying regional carrying capacity based normalization references as distance-to-target values in LCA. The scenarios are based on policy measurements of the German Fertilizer Ordinance applied to rapeseed cropping systems for five case study regions. Furthermore, for all German regions on NUTS-3 level (smallest regional division in Eurostat’s Nomenclature of Territorial Units for Statistics), values for regional normalization references and background interventions of non-crop producing sectors contributing to acidification and eutrophication impacts were derived. The overall aim of the study is to evaluate the applicability of a newly developed approach for assessing policy reduction measures and N management strategies by considering regional N resilience in LCA. Therefore, regional differences in N management options and reduction potentials for minimizing impacts on regional N resilience will be identified. Herewith, the improved decision support for agricultural stakeholders on the regional level for a wide range of research questions in the evaluation of cropping systems shall be proven.

2. Materials and Methods

The study’s main goal was to comprehensively assess the environmental intervention considering regional N resilience of rapeseed production systems in five different case study regions. Therefore, the environmental performance for four N management scenarios was assessed by combining LCA and a recently developed distance-to-target approach [34]. An attributional LCA is applied following ISO 14040/44 [32,37]. The study focused on regional N-related impacts of terrestrial acidification potential (TAP) and terrestrial eutrophication potential (TEP). The distance-to-target approach applies regional carrying capacity based normalization references (rNR) for N-related impact categories, and considers background interventions (BI) of non-crop producing sectors in NUTS-3 regions. For the application of the approach, three steps have to be undertaken: first, the calculation of N-related background interventions of non-crop producing sectors; secondly, the definition of N-related thresholds; and based on these, in a third step, the derivation of carrying capacity based normalization references. A detailed description of each step, background data and data sources is provided by Wowra et al. [34]. The subsequent sections briefly describe the steps required for assessing the environmental performance of the cropping systems with the distance-to-target approach, the case study design, and each life cycle phase.

2.1. Distance-to-Target Approach for Assessing the Environmental Performance

The relevant steps for assessing the environmental performance and derivation of the relevant parameters are the following:
  • Calculation of background interventions (BI).
Based on data for air pollutant emissions of the German Environment Agency (Umweltbundesamt) [38], background interventions of non-crop production sectors are calculated for N emissions contributing to acidification and eutrophication impacts. The background interventions are aggregated on a NUTS-3 level and derived for TAP and TEP impact categories.
2.
Definition of regional N-related thresholds.
The calculation of regional thresholds is based on data for critical N losses for German NUTS-3 regions provided by De Vries based on de Vries and Schulte-Uebbing [39] and de Vries et al. [40]. The thresholds are derived from values for the critical loss of NH3 emissions leading to critical N deposition, depending on the ecosystem’s critical load [40]. These values are used as thresholds for TAP and TEP mid-point impact categories.
3.
Derivation of regional carrying capacity based normalization reference (rNR).
The regional normalization references (rNR) are based on the derived regional N-related thresholds. According to an equal per capita sharing principle, the threshold for a specific NUTS-3 region j and impact category i is related to the number of persons (pers) P living in the region j as described in Equation (1).
r N R i , j   = T h r e s h o l d i , j P j
The indicator rNR does not consider background interventions related to non-crop producing sectors in a specific region. Therefore, the background intervention BI of an impact i in a region j is subtracted from the rNR as shown in Equation (2),
r N R B I i , j = r N R i , j − B I i , j
where the indicator rNRBI is defined as the regional normalization reference of a specific region j, contributing to an impact category i and considering the background intervention (BI) of non-crop producing sectors expressed in pers/year.
4.
Assessment of the environmental performance.
After completing steps 1 to 3, the environmental performance is assessed for the defined scenarios of the crop production system using the indicator rNRBI. This is applied as a distance-to-target value to an impact I resulting from an LCIA category i. Thus, the environmental performance is described as the environmental intervention (EI), namely, the specific personal share (in pers/year) of a cropping system’s impact I to the N resilience (rNRBI) in a region j contributing to an impact category i—as shown in Equation (3). If the EI results in a value above 1, the EI of the environmental performance of the respective cropping system is assessed as "unsustainable" since it exceeds regional N resilience.
E I = I i r N R B I i , j
All parameters are derived for each NUTS-3 region in Germany and include an impact characterization focusing on TAP and TEP impacts. To assess the TAP, we applied the IMPACT World+ [41] method using characterization factors (CF) on regional levels. For TEP, we applied the Environmental Footprint (EF) method (reference package 2.0) [42,43] using CF on the national level since no finer scale of CF is available.

2.2. Case Study Regions

The case study regions are related to German NUTS-3 regions. First, background interventions of non-crop producing sectors were derived for all German NUTS-3 regions contributing to terrestrial eutrophication and terrestrial acidification impacts. Five case study regions were selected based on the following criteria:
  • NUTS-3 region with the highest background intervention affecting TAP;
  • NUTS-3 region with the highest background intervention affecting TEP;
  • NUTS-3 region with the largest share of agricultural area.
  • In addition, for comparability, two regions were selected, representing average population and agricultural area.
Accordingly, the following regions were further assessed:
  • Emsland (DE949), located in Lower Saxony, North West Germany;
  • Mecklenburgische Seenplatte (DE80J) located in Mecklenburg-Western Pomerania, North-East Germany;
  • Kassel (DE734), located in Hesse, Mid-West-Germany;
  • Spree-Neiße (DE40G), located in Brandenburg, East-Germany;
  • Ansbach (DE256), located in Bavaria, South Germany.
Table 1 shows the main characteristics of the regions and the related criteria.

2.3. Goal, Scope and Functional Unit

Figure 1 shows the considered system boundary comprising all relevant process steps of the fore- and background system, from cradle-to-farm-gate. The system included the agricultural production of rapeseed until its provision as raw material. The analysis focused on the vegetation period of rapeseed production, assuming winter barley as the previous crop, including catch-crop cultivation (if applied), sowing of rapeseed and the harvest, but not including further crop rotations. The functional unit (FU) describes the management of 1 hectare of arable land indicated for rapeseed within a NUTS-3 region. Data for background processes were based on the ecoinvent database v.3.5 (cut-off) [48].

2.4. Life Cycle Inventory and Assumptions

2.4.1. Nitrogen Management Scenarios and Effects of Nitrogen-Reducing Management

One of the most important measures to reduce N emissions within the German Fertilizer Ordinance is a binding N demand analysis to determine fertilization requirements [15,49]. According to a defined procedure, farmers must calculate the specific N requirement depending on parameters influencing the requirement of the respective cultivated crop.
For the case study assessment, four different N management or reduction scenarios were defined on the basis of neglecting or considering the N demand analysis and applying different N mitigation options:
  • N-base scenario: This scenario does not include an N demand analysis, and potential yields are based on general recommendations of the German fertilizer ordinance.
  • N-management: In this scenario, the fertilizer amount is determined according to the N demand analysis, and the considered yields are based on the region’s three-year average. Furthermore, soil humus content is considered, and no further N mitigation options are included.
  • N-catch crop: The calculated fertilizer amount is determined according to the N demand analysis, and considered yields are based on the region’s three-year average. The cultivation of a catch crop as an N reduction option is assumed.
  • N-organic: This scenario applies the same measurement as the N-catch crop. In addition, it is assumed that the crop production system proportionally replaces its mineral fertilizer quantity by importing organic fertilizers (liquid manure). Thus, it also accounts for the organic fertilizer application of the previous year.
As further N reduction management, we considered specific characteristics such as low tillage soil cultivation technique for all scenarios, and N reducing application technique for manure. We included data based on regional agricultural statistics for each region to derive values for yields, N mineralization (Nmin) in soil, and applied fertilizer types. Table 2 lists all relevant scenario parameters and considered values for the respective regions.

2.4.2. Consideration of Direct and Indirect Emissions

We applied an emission factor-based approach to quantify direct and indirect field emissions, including data on the regional NUTS-3 level where possible. N2O emissions were estimated according to the Tier 1 approach of the Intergovernmental Panel on Climate Change [61] as proposed in Nemecek et al. [62]. In addition, we calculated the amount of leached N that leads to indirect N2O emissions [63]. Losses of ammonia (NH3) from mineral nitrogen [64] fertilizers were calculated according to the Tier 2 approach and emission factors provided by the European Environment Agency [65]. We calculated emissions for mineral fertilizers considering: calcium ammonium nitrate (CAN), urea ammonia nitrate (UAN) and urea. Furthermore, NMVOC emissions were calculated based on the Tier 2 methodology described in [65]. Nutrient losses via leaching were considered for nitrate (NO3−) [66], and via leaching and run-off for phosphorus (P2O5) [67]. According to the Guidance for the development of Product Environmental Footprint Category Rules provided by European Commission [68], emissions of pesticides, fungicides, and insecticides were estimated. We also calculated nitrogen and phosphor emissions from manure application for the N-organic scenario. We assumed the application with a trailing shoe and an immediate (<4 h) incorporation of manure [63].

2.5. Life Cycle Impact Assessment

The Life Cycle Impact Assessment was carried out using the LCIA methods Environmental Footprint method (reference package 2.0) [42,43] and IMPACT World+ [41]. Regional CFs were applied for TAP calculated with IMPACT World+.

2.6. Sensitivity and Uncertainty Analysis

Sensitivity and uncertainty analysis of the developed normalization references, chosen LCIA methods, and life cycle inventory (LCI) input parameters were done to test the influence of the assumptions made [69]. Therefore, the uncertainty of the developed indicator rNRBI was assessed across all NUTS-3 regions with a quantitative uncertainty approach. Furthermore, to account for the sensitivity of spatial variability for the TAP category, regional normalization references and background interventions were additionally calculated with the national CF of IMPACT World+ and the EF method.
Uncertainty analysis of LCI input parameters was done to evaluate statistical significance. However, a complete statistical significance analysis could not be conducted since uncertainty of all parameters is unknown. Therefore, only the most sensitive input parameters for agricultural LCI calculation, namely Nmin values and agricultural yields, were taken into account due to their high spatial variability and dependency on biogeographical conditions [70]. Both parameters define fertilization requirements. Monte Carlo simulations were carried out to account for these uncertainties, including 1000 iterations for all scenarios and regions and two LCIA methods. Since yield variabilities are already considered in the different scenarios, only the parameter Nmin was varied. We considered a standard deviation of 10% and log-normal distribution. Results of the sensitivity and uncertainty analysis are provided in detail in the Supplementary Materials File S2.

3. Results and Discussion

3.1. Current Background Interventions and Regional Normalization References

Figure 2 shows the background intervention for all German NUTS-3 regions for the impact categories TAP and TEP of non-crop producing sectors displaying their share in relation to the highest interventions. The main share of the regions is distributed within lower background interventions for TAP impacts, whereas for TEP, the regions’ background interventions are mainly distributed within a share of more than 25% of the highest background intervention. Emissions contributing to the N-related background interventions of non-crop producing sectors are NOx emissions from the public energy sector or transportation, and NH3 emissions from the livestock sector. Both compounds cause acidification and eutrophication impacts. Regions with the highest background intervention contributing to TEP are located in the northwestern part of Germany. The reason is the high importance of the livestock sector in these regions. Consequently, their NH3 emissions contribute considerably to the background interventions [38]. The region with the highest background intervention contributing to TAP and TEP is Spree-Neiße, located in the eastern part of Germany. Here, in contrast, the high background intervention results from NOx emissions from the public power sector due to the location of a large-scale lignite power plant [71].
Based on evaluations of the background interventions, the case study regions were chosen as described in Section 2.2. The results of the derivation of regional normalization references (rNR), background interventions (BI) and the resulting regional normalization references considering BI (rNRBI) are listed in the Supplementary Materials File S1. The values for the five case study regions are shown in Figure 3. The uncertainty analysis revealed that rNR and BI values are similar for most regions, meaning rNRBI is predominantly positive and nearly evenly distributed around zero (see Supplementary Materials File S2). However, if the rNR is lower than current background interventions, rNRBI may result in negative values, as in the case of Emsland and Spree-Neiße for eutrophication impacts. The same holds for Spree-Neiße for acidification impacts. In fact, if background interventions of the non-crop producing sectors are considered, the calculated regional rNRBI values vary between minus 25% to more than minus 600% (Figure 3). This underpins the relevance of considering N-related background interventions in developing regional carrying capacity based normalization references. Likewise, based on this information, possible reduction levels and strategies for regions with high background interventions may be elaborated.

3.2. Environmental Performance of Rapeseed Production Systems

3.2.1. Regional Differences in Nitrogen Management Scenarios

The results of the environmental performance analysis of the N management scenarios for TAP and TEP impacts are presented in Figure 4 for three of the case study regions, Ansbach, Kassel and Mecklenburgische Seenplatte, and in Figure 5 for the other two regions, Spree-Neiße and Emsland. As indicated in Figure 3, Spree-Neiße and Emsland showed higher background intervention than normalization references. Therefore, separate analyses including reduction of background interventions have been carried out.
Comparing the three case studies in Ansbach, Kassel and Mecklenburgische Seenplatte, Kassel showed the lowest environmental intervention and Ansbach the highest (Figure 4). In all N-base scenarios, environmental interventions (EI) exceeded regional N resilience for TEP impacts, whereas for TAP impacts, this was only the case for Ansbach and Mecklenburgische Seenplatte. We generally observed a decrease in EI from the N-base to the N-catch crop scenario for all N scenarios. The highest decrease appeared for the Ansbach region, with a more than 40% reduction of EI from the N-base to the N-catch crop scenario. Due to the application of catch crops, lower mineral fertilizer amounts are necessary, leading to lower environmental intervention.
For all three regions depicted in Figure 4, the lowest reduction of EI compared to the N-base scenario is observed for the N-management scenario. This lies notably in the fact that only the basic measurements of implementing and calculating fertilizer requirements according to the Fertilizer Ordinance were applied, and no further measurements were considered. Only for the region Kassel the N-base scenario remain under current background interventions in the sustainable area, resulting from high yields in the region and low background interventions (Figure 4). The region Mecklenburgische Seenplatte showed a minor influence amongst the different management scenarios, with a reduction of only 18% between the N-base and the N-catch crop scenario compared to the other regions, and no reduction from N-base to N-management. High yield assumptions based on a three-year average were the main reason. Moreover, although in Mecklenburgische Seenplatte, N-input assumptions were also highest amongst the regions due to high yields and low assumed Nmin in the soil, the environmental interventions only slightly exceeded regional N resilience, e.g., in the base scenario with EI = 1.1 pers/year. A primary reason for this was, as in the case of Kassel, the low background interventions in this region (Figure 3). The uncertainty analysis of the LCI inventory revealed that the differences between all scenarios and regions based on the 95% confidence interval were deemed significant. The highest uncertainty was observed for the Ansbach region as displayed in the larger 95% probability range in Figure 4a,b (see Supplementary Materials File S2).
We assumed the background intervention for the non-agricultural sectors to remain static. However, background interventions are higher for some regions than the calculated rNR. This means that none of the agricultural management scenarios would perform environmentally friendlier without reduction from these sectors in view of regional N resiliencies. Therefore, to achieve an equal share amongst the non-crop and the crop-producing sectors, we likewise assessed the necessary reductions in non-crop producing sectors by setting the EI = 1. The environmental performance assuming environmental interventions resulting in EI = 1 are shown in Figure 5a,b for the Spree-Neiße region, and in Figure 6a,b for Emsland. We highlighted these regions for demonstration due to their negative rNRBI values. Results of the necessary reduction levels for the remaining regions are displayed in the Supplementary Materials File S1.
None of the scenarios in the Spree-Neiße and Emsland regions exceeded the regional N resilience for either impact categories if the target EI = 1 was set for N-base. The highest necessary reduction (−173%) of background interventions resulting from non-crop producing sectors is for the Emsland for TAP impacts. The lowest reduction (−43%) of background intervention is displayed for TEP impacts in the Emsland region, if the minimum set of EI = 1 for the N-catch crop scenario is fulfilled. The N-catch crop scenario showed the best overall performance compared to the other regions. By contrast, in the Emsland region the N-organic scenario revealed the lowest environmental intervention in the case of TAP impacts (Figure 6a). In this case, a reduction of up to 26% was achieved by applying the N-organic scenario. When comparing all N-scenarios within the Spree-Neiße region, it can be noted that the reductions in environmental interventions were highest between the N-base and the other scenarios. For instance, EI leading to TEP impacts decreased by 55% and for TAP by 54% from N-base to N-catch crop (Figure 5a,b). Since the three-year average yield was lowest for Spree-Neiße, applying demand specific N fertilization based on the N demand analysis resulted in the highest reduction in environmental intervention. Thus, the application of region specific N measures was thereby demonstrated to reduce EI.
In general terms, the cultivation of catch crops is the most recommended option for reducing overall N impacts in all regions, with the highest reduction potential of more than 40% in Ansbach (Figure 4) and Spree-Neiße (Figure 5) compared to the base scenario. Using catch crops significantly impacts N availability in soil, reducing the necessary mineral N fertilizer amount. Several LCA studies analyzing crop production systems have also reported the positive impact of catch crops on reducing environmental intervention [72,73,74,75]. Although catch crop cultivation was also assumed in the N-organic scenario, and the amount of mineral fertilizer applied was the lowest amongst all management scenarios, this option was observed to be the best solely for the Emsland region, given TAP impacts (Figure 6a). In Kassel, for instance, the N-organic scenario performed the worst. The reasons for this lie notably in the combination of low background interventions, high Nmin amounts, medium yield potential, and lower N fertilizer requirements compared to the other regions. Furthermore, the share of urea fertilizer in mineral N fertilizer application in Kassel was lower than in other regions. Urea has a higher emission factor than other N fertilizers [63]. Therefore, the NH3 emission from organic fertilizer application caused higher environmental interventions in the N-organic scenario in Kassel than in the other N scenarios, where the overall impact was lower due to fewer NH3 emissions.
A pattern between the regions can be observed comparing TAP and TEP results. TEP impact results were nearly similar (Mecklenburgische Seenplatte and Spree-Neiße) or slightly higher (Ansbach, Kassel and Emsland) than TAP for EI. This is due to the fact that the same critical N-thresholds for deriving regional normalization references were assumed for both impact categories [34]. The thresholds are derived from values for the critical loss of NH3 emissions leading to critical N deposition, depending on the ecosystem’s critical load [40].
The assessed environmental interventions result from background intervention, regional parameters such as yields and fertilizer input, and impact characterization. For instance, although N inputs were lower in Ansbach compared to other regions, the EI was higher. This is explained by the combination of high background intervention and the calculated normalization references, resulting in a low rNRBI. The smaller rNR and rNRBI values arise within LCIA normalization in higher environmental intervention.

3.2.2. Contribution of Agricultural Processes in N-Management Scenarios

Figure 7 displays the contribution of agricultural activities to each scenario and, thus, environmental intervention to the depicted impact categories TAP and TEP. In both impact categories, fertilizer application, including field emission, contributed mainly to TAP and TEP impacts. These results correspond with previous studies showing that due to large amounts of mineral fertilizer applications, field emissions are the main contributor to TAP [76,77,78] and TEP impacts on rapeseed production systems [79]. Furthermore, all regions, besides Kassel, have a share of more than 25% urea in mineral fertilizer use (see Table 2). As indicated earlier, urea has the highest emission factor corresponding to NH3 emissions. Therefore, for both TAP and TEP impacts, Kassel showed the lowest emission contributions from mineral fertilizer application.
Regional differences could be observed for Emsland, where impacts from mineral fertilizer application were lowest compared to the other regions for TAP impacts. This is by reason of, firstly, a lower fertilizer application and, secondly, the use of regional CFs, which in Emsland resulted in a reduced impact of the field emissions. In this case, the choice of spatial level influenced the results, as also shown by the sensitivity results (Supplementary Materials File S2).
An interesting observation can also be made by looking at contributions from organic fertilizer applications. Here regional differences are most visible. For instance, Kassel’s highest contribution was observed with 66% and the lowest for Emsland with merely 13% for TAP. Moreover, in Kassel, organic fertilizer application contributed most (61%) to TEP, whereas Mecklenburgische Seenplatte showed a minor contribution within the organic scenario reaching only 26%. The main reason for this is the lower amount of mineral fertilizer and the higher NH3 emissions from organic fertilizers for Kassel. The direct emissions from mineral fertilizer application and production were reduced within the organic fertilizer scenario [74]. However, it has to be noted that this was mainly influenced by the fertilizer type applied [80].

3.3. Recommendation for Decision Makers and Added Value for Decision Support

Recommendations on which mitigation measures are most suitable may vary across regions due to regional dependent parameters such as Nmin amounts, yields, other biogeographical parameters or agricultural inputs. For instance, although the N-organic scenario showed high reduction potential in other regions, it had the lowest environmental performance in Kassel and exceeded the regional N resilience. As a result, the N-organic scenario would not be recommended as the best option for Kassel to reduce regional impacts on N. The impacts of introducing mitigation measures on N resilience were also apparent in the Ansbach and Spree-Neiße regions. Both regions showed high reduction potentials compared to the N-base scenario when applying N reduction strategies. This demonstrates the ability of the approach to serve policymakers on the federal level to evaluate and compare different regions and elaborate the most suitable N management measures or reduction strategies for a region, based on regional requirements. Furthermore, the approach herewith serves decision-makers in improving the environmental performance of regional cropping systems in consideration of N resilience.
Beyond this, the applied methodology adds value for stakeholders from LCA by making it possible to analyze N mitigation measures that take into account different targets on environmental interventions affecting regional N resilience. For example, in the case of achieving an equal share amongst background emissions, we showed the necessary reduction also from non-crop producing sectors, if environmental interventions given regional N resilience remain in a sustainable area (EI = 1). On this basis, recommendations for reducing N impacts for policymakers on the regional and federal levels are given to achieve environmental sustainability targets.
Furthermore, an added value is provided by improving the communication of LCIA results to non-LCA practitioners, for instance, farmers. The regional normalization references are based on the LCIA frameworks and are compatible with frequently used LCIA methods and can thus complement existing approaches, e.g., European references based on the LCIA concept. Finally, by applying the indicator rNRBI, LCIA results might be easily translated into single indicators, allowing a simplified presentation of the environmental intervention of cropping systems and management strategies. In the light of this, consulting companies for farmers, agricultural authorities responsible for monitoring sustainable fertilizer use and N management strategies, and other stakeholders may be guided in decision support and consultancy.

3.4. Limitations and Improvement of the Approach

Although the case study proved the applicability of regional normalization references as a distance-to-target approach for regional N resilience, several areas require further research. First, the approach focused on regional N impacts and, therefore, on only two N-related impact categories, TAP and TEP. Further research should elaborate on integrating and combining existing normalization references based on absolute references for other impact categories also, aiming to improve the overall impact assessment and to avoid possible burden shifting. Second, it has to be noted that we considered background interventions of non-crop producing sectors only. In order to evaluate the impacts of each contributing sector, a scenario analysis is recommended to assess the resilience considering single sectors, such as the livestock sector, or combinations of sectors. Moreover, the case study showed sensitive results when regional CF had been applied, as in the case of, e.g., Emsland. The choice of LCIA method and spatial level may influence the results given a regional assessment considering N resilience (see Supplementary Materials File S2). The practitioner should therefore base the choice of LCIA method and spatial level on data requirements and regional focus of the study. Likewise, scenario analysis and parameter sensitivity should be considered. For instance, the uncertainty analysis of LCI data and variation of the parameter Nmin showed variability of the impact results (see Supplementary Materials File S2). The amount of fertilizer applied and, thus, resulting emissions are highly dependent on the considered Nmin. Therefore, the sensitivity of Nmin should be taken into account in the uncertainty analysis of agricultural LCA due to its spatial variability and dependency.

4. Conclusions

In order to reduce human alteration of global and regional N cycles and enable the transition to sustainable agriculture, it is crucial to develop appropriate measurement tools and monitor environmental impacts for deriving necessary targets, trajectories and relevant policy measures [81].
The study proved the applicability of considering regional N resilience in LCA to evaluate N-management scenarios based on policy measurements. Therefore, it provides improved guidance for evaluation and decision support for regional stakeholders on N management strategies. Overall, the LCA results confirmed the reduction of N impacts by applying policy measures such as the N demand analysis. Moreover, the study revealed the necessity to account for regional differences in an overall N assessment for recommendations of N management options. We displayed necessary reduction levels for background interventions from non-crop producing sectors. With this, we showed that cropping systems would not remain sustainable for any N management option, given regional N resilience, without additional reduction in other sectors, especially in regions with high background interventions. Furthermore, the study is the first to provide regional carrying capacity based normalization references considering background interventions (rNRBI) for the impact categories of terrestrial acidification and terrestrial eutrophication potential for all German NUTS-3 regions.
Finally, this work highlights the need to consider absolute sustainability metrics such as regional N resilience to stress the importance of regional impacts such as terrestrial acidification and eutrophication in assessing crop production systems as an additional tool and an alternative for standard LCA interpretation. However, further research is needed to explore the approach for different countries and regions and complement existing methods that focus on the global or European level.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142215286/s1, File S1: Results of background intervention (BI), regional normalization reference (rNR), regional normalization reference considering BI (rNRBI), results of environmental interventions (EI) for all regions and scenarios including sensitivity of LCIA methods, analysis of reduction of background interventions for all regions and scenarios including sensitivity for LCIA methods, LCA results contribution analysis, LCIA results of all impact categories, regions and scenarios. File S2: Results of sensitivity and uncertainty analysis.

Author Contributions

Conceptualization, K.W.; methodology, K.W.; validation, K.W; formal analysis, K.W.; investigation, K.W.; resources, K.W.; data curation, K.W.; writing—original draft preparation, K.W.; writing—review and editing, K.W., V.Z. and L.S.; supervision, V.Z. and L.S. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG—German Research Foundation) and the Open Access Publishing Fund of Technical University of Darmstadt.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is available in the Supplementary Materials.

Acknowledgments

The authors thank Wim de Vries for supplying data on critical N losses for European NUTS regions and the German Environment Agency (Umweltbundesamt—UBA) for providing data on regional air emissions. We also thank W. Bulach for support and advice.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Erisman, J.W.; Galloway, J.N.; Seitzinger, S.; Bleeker, A.; Dise, N.B.; Am Petrescu, R.; Leach, A.M.; de Vries, W. Consequences of human modification of the global nitrogen cycle. Philos. Trans. R. Soc. B 2013, 368, 20130116. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Chen, X.; Cui, Z.; Fan, M.; Vitousek, P.; Zhao, M.; Ma, W.; Wang, Z.; Zhang, W.; Yan, X.; Yang, J.; et al. Producing more grain with lower environmental costs. Nature 2014, 514, 486–489. [Google Scholar] [CrossRef] [PubMed]
  3. Foley, J.A.; Ramankutty, N.; Brauman, K.A.; Cassidy, E.S.; Gerber, J.S.; Johnston, M.; Mueller, N.D.; O’Connell, C.; Ray, D.K.; West, P.C.; et al. Solutions for a cultivated planet. Nature 2011, 478, 337–342. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Sutton, M.A.; Bleeker, A.; Howard, C.M.; Bekunda, M.; Grizzetti, B.; de Vries, W.; van Grinsven, H.J.; Abrol, Y.P.; Adhya, T.K.; Billen, G. Our Nutrient World: The Challenge to Produce More Food and Energy with Less Pollution; No. 1906698406; Centre for Ecology & Hydrology: Lancaster, UK, 2013. [Google Scholar]
  5. Bodirsky, B.L.; Popp, A.; Lotze-Campen, H.; Dietrich, J.P.; Rolinski, S.; Weindl, I.; Schmitz, C.; Müller, C.; Bonsch, M.; Humpenöder, F. Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution. Nat. Clin. Pract. Endocrinol. Metab. 2014, 5, 3858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Bouwman, L.; Goldewijk, K.K.; van der Hoek, K.W.; Beusen, A.H.W.; van Vuuren, D.P.; Willems, J.; Rufino, M.C.; Stehfest, E. Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period. Proc. Natl. Acad. Sci. USA 2013, 110, 20882–20887. [Google Scholar] [CrossRef] [Green Version]
  7. Tian, H.; Xu, R.; Canadell, J.G.; Thompson, R.L.; Winiwarter, W.; Suntharalingam, P.; Davidson, E.A.; Ciais, P.; Jackson, R.B.; Janssens-Maenhout, G.; et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 2020, 586, 248–256. [Google Scholar] [CrossRef]
  8. Erisman, J.W.; Galloway, J.N.; Dise, N.B.; Sutton, M.A.; Bleeker, A.; Grizzetti, B.; Leach, A.M.; de Vries, W. Nitrogen: Too Much of a Vital Resource: Science Brief; No. 9074595227; WWF Netherlands: Zeist, The Netherlands, 2015. [Google Scholar]
  9. de Vries, W.; Kros, J.; Kroeze, C.; Seitzinger, S.P. Assessing planetary and regional nitrogen boundaries related to food security and adverse environmental impacts. Curr. Opin. Environ. Sustain. 2013, 5, 392–402. [Google Scholar] [CrossRef]
  10. Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin, F.S., III; Lambin, E.; Lenton, T.; Scheffer, M.; Folke, C.; Schellnhuber, H.J. Planetary boundaries: Exploring the safe operating space for humanity. Ecol. Soc. 2009, 14, 32. [Google Scholar] [CrossRef]
  11. Steffen, W.; Richardson, K.; Rockström, J.; Cornell, S.E.; Fetzer, I.; Bennett, E.M.; Biggs, R.; Carpenter, S.R.; de Vries, W.; de Wit, C.A.; et al. Planetary boundaries: Guiding human development on a changing planet. Science 2015, 347, 1259855. [Google Scholar] [CrossRef] [Green Version]
  12. Salomon, M.; Schmid, E.; Volkens, A.; Hey, C.; Holm-Müller, K.; Foth, H. Towards an integrated nitrogen strategy for Germany. Environ. Sci. Policy 2016, 55, 158–166. [Google Scholar] [CrossRef]
  13. Döhler, H.; Eurich-Menden, B.; Rößler, R.; Vandré, R.; Wulf, S. UN ECE-Luftreinhaltekonvention Task Force on Reactive Nitrogen; Umweltbundesamt: Dessau-Roßlau, Germany, 2011. [Google Scholar]
  14. Schütze, G.; Geupel, M. Stickstoff—Zuviel des Guten? Umweltbundesamt: Dessau-Roßlau, Germany, 2011. [Google Scholar]
  15. Bundesministerium für Ernährung und Landwirtschaft. Verordnung über die Anwendung von Düngemitteln, Bodenhilfsstoffen, Kultursubstraten und Pflanzenhilfsmitteln nach den Grundsätzen der Guten Fachlichen Praxis beim Düngen (Düngeverordnung—DüV): DüV. Available online: http://www.gesetze-im-internet.de/d_v_2017/ (accessed on 25 October 2022).
  16. Olfs, H.-W.; Blankenau, K.; Brentrup, F.; Jasper, J.; Link, A.; Lammel, J. Soil- and plant-based nitrogen-fertilizer recommendations in arable farming. J. Plant Nutr. Soil Sci. 2005, 168, 414–431. [Google Scholar] [CrossRef]
  17. Sala, S.; Amadei, A.M.; Beylot, A.; Ardente, F. The evolution of life cycle assessment in European policies over three decades. Int. J. Life Cycle Assess. 2021, 26, 2295–2314. [Google Scholar] [CrossRef]
  18. Potting, J.; Schöpp, W.; Blok, K.; Hauschild, M. Site-Dependent Life-Cycle Impact Assessment of Acidification. J. Ind. Ecol. 1998, 2, 63–87. [Google Scholar] [CrossRef]
  19. Patouillard, L.; Bulle, C.; Querleu, C.; Maxime, D.; Osset, P.; Margni, M. Critical review and practical recommendations to integrate the spatial dimension into life cycle assessment. J. Clean. Prod. 2018, 177, 398–412. [Google Scholar] [CrossRef] [Green Version]
  20. Yang, Y.; Heijungs, R. A generalized computational structure for regional life-cycle assessment. Int. J. Life Cycle Assess. 2017, 22, 213–221. [Google Scholar] [CrossRef]
  21. Norris, G.A. Impact Characterization in the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts. J. Ind. Ecol. 2002, 6, 79–101. [Google Scholar] [CrossRef]
  22. Roy, P.-O.; Deschênes, L.; Margni, M. Uncertainty and spatial variability in characterization factors for aquatic acidification at the global scale. Int. J. Life Cycle Assess. 2013, 19, 882–890. [Google Scholar] [CrossRef]
  23. Roy, P.O.; Azevedo, L.B.; Margni, M.; van Zelm, R.; Deschenes, L.; Huijbregts, M.A.J. Characterization factors for terrestrial acidification at the global scale: A systematic analysis of spatial variability and uncertainty. Sci. Total Environ. 2014, 500, 270–276. [Google Scholar] [CrossRef]
  24. Roy, P.-O.; Deschênes, L.; Margni, M. Life cycle impact assessment of terrestrial acidification: Modeling spatially explicit soil sensitivity at the global scale. Environ. Sci. Technol. 2012, 46, 8270–8278. [Google Scholar] [CrossRef]
  25. Seppälä, J.; Posch, M.; Johansson, M.; Hettelingh, J.P. Country-dependent characterisation factors for acidification and terrestrial eutrophication based on accumulated exceedance as an impact category indicator. Int. J. Life Cycle Assess. 2006, 11, 403–416. [Google Scholar] [CrossRef]
  26. Bjørn, A.; Sim, S.; Boulay, A.-M.; King, H.; Clavreul, J.; Lam, W.Y.; Barbarossa, V.; Bulle, C.; Margni, M. A planetary boundary-based method for freshwater use in life cycle assessment: Development and application to a tomato production case study. Ecol. Indic. 2020, 110, 105865. [Google Scholar] [CrossRef]
  27. Wowra, K.; Zeller, V.; Schebek, L. Nitrogen in Life Cycle Assessment (LCA) of agricultural crop production systems: Comparative analysis of regionalization approaches. Sci. Total Environ. 2021, 763, 143009. [Google Scholar] [CrossRef] [PubMed]
  28. Bjørn, A.; Sim, S.; King, H.; Margni, M.; Henderson, A.D.; Payen, S.; Bulle, C. A comprehensive planetary boundary-based method for the nitrogen cycle in life cycle assessment: Development and application to a tomato production case study. Sci. Total Environ. 2020, 715, 136813. [Google Scholar] [CrossRef] [PubMed]
  29. Ryberg, M.W.; Owsianiak, M.; Richardson, K.; Hauschild, M.Z. Development of a life-cycle impact assessment methodology linked to the Planetary Boundaries framework. Ecol. Indic. 2018, 88, 250–262. [Google Scholar] [CrossRef] [Green Version]
  30. Ryberg, M.W.; Owsianiak, M.; Richardson, K.; Hauschild, M.Z. Challenges in implementing a Planetary Boundaries based Life-Cycle Impact Assessment methodology. J. Clean. Prod. 2016, 139, 450–459. [Google Scholar] [CrossRef] [Green Version]
  31. Bjørn, A.; Hauschild, M.Z. Introducing carrying capacity-based normalisation in LCA: Framework and development of references at midpoint level. Int. J. Life Cycle Assess. 2015, 20, 1005–1018. [Google Scholar] [CrossRef] [Green Version]
  32. ISO 14044; Environmental Management—Life Cycle Assessment—Requirements and Guidelines. International Organization for Standardization: Geneva, Switzerland, 2006.
  33. Pizzol, M.; Laurent, A.; Sala, S.; Weidema, B.; Verones, F.; Koffler, C. Normalisation and weighting in life cycle assessment: Quo vadis? Int. J. Life Cycle Assess. 2017, 22, 853–866. [Google Scholar] [CrossRef] [Green Version]
  34. Wowra, K.; Zeller, V.; Schebek, L. Regional nitrogen resilience as distance-to-target approach in LCA of crop production systems. Environ. Impact Assess. Rev. 2022, 97, 106869. [Google Scholar] [CrossRef]
  35. Bjørn, A. Better, but Good Enough? Indicators for Absolute Environmental Sustainability in a Life Cycle Perspective. Ph.D. Thesis, DTU Management Engineering, Lyngby, Denmark, 2015. Available online: https://orbit.dtu.dk/en/publications/better-but-good-enough-indicators-for-absolute-environmental-sust (accessed on 10 October 2022).
  36. Walker, B.; Holling, C.S.; Carpenter, S.R.; Kinzig, A. Resilience, adaptability and transformability in social–ecological systems. Ecol. Soc. 2004, 9, 5. [Google Scholar] [CrossRef]
  37. ISO 14040; Environmental Management—Life Cycle Assessment—Principles and Framework. International Organization for Standardization: Geneva, Switzerland, 2006.
  38. Schneider, C.; Pelzer, M.; Toenges-Schuller, N.; Nacken, M.; Niederau, A. ArcGIS Basierte Lösung zur Detaillierten, Deutschlandweiten Verteilung (Gridding) Nationaler Emissionsjahreswerte auf Basis des Inventars zur Emissionsberichterstattung 71/206. 2016. Available online: https://www.umweltbundesamt.de/publikationen/arcgis-basierte-loesung-zur-detaillierten (accessed on 25 October 2022).
  39. De Vries, W.; Schulte-Uebbing, L. Required Changes in Nitrogen Inputs and Nitrogen Use Efficiencies to Reconcile Agricultural Productivity with Water and Air Quality Objectives in the EU-27; Proceedings 842. 2020. Available online: https://www.fertilizerseurope.com/news/required-changes-in-nitrogen-inputs-and-nitrogen-use-efficiencies-to-reconcile-agricultural-productivity-with-water-and-air-quality-objectives-in-the-eu-27/ (accessed on 25 October 2022).
  40. De Vries, W.; Schulte-Uebbing, L.; Kros, H.; Voogd, J.C.; Louwagie, G. Spatially explicit boundaries for agricultural nitrogen inputs in the European Union to meet air and water quality targets. Sci. Total Environ. 2021, 786, 147283. [Google Scholar] [CrossRef]
  41. Bulle, C.; Margni, M.; Patouillard, L.; Boulay, A.-M.; Bourgault, G.; de Bruille, V.; Cao, V.; Hauschild, M.; Henderson, A.; Humbert, S.; et al. IMPACT World+: A globally regionalized life cycle impact assessment method. Int. J. Life Cycle Assess. 2019, 24, 1653–1674. [Google Scholar] [CrossRef] [Green Version]
  42. Fazio, S.; Castellani, V.; Sala, S.; Schau, E.M.; Secchi, M.; Zampori, L.; Diaconu, E. Supporting Information to the Characterisation Factors of Recommended EF Life Cycle Impact Assessment Methods, Version 2, from ILCD to EF 3.0; European Comission: Rome, Italy, 2018. [Google Scholar] [CrossRef]
  43. Sala, S.; Benini, L.; Castellani, V.; Vidal Legaz, B.; de Laurentiis, V.; Pant, R. Suggestions for the update of the Environmental Footprint Life Cycle Impact Assessment: Impacts due to Resource Use, Water Use, Land Use, and Particulate Matter. EUR 28636 EN 2019. Publications Office of the European Union, Luxembourg. Available online: https://publications.jrc.ec.europa.eu/repository/handle/JRC106939 (accessed on 10 October 2022).
  44. DeStatis. Daten aus dem Gemeindeverzeichnis: Kreisfreie Städte und Landkreise nach Fläche, Bevölkerung und Bevölkerungsdichte. Available online: https://www.destatis.de/DE/Themen/Laender-Regionen/Regionales/Gemeindeverzeichnis/_inhalt.html (accessed on 25 October 2022).
  45. DeStatis. Bodenfläche nach Art der Tatsächlichen Nutzung—Stichtag 31.12.—Kreise und Kreisfr. Städte (ab 2016). Available online: https://www.regionalstatistik.de/genesis/online?operation=statistic&levelindex=0&levelid=1635625816774&code=33111#abreadcrumb (accessed on 25 October 2022).
  46. Düwel, O.; Siebner, C.; Utermann, J.; Krone, F. Bodenarten der Böden Deutschlands Bericht über Länderübergreifende Auswertungen von Punktinformationen im FISBo BGR: Karte der Bodenarten in Oberböden 1:1.000.000 (BOART1000OB). Available online: https://www.bgr.bund.de/DE/Themen/Boden/Informationsgrundlagen/Bodenkundliche_Karten_Datenbanken/Themenkarten/BOART1000OB/boart1000ob_node.html (accessed on 25 October 2022).
  47. Wetterkontor.de. Wetterrückblick—Jahreswerte Deutschland. Available online: https://www.wetterkontor.de/wetter-rueckblick/jahreswerte/deutschland?jahr=2020 (accessed on 7 June 2022).
  48. Wernet, G.; Bauer, C.; Steubing, B.; Reinhard, J.; Moreno-Ruiz, E.; Weidema, B. The ecoinvent database version 3 (part I): Overview and methodology. Int. J. Life Cycle Assess. 2016, 21, 1218–1230. [Google Scholar] [CrossRef]
  49. Landesbetrieb Landwirtschaft Hessen. Düngebedarfsermittlung Frühjahr. 2021. Available online: https://llh.hessen.de/pflanze/boden-und-duengung/duengeverordnung/neue-rahmenbedingungen-fuer-die-duengebedarfsermittlung-im-fruehjahr-2021 (accessed on 25 October 2022).
  50. DeStatis. Düngemittelversorgung—Fachserie 4 Reihe 8.2—3. Vierteljahr. 2020. Available online: https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Industrie-Verarbeitendes-Gewerbe/Publikationen/Downloads-Fachstatistiken/duengemittelversorgung-vierteljahr-2040820203234.html (accessed on 25 October 2022).
  51. Bayrisches Landesamt für Statistik. Land-und Forstwirtschaft, Fischerei: Pflanzliche Erzeugung (Erntestatistiken). Available online: https://www.statistik.bayern.de/statistik/wirtschaft_handel/landwirtschaft/index.html#link_2 (accessed on 25 October 2022).
  52. Bayerische Landesanstalt für Landwirtschaft. Nmin-Werte im Jahr 2021 in Bayern. Available online: https://www.lfl.bayern.de/mam/cms07/iab/dateien/alle_nmin_werte_2021.pdf (accessed on 25 October 2022).
  53. Amt für Statistik Berlin-Brandenburg. Ernteberichterstattung über Feldfrüchte und Grünland im Land Brandenburg (endgültiges Ergebnis). Available online: https://www.statistik-berlin-brandenburg.de/c-ii-2-j (accessed on 25 October 2022).
  54. Landesamt für Ländliche Entwicklung, Landwirtschaft und Flurneuordnung. Empfehlung 2021 Entsprechend Düngeverordnung § 4 (4) für Winterungen und Sommergetreide. Available online: https://lelf.brandenburg.de/sixcms/media.php/9/Empfehlung-2021-entsprechend-Duengeverordnung-fuer-Winterungen-und-Sommergetreide.pdf (accessed on 25 October 2022).
  55. Hessisches Statistisches Landesamt. Regionale Erntedaten. Available online: https://statistik.hessen.de/unsere-zahlen/land-und-forstwirtschaft (accessed on 25 October 2022).
  56. Landesbetrieb Landwirtschaft Hessen. Nmin-Wert zu Vegetationsbeginn. Available online: https://llh.hessen.de/pflanze/boden-und-duengung/n-duengung/nmin-wert-zu-vegetationsbeginn/ (accessed on 25 October 2022).
  57. Landesamt für innere Verwaltung Mecklenburg-Vorpommern. Land & Forstwirtschaft, Fischerei. Available online: https://www.laiv-mv.de/Statistik/Zahlen-und-Fakten/Wirtschaftsbereiche/Land%E2%80%93-und-Forstwirtschaft,-Fischerei (accessed on 25 October 2022).
  58. LMS Agrarberatung GmbH. Ergebnisse der Nmin-Untersuchungen für Mecklenburg-Vorpommern—Winterungen 2021. Available online: https://www.lms-beratung.de/export/sites/lms/de/.galleries/Downloads_LFB/DueV/Nmin-Winterungen-2021.pdf (accessed on 25 October 2022).
  59. Landesamt für Statistik Niedersachsen. Erntestatistik Online—Ernteergebnisse in Niedersachsen seit 1991. Available online: https://www.statistik.niedersachsen.de/landwirtschaft_forstwirtschaft_fischerei/landwirtschaft_in_niedersachsen/wachstum_und_ernte/erntestatistik_online/ernteergebnisse_seit_1991/erntestatistik-online-ernteergebnisse-in-niedersachsen-seit-1991-152870.html (accessed on 30 October 2021).
  60. Landwirtschaftskammer Niedersachsen. Nmin-Richtwerte für das Jahr 2021. Available online: https://www.lwk-niedersachsen.de/index.cfm/portal/duengebehoerde/nav/2280/article/37824.html (accessed on 25 October 2022).
  61. Intergovernmental Panel on Climate Change. 2006 IPCC Guidelines for National Greenhouse Gas Inventories; Agriculture, Forestry and Other Land Use; Institute for Global Environmental Strategies (IGES): Hayama, Japan, 2006; Volume 4. [Google Scholar]
  62. Nemecek, T.; Schnetzer, J.; Reinhard, J. Updated and harmonised greenhouse gas emissions for crop inventories. Int. J. Life Cycle Assess. 2016, 21, 1361–1378. [Google Scholar] [CrossRef]
  63. Rösemann, C.; Hänel, H.-D.; Dämmgen, U.; Voss, C.; Freibauer, A.; Wulf, S.; Eurich-Menden, B.; Döhler, H.; Schreiner, C.; Döring, U.; et al. Calculations of Gaseous and Particulate Emissions from German Agriculture 1990–2019: Report on Methods and Data (RMD) Submission 2021; Johann-Heinrich-von-Thünen-Institut: Braunschweig, Germany, 2021. [Google Scholar]
  64. Audsley, E.; Alber, S.; Clift, R.; Cowell, S.; Crettaz, P.; Gaillard, G.; Hausheer, J.; Jolliet, O.; Kleijn, R.; Mortensen, B. Harmonisation of Environmental Life Cycle Assessment for Agriculture; Final Report, Concerted Action AIR3-CT94-2028. European Commission, DG VI Agriculture; European Commission: Brussels, Belgium, 1997. [Google Scholar]
  65. European Environment Agency. EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019: Technical Guidance to Prepare National Emission Inventories; Publications Office: Luxemburg, 2019. [Google Scholar]
  66. Emmenegger, M.F.; Reinhard, J.; Zah, R. Sustainability Quick Check for Biofuels—Intermediate Background Report; EMPA: Dübendorf, Switzerland, 2009. [Google Scholar]
  67. Nemecek, T.; Kägi, T.; Blaser, S. Life Cycle Inventories of Agricultural Production Systems; Final Report Ecoinvent v2.0 No; Agroscope Reckenholz-Taenikon Research Station ART, Swiss Centre for Life Cycle Inventories: Zurich, Switzerland, 2007. [Google Scholar]
  68. European Commission. PEFCR Guidance Document: Guidance for the Development of Product Environmental Footprint Category Rules (PEFCRs), Version 6.3. Available online: https://ec.europa.eu/environment/eussd/smgp/pdf/PEFCR_guidance_v6.3.pdf (accessed on 10 October 2022).
  69. Rosenbaum, R.K.; Georgiadis, S.; Fantke, P. Uncertainty Management and Sensitivity Analysis. In Life Cycle Assessment: Theory and Practice; Hauschild, M.Z., Rosenbaum, R.K., Olsen, S.I., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 271–321. ISBN 978-3-319-56475-3. [Google Scholar]
  70. Nemecek, T.; Huguenin-Elie, O.; Dubois, D.; Gaillard, G. Ökobilanzierung von Anbausystemen im Schweizerischen Acker-und Futterbau; Agroscope FAL Reckenholz: Zürich, Switzerland, 2005; ISBN 3905608820. [Google Scholar]
  71. Bundesnetzagentur. Power Plant List (from 15 November 2021). Available online: https://www.bundesnetzagentur.de/EN/Areas/Energy/Companies/SecurityOfSupply/GeneratingCapacity/PowerPlantList/start.html (accessed on 1 May 2022).
  72. Nemecek, T.; Huguenin-Elie, O.; Dubois, D.; Gaillard, G.; Schaller, B.; Chervet, A. Life cycle assessment of Swiss farming systems: II. Extensive and intensive production. Agric. Syst. 2011, 104, 233–245. [Google Scholar] [CrossRef]
  73. Nemecek, T.; Dubois, D.; Huguenin-Elie, O.; Gaillard, G. Life cycle assessment of Swiss farming systems: I. Integrated and organic farming. Agric. Syst. 2011, 104, 217–232. [Google Scholar] [CrossRef]
  74. Câmara-Salim, I.; Almeida-García, F.; Feijoo, G.; Moreira, M.T.; González-García, S. Environmental consequences of wheat-based crop rotation in potato farming systems in galicia, Spain. J. Environ. Manag. 2021, 287, 112351. [Google Scholar] [CrossRef]
  75. Knudsen, M.T.; Meyer-Aurich, A.; Olesen, J.E.; Chirinda, N.; Hermansen, J.E. Carbon footprints of crops from organic and conventional arable crop rotations—Using a life cycle assessment approach. J. Clean. Prod. 2014, 64, 609–618. [Google Scholar] [CrossRef]
  76. González-García, S.; Almeida, F.; Moreira, M.T.; Brandão, M. Evaluating the environmental profiles of winter wheat rotation systems under different management strategies. Sci. Total Environ. 2021, 770, 145270. [Google Scholar] [CrossRef]
  77. Noya, I.; Gonzalez-Garcia, S.; Bacenetti, J.; Arroja, L.; Moreira, M.T. Comparative life cycle assessment of three representative feed cereals production in the Po Valley (Italy). J. Clean. Prod. 2015, 99, 250–265. [Google Scholar] [CrossRef]
  78. Bacenetti, J.; Fusi, A.; Negri, M.; Guidetti, R.; Fiala, M. Environmental assessment of two different crop systems in terms of biomethane potential production. Sci. Total Environ. 2014, 466, 1066–1077. [Google Scholar] [CrossRef]
  79. Mousavi-Avval, S.H.; Rafiee, S.; Sharifi, M.; Hosseinpour, S.; Notarnicola, B.; Tassielli, G.; Renzulli, P.A.; Khanali, M. Use of LCA indicators to assess Iranian rapeseed production systems with different residue management practices. Ecol. Indic. 2017, 80, 31–39. [Google Scholar] [CrossRef]
  80. Basosi, R.; Spinelli, D.; Fierro, A.; Jez, S. Mineral nitrogen fertilizers: Environmental impact of production and use. In Fertilizers: Components, Uses in Agriculture and Environmental Impacts; Lòpez-Valdez, F., Luqueno, F.F., Eds.; NOVA Science Publishers: New York, NY, USA, 2014; pp. 3–44. [Google Scholar]
  81. Leach, A.M.; Galloway, J.N.; Bleeker, A.; Erisman, J.W.; Kohn, R.; Kitzes, J. A nitrogen footprint model to help consumers understand their role in nitrogen losses to the environment. Environ. Dev. 2012, 1, 40–66. [Google Scholar] [CrossRef]
Sustainability 14 15286 g001 550
Figure 1. System boundary and considered processes for LCA of rapeseed production systems for a NUTS-3 region. The dotted lines show optional process steps depending on the scenario considered.
Figure 1. System boundary and considered processes for LCA of rapeseed production systems for a NUTS-3 region. The dotted lines show optional process steps depending on the scenario considered.
Sustainability 14 15286 g001
Sustainability 14 15286 g002 550
Figure 2. Background intervention in Germany for the impact categories of terrestrial acidification potential, regional and terrestrial eutrophication potential, national. The intervention of NUTS-3 regions is shown as a share of distribution from low to high.
Figure 2. Background intervention in Germany for the impact categories of terrestrial acidification potential, regional and terrestrial eutrophication potential, national. The intervention of NUTS-3 regions is shown as a share of distribution from low to high.
Sustainability 14 15286 g002
Sustainability 14 15286 g003 550
Figure 3. Regional normalization reference (rNR), background intervention (BI) and regional normalization references considering background interventions (rNRBI) for the five case study regions for terrestrial acidification potential, TAP, and terrestrial eutrophication potential, TEP. Relative differences from rNR for the resulting rNRBI are shown in color.
Figure 3. Regional normalization reference (rNR), background intervention (BI) and regional normalization references considering background interventions (rNRBI) for the five case study regions for terrestrial acidification potential, TAP, and terrestrial eutrophication potential, TEP. Relative differences from rNR for the resulting rNRBI are shown in color.
Sustainability 14 15286 g003
Sustainability 14 15286 g004 550
Figure 4. Environmental performance expressed as environmental intervention (EI, as annual personal share per functional unit, FU) for the case study regions Ansbach, Kassel and Mecklenburgische Seenplatte for (a) terrestrial acidification potential, regional and (b) terrestrial eutrophication potential, national. Results are displayed for the different N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario). Error bars represent 95% probability range based on Monte Carlo analysis (see Supplementary Materials File S2).
Figure 4. Environmental performance expressed as environmental intervention (EI, as annual personal share per functional unit, FU) for the case study regions Ansbach, Kassel and Mecklenburgische Seenplatte for (a) terrestrial acidification potential, regional and (b) terrestrial eutrophication potential, national. Results are displayed for the different N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario). Error bars represent 95% probability range based on Monte Carlo analysis (see Supplementary Materials File S2).
Sustainability 14 15286 g004
Sustainability 14 15286 g005 550
Figure 5. Environmental performance expressed as environmental intervention (EI, as annual personal share per functional unit, FU) for the case study region Spree-Neiße for (a) terrestrial acidification potential, regional and (b) terrestrial eutrophication potential, national. Results are displayed for the different N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario). For each scenario, the necessary reduction in background interventions (BI) is displayed (in %) if EI = 1.
Figure 5. Environmental performance expressed as environmental intervention (EI, as annual personal share per functional unit, FU) for the case study region Spree-Neiße for (a) terrestrial acidification potential, regional and (b) terrestrial eutrophication potential, national. Results are displayed for the different N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario). For each scenario, the necessary reduction in background interventions (BI) is displayed (in %) if EI = 1.
Sustainability 14 15286 g005
Sustainability 14 15286 g006 550
Figure 6. Environmental performance expressed as environmental intervention (EI, as annual personal share per functional unit, FU) for the case study region Emsland for (a) terrestrial acidification potential, regional and (b) terrestrial eutrophication potential, national. Results are displayed for the different N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario). For each scenario, the necessary reduction in background interventions (BI) is displayed (in %) if EI = 1.
Figure 6. Environmental performance expressed as environmental intervention (EI, as annual personal share per functional unit, FU) for the case study region Emsland for (a) terrestrial acidification potential, regional and (b) terrestrial eutrophication potential, national. Results are displayed for the different N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario). For each scenario, the necessary reduction in background interventions (BI) is displayed (in %) if EI = 1.
Sustainability 14 15286 g006
Sustainability 14 15286 g007 550
Figure 7. Process contribution analysis of rape seed production system for all N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario), for terrestrial acidification potential, regional and terrestrial eutrophication potential, national.
Figure 7. Process contribution analysis of rape seed production system for all N-management scenarios (Nbase = N-base scenario; Nman = N-management scenario; Ncc = N-catch crop scenario; Norg = N-organic scenario), for terrestrial acidification potential, regional and terrestrial eutrophication potential, national.
Sustainability 14 15286 g007
Table
Table 1. Case study regions and biogeographical characteristics.
Table 1. Case study regions and biogeographical characteristics.
Name and NUTS
Identification		Emsland (DE949)Mecklenburgische Seenplatte (DE80J)Kassel (DE734)Spree-Neiße (DE40G)Ansbach (DE256)
RegionLower SaxonyMecklenburg-Western PomeraniaHesseBrandenburgBavaria
Population (amount) a)325,657259,130236,633114,429183,949
Total land area (in ha)288,366549,560129,333165,698197,133
Agricultural area (in ha) b)174,440316,59757,70457,942111,284
Soil characteristicsPara-brown earths, pseudogley characterized by periodically stagnated surface water, with soils of mainly sand to loamy sands, poor to moderate in nutrients, well-drainedPseudogley and para-brown earths with loamy sands to sandy loam structure, moderate nutrient demandPara-brown earths, Podzolic brown earths, with clay slit to slit clay textures, good water and nutrient capacityPodzolic brown earths, para-brown earths,
with mainly sand to loamy sand structure, dry and excessively drained, acidic and nutrient-poor		Podzolic brown earths with slit-clay to clay sand texture, moderate nutrients, well-drained
Clay amount (in %) c)0–85–12;
8–17		17–30;
8 – 17		0–825–45;
8 – 17
Precipitation (average in mm) d)~782~530~622~603~625
Related criteria selectioniiiiiiviiv
a) [44]; b) [45]; c) [46]; d) [47]
Table
Table 2. Scenario parameters and calculated values for LCI for the NUTS-3 regions, Ansbach, Spree-Neiße, Kassel, Mecklenburgische Seenplatte and Emsland. Unless N-base, all scenarios are based on a N demand analysis. (Nbase = N-base scenario; Nman = N-management scenario, Ncc = N-catch crop scenario, Norg = N-organic scenario; Nmin = N-mineralization in soil; CAN = Calcium ammonia nitrate; UAN = urea ammonium nitrate.)
Table 2. Scenario parameters and calculated values for LCI for the NUTS-3 regions, Ansbach, Spree-Neiße, Kassel, Mecklenburgische Seenplatte and Emsland. Unless N-base, all scenarios are based on a N demand analysis. (Nbase = N-base scenario; Nman = N-management scenario, Ncc = N-catch crop scenario, Norg = N-organic scenario; Nmin = N-mineralization in soil; CAN = Calcium ammonia nitrate; UAN = urea ammonium nitrate.)
AnsbachSpree-NeißeKasselMecklenburgische SeenplatteEmsland
N
Base		N
Man		N
cc		N
Org		N
Base		N
Man		N
cc		N
Org		N
Base		N
Man		N
cc		N
Org		N
Base		N
Man		N
cc		N
Org		N
Base		N
Man		N
cc		N
Org
Yield potentialhigh3-year average
middle f)		high3-year average
middle h)		high3-year average
middle j)		high3-year average
middle l)		high3-year average
middle n)
Yield rapeseed [tha−1]43.553.553.5541.741.741.7443.293.293.2944.044.044.0443.213.213.21
Yield dependent N demand a)
[kg Nha−1]		200187187187200132132132200179179179200201201201200176176176
Nmin
[kg NO3-Nha−1]		44 g)18 i)32 k)7 m)35 o)
N demand
[minus Nmin kg Nha−1]		156143143143182114114114168147147147193194194194165141141141
Catch crop
[minus 40 kg Nha−1] b)		--✓✓--✓✓--✓✓--✓✓--✓✓
Organic fertilizer previous year
[minus 5.4 kg Nha−1]		---✓---✓---✓---✓---✓
Humus amount more than 4%
[minus 20 kg Nha−1] c)		-✓✓✓-----✓✓✓-------✓
N-requirement for fertilization
[kg Nha−1]		1561238377.61821147468.61681278781.619319415414816514110195.6
Fertilization
Mineral fertilizer
[kg Nha−1] d)
CAN766041229157371814611076433334262049423019
UAN3321462819976321717141031271912
Urea7760402245291891511841431431148685725232
Liquid manure
[kg Nha−1] e)		 32.4 32.4 32.4 32.4 32.4
Mineral N
[kg Nha−1]		1561238345.21821147436.2168127874919319415411616514110163.2
a) according to Fertilizer Ordinance, 4 t ha−1 [15]; b) yield share legumes > 20–40% [49]; c) humus amount based on [46]; d) ratio for each region based on [50]; e) application of 15 m3, 3.6 kg total N [49]; f) [51]; g) [52]; h) [53]; i) [54]; j) [55]; k) [56]; l) [57]; m) [58]; n) [59]; o) [60]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wowra, K.; Zeller, V.; Schebek, L. Evaluation of the Environmental Performance of Cropping Systems under Different Nitrogen Management Scenarios Considering Regional Nitrogen Resilience. Sustainability 2022, 14, 15286. https://doi.org/10.3390/su142215286

AMA Style

Wowra K, Zeller V, Schebek L. Evaluation of the Environmental Performance of Cropping Systems under Different Nitrogen Management Scenarios Considering Regional Nitrogen Resilience. Sustainability. 2022; 14(22):15286. https://doi.org/10.3390/su142215286

Chicago/Turabian Style

Wowra, Karoline, Vanessa Zeller, and Liselotte Schebek. 2022. "Evaluation of the Environmental Performance of Cropping Systems under Different Nitrogen Management Scenarios Considering Regional Nitrogen Resilience" Sustainability 14, no. 22: 15286. https://doi.org/10.3390/su142215286

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop