County-level CFs are presented in Section 3.1. Land use occupation impacts in the case studies are presented in Section 3.2. Results from the indicator evaluation are presented in Section 3.3.
Here, we present the calculated CFs (Table 3) and analyze and explain the reasons for the observed differences between county- and biome-level CFs, between county-level CFs for cropland in VG and OG, and between county-level CFs for cropland and grassland in VG. For GWR, MFC, PFC, and SL, the observed differences between county- and biome-level CFs could not easily be explained; this is discussed further in Section 4.1.
County- and biome-level CFs for CFC, MFC, SL, and SOC fall within the same order of magnitude, while CFs for GWR and PFC differ by up to one order of magnitude (Table 3). County-level CFs are consistently lower than biome-level CFs, indicating lesser negative impacts or greater positive effects (i.e., ecosystem service improvements).
Comparing county-level CFs for cropland and grassland in VG for GWR, MFC, and PFC, the differences are entirely due to a higher degree of sealing in cropland than in grassland, i.e., less water can infiltrate cropland soils (due to compaction) and become available for purification and groundwater recharge.
Carbon flow change
Positive CFs represent a loss in the carbon sequestration potential of ecosystems. County-level CFs indicate lesser negative impacts than biome-level CFs (Table 3) due to lower carbon stocks in soil and vegetation in the reference situations in VG and OG than the stocks that Müller-Wenk and Brandão (2010) used for biome 4.
There is considerable difference between county-level CFs for cropland in VG and OG. CFs at both scales indicate a greater negative impact on cropland than on grassland, which is in line with the view that conversion of forest to cropland causes larger ecosystem carbon losses than conversion to grassland (Guo and Gifford 2002).
Positive CFs represent the amount of groundwater that is potentially not recharged annually, due to land occupation (ecosystem service impairment), while negative CFs represent the additional amount of water that is potentially recharged annually due to land occupation (ecosystem service improvement).
Biome-level CFs indicate greater negative impacts than county-level CFs (Table 3). At the county level, the CF for cropland in OG is three times larger than in VG, due to a greater relative reduction in recharging capacity in OG.
The difference between county-level CFs for cropland in VG and OG is mainly due to that the difference in evapotranspiration levels between the reference and the assessed land use situation is larger in OG than in VG (precipitation levels do not contribute since they were kept fixed, see Electronic supplementary material S8). The influence of different choices in parameterizing the reference situations with evapotranspiration data is evaluated and further discussed in Section 4.2.2.
The CF for grassland in VG indicates an ecosystem service improvement compared to the reference situation. This is because evapotranspiration is lower in the assessed land use situation than in the reference situation (Table S8, Electronic supplementary material S8.1); hence, more water becomes available for groundwater recharge, ceteris paribus.
Mechanical filtration capacity
Positive CFs represent the amount of water that is potentially not mechanically filtrated, due to land occupation (an ecosystem service impairment). All CFs are positive, but county-level CFs indicate lesser negative impacts than biome-level CFs (Table 3).
County-level CFs for cropland indicate a greater negative impact in VG than in OG, due to more sandy—and hence more permeable—cropland soils in VG (Table S11, Electronic supplementary material S9.1) (further discussed in Section 4.3). The CF for grassland in VG indicates no impact, compared to the reference situation. This is because it was assumed that the soil texture remains fixed over time, and because both grasslands and woodlands are considered equally capable of infiltrating water in LANCA (Table S29, Electronic supplementary material S14).
Physicochemical filtration capacity
Positive biome-level CFs represent the moles of cation charges that are potentially lost, due to land occupation (ecosystem service impairment), while negative county-level CFs represent the moles of additional cation charges that are potentially fixed due to land occupation (ecosystem service improvement).
County- and biome-level CFs differ both in sign and order of magnitude (Table 3). County-level CFs are negative due to the use of CECeff data that most probably underestimate reference conditions, caused by challenges in finding suitable land areas from which to derive representative data, further discussed in Section 4.2.2.
County-level CFs indicate a more positive effect on cropland in OG, than in VG, due to more clayey cropland soils in OG (associated with a higher CECeff) in combination with almost similar quality levels in the respective reference situations. Thus, cropland use in OG is associated with a relatively greater increase in the capacity of the soil to physicochemically purify water. However, this result should be interpreted with caution considering that reference conditions are probably underestimated (see Section 4.2.2).
Positive CFs indicate the additional mass of soil potentially eroded, due to land occupation (ecosystem service impairment). All CFs are positive (Table 3), but the magnitudes are different across the three sets of CFs, and decrease as the spatial resolution increases. The CF for cropland in VG calculated with LANCA (SL-LANCA) indicates an annual soil loss of 7 tonnes ha−1 (compared to the reference situation), while the corresponding value calculated with RUSLE (SL-RUSLE) indicates an annual soil loss of only 0.07 tonnes ha−1 (Table 3). This difference is mainly due to RUSLE taking reduced tillage into account (which LANCA does not). These results can be compared with modeled annual erosion rates of 0–2 tonnes ha−1 for arable land in VG, based on RUSLE (JRC 2012), and measured annual erosion rates of 0–2 tonnes ha−1 for arable land in Sweden (Cerdan et al. 2010). When we considered a mix of tillage systems, the CFs calculated with RUSLE increased by a factor of 10, making them comparable to the results from JRC (2012) and Cerdan et al. (2010).
County-level CFs calculated with RUSLE indicate a greater negative impact on cropland in VG than in OG, mainly due to a higher precipitation rate in VG (Table S9, Electronic supplementary material S8.1). County-level CFs calculated with LANCA do not capture these relatively small, but significant, regional differences in precipitation.
County-level CFs for grassland are considerably lower than for cropland, indicating lower soil erosion for roughage fodder crops than for annual feed grains. This is expected since grasslands provide a more complete vegetation cover over the year than cropland.
Soil organic carbon
Negative CFs indicate an increase in the potential capacity of ecosystems to support biomass production, due to larger SOC stocks in the assessed land use situations than the reference situations (Table 3). County-level CFs indicate a greater positive effect on cropland in VG than in OG, due to an average 0.5–1 percentage points higher content of organic matter in soils on dairy farms than on arable and pig farms, mainly due to grass cultivation on dairy farms (Eriksson et al. 2010). Trends in Danish agricultural soils over a period of 10–12 years have shown similar results: SOC stocks increased on dairy farms and decreased on arable and pig farms (Heidmann et al. 2002).
County-level CFs indicate greater positive effects than biome-level CFs, but these positive effects are probably exaggerated, due to challenges in finding suitable land areas from which to derive representative data (see Section 4.2.2).
Improved biotic production potentials, as suggested by the CFs for SOC, contradict research showing that conversion of natural lands into agricultural land has resulted in significant losses of SOC globally (Wei et al. 2014; Yang et al. 2003). One explanation is that we considered cropland with a high input of manure. At the biome level, this is also due to that Brandão and Milà i Canals (2013) probably exaggerated the benefits of reduced tillage on SOC stocks (see Section 4.3).
Here, we present land use occupation impact results (Fig. 2) and analyze the reasons for the differences between results at the two geographic scales (county and biome level) and between the two production cases (VG-dairy and OG-pork).
Occupation impacts calculated with CFs at the two geographic scales, and for the two production cases, differ both in absolute numbers, and—for two indicators of water purification (MFC and PFC)—in the ranking of cases, i.e., results flip across scales and cases (Fig. 2).
Occupation impacts at both scales calculated with CFs for CFC, GWR, SL, and SOC are consistent in ranking the cases. Results for CFC, GWR, and SL show that OG-pork has greater negative impacts per unit protein than VG-dairy, for both sets of CFs. This is because (1) VG-dairy is a more area-efficient form of protein production than OG-pork, (2) 40 % of the land use in VG-dairy consists of grassland, and (3) grass production is less damaging, or more beneficial, than crop production, as indicated by the CFs for CFC, GWR, and SL (Table 3).
For MFC and PFC, occupation impacts calculated with the two sets of CFs are inconsistent. For MFC, results at the biome level indicate that VG-dairy is associated with a lesser negative impact than OG-pork, while results at the county level indicate the opposite, despite that VG-dairy requires less land than OG-pork (Section 2.1.1) and no impact is attributed to grassland (Table 3). These county-level results are due to a greater negative impact associated with cropland in VG than in OG (Table 3), which outweighs the lower use of cropland in VG. For PFC, occupation impact results are inconsistent across scales and cases primarily because biome-level CFs are positive, while county-level CFs are negative (Section 3.1.4).
Only one indicator (SOC) consistently shows that OG-pork is more beneficial than VG-dairy, at both scales, despite higher content of organic matter in soils on dairy farms (Section 3.1.6). Recalling that occupation impacts are the product of CFs and inventory flows (Section 2.1.4), this somewhat counterintuitive result is mainly due to OG-pork requiring more land per unit protein, i.e., the (relatively smaller) positive effect of higher content of organic matter on dairy farms is outweighed by more land use in OG-pork.
Occupation impacts for SL calculated with RUSLE yield the same ranking of the two cases as LANCA but with much smaller values (Electronic supplementary material S12).
Considering the results for SL and SOC, Fig. 2 suggests that the assessed land use situations simultaneously cause soil loss and increased biotic production potential relative to the reference situations (at both scales). This is inconsistent with research showing that soil erosion is associated with a loss in SOC stocks, hence a loss in the potential capacity of ecosystems to support biomass production (Yang et al. 2003). These inconsistent results stem from considering cropland with a high input of manure in combination with, at the county level, probably underestimated reference SOC stocks (see Section 4.2.2) and, at the biome level, a probably exaggerated benefit of reduced tillage on SOC stocks in the assessed land use situations (Section 3.1.6).
Taken together, occupation impacts at both geographic scales indicate positive effects—or lower negative impacts—in protein production from dairy milk compared to pork, due to grass production on dairy farms, and lower use of land per unit protein. However, some of the observed benefits (for PFC and SOC) may be exaggerated due to challenges in adequately representing the reference situations.
Evaluation of ecosystem service indicators
All indicators were assigned medium degrees of representativeness (Table 1); all are relevant indicators (on a conceptual level), but none is “a sign of the degree to which an objective of an ecosystem service is met” (Section 2.2.1), i.e., relates the result to a goal or a threshold, thereby supporting an evaluation of the performance in relative terms. Relating conditions to a goal or a threshold is important due to varying environmental conditions and sensitivities, and because a certain level of impact may cause large damage in one region and less damage in another. The construction of impact indicators from ecosystem service indicators, i.e., the calculation of CFs, aims to fulfill this criterion, but challenges associated with setting a relevant baseline (i.e., a reference situation) potentially undermine the relevance of the CFs.
All indicators are (fully or partially) capable of capturing “trends in ecosystems and their services over time, as well as differences between places.” For example, CFC only partially captures spatiotemporal differences, due to the assumption in Müller-Wenk and Brandão (2010) that land use management is carbon neutral, which prevents different forms of land management from being distinguished in this regard.
Most indicators were assigned low reliability scores, except SOC and SL-RUSLE (Table 1). In order for ecosystem service indicators to be successfully adopted, it is important that users (in this case LCA practitioners) understand why the indicators are chosen, and why they are assessed as they are. For GWR, MFC, PFC, and SL, Koellner et al. (2013) refer to Saad et al. (2013), who adopted an existing model, LANCA, described in Beck et al. (2010), and developed based on Baitz (2002). The scientific basis for adopting these indicators could not be established by reviewing these publications. On the other hand, Brandão and Milà i Canals (2013) convincingly argue that SOC is a relevant indicator for biotic production potential, as do Renard et al. (1997) for soil loss calculated with RUSLE. Therefore, only SOC and SL-RUSLE were found to be well-founded.
With regard to accuracy, LANCA assesses SL less accurately than RUSLE, since the former is based on a simplified version of USLE that uses coarse data to represent large regions (Section 2.1.3). Also, the simplified version of GWR that we used (Electronic supplementary material S8) has a low degree of accuracy since it does not take into account the available field capacity, influence by the soil texture. Further, the accuracy of MFC is uncertain, as discussed in Section 4.2.1.
With regard to standardization, Renard et al. (1997) and Brandão and Milà i Canals (2013) describe in sufficient detail how SL-RUSLE and SOC should be assessed (what data to use, etc.). In contrast, the original publications for MFC, PFC, and SL-LANCA (Table 1) do not state what soil depth to derive soil data from, which is a serious shortcoming.
Most indicators were assigned medium (CFC, GWR, MFC, and PFC) or high (SL-LANCA, SOC) degrees of feasibility, except SL-RUSLE (low degree; Table 1). The reason is that RUSLE requires large amounts of data that are not always readily available or obtainable at reasonable cost (time, effort). For example, rainfall runoff erosivity requires data on rainfall intensity and temperature with a high level of spatiotemporal resolution (data records at 15 min intervals over a period of 20 years). These data are seldom available, and when they are, they are very resource-intensive to process. The feasibility scores can be interpreted as an indication of the time required by LCA practitioners to calculate new CFs in LCA studies.
The medium to high feasibility is partly a consequence of performing this case study in Sweden. The Swedish Environmental Protection Agency’s national environmental monitoring program has time series for some environmental variables that are among the longest in the world (SEPA 2014a). The Swedish forest biomass has for example been regularly monitored since 1923 (Fridman et al. 2014). Despite the relatively favorable situation, data that represent the reference situation are nevertheless limited: 14 and 18 soil samples may not be sufficient for deriving representative averages, as done for CECeff in the reference situations in OG and VG, respectively (Table S12, Electronic supplementary material S10.1).
Finding data that are both representative of the situations under study, and at the required resolution, is one of the main challenges in developing regionalized CFs. However, gradually, data availability increases, whether it is measured, remotely sensed, modeled, or open source with multiple spatial and temporal resolutions (such as Hijmans et al. 2005, Jarvis et al. 2008, and Shangguan et al. 2014). While this development is beneficial, it introduces another challenge when it comes to combining data from different sources: datasets with similar resolutions can display significant variations in values associated with different methods to derive data (Herold et al. 2006; Verburg et al. 2011). It is important to ensure consistency when using data from different sources and to communicate the uncertainties that may arise from any inconsistencies.
Most indicators were assigned medium (PFC) or high (GWR, MFC, SL-LANCA, SL-RUSLE, and SOC) degrees of transparency, i.e., the indicators are considered communicable to stakeholders and fairly easily interpretable (Table 1). This is because water volume, soil mass, and SOC are quantities to which most stakeholders can intuitively relate. PFC has been assigned a medium degree of transparency because the CECeff concept is less well known. Only one indicator, CFC, has been assigned a low degree of transparency, because the concept of a duration time of carbon in the atmosphere, in relation to fossil-combustion-equivalent carbon, requires some level of expertise to comprehend.