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Improved Environmental Life Cycle Assessment of Crop Production at the Catchment Scale via a Process-Based Nitrogen Simulation Model Wenjie Liao,* Hayo M. G. van der Werf, and Jordy Salmon-Monviola INRA/Agrocampus Ouest, UMR1069, Soil, Agro and hydroSystem, F-35000 Rennes, France S Supporting Information *

ABSTRACT: One of the major challenges in environmental life cycle assessment (LCA) of crop production is the nonlinearity between nitrogen (N) fertilizer inputs and onsite N emissions resulting from complex biogeochemical processes. A few studies have addressed this nonlinearity by combining process-based N simulation models with LCA, but none accounted for nitrate (NO3−) flows across fields. In this study, we present a new method, TNT2-LCA, that couples the topography-based simulation of nitrogen transfer and transformation (TNT2) model with LCA, and compare the new method with a current LCA method based on a French life cycle inventory database. Application of the two methods to a case study of crop production in a catchment in France showed that, compared to the current method, TNT2-LCA allows delineation of more appropriate temporal limits when developing data for on-site N emissions associated with specific crops in this catchment. It also improves estimates of NO3− emissions by better consideration of agricultural practices, soil-climatic conditions, and spatial interactions of NO3− flows across fields, and by providing predicted crop yield. The new method presented in this study provides improved LCA of crop production at the catchment scale.

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N from terrestrial to aquatic systems and across fields that is strongly affected by different water pathways (overland flow, percolation, groundwater flow, etc.) needs to be considered.15,16 Few studies so far have addressed the nonlinearity between N-fertilizer inputs and N emissions by combining simulation models with LCA. Process-based N models have been used to account for local factors in LCA studies of biofuels produced from crops, such as sugar beet in France,4,17,18 wheat in France18 and Spain,19 and rapeseed in France18 and Spain.19 However, the SIMSNIC model used in Gallejones et al.19 only simulates N emissions at a monthly time step and at the field scale.20 The CERES-EGC model used in Bessou et al.,4 Dufossé et al.,17 and Gabrielle et al.18 simulates daily N emissions at the landscape scale, but NO3− flows across fields are not considered.17 No study has combined an N simulation model with LCA and accounted for NO3− flows across fields at the landscape scale. The objective of this article is to offer a new method for crop-LCA studies: how to consider local soil-climatic conditions and agricultural practices and how best to estimate N emissions, especially NO3−. We thus present a method that

INTRODUCTION The large increase in nitrogen (N) fertilizer in crop production has triggered a cascade process that generates a variety of N emissions (dinitrogen monoxide N2O, ammonia NH3, nitrate NO3−, etc.) into the environment.1−3 These N emissions are crucial for environmental impacts from crop production (climate change, acidification, eutrophication, etc.) and have been included in environmental life cycle assessment (LCA) of crop production (“crop LCA”). However, the current practice of life cycle inventory analysis (LCI) of N flows in crop-LCA studies has encountered the nonlinearity between N-fertilizer inputs and on-site N emissions, which mainly results from complex biogeochemical processes.4−6 Local soil-climatic conditions and agricultural practices affecting these processes are considered only to a limited extent in the simple models generally used to estimate N emissions in LCA studies.7−10 Models based on emission factors (EFs) derived from empirical relations between inputs and emissions at relatively high aggregation levels (e.g, the national scale) are not satisfactory for calculating emissions from individual sources. For instance, methods from Tiers 1 and 2 in reports by the International Panel for Climate Change (IPCC) up to 200611 were mostly used to calculate N2O emissions from fertilizers applied to soil;12−14 however, they are suitable only at the (supra-) national scale. The spatial scale of crop production also plays a role in the complexity. When a cropping system is studied at the landscape scale, for example, in a catchment, the transfer of © 2015 American Chemical Society

Received: Revised: Accepted: Published: 10790

March 16, 2015 July 16, 2015 August 25, 2015 August 25, 2015 DOI: 10.1021/acs.est.5b01347 Environ. Sci. Technol. 2015, 49, 10790−10796

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Environmental Science & Technology

NO3 (see the Supporting Information for the equation to calculate N2O emissions). More detailed information on calculation of N2O emissions in the AGRIBALYSE database can be found in Koch and Salou.23 TNT2-LCA Method. TNT2 The process-based model combined with LCA in this study is the topography-based simulation of nitrogen transfer and transformation (TNT2) model consisting of the crop model STICS26 that is adapted and validated for grassland and for sequences of annual crops, a hydrological model based on TOPMODEL assumptions,27 and the NEMIS model28 for the denitrification process. The main feature of TNT2 is its ability to spatially simulate interactions between soil and shallow groundwater and their influence on N dynamics. The model has been thoroughly tested and used to study N dynamics in agricultural catchments.29−31 A detailed description of the model can be found in Beaujouan et al. (see the Supporting Information for parametrization and calibration of the model)32 It is fully distributed, with multiple levels of spatial discretization: pixels, soil units, fields, climatic zones, and the catchment. Agricultural practices are input into the model as a succession of individual crop technical operations (sowing, fertilization, grazing, harvesting, etc.) for individual fields. It runs at a daily time step for multiple-year simulations. Output from its simulations includes the daily NH3 flux due to volatilization (kg NH3−N/ha of agricultural land), the daily water flow at the outlet of a catchment (in water height, m), the N concentration of the flow (g NO3−-N/m3), and daily N loss due to denitrification (“total denitrification loss”, g N/m2) in the catchment (including both hillslope and riparian zones). Description of TNT2-LCA. In TNT2-LCA, on-site and offsite emissions are distinguished. On-site emissions are defined as flows of potentially polluting substances due to agricultural practices and biogeochemical processes within the catchment. Off-site emissions are defined as flows of potentially polluting substances associated with production of inputs for crop production and occurring outside of the catchment. For a given crop, the indicator result of a certain impact category (cat) is calculated by linking inventory results of relevant substances (subs) with corresponding characterization factors (CF) as

combines a process-based N simulation model with LCA to improve environmental assessment of crop production at the catchment scale. It compares the combined LCA method with a current LCA method (AGRIBALYSE) and applies both methods to a case study of crop production in a catchment in France.

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MATERIALS AND METHODS AGRIBALYSE Method. AGRIBALYSE is a public LCI database of the main French agro-products, including, among others, annual crops and grass. In its delineation of temporal scope, the period used to develop the LCI of a crop (“inventory period”) depended on the crop type. For annual crops, the standard inventory period was set to “harvest to harvest (HtH)”, that is, the pollutant emission inventory data set for a given crop starts when the previous crop was harvested and ends when the given crop is harvested. The HtH period describes the temporal boundaries of all on-site emissions except NO3−. Since NO3− leaching requires drainage (a downward flow of water in the soil), which in temperate regions occurs mainly during autumn and winter, the period for NO3− was set from sowing of the given crop to sowing of the following crop (“sowing to sowing (StS)”). This period thus includes all or part of the drainage period following harvest of the crop that received the fertilization that was the primary source of NO3−. For permanent grassland, the period was set to one calendar year. NO3− leaching for annual crops was estimated using the qualitative COMIFER model,21,22 which is based on expert knowledge. COMIFER estimates the amount of NO3− leached for a crop by considering several crop factors (i.e., duration without crop cover, amount of N released by crop residues, Nabsorption capacity of the following crop, and application of organic fertilizers in autumn) and soil factors (i.e., drainage amount and soil organic-matter content). It attributes an amount of leached NO3− to each combination of crop and soil factors without specifying temporal dynamics. NO3− emissions were estimated first at the field scale and then aggregated by administrative region. For each crop, mean emissions at the French national level were calculated from regional means that were weighted by production volume of the crop. COMIFER assumes that N-fertilizer inputs do not exceed crop requirements, which does not always hold true, in particular in regions with excess organic fertilizers (including animal excretions); it thus constitutes a limit of the model. NO3− leaching for permanent grassland was estimated using the mechanistic DEAC model.23 DEAC estimates the amount of NO3− leached by considering the amount of N fertilizers, timing of fertilizer application, grazing, and drainage amount. It does not specify temporal dynamics. On-site NH3 emissions due to volatilization of mineral and organic N fertilizers were estimated according to EFs suggested by the models EMEP/CORINAIR24 and EMEP/EEA,25 respectively. Different EFs were used depending on the emission source (during fertilizer application or during grazing), the fertilizer type (mineral or organic) and form (liquid or solid), and the animal type. A list of EFs used in AGRIBALYSE can be found in Koch and Salou.23 N2O emissions were calculated according to EFs from the IPCC Tier 1 method.11 Following the IPCC definition,11 N2O emissions include direct emissions due to N-fertilizer inputs and from crop residues and indirect emissions due to transformations of volatilized NH3 and NOx and leached

indicator resultcat =

∑ CFcat,subs subs

× (inventory result subs,on − site + inventory result subs,off − site)

(1)

The inventory result of a substance refers to the amount of the substance emitted into the environment. In TNT2-LCA, substances were inventoried, grouped into impacts of climate change (using the category indicator of global warming potential (GWP)) and eutrophication (using the category indicator of eutrophication potential (EP)), and then characterized using CFs of Forster et al.33 and Heijungs et al.34 (Table S1). N2O from Denitrification According to TNT2-LCA. In the real world, N2O emitted within a catchment (i.e., N2Oonsite) comes from both nitrification and denitrification. However, TNT2 predicts only N emissions from denitrification (i.e., N2Odenitri + N2) occurring within the catchment (i.e., hillslope and riparian zones), excluding N emissions from denitrification downstream of the catchment outlet (and in the ocean) and those from nitrification. In addition, N2O from deposition of NH3 and NOx, most of which occurs outside the catchment, is 10791

DOI: 10.1021/acs.est.5b01347 Environ. Sci. Technol. 2015, 49, 10790−10796

Policy Analysis


m thick). A shallow and perennial groundwater body develops in the soil and weathered bedrock. Its topography is moderate (slopes ≤5%, elevation 98−140 m). Local climate is temperate (mean daily Tmax = 11.2 °C, 1994−2001). Mean annual rainfall is 814 mm, with the maximum and minimum monthly means reached in November (100 mm) and June (38.5 mm), respectively (1994−2001). The catchment has been investigated in several studies (e.g., Benhamou et al.,40 McDowell et al.,41 and Salmon-Monviola et al.42) using TNT2. Three virtual cropping systems (S1, S1, and S3) with real agricultural practice data were configured for the agricultural area of the catchment to explore the influence of different crops (Ci, i = 1−6, 30 August 1994 to 30 May 2001): S1 contains grazed permanent grassland, S2 contains continuous silage maize (Zea mays L.), and S3 contains a sequence of annual crops (C1 = C6 = silage maize, C2 = wheat (Triticum aestivum), C3 = pea (Pisum sativum L.), C4 = potato (Solanum tuberosum L.), C5 = rapeseed (Brassica napus L.), with white mustard (Sinapis alba L.) as a catch crop whenever possible) (Figure 2). Corresponding crop technical operations (Table S2) based on survey data in Kervidy-Naizin were represented in TNT2 simulations.

not included. Therefore, in TNT2-LCA, N2Oonsite is assumed to equal N2Odenitri. Estimates of NO3−onsite, NH3onsite, and N2Oonsite were obtained by integrating daily N flows predicted by TNT2 over a specific inventory period and applying a range of values for the percentage of total denitrification loss that is N2Odenitri. Estimates of on-site emissions of other substances and of offsite emissions of all substances (Table S1) were obtained from the databases AGRIBALYSE v1.123 and ecoinvent v2.235 (Figure 1).

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Figure 1. Models, databases, and sources of characterization factors used to estimate potential climate change and eutrophication impacts in TNT2-LCA. cat: impact category, subs: substances, CF: characterization factor.

RESULTS

On-Site Emissions. When AGRIBALYSE was used (Table 1), a single value for NO3−onsite was estimated for a given crop present in any year in either a continuous cropping system (SDS1 = SDS2 = 0; SD stands for “standard deviation”) or a system containing a sequence of annual crops (NO3−SilageMaize = 36.0 kg NO3−-N/ha/yr, S2 and S3); different crops typically had different values (SDS3 ≠ 0). When TNT2 was used, a pattern of annual drainage “waves” was observed for NO3− emissions (Figure 3), with high emissions occurring in winter and low emissions in summer, which corresponds to the seasonal variations exhibited in many humid and temperate catchments (Molénat et al., 2002). These waves represent the integral of daily NO3− fluxes over a time interval that was similar for the three systems: August to August. The amount of NO3− leached varied among drainage waves and tended to be lowest for S1 (permanent grassland) and highest for S2 (continuous silage maize), with S3 (annual crop sequence) in-between (Figure 3 and Table 1). A pattern of annual “waves” was also observed for total denitrification loss (i.e., N 2 O denitr + N 2 ) for the three systems, with a corresponding time interval: January-to-January (Figure S2). When TNT2 was used, predicted NO3− emissions varied among years for S1 (SDS1 = 10.8) and S2 (SDS2 = 27.6), because although the same crop was grown each year in these

Denitrification is influenced by many factors and is highly variable over space and time.36−39 NEMIS,28 as used in TNT2LCA, predicts a daily denitrification rate as a function of soil temperature, NO3− content, and water saturation and residence time.39 According to Bouwman et al.,36 the percentage of total denitrification loss that is N2O in riparian zones and agricultural soils ranges from 0.3 to 73.0%. Oehler et al.39 applied the acetylene blockage technique to incubated soil cores sampled from a catchment in Brittany, and reported that 60% of total denitrification loss was N2O. Thus, 60% was assumed to be the maximum percentage of total denitrification loss that is N2O in catchments of Brittany. To explore the influence of variability and uncertainty in N2O emissions, the percentage was set from 0.3 to 60.0% in the study. Case Study of Crop Production in the Kervidy-Naizin Catchment. Kervidy-Naizin (Figure S1) is a 4.82 k2 headwater catchment located in Brittany, France (48°N, 3°W). Its agricultural area is 3.88 km2. The soil is silty loam (0.5−1.5 m deep), with well-drained hillslope areas and a poorly drained zone near the channel network. The bedrock is Brioverian schist, with a weathered layer of variable thickness (a few to 30

Figure 2. Crop sequences of the three cropping systems under study from 30 August 1994 to 30 May 2001, configured for the Kervidy-Naizin catchment in Brittany, western France. CC: catch crop. Horizontal lines indicate the presence of vegetation. 10792


Policy Analysis


Table 1. On-Site NO3− Emissions (kg NO3−-N/ha/yr) Estimated by AGRIBALYSE or TNT2 for Three Cropping Systems (S1− S3) Consisting of Six Annual Crops/Grass (C1−C6) Eacha model

syst.

period

C1

C2

C3

C4

C5

C6

AGRIBALYSE

S1 S2 S3 S1 S2 S3 S1 S2 S3

calendar year StS StS calendar year StS StS drainagewave-based period

20.0 36.0 36.0 43.0 28.3 2.4 15.8 31.1 33.2

20.0 36.0 30.5 14.6 23.0 50.0 6.7 19.7 19.8

20.0 36.0 33.0 6.5 36.2 29.1 16.0 47.7 36.9

20.0 36.0 25.6 19.2 60.5 10.3 17.9 52.5 52.2

20.0 36.0 32.0 21.5 58.6 93.5 22.2 62.6 47.9

20.0 36.0 36.0 26.1 115.6 84.8 41.9 106.5 78.3

TNT2

a

mean 20.0 36.0 32.2 21.8 53.7 45.0 20.1 53.3 44.7

(0.0) (0.0) (3.6) (11.3) (31.1) (34.7) (10.8) (27.6) (18.3)

Mean values are followed by the standard deviation in brackets. StS: Sowing to sowing.

Figure 3. Time series of daily NO3− fluxes at the outlet of the catchment (kg NO3−-N/day). Vertical red lines represent the sowing-to-sowing inventory period, while purple lines represent the drainage-wave-based inventory period. CC: catch crop.

Table 2. On-Site NH3 Emissions (kg NH3−N/ha/yr) Estimated by AGRIBALYSE or TNT2 for Three Cropping Systems (S1− S3) Consisting of Six Annual Crops/Grass (C1−C6) Eacha model

syst.

period

C1

C2

C3

C4

C5

C6

mean

AGRIBALYSE

S1 S2 S3 S1 S2 S3

calendar year HtH HtH calendar year HtH HtH

8.7 22.9 22.9 17.8 36.4 36.4

8.7 22.9 12.5 18.2 36.4 5.6

8.7 22.9 1.3 18.2 36.4 1.0

8.7 22.9 12.0 18.1 36.4 2.5

8.7 22.9 10.8 18.6 36.4 7.1

8.7 22.9 22.9 19.2 36.4 36.4

8.7 (0.0) 22.9 (0.0) 13.7 (7.5) 18.4 (0.4) 36.4 (0.0) 14.8 (15.4)

TNT2

a

Mean values are followed by the standard deviation in brackets. HtH: Harvest to harvest.

Table 3. On-Site N2O Emissions (kg N2O−N/ha/yr) Estimated by AGRIBALYSE or TNT2-LCA for Three Cropping Systems (S1−S3) Consisting of Six Annual Crops/Grass (C1−C6) Eacha model

syst.

period

C1

C2

C3

C4

C5

C6

mean

AGRIBALYSE

S1 S2 S3

calendar year HtH HtH

0.10 2.74 2.74

0.10 2.74 2.58

0.10 2.74 0.84

0.10 2.74 2.46

0.10 2.74 2.75

0.10 2.74 2.74

0.10 (0.00) 2.74 (0.00) 2.35 (0.68)

TNT2-LCA

S1 S2 S3

emissionwave-based period

0.07−13.1 0.11−21.6 0.11−22.8

0.03−5.5 0.06−11.8 0.02−4.8

0.05−9.8 0.09−17.0 0.06−12.5

0.03−5.1 0.06−11.3 0.09−18.2

0.05−9.1 0.08−15.6 0.03−5.5

0.03−5.5 0.06−12.4 0.06−12.2

0.04 (0.01)−8.0 (2.9) 0.07 (0.02)−14.9 (3.6) 0.06 (0.03)−12.7 (6.4)

a

TNT2-LCA results are based on the range of percentage of total denitrification loss, that is, N2O (0.3−-60%). Mean values are followed by the standard deviation in brackets. HtH: Harvest to harvest.

(Table 4). This is in line with the ranks of mean on-site NO3− emissions for the three systems according to the two models (S1 < S3 < S2, Table 1). TNT2-LCA predicted EP impacts 18−43% higher than those of AGRIBALYSE. The permanent grassland system (S1) had the lowest GWP. However, depending on percentage of total denitrification loss that is N2O, it was uncertain whether S2 or S3 had lower GWP. When using TNT2-LCA for grazed grassland or an annual crop in a monoculture system, like S1 or S2, impacts per ha varied

systems, weather varied among years, affecting crop yields (and consequently N uptake) and drainage amounts. NO 3 − emissions varied even more among years for S3 (SDTNT2‑LCA > SDAGRIBALYSE), because not only did weather vary among years, but crops and their agricultural practices also varied. Similar predictions were made for NH3 and N2O emissions (Tables 2 and 3, respectively). Potential Environmental Impacts. AGRIBALYSE and TNT2 agreed that the permanent grassland system (S1) had the lowest EP and the silage maize system the highest (S2) 10793


Policy Analysis


vulnerable zone, with higher risk of NO3− leaching than the average situation in France. One cause of different estimates of NO3− emissions by AGRIBALYSE and TNT2 (Table 1) is that they model NO3− flows using different system limits. In AGRIBALYSE, the system limit for on-site NO3− emissions is set 1 m below the soil surface,23 while that for TNT2 is at the catchment outlet and includes transfer of NO3− flows and the resulting temporary storage of NO3− in the soil below 1 m, that vadose zone, and groundwater (e.g., as studied by Basset-Mens et al.44). These NO3− dynamics can be simulated by dynamic models but not by a static model, as in AGRIBALYSE. Another cause of difference is excess N due to overfertilization of crops (which is common in Brittany, which has intensive animal production and thus abundant organic fertilizer); the excess N is not considered in COMIFER (used in AGRIBALYSE). Compared to COMIFER or DEAC used in AGRIBALYSE,23 TNT2 improves estimation of NO3− emissions by better accounting for agricultural practices (including fertilization amounts), soil-climatic conditions, and spatial interactions of NO3− flows between fields within the catchment. As a result, TNT2-LCA is a major improvement in estimating eutrophication impact in crop-LCA studies at the catchment scale. N2O Emissions. TNT2 predicts total denitrification loss but does not really predict N2O emissions, as the percentage that is N2O is uncertain.36 Consequently, we can only estimate a wide range of values for N2O emissions using TNT2-LCA (Table 3). N2O emissions estimated by AGRIBALYSE (IPCC method) are at the lower bound of this range (Table 3) and correspond to an N2O percentage of 11% for S2 and S3 (Table S5) and 0.8% for S1 (Table S6). Given the wide range of estimates, TNT2 does not help much to improve estimates of N2O emissions or climate-change impact. The range of values for on-site N2O emissions for wheat and rapeseed produced in Brittany, as estimated by TNT2, were similar to those for the two crops produced in another region in France (Ile de France), as estimated by CERES-EGC18 (cf. detailed data in Table S7). However, considering that N2O emissions from the nitrification process are not simulated by TNT2, using CERES-EGC, which includes a dedicated model to simulate N2O from both denitrification and nitrification, would provide more robust results for on-site N2O emissions of cropping systems. It is expected that using TNT2 along with other process-based models for N2O (e.g., CERES-EGC) can improve assessment of both eutrophication and climate-change impacts in crop-LCA studies, if the internal consistency of this type of combined LCA model is ensured to maintain the mass and energy balance of cropping systems. Crop Yield. Estimation of crop productivity for different years and sites is also relevant for crop-LCA studies (e.g., crops

Table 4. Mean (and Standard Deviation) of Climate Change (GWP) and Eutrophication (EP) Impacts for Six Annual Crops/Grass in the Three Systems (S1−S3) According to AGRIBALYSE and TNT2-LCAa GWP, kg CO2 eq/ha/yr

EP, kg PO4− eq/ ha/yr

method

syst.

AGRIBALYSE

S1 S2 S3

1667 (0) 2342 (0) 2469 (792)

13.4 (0.0) 31.3 (0.0) 26.5 (5.8)

TNT2-LCA

S1 S2 S3

1641 (7)−5384 (1377) 1093 (9)−8055 (1692) 1395 (569)−7294 (3026)

17.8 (4.9) 44.7 (12.2) 31.4 (12.8)

a

Ranges of climate change impact depend on percentage of denitrification loss that Is N2O, which ranged from 0.3 to 60%.

among years (Tables S3 and S4) because weather varied among years (Figure S3).

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DISCUSSION Inventory Period. To establish LCIs for individual crops, NO3− emissions predicted by TNT2 must be attributed to them. This can be done in two ways: StS, as used in AGRIBALYSE (Figure 3, vertical red lines), or by drainage waves (Figure 3, vertical purple lines). With the former, crops with a short StS interval (silage maize in 1995 and potato) are attributed much smaller amounts of NO3− than crops with a long StS interval (wheat, pea, rapeseed, and silage maize in 2000) (Table 1). With the latter, a more balanced picture arises, with each crop paired with a drainage wave (Table 1), making it more appropriate than StS for developing the NO3− inventory. The time intervals corresponding to waves of on-site N2O emissions (January to January, Figure S2) differed from those for on-site NO3− emissions (August to August, Figure S3), as temperature may have greater effect on denitrification than NO3− content and water retention time is longer in summer than in winter in Kervidy-Naizin. They are more appropriate for developing inventory data for on-site N2O emissions from denitrification of cropping systems than the HtH inventory periods used in AGRIBALYSE, at least in the case of the Kervidy-Naizin catchment. Nevertheless, this type of attribution assumes a steady state of N flows in the catchment; the real retention time of N-fertilizer inputs can be decades,43 which is much longer than the drainage/emission-wave-based period. System Limit for NO3−. Applying TNT2 to the three systems resulted in higher mean values for NO3− emissions than those estimated by AGRIBALYSE (Table 1), which is in line with the current classification of Brittany as a NO3−

Table 5. Yield-Scaled EP Impacts (g PO4− eq./kg dry matter/yr) for the Three Systems under Studya method

syst.

C1

C2

C3

C4

C5

C6

mean

AGRIBALYSE

S1 S2 S3

1.81 2.57 2.57

1.81 2.57 3.98

1.81 2.57 4.77

1.81 2.57 6.07

1.81 2.57 8.25

1.81 2.57 2.57

1.81 (0.00) 2.57 (0.00) 4.70 (2.01)

TNT2-LCA

S1 S2 S3

2.15 1.89 1.93

1.54 2.17 1.99

1.86 2.51 3.52

1.80 2.59 3.46

2.23 2.39 5.92

2.94 3.78 3.09

2.09 (0.44) 2.56 (0.59) 3.32 (1.33)

a

AGRIBALYSE results are based on yield as provided in its database; TNT2-LCA results are based on predicted crop yields. Mean values are followed by the standard deviation in brackets. 10794



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used as feedstock for biofuel production19,45). For instance, a wet year can result in a higher yield of silage maize than a drier year (Table S8) but also in increased emissions of eutrophying substances (e.g., NO3−, Table 1) and/or greenhouse gases. However, in most studies, data about crop yields were obtained from nonsite-specific literature (e.g., Brandão et al.46) or from databases which only provide single mean values (e.g., AGRIBALYSE23 or ecoinvent35) and are not tailored to specific soil-climate conditions, thus introducing high uncertainty. Averaged across the six years and three systems, EP impacts of TNT2-LCA were 31% higher than those of AGIBALYSE when expressed per ha (Table 4) and 4% lower when expressed per kg of dry matter (DM) (Table 5). This resulted from higher crop yields for TNT2-LCA than for AGRIBALYSE, in particular for S2 and S3 (Table S8). For TNT2-LCA, crop yields varied among years (Table S8), consequently EP impacts per kg DM for a monoculture cropping system (like S1 or S2) also varied among years (Table 5). When emissions per ha do not change substantially, higher crop yields lead to lower emissions per kg DM. Compared to AGRIBALYSE, TNT2-LCA also improves environmental LCA of crop production by predicting year-and-site-specific impacts of crop products. Generalizability and Evaluation of TNT2-LCA. We present TNT2-LCA that addresses the nonlinearity between N-fertilizer inputs and NO3− emissions in crop-LCA studies by considering local soil-climate conditions and agricultural practices via the simulation model TNT2. This method is original in its combination of LCA with TNT2 that runs at a daily time step and accounts for NO3− flows across fields; such a combination does not exist in the literature. The generalizability of the method to studies of other systems depends on: (1) the goal and scope of these studies (as assessment of terrestrial and aquatic eutrophication and climate change associated with catchment-scale crop production), (2) the similarity between these systems and the systems in this study in terms of crop types (Table S9, copes that can be studied using TNT2-LCA) and soil-climatic conditions, and (3) available resources for the data collection of a variety of parameters (climate and catchment physical description) and input data (crop technical operations) that can be used to feed TNT2 (Figure S4). TNT2-LCA combines TNT2 with empirical data sets (from AGRIBALYSE23) to estimate onsite emissions; this introduces the inconsistency of target-data representativeness between TNT2 and empirical data sets, which are considered as two ways to develop unit process data from raw data47 at the foreground-system level (i.e., on-site crop production). Evaluation of TNT2-LCA deals with the uncertainty estimates around results and many methods are available to propagate statistical uncertainties around the results, including Monte Carlo analysis, analytical error propagation, and fuzzy logic.48−50

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Policy Analysis

AUTHOR INFORMATION

Corresponding Author

*Phone: +33 (0)2 23 48 54 31; fax: +33 (0)2 23 48 54 30; email: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS W.L. received support from the European Union Marie-Curie FP7 COFUND People Program through the award of an AgreenSkills fellowship (grant agreement no. 267196).

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REFERENCES

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b01347. Details on equation for N2O, parametrization and calibration of TNT2, and complementary figures and tables (PDF) 10795


Policy Analysis


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