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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 Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b01347 • Publication Date (Web): 25 Aug 2015 Downloaded from http://pubs.acs.org on August 29, 2015

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Environmental Science & Technology

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Improved environmental life cycle assessment of crop

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production at the catchment scale via a process-based

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nitrogen simulation model

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Wenjie Liao*, Hayo M. G. van der Werf, Jordy Salmon-Monviola

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INRA/Agrocampus Ouest, UMR1069, Soil, Agro and hydroSystem, F-35000 Rennes, France

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* Corresponding author: Tel.: +33 (0)2 23 48 54 31; Fax: +33 (0)2 23 48 54 30

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E-mail: [email protected] (W. Liao)

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ACS Paragon Plus Environment


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ABSTRACT One of the major challenges in environmental life cycle assessment (LCA) of crop

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production is the non-linearity between nitrogen (N) fertiliser inputs and on-site N emissions resulting

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from complex biogeochemical processes. A few studies have addressed this non-linearity by combining

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process-based N simulation models with LCA, but none accounted for nitrate (NO3-) flows across fields.

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In this study, we present a new method, TNT2-LCA, that couples the Topography-based simulation of

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Nitrogen Transfer and Transformation (TNT2) model with LCA, and compare the new method with a

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current LCA method based on a French life cycle inventory database. Application of the two methods to a

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case study of crop production in a catchment in France showed that, compared to the current method,

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TNT2-LCA allows delineation of more appropriate temporal limits when developing data for on-site N

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emissions associated with specific crops in this catchment. It also improves estimates of NO3- emissions

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by better consideration of agricultural practices, soil-climatic conditions, and spatial interactions of NO3-

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flows across fields, and by providing predicted crop yield. The new method presented in this study

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provides improved LCA of crop production at the catchment scale.

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INTRODUCTION

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The large increase in nitrogen (N) fertiliser in crop production has triggered a cascade process that

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generates a variety of N emissions (dinitrogen monoxide N2O, ammonia NH3, nitrate NO3-, etc.) into

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the environment.1-3 These N emissions are crucial for environmental impacts from crop production

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(climate change, acidification, eutrophication, etc) and have been included in environmental life cycle

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assessment (LCA) of crop production (“crop LCA”). However, the current practice of life cycle

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inventory analysis (LCI) of N flows in crop-LCA studies has encountered the non-linearity between

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N-fertiliser inputs and on-site N emissions, which mainly results from complex biogeochemical

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processes.4-6 Local soil-climatic conditions and agricultural practices affecting these processes are

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considered only to a limited extent in the simple models generally used to estimate N emissions in

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LCA studies.7-10 Models based on emission factors (EFs) derived from empirical relations between

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inputs and emissions at relatively high aggregation levels (e.g, the national scale) are not satisfactory

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for calculating emissions from individual sources. For instance, methods from Tiers 1 and 2 in reports

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by the International Panel for Climate Change (IPCC) up to 200611 were mostly used to calculate N2O

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emissions from fertilisers applied to soil;12-14 however, they are suitable only at the (supra-) national

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scale. The spatial scale of crop production also plays a role in the complexity. When a cropping

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system is studied at the landscape scale, e.g., in a catchment, the transfer of N from terrestrial to

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aquatic systems and across fields that is strongly affected by different water pathways (overland flow,

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percolation, groundwater flow, etc.) needs to be considered.15, 16

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Few studies so far have addressed the non-linearity between N-fertiliser inputs and N

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emissions by combining simulation models with LCA. Process-based N models have been used to

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account for local factors in LCA studies of biofuels produced from crops, such as sugar beet in

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France,4,

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SIMSNIC model used in Gallejones et al.19 only simulates N emissions at a monthly time step and at

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the field scale.20 The CERES-EGC model used in Bessou et al.,4 Dufossé et al.,17 and Gabrielle et al.18

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simulates daily N emissions at the landscape scale, but NO3- flows across fields are not considered.17

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No study has combined an N simulation model with LCA and accounted for NO3- flows across fields

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at the landscape scale.

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wheat in France18 and Spain,19 and rapeseed in France18 and Spain.19 However, the

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The objective of this article is to offer a new method for crop-LCA studies: how to consider

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local soil-climatic conditions and agricultural practices and how best to estimate N emissions,

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especially NO3-. We thus present a method that combines a process-based N simulation model with

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LCA to improve environmental assessment of crop production at the catchment scale. It compares the

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combined LCA method with a current LCA method (AGRIBALYSE) and applies both methods to a

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case study of crop production in a catchment in France.

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MATERIALS AND METHODS

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AGRIBALYSE Method

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AGRIBALYSE is a public LCI database of the main French agro-products, including, among others,

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annual crops and grass. In its delineation of temporal scope, the period used to develop the LCI of a crop

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(“inventory period”) depended on the crop type. For annual crops, the standard inventory period was set to

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“harvest to harvest (HtH)”, i.e., the pollutant emission inventory dataset for a given crop starts when the

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previous crop was harvested and ends when the given crop is harvested. The HtH period describes the

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temporal boundaries of all on-site emissions except NO3-. Since NO3- leaching requires drainage (a

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downward flow of water in the soil), which in temperate regions occurs mainly during autumn and winter,

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the period for NO3- was set from sowing of the given crop to sowing of the following crop (“sowing to

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sowing (StS)”). This period thus includes all or part of the drainage period following harvest of the crop

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that received the fertilisation that was the primary source of NO3-. For permanent grassland, the period

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was set to one calendar year.

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NO3- leaching for annual crops was estimated using the qualitative COMIFER model,21, 22 which

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is based on expert knowledge. COMIFER estimates the amount of NO3- leached for a crop by considering

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several crop factors (i.e., duration without crop cover, amount of N released by crop residues, N-

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absorption capacity of the following crop, and application of organic fertilisers in autumn) and soil factors

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(i.e., drainage amount and soil organic-matter content). It attributes an amount of leached NO3- to each

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combination of crop and soil factors without specifying temporal dynamics. NO3- emissions were

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estimated first at the field scale and then aggregated by administrative region. For each crop, mean

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emissions at the French national level were calculated from regional means that were weighted by

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production volume of the crop. COMIFER assumes that N-fertiliser inputs do not exceed crop

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requirements, which does not always hold true, in particular in regions with excess organic fertilisers

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(including animal excretions); it thus constitutes a limit of the model. NO3- leaching for permanent

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grassland was estimated using the mechanistic DEAC model.23 DEAC estimates the amount of NO3-

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leached by considering the amount of N fertilisers, timing of fertiliser application, grazing, and drainage

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amount. It does not specify temporal dynamics.

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On-site NH3 emissions due to volatilisation of mineral and organic N fertilisers were estimated

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according to EFs suggested by the models EMEP/CORINAIR24 and EMEP/EEA,25 respectively. Different

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EFs were used depending on the emission source (during fertiliser application or during grazing), the

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fertiliser type (mineral or organic) and form (liquid or solid), and the animal type. A list of EFs used in

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AGRIBALYSE can be found in Koch and Salou.23

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N2O emissions were calculated according to EFs from the IPCC Tier 1 method.11 Following the

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IPCC definition,11 N2O emissions include direct emissions due to N-fertiliser inputs and from crop

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residues and indirect emissions due to transformations of volatilised NH3 and NOx and leached NO3 (see

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the Supporting Information for the equation to calculate N2O emissions). More detailed information on

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calculation of N2O emissions in the AGRIBALYSE database can be found in Koch and Salou.23

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TNT2-LCA Method

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TNT2 The process-based model combined with LCA in this study is the Topography-based simulation of

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Nitrogen Transfer and Transformation (TNT2) model consisting of the crop model STICS26 that is adapted

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and validated for grassland and for sequences of annual crops, a hydrological model based on

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TOPMODEL assumptions,27 and the NEMIS model28 for the denitrification process. The main feature of

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TNT2 is its ability to spatially simulate interactions between soil and shallow groundwater and their

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influence on N dynamics. The model has been thoroughly tested and used to study N dynamics in

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agricultural catchments.29-31 A detailed description of the model can be found in Beaujouan et al. (see the

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Supporting Information for parameterisation and calibration of the model)32 It is fully distributed, with

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multiple levels of spatial discretisation: pixels, soil units, fields, climatic zones, and the catchment.

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Agricultural practices are input into the model as a succession of individual crop technical operations

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(sowing, fertilisation, grazing, harvesting, etc.) for individual fields. It runs at a daily time step for

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multiple-year simulations. Output from its simulations includes the daily NH3 flux due to volatilisation (kg

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NH3-N/ha of agricultural land), the daily water flow at the outlet of a catchment (in water height, m), the

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N concentration of the flow (g NO3--N/m3), and daily N loss due to denitrification (“total denitrification

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loss”, g N/m2) in the catchment (including both hillslope and riparian zones).

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Description of TNT2-LCA In TNT2-LCA, on-site and off-site emissions are distinguished. On-site

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emissions are defined as flows of potentially polluting substances due to agricultural practices and

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biogeochemical processes within the catchment. Off-site emissions are defined as flows of potentially

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polluting substances associated with production of inputs for crop production and occurring outside of the

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catchment. For a given crop, the indicator result of a certain impact category (cat) is calculated by linking

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inventory results of relevant substances (subs) with corresponding characterisation factors (CF) as:

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Indicator Resultcat =

∑ CF

cat, subs

× (Inventory Resultsubs, on-site + Inventory Resultsubs, off -site )

Equation (1)

subs

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The inventory result of a substance refers to the amount of the substance emitted into the

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environment. In TNT2-LCA, substances were inventoried, grouped into impacts of climate change (using

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the category indicator of global warming potential (GWP)) and eutrophication (using the category

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indicator of eutrophication potential (EP)), and then characterised using CFs of Forster et al.33 and

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Heijungs et al.34 (Table S1).

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N2O from denitrification according to TNT2-LCA In the real world, N2O emitted within a catchment

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(i.e., N2Oonsite) comes from both nitrification and denitrification. However, TNT2 predicts only N

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emissions from denitrification (i.e., N2Odenitri+N2) occurring within the catchment (i.e., hillslope and

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riparian zones), excluding N emissions from denitrification downstream of the catchment outlet (and in

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the ocean) and those from nitrification. In addition, N2O from deposition of NH3 and NOx, most of which

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occurs outside the catchment, is not included. Therefore, in TNT2-LCA, N2Oonsite is assumed to equal

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N2Odenitri. Estimates of NO3-onsite, NH3onsite, and N2Oonsite were obtained by integrating daily N flows

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predicted by TNT2 over a specific inventory period and applying a range of values for the percentage of

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total denitrification loss that is N2Odenitri. Estimates of on-site emissions of other substances and of off-site

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emissions of all substances (Table S1) were obtained from the databases AGRIBALYSE v1.123 and

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ecoinvent v2.235 (Fig. 1).

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Denitrification is influenced by many factors and is highly variable over space and time.36-39

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NEMIS,28 as used in TNT2-LCA, predicts a daily denitrification rate as a function of soil temperature,

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NO3- content, and water saturation and residence time.39 According to Bouwman et al.,36 the percentage of

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total denitrification loss that is N2O in riparian zones and agricultural soils ranges from 0.3-73.0%. Oehler

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et al.39 applied the acetylene blockage technique to incubated soil cores sampled from a catchment in

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Brittany, and reported that 60% of total denitrification loss was N2O. Thus, 60% was assumed to be the

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maximum percentage of total denitrification loss that is N2O in catchments of Brittany. To explore the

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influence of variability and uncertainty in N2O emissions, the percentage was set from 0.3-60.0% in the

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study.

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Case Study of Crop Production in the Kervidy-Naizin Catchment

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Kervidy-Naizin (Fig. S1) is a 4.82 km² headwater catchment located in Brittany, France (48°N, 3°W).

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Its agricultural area is 3.88 km². The soil is silty loam (0.5-1.5 m deep), with well-drained hillslope

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areas and a poorly drained zone near the channel network. The bedrock is Brioverian schist, with a

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weathered layer of variable thickness (a few to 30 m thick). A shallow and perennial groundwater

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body develops in the soil and weathered bedrock. Its topography is moderate (slopes ≤ 5%, elevation

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98-140 m). Local climate is temperate (mean daily Tmax = 11.2°C, 1994-2001). Mean annual rainfall

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is 814 mm, with the maximum and minimum monthly means reached in November (100 mm) and

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June (38.5 mm), respectively (1994-2001). The catchment has been investigated in several studies

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(e.g., Benhamou et al.,40 McDowell et al.,41 and Salmon-Monviola et al.42) using TNT2.

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Three virtual cropping systems (S1, S1, and S3) with real agricultural practice data were

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configured for the agricultural area of the catchment to explore the influence of different crops (Ci,

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i=1-6, 30 Aug 1994 - 30 May 2001): S1 contains grazed permanent grassland, S2 contains continuous

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silage maize (Zea mays L.), and S3 contains a sequence of annual crops (C1=C6=silage maize,

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C2=wheat (Triticum aestivum), C3=pea (Pisum sativum L.), C4=potato (Solanum tuberosum L.),

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C5=rapeseed (Brassica napus L.), with white mustard (Sinapis alba L.) as a catch crop whenever

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possible) (Fig. 2). Corresponding crop technical operations (Table S2) based on survey data in

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Kervidy-Naizin were represented in TNT2 simulations.

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RESULTS

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On-site Emissions

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When AGRIBALYSE was used (Table 1), a single value for NO3-onsite was estimated for a given crop

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present in any year in either a continuous cropping system (SDS1=SDS2=0; SD stands for “standard

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deviation”) or a system containing a sequence of annual crops (NO3-SilageMaize=36.0 kg NO3--N/ha/yr,

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S2 and S3); different crops typically had different values (SDS3≠0).

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When TNT2 was used, a pattern of annual drainage “waves” was observed for NO3- emissions

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(Fig. 3), with high emissions occurring in winter and low emissions in summer, which corresponds to

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the seasonal variations exhibited in many humid and temperate catchments (Molénat et al., 2002).

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These waves represent the integral of daily NO3- fluxes over a time interval that was similar for the

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three systems: August-to-August. The amount of NO3- leached varied among drainage waves and

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tended to be lowest for S1 (permanent grassland) and highest for S2 (continuous silage maize), with

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S3 (annual crop sequence) in-between (Fig. 3 and Table 1). A pattern of annual “waves” was also

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observed for total denitrification loss (i.e., N2Odenitr+N2) for the three systems, with a corresponding

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time interval: January-to-January (Fig. S2).

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When TNT2 was used, predicted NO3- emissions varied among years for S1 (SDS1=10.8) and

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S2 (SDS2=27.6), because although the same crop was grown each year in these systems, weather

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varied among years, affecting crop yields (and consequently N uptake) and drainage amounts. NO3-

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emissions varied even more among years for S3 (SDTNT2-LCA>SDAGRIBALYSE), because not only did

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weather vary among years, but crops and their agricultural practices also varied. Similar predictions

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were made for NH3 and N2O emissions (Tables 2 and 3, respectively).

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Potential Environmental Impacts

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AGRIBALYSE and TNT2 agreed that the permanent grassland system (S1) had the lowest EP and the

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silage maize system the highest (S2) (Table 4). This is in line with the ranks of mean on-site NO3-

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emissions for the three systems according to the two models (S1

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