Improved Environmental Life Cycle Assessment of Crop Production at

Subscriber access provided by FLORIDA ATLANTIC UNIV

Policy Analysis

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 32

Environmental Science & Technology

1

Improved environmental life cycle assessment of crop

2

production at the catchment scale via a process-based

3

nitrogen simulation model

4

5

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

6

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

7

8

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

9

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

10

11

12

13

14

15

16

17

18

1

ACS Paragon Plus Environment


Page 2 of 32

19

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

20

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

21

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

22

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

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

provides improved LCA of crop production at the catchment scale.

32

33

34

35

36

2


Page 3 of 32


37

INTRODUCTION

38

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

39

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

40

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

41

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

42

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

43

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

44

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

45

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

46

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

47

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

48

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

49

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

50

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

51

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

52

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

53

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

3



Page 4 of 32

54

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

55

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

56

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

57

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

58

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

59

France,4,

60

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

61

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

62

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

63

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

64

at the landscape scale.

17, 18

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

65

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

66

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

67

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

68

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

69

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

70

case study of crop production in a catchment in France.

71

4


Page 5 of 32


72

MATERIALS AND METHODS

73

AGRIBALYSE Method

74

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

75

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

76

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

77

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

78

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

79

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

80

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

81

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

82

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

83

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

84

was set to one calendar year.

85

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

86

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

87

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

88

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

89

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

5



Page 6 of 32

90

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

91

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

92

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

93

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

94

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

95

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

96

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

97

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

98

amount. It does not specify temporal dynamics.

99

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

100

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

101

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

102

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

103

AGRIBALYSE can be found in Koch and Salou.23

104

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

105

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

106

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

6


Page 7 of 32


107

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

108

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

109

110

TNT2-LCA Method

111

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

112

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

113

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

114

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

115

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

116

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

117

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

118

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

119

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

120

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

121

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

122

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

123

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

7



Page 8 of 32

124

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

125

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

126

127

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

128

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

129

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

130

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

131

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

132

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

133

Indicator Resultcat =

∑ CF

cat, subs

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

Equation (1)

subs

134

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

135

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

136

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

137

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

138

Heijungs et al.34 (Table S1).

139

140

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

141

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

8


Page 9 of 32


142

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

143

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

144

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

145

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

146

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

147

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

148

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

149

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

150

ecoinvent v2.235 (Fig. 1).

151

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

152

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

153

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

154

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

155

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

156

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

157

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

158

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

159

study.

9



Page 10 of 32

160

161

Case Study of Crop Production in the Kervidy-Naizin Catchment

162

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

163

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

164

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

165

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

166

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

167

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

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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

176

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

10


Page 11 of 32


177

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

178

Kervidy-Naizin were represented in TNT2 simulations.

179

180

RESULTS

181

On-site Emissions

182

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

183

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

184

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

185

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

186

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

187

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

188

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

189

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

190

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

191

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

192

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

193

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

194

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

11



Page 12 of 32

195

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

196

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

197

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

198

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

199

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

200

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

201

202

Potential Environmental Impacts

203

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

204

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

205

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

Improved Environmental Life Cycle Assessment of Crop Production at

Recommend Documents