Using rye as cover crop for bioenergy production: An environmental and economic assessment

Biomass and Bioenergy 95 (2016) 116e123

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Research paper

Using rye as cover crop for bioenergy production: An environmental and economic assessment Elorri Igos a, *, Katarzyna Golkowska a, Daniel Koster a, Bram Vervisch b, Enrico Benetto a a

Luxembourg Institute of Science and Technology (LIST), Environmental Research and Innovation (ERIN), 5, av. des Hauts-Fourneaux, L-4362 Esch-surAlzette, Luxembourg b Inagro vzw, Ieperseweg 87, B- 8800 Rumbeke-Beitem, Belgium

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 March 2016 Received in revised form 16 September 2016 Accepted 30 September 2016

The use of cover crops (CCs) during winter can improve the structure and water retention capacity of the soil. Additionally, the harvested CCs could be used as substrate in an anaerobic digestion (AD) plant. This paper aims at assessing the environmental and economic consequences of planting rye as a winter CC (after maize) and its use as co-substrate in an AD plant (Rye scenario) instead of leaving the land fallow during winter and use solely maize for co-digestion with manure (NoRye scenario). The life cycle assessment (LCA) of 1 MJ of produced bioenergy (36% electricity and 64% heat) shows significant benefits for marine eutrophication for the Rye scenario due to reductions in nitrate leaching. However, the lower specific yield of rye and the biogas potential for the Rye scenario resulted in higher total impacts on climate change and resource depletion (higher use of machinery and infrastructures for 1 MJ of produced bioenergy), as compared to the use of maize in the NoRye scenario. Based on the analysis, possible methodological improvements are highlighted, in particular for the simulation of field emissions and regionalization of impacts. From an economic point-of-view, planting rye during winter could generate additional revenues for the farmer. However, the calculation incorporates large uncertainties, linked mainly to price volatility, seasonal weather conditions (and related yield variations), and to the possible influence of CCs on the summer crop yield. In conclusion, this paper presents a first overview of the sustainability performances of using rye as a CC for energy purposes. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Cover crops Harvest Bioenergy Life cycle assessment Costs Maize-rye rotation

1. Introduction Cover crops (CC) are planted on the field during the part of the year when the land would usually be left fallow, with the primary aim to reduce erosion and improve the structure and water retention capacity of the soil. The management of cropping systems with CCs also brings benefits with regard to soil characteristics, such as increased humus building, reduced compaction, biological weed and pest control, moisture conservation, as well as improved nutrient cycling through avoidance of leaching and increase of nitrogen sequestration [1e4]. Although previous authors have investigated the agricultural performances of CC, e.g. nitrate leaching, soil productivity or crop yield [1,3e8], the overall environmental impacts of this practice were only assessed in a few recent studies [9,10]. In these studies, life cycle assessments (LCAs)

* Corresponding author. E-mail address: [email protected] (E. Igos). http://dx.doi.org/10.1016/j.biombioe.2016.09.023 0961-9534/© 2016 Elsevier Ltd. All rights reserved.

of different bioenergy production systems were performed using CCs as a soil protection measure [9,10]. However, these two research works do not evaluate the environmental consequences of using CC during the winter as a substrate for energy production purposes. Kim & Dale [10] performed a comparison of different cropping management systems (including one scenario with wheat as a CC but not as a substrate) for biofuel production. Bühle et al. [9] compared different techniques of biomass (maize and winter rye) transformation into energy but did not focus on the cropping system itself. This lack of evaluation of CC effects for energy production can be explained by the fact that, in the past, CCs were mainly cultivated as co called “green manure.” This meant that the CCs were not harvested, but instead incorporated into the soil before the main crop was sowndthus contributing to higher yields of the main crop. Nevertheless, there is an increasing need to develop such bioenergy solutions due to the growing scarcity and the societal dependence on fossil fuels as well as the increasing biomass demand. In this context, and considering the modern, intensive

E. Igos et al. / Biomass and Bioenergy 95 (2016) 116e123

agricultural constraints, a pilot study was conducted in the framework of the ARBOR project (INTERREG IVB NWE) in order to optimize cultivation of CCs as additional biomass streams (not competing with food and feed production) for energetic use. The field trials with maize as a main crop and winter rye as a CC were conducted in Flanders (Belgium). These crops were chosen due to their common use in Flanders (and Europe) and the crop succession feasibility. This allows for the possible late sowing of rye in the autumn with still good yields in late spring, combined with possible late sowing of maize in spring and still good harvests in autumn. Moreover, rye was in particular selected as a CC because of its frost resistance and low nitrogen requirements, and therefore its decreased fertilizer demand [11,12]. The objective of this paper is to evaluate the environmental consequences of planting rye as a winter CC (after maize cultivation) and its use as a co-substrate with maize and manure in an anaerobic digestion (AD) plant, instead of leaving the land fallow during winter and only using maize for co-digestion in the AD plant. The analysis is based on the LCA methodology, which is a standardised approach [13] to estimate the environmental impacts of a product or process along its life cycle. To the author's knowledge, such an assessment has not been performed yet. In order to get a more complete view on the sustainability performances of the analysed system, the economic performance of the CCs for bioenergy concept is additionally assessed. 2. Materials and methods 2.1. Goal and scope definition The aim of the study is to compare the environmental and economic impacts of the cultivation of rye during winter, its harvest, and subsequent use together with maize silage in AD (scenario “Rye”) versus leaving the land fallow in the winter and co-digesting maize silage only (scenario “NoRye”). For the LCA study, the whole lifecycle of the system was considered, from crops cultivation, their co-digestion with pig manure, to the final energy production stage at the combined heat and power (CHP) plant (Fig. 1). The functional unit considered was the production of 1 MJ of energy (36% electricity and 64% heat). The above mentioned foreground processes were linked to background processes from the ecoinvent database v.2.2 [15] to describe the lifecycle of the transformed inputs (e.g. fertilizer, digester unit, etc.). For the economic evaluation, the farmer perspective was chosen. Based on the cultivation costs for the winter rye and the

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revenues from selling the marketable substrate for biogas production, the potential net benefits of farmer have been calculated. This approach does not consider potential externalities, like water or air pollution, which could affect the costs of other sectors, such as water treatment. 2.2. Life cycle inventory (LCI) This section gives an overview of the studied processes. While data regarding the crops cultivation were used for both the environmental and economic evaluation, the inventory data for the codigestion and energy production were considered only in the LCA study. 2.2.1. Crops cultivation Maize was sown in the middle of May and harvested at the end of October 2013. Winter rye was sown at the beginning of November 2013 and harvested mid-May 2014. The characteristics of the harvested crops are displayed in Table 1. Since rye was cultivated as a CC during the winter and harvested as green, wholeplant silage at an early plant maturity stage, its productivity per hectare (ha) was much lower than for regular rye silage. The slight decrease in maize maturity at harvest time in the Rye scenario as compared to the NoRye scenario explains the slightly lower yield, as also reported by other researchers [3]. Moreover, the specific energy captured in one kilogram (kg) of fresh matter (FM) for rye silage was 20% lower than for maize silage. However, it has to be mentioned that the maize yields considered for this study (based on field data) are considered relatively high as compared to literature [8,14] (see Table S1 of the Supplementary Material). The uncertainties linked to the measurements of yield, dry matter content, and biogas potentials have been evaluated in Section 3.2. Maize cultivation included ploughing, power harrow, rotary harrow, sowing, fertilizing with mineral fertilizer, herbicide application, digestate application, and chopping. For rye, the conducted operations were ploughing, harrowing, sowing, fertilizing, mowing, tedding, raking, and chopping. For the background LCI modelling, the agricultural machinery operations were described by ecoinvent processes [16], in which diesel consumption was modified based on data collected on-site (Table 2). The air emissions due to diesel combustion were also respectively adapted. The agricultural operation inputs were calculated based on yield data (Table 1). The amounts of ammonium nitrate, digestate, seeds, and herbicides (only applied to maize) were based on the pilot site data (Table 3). It should be

Environment Background processes Foreground processes Herbicides Fertilizer

Digester

Transport

Diesel Oil

CHP

Pig manure Maize cultivation

Maize

Codigestion

Biogas

Energy production

Electricity Heat

Seeds Digestate Machinery operations

Rye cultivation

Rye

Fig. 1. System boundaries for the LCA of the bioenergy production. Italic font for process and flow only considered for Rye scenario. Plain arrows for intermediary flows (exchanges of transformed products) and doted arrows for elementary flows (direct exchanges with the environment).

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Table 1 Characteristics of the crops included in the assessment.

Yield Dry matter content Specific biogas potential Specific energy

Unit

Maize silage NoRye scenario

Maize silage Rye scenario

Rye silage Rye scenario

ton FM ha
1 % FM L kg FM
1 MJ kg FM
1

80 30 212 3.6

72 28 200 3.4

32 26 170 2.8

Table 2 Specific diesel consumption for the agricultural machinery operations. Operation

Diesel consumption (L ha
1) Maize

Rye

Ploughing Power harrow Rotary harrowing þ sowing Fertilizing Herbicides application Digestate application Mowing Tedding Raking Chopping

30 45 60 60 10 45 e e e 75

30 e 60 5 e e 10 7.5 5 50

noted that the maize field management was not affected by the planting of CC during winter (same amounts used per hectare). The inputs production was modelled through the corresponding ecoinvent process, except for the digestate application. The digestate supplied by the AD plant (see Fig. 1) represents a closed loop system and no further impacts were considered to avoid doublecounting. Due to a lack of ecoinvent background processes for the different types of herbicides used, the generic process for herbicide production was used. The characteristics of ammonium nitrate and seeds (including trace elements content) were taken from Ref. [16] while the compositions of digestate and herbicides were provided by Inagro. Carbon dioxide sequestered during crop growth, is assumed to be 1.29 kg CO2/kg for rye and 0.48 kg CO2/kg for maize silage [16]. Land use was modelled with the ecoinvent flow “Occupation of arable land, non-irrigated”, considering an occupation period of one year for maize cultivation in the NoRye scenario and a half-year each, both for rye and for maize in the Rye scenario. Effects of land transformation were not taken into account because it was assumed that the land had the same function before and after the cultivation of the crops on the pilot field. Equations from Ref. [16] were used to calculate the emissions of

the following: ammonia to air; nitrate to groundwater (via leaching); nitrous oxide to air; mono-nitrogen oxides to air; phosphorous to groundwater (via leaching); phosphorous to rivers (via runoff and erosion); and heavy metals (cadmium, copper, zinc, lead, nickel, chromium and mercury) to groundwater (via leaching), to rivers (via erosion), and to soil. The parameters from Table 3 were applied to identify the inputs of pollutants to the field. For nitrate €gi [16] consider a leaching leaching to groundwater, Nemecek & Ka risk of 10% for fertilizer application in March for rye and 70% for application in May for maize. However, it should be noted that €gi [16], the leaching risk for maize in according to Nemecek & Ka June (one month later than applied) is equal zero. Such abrupt change of the parameter could significantly affect the results. The leaching risk was adapted for the Rye scenario. One of the main advantages of using CCs during winter is the nitrogen sequestration in soil, which can reduce the leaching effect by nearly 60% [17]. This literature-based value was therefore applied to the nitrate emissions to groundwater during maize cultivation when CCs were planted. For phosphorous and heavy metal emissions to river, the erosion value provided by Inagro (1000 kg ha
1) was assumed to decrease by 20% in the Rye scenario, based on the influence of CCs on C-factor (cover management factor) reduction from Ref. [18]. To analyse the impacts of pesticide emissions after application, the PestLCI 2.0 model [19] determined the fraction of the applied active substance emitted to air, surface water, and groundwater, as well as the fraction degraded or up taken by the crops. From all active ingredients applied on the maize crop, only the herbicide terbuthylazine was available in the PestLCI database and could therefore be modelled. Since this ingredient represented only 40% of herbicides applied on maize crops, the total herbicide impact during maize cultivation has been underestimated in this study. The model required several inputs: active ingredient (terbuthylazine), crop type (maize), soil texture (type 7 corresponding to high sand and pH 4.6), climate (temperate maritime II representative from De Bilt in the Netherlands), month (May), application rate (0.666 kg/ha), application mode (conventional for cereals) and slope (0%). The other parameters, such as field length, depth of

Table 3 Materials applied on the cultivation fields.

Inputs for maize Inputs for rye Ecoinvent process Composition

a

Ammonium nitrate

Digestate

Rye seeds

Maize seeds

Herbicides

148 kg ha
1

62.5 m3 ha
1

e

31 kg ha
1

4.3 kg ha
1

e Maize seed IP, at regional storehouse/CH Heavy metals [mg kg DM
1] Cd: 0.1 Cu: 5 Zn: 34.5 Pb: 1.61 Ni: 0.48 Cr: 0.7 Hg: 0.01

e Herbicides, at regional storehouse/RER Active substances [g kg
1] Flufenacet: 93 Terbuthylazine: 155 Tembotrione: 93 Isoxadifen-ethyl: 47 Nicosulfuron: 4

296 kg ha
1 Ammonium nitrate, as N, at regional storehouse/RER Nutrients [kg kg
1] N: 0.35 Heavy metals [mg kg
1] Cd: 0.06 Cu: 8.91 Zn: 63.6 Pb: 2.42 Ni: 16.5 Cr: 5.09

Closed loop from the AD.

e ea Nutrients [kg m
3] N: 2.4 P: 2.4 Heavy metals [mg m
3] Cu: 17.57 Zn: 38.13

100 kg ha
1 Rye seed IP, at regional storehouse/CH Heavy metals [mg kg DM
1] Cd: 0.1 Cu: 3.2 Zn: 13 Pb: 0.4 Ni: 0.7 Cr: 0.5

E. Igos et al. / Biomass and Bioenergy 95 (2016) 116e123

drainage system, soil material density, were kept with the default values due to lack of more particular information. The PestLCI 2.0 model calculated the following emission fractions: 3.7% emitted to air, 0.00094% emitted to surface water, 3.1% emitted to groundwater, and 93.2% considered as biodegraded (no impact considered for these effects). 2.2.2. Co-digestion and energy production Both maize and rye silage were co-digested with pig manure in a small scale AD plant located 4.5 km from the fields. Pig manure, coming from a farm nearby, was stored in closed tanks next to the stable. No impact related to the production of manure was included since it was considered a waste [20]. Without using rye as a CC (NoRye scenario), the digester was fed daily with 1600 kg of maize silage and 1400 kg of pig manure, while in the Rye scenario the daily biomass input to the digester consisted of 500 kg of rye silage, 1100 kg of maize silage and 1400 kg of pig manure. The additional 500 kg of maize silage for the scenario NoRye were assumed to be provided by another field located at a 15 km distance to the AD plant (additional transport included). The biogas production expressed according to the functional unit was calculated from the measured biogas potential (Table 1). The AD plant, consisting of one stirred digestion tank, with a retention time of approximately 40 days, was modelled with the ecoinvent process “Anaerobic digestion plant covered, agriculture/CH” with an associated lifetime of 20 years [21]. The digestion step required 100 MWh of heat and 28 MWh of electricity per year (of which 10 MWh are for internal use of the digester and its auxiliaries and 18 MWh for the pumps), which were provided by the CHP (closed loop system). Emissions of carbon dioxide, methane, ammonia, hydrogen sulphide, dinitrogen monoxide, and waste heat were derived from the dataset “Biogas, from biowaste, at agricultural co-fermentation, covered/CH” [21]. The biogas was burned in a CHP unit with a 32% electrical and 58% thermal efficiency. The multi-output process from ecoinvent “Biogas, agriculture covered, burned in cogen with ignition biogas engine [CH]” [21] was used as a reference (similar characteristics to the pilot installation) to model the use of infrastructures, oil and diesel, as well as the air emissions. 2.3. Life cycle impact assessment (LCIA) The LCA model was implemented in the software SimaPro® v7.3.3. The LCIA was based on the ILCD recommendations [22]. The chosen environmental indicators reflect the major concerns linked to the agricultural systems. First, the categories “water resource depletion” (WD), based on local scarcity of water from the Swiss Ecoscarcity model [23], and “mineral & fossil resource depletion” (MFD), are based on the ultimate stock reserves compared to the ones of antimony (chosen as reference element) from CML 2002 model [24] to assess the impacts linked to resource use. “Global warming potential” (GWP) quantifies the effects of greenhouse

119

gases [25]. The impacts on soil are quantified through the categories “land use” (LU), indicating the change of soil organic matter [26] and “acidification” (AC), characterizing the accumulated exceedance (emissions above critical loads accumulated by all ecosystems) due to acidifying substance deposition [27,28]. “Freshwater eutrophication” (FEP) and “marine eutrophication” (MEP) express the change of the nutrient load (Phosphorous-based and Nitrogen-based, respectively) in water streams (from ReCiPe method [29]). Finally, “freshwater ecotoxicity” (FET) is finally chosen to evaluate the impact of toxic substances based on the scientific consensus model USEtox [30] because this indicator provides less uncertainties to represent toxicity (as compared to human toxicity, terrestrial and marine ecotoxicity). No impact aggregation was performed (either through endpoint characterization, normalization, or weighting) to avoid adding uncertainties to the impact characterization step and losing information related to the different indicators. 2.4. Economic evaluation The analysis was conducted based on the data from the experimental field and also to a limited extent on other sources of agricultural data. The study aimed at assessing the costs of CC cultivation and whether they can be compensated by the possible revenues by selling the ensiled CC. The maize cultivation costs were not included as such because they are not expected to change with CC use; however, the potential loss of revenues due to maize yield reduction (see Table 1) was evaluated. The economic assessment of rye cultivation included all the costs linked to field management works (fuel costs, fixed machine costs and maintenance), materials (seeds and fertilizer), fuels, and manpower (Table 4). Fuel costs were provided by Inagro, while maintenance, manpower and fixed costs were determined from Ref. [31], adapted with the inflation rate from Ref. [32]. Additionally, for the ensiling process, it was assumed that there were no mass losses, i.e. the total weight of the fresh rye equals the total weight of the silage. The costs for ensiling were taken from Ref. [14]. The market prices of 35 V.ton FM
1 for maize silage [33] and of 27 V.ton FM
1 for rye silage were assumed. This latter assumption was based on the 78% ratio between the specific energy content of rye silage as compared to the one of maize silage (see Table 1). The subsidies resulting from the compulsory, direct payment schemes [34] have not been included in the assessment. This is because they are land attributed, meaning that the farmers receive them independently whether the particular land is covered with CCs during the winter or not. 3. Results & discussion 3.1. LCA results and interpretation The results calculated with the SimaPro® software for 1 MJ of energy produced are presented in Fig. 2. For some indicators (AC

Table 4 Costs of different agricultural activities assumed in the study (in V/ha). Field management works

Fuel costs (V ha
1)

Maintenance costs (V ha
1)

Fixed costs (V ha
1)

Manpower costs (V ha
1)

Ploughing Harrowing þ Sowing Fertilizing Mowing Tedding Raking Picking up þ Chopping Transport Ensiling

24 48 87 8.0 6.0 4.0 40 3.6 0.0a

16 23 4.0 8.1 11 4.0 15 1.7 10

30 53 5.1 10 7.7 5.1 49 4.0 31

27 35 8.8 18 13 8.8 18 2.6 0.0a

a

Costs are included in the fixed costs and maintenance costs.

E. Igos et al. / Biomass and Bioenergy 95 (2016) 116e123

4.E-02 3.E-02 2.E-02 1.E-02

2.0E-01

1.5E-01 1.0E-01 5.0E-02

NoRye

5.E-06

- 47%

2.E-04 1.E-04

- 4%

3.E-06 2.E-06 1.E-06

NoRye

Rye

MFD results (kg Sb eq)

6.E-06 4.E-06 2.E-06

NoRye

Rye

Rye

6.E-01

+ 1%

5.E-01 4.E-01 3.E-01 2.E-01 1.E-01

NoRye

Rye

Legend: + 22%

3.E-07 2.E-07

Maize cultivation (indirect) Maize cultivation (direct)

2.E-07

Rye cultivation (indirect) Rye cultivation (direct)

1.E-07

Digester (indirect) Digester (direct)

5.E-08

0.E+00

0.E+00

NoRye

Rye

3.E-07

+ 17%

8.E-06

4.E-04

0.E+00

0.E+00

1.E-05

6.E-04

Rye

4.E-06

0.E+00

NoRye

8.E-04

2.E-04

6.E-04

3.E-04

1.E-03

0.E+00

6.E-06

4.E-04

1.E-03

-5.0E-02

Rye

- 1%

1.E-03

0.0E+00

7.E-04

5.E-04

2.E-03

AC results (mol H+ eq)

FET results (CTUe)

5.E-02

NoRye

WD results (m3 water eq)

- 7%

2.5E-01

0.E+00

MEP results (kg N eq)

3.0E-01

+ 12%

FEP results (kg P eq)

GWP results (kg CO2 eq)

6.E-02

LU results (kg C deficit eq)

120

NoRye

Rye

CHP (indirect) CHP (direct)

Fig. 2. LCA results differentiated between the direct (foreground emissions or resources) and indirect (emissions and resources coming from background processes) impacts of the system processes.

and LU), changes observed between the two scenarios are negligible because the Rye scenario generates benefits and drawbacks that compensate each other. Indeed, the lower field application of nitrogen to rye crops decreases the emissions of ammonia to air (impacting AC). However, the lower crop yield and biogas potential for the Rye scenario (see Table 1) contribute to increased use of machinery and chemical fertilizer for the functional unit (1 MJ of energy produced), thus generating additional emissions of nitrogen oxides and sulphur dioxide to air (AC). Concerning LU impacts, scenario Rye allows optimizing land use (decreasing the amount of arable land occupation) on the cultivation site, but generates additional land demand linked to the higher consumption of seeds (see Table 3). Consequently, this contributes to increased impacts with regard to the transformation of arable land. In the Rye scenario, a considerable decrease of the impacts on MEP could be observed, while the reduction trend was not so strong but still perceivable for FET and FEP. Effectively, the CCs emit less nitrates to groundwater due to leaching mitigation and lower nitrogen input on the field, leading to a significant reduction to MEP. FET and FEP impacts are lower for the Rye scenario due to reduced digestate and herbicide application per functional unit, as those are only applied during maize cultivation (FEP caused by phosphate emissions to water and FET mainly caused by the emissions of copper and zinc to soil and of terbuthylazin to water). Finally, the background processes of the Rye scenario were

mainly responsible for the increase in GWP, WD and MFD. As shown in Fig. 2, this scenario leads to higher use of machinery and infrastructures because of the lower yield and biogas potential of maize and rye. This affects the emissions of fossil carbon dioxide to air (GWP), the extraction of water to produce materials or crude oil (WD), and the extraction of zinc for infrastructure and tractor construction (MFD). For WD and MFD, a small decrease for the scenario Rye is observed on the digester process due to the avoided transport of additional maize to the digester. Based on the outcomes, it can be observed that the main benefits of the Rye scenario mainly come from local effects, such as the decreased emissions from the crop field. The drawbacks are generally due to indirect impacts, like the increased use of transformed products, which are linked to a lower yield and biogas potential of the rye crop. 3.2. Sensitivity and uncertainty analysis Due to SimaPro® limitations, only a local sensitivity analysis could be applied and not a global analysis to identify the contribution of inputs' uncertainties and interactions on results uncertainty. Variations one-at-a-time (arbitrarily set at ± 20%) were performed for crop yield and dry matter content, specific biogas potential, fertilizer consumption, herbicides and digestate use (only for maize), erosion, C-factor and nitrate leaching reduction with

E. Igos et al. / Biomass and Bioenergy 95 (2016) 116e123

rye, and additional transport distance for maize in the NoRye scenario. Negligible effects (variation < 5%) were observed for rye dry matter content, erosion, C-factor, fertilizer and herbicides use and additional maize distance. Some parameters significantly affected the results only for some categories: maize dry matter content for FET (heavy metals content of grain); nitrate leaching reduction for MEP; rye yield for LU and MFD (calculation for land occupation and machinery use); digestate application rate for AC, MEP and FET (nutrients and heavy metals content of digestate); and rye's specific biogas potential for FEP, FET and LU (calculation for rye input to the digester). Finally, all impacts indicated high level of sensitivity (up to 30% of variation) to specific biogas potential and yield of maize. Indeed, these inputs mostly influence the environmental profiles (included in many calculations such as biogas production and machinery use). The maize cultivation process shows high contributions to the results (see Fig. 2). The LCA outcomes were observed to be stable with this sensitivity analysis, except for the influence of crop characteristics (mainly crop yield and biogas potential), as well as the nitrate leaching reduction for rye on MEP and the digestate application rate on FET. The ranking between the Rye and NoRye scenarios was only affected by the categories with very similar scores (FEP, LU and AC, see Fig. 2), and therefore sensitive to small changes. An additional analysis was conducted to estimate the uncertainty of the results based on the propagation of the uncertainties of the inputs. The Monte Carlo method, available in the SimaPro® software, was used to randomly sample the inputs on their defined probability distributions during a specified number of simulations (5000) in order to generate the results distribution. Within this case study, the most influencing parameters identified in the sensitivity analysis (crop characteristics), as well as the most uncertain parameters for which no measured and specific data were available (nitrate leaching reduction, C-factor reduction and additional maize transporting distance), were investigated (see Supplementary Material). Background uncertainties from the ecoinvent database were also considered. Only the indicator MEP presents a statistically significant advantage (98% probability) for the scenario Rye. For the other categories, the 95% confidence interval between the difference of the two scenarios, including the zero value, means that a clear preference for one scenario cannot be statistically proven. It can, however, be observed that the benefits of scenario Rye for FET and the scenario NoRye for GWP and WD is quite probable (>75%), while results for the other categories do not allow such a conclusion (probability for the preferred scenario is lower than 67%). The environmental performances of the two scenarios for these latter impacts are therefore equivalent in terms of uncertainty range.

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time difference of three to ten years from the study period). Only for the different types of herbicides a proxy was considered (generic dataset). Moreover, the agricultural machinery operations were adapted to match the real data (diesel consumption and related emissions). Concerning the evaluation of environmental impacts, the ILCD handbook [22] assesses the quality level of the recommended indicators. GWP is considered satisfactory, while other categories need some improvements or should be applied with caution, as in the case of FET, LU and WD. It means that even if the selected methods were recommended from ILCD handbook, there is still space for improvement in the field of life cycle impact assessment and results should always be interpreted with caution depending on the quality of data and the model used.

3.4. Costs & revenues The total costs of rye cultivation as a CC during winter and the corresponding possible revenues are presented in Table 5. The market value of the rye silage produced from 1 ha of land is estimated at 861 V. The more profitable scenario considers that the field work is done by the farmer himself, and the CC does not affect the agricultural performances of maize. In that case, the possible revenues could reach 181 V ha
1. The highest costs related to cultivation of CC are linked to machinery (288 V ha
1) and diesel (221 V ha
1) necessary to conduct the agricultural operations, while the seeds, fertilizers and ensiling linked material costs sum up to 171 V ha
1. If external personnel needs to be hired, the manpower costs can reach up to 131 V ha
1. Moreover, the 10% maize yield loss as given in Table 1 would the profit of 280 V ha
1. Taking into account these two factors, the cultivation of CC for harvest would not be financially feasible without additional subsidies. In the new Common Agricultural Policy [34], a part of the direct payments is coupled to climate and environmental friendly agricultural management. These greening measures include the use of CC. Therefore, additional revenue through a greening bonus could be generated if this measure would be implemented by the farmer. Moreover, it needs to be considered that any estimation related to cultivation costs is very volatile and suffers from huge uncertainties, particularly with regard to the prices of agricultural products (rye seeds and silage) as well as fossil fuels (diesel but also mineral fertilizer strongly depending on fuel prices). These products can be influenced by strong price fluctuations linked to an unstable global, political and economic situation related to resources availability as well as unpredictable weather conditions within each cropping year. Finally, the direct influence of the CC on

3.3. Data and model quality For most of the foreground data for intermediary flows (e.g. consumption of herbicides or fertilizer), specific data of the case study could be collected, however, they represent solely single values. Missing information (digester and CHP infrastructures, diesel and oil consumption, as well as emissions of CHP) was complemented with generic ecoinvent data, representative from the analysed technologies in Switzerland in 2007e2009. Concerning the field emissions, the model from ecoinvent was used for nutrients and heavy metals, but the latter can be considered as basic (static, generic constants and basic mass balances). Regarding pesticide emissions, the more complex model, PestLCI 2.0, was applied with specifications from the case study (application rate, type of crop, soil and climate) but only one active ingredient is included in the calculation. Background processes were chosen to be representative for the case study (European or Swiss data with a

Table 5 Results of the economic assessment for cultivation of winter rye as cover crop. Italic fonts represent sub-items for the costs and bold fonts represent the calculated revenues. Parameter

Costs/revenues [V ha
1]

Costs Seeds, fertilizers, materials Fuel Machinesa Total costs

171 221 288 680

Market value of the silage

861

Possible revenues (max) Additional costs: manpower Additional costs: maize yield losses Possible revenues (min)

181 131 280 ¡230

a

Fixed and variable costs excl. fuel.

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the main crop yield loss cannot be easily proven, since the influence of the weather conditions (a cold, wet year or a hot, dry year) can generate much stronger yield effects than those linked to either delay in sowing or earlier harvest of the main culture. Although, the opposite trend, i.e. increased yield with cover crops, was also observed in literature [35]. 4. Conclusions This research work analyses the potential environmental and economic consequences of planting rye as CC during winter for energy production. Based on the results of the study, the use of rye as a winter CC could generate significant environmental benefits on a local scale due to a reduction in nitrate leaching, lower nutrients application (and therefore heavy metals), optimized land use, and avoid use of herbicides. These parameters also lead to potential revenues for the farmer supposing that the yield of the summer crop is not influenced by the CC. However, the lower rye productivity and specific biogas potential as compared to maize, also observed by Ref. [9], contribute to increased, indirect environmental impacts (in particular resources depletion) due to higher materials and fuel consumption for the production of 1 MJ of bioenergy. These conclusions suggest a trade-off regarding CC sustainability between mitigation of local environmental effects (soil and air quality) and reduced energy productivity. Different types of energy production modes (e.g. Integrated Generation of Solid Fuel and Biogas from Biomass (IFBB) studied in Ref. [9]) or of CC (e.g. promising results from wheat in Ref. [10]) could contribute to the optimisation of CC valorisation as an energetic product. The presented results, specific to the case study, would need to be enriched with feedback from other sites, as well as further investigation on model uncertainties. For example, averaged multi-annual data for crop characteristics (the main drivers of environmental profile) have to be collected and analysed in order to decrease uncertainties linked to these parameters. In this study, the defined probability distributions (based on the literature and the expert judgements) could partly represent the variability of agronomic practices and weather conditions. However, data from literature or agricultural databases does not incorporate the influence of winter rye on performance of maize crop, which is a key element differentiating the analysed scenarios, in particular regarding economic impacts. Indeed, the loss of maize yield can change the possible revenues from a CC into potential losses for the farmer. Nevertheless, the lack of long-term recorded data (e.g. at least three years) is a common issue in LCA studies of agricultural products [see e.g. 9, 10]. From an LCA methodological point-of-view, this case study highlighted some limitations that could be improved in the future. First, a more complex emission model could be used, including a higher number of specific data collected on site (e.g. soil samples), and by considering interactions or dynamic effects. The need for concentrating efforts to better simulate nutrient emissions from crop cultivation has also recently been expressed in literature [36,37]. Finally, the local character of the impacts raises the interest for regionalizing the inventory and the characterization factors. Current efforts are being developed in the LCA community to spatialize LCIA methods such as eutrophication, acidification, or toxicity. Based on Geographic Information Systems (GIS), LCA models for agricultural systems have been spatialized for data such as soil characteristics or phosphorous emissions and their subsequent effects [38,39]. Advancements in data collection and modelling could therefore increase the accuracy and reliability of environmental and economic evaluation. This paper is a first milestone to assess sustainability performances of using rye as a CC for energetic purposes and therefore to support decision makers. The transparent data used for the calculations could be reused by

LCA practitioners for comparing results and evaluating further CC methods. Acknowledgements This study was conducted in the framework of the ARBOR project (INTERREG IVB NWE) which was co-financed by the European Commission. The authors wish to express their gratitude to Anke De Dobbelaere and Bart Ryckaert from Inagro for their contribution to the study, as well as to Alya Bolowich for the English language revision. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.biombioe.2016.09.023. References [1] S.M. Dabney, J.A. Delgado, D.W. Reeves, Using winter cover crops to improve soil and water quality, Commun. Soil Sci. Plant Anal. 32 (7e8) (2001) 1221e1250. [2] Y.C. Lu, K.B. Watkins, J.R. Teasdale, A.A. Abdul-Baki, Cover crops in sustainable food production, Food Rev. Int. 16 (2000) 121e157. [3] N.K. Fageria, V.C. Baligar, B.A. Bailey, Role of cover crops in improving soil and row crop productivity, Commun. Soil Sci. Plant Anal. 36 (2005) 2733e2757. [4] L.J. Wyland, L.E. Jackson, W.E. Chaney, K. Klonsky, S.T. Koike, B. Kimple, Winter cover crops in a vegetable cropping system: impacts on nitrate leaching, soil water, crop yield, pests and management costs, Agric. Ecosyst. Environ. 59 (1996) 2e17. [5] G.L. Wilson, R. Lal, B.N. Okigbo, Effects of cover crops on soil structure and on yield of subsequent arable crops grown under strip tillage on an eroded alfisol, Soil Till Reas 2 (3) (1982) 233e250. [6] J.R. Teasdale, Contribution of cover crops to weed management in sustainable agricultural systems, J. Prod. Agric. 9 (4) (1996) 475e479. [7] K.J. Dagel, Improving soybean performance in the northern Great Plains through the use of cover crops, Commun. Soil Sci. Plant Anal. 45 (10) (2014) 1369e1384. [8] T. Amon, B. Amon, V. Kryvoruchko, W. Zollitsch, K. Mayer, L. Gruber, Biogas production from maize and dairy cattle manuredInfluence of biomass composition on the methane yield, Agric. Ecosyst. Environ. 118 (2007) 173e182. [9] L. Bühle, R. Stülpnagel, M. Wachendorf, Comparative life cycle assessment of the integrated generation of solid fuel and biogas from biomass (IFBB) and whole crop digestion (WCD) in Germany, Biomass Bioenerg. 35 (2011) 363e373. [10] S. Kim, B.E. Dale, Life cycle assessment of various cropping systems utilized for producing biofuels: bioethanol and biodiesel, Biomass Bioenerg. 29 (2005) 426e439. [11] M. Griffith, M. Antikainen, W.C. Hon, K. Pihakaski-Maunsbach, X.M. Yu, J.U. Chun, D.S.C. Yang, Antifreeze proteins in winter rye, Physiol. Plant 100 (1997) 327e332. [12] J. Stute, K. Shelley, D. Mueller, T. Wood, Planting Winter Rye after Corn Silage: Managing for Forage, University of Wisconsin, Nutrient & Pest Mgmt Program, 2007. [13] ISO-International Organisation For Standardisation, Environmental Management e Life Cycle Assessment ISO 14040 Principles and Framework ISO, 14044 Requirements and Guidelines, Geneva, 2006. [14] KTBL e Kuratorium für Technik und Bauwesen in der Landwirtschaft, Energiepflanzen - Daten für die Plannung des Energiepflanzenbaus, 2006. [15] R. Frischknecht, N. Jungbluth, H.-J. Althaus, G. Doka, R. Dones, T. Heck, S. Hellweg, R. Hischier, T. Nemecek, G. Rebitzer, M. Spielmann, The ecoinvent database: overview and methodological framework, Int. J. LCA 10 (2005) 3e9. €gi, Life Cycle Inventories of Agricultural Production Systems [16] T. Nemecek, T. Ka e Ecoinvent Report No. 15, 2007. Zürich and Dübendorf. [17] C.S. Snyder, J.J. Meisinger, Capturing residual soil nitrogen with winter cereal cover crops, Newsl. Publ. Int. Plant Nutr. Inst. (IPNI) (September 2012) 1e6. [18] P. Panagos, P. Borrelli, K. Meusburger, C. Alewell, E. Lugato, L. Montanarella, Estimating the soil erosion cover-management factor at the European scale, Land Use Policy 48 (2015) 38e50. [19] T.J. Dijkman, M. Birkved, M.Z. Hauschild, PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA, Int. J. LCA 17 (2012) 973e986. [20] European Commission - Joint Research Centre - Institute for Environment and Sustainability, International Reference Life Cycle Data System (ILCD) Handbook - General Guide for Life Cycle Assessment - Detailed Guidance, first ed., Publications Office of the European Union, Luxembourg, March 2010. EUR 24708 EN 2010. [21] N. Jungbluth, M. Faist Emmenegger, F. Dinkel, C. Stettler, G. Doka,

E. Igos et al. / Biomass and Bioenergy 95 (2016) 116e123

[22]

[23] [24]

[25]

[26]

[27]

[28]

[29]

M. Chudacoff, A. Dauriat, E. Gnansounou, M. Spielmann, J. Sutter, N. Kljun, M. Keller, K. Schleiss, Life Cycle Inventories of Bioenergy e Ecoinvent Report No. 17, Uster, 2007. European Commission-Joint Research Centre - Institute for Environment and Sustainability, International Reference Life Cycle Data System (ILCD) Handbook- Recommendations for Life Cycle Impact Assessment in the European Context, first ed., Publications Office of the European Union, Luxemburg, November 2011. EUR 24571 EN, 2011. €kologischen Knappheit R. Frischknecht, R. Steiner, N. Jungbluth, Methode der o € € .b.u. und Bundesamt für Umwelt, Bern, 2008. e Okofaktoren 2006, o e, M. Gorre e, R. Heijungs, G. Huppes, R. Kleijn, A. de Koning, L. Van J.B. Guine Oers, A. Wegener Sleeswijk, S. Suh, H.A. Udo de Haes, J.A. De Bruijn, R. Van Duin, M.A.J. Huijbregts, Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards. Series: Eco-efficiency in Industry and Science, Kluwer Academic Publishers, 2002. ISBN 1-4020-0228-9. IPCC e International Panel on Climate Change, IPCC Climate Change Fourth Assessment Report: Climate Change, 2007. http://www.ipcc.ch/ipccreports/ assessments-reports.htm.  i Canals, C. Bauer, J. Depestele, A. Dubreuil, R. Freiermuth Knuchel, L. Mila G. Gaillard, O. Michelsen, R. Müller-Wenk, B. Rydgren, Key elements in a framework for land use impact assessment within LCA, Int. J. LCA 12 (2007) 5e15. J. Sepp€ al€ a, M. Posch, M. Johansson, J.P. Hettelingh, Country-dependent characterisation factors for acidification and terrestrial eutrophication based on accumulated exceedance as an impact category indicator, Int. J. LCA 11 (6) (2006) 403e416. €l€ M. Posch, J. Seppa a, J.P. Hettelingh, M. Johansson, M. Margni, O. Jolliet, The role of atmospheric dispersion models and ecosystem sensitivity in the determination of characterisation factors for acidifying and eutrophying emissions in LCIA, Int. J. LCA 13 (2008) 477e486. M. Goedkoop, R. Heijungs, M.A.J. Huijbregts, A. De Schryver, J. Struijs, R. Van Zelm, ReCiPe 2008 a Life Cycle Impact Assessment Method Which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level. Report I: Characterisation Factors, first ed., 2009.

123

[30] R.K. Rosenbaum, T.M. Bachmann, L.S. Gold, M.A.J. Huijbregts, O. Jolliet, R. Juraske, et al., USEtox - the UNEPSETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in Life Cycle Impact Assessment, Int. J. LCA 13 (7) (2008) 532e546. [31] KTBL - Kuratorium für Technik und Bauwesen in der Landwirtschaft, Betriebsplannung Landwirtschaft 2006/2007, 2006. Darmstadt, ISBN-13: 9783-939371-07-6. [32] Eurostat, HICP Inflation Rate for EU, 2014. http://ec.europa.eu/eurostat/tgm/ table.do?tab¼table&init¼1&language¼en&pcode¼tec00118&plugin¼1 (last accessed in August 2015). €ndliche Entwicklung, Umwelt und Ver[33] MLUV - Ministerium für La braucherschutz des Landes Brandenburg. Biogas in der Landwirtschaft, Leitfaden für Landwi rte und Investoren im Land Brandenburg, Potsdam, 2006. [34] European Commission, Direct Payment Schemes. Agricultural and Rural Development, January 2015. Available at: http://ec.europa.eu/agriculture/ direct-support/direct-payments/docs/direct-payments-schemes_en.pdf (last accessed in August 2015). [35] B. Molinuevo-Salces, S.U. Larsen, B.K. Ahring, H. Uellendahl, Biogas production from catch crops: increased yield by combined harvest of catch crops and straw and preservation by ensiling, Biomass Bioenerg. 79 (2015) 3e11. € [36] A. Leip, F.S. Ozbek, O. Hutton, A. Leach, Galloway. The importance and challenge for accounting of soil stock changes in N footprint and N budget studies, in: Proceedings of SETAC Europe 25th Annual Meeting, Barcelona, 2015. [37] W. Liao, H. Van der Werf, J. Salmon-Monviola, Using a process-based simulation model for nitrogen emissions in life cycle inventory analysis of crop production, in: Proceedings of SETAC Europe 25th Annual Meeting, Barcelona, 2015. [38] O. Arodudu, E. Ibrahim, A. Voinov, I. van Duren, Exploring bioenergy potentials of built-up areas based on NEG-EROEI indicators, Ecol. Indic. 47 (2014) 67e79. [39] L. Scherer, S. Pfister, Regionalised aquatic eutrophication impacts from phosphorous emission in crop production, in: Proceedings of SETAC Europe 25th Annual Meeting, Barcelona, 2015.