Integrated Environmental Assessment and Management — Volume 11, Number 3—pp. 397–403 © 2014 SETAC

397

Comparative Attributional Life Cycle Assessment of Annual and Perennial Lignocellulosic Feedstocks Production Under Mediterranean Climate for Biorefinery Framework Amalia Zucaro,y Annachiara Forte,y Massimo Fagnano,z Simone Bastianoni,§ Riccardo Basosi,k and Angelo Fierroy yDepartment of Biology, University of Naples Federico II, Naples, Italy zDepartment of Agriculture, University of Naples Federico II, Naples, Italy §Department of Physical Sciences, Earth and Environment, University of Siena, Siena, Italy kDepartment of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy

(Submitted 11 June 2014; Returned for Revision 24 September 2014; Accepted 28 October 2014)

This paper represents 1 of 7 articles in the special series “LCA Case Study Symposium 2013,” which was generated from the 19th SETAC LCA Case Study Symposium “LCA in market research and policy: Harmonisation beyond standardization,” held in November 2013, in Rome, Italy. This collection of invited papers reflect the purpose of the symposium and focus on how LCA can support the decision-making process at all levels, that is, industry and policy contexts, and how LCA results can be efficiently communicated and be used to support market strategies.

ABSTRACT Annual fiber sorghum (FS) and perennial giant reed (GR) cultivated in the Mediterranean area are interesting due to their high productivity under drought conditions and their potential use as lignocellulosic feedstock for biorefinery purposes. This study compares environmental constraints related to FS and GR produced on experimental farms (in the Campania region) using an attributional life cycle assessment (LCA) approach through appropriate modeling of the perennial cultivation. For both crops, primary data were available for agricultural management. Direct field emissions (DFEs) were computed, including the potential soil carbon storage (SCS). Giant reed showed the lowest burdens for all impact categories analyzed (most were in the range of 40%–80% of FS values). More apparent were the differences for climate change and freshwater eutrophication (respectively 80% and 81% lower for GR compared to FS). These results are due to the short-term SCS, experimentally detected in the perennial GR crop (about 0.25 ton C ha1yr1, with a global warming offsetting potential of about 0.03 ton CO2/tonGR dry biomass). The results are also due to the annual application of triple superphosphate at the sowing fertilization phase for FS, which occurs differently than it does for GR. Phosphorous fertilization was performed only when crops were being established and therefore properly spread along the overall crop lifetime. For both crops, after normalization, terrestrial acidification and particulate matter formation were relevant impact categories, as a consequence of the NH3 DFE by volatilization after urea were spread superficially. Therefore, the results suggest higher environmental benefits of the perennial crop than the annual crop. Integr Environ Assess Manag 2015;11:397–403. © 2014 SETAC Keywords: Life cycle assessment

Energy crops

Perennial crops

INTRODUCTION Following European Union goals to blend biofuels with fossil fuels by a further 10% by 2020 (2009/28/CE), energy production from biomass is generating great interest. Recently, the European Commission gave directives about how to minimize indirect landuse change, and made it clear that first generation biofuels play a limited role. Instead, second and third generation biofuels and other alternatives should be considered as the only strategy in the future (COM 15 final 2014). Lignocellulosic biomass has been identified as a promising feedstock, not only for bioethanol

* Address correspondence to: [email protected] Published online 6 November 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ieam.1604

Annual crops

Direct field emissions

production but in a broader framework of integrated biorefinery systems (Cherubini and Jungmeier 2010). In the context of lowinput lignocellulosic cultivation on marginal lands (those that cannot profitably cultivate food) for biorefinery purposes, the annual fiber sorghum Sorghum bicolor L. (and the perennial giant reed Arundo donax L.), appear of great interest in the Mediterranean basin thanks to their high production under drought conditions (Monti et al. 2009; Buratti and Fantozzi 2010; Fazio and Monti 2011; Barbanti et al. 2006; Angelini et al. 2009). The environmental performance of these feedstock supplies is of great concern in light of their relevant contribution to the overall impact of bioenergy and biomaterial production chains (Cherubini and Jungmeier 2010; Fazio and Monti 2011; Godard et al. 2013). The aim of the present study is a comparative LCA of FS and GR production. The environmental impacts of agricultural practices and related direct field emissions (DFEs) of both crops were calculated, taking into account GR perennial crop

Special Series

EDITOR'S NOTE:

398

Integr Environ Assess Manag 11, 2015—A Zucaro et al

specificities (crop establishment, final removal, soil carbon storage) for a proper comparison with the annual FS.

MATERIALS AND METHODS This study applied a “cradle-to-gate” attributional LCA to compare the environmental constraints related to FS and GR production in a Mediterranean climate. The LCA was applied to experimental cultivations in the Campania region of Southern Italy according to standard procedures (ISO 2006) and implemented by means of SimaPro 8.0.1 software coupled

with the ReCiPe H Ver 1.08 as midpoint hierarchic impact assessment method and EcoInvent database (Ver 2.0). The analysis assumed large-scale crop establishment for the requalification of unpolluted marginal land in a hypothetical biorefinery framework on a regional scale. Primary data of agricultural management were available for both crops (Table 1) and the linked DFEs were computed (Nemecek and Shnetzer 2011). System boundary and functional unit were set as 1 ha of cropped land and 1 kg of dry biomass harvested, respectively, in the year 2012 (Figure 1).

Table 1. Input and output flows for the annual fiber sorghum (FS) and perennial giant reed (GR) cultivations Input Seedbed preparation

GR

FS

Unit

Diesel oil

12

20.5

L ha1

Diesel oil

5

5

L ha1

Urea 46% (as N)



50

kg ha1

Triple superphosphate (as P2O5)

150

150

kg ha1

Potassium sulphate (as K2O)

150

150

kg ha1

12

10

L ha1



17

kg ha1

10 000



rhizomes ha1

Diesel oil

26

26

L ha1

Water (well in ground)

560

560

m3 ha1

5

5

L ha1

Urea 46% (as N)

120

100

kg ha1

Mower

Diesel oil

15.8

16.5

L ha1

Swath, by rotary windrower

Diesel oil



8

L ha1

Baling

Diesel oil

66

46

L ha1

Loading bales

Diesel oil

70.5

40

L ha1

Mower

Diesel oil

6.4



L ha1

Pressurized sprayer

Diesel oil

2.6



L ha1

Roundup (as glyphosate)

2.7



kg ha1

Potato digger

Diesel oil

70



L ha1

Combined rotary harrow

Diesel oil

70



L ha1

30

30

t ha1

a

Ripping and hoeing

Combined rotary harrow

Pre-plant fertilization Combined fertilizer spreader

Crop establishmenta Seeding

Rhizomes planting GR; seeds machinery FS Diesel oil Seeds Rhizomesb

Rescue irrigation

Pump irrigation system

Field mantainance Late N fertilization

Combined fertilizer spreader

Diesel oil

Harvest

Final removalc

Output: Harvested biomass (d.w.)¼–dry weight a

Agricultural practices performed for GR only in the first year of cultivation and therefore to be properly shared for the whole lifetime. A specific record for GR rhizome preparation was implemented in SimaPro based on primary data about machineries and related fuel consumption, used for rhizomes explantation (earthing up, 70L diesel oil ha1), collection and transport (combine harvester, 2.2 L diesel oil ha1), and cutting (band saw by tractor supply). c Agricultural practice performed only for GR at the end of the fifteenth year of cultivation and therefore to be properly shared for the whole lifetime. b

Comparative LCA of Lignocellulosic Feedstocks Production—Integr Environ Assess Manag 11, 2015

399

Figure 1. System boundaries and functional units for fiber sorghum (FS) and giant reed (GR) from raw materials and energy to create 1 kg of dry biomass per hectare. Blue boxes include the annual practices for crop management, whereas the white dotted boxes represent the farm boundaries. For GR, the agricultural practices were performed once for crop establishment and final dismissing, which were properly shared for the crop’s lifetime (15 years). DFE ¼ direct field emissions; SOC ¼ soil organic carbon

Environmental burdens were evaluated considering the following impact categories: climate change (CC; kg CO2 eq; 100-yr time frame); ozone depletion (OD; kg CFC- 11 eq); terrestrial acidification (TA; kg SO2 eq); freshwater eutrophication (FE; kg P eq); marine eutrophication (ME; kg N eq); photochemical oxidant formation (POF; kg NMVOC eq); particulate matter formation (PMF; kg PM10 eq); water depletion (WD; m3); and fossil depletion (FD; kg Oil eq). Human and ecotoxicity impact categories were not included in the present study, due to large differences between the impact assessment methods and large uncertainties related to data sets (European Bioplastics 2012; Rosenbaum et al. 2008). Description of experimental areas The field trials were located in Torre Lama (Bellizzi; 40° 37’N, 14°58’E) and Acerra (Napoli; 40°57’N, 14°25’E) for GR and FS, respectively. The climate at both sites is Mediterranean. The values of some soil characteristics at Torre Lama are: bulk density 1.42, sand (47%), silt (22%), clay (31%), pH 7.4, organic matter (OM) 2.0%. The values of some soil characteristics at Acerra are: bulk density 1.35, sand (63%), silt (23%), clay (14%), pH 7.4, OM 3.2%. Arundo donax L. was grown from 2009 to 2012 in 5000 m2 (50 m  100 m) plots, whereas Sorghum bicolor L. was grown in 2012 in 1080 m2 (18 m  60 m) plots. Inventory The environmental performance of GR cultivation was carried out on productivity data referring to the third year as a preliminary estimate of average productivity. This was done according to Fagnano et al. (2010) reporting for GR, increasing yields up to the third to fifth year of cultivation, followed by a stationary phase and then by a significant decline starting from the ninth year. Giant reed’s annual impact was calculated as an annual equivalent, that is, the annual impact related to field maintenance (FM) and harvest operation (HO), plus the contribution from seedbed preparation (SP), crop establishment (CE), and final removal (FR; eradication) properly spread over the lifetime (15 years) (Monti et al. 2009). The environmental performance of FS

was evaluated considering the different annual management practices (SP, CE, FM, HO). For both crops, primary data were available for the agronomic characteristics (e.g., yield), diesel consumption of machinery, typology, and application rate of mineral fertilizers, irrigation system, and the amount of water applied (Table 1). Emissions from using agricultural machinery were derived according to EcoInvent guidelines (Nemecek and Kägi 2007) on the basis of primary fuel consumption and working hours data. All inputs related to the emissions background data derived from the EcoInvent unit process database (Ver 2.02), and reflected the European context for extraction and treatment of raw materials, the manufacturing process, transportation, distribution, use, and final disposal. Direct field emissions (DFEs) were calculated according to the EcoInvent guidelines (Nemecek and Shnetzer 2011), through the use (if applicable) of primary data related to sitespecific pedo-climatic characteristics and crops growth: NH3-N (15% of N input from urea fertilizer); NOx (21% of total biogenic N2O emissions); P losses (on the base of standard runoff or leaching constants and site-specific soil erosion rates calculated using the RUSLE equation); CO2 (fossil fuel, calculated assuming a release of 1.57 kg of fossil CO2 per kg of applied urea-N); and NO3– leaching (assessed through a nitrogen balance according to Nemecek and Shnetzer [2011]). For the latter, the expected monthly N mineralization in soil (ENM) was determined on the basis of a potential nitrogen mineralization of about 71 kg ha1 (as derived from Moodie [2012] according to soil characteristics), further corrected for the monthly pattern of soil temperature and moisture under Mediterranean conditions (Marion 1982). Nitrogen uptake by crop (NUV) was derived from Nassi o Di Nasso et al. (2013) and Barbanti et al. (2006) for GR and FS, respectively. Nitrate leaching was derived as the potentially leachable nitrate-N surplus from fertilizer starting in October, which is the first month that rain exceeded evapotranspiration, on the basis of N mineral deficit in soil for plant growth (NUV-ENM), balanced by fertilization. According to the computation, NO3– leaching resulted in null for both crops. N2O emissions were calculated from local emission factors (EF%) starting from the pertinent

400

Integr Environ Assess Manag 11, 2015—A Zucaro et al

scientific literature dealing with Mediterranean crops under low water input and spring urea fertilization (Fierro and Forte 2012) conditions. Emission factors derived for FS and GR are 0.83% (conventional tillage þ urea fertilization) and 0.67% (minimum tillage þ urea fertilization), respectively. CO2 uptake of GR was computed through direct data of short-term SCS, amounting to about 0.75 ton organic-C ha1 after the first three years of cultivation (taken as the average value from 3, 0–20 cm field replicates by chromic acid digestion method [Walkley and Black 1934]). Therefore, in the present study, an average annual sequestration of 0.25 ton C ha1yr1 was suggested as the preliminary estimate of annual SCS, throughout the crop lifetime. We assumed a plateau occurred toward the end of the investigated timeframe, also according to the 20-year payback time suggested for greenhouse gases (GHG) balance of biofuels by the IPCC guidelines (IPCC 2006). Parameters uncertainty An uncertainty analysis was carried out to evaluate the robustness of the results, because they may have been influenced by variability in DFEs and data collection. The source of variation for N losses, retrieved from pertinent scientific literature and the uncertainty of the experimental data detected (e.g., SCS) were propagated using the Monte Carlo function within SimaPro software, together with the probability distribution given in the EcoInvent database for the background inputs. A 10 000-trial simulation was then run to generate results falling within the 95% confidence limits. The range of foreground parameters is shown in Table 2.

RESULTS AND DISCUSSION Total burdens of each agricultural practice and related DFEs are shown for both FS and GR in the characterization diagrams of Figure 2a and Figure 3a, respectively. The total effects of FS cultivation were shared by the different agronomic practices, with specific impacts in target categories (Figure 2a, b). In detail, the highest contribution to terrestrial acidification, the most relevant category after normalization (Figure 2b), came from FM, due to DFE of ammonia after urea spreading, while also significantly affecting marine eutrophication. Seedbed preparation was the main cause of freshwater eutrophication, as a result of upstream (to prepare the seedbed

for rhizomes planting) and downstream (P losses by runoff and leaching) emissions related to phosphorus input. Photochemical oxidant formation and particulate matter formation were mainly affected by HO, 84% and 44%, respectively. Water depletion was markedly linked to the rescue irrigation performed during CE (64%). Climate change was related to SP (33%) and FM (28%), due to urea fertilizer spreading and the consequent DFEs, mainly biogenic N2O, amounting to about 8% and 14% of the total climate change, for SP and FM respectively. Finally, the contribution to fossil depletion was shared by the different crop practices, according to the different level of mechanization involved in each phase (Table 1). The highest total effects of GR cultivation were due to the practices performed yearly for FM (urea-N fertilization) and HO (mowing, baling, and loading bales) (Figure 3a, b). Seedbed preparation, CE, and FR, even if to a lesser extent, significantly affected total burdens (about 4%, 8%, and 5% respectively, as average values for all impact categories). More marked effects of SP were recorded for the freshwater eutrophication impact category (about 23%) due to the use of triple superphosphate, related upstream emissions, and DFEs. For both FS and GR, terrestrial acidification, particulate matter formation, and marine eutrophication appeared greatly affected by NH3 volatilization after urea supply (Figure 3a). The greatest contribution to climate change originated from FM, as a consequence of remarkable biogenic N2O losses (sharing about 24% of total climate change) and both upstream and local emissions of fossil CO2 (Figure 3a). The short-term SCS, (about 0.25 ton C ha1 yr1) entailed a global warming offsetting potential of about 0.031 ton CO2 tondb1, reducing total contributions toward climate change of GR cultivation to about 70%. For both GR and FS biomass production, a high contribution of DFEs to total burdens was detected, which agrees with relevant findings from the literature stressing the importance of DFEs of GHGs (Cherubini and Jungmeier 2010; Godard et al. 2013). Likewise, both reactive N and P losses affected the final outcome of the LCA analyses applied to bioenergy crops, in terms of climate change, eutrophication, and acidification (Renouf et al. 2010; Godard et al. 2013). Regarding climate change, an average contribution of about 40% to 50% to total impact related to different perennial biomass production chains (sugar cane, miscanthus, and

Table 2. Range of calculated parameters: Best guess; upper and lower values, type of distributions Range of uncertainty Parameters NH3-N volatilization factor N2O-N emission factore

Best Guess (BS)a 15%

b

CO2 fossil from applied urea-N SCS for GR (ton C ha1yr1) a

1.57b,h 0.25f

Calculated best estimated values. Nemecek and Shnetzer (2011). c Sanz-Cobena et al. (2008) and IPCC (2006). d CORINAIR, 2006. e Range for NOx derived according to range of N2O variability. f Experimentally detected in the present study. g Aguilera et al. (2013). h IPCC (2006). b

10.1%

0.83% (FS)f 0.67% (GR)

Lower

Higher

c

20%

0.08%g

d

Type of distribution Uniform

1%g,h

Triangular

BS

Uniform

0.00b

Triangular

f

50% of BS 3.16f

b,h

Comparative LCA of Lignocellulosic Feedstocks Production—Integr Environ Assess Manag 11, 2015

401

Figure 2. Characterization (a) and normalization (b) graphs for fiber sorghum cultivation, showing the relative contribution of agricultural practices and linked direct field emissions (DFE) to total burdens (DFE of NOx negligible). Seedbed preparation (SP), crop establishment (CE), field maintenance (FM) and harvest operation (HO). Absolute values of total impacts in each category are reported at the top of columns and referred to 1 kg of dry biomass ha-1.

switchgrass), which was attributed to N2O biogenic emissions (Cherubini and Jungmeier 2010; Renouf et al. 2010; Godard et al. 2013). In this study, the contribution of microbial N2O evolution appeared restrained, also in consequence of the lower assumed local EF under Mediterranean conditions. Indeed, the use of the default values reported in the IPCC Guidelines (IPCC 2006) would have switched the share of microbial N-N2O losses under GR from about 24% up to about 33%. Nevertheless, new studies are required to gain representative estimates of site- and crop-specific N2O-EF to further improve and validate the results. In addition, for a GR cropping system, a further reduction of influence on climate change could be achieved, considering the

amount of CO2 sequestered in the stable pool of soil organic matter (SOM). The relevance of SCS has been pointed out in the context of bioenergy (Godard et al. 2013) and biorefinery purposes (Cherubini and Jungmeier 2010). Standardizing and harmonizing procedures to include SCS in LCA analyses are still debated, above all in relation to spatial variability and the time-horizon after which a new equilibrium is reached in soil. In this study, a mean C sequestration rate of 0.25 ton organic-C ha1 yr1 could be suggested as a preliminary estimate for giant reed cultivation in the Mediterranean climate. This estimate is in line with the average annual soil carbon storage that Arrouays et al. (2002) highlighted for land conversion from arable to grassland (0.5  0.25 ton ha1 over a 20-year period).

Figure 3. Characterization (a) and normalization (b) graphs for giant reed cultivation, showing the relative contribution of agricultural practices and linked direct field emissions (DFE) to total burdens (DFE of NOx negligible). Seedbed preparation (SP), crop establishment (CE), field maintenance (FM) and harvest operation (HO). Absolute values of total impacts in each category are reported at the top of columns and referred to 1 kg of dry biomass ha-1.

402

Figure 4. Comparison of environmental impacts of the perennial Giant Reed (GR) and the annual Fiber Sorghum (FS) (functional unit: 1 kg of dry matter; system boundary: 1 ha of cropped land) including 95% confidence interval from the Monte Carlo analysis.

The C sequestration rate appeared consistently lower compared to available estimates from literature, ranging from 1.00 ton organic-C ha1 yr1 (Sarkhot et al. 2012) to 1.70 ton organic-C ha1 yr1 (Ceotto and Di Candilo 2011). Such amounts would have led to very different results if implemented in the present analyses (also to a net sink, in the latter instance). This stresses the importance of site-specific data. Comparing FS and GR at equal yields (30 ton ha1 yr1 for both crops), GR showed the lowest burdens for all impact categories, mainly due to the reduced environmental loads of SP and CE, which were shared for the overall crop cycle (Figure 4). The comparable crop yield pattern experimentally detected along with the field trial was in line with pertinent scientific literature. In fact, for fertilized crops under Mediterranean conditions, GR biomass at full yield production ranged mostly from about 20 t ha1 to about 27 t ha1 dry biomass (Nassi o di Nasso et al. 2013; Angelini et al. 2005; Fagnano et al. 2010) and ranking to about 38 t ha1 according to Angelini et al. (2009). Meanwhile, the yield pattern for FS was in the range of 20 t ha1 to 29.8 t ha1 dry biomass (Fazio and Monti 2011; Amaducci et al. 2000; Barbanti et al. 2006; Di Candilo and Ceotto 2011). Therefore, the results appear representative of standard cultivation scenarios. The variability of the final outcome, due to uncertainty in both background and foreground data, is depicted for each impact category by the error bars in Figure 4. The significant differences in climate change and freshwater eutrophication are a result, respectively, of the short-term SCS for GR and the annual application of triple superphosphate at the sowing fertilization for FS, even if the large variability of SCS (which explains the elevated GR uncertainty range for climate change), once again underscores that this parameter needs to be considered carefully when reporting LCA results. In addition, the reduction of water depletion, entailed by the perennial crop, appeared significant (low water input). For the other impact categories, GR displayed an average (not significant) decrease mainly as a consequence of uncertainty in foreground parameters (Table 2) for particulate matter formation, marine eutrophication, and terrestrial acidification (with a low uncertainty range: coefficient of variation in the range of 10%–12%). Notably, the ozone depletion impact category highlighted for both GR and FS a high uncertainty

Integr Environ Assess Manag 11, 2015—A Zucaro et al

(coefficient of variation around 25% for both crops) as a consequence of EcoInvent background data uncertainty. Therefore, the perennial GR proves to be a promising lignocelluloses feedstock for bioethanol production and integrated biorefinery. These findings agree with monitoring studies reporting ecological advantages for the perennial grasses compared with annual crops in terms of four factors: (1) both biomass yield and energy efficiency (Angelini et al. 2009; Fazio and Monti 2011); (2) reduced nutrient leaching (Pimentel et al. 2012) and a longer C turnover (Monti and Zatta 2009) due to the more extensive rooting systems; (3) limited soil management (planting and related tillage, to be shared for the whole lifetime) (Monti et al. 2009) and a reduced risk of soil erosion (Angelini et al. 2009; Pimentel et al. 2012); and (4) increase in soil carbon content and biodiversity (Angelini et al. 2009). From an integrated biorefinery perspective, the results of this study could be useful for defining suitable feedstock supply to bioenergy–biomaterial local agro-production chains, in line with the most recent national and regional environmental policy strategies (D. Lgs. 28/2011; DGR 2013).

CONCLUSION Through the LCA process, the environmental impact and the most critical agricultural practices related to annual and perianal biomass chains can be profitably assessed and compared. For both feedstock productions analyzed—fiber sorghum and giant reed—the crucial factors of unsustainability and resilience were related to the use of N, K, and P fertilizers and their downstream impact after field application. This finding underscores the need to improve fertilizer management and to account properly for links to direct field emissions. Nonetheless, the final outcome of the comparative analyses highlighted that the perennial cultivation resulted in substantially higher environmental benefits than annual crops due to reduced input and emissions for establishing crops (to be shared for the crop’s whole life time) and the potential of CO2 sequestration in soil organic matter. Therefore, our results show that Arundo donax L. proves to be a promising lignocellulosic feedstock for local bioethanol and biorefinery supply chains. Still, for a comprehensive evaluation of environmental constraints related to cultivating the perennial giant reed, there remains a need to investigate the pattern of both soil carbon storage and crop productivity in the long term, and throughout the whole crop life cycle. In doing so, we can more properly perform assessments of environmental burdens on an average annual base, and further compare annual crop systems. Acknowledgment—The authors gratefully acknowledge the financial support received from the PON-REC ENERBIOCHEM Project no. 881/Ric - Programma Operativo Nazionale.

REFERENCES Aguilera E, Lassaletta L, Sanz-Cobenad A, Garniere J, Vallejod A. 2013. The potential of organic fertilizers and water management to reduce N2O emissions in Mediterranean climate cropping systems. Agric Ecosyst Environ 164:32–52. Amaducci S, Amaducci MT, Benati R, Venturi G. 2000. Crop yield and quality parameters of four annual fibre crops (hemp, kenaf, maize and sorghum) in the North of Italy. Ind Crop Prod 11:179–186.

Comparative LCA of Lignocellulosic Feedstocks Production—Integr Environ Assess Manag 11, 2015 Angelini LG, Ceccarini L, Bonari E. 2005. Biomass yield and energy balance of giant reed (Arundo donax L.) cropped in central Italy as related to different management practices. Eur J Agron 22:375–389. Angelini LG, Ceccarini L, Nassi o, Di Nasso N, Bonari E. 2009. Comparison of Arundo donax L. and Miscanthus x giganteus in a long-term field experiment in Central Italy: Analysis of productive characteristics and energy balance. Biomass Bioenerg 33:635–643. Arrouays D, Balesdent J, Germon JC, Jayet PA, Soussana JF, Stengel P. 2002. Mitigation of the greenhouse effect. Increasing carbon stocks in French agricultural soils? An assessment report compiled by the French Institute for Agricultural Research(INRA) on the request of the French Ministry for Ecology and Sustainable Development, Scientific Assessment Unit for Expertise, Paris, France: p 32. Available from: http://inra.dam.front.pad.brainsonic.com/ ressources/afile/225456-9686b-resource-synthese-en-anglais.html Barbanti L, Grandi S, Vecchi A, Venturi G. 2006. Sweet and fibre sorghum (Sorghum bicolor (L.) Moench), energy crops in the frame of environmental protection from excessive nitrogen loads. Eur J Agron 25:30–39. Buratti C, Fantozzi F. 2010. Lifecycle assessment of biomass production: Development of a methodology to improve the environmental indicators and testing with fiber sorghum energy crop. Biomass Bioenerg 34:1513– 1522. Ceotto E, Di Candilo M. 2011. Medium term effect of perennial energy crops on soil carbon storage. Ital J Agron 6:212–217. Cherubini F, Jungmeier G. 2010. LCA of a biorefinery concept producing bioethanol, bioenergy, and chemicals from switchgrass. Int J Life Cycle Ass 15:53–66. CORINAIR. 2006. Emission Inventory Guidebook. Technical report 11/2006. Available from: http://reports.eea.europa.eu/EMEPCORINAIR4/en/B1010vs4.0.pdf DGR. 2013. DGR 142/2013, Documento stategico regionale. Available from: http://www.agricoltura.regione.campania.it/PSR_2014_2020/pdf/documento_strategico_programmazione_2014-2020.pdf Di Candilo M, Ceotto E. 2011. Biomass Yield of Sweet and Fiber Sorghum (Sorghum Bicolor L. Moench) under Nine Combinations of Sowing and Harvest Timing. In proceedings of 19th European Biomass Conference and Exhibition (2011, 06–10 June. Berlin (DE): p 634–638. Available from: DOI: 10.5071/ 19thEUBCE2011-VP1.3.29 European Bioplastics. 2012. LCA secondary data: A major problem for comparison among plastic materials - position paper p. 1–10. http://en. european-bioplastics.org/wp-content/uploads/2012/11/LCA%20Secondary% 20Data_201112.pdf Fagnano M, Impagliazzo A, Mori M, Fiorentino N. 2010. Produzione dell'Arundo donax in Ambiente Collinare Meridionale. In proceedings of XXXIX SIA (2010, 20–22 September). Rome (IT): Mastrorilli Ed. p 207–207. Fazio S, Monti A. 2011. Lifecycle assessment of different bioenergy production systems including perennial and annual crops. Biomass Bioenerg 35:4868– 4878. Fierro A, Forte A. 2012. Measurements of CO2 and N2O Emissions in the Agricultural Field Experiments of the MESCOSAGR project. In: Piccolo A, editor. Carbon Sequestration in Agricultural Soils. Berlin (DE): Springer-Verlag. p 229– 259. Godard C, Boissy J, Gabrielle B. 2013. Lifecycle assessment of local feedstock supply scenarios to compare candidate biomass sources. Glob Change Biol 5:16–29.

403

International Organization for Standardization. 2006. Environmental management—Life cycle assessment—Principles and framework. ISO 14040 and 44. Geneva (CH): ISO. IPCC. 2006. Intergovernmental Panel of Climate Change Guidelines for National Greenhouse Gas Inventories, 2006. Vol 4. Agriculture, Forestry, and Other Land Use. Chap 11: N2O emissions from managed soils, and CO2 emissions from lime and urea application. In: Eggleston HS, Buendia L, Miwa K, Ngara T. and Tanabe K, editors. IGES, Japan: National Greenhouse Gas Inventories Programme. Marion GM. 1982. Nutrient mineralization processes in Mediterranean-type ecosystems. USDA Forest Service General Technical Report. Pacific Southwest Forest and Range Experiment Station 58:313–320. Monti A, Fazio S, Venturi G. 2009. Cradle-to-farm gate life cycle assessment in perennial energy crops. Eur J Agron 31:77–84. Monti A, Zatta A. 2009. Root distribution and soil moisture retrieval in perennial and annual energy crops in Northern Italy. Agr Ecosyst Environ 132:252–259. Moodie M. 2012. February 18. Improving knowledge of mineralisation potential of Mallee soils. Mallee Catchment Management Authority Publication. February. Final Report. Available at http://www.malleecma.vic.gov.au/resources/reports/ land.html Nassi o Di Nasso N, Roncucci N, Bonari E. 2013. Seasonal dynamics of aboveground and belowground biomass and nutrient accumulation and remobilization in giant reed (Arundo donax L.): A three year-study on marginal land. Bio Energ Res 6:725–736. Nemecek T, Kägi T. 2007.Life cycle inventory of agricultural production systems. In: Final report Ecoinvent Ver 2.0 No 15. Dübendorf (CH): Swiss Centre for Life Cycle Inventories. p 1–360. Nemecek T, Shnetzer J. 2011. Direct field emissions and elementary flows in LCIs of agricultural production systems. In: Updating of agricultural LCIs for EcoInvent data Ver 3. 0. Agroscope Reckenholz-Tänikon Research Station. p 1–34. Pimentel D, Cerasalea D, Stanleya RC, Perlmana R, Newmanb EM, Brenta LC, Mullana A, Changa DTI. 2012. Annual vs. perennial grain production. Agr Ecosyst Environ 161:1–9. Renouf MA, Wegener MK, Pagan RJ. 2010. Life cycle assessment of Australian sugarcane production with a focus on sugarcane growing. Int J Life Cycle Ass 15:927–937. Rosenbaum RK, Bachmann TM, Swirsky Gold L, Huijbregts MAJ, Jolliet O, Juraske O, Koehler A, Larsen HF, MacLeod M, Margni M, McKone TE, Payet J, Schuhmacher M, van de Meent D, Hauschild MZ. 2008. USEtox—the UNEP– SETAC toxicity model: Recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int J Life Cycle Ass 13:532–546. Sanz-Cobena A, Misselbrook TH, Arce A, Mingot JI, Diez JA, Vallejo A. 2008. An inhibitor of urease activity effectively reduces ammonia emissions from soil treated with urea under Mediterranean conditions. Agr Ecosyst Environ 126:243–249. Sarkhot DV, Grunwald S, Morgan Y. 2012. Total and available soil carbon fractions under the perennial grass Cynodon dactylon (L.) Pers and the bioenergy crop Arundo donax L. Biomass Bioenerg 41:122–130. Walkley A, Black IA. 1934. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci 37:29–38.

Comparative attributional life cycle assessment of annual and perennial lignocellulosic feedstocks production under Mediterranean climate for biorefinery framework.

Annual fiber sorghum (FS) and perennial giant reed (GR) cultivated in the Mediterranean area are interesting due to their high productivity under drou...
1MB Sizes 5 Downloads 5 Views