Impact of management strategies on the global warming potential at the cropping system level

Science of the Total Environment 490 (2014) 921–933

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Impact of management strategies on the global warming potential at the cropping system level Pietro Goglio a, Brian B. Grant a, Ward N. Smith a, Raymond L. Desjardins a,⁎, Devon E. Worth a, Robert Zentner b, Sukhdev S. Malhi c a b c

Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, K.W. Neatby Building, Ottawa, Ontario K1A 0C6, Canada Swift Current Research Station, Swift Current, Saskatchewan S0E 1A0, Canada Melfort Research Farm, PO Box 1240, Melfort, Saskatchewan S0E 1A0, Canada

H I G H L I G H T S • • • • •

LCA was combined with DNDC model to estimate the GWP of a cropping system. N2O, NO and NH3 flux increased by 39% under the higher fertilizer rate. A change from 75 to 50 kg N ha− 1 reduced the GWP per ha and GJ basis by 18%. N2O emissions contributed 67% to the overall GWP of the cropping system. Small changes in N fertilizer can have a substantial environmental impact.

a r t i c l e

i n f o

Article history: Received 19 February 2014 Received in revised form 29 April 2014 Accepted 16 May 2014 Available online xxxx Editor: P. Kassomenos Keywords: LCA GWP Cropping systems DNDC Crop management Energy demand

a b s t r a c t Estimating the greenhouse gas (GHG) emissions from agricultural systems is important in order to assess the impact of agriculture on climate change. In this study experimental data supplemented with results from a biophysical model (DNDC) were combined with life cycle assessment (LCA) to investigate the impact of management strategies on global warming potential of long-term cropping systems at two locations (Breton and Ellerslie) in Alberta, Canada. The aim was to estimate the difference in global warming potential (GWP) of cropping systems due to N fertilizer reduction and residue removal. Reducing the nitrogen fertilizer rate from 75 to 50 kg N ha−1 decreased on average the emissions of N2O by 39%, NO by 59% and ammonia volatilisation by 57%. No clear trend for soil CO2 emissions was determined among cropping systems. When evaluated on a per hectare basis, cropping systems with residue removal required 6% more energy and had a little change in GWP. Conversely, when evaluated on the basis of gigajoules of harvestable biomass, residue removal resulted in 28% less energy requirement and 33% lower GWP. Reducing nitrogen fertilizer rate resulted in 18% less GWP on average for both functional units at Breton and 39% less GWP at Ellerslie. Nitrous oxide emissions contributed on average 67% to the overall GWP per ha. This study demonstrated that small changes in N fertilizer have a minimal impact on the productivity of the cropping systems but can still have a substantial environmental impact. Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.

1. Introduction There is increasing demand on agricultural systems to produce food and fuel in a sustainable manner. The challenge is to quantify the impacts and synergies of agricultural systems on ecosystem services, of which productivity, soil and air quality, water use, and biodiversity are

⁎ Corresponding author at: Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, K.W. Neatby Building, Ottawa, Ontario K1A 0C6, Canada. Tel.: +1 613 759 1522; fax: +1 613 759 1432. E-mail address: [email protected] (R.L. Desjardins).

http://dx.doi.org/10.1016/j.scitotenv.2014.05.070 0048-9697/Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.

important components. It is well recognized that agriculture can either be a source or a sink of greenhouse gases (GHG) (Brady and Weil, 2002; West and Six, 2007), but the relative contribution varies greatly depending on management practices, climate and soil conditions (De Klein et al., 2006; Smith et al., 2007; Snyder et al., 2009). When evaluating agricultural systems, it is crucial to consider and include all important agriculturally related GHGs (CO2, N2O, and CH4) in order to understand the inter-relationships contributing to the net GHG emissions (Guinée et al., 2009; Guinée et al., 2006; Tuomisto et al., 2012). Agriculture has the potential to fix large amounts of CO 2 from the atmosphere, some of which evolve into a more stable form of soil organic matter (Brady and Weil, 2002; Johnson et al., 2007). The extent of the benefit

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should, however, be precisely evaluated, since soil organic carbon (SOC) can be gained or lost over time depending on changes in tillage or residue management and changes in fertilizer application can increase N2O and other N emissions (sources of indirect N2O emissions, i.e. NH3, NO, NO− 3 ) (De Klein et al., 2006; Hillier et al., 2012). The farming practices that have the greatest influence on GHG emissions from drained agricultural soils are tillage operations, cover crops, dead and living mulches, and fertilizer applications (Archer and Halvorson, 2010; Johnson et al., 2007). Tillage operations influence a wide range of soil properties that affect soil respiration and mineralization (Brady and Weil, 2002). These biochemical processes affect the rate of CO2 and N2O production both as co-products from the process itself as well as through provision of substrates for additional processes such as denitrification (Archer and Halvorson, 2010; Brady and Weil, 2002; Hénault et al., 2005). Cover crops can enhance C storage by fixing atmospheric CO2 during their growth and storing it in residues (Brady and Weil, 2002; Franzluebbers, 2010). Nitrogen fertilizer application increases soil N availability and can enhance denitrification, which is the main process responsible for N2O emissions (Johnson et al., 2007; Lehuger et al., 2007; Snyder et al., 2009). Such emissions are regulated by the quantity, type, placement, speed of release, and timing of application (Goglio et al., 2013; Pappa et al., 2011; Saggar, 2010). Within the context of Life Cycle Assessment (LCA) applied to agricultural production systems, there is no general consensus on the procedure to follow in estimating agricultural impacts, including GHG emissions (Guinée et al., 2009; Guinée et al., 2006). Cropping system effects have seldom been investigated with LCA (Brentrup et al., 2004a; Charles et al., 2006; JRC et al., 2007). In practice, most of the environmental impacts related to the management of a particular crop within a cropping system occur in subsequent seasons (Goglio et al., 2012; Mazzoncini et al., 2008). Hence, assessments need to be carried out for several years and should be supported by results from either long-term field trials or with modelling that can characterize multiyear C & N dynamics (Adler et al., 2007; Del Grosso et al., 2005). The majority of LCA assessments on agricultural systems employ IPCC (Intergovernmental Panel for Climate Change) Tier I/II methods to quantify GHG emissions, in particular soil CO2 and N2O emissions derived from soil C and N dynamics. This method provides a consistent approach towards satisfying the requirements of the LCA framework; however, the IPCC methodology only provides gross estimates and is limited in its functionality for considering the influence of interannual climate variability, soil properties, and multiple management changes on C & N cycles (De Klein et al., 2006; Gabrielle and Gagnaire, 2008). Employing a validated process-based model allows us to consider the interdependencies of the evolution of all the GHGs under varying management, for which having a full array of measurements is not feasible (Del Grosso et al., 2008). Process-based models have the advantage of simulating C & N interactions and maintain a mass balance of C, N and water (De Klein et al., 2006; Del Grosso et al., 2008; Gabrielle and Gagnaire, 2008). They are particularly useful for estimating outputs when limitations in the availability of measured data prevent the development of empirical relationships. However, process-based models require more detailed inputs and more expertise than simple empirical models. A number of studies have already utilised process-based models for LCAs of cropping systems (Adler et al., 2007; Gabrielle and Gagnaire, 2008; JRC et al., 2007; Kim et al., 2009; Zaher et al., 2013). In the present study, measurement data recorded from two field experiments supplemented with results from a regionally validated biophysical model (DNDC) were combined with LCA to estimate the difference in GWP of a common cropping system for two locations in Alberta, Canada. The specific objectives were: i) to estimate the effect of fertilizer and residue management on net GHG emissions of a cropping system at two field sites in Alberta, using the DNDC model; ii) to determine the overall GWP and energy use by performing a LCA of the cropping system

and iii) to estimate the contribution of each source of GHG towards the GWP using a LCA.

2. Material and methods 2.1. Field sites Field trials were conducted in Alberta, Canada, on a Grey Luvisol (Typic Cryoboralf; United States Department of Agriculture (USDA) taxonomy) in Breton (53°07′N, 114°28′W; elevation of 830 m) and a Black Chernozem (Haplustoll; USDA) in Ellerslie (53°25′N, 113°33′W; elevation of 692 m) (AAFC, 2008; USDA, 1999). In Breton, the study was on a silty loam soil with a particle size distribution: 120 g clay kg− 1, 620 g silt kg−1, and 260 g sand kg−1. In Ellerslie, the study was on a clay loam soil with a particle size distribution: 360 g clay kg− 1 , 420 g silt kg− 1, and 220 g sand kg− 1. In autumn 1982, the soil in Breton had a pH of 6.6 and an initial soil organic C content of 13.1 g C kg−1, and the soil in Ellerslie had a pH of 6.0 and a soil organic C content of 60.9 g C kg−1. The mean annual rainfall is 475 mm in the Breton area and 450 mm in the Ellerslie area (Malhi et al., 2011a, 2011b). At both sites, approximately 60% of the total rainfall occurs during the growing season (May to August). Both experiments began in autumn 1982. The LCA assessment was conducted for the period 1996 to 2009. Measurements prior to 1996 were used to initialize DNDC. The long-term cropping systems consisted of the following crop sequence from 1996 to 2009: barley (Hordeum vulgare L.)–wheat (Triticum aestivum L.)–wheat–canola (Brassica napus L.)–triticale (× Triticosecale [Camus] Wittm.)–pea (Pisum sativum L.)–barley– canola–triticale–pea–wheat–canola–triticale–pea. These crops are commonly grown in Western Canada (CANSTAT, 2013). The individual cropping systems were employed in a factorial combination of two residue management treatments (residues removed [or] and residues retained [1r]) and two N rate treatments (50 and 75 kg N ha−1 y−1, Table 1) and were arranged in a randomized complete block design in four replicates. The experimental design of the field setup has been extensively described in previous publications (Malhi et al., 2011a, 2011b), while here a brief summary is presented. In all plots, tillage (8 cm depth) was carried out in the fall and in the spring using a chisel cultivator and a coil packer roll implement (Table 2). Each crop was sampled annually for grain and straw yield and in the fall of 1996 and 2009 soil samples were taken. Laboratory measurements were carried out for total organic C, total organic N, light-fraction organic C, light-fraction organic N, pH, extractable P, ammonium-N, and nitrate-N in soil samples taken from the experimental units (4 treatments × 4 replications = 16 experimental units) at different soil depths (Malhi et al., 2011b). These data were used as inputs in the DNDC model simulations.

Table 1 Cropping systems analysed at Breton and Ellerslie. The reference systems are highlighted in grey. Amount of N Residue management

fertilizer Field trial

applied (kg N −1 −1

ha

y )

50 Breton (B)

75

50 Ellerslie (E)

75

Residue removal (0r)

Residue retained (1r)

50N fertilizer applied, residue

50N fertilizer applied, residue

removal

retained

75N fertilizer applied, residue

75N fertilizer applied, residue

removal

retained

50N fertilizer applied, residue

50N fertilizer applied, residue

removal

retained

75N fertilizer applied, residue

75N fertilizer applied, residue

removal

retained

Table 2 Main farming inputs used during cultivation at the two locations (W: spring wheat, Tri: triticale; Ba: barley, P: field pea, Ca: canola). Crop

a b c d e f g h

All All Tri All All All Ba + Tri + P 50N Ba + Tri + P 75N 50N 75N Tri All All All All All All All All All All 0r 0r 0r

Cultivator and roll packer pass Cultivator and roll packer pass Herbicide treatment Herbicide treatment Herbicide treatment Seeding + fertilizer application Seeding + fertilizer application Seeding + fertilizer application Seeding + fertilizer application Herbicide treatment Herbicide treatment Herbicide treatment Herbicide treatment Herbicide treatment Fungicide treatment Insecticide treatment Fungicide treatment Cutting and windrowing Harvest Windrowing Baling Bale collecting

Engine Operating machine power (KW) 265a 265a 121b 121b 121b 265a 265a 265a 265a 121b 121b 121b 121b 121b 121b 121b 121b 147b 235f 147b 154a 154a

Amount of fertilizer Diesel consumption nutrient (kg ha−1) (kg ha−1)

Small sweep-chisel cultivator + coil packer Small sweep-chisel cultivator + coil packer Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Seed drill + fertilizer spreader 6 t capacity Seed drill + fertilizer spreader 6 t capacity Seed drill + fertilizer spreader 6 t capacity Seed drill + fertilizer spreader 6 t capacity Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Self-propelled sprayer Self-propelled windrowere

6.51 6.51 2.43 2.43 2.43 7.47 7.47 9.9 9.9 2.43 2.43 2.43 2.43 2.43 2.43 2.43 2.43 4.33

g

h

Self-propelled windrowere Baler Pick up trailer

4.33 6.45 11.8

4DW tractor. Machine engine. With superphosphate, KCl and K2SO4. With superphosphate and KCl. Also called swather. Harvester power; tractor power 154 KW. Combined harvester + tractor and 20 t trailer + 20 t truck. Harvester diesel consumption 11.3 kg ha−1; tractor and trailer diesel consumption 1.71 kg ha−1.

Amount and type of active ingredient (kg ha−1)

0.54 2.4D +0.902 glyphosate 0.63 MCPA + 0.902 glyphosate 0.902 glyphosate Urea N 50; B PKS P 22 K 33 S 11 Urea N 75; B PKS P 22 K 33 S 11 Urea N 50; B PKS P 22 K 33 S 11 Urea N 75; B PKS P 22 K 33 S 11

kgc; E P 22 K 33d kgc; E P 22 K 33d kgc; E P 22 K 33d kgc; E P 22 K 33d 0.0289 pyrasulfotole + 0.162 bromoxynil 0.075 florasulam + 0.075 clopyralid + 0.42 MCPA 0.902 glyphosate 0.902 glyphosate 0.0151 imazamox + 0.0151 imazethapyr 0.25 azoxystrombin 0.72 chlorpyrifos 0.294 boscalid

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All All W+ Ba P W+ W+ Ca Ca W+ Ba Ca Ca P Ca Ca P All All All All All

Cropping Crop management system operation

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2.2. Life cycle assessment assumptions, system boundary, and cropping systems Life cycle assessment was carried out, using the SimaPro software (SimaPro 7.3, 2012), to compare the effect of two fertilizer rates and residue managements of the cropping system on energy consumption and GWP using a 100 year horizon (Tables 1 and 2). For the two sites, crop management details were obtained from regional experts as well as from Alberta Agricultural and Rural Development (AARD) databases and previous research (AARD, 2013; Dyer and Desjardins, 2006; 2005; 2003). The analysed system included all the upstream processes of the agricultural phase, the agricultural phase, and farm transport, according to Goglio et al. (2012) and Gelfand et al. (2010) (Fig. 1). No artificial drying was accounted for because of local conditions: grains were sold at field moisture, while straw and residues were dried in the field with cultivation operations. The main agricultural product (output) was grain, although in the cropping systems with residue removal, cereal straw and residues were also considered outputs of the agricultural system as co-products (Fig. 1). Average measured grain yields were used as inputs for the LCA (Table 3). Four crop management combinations were analysed at each location (Table 1) with the reference system being represented by the 75 kg N ha − 1 fertilizer treatment under no residue removal (shaded cells). This reference system best represented the typical agricultural practices in the surrounding area. The main inputs during cultivation are given in Tables 1 and 2. Energy consumption and emission intensities (GWP) were estimated based on hectares, gigajoules of energy content in the harvested biomass (grain for all systems and straw for 0r systems only) and just in grain (for GWP only) using the CML 2001 impact assessment method (Börjesson and Tufvesson, 2011; Camargo et al., 2013; Goglio et al., 2012; Guinée et al., 2001; Nemecek et al., 2011a, 2011b). 2.3. Simulations using the DNDC model The DNDC (DeNitrification–DeComposition) model was used to estimate trace gas emissions as well as C and N dynamics (Li et al., 1992; Smith et al., 2012). The model is composed of six modules: soil/climate, crop growth, organic matter decomposition, nitrification, denitrification, and fermentation (Smith et al., 2012). It simulates a wide range of agricultural management and has been used worldwide

in numerous studies. Country specific versions have been developed to simulate crops and management that occur regionally. In this study we used our own country-specific version of DNDC, derived from DNDC 9.5, that has been developed and evaluated to simulate above and belowground biomass production (including root exudates), soil carbon and trace gas emissions in Canada (Grant et al., 2014; Kröbel et al., 2011; Smith et al., 2013; 2012). The DNDC model simulates the C & N dynamics across a wide spectrum of management. It includes a mass balance approach to account for C, N and water between plants, soil and air. To ensure that model outputs of N2O, NO, NH3, CH4 and soil carbon change were appropriate for use in the LCA framework, comparisons against on site measurements as well as regionally reported values, for major model outcomes (crop C inputs, carbon change, grain yields, N leaching, N2O emissions) were carried out. Previous investigation of DNDC including these two sites and treatments by Smith et al. (2012) demonstrated that DNDC was able to estimate the relative influence of residue removal on soil organic carbon change within the 95% confidence interval of measurements. It was found however that, due to the high sampling variability in carbon measurements, specific treatment comparisons were not appropriate for assessing model performance and aggregated assessments across multiple studies would be more insightful. Considering this, we chose to use model estimated CO2 emission rates in our study, rather than measurements, to ensure that the effect of residue management on soil carbon was appropriately represented for these sites. DNDC was initialized by simulating barley production that occurred at the Breton and Ellerslie sites from 1983 to 1995 (Malhi et al., 2011b). The model was then calibrated against annual measurements of grain and straw yields for spring wheat, canola, triticale, barley and field pea during the 14 year study (1996–2009) and employed to predict daily N2O, NO, NH3, and CH4 emissions and changes in soil carbon from 1996 to 2009, with simulations being conducted in a similar manner as employed in Smith et al. (2012). Greenhouse gas emissions were accounted for on a yearly basis using DNDC and emissions of CO2 were estimated from the difference in soil organic C content from the first day of the year until the last day of the year. The mean GHG emissions were subsequently used in SimaPro to build the life cycle inventory for the analysed cropping systems. Data describing the cropping systems, tillage, residue and fertilizer management were taken from Malhi et al. (2011b). Daily maximum and minimum temperature and precipitation data were obtained from the CanSIS weather database (AAFC, 2006) and monthly wind speed

Grain Fuel and lubricating oil production

Agricultural phase

Fertilizer production Transport

Pesticide production System boundary

Straw* Farm transport

Machinery production Machinery maintenance and repairs

Residues*

*Only considered in the cropping system where residue removal was performed. Fig. 1. System boundary for the life cycle assessment of the cropping systems at the two locations (Breton and Ellerslie).

SE of grain yielda (Mg ha−1)

0.14 0.15 0.12 0.07 1.05 1.09 1.16 1.26 2.50 2.84 2.99 2.97 3.69 4.03 4.10 4.24

and humidity were derived from the National Climate Data and Information Archive (Environment Canada, 2013). The DNDC model employs the Penman–Monteith equation for estimating evapotranspiration and benefits from using as many available climate drivers as possible. Grant et al. (2014) modified DNDC to read in monthly wind speed and humidity when daily data are not available. Required soil water characteristics (e.g., field capacity and field saturation) that were not measured in the field trial were determined using the SPAW (Soil–Plant–Atmosphere– Water) model (USDA, 2012).

1.52 1.62 1.56 1.67 0.56 0.56 0.40 0.51

The agricultural systems were analysed using a combination of database values, DNDC model results, data obtained from literature, recorded data, expert knowledge, and information from a survey of agricultural machinery manufacturers in Canada. Database values, available in SimaPro 7.3, were used for all upstream processes (SimaPro 7.3, 2012) together with previously gathered data (Dyer and Desjardins, 2003, 2005, 2006). Soil GHG emissions were based on DNDC results and determined as described in Section 2.3. Technical operations during cultivation were assessed on the basis of the experimental protocol and expert knowledge (Tables 1 and 2). Transport inputs were obtained using a combination of communications with experts and databases. Finally, fuel and material consumption for field operations were estimated on the basis of the type of machinery, expert knowledge, the experimental protocol, data previously gathered for Alberta (Dyer and Desjardins, 2006; 2003), and AARD databases (AARD, 2013). Data for different inputs used during cultivation were treated as set out below.

0.05 0.05 0.05 0.06 0.03 0.15 0.27 0.26 SE, standard errors. a

Ellerslie (E)

Breton (B)

50 50 75 75 50 50 75 75

Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r)

2.44 2.55 2.92 2.89 3.14 4.08 2.87 3.38

1.75 1.71 1.94 1.90 0.11 0.61 0.19 0.04

2.51 3.02 2.77 3.13 2.34 2.79 2.26 2.59

0.29 0.32 0.35 0.33 0.41 0.51 0.40 0.36

0.63 0.68 0.87 0.84 1.82 1.89 2.00 2.18

3.35 3.59 3.82 4.00 2.18 2.30 2.05 2.16

SE of grain yielda (Mg ha−1)

Triticale Canola

SE of grain yielda (Mg ha−1) SE of grain yielda (Mg ha−1)

Wheat

Grain yield (Mg ha−1) SE of grain yielda (Mg ha−1)

Barley

Grain yield (Mg ha−1) Residue management Amount of N fertilizer applied (kg N ha−1 y−1)

Table 3 Observed grain yields and standard errors on a dry matter basis for the cropping systems at the two locations.

925

2.4. Data sources and treatment for LCA

Grain yield (Mg ha−1)

Grain yield (Mg ha−1)

Field pea

Grain yield (Mg ha−1)

P. Goglio et al. / Science of the Total Environment 490 (2014) 921–933

2.4.1. Machinery use Machinery use impacts were accounted for on the basis of the field cultivation phase, the transport of machinery from the farm centre to the field, the production and transport of machinery, and the maintenance and repair of machinery, with data obtained previously under Canadian conditions (AARD, 2013; Dyer and Desjardins, 2006; 2003). Field cultivation phase impacts were accounted for on the basis of field working capacity and power needed to carry out the specific field operation. Power and working capacity were then used to establish fuel and lubricating oil consumption. Based on expert knowledge, it was assumed that the transport of machinery from the farm centre to the field involved a 10 km distance at an average speed of 35 km h−1 with the same tractor used during field operations. Fuel and lubricating oil were transported 25 km from the local storehouse to the farm centre using a truck with a full load capacity of more than 16 Mg. In all transport processes, the return journey of the machinery was considered, in accordance with Gasol et al. (2012) and Goglio et al. (2012). The impacts of fuel and lubricating oil production from the extraction of crude oil to the delivery to the local storehouse were accounted for using databases implemented in SimaPro 7.3 (SimaPro 7.3, 2012), along with specific data for the oil products production system in North America (Sheehan et al., 1998). Machinery production impacts were estimated considering weight, working capacity, and total lifetime of the machine (ASABE, 2003; Audsley et al., 1997; Brentrup et al., 2004b), together with other data previously collected and processed for Canadian conditions (Dyer and Desjardins, 2006; 2003). The machine weight was derived from literature, technical reports, and a survey of machinery manufacturers in Canada. The impacts of production, transport, maintenance and repairs of tractors were taken from SimaPro 7.3 databases (SimaPro 7.3, 2012). Production impacts for machinery other than tractors were subdivided into the impacts related to material extraction and those related to machinery manufacture. In accordance with Audsley et al. (1997), all operating machinery was assumed to be completely composed of steel, and combined harvesters, self-propelled sprayers, and trailers were assumed to be manufactured from 95% steel and 5% rubber. For

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manufacture, the only energy source was electricity (Audsley et al., 1997). It was assumed that machinery and raw materials for machinery production were transported 1000 km by a truck with a full load capacity of more than 16 Mg. The impacts of maintenance and repairs for machinery other than tractors were accounted for on the basis of the overall energy used during machinery manufacture and the production of raw materials for machinery, in accordance with Audsley et al. (1997). In our accounting process, the overall energy was split among four different primary energy sources to evaluate energy consumption and GHGs from the production of materials used during maintenance and repairs. 2.4.2. Fertilizer consumption The impacts of fertilizer use include production and transport from the factory to the local storehouse, from the local storehouse to the farm centre, and from the farm centre to the field, and field application (Fig. 1). For P and K fertilizers, the fate of these nutrients was accounted for by applying the procedure of Audsley et al. (1997). Fertilizer production impacts were evaluated on the basis of the amount of nutrient applied (e.g., for urea, the amount of N applied to the field). This amount was established for the various cropping systems on the basis of the experimental protocol. Data for impacts related to fertilizer manufacture were taken from the literature for N fertilizer, whereas SimaPro 7.3 databases were used for P, K, and S fertilizer production and transport to local storehouse (Brentrup et al., 2004a, 2004b; SimaPro 7.3, 2012). It was assumed that N fertilizer was transported 500 km from the factory to the local storehouse in a truck with a full load capacity of more than 16 Mg, with fertilizer weight taken into consideration. Transport of all fertilizers from the local storehouse to the farm centre was performed in a similar truck for a distance of 25 km. Transport from the farm centre to the field was assumed to be carried out with the air cart that was used during seeding and was attached to the seeding drill. The tractor and the operating machine were considered to cover a 10 km distance from the farm centre to the field at a speed of 35 km h− 1. For field application, the impacts were computed as described in the Machinery Use section (section 2.4.1). 2.4.3. Pesticide use The estimated impacts of pesticide use were based on the number of pesticide treatments, which was established on the basis of expert knowledge and AARD guidelines (AARD, 2013). The amount of active ingredient used was assessed on the basis of the amount of commercial formulation applied. Pesticide use impacts involved different aspects related to pesticide production and transport, from raw material extraction up to transport to the local storehouse, transport from the local storehouse to the farm centre, spraying, and fate emissions at the field level. The impacts of pesticide production and transport to the local storehouse were accounted for on the basis of the type and amount of the active ingredient applied to the field. For this process, if data were not available, the impacts of the chemical class of active ingredients rather than of the active ingredient itself were used. When the active ingredient class was not available in the database, a general value for pesticides was utilised. Pesticide transport from the local storehouse to the farm centre was carried out with a truck with a full load capacity of more than 16 Mg over a 25 km distance. Pesticide transport from the farm centre to the field was assumed to have been performed with the self-propelled sprayer, considering a distance of 10 km and a speed of 35 km h−1. Both these transport processes were calculated on the basis of the amount of pesticide applied in the field. Pesticide fate was accounted for in three compartments: soil, water, and air. The assessment was carried out on the basis of coefficients developed by Audsley et al. (1997), which were multiplied by the amount of active ingredient applied to the field.

2.5. Statistical and contribution analyses Statistical analysis of GHG emissions estimated with DNDC was carried out using the R software program to determine the significance of crop management effects for the cropping systems at the two locations on GHG emissions (R Development Core Team, 2005). A statistical analysis was first done with the Friedman test followed by pair-wise non-parametric comparisons, considering each year separately (Siegel and Castellan, 1988). Thus crop management for each specific cropping system was compared year by year, largely independent of annual climate variability. For each cropping system 14 sample elements (years) were available to carry out the statistical analysis. A contribution analysis was performed to evaluate the different contributions of soil emissions, in particular GHGs (soil CO2, CH4, and N2O), and machinery (including upstream processes), as suggested by the International Organisation for Standardisation (ISO, 2006a, b). 3. Results 3.1. Soil emissions The analysis of CO2 exchange for the two sites indicated that for Breton every cropping system resulted in a slight CO2 uptake, corresponding to soil C accumulation (−83 kg CO2–C ha−1 y−1 on average), whereas at Ellerslie, there was a small C loss (118 kg CO2–C ha−1 y−1) (Fig. 2a). For both locations, the lowest emissions on average (or most carbon sequestered in the case of Breton) occurred when residues were returned. However, the paired test across the 14 year study indicated that no significant difference (P b 0.05) was observed among the cropping systems analysed. Treatment effects were masked by inter-annual variability due to changes in weather and crops from year to year as well as relatively small treatment differences in crop carbon inputs. For every cropping system examined, CH4 exchange obtained using DNDC was negative, indicating a soil uptake of atmospheric CH4 due to methane oxidation, with values ranging from −0.625 to − 1.18 CH4– C ha−1 y−1 across sites and treatments (Fig. 2b). There was very little difference in simulated uptake (b 2%) within a particular site, but there was on average 46% less uptake at Breton than Ellerslie. The Friedman test was significant and pair-wise comparisons confirmed the difference between sites (P b 0.05) (Fig. 2d). Also, some significant (P b 0.05) discrepancies among fertilizer and crop residue management were observed. Interactions between N fertilizer, residue management and location were discerned for N2O (Fig. 2c). Interestingly, increased N fertilizer rate resulted in 39% more N2 O emissions; while changing the location and residue management accounted for 20% higher emissions from Ellerslie and 18% greater emissions when residues were retained, respectively. The highest N2 O emissions were obtained from the Ellerslie reference system (E 75 N 1r, 3.10 kg N2O–N ha−1 y−1), whereas the Breton 50 N or treatment emitted the lowest amount of N2O (1.33 kg N2O–N ha−1 y−1). At both locations the difference across treatments was statistically significant, with the greatest emissions occurring in the cropping system with the higher fertilizer rate and with residues retained. Pair-wise comparisons indicated that changes in the amount of N fertilizer in most cases resulted in significantly different emissions (P b 0.05) (Fig. 2e). Furthermore, residue management and location had a significant effect on N2O emissions. Similarly to N2O, nitric oxide (NO) and ammonia emissions were largely influenced by N management while NO3 leaching had no clear trend (Fig. 3). The largest estimated NO emissions for both sites occurred when residues were retained and under the highest fertilizer rate. Nitric oxide emissions ranged from 0.207 to 0.794 kg NO–N ha−1 y−1 across all the cropping systems. The statistical test revealed a significant difference (P b 0.05) among various cropping systems. The pairwise test confirmed that the change due to fertilizer rate was significant for most of the comparisons (P b 0.05) (Fig. 3d).

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Fig. 2. Emissions of CO2 (a); N2O (c) and methane uptake (b) per hectare for the cropping systems analysed at two locations (B, Breton; E, Ellerslie) with the corresponding p-value tables (d, e) for non parametric post-hoc comparisons when the Friedman test was significant. 50 kg N ha−1 y−1; 75 kg N ha−1 y−1; or, residue removal; 1r, residue retained (* indicates statistical significance for the Friedman test with P b 0.05).

Ammonia volatilization was largely affected by N fertilizer rate, being more than twice the emissions under the 75 kg N ha−1 treatment (Fig. 3b). Ammonia volatilization was highest in the E 75 N 0r system (14.7 kg NH3–N ha− 1 y− 1) and the lowest for the B 50 N 1r system (4.87 kg NH3–N ha−1 y−1) (Fig. 3b). A significant difference (P b 0.05) was observed among the cropping systems evaluated. Pair-wise comparisons showed a significant difference due to N management with some exceptions (Fig. 3e). Nitrate leaching at Ellerslie was 60% less than at Breton (Fig. 3c), while fertilizer management resulted in a 42% reduction on average for the 50 kg N ha − 1 treatment. Nitrate leaching was, however, small across both sites (Fig. 3c) with values ranging from −1 y − 1 for the Ellerslie 50N 0r system to 1.77 kg NO − 3 –N ha − −1 −1 13.4 kg NO3 –N ha y for the Breton reference system (B 75N 1r). The cropping systems showed significant differences in nitrate leaching

(P b 0.05) but pair-wise comparisons did not reveal a clear trend among cropping systems (P b 0.05) (Fig. 3f). 3.2. Energy consumption Change in energy use was marginally affected by residue management (6% difference on average), on a hectare basis (Table 4). However, decreasing the amount of N fertilizer applied resulted in a 14% reduction of energy consumption per hectare. There were limited differences in energy consumption for each location (2.8% difference on average, on a per hectare basis). The largest energy demand on a per hectare basis was estimated for the B 75N 0r system, whereas the E 50N 1r system accounted for the least energy requirement (Table 4). On the basis of gigajoules of harvestable biomass, the situation is reversed, with estimated 28% lower energy consumption for residue removal in comparison to

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Fig. 3. Annual emissions per ha of N compounds (a: NO; b: NH3; c: NO− 3 ) causing N2O emissions indirectly for the cropping systems analysed at two locations (B, Breton; E, Ellerslie) with the corresponding p-value tables (d, e, f) for non parametric post-hoc comparisons when the Friedman test was significant. 50N, 50 kg N ha−1 y−1; 75N, 75 kg N ha−1 y−1; 0r, residue removal; 1r, residue retained (* indicates statistical significance for the Friedman test with P b 0.05).

Table 4 Cumulative energy consumption per hectare and per gigajoule of harvestable biomass for the cropping systems at two locations. Field site Breton (B)

Amount of N fertilizer applied (kg N ha−1 y−1)

Residue management

50

Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r)

75 Ellerslie (E)

50 75

a

1 GJeq corresponds to 1 GJ energy from different sources.

Cumulative energy demand (GJeq ha−1 y−1)a Cumulative energy demand (GJeq GJ−1 y−1)a 16.9 15.9 19.6 18.6 16.4 15.4 19.1 18.1

0.285 0.359 0.280 0.387 0.249 0.302 0.287 0.364

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largest GWP (2400 kg CO2eq ha−1 y−1) and the E 50N 0r showed the least global warming potential (1460 kg CO2eq ha−1 y−1). A similar trend was observed for GWP results on the basis of gigajoules of harvested biomass, with Ellerslie field site emitting 71% more than Breton on average (Fig. 4b). The lowest GWP was obtained under the B 50N 0r cropping system (16.0 kg CO2eq GJ−1 y−1), whereas the greatest emitter was the reference system in Ellerslie (E 75N 1r, 48.2 kg CO2eq GJ−1 y−1). Removing crop residues lowered GWP per gigajoule of harvested biomass by 33% on average among systems. When using GWP per GJ of grain instead of GWP per GJ of harvested biomass, the removal of crop residues resulted in a 10% increase. Reducing the amount of N fertilizer decreased GWP per GJ of harvested biomass by 28%. In Breton, the lowest emitter, on a GJ of harvested biomass basis, was the B 50N 0r system whereas the reference system (B 75N 1r) was the greatest emitter, with 25.7 kg CO2eq GJ− 1 y− 1 (Fig. 4b). At Ellerslie, the lowest value for GWP was observed for the E 50N 0r cropping system (22.2 kg CO2eq GJ−1 y−1), whereas the greatest GWP was also for the reference system (E 75N 1r).

3.4. Contribution analysis of global warming potential

Fig. 4. Emission intensities as global warming potentials (GWP; with a 100-y horizon) on a hectare (a) and gigajoules of harvestable biomass basis (b) for the cropping systems analysed at two locations (B, Breton; E, Ellerslie). 50N, 50 kg N ha−1 y−1; 75N, 75 kg N ha−1 y−1; 0r, residue removal; 1r, residue retained.

residue retained (Table 4). Increasing the amount of N fertilizer applied and changing the location caused less than 8.8% difference on average in energy consumption. The largest energy demand was estimated for Breton reference (B 75N 1r), whereas the lowest energy consumption was obtained with the E 50N 0r system (Table 4).

3.3. Global warming potential Emission intensities on a hectare basis were on average 44% greater for Ellerslie than at Breton (Fig. 4). Overall, the reduction in N fertilizer application caused a 33% decrease in GWP on a hectare basis (Fig. 4). At Breton, emission intensities in GWP per hectare ranged from 815 to 1240 kg CO2eq ha−1 y−1 for the B 50N 1r and B 75N 1r systems respectively (Fig. 4a). At Ellerslie, the reference system (E 75N 1r) had the

Global warming potential of all cropping systems evaluated in this study clearly showed that 67% of the GWP was due to direct N 2O emissions, whereas the lowest contribution was from NO-derived indirect N2O emissions (0.2%) (Table 5). Machinery contribution, due to the use of fuel and lubricating oil, transport and production, machinery production, maintenance repairs, and transport of machinery accounted for 33% of the overall emission intensities. When the two locations were analysed separately, direct N2O emissions contributed 81% of the overall GWP in Breton and 53% in Ellerslie. Machinery accounted for 43% of the total GWP in Breton and 23% in Ellerslie. Soil CO2 emissions represented −30% (CO2 negative value represents a net CO2 uptake from the atmosphere) of Breton GWP and 23% of Ellerslie GWP (Table 5). Increased N fertilizer application rate resulted in a change in the contribution of N2O emissions from 65% (50N) to 69% (75N); whereas machinery contributed from 38% to 29% for the 50N and 75N cropping systems, respectively; and direct soil CO2 emissions represented contributions of − 4.5% for 50N and − 2.5% for 75N systems (Table 5). For 75N systems, indirect N2O emissions from ammonia volatilisation had a 3.7% contribution towards the overall estimate. For residue removal, the main contributions were, on average, as follows: direct N2O emissions, 60%; machinery, 35%; and NH3 driven N2O emissions, 2.9%. Different contributions were found with no crop removal: N2O emissions, 74%; machinery, 31% and CO2 uptake, −8.8% (Table 5).

Table 5 Percentage contribution to the global warming potential on a hectare basis with a 100-y horizon for the cropping systems at the two locations. Field site

Breton (B)

Amount of N fertilizer applied (kg N ha−1 y−1)

RMa

Mb

50

Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r) Residue removal (or) Residue retained (1r)

48.5 47.6 42.2 35.5 29.6 24.5 20.7 17.0

75 Ellerslie (E)

50 75

a

CO2c

Direct N2O

CH4c

Indirect N2O from NH3

Indirect N2O from NOx

Indirect N2O from NO− 3

−20.0 −42.7 −23.0 −32.1 25.8 19.1 24.8 20.4

68.0 90.1 75.0 89.5 44.5 56.0 52.1 60.1

−2.0 −2.4 −1.6 −1.6 −2.5 −2.4 −1.5 −1.5

2.4 2.8 4.4 4.6 2.1 2.1 2.9 2.8

0.1 0.2 0.3 0.3 0.1 0.1 0.1 0.1

2.9 4.5 2.8 3.8 0.4 0.7 0.9 1.0

(%)

RM, residue management. M, Machinery, including fuel and lubricating oil use, transport and production, machinery production, maintenance repairs, transport of machinery and of raw materials for machinery manufacture. c Negative values indicate a GWP reduction, due to CO2 uptake and/or CH4 oxidation. Soil CO2 emissions are due to soil carbon dynamics. b

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4. Discussion 4.1. Soil emissions Removal of crop residues (35% removal rate) resulted in limited changes in CO2 emissions, in agreement with previous findings at the same sites (Smith et al., 2012). As expected, more CO2 emissions occurred in treatments with residue removal. The rates of change in soil organic carbon (SOC) estimated by the DNDC model in this study would not likely be detectable in field scale measurements as detectable differences between treatments are typically 1–3 Mg C ha−1 (VandenBygaart et al., 2010, 2011). Total carbon inputs from roots and straw, which heavily regulate SOC change, were well simulated by the DNDC model being within 3–4% of values derived using measured straw and grain yields (Fig. 5) (Bolinder et al., 2007). The Bolinder et al. (2007) method includes estimates of root C inputs and exudates based on empirical relationships. Additionally, the estimated effect of fertilizer rates (N kg ha−1 rates 0, 25, 50 & 75) on grain yields for wheat, canola, triticale and barley from DNDC at Ellerslie and Breton was shown to agree well with onsite measurements (Smith et al., 2012). Nitrogen fertilizer application rates had only a marginal effect on soil CO2 emissions. The C inputs between these treatments were reasonably similar, particularly at Ellerslie (Fig. 5). In similar pedoclimatic conditions, Campbell et al. (1997) found that water use was responsible for nearly 65% of the variability in wheat yields while soil N level was responsible for 13% and degree days accounted for only 3%. Methane emissions, similar to CO2 emissions, are affected by soil C dynamics (Brady and Weil, 2002). However most agricultural soils act as a sink for methane as oxidation is the dominant process under aerobic conditions. (Dendooven et al., 2012; Hastings et al., 2010). The estimated differences between methane oxidation rates for Breton and Ellerslie were mainly attributed to the soil textural differences between sites, in agreement with Sullivan et al. (2013) and Brady and Weil (2002). The CH4 uptake at Ellerslie was on average 38% higher than the observations made by Ellert and Janzen (2008) with direct field measurements in

cropping systems that included wheat and barley under Alberta conditions. Ellert and Janzen (2008) estimated a net CH4 oxidation of − 0.394 kg CH4–C ha− 1 y− 1. The cropping systems differed from those in the current study because they were irrigated and had a different crop sequence. Nitrous oxide emissions were 39% larger with higher N fertilizer rates. This trend has been confirmed in other studies. For instance, Goglio et al. (2013) found an increase in emissions of 29% when greater amounts of fertilizer were applied for cropping systems with barley, wheat, and triticale under conditions in France. On site N2O flux measurements were not available for model validation; however, estimated emissions were within the range of measurements used to derive country specific IPCC Tier II emission factors. For these soils, measurements of up to 3 kg N2O–N ha−1 y−1 were reported (Rochette et al., 2008). These compare well with the DNDC estimated values of 1.33–3.10 kg N2O–N ha−1 y−1 across treatments in both sites. Note that these measurements are typically from chamber studies and do not include emissions due to spring thaw which would increase the observed emission rates. Additionally, the results for N2O emissions were comparable to those of similar cropping systems (including barley, wheat, and canola) under different crop management practices in Alberta (0.15–4.04 kg N2O–N ha− 1, based on direct monitoring (Ellert and Janzen, 2008; Li et al., 2012)). Nitric oxide emissions, which are responsible for indirect N2O emissions together with ammonia volatilization (De Klein et al., 2006), showed a 59% average increase when N fertilizer application rate increased from 50 to 75 kg N ha− 1. This result is in agreement with Stehfest and Bouwman (2006). The range obtained here, namely 0.21 to 0.79 kg NO–N ha−1 y−1, was similar to the measured NO flux (0.2 to 0.7 kg NO–N ha−1) during maize cultivation under varying types of fertilizer in Minnesota during the growing season (Fujinuma et al., 2011). In agreement with NO emissions, NH3 emissions were 57% larger under the higher fertilizer rate. Increase in N fertilizer rate did not cause a statistically significant increase in nitrate leaching. The additional N fertilizer (25 kg N ha−1) was

Fig. 5. Estimated cumulative crop carbon inputs for the cropping systems analysed at two locations (B, Breton; E, Ellerslie) from 1996 to 2009. 50N, 50 kg N ha−1 y−1; 75N, 75 kg N ha−1 y−1; 0r, residue removal; 1r, no residue retained; DNDC results; obs: calculated from observations.

P. Goglio et al. / Science of the Total Environment 490 (2014) 921–933

mostly taken up by crops or lost as N gas (Brady and Weil, 2002). These soils in the semi-arid prairies are not normally subject to frequent leaching events (Campbell et al, 2006a). When leaching does occur in these soils it is commonly associated with cropping systems containing fallow or where fertilizer rates exceed crop demands, neither of which were the case at the study sites. DNDC estimated annual average leaching of nitrate below 1 m ranging from 2 to 13 kg N ha−1 y−1 across the treatments at Breton and Ellerslie. These nitrate losses appear consistent with values reported by Campbell et al. (2006a, 2006b) at Swift Current (Saskatchewan, Canada) where no significant leaching was observed in 17 years and infrequent leaching (b40 kg N ha−1) was observed over 37 years from 1.2 to 1.5 m depth. In another study by Chang and Entz (1996) at Lethbridge, Alberta, for barley monoculture in nonirrigated systems, nitrate losses due to leaching were observed to be minimal. 4.2. Energy input Energy demand was affected (more than 8.8% increase) by the rise in N fertilizer rates on both a per hectare and a per gigajoule of harvested biomass basis, even though there was only a moderate change in amount of fertilizer applied (Brentrup et al., 2004b; Gan et al., 2011; Goglio et al., 2012). Crop residue harvest was responsible for a 6% increase in energy consumption on a hectare basis and a 28% decrease on a gigajoule basis. This change can be attributed to field operations during residue harvest, which involved more fuel consumption on a hectare basis; however, the residue collected contributed towards a greater output, which offset the extra input for harvesting operations, in agreement with Kim et al. (2009). Energy consumption was comparable with the range (6.92– 21.2 GJ ha−1 y−1) reported by Camargo et al. (2013) for several crops in the US including wheat, barley and canola. Present data were also comparable with LCA results reported for maize cultivation with and without tillage in a series of locations in the US corn belt (14.2–27.2 GJ ha−1 y−1) (Kim et al., 2009), but larger than the range reported by Kim et al. (2009) on a gigajoule basis (143–224 MJ GJ−1 y−1). This difference could be attributed to varying grain and biomass yields. Maize yield was, as can be expected, substantially greater than the crop yields evaluated in our study (Kim et al., 2009). Estimated energy consumption in this research (expressed as energy demand in GJeq) was within the range of estimates from other studies derived for similar crops on ha and GJ basis (1.94–50.5 GJ ha− 1 vs 15.4–19.6 GJ ha−1 here; 0.047–0.385 GJ GJ−1 vs. 0.249–0.387 GJ GJ−1 in this study), across various climatic zones (Börjesson and Tufvesson, 2011; Brentrup et al., 2004b; Charles et al., 2006; Goglio et al., 2012; Pelletier et al., 2008). 4.3. Effect of fertilizer and residue management on GWP The main purpose of this study was to assess the effects of management on GHG emissions and overall GWP (Figs. 4a & b) and also to determine the contribution of each source of GHG and energy to the GWP within each management scenario (Table 5). It was found that there was a sizable difference in GWP between sites, due primarily to differences in the initial SOC content at each location, together with soil characteristics affecting the overall emissions of N compounds which contributed to GWP (Brady and Weil, 2002; Saggar, 2010; Snyder et al., 2009). In particular SOC at Breton is slowly increasing while at Ellerslie SOC is slowly decreasing which impacted the CO2 contribution towards the GWP. Measurements at the site indicated that there was on average a slight gain in SOC over the 27 year study for 50 and 75 kg N ha−1 treatments at Breton and a loss at Ellerslie but there was considerable variability in the results (Smith et al., 2012). The LCA indicated that the GWP, for both functional units ha and GJ, was at least 71% greater for Ellerslie cropping systems than at Breton. When the effect of management was included, more carbon was sequestered when residue was retained at

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Breton and less carbon was lost when residue was retained at Ellerslie (Table 5). Little effect on soil CO2 emissions could be discerned for the two fertilizer rates. Increased fertilizer application resulted in at least 28% more GWP for both functional units (ha and GJ), in agreement with other research (Adler et al., 2007; Biswas et al., 2008; MacWilliam et al., 2014). At Breton and Ellerslie there was respectively 28% and 38% less GWP on a hectare basis for 50N in comparison to 75N treatments (Fig. 4). Likewise, a similar trend was observed for GWP on a GJ of harvested biomass basis. As expected, the higher fertilizer rate significantly increased N2O and NO emissions, ammonia volatilisation, and leaching of NO− 3 (De Klein et al., 2006; Hillier et al., 2012; Saggar, 2010). 0.09pt?>Therefore, considering that both measured and modelled yields were similar for 50 and 75 kg N ha−1 treatments at Ellerslie, fertilizer reduction appears to be a viable practice at this location. Substantial N was likely to have been made available through mineralization since the Ellerslie site had very high SOC levels of about 90 t ha−1 at the end of the field trials. Removal of residues resulted in limited changes in GWP (Fig. 4, 2% increase), without causing a substantial difference in energy demand (6.0% more energy consumption, Table 4) on a hectare basis, although on gigajoule basis a sensible reduction in global warming (33%) and energy consumption was observed (28%) (Fig. 4). The small change on a hectare basis was due in part to the limited amount of residue collected (only 35%) (Dendooven et al., 2012); but this removal rate is not uncommon for farming systems in the semi-arid prairies. For instance, Campbell et al. (2001) estimated a 22% average wheat residue removal rate for a 50-year study on a similar soil at Indian Head, Saskatchewan, Canada. While comparing GWP between residue treatments on a GJ of crop/ residue energy basis (Fig. 4b), results should be regarded with caution. The LCA assessment indicates that there is substantially less GWP for residue removal than residue retained which appears to be counterintuitive. However, these values were estimated based on both total energy in grain and residue. When GWP was estimated per GJ of grain product exclusively, GWP was found to be 10% larger for residue removal. Indeed, it could be argued that production is predominantly based on grain yields; while straw and residue yields are just a by-product worth little on the market. However, estimates per GJ of total biomass production can be more effective in assessing the overall productivity of the system (Camargo et al., 2013; Nemecek et al., 2011a, 2011b); although it may be more sensible to do the estimate based on crop value (ie. monetary or protein content) (Charles et al., 2006; Nemecek et al., 2011b; 2011b). A study whereby the DayCent model was used to provide inputs for a LCA of maize production for various locations in the U.S. corn belt (Kim et al. 2009) indicated that the GWP was about twice as high on a hectare basis in comparison to values in our study (1.9–5.2 Mg CO2eq ha−1 y−1 vs. 0.82–2.4 Mg CO2eq ha−1 y−1 here). This is not surprising since corn production in humid climatic zones requires more fertilizer and is more susceptible to leaching, runoff, and GHG emissions. Several other studies, some of which were performed for prairie soils and crops that are similar to our case have focused on estimating emission intensities. For instance, Dyer et al. (2010) reported emission intensities of 0.96 Mg of CO2eq ha− 1 for spring wheat, barley and mixed grains, 0.98 Mg CO2eq ha−1 for dry pea and 1.28 Mg CO2eq ha−1 for canola. The mean GWP per ha across all the cropping systems for our study was 30% larger than the weighted average on a crop frequency basis, derived from GHG emission intensities reported by Dyer et al. (2010). However, Dyer et al. (2010) estimated emission intensity based on average crop management for Alberta, whereas in our study a LCA assessment specific to the two field experiments was performed. Shrestha et al. (2014) used empirical methodologies to estimate emission intensities for canola production on the Canadian prairies. On an area basis, we estimate on average 33% more emissions from the Ellerslie cropping system than from canola production on sub-humid soils and 30% more emissions from the Breton cropping system than

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for canola production on semi-arid soils. It is not surprising that we estimate more emissions considering that the tier II IPCC methodologies would estimate low emissions from canola due to low residue inputs from the empirical equation, in comparison to other crops. Most of the published LCAs on cropping systems use IPCC emission factors and simple models to evaluate reactive N species and often disregard soil C dynamics (Börjesson and Tufvesson, 2011; Brentrup et al., 2004b; MacWilliam et al., 2014; Nemecek et al., 2011a, 2011b; Pelletier et al., 2008). On this basis, comparable results to the present study were obtained by Pelletier et al. (2008) in conventional and organic wheat and canola under Canadian conditions (17.1–39.3 kg CO2eq GJ−1 y−1). Results from our contribution analysis (Table 5) stress the importance of including soil-borne emissions in LCA assessments of cropping systems. In particular, N2O emissions greatly contributed to the overall GWP in our study as well as in several others (Camargo et al., 2013; MacWilliam et al., 2014). Nitrous oxide emission contribution to GWP (67%) was in agreement with Zaher et al. (2013) who estimated a 60–70% range for N2O contribution in cropping systems with barley and wheat in Eastern Washington State. The other indirect N2O produced from nitrate leaching, ammonia volatilization, and NO emissions had a limited impact on the overall GWP. 5. Conclusion In this study, GHG emission estimates from the DNDC model were combined with LCA to estimate the GWP and energy demand of crop management in cropping systems for two Alberta locations. As expected, nitrogen fertilizer reduction resulted in less GWP both on ha and GJ basis for the cropping systems assessed. The statistical analysis indicated that a reduction in fertilizer rate resulted in a decrease in N2O emissions. Fertilizer reduction at Ellerslie appears to be a viable and recommended practice considering that measured yields were not affected during the course of our study. Energy consumption was marginally affected by a reduction in N fertilizer rate and crop residue management at the two sites on hectare basis. However, on productivity basis, considering the energy in both grain and straw biomass, significant reductions in energy consumption were achieved for crop residue removal. The GHG emissions in this assessment were based on results from DNDC, which carry uncertainties in emission prediction. However, since components of the model have been validated for these sites as well as for similar soils in the region, a certain level of confidence is conferred when integrating the estimated GHG emissions within the LCA of cropping systems. Further research should focus on evaluating other combinations of management practices carried out to reduce GHG emissions. Furthermore, a thorough assessment of the effects of climate variability within a season is necessary to better evaluate the overall impact of crop management on GWP. Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2014.05.070. These data include Google map of the most important areas described in this article. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2014.05.070. References AAFC. The Canadian system of soil classification, 3rd edition. Canadian soil resource group. Agriculture agri-food Canada. Available online: http://sis.agr.gc.ca/cansis/ taxa/cssc3/intro.html, 2008. [accessed on 15 Apr 2013].

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