Energy Use and Greenhouse Gas Emissions from Crop Production

Articles

Energy Use and Greenhouse Gas Emissions from Crop Production Using the Farm Energy Analysis Tool GUSTAVO G. T. CAMARGO, MATTHEW R. RYAN, AND TOM L. RICHARD Using the Farm Energy Analysis Tool (FEAT), we compare energy use and greenhouse gas (GHG) emissions from the cultivation of different crops, highlight the role of sustainable management practices, and discuss the impact of soil nitrous oxide (N2O) emissions and the uncertainty associated with denitrification estimates in the northeastern United States. FEAT is a transparent, open-source model that allows users to choose parameter estimates from an evolving database. The results show that nitrogen fertilizer and N2O emissions accounted for the majority of differences between crop energy use and GHG emissions, respectively. Integrating sustainable practices such as no tillage and a legume cover crop reduced energy use and GHG emissions from corn production by 37% and 42%, respectively. Our comparisons of diverse crops and management practices illustrate important trade-offs and can inform decisions about agriculture. We also compared methods of estimating N2O emissions and suggest additional research on this potent GHG. Keywords: agriculture, sustainability, efficiency, biomass, nitrous oxide emissions

I

n order to meet the grand challenges of agriculture and to

increase production without excessively compromising environmental integrity, farmers, agricultural researchers, educators, and policymakers need tools to help them compare the impacts of different options. The agricultural sector is already a large contributor to global energy use and greenhouse gas (GHG) emissions, and the environmental impact of agriculture is likely to increase as our population grows to nine billion and requires more protein and calories (Beddington et al. 2012, Kastner et al. 2012). On-farm energy use in the United States has been increasing since 1990, reaching 0.84 exajoules in 2008, which accounted for 0.8% of the total US energy use (USDA 2011). More alarming is that the agricultural sector was responsible for emitting 6.3% of the total amount of US GHG emissions in 2009—a distressing 413 teragrams of carbon dioxide equivalents (CO2e; USEPA 2012). Given the myriad food, feed, fiber, and bioenergy crops that can be grown in the United States, it is important to understand how different crops compare in terms of energy use and GHG emissions and to synthesize quantitative information on their production. The energy required to grow a crop can be calculated by accounting for the energy associated with the inputs required for production. Energy and GHG emissions from agricultural inputs can be divided into primary (e.g., fuel for machinery operations), secondary (e.g., production and transportation of inputs), and tertiary (e.g., raw materials to produce items such as machinery and buildings) sources (Gifford 1984).

Comparisons can be made on a land-area basis (West and Marland 2002, Adler et al. 2007, Nelson et al. 2009), thus normalizing for the size of a farm, or they can be made on an output basis (e.g., per ton of biomass or per megajoule [MJ] of biofuel; Wang et al. 1999, Farrell et al. 2006, Chianese et al. 2009). It is increasingly common for GHG analyses to be linked with energy analyses of agricultural systems because of global climate change. The methodology for GHG evaluation is similar to energy analysis, in which the inputs are converted to one unit, such as the kilograms (kg) of CO2e (Lal 2004, Farrell et al. 2006). Unfortunately, information on energy and GHG emissions from agricultural crops is scattered throughout the literature, and estimates for the same crop species can be quite variable. For example, Pradhan and colleagues (2008) reviewed four soybean (Glycine max [L.] Merr.) studies and found that the energy associated with soybean production ranged from 4032 to 15,506 MJ per hectare (ha) per year. Farrell and colleagues (2006) compared six publications and found that the energy use and GHG emissions associated with corn (Zea mays L.) production ranged from 5728 to 12,066 MJ per ha per year and from 2441 to 4201 kg CO2e per ha per year, respectively. One major factor causing variability in the results is the different energy and GHG emissions parameters used in the studies (Hülsbergen et al. 2001, Pimentel et al. 2005, Hoeppner et al. 2006, Rathke et al. 2007, Baum et al. 2009, Gelfand et al. 2010). In some cases, a parameter value was used multiple times

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Articles without referencing all of the original sources (Gelfand et al. 2010), and in other cases, the parameters were derived from complied data sets that were not publically available (West and Marland 2002, Lal 2004). Some of these differences are also a result of different management practices, such as tillage, ferti lizer use, or crop rotations. In addition to variable estimates in the literature and continued disagreement about crop yields under such different management practices, the different approaches and interfaces that are used by these models make it difficult to identify the specific model components causing discrepancies among studies. We attempt to address these shortcomings by introducing the Farm Energy Analysis Tool (FEAT), which is a new database model that organizes information from the literature into a functional and transparent structure that can be used to estimate the energy use and GHG emissions of individual crops and cropping systems. We developed FEAT to facilitate energy and GHG analyses, to extend the inferences of previous research and make this information accessible, and to identify know ledge gaps and areas in which additional research is needed. To illustrate the utility of FEAT, we compared 13 different crops that can be grown for food, feed, or fuel in the United States. The crops included barley (Hordeum vulgare L.) harvested for grain, corn harvested for grain and silage, rye (Secale cereale L.) harvested for silage, wheat (Triticum aestivum L.) harvested for grain and silage, alfalfa (Medicago sativa L.), red clover (Trifolium pratense L.), canola (Brassica napus L.), soybeans, sugar beets (Beta vulgaris L.), miscanthus (Miscanthus × giganteus Greef et Deu.), switchgrass (Panicum virgatum L.), hybrid poplar (Populus spp.), and willow (Salix spp). In this article, we compare the crop energy production efficiency (energy output divided by energy input) and GHG intensity (GHG output divided by energy output) of these crops and discuss the impact of different methods and knowledge gaps. We also compared three corn management systems and estimated energy use and GHG emissions in corn production using three different management types: standard management with tillage and synthetic fertilizer, no-till management with synthetic fertilizer, and no-till management with a legume cover crop. An overview of the FEAT model FEAT is an open-source database model that uses a spreadsheet program to store data, perform calculations, and present results graphically (available at www.ecologicalmodels.psu.edu/ agroecology/feat/download.htm). The values for energy use and GHG emissions used in the analysis were selected from an extensive literature review (available at www.ecologicalmodels. psu.edu/agroecology/feat/Supplemental_materials.zip). Sources, including peer-reviewed literature, online documents, and simulations, were used to develop the FEAT database, which includes crop management characteristics, energy inputs, energy outputs, and GHG emissions. Summary statistics, including the mean, range, and 95% confidence interval, are also provided in the database to show users the uncertainty associated with each input parameter. Conducting an analysis with FEAT involves specifying assumptions, including crop inputs and their rates, crop yield, and management practices. One of the unique features of FEAT is that users can also select

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specific energy and GHG emission conversion values or use the average from all entries in the database. Crop production inputs are then converted to common units of MJ for energy use and kg CO2e for GHG emissions (see supplementary tables S1 and S2, available online at http://dx.doi.org/10.1525/ bio.2013.63.4.6, for the equations used to perform the energy and GHG analyses). Results and graphs are automatically generated in the spreadsheet. The database approach used for developing FEAT is similar to several existing models that estimate biofuel impacts, including the Greenhouse, Regulated Emissions, and Energy Use in Transportation (GREET) model, the Energy and Resources Group Biofuel Analysis Meta-Model (EBAMM), and the Biofuel Energy Systems Simulator (BESS) model (Wang 2001, Farrell et al. 2006, Liska et al. 2009). These database models are different from mechanistic agricultural models that simulate biophysical processes with mathematical equations and weather data. Although they are less representative of the interactions among biophysical processes in complex agroecosystems, database models can perform well, are generally easier to use, and provide greater model modification flexibility. Life cycle assessment (LCA) represents another class of tools that is formally defined by the International Standards Office. Proprietary software such as SimaPro (www.pre-sustainability. com/software) or GaBi (www.gabi-software.com/america/index) is typically used for LCA, which is performed for diverse consumer products. In agricultural research, an LCA might include additional impact categories, such as water quality, biodiversity, toxicology, and aesthetics (Haas et al. 2000). In table 1, the features and capabilities of six farm models that are used to estimate energy and GHG emissions are compared in order to illustrate the distinctions of the FEAT approach. The benefits of FEAT include its transparency, flexibility, and capacity to evolve as an open-source tool that can integrate new information from a diverse community of users. The ability to easily learn and manipulate FEAT also makes it a useful educational tool for exploring the effects of different crop and management options on energy use and GHG emissions with students and farmers. Crop comparisons using FEAT Below, we outline the various analyses we performed using FEAT to estimate energy use and GHG emissions, indicating assumptions associated with system boundaries, inputs, and crop characteristics. We also define two specific output variables: energy efficiency and GHG intensity. Energy use analysis. Energy use was calculated by multiplying each input rate by its respective energy parameter, which represents the embodied energy to produce the input or the energy required to perform an activity (IFIAS 1978, Farrell et al. 2006). We used the mean of reported parameter values for embodied energy associated with major crop inputs from the northeastern United States for soil and pest management; seed, rhizome, and cutting production; and grain drying for corn, barley, and canola. The transportation of inputs and on-farm fuel needed for crop production were also included in the analysis. We defined the transportation of inputs as the energy required to transfer inputs from manufacturing facilities to the farm.


Articles Table 1. Comparison of farm models used to estimate energy and greenhouse gas (GHG) emissions. Farm Energy Analysis Tool

Cool Farm Toola,b,c

Farming Systems Greenhouse Gas Emissions Calculator d

Fieldprint Calculator e

Integrated Farm System Modelf

Holosi

Life cycle assessment tools

Platform

Spreadsheet

Spreadsheet

Web based

Web based

Program

Program

Program

Availability

Free

Free

Free

Free

Free

Free

Free or paid

Code manipulation

Yes

No

No

No

No

No

No

Simulation type

Database

Database

Database, mechanistic

Database

Mechanistic

Mechanistic

Database

Carbon soil emissions and sequestration

No

Yes

Yes

No

No

Yes

No

Weather and soils data input

No

Yes

Yes

Soils only

Yes

Yes

No

Energy analysis

Yes

Yes

No

Yes

No

No

Yes

GHG analysis

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Economic analysis

No

No

No

Yes

Yes

No

Yes

Other environmental results

No

Land-use change

No

Erosion

Erosion, water quality

No

Water quality, biodiversity, toxicology

N2O emissions method

IPCC Tier 1

Bouwman et al. 2002a, 2002b

IPCC Tier 1 and 2

Method not reported

DAYCENTg,h

Rochette et al. 2008j

No

Crop rotations

Yes

No

Yes

No

Yes

Yes

No

Complex rotations, crop mix, and custom operations

Yes

No

No

No

Yes

No

No

Crops available

13

20 or more

6

5

17

Livestock integration

Under development

Yes

No

Manure use only

Yes

Yes

Yes

Other environmental performance results

No

No

No

No

No

No

Yes

–

Abbreviation: N2O, nitrous oxide. a Hillier et al. 2011. bBouwman et al. 2002a. cBouwman et al. 2002b. dMcSwiney et al. 2010. ewww.fieldtomarket.org/fieldprint-calculator. f www.ars.usda.gov/main/docs.htm?docid=8519. gDel Grosso et al. 2000. hParton et al. 2001. iLittle et al. 2008. jRochette et al. 2008.

Greenhouse gas emission analysis. Following the energy

methodology, mean emissions from selected farm inputs (nitrogen [N], phosphate, potash, lime, herbicide, insecticide, seed, crop drying) production, input transportation, on-farm fuel consumption, and soil nitrous oxide (N2O) emissions were converted to kg CO2e. Additional GHGs, such as methane and N2O were converted to kg CO2e on the basis of their 100-year global warming potentials (GWPs), which are 25 for methane and 298 for N2O (Eggleston et al. 2006). After GWP conversion, GHGs can be added, because they have the same units of kg CO2e. We used the Intergovernmental Panel on Climate Change (IPCC) Tier 1 as our default method to account for N2O emissions (Eggleston et al. 2006), which is appropriate for the database modeling approach used in FEAT.


We accounted for both direct and indirect N2O emissions in our analysis. Direct (i.e., on-site) emissions included N2O from nitrification and denitrification of N from synthetic fertilizer and crop residues. Indirect (i.e., off-site) emissions included N2O from volatilization in N production and from leaching and runoff. For direct emissions, we used a default value such that 1% of the N added to the system as synthetic fertilizer or crop residue is assumed to be converted to N2O-N. For subsequent N2O and CO2e calculations the N2O molecular mass is set equal to 44/28 of this elemental N2O-N mass. The N content of crop residues was calculated using a linear relationship between crop yield and residue. For indirect emissions, we assumed that 10% of the N that is volatilized into NH3-N and NOX-N and their products, NH4+ and NO3–, is deposited back into soil and water and that 1% of the N that is redeposited is converted to


Articles N2O-N. In addition, 30% of the synthetic N added to the soil was assumed to be lost to leaching and runoff, and 0.75% of this N is converted to N2O-N through denitrification. System boundary and inputs. In this analysis, we assumed a

cradle-to-farm-gate boundary, which provided flexibility for evaluating different crops with multiple end uses (e.g., food, feed, forage, fuel). We did not include additional on-farm processing beyond grain drying and assumed that the crops were sold as commodities. The input requirements for each crop included N, phosphorous measured as phosphorus pentoxide (P2O5), potassium measured as potassium oxide (K2O), lime, seed, herbicides, insecticides, and on-farm diesel consumption. Crop characteristics. Crop yield can vary because of condi-

tions such as weather, soil, location, input intensity, irrigation, tillage, seed variety, and rotation. However, in our analysis, we assigned a yield to each crop on the basis of reported values. In the case of emerging energy crops, such as miscanthus, yields were assigned on the basis of the results from carefully managed research plots and may therefore overestimate typical values. Similarly, input rates may vary in terms of crop variety, location, rotation, and tillage practices. In our analysis, inputs such as seed, pesticides, and fertilizer rates were set at standard values intended to be representative of the northeastern United States (Beegle 2011). When input values from the northeastern United States were not available (i.e., miscanthus, willow, and hybrid poplar), a mean literature value from several locations was used. We did not assume that crops were grown with particular management practices but, rather, attempted to model an average of the most common practices found in our literature review. However, FEAT does provide the flexibility to select and modify management practices to evaluate specific situations. Because not all crop inputs are required every year (e.g., lime), the input rates were amortized to an annual basis. Similarly, we amortized energy use and GHG emissions for perennial crops, assuming that alfalfa is grown for 3 years, red clover for 1 year, miscanthus for 20 years, switchgrass for 15 years, and hybrid poplar and willow for 18 years. Defining energy efficiency and greenhouse gas intensity. In addi-

tion to reporting total energy use and GHG emissions for each crop by input, we also calculated energy production efficiency (energy output divided by energy input) and GHG intensity (GHG output divided by energy output). Efficiency metrics provide a useful indication of crop outputs relative to their inputs, whereas GHG intensity provides an indication of emissions per unit energy production. The output energy type considered in the analysis was higher heating value, which is the energy released after reaction with oxygen under isothermal conditions (Brown 2003). This energy is the chemical energy contained in plant tissue that was converted from photon energy by photosynthesis. Downstream uses of this chemical energy might include providing metabolic energy to cows for lactation, burning pellets for heat, or making food ingredients or biofuels in an industrial biorefinery, and each of these downstream processes will have a different conversion efficiency. However, before that conversion occurs, higher


heating value represents the maximum theoretical energy possible from a unit of crop biomass and, therefore, provides a way to normalize on-farm energy yield for different crops. For both energy and GHG impacts, the unit basis was annual crop area (per ha per year), which is the same unit basis used for other crop performance measures, such as yield. Efficiency calculations were reported as a biomass energy output per fossil energy input (MJoutput per MJinput), whereas GHG intensity is reported as GWP per unit of biomass energy output (kg CO2e per MJoutput). Energy use Across all nonleguminous crops, the single greatest contribution to energy use was from N fertilizer, which ranged from 3175 MJ per ha per year (40% of the total) in hybrid poplar to 9209 MJ per ha per year (43% of the total) in corn silage (figure 1). The single greatest energy use contribution across the three legume crops was on-farm fuel use, which was also the second largest single energy use input across all nonleguminous crops. When averaged across all crops, N fertilizer accounted for 36% of the total energy use, followed by on-farm fuel (30%), then K2O (7%), lime (6%), the transportation of inputs (6%), P2O5 (5%), seed (5%), herbicide (4%), drying (2%), and insecticide (1%). These results show that N and on-farm fuel consumption are the main energy input drivers, representing 66% of the total energy across crops. Energy use was lower for legume crops because they fix atmospheric N and, therefore, do not require energy-expensive N fertilizer. Energy use ranged from 6922 to 21,235 MJ per ha per year, with red clover requiring the lowest amount of energy for production, followed by soybeans, then hybrid poplar, willow, alfalfa, barley grain, switchgrass, wheat grain, rye silage, wheat silage, miscanthus, canola, sugar beets, corn grain, and corn silage (figure 1). The crops that did not require large amounts of N fertilizer used relatively little energy; however, other fertilizer inputs were also important. For example, if we compare the two perennial legume crops, we can see that the energy required to grow alfalfa was estimated to be 26% greater than that required for red clover, which was mainly because of greater potash use in alfalfa (90 kg per ha) than in red clover (67 kg per ha). Although some of these results may seem intuitive or predictable, quantitative information across such a large range of crops is useful for understanding the impact of different inputs. Moreover, plotting the results as a function of crop type helps to illustrate underlying drivers that are responsible for the major effects. Energy production efficiency In our analysis, miscanthus was assumed to produce the greatest amount of energy (i.e., MJoutput), followed by corn silage, then willow, hybrid poplar, alfalfa, wheat silage, red clover, switchgrass, sugar beets, rye silage, corn grain, barley grain, wheat grain, soybeans, and canola (figure 2). Crop residues, such as corn stover and wheat straw, were not included as output. In addition to the energy requirements associated with crop production and the amount of crop energy produced, we also compared crops in terms of energy production efficiency (output divided by input), which ranged from 3.6 to


Articles whereas canola requires a large amount of N fertilizer and, therefore, requires more energy. Energy use analysis considerations The efficiency of manufacturing various crop inputs has increased over time through various technical and management innovations. One concern about using the mean of published parameter values is the inclusion of dated para meter values representing older, inefficient manufacturing facilities. For example, Smil (2001) reported that N fertilizer production now requires less than half as much energy as it did in the early 1900s. We tested for a correlation between publication date and the FEAT database values for the amount of energy required to produce fertilizer in an effort to account for potential increases in manufacturing efficiency. The results from a regression analysis indicate that the energy necessary to manufacture fertilizer was consistent over the period represented in our database (i.e., there was no significant correlation with time), which supports our decision to use the mean of reported parameter values Figure 1. (a) Estimated energy use associated with each crop. (b) Energy production (figure 3). One explanation for not efficiency calculated as a ratio of energy output to energy inputs. Abbreviations: gr., grain; detecting an increase in efficiency K2O, potassium oxide; MJinput, fossil energy input, in megajoules; MJoutput, biomass energy is that the majority of the increase output, in megajoules; N, nitrogen; P2O5, phosphorus pentoxide; sil., silage; Transp., in efficiency over time occurred in transportation. the middle of the twentieth century (Smil 2001). Therefore, because our 34.5 MJoutput per MJinput. Canola had the lowest value, followed by parameter estimates were found in publications from between wheat grain, then barley grain, corn grain, soybeans, sugar beets, 1977 and 2005, the majority of improvements in manufacrye silage, corn silage, wheat silage, switchgrass, alfalfa, red cloturing efficiency had already taken place. However, given the ver, willow, hybrid poplar, and miscanthus (figure 1). In general, importance of N fertilizer in our analysis, using a specific perennial legume, perennial grass, and short-rotation woody fertilizer sourced from a modern, efficient facility could subcoppice crops were the most efficient, followed by annual silage stantially reduce energy use and GHG impacts relative to our crops, annual grain, and oilseed crops. The large difference in results. For example, data on fertilizer manufacturing in energy production efficiency between miscanthus and switchEurope indicates that the best available technology could lower grass was due to differences in assumed yield; switchgrass yield the energy required for ammonia N production from 39 MJ was estimated at 8.52 megagrams (Mg) of dry matter per ha per per kg (the 2003 European Union average) to 25 MJ per kg year, whereas miscanthus yield was 24.63 Mg of dry matter per (the 2000 best available technology estimate), a reduction ha per year. Recall that the miscanthus yields used in this analyof 36% (EFMA 2000, Kongshaug and Jenssen 2003). The N sis were reported from experimental sites and, therefore, might component of crop energy requirements can also be reduced be higher than typical management would achieve. Between through the use of alternative N sources. For example, if the two oilseed crops that can be used to produce biodiesel, manure were used instead of a different fertilizer, the contrisoybeans were more efficient than canola, despite the fact that bution of N to total energy use for crop production could be canola can produce more oil per hectare than soybeans can. greatly reduced, but this depends on the attribution framework This is because soybeans are a legume that fixes atmospheric N, (Thomassen et al. 2008).



Articles and included in the FEAT database, so they can be readily incorporated into other crop or cropping system analyses. They were excluded from this analysis because of the small impact on the results and also because limited data for less common crops made the comparison difficult. The embedded energy necessary to manufacture machinery for crop pro duction is a tertiary input that typically has a minor impact on the total energy. Farrell and colleagues (2006) reported that machinery accounted for only 1.7% of the total energy associated with corn production. However, others have reported that the energy embedded in machinery can be a much greater factor. For example, Hoeppner and colleagues (2006) conducted an energy analysis of organic and conventional cropping systems and found that the energy embedded in machinery accounted for 3.5%–10.4% of the total Figure 2. Estimated energy output associated with each crop. Abbreviations: energy use across systems. Although it gr., grain; MJoutput, biomass energy output, in megajoules; sil., silage. was not included in our analysis, the FEAT database default mean value for the embedded energy in machinery was 75 MJ per kg (Berry and Fulton 1972, Chancellor 1978). Therefore, once the equipment used to produce crops is determined, the contribution to total energy use can be calculated by multiplying the machinery’s weight by the default value and then amortizing that value over the expected life of the equipment. Irrigation was also excluded from our analysis, because it is rarely used for field crop production in the humid northeastern United States. However, we realize that pumping can have a significant impact on energy use in more arid regions and that water withdrawals should be taken into account when con sidering irrigated cropping systems (King and Webber 2008). The FEAT database default value for irrigation is 0.014 MJ per cubic meter of water per meter depth of water pumped from Figure 3. Estimates of fertilizer manufacturing energy a well (Batty and Keller 1980). Other than irrigation, the elec(in megajoules per kilogram) over time (sampled between tricity required for agronomic crop production typically rep1977 and 2005) in the Farm Energy Analysis Tool database. resents only a small fraction of the total energy use, and there A linear regression showed no significant improvement are few citations that quantify electricity consumption from in efficiency in the literature values for nitrogen (r2 = .02, crop production. Other models with a similar methodology p = .643), phosphate (r2 = .002, p = .893), or potash (r2 = .19, (e.g., I-FARM; http://i-farmtools.iastate.edu) do not account p = .157) during the period when estimates were published in for the electricity use associated with crop production, and in the literature. models that do account for electricity (e.g., IFSM; www.ars. usda.gov/main/docs.htm?docid=8519), its contribution is negligible. Labor as metabolic energy is often excluded from energy In addition to the impact of improvements in efficiency, some analyses (Ahmed et al. 1994, Wang 2001, Dias de Oliveira et al. inputs are often excluded from energy analyses. The inputs 2005) but can be added if the amount of time that is required that were excluded from the results presented here include the to produce a crop is known. The FEAT database default value effects of improvements in farm machinery, irrigation, elecfor labor is 6 MJ per hour (Patzek 2004, Zhang and Dornfeld tricity, and labor. Our analysis indicates that these excluded 2007). Although labor typically represents only a small fracinputs represent a relatively small contribution to energy use or tion of the total energy use and although the methods used GHG emissions, but circumstances may vary in other cropping to calculate the contribution of labor to energy use have long systems. Information about these inputs is summarized below



Articles been criticized (see Jones 1989), labor, in itself, is a useful metric for comparing crops and cropping systems and is a critical factor in farm management decisions independent of the energy or GHG implications. Greenhouse gas emissions. Across all crops, the largest con-

tributor to the total amount of GHG emissions was N2O, which ranged from 157 kg CO2e per ha per year (17% of the total) in soybeans to 1539 kg CO2e per ha per year (45% of total) in corn silage. N production and on-farm fuel followed N2O emissions, representing 16% and 14%, respectively, across all crops. When it was averaged across crops, N2O emissions accounted for the greatest GHG contribution, with 44% of the total, followed by N production (16%), on-farm fuel (14%), lime (12%), K2O

(4%), P2O5 (3%), transportation of inputs (3%), seed (2%), herbicide (1%), drying (1%), and insecticide (0.6%). GHG emissions from crop inputs ranged from 847 to 3283 kg CO2e per ha per year, and soybeans had the lowest input, followed by red clover, alfalfa, hybrid poplar, barley grain, wheat grain, rye silage, willow, wheat silage, switchgrass, canola, sugar beets, miscanthus, corn grain, and corn silage (figure 4). N fertilizer had a large impact on GHG emissions, because a small portion of the N that is applied to soil is converted to N2O, which has a high GWP. Greenhouse gas intensity. Canola and the crops grown for grain (barley, corn, and wheat) were estimated to have the greatest GHG intensity (figure 4). Although soybeans did not require N fertilizer, the relatively low yield of this crop increased its intensity. For the other crops, high N requirements and the associated soil N2O emissions contributed to the higher GHG intensity. The higher-yield crops tended to have lower GHG intensities. Miscanthus had the lowest GHG intensity (0.006 kg CO2e per MJoutput), which was mainly a function of its high yield potential. The two perennial legume forage crops, alfalfa and red clover, had relatively low GHG intensities, which was a function of their relatively high yields and low input requirements, whereas silage crops with comparable yields but greater input requirements had higher GHG intensities. Although corn silage production was estimated to result in the greatest GHG emissions across all crops, the high energy output from corn silage caused its GHG intensity to be relatively low.

Figure 4. (a) Greenhouse gas (GHG) emissions (in kilograms of carbon dioxide equivalents per hectare per year) associated with each crop. (b) GHG intensity calculated as the ratio of GHG emissions to the output of energy for each crop (in kilograms of carbon dioxide equivalents per megajoule of biomass energy output). Abbreviations: gr., grain; K2O, potassium oxide; N, the amount of carbon dioxide emissions necessary to produce and transport nitrogen fertilizer; N2O, nitrous oxide emissions from nitrification and denitrification occurring on and off the farm; P2O5, phosphorus pentoxide; sil., silage; Transp., transportation.


Sustainable management practices In addition to the choice of crop, decisions about management practices can have a large impact on energy use and GHG emissions. To illustrate the effects of integrating sustainable management practices and the flexibility of FEAT, we compared three different corn production scenarios: tillage farming and synthetic fertilizer (the standard scenario), no-till farming with synthetic fertilizer, and no-till farming with a legume cover crop. The details of the management of and inputs to these scenarios were


Articles reported by Ryan (2010). Synthetic N fertilizer was the single largest energy input, constituting 51%, 53%, and 19% of the total energy budget in the standard, no-till, and no-till with cover crop corn production scenarios, respectively (figure 5). The no-till scenario resulted in a 6% reduction in overall energy use and a 3% reduction in GHG emissions compared with the standard practices. Our findings are congruent with Rathke and colleagues’ (2007) and West and Marland’s (2002), who reported an 8% reduction in energy use and a 1% reduction in GHG emissions when comparing standard tillage-based corn management with no-till management. Compared with the standard practices, the no-till cover crop scenario resulted in a 37% decrease in energy use and a 42% reduction in GHG emissions (figure 6). The energy embodied in crop seed was 1335 MJ per ha greater because of the hairy vetch cover crop seed in this scenario; however, the energy from N fertilizer production was 7011 MJ per ha lower than that in the standard scenario, because the N-fixing cover crop reduced the need for

synthetic fertilizer. Assuming similar corn yields across scenarios (which is admittedly controversial), the cover crop scenario resulted in a 59% increase in energy production efficiency and a 42% reduction in GHG intensity relative to the standard practice. These results illustrate the importance of considering not only different crop choices but also the management systems that are used to grow them.

Nitrous oxide uncertainty As we developed FEAT and conducted the analyses above, it became increasingly clear that the method used to calculate N2O emissions has a profound impact on GHG emission budgets and the GWP associated with crop production. Because of the high GWP, N2O is the most important factor to consider and, unfortunately, the least well understood. In a comparison of different bioenergy crops (corn, soybeans, alfalfa, reed canary grass, switchgrass, and hybrid poplar) in Pennsylvania, Adler and colleagues (2007) reported that N2O emissions were the single largest contribution to GWP, which is consistent with a our findings and those of Robertson and colleagues (2000). Yet, there is considerable variation and uncertainty in the literature related to N2O emissions from crop production. For instance, the estimated IPCC Tier 1 soil emissions default value is 1% of the N added converted to N2O-N, within a range of 0.3% to 3% (Eggleston et al. 2006). In other research, the value was reported to range from 2% to 2.5% (Ogle et al. 2009) and from 3% to 5% (Crutzen et al. 2008). Differences in application rates b and complex soil biogeochemical processes both affect the amount of N fertilizer that is observed to be converted into N2O in empirical research. The IPCC Tier 1 default method used in FEAT assumes a linear relationship between N2O emissions and N from fertilizer applications and crop residues (figure 7). However, Van Groenigen and colleagues (2010) and Hoben and colleagues (2011) developed nonlinear models that relate N fertilizer application with N2O emissions (see supplementary tables S1 and S2 for the relevant equations). We compared Figure 5. (a) Estimated energy use (in megajoules of fossil fuel input per hectare per year) the N2O emission estimates for associated with corn management practices. (b) Energy production efficiency calculated each of the 13 crops using these as a ratio of energy output to energy input (in megajoules of biomass energy output per two nonlinear methods, with the megajoule of energy input). The corn management scenarios were tillage farming with linear IPCC Tier 1 method servsynthetic fertilizer (the standard scenario), no-till farming with synthetic fertilizer, and ing as a baseline. Except in legume no-till farming with a legume cover crop. Abbreviations: K2O, potassium oxide; crops, the Hoben and colleagues N, nitrogen; P2O5, phosphorus pentoxide; Transp., transportation.



Articles Conclusions Our results demonstrate the utility of FEAT to provide useful quantitative information in a clear and transparent manner. Using a cradle-to-farm-gate analysis, we found that N had the largest impact on energy use and GHG emissions for a wide range of crops, resulting from the large amount of energy required to produce N fertilizer through the Haber–Bosch process (54.8 MJ per kg) and from the large GWP associated with N2O emissions (298 kg CO2e per kg b N2O). For agricultural producers and consumers striving to dramatically reduce these impacts to meet renewable fuel or other sustainability criteria, sourcing N fertilizer from less energy-intensive sources can offer significant benefits. The results from our comparison of energy production efficiency and GHG intensity illustrate the important role that legume crops can play in reducing energy use and GHG emissions—either directly, when legumes are used as energy crops, or indirectly, when N is provided as an ecosystem service in crop rotations. Our results suggest Figure 6. (a) Greenhouse gas (GHG) emissions (in kilograms of carbon dioxide equivalents per that crop diversification with highhectare per year) associated with each corn management scenario. (b) GHG intensity calculated yielding biomass crops that require as a ratio of GHG emissions to the output of energy for each crop (in kilograms of carbon relatively small N inputs could help dioxide equivalents per megajoule of biomass energy output). Abbreviations: K2O, potassium mitigate global warming. oxide; N, the amount of carbon dioxide emissions necessary to produce and transport nitrogen Although energy production fertilizer; N2O, nitrous oxide emissions from nitrification and denitrification occurring on and efficiency is a useful metric, conoff the farm; P2O5, phosphorus pentoxide; Transp., transportation. verting crop biomass to MJ fails to convey the inherent value differences that exist between crop products. For example, 1 MJ (2011) method estimated greater N2O emissions than did the of canola has a mass of 0.049 kg, which would be worth IPCC method. However, the Van Groenigen and colleagues approximately $0.011, whereas 1 MJ of miscanthus has a mass (2010) method estimated lower N2O emissions than did the of 0.053 kg and would be worth approximately $0.003 (CALU IPCC method. The legume crops did not emit N2O in Van 2006; see also the table “Canola: Acreage, Yield, Production, Groenigen and colleagues (2010) and Hoben and colleagues Price and Value North Dakota, 1997–2006” in NASS 2007). (2011), because their methodology accounts for N from only Canola, other oilseeds, and starch grains command a much fertilizer applications and excludes emissions from crop resihigher price per unit mass (or energy) than cellulosic biomass dues. Another concern regarding Tier 1 IPCC methodologies because of the easy and less expensive conversion of lipids, is the lack of site specificity (Eggleston et al. 2006), although protein, and starch into valuable commodities like milk; meat; some mechanistic models, such as DAYCENT (Ogle et al. and, with current technologies, biofuels. Assuming that the 2009), account for site variation and can simulate N 2O costs of cellulosic biofuel conversion decrease as technologies emissions. Comparing different N2O-emission-accounting mature, this threefold price discrepancy is likely to decrease. methods underscores the need for additional research on Although smaller price differentials will be advantageous to quantifying and estimating N2O emissions. Future research crops with greater energy production efficiencies, the relative should be aimed at determining the importance of site speciprices of food, feed, fiber, and fuel will continue to reflect a ficity, the effects of legume crop residues, and the threshold combination of market demand and public policy incentives range under which linear methods are appropriate for estifor those different end uses. Regardless of the end use, energy mating emissions from fertilizer applications.

a



Articles Northeast Sun Grant Initiative, the Richard King Mellon Foundation, and the Penn State Institutes of Energy and the Environment. We thank Glenna Malcolm and several anonymous reviewers for their thoughtful comments on an earlier draft of this article. References cited

Figure 7. Differences in nitrous oxide (N2O) emissions (in kilograms of carbon dioxide equivalents per hectare per year) between Hoben and colleagues’ (2011) and Van Groenigen and colleagues’ (2010) nonlinear methods and the Farm Energy Analysis Tool’s default linear Intergovernmental Panel on Climate Change Tier 1 method, which serves as the y-axis baseline. Abbreviations: gr., grain; sil., silage.

and GHG analyses should always be used in conjunction with other metrics, such as profitability, biodiversity, and nutrientand water-use efficiency to inform decisions about agricultural sustainability. In addition to the analyses described above, FEAT offers flexibility and a community-based evolving framework that can be adapted, expanded, and improved by users. Unlike some models, FEAT is transparent and allows users to clearly see calculations and how each component affects the overall results. In addition to comparing individual crops, FEAT can be used to compare different crop rotations, cropping systems, and integrated crop and livestock systems. For example, Ryan (2010) compared five different corn–soybean–wheat management systems, ranging from conventional to cover-crop-based organic rotational no-till farming, using a prerelease version of FEAT. Most energy and GHG analysis models cannot simulate novel management systems, such as cover-crop-based organic rotational no-till farming, whereas the flexibility provided by FEAT to tailor analyses makes it a valuable tool for comparing alternative practices and systems (Mirsky et al. 2012). These characteristics could also make FEAT an important complement to biophysical models of soil carbon dynamics for managementspecific accounting of the net benefits of carbon sequestration strategies for carbon markets. Knowing the different energy and GHG options for crop production and empowering educators and decisionmakers with such tools will help the agricultural community meet the food, feed, fiber, and bioenergy goals of the future without excessively degrading our natural resources. Acknowledgments Support for this research was generously provided by the Morgan Family Foundation, the Department of Transportation 272 BioScience • April 2013 / Vol. 63 No. 4

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Gustavo G. T. Camargo ([email protected]) and Tom L. Richard (trichard@ psu.edu) are affiliated with the Department of Agricultural and Biological Engineering at Pennsylvania State University, in University Park. Matthew R. Ryan is affiliated with the Department of Crop and Soil Sciences at Cornell University, in Ithaca, New York.
