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Energy and Carbon Accounting to Compare Bioenergy Crops

Current Opinion in Biotechnology
Publication Date: 
Tuesday, March 19, 2013
Related Program(s): 
Donald R.
Jonathan J.


In production of liquid fuels, the enormous barriers that face plausible substitutes for fossil fuel sources are derived from two factors: the lowest-cost economics of commodities and the logistics of implementation of new technologies at immense scale. These barriers make the development of alternatives to petroleum one of the most challenging problems faced by human society [1]. Several credible approaches seek to exploit other non-renewable resources, such as the use of natural gas in vehicles or the conversion of coal-derived or gas-derived syngas to drop-in synthetic fuels via the Fischer–Tropsch process. Other approaches seek to shift from internal combustion engines to electric motors, a shift that increases the number of substitutes for petroleum-based energy. However, even electric vehicles such as the Chevrolet Volt or the Nissan Leaf are not substantially more environmentally friendly or sustainable compared to conventional vehicles when connected to an electrical grid dominated by non-renewable carbon-emitting generation facilities [2].

In theory, strategies that produce renewable biofuels both at low cost (relative to increasingly scarce petroleum) and at large scale will help lead to a cleaner, more sustainable future. However, transitioning to a higher share of renewable biofuels carries profound implications: On a fundamental level, photosynthetic biofuels replace the process of mining (i.e. underground energy extraction) with the process of agriculture (i.e. above-ground energy capture). Such a process shift is significant, not only because the methods of energy harvesting are divergent, but also because biomass has both significantly lower energy density and significantly higher carbon oxidation state than crude fossil energy feedstocks.

The energy stored in biofuels is derived from current biological carbon fixation, a process that accounts for nearly all of the gross primary production of the planet and at least half of the annual global absorption of atmospheric carbon dioxide [4]. Biofuels can be formed from terrestrial plant matter, by the aquaculture of cyanobacteria, microalgae, or macroalgae, or even by the non-photosynthetic fixation of carbon by chemolithoautotrophs [5]. All of these approaches essentially reverse the combustion of carbon-based liquid fuels, using an external energy source to convert carbon dioxide into energy dense hydrocarbons. This process is complex but is fairly well understood in terms of energy capture, carbon yield, and process economics. For the past 2.4 billion years (nearly half the age of the earth) [6], photosynthetic biology has acted to reduce atmospheric carbon concentrations and increase atmospheric oxygen using energy from sunlight and electrons from water to sequester carbon into more highly reduced compounds, providing the basis for life on the planet4 as well as storing energy in previous geological era as fossil fuels.

Conventional genetic selection has significantly improved the yields of biomass crops [7], while the technologies for engineering crops with improved agronomic properties have begun to mature more rapidly [8]. Tools to engineer biological systems for higher productivity have been developed in microbial systems [9], providing a tantalizing prospect to engineer energy crops with characteristics more favorable for energy capture.

The development of methods to engineer energy crops is necessary, but not sufficient, to impact the future of renewable biofuels. Technologies to measure and model the impact of proposed engineering improvements in complex biological systems are also needed. These methods are nearly impossible to validate, because they depend heavily on assumptions of energy flows and rates of individual steps within particular parts of the organism, built from ex vivo or laboratory measurements. Biochemical optimization of photosynthesis builds up from the biological or biochemical components of the system [e.g. [10]], while agronomic optimization of biomass yields is derived primarily from domestication and breeding of wild plant varieties. These paths must eventually converge.

As a positive step in this direction, while developing the Plants Engineered To Replace Oil (PETRO) program, ARPA-E developed a holistic approach that looks simultaneously at both energy and mass balances to evaluate different paths toward improved, dedicated, renewable biofuels crops. This ‘PETRO approach’ provides a means for tracking both energy and carbon from solar photons to liquid fuels.


The modern diversity of photosynthetic organisms derives from the capture of a cyanobacterium by a eukaryotic cell as a protochloroplast [11]. Essentially, the fundamental biochemical structures and pathways used to capture and store solar energy (i.e. the RuBisCO-based carbon reduction cycle) was inherited by eukaryotes from endosymbotic cyanobacteria resulting in the adoption, by plants, of a biochemical strategy for carbon assimilation that has been conserved over the past 2 billion years [12]. As a consequence, carbon capture and storage varies only slightly from one plant species to another, primarily in the differentiation among C3, C4, and CAM plants [13]. However, subsequent to the highly conserved process of photosynthesis is an immense metabolic diversity shaped by species, environment and development, which has created profound differences in the bioproducts of different plants under different environments at different developmental stages. While the carbohydrates in biomass have been a primary target for conversion into fuels due to their abundance in the biosphere, the relatively high oxidation state of carbon in these molecules (nominally 0) [3] mandates either the addition of reducing equivalents or the loss of carbon in a higher oxidation state, if an energy dense fuel is to result. This contrasts with processing of traditional fossil-derived hydrocarbons (the carbon oxidation state of methane is −4, alkanes are −2 to −3, while gasoline is about −1.75). Energetically (and therefore economically) costly conversion steps are thus unavoidable when starting with biomass, if the target is a more energy dense (less oxidized) fuel. Fortunately, many plants already produce natural products with lower oxidation states and thus higher energy value (e.g. lipids, terpenes) [14], but their amounts can vary widely both among plant species as well as within the different tissues of a given plant, complicating the calculation of energy yields.

Where we are today

An accurate, quantitative analysis of biological systems, accounting for both process and economics, would allow comparative analysis of new biofuel crops, but a systematic methodology is currently lacking. Economic performance metrics in bioenergy (e.g. ‘barrel of oil equivalent’, ‘tons of biomass per acre’, ‘feedstock costs’) are frequently used, but these metrics tend to finesse the central issue of objective comparison. Different disciplines, and even different research groups within a particular discipline, make different assumptions and use different comparators in the calculation of efficiencies and yields. Key physical data, including feedstock composition (particularly moisture content), seasonal yields, regional climatic conditions and year-over-year variability, are frequently not reported. These reporting inconsistencies make it difficult to derive an objective basis of comparison from diverse literature sources. What is needed is a detailed accounting of the flow of both energy and mass from raw materials (sunlight, carbon dioxide, and water), through a conversion process (plants), into finished goods (fuel). This requirement implies a model based on chemical processes, with a series of connected steps that each has inputs, outputs, and conversions. This system is usually discussed in terms of energy flow, where energy losses are tracked from inputs to output, with conversions described in terms of efficiency [15]. However, there is another key dimension, beyond the transduction of light energy: the flow of carbon from atmospheric capture to conversion into bioproducts. Carbon dioxide is absorbed from the atmosphere and proceeds through a series of conversion steps to produce a liquid fuel. These processes happen both during the growth of the plant and during the processing of biomaterial after harvest. This stepwise formalism promotes a discussion on the basis of standard units (e.g. tonnes of carbon per hectare per year, MgC ha−1 y−1) and on the chemical stoichiometry of carbon flow directed toward the products of interest. From the energy content of the final fuel product, an areal energy yield (in GJ ha−1 y−1) can then be calculated to provide a robust, holistic view of an agricultural crop.

Tracking energy efficiency

Although plants have evolved for optimal light and carbon use in various ecological niches over the last ∼160 million years [16], the result of natural selection does not inevitably optimize agriculture for the benefit of humans, either for food or for biofuels [17]. Natural selection works at the level of groups or species to maximize survival, but does not act to maximize the overall productivity of plant life in a particular area. For example, determinants of survival during natural selection include the propensity to hoard resources by the individual, to increase height and leaf area over competing plants. Both light and carbon are wasted, reducing overall areal productivity [18 and 19]. In contrast, maximum stand productivity results from a population of plants that are good neighbors and share resources optimally. Consequently, it does not contradict established scientific principles to develop improved light utilization for agricultural crops that transcend their natural evolutionarily determined optimum [20]. Further, the recent, significant increase in atmospheric [CO2] has changed the environment more rapidly than modern crops have been able to adjust. This offers additional opportunities to improve light utilization efficiency [21].

The energy captured as reduced carbon in biomass depends on both the amount of solar energy captured and the efficiency of its conversion [22, 23 and 24]. To evaluate efficiency, a theoretical upper limit for the energy capture of plant photosynthesis is needed. This limit has been estimated from a detailed stepwise analysis of the biophysical and biochemical processes to be about 4.6% for C3 and 6.0% C4 plants [25 and 26••] (Figure 1). Slightly lower theoretical upper limit efficiency values are obtained for C3 and C4 plants if photophosphorylative H+/ATP ratio is actually 4.67 rather than 4.0 as recent mechanistic structure data suggest [27••].

The highest short run efficiencies observed for plants in the field, assessed from maximum growth rates, are about 3.5% for C3 and 4.3% for C4 plants and these maximum values drop to 2.4% and 3.4% when variations over a full growing season are considered [25].6 For crop plants, these efficiencies define the yield potential [28•] or maximum yield of the crop; in practice, they are at least twice the observed photosynthetic efficiencies under most commercial farming conditions.

An integrated model based on our most up-to-date understanding of photosynthesis [10], when combined with transgenic technologies to modify metabolic pathways, has potential to refine or even design new crops (see Table 2 in [26]). Some of the near term prospects (for which there is already proof of concept) include improved photorespiratory pathways [29•], optimization of photosynthetic pigments [30] and rebalancing of photosynthesis [31 and 32] in response to increased atmospheric [CO2]. Other alterations that are well grounded in theory require the development of new technologies, for example, transferring C4 RuBisCO into the chloroplasts of C3 plants [33] will require techniques to coordinately transform both plastid and nuclear genomes. Nevertheless, the solutions to implementation hurdles for this and numerous other improvements to photosynthesis seem very plausible in a 20-year or shorter timeframe with sufficient investment. In addition there are possibilities to improve photosynthesis, such as extending the photosynthetically active spectrum [34] for which there currently exists too little science to judge feasibility [15].

The measurement of energy efficiency (i.e. the output/input ratio, expressed as J J−1) is an intuitive and effective way to compare plants with different composition and therefore differing energy content. However such comparisons work best for similar plants in similar growing regions under similar conditions. To compare different types of crops growing in different regions requires a more in-depth approach.

Carbon accounting

To compare crops grown in different regions quantitatively, it is useful to track the plant's elemental carbon composition as an absolute quantity that avoids the ambiguities of other metrics. This requires knowledge of the chemical composition, but normalizes the considerable differences among agricultural crops to an absolute value (in metric tonnes [Mg] per ha) based on the molar quantity of a known physicochemical energy carrier, carbon (atomic mass 12). Since photosynthesis uses sunlight, the areal exposure (measured in area units and insolation during the growing season) is also an input. Different crops require different amounts of land to produce a given quantity of energy; therefore, land use must also be normalized. The PETRO approach to carbon accounting analysis breaks down the carbon quantitation into four distinct parts (see Figure 2; Captured, Harvested, Purified, and Processed), and tracks the mass of carbon per unit area per unit time through each of the various processes from raw materials to a final liquid fuel. Using the lower heating value (LHV, in ) for the final fuel, one can derive a final fuel energy yield in GJ ha−1 yr−1 to facilitate comparison among crops in different regions. For the purposes of this analysis, other salient parameters such as water and nitrogen use are not included, to focus on the isolation and conversion of carbon-based compounds for use as fuels.

As discussed above, data for various steps in the process are not readily available; therefore significant amounts of data synthesis and extrapolation are required to piece together a working model. A sample of the calculation for corn-based ethanol can serve as an illustration of the analysis process (Figure 2, calculations provided in supporting information). Using the final carbon yield from ethanol in this process, 78 GJ ha−1 yr−1 fuel energy yield results. Similar analyses were performed on several biofuels crops of interest when determining the scope of the ARPA-E PETRO program metrics (Table 1). As seen from the data, sugarcane-based ethanol and corn-based ethanol have a clear productivity advantage over soybean-based biodiesel in this analysis. Table 1 also suggests which steps in the biofuel production process differentiate one crop from another. Those who wish to engineer crops for higher energy yields can use this analysis to facilitate a structured discussion of the impact of various genetic modifications or breeding approaches and then confirm the analysis with actual data from the field.


Combined carbon and energy accounting is a valuable tool to assess biofuels. Researchers, investors, and policymakers who employ this approach will gain a more accurate understanding of different approaches (e.g. the use of genetic engineering or the objectives of a breeding program), and how these approaches could impact choices of alternatives. It should be noted that, as with all modeling approaches, the output (in this case, a plausible range for areal energy yield) is only as good as the input data. When additional data for new biofuels approaches are collected, using the PETRO model will help the scientific community to have objective, data-driven discussions that weigh the merits of diverse approaches, and to more accurately inform their sponsors about the merits of their proposals.

Works Cited: 

References and Recommended Reading

Papers of particular interest, published within the period of review, have been highlighted as:

• of special interest

•• of outstanding interest


The authors would like to thank Drs Carl Bernacchi, Edward Richard, Jack Okamuro, and Robert Fireovid from the USDA/ARS, Dr Dan Robertson of Joule Unlimited, Dr Alex Aravanis of Sapphire Energy, Professor Thomas D. Sharkey of Michigan State University, and Professors Himadri Pakrasi and Robert Blankenship of Washington University, for their consultation in building the fuel energy yield table, Drs Arun Majumdar, Chad Haynes, and Eric Toone of ARPA-E for their support, and Kacy Littlehale for her assistance in preparing the graphics.


1) S. Chu, A. Majumdar

Opportunities and challenges for a sustainable energy future

Nature, 488 (2012), pp. 294–303

2) U.S. Energy Information Administration

Annual Energy Outlook 2012

(June 2012) 87

3) C.A. Masiello, M.E. Gallagher, J.T. Randerson, R.M. Deco, O.A. Chadwick

Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio

J Geophys Res, 113 (2008), p. G03010

4) C.D. Keeling

The concentration and isotopic abundances of carbon dioxide in the atmosphere

Tellus, 12 (1960), pp. 200–203

5) R.J. Conrado, C.A. Haynes, B.E. Haendler, E.J. Toone

Electrofuels: a new paradigm for renewable fuels

J.W. Lee (Ed.), Advanced Biofuels and Bioproducts, Springer Science+Business Media (2013), pp. 1037–1064

6) R.A. Fischer, G.O. Edmeades

Breeding and cereal yield progress

Crop Sci, 50 (2010), pp. S85–S98

7) R. Mittler, E. Blumwald

Genetic engineering for modern agriculture: challenges and perspectives

Annu Rev Plant Biol, 61 (2010), pp. 443–462

8) R.E. Blankenship

Early evolution of photosynthesis

Plant Physiol, 154 (2010), pp. 434–438

9) H.H. Wang, F.J. Isaacs, P.A. Carr, Z.Z. Sun, G. Xu, C.R. Forest, G.M. Church

Programming cells by multiplex genome engineering and accelerated evolution

Nature, 460 (2009), pp. 894–898

10) Zhu X-G, Wang U, Ort DR, Long SP: e-Photosynthesis: a comprehensive dynamic mechanistic model of C3photosynthesis: from light capture to sucrose synthesis. Plant Cell Environ. 2012,

11) P.J. Keeling

The endosymbiotic origin, diversification and fate of plastids

Phil Trans R Soc B, 365 (2011), pp. 729–748

12) P.G. Falkowski

The biological and geological contingencies for the rise of oxygen on Earth

Photosynth Res, 107 (2011), pp. 7–10

13) S.E. Weise, K.J. van Wijk, T.D. Sharkey

The role of transitory starch in C3, CAM, and C4metabolism and opportunities for engineering leaf starch accumulation

J Exp Bot, 62 (2011), pp. 3109–3118

14) S.T. Withers, J.D. Keasling

Biosynthesis and engineering of isoprenoid small molecules

Appl Microbiol Biotechnol, 73 (2007), pp. 980–990

15) R.E. Blankenship, D.M. Tiede, J. Barber, G.W. Brudvig, G. Fleming, M. Ghirardi, M.R. Gunner, W. Junge, D.M. Kramer, A. Melis et al.

Comparing photosynthetic and photovoltaic efficiencies and recognizing the potential for improvement

Science, 332 (2011), pp. 805–809

16) C.D. Bell, D.E. Soltis, P.S. Soltis

The age of the angiosperms: a molecular timescale without a clock

Evolution, 6 (2005), pp. 1245–1258

17) R.S. Meyer, A.E. DuVal, H.R. Jensen

Patterns and processes in crop domestication: an historical review and quantitative analysis of 203 global food crops

New Phytol, 196 (2012), pp. 29–48

18) N.P.R. Anten

Optimal photosynthetic characteristics of individual plants in vegetation stands and implications for species coexistence

Ann Bot, 95 (2005), pp. 495–506

19) D.-Y. Zhang, G.-J. Sun, X.-H. Jiang

Donald's ideotype and growth redundancy: a game theoretical analysis

Field Crops Res, 61 (1999), pp. 179–187

20) P.C. Mangelsdorf

The origin of corn

Sci Am, 255 (1986), pp. 80–86

21) S.P. Long, D.R. Ort

More than taking the heat: crops and global change

Curr Opin Plant Biol, 13 (2010), pp. 241–248

View Record in Scopus | Cited By in Scopus (41)

22) J.L. Monteith

Solar radiation and productivity in tropical ecosystems

J Appl Ecol, 9 (1972), pp. 747–766

23) J.L. Monteith

Climate and the efficiency of crop production in Britain

Philos Trans R Soc Lond B: Biol Sci, 281 (1977), pp. 277–294

24) F.G. Dohleman, S.P. Long

More productive than maize in the Midwest: how does Miscanthus do it?

Plant Physiol, 150 (2009), pp. 2104–2115

25) X.G. Zhu, S.P. Long, D.R. Ort

What is the maximum efficiency with which photosynthesis can convert solar energy into biomass?

Curr Opin Biotechnol, 19 (2008), pp. 153–159

26)•• X.G. Zhu, S.P. Long, D.R. Ort

Improving photosynthetic efficiency for greater yield

Annu Rev Plant Biol, 61 (2010), pp. 235–261

26)••This article provides both the rationale for focusing on improved photosynthesis to enhance biomass and yield as well as the identification of both near and long-term opportunities to improve photosynthetic efficiency well beyond that accomplished by evolution.

27)••J.S. Amthor

From sunlight to phytomass: on the potential efficiency of converting solar radiation to phyto-energy

New Phytol, 188 (2010), pp. 939–959

27)••This article provides a quantitative and insightful summary of the how output:input stoichiometries of photosynthesis and photorespiration in C3 and C4 systems constrain and define overall efficiency.

28)•R.A. Fischer, G.O. Edmeades

Breeding and cereal yield progress

Crop Sci, 50 (2010), pp. S85–S98

28)•This paper presents an analysis of the historic and future potential for improving radiation use efficiency in lifting crop yield potential.

29)•C. Peterhansel, V.G. Maurino

Photorespiration redesigned

Plant Physiol, 155 (2010), pp. 49–55

29)•The engineering of photorespiratory ‘bypass pathways’ into the chloroplasts of plants is a powerful proof of concept for using synthetic biology to introduce new pathways to improve plant photosynthetic efficiency.

30) D.R. Ort, X.G. Zhu, A. Melis

Optimizing antenna size to maximize photosynthetic efficiency

Plant Physiol, 155 (2010), pp. 79–85

31) S. Lefebvre, T. Lawson, O.V. Zakhleniuk, J.C. Lloyd, C.A. Raines

Increased sedoheptulose-1,7-bisphosphatase activity in transgenic tobacco plants stimulates photosynthesis and growth from an early stage in development

Plant Physiol, 138 (2005) 451–460

32) D.M. Rosenthal, A.M. Locke, M. Khozaei, C.A. Raines, S.P. Long, D.R. Ort

Over-expressing the C3 photosynthesis cycle enzyme sedoheptulose-1-7 bisphosphatase

BMC Plant Biol, 11 (2011), p. 123

33) X. Zhu, A.R. Portis Jr, S.P. Long

Would transformation of C3 crop plants with foreign Rubisco increase productivity? A computational analysis extrapolating from kinetic properties to canopy photosynthesis

Plant Cell Environ, 27 (2004), pp. 155–165

34) M. Chen, R.E. Blankenship

Expanding the solar spectrum used by photosynthesis

Trends Plant Sci, 16 (2011), pp. 427–431