Farm systems assessment of bioenergy feedstock production: Integrating bio-economic models and life cycle analysis approaches
Climate change and energy security concerns have driven the development of policies that encourage bioenergy production. Meeting EU targets for the consumption of transport fuels from bioenergy by 2020 will require a large increase in the production of bioenergy feedstock. Initially an increase in ‘first generation’ biofuels was observed, however ‘food competition’ concerns have generated interest in second generation biofuels (SGBs). These SGBs can be produced from co-products (e.g. cereal straw) or energy crops (e.g. miscanthus), with the former largely negating food competition concerns. In order to assess the sustainability of feedstock supply for SGBs, the financial, environmental and energy costs and benefits of the farm system must be quantified. Previous research has captured financial costs and benefits through linear programming (LP) approaches, whilst environmental and energy metrics have been largely been undertaken within life cycle analysis (LCA) frameworks. Assessing aspects of the financial, environmental and energy sustainability of supplying co-product second generation biofuel (CPSGB) feedstocks at the farm level requires a framework that permits the trade-offs between these objectives to be quantified and understood. The development of a modelling framework for Managing Energy and Emissions Trade-Offs in Agriculture (MEETA Model) that combines bio-economic process modelling and LCA is presented together with input data parameters obtained from literature and industry sources. The MEETA model quantifies arable farm inputs and outputs in terms of financial, energy and emissions results. The model explicitly captures fertiliser: crop-yield relationships, plus the incorporation of straw or removal for sale, with associated nutrient impacts of incorporation/removal on the following crop in the rotation. Key results of crop-mix, machinery use, greenhouse gas (GHG) emissions per kg of crop product and energy use per hectare are in line with previous research and industry survey findings. Results show that the gross margin – energy trade-off is £36GJ−1, representing the gross margin forgone by maximising net farm energy cf. maximising farm gross margin. The gross margin–GHG emission trade-off is £0.15kg−1 CO2 eq, representing the gross margin forgone per kg of CO2 eq reduced when GHG emissions are minimised cf. maximising farm gross margin. The energy–GHG emission trade-off is 0.03GJkg−1CO2 eq quantifying the reduction in net energy from the farm system per kg of CO2 eq reduced when minimising GHG emissions cf. maximising net farm energy. When both farm gross margin and net farm energy are maximised all the cereal straw is baled for sale. Sensitivity analysis of the model in relation to different prices of cereal straw shows that it becomes financially optimal to incorporate wheat straw at price of £11t−1 for this co-product. Local market conditions for straw and farmer attitudes towards incorporation or sale of straw will impact on the straw price at which farmers will supply this potential bioenergy feedstock and represent important areas for future research.
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- RW Ackrill & SJ Ramsden & JM Gibbons, 2001. "CAP reform and the rebalancing of support for cereals and oilseeds: a farm-level analysis," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 28(2), pages 207-226, June.
- Janssen, Sander & van Ittersum, Martin K., 2007. "Assessing farm innovations and responses to policies: A review of bio-economic farm models," Agricultural Systems, Elsevier, vol. 94(3), pages 622-636, June.
- Cherubini, Francesco & Ulgiati, Sergio, 2010. "Crop residues as raw materials for biorefinery systems - A LCA case study," Applied Energy, Elsevier, vol. 87(1), pages 47-57, January.
- Pehnt, Martin, 2006. "Dynamic life cycle assessment (LCA) of renewable energy technologies," Renewable Energy, Elsevier, vol. 31(1), pages 55-71.
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