Glithero N J, Ramsden S J, Wilson P
Division of Agricultural and Environmental Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, LE12 5RD, United Kingdom.
Agric Syst. 2012 Jun;109:53-64. doi: 10.1016/j.agsy.2012.02.005.
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. ), 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 £36 GJ, representing the gross margin forgone by maximising net farm energy maximising farm gross margin. The gross margin-GHG emission trade-off is £0.15 kg CO eq, representing the gross margin forgone per kg of CO eq reduced when GHG emissions are minimised maximising farm gross margin. The energy-GHG emission trade-off is 0.03 GJ kg CO eq quantifying the reduction in net energy from the farm system per kg of CO eq reduced when minimising GHG emissions 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 £11 t 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.
气候变化和能源安全问题推动了鼓励生物能源生产政策的发展。要实现欧盟到2020年生物能源在运输燃料消费中所占比例的目标,生物能源原料的产量需要大幅增加。最初观察到“第一代”生物燃料产量有所增加,然而,“粮食竞争”问题引发了人们对第二代生物燃料(SGBs)的兴趣。这些第二代生物燃料可以由副产品(如谷物秸秆)或能源作物(如 )生产,前者在很大程度上消除了粮食竞争问题。为了评估第二代生物燃料原料供应的可持续性,必须对农场系统的财务、环境和能源成本及效益进行量化。先前的研究通过线性规划(LP)方法获取了财务成本和效益,而环境和能源指标大多是在生命周期分析(LCA)框架内进行的。在农场层面评估供应副产品第二代生物燃料(CPSGB)原料的财务、环境和能源可持续性的各个方面,需要一个能够量化和理解这些目标之间权衡的框架。本文介绍了一个用于管理农业能源与排放权衡的建模框架(MEETA模型)的开发,该模型结合了生物经济过程建模和生命周期分析,并给出了从文献和行业来源获取的输入数据参数。MEETA模型从财务、能源和排放结果方面对耕地农场的投入和产出进行量化分析。该模型明确考虑了肥料与作物产量的关系,以及秸秆的混入或出售,同时考虑了混入/移除秸秆对轮作中后续作物的养分影响。作物组合、机械使用、每千克作物产品的温室气体(GHG)排放量以及每公顷能源使用量的关键结果与先前的研究和行业调查结果一致。结果表明,毛利润与能源的权衡为每吉焦36英镑,这代表了通过最大化农场净能源而放弃的毛利润 最大化农场毛利润。毛利润与温室气体排放的权衡为每千克二氧化碳当量0.15英镑,这代表了在将温室气体排放降至最低 最大化农场毛利润时,每减少一千克二氧化碳当量所放弃的毛利润。能源与温室气体排放的权衡为每千克二氧化碳当量0.03吉焦,量化了在将温室气体排放降至最低 最大化农场净能源时,每减少一千克二氧化碳当量农场系统净能源的减少量。当农场毛利润和农场净能源都最大化时,所有谷物秸秆都打成捆出售。该模型针对不同谷物秸秆价格的敏感性分析表明,对于这种副产品,当小麦秸秆价格为每吨11英镑时,混入秸秆在财务上是最优的。当地秸秆市场条件以及农民对秸秆混入或出售的态度将影响农民供应这种潜在生物能源原料的秸秆价格,这是未来研究的重要领域。