Consequential Life Cycle Assessment of Grain and Oilseed Crops: Review and Recommendations

The field crop industry in Canada is a source of both significant economic benefits and environmental impacts. Environmental impacts include land and energy use, as well as greenhouse gas (GHG) and other emissions. Impacts also accrue upstream of the field in the product supply chain, from the production of such inputs as fertilizers and pesticides. There are currently two types of environmental life cycle assessment (LCA)-attributional LCA (ALCA) and consequential LCA (CLCA)-that may be used to study the life cycle impacts of products such as field crops. ALCA is a retrospective methodology that presents a snapshot of average, "status quo" conditions. CLCA is a prospective methodology that presents the potential implications of changes in a product system, including any associated market-mediated changes in supply or demand in other product systems. Thus, CLCAs can be used to assess large-scale changes in the field crop industry, including its relationship to other sectors and processes, such as the production of biofuel or of food for both human and animal consumption. The aim of this paper is to review and curate the knowledge derived through published CLCA studies that assessed the impacts of changes to field crop production systems on the life cycle resource use and emissions associated with the agricultural products, with a focus on their relevance to temperate climate conditions. The current study also highlights how previous studies, including ALCAs and farm management recommendations, can be used to inform the changes that should be studied using CLCA. The main challenges to conducting CLCAs include identifying the system boundaries, marginal products and processes that would be impacted by changes to field crop production. Marginal markets and product systems to include can be determined using economic equilibrium models, or information from local experts and industry reports. In order to conduct ISO-compliant CLCAs, it is necessary to include multiple relevant environmental impact categories, and to perform robust data quality and uncertainty analyses.