Abstract
Pulses are nutritionally beneficial and cost-effective food/feed products, and are also nitrogen-fixing crops that can improve the environmental sustainability of annual cropping systems when included in crop rotations – particularly with respect to GHG emissions. Canada is a major producer and exporter of pulses, therefore the goals of this study were to (a) develop regionalized life cycle inventories (LCIs) for Canadian faba and dry beans suitable for incorporation into public LCI databases and (b) conduct ISO 14044-compliant LCAs of Canadian faba, navy, pinto, and kidney beans at provincial, ecozone, and national scales. LCI data were collected via survey from ~200 Canadian faba and dry bean farmers.At the national scale of aggregation, faba beans had considerably lower impacts across all impact categories considered than the three dry bean types. This is due to their lower input requirements (particularly fertilizer), and higher nitrogen fixation capabilities. Among the three dry bean types considered, pinto beans had the lowest environmental impacts across most impact categories. Fertilizer production and field-level emissions from fertilizer use were the major contributors to the overall environmental impacts of the production of all bean types. In addition to differences between bean types, there were also differences within bean types, between regions of production and levels of regional aggregation. The results of the uncertainty analysis revealed that variability was lowest when aggregated at the ecozone level, based on soil and climate factors, and highest when aggregated to the national average. Taken together, these results demonstrate the utility of spatially-resolved LCI and LCA modelling that distinguishes between different types of pulse crops in order to accurately estimate impacts, and to support decision-making in pursuit of more sustainable food systems. Future research needs include more primary data on field-level biological nitrogen fixation and soil carbon fluxes, since these can significantly influence estimated environmental impacts.
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