Geographic variability of agriculture requires sector-specific uncertainty characterization
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Regionalization in life cycle assessment (LCA) has focused on spatially differentiated environmental variables for regional impact assessment models. Relatively less attention has been paid to spatial disparities in intermediate flows for life cycle inventory (LCI).
First, we compiled state-specific LCIs for four major crops in the USA and evaluated their geographic variability in the characterized results due to the differences in intermediate inputs. Second, we evaluated the consequence of choosing average or region-specific LCIs in understanding the life cycle environmental implications of land use change from cotton to corn or soybean. Finally, we analyzed the implications of our findings in characterizing the uncertainties associated with geographic variability under the conventional pedigree approach.
Results and discussion
Our results show that spatial disparities in LCI alone lead to two to fourfold differences in characterized results for most impact categories. The differences, however, increase to over an order of magnitude for freshwater ecotoxicity and human health non-cancer. Among the crops analyzed, winter wheat shows higher variability partly due to a larger difference in yield. As a result, the use of national average data derived from top corn and soybean producing states significantly underestimates the characterized impacts of corn and soybean in the states where land conversion from cotton to corn or soybean actually took place. The results also show that the conventional pedigree approach to uncertainty characterization in LCA substantially underestimates uncertainties arising from geographic variability of agriculture. Compared to the highest geometric standard deviation (GSD) value of 1.11 under the pedigree approach, the GSDs that we derived are as high as 7.1, with the median around 1.9.
The results highlight the importance of building regional life cycle inventory for understanding the environmental impacts of crops at the regional level. The high geographic variability of crops also indicates the need for sector-specific approaches to uncertainty characterization. Our results also suggest that the uncertainty values in the existing LCI databases might have been signficantly underestimated especially for those products with high geographic variability, demanding a cautious interpretation of the results derived from them.
KeywordsLife cycle inventory Pedigree approach Spatial variability Staple crops Uncertainty
All data are available upon request. The authors thank the two anonymous reviewers for their valuable comments and suggestions, which greatly improved the quality of the paper. This work was partly funded by US Environmental Protection Agency through Science to Achieve Results (STAR) Program Grant No.83557907 (MT and SS). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of US EPA. This work has not been subjected to US EPA review, and no official endorsement should be inferred.
- Ecoinvent (2014) Database. In: Ecoinvent database V22. http://www.ecoinvent.org/database/
- Fulton J, Cooley H, Cardenas S, Shilling F (2013) Trends and variation in California’s water footprintGoogle Scholar
- Krauter C, Goorahoo D, Potter C, Klooster S (2002) Ammonia emissions and fertilizer applications in California’ s Central Valley. Atlanta GAGoogle Scholar
- Mekonnen MM, Hoekstra AY (2010) The green, blue and grey water footprint of crops and derived crop products—volume 1: main report. UNESCO-IHE Institute for Water Education, DelftGoogle Scholar
- Mortvedt J (1995) Heavy metal contaminants in inorganic and organic fertilizers. Nutr Cycl Agroecosyst 43:55–61Google Scholar
- Ogle S, Del Grosso S, Adler P, Parton W (2008) Soil nitrous oxide emissions with crop production for biofuel: implications for greenhouse gas mitigationGoogle Scholar
- Shapouri H, Gallagher PW, Nefstead W et al (2010) 2008 energy balance for the corn-ethanol industry. U.S. Department of Agriculture, Washington, DCGoogle Scholar
- USDA (2004) Energy use on major field crops in surveyed states. Economic Research Service, US Department of Agriculture, Washington, DCGoogle Scholar
- USDA (2006) Model simulation of soil loss, nutrient loss, and change in soil organic carbon Associated with crop production. Natural Resource Conservation Service, US Department of AgricultureGoogle Scholar
- USDA (2016) Agricultural chemical use program. http://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Chemical_Use/. Accessed 12 Dec 2013
- Wallander S, Claassen R, Nickerson C (2011) The ethanol decade: an expansion of US corn production, 2000–09. US Department of Agriculture, Economic Research Service, Washington DCGoogle Scholar
- Wang M (2013) The greenhouse gases, regulated emissions, and energy use in transportation (GREET) model, 2012Google Scholar