Goals, Strengths and Limitations Governing the Use of Life Cycle Assessment (LCA) in Food and Agriculture
DOI:
https://doi.org/10.62300/0kzszt49Keywords:
Life Cycle Assessment (LCA), Agricultural Supply Chains, Environmental Impact Assessment, Data Quality & Uncertainty, Sustainability MetricsAbstract
This paper examines the goals, strengths, and limitations of using Life Cycle Assessment (LCA) within food and agricultural systems. It outlines how LCA provides a systematic, science‑based approach for quantifying environmental impacts across complex agricultural supply chains, enabling improved decision-making, transparency, and sustainability performance. The authors describe the methodological foundations of LCA—including goal and scope definition, life cycle inventory development, and impact assessment—while emphasizing challenges such as data quality, uncertainty, system boundary selection, and allocation procedures. Although LCA cannot address normative or ethical questions, it remains a powerful tool for identifying environmental hotspots, evaluating trade‑offs, and guiding policy and management strategies. The paper concludes by identifying research needs in areas such as big data integration, advanced greenhouse gas modeling, geospatial analysis, and improved uncertainty characterization to enhance the reliability and relevance of LCA in agriculture.
Downloads
References
Audsley, E., Alber, R., Clift, S., Cowell, P., Crettaz, G., Gaillard, J., Hausheer, O., Jolliet, O., Kleijn, R., Mortensen, D., Pearce, E., Roger, E., Teulon, H., Weidma, B., & van Zeijts, H. (1997). Harmonisation of environmental life cycle assessment for agriculture. Final Report, Concerted Action AIR3-CT94-2028. European Commission, DG VI Agriculture.
Bamber, N., Turner, I., Arulnathan, V., Li, Y., Zargar Ershadi, S., Smart, A., & Pelletier, N. (2020). Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: review of current practice and recommendations. The International Journal of Life Cycle Assessment, 25(1), 168–180.
Bare, J. (2011). TRACI 2.0: the tool for the reduction and assessment of chemical and other environmental impacts 2.0. Clean Technologies and Environmental Policy, 13(5), 687–696.
Bouwman, A. F., Boumans, L. J. M., & Batjes, N. H. (2002). Emissions of N2O and NO from fertilized fields: Summary of available measurement data. Global Biogeochemical Cycles, 16(4), 6–1–6–13.
Brentrup, F., Küsters, J., Kuhlmann, H., & Lammel, J. (2004). Environmental impact assessment of agricultural production systems using the life cycle assessment methodology: I. Theoretical concept of a LCA method tailored to crop production. European Journal of Agronomy, 20(3), 247–264.
Buckingham, S., Anthony, S., Bellamy, P. H., Cardenas, L. M., Higgins, S., McGeough, K., & Topp, C. F. E. (2014). Review and analysis of global agricultural N₂O emissions relevant to the UK. Science of The Total Environment, 487, 164–172.
Cai, H., & Xu, M. (2013). Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns. Environmental Science & Technology, 47(16), 9035–9043.
Cashman, S. A., Meyer, D. E., Edelen, A. N., Ingwersen, W. W., Abraham, J. P., Barrett, W. M., Gonzalez, M. A., Randall, P. M., Ruiz-Mercado, G., & Smith, R. L. (2016). Mining Available Data from the United States Environmental Protection Agency to Support Rapid Life Cycle Inventory Modeling of Chemical Manufacturing. Environmental Science & Technology, 50(17), 9013–9025.
Chaplin-Kramer, R., Sim, S., Hamel, P., Bryant, B., Noe, R., Mueller, C., Rigarlsford, G., Kulak, V., Kowal, V., Sharp, R., Clavreul, J., Price, E., Polasky, S., Ruckelshaus, M., & Daily, G. (2017). Life cycle assessment needs predictive spatial modelling for biodiversity and ecosystem services. Nature Communications, 8(1), 15065.
Charmley, E., Williams, S. R. O., Moate, P. J., Hegarty, R. S., Herd, R. M., Oddy, V. H., Reyenga, P., Staunton, K. M., Anderson, A., & Hannah, M. C. (2016). A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science, 56(3), 169–180.
Cooper, J., & Cooper, J. S. (2015). LCA Digital Commons Unit Process Data: field operations/work processes and farm implements.
De Klein, C., Novoa, R. S. A., Ogle, S., Smith, K. A., Rochette, P., Wirth, T. C., McConkey, B. G., Mosier, A., & Rypdal, K. (2006). N₂O Emissions From Managed Soils, and CO₂ Emissions from Lime and Urea Application. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
Ekvall, T. (2020). Attributional and consequential life cycle assessment. In M. J. Bastante-Ceca (Ed.), Sustainability Assessment in the 21st Century. IntechOpen.
Frischknecht, R., Pfister, S., Bunsen, J., Haas, A., Känzig, J., Kilga, M., Lansche, J., Margni, M., Mutel, C., Reinhard, J., Stolz, P., van Zelm, R., Vieira, M., & Wernet, G. (2019). Regionalization in LCA: Current status in concepts, software and databases—69th LCA forum. The International Journal of Life Cycle Assessment, 24(2), 364–369.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Goglio, P., Smith, W. N., Grant, B. B., Desjardins, R. L., McConkey, B. G., Campbell, C. A., & Nemecek, T. (2015). Accounting for soil carbon changes in agricultural life cycle assessment (LCA): A review. Journal of Cleaner Production, 104, 23–39.
Groen, E. A., Heijungs, R., Bokkers, E. A. M., & de Boer, I. J. M. (2014). Methods for uncertainty propagation in life cycle assessment. Environmental Modelling & Software, 62, 316–325.
Groen, E. A., & Heijungs, R. (2017). Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk? Environmental Impact Assessment Review, 62, 98–109.
Hauschild, M. Z., Rosenbaum, R. K., & Olsen, S. I. (2018). Life cycle assessment. Springer.
Heijungs, R. (2020). On the number of Monte Carlo runs in comparative probabilistic LCA. The International Journal of Life Cycle Assessment, 25(2), 394–402.
Hellweg, S., & Milà i Canals, L. (2014). Emerging approaches, challenges and opportunities in life cycle assessment. Science, 344(6188), 1109–1113.
Hörtenhuber, S., Piringer, G., Zollitsch, W., Lindenthal, T., & Winiwarter, W. (2014). Land use and land use change in agricultural life cycle assessments and carbon footprints. Journal of Cleaner Production, 73, 31–39.
i Canals, L. M., Azapagic, A., Doka, G., Jefferies, D., King, H., Mutel, C., Nemecek, T., Roches, A., Sim, S., Stichnothe, H., Thoma, G., & Williams, A. (2011). Approaches for Addressing Life Cycle Assessment Data Gaps for Bio-based Products. Journal of Industrial Ecology, 15(5), 707–725.
IPCC. (2014). 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol.
ISO (2006–2018). Series of standards on LCA (14040, 14044, 14046, 14067, 14071, 14072).
Kebreab, E., Johnson, K. A., Archibeque, S. L., Pape, D., & Wirth, T. (2008). Model for estimating enteric methane emissions from United States dairy and feedlot cattle. Journal of Animal Science, 86(10), 2738–2748.
Koellner, T., de Baan, L., Beck, T., Brandão, M., et al. (2013). Principles for life cycle inventories of land use on a global scale. The International Journal of Life Cycle Assessment, 18(6), 1203–1215.
Lan, K., & Yao, Y. (2019). Integrating Life Cycle Assessment and Agent-Based Modeling: A Dynamic Modeling Framework for Sustainable Agricultural Systems. Journal of Cleaner Production, 238, 117853.
Lan, K., Kelley, S. S., Nepal, P., & Yao, Y. (2020). Dynamic life cycle carbon and energy analysis for cross-laminated timber. Environmental Research Letters, 15(12), 124036.
Lesage, P., Mutel, C., Schenker, U., & Margni, M. (2018). Uncertainty analysis in LCA using precalculated aggregated datasets. The International Journal of Life Cycle Assessment, 23(11), 2248–2265.
Liang, C., MacDonald, D., Thiagarajan, A., Flemming, C., Cerkowniak, D., & Desjardins, R. (2020). Developing a country-specific method for estimating nitrous oxide emissions from agricultural soils in Canada. Nutrient Cycling in Agroecosystems, 117(2), 145–167.
Liao, M., & Yao, Y. (2021). Applications of Artificial Intelligence-Based Modeling for Bioenergy Systems: A Review. GCB Bioenergy.
Liao, M., Kelley, S., & Yao, Y. (2019). Generating Energy and Greenhouse Gas Inventory Data of Activated Carbon Production Using Machine Learning and Kinetic Based Process Simulation. ACS Sustainable Chemistry & Engineering, 8(2), 1252–1261.
Lloyd, S. M., & Ries, R. (2007). Characterizing, Propagating, and Analyzing Uncertainty in Life-Cycle Assessment: A Survey of Quantitative Approaches. Journal of Industrial Ecology, 11(1), 161–179.
Mendoza Beltran, A., Prado, V., Vivanco, D. F., Henriksson, P. J. G., Guinée, J. B., & Heijungs, R. (2018). Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded? Environmental Science & Technology, 52(4), 2152–2161.
Meyer, D. E., Cashman, S., & Gaglione, A. (2020). Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data. Journal of Industrial Ecology.
Mutel, C. L., Pfister, S., & Hellweg, S. (2012). GIS-Based Regionalized Life Cycle Assessment. Environmental Science & Technology, 46(2), 1096–1103.
Mutel, C., Liao, X., Patouillard, L., Bare, J., Fantke, P., et al. (2019). Overview and recommendations for regionalized life cycle impact assessment. The International Journal of Life Cycle Assessment, 24(5), 856–865.
Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., et al. (2018). Prediction of enteric methane production in dairy cattle using an intercontinental database. Global Change Biology, 24(8), 3368–3389.
Ogle, S. M., Butterbach-Bahl, K., Cardenas, L., Skiba, U., & Scheer, C. (2020). From research to policy: optimizing national monitoring systems for soil nitrous oxide emissions. Current Opinion in Environmental Sustainability, 47, 28–36.
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., et al. (2011). A Large and Persistent Carbon Sink in the World’s Forests. Science, 333(6045), 988–993.
Pfister, S., Oberschelp, C., & Sonderegger, T. (2020). Regionalized LCA in practice: the need for a universal shapefile to match LCI and LCIA. The International Journal of Life Cycle Assessment, 25(10), 1867–1871.
Reinhard, J., Zah, R., & Hilty, L. M. (2017). Regionalized LCI Modeling: A Framework for the Integration of Spatial Data in Life Cycle Assessment.
Romeiko, X. X., Lee, E. K., Sorunmu, Y., & Zhang, X. (2020). Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest. Environmental Science & Technology, 54(8), 4758–4768.
Schmidinger, K., & Stehfest, E. (2012). Including CO₂ implications of land occupation in LCAs—method and example for livestock products. The International Journal of Life Cycle Assessment, 17(8), 962–972.
Shepherd, A., Yan, X., Nayak, D., Newbold, J., Moran, D., Singh Dhanoa, M., Goulding, K., Smith, P., & Cardenas, L. M. (2015). Disaggregated N₂O emission factors in China. Atmospheric Environment, 122, 272–281.
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture: recent advances and future challenges. Biosystems Engineering, 164, 31–48.
U.S. EPA. (2020). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2019.
van Lingen, H. J., Niu, M., Kebreab, E., Valadares Filho, S. C., et al. (2019). Prediction of enteric methane production of beef cattle using an intercontinental database. Agriculture, Ecosystems & Environment, 283, 106575.
van Zelm, R., & Huijbregts, M. A. J. (2013). Quantifying the trade-off between parameter and model structure uncertainty in LCIA. Environmental Science & Technology, 47(16), 9274–9280.
Verones, F., Hellweg, S., Antón, A., Azevedo, L. B., et al. (2020). LC-IMPACT: A regionalized life cycle damage assessment method. Journal of Industrial Ecology.
Wei, W., Larrey-Lassalle, T., Faure, T., Dumoulin, N., Roux, P., & Mathias, J.-D. (2015). How to Conduct a Proper Sensitivity Analysis in LCA. Environmental Science & Technology, 49(1), 377–385.
Wernet, G., Hellweg, S., Fischer, U., Papadokonstantakis, S., & Hungerbühler, K. (2008). Molecular-Structure-Based Models of Chemical Inventories Using Neural Networks. Environmental Science & Technology, 42(17), 6717–6722.
Xu, M., Cai, H., & Liang, S. (2015). Big Data and Industrial Ecology. Journal of Industrial Ecology, 19(2), 205–210.
Yang, Y., Tao, M., & Suh, S. (2018). Geographic variability of agriculture requires sector-specific uncertainty characterization. The International Journal of Life Cycle Assessment, 23(8), 1581–1589.
Ziyadi, M., & Al-Qadi, I. L. (2019). Model uncertainty analysis using data analytics for LCA applications. The International Journal of Life Cycle Assessment, 24(5), 945–959.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Council for Agricultural Science and Technology (CAST)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License Terms for CC Attribution-NonCommercial 4.0:
License URL: https://creativecommons.org/licenses/by-nc/4.0/
License Terms Statement:
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following conditions:
- Attribution — you must give appropriate credit, provide a link to the license, and indicate if changes were made
- NonCommercial — you may not use the material for commercial purposes
No additional restrictions — you may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
