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Evaluating agricultural management practices to improve the environmental footprint of corn-derived ethanol

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  • Xue, Xiaobo
  • Pang, YuLei
  • Landis, Amy E.

Abstract

This study examines three agriculture management practices with the aim of improving the environmental performance of corn-derived products such as bioethanol. Corn production is energy intensive and contributes to water quality degradation and global warming, thus affecting the environmental impact of corn-derived ethanol. Life Cycle Assessment (LCA) is used to quantify and compare the environmental impacts of three management strategies: tillage, fertilizer choices and the use of buffer strips to sequester nutrients. Detailed energy, carbon, nitrogen and phosphorus inventories are compiled to represent corn production scenarios within the US Corn Belt. The LCA was developed using GREET 1.8 (Greenhouse Gases, Regulated Emissions, and Energy use in Transportation) and emission factors with statistical analyses to estimate energy consumption, associated air emissions, and aqueous nutrient runoff potentials. Results show that using manure fertilizers as opposed to synthetic fertilizers requires less energy, however the use of manure generates more CH4, N2O, CO2 and results in more variable concentrations of nitrogen and phosphorus leaching from farmlands. No tillage emits less greenhouse gas emissions, sequesters more soil organic carbon and slightly reduces nutrient runoff compared with conventional tillage practices. Building buffer strips of certain widths is an efficient way to reduce N and P discharge to surrounding waters with minimal effect on the energy or global warming profile. Based on the results of the LCA studies, replacing conventional tillage with no till, and installing buffer strips can improve environmental performances of corn derived ethanol.

Suggested Citation

  • Xue, Xiaobo & Pang, YuLei & Landis, Amy E., 2014. "Evaluating agricultural management practices to improve the environmental footprint of corn-derived ethanol," Renewable Energy, Elsevier, vol. 66(C), pages 454-460.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:454-460
    DOI: 10.1016/j.renene.2013.12.026
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    References listed on IDEAS

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    3. Xiaobo Xue Romeiko & Zhijian Guo & Yulei Pang & Eun Kyung Lee & Xuesong Zhang, 2020. "Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    4. Muntwyler, Anna & Panagos, Panos & Morari, Francesco & Berti, Antonio & Jarosch, Klaus A. & Mayer, Jochen & Lugato, Emanuele, 2023. "Modelling phosphorus dynamics in four European long-term experiments," Agricultural Systems, Elsevier, vol. 206(C).
    5. Machado, Karina Scurupa & Seleme, Robson & Maceno, Marcell M.C. & Zattar, Izabel C., 2017. "Carbon footprint in the ethanol feedstocks cultivation – Agricultural CO2 emission assessment," Agricultural Systems, Elsevier, vol. 157(C), pages 140-145.
    6. Mainul Hoque & Catherine L. Kling, 2016. "Economic Valuation of Ecosystem Benefits from Conservation Practices Targeted in Iowa Nutrient Reduction Strategy 2013: A Non Market Valuation Approach," Center for Agricultural and Rural Development (CARD) Publications 16-wp561, Center for Agricultural and Rural Development (CARD) at Iowa State University.

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