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• W2899454866 abstract "Agricultural soil management is responsible for a significant portion of net greenhouse gas emission in the United States. Alternative and sustainable agricultural practices are important, as they may help minimize the carbon footprint from agriculture. We developed a framework to account for the carbon footprint (C-footprint), i.e., cumulative greenhouse gas generation of various stages in the cultivation of the crop rotation system of corn-soybean-oats. We obtained real-farm data for this analysis from a biological farming and management company (referred to as bio-farming), and benchmarked this footprint against a conventional farm baseline. The analysis included process modeling i.e., the upstream manufacture of fertilizers and crop applications, local mixing and transport of solid fertilizers (in the bio-farming scenario), on-farm fuel use by farm machinery, as well as agricultural modeling of on-farm soil interactions using the soil & water assessment tool (SWAT). Results varied based on individual crops, and showed that bio-farming practices generated a 44% lower C-footprint for corn, 69% lower footprint for soy and 70% lower footprint for oats compared to conventional farming on a kgCO2e/bushel basis. The on-field (N2O) emissions contributed most significantly to the total carbon footprint at 70%, 96%, and 89% of the total emissions in corn, soy and oats, respectively. C-footprint results of all crops showed a high dependence on the yield of the respective crop. Sensitivity analyses showed that the application of lime and lack of cover crop increased C-footprint. We identified strategies to reduce emissions from the bio-farming scenario further by implementing management strategies." @default.
• W2899454866 created "2018-11-09" @default.
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• W2899454866 date "2019-02-01" @default.
• W2899454866 modified "2023-09-26" @default.
• W2899454866 title "Carbon footprint of corn-soy-oats rotations in the US Midwest using data from real biological farm management practices" @default.
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• W2899454866 doi "https://doi.org/10.1016/j.jclepro.2018.11.002" @default.
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