DOI: 10.1016/j.scitotenv.2020.136697
论文题名: Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning approach
作者: Lee E.K. ; Zhang W.-J. ; Zhang X. ; Adler P.R. ; Lin S. ; Feingold B.J. ; Khwaja H.A. ; Romeiko X.X.
刊名: Science of the Total Environment
ISSN: 489697
出版年: 2020
卷: 714 语种: 英语
英文关键词: Climate change
; Corn production
; Environmental impacts
; Life cycle assessment
; Machine learning
; US Midwest
Scopus关键词: Climate change
; Climate models
; Cultivation
; Environmental impact
; Eutrophication
; Forecasting
; Global warming
; Learning systems
; Machine learning
; Agricultural productions
; Corn production
; Environmental mitigation
; Future climate scenarios
; Life Cycle Assessment (LCA)
; Life-cycle environmental impact
; Machine learning approaches
; Spatial and temporal heterogeneity
; Life cycle
; climate change
; climate effect
; crop production
; environmental impact assessment
; future prospect
; life cycle analysis
; machine learning
; maize
; article
; climate change
; eutrophication
; greenhouse effect
; life cycle assessment
; machine learning
; plant yield
; precipitation
; prediction
; quantitative analysis
; cross validation
; Midwest
; United States
; Zea mays
英文摘要: Climate change is exacerbating environmental pollution from crop production. Spatially and temporally explicit estimates of life-cycle environmental impacts are therefore needed for suggesting location and time relevant environmental mitigations strategies. Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanistic models, capable of capturing the heterogeneity, tend to be very complicated and time-consuming. Efficient prediction of life-cycle environmental impacts from agricultural production is lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW) and eutrophication (EU) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate future life-cycle environmental impacts of corn production in U.S. Midwest counties under four emissions scenarios for years 2022–2100. Results from BRT models indicate that the cross-validation (R2) for predicting life cycle GW and EU impacts ranged from 0.78 to 0.82, respectively. Furthermore, results show that future life-cycle GW and EU impacts of corn production will increase in magnitude under all four emissions scenarios, with the highest environmental impacts shown under the high-emissions scenario. Moreover, this study found that changes in precipitation and temperature played a significant role in influencing the spatial heterogeneity in all life-cycle impacts across Midwest counties. The BRT model results indicate that machine learning can be a useful tool for predicting spatially and temporally explicit future life-cycle environmental impacts associated with corn production under different climate scenarios. © 2020 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158356
Appears in Collections: 气候变化与战略
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作者单位: Department of Environmental Health Sciences, University at Albany, State University of New York, One University Place, Rensselaer, NY 12144, United States; Joint Global Change Research Institute, Pacific Northwest National Laboratory, 5825 University Research Court, College ParkMD 20740, United States; Pasture Systems and Watershed Management Research Unit, USDA-ARS, Curtin Road, University ParkPA 16807, United States; Department of Epidemiology and Biostatistics, University at Albany, State University of New York, One University Place, Rensselaer, NY 12144, United States; Wadsworth Center, New York State Department of Health, Empire State Plaza, Albany, NY 12201, United States
Recommended Citation:
Lee E.K.,Zhang W.-J.,Zhang X.,et al. Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning approach[J]. Science of the Total Environment,2020-01-01,714