Abstract
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 (R-2) 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. (C) 2020 Elsevier B.V. All rights reserved.