Machine learning to predict biomass sorghum yields under future climate scenarios
[1];
[4]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Huazhong Univ. of Science and Technology, Wuhan (China)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Univ. of California, Berkeley, CA (United States)
Abstract Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near‐term and long‐term pathways for scaling up bio‐based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. We used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC02-06CH11357; AC02-05CH11231
- OSTI ID:
- 1631977
- Alternate ID(s):
- OSTI ID: 1597542
- Journal Information:
- Biofuels, Bioproducts & Biorefining, Vol. 14, Issue 3; ISSN 1932-104X
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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