Machine learning to predict biomass sorghum yields under future climate scenarios
Machine learning to predict biomass sorghum yields under future climate scenarios
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DOI：
10.1002/bbb.2087
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发表时间：
2020-02-05
影响因子：
3.9
通讯作者：
Scown, Corinne D.
中科院分区：
文献类型：
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作者：
Huntington, Tyler;Cui, Xinguang;Scown, Corinne D.
作物产量模型在设计国家农业生产战略方面至关重要，特别是在气候变化的背景下。以精细的空间分辨率预测生物能源作物的产量有助于评估扩大生物燃料和化学品生产的短期和长期途径，并有助于了解严重干旱和极端温度等非生物胁迫因素对潜在生物质供应的影响。我们使用了28,364个高粱双色产量样本的大型数据集(由县和观察年份唯一确定)、环境变量和多种方法来分析美国高粱产量的历史趋势。我们选择了最准确的机器学习方法(随机森林方法的一种变体)来预测四种温室气体排放情景和两种灌溉制度下高粱产量的未来趋势。我们确定灌溉措施、水汽压差和时间(技术改进的替代指标)是高粱产量最重要的预测因子。我们的结果表明，高粱产量在未来几年呈下降趋势(2018-2099年平均为2.7%)，在温室气体排放较高的情况下(3.8%)和在不灌溉的情况下(4.6%)，下降幅度更大。在地理上，我们观察到五大湖(8.2%)、中西部上段(7.5%)和哈特兰(6.7%)地区的预测降幅最大。我们的研究表明，使用机器学习来识别高粱生物量产量的环境控制因素，并以合理的精度预测产量。这些结果可以为制定更现实的生物质供应预测提供信息，如果高粱生产扩大的话。(C)2020年化学工业协会和John Wiley&Sons，Ltd.
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. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd