气候变化领域集成服务门户(CCPortal): Machine learning based estimation of land productivity in the contiguous US using biophysical predictors

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DOI	10.1088/1748-9326/ab865f
	Machine learning based estimation of land productivity in the contiguous US using biophysical predictors
	Yang P.; Zhao Q.; Cai X.
发表日期	2020
ISSN	17489318
卷号	15 期号:7
英文摘要	Estimation of land productivity and availability is necessary to predict land production potential, especially for the emerging bioenergy crop production, which may compete land with food crop production. This study provides land productivity estimates in the contiguous United States (CONUS) through a machine learning approach. Land productivity is defined as the potential in producing agricultural outputs given biophysical properties including climate, soil, and land slope. The land productivity is approximated by the potential yields of six major crops in the CONUS, i.e. corn, soybean, winter wheat, spring wheat, cotton, and alfalfa. This quantitative relationship is then applied to estimating the availability of marginal land for bioenergy crop production in the CONUS. Furthermore, the levels of uncertainties associated with land productivity and marginal land estimates are quantified and discussed. Based on the modeling results, the total marginal land of the CONUS ranges 55.0-172.8 mha, but the 95% inter-percentile distance of the estimated productivity index reaches up to 60% of its expected value in data-scarce regions. Finally, in a cross-check analysis, marginal lands estimated based on biophysical criteria are found to be comparable to those based on an economic criterion. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	land productivity; land use; machine learning; marginal land
语种	英语
scopus关键词	Agricultural robots; Biofuels; Biophysics; Crops; Cultivation; Machine learning; Uncertainty analysis; Agricultural output; Bioenergy crops; Biophysical properties; Economic criteria; Expected values; Land productivities; Machine learning approaches; Productivity index; Productivity; agricultural land; crop plant; crop production; crop yield; estimation method; machine learning; United States; Glycine max; Gossypium hirsutum; Medicago sativa; Triticum aestivum; Zea mays
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153953
作者单位	Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, United States
推荐引用方式 GB/T 7714	Yang P.,Zhao Q.,Cai X.. Machine learning based estimation of land productivity in the contiguous US using biophysical predictors[J],2020,15(7).
APA	Yang P.,Zhao Q.,&Cai X..(2020).Machine learning based estimation of land productivity in the contiguous US using biophysical predictors.Environmental Research Letters,15(7).
MLA	Yang P.,et al."Machine learning based estimation of land productivity in the contiguous US using biophysical predictors".Environmental Research Letters 15.7(2020).