Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab865f |
Machine learning based estimation of land productivity in the contiguous US using biophysical predictors | |
Yang, Pan1,2; Zhao, Qiankun1,2; Cai, Ximing1,2 | |
2020-07-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS
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ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:7 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
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. |
英文关键词 | land productivity marginal land land use machine learning |
领域 | 气候变化 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000547010300001 |
WOS关键词 | MARGINAL LAND ; FEEDSTOCK PRODUCTION ; GAUSSIAN-PROCESSES ; UNITED-STATES ; CROP YIELD ; BIOENERGY ; SOIL ; CARBON ; MODEL ; MISCANTHUS |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/289394 |
专题 | 气候变化 |
作者单位 | 1.Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA; 2.Univ Illinois, DOE Ctr Adv Bioenergy & Bioprod Innovat, Urbana, IL 61801 USA |
推荐引用方式 GB/T 7714 | Yang, Pan,Zhao, Qiankun,Cai, Ximing. Machine learning based estimation of land productivity in the contiguous US using biophysical predictors[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(7). |
APA | Yang, Pan,Zhao, Qiankun,&Cai, Ximing.(2020).Machine learning based estimation of land productivity in the contiguous US using biophysical predictors.ENVIRONMENTAL RESEARCH LETTERS,15(7). |
MLA | Yang, Pan,et al."Machine learning based estimation of land productivity in the contiguous US using biophysical predictors".ENVIRONMENTAL RESEARCH LETTERS 15.7(2020). |
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