Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus x giganteus)
文献类型：期刊论文
| 作者 | Hao, Mengmeng2,3; Chen, Shuai2,3; Qian, Yushu2; Jiang, Dong1,2,3; Ding, Fangyu2,3 |
| 刊名 | ENERGIES
 |
| 出版日期 | 2022 |
| 卷号 | 15期号:2页码:13 |
| 关键词 | giant silvergrass boosting regression tree marginal land environmental suitability |
| DOI | 10.3390/en15020591 |
| 通讯作者 | Jiang, Dong(jiangd@igsnrr.ac.cn) ; Ding, Fangyu(dingfy@igsnrr.ac.cn) |
| 英文摘要 | Developing biomass energy, seen as the most important renewable energy, is becoming a prospective solution in attempting to deal with the world's sustainability-related challenges, such as climate change, energy crisis, and carbon emission reduction. As one of the most promising second-generation energy crops, giant silvergrass (Miscanthus x giganteus) is highly valued for its high potential for biomass production and low maintenance requirements. Mapping the potential global distribution of marginal land suitable for giant silvergrass is an essential prerequisite for the development of giant silvergrass-based biomass energy. In this study, a boosting regression tree was used to identify the marginal land resources for giant silvergrass cultivation using influencing factors, which include climate conditions, soil conditions, topography conditions, and land use. The results indicate that there are 3068.25 million hectares of land resources worldwide suitable for giant silvergrass cultivation, which are mainly located in Africa (902.05 million hectares), Asia (620.32 million hectares), South America (547.60 million hectares), and North America (529.26 million hectares). Among them, countries with the most land resources, Russia and Brazil, have the first- and second-highest amounts of suitable marginal land for giant silvergrass, with areas of 373.35 and 332.37 million hectares, respectively. Our results also rank the involved factors by their contribution. Climatic conditions have the greatest influence on the spatial distribution of giant silvergrass, with an average contribution of 74.38%, followed by land use, with a contribution of 17.38%. The contribution of the soil conditions is 7.26%. The results of this study provide instructive support for future biomass energy policy development. |
| WOS关键词 | BIOETHANOL PRODUCTION ; BIOENERGY ; RESOURCES ; CROPS |
| WOS研究方向 | Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000748141900001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/170084]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Jiang, Dong; Ding, Fangyu |
| 作者单位 | 1.Minist Land & Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hao, Mengmeng,Chen, Shuai,Qian, Yushu,et al. Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus x giganteus)[J]. ENERGIES,2022,15(2):13. |
| APA | Hao, Mengmeng,Chen, Shuai,Qian, Yushu,Jiang, Dong,&Ding, Fangyu.(2022).Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus x giganteus).ENERGIES,15(2),13. |
| MLA | Hao, Mengmeng,et al."Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus x giganteus)".ENERGIES 15.2(2022):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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