Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Energies
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Energies
Article
License: CC BY
Data sources: UnpayWall
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Energies
Article . 2022
Data sources: DOAJ
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

Authors: Mengmeng Hao; Shuai Chen; Yushu Qian; Dong Jiang; Fangyu Ding;

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

Abstract

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 × 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.

Related Organizations
Keywords

Technology, boosting regression tree, marginal land, T, giant silvergrass, giant silvergrass; boosting regression tree; marginal land; environmental suitability, environmental suitability

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Top 10%
Top 10%
gold