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.

The implications for potential marginal land resources of cassava across worldwide under climate change challenges

“…In the present study, an ensemble boosted regression tree (BRT) modeling framework that has been used for mapping the environmental suitability of several bioenergy plants 23 – 27 and medicinal plants 28 , 29 was adopted. This modeling approach allows multicollinearity among covariates and can establish a multivariate empirical relationship between known occurrence points and the suitable environmental conditions in the corresponding locations where the target has been confirmed to have occurred.…”

“…To obtain the accurate distribution of marginal land suitable for energy plants under current and future climate scenarios, more advanced methods should be employed. The machine learning methods that have proven to be effective in extracting land distribution were applied in this research, combining the occurrence records and growing conditions of energy plants to identify the global marginal land suitable for growing energy plants 22 , 23 . This in-depth analysis of the potential distribution of energy plants under the challenge of climate change proceeds in three steps: (1) choosing cassava, which is one of the most promising energy plants for bioenergy production, as a research object; (2) structuring the dataset with occurrence records and environmental covariates; (3) applying Boosted Regression Trees (BRT), a machine learning method, to evaluate the distribution of potential marginal land suitable for cassava from the perspective of environmental suitability under current and future climate scenarios.…”

Recent Advances in Miscanthus Macromolecule Conversion: A Brief Overview

“…The machine learning study results [ 18 ] showed that globally there exist 3068.25 million ha marginal land resources eligible for M. × giganteus cultivation, which are basically located in Africa (902.05 million ha), Asia (620.32 million ha), South America (547.60 million ha) and North America (529.26 million ha). The countries with the largest land resources, Russia and Brazil, hold the first and second places based on the amount of marginal lands suitable for M. × giganteus , with areas of 373.35 and 332.37 million ha, respectively.…”

Development of a Land Suitability Analysis Model for Slums Relocation: The Case of Cairo, Egypt

“…Subsequently, LSA has been used in studies associated with a range of different environmental phenomena. For instance, it has been used to map and model urban areas for different particular purposes (Bamrungkhul and Tanaka, 2022; Foroozesh et al, 2022; Hao et al, 2022; Ismaeel and Elsayed, 2022; Saleem et al, 2022) . The driving concept of LSA is to provide decision-makers with the necessary information to evaluate a site suitability for a particular purpose.…”