LAPSE:2023.4059
Published Article
LAPSE:2023.4059
Mapping Global Environmental Suitability for Sorghum bicolor (L.) Moench
February 22, 2023
Abstract
(L.) Moench, called sweet sorghum, is a drought-resistant and heat-tolerant plant used for ethanol bioenergy production, and is able to reduce the competition between growing crops for energy vs. growing crops for food. Quantitatively mapping the marginal lands of sweet sorghum is essential for the development of sorghum-based fuel ethanol production. However, knowledge of the contemporary marginal lands of sweet sorghum remains incomplete, and usually relies on sample data or is evaluated at a national or regional scale based on established rules. In this study, a novel method was demonstrated for mapping the global marginal lands of sweet sorghum based on a machine learning model. The total amount of global marginal lands suitable for sweet sorghum is 4802.21 million hectares. The model was applied to training and validation samples, and achieved high predictive performance, with the area under the receiver operating characteristic (ROC) curve (AUC) values of 0.984 and 0.978, respectively. In addition, the results illustrate that maximum annual temperature contributes more than do other variables to the predicted distribution of sweet sorghum and has a contribution rate of 40.2%.
(L.) Moench, called sweet sorghum, is a drought-resistant and heat-tolerant plant used for ethanol bioenergy production, and is able to reduce the competition between growing crops for energy vs. growing crops for food. Quantitatively mapping the marginal lands of sweet sorghum is essential for the development of sorghum-based fuel ethanol production. However, knowledge of the contemporary marginal lands of sweet sorghum remains incomplete, and usually relies on sample data or is evaluated at a national or regional scale based on established rules. In this study, a novel method was demonstrated for mapping the global marginal lands of sweet sorghum based on a machine learning model. The total amount of global marginal lands suitable for sweet sorghum is 4802.21 million hectares. The model was applied to training and validation samples, and achieved high predictive performance, with the area under the receiver operating characteristic (ROC) curve (AUC) values of 0.984 and 0.978, respectively. In addition, the results illustrate that maximum annual temperature contributes more than do other variables to the predicted distribution of sweet sorghum and has a contribution rate of 40.2%.
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Keywords
AUC, high predictive performance, machine learning model, marginal lands, sweet sorghum
Subject
Suggested Citation
Jiang D, Ma T, Ding F, Fu J, Hao M, Wang Q, Chen S. Mapping Global Environmental Suitability for Sorghum bicolor (L.) Moench. (2023). LAPSE:2023.4059
Author Affiliations
Jiang D: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad [ORCID]
Ma T: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Ding F: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Fu J: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Hao M: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Wang Q: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Chen S: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
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Ma T: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Ding F: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Fu J: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Hao M: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Wang Q: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
Chen S: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Acad
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Journal Name
Energies
Volume
12
Issue
10
Article Number
E1928
Year
2019
Publication Date
2019-05-20
ISSN
1996-1073
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Original Submission
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PII: en12101928, Publication Type: Journal Article
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LAPSE:2023.4059
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https://doi.org/10.3390/en12101928
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