文献类型：期刊文献

英文题名：Productivity-Based Land Suitability and Management Sensitivity Analysis: The Eucalyptus E. urophylla × E. grandis Case

作者：Shi, Miaoying[1]; Xu, Jintao[2]; Liu, Shilei[3]; Xu, Zhenci[4,5]

机构：[1] School of Social and Public Administration, East China University of Science and Technology, Xuhui District, Shanghai, 200237, China; [2] National School of Development, Peking University, Beijing, 100871, China; [3] School of Environment & Natural Resources, Renmin University of China, Beijing, 100872, China; [4] Department of Geography, The University of Hong Kong [HKU], 999077, Hong Kong; [5] HKU Shenzhen Institute of Research and Innovation, Nanshan District, Shenzhen, 518057, China

年份：2022

卷号：13

期号：2

外文期刊名：Forests

收录：EI(收录号：20220911727880)

语种：英文

外文关键词：Decision trees - Learning algorithms - Sensitivity analysis - Tropics - Forestry - Machine learning

摘要：Eucalyptus plantations are productive and short rotation forests prevalent in tropical areas that experience fast expansion and face controversies in ecological issues. In this study, we perform a systematic analysis of factors influencing eucalyptus growth through plot records from the National Forest Inventories and satellite images. We find primary restricting factors for eucalyptus growth via machine learning algorithms with random forests and accumulated local effects plots, as conventional forest growth models are inadequate to calculate the causal effect with the large number of environmental and socioeconomic factors. As a result, despite common belief that temperature affects eucalyptus growth the most, we find that precipitation is the most evident restricting factor for eucalyptus growth. We then identify and rank key factors that affect timber growth, such as tree density, rotation period, and wood ownership. Finally, we suggest optimal management and planting strategies for local farmers and policymakers to facilitate eucalyptus growth. ? 2022 by the authors. Licensee MDPI, Basel, Switzerland.