Abstract
The construction of artificial shelter forests (ASFs) has resulted in substantial ecological, economic, and societal benefits to the Chinese Loess Plateau (CLP). However, the health and benefits of ASFs are being increasingly threatened by the formation of low-efficiency artificial shelter forests (LEASFs). In this study, LEASFs are systematically analyzed in terms of their status, formation mechanisms, and developmental obstacles. The key restoration techniques and schemes were summarized to improve the quality and efficiency of LEASFs. LEASFs are formed by relatively complex mechanisms, but they arise mainly due to poor habitat conditions, improper tree species selections, mismatch between stands and habitat, extensive forest management measures, and human interferences. The restoration and improvement of LEASFs are hindered by water deficits, mismatch between stands and habitat, single management purpose, and low efficiency. LEASFs are becoming more complex due to their wide range, the challenges associated with their restoration, and insufficient technological measures for their restoration. The key techniques of the quality and efficiency improvement of LEASFs include basic forest tending methods, near-natural restoration, multifunction-oriented improvement, and systematic restoration. An understanding on the formation mechanisms of LEASFs and a scientific approach toward their restoration are urgently needed and critical for the ecological protection and high-quality development of LEASFs on the CLP. Based on these analyses, we recommend strengthening the monitoring and supervision of LEASFs, considering the bearing capacity of regional water resources, implementing multiple restoration techniques, promoting multifunction-oriented ecological development, and exploring new management concepts to achieve the sustainable development of ASFs on the CLP.
基金项目
Science and Technology Innovation Program of the Shaanxi Academy of Forestry(SXLK2022-02)
国家自然科学基金(42077452)
Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23070201)
国家科技期刊平台
NSTL
维普
万方数据