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Reorienting paradoxical land use policies towards coherence: A self-adaptive ensemble learning geo-simulation of tea expansion under different scenarios in subtropical China
Affiliation:1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China;3. Key Laboratory of Geographical Information Systems, Ministry of Education, Wuhan University, Wuhan, China;4. Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan, China;1. Environmental Planning Laboratory (LABPLAM), Department of Urbanism and Spatial Planning, University of Granada, Spain;2. Environmental Planning Laboratory (LABPLAM), Department of Urbanism and Spatial, Planning, University of Granada, Spain;3. E.T.S. Ingeniería de Caminos, Canales y Puertos, Campus de Fuentenueva s/n, 18071, Granada, Spain, Spain;1. Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210093, China;2. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;1. State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China;2. Department of Zoology, University of British Columbia, 6270 University Blvd., Vancouver, BC V6T 1Z4, Canada;1. Department of Cardiovascular Medicine, University of Arkansas for Medical Science, Little Rock, Arkansas, USA;2. Division of Cardiology, Central Arkansas Veterans Healthcare System, Little Rock, Arkansas, USA
Abstract:The expansion of cash crops has raised contradicting interests between two bureaucratic bodies (the economy-oriented one that advocates cash crop production and the conservation-oriented one that focuses on natural resources protection) in many places around the world. Recent past has saw growing efforts on the theoretical linkages between cash crop production and conservation, but the solutions to the cash cropping −related land use conflicts remain as violent controversy. Using a geo-simulation approach, this paper models the tea expansion under different policy scenarios and evaluates the effectiveness of these policies in Anji County (China), as a contribution to the scientific basis for formulating sustainable cash cropping practices and alternative land use policies. In particular, a new self-adaptive cellular automaton model based on ensemble learning (EL-CA) is developed and three policy scenarios (economy-over-conservation (EOC), conversion-over-economy (COE), and economy-balance-conservation (EBC)) are set to predict the tea expansion patterns in 2025. Results show that the EL-CA model significantly outperforms the traditional CA models based on empirical statistics. We find that the tea expansion under the EOC scenario is much more intensive than that under the COE and EBC scenarios. The most outstanding ecological consequence of tea expansion is the occupation of forests. Employing an equivalent coefficient approach, we further quantify the trade-offs between economic incomes (from tea expansion) and ecological loss (due to ecosystem service value (ESV) declines) under the three policy scenarios. In the EOC scenario, the loss in ESV far exceeds the benefit of tea expansion. Net change of ESV is higher than that of economic return under the COE. The economic benefit is approximately equal to the ecological loss in the EBC scenario. The EBC should be a socially preferred scenario, since it leads to sustainable tea expansion and minimal ecological impacts. Though the EBC scenario is a desirable choice, how to enforce these policies is an important consideration. Given the complexity in the Chinese policy context, we finally propose several possible measures to promote the coherence of paradoxical policies involving the allocation of land for cash crop cultivation.
Keywords:Land use conflicts  Cash crop  Tea cultivation  Trade-off  Cellular automaton  Land use change modeling  Ensemble learning
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