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耦合土地质量与立地条件的耕地恢复时序分区

Sequential zoning of cultivated land restoration based on the coupling of land quality and site conditions

  • 摘要: 耕地恢复是保障粮食安全的基础。然而,目前存在无序恢复的情况。现有的研究忽略了耕地恢复后的稳定性。因此,为了合理确定耕地恢复的次序,该研究考虑了土地地力和稳定利用的可能性,构建了包含土地质量和立地条件的评价体系。并运用了耦合协调度模型,探索土地质量评估(land evaluation, LE)和立地条件分析(site assessment, SA)的动态权重。在此基础上,在河南省濮阳市开展实证研究。结果表明:LE与SA之间的最优耦合系数为6:4。濮阳市可恢复耕地可划分为优先恢复区、次先恢复区、一般恢复区与滞后恢复区4个类型,面积分别为25.63、91.85、79.06与113.68 km2。另外,城镇建成区周边恢复次序较为优先,黄河周边与之相反。该研究所建立的耕地恢复次序协同评估框架,能够为区域耕地恢复优先次序安排与可持续利用提供理论借鉴与实践参考。

     

    Abstract: Orderly restoration of cultivated land is essential to regulate cultivated land use for national food security. However, it is lacking in natural land endowments, particularly for the long-term stable use of restored cultivated land. It is often required to more rationally determine restoration priorities for the high stability of post-restoration agricultural use. In this study, a sequential zoning was developed for cultivated land restoration using land quality and site conditions. A study area was taken as Puyang City, Henan Province, China. An evaluation framework was constructed using the Land Evaluation and Site Assessment (LESA). The framework also consisted of Land Evaluation (LE) and Site Assessment (SA) subsystems. Among them, the Land Evaluation (LE) subsystem was used to characterize the natural quality of restorable land parcels, including obstacle-layer depth, soil profile configuration, top-soil texture, soil organic matter content, soil pH, salinization degree, irrigation guarantee rate, and irrigation water source. The Site Assessment (SA) subsystem was designed to evaluate the external conditions affecting the long-term stable utilization of restored cultivated land, including location advantage, economic benefit, cultivated land use condition, landscape value, and environmental index. Multiple data sources were integrated, including land use, restorable land category, agricultural land grading, and socio-economic data in 2022. The LE subsystem was scored, according to agricultural land quality grading, while the SA subsystem was calculated after indicator standardization and the entropy weight. A coupling coordination degree model was also introduced to identify the relative contribution of land quality and site conditions. The interaction between the LE and SA subsystems was quantified to determine the optimal dynamic weight ratio. According to the optimal weights, the integrated LESA score was calculated for each restorable land parcel. The natural breaks method was then used to classify the restorable cultivated land into different restoration priority zones. The results showed that the optimal weight ratio between LE and SA was 6:4, and the coupling coordination degree reached 0.913, indicating a high level of coordination between land quality and site conditions. The land quality provided the fundamental basis for cultivated land restoration, whereas the site conditions were important constraints to maintain stable use after restoration. The integrated LESA evaluation scores of restorable cultivated lands ranged from 52.28 to 91.61. The restorable cultivated land was divided into four categories after zoning: priority, secondary priority, general, and lagging restoration zone. Their areas were 25.63, 91.85, 79.06, and 113.68 km², respectively, accounting for 8.26%, 29.61%, 25.48%, and 36.65% of the total restorable cultivated land area, respectively. Spatially, the priority and secondary-priority restoration zones were distributed around urban built-up areas and county hinterlands, where there were relatively favorable land quality, irrigation conditions, accessibility, and cultivated land connectivity. In contrast, the general and lagging restoration zones were located along the Yellow River and the old Yellow River course, where the restoration priority was reduced by soil salinization, land fragmentation, weaker production conditions, and ecological constraints. The natural land suitability was integrated with the possibility of long-term stable utilization, thereby limiting the restoration decisions on land quality or suitability evaluation. The finding can provide a quantitative basis to prioritize differentiated restoration of farmland to support its sustainable resources.

     

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