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Evaluation of Rice–Crayfish Field Fragmentation Based on Landscape Indices: A Case Study of Qianjiang City, China

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  • Lei Shi

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Xu He

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Bo Hu

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Jiuwei Li

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Lei Yu

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

Abstract

Since the 21st century, rice–crayfish fields have been widely distributed in the Yangtze River Basin in China. However, the spontaneous construction of these fields by farmers has given rise to the issue of rice–crayfish field fragmentation (RCFF) in certain areas. This study introduced a novel method for evaluating RCFF using township-level administrative regions as the evaluation units. Three key evaluation elements, including five landscape indices, were employed: area and edge metrics (rice–crayfish area ratio), shape metrics (perimeter–area ratio distribution), and aggregation metrics (rice–crayfish patch density, percentage of like adjacencies, and rice–crayfish contagion index). The RCFF was quantified and its spatial distribution pattern was analyzed through the entropy method and GIS spatial analysis. Empirical studies conducted in Qianjiang city yielded insightful results: (1) The contribution of evaluation elements to the RCFF was ranked in descending order as follows: aggregation metrics > shape metrics > area and edge metrics. (2) The RCFF of Yunlianghu farm was the lowest at 0.06, while the RCFF of Yangshi subdistrict 2 was the highest at 0.94. The spatial distribution of the RCFF exhibited a distinct trend, showing a gradual decrease from the northeast to the southwest in Qianjiang, and a low-RCFF area in the southwest. This evaluation system enables local government decisionmakers to comprehend the current status of rice–crayfish field management and construction. It facilitates the scientific planning of rice–crayfish field layouts and provides guidance for farmers in their expansion strategies. This method can be promoted in counties (cities) where rice–crayfish fields are primarily distributed in the Yangtze River Basin, promoting the transition of traditional agriculture to environmentally friendly agriculture in China.

Suggested Citation

  • Lei Shi & Xu He & Bo Hu & Jiuwei Li & Lei Yu, 2024. "Evaluation of Rice–Crayfish Field Fragmentation Based on Landscape Indices: A Case Study of Qianjiang City, China," Land, MDPI, vol. 13(7), pages 1-17, July.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:7:p:1001-:d:1430177
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    References listed on IDEAS

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    1. Georgina Milne & Andrew William Byrne & Emma Campbell & Jordon Graham & John McGrath & Raymond Kirke & Wilma McMaster & Jesko Zimmermann & Adewale Henry Adenuga, 2022. "Quantifying Land Fragmentation in Northern Irish Cattle Enterprises," Land, MDPI, vol. 11(3), pages 1-16, March.
    2. Gang Fu & Wei Wang & Junsheng Li & Nengwen Xiao & Yue Qi, 2021. "Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis," Land, MDPI, vol. 10(11), pages 1-21, November.
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