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<p>Sustainable agro-economic development is critical yet challenging in ecologically fragile regions like China’s rocky desertification control area (DC). This research investigates agricultural eco-efficiency (AEE) and its drivers in the Southern China Karst (SCK) core area to guide targeted sustainability policies. Using the Super-SBM and Malmquist index models, we analyze static and dynamic AEE trends from 2010 to 2020. Results reveal a rising but uneven AEE trajectory, with regional disparities and an “N”-shaped growth pattern. High-efficiency cities shifted southwestward, while the Malmquist index consistently surpassed 1, primarily due to technological advancements. Tobit regression identifies land reclamation rate (LRR), farmer income level (FLS), financial support for agriculture (FSA), scale level of agriculture (SLA), per capita GDP (GPC), and agricultural economic development level (AEDL) as positive AEE influences, whereas agricultural industrial structure(AIS), urbanization level (UL), and agricultural mechanization level (AML) exert negative effects. Key recommendations include optimizing agricultural machinery investment, fostering urban–rural resource flows, and restructuring agricultural systems to enhance AEE. These measures can advance sustainable development in ecologically vulnerable regions.</p> Graphical abstract <p></p>

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Quantification and attribution of agriculture eco-efficiency in the desertification control of South China Karst core area

  • 期刊论文

  • 发表日期：

    2025年5月29日

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  • Nana Yu,
  • Kangning Xiong,
  • Rong Zhao,
  • Fangli Feng

摘要

Sustainable agro-economic development is critical yet challenging in ecologically fragile regions like China’s rocky desertification control area (DC). This research investigates agricultural eco-efficiency (AEE) and its drivers in the Southern China Karst (SCK) core area to guide targeted sustainability policies. Using the Super-SBM and Malmquist index models, we analyze static and dynamic AEE trends from 2010 to 2020. Results reveal a rising but uneven AEE trajectory, with regional disparities and an “N”-shaped growth pattern. High-efficiency cities shifted southwestward, while the Malmquist index consistently surpassed 1, primarily due to technological advancements. Tobit regression identifies land reclamation rate (LRR), farmer income level (FLS), financial support for agriculture (FSA), scale level of agriculture (SLA), per capita GDP (GPC), and agricultural economic development level (AEDL) as positive AEE influences, whereas agricultural industrial structure(AIS), urbanization level (UL), and agricultural mechanization level (AML) exert negative effects. Key recommendations include optimizing agricultural machinery investment, fostering urban–rural resource flows, and restructuring agricultural systems to enhance AEE. These measures can advance sustainable development in ecologically vulnerable regions.

Graphical abstract