地貌分异视角下喀斯特山区耕地细碎化及其影响因素

段雨洁; 赵宇鸾; 周春芳

doi:10.11932/karst20250608

地貌分异视角下喀斯特山区耕地细碎化及其影响因素

doi: 10.11932/karst20250608

段雨洁^1,,
赵宇鸾^{1, 2, ,},
周春芳¹

1.
贵州师范大学地理与环境科学学院, 贵州贵阳 550025
2.
贵州省喀斯特山地生态环境国家重点实验室培育基地, 贵州贵阳 550025

基金项目: 贵州省研究生科研基金项目（2024YJSKYJJ145）；贵州省科技计划项目（黔科合基础-ZK[2025]重点045）；贵州科技创新基地建设项目（黔科合中引地[2023]005）

详细信息

作者简介:
段雨洁（2001－），女，硕士研究生，研究方向为喀斯特生态环境保护。E-mail：dyj39272023@163.com

通讯作者:
赵宇鸾（1985－），男，博士，教授，博士研究生导师，研究方向为土地利用与山区发展。E-mail：zhaoyl.09b@igsnrr.ac.cn。

中图分类号: F323.211
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出版历程
- 收稿日期: 2025-01-16
- 录用日期: 2025-12-31
- 修回日期: 2025-11-16
- 网络出版日期: 2026-02-12
- 刊出日期: 2025-12-25

Cultivated land fragmentation and its influencing factors in karst mountainous areas from the perspective of geomorphic differentiation

DUAN Yujie^1
,,
ZHAO Yuluan^{1, 2
, ,},
ZHOU Chunfang¹

1.
School of Geography & Environmental Science, Guizhou Normal University, Guiyang, Guizhou 550025, China
2.
The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province, Guiyang, Guizhou 550025, China

摘要

摘要: 文章以毕节市七星关区为研究对象，综合分析不同地貌类型下的耕地细碎化特征及其空间关联性，同时利用多元线性回归模型和地理探测器模型测算各影响因素的作用强度。结果表明：（1）七星关区地貌可划分为低山河谷坡地、中山丘陵坝地、中山坡谷地和中山丘陵坡地4类，各地貌类型涵盖的行政村个数分别为49个、162个、81个和185个；（2）七星关区耕地细碎化程度较高，权属细碎化程度高于景观细碎化程度，且存在明显的空间分异；（3）不同地貌类型组合特征鲜明，低山河谷坡地景观细碎化高、权属细碎化低，中山坡谷地权属细碎化高、景观细碎化低，中山丘陵坝地景观与权属细碎化双高，中山丘陵坡地两者均较低；（4）景观细碎化主要受建设用地比例、高程和地形切割度的影响，其中低山河谷坡地、中山丘陵坡地与坡度呈正相关，中山坡谷地与地形切割度呈正相关，中山丘陵坝地由建设用地比例驱动；权属细碎化的关键驱动因子为建设用地比例和农村人口密度，其中低山河谷坡地、中山坡谷地及中山丘陵坡地核心驱动因子为农村人口密度，中山丘陵坝地主要与人均耕地面积呈正相关。建议基于不同地貌类型区耕地资源禀赋特点，从景观与权属双重视角下开展土地资源综合治理，以推动七星关区农业现代化发展。
- 耕地细碎化 /
- 地貌类型 /
- 空间格局 /
- 影响因素 /
- 贵州七星关 /
- 喀斯特山区
Abstract: The fragmentation of cultivated land, as a land use pattern contrasting with large-scale farming operations, constrains agricultural modernisation and sustainable development. This is particularly pronounced in the karst mountainous regions of southwestern China, where factors such as the fundamental agricultural conditions of large population and limited land, the egalitrian allocation method of matching fertile and infertile plots, combining with distant and nearby plots, and the complex natural environment dominated by mountainous terrain have led to a marked pattern of cultivated land fragmentation. Consequently, targeted research holds significant importance for ensuring food security and achieving cultivated land comprehensive governance in karst mountainous areas. The Qixingguan District, situated in northwestern Guizhou Province, exhibits a topography that slopes from southwest to northeast in a terraced descent. The terrain is complex, characterised by extensive mountainous hills and limited plains or basins, with karst landforms such as peak clusters and trough valleys developed throughout. The region's geology is dominated by carbonate rocks and basalt, with pronounced rock desertification, making it a quintessential example of China's ecologically fragile karst areas in the southwest. Furthermore, the spatial distribution of cultivated land presents the characteristics of scattered, small, and fragmented, with sharp contradictions between people and land, providing an ideal case area for the study of cultivated land fragmentation. This study employs 37 townships and sub-districts, encompassing 477 administrative villages within Qixingguan District, as its research units. Drawing upon data from the Third National Land Survey, rural land contract management rights, and farm household surveys datas, it aims to systematically reveal the characteristics, patterns and spatial correlations of cultivated fragmentation across different landform types, accurately identify the key influencing factors and their respective intensities, and thereby provide a scientific basis for the comprehensive governance of cultivated land fragmentation in karst mountainous areas. The research methodology primarily comprises, (1) Delineating landform types within the study area by integrating regional topographic characteristics and relevant classification standards; (2) Constructing an evaluation index system for cultivated land fragmentation from dual perspectives of landscape and ownership to comprehensively characterise fragmentation status; (3) Employing spatial autocorrelation analysis to reveal the spatial distribution patterns and correlation characteristics of cultivated land fragmentation; (4) Utilising multiple linear regression models and geographic detector models to quantify the intensity of influence and regional variations of each factor on landscape fragmentation and ownership fragmentation. The research findings indicate, (1) The landforms of Qixingguan District can be categorised into four types: low mountain valley slopes, medium mountain hilly terraces, medium mountain valley terraces, and medium mountain hilly slopes. The number of administrative villages covered by each landform type is 49, 162, 81, and 185, respectively; (2) Qixingguan District exhibits high levels of cultivated land fragmentation, with ownership fragmentation exceeding landscape fragmentation. Distinct high-value and low-value zones are discernible. Administrative villages in medium-mountain valley slopes and medium-mountain hilly slopes demonstrate stronger cultivated land resource scale and spatial aggregation, while those in low-mountain river valley slopes and medium-mountain hilly slopes exhibit greater operational intensity; (3) The spatial distribution of cultivated land fragmentation in Qixingguan District exhibits correlations, with distinct combinations of topographic features: low mountain river valley slopes show high landscape fragmentation and low ownership fragmentation; medium mountain valley slopes exhibit high ownership fragmentation and low landscape fragmentation; medium mountain upland terraces demonstrate high levels of both landscape and ownership fragmentation; while medium mountain upland slopes display relatively low levels of both; (4) Landscape fragmentation is primarily influenced by the proportion of construction land, elevation, and terrain dissection. Specifically: low mountain river valley slopes and medium mountain hilly slopes show positive correlations with slope gradient; medium mountain slope valleys correlate positively with terrain dissection; while medium mountain hilly terraces are driven by the proportion of construction land. The key drivers of ownership fragmentation are the proportion of construction land and rural population density. Among these, rural population density is the core driver for low mountain river valley slopes, medium mountain slope valleys, and medium mountain hilly slopes, while medium mountain terraced land primarily exhibits a positive correlation with per capita cultivated land area. Based on the distinctive characteristics of cultivated land resources across different topographic zones, this study proposes a comprehensive approach to cultivated land resource goverance encompassing landscape, land tenure, and land use practices. This framework provides scientific support for optimising cultivated land distribution, advancing land transfer and consolidation, and enhancing agricultural scale in Qixingguan District and karst mountainous areas of Southwest China. It holds significant practical value for safeguarding regional food security and propelling agricultural modernisation.
- farmland fragmentation /
- landform types /
- spatial pattern /
- influencing factors /
- Qixingguan District in Guizhou /
- karst mountainous area

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图 1 研究区地理位置

Figure 1. Geographical location of the study area

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图 2 七星关区各乡镇耕地面积与地块数

Figure 2. Cultivated land area and the number of plots in each township in Qixingguan District

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图 3 七星关区地貌类型空间分布图

Figure 3. Spatial distribution map of landform types in Qixingguan District

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图 4 不同地貌区耕地细碎化空间格局及其占比情况

Figure 4. Spatial pattern and proportion of cultivated land fragmentation in different geomorphic zones

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图 5 不同地貌区耕地细碎化分异特征

Figure 5. Differentiation characteristics of cultivated land fragmentation in different geomorphic zones

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图 6 不同地貌分区综合细碎化模式的空间格局

Figure 6. Spatial pattern of integrated fragmentation patterns in different geomorphic zones

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图 7 耕地细碎化指数空间关联局部指标（LISA）集聚图

Figure 7. LISA cluster map of cultivated land fragmentation

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图 8 耕地细碎化各影响因素Beta值与q值

Figure 8. Beta values and q values of each influencing factor on cultivated land fragmentation

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表 1 地貌类型划分标准

Table 1. Classification criteria for landform types

地貌类型	海拔/m	地形起伏度/m	坡度/°
坝地	−	＜50	＜8
丘陵	−	[50，200)	[8，30)
低山	[500，1000)	≥200	≥30
中山	[1000，2500)	−	−

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表 2 七星关区耕地景观和权属细碎化评价指标体系

Table 2. Evaluation index system for cultivated landscape and tenure fragmentation in Qixingguan district

指标体系	指标层	计算公式	指标说明	指标性质	权重
景观细碎化	耕地斑块密度	$ PD=\dfrac{n}{A} $	表征单位面积的耕地斑块数	+	0.2118
	耕地斑块破碎度	$ FN=\dfrac{(n-1)}{{N}_{c}} $	表征耕地整体上的细碎化程度	+	0.5208
	边界密度	$ ED=\dfrac{E}{A} $	表征耕地地块的分割程度	+	0.1400
	面积加权形状指数	$ AWMSI={\displaystyle\sum}_{i=1}^{n}\left[\left(\dfrac{0.25{P}_{i}}{\sqrt{{a}_{i}}}\right)\left(\dfrac{{a}_{i}}{A}\right)\right] $	表征耕地地块的形状状况	+	0.1275
权属细碎化	地块数量	$ NP=N $	表征研究范围内耕地地块数量	+	0.3118
	承包农户户均地块数	$ PCN=\dfrac{N}{{F}_{i}} $	表征已经确权颁证的承包户拥有的耕作地块平均数量	+	0.2097
	现有农户户均地块数	$ PNC=\dfrac{N}{F} $	表征研究区内现有农户拥有的耕作地块平均数量	+	0.2395
	户均耕地面积	$ HCA=\dfrac{LA}{F} $	表征研究区内单个农户拥有的耕作地块面积	−	0.0880
	平均地块面积	$ PCA=\dfrac{LA}{N} $	表征研究区内耕作地块的平均面积	−	0.1510
注：$ A $为研究单元内的三调耕地总面积；$ {N}_{c} $为研究单元内耕地总面积与最小耕地斑块面积的比值；$ E $为研究单元内斑块的总周长; $ {P}_{i} $为斑块周长；$ {a}_{i} $为斑块面积；$ N $为耕作地块数；$ {F}_{i} $为二轮承包确权总户数；$ F $为2021年末统计的评价单元内实际总户数；$ LA $为研究单元内的农村土地承包经营权确权的耕地总面积；+表示指标为正向指标，即值越大，耕地细碎化程度越高；–表示指标为负向指标，情况则与正向指标相反。

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表 3 耕地细碎化影响因素

Table 3. Factors influencing cultivated land fragmentation

影响因素类别	影响因素名称	单位	标志
自然环境	高程	m	x₁
	坡度	°	x₂
	地形切割度	m	x₃
	石漠化程度	/	x₄
社会经济发展	道路面积	km²	x₅
	建设用地比例	%	x₆
	农村人口密度	人·hm⁻²	x₇
	人均耕地面积	hm²·人⁻¹	x₈

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表 4 不同地貌分区行政村个数及占比情况

Table 4. Number and proportion of administrative villages by different geomorphic divisions

细碎化	地貌分区	低度	较低度	中度	较重度	重度
景观细碎化	低山河谷坡地	5（10.20）	10（20.41）	15（30.61）	14（28.57）	5（10.20）
	中山丘陵坝地	47（29.01）	47（29.01）	36（22.22）	21（12.96）	11（6.79）
	中山坡谷地	46（56.79）	25（30.86）	9（11.11）	1（1.23）	0（0.00）
	中山丘陵坡地	84（45.41）	65（35.14）	28（15.14）	7（3.78）	1（0.54）
		182（38.16）	147（30.82）	88（18.45）	43（9.22）	17（3.35）
权属细碎化	低山河谷坡地	10(20.41)	11(22.45)	11(22.45)	16(32.65)	1(2.04)
	中山丘陵坝地	21(12.96)	47(29.01)	41(25.31)	32(19.75)	21(12.96)
	中山坡谷地	3(3.70)	8(9.88)	20(24.69)	19(23.46)	31(38.27)
	中山丘陵坡地	22(11.89)	56(30.27)	70(37.84)	30(37.84)	7(3.78)
		55(11.74)	122(25.58)	142(29.77)	97(20.34)	60(12.58)

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表 5 不同地貌分区行政村个数及占比情况

Table 5. Number and proportion of administrative villages by different geomorpic divisions

	景观低度细碎– 权属低度细碎	景观低度细碎– 权属高度细碎	景观中度细碎– 权属中度细碎	景观高度细碎– 权属低度细碎	景观高度细碎– 权属高度细碎
低山河谷坡地	3（10.34）	8（27.57）	2（6.90）	12（41.38）	4（13.79）
中山丘陵坝地	35（35.35）	29（29.29）	12（12.12）	18（18.18）	5（5.05）
中山坡谷地	11（20.37）	41（75.93）	1（1.85）	0（0.00）	1（1.85）
中山丘陵坡地	62（57.94）	32（29.91）	10（9.35）	3（2.80）	0（0.00）
总计	111（23.27）	110（23.06）	25（5.24）	33（6.92）	10（2.10）

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表 6 全局自相关分析报表

Table 6. Global spatial autocorrelation analysis

类型	景观指数	权属指数
全局Moran I 指数	0.2882	0.5481
预期指数	−0.0021	−0.0021
检验统计量z	10.6733	19.9652
p-value	0	0

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表 7 地貌分区与耕地细碎化指数空间自相关对应的行政村数量

Table 7. Number of administrative villages corresponding to the spatial autocorrelation between geomorphic divisions and cultivated land fragmentation index

类型	景观指数				权属指数
类型	低山河谷坡地	中山丘陵坝地	中山坡谷地	中山丘陵坡地	低山河谷坡地	中山丘陵坝地	中山坡谷地	中山丘陵坡地
H-H	15	27	1	0	2	43	36	6
H-L	0	1	3	3	1	2	0	0
L-L	1	8	30	27	11	42	5	32
L-H	4	9	0	3	1	3	3	3

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