中国北方农牧交错带耕地时空分异及驱动因素

doi:10.12118/j.issn.1000–6060.2021.153

干旱区地理 ›› 2022, Vol. 45 ›› Issue (1): 153-163.doi: 10.12118/j.issn.1000–6060.2021.153

中国北方农牧交错带耕地时空分异及驱动因素

陶泽涪^1,²(),王世清^1,²,孙丕苓^1,^2,³(),李凯迪^1,²,田文^1,²,韩潇潇^1,²

1.曲阜师范大学地理与旅游学院,山东日照 276826
2.日照市国土空间规划与生态建设重点实验室,山东日照 276826
3.中国农业大学土地科学与技术学院,北京海淀 100193

收稿日期:2021-04-06 修回日期:2021-07-30 出版日期:2022-01-25 发布日期:2022-01-21
通讯作者: 孙丕苓
作者简介:陶泽涪（2000-）,男,本科生,主要从事土地变化及其生态效应研究. E-mail: tzf0824@126.com
基金资助:
教育部人文社会科学研究青年基金项目(19YJCZH144);山东省自然科学青年基金项目(ZR2019QD006)

Spatio-temporal differentiation and driving factors of cropland in the agro-pastoral ecotone of northern China

TAO Zefu^1,²(),WANG Shiqing^1,²,SUN Piling^1,^2,³(),LI Kaidi^1,²,TIAN Wen^1,²,HAN Xiaoxiao^1,²

1. School of Geography and Tourism, Qufu Normal University, Rizhao 276826, Shandong, China
2. Rizhao Key Laboratory of Territory Spatial Planning and Ecological Construction, Rizhao 276826, Shandong, China
3. College of Land Science and Technology, China Agriultural University, Beijing 100193, China

Received:2021-04-06 Revised:2021-07-30 Online:2022-01-25 Published:2022-01-21
Contact: Piling SUN

摘要/Abstract

摘要：

耕地是保障国家粮食安全的重要载体。以中国北方农牧交错带为例,基于2000、2010、2020年3期遥感影像数据,综合运用转移矩阵、空间自相关分析、空间计量模型等方法系统分析了2000—2020年中国北方农牧交错带耕地时空分异特征及影响因素。研究结果表明：（1） 2000—2020年中国北方农牧交错带耕地面积由268959.49 km²增加到269471.62 km²,耕地变化主要表现为耕地与林地、草地之间的相互转化。在垂直方向上,耕地变化主要发生于海拔1000~2000 m以及坡度0~5°的地区。（2） 2000—2020年中国北方农牧交错带耕地分布具有明显的空间集聚特征,整体呈现东高西低、北高南低的空间格局。耕地主要分布于研究区东北部的河谷平原及中西部的黄河谷地,地形较为复杂的高原边缘山区、丘陵沟壑区耕地分布较少。（3） 2000—2020年气候、地形等自然环境因素是中国北方农牧交错带耕地空间分布的基础条件,社会经济因素和区域政策因素是耕地时空分异的重要推动因素。其中,年均气温、道路密度以及生态退耕政策、土地利用管控政策等对耕地时空分异的影响为正,坡度、经济密度对耕地时空分异的影响为负。

关键词: 耕地, 时空分布, 驱动因素, 中国北方农牧交错带

Abstract:

Cropland plays an important role in guaranteeing the food security of China. On the basis of remote sensing images from 2000, 2010, and 2020, transfer matrix, spatial autocorrelation analysis, and spatial statistics model were used to analyze the spatiotemporal differentiation characteristics of cropland and its driving factors during the period of 2000 to 2020 in the agro-pastoral ecotone of northern China. The results showed the following: (1) From 2000 to 2020, the area of cropland increased from 268959.49 km² to 269471.62 km² in the agro-pastoral ecotone of northern China. The changes in cropland exhibited transformations between cropland and forest land, and grassland, which were mainly distributed in the regions with an altitude of 1000-2000 m and a slope of 0-5°. (2) Obvious spatial agglomeration characteristics of the distribution of cropland were observed during the period of 2000 to 2020 in the agro-pastoral ecotone of northern China. Meanwhile, the spatial pattern of cropland was higher in the east than in the west and was higher in the north than in the south. The cropland was mainly distributed in the junction areas among Jilin, Liaoning, and Inner Mongolia, as well as the river valleys within Gansu and Ningxia, where a plate with a low altitude is found. However, the cropland was scattered in the edge mountains of the plateau and the loess hilly and gully regions, where the terrain was complex, including Huangzhong County, Jianzha County, Fenxi County, Xiangning County, Loufan County, and Gujiao County. (3) From 2000 to 2020, the spatiotemporal differentiation of cropland in the agro-pastoral ecotone of northern China resulted from the interaction of the natural environment, socioeconomic factors, and regional policy factors. The national environment was the primary factor in the spatial distribution of cropland, and socioeconomic factors and regional policy factors were the key driving factors in the spatiotemporal differentiation of cropland in the agro-pastoral ecotone of northern China. The factors of annual average temperature, road density, ecological conversion policy, and land use control system had a positive influence on the spatiotemporal differentiation of cropland, whereas the factors of slope and economic density had a negative impact. This study will provide a scientific reference for cropland protection and sustainable development in the agro-pastoral ecotone of northern China.

Key words: cropland, spatiotemporal pattern, driving factors, agro-pastoral ecotone of northern China

陶泽涪,王世清,孙丕苓,李凯迪,田文,韩潇潇. 中国北方农牧交错带耕地时空分异及驱动因素[J]. 干旱区地理, 2022, 45(1): 153-163.

TAO Zefu,WANG Shiqing,SUN Piling,LI Kaidi,TIAN Wen,HAN Xiaoxiao. Spatio-temporal differentiation and driving factors of cropland in the agro-pastoral ecotone of northern China[J]. Arid Land Geography, 2022, 45(1): 153-163.

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参考文献 30

[1]	刘纪远, 匡文慧, 张增祥, 等. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局[J]. 地理学报, 2014, 69(1):3-14.
	[Liu Jiyuan, Kuang Wenhui, Zhang Zengxiang, et al. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s[J]. Acta Geographica Sinica, 2014, 69(1):3-14. ]
[2]	刘超, 霍永伟, 许月卿, 等. 生态退耕前后张家口市耕地变化及影响因素识别[J]. 自然资源学报, 2018, 33(10):1806-1820.
	[Liu Chao, Huo Yongwei, Xu Yueqing, et al. Changes in cultivated land and influencing factors before and after the implementation of grain for green project in Zhangjiakou City[J]. Journal of Natural Resources, 2018, 33(10):1806-1820. ]
[3]	Chen X, Jiang L, Zhang G L, et al. Green-depressing cropping system: A referential land use practice for fallow to ensure a harmonious human-land relationship in the farming-pastoral ecotone of northern China[J]. Land Use Policy, 2021, 100:10497, doi: 10.1016/J.LANDUSEPOL.2020.104917. doi: 10.1016/J.LANDUSEPOL.2020.104917
[4]	彭建, 杜悦悦, 刘焱序, 等. 从自然区划、土地变化到景观服: 发展中的中国综合自然地理学[J]. 地理研究, 2017, 36(10):1819-1833. doi: 10.11821/dlyj201710001
	[Peng Jian, Du Yueyue, Liu Yanxu, et al. From natural regionalization, land change to landscape service: The development of integrated physical geography in China[J]. Geographical Research, 2017, 36(10):1819-1833. ] doi: 10.11821/dlyj201710001
[5]	Wang C, Ouyang H, Maclaren V, et al. Evaluation of the economic and environmental impact of converting cropland to forest: A case study in Dunhua County, China[J]. Journal of Environmental Management, 2007, 85(3):746-756. pmid: 17188798
[6]	Zhu Y Y, Zhou X Q, Gan Y L, et al. Spatio-temporal differentiation and driving mechanism of the resource curse of the cultivated land in main agricultural production regions: A case study of Jianghan Plain, central China[J]. International Journal of Environmental Research and Public Health, 2021, 18(3):858-858. doi: 10.3390/ijerph18030858
[7]	Li F, Zhou M J, Shao J Q, et al. Maize, wheat and rice production potential changes in China under the background of climate change[J]. Agricultural Systems, 2020, 182:102853, doi: 10.1016/j.agsy.2020.102853. doi: 10.1016/j.agsy.2020.102853
[8]	Sannigrahi S. Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models[J]. Ecological Modelling, 2017, 356:73-90. doi: 10.1016/j.ecolmodel.2017.03.003
[9]	Uisso A, Tanrvermi H. Driving factors and assessment of changes in the use of arable land in Tanzania[J]. Land Use Policy, 2021, 104(2):105359, doi: 10.1016/J.LANDUSEPOL.2021.105359. doi: 10.1016/J.LANDUSEPOL.2021.105359
[10]	Liu Z J, Liu Y S, Liu Y R. Anthropogenic contributions dominate trends of vegetation cover change over the farming-pastoral ecotone of northern China[J]. Ecological Indicators, 2018, 95(1):370-378. doi: 10.1016/j.ecolind.2018.07.063
[11]	张兆彤, 李源, 刘国宏, 等. 新疆昌吉州耕地土壤有机质空间变异动态研究[J]. 地理研究, 2021, 40(3):643-651. doi: 10.11821/dlyj020200915
	[Zhang Zhaotong, Li Yuan, Liu Guohong, et al. Dynamic research on spatial variability of cultivated soil organic matter in Changji Prefecture, Xinjiang[J]. Geographical Research, 2021, 40(3):643-651. ] doi: 10.11821/dlyj020200915
[12]	Kadiri W, Fasina A S, Babalola T S. Soil organic carbon concentration and stock of arable land use of two agro-ecological zones of Nigeria[J]. Journal of the Saudi Society of Agricultural Sciences, 2021, 20(3):180-189. doi: 10.1016/j.jssas.2021.01.004
[13]	Prvlie R, Patriche C, Borrelli P, et al. Arable lands under the pressure of multiple land degradation processes: A global perspective[J]. Environmental Research, 2021, 194:110697, doi: 10.1016/J.ENVRES.2020.110697. doi: 10.1016/J.ENVRES.2020.110697
[14]	Cao Q, Wu J G, Yu D Y, et al. Regional landscape futures to moderate projected climate change: A case study in the agro-pastoral transitional zone of north China[J]. Regional Environmental Change, 2020, 20(2):1847-1856.
[15]	农业部. 农业部关于北方农牧交错带农业结构调整的指导意见[J]. 中华人民共和国农业部公报, 2016(12):7-12.
	[Ministry of Agriculture. Guiding opinions of Ministry of Agriculture on agricultural structure adjustment in northern farming and pastoral ecotone[J]. Gazette of the Ministry of Agriculture of the People’s Republic of China, 2016(12):7-12. ]
[16]	王介勇, 刘彦随. 三亚市土地利用/覆被变化及其驱动机制研究[J]. 自然资源学报, 2009, 24(8):1458-1466.
	[Wang Jieyong, Liu Yansui. Land use and cover change and its driving forces in Sanya[J]. Journal of Natural Resources, 2009, 24(8):1458-1466. ]
[17]	蒋姣, 孙哲, 赵思远, 等. 淮安市耕地时空变化特征及其驱动因素[J]. 农业工程, 2020, 10(12):63-69.
	[Jiang Jiao, Sun Zhe, Zhao Siyuan, et al. Spatial and temporal variation characteristics of cultivated land and its driving factors in Huai’an City[J]. Agricultural Engineering, 2020, 10(12):63-69. ]
[18]	张钥, 许端阳, 王子玉, 等. 2000—2015年锡林郭勒盟防风固沙服务功能变化驱动因素分析[J]. 生态学报, 2021, 41(2):603-614.
	[Zhang Yue, Xu Duanyang, Wang Ziyu, et al. The interaction of driving factors for the change of windbreak and sand-fixing service function in Xilingol League between 2000 and 2015[J]. Acta Ecologica Sinica, 2021, 41(2):603-614. ]
[19]	刘敏, 赵翠薇, 施明辉. 贵州山区土地利用变化多尺度空间自相关分析[J]. 农业工程学报, 2012, 28(20):239-246.
	[Liu Min, Zhao Cuiwei, Shi Minghui. Spatial autocorrelation analysis of multi-scale land use change at mountainous areas in Guizhou Province[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(20):239-246. ]
[20]	程浩然, 蒙吉军, 朱利凯. 基于多源地理数据融合的黑河中游土地多功能性时空格局与权衡研究[J]. 干旱区地理, 2021, 44(1):208-220.
	[Cheng Haoran, Meng Jijun, Zhu Likai. Spatial-temporal pattern and trade-offs of land multi-function in the middle reaches of the Heihe River based on multi-source geographic data fussion[J]. Arid Land Geography, 2021, 44(1):208-220. ]
[21]	陶荣, 孔雪松, 陈翠芳, 等. “二调”以来湖北省耕地变化的时空特征及其驱动因子识别[J]. 水土保持研究, 2019, 26(6):290-295.
	[Tao Rong, Kong Xuesong, Chen Cuifang, et al. Spatiotemporal change of cultivated land and its driving factors in Hubei Province since the second land resource survey[J]. Research of Soil and Water Conservation, 2019, 26(6):290-295. ]
[22]	李宏, 王红梅. 黑龙江省耕地数量变化及驱动因子分析[J]. 农机化研究, 2007(12):12-15.
	[Li Hong, Wang Hongmei. An analysis of dynamic change and driving forces of cultivated land in Heilongjiang Province[J]. Journal of Agricultural Mechanization Research, 2007(12):12-15. ]
[23]	邹欣怡, 赵伟, 蒲海霞. 三峡库区重庆段土地利用转型及生态服务功能价值时空分异特征[J]. 水土保持研究, 2021, 28(2):267-275.
	[Zou Xinyi, Zhao Wei, Pu Haixia. Spatial-temporal differentiation of land use transformation and ecological service function value in Chongqing Section of the Three Gorges Reservoir[J]. Research of Soil and Water Conservation, 2021, 28(2):267-275. ]
[24]	曾金伟, 刘辉. 就地城镇化地区耕地细碎化及其影响因素分析——以福建省晋江市为例[J]. 海南大学学报自然科学版, 2020, 38(4):397-408.
	[Zeng Jinwei, Liu Hui. Cultivated land fragmentation and influencing factors in in-situ urbanization areas: A case study on Jinjiang, Fujian[J]. Natural Science Journal of Hainan University, 2020, 38(4):397-408. ]
[25]	付慧, 刘艳军, 孙宏日, 等. 京津冀地区耕地利用转型时空分异及驱动机制[J]. 地理科学进展, 2020, 39(12):1985-1998. doi: 10.18306/dlkxjz.2020.12.003
	[Fu Hui, Liu Yanjun, Sun Hongri, et al. Spatiotemporal characteristics and dynamic mechanism of cultivated land use transition in the Beijing-Tianjin-Hebei region[J]. Progress in Geography, 2020, 39(12):1985-1998. ] doi: 10.18306/dlkxjz.2020.12.003
[26]	孙丕苓, 许月卿, 王数. 环京津贫困带土地利用变化的地形梯度效应分析[J]. 农业工程学报, 2014, 30(14):277-288.
	[Sun Piling, Xu Yueqing, Wang Shu. Terrian gradient effect analysis of land use change in poverty area around Beijing and Tianjin[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(14):277-288. ]
[27]	张翀, 白子怡, 李学梅, 等. 2001—2018年黄土高原植被覆盖人为影响时空演变及归因分析[J]. 干旱区地理, 2021, 44(1):188-196.
	[Zhang Chong, Bai Ziyi, Li Xuemei, et al. Spatio-temporal evolution and attribution analysis of human effects of vegetation cover on the Loess Plateau from 2001 to 2018[J]. Arid Land Geography, 2021, 44(1):188-196. ]
[28]	刘孟竹, 王彦芳, 裴宏伟. 退耕还林(草)背景下中国北方农牧交错带土地利用及碳储量变化[J]. 中国沙漠, 2021, 41(1):174-182.
	[Liu Mengzhu, Wang Yanfang, Pei Hongwei. The changes of land use and carbon storage in the northern farming-pastoral ecotone under the background of returning farmland to forest (grass)[J]. Journal of Desert Research, 2021, 41(1):174-182. ]
[29]	刘孟竹, 张红娟, 王彦芳, 等. 基于土地利用的北方农牧交错带生境质量研究[J]. 水土保持研究, 2021, 28(3):156-162.
	[Liu Mengzhu, Zhang Hongjuan, Wang Yanfang, et al. Characteristics of habitat quality in the agro-pastoral ecotone of northern China based on land uses[J]. Research of Soil and Water Conservation, 2021, 28(3):156-162. ]
[30]	黄越, 程静, 王鹏. 中国北方农牧交错区生态脆弱性时空演变格局与驱动因素——以盐池县为例[J]. 干旱区地理, 2021, 44(4):1175-1185.
	[Huang Yue, Cheng Jing, Wang Peng. Spatiotemporal evolution pattern and driving factors of ecological vulnerability in agro-pastoral region in northern China: A case study of Yanchi County in Ningxia[J]. Arid Land Geography, 2021, 44(4):1175-1185. ]

驱动因素		解释变量	变量解释
自然环境因素	气候	年均降水量/mm	基于ArcGIS空间插值法获取各地域单元的年均降水量
	气候	年均气温/℃	基于ArcGIS空间插值法获取各地域单元的年均气温
	地形	高程/m	基于ArcGIS分析工具获取农牧交错带的数字高程模型（DEM）数据
		坡度/(°)	基于DEM数据提取各地域单元的坡度平均值
		坡向	基于DEM数据提取各栅格单元的坡向
		地形起伏度/m	基于ArcGIS的分区统计获取各地域单元的地形起伏度
社会经济因素		人口密度/10⁴人·km^-2	各县（市、区、旗）的国民经济和社会发展统计公报
		经济密度/10⁸元·km^-2	各县（市、区、旗）的国民经济和社会发展统计公报
		路网密度/km·km^-2	各县（市、区、旗）的国民经济和社会发展统计公报
区域政策因素		生态退耕工程	根据每个格网单元内生态退耕面积是否为0。若不为0,表明实施了生态退耕工程,赋值为1;若为0,表明未实施生态退耕工程,赋值为0。
区域政策因素		土地利用管制	根据每个格网单元内土地利用管制面积是否为0。若不为0,表明实施了土地利用管制,赋值为1;若为0,表明未实施土地利用管制,赋值为0。

高程			坡度
高程分级/m	各高程等级比例/%	耕地变化比例/%	坡度分级/(°)	各坡度等级比例/%	耕地变化比例/%
<200	7.34	7.97	0~5	41.21	50.54
200~500	13.13	17.16	5~10	22.26	23.27
500~1000	17.78	17.28	10~15	15.30	12.72
1000~2000	52.68	53.40	15~25	16.17	11.10
>2000	9.07	4.19	>25	5.06	2.37

年份	Moran’s I	预期指数	方差	Z得分（标准差）
2000	0.294	-0.004	0.001	7.942
2010	0.289	-0.004	0.001	7.795
2020	0.293	-0.004	0.001	7.905

变量		2000年影响因素		2020年影响因素
变量		系数	P	系数	P
常数		228.510	0.376	-104.804	0.968
自然因素	年平均降水量	-0.582	0.515	0.752	0.370
	年平均气温	437.466^***	0.000	426.409^***	0.000
	高程	3.900^**	0.050	-0.516	0.777
	坡度	-5202.760^***	0.000	-5242.810^***	0.000
	坡向	8.811^*	0.075	7.518	0.134
	地形起伏度	16.015	0.278	25.655^*	0.087
社会经济因素	经济密度	-29.564^***	0.000	-2.218^***	0.000
	人口密度	4.878	0.267	-2.616	0.167
	路网密度	6.080^***	0.000	3.175^***	0.000
区域政策因素	生态退耕政策	3396.050^***	0.000	1999.360^**	0.017
区域政策因素	土地利用管制	9171.290^***	0.000	8162.700^***	0.000
拟合系数R²		0.755		0.741
对数似然值		-83642.578		-83752.834
赤池信息准则		12		167530
施瓦茨准则		7517		167613

中国北方农牧交错带耕地时空分异及驱动因素

Spatio-temporal differentiation and driving factors of cropland in the agro-pastoral ecotone of northern China

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[4]	潘雪, 关宇淇, 潘占东, 刘杰, 蔡立群, 董博, 杜健. 干旱区耕地质量等级时空变化及其评价——以西宁市为例[J]. 干旱区地理, 2023, 46(5): 793-803.
[5]	王新友,王玉娇. 耕地撂荒的文献计量分析：脉络和展望[J]. 干旱区地理, 2023, 46(5): 804-813.
[6]	马文江, 白妙琴, 阿迪力·艾合买提, 张德平, 杨忠娜. “双碳”目标下新疆粮食绿色全要素生产率的时空分异及驱动因素分析[J]. 干旱区地理, 2023, 46(12): 2029-2041.
[7]	吴志祥, 张志斌, 赵学伟, 陈龙, 马晓敏, 柴姣. 中国西北地区A级旅游景区时空分布格局及影响因素[J]. 干旱区地理, 2023, 46(12): 2061-2073.
[8]	卓静,胡皓,何慧娟,王智,杨承睿. 陕北黄土高原生态脆弱性时空变异及驱动因素分析[J]. 干旱区地理, 2023, 46(11): 1768-1777.
[9]	杨丽,付玉慧,王姣姣. “一带一路”沿线中国省份旅游信息流时空分布格局及驱动因素分析[J]. 干旱区地理, 2023, 46(10): 1714-1722.
[10]	魏娟娟, 万瑜, 张俊兰, 赵凤环, 李桉孛. 近20 a塔城地区暖区暴雪环流分型及成因分析[J]. 干旱区地理, 2022, 45(6): 1718-1728.
[11]	刘兆旭,刘晶,范子昂. 2005—2020年新疆雷电灾害特征分析[J]. 干旱区地理, 2022, 45(5): 1402-1414.
[12]	买合木提江·维吉旦,玉素甫江·如素力,仇忠丽. 2000—2019年新疆积雪终日时空变化特征[J]. 干旱区地理, 2022, 45(4): 1061-1070.
[13]	杜军,高佳佳,王挺,平措桑旦. 2007—2020年西藏草面温度时空分布特征[J]. 干旱区地理, 2022, 45(4): 1103-1113.
[14]	杨涛,杨莲梅,周鸿奎,余行杰,李元鹏. 新疆北部小时降雪特征及大暴雪天气影响系统研究[J]. 干旱区地理, 2022, 45(3): 725-733.
[15]	龙小翠,刘京,徐仲炜,尉芳. 陕西渭北旱塬区县域耕地时空变化及其对粮食生产影响[J]. 干旱区地理, 2022, 45(2): 423-434.