Modeling of spatial pattern and influencing factors of cultivated land quality in Henan Province based on spatial big data

被引:13
|
作者
Wang, Hua [1 ]
Zhu, Yuxin [1 ]
Wang, Jinghao [1 ]
Han, Hubiao [1 ]
Niu, Jiqiang [2 ]
Chen, Xueye [3 ]
机构
[1] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou, Peoples R China
[2] Xinyang Normal Univ, Key Lab Synergist Prevent Water & Soil Environm P, Xinyang, Peoples R China
[3] Minist Nat Resources, Key Lab Urban Land Resources Monitoring & Simulat, Shenzhen, Peoples R China
来源
PLOS ONE | 2022年 / 17卷 / 04期
基金
中国国家自然科学基金;
关键词
COVER CHANGE; SOUTH; CHINA;
D O I
10.1371/journal.pone.0265613
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I approximate to 0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I approximate to-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province.
引用
收藏
页数:17
相关论文
共 50 条
  • [21] Spatial variation of soil quality and pollution assessment of heavy metals in cultivated soils of Henan Province, China
    Cheng, Qingli
    Guo, Yujie
    Wang, Wenlin
    Hao, Shilong
    [J]. CHEMICAL SPECIATION AND BIOAVAILABILITY, 2014, 26 (03): : 184 - 190
  • [22] Spatial-temporal differentiation pattern and influencing factors of land economic density at the township scale in Zhejiang Province
    Ma, Fangfang
    Hu, Yiping
    Ding, Zhiwei
    [J]. PLOS ONE, 2024, 19 (05):
  • [23] Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data
    Hua, Wang
    Yuxin, Zhu
    Mengyu, Wang
    Jiqiang, Niu
    Xueye, Chen
    Yang, Zhang
    [J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2021, 15 (02): : 767 - 785
  • [24] Temporal-spatial pattern and driving factors of cultivated land use transition at country level in Shaanxi province, China
    Chen, Zhe
    Li, Xiaojing
    Xia, Xianli
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2022, 194 (05)
  • [25] Temporal-spatial pattern and driving factors of cultivated land use transition at country level in Shaanxi province, China
    Zhe Chen
    Xiaojing Li
    Xianli Xia
    [J]. Environmental Monitoring and Assessment, 2022, 194
  • [26] Spatial Pattern and Influencing Factors of Agricultural Leading Enterprises in Heilongjiang Province, China
    Wang, Tianli
    Ma, Yanji
    Luo, Siqi
    [J]. AGRICULTURE-BASEL, 2023, 13 (11):
  • [27] Identifying spatial and temporal dynamics and driving factors of cultivated land fragmentation in Shaanxi province
    Zhao, Yu
    Feng, Qi
    [J]. AGRICULTURAL SYSTEMS, 2024, 217
  • [28] Diagnosis of rural poverty pattern and its influencing factors in Yunnan province based on spatial econometric model
    Zhang, Bosheng
    Yang, Zisheng
    [J]. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2019, 35 (07): : 276 - 287
  • [29] Spatial pattern of cultivated land fragmentation in mainland China: Characteristics, dominant factors, and countermeasures
    Ye, Sijing
    Ren, Shuyi
    Song, Changqing
    Du, Zhenbo
    Wang, Kuangxu
    Du, Bin
    Cheng, Feng
    Zhu, Dehai
    [J]. LAND USE POLICY, 2024, 139
  • [30] Spatial-Temporal Pattern and Driving Factors of Carbon Emission Intensity of Main Crops in Henan Province
    Li, Zhi
    Cao, Tingting
    Sun, Zhongye
    [J]. SUSTAINABILITY, 2022, 14 (24)