Influencing factors of hog production based on geographical detection, OLS, and GWR-a case study in Jilin Province
文献类型：期刊论文
| 作者 | Yan, Bojie5; Li, Yaxing4; Shi, Wenjiao2,3; Yan, Jingjie1 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2025-04-01 |
| 卷号 | 231页码:109974 |
| 关键词 | Hog production Geographic detector Ordinary least squares model Geographically weighted regression model |
| ISSN号 | 0168-1699 |
| DOI | 10.1016/j.compag.2025.109974 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | China is the world's largest producer and consumer of pork. Thus, identifying the dominant factors influencing hog production is important to promote the sustainable development of hog production. However, few studies have analyzed the dominant factors influencing and their degree of influence on hog production. This study investigated the spatial distribution of hog production in Jilin Province at county scale using a geographic information system (GIS). Then, the dominant factors influencing hog production were analyzed by geographical detection. Moreover, the degree of influence of dominant factors on hog production was measured using the ordinary least squares (OLS) model and geographically weighted regression (GWR) model. Finally, the spatial heterogeneity of dominant factors influencing hog production was measured using the GWR model. Results indicated that the total, largest, and least pork in 2019 were 1,070,409 t in Jilin Province, 99,810 t in Lishu County, and 637 t in Korean Autonomous County of Changbai, respectively. The results also showed that the total output of agriculture, forestry, animal husbandry, and fishery; employees in the primary industry; vegetable yield; cultivated land area; elevation; maize yield, and rural population were dominant factors influencing hog production. Furthermore, the results demonstrated that the accuracy of the regression results of the GWR model was better than that of the OLS model. The results also indicated that the GWR model had the advantage of spatially non-stationary detection, which can accurately explain the relationship between hog production and its influencing factors in local fine detection and significantly improve the accuracy of regression results. Such results could improve spatial layout planning of hog production, and structural adjustment of animal husbandry. |
| URL标识 | 查看原文 |
| WOS关键词 | WEIGHTED REGRESSION ; QUALITY |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001408042400001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212283]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Li, Yaxing |
| 作者单位 | 1.Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 4.Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China; 5.Minjiang Univ, Coll Geog & Oceanog, Fuzhou 350108, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yan, Bojie,Li, Yaxing,Shi, Wenjiao,et al. Influencing factors of hog production based on geographical detection, OLS, and GWR-a case study in Jilin Province[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2025,231:109974. |
| APA | Yan, Bojie,Li, Yaxing,Shi, Wenjiao,&Yan, Jingjie.(2025).Influencing factors of hog production based on geographical detection, OLS, and GWR-a case study in Jilin Province.COMPUTERS AND ELECTRONICS IN AGRICULTURE,231,109974. |
| MLA | Yan, Bojie,et al."Influencing factors of hog production based on geographical detection, OLS, and GWR-a case study in Jilin Province".COMPUTERS AND ELECTRONICS IN AGRICULTURE 231(2025):109974. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。