{"title":"利用数据挖掘技术探索水土保持措施的决定因素","authors":"D. Fu-qiang, Liu Gang-cai","doi":"10.1109/IFCSTA.2009.215","DOIUrl":null,"url":null,"abstract":"This study explores different socioeconomic and environmental factors influencing the adoption of SWC (soil and water conservation) measures. As a consortium of national and international institutions, WOCAT (World Overview of Conservation Approaches and Technologies) has developed a database system to record specific details about the environmental and socioeconomic setting in soil and water management. Factor analysis, as a data mining technique, was used in the present work and the results show that adoption of SWC measures were influenced primarily by four factors including economic efficiency, human setting and land use, natural environment and soil quality. The first factor named as economic efficiency explained 18.888% of the total variance which comprised of variables like short term returns to establishment, long term returns to establishment, short term returns to maintenance and long term returns to maintenance. The second factor called human setting and land use explained 17.546% (comprised of variables like land ownership, off-farm income, market orientation, wealth, production subsidy and size of crop land per household) and the third factor called natural environment explained 14.945% (comprised of variables like average annual rainfall and slope) of the total variance, respectively. Furthermore, forth factor called soil quality explained 13.483% of the total variance comprised of soil fertility and topsoil.","PeriodicalId":256032,"journal":{"name":"2009 International Forum on Computer Science-Technology and Applications","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the Determinants of Soil and Water Conservation Measures with Data Mining Techniques\",\"authors\":\"D. Fu-qiang, Liu Gang-cai\",\"doi\":\"10.1109/IFCSTA.2009.215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study explores different socioeconomic and environmental factors influencing the adoption of SWC (soil and water conservation) measures. As a consortium of national and international institutions, WOCAT (World Overview of Conservation Approaches and Technologies) has developed a database system to record specific details about the environmental and socioeconomic setting in soil and water management. Factor analysis, as a data mining technique, was used in the present work and the results show that adoption of SWC measures were influenced primarily by four factors including economic efficiency, human setting and land use, natural environment and soil quality. The first factor named as economic efficiency explained 18.888% of the total variance which comprised of variables like short term returns to establishment, long term returns to establishment, short term returns to maintenance and long term returns to maintenance. The second factor called human setting and land use explained 17.546% (comprised of variables like land ownership, off-farm income, market orientation, wealth, production subsidy and size of crop land per household) and the third factor called natural environment explained 14.945% (comprised of variables like average annual rainfall and slope) of the total variance, respectively. Furthermore, forth factor called soil quality explained 13.483% of the total variance comprised of soil fertility and topsoil.\",\"PeriodicalId\":256032,\"journal\":{\"name\":\"2009 International Forum on Computer Science-Technology and Applications\",\"volume\":\"111 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Forum on Computer Science-Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IFCSTA.2009.215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Forum on Computer Science-Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IFCSTA.2009.215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploring the Determinants of Soil and Water Conservation Measures with Data Mining Techniques
This study explores different socioeconomic and environmental factors influencing the adoption of SWC (soil and water conservation) measures. As a consortium of national and international institutions, WOCAT (World Overview of Conservation Approaches and Technologies) has developed a database system to record specific details about the environmental and socioeconomic setting in soil and water management. Factor analysis, as a data mining technique, was used in the present work and the results show that adoption of SWC measures were influenced primarily by four factors including economic efficiency, human setting and land use, natural environment and soil quality. The first factor named as economic efficiency explained 18.888% of the total variance which comprised of variables like short term returns to establishment, long term returns to establishment, short term returns to maintenance and long term returns to maintenance. The second factor called human setting and land use explained 17.546% (comprised of variables like land ownership, off-farm income, market orientation, wealth, production subsidy and size of crop land per household) and the third factor called natural environment explained 14.945% (comprised of variables like average annual rainfall and slope) of the total variance, respectively. Furthermore, forth factor called soil quality explained 13.483% of the total variance comprised of soil fertility and topsoil.