{"title":"使用混合DEA和机器学习方法的女性家禽养殖户效率管理:以喜马拉雅北孟加拉邦shg为基础的生产为例","authors":"A. Nandy, Poulomi Chaki Nandi, Mousumi Chatterjee","doi":"10.1177/09722629231159708","DOIUrl":null,"url":null,"abstract":"Data envelopment analysis (DEA) offers a linear programming approach to evaluate the efficiency in diverse fields of production and service sectors with wide utilization for effective performance measurement operations. DEA has found its useful applications in agriculture to examine optimal resource use for sustainable consumption. The popularly used two-step process where DEA is employed along with a regression model to explain the impact of exogenous factors on efficiency has been employed in past studies. This article aims to combine the conventional DEA approach with machine learning (ML) models for establishing a novel alternative method for performance measurement as well as the prediction of key exogenous factors affecting the efficiency of the women self-help groups (SHGs) led poultry farmers in sub-Himalayan North Bengal surrounding the Siliguri region of Darjeeling district. For this purpose, in the first step, DEA was employed to measure the efficiency of 80 women poultry farmers belonging to 20 SHGs and in the second step, the state-of-the-art random forest (RF) technique has been employed to predict the most important efficiency influencing variables. The results suggested inefficiencies among the SHG women with wide variation between the efficient and inefficient units. The use of the RF model predicted important factors such as the role of non-governmental organizations, educational level, financial inclusion, landholding and poultry rearing experience in years to impact the efficiency of these women farmers. As a result, the hybrid DEA-ML approach is useful to tackle ill adversities in poultry production that may help the women SHGs to develop agriculture-based income.","PeriodicalId":44860,"journal":{"name":"Vision-The Journal of Business Perspective","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficiency Management of Women Poultry Farmers Using Hybrid DEA and Machine Learning Approach: A Case of SHG-based Production in Sub-Himalayan North Bengal\",\"authors\":\"A. Nandy, Poulomi Chaki Nandi, Mousumi Chatterjee\",\"doi\":\"10.1177/09722629231159708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data envelopment analysis (DEA) offers a linear programming approach to evaluate the efficiency in diverse fields of production and service sectors with wide utilization for effective performance measurement operations. DEA has found its useful applications in agriculture to examine optimal resource use for sustainable consumption. The popularly used two-step process where DEA is employed along with a regression model to explain the impact of exogenous factors on efficiency has been employed in past studies. This article aims to combine the conventional DEA approach with machine learning (ML) models for establishing a novel alternative method for performance measurement as well as the prediction of key exogenous factors affecting the efficiency of the women self-help groups (SHGs) led poultry farmers in sub-Himalayan North Bengal surrounding the Siliguri region of Darjeeling district. For this purpose, in the first step, DEA was employed to measure the efficiency of 80 women poultry farmers belonging to 20 SHGs and in the second step, the state-of-the-art random forest (RF) technique has been employed to predict the most important efficiency influencing variables. The results suggested inefficiencies among the SHG women with wide variation between the efficient and inefficient units. The use of the RF model predicted important factors such as the role of non-governmental organizations, educational level, financial inclusion, landholding and poultry rearing experience in years to impact the efficiency of these women farmers. As a result, the hybrid DEA-ML approach is useful to tackle ill adversities in poultry production that may help the women SHGs to develop agriculture-based income.\",\"PeriodicalId\":44860,\"journal\":{\"name\":\"Vision-The Journal of Business Perspective\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vision-The Journal of Business Perspective\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/09722629231159708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vision-The Journal of Business Perspective","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09722629231159708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Efficiency Management of Women Poultry Farmers Using Hybrid DEA and Machine Learning Approach: A Case of SHG-based Production in Sub-Himalayan North Bengal
Data envelopment analysis (DEA) offers a linear programming approach to evaluate the efficiency in diverse fields of production and service sectors with wide utilization for effective performance measurement operations. DEA has found its useful applications in agriculture to examine optimal resource use for sustainable consumption. The popularly used two-step process where DEA is employed along with a regression model to explain the impact of exogenous factors on efficiency has been employed in past studies. This article aims to combine the conventional DEA approach with machine learning (ML) models for establishing a novel alternative method for performance measurement as well as the prediction of key exogenous factors affecting the efficiency of the women self-help groups (SHGs) led poultry farmers in sub-Himalayan North Bengal surrounding the Siliguri region of Darjeeling district. For this purpose, in the first step, DEA was employed to measure the efficiency of 80 women poultry farmers belonging to 20 SHGs and in the second step, the state-of-the-art random forest (RF) technique has been employed to predict the most important efficiency influencing variables. The results suggested inefficiencies among the SHG women with wide variation between the efficient and inefficient units. The use of the RF model predicted important factors such as the role of non-governmental organizations, educational level, financial inclusion, landholding and poultry rearing experience in years to impact the efficiency of these women farmers. As a result, the hybrid DEA-ML approach is useful to tackle ill adversities in poultry production that may help the women SHGs to develop agriculture-based income.
期刊介绍:
Vision-The Journal of Business Perspective is a quarterly peer-reviewed journal of the Management Development Institute, Gurgaon, India published by SAGE Publications. This journal contains papers in all functional areas of management, including economic and business environment. The journal is premised on creating influence on the academic as well as corporate thinkers. Vision-The Journal of Business Perspective is published in March, June, September and December every year. Its targeted readers are researchers, academics involved in research, and corporates with excellent professional backgrounds from India and other parts of the globe. Its contents have been often used as supportive course materials by the academics and corporate professionals. The journal has been providing opportunity for discussion and exchange of ideas across the widest spectrum of scholarly opinions to promote theoretical, empirical and comparative research on problems confronting the business world. Most of the contributors to this journal range from the outstanding and the well published to the upcoming young academics and corporate functionaries. The journal publishes theoretical as well as applied research works.