{"title":"北孟加拉亚喜马拉雅地区的阿迪瓦西水稻种植者是否足够高效?COVID-19 之后机器学习在农场经营中的作用","authors":"Anirban Nandy, Poulomi Chaki Nandi, Mousumi Chatterjee, Shankhadeep Mahato, Souradipt Bandyopadhyay","doi":"10.1177/09721509241250229","DOIUrl":null,"url":null,"abstract":"The COVID-19 pandemic and the consequent lockdown have disrupted the farming communities across the planet; however, the indigenous communities such as the Adivasi people of tea garden areas in India have been hit hard. It becomes more crucial when we talk about marginalized communities. In India, the Adivasi tribe found in the sub-Himalayan North Bengal cultivates folk rice that needs immediate energy-efficient measures as the production has been increased after receiving the geographical indication tag. Energy efficiency estimation often applied a two-step data envelopment analysis model in agricultural production. However, in most of the previous articles, the applications discussed the factors affecting energy use efficiency with rare studies on efficiency prediction. In this article, first, data envelopment analysis was used to estimate the energy efficiency of rice growers, and in the second stage, extreme gradient boosting, a state-of-the-art machine learning algorithm, was employed to derive the key leading efficiency determinants. The findings revealed wide variation among efficient and inefficient rice growers in the first stage and derived the most salient factors predicting energy efficiency in the second stage. The optimal use of energy inputs combined with effective education, better credit delivery mechanism, arable land availability and years of farming experience provided improvement for the future energy use efficiency of the Adivasi farmers. Further, the novel application of extreme gradient boosting as a machine learning algorithm in energy efficiency evaluation suggests decision-making solutions with a prediction accuracy of 80.91%. Moreover, this study aims to assist future researchers in examining and predicting the key leading determinants to affect energy utilization.","PeriodicalId":47569,"journal":{"name":"Global Business Review","volume":"94 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Are the Adivasi Rice Growers of Sub-Himalayan North Bengal Efficient Enough? Role of Machine Learning in Farm Business After COVID-19\",\"authors\":\"Anirban Nandy, Poulomi Chaki Nandi, Mousumi Chatterjee, Shankhadeep Mahato, Souradipt Bandyopadhyay\",\"doi\":\"10.1177/09721509241250229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The COVID-19 pandemic and the consequent lockdown have disrupted the farming communities across the planet; however, the indigenous communities such as the Adivasi people of tea garden areas in India have been hit hard. It becomes more crucial when we talk about marginalized communities. In India, the Adivasi tribe found in the sub-Himalayan North Bengal cultivates folk rice that needs immediate energy-efficient measures as the production has been increased after receiving the geographical indication tag. Energy efficiency estimation often applied a two-step data envelopment analysis model in agricultural production. However, in most of the previous articles, the applications discussed the factors affecting energy use efficiency with rare studies on efficiency prediction. In this article, first, data envelopment analysis was used to estimate the energy efficiency of rice growers, and in the second stage, extreme gradient boosting, a state-of-the-art machine learning algorithm, was employed to derive the key leading efficiency determinants. The findings revealed wide variation among efficient and inefficient rice growers in the first stage and derived the most salient factors predicting energy efficiency in the second stage. The optimal use of energy inputs combined with effective education, better credit delivery mechanism, arable land availability and years of farming experience provided improvement for the future energy use efficiency of the Adivasi farmers. Further, the novel application of extreme gradient boosting as a machine learning algorithm in energy efficiency evaluation suggests decision-making solutions with a prediction accuracy of 80.91%. Moreover, this study aims to assist future researchers in examining and predicting the key leading determinants to affect energy utilization.\",\"PeriodicalId\":47569,\"journal\":{\"name\":\"Global Business Review\",\"volume\":\"94 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Business Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/09721509241250229\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Business Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09721509241250229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
Are the Adivasi Rice Growers of Sub-Himalayan North Bengal Efficient Enough? Role of Machine Learning in Farm Business After COVID-19
The COVID-19 pandemic and the consequent lockdown have disrupted the farming communities across the planet; however, the indigenous communities such as the Adivasi people of tea garden areas in India have been hit hard. It becomes more crucial when we talk about marginalized communities. In India, the Adivasi tribe found in the sub-Himalayan North Bengal cultivates folk rice that needs immediate energy-efficient measures as the production has been increased after receiving the geographical indication tag. Energy efficiency estimation often applied a two-step data envelopment analysis model in agricultural production. However, in most of the previous articles, the applications discussed the factors affecting energy use efficiency with rare studies on efficiency prediction. In this article, first, data envelopment analysis was used to estimate the energy efficiency of rice growers, and in the second stage, extreme gradient boosting, a state-of-the-art machine learning algorithm, was employed to derive the key leading efficiency determinants. The findings revealed wide variation among efficient and inefficient rice growers in the first stage and derived the most salient factors predicting energy efficiency in the second stage. The optimal use of energy inputs combined with effective education, better credit delivery mechanism, arable land availability and years of farming experience provided improvement for the future energy use efficiency of the Adivasi farmers. Further, the novel application of extreme gradient boosting as a machine learning algorithm in energy efficiency evaluation suggests decision-making solutions with a prediction accuracy of 80.91%. Moreover, this study aims to assist future researchers in examining and predicting the key leading determinants to affect energy utilization.
期刊介绍:
Global Business Review is designed to be a forum for the wider dissemination of current management and business practice and research drawn from around the globe but with an emphasis on Asian and Indian perspectives. An important feature is its cross-cultural and comparative approach. Multidisciplinary in nature and with a strong practical orientation, this refereed journal publishes surveys relating to and report significant developments in management practice drawn from business/commerce, the public and the private sector, and non-profit organisations. The journal also publishes articles which provide practical insights on doing business in India/Asia from local and global and macro and micro perspectives.