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

IF 2.3 Q3 BUSINESS Global Business Review Pub Date : 2024-05-31 DOI:10.1177/09721509241250229
Anirban Nandy, Poulomi Chaki Nandi, Mousumi Chatterjee, Shankhadeep Mahato, Souradipt Bandyopadhyay
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引用次数: 0

摘要

COVID-19 大流行病和随之而来的封锁扰乱了全球各地的农业社区;然而,印度茶园地区的阿迪瓦西人等土著社区受到了严重打击。当我们谈到边缘化社区时,这一点变得更加重要。在印度,喜马拉雅山下北孟加拉的阿迪瓦西部落种植民间稻米,由于获得地理标志标签后产量增加,他们需要立即采取节能措施。在农业生产中,能效估算通常采用两步数据包络分析模型。然而,在以往的文章中,大多数应用都是讨论影响能源利用效率的因素,很少有关于效率预测的研究。本文首先采用数据包络分析法估算水稻种植者的能源利用效率,在第二阶段采用最先进的机器学习算法--极梯度提升法得出主要的效率决定因素。研究结果表明,在第一阶段,高效和低效水稻种植者之间存在很大差异,并在第二阶段得出了预测能源效率的最突出因素。能源投入的优化使用与有效的教育、更好的信贷机制、耕地可用性和多年的耕作经验相结合,提高了阿迪瓦西农民未来的能源使用效率。此外,极端梯度提升作为一种机器学习算法,在能效评估中的新颖应用提出了决策解决方案,预测准确率高达 80.91%。此外,本研究旨在帮助未来的研究人员检查和预测影响能源利用的主要决定因素。
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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.
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来源期刊
CiteScore
7.10
自引率
12.50%
发文量
107
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.
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