Sales forecasting of selected fresh vegetables in multiple channels for marginal and small-scale farmers in Kerala, India

IF 2.4 Q2 AGRICULTURAL ECONOMICS & POLICY
R.S. Sreerag, Prasanna Venkatesan Shanmugam
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Abstract

Purpose The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life. Design/methodology/approach Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp). Findings The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels. Research limitations/implications The price of vegetables is not considered as the government sets the base price for the vegetables. Originality/value The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.
印度喀拉拉邦边缘和小规模农民多渠道精选新鲜蔬菜的销售预测
生鲜蔬菜销售渠道的选择是农民可以做出的重要决策。通常情况下,农民依靠他们的个人经验来引导农产品进入销售渠道。本研究考察了沿多个渠道的新鲜蔬菜销售预测如何使边际和小规模农民通过考虑其短保质期的产品按比例分配来最大化其收入。设计/方法/方法机器学习模型,即长短期记忆(LSTM),卷积神经网络(CNN)和传统方法,如自回归综合移动平均(ARIMA)和加权移动平均(WMA)通过三个不同的渠道开发和测试蔬菜需求预测,即直接(Jaivasree),调节(世界市场)和合作(Horticorp)。结果表明,机器学习方法(LSTM/CNN)为监管(World market)和合作(Horticorp)渠道提供了更好的预测,而传统的移动平均线对直接(Jaivasree)渠道产生了更好的结果,因为与其他两个渠道相比,直接(Jaivasree)渠道的销量较少。研究限制/启示由于政府设定了蔬菜的基本价格,因此不考虑蔬菜的价格。现有文献缺乏模型和方法来预测像印度这样的发展中经济体的边缘和小规模农民的新鲜蔬菜销售。在这项研究中,作者根据从喀拉拉邦园艺公司获得的一组130周的时间序列数据,预测了印度喀拉拉邦小农常用新鲜蔬菜的销售。
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来源期刊
CiteScore
4.60
自引率
37.50%
发文量
58
期刊介绍: The Journal of Agribusiness in Developing and Emerging Economies publishes double-blind peer-reviewed research on issues relevant to agriculture and food value chain in emerging economies in Asia, Africa, Latin America and Eastern Europe. The journal welcomes original research, particularly empirical/applied, quantitative and qualitative work on topics pertaining to policies, processes, and practices in the agribusiness arena in emerging economies to inform researchers, practitioners and policy makers
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