PDF(Pubmed)
Abstract：
Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of protein needed for human health. India\'s pulses production guidelines must be based on accurate and best forecast models. Comparing classical statistical and machine learning models based on different scientific data series is the subject of high-level research today. This study focused on the forecasting behaviour of pulses production for India, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh. The data series was split into a training dataset (1950-2014) and a testing dataset (2015-2019) for model building and validation purposes, respectively. ARIMA, NNAR and hybrid models were used and compared on training and validation datasets based on goodness of fit (RMSE, MAE and MASE). This research demonstrates that due to the diverse agricultural conditions across different provinces in India, there is no single model that can accurately predict pulse production in all regions. This study\'s highest accuracy model is ARIMA. ARIMA outperforms NNAR, a machine learning model. Pulse production in India, Rajasthan, and Madhya Pradesh will expand by 26.11%, 12.62%, and 0.51% from 2020 to 2030, whereas it would decline by - 6.5%, - 6.21%, and - 6.76 per cent in Karnataka, Maharashtra, and Uttar Pradesh, respectively. The current forecast results could allow policymakers to develop more aggressive food security and sustainability plans and better Indian pulses production policies in the future.
摘要：
预测对各国做出明智的商业决策和制定数据驱动战略很有价值。豆类的生产是农业多样化举措的一个组成部分，因为它为减少发展中国家的农村贫困和失业提供了有希望的经济机会。豆类是人类健康所需的最便宜的蛋白质来源。印度的豆类生产指南必须基于准确和最佳的预测模型。比较基于不同科学数据系列的经典统计和机器学习模型是当今高级研究的主题。这项研究的重点是预测印度豆类生产的行为，卡纳塔克邦,中央邦,马哈拉施特拉邦,拉贾斯坦邦和北方邦。将数据序列拆分为训练数据集（1950-2014）和测试数据集（2015-2019），用于模型构建和验证，分别。阿丽玛,使用NNAR和混合模型，并在基于拟合优度的训练和验证数据集上进行比较(RMSE，MAE和MASE）。这项研究表明，由于印度不同省份的农业条件不同，没有一个单一的模型可以准确预测所有地区的脉冲产生。本研究的最高精度模型是ARIMA。ARIMA优于NAR，机器学习模型。印度的脉冲生产，拉贾斯坦邦,中央邦将扩大26.11%，12.62%,从2020年到2030年，将下降0.51%，而下降6.5%，-6.21%，卡纳塔克邦为6.76%，马哈拉施特拉邦,北方邦,分别。目前的预测结果可以让政策制定者在未来制定更积极的粮食安全和可持续性计划，以及更好的印度豆类生产政策。
