Time series analysis of wheat moisture content variations for grain storage systems

IF 2.7 2区 农林科学 Q1 ENTOMOLOGY
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Abstract

The Philippines is one of the many countries that consume and produce wheat grains. Despite having agricultural resources, the country still struggles to supply wheat and continues to import tons of wheat from different countries. Moreover, being a tropical country, the weather conditions exacerbate the parameters in defining grain quality, and thus, impact the preservation of grains in bulk storage systems. This study aims to understand the fluctuations of moisture content over time and its impact on grain quality and identify trends to utilize in optimizing storage systems management practices. Furthermore, this study investigated the moisture content variations, analyzed the temporal dynamics, and forecasted future trends using the ARIMA model, which can effectively handle time series data and has been applied in several agricultural applications. From the correlation patterns from lag intervals of the correlogram, there is a total of 24 ARIMA models generated with varying (p, d, q) values. These models were then evaluated using log-likelihood, AIC, RMSE, and MAE to select the best-performing and most appropriate model for forecasting wheat moisture content data. Out of these 24 models, Models (4,0,3), (4,0,4), and (1,0,0) are the top performing models, however, Model (4,0,3) emerged as a strong forecasting model in predicting wheat moisture content trends for the forthcoming two-year horizon. Its evaluation performance resulted in a log-likelihood of −69.38384, AIC of 156.7677, RMSE of 0.4248, and MAE of 0.30427, having the least values in three of the validation methods. When used in forecasting, the trend of the moisture content continues to consistently fall within the lower end of the accepted range. The distribution of the predicted values also shows a perfectly normal distribution, showing its effectiveness of the model for forecasting. These findings will serve as a guide towards enhancing operational efficiencies and sustainable growth within the flour milling landscape.

谷物储存系统的小麦水分含量变化时间序列分析
菲律宾是许多消费和生产小麦的国家之一。尽管该国拥有农业资源,但在小麦供应方面仍然举步维艰,并继续从不同国家进口成吨的小麦。此外,作为一个热带国家,气候条件加剧了确定谷物质量的参数,从而影响了谷物在散装储存系统中的保存。本研究旨在了解水分含量随时间的波动及其对谷物质量的影响,并确定趋势,以用于优化存储系统的管理实践。此外,本研究还使用 ARIMA 模型调查水分含量的变化、分析时间动态并预测未来趋势,该模型可有效处理时间序列数据,已在多个农业应用中得到应用。根据相关图滞后间隔的相关模式,共生成了 24 个不同(p、d、q)值的 ARIMA 模型。然后使用对数似然、AIC、RMSE 和 MAE 对这些模型进行评估,以选出性能最好、最适合预测小麦含水量数据的模型。在这 24 个模型中,模型(4,0,3)、(4,0,4)和(1,0,0)是表现最好的模型,然而,模型(4,0,3)在预测未来两年小麦含水率趋势方面表现突出。其评估结果为:对数似然值为 -69.38384,AIC 为 156.7677,RMSE 为 0.4248,MAE 为 0.30427,在三种验证方法中数值最小。在进行预测时,含水量的趋势始终处于可接受范围的下限。预测值的分布也呈现出完全正态分布,显示了该模型在预测方面的有效性。这些发现将为提高面粉加工业的运营效率和可持续增长提供指导。
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来源期刊
CiteScore
5.70
自引率
18.50%
发文量
112
审稿时长
45 days
期刊介绍: The Journal of Stored Products Research provides an international medium for the publication of both reviews and original results from laboratory and field studies on the preservation and safety of stored products, notably food stocks, covering storage-related problems from the producer through the supply chain to the consumer. Stored products are characterised by having relatively low moisture content and include raw and semi-processed foods, animal feedstuffs, and a range of other durable items, including materials such as clothing or museum artefacts.
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