基于ARIMA模型的中亚粮食生产量时空变化分析与预测

高雪梅; 董晔; 许文强; 包安明; 钟秀凤

doi:10.7523/j.ucas.2024.017

中国科学院大学学报 >

2025 , Vol. 42 >Issue 4: 472 - 486

DOI: https://doi.org/10.7523/j.ucas.2024.017

环境科学与地理学

基于ARIMA模型的中亚粮食生产量时空变化分析与预测

高雪梅 ,
董晔 ,
许文强 ,
包安明 ,
钟秀凤

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1. 新疆师范大学, 乌鲁木齐 830054;
2. 中国科学院新疆生态与地理研究所干旱区生态安全与可持续发展重点实验室/荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011;
3. 中国科学院新疆生态与地理研究所新疆遥感与地理信息系统应用重点实验室, 乌鲁木齐 830011

收稿日期: 2023-10-30

修回日期: 2024-04-01

网络出版日期: 2024-05-09

基金资助

第三次新疆综合科学考察项目(2022xjkk0701)、青海省“昆仑英才·高端创新创业人才-领军人才”项目(2020-LCJ-02)和新疆维吾尔自治区“天山英才培养”计划(2022TSYCLJ0011)资助

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Spatial-temporal variation analysis and prediction of grain production in Central Asia based on ARIMA model

GAO Xuemei ,
DONG Ye ,
XU Wenqiang ,
BAO Anming ,
ZHONG Xiufeng

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1. Xinjiang Normal University,Urumqi 830054, China;
2. State Key Laboratory of Desert and Oasis Ecology/Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences,Urumqi 830011, China;
3. Xinjiang Key Laboratory of Remote Sensing and Geographic Information System Application, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

Received date: 2023-10-30

Revised date: 2024-04-01

Online published: 2024-05-09

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摘要

选取中亚地区最为重要的小麦、大麦、玉米、燕麦、水稻等5类粮食作物作为研究对象，分析1992—2021年中亚粮食的单产、总量及播种面积的变化，研究中亚地区粮食波动性的区域化差异，并借助ARIMA模型对中亚未来粮食生产量进行预测。结果表明：1)1992—2021年中亚的粮食单产、总量及播种面积整体呈先减少后增加的趋势，三者的变化区间分别为：0.79～1.96 t/hm²、(0.14～0.37)×10⁸ t、(0.14～0.23)×10⁸ hm²，粮食单产及总量在2011年达到高点，分别为：1.96 t/hm²、0.37×10⁸ t，粮食播种面积在1993年达到高点，为0.23×10⁸ hm²；2)中亚地区的粮食波动性和周期性特征主要表现为粮食产量波动频繁，波动指数的绝对值超过5%的年份占比大，波动幅度较大，平均波动周期为2～4 a，属于短期波动，以古典波动为主，增长型波动极少；3)未来几年中亚的小麦、大麦、玉米、燕麦及粮食总产量均将呈上升趋势，而水稻产量呈下降趋势，与2021年相比，到2030年中亚的小麦、大麦、玉米、燕麦分别可增产(410.15，91.6，795.26，8.91)×10⁴ t，增幅分别为20.1%、31%、299.2%、37.1%，水稻可能减产15.99×10⁴ t，降幅为15.5%。

关键词： ARIMA模型; 粮食生产量; 粮食波动性; 产量预测

本文引用格式

高雪梅 , 董晔 , 许文强 , 包安明 , 钟秀凤 . 基于ARIMA模型的中亚粮食生产量时空变化分析与预测[J]. 中国科学院大学学报, 2025 , 42(4) : 472 -486 . DOI: 10.7523/j.ucas.2024.017

Abstract

The production and supply of food are core components of sustainable development. Ensuring the sustainability of global food production and supply is crucial for maintaining human survival and socioeconomic stability, and it holds significant importance in advancing the “Zero Hunger” goal within the framework of global sustainable development. This paper selects the five key cereal crops, including wheat, barley, maize, oats, and rice, as the subjects of study, focusing on the Central Asian region. It analyzes the variations in yield per hectare, total production, and cultivated area for these cereals from 1992 to 2021, investigates regional disparities in food production fluctuations within Central Asia, and employs the ARIMA model to forecast future grain production in Central Asia. The results showed that: 1) From 1992 to 2021, the grain yield, total output and sown area in Central Asia showed a trend of first decreasing and then increasing, and the three changes ranged from 0.79～1.96 t/hm², (0.14～0.37)×10⁸ t and (0.14～0.23)×10⁸ hm², respectively. Grain yield and total production reached their peaks in 2011 at 1.96 t/hm², and 0.37×10⁸ t, respectively, while the cultivated grain area peaked in 1993 at 0.23×10⁸ hm². 2) The grain volatility in Central Asia is characterized by frequent fluctuations in grain production, with a significant proportion of years experiencing fluctuations exceeding 5%. The amplitude of these fluctuations is substantial, and the average fluctuation cycle is 2-4 years, indicating a short-term cyclical pattern dominated by classical rather than growth-oriented fluctuations. 3) In the coming years, Central Asia is projected to experience an upward trend in wheat, barley, maize, and oats production, while rice production is expected to decline. Compared to the year 2021, by 2030, Central Asia’s wheat, barley, maize, and oats production is estimated to increase by (410.15, 91.6, 795.26, and 8.91)×10⁴ t, respectively, representing growth rates of 20.1%, 31%, 299.2%, and 37.1%. Conversely, rice production may decrease by 15.99×10⁴ t, with a decline of 15.5%.

Key words： ARIMA model; grain production; grain production volatility; production forecast

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