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

doi:10.7523/j.ucas.2024.017

Abstract

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

CLC Number:

F33
F307.11

GAO Xuemei, DONG Ye, XU Wenqiang, BAO Anming, ZHONG Xiufeng. Spatial-temporal variation analysis and prediction of grain production in Central Asia based on ARIMA model[J]. Journal of University of Chinese Academy of Sciences, 2025, 42(4): 472-486.

References

[1] 青平, 邓秀新, 闵师, 等. “双循环”背景下我国粮食安全韧性及风险管控战略研究[J]. 中国工程科学, 2023, 25(4): 26-38. DOI: 10.15302/J-SSCAE-2023.04.002.
[2] 张宁. 中国与中亚国家的粮食贸易分析[J]. 欧亚经济, 2019(2): 8-21, 125, 127.
[3] 廖梦婷. 中亚地区粮食生产及出口潜力研究: 基于中国粮食安全视角[D]. 杨凌: 西北农林科技大学, 2020. DOI:10.27409/d.cnki.gxbnu.2020.000345.
[4] Babu S C, Tashmatov A. Attaining food security in Central Asia:emerging issues and challenges for policy research[J]. Food Policy, 1999, 24(4): 357-362. DOI: 10.1016/s0306-9192(99)00052-4.
[5] Babu S, Pinstrup-Andersen P. Achieving food security in Central Asia:current challenges and policy research needs[J]. Food Policy, 2000, 25(6):629-635. DOI:10.1016/s0306-9192(00)00031-2.
[6] Pandya-Lorch R, Rosegrant M W. Prospects for food demand and supply in Central Asia[J]. Food Policy, 2000, 25(6): 637-646. DOI: 10.1016/s0306-9192(00)00032-4.
[7] Li Q Q, Liu G L. Is land nationalization more conducive to sustainable development of cultivated land and food security than land privatization in post-socialist Central Asia?[J]. Global Food Security, 2021, 30: 100560. DOI: 10.1016/j.gfs.2021.100560.
[8] 廖梦婷, 魏凤. 中亚地区粮食生产潜力及发展潜力分析:基于GAEZ方法[J]. 自然资源学报, 2021,36(3): 582-593. DOI:10.31497/zrzyxb.20210304.
[9] 梁鑫源, 金晓斌, 韩博, 等. 新时期 “藏粮于地、藏粮于技” 战略解析与路径探索[J]. 中国农业资源与区划, 2022, 43(4): 1-12. DOI: 10.7621/cjarrp.1005-9121.20220401.
[10] 陈印军, 易小燕, 陈金强, 等. 藏粮于地战略与路径选择[J]. 中国农业资源与区划, 2016, 37(12): 8-14. DOI: 10.7621/cjarrp.1005-9121.20161202.
[11] Liu Y, Gao B B, Pan Y C. Assessing the fluctuation characteristics of grain output in China[J]. Outlook on Agriculture, 2015, 44(3): 243-251. DOI:10.5367/oa.2015.0213.
[12] 罗海平, 邹楠, 胡学英, 等. 1980—2019年中国粮食主产区主要粮食作物气候生产潜力与气候资源利用效率[J]. 资源科学, 2021, 43(6): 1234-1247. DOI: 10.18402/resci.2021.06.14.
[13] 解纯芬. 气候条件对粮食产量的影响:以潍坊为例[J]. 分子植物育种, 2017, 15(5): 2022-2027. DOI:10.13271/j.mpb.015.002022.
[14] 于昊辰, 曾思燕, 王庆宾, 等. 多情景模拟下新时代中国耕地保护底线预测[J]. 资源科学, 2021, 43(6): 1222-1233. DOI: 10.18402/resci.2021.06.13.
[15] Wang J Y, Zhang Z W, Liu Y S. Spatial shifts in grain production increases in China and implications for food security[J]. Land Use Policy, 2018, 74: 204-213. DOI:10.1016/j.landusepol.2017.11.037.
[16] 李雨凌, 马雯秋, 姜广辉, 等. 中国粮食主产区耕地撂荒程度及其对粮食产量的影响[J]. 自然资源学报, 2021, 36(6): 1439-1454. DOI: 10.31497/zrzyxb.20210607.
[17] 田媛媛, 石淑芹, 李正国. 黑龙江省农业投入对粮食单产影响的研究[J]. 干旱区资源与环境, 2014, 28(5): 145-150. DOI:10.13448/j.cnki.jalre.2014.05.024.
[18] 曹志宏, 郝晋珉, 梁流涛. 黄淮海平原粮食产量与主要投入要素的灰色关联分析[J]. 农业现代化研究, 2008, 29(3): 310-313. DOI: 10.3969/j.issn.1000-0275.2008.03.013.
[19] Yang J, Wan Q, Bi W. Off-farm employment and grain production change: new evidence from China[J]. China Economic Review, 2020, 63: 101519. DOI:10.1016/j.chieco.2020.101519.
[20] 陈振坤, 贾积身. 基于分数阶灰色模型的河南省粮食产量预测[J]. 中国农机化学报, 2022, 43(6): 135-141. DOI:10.13733/j.jcam.issn.2095-5553.2022.06.018.
[21] 李静, 朱农, 李凤桂, 等. 近10年黄河三角洲地区粮食产量及灰色预测[J]. 干旱地区农业研究, 2012,30(5): 15-19.DOI:10.3969/j.issn.1000-7601.2012.05.003.
[22] 傅洪亮, 王少航, 曹培格, 等. 基于指数平滑及差分处理的粮食产量组合预测方法[J]. 中原工学院学报, 2018, 29(6): 50-55. DOI: 10.3969/j.issn.1671-6906.2018.06.010.
[23] 张成才, 陈少丹.BP神经网络在河南省粮食产量预测中的应用[J]. 湖北农业科学, 2014, 53(8): 1969-1971. DOI:10.14088/j.cnki.issn0439-8114.2014.08.062.
[24] 庄星, 韩飞. 基于混合群智能算法优化BP神经网络的粮食产量预测[J]. 江苏大学学报(自然科学版),2019, 40(2): 209-215. DOI: 10.3969/j.issn.1671-7775.2019.02.014.
[25] 马云倩, 郭燕枝, 王秀丽, 等. 基于LASSO与GM(1,N)模型的中国粮食产量预测[J]. 干旱区资源与环境, 2018, 32(7): 30-35. DOI:10.13448/j.cnki.jalre.2018.200.
[26] 孙东升, 梁仕莹. 我国粮食产量预测的时间序列模型与应用研究[J]. 农业技术经济, 2010(3): 97-106. DOI:10.13246/j.cnki.jae.2010.03.005.
[27] 胡程磊, 刘永华, 高菊玲. 基于IPSO-BP模型的粮食产量预测方法研究[J]. 中国农机化学报, 2021, 42(3): 136-141. DOI:10.13733/j.jcam.issn.2095-5553.2021.03.019.
[28] Zhang Y S, Chipanshi A, Daneshfar B, et al. Effect of using crop specific masks on earth observation based crop yield forecasting across Canada[J]. Remote Sensing Applications: Society and Environment, 2019, 13: 121-137. DOI:10.1016/j.rsase.2018.10.002.
[29] Torriani D, Calanca P, Lips M, et al. Regional assessment of climate change impacts on maize productivity and associated production risk in Switzerland[J]. Regional Environmental Change,2007, 7(4): 209-221. DOI:10.1007/s10113-007-0039-z.
[30] Shekhar C, Singh D, Singh R, et al. Prediction of wheat growth and yield using WOFOST model[J]. Journal of Agrometeorology, 2008(SPECIAL ISSUE 2): 400-402.
[31] Qader S H, Dash J, Atkinson P M. Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: a case study in Iraq[J]. The Science of the Total Environment, 2018, 613/614: 250-262. DOI: 10.1016/j.scitotenv.2017.09.057.
[32] Meroni M, Waldner F, Seguini L, et al. Yield forecasting with machine learning and small data: What gains for grains?[J]. Agricultural and Forest Meteorology, 2021, 308: 108555. DOI:10.1016/j.agrformet.2021.108555.
[33] Lecerf R, Ceglar A, López-Lozano R, et al. Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe[J]. Agricultural Systems, 2019, 168: 191-202. DOI:10.1016/j.agsy.2018.03.002.
[34] 罗巍, 杨玄酯, 杨永芳, 等. 黄河流域水-能源-粮食纽带关系协同演化及预测[J]. 资源科学, 2022, 44(3): 608-619. DOI: 10.18402/resci.2021.03.14.
[35] 李晶, 任志远, 周自翔. 区域粮食安全性分析与预测: 以陕西省关中地区为例[J]. 资源科学, 2005, 27(4): 89-94. DOI: 10.3321/j.issn: 1007-7588.2005.04.016.
[36] 于宏源, 李坤海. 中亚“水-能源-粮食” 安全纽带：困境、治理及中国参与[J]. 俄罗斯东欧中亚研究, 2021(1): 84-105, 157.DOI:10.20018/j.cnki.reecas.2021.01.005.
[37] 闫祥祥. 使用ARIMA模型预测公园绿地面积[J]. 计算机科学, 2020, 47(S2): 531-534, 556. DOI: 10.11896/jsjkx.200300099.
[38] 蔡承智, 黄军结, 梁颖. 基于ARIMA模型的世界大豆总产预测分析[J]. 大豆科学, 2019, 38(2): 298-303. DOI: 10.11861/j.issn.1000-9841.2019.02.0298.
[39] Tipi T, Erdal B. The forecast of corn production fields in Turkey with ARIMA model[J]. Romanian Agricultural Research, 2021, 38: 479-485. DOI: 10.59665/rar3850.
[40] 徐利岗, 杜历, 姚海娇, 等. 中亚干旱区降水时空变化特征及趋势分析[J]. 干旱区资源与环境, 2015, 29(11): 121-127. DOI:10.13448/j.cnki.jalre.2015.373.
[41] 魏凤. 中亚五国农业[M]. 北京：中国农业出版社，2021.
[42] 王洪丽, 杨双, 王军, 等. 吉林省玉米产量波动分析[J]. 玉米科学, 2011, 19(5): 134-136, 142. DOI:10.13597/j.cnki.maize.science.2011.05.002.
[43] 杨铁军, 杨娜, 朱春华, 等. 一种新的基于ARIMA模型的粮食产量预测[J]. 河南工业大学学报(自然科学版), 2015, 36(5): 19-22. DOI:10.16433/j.cnki.issn1673-2383.2015.05.004.
[44] Rakhmatullaev S, Abdullaev I. Central Asian irrigation sector in a climate change context: some reflections[J]. Journal of Water and Climate Change, 2014, 5(3): 341-356. DOI:10.2166/wcc.2014.120.
[45] 尹成杰. 关于我国粮食生产波动的思考及建议[J]. 农业经济问题, 2003, 24(10): 4-9, 70. DOI: 10.3969/j.issn.1000-6389.2003.10.001.
[46] 田德斌, 车明诚. 黑龙江省粮食产量波动分析与政策建议[J]. 农业现代化研究, 2009, 30(3): 284-287. DOI: 10.3969/j.issn.1000-0275.2009.03.007.
[47] 苏芳, 刘钰, 陈律凡, 等. 气候变化对中亚五国粮食安全的影响[J]. 中国科学:地球科学2024, 54(1):281-293. DOI: 10.1360/SSTe-2022-0316.
[48] 韩冬, 钟钰. 地缘因素对我国粮食进口韧性的冲击与政策响应[J]. 国际贸易, 2023(9): 52-61. DOI:10.14114/j.cnki.itrade.2023.09.009.