MA Hong-wei,BAI Di,LI Jing,et al.Prediction and Analysis of China’s Soybean Consumption and Production in 2021—2025[J].Soybean Science,2022,41(03):358-362.[doi:10.11861/j.issn.1000-9841.2022.03.0358]
中国大豆2021—2025年消费量和生产量预测分析
- Title:
- Prediction and Analysis of China’s Soybean Consumption and Production in 2021—2025
- 关键词:
- 大豆; Grey-Markov模型; 预测; 消费量; 生产量
- Keywords:
- soybean; Grey-Markov model; forecast; consumption; production
- 文献标志码:
- A
- 摘要:
- 中国是世界上大豆消费量和进口量最大的国家，为了较为准确地预测我国大豆未来的消费量和生产量，维护我国大豆供给安全，本研究提出Grey-Markov大豆消费量预测模型，采用GM(1， 1)模型和Grey-Markov模型对2017—2020年我国大豆消费量进行拟合，并利用其中预测精度较高的模型预测我国2021—2025年的大豆消费量和生产量。结果表明：Grey-Markov模型预测精度较高，利用该模型的进一步预测结果表明，2017—2020年我国大豆供给量与需求量之间的缺口分别为10.37，10.87，11.40，11.95和12.53亿t，2021—2025年我国大豆生产量和消费量年增长率将分别为6.06%和5.01%。最后，根据预测数据提出建议：我国应力争实现大豆进口多元化、大力发展国内大豆产业，以提高粮食安全性。
- Abstract:
- China is the country with the largest soybean consumption and import in the world. In order to accurately predict the future consumption and production of soybean in China and maintain the safety of soybean supply in China， this study proposed the Grey-Markov prediction model for soybean consumption prediction， used GM(1，1) model and Grey-Markov model to fit the China′s soybean consumption from 2017 to 2020. Then， we used the model with higher precision to predict China′s soybean consumption and production from 2021 to 2025. The results showed that the Grey-Markov model had higher prediction accuracy. The forecast results with Grey-Markov model showed the gap between China′s soybean supply and demand of 2021—2025 will be 1 037， 1 087， 1 140， 1 195 and 1 253 million tons respectively， and the annual growth rates of China′s soybean production and consumption will be 6.06% and 5.01% respectively. Finally， according to the forecast data， it was suggested that China should make great efforts to diversify soybean imports and develop domestic soybean industry to improve food security.
参考文献/References:
［1］何朋飞， 李静， 张冬青. APSO_SVR模型在我国大豆价格预测的应用研究[J]. 大豆科学， 2017，36(4)： 632-638. (HE P F， LI J， ZHANG D Q. Predicting Chinese soybean price based on APSO_SVR[J]. Soybean Science， 2017， 36(4): 632-638.)[2]石波， 张冬青， 马开平， 等. 改进RBF神经网络在我国大豆价格预测中的应用研究[J].大豆科学， 2016， 35(2): 310-314. (SHI B， ZHANG D Q， MA K P， et al. Soybean price prediction in China based on improved RBF neural network[J]. Soybean Science， 2016，35(2): 310-314.)[3]徐鑫洲， 马开平. 基于系统动力学的我国大豆价格预测分析[J].大豆科学， 2018， 37(5): 787-793. (XU X Z， MA K P. Prediction and analysis of soybean price in China based on system dynamics[J]. Soybean Science， 2018， 37(5): 787-793.)[4]朱婧， 范亚东， 徐勇. 基于改进GM(1， 1)模型的中国大豆价格预测[J].大豆科学， 2016，35(2): 315-319. (ZHU J， FAN Y D， XU Y. Soybean price prediction in China based on modified GM(1， 1) model[J]. Soybean Science， 2016，35(2): 315-319.)[5]范震， 马开平， 姜顺婕， 等. 基于改进 GM(1， N) 模型的我国大豆价格影响因素分析及预测研究[J].大豆科学， 2016， 35(5): 847-852. (FAN Z， MA K P， JIANG S J， et al. Influence factors analysis and price prediction of soybean in China based on improved GM (1，N) model[J]. Soybean Science， 2016， 35(5): 847-852.)[6]蔡承智， 张建威， 梁颖. 基于ARIMA模型的世界大豆单产预测分析[J].大豆科学， 2018， 37(5): 452-457. (CAI C Z， ZHANG J W， LIANG Y. Analysis of potential yield of global soybean forecasted by ARIMA model[J]. Soybean Science， 2018， 37(5): 452-457.)[7]蔡承智， 莫洪兰， 梁颖. 基于AＲIMA模型的我国大豆单产预测分析[J].大豆科学， 2017， 36(5): 789-795. (CAI C Z， MO H L， LIANG Y. Prediction on Chinese soybean yield based on ARIMA model[J]. Soybean Science， 2017， 36(5): 789-795.)[8]彭世广， 耿献辉. 基于ARIMA和GM(1， 1)模型的中国大豆进口量及进口额预测[J].大豆科学， 2020， 39(4): 426-432. (PENG S G， GENG X H. Forecasting for import quantity and value of China′ s soybean based on ARIMA and GM(1， 1) models[J]. Soybean Science， 2020， 39(4): 426-432.)[9]齐波， 张宁， 赵团结， 等. 长江中下游生态区大豆生长性状及产量的冠层高光谱偏最小二乘回归预测[J]. 大豆科学， 2015， 34(3): 414-419. (QI B， ZHANG N， ZHAO T J， et al. Using canopy hyperspectral reflectance to predict growth traits and seed yield of soybeans from middle and lower Yangtze valleys through partial least squares regression[J]. Soybean Science， 2015， 34(3): 414-419.)[10]郑岩岩， 舒彤， 陈收，等. 外商直接投资趋势研究-基于灰色马尔可夫预测模型与时间序列模型的对比[J]. 系统工程理论与实践， 2016， 36(4): 897-909. (ZHENG Y Y， SHU T， CHEN S， et al.Ａstudy on the forecasting trend of foreign direct investment chain system-based on the comparison between grey Markov forecasting model and time series forecasting model[J]. Systems Engineering-Theory & Practice， 2016， 36(4): 897-909.)[11]李炳军， 杨卫明．基于灰色区间预测和GM(1， N)模型的我国粮食供需结构平衡分析[J]. 江苏农业科学， 2019， 47(18): 325-329. (LI B J， YANG W M．Analysis of grain supply and demand structure balance in China based on grey interval forecast and GM (1， N) model[J]. Jiangsu Agricultural Sciences， 2019， 47(18): 325-329.)[12]田梓辰， 刘淼. 基于改进灰色GM(1， 1)模型的GDP预测实证[J]. 统计与决策， 2018， 34(11): 83-85. (TIAN Z C， LIU M. Empirical study of GDP forecast based on improved grey GM(1， 1)model[J]. Statistics and Decision， 2018， 34(11): 83-85.)
相似文献/References:
[1]刘章雄,李卫东,孙石,等.1983～2010年北京大豆育成品种的亲本地理来源及其遗传贡献[J].大豆科学,2013,32(01):1.[doi:10.3969/j.issn.1000-9841.2013.01.002]
LIU Zhang-xiong,LI Wei-dong,SUN Shi,et al.Geographical Sources of Germplasm and Their Nuclear Contribution to Soybean Cultivars Released during 1983 to 2010 in Beijing[J].Soybean Science,2013,32(03):1.[doi:10.3969/j.issn.1000-9841.2013.01.002]
[2]李彩云,余永亮,杨红旗,等.大豆脂质转运蛋白基因GmLTP3的特征分析[J].大豆科学,2013,32(01):8.[doi:10.3969/j.issn.1000-9841.2013.01.003]
LI Cai-yun,YU Yong-liang,YANG Hong-qi,et al.Characteristics of a Lipid-transfer Protein Gene GmLTP3 in Glycine max[J].Soybean Science,2013,32(03):8.[doi:10.3969/j.issn.1000-9841.2013.01.003]
[3]王明霞,崔晓霞,薛晨晨,等.大豆耐盐基因GmHAL3a的克隆及RNAi载体的构建[J].大豆科学,2013,32(01):12.[doi:10.3969/j.issn.1000-9841.2013.01.004]
WANG Ming-xia,CUI Xiao-xia,XUE Chen-chen,et al.Cloning of Halotolerance 3 Gene and Construction of Its RNAi Vector in Soybean (Glycine max)[J].Soybean Science,2013,32(03):12.[doi:10.3969/j.issn.1000-9841.2013.01.004]
[4]张春宝,李玉秋,彭宝,等.线粒体ISSR与SCAR标记鉴定大豆细胞质雄性不育系与保持系[J].大豆科学,2013,32(01):19.[doi:10.3969/j.issn.1000-9841.2013.01.005]
ZHANG Chun-bao,LI Yu-qiu,PENG Bao,et al.Identification of Soybean Cytoplasmic Male Sterile Line and Maintainer Line with Mitochondrial ISSR and SCAR Markers[J].Soybean Science,2013,32(03):19.[doi:10.3969/j.issn.1000-9841.2013.01.005]
[5]卢清瑶,赵琳,李冬梅,等.RAV基因对拟南芥和大豆不定芽再生的影响[J].大豆科学,2013,32(01):23.[doi:10.3969/j.issn.1000-9841.2013.01.006]
LU Qing-yao,ZHAO Lin,LI Dong-mei,et al.Effects of RAV gene on Shoot Regeneration of Arabidopsis and Soybean[J].Soybean Science,2013,32(03):23.[doi:10.3969/j.issn.1000-9841.2013.01.006]
[6]杜景红,刘丽君.大豆fad3c基因沉默载体的构建[J].大豆科学,2013,32(01):28.[doi:10.3969/j.issn.1000-9841.2013.01.007]
DU Jing-hong,LIU Li-jun.Construction of fad3c Gene Silencing Vector in Soybean[J].Soybean Science,2013,32(03):28.[doi:10.3969/j.issn.1000-9841.2013.01.007]
[7]张力伟,樊颖伦,牛腾飞,等.大豆“冀黄13”突变体筛选及突变体库的建立[J].大豆科学,2013,32(01):33.[doi:10.3969/j.issn.1000-9841.2013.01.008]
ZHANG Li-wei,FAN Ying-lun,NIU Teng-fei?,et al.Screening of Mutants and Construction of Mutant Population for Soybean Cultivar "Jihuang13”[J].Soybean Science,2013,32(03):33.[doi:10.3969/j.issn.1000-9841.2013.01.008]
[8]盖江南,张彬彬,吴瑶,等.大豆不定胚悬浮培养基因型筛选及基因枪遗传转化的研究[J].大豆科学,2013,32(01):38.[doi:10.3969/j.issn.1000-9841.2013.01.009]
GAI Jiang-nan,ZHANG Bin-bin,WU Yao,et al.Screening of Soybean Genotypes Suitable for Suspension Culture with Adventitious Embryos and Genetic Transformation by Particle Bombardment[J].Soybean Science,2013,32(03):38.[doi:10.3969/j.issn.1000-9841.2013.01.009]
[9]王鹏飞,刘丽君,唐晓飞,等.适于体细胞胚发生的大豆基因型筛选[J].大豆科学,2013,32(01):43.[doi:10.3969/j.issn.1000-9841.2013.01.010]
WANG Peng-fei,LIU Li-jun,TANG Xiao-fei,et al.Screening of Soybean Genotypes Suitable for Somatic Embryogenesis[J].Soybean Science,2013,32(03):43.[doi:10.3969/j.issn.1000-9841.2013.01.010]
[10]刘德兴,年海,杨存义,等.耐酸铝大豆品种资源的筛选与鉴定[J].大豆科学,2013,32(01):46.[doi:10.3969/j.issn.1000-9841.2013.01.011]
LIU De-xing,NIAN Hai,YANG Cun-yi,et al.Screening and Identifying Soybean Germplasm Tolerant to Acid Aluminum[J].Soybean Science,2013,32(03):46.[doi:10.3969/j.issn.1000-9841.2013.01.011]
备注/Memo
收稿日期：2020-09-19