|Table of Contents|

Analysis of Potential Yield of Global Soybean Forecasted by ARIMA Model(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2018年03期
Page:
452-457
Research Field:
Publishing date:

Info

Title:
Analysis of Potential Yield of Global Soybean Forecasted by ARIMA Model
Author(s):
CAI Cheng-zhi1;  ZHANG Jian-wei1;  LIANG Ying2
(1.Faculty of Economics, Guizhou University of Finance and Economics, Guiyang 550025, China; 2.Public Administration School of Guizhou University, Guiyang 550025, China)
Keywords:
ARIMA model;  Global soybean;  Yield;  Yield potential
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2018.03.0452
Abstract:
Soybean is one of the world’s most important food oil and economic crops, with the continuous increase of the world’s population and ever declining farmland, attention has increasingly focused on improving the potential yield of soybean. Therefore, analyzing the yield of global soybean in the future is of great significance to the production of soybean in the world. However up to now, there were few reports on forecasting potential yield of global soybean on the bases of ‘time series’ model. In this paper, potential yield of global soybean is forecasted on ARIMA (auto-regression integrated moving average) model basis. The results showed that, in 2017, 2018, 2019, 2020 and 2021, average yields of global soybean would be 2 825,2 872,2 920,2 968 and 3 017 kg·ha-1 while top ones would reach 4 037,4 081,4 129,4 171 and 4 217 kg·ha-1, respectively, the former was 69.98%, 70.37%, 70.72%, 71.16% and 71.54% of the later, correspondingly. The results signify that, as for global soybean production in the future, higher improvement opportunities will increasingly come from raising the potential of middle & low yield countries rather than high ones. In comparison, there is still a considerable space for China to increase future yield of soybean, and the importance should be equally paid to both sustaining the productivity of high yield fields and ameliorating middle & low ones.

References:

［1］蔡承智, Harrij van V, Guenther F. 基于AEZ模型的我国大豆产量潜力的农作制区划分析［J］. 河南农业科学, 2006, 32(5): 27-31. （Cai C Z, Harrij van V, Guenther F. Analysis of soybean yield potential of Chinese farming system zoning based on AEZ model［J］. Journal of Henan Agricultural Sciences, 2006, 32(5): 27-31.）

［2］Cai C Z, Shao J B, Liang Y. Analyses on soybean yield in China based on the prediction of yield potential［J］. Crop Research, 2012, 43(3): 47-51.
［3］Roekel van R J, Purcell L C, Salmeron M. Physiological and management factors contributing to soybean potential yield［J］. Field Crops Research, 2015, 182(S1): 86-97.
［4］Bhatia V S, Jumrani K. A maximin-minimax approach for classifying soybean genotypes for drought tolerance based on yield potential and loss［J］. Plant Breeding, 2016, 135(6): 691-700.
［5］Kuswantoro H, Hapsari R T, Sulistyo A, et al. Potential yield of tidal swamp-adaptive soybean promising lines［J］. Legume Research, 2017, 40(3): 514-519.
［6］Soltani N, Dille J A, Burke I C, et al. Perspectives on potential soybean yield losses from weeds in North America ［J］. Weed Technology, 2017, 31(1): 148-154.
［7］Muleta D, Ryder M H, Denton M D. The potential for rhizobial inoculation to increase soybean grain yields on acid soils in Ethiopia ［J］. Soil Science and Plant Nutrition, 2017, 63(5): 441-451.?
［8］Zhang B B, Feng G, Kong X B, et al. Simulating yield potential by irrigation and yield gap of rainfed soybean using APEX model in a humid region［J］. Agricultural Water Management, 2016, 177: 440-453.
［9］Zanon A J, Streck N A, Grassini P. Climate and management factors influence soybean yield potential in a subtropical environment［J］. Agronomy Journal, 2016, 108(4): 1447-1454.
［10］ Yield potentials of rice and soybean as affected by cropping systems in mid-mountainous paddy soils of Korea［J］. Korean Journal of Soil Science & Fertilizer, 2017, 50(4): 259-274.
［11］Slattery R A, VanLoocke A, Bernacchi C J, et al. Photosynthesis, light use efficiency, and yield of reduced-chlorophyll soybean mutants in field conditions［J］. Frontiers in Plant Science, 2017, 8(18):549.
［12］薛庆喜. 中国及东北三省30年大豆种植面积、总产、单产变化分析［J］. 中国农学通报, 2013, 28(35): 102-106. (Xue Q X. Analysis on the change of 30 year’s soybean areas, production and yield in China and northeast China ［J］. Chinese Agricultural Science Bulletin, 2013, 28(35): 102-106.)
［13］张战国, 杨振华, 左鹏, 等. 黑龙江省大豆单产影响因素和技术效率分析［J］. 湖北农业科学, 2014, 59(13): 3191-3196. (Zhang Z G, Yang Z H, Zuo P, et al. Influencing factors and technical efficiency of soybean single yield in Heilongjiang province［J］. Hubei Agricultural Sciences, 2014, 59(13): 3191-3196.)
［14］Bishop K A, Betzelberger A M, Long S P, et al. Is there potential to adapt soybean (Glycine max Merr.) to future ［CO2］ An analysis of the yield response of 18 genotypes in free-air CO2 enrichment［J］. Plant Cell And Environment, 2015, 38(9): 1765-1774.
［15］Chung U. Exploring ways to improve the predictability of flowering time and potential yield of soybean in the crop model simulation［J］. Korean Journal of Agricultural and Forest Meteorology, 2017, 19(4): 203-214.
［16］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): 789-796. (Cai C Z, Mo H L, Liang Y. Chinese soybean yield projected on ARIMA model［J］. Soybean Science, 2017, 36(5): 789-796.)

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Last Update: 2018-06-08