Potential yields of two staple cereal crops worldwide under global warming

DOI: 10.31830/2456-8724.2024.FM-133    | Article Id: FM-133 | Page : 1-11
Citation :- Potential yields of two staple cereal crops worldwide under global warming. Farm. Manage. 9: 1-11
CAI CHENGZHI, CHEN JIDONG AND CAO WENFANG caichengzhi@mail.gufe.edu.cn
Address : Economic Institute, Guizhou University of Finance and Economics, Guiyang 550025, China
Submitted Date : 1-02-2024
Accepted Date : 22-05-2024


Most studies on model-estimated yield potential of crop under climate change are based on the principle of production function, for specific variety, from static biological dimension and at local or regional level, while few theoretically are based on time-series approach for world crop from dynamic evolutionary angle and on global scale. Therefore we conducted following research in 2019 to 2023 at Guiyang City of China: potential yields of rice and wheat worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend-regressed model based on their history since 1961, in which the projections are tested by actual yields in recent two years and further validated by Gray System model; the impacts of global warming on past production of rice and wheat worldwide since 1961 are analyzed using binary regression model in which global mean temperature is treated as the independent variable while the crop yield as the dependent variable. Our results show that: in the future between 2022 and 2030, average yield of world rice is projected to be from 4876 kg/ha to 5195 kg/ha whereas that of world wheat from 3548 kg/ha to 3817 kg/ha; top (national) yield of world rice is projected to be from 10148 kg/ha to 10269 kg/ha whereas that of world wheat from 9914 kg/ha to 10042 kg/ha; or the ratio between average and top yields of world rice will be from 48.05% to 50.59% whereas that of world wheat from 35.79% to 38.01%; since 1961, global warming exerts a negative impact on average yield of world rice less than on its top, and a positive effect on average yield of world wheat while a negative one on its top, which partially drives the gap between average and top yields of world rice slightly narrowed and that of world wheat gradually closed. These findings indicate that: given that top yield is considered potential limit of average yield, to improve global production of these two staple cereal crops by 2030, the priorities should be given to rice as well as wheat worldwide in their both high and low yield countries.


Global warming potential yield world rice world wheat


Cai, C. Z., Yang, H. Y., Zhang, L. and Wenfang Cao (2022). Potential yield of world rice under global warming based on the ARIMA-TR Model. Atmosphere 13: doi:10. 3390/atmos13081336.
Divya, K. L., Mhatre, P. H., Venkatasalam, E. P. and R. Sudha (2021). Crop simulation models as decision-supporting tools for sustainable potato production: A Review. Potato Res. 64: 387-419. doi:10.3390/atmos13081336.       
Dong, J., Lu, H. B., Wang, Y. W., Ye, T. and Yuan, W. (2020). Estimating winter wheat yield based on a light use efficiency model and wheat variety data. ISPRS J.  Photogramm. Remote Sens. 160: 18-32. doi:10.1016/j.isprsjprs.2019.12.005.
Espe, M. B., Yang, H. S., Cassman, K. G. et al. (2016). Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems. Field Crops Res. 193: 123-32. doi:10.1016/j.fcr.2016.04.003.
Huang, M., Tang, Q. Y., Ao, H. J.  and Zou, Y. B. (2017). Yield potential and stability in super hybrid rice and its production strategies. J. Integr. Agric. 16: 1009-017. doi:10.1016/S2095-3119(16)61535-6.
Jensen, L. (1990). Guidelines for the application of ARIMA models in time series. Res. Nurs. Health 13: 429-35.
Lai, Y. R., Pringle, M. J., Kopittke, P. et al. (2018). An empirical model for prediction of wheat yield, sing time-integrated Landsat NDVI. Int. J. Appl. Earth Observ. Geoinformation 72: 99-108. doi:10.1016/j.jag.2018.07.013.
Liu, Z. C., Xu, Z. J., Bi, R. et al., (2021). Estimation of winter wheat yield in arid and semiarid regions based on assimilated multi-source sentinel data and the CERES-wheat model. Sensors 21: doi:10.3390/s21041247.
Ma, X., Wu, W. and Zhang, Y. (2019). Improved GM (1,1) Model Based on Simpson Formula and its applications. J. Gray System 31: 33-46. doi:10.48550/arXiv.1908.03493.
Sagar Maitra, Upasana Sahoo, Masina Sairam, Harun I. Gitari, Esmaeil Rezaei-Chiyaneh, Martin Leonardo Battaglia and Akbar Hossain (2023). Cultivating sustainability: A comprehensive review on intercropping in a changing climate. Res. Crop. 24: 702-15.
Ojeda, J. J., Huth, N., Holzworth, D. et al. (2021). Assessing errors during simulation configuration in crop models-A global case study using APSIM-Potato. Ecol. Modell. 458: doi:10.1016/j.ecolmodel.2021.109703.
Poudel, M. R., Neupane, M. P., Paudel, H., Bhandari, R. Nyaupane, S., Dhakal, A. and Panthi, B. (2023). Agromorphological analysis of wheat (Triticum aestivum L.) genotypes under combined heat and drought stress conditions. Farm. Manage. 8: 72-80.
Setiyono, T. D., Quicho, E. D., Holecz, F. H. et al., (2019). Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries. Int. J. Remote Sens. 40: 8093-124. doi:10.1080/01431161.2018.1547457.
Singh, P. K., Singh, K. K., Bhan, S. C. and A. K. Baxla. (2016). Potential yield and yield gap analysis of rice (Oryza Saliva L) in eastern and north eastern regions of India using CERES-rice model. J. Agrometeorol. 17: 194-98. doi:10.54386/jam.v17i2.1005.
Singh, P. K., Singh, K. K., Singh, P. and R. Subramanian. (2017). Forecasting of wheat yield in various agro-climatic regions of Bihar by using CERES-Wheat model. J. Agrometeorol. 19: 346-49. doi:10.54386/jam.v19i4.604.
Sun, T., Hasegawa, T., Liu, B., et al., (2021). Current rice models underestimate yield losses from short-term heat stresses. Global Change Biol. 27: 402-16. doi:10.1111/gcb.15393.
Tian, L. Y., Li, Z. X., Huang, J. X., et al., (2013). Comparison of two optimization algorithms for estimating regional winter wheat yield by integrating MODIS leaf area index and world food studies model. Sensor Lett. 11: 1261-68. doi:10.1166/sl.2013.2871.
Turko, S. Y. (2023). Influence of weather conditions on the productive potential of pasture ecosystems in the south of Russia (Based on artificially created models). Res. Crop. 24: 774-78.
Wang, J. W., Zhang, J. H., Bai, Y., et al., (2020). Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crops Res. 246: doi:10.1016/j.fcr.2019.107682.
Wang, L. Z. (2018). Rice yield potential, gaps and constraints during the past three decades in a climate-changing Northeast China. Agric. Forest Meteorol. 259: 173-83. doi:10.1016/j.agrformet.2018.04.023.
Wang, Y. L., Xu, XG., Huang, L. S., et al., (2019). An Improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sens. 11: doi:10.3390/rs11091088.

Global Footprints