The need to make more accurate grain demand (GD) forecasting has become a major topic in the current international grain security discussion. Our research aims to improve short term GD prediction by establishing a multi-factor model that integrates the key factors: shifts in dietary structures, population size and age structure, urbanization, food waste, and the impact of COVID-19. These factors were not considered simultaneously in previous research. To illustrate the model, we projected China’s annual GDP from 2022 to 2025. We calibrated key parameters such as conversion coef？cients from animal foods to feed grain, standard person consumption ratios, and population size using the latest surveys and statistical data that were either out of date or missing in previous research. Results indicate that if the change in diets continued at the rate as observed during 2013–2019 (scenario 1), China’s GD is projected to be 629.35 million tons in 2022 and 658.16 million tons in 2025. However, if diets shift to align with the recommendations in the Dietary Guideline for Chinese Residents 2022 (scenario 2), GD would be lower by 5.9–11.1% annually compared to scenario 1. A reduction in feed grain accounts for 68% of this change. Furthermore, for every 1 percentage point increase in the population adopting a balanced diet, GD would fall by 0.44–0.73 million tons annually during that period. Overlooking changes in the population age structure could lead to an overprediction of annual GDP by 3.8% from 2022 to 2025. With an aging population, China’s GD would fall slightly, and adopting a balanced diet would not lead to an increase in GD but would have positive impacts on human health and the environment. Our sensitivity analysis indicated that reducing food waste, particularly cereal, livestock, and poultry waste, would have signi？cant effects on reducing GD, offsetting the higher demand due to rising urbanization and higher incomes. These results underscore the signi？cance of simultaneous consideration of multiple factors, particularly the dietary structure and demographic composition, resulting in a more accurate prediction of GD. Our ？ndings should be useful for policymakers concerning grain security, health, and environmental protection.
Publication:
Nutrients 15, 2877 (2023).
https://doi.org/10.3390/nu15132877
Authors:
Xiuli Liu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190, China
E-mail: xiuli.liu@amss.ac.cn
Mun S. Ho
Harvard China Project on Energy, Economy and Environment, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Geoffrey J. D. Hewings
Regional Economics Applications Laboratory, University of Illinois at Urbana-Champaign,
Yuxing Dou
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
University of Chinese Academy of Sciences, Beijing 100049, China
Shouyang Wang
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Center for Forecasting Science, Chinese Academy of Sciences, Beijing 100190, China
Guangzhou Wang
Institute of Population and Labor Economics, Chinese Academy of Social Sciences, Beijing 100006, China
Dabo Guan
Department of Earth System Science, Tsinghua University, Beijing 100084, China
Shantong Li
Development Research Center of the State Council, Beijing 100010, China
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