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Liu X, Ho MS, Hewings GJD, Dou Y, Wang S, Wang G, Guan D, Li S. Aging Population, Balanced Diet and China's Grain Demand. Nutrients 2023; 15:2877. [PMID: 37447204 DOI: 10.3390/nu15132877] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
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 coefficients 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standard person consumption ratios, and population size using the latest surveys and statistical data that were either out of date or missing in previous research.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 significant effects on reducing GD, offsetting the higher demand due to rising urbanization and higher incomes. These results underscore the significance of simultaneous consideration of multiple factors, particularly the dietary structure and demographic composition, resulting in a more accurate prediction of GD. Our findings should be useful for policymakers concerning grain security, health, and environmental protection.
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Affiliation(s)
- 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
- Harvard China Project on Energy, Economy and Environment, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - 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, Urbana, IL 61801, USA
| | - 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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Zhou X, Zou X, Tang W, Yan Z, Meng H, Luo X. Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm. FRONTIERS IN PLANT SCIENCE 2023; 14:1103276. [PMID: 37332733 PMCID: PMC10272741 DOI: 10.3389/fpls.2023.1103276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems.
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Affiliation(s)
- Xinzhao Zhou
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture, Foshan, China
| | - Xiangjun Zou
- Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture, Foshan, China
- Foshan Sino-tech Industrial Technology Research Institute, Foshan, China
| | - Wei Tang
- Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture, Foshan, China
| | - Zhiwei Yan
- Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture, Foshan, China
| | - Hewei Meng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Xiwen Luo
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- College of Engineering, South China Agricultural University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou, China
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