Abstract
The growing global meat consumption has serious consequences on human health, the environment and ultimately impacts global food security. Therefore, identifying the drivers of meat consumption and predicting its evolution is necessary. We compared four machine learning methods in modelling meat consumption, leading to the selection of a random forest-based model to detect main drivers for global meat consumption. Our results show that per capita meat consumption is mainly driven by socioeconomic factors, such as national GDP and urbanization. However, the strength of these drivers declined between 1990 and 2018. Pork, beef, and poultry consumption are mainly driven by socioeconomic factors, whereas mutton consumption appears driven by other factors such as the per capita agricultural land. In this work, the model-agnostic interpretability method is introduced to measure the marginal effect of each driver on meat consumption. We found that there may be insufficient evidence to support the inverted U-shaped relationship between per capita GDP and meat consumption, which is reported in previous studies. Our analysis may provide avenues for predicting meat consumption at the national scale.
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The data and code that support the findings of this study are available from the corresponding author upon request.
Abbreviations
- AEF:
-
Agro-environmental factors
- ANN:
-
Artificial neural network
- CF:
-
Cultural factors
- DF:
-
Demographic factors
- EVS:
-
Explained variance score
- \(\widehat{f}\) :
-
Machine learning generated model
- GDP:
-
Gross domestic product
- GF:
-
Globalization factors
- GPR:
-
Gaussian process regression
- MAPE:
-
Mean absolute percentage error (%)
- ML:
-
Machine learning
- N:
-
Total number of samples
- PDPs:
-
Partial dependence plots
- PF:
-
Price factors
- PPPGKD:
-
Purchasing-power-parity-adjusted Geary–Khamis dollar, constant 2011
- R2 :
-
Determination coefficients
- RF:
-
Random forest
- RMSE:
-
Root mean square error
- SEF:
-
Socioeconomic factors
- SVM:
-
Support vector machine
- \({\text{Var}}\) :
-
Calculation of variance
- \({x}_{s}\) :
-
One or two input features of interest in the model
- \({x}_{c}\) :
-
Other input features outside the input features of interest in the model
- \({Y}_{j}^{model}\) :
-
Simulated value of the sample
- \({Y}_{j}\) :
-
Observed value of the sample
- \({Y}_{ave}^{act}\) :
-
Average value of the observed value
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Acknowledgements
This work is jointly supported by National Key Research and Development Program of China (Grant No. 2023YFF1303702), China Postdoctoral Science Foundation (Grant No. 2023M740287), National Natural Science Foundation of China (Grant No. 42301070 and 42301101). Special thanks to Dr. Zifei Yang at School of Earth and Environmental Sciences, Cardiff University for assistance on English edit of this study.
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Junwen Jia: Conceptualization, Writing – original draft, Methodology, Visualization, Writing – review & editing. Fang Wu: Visualization, Writing – review & editing, Funding acquisition. Hao Yu: Visualization, Writing – review & editing. Jieming Chou: Validation, Supervision, Funding acquisition. Qinmei Han: Funding acquisition. Xuefeng Cui: Funding acquisition, Conceptualization, Validation, Investigation, Writing – review & editing, Supervision.
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All authors: Junwen Jia, Fang Wu, Hao Yu, Jieming Chou, Qinmei Han, Xuefeng Cui. This material has not been published in whole or in part elsewhere. The manuscript is not currently being considered for publication in another journal. All authors have been personally and actively involved in substantive work leading to the manuscript and will hold themselves jointly and individually responsible for its content.
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Jia, J., Wu, F., Yu, H. et al. Global meat consumption driver analysis with machine learning methods. Food Sec. (2024). https://doi.org/10.1007/s12571-024-01455-y
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DOI: https://doi.org/10.1007/s12571-024-01455-y