Global meat consumption driver analysis with machine learning methods
Author:
Funder
National Key Research and Development Program of China
China Postdoctoral Science Foundation
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12571-024-01455-y.pdf
Reference68 articles.
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2. Andriamparany, J. N., Hänke, H., & Schlecht, E. (2021). Food security and food quality among vanilla farmers in Madagascar: The role of contract farming and livestock keeping. Food Security, 13(4), 981–1012. https://doi.org/10.1007/s12571-021-01153-z
3. Arteaga-Pérez, L. E., Casas-Ledón, Y., Pérez-Bermúdez, R., Peralta, L. M., Dewulf, J., & Prins, W. (2013). Energy and exergy analysis of a sugar cane bagasse gasifier integrated to a solid oxide fuel cell based on a quasi-equilibrium approach. Chemical Engineering Journal, 228, 1121–1132. https://doi.org/10.1016/j.cej.2013.05.077
4. Aston, L. M., Smith, J. N., & Powles, J. W. (2013). Meat intake in Britain in relation to other dietary components and to demographic and risk factor variables: Analyses based on the National Diet and Nutrition Survey of 2000/2001. Journal of Human Nutrition and Dietetics, 26(1), 96–106. https://doi.org/10.1111/j.1365-277X.2012.01278.x
5. Azodi, C. B., Tang, J., & Shiu, S. H. (2020). Opening the black box: Interpretable machine learning for geneticists. Trends in Genetics, 36(6), 442–455. https://doi.org/10.1016/j.tig.2020.03.005
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