Food Sovereignty Index in Mexico Through Principal Component Analysis: Is There Still Hope for the Mexican Case?
DOI:
https://doi.org/10.47847/fagropec.v18n1a4Keywords:
Neural network, estimation, food systemAbstract
The measurement of Food Sovereignty is essential to assess the extent to which an economy can control its most diverse and sustainable food systems without relying excessively on external markets. It is crucial for evaluating food access, availability, and quality. The objective of this research was to estimate the Food Sovereignty Index in Mexico for the period from 1995 to 2023. The analysis was conducted using data from the World Bank. Principal Component Analysis (PCA) techniques were employed to reduce data dimensionality and to identify and group key variables related to Food Sovereignty; a total of 38 variables were used. Despite the programs implemented to achieve Food Sovereignty, the trend was negative. Furthermore, for comparison and validation of the results, the study relied on the yellow corn series and Neural Network analysis.
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