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Memorias de investigación
Ponencias en congresos:
Assessment of a sustainable food security with a multivariate aggregated indicator
Año:2013
Áreas de investigación
  • Producción vegetal
Datos
Descripción
A sustainable food production and supply for a growing human population is a majorchallenge for the next years and decades. Food security, including supply and demand factors as physical and economic access and wealth and assimilation of nutrients, must be performed by a sustainable agricultural production that ensures the long term agricultural productivity and ecosystem services. This challenge must be studied with a holistic approach, where sustainability and food security may be assessed by a number of indicators that reveal the strengths and weaknesses, as well barriers and drivers operating in each country in a multinational scenario and a multilevel assessment. This assortment of indicators should be synthesized into an appropriate unique indicator that in spite of containing much information, is easy to understand by the end-users (policy-makers, scientific, technicians, etc.). Aggregated indicators help to communicate the information succinctly and to make easier to distinguish patterns in the data by formalizing the aggregation process that is often done implicitly, subjectively and intuitively. The aim of this work is to assess national sustainability of the agricultural production and food security with and aggregated indicator using multivariate statistical tools. The hierarchical structure of the framework was based upon three subsystems: human (food security and social dimension), natural (environmental dimension) and support (production and economic dimension). Indicators were organized according to Bossel¿s seven basic orientors: existence, effectiveness, freedom of action, security, adaptability, coexistence and psychological needs. A matrix of 60 countries x 21 variables was built up. This analysis was multinational including countries from Europe, Mediterranean basin, Maghreb, Mashreq, Middle East, Sahel and OPEC. These countries have been selected as the observations set. For each country 21 indicators were computed (7 orientors x 3 subsystems). The data were obtained from FAO, United Nations, Worldwatch Institute, World Resources Institute and other international organizations. In the food security subsystem were included variables about dietary energy and protein requirements, percentage of cereals in energy intake and well being. In the environmental susbsystem were included variables about water footprint, greenhouse gas emissions and ecological footprint. Dry matter yield and stability, agricultural inputs and food imports were included as economic variables. A principal component analysis (PCA) was performed with the data. This multivariate statistical tool used to study large sets of data. This method reproduces a great proportion of variance among a big number of variables by using a small number of new variables called principal components (PCs). The PCs are linear combinations of the original variables, and the analysis of multidimensional data is simplified when these are correlated. The first PC explains maximum variance between data, while the second component is a new combination of the original variables being orthogonal to the first component and explaining the second largest value of variation among observations, and so forth. The absorption of variance in each component is computed with the so-called eigenvalues. High absolute values of loadings of the variables (i.e. indicators) on the PCs imply that the indicator has a large bearing on the creation of that component. Thus, the most important indicators in each component, that best explain variance; will also be more useful in explaining variability between observations (i.e. countries). Each component will be a linear combination of indicators (variables) multiplied by their loadings on that component. Observations (countries) will have coordinates in each axis or component, computed with the standardized value of each variable (zero mean and unit variance) for that observation using the linear combination of variables with PCs obtained
Internacional
Si
Nombre congreso
1st International Conference on Global Food Security
Tipo de participación
960
Lugar del congreso
Noordwijkerhout The Netherlands
Revisores
Si
ISBN o ISSN
CDP08UPM
DOI
Fecha inicio congreso
29/09/2013
Fecha fin congreso
02/10/2013
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Título de las actas
Book of Abstracts 1st International Conference on Global Food Security
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Jose Soler Rovira (UPM)
  • Autor: Juan Manuel Arroyo Sanz (UPM)
  • Autor: Francisco Gonzalez Torres (UPM)
  • Autor: Carlos Rojo Hernandez (UPM)
  • Autor: Antonio Marquina Barrio (UCM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Sistemas de producción y protección vegetal sostenibles
  • Centro o Instituto I+D+i: Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios Medioambientales (CEIGRAM). Centro Mixto UPM-AGROMUTUA-ENESA
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