Concordancia entre el Índice de Capacidad Laboral y los Años de Discapacidad Sobrevenida Estimados mediante metodología PoRT-9LSQ

Objetivo: Analizar la asociación entre los estilos de vida y factores de riesgo para la salud que pueden suponer un abandono prematuro del trabajo, con los años de discapacidad sobrevenida estimados (ADSE) en población laboral, y calcular la correlación entre el Índice de Capacidad Laboral (ICL) y el Work Ability Score (WAS), y ambos con los ADSE y su coste económico. Métodos: Estudio transversal en una muestra de trabajadores a los que se realizó un examen de salud. La información se recogió mediante los cuestionarios ICL y WAS, y la metodología PoRT-9LSQ. Se realizó un análisis de la asociación entre los factores de riesgo analizados y los ADSE mediante regresión lineal y análisis de la varianza (ANOVA). Se analizó la correlación entre ICL y WAS usando el coeficiente de correlación intraclase (CCI), y con los ADSE y su coste económico mediante regresión lineal ajustada.  Resultados: Se incluyeron 590 trabajadores. Los factores que más influyeron en la media de ADSE fueron el sedentarismo, la mala alimentación y el sobrepeso/obesidad, con diferencias estadísticamente significativas según sexo, turno y ocupación (p<0,05). El CCI entre ICL y WAS fue del 93,0% para una valoración excelente/buena. La regresión lineal ajustada entre ICL y los ADSE fue de 7,982-0,136xICL (p<0,05), siendo similar para el WAS.  Conclusiones: El ICL se ha mostrado útil para la predictibilidad de los ADSE en población laboral, lo que facilitará la toma de decisiones del personal sanitario para identificar personas vulnerables favoreciendo cambios en los estilos de vida y el autocuidado.

Concordancia entre el Índice de Capacidad Laboral y los Años de Discapacidad Sobrevenida Estimados mediante metodología PoRT-9LSQ

Objetivo: Analizar la asociación entre los estilos de vida y factores de riesgo para la salud que pueden suponer un abandono prematuro del trabajo, con los años de discapacidad sobrevenida estimados (ADSE) en población laboral, y calcular la correlación entre el Índice de Capacidad Laboral (ICL) y el Work Ability Score (WAS), y ambos con los ADSE y su coste económico. Métodos: Estudio transversal en una muestra de trabajadores a los que se realizó un examen de salud. La información se recogió mediante los cuestionarios ICL y WAS, y la metodología PoRT-9LSQ. Se realizó un análisis de la asociación entre los factores de riesgo analizados y los ADSE mediante regresión lineal y análisis de la varianza (ANOVA). Se analizó la correlación entre ICL y WAS usando el coeficiente de correlación intraclase (CCI), y con los ADSE y su coste económico mediante regresión lineal ajustada.  Resultados: Se incluyeron 590 trabajadores. Los factores que más influyeron en la media de ADSE fueron el sedentarismo, la mala alimentación y el sobrepeso/obesidad, con diferencias estadísticamente significativas según sexo, turno y ocupación (p<0,05). El CCI entre ICL y WAS fue del 93,0% para una valoración excelente/buena. La regresión lineal ajustada entre ICL y los ADSE fue de 7,982-0,136xICL (p<0,05), siendo similar para el WAS.  Conclusiones: El ICL se ha mostrado útil para la predictibilidad de los ADSE en población laboral, lo que facilitará la toma de decisiones del personal sanitario para identificar personas vulnerables favoreciendo cambios en los estilos de vida y el autocuidado.

Valuation of livestock eco-agri-food systems: poultry, beef and dairy

As input for the TEEBAgriFood study, TEEB asked for a series of studies on livestock, rice, palm oil, inland fisheries and agro-forestry. This report deals with livestock production and aims to improve decision-making in livestock production policies, to enhance its viability, not just economically but also socially and environmentally. Livestock sector is important because they have high externalities and it is expected that livestock consumption will be 76% higher in 2050 compared to 2005 (Alexandratos and Bruisma, 2012) because of population growth, income growth, urbanization and preference shifts. This report aims to provide evidence that will help to identify policy options for the transition towards increased food security with sustainable livestock production systems, with particular emphasis on the role of smallholder farmers

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives