No abstract

SUMMARYCapturing agricultural heterogeneity through the analysis of farm typologies is key with regard to the design of sustainable policies and to the adoptability of new technologies. An optimal balance needs to be found between, on the one hand, the requirement to consider local stakeholder and expert knowledge for typology identification, and on the other hand, the need to identify typologies that transcend the local boundaries of single studies and can be used for comparisons. In this paper, we propose a method that supports expert-driven identification of farm typologies, while at the same time keeping the characteristics of objectivity and reproducibility of statistical tools. The method uses a range of multivariate analysis techniques and it is based on a protocol that favours the use of stakeholder and expert knowledge in the process of typology identification by means of visualization of farm groups and relevant statistics. Results of two studies in Zimbabwe and Kenya are shown. Findings obtained with the method proposed are contrasted with those obtained through a parametric method based on latent class analysis. The method is compared to alternative approaches with regard to stakeholder-orientation and statistical reliability.

La identificación de tipologías prediales es una herramienta útil para sintetizar la diversidad intrínseca de cada sistema de producción y puede ser eficientemente usada para realizar una selección racional y metódica de las fincas representativas en el contexto de proyectos de investigación y extensión. Sin embargo, las metodologías que se utilizan más comúnmente para producir una tipología presentan algunas características que limitan su aplicación expeditiva para la selección de fincas piloto. El objetivo de este trabajo fue realizar una identificación cuantitativa y una caracterización de tipologías prediales, sobre la base de análisis multivariado de fincas productoras de cerezas en el sur de la Patagonia argentina. Se aplicó una metodología innovadora combinando escala multidimensional, análisis de conglomerados y análisis de porcentajes de semejanza, sobre la base de la cual se identificaron seis diferentes tipos de fincas. Las ventajas de este método para la selección de fincas representativas son mostradas y discutidas a través de la individuación de una finca piloto dentro de cada tipo.

Summary Large-scale sharing of genomic quantification data requires standardized access interfaces. In this Global Alliance for Genomics and Health (GA4GH) project we developed RNAget, an API for secure access to genomic quantification data in matrix form. RNAget provides for slicing matrices to extract desired subsets of data and is applicable to all expression matrix-format data, including RNA-seq and microarrays. Further, it generalizes to quantification matrices of other sequence-based genomics such as ATAC-seq and ChIP-seq. Availability and Implementation https://ga4gh-rnaseq.github.io/schema/docs/index.html Supplementary information Supplementary data are available at Bioinformatics online.