Browsing by Subject "Spatial autocorrelation"

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    Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems
    (2023) Golicz, Karolina; Piepho, Hans-Peter; Minarsch, Eva-Maria L.; Niether, Wiebke; Große-Stoltenberg, André; Oldeland, Jens; Breuer, Lutz; Gattinger, Andreas; Jacobs, Suzanne; Golicz, Karolina; Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Giessen, Germany; Piepho, Hans-Peter; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Minarsch, Eva-Maria L.; Department of Agronomy and Plant Breeding II, Organic Farming, Justus Liebig University Giessen, Giessen, Germany; Niether, Wiebke; Department of Agronomy and Plant Breeding II, Organic Farming, Justus Liebig University Giessen, Giessen, Germany; Große-Stoltenberg, André; Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Giessen, Germany; Oldeland, Jens; Institute for Globally Distributed Open Research and Education (IGDORE), Hamburg, Germany; Breuer, Lutz; Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Giessen, Germany; Gattinger, Andreas; Department of Agronomy and Plant Breeding II, Organic Farming, Justus Liebig University Giessen, Giessen, Germany; Jacobs, Suzanne; Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Giessen, Germany
    Agroforestry is a land-use system that combines arable and/or livestock management with tree cultivation, which has been shown to provide a wide range of socio-economic and ecological benefits. It is considered a promising strategy for enhancing resilience of agricultural systems that must remain productive despite increasing environmental and societal pressures. However, agroforestry systems pose a number of challenges for experimental research and scientific hypothesis testing because of their inherent spatiotemporal complexity. We reviewed current approaches to data analysis and sampling strategies of bio-physico-chemical indicators, including crop yield, in European temperate agroforestry systems to examine the existing statistical methods used in agroforestry experiments. We found multilevel models, which are commonly employed in ecology, to be underused and under-described in agroforestry system analysis. This Short Communication together with a companion R script are designed to act as an introduction to multilevel models and to promote their use in agroforestry research.
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    Modelling and diagnostics of spatially autocorrelated counts
    (2022) Jung, Robert C.; Glaser, Stephanie
    This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.