02758naa a2200253 a 450000100080000000500110000800800410001902200200006002400370008010000150011724501350013226000090026750004700027652015500074665300230229665300090231965300150232865300150234365300230235870000180238170000110239970000150241077300790242510612312020-07-16       2020    bl uuuu     u00u1 u    #d  aeISSN 2077-04727 a10.3390/agriculture100702992DOI1 aHARRIS, P.  aInfluence of geographical effects in hedonic pricing models for grass-fed cattle in Uruguay. [OPEN ACCESS].h[electronic resource]  c2020  aArticle history: Received: 27 June 2020 / Revised: 12 July 2020 / Accepted: 13 July 2020 / Published: 15 July 2020. This article belongs to the Section Agricultural Economics, Policies and Rural Management. The article contains supplementary material. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  aABSTRACT. A series of non-spatial and spatial hedonic models of feeding and replacement cattle prices at video auctions in Uruguay (2002 to 2009) were specified with predictors measuring marketing conditions (e.g., steer price), cattle characteristics (e.g., breed) and agro-ecological factors (e.g., soil productivity, water characteristics, pasture condition, season). Results indicated that cattle prices produced under extensive production systems were influenced by all of predictor categories, confirming that found previously. Although many of the agro-ecological predictors were inherently spatial in nature, the incorporation of spatial effects into the estimation of the hedonic model itself, through either a spatially-autocorrelated error term or allowing the regression coefficients to vary spatially and at different scales, was able to provide greater insight into the cattle price process. Through the latter extension, using a multiscale geographically weighted regression, which was the most informative and most accurate model, relationships between cattle price and predictors operated at a mixture of global, regional, local and highly local spatial scales. This result is considered a key advance, where uncovering, interpreting, and utilizing such rich spatial information can help improve the geographical provenance of Uruguayan beef and is critically important for maintaining Uruguay´s status as a key exporter of beef with respect to the health and safety benefits of natural, open-sky, grass-fed production systems.  aBeef cattle prices  aMGWR  aMultiscale  aProvenance  aSpatial regression1 aLANFRANCO, B.1 aLU, B.1 aCOMBER, A.  tAgriculture, 2020, 10(7), 299; https://doi.org/10.3390/agriculture10070299