Fecha/hora: 07/06/2024 01:30:12

Biblioteca(s): | INIA Las Brujas.
Tipo de la producción científica: | Artículos en Revistas Indexadas Internacionales
Circulación/Nivel: | Internacional - --
Fecha corriente: | 16/07/2020
Fecha de la última actualización: | 16/07/2020
Autoria: | HARRIS, P.; LANFRANCO, B.; LU, B.; COMBER, A.
Afiliación: | PAUL HARRIS, Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton EX20 2SB, UK; BRUNO ANTONIO LANFRANCO CRESPO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BINBIN LU, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; ALEXIS COMBER, School of Geography, University of Leeds, Leeds LS2 9JT, UK.
Título: | Influence of geographical effects in hedonic pricing models for grass-fed cattle in Uruguay. [OPEN ACCESS].
Año de publicación: | 2020
Fuente/Imprenta: | Agriculture, 2020, 10(7), 299; https://doi.org/10.3390/agriculture10070299
ISSN: | eISSN 2077-0472
DOI: | 10.3390/agriculture10070299
Idioma: | Inglés

Notas:

Article 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.

Contenido:

ABSTRACT.
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.

Palabras-Clave: | Beef cattle prices; MGWR; Multiscale; Provenance; Spatial regression.
Categoría del tema: | A50 Investigación agraria

URL:

http://www.ainfo.inia.uy/digital/bitstream/item/14550/1/Harris-et-al-2020-Agriculture-107-spatial-hedonic.pdf
https://www.mdpi.com/2077-0472/10/7/299/pdf
https://www.mdpi.com/2077-0472/10/7/299/s1

Registro original: | INIA Las Brujas (LB)
Ejemplares: | LB - (102373-1) - PXI - AP - DD - PP/AGRICULTURE/2020 -  -  - DIS