|
|
Registro completo
|
Biblioteca (s) : |
INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
22/02/2014 |
Autor : |
Congreso Mundial del Merino 4:1994: Montevideo - Benítez, D.;Cardellino, Ricardo A. |
Título : |
Trabajos presentados |
Fecha de publicación : |
1994 |
Fuente / Imprenta : |
Montevideo: S.U.L., 1994. |
Páginas : |
p. 292 |
Idioma : |
Español |
Asunto categoría : |
-- |
Marc : |
LEADER 00324nam a2200109 a 4500 001 1023091 005 2014-02-22 008 1994 bl uuuu u01u1 u #d 100 1 $aCONGRESO MUNDIAL DEL MERINO 4:1994: MONTEVIDEO - BENÍTEZ, D.;CARDELLINO, RICARDO A. 245 $aTrabajos presentados 260 $aMontevideo: S.U.L.$c1994 300 $ap. 292
Descargar
Esconder MarcPresentar Marc Completo |
Registro original : |
INIA Tacuarembó (TBO) |
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
|
Registro completo
|
Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
16/07/2020 |
Actualizado : |
16/07/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
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]. |
Fecha 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. MenosABSTRACT.
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 nat... Presentar Todo |
Palabras claves : |
Beef cattle prices; MGWR; Multiscale; Provenance; Spatial regression. |
Asunto categoría : |
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
|
Marc : |
LEADER 02758naa a2200253 a 4500 001 1061231 005 2020-07-16 008 2020 bl uuuu u00u1 u #d 022 $aeISSN 2077-0472 024 7 $a10.3390/agriculture10070299$2DOI 100 1 $aHARRIS, P. 245 $aInfluence of geographical effects in hedonic pricing models for grass-fed cattle in Uruguay. [OPEN ACCESS].$h[electronic resource] 260 $c2020 500 $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. 520 $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. 653 $aBeef cattle prices 653 $aMGWR 653 $aMultiscale 653 $aProvenance 653 $aSpatial regression 700 1 $aLANFRANCO, B. 700 1 $aLU, B. 700 1 $aCOMBER, A. 773 $tAgriculture, 2020, 10(7), 299; https://doi.org/10.3390/agriculture10070299
Descargar
Esconder MarcPresentar Marc Completo |
Registro original : |
INIA Las Brujas (LB) |
|
Biblioteca
|
Identificación
|
Origen
|
Tipo / Formato
|
Clasificación
|
Cutter
|
Registro
|
Volumen
|
Estado
|
Volver
|
Expresión de búsqueda válido. Check! |
|
|