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Article

Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay

1
Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton EX20 2SB, UK
2
Instituto Nacional de Investigación Agropecuaria (INIA), INIA-Las Brujas, Canelones 90200, Uruguay
3
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
4
School of Geography, University of Leeds, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Agriculture 2020, 10(7), 299; https://doi.org/10.3390/agriculture10070299
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)
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. View Full-Text
Keywords: beef cattle prices; spatial regression; multiscale; provenance; MGWR beef cattle prices; spatial regression; multiscale; provenance; MGWR
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MDPI and ACS Style

Harris, P.; Lanfranco, B.; Lu, B.; Comber, A. Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay. Agriculture 2020, 10, 299. https://doi.org/10.3390/agriculture10070299

AMA Style

Harris P, Lanfranco B, Lu B, Comber A. Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay. Agriculture. 2020; 10(7):299. https://doi.org/10.3390/agriculture10070299

Chicago/Turabian Style

Harris, Paul; Lanfranco, Bruno; Lu, Binbin; Comber, Alexis. 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay" Agriculture 10, no. 7: 299. https://doi.org/10.3390/agriculture10070299

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