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	Provedor de dados AgEcon (5) Autor English, Burton C. (3) Roberts, Roland K. (3) Larson, James A. (2) Babcock, Bruce A. (1) Breidt, F. Jay (1) Bullock, David S. (1) Lambert, Dayton M. (1) Larkin, Sherry L. (1) Lowenberg-DeBoer, James (1) Mahajanashetti, S.B. (1) Marra, Michele C. (1) Martin, Steven W. (1) Mooney, Daniel F. (1) Pautsch, Gregory R. (1) Paxton, Kenneth W. (1) Reeves, Jeanne M. (1) Walton, Jonathan C. (1) Mais... Palavra-chave Variable rate technology (5) Farm Management (3) Q16 (3) Precision agriculture (2) Precision farming (2) Research and Development/Tech Change/Emerging Technologies (2) Site-specific management (2) Agribusiness (1) C31 (1) Capital budgeting (1) Community/Rural/Urban Development (1) Crop Production/Industries (1) D21 (1) Decision aid (1) Decision support (1) Estimation risk (1) Farm management (1) Geostatistics (1) Information technology (1) Land Economics/Use (1) Mais... Tipo do documento Conference Paper or Presentation (3) Journal Article (2) Ano 2010 (1) 2009 (1) 2006 (1) 2001 (1) 1999 (1) País United States (5) Idioma Inglês (5)		Registro completo Provedor de dados:  AgEcon País:  United States Título:  Optimal Information Acquisition under a Geostatistical Model Autores:  Pautsch, Gregory R. Babcock, Bruce A. Breidt, F. Jay Data:  2006-07-31 Ano:  1999 Palavras-chave:  Estimation risk Geostatistics Nitrogen fertilizer Optimal sample sizes Precision farming Single rate technology Variable rate technology Variogram Research Methods/ Statistical Methods Resumo:  Studies examining the value of switching to a variable rate technologies (VRT) fertilizer program assume producers possess perfect soil nitrate information. In reality, producers estimate soil nitrate levels with soil sampling. The value of switching to a VRT program depends on the quality of the estimates and on how the estimates are used. Larger sample sizes, increased spatial correlation, and decreased variability improve the estimates and increase returns. Fertilizing strictly to the estimated field map fails to account for estimation risk. Returns increase if the soil sample information is used in a Bayesian fashion to update the soil nitrate beliefs in non-sampled sites. Tipo:  Conference Paper or Presentation Idioma:  Inglês Identificador:  23186 http://purl.umn.edu/18358 Editor:  AgEcon Search Relação:  Iowa State University>Center for Agricultural and Rural Development>CARD Working Paper Series CARD Working Paper 99-WP 217 Formato:  31 application/pdf Fechar

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