




Babcock, Bruce A.; Carriquiry, Alicia L.; Stern, Hal S.. 
The value of soiltest information in planning fertilizer application levels is determined by using agricultural fieldplot data to estimate the posterior distribution of mean soilnitrate concentrations at a give location. Optimal decisions concerning fertilizer application levels are made with respect to this posterior distribution. Average reductions in fertilizer application rates range from 15 to 41 percent, depending on the form of prior information that is available. These reductions are achieved by increasing the variability of application rates over time. Disregarding the uncertainty that remains after the soil testing significantly overstates the expected benefits of soil testing. 
Tipo: Working or Discussion Paper 
Palavraschave: Bayesian methods; Fertilizer rates; Posterior distributions; Soil tests; Farm Management. 
Ano: 1996 
URL: http://purl.umn.edu/18633 
 

 

 


AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; MUÑOZ, P.. 
Background: A complete approach for genomewide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additivedominance models. The objective of this paper was (i) to compare the performance of 10 additivedominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), cosegregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated... 
Tipo: Artigo em periódico indexado (ALICE) 
Palavraschave: Modelo Bayesiano; Genética quantitativa; Melhoramento genético; Parâmetro genético; Dominance genomic models; Bayesian methods; Lasso methods; Selection accuracy. 
Ano: 2015 
URL: http://www.alice.cnptia.embrapa.br/handle/doc/1022575 
 

 


