03148naa a2200313 a 450000100080000000500110000800800410001910000230006024501530008326000090023652021730024565000250241865000240244365000210246765000190248865000190250765300260252665300260255265300400257865300290261865300170264765300290266465300330269370000150272670000180274170000190275970000150277877300410279315296262023-08-28       2001    bl uuuu     u00u1 u    #d1 aEEUWIJK, F. A. van  aSome voculabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials.h[electronic resource]  c2001  aAbstract: For the improvement of genetic material suitable for on farm use under low-input conditions. participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model tit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor- analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. The statistical machinery involved is illustrated on an example data set consisting of barley trials from the Inter- national Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.  aCorrelated responses  aGenetic correlation  aGenetic variance  aPlant breeding  aPlant genetics  aFactor-analytic model  aFormal plant breeding  aGenotype by environment interaction  aHeterogeneity of varianc  aMixed models  aMulti-environment trials  aParticipatory plant breeding1 aCOOPER, M.1 aDeLACY, I. H.1 aCECCARELLI, S.1 aGRANDO, S.  tEuphyticagv. 122, p. 477-490, 2001.