Walsh, K. D., Puttick, D. M., Hills M. J., Yang R.-C., Topinka, K. C. and Hall, L. M. 2012. Short Communication: First report of outcrossing rates in camelina [ Camelina sativa (L.) Crantz], a potential platform for bioindustrial oils. Can. J. Plant Sci. 92: 681–685. Outcrossing rates in camelina were low (0.09–0.28%), suggesting camelina is a primarily self-pollinated species. Outcrossing was affected by flowering synchrony influenced by planting date as well as direction and distance (20, 40 or 60 cm) from the pollen source. Pollen-mediated intra-specific gene flow is unlikely to prohibit the development of camelina as a bioindustrial platform. Short distance outcrossing results will be used to design experiments to evaluate outcrossing rates at a field scale.

P. 2011. Analysis of covariance in agronomy and crop research. Can. J. Plant Sci. 91: 621Á641.Analysis of covariance (ANCOVA) is a statistical technique that combines the methods of the analysis of variance (ANOVA) and regression analysis. However, ANCOVA is an advanced topic that often appears towards the end of many textbooks, and thus, it is either taught cursorily or ignored completely in many statistics classes. Additionally, many elaborated applications of ANCOVA to agronomy and crop research along with uses of the latest statistical software are rarely described in textbooks or classes. The objectives of this paper are to provide an overview on conventional ANCOVA and to introduce more advanced uses of ANCOVA under mixed models. We describe three elaborate applications including (i) the use of ANCOVA for dissecting dosage responses for different treatments, (ii) stability of treatments across multiple environments and (iii) removal of spatial variation that is not effectively controlled by blocking. These analyses illustrate that ANCOVA is either a simpler analysis or provides more information than conventional statistical methods. We provide a technical appendix (Appendix A) on principles and theory underlying mixed-model analysis of ANCOVA along with SAS programs (Appendix B) for more uses and in-depth understanding of this powerful technique in agronomy and crop research. Key words: Analysis of covariance, dosage response, mixed models, nearest neighbour analysis, orthogonal polynomials, spatial variability, stability analysis, statistical control of errors Yang, R.-C. et Juskiw, P. 2011. Analyse de la covariance en recherche agronomique et agricole. Can. J. Plant Sci. 91:621Á641. L'analyse de la covariance (ANCOVA) est une me´thode statistique qui combine l'analyse de la variance (ANOVA) et l'analyse de re´gression. Pourtant, l'ANCOVA est un sujet avance´que den nombreux ouvrages de statistique n'abordent souvent qu'a`la fin, donc qui n'est enseigne´qu'en diagonale, voire ignore´totalement dans maints cours de statistique. D'autre part, rares sont les ouvrages et les cours qui de´crivent les multiples applications e´volue´es de l'ANCOVA en recherche agronomique et agricole ou les plus re´cents logiciels de statistique. Cet article donne un aperc¸u de l'ANCOVA classique et pre´sente des applications plus complexes de cette dernie`re dans des mode`les mixtes. Les auteurs de´crivent trois applications e´volue´es, soit (i) le recours a`l'ANCOVA pour de´tailler la re´action au dosage dans divers traitements, (ii) la stabilite´du traitement dans de multiples environnements et (iii) la suppression de la variation spatiale que le blocage ne controˆle pas entie`rement. Ces applications re´ve`lent que l'ANCOVA soit est plus simple, soit donne plus de renseignements que les me´thodes statistiques usuelles. Les auteurs pre´sentent les principes et la the´orie sous-jacents al 'analyse par mode`le mixte de l'ANCOVA ainsi que des programmes SAS dans deux annexes afin de mieux illustrer l'usage de cette puissante tech...