Anbessa, Y. and Juskiw, P. 2012. Review: Strategies to increase nitrogen use efficiency of spring barley. Can. J. Plant Sci. 92: 617–625. Improvement in nitrogen use efficiency (NUE) is important to reduce input costs and the negative impact of excessive N on the environment. This review found that barley growers in western Canada have over the years adopted a number of improved N management strategies including soil testing and adjusting rate of N fertilization accordingly, switching from fall application to spring application of N fertilizers, and side-dressing placement of N that gives plant roots easier access to N nutrition. However, it is our opinion that use of variable N rates, choice of N fertilizer type that is less susceptible to losses, and improved manure management are some of the areas where further increase in NUE should be sought. As well, barley germplasms show substantial differences in NUE and genetic selection could increase NUE. Genetic improvement of NUE in barley should be possible both by the traditional breeding approach of crossing and pyramiding NUE genes from across different sources as well as through the development of transgenic barley. The integration of improved N management practices and more efficient cultivars may bring about a significant increase in NUE and ultimately grain yield of barley under the target moderate rate of N application.

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...