Blackshaw, R. E., Johnson, E. N., Gan, Y., May, W. E., McAndrew, D. W., Barthet, V., McDonald, T. and Wispinski, D. 2011. Alternative oilseed crops for biodiesel feedstock on the Canadian prairies. Can. J. Plant Sci. 91: 889–896. Increased demand for biodiesel feedstock has encouraged greater napus canola (Brassica napus L.) production, but there may be a need for greater production of other oilseed crops for this purpose. A multi-site field study was conducted to determine the oil yield potential of various crops relative to that of napus canola in the semi-arid, short-season environment of the Canadian prairies. Oilseed crops evaluated included rapa canola (Brassica rapa L.), juncea canola (Brassica juncea L.), Ethiopian mustard (Brassica carinata L.), oriental mustard (Brassica juncea L.), yellow mustard (Sinapis alba L.), camelina (Camelina sativa L.), flax (Linum usitatissimum L.), and soybean [Glycine max (L.) Max.]. Crop emergence and growth were generally good for all crops, but soybean did not fully mature at some locations. The number of site-years (out of a total of 9) that crops attained similar or greater yields compared to napus canola were camelina (6), oriental mustard (5), juncea canola (3), flax (3), soybean (3), rapa canola (2), yellow mustard (2), and Ethiopian mustard (1). The ranking of seed oil concentration was napus canola=rapa canola= juncea canola=flax>camelina=oriental mustard>Ethiopian mustard>yellow mustard>soybean. Considering yield and oil concentration, the alternative oilseed crops exhibiting the most potential for biodiesel feedstock were camelina, flax, rapa canola and oriental mustard. Oils of all crops were easily converted to biodiesel and quality analyses indicated that all crops would be suitable for biodiesel feedstock with the addition of antioxidants that are routinely utilized in biodiesel fuels.

Structural equation modeling in the plant sciences: An example using yield components in oat. Can. J. Plant Sci. 91: 603Á619. Structural equation modeling (SEM) is a powerful statistical approach for the analysis of complex intercorrelated data with a wide range of potential applications in the plant sciences. In this paper we introduce plant scientists to the principles and practice of SEM using as an example an agronomic field trial. We briefly review the history of SEM and path analysis and introduce the statistical concepts underlying SEM. We demonstrate the use of observed and latent variable structural equation models using a multi-site multi-year field trial examining the effects of seed size and seeding density on the plant density and yield of oat in Saskatchewan. Using SEM allowed for insights that a standard univariate analysis would not have revealed. We show that seeding density has strong effects on plant and panicle density, but has very limited effects on final yield. Plant density has a consistent non-linear effect on panicle density across location that was not affected by precipitation. In contrast, the implicit effect of precipitation on seed number appears to be the main driver for final yield. Incorporating precipitation data into the model demonstrates how mechanistic models can be developed by including in the path diagram variables that would normally treated as random factors in a mixed model analysis. Finally, we provide a guideline to assist plant scientists in determining whether SEM is an appropriate method to be used for the analysis of their data.Can. J. Plant Sci. Downloaded from pubs.aic.ca by University of P.E.I. on 08/11/15For personal use only.Can. J. Plant Sci. Downloaded from pubs.aic.ca by University of P.E.I. on 08/11/15For personal use only.