An F1 derived doubled haploid (DH) population of 402 lines from the adapted spring wheat cross Superb (high yielding)/BW278 (low yielding) was developed to identify quantitative trait loci (QTL) associated with yield and yield components. A subset of the population (186 lines) was evaluated in replicated field trials in 2001 and 2002 at six locations in Manitoba and Saskatchewan, Canada. Agronomic parameters, grain yield and yield components including 1,000 grain weight, harvest index, average seed weight spike(-1), seed number spike(-1) and spikes number m(-2) were measured. A genetic map was constructed with 268 microsatellite marker loci and included two morphological genes, reduced plant height, Rht-B1b, and the presence/absence of awns, B1. Composite interval mapping was conducted to estimate the location and effect of QTL associated with the evaluated traits. A total of 53 QTL were identified on 12 chromosomes for the 9 evaluated traits with the coefficient of determination ranging from 0.03 to 0.21 of the total variation. The increase in yield and yield components ranged from 4.5 to 17.1% over the population mean. The five grain yield QTL were detected on chromosomes 1A, 2D, 3B, and 5A and showed a combined increase of 34.4%, over the population mean. The alleles from Superb were associated with increased yield for four of the five QTL. This study identified potential chromosome segments for use in marker-assisted selection to improve yield and yield components in spring wheat.

The objective of the study was to determine the effects of feed delivery time and its interactions with dietary concentrate inclusion and parity on milk production and on 24-h averages and patterns of feed intake and blood metabolites. Four multiparous and 4 primiparous lactating Holstein cows were used in a 4 x 4 Latin square design with a 2 x 2 factorial arrangement of treatments. Experimental periods included 14 d of adaptation and 7 d of sampling. A higher concentrate diet with a forage:concentrate ratio (dry matter basis) of 38:62 or a lower-concentrate diet with a forage:concentrate ratio of 51:49 was delivered at either 0900 or 2100 h. During sampling periods, daily feed intakes, as well as feed intakes during 3-h intervals relative to feed delivery, were determined. During 2 nonconsecutive days of the sampling period, jugular blood was sampled every 2 h. Average temperature and relative humidity in the experimental facility were 20.4 degrees C and 68.1%, and the maximum daily air temperature did not exceed 25 degrees C. This data does not suggest that cows were heat-stressed. Changing feed delivery time from 0900 to 2100 h increased the amount of feed consumed within 3 h after feeding from 27 to 37% of total daily intake but did not affect daily dry matter intake. The cows fed at 2100 h had lower blood glucose at 2 h after feeding but greater blood lactate and beta-hydroxybutyrate acid at 2 and 4 h after feeding than cows fed at 0900 h. These effects of feed delivery time on the 24-h patterns in blood metabolites may be caused by the greater feed intake during the 3 h after feed delivery of the cows fed at 2100 h. Daily averages of glucose, urea, lactate, and beta-hydroxybutyrate acid and nonesterified fatty acids in peripheral blood were not affected by time of feeding. The change in feed delivery time did not affect milk yield and milk protein but increased milk fat percentage from 2.5 to 2.9% and milk fat yield from 0.98 to 1.20 kg/d in multiparous cows, without affecting milk fat in primiparous cows. The interactions between diet and time of feeding on daily feed intake, milk production, and blood metabolites were not significant. The effects of the time of feed delivery on the 24-h patterns in blood metabolites suggest that this time may affect peripheral nutrient availability. Results of this study suggest beneficial effects of feeding at 2100 h instead of at 0900 h on milk fat production of lactating cows, but parity appears to mediate this effect.

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Summary A situation is described where breeding values (BVs) predicted by Best Linear Unbiased Prediction for selection criteria are used to predict an aggregate breeding objective consisting of traits that may or may not have been measured. The index weights by which the predicted BVs are multiplied are the same for all animals with the same available predicted BVs, and they depend only on the genetic variances and covariances among the selection criteria and the traits in the objective, and on the economic values of these traits. The variance of the index and expected genetic gains in both the selection criteria and the traits of the objective, however, depend on the inbreeding coefficient and the prediction error variances and covariances of the predicted BVs for each animal. When these prediction error variances and covariances are not available for all animals approximations based on assumed “standard” animals might be used to give an indication of the direction in which the population will move under selection. Zusammenfassung Wirtschaftliche Indices aus mit BLUP geschätzten Zuchtwerten Mit BLUP (Best Linear Unbiased Prediction) geschätzte Zuchtwerte (ZW) für meßbare Selektionskriterien werden zum Schätzen eines Zuchtzieles verwendet, welches sowohl gemessene als auch nicht gemessene Merkmale enthalten kann. Die Indexgewichte, mit denen die geschätzten ZW multipliziert werden, sind für alle Tiere mit den gleichen verfügbaren ZW identisch. Sie hängen nur von den genetischen Varianzen und Kovarianzen der Selektionskriterien und der Merkmale im Zuchtziel und den wirtschaftlichen Gewichten für diese Merkmale ab. Die Varianz des Indexes und die erwarteten Zuchtfortschritte in den einzelnen Indexkriterien und Merkmalen im Zuchtziel hängen dagegen vom Inzuchtkoeffizienten und den Varianzen und Kovarianzen der geschätzten ZW (prediction error variances and covariances) für jedes Tier ab. Wenn die Varianzen und Kovarianzen der geschätzten ZW nicht für jedes Tier verfügbar sind, können auf angenommenen “Standardtieren” beruhende Näherungen verwendet werden, um einen Hinweis zu erhalten, in welche Richtung sich die Population bei Selektion nach diesem Index verschieben wird.