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The objective of this study was to quantify the effect of heat stress during the life of a pig on its final weight, as a first step toward a genetic evaluation for heat tolerance. Data included carcass weights of 23,556 crossbred pigs [Duroc x (Landrace x Large White)] raised on 2 farms in North Carolina and slaughtered from May 2005 through December 2006. Weather data were available from a nearby weather station. Lifetime of a pig was assumed to be partitioned into 2 periods. During an initial period, the effect of heat stress was assumed to be negligible or compensated for later. During the second period ending in slaughtering, the ADG was assumed to be affected linearly by heat load. Weekly heat load was calculated as degrees of average temperature-humidity index in excess of a threshold (18[degrees]C). The total heat load (H) was the sum of weekly heat loads during the second period. During the months of January to May H was 0; H reached a peak in September. The final BW during the peak of heat stress decreased about 6 kg compared with BW during months of non-heat stress. Weekly and monthly averages of carcass weight generally moved similarly to H. However, there were large fluctuations unrelated to H; the fluctuations were different on the 2 farms. The model included the effects of farm-year of slaughter, sex, age at slaughter, and H, where age at slaughter and H were linear regressions. In analyses, the threshold was varied from 16 to 20[degrees]C, and the second period was varied from 8 to 16 wk. The greatest [R.sup.2] (10.4%) was at the threshold of temperature-humidity index = 18[degrees]C for a period of 10 wk. Varying the threshold and the length of time reduced [R.sup.2] less than 1%. Least squares means of year-month and year-week of carcass weight were calculated using a model with the fixed effects farm-year-month or farm-year-week of slaughter, sex, and age at slaughter (linear covariate), and the random effect of birth litter. Changes in BW of finisher pigs due to heat stress can be quantified by H during the last 10 wk of the life of the pig. Key words: carcass weight, heat stress, pig

The objective of this study was to describe genetic variability of pig carcass weight as a function of heat stress. Data included carcass weights of 23,556 crossbred pigs [Duroc x (Landrace x Large White)] raised on 2 farms in North Carolina and harvested from May 2005 through December 2006. Weather data were obtained from a weather station located about 20 km from the furthest farm. Weekly heat load was calculated as degrees of average temperature-humidity index (THI) in excess of 18[degrees]C. The total heat load (H) was the sum of heat loads for 10 wk before harvest. Variance components were estimated with 3 models: univariate (UNI)--not accounting for heat stress, 2-trait (MT2), and random regression (RR). In all of the models, effects included contemporary group, sex, age at harvest, sire, and litter. In MT2, observations in months in which heat stress was observed ('hot') and not observed ('cold') were treated as separate traits. Heat stress was observed in the months of August to November 2005, as well as July to October 2006. No heat stress was observed in the months of May to July 2005, January to June 2006, and November to December 2006. The RR model added a random regression on heat load for the sire effect. Heat load was adjusted to a scale ranging from 0 (no heat stress) to 5 (greatest heat stress). The heritability estimate [+ or -] SE of carcass weight in UNI was 0.17 [+ or -] 0.01. In MT2, the estimates were 0.14 [+ or -] 0.01 for 'cold' and 0.28 [+ or -] 0.01 for 'hot'; the genetic correlation between carcass weight in 'hot' and 'cold' months was 0.42 [+ or -] 0.13. The heritability estimates obtained with RR were 0.20 [+ or -] 0.11, 0.19 [+ or -] 0.15, and 0.51 [+ or -] 0.17 for H = 0, 2.5, and 5, respectively. The genetic correlation between the performance in 'cold' months (H = 0), and performance under maximum heat load (H = 5) was 0.02, between H = 0 and intermediate heat load (H = 2.5) was 0.52, and between H = 2.5 and H = 5 was 0.86. Rank correlations between EPD derived from the different models ranged from 0.82 to 0.94 between carcass weights under similar H, 0.18 to 0.54 between carcass weights under high and low H, and 0.66 to 0.91 between carcass weights of intermediate and high/low H. Heritability for growth was greater under heat stress. Selection for crossbred performance would be optimal when data for periods both in the absence and presence of heat stress were considered. Key words: carcass weight, genetic parameter, heat stress, pig, random regression

The objective of this study was to determine the suitability of 2 methods for computing approximate accuracies of predicted breeding values, in which accuracy was defined as the squared correlation between the predicted and true breeding value, when modeling growth traits in beef cattle using random regression (RR) models. The first method (Strabel et al., S-M-B) was designed for use with multitrait models; thus, its use with RR models requires the clustering of measurements into different traits. The second method (Tier and Meyer, T-M) was more general, because it accounted for random coefficients other than zeros and ones and thus it could be used directly when fitting RR models. To investigate the performance of both methods, their results were compared with the true accuracies using a balanced simulated data set. The largest difference between approximate and true average accuracies for direct effects was observed at 205 d when SM-B was used (4.6% males and 8.8% females). With regard to maternal effects, the largest differences in average accuracies were observed at 205 d in males when S-M-B was used (31.8%) and at the same age in females but when using T-M (33.3%). In general, bias increased for direct effect accuracies in males at the tails of the accuracy range, but for females and for maternal effect accuracies in both sexes, bias increased as accuracy increased. When a population was simulated to create large numbers of progeny for base females that did not have individual records, much greater errors were observed in the regression of approximate values on the true ones. When both approximate methods were compared using a real beef cattle data set, a good agreement was observed, particularly for direct effect accuracies in sires [i.e., at 205 d, the regressions were 0.98 (direct) and 0.95 (maternal) with [r.sup.2] over 0.99]. The largest discrepancies for sires between the methods were observed at 205 d for direct (2.7%) and maternal (16.3%) effect accuracies. For dams, the largest differences between methods were also observed at 205 d, 9.3% (direct), and 15.2% (maternal). The differences between methods for nonparent cattle were greater than for dams for maternal effect accuracies but intermediate between sires and dams for direct effect accuracies. In spite of the less biased results provided by T-M, its use could be problematic when employed in evaluations of large populations due to its greater memory and computation requirements (e.g., 170 and 478% more than S-M-B for a population of 11 million). Key words: accuracy, beef cattle, growth, linear spline, random regression

An evaluation of a new maternal line (LP) of rabbits was carried out. This new line was founded following a scheme similar to that applied in the selection for hyperprolificacy in rabbits or pigs. In this case, the selection criteria were hyperlongevity and an independent culling level near the average for prolificacy. Evaluation was carried out by comparison of the reproductive and longevity performance of the LP line with another maternal line recognized for good reproductive performance and standard longevity (V line). The results indicate that the LP line could be a valuable resource for inclusion in the current 3-way cross schema used in rabbit production, because females showed better survival ability and nearly the same prolificacy as the well-reputed V line. A V doe was 1.3 times more likely to leave the herd than an LP doe, and the probability of the differences in prolificacy between lines being greater than 0 was not extreme (no more than 0.22). Differences in relative performance of the lines were observed across farms for prolificacy, longevity, cumulative production, and fertility; however, based on deviance information criterion results, the data supported the hypothesis of only these differences being generated under a genotype x environment interaction for prolificacy traits. The longer productive life of LP females could partially be understood as an indication of success of the selection procedure during the foundation of this line. Key words: constitution of line, cumulative production, fertility, longevity, prolificacy, rabbit breeding

The objective of this study was to examine the feasibility of using random regression-spline (RR-spline) models for fitting growth traits in a multibreed beef cattle population. To meet the objective, the results from the RR-spline model were compared with the widely used multitrait (MT) model when both were fit to a data set (1.8 million records and 1.1 million animals) provided by the American Gelbvieh Association. The effect of prior information on the EBV of sires was also investigated. In both RR-spline and MT models, the following effects were considered: individual direct and maternal additive genetic effects, contemporary group, age of the animal at measurement, direct and maternal heterosis, and direct and maternal additive genetic mean effect of the breed. Additionally, the RR-spline model included an individual direct permanent environmental effect. When both MT and RR-spline models were applied to a data set containing records for weaning weight (WWT) and yearling weight (YWT) within specified age ranges, the rankings of bulls' direct EBV (as measured via Pearson correlations) provided by both models were comparable, with slightly greater differences in the reranking of bulls observed for YWT evaluations ([greater than or equal to] 0.99 for BWT and WWT and [greater than or equal to] 0.98 for YWT); also, some bulls dropped from the top 100 list when these lists were compared across methods. For maternal effects, the estimated correlations were slightly smaller, particularly for YWT; again, some drops from the top 100 animals were observed. As in regular MT multibreed genetic evaluations, the heterosis effects and the additive genetic effects of the breed could not be estimated from field data, because there were not enough contemporary groups with the proper composition of purebred and crossbred animals; thus, prior information based on literature values had to be included. The inclusion of prior information had a negligible effect in the overall ranking for bulls with greater than 20 birth weight progeny records; however, the effect of prior information for breeds or groups poorly represented in the data was important. The Pearson correlations for direct and maternal WWT and YWT ranged from 0.95 to 0.98 when comparing evaluations of data sets for which the out-of-range age records were removed or retained. Random regression allows for avoiding the discarding of records that are outside the usual age ranges of measurement; thus, greater accuracies are achieved, and greater genetic progress could be expected. Key words: beef cattle, growth, multibreed, random regression model, spline

A complete diallel cross involving 3 maternal lines of rabbit was performed to estimate crossbreeding parameters for functional longevity. This trait was defined as the ability to delay involuntary culling. The lines considered, A, V, and Prat, had all been selected by litter size at weaning for a long period. Data were related to a total of 653 does belonging to the 9 genetic types from the diallel cross; does were reared and bred on the same Commercial farm. Survival analysis was performed using a Cox proportional hazard model. The model incorporated time-dependent factors, such as year-season, litter size, and the interaction between cycle and physiological status of the female; time-independent factors, such as the genetic type of the doe; and sire and dam random factors. Crossbreeding parameters were estimated from the solutions obtained for the type of doe and its estimated variance-covariance matrix, using a generalized least squares procedure. The estimated parameters were the differences between lines in direct genetic effects and maternal genetic effects and individual heterosis. Relevant differences were observed in direct genetic effects between lines A and Prat but not in any maternal effects. Heterosis was found to be significant and favorable between lines A and Prat, and between the lines V and Prat. The magnitude of this effect was variable but important, especially in the first cross. Results stress the importance of using crosses between specialized lines to produce does for intensive meat rabbit production. Key words: crossbreeding parameter, longevity, rabbit, survival analysis