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Left- and right-turn movements at signalized intersections have been found to be three to six times more hazardous to pedestrians than through movements mainly because drivers fail to observe or yield the right of way to pedestrians. The objective was to evaluate the Turning Traffic Must Yield to Pedestrians sign, which was aimed at reminding turning motorists of their legal obligation to yield the right of way to pedestrians and, consequently, reducing vehicle-pedestrian conflicts. The sign was installed at 12 marked crosswalks in two cities, and vehicle-pedestrian conflict data were collected before and after its installation. The sign was effective in reducing left-turn conflicts 20 to 65 percent and right-turn conflicts 15 to 30 percent; both reductions were statistically significant at the 0.05 level. The sign was significantly more effective in reducing left-turn conflicts than it was for right-turn conflicts. Percentage conflicts between pedestrians and turning traffic decreased as the pedestrian group size increased. In other words, larger pedestrian group sizes encountered fewer conflicts with turning traffic. This result is true for both left- and right-turn movements, regardless of the presence of the sign. Based on the results of this study, it was recommended that the “Turning Traffic Must Yield to Pedestrians” sign be considered for inclusion in the Manual on Uniform Traffic Control Devices.

Genetic parameters were estimated with restricted maximum likelihood for individual test-day milk, fat, and protein yields and somatic cell scores with a random regression cubic spline model. Test-day records of Holstein cows that calved from 1994 through early 1999 were obtained from Dairy Records Management Systems in Raleigh, North Carolina, for the analysis. Estimates of heritability for individual test-days and estimates of genetic and phenotypic correlations between test-days were obtained from estimates of variances and covariances from the cubic spline analysis. Estimates were calculated of genetic parameters for the averages of the test days within each of the ten 30-day test intervals. The model included herd test-day, age at first calving, and bovine somatropin treatment as fixed factors. Cubic splines were fitted for the overall lactation curve and for random additive genetic and permanent environmental effects, with five predetermined knots or four intervals between days 0, 50, 135, 220, and 305. Estimates of heritability for lactation one ranged from 0.10 to 0.15, 0.06 to 0.10, 0.09 to 0.15, and 0.02 to 0.06 for test-day one to test-day 10 for milk, fat, and protein yields and somatic cell scores, respectively. Estimates of heritability were greater in lactations two and three. Estimates of heritability increased over the course of the lactation. Estimates of genetic and phenotypic correlations were smaller for test-days further apart.

A 2-yr study using primiparous and multiparous, spring-calving, crossbred beef cows was conducted to evaluate the effects of supplemental whole corn germ on reproductive performance, calf performance, and serum leptin concentrations. Each year, cows were blocked by age and BCS and assigned randomly to one of three treatments: PRE (n = 115) cows received 1.14 kg/d (DM basis) of whole corn germ for approximately 45 d before calving; POST (n = 109) cows were fed 1.14 kg/d of whole corn germ for approximately 45 d after calving; and control cows (n = 118) were fed similar energy and protein from dry-rolled corn (1.82 kg of DM/d) for 45 d before and after calving. Additionally, PRE cows were grouped with controls after calving, and POST cows were grouped with control cows before calving, so that corn germ-supplemented cows received the control supplement in the alternate feeding period. Cow BW (538 +/- 13 kg) and BCS (5.4 +/- 0.13) did not differ among treatments at any time during the experiment. Calf birth weight (39 +/- 2 kg), weaning weight (225 +/- 7 kg), and age-adjusted weaning weight (234 +/- 8 kg) did not differ because of dam supplementation regimen. Treatment did not affect the proportion of cows exhibiting ovarian luteal activity before the start of the breeding season (67%) or pregnancy rate (91%). The interval from exposure to bulls until subsequent calving did not differ (P = 0.16) among PRE (298 +/- 2.3 d), POST (303 +/- 2.6 d), and control (304 +/- 2.3 d) cows. Leptin concentrations did not differ among treatments and were 2.15 +/- 0.75, 1.88 +/- 0.76, and 1.91 +/- 0.75 ng/mL for control, POST, and PRE cows, respectively. Age and week relative to calving influenced leptin concentration. Primiparous cows had similar leptin concentrations to 3-yr-old and mature cows for wk -7 and -6 relative to calving, but lower (P0.10) concentrations than mature cows for wk -5, and lower (P0.05) concentrations than either 3-yr-old or mature cows for wk -4 to +7 relative to calving. Serum leptin was correlated with BCS (P0.0001; r = 0.35) at initiation of the feeding period and was correlated with BCS (P = 0.02; r = 0.12) and weight (P0.01; r = 0.14) at the completion of the supplement period, but it was not correlated with initial BW or interim BCS. Calving interval was not correlated (P0.12) with weekly measures of serum leptin concentration. Supplementing beef cows with whole corn germ had no effect on cow performance, calf performance, or serum leptin concentrations of cows.

Advances in precision agriculture allow researchers to capture data more frequently and in more detail. For example, it is typical to collect "on-the-go" data such as soil electrical conductivity readings. This creates the opportunity to use these measurements as covariates for the primary response variable to possibly increase experimental precision. Moreover, these measurements are also spatially referenced to one another, creating the need for methods in which spatial locations play an explicit role in the analysis of the data. Data sets which contain measurements on a spatially referenced response and covariate are analyzed using either cokriging or spatial analysis of covariance. While cokriging accounts for the correlation structure of the covariate, it is purely a predictive tool. Alternatively, spatial analysis of covariance allows for parameter estimation yet disregards the correlation structure of the covariate. A method is proposed which both accounts for the correlation in and between the response and covariate and allows for the estimation of model parameters; also, this method allows for analysis of covariance when the response and covariate are not colocated.Los avances en agricultura de precisión permiten a los investigadores obtener datos con más frecuencia y en detalle. Por ejemplo, es común colectar "en el transcurso" datos como lecturas de electro-conductividad del suelo. Esto crea la oportunidad de usar estas medidas como covariables para incrementar la precisión experimental de la variable de respuesta. Aún más, estas medidas están espacialmente relacionadas entre sí, creando la necesidad de métodos en los cuales la ubicación espacial representa un papel explícito en el análisis de los datos. Se analizan conjuntos de datos que contienen variables de respuesta y covariables espacialmente relacionadas, usando el método cokriging o el análisis espacial de covarianza. Aunque el método cokriging usa la estructura de correlación de la covariable, es una herramienta puramente predictiva. Alternativamente, el análisis espacial de covarianza permite la estimación de parámetros pero sin tener en cuenta la estructura de correlación de la covariable. El presente artículo propone un método que tiene en cuenta la correlación en la covariable, así como la correlación entre la covariable y la variable de respuesta, permitiendo la estimación de los parámetros del modelo. De la misma manera, este método permite el análisis espacial de covarianza cuando la variable de respuesta y la covariable no están colocalizadas.

In the context of linear models, an optimality criterion is developed for models that include random effects. Traditional information-based criteria are premised on all model effects being regarded as fixed. When treatments and/or nuisance parameters are assumed to be random effects, an appropriate optimality criterion can be developed under the same conditions. This paper introduces such a criterion, and this criterion also allows for the inclusion of fixed and/or random nuisance parameters in the model and for the presence of a general covariance structure. Also, a general formula is presented for which all previously published optimality criteria are special cases.En el contexto de modelos lineales, los criterios de optimalidad se cons- truyen para los modelos que incluyen efectos aleatorios. Tradicionalmente los criterios basados en la información asumen que todos los efectos en el modelo se consideran fijos. Cuando los parámetros, tratamientos o molestias son considerados efectos aleatorios, un criterio adecuado de optimalidad se puede desarrollar en las mismas condiciones. En este trabajo se introduce ese criterio, que permite la inclusión en el modelo de parámetros que representan molestias fijas o al azar, además de una estructura general de covarianza. También, se presenta una fórmula general para la cual en todos los casos publicados anteriormente, los criterios de optimalidad son casos especiales.

Genetic parameters for stayability to six ages (ST1, . . ., ST6), for five measures of stayability to calving (SC2, . . ., SC6), and for five measures of stayability to weaning (SW2, . . ., SW6), were estimated using records of 2,019 Hereford cows collected from 1964 to 1979 from a selection experiment with a control line and three lines selected for weaning weight, yearling weight, and an index of yearling weight and muscle score. The model included birth year of the cow as a fixed effect and the cow's sire as a random effect. Analyses were performed with 1) a generalized linear mixed model for binary data using a probit link with a penalized quasi-likelihood function, and 2) with a linear mixed model using REML. Genetic trends were estimated by regressing weighted means of estimated transmitting abilities (ETA) of sires by birth year of their daughters on birth year. Environmental trends were estimated by regressing solutions for year of birth on birth year. Estimates of heritability (SE) for ST were between 0.09 (0.08) and 0.30 (0.14) for threshold model and between 0.05 (0.04) and 0.19 (0.09) for linear model. Estimates of heritability from linear model analyses transformed to an underlying normal scale were between 0.09 and 0.35. Estimates of heritability (SE) for SC were between 0.29 (0.10) and 0.39 (0.11) and between 0.18 (0.09) and 0.25 (0.08) with threshold and linear models. Estimates of heritability transformed to an underlying normal scale were between 0.30 and 0.40. Estimates of heritability (SE) for SW were between 0.21 (0.14) and 0.47 (0.19) and between 0.12 (0.08) and 0.26 (0.12) with threshold and linear models, respectively. Estimates of heritability transformed to an underlying normal scale were between 0.21 and 0.50. Estimates of genetic and environmental trends for all lines were nearly zero for all traits. Correlations between ETA of sires for stayability to specific ages, for stayability to calving, and for stayability to weaning with threshold and linear models ranged from 0.09 to 0.82, from 0.68 to 0.90, and from 0.67 to 0.87, respectively. Selection for stayability would be possible in a breeding program and could be relatively effective as a result of the moderate estimates of heritability, which would allow selection of sires whose daughters are more likely to remain longer in the herd. Selection for weaning and yearling weights resulted in little correlated response for any of the measures of stayability.