Genetic‐Quantitative Study of the First‐Service Pregnancy Probability of Murrah Heifers

Because of the importance of reproduction in stock breeding systems, it is necessary to find selection criteria that increase reproductive efficiency. The aim of this study was to estimate genetic parameters for the probability of conception on first service (PROB) in Murrah heifers, and its association with other traits of economic interest [age at first calving (AFC), service period, calving interval and milk yield at 270 days], with the purpose of evaluating their use as selection criteria. Reproductive information and first lactation records of 1200 Murrah heifers were used to perform two‐trait analyses between PROB and the other characteristics. Bayesian inference was used to estimate the variance components, considering PROB as threshold and the other as linear factors. The results demonstrate that this trait has heritability of 0.15, indicating the possibility of a genetic gain by using it for selection. With respect to the genetic correlation estimates, the only high‐magnitude association was with AFC (?0.899), which is the current criterion indicating sexual precocity of females. In the light of the parameters estimated, the first‐service pregnancy rate is an alternative for indication of sexual precocity, although presenting a smaller genetic gain than the current standard AFC. Nevertheless, additional research should be conducted regarding this trait to assess the economic importance of its use in dairy buffalo production systems.     

Seasonal and temporal changes in species use of the landscape: how do they impact the inferences from multi-scale habitat modeling?

Context

Multi-scale approaches to habitat modeling have been shown to provide more accurate understanding and predictions of species-habitat associations. It remains however unexplored how spatial and temporal variations in habitat use may affect multi-scale habitat modeling.

Objectives

We aimed at assessing how seasonal and temporal differences in species habitat use and distribution impact operational scales, variable influence, habitat suitability spatial patterns, and performance of multi-scale models.

Methods

We evaluated the environmental factors driving brown bear habitat relationships in the Cantabrian Range (Spain) based on species presence records (ground observations) for the period 2000–2010, LiDAR data on forest structure, and seasonal estimates of foraging resources. We separately developed multi-scale habitat models for (i) each season (spring, summer, fall and winter) (ii) two sub-periods with different population status: 2000–2004 (with brown bear distribution restricted to the main population nuclei) and 2005–2010 (with expanding bear population and range); and (iii) the entire 2000–2010 period.

Results

Scales of effect remained considerably stable across seasonal and temporal variations, but not the influence of certain environmental variables. The predictive ability of multi-scale models was lower in the seasons or periods in which populations used larger areas and a broader variety of environmental conditions. Seasonal estimates of foraging resources, together with LiDAR data, appeared to improve the performance of multi-scale habitat models.

Conclusions

We highlight that the understanding of multi-scale behavioral responses of species to spatial patterns that continually shift over time may be essential to unravel habitat relationships and produce reliable estimates of species distributions.

     

Prediction of β‐Glucan Concentration Based on Viscosity Evaluations of Raw Oat Flours from High β‐Glucan and Traditional Oat Lines

Rheological properties of raw oat flour slurries were determined in experimental high β‐glucan (≤7.8%) and traditional oat lines (4–5% β‐glucan) grown in two consecutive years. Three different media were used to disperse oat flours: deionized water, silver nitrate solution (to inactivate endogenous enzymes), and alkali solution (to solubilize both water‐soluble and water‐insoluble β‐glucans). Significant correlations (P < 0.05) between viscosity of slurries and β‐glucan concentration obtained in either deionized water (r = 0.833), silver nitrate (r = 0.940), or alkali (r = 0.896) solutions showed that β‐glucans were the main contributor to oat extract viscosity. The highest correlation was obtained in silver nitrate solution, suggesting that inactivating endogenous enzymes is important to obtain high correlations. Predictive models of oat β‐glucan concentration based on the viscosity profile were developed using partial least squares (PLS) regression. Prediction of β‐glucan concentration based on viscosity was most effective in the silver nitrate solution (r = 0.949, correlation coefficient of predicted vs. analyzed β‐glucans) and least effective in the alkali solution (r = 0.870). These findings demonstrate that the β‐glucan in oat could be predicted by measuring the viscosity of raw flours in silver nitrate solution, and this method could be used as a screening tool for selective breeding.