| dc.description.abstract | Efficient sampling strategies expedite behavioral data collection. While multiple studies have evaluated sampling strategies for core behaviors in cattle, few have focused on social interactions or stereotypic behavior. Therefore, for the first chapter, to identify sampling strategies that accurately capture feedlot cattle social behavior and stereotypic behaviors such as brush use, steers (n = 3 pens; 9 steers/pen) were observed from 8:00 to 17:00. Differences among sampling strategies were evaluated using a non-parametric one-way ANOVA Kruskal-Wallis Test. Pearson correlation evaluated the strength of association between a specific sampling strategy and continuous observations. Observing cattle for 15 minutes every 30 minutes yielded the highest accuracy for all behavioral metrics and was considered the most effective strategy for comprehensively evaluating cattle social behavior (r² > 75; P < 0.05). For the second chapter, because an empirical examination of how sex influences cattle social behavior has been absent, our objective was to evaluate the impact of sex on feeding behavior and the performance of social behaviors. Social behaviors and brush use were decoded using continuous observation for 15 minutes of every 30 minutes on d 1, 2, 4, 8, 16, 32, and 64. A Generalized Linear Mixed Model (PROC GLIMMIX) with a Poisson distribution and log transform link (to normalize the data) was used to evaluate differences between sexes for each behavior. The model included sex, sex within pen, and research day as fixed effects, and pen as the random effect. Exit velocity was evaluated in a separate GLIMMIX model that included sex within pen and research day as fixed effect and the animal’s individual ID as random effect. Lastly, productivity and feeding behavior traits were evaluated using a GLIMMIX model that included sex as fixed effect and sex within pen as random effect. Heifers showed higher temperament scores than steers (P < 0.05). Steers performed on average and total duration/d more social (P < 0.001) and stereotypic behaviors (P < 0.04) and used the brush (P < 0.04) more than heifers. In bout frequency/d, steers performed more allogrooming (P = 0.002), brush displacement (P =0.02), bunk displacement (P < 0.001), head butt (P < 0.001), and brush use (P < 0.001). Lastly, the third chapter address the gap of understanding regarding the relationships among environmental enrichment use, temperament, productivity, and feeding behavior for Bos indicus cattle. To examine the effect of divergent behavioral phenotypes for brush usage on temperament, productivity, and feeding behavior patterns, four pens of Brahman yearling cattle (Pen 1 & 2: 15 heifers/pen, and Pen 3 & 4:19 steers/pen) were housed in dry lots. Ten animals from each pen were marked with a colored ear tag for individual identification. Animals were video recorded for the same time and days as chapter 2, as well as EV and body weight. Animals were categorized into one of three phenotypes (high, medium, low brush use). A PROC GLIMMIX evaluated the interaction of brush usage phenotype and gender as fixed effect, and pen as random effect on final BW (kg), average daily gain (kg/d), dry matter intake (kg/d), feed to gain ratio, bunk visit frequency (events/d), bunk visit duration (min/d), bunk visit eating rate (g/min), head down duration (min/d), head down duration per bunk visit duration, and exit velocity. High brush use steers showed to be the most productive ones, presenting higher FBW (P = 0.0005), higher ADG (P = 0.0002), and higher DMI (P = 0.0003) than low brush use steers, and as expected, steers were more productive than heifers while also being less temperamental than heifers (P = 0.0008). These results provide insight into accurate and efficient sampling strategies that expedite social behavior data collection in cattle. Also, the study presents novel empirical information regarding the impact of sex on cattle social behavior, environmental enrichment, productivity, and feeding behavior, suggesting the need for sex-specific statistics in research and management practices and highlighting the importance of incorporating sex-specific behavioral profiles into sensor technology and algorithm development. | en |
