Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported.

The physical form of feeds can influence dairy cow chewing behavior, rumen characteristics, and ruminal passage rate. Changing particle size of feeds is usually done through grinding or chopping forages, but pelleting feed ingredients also changes particle size. Our objective was to determine if pelleted dried distillers grains and solubles (DDGS) affected the feeding value for lactating dairy cattle. Seven lactating Jersey cows that were each fitted with a ruminal cannula averaging (± standard deviation) 56 ± 10.3 d in milk and 462 ± 75.3 kg were used in a crossover design. The treatments contained 15% DDGS in either meal or pelleted form with 45% or 55% forage on a dry matter basis. The forages were alfalfa hay, corn silage, and wheat straw. The factorial treatment arrangement was meal DDGS and low forage (mDDGS-LF), pelleted DDGS and low forage (pDDGS-LF), meal DDGS and high forage (mDDGS-HF), and pelleted DDGS and high forage (pDDGS-HF). Dry matter intake and energy-corrected milk were both unaffected by treatment averaging 19.8 ± 2.10 kg/d and 33.9 ± 1.02 kg/d, respectively. Fat yield was unaffected averaging 1.7 ± 0.13 kg/d, but protein yield was affected by the interaction of forage and DDGS. Protein yield was similar for both low forage treatments but was increased by when pDDGS was fed in the high forage treatment (1.05 vs. 0.99 ± 0.035 kg/d). When forage concentration was increased, starch digestibility increased by 1.9 percentage units, crude protein digestibility tended to increase 1.1 percentage units, and residual organic matter digestibility decreased 3.4 percentage units. Pelleting DDGS increased digestibility of neutral detergent fiber (NDF) digestibility (49.2 vs. 47.5 ± 1.85%) and gross energy (68.2 vs. 67.1 ± 1.18%). Increasing forage increased ruminal pH (5.85 to 5.94 ± 0.052). Passage rate slowed from 2.84 to 2.65 ± 0.205 %/h when feeding HF compared with LF. Rumination time increased from 417 to 454 ± 49.4 min with increasing forage concentration but was unaffected by the form of DDGS or the interaction of forage and DDGS. Eating time increased with pDDGS (235 vs. 209 ± 19.8 min), which may be a result of increased feed sorting behavior. Pelleting DDGS increased preference for particles retained on the 8-mm sieve and decreased preference for particles on the 1.18-mm sieve and in the pan (<1.18 mm). Results confirm that increasing forage concentration increases ruminal pH, rumination time, and slows passage rate, but contrary to our hypothesis increasing forage concentration did not increase NDF digestibility. Results also suggest that pelleted DDGS do not appear to affect milk production, ruminal characteristics, or passage rate, but pelleted DDGS may increase sorting behavior of lactating Jersey cows and increase NDF and gross energy digestibility.

Statisticians with a primary collaborative position at their academic institution have a unique role in combining domain science and statistical research and training and mentoring graduate students. As a result, it is important that their responsibilities be clearly understood by both the collaborative statisticians and their departments. In this paper, we discuss various steps that these statisticians can take to be successful and to be appropriately evaluated for their work. These steps are even more important now because a combination of new technologies, quantitative experts in other fields and the growth of data science in general seems to have put the field of Statistics at a crossroads. For the discipline to continue to flourish, we argue that it is critical that the role of the collaborative statistician be maintained and valued in statistics departments.

The objective of this pooled statistical analysis was to evaluate Syngenta Enogen^® Feed Corn (EFC) versus conventional corn (CON) when fed as either dry-rolled corn (DRC) or high-moisture corn (HMC) for effects on finishing beef cattle performance and carcass characteristics. Corns were evaluated in diets with byproduct inclusion rates of 0, 15, 18, 20, and 30% distiller grains or 25 and 35% Sweet Bran^® (a commercial corn gluten feed product). Seven trials (n = 1856) consisting of 200 pen means comparing 26 diet treatments were analyzed using regression in a pooled analysis. When EFC was processed as DRC, the gain efficiency (G:F) improved compared with CON, but the response to feeding EFC decreased from a 4.8% improvement to no improvement compared to CON as distiller grains increased from 0 to 30%, but was significantly improved due to feeding EFC in diets with 0 to 18% distiller grains. Feeding cattle EFC as DRC increased the average daily gain (ADG) and G:F by 4.5% compared with CON corn in diets containing Sweet Bran^®. No improvements in animal performance were observed when cattle were fed EFC compared to CON when processed as HMC in any situation. Feeding Enogen^® corn improved the gain efficiency of finishing cattle compared with conventional corn when processed as dry-rolled corn and fed in diets with less than 20% distillers or diets that include Sweet Bran^®.

Diets formulated for dairy cattle are designed to supply nutrients, but rations delivered to the animal may deviate from original formulations, resulting in implications for milk production, composition, and even pregnancy rate. The objective of this study was to evaluate retrospective feed mixing records collected from 8 commercial dairy farms over 52 wk. Farms ranged from 2,982 to 29,783 animals, producing 31.8 +/- 5.93 (+/- SD) kg of milk with 4.14 +/- 0.461% fat and 3.26 +/- 0.218% protein. Data collected included daily TMR nutrient deviation, calculated as diet nutrient content minus the observed divided by the formulated diet concentrations of CP, fat, NDF, and starch. Data were analyzed with fixed effects of time (modeled as 28-d periods) and days of positive nutrient deviation in fat, CP, NDF, and starch with each nutrient alone and in combination with the other nutrients, random effects of herd, and repeated measure of time within herd. The nutrient deviation by farm averaged (+/- SD) 1.68 +/- 2.655, 2.28 +/- 4.473, 1.75 +/- 2.621, and 1.47 +/- 2.147% for CP, fat, NDF, and starch, respectively. Days of positive nutrient deviation per period averaged (+/- SD) 25.5 +/- 3.47, 25.5 +/- 3.72, 25.6 +/- 3.49, and 25.9 +/- 2.61, respectively. Dry matter intake (DMI; 17.2 +/- 1.78 kg/d) decreased with increasing positive deviation days in starch (-0.0483 +/- 0.01265) and increased with increasing positive deviation days in CP (0.0211 +/- 0.00942). Milk yield (31.4 +/- 2.50 kg/d) increased with positive deviation days in starch (0.0486 +/- 0.02110) and decreased with increased positive deviation days in NDF (-0.0298 +/- 0.02202). Finally, pregnancy rate (21.7 +/- 4.34) increased with increasing positive deviation days in fat (0.385 +/- 0.1635) and decreased with increasing positive deviation days in CP (-0.420 +/- 0.1879). For these models, period significantly affected milk yield but was not observed to affect pregnancy rate and DMI. Results suggest farm and nutrient type were significant sources of variation, and overall, farms overfeed nutrients CP, fat, NDF, and starch 92% of the days of a 28-d period.

In hybrid sunflower, bee pollination can improve productivity, but the contribution of bees to productivity may be over or underestimated. To estimate bee effects (seed trait gains from exposure to bees during anthesis), single capitula are commonly covered with a porous material to exclude bees. However, depending on the exclosure porosity, estimates of the magnitude of bee effects will vary. In two studies, porosity size and bee effect gains in two sunflower types were tested. In the exclosure study, Delnet exclosures severely reduced seed set and exclosures with larger porosities and had smaller and similar effects. However, since a few small bees penetrated the largest porosity size tested, exclosures with porosity sizes < 7 mm are recommended. With an exclosure porosity of 5 X 5 mm, the estimated bee effect contribution to the yield was 323 kg per hectare. Effects of exclosures on seed traits were similar in the oilseed and confectionary hybrids tested. Insecticide use did not affect seed traits but did lower insect damage to seeds. Bees from three families, mostly Apidae, were collected while foraging on sunflower. In summary, we recommend the use of exclosures with porosities of about 3 to 5 mm to avoid over or underestimating bee effects. And we recommend holistic insect management for sunflower cropping systems that balances the benefits of bee effects on seed traits with management of pest insects.