Live Yeast Supplementation and Heat Stress on Ruminal Fusobacterium necrophorum Counts

Reduced average daily gains and feed efficiencies, as well as liver condemnations associated with severe liver abscesses in feedlot cattle, are economic liabilities to producers and packers. Fusobacterium necrophorum, a Gram-negative ruminal bacterium, is the primary etiological agent of liver abscesses in grain-fed cattle. F. necrophorum survives elevated rumen temperatures during heat stress and exploits ruminal acidosis in conjunction with rumenitis as an opportunity to invade ruminal epithelium and enter portal circulation to reach the parenchyma of the liver. Live yeast supplementation has been shown to stabilize ruminal pH levels away from acidotic conditions during heat stress in dairy cattle

An Overview of Dairy Cattle Models for Predicting Milk Production: Their Evolution, Evaluation, and Application for the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Livestock.

The contemporary concern about anthropogenic release of greenhouse gas (GHG) into the environment and the contribution of livestock to this phenomenon have sparked animal scientists’ interest in predicting methane (CH4) emissions by ruminants. Focusing on milk production, we address six basic nutrition models or feeding standards (mostly empirical systems) and five complex nutrition models (mostly mechanistic systems), describe their key characteristics, and highlight their similarities and differences. Four models were selected to predict milk production in lactating dairy cows, and the adequacy of their predictions was measured against the observed milk production from a database that was compiled from 37 published studies from six regions of the world, totalling 173 data points. We concluded that not all models were suitable for predicting predict milk production and that simpler systems might be more resilient to variations in studies and production conditions around the world. Improving the predictability of milk production by mathematical nutrition models is a prerequisite to further development of systems that can effectively and correctly estimate the contribution of ruminants to GHG emissions and their true share of the global warming even

Evaluation of the Small Ruminant Nutrition System model (SRNS) for goat production in Vietnam

Applied nutrition models that can accurately predict goat performance under different feed intake regimes play a crucial role in developing improved feeding strategies. The primary objective of this study was to evaluate the ability of the SRNS model to predict the dry matter intake (DMI), average daily gain (ADG), nutrient digestibility, and faecal output characteristics of Vietnamese goats.The SRNS version 1.9.4468 (http://nutritionmodels.tamu.edu/srns.html) was used to simulate animal intake and performance of two local Vietnamese goat breeds for four feeding experiments.The model under-predicted DMI (kg/d) for most treatments (R2 = 0.70) and under-predicted ADG (g/d) for all treatments (R2 = 0.69) (Table 1). Nutrient digestibility and faecal outputs were generally under-predicted. Coefficients of determination for DM (0.94) and CP digestibility (0.93) were high.Our evaluation indicated that the SRNS model can predict the DMI and ADG of Vietnamese goats when nutritive values of the feeds are known. The regression equations developed in this study could be used to adjust the outputs of the SRNS model to predict the results of feeding systems