Rumen fermentation of meal-fed sheep in response to diets formulated to vary in fiber and protein degradability.

The concentration of volatile fatty acid (VFA) provides an imprecise view of VFA dynamics due to the confounding effects of fluid pool size and dynamics. Determination of VFA flux using isotope is expensive and a complex methodology. Therefore, a rapid and affordable approach to explore VFA dynamics may allow comprehensive characterization of VFA availability. The objective of this study was to explore the use of VFA dynamics generated by meal feeding to derive time-series rates of VFA apparent appearance and disappearance driven by different protein and fiber sources. Six ruminally cannulated wethers were fed diets containing timothy hay or beet pulp (TH and BP) and soybean meal (SBM) or heated soybean meal (HSBM). Diets were, TH + HSBM; TH + SBM; BP + HSBM; and BP + SBM and the experimental design was a partially replicated 4 × 4 Latin Square. Concentrations of VFA and polyethylene glycol (PEG) in rumen fluid samples were estimated. Concentrations of PEG were used to estimate fluid passage and volume to calculate VFA mass, and fluid-mediated exit. Maximum apparent appearance rate (mmol/h), the rate of apparent appearance decline (mmol/mmol/h), mean apparent appearance flux (mmol/h), mean apparent disappearance (mmol/h), and apparent disappearance rate (mmol/mmol/h) were estimated by deriving a 1 pool model for each VFA on a mass basis where appearance was assumed to follow an exponential decay pattern and disappearance followed mass-action kinetics. Statistical analyses were conducted using a linear mixed effect regression with fixed effects for fiber source, protein source, and their interaction, as well as random effects for animal and period. Rumen fluid volume (L) was greater in HSBM diets (P = 0.033) and fluid passage (%/h) was greater in SBM diets (P = 0.048). Concentrations (higher acetate and butyrate, P = 0.002 and 0.004, respectively) and molar proportions (higher valerate, P = 0.035) of VFA were affected only by fiber source; however, protein source and fiber source interacted to significantly influence apparent appearance rates and absorption rates of many major VFA. On a flux basis, HSBM supported significantly elevated mean disappearance of propionate (P = 0.033). This data demonstrates that time-series evaluation of fermentation dynamics, including fluid dynamics and VFA concentrations can be used to estimate apparent appearance and disappearance of VFA. Although further work is needed to confirm the alignment of these estimates with measurements of VFA supplies to the animal, this modeling approach may provide a simpler way to better understand the kinetics of rumen.

We estimated apparent appearance, apparent disappearance, appearance, and disappearance rates of rumen volatile fatty acid (VFA) of sheep in response to the different degradability of nutrients using time-series fermentation indicators with regular meal feeding. Two fiber sources (timothy hay [TH], and beet pulp [BP]) and two protein sources (soybean meal [SBM], and heat-treated soybean meal [HSBM]) were used in combination to prepare four dietary treatments. Polyethylene glycol (PEG) was used as the fluid marker to estimate rumen fluid volume and passage rate. The dynamics of VFA were estimated by deriving one pool model for individual VFA and concentrations of VFA, rumen fluid volume, and fluid passage rate were used in calculations. The interaction effect of protein and fiber source significantly influenced apparent appearance rates and disappearance rates of many major VFA. Significantly altered VFA dynamics, especially apparent disappearance was associated with HSBM-based diets. In conclusion, use of time-series evaluation of fermentation dynamics provides a minimal approach to integrate fluid dynamics and VFA concentrations to estimate apparent appearance and disappearance of VFA. With further development of this approach, we assume that estimated VFA dynamics will provide a better depiction of rumen VFA beyond concentrations and molar proportions in making inferences on rumen fermentation.

Finisher lamb growth and rumen fermentation responses to the plane of nutrition and naturally occurring coccidiosis.

The objective of the present study was to investigate the interaction of plane of nutrition and naturally occurring coccidiosis on finisher lamb growth performance, FAMACHA score, and rumen volatile fatty acid profile. The study included 30 Suffolk, Dorset or Suffolk x Dorset lambs and were divided into 2 groups based on their initial body weight and assigned to 2 feeding groups differing in dietary energy intake to create lambs representing divergent growth curves due to differing nutritional management. Lambs with naturally occurring coccidiosis and healthy lambs were present in both feeding groups making a 2 × 2 factorial arrangement of treatments, (a) high plane of nutrition (HPN) lambs with no clinical coccidiosis diagnosis (HPNH), (b) HPN lambs with clinical coccidiosis (HPNC), (c) low plane of nutrition (LPN) lambs with no clinical coccidiosis diagnosis (LPNH), (d) LPN lambs with clinical coccidiosis (LPNC). Body weight and FAMACHA scores were recorded once every 2 weeks. On d 65 of feeding, lambs were slaughtered, and rumen fluid samples were collected and analyzed for volatile fatty acid concentrations. All response variables were analyzed statistically using a linear mixed effects model with fixed effects for plane of nutrition, health status, and a random effect for initial body weight nested within the pen. The total and average weight gain were not associated with planes of nutrition, health status, or the interaction. Health status had an impact on FAMACHA© score (P = 0.047) and concentration of isobutyrate (P = 0.037) and tended to affect total VFA (P = 0.085) and acetate (P = 0.071) concentrations. The interaction between the plane of nutrition and the health status tended to affect butyrate concentration (P = 0.058). These data support the conclusion that coccidiosis infection impacted on rumen fermentation in a manner independent of the plane of nutrition; however, the translation of these rumen level impacts did not translate to the production responses.

Network analysis to evaluate complexities in relationships among fermentation variables measured within continuous culture experiments.

The objective of this study was to leverage a frequentist (ELN) and Bayesian learning (BLN) network analyses to summarize quantitative associations among variables measured in 4 previously published dual-flow continuous culture fermentation experiments. Experiments were originally designed to evaluate effects of nitrate, defaunation, yeast, and/or physiological shifts associated with pH or solids passage rates on rumen conditions. Measurements from these experiments that were used as nodes within the networks included concentrations of individual volatile fatty acids, mM and nitrate, NO3-,%; outflows of non-ammonia nitrogen (NAN, g/d), bacterial N (BN, g/d), residual N (RN, g/d), and ammonia N (NH3-N, mg/dL); degradability of neutral detergent fiber (NDFd, %) and degradability of organic matter (OMd, %); dry matter intake (DMI, kg/d); urea in buffer (%); fluid passage rate (FF, L/d); total protozoa count (PZ, cells/mL); and methane production (CH4, mmol/d). A frequentist network (ELN) derived using a graphical LASSO (least absolute shrinkage and selection operator) technique with tuning parameters selected by Extended Bayesian Information Criteria (EBIC) and a BLN were constructed from these data. The illustrated associations in the ELN were unidirectional yet assisted in identifying prominent relationships within the rumen that were largely consistent with current understanding of fermentation mechanisms. Another advantage of the ELN approach was that it focused on understanding the role of individual nodes within the network. Such understanding may be critical in exploring candidates for biomarkers, indicator variables, model targets, or other measurement-focused explorations. As an example, acetate was highly central in the network suggesting it may be a strong candidate as a rumen biomarker. Alternatively, the major advantage of the BLN was its unique ability to imply causal directionality in relationships. Because the BLN identified directional, cascading relationships, this analytics approach was uniquely suited to exploring the edges within the network as a strategy to direct future work researching mechanisms of fermentation. For example, in the BLN acetate responded to treatment conditions such as the source of N used and the quantity of substrate provided, while acetate drove changes in the protozoal populations, non-NH3-N and residual N flows. In conclusion, the analyses exhibit complementary strengths in supporting inference on the connectedness and directionality of quantitative associations among fermentation variables that may be useful in driving future studies.

This study leveraged frequentist (ELN) and Bayesian networks (BLN) to evaluate the potential of network analysis to explore complex rumen environments with interconnected quantitative associations. The approaches were selected based on their capacity for holistic exploration of all possible quantitative associations among variables, including opportunities to explore the potential strength and directionality of those associations. Data from 4 continuous culture experiments, involving 18 rumen variables [major and minor volatile fatty acid (VFA), degradability variables and nitrogen related variables], were used for network derivation. Variables within a network are denoted as nodes and relationships between two nodes are referred to as edges. The different networking approaches had different strengths for biological interpretation. Although the ELN approach was useful for exploring the role and importance of specific variables in the network, the BLN had more relevance in selecting edges or relationships linking those variables. These strengths are complementary and make a case for joint exploration of datasets using both approaches. Many of the biological inferences derived from the networks are well-acknowledged within the literature, acetate, valerate, isobutyrate, and isovalerate were important nodes within both networks, and important edges focused on the driving role of N dynamics within the rumen. Overall, these analyses demonstrated potential to illustrate associations and directionality of quantitative associations among fermentation variables. These associations can be used to direct future studies based on more comprehensive datasets.

Development of Tetrapod Zinc Oxide-Based UV Sensor for Precision Livestock Farming and Productivity.

In order to ensure the health and welfare of livestock, there has been an emphasis on precision farming of ruminant animals. Monitoring the life index of ruminant animals is of importance for intelligent farming. Here, a wearable sensor for monitoring ultraviolet (UV) radiation is demonstrated to understand the effect of primary and secondary photosensitization on dairy animals. Thin films of wide bandgap semiconductor zinc oxide (ZnO) comprising multilevel of nanostructures from microparticles (MP) to nanoparticles (NP), and tetrapod (T-ZnO), were prepared as the UV sensing active materials. The sensitivity was evaluated by exposing the films to various radiation sources, i.e., 365 nm (UV A), 302 nm (UV B), and 254 nm (UV C), and measuring the electrical resistance change. T-ZnO is found to exhibit higher sensitivity and stable response (on/off) upon exposure to UV A and UV B radiation, which is attributed to their higher surface area, aspect ratio, porosity, and interconnective networks inducing a high density of chemical interaction sites and consequently improved photocurrent generation. A wearable sensor using T-ZnO is packaged and attached to a collar for dynamic monitoring of UV response on ruminant animals (e.g., sheep in this study). The excellent performance of T-ZnO wearable sensors for ruminant animals also holds the potential for a wider range of applications such as residential buildings and public spaces.