A mechanistic model for predicting intake of forage diets by ruminants

Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d-1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Effect of metabolizable protein intake on growth performance, carcass characteristics, and feeding behavior in finishing steers

One-hundred thirty-two finishing steers (300 ± 2.7 kg body weight [BW]) predominately of Angus, Simmental, and Shorthorn breeding were used to study the effect of metabolizable protein (MP) intake on growth performance, carcass characteristics, and feeding behavior. Steers were stratified by initial BW across five pens and randomly assigned to one of four dietary treatments to supply an average of 626, 906, 1,209, and 1,444 g MP/d (n = 33 per treatment). Feed intake and feeding behavior were measured using radio frequency identification tags and the Insentec feeding system. For feeding behavior, a visit was defined as each time the Insentec system detected a steer at the feed bunk. A meal was defined as eating periods by intervals no longer than 7 min. Steers were fed until they reached an average BW of 598 ± 3.1 kg. Average daily gain (ADG) responded quadratically (P < 0.01) with ADG increasing in steers fed 906 g MP/d and plateauing thereafter. Dry-matter intake (DMI; kg) responded quadratically (P = 0.009) with DMI increasing with MP intake up to 1,209 g/d MP and decreasing thereafter. Gain to feed ratio (G:F) increased linearly (P = 0.04) and tended (P = 0.10) to respond quadratically, as G:F increased up to 906 g MP/d and plateaued thereafter. A quadratic response (P = 0.04 and P = 0.02, respectively) was observed for marbling score and 12th rib subcutaneous fat thickness with steers fed 1,209 g MP/d having the greatest marbling score and back fat thickness. A quadratic effect for visits and meals per day was observed (P < 0.01) with steers fed the 1,209 g MP/d treatment having the least visits and meals per day. In addition, time eating per visit responded quadratically (P = 0.05) with time increasing from 626 to 906 g MP/d. There was a linear increase (P ≤ 0.02) in time eating per meal and per day with increasing MP intake. A quadratic effect (P < 0.03) was observed for DMI per visit, meal, and minute with steers fed 1,209 g MP/d having the greatest DMI. In summary, steers fed 626 g MP/d had increased visits and meals per day. However, DMI per visit, meal, and minute were greater in steers fed 1,209 g MP/d. A day × treatment interaction (P < 0.001) was observed for plasma urea N as concentrations increased to a greater extent over time in the higher MP treatments than in the lower MP treatments. These data indicate that MP supply (from deficient to excess) influences growth performance, carcass characteristics, and feeding behavior of finishing steers.

The assessment of supplementation requirements of grazing ruminants using nutrition models

This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.

Maximizing efficiency of rumen microbial protein production

Rumen microbes produce cellular protein inefficiently partly because they do not direct all ATP toward growth. They direct some ATP toward maintenance functions, as long-recognized, but they also direct ATP toward reserve carbohydrate synthesis and energy spilling (futile cycles that dissipate heat). Rumen microbes expend ATP by vacillating between (1) accumulation of reserve carbohydrate after feeding (during carbohydrate excess) and (2) mobilization of that carbohydrate thereafter (during carbohydrate limitation). Protozoa account for most accumulation of reserve carbohydrate, and in competition experiments, protozoa accumulated nearly 35-fold more reserve carbohydrate than bacteria. Some pure cultures of bacteria spill energy, but only recently have mixed rumen communities been recognized as capable of the same. When these communities were dosed glucose in vitro, energy spilling could account for nearly 40% of heat production. We suspect that cycling of glycogen (a major reserve carbohydrate) is a major mechanism of spilling; such cycling has already been observed in single-species cultures of protozoa and bacteria. Interconversions of short-chain fatty acids (SCFA) may also expend ATP and depress efficiency of microbial protein production. These interconversions may involve extensive cycling of intermediates, such as cycling of acetate during butyrate production in certain butyrivibrios. We speculate this cycling may expend ATP directly or indirectly. By further quantifying the impact of reserve carbohydrate accumulation, energy spilling, and SCFA interconversions on growth efficiency, we can improve prediction of microbial protein production and guide efforts to improve efficiency of microbial protein production in the rumen.