-Protein supply and requirements by ruminants have been studied for more than a century. These studies led to the accumulation of lots of scientific information about digestion and metabolism of protein by ruminants as well as the characterization of the dietary protein in order to maximize animal performance. During the 1980s and 1990s, when computers became more accessible and powerful, scientists began to conceptualize and develop mathematical nutrition models, and to program them into computers to assist with ration balancing and formulation for domesticated ruminants, specifically dairy and beef cattle. The most commonly known nutrition models developed during this period were the National Research Council (NRC) in the United States, Agricultural Research Council (ARC) in the United Kingdom, Institut National de la Recherche Agronomique (INRA) in France, and the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. Others were derivative works from these models with different degrees of modifications in the supply or requirement calculations, and the modeling nature (e.g., static or dynamic, mechanistic, or deterministic). Circa 1990s, most models adopted the metabolizable protein (MP) system over the crude protein (CP) and digestible CP systems to estimate supply of MP and the factorial system to calculate MP required by the animal. The MP system included two portions of protein (i.e., the rumen-undegraded dietary CP -RUP -and the contributions of microbial CP -MCP) as the main sources of MP for the animal. Some models would explicitly account for the impact of dry matter intake (DMI) on the MP required for maintenance (MPm; e.g., Cornell Net Carbohydrate and Protein System -CNCPS, the Dutch system -DVE/OEB), while others would simply account for scurf, urinary, metabolic fecal, and endogenous contributions independently of DMI. All models included milk yield and its components in estimating MP required for lactation (MPl) and calf birth weight and some form of an empirical, exponential equation to compute MP for pregnancy (MPp). The MP required for growth (MPg) varied tremendously among the original models and their derivative works mainly due to the differences in computing growth pattern and the composition of the gain. The calculation of MCP differs among models; some rely on the total digestible nutrient (TDN; e.g., NRC, CNCPS level 1) intake to estimate MCP, while others use fermentable organic matter (FOM; e.g., INRA, DVE/OEB), fermentable carbohydrate (e.g., CNCPS level 2, NorFor), or metabolizable energy (ME; e.g., ARC, CSIRO, Rostock). Most models acknowledged the importance of ruminal recycled N, but not all accounted for it. Our Monte Carlo simulation indicated the prediction of most models for required MPl overlapped, confirming uniformity among models when predicting requirements for lactating animals, but a large variation in required MPg for growing animals exists.

RESUMO -Objetivou-se avaliar os efeitos de classe sexual e nível de oferta de concentrado sobre o desempenho, o consumo e a digestibilidade dos nutrientes em bovinos Nelore. Trinta e cinco animais, 12 machos inteiros, 11 machos castrados e 12 fêmeas, provenientes de um mesmo grupo contemporâneo, foram distribuídos aleatoriamente a dois tratamentos, correspondentes aos níveis de oferta de concentrado, 0,6 e 1,2% do peso corporal, respectivamente. Os animais foram alimentados individualmente durante 112 dias, sendo abatidos ao final do experimento, com um grupo referência abatido ao início. Os machos inteiros foram mais eficientes, pois apresentaram maior peso final e de corpo vazio, como resultado da maior taxa de crescimento em relação às fêmeas, ficando os machos castrados em posição intermediária. Os consumos relativos de matéria seca e de todos os demais nutrientes foram superiores nas fêmeas em relação aos machos inteiros, de modo que os castrados apresentaram valores intermediários. As digestibilidades, à exceção do extrato etéreo, não foram afetadas pela classe sexual. As digestibilidades da matéria seca e matéria orgânica foram superiores para a dieta com nível de oferta de concentrado de 1,2% do PV. Classe sexual afetou o crescimento e a capacidade de consumo dos animais, porém não teve efeito sobre a digestibilidade dos nutrientes da dieta. Níveis de oferta de concentrado de 0,6 e 1,2% do PV não foram suficientes para promover diferenças no desempenho de bovinos Nelore, independentemente de classe sexual.Palavras-chave: bovinos de corte, crescimento, consumo, digestibilidade Productive performance of Nellore cattle of different gender fed diets containing two levels of concentrate allowanceABSTRACT -The objective of this work was to evaluate the effects of sex class and concentrate allowance level on performance and digestibility of the nutrients in Nellore cattle. Thirty five animals were used, 12 young bulls, 11 steers and 12 heifers, from the same contemporary group, were randomly allotted into two treatments: concentrate allowance levels of 0.6 and 1.2% BW, respectively. The animals were individually fed during 112 days, and slaughtered at the end of the trial with a reference group slaughtered at the beginning of the trial. The young bulls were more efficient and had heavier body weight (BW) and empty body weight (EBW), as a result of their increased growth rate in relation to the heifers, with the steers presenting intermediate values. The relative intakes (% BW) of dry matter and of all other nutrients were superior in the heifers in relations to the young bulls, while the steers showed intermediate values. The digestibilities, except EE, were unaffected by sexual class. The digestibilities of dry matter and organic matter were higher for the diet with concentrate allowance of 1.2% BW. Sexual class affected the growth and the intake capacity of the animals, however with no effect on the digestibility of the nutrients of the diet. Concentrate allowance levels of 0.6 and 1.2% BW were not sufficient...

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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. We contend that improving the adequacy of mathematical nutrition model estimates of production of meat and milk is a sine qua non condition to reliably determine ruminants’ worldwide contribution to GHG. 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. We also present derivative systems. We compiled a database of milk production information from 37 published studies from six regions of the world, totalling 173 data points: 19 for Africa, 45 for Asia, 16 for Europe, 12 for Latin America, 44 for North America and 37 for Oceania. Four models were used to predict milk production in lactating dairy cows, and the adequacy of their predictions was measured against the observed milk production from our database. Even though these mathematical nutrition models shared similar assumptions and calculations, they have different conceptual and structural foundations inherent to their intended purposes. A direct comparison among these models was further complicated by the different models requiring unique inputs that are very often not available, and the low reliability of the inputs prevents an unbiased assessment of the model predictions. Very few studies have collected the necessary information to run more mechanistic systems, and users have to rely on standard information to populate many model inputs. Study effect was a critical source of variation that limited our ability to conclusively evaluate the models’ applicability under different scenarios of production around the world. Only after study variation was removed from the database did the adequacy of the model predictions of milk production improved, but deficiencies still existed. On the basis of these analyses, we conclude that not all models were suitable for predicting 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 event.

Abstract 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.