The assessment of supplementation requirements of grazing ruminants using nutrition models

Abstract

<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>

Consumo, comportamento ingestivo e perfil metabólico de ovinos alimentados com ração extrusada com diferentes relações volumoso:concentrado

“…As diferentes relações V:C não comprometeram o tempo gasto em ingestão, ruminação e mastigação (P>0,05; Tabela 2), provavelmente em decorrência da ausência de efeito dos tratamentos sobre o CMS e possível seleção de alimentos realizada por ovinos, uma maneira de compensarem a reduzida capacidade fermentativa. Quando permitido, esses animais escolhem partes da forrageira que são mais nutritivas, como caules, folhas e brotos de boa qualidade, realizando maior seleção por apresentarem focinho estreito e alta mobilidade de língua e lábios (Tedeschi et al, 2019). Nesse sentido, os ovinos podem ter selecionado a favor do concentrado ou do volumoso, disponibilizados em dois tipos de pellets, não permitindo identificar diferenças da relação volumoso:concentrado sobre as variáveis do comportamento ingestivo.…”

The characterization of the cow-calf, stocker and feedlot cattle industry water footprint to assess the impact of livestock water use sustainability

“…Many models to estimate cattle growth (kg/day) exist and take into consideration different factors such as breed type, climate, environmental conditions (e.g. mud or humidity) and utilize a wide array of empirical, probabilistic, deterministic and mechanistic equations (Oltjen et al ., 1986; Leon-Velarde and Quiroz, 1999; Tedeschi et al ., 2019; 2004; Tedeschi, 2019 a , b ; Tedeschi and Menendez, 2020). Baudracco et al .…”

“…Tedeschi et al . (2019) provide a comprehensive review of models for ruminant grazing and supplementation. The forage component in the TXWFB model is simple because its level of aggregation is considerable, at a regional level (Fig.…”

“…The majority of cattle inputs to achieve nutrient requirements and growth are endogenous within the cow-calf and stocker phases of Texas cattle production, meaning that pasture and hay resource management is a critical component. Tedeschi et al (2019) provide a comprehensive review of models for ruminant grazing and supplementation. The forage component in the TXWFB model is simple because its level of aggregation is considerable, at a regional level (Fig.…”

“…Growth dynamics in the TXWFB model were linked to the nutrition model, and feedback existed between these two structures. Tedeschi et al (2019) provide a comprehensive review of nutrition models for grazing and feedlot ruminants and identify the commonalities and differences in parameterization, application and synthesis of results. Since growth and nutrition for cattle go hand in hand, most mathematical nutrition models that simulate cattle growth include nutritional components, though degrees of complexity and aggregation differ.…”