Short communication: Prediction of intake in dairy cows under tropical conditions

Souza, Márcia Cristina de; Oliveira, André Soares de; Araújo, Cláudio Vieira de; Brito, A.F.; Teixeira, Rafael Monteiro Araújo; Moares, E.H.B.K.; Moura, Daiane Caroline de

doi:10.3168/jds.2013-7652

Cited by 18 publications

(5 citation statements)

References 75 publications

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“…Mathematical models (Tedeschi andFox, 2009 and2018;Tedeschi et al, 2013) and empirical equations (NRC, 2001;Hoffman et al, 2008) have been developed to predict dry matter intake for animals of all categories, such as lactating cows and growing heifers. Further, a series of management and environmental conditions influencing those intake models have also been evaluated (Hayirli et al, 2003;Krizsan et al, 2014;Souza et al, 2014). However, no models or equations have been developed to estimate dry matter intake, or more appropriately, SFI of preweaned dairy calves.…”

Section: Introductionmentioning

confidence: 99%

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

Silva

DeVries

Tedeschi

et al. 2019

Animal

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There is a lack of studies that provide models or equations capable of predicting starter feed intake (SFI) for milk-fed dairy calves. Therefore, a multi-study analysis was conducted to identify variables that influence SFI, and to develop equations to predict SFI in milk-fed dairy calves up to 64 days of age. The database was composed of individual data of 176 calves from eight experiments, totaling 6426 daily observations of intake. The information collected from the studies were: birth BW (kg), SFI (kg/day), fluid milk or milk replacer intake (MI; l/day), sex (male or female), breed (Holstein or Holstein×Gyr crossbred) and age (days). Correlations between SFI and the quantitative variables MI, birth BW, metabolic birth BW, fat intake, CP intake, metabolizable energy intake, and age were calculated. Subsequently, data were graphed, and based on a visual appraisal of the pattern of the data, an exponential function was chosen. Data were evaluated using a meta-analysis approach to estimate fixed and random effects of the experiments using nonlinear mixed coefficient statistical models. A negative correlation between SFI and MI was observed (r=-0.39), but age was positively correlated with SFI (r=0.66). No effect of liquid feed source (milk or milk replacer) was observed in developing the equation. Two equations, significantly different for all parameters, were fit to predict SFI for calves that consume less than 5 (SFI5) l/day of milk or milk replacer: ${\rm SFI}_{{\,\lt\,5}} {\equals}0.1839_{{\,\pm\,0.0581}} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0333_{{\,\pm\,0.0021 }} {\minus}0.0040_{{\,\pm\,0.0011}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}{\rm }\left( {0.8302_{{\,\pm\,0.5092}} {\plus}6.0332_{{\,\pm\,0.3583}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ ; ${\rm SFI}_{{\,\gt\,5}} {\equals}0.1225_{{\,\pm\,0.0005 }} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0217_{{\,\pm\,0.0006 }} {\minus}0.0015_{{\,\pm\,0.0001}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}\left( {3.5382_{{\,\pm\,1.3140 }} {\plus}1.9508_{{\,\pm\,0.1710}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ where MI is the milk or milk replacer intake (l/day) and A the age (days). Cross-validation and bootstrap analyses demonstrated that these equations had high accuracy and moderate precision. In conclusion, the use of milk or milk replacer as liquid feed did not affect SFI, or development of SFI over time, which increased exponentially with calf age. Because SFI of calves receiving more than 5 l/day of milk/milk replacer had a different pattern over time than those receiving <5 l/day, separate prediction equations are recommended.

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Section: Introductionmentioning

confidence: 99%

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

Silva

DeVries

Tedeschi

et al. 2019

Animal

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“…The accurate prediction of feed intake by cattle is essential to optimize the use of nutrients and the productive, economic, and environmental performance of dairy cattle systems (Souza et al, 2014). However, the accurate estimation of the DMI remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Prediction of intake in growing dairy heifers under tropical conditions

Oliveira

Ferreira

2016

Journal of Dairy Science

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A meta-analysis was conducted to develop models of the prediction of dry matter intake (DMI) in growing dairy heifers [postweaning to 390 kg of body weight (BW)] under tropical conditions. The adequacy of the models was assessed in a comparison with the 4 US models currently used to predict DMI [Quigley; National Research Council; and 2 Hoffman models]. The data set was created using 95 treatment means from 28 studies published in journals. The data set (studies) was randomly divided into 2 data subsets for the statistical analyses. The first data subset was used to develop the prediction equations for DMI (17 studies; 58 treatment means), and the second data subset was used to assess the adequacy of the predictive models (11 studies; 37 treatment means). The models were developed using nonlinear and linear mixed analyses. Breed (Bos taurus vs. Bos taurus × Bos indicus), BW (240.2±62.2 kg), and average daily gain (ADG, 0.83±0.28 kg/d) were the independent variables. No significant effects of the breed or the interactions between the breed and metabolic BW (BW(0.75)) or breed and ADG were detected. Thus, nonlinear [DMI=0.1175 × BW(0.75) - 3.4984 × e((-2.4690 × ADG))] and linear models [DMI=8.7147 - 0.2402 × BW(0.75) + 0.0027 × (BW(0.75))(2) + 3.6050 × ADG - 1.4168 × ADG(2)] were proposed for both breeds. The nonlinear model explained 81% of the variation in the DMI, over-predicted the DMI by 0.21 kg/d and predicted the DMI with a higher accuracy and precision than the linear model [root mean square error of prediction (RMSEP)=8.82 vs. 10.71% of the observed DMI, respectively]. The Quigley model explained only 54% of the variation in the DMI and was the fourth most accurate and precise model (RMSEP=11.21% of the observed DMI). The National Research Council model explained 69% of the variation in the DMI but under-predicted the DMI by 0.53 kg/d, with an RMSEP of 12.72% of the observed DMI and presence of systematic constant bias. The Hoffman exponential model I (BW as the input) adequately predicted the DMI with an accuracy that was similar to the proposed nonlinear model. The equation of the Hoffman exponential model I explained 75% of the variation in the DMI and over-predicted the DMI by 0.07 kg/d, which was the second most accurate and precise equation (RMSEP=9.35% of the observed DMI). However, the Hoffman exponential model II (BW and diet NDF as the inputs) did not adequately predict the DMI, because it explained only 54% of the variation in the DMI, under-predicted the DMI by 0.72 kg/d, and had a high RMSEP (17.96% of the observed DMI). The use of nonlinear models increase the accuracy and the precision of the prediction of DMI compared with the linear models. Only the models proposed in the present study, the Hoffman exponential model I (BW as the input), and the Quigley model were adequate for the prediction of the DMI of growing dairy heifers under tropical conditions.

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“…En este estudio el consumo de forraje se estimó utilizando cromo como marcador externo. Una de las limitantes cuando se trabaja con animales bajo pastoreo es el cálculo de consumo de forraje y a pesar que se han ideado diferentes técnicas que permiten estimar el consumo de MS en animales bajo pastoreo, que se han publicado en diferentes revisiones y trabajos (CHÁVEZ et al, 1981, CORDOVA et al, 1978, LIPPKE 2002, MEJÍA 2002, SOUZA et al, 2014, todos los autores coinciden en que ningún método desarrollado hasta el presente, cuantifica con exactitud el consumo de forraje por rumiantes bajo condiciones de pastoreo, lo que pudo influir en la estimación de la participación del aceite como porcentaje de la MS (1.54% y 3.43%), no obstante estos valores están cercanos a los proyectados (2 y 4%).…”

Section: Resultados Y Discusiónunclassified

Suplementación con aceite de girasol sobre ácidos grasos de la leche en una lechería tropical

Prieto-Manrique¹,

Vargas-Sánchez²,

Angulo-Arizala³

et al. 2016

Rev Colombiana Cienc Anim. RECIA

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El objetivo de este trabajo fue evaluar el efecto de la suplementación alimenticia con aceite de girasol (0, 2 y 4% de la materia seca), sobre el consumo de forraje, la producción, composición, concentración de ácidos grasos de la leche y sobre la relación beneficio-costo, en una finca del sistema de producción lechería tropical donde los animales pastoreaban pasto Estrella (Cynodon plectostachyus). Se trabajó con nueve vacas, con más de dos partos y entre 70 -110 días de lactancia. Se utilizó un diseño de cuadrado latino de sobrecambio, 3 X 3 (periodos de 21 días), repitiendo cada tratamiento tres veces. En el tratamiento 0%, no se ofreció suplementación con aceite. Las vacas suplementadas con los niveles 2 y 4% de aceite de girasol (63.32% de ácido linoleico y 28.32% de ácido oleico) recibieron 250 y 500 g aceite/animal/día, respectivamente. El consumo de forraje, la producción y composición de la leche no fueron afectadas (P>0.05) por los tratamientos. La proporción de ácido linoleico conjugado c9t11 (CLA-c9t11) en la leche tendió (P=0.0799) a aumentar, los ácidos transvaccénico y oleico aumentaron linealmente con los dos niveles de suplementación y los ácidos grasos aterogénicos C12:0, C14:0 y C16:0 disminuyeron (P<0.05), obteniéndose una leche con mayor cantidad de ácidos grasos insaturados y menor índice de aterogenicidad, que ofrece beneficios para la salud humana. La relación beneficio-costo fue mayor a nivel de suplementación de 2%, si la leche se mercadea con un valor diferencial por contenido de CLA-c9t11.

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Short communication: Prediction of intake in dairy cows under tropical conditions

Cited by 18 publications

References 75 publications

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

Prediction of intake in growing dairy heifers under tropical conditions

Suplementación con aceite de girasol sobre ácidos grasos de la leche en una lechería tropical

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