Predicting Average Feed Intake of Lactating Holstein Cows Fed Totally Mixed Rations

Abstract

A dry matter intake (DMI) prediction equation was estimated by using a data file that contained 124 treatment means collected from published studies. Animal factors considered for inclusion in the prediction model were body weight (BW) and its natural logarithm, BW(0.75), milk yield (MY) and its natural logarithm, milk fat and protein yields, month of lactation and its square, as well as its natural logarithm. The dietary factors considered were the percentages of neutral detergent fiber, acid detergent fiber, crude protein and hemicellulose in the ration dry matter together with the square of all these predictors. The multiple regression model selected by using the maximum R2 method include both animal and dietary factors as independent variables. The accuracy of this DMI prediction equation was evaluated and compared with that of five other equations previously published by using three independent datasets also containing treatment means collected from literature. Even though the latest NRC equation was slightly more accurate than the equation proposed in this study with the three evaluation datasets, the latter can be used for some applications for which the NRC equation is not appropriate.

Genetic variation of maize silage ingestibility in dairy cattle

“…Ruminants consuming diets high in cell wall content are often unable to eat sufficient quantities of forage to meet their energy demands, especially for perennial grasses and alfalfa, but also for forage with a high energy content such as whole-plant maize. Different equations of intake prediction have been established by ruminant nutritionists, allowing the formulation of relevant and economical diets by farmers (reviews in [12,15]). However, due mostly to the great impossibility for plant breeders to work with cattle, there was "a failure of most scientists to recognize the importance of voluntary intake, that has led to an unnecessary and undesirable gulf between the science and the practice" [24].…”

Evaluation of nutrition models to estimate performance of young dairy calves: a meta-analytical study under tropical conditions

“…Methods for the literature search and screening process have been described previously (Olsen, 1995;Sauvant et al, 2008). The meta-analysis methods may be more economically viable when compared with high cost of using experimental herds to carry out studies in dairy science (Fuentes-Pila et al, 2003).…”

“…Methods for the literature search and screening process have been described previously (Olsen, 1995;Sauvant et al, 2008). The meta-analysis methods may be more economically viable when compared with high cost of using experimental herds to carry out studies in dairy science (Fuentes-Pila et al, 2003).The data set was compiled using data from dairy calves in the pre-weaning phase. Studies included in this metaanalysis were selected based on specific criteria: studies conducted in Brazil using dairy calves during the preweaning phase; studies with description of the starter composition during the entire experimental period; studies that included weaning weights; studies that included liquid diet (milk and milk replacer) composition, starter intake, liquid diet intake and average daily gain (ADG).…”

e-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems

“…Outputs compared were: yields of milk and MS (kg/cow per year), BCS and LW at day 365-day of simulation and annual pasture utilisation. The concordance correlation coefficient (CCC; Lin, 1989) and the relative prediction error (RPE) (Fuentes-Pila et al, 2003) were used to evaluate the extent of agreement between actual and predicted values. The Landis and Koch (1977) scale has been used here to describe the degree of concordance, with: 0.21 to 0.40 being 'Fair'; 0.41 to 0.60 being 'Moderate'; 0.61 to 0.80 being 'Substantial'; and 0.81 to 1.00 being 'Almost perfect'.…”

“…The RPE is defined as the positive square root of the mean square prediction error (equation (5)), the latter expressed as a percentage of the mean of actual values (mA) (Fuentes-Pila et al, 2003):…”