Factors Affecting Water Consumption by Holstein Cows in Early Lactation

Murphy, Michael R.; Davis, C.L.; McCoy, G.C.

doi:10.3168/jds.s0022-0302(83)81750-0

Cited by 129 publications

(102 citation statements)

References 9 publications

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“…The lactation curve for water intake showed a path similar to the curve for milk yield with only a shift in level, which was also observed by Murphy et al (1983). Similar lactation curves for dry matter intake were also found by Veerkamp and Thompson (1999) and Hü ttmann (2007).…”

Section: Lactation Curvessupporting

confidence: 80%

Analysis of water intake, dry matter intake and daily milk yield using different error covariance structures

Kramer

Stamer²,

Spilke

et al. 2008

Animal

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The aim of the present study was to investigate the daily measured traits milk yield, water intake and dry matter intake with fixed and random regression models added with different error covariance structures. It was analysed whether these models deliver better model fitting in contrast to conventional fixed and random regression models. Furthermore, possible autocorrelation between repeated measures was investigated. The effect of model choice on statistical inference was also tested. Data recording was performed on the Futterkamp dairy research farm of the Chamber of Agriculture of SchleswigHolstein. A dataset of about 21 000 observations from 178 Holstein cows was used. Average milk yield, water intake and dry matter intake were 34.9, 82.4 and 19.8 kg, respectively. Statistical analysis was performed using different linear mixed models. Lactation number, test day and the parameters to model the function of lactation day were included as fixed effects. Different structures were tested for the residuals; they were compared for their ability to fit the model using the likelihood ratio test, and Akaike's and Bayesian's information criteria. Different autocorrelation patterns were found. Adjacent repeated measures of daily milk yield were highest correlated (p 1 5 0.32) in contrast to measures further apart, while for water intake and dry matter intake, the measurements with a lag of two units had the highest correlations with p 2 5 0.11 and 0.12. The covariance structure of TOEPLITZ was most suitable to indicate the dependencies of the repeated measures for all traits. Generally, the most complex model, random regression with the additional covariance structure TOEPLITZ(4), provided the lowest information criteria. Furthermore, the model choice influenced the significance values of one fixed effect and therefore the general inference of the data analysis. Thus, the random regression 1 TOEPLITZ(4) model is recommended for use for the analysis of equally spaced datasets of milk yield, water intake and dry matter intake.

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Section: Lactation Curvessupporting

confidence: 80%

Analysis of water intake, dry matter intake and daily milk yield using different error covariance structures

Kramer

Stamer²,

Spilke

et al. 2008

Animal

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“…Estimates of FWI may also be useful when attempting to match available resources to newly constructed facilities. To do so, several mathematical models have been published and may be used to predict FWI in dairy cattle (e.g., Castle and Thomas, 1975;Little and Shaw, 1978;Murphy et al, 1983;Stockdale and King, 1983;Dahlborn et al, 1998;Meyer et al, 2004;Cardot et al, 2008;Khelil-Arfa et al, 2012;Appuhamy et al, 2014b). The majority of extant models require DMI of individual cows as an input, which may not be routinely available in commercial dairy farms.…”

Section: Introductionmentioning

confidence: 99%

“…When multiple models had similar likelihood values, the model associated with the least Bayesian information criteria was chosen as the best prediction model. All the meta-analyses were Castle and Watson, 1973;Castle and Thomas, 1975;Little et al, 1976;Holter et al, 1982;Murphy et al, 1983;Stockdale and King, 1983;Andersson et al, 1984;Woodford et al, 1984;Andersson, 1985;Janicki et al, 1985;Nocek and Braund, 1985;Richards, 1985;Anderson, 1987;Gorewit et al, 1989;Holter et al, 1990Shalit et al, 1991;Bahman et al, 1993;Dado and Allen, 1993, 1995, 1996Silanikove et al, 1997;Dahlborn et al, 1998;Dewhurst et al, 1998;Muller et al, 1994;Mooney and Allen, 1997;Burgos et al, 2001;Osborne et al, 2002aOsborne et al, , 2009Voelker and Allen, 2003;Cottee et al, 2004;Meyer et al, 2004;Taylor and Allen, 2005;Harvatine and Allen, 2006;Chaiyabutr et al, 2007Chaiyabutr et al, , 2008Thomas et al, 2007;Cardot et al, 2008;Kume et al, 2008Kume et al, , 2010Longuski et al, 2009;Kramer et al, 2009;…”

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confidence: 99%

Prediction of drinking water intake by dairy cows

Appuhamy

Judy

Kebreab

et al. 2016

Journal of Dairy Science

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Mathematical models that predict water intake by drinking, also known as free water intake (FWI), are useful in understanding water supply needed by animals on dairy farms. The majority of extant mathematical models for predicting FWI of dairy cows have been developed with data sets representing similar experimental conditions, not evaluated with modern cows, and often require dry matter intake (DMI) data, which may not be routinely available. The objectives of the study were to (1) develop a set of new empirical models for predicting FWI of lactating and dry cows with and without DMI using literature data, and (2) evaluate the new and the extant models using an independent set of FWI measurements made on modern cows. Random effect meta-regression analyses were conducted using 72 and 188 FWI treatment means with and without dietary electrolyte and daily mean ambient temperature (TMP) records, respectively, for lactating cows, and 19 FWI treatment means for dry cows. Milk yield, DMI, body weight, days in milk, dietary macro-nutrient contents, an aggregate milliequivalent concentration of dietary sodium and potassium (NaK), and TMP were used as potential covariates to the models. A model having positive relationships of DMI, dietary dry matter (DM%), and CP (CP%) contents, NaK, and TMP explained 76% of variability in FWI treatment means of lactating cows. When challenged on an independent data set (n = 261), the model more accurately predicted FWI [root mean square prediction error as a percentage of average observed value (RMSPE%) = 14.4%] compared with a model developed without NaK and TMP (RMSPE% = 17.3%), and all extant models (RMSPE% ≥ 15.7%). A model without DMI included positive relationships of milk yield, DM%, NaK, TMP, and days in milk, and explained 63% of variability in the FWI treatment means and performed well (RMSPE% = 17.9%), when challenged on the independent data. New models for dry cows included positive relationships of DM% and TMP along with DMI or body weight. The new models with and without DMI explained 75 and 54% of the variability in FWI treatment means of dry cows and had RMSPE% of 12.8 and 15.2%, respectively, when evaluated with the literature data. The study offers a set of empirical models that can assist in determining drinking water needs of dairy farms.

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“…Temperature and humidity have a direct relation with water consumption among cattle (Murphy et al, 1983;Murphy, 1992;Rouda et al, 1994). Additional water requirements of young calves are 0.5 l/day for each additional degree Celsius of air temperature (Meyer et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Effect of water trough type on the drinking behaviour of pasture-based beef heifers

Coimbra

Filho

Nunes

et al. 2010

Animal

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The objective of this study was to investigate the effects of different trough types on the water consumption and drinking behaviour of pasture-based beef heifers. Two trials were implemented with 32 beef heifers to test two different types of water troughs, namely a rectangular concrete trough (RC) and a round polyvinyl chloride water tank (PVC). In Trial 1, both troughs were simultaneously available to groups of four animals within eight paddocks. In Trial 2, the animals were distributed in pairs throughout 16 paddocks and, in a crossover design, were exposed to one type of trough at a time. In both trials, estimated water intake was per four animals. Number of drinking bouts, time spent drinking and amount of water intake from the RC and PVC trough were recorded in both trials. Data were statistically analysed by analysis of variance. In Trial 1, group and trough effect were in the model. In Trial 2, stage, pair and trough were tested. In Trial 1, where both types of troughs were available, animals had a higher number of drinking bouts (3.32 v. 0.57 6 0.09; P , 0.01), longer drinking periods (144.21 v. 22.81 6 7.3 s; P , 0.01) and greater intake (160.21 v. 23.76 6 13.06 l; P , 0.01) from the PVC water tank, compared to the RC trough. In Trial 2, all groups drank more often (5.10 v. 3.28 6 0.32; P , 0.001), for longer periods (167.23 v. 115.23 6 15.61 s; P , 0.02) and with higher intake (141.36 v. 118.47 6 5.01 l; P , 0.02) from the PVC than from the RC trough. Thus, heifers not only prefer, but also drink more from a PVC water tank in comparison to a RC trough.

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Factors Affecting Water Consumption by Holstein Cows in Early Lactation

Cited by 129 publications

References 9 publications

Analysis of water intake, dry matter intake and daily milk yield using different error covariance structures

Analysis of water intake, dry matter intake and daily milk yield using different error covariance structures

Prediction of drinking water intake by dairy cows

Effect of water trough type on the drinking behaviour of pasture-based beef heifers

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