Effects of heated drinking water on the production responses of lactating Holstein and Jersey cows

Osborne, V.R.; Hacker, R. R.; McBride, B.W.

doi:10.4141/a01-055

Cited by 27 publications

(23 citation statements)

References 21 publications

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“…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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confidence: 99%

Prediction of drinking water intake by dairy cows

Appuhamy

Judy

Kebreab

et al. 2016

Journal of Dairy Science

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“…Differences in water temperature may affect intake by cattle (Andersson, 1985;Lardy and Stoltenow, 1999;Osborne et al, 2002). However, minor differences in water temperature are not likely to influence intake (Murphy, 1992).…”

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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“…Cardot et al (2008) found 2 main and 3 smaller consumption peaks and 72.70% of NDE per day during working hours and 27.00% during night on the farm was achieved. Osborne et al (2002) claims that 25.00 of NDE during night was realised. NDE occurred during the whole day but NDE peaks were in relationship with feeding and milking (Nocek and Braun, 1985;Osborne, 2002).…”

Section: Figurementioning

confidence: 99%

“…Osborne et al (2002) claims that 25.00 of NDE during night was realised. NDE occurred during the whole day but NDE peaks were in relationship with feeding and milking (Nocek and Braun, 1985;Osborne, 2002).…”

Section: Figurementioning

confidence: 99%

Drinking regime evaluation with continuous ruminal monitoring boluses

Hanušovský

Bíro

Šimko

et al. 2017

Acta fytotechn zootechn

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The aim of this study was to continuously monitored drinking regime of 7 dairy cows of Holstein breed using boluses during 24 weeks of lactation in relation to the outside temperature and observed daily drinking regime with the impact of drinking on rumen temperature at University Experimental Farm in Oponice. Animals were fed once daily and milked 3 times per day. The bolus pH and temperature values implemented via esophagus were measured every 15 minutes (96 data points per day) with accuracy ±0.1 ph and °C. Outside temperature by FREEMETEO meteorological server (48 times per day) was measured. Outside temperature can affect the drinking regime of dairy cows. During lactation weeks with higher outside temperature higher average number of drinking events (ANDE) was determined. The biggest difference between weeks in ANDE 18.33% (p = 0.000) was found. Daily ANDE 9.25±1.85 and average daily temperature (ADT) 19.03±5.19 °C were observed. The most of the drinking events (NDE) concentrated to 4 main peaks (25.17%) during working hours (74.98%) was found. After the feed intake and milking the highest frequencies of NDE were observed. The highest average ruminal temperature after drinking (ARTAD) during night before first feeding due to lower NDE in this time were found. Overall ARTAD 36.86 °C was observed. The most measured ruminal temperatures after drinking (RTAD) (51.53%) in the interval 35-37 °C were found. This research proved that continuous ruminal monitoring with boluses is an appropriate tool for drinking regime evaluation and heat stress determination in herd of dairy cows.

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Effects of heated drinking water on the production responses of lactating Holstein and Jersey cows

Cited by 27 publications

References 21 publications

Prediction of drinking water intake by dairy cows

Prediction of drinking water intake by dairy cows

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

Drinking regime evaluation with continuous ruminal monitoring boluses

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