Effects of urinary excretion of nitrogen, potassium and sodium on urine volume in dairy cows

Kume, Shinji; Nonaka, K.; Oshita, Takafumi; Kozakai, Takaharu; Hirooka, Hiroyuki

doi:10.1016/j.livsci.2007.06.003

Cited by 20 publications

(29 citation statements)

References 10 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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“…However, it is likely that our predictive equation of urine water may have been more precise if Na, and possibly K intake were included as candidate regressors because these minerals also contribute to the osmotic pressure of urine (Bannink et al, 1999;Nennich et al, 2006;Kume et al, 2008). Maltz and Silanikove (1996) reported an inversely proportional relationship between urea and electrolyte solute concentration.…”

Section: Discussionmentioning

confidence: 99%

“…However, the inclusion of the content of CP ingested with forage as a regressor in the predictive equation presented in this paper probably allowed to partially account for the electrolyte content of the diet and more specifically for the K content. Kume et al (2008) found a high positive correlation between CP and K in first-cut alfalfa, but a clear correlation could also be observed between the CP content and the mineral content of forages and more specifically the K content when different forages were compared (INRA, 2007). This is particularly clear when comparing grazing and conserved forage-based diets (de Boer et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…The variables DMI, DM, BW and MY were chosen to be included in the regression model because it has been shown that they are related to water flows at the animal scale Meyer et al, 2004;. The contents of CP ingested with forage and concentrate were added because elimination of urea in urine can partly drive water excretion and thus water balance at the animal scale (Bannink et al, 1999;Nennich et al, 2006;Kume et al, 2008). These variables were also used because the mineral load (K and Mg) is partly positively correlated to the CP content among and between feedstuffs (Institut National de la Recherche Agronomique (INRA), 2007) and the CP content is generally better known than the mineral content.…”

Section: Methodsmentioning

confidence: 99%

“…Construction of equations able to predict fluxes of water excretion in dairy cow could be useful to predict the concentration of some minerals and the DM of freshly produced manure and slurry, which can impact certain processes such as volatilization of ammonia and nitrous oxide from effluents (Brown et al, 2000). Although numerous empirical equations have been developed to predict water intake in dairy cows (Castle and Thomas, 1970;Paquay et al, 1970;Stockdale and King, 1983;Holter and Urban, 1992;Dahlborn et al, 1998;Meyer et al, 2004;, along with several empirical equations to predict urinary water flow (Bannink et al, 1999;Fox et al, 2004;Nennich et al, 2006;Kume et al, 2008), only one study has addressed, through an integrated approach, the water balance (intake and excretions) in dairy cattle (Holter and Urban, 1992). An advantage of this integrative approach is that it allows validation of water flow predictions consistent with the whole water balance of dairy cows.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of water intake and excretion flows in Holstein dairy cows under thermoneutral conditions

Khelil-Arfa

Boudon

Maxin

et al. 2012

animal

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The increase in the worldwide demand for dairy products, associated with global warming, will emphasize the issue of water use efficiency in dairy systems. The evaluation of environmental issues related to the management of animal dejections will also require precise biotechnical models that can predict effluent management in farms. In this study, equations were developed and evaluated for predicting the main water flows at the dairy cow level, based on parameters related to cow productive performance and diet under thermoneutral conditions. Two datasets were gathered. The first one comprised 342 individual measurements of water balance in dairy cows obtained during 18 trials at the experimental farm of Mé jussaume (INRA, France). Predictive equations of water intake, urine and fecal water excretion were developed by multiple regression using a stepwise selection of regressors from a list of seven candidate parameters, which were milk yield, dry matter intake (DMI), body weight, diet dry matter content (DM), proportion of concentrate (CONC) and content of crude protein (CP) ingested with forage and concentrate (CPf and CPc, g/kg DM). The second dataset was used for external validation of the developed equations and comprised 196 water flow measurements on experimental lots obtained from 43 published papers related to water balance or digestibility measurements in dairy cows. Although DMI was the first predictor of the total water intake (TWI), with a partial r 2 of 0.51, DM was the first predictive parameter of free water intake (FWI), with a partial r 2 of 0.57, likely due to the large variability of DM in the first dataset (from 11.5 to 91.4 g/100 g). This confirmed the compensation between water drunk and ingested with diet when DM changes. The variability of urine volume was explained mainly by the CPf associated with DMI (r.s.d. 5.4 kg/day for an average flow of 24.0 kg/day) and that of fecal water was explained by the proportion of CONC in the diet and DMI. External validation showed that predictive equations excluding DMI as predictive parameters could be used for FWI, urine and fecal water predictions if cows were fed a well-known total mixed ration. It also appeared that TWI and FWI were underestimated when ambient temperature increased above 258C and possible means of including climatic parameters in future predictive equations were proposed.

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