Meta-analysis of factors that affect the utilization efficiency of phosphorus in lactating dairy cows

Abstract

A meta-analysis investigation based on literature data was conducted to estimate the effect size of nutritional and animal factors on phosphorus (P) excretion in feces and concentrations of P in milk. Two data sets were created for statistical analysis: One to derive prediction equations for P in feces (25 studies; 130 treatments) and another for P in milk (19 studies; 94 treatments). Prediction equations were derived using mixed model regression analysis with a random effect for study, and equations were evaluated based on values for Bayesian information criterion (BIC), root mean square prediction error (RMSPE), and concordance correlation coefficient (CCC) statistics. In terms of RMSPE and CCC values, fecal P excretion was best predicted by P intake, where P in feces (g/d) = −3.8(±3.45) + 0.64(±0.038) × P intake (g/d) (RMSPE: 18.3%, CCC: 0.869). However, significant effects of crude protein [g/ kg of dry matter (DM)], neutral detergent fiber (g/kg of DM), and milk yield (kg/d) on fecal P excretion were also found. Despite a lack of improvement in terms of RMSPE and CCC values, these parameters may still explain part of the variation in fecal P excretion. For milk P, expressed as a fraction of P intake, the following equation had the highest CCC and the lowest RMSPE value: P in milk as a fraction of P intake (g/g) = 0.42(±0.065) + 0.23(±0.018) × feed efficiency (i.e., fat-and protein-corrected milk yield/dry matter intake) − 0.11(±0.0199) × P in feed (g/kg of DM) (RMSPE: 19.7%; CCC: 0.761). Equations derived to predict fecal P as a fraction of P intake (g/g) or milk P content (g/ kg) could not adequately explain the observed variation and did not perform well in terms of RMSPE and CCC values. Examination of the residuals showed that P balance was a seemingly confounding factor in some of the models. The results presented here can be used to estimate P in feces and milk based on commonly measured dietary and milk variables, but could also be used to guide development of mechanistic models on P metabolism in lactating dairy cattle. Factors to consider in future research and modeling efforts regarding efficiency of P use include the effects of dietary neutral detergent fiber, crude protein, starch, variation in P content of milk, and effects of P resorption from bone and body tissues during early lactation.

Prediction of phosphorus output in manure and milk by lactating dairy cows

“…The negative relationship of milk yield with P f , independent of P i , indicate that P i partitioning to P f diminishes as milk production increases. Comparable effects have been previously reported in Valk et al (2002) and Klop et al (2013). Moreover, the final prediction model for P f also included a positive relationship with dietary ash.…”

“…Therefore, the new model including P i and milk yield is recommended for predicting P f if DMI data are available. The Weiss and Wyatt (2004a) model using only P i and the Klop et al (2013) model using P i and milk yield also predicted P f successfully, although the RMSPE% were slightly greater (19.3 to 19.8%). The new model including linear relationships of P i , milk yield, dietary ash content, and milk protein concentration predicted P Ma better compared with extant models, so it is recommended to predict P Ma if DMI data are available.…”

“…The mean and slope biases were negligible in both cases, where 98 to 99% of the prediction error were due to random variability of data. The extant models including only P i or both P i and milk yield (Klop et al, 2013;Weiss and Wyatt, 2004a) also performed well, although the RMSPE% were slightly greater than the new model (RMSPE% = 19.3 to 20.0%). The new model not requiring DMI data for predicting P f was associated with an acceptable (22.3%) RMSPE%, 95% of which came from random sources (Table 5).…”

“…New models developed for predicting P output (g/d) in feces (P f ), urine (P u ), manure (P Ma ), and milk (P m ), and factors affecting P concentration in milk (P MilkC , g/kg of milk) Table 5. Performance of models challenged with independent data including 354 measurements of fecal P output (P f ) and 177 measurements of manure P output (P Ma ) 19.6 7.1 0.6 99.2 Extant models P f (g/ d) = −2.3 + 0.63 × Pi (Weiss and Wyatt, 2004a) 19.8 4.0 1.3 94.7 = 27.1 + 0.99 × DMI (Klop et al, 2013) 26.5 16.1 3.2 80.7 = −31.3 + 0.73 × DMI + 16.78 × P (Klop et al, 2013) 22.6 13.4 6.6 80.0 = −3.8 + 0.64 × P i (Klop et al, 2013) 20.0 6.4 1.0 92.5 = −15.6 + 0.82 × DMI + 16.47 × P − 0.096 × CP (Klop et al, 2013) 22.5 13.2 8.7 78.1 = −48.9 + 0.84 × DMI + 16.96 × P + 0.043 × NDF (Klop et al, 2013) 22.4 22.8 11.8 65.4 = −34.7 + 0.90 × DMI + 17.28 × P − 0.093 × CP + 0.041 × NDF (Klop et al, 2013) 22.4 20.7 9.6 67.7 = 19.9 + 0.79 × P i − 1.04 × milk (Klop et al, 2013) 19.3 4.8 0.2 95.00 = −58.3 + 1.85 × DMI + 16.90 × P + 0.056 × NDF -0.48 × milk (Klop et al, 2013) 20.7 23.7 6.9 69.4 = −43.9 +1.72 × DMI+ 17.15 × P -0.082 × CP + 0.052 × NDF -0.39 × milk (Klop et al, 2013) 20.9 20.9 6.6 72.5 P Ma (g/ d) = (P i × 560.7) + 21.1 24.7 55.1 0.1 44.7 = 7.5 + 0.78 × P i − 0.702 × milk (Weiss and Wyatt, 2004a) 26.7 53.6 4.0 42.3 = −2.5 + 0.64 × P i (Weiss and Wyatt, 2004a) 21.2 11.2 0.5 88.7 = P i − (Milk × 0.9) (Van Horn et al, 1994) 20.9 0.8 8.5 90.6 = 14.67 + 0.6786 × P i + 0.00196 × P i 2 -0.317 × milk (Morse et al, 1992) 35.4 73.8 38.6 2.23 = (milk × 0.781) + 50.4 46.7 73.4 3.0 23.6 1 DMI in kg/d; P i = P intake (g/d); milk = milk yield (kg/d); ash% and P% = dietary total ash and P, respectively (% of DM); CP% = dietary CP (% of DM); NDF, CP, and P = dietary NDF, CP, and P, respectively (g/kg of DM); MProt = milk protein (%); RMSPE% = root mean square prediction error as a percentage of observed mean; MB = mean bias; SB = slope bias; RB = random bias (all as percentage of total bias). this study performed well when evaluated using an independent data set.…”

The investigation of correlation among selected biochemical parameters and vital signs in dairy herd to design the bio-cybernetic dairy cow model

“…Subsequently the comparison of different lactation curve models were described by several authors, among others (Silvestre et al 2006, Cankaya et al 2011) are worth mentioning. Mathematical analysis of milk production represents a very successful application of modeling systems in agriculture and animal breeding (Vargas et al 2000, Macciotta et al 2005b, Klop et al 2013, Haile-Mariam et al 2015. It is a valuable tool for development and validation of mechanistic models explaining features of milk production pattern.…”