Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis.

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

High-digestible silages allow low concentrate supply without affecting milk production or methane emissions.

In this study, we tested a response function comprising responses in milk to changes in organic matter digestibility of silages and concentrate supply. We studied the effect of changes in silage digestibility and concentrate supply on milk yield, feed intake, body weight, and methane production using 60 Norwegian Red cows. The experiment was a complete randomized block design comprising 3 periods. The pre-experimental period lasted 20 d and all the cows were fed a common silage for ad libitum intake and concentrate according to yield. Next, response period 1 lasted 17 d and the cows were divided into 2 treatments, where a low-digestible silage (LDS) was fed to half of the cows, and the other half were fed a high-digestible silage (HDS). Both groups were fed silage for ad libitum silage intake. Concentrate was optimized according to the yield and type of silage offered. In this period, the effect of silage was evaluated using a mixed model, including the results from pre-experimental period, with parity as a covariate and animal as a random effect. In response period 2, which lasted 20 d, the concentrate level was evaluated by dividing the silage digestibility treatments further into 3 subgroups. Concentrate was increased by 2 kg of dry matter (DM) per day, decreased by 2 kg of DM/d, or remained unchanged. In response period 1, silage treatments were optimized to obtain similar yields and resulted in a lower concentrate offer to HDS treatment. However, the HDS treatment showed a 3.0 kg of DM/d higher total feed intake due to a higher than expected silage intake. This resulted in 3.5 kg higher energy-corrected milk (ECM). Methane emissions were similar between silage treatments, but HDS showed lower methane per kilogram of DM due to its higher intake. The effect of concentrate supply level and interaction with silage digestibility was evaluated using mixed models, including the results for response period 1, with parity as a covariate and animal as a random effect. The reduction in concentrate offer by 2 kg/d in response period 2 was compensated for by increased 1.3 kg of DM/d of silage intake for HDS, resulting in similar intake (22.1 kg of DM/d and 21.7 kg of DM/d without and with concentrate reduction, respectively) and ECM yields (29.4 and 29 kg of ECM without and with concentrate reduction, respectively). However, concentrate offer reduction could not be compensated for by increased silage intake for LDS and resulted in lower milk yields (27.5 kg of ECM). Increased concentrate showed a higher marginal ECM response (kg of ECM per kg of additional concentrate intake) for LDS (1.8 vs. 3.3 kg of ECM for HDS and LDS, respectively). Thus, the drop in milk yields could be compensated for by increased concentrate offers if LDS are fed. Total methane production increased with increased concentrate intake, regardless of silage digestibility. Methane emissions per unit of milk were affected by total DM intake rather than by changes in silage digestibility and concentrate level. The results of this study are based on short-term periods and could show differences if study periods were longer; the results should be interpreted accordingly.

Precision and additivity of organic matter digestibility obtained via in vitro multi-enzymatic method.

The enzymatic digestibility of organic matter (EDOM) method is an in vitro multi-enzymatic method for estimating the organic matter (OM) digestibility of feeds. The EDOM method previously showed high accuracy with in vivo values for compound feeds. The aim of this study was to evaluate the precision of the EDOM method and determine its additivity, compared with the long-assumed additive property of the chemical components of compound feeds. 149 feed samples, 70 commercial compound feeds and 79 associated ingredients, were analyzed in a laboratory (lab1) for OM digestibility measured by EDOM (OMDEDOM) with 2 repetitions separated in time to estimate repeatability. Of the total samples, 49 compound feeds were further analyzed in a commercial laboratory (lab2) for OMDEDOM to determine reproducibility. The 49 compounds and their 69 associated ingredients were also analyzed by lab2 for dry matter (DM), ash, crude protein (CP), neutral detergent fiber (NDF), and starch. The EDOM method resulted in an intralaboratory correlation of 98.9% and an interlaboratory correlation of 92.6%, with no significant mean bias between the 2 laboratories tested. The formulation of compound feeds, total mixed rations, and mixtures in general assumes that their nutrient content can be calculated by adding together the nutrient supply of individual ingredients. This is of great importance in the feed industry for the creation of compound feeds. Additivity of OMDEDOM for the compound feed samples was evaluated by comparing the sum of the digestible OM (DOMEDOM) of the ingredients (predicted) with DOMEDOM estimated directly in the compound feed (observed). The regression of predicted versus observed showed a coefficient of determination (R2) of 0.93 and root mean square error (RMSE) of 1.07% of total DM, with no linear bias but with a mean bias (0.83% of DM). Additivity of CP, starch, crude fat, and NDF showed an R2 of 0.95, 0.98, 0.95, and 0.93, and RMSE of 1.56, 1.90, 0.39, and 1.46% of DM, respectively, all presenting linear bias. Crude fat also presented mean bias. Although significant, all linear and mean bias for DOMEDOM and chemical components were within the acceptable error limits for declaration of feeds. The results demonstrate the high precision of the EDOM method and its additive property, which is an advantage for the estimation of OM digestibility in compound feeds. Moreover, results of the tests of chemical components confirm their additive property.

Predicting feed intake and feed efficiency in lactating dairy cows using digesta marker techniques.

Direct measurement of individual animal dry matter intake (DMI) remains a fundamental challenge to assessing dairy feed efficiency (FE). Digesta marker, is currently the most used indirect technique for estimating DMI in production animals. In this meta-analysis we evaluated the performance of marker-based estimates against direct or observed measurements and developed equations for the prediction of FE (g energy-corrected milk (ECM)/kg DMI). Data were taken from 29 change-over studies consisting of 416 cow-within period observations. Most studies used more than one digesta marker. So, for each observed measurement of DMI, faecal dry matter output (FDMO) and apparent total tract dry matter digestibility (DMD), there was one or more corresponding marker estimate. There were 924, 409 and 846 observations for estimated FDMO (eFDMO), estimated apparent total tract DMD (eDMD) and estimated DMI (eDMI), respectively. The experimental diets were based mainly on grass silage, with soya bean or rapeseed meal as protein supplements and cereal grains or by-products as energy supplements. Across all diets, average forage to concentrate ratio on a dry matter (DM) basis was 59 : 41. Variance component and repeatability estimates of observed and marker estimations were determined using random factors in mixed procedures of SAS. Between-cow CV in observed FDMO, DMD and DMI was, 10.3, 1.69 and 8.04, respectively. Overall, the repeatability estimates of observed variables were greater than their corresponding marker-based estimates of repeatability. Regression of observed measurements on marker-based estimates gave good relationships (R2=0.87, 0.68, 0.74 and 0.74, relative prediction error =10.9%, 6.5%, 15.4% and 18.7%for FDMO, DMD, DMI and FE predictions, respectively). Despite this, the mean and slope biases were statistically significant (P<0.001) for all regressions. More than half of the errors in all regressions were due to mean and slope biases (52.4% 87.4%, 82.9% and 85.8% for FDMO, DMD, DMI and FE, respectively), whereas the contributions of random errors were small. Based on residual variance, the best model for predicting FE developed from the dataset was FE (g ECM/kg DMI)=1179(±54.1) +38.2(±2.05)×ECM(kg/day)-0.64(±0.051)×BW (kg)-75.6(±4.39)×eFDMO (kg/day). Although eDMD was positively related to FE, it only showed a tendency to reduce the residual variance. Despite inaccuracy in marker procedures, eFDMO from external markers provided a reliable determination for FE measurement. However, DMD estimated by internal markers did not improve prediction of FE, probably reflecting small variability.

Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows.

Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.

Characterization of progesterone profiles in fall-calving Norwegian Red cows.

Progesterone profiles in Norwegian Red cows were categorized, and associations between the occurrence of irregularities in the profiles and the commencement of luteal activity were investigated. The cows were managed in 3 feeding trials from 1994 to 2001 and from 2005 to 2008 at the Norwegian University of Life Sciences. The cows were followed from calving, and the milk samples collected represented 502 lactations from 302 cows. Milk samples for progesterone analysis were taken 3 times weekly from 1994 throughout 1998 and from 2005 to 2008 and 2 times weekly from 1999 to 2001. Commencement of luteal activity was defined as the first day of 2 consecutive measurements of progesterone concentration >or=3 ng/mL not earlier than 10 d after calving. Delayed ovulation type I was defined as consistently low progesterone concentration, <3 ng/mL for >or=50 d postpartum. Delayed ovulation type II was defined as prolonged interluteal interval with milk progesterone measurements <3 ng/mL for >or=12 d between 2 luteal phases. Persistent corpus luteum (PCL) type I was defined as delayed luteolysis with milk progesterone >or=3 ng/mL for >or=19 d during the first estrous cycle postpartum. Persistent corpus luteum type II was defined as delayed luteolysis with milk progesterone >or=3 ng/mL for >or=19 d during subsequent estrous cycles before first artificial insemination. Delayed ovulation type I was present in 14.7%, delayed ovulation type II in 2.8%, PCL type I in 6.7%, and PCL type II in 3.3% of the profiles. Commencement of luteal activity was related to milk yield, parity, PCL type I, and the summated occurrence of PCL type I and II. The least squares means for the interval to commencement of luteal activity were 24.2 d when PCL type I and II were present and 29.5 d when PCL type I and II were absent. The likelihood of pregnancy to first service was not affected in cows with a history of PCL when artificial insemination was carried out at progesterone concentrations <3 ng/mL (i.e., during estrus); however, cows that had experienced PCL were more likely to be inseminated during a luteal phase. The occurrence of delayed ovulation and PCL in Norwegian Red cows was less than that reported in most other dairy populations.

The effect of energy balance on ovarian activity in a herd of Norwegian cattle.

The study involved 34 primiparous cows fed ad libitum grass silage and fixed amounts of concentrate per cow and stage of lactation. It revealed that number of days from calving to maximum progesterone concentration in first luteal phase was negatively related to (p < 0.05) energy balance summarized over weeks 3-12 post-partum. One standard deviation improvement of the summarized energy balance relative to the mean reduced the length of the anovulatory period by 12 days. Similarly, an improved energy balance enhanced progesterone secretion during the oestrus cycle and early pregnancy, as measured by 3 variables; 1) maximum progesterone concentration in first luteal phase, 2) cumulative progesterone secretion bounded by the maximum concentrations in first and in third luteal phase and 3) cumulative progesterone secretion in the first month of pregnancy. All results were supported by the estimated regression coefficients of the 4 ovarian activity variables on summarized non-estrified fatty acids and acetoacetate variables.