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ABSTRACT: Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called “sniffer technique”) and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 (“best model”), where all significant traits were included; model 2 (“on-farm model”), where DMI was excluded; and model 3 (“reduced on-farm model”), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (−0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

ABSTRACT: This study consists of milk fatty acid (FA) data collected during 2 in vivo experiments. For this study, 8 cows from each experiment were included in a replicated 4 × 4 Latin square design. At the start of experiment 1 (Exp1) cows were at (mean ± standard deviation) 87 ± 34.6 d in milk, 625 ± 85.0 kg of body weight, and 32.1 ± 4.17 kg/d milk yield and at the start of experiment 2 (Exp2) cows were at 74 ± 18.2 d in milk, 629 ± 87.0 kg of body weight, and 37.0 ± 3.2 kg/d milk yield. In Exp1, we examined the effects of gradual replacement of barley with hulled oats (oats with hulls) on milk FA composition. The basal diet was grass silage and rapeseed meal (58 and 10% of diet DM, respectively), and the 4 grain supplements were formulated so that barley was gradually replaced by hulled oats at levels of 0, 33, 67, and 100% on dry matter basis. In Exp2, we examined (1) the effects of replacing barley with both hulled and dehulled oats (oats without hulls) and (2) the effects of gradual replacement of hulled oats with dehulled oats on milk FA composition. The basal diet was grass silage and rapeseed meal (60 and 10% of diet DM, respectively), and the 4 pelleted experimental concentrates were barley, hulled oats, a 50:50 mixture of hulled and dehulled oats, and dehulled oats on dry matter basis. In Exp1, gradual replacement of barley with hulled oats decreased relative proportions of 14:0, 16:0, and total saturated FA (SFA) in milk fat linearly, whereas proportions of 18:0, 18:1, total monounsaturated FA, and total cis unsaturated FA increased linearly. Transfer efficiency of total C18 decreased linearly when barley was replaced by hulled oats in Exp1. In Exp2, relative proportions of 14:0, 16:0, and total SFA were lower, whereas proportions of 18:0, 18:1, monounsaturated FA, and cis unsaturated FA were higher in milk from cows fed the oat diets than in milk from cows fed the barley diet. Moreover, in Exp2, gradual replacement of hulled oats with dehulled oats slightly decreased the relative proportion of 14:0 in milk fat but did not affect the proportions of 16:0, 18:0, 18:1, total SFA, monounsaturated FA, trans FA, or polyunsaturated FA. In Exp2, transfer efficiency of total C18 was lower when cows were fed the oat diets than when fed the barley diet and decreased linearly when hulled oats were replaced with dehulled oats. Predictions of daily CH4 emissions (g/d) using the on-farm available variables energy-corrected milk yield and body weight were not markedly improved by including milk concentrations of individual milk FA in prediction equations. In conclusion, replacement of barley with oats as a concentrate supplement for dairy cows fed a grass silage-based diet could offer a practical strategy to change the FA composition of milk to be more in accordance with international dietary guidelines regarding consumption of SFA.

ABSTRACT: The effects of different ruminal protozoa (RP) on CH4 emissions from ruminants were evaluated in a meta-analysis, using 64 publications reporting data from 79 in vivo experiments. Experiments included in the database reported CH4 emissions (g/d) and total RP (TRP, log10 cells/mL) from the same group of animals. The relationship between CH4 emissions and RP (TRP, entodiniomorphids, and isotrichids), and TRP-, entodiniomorphid-, and isotrichid-based CH4 emission prediction models, were evaluated as mixed models with experiment as a random effect and weighted by the reciprocal of the standard error of the mean and centered around one. Positive associations existed between TRP and isotrichids with CH4 emissions but not between entodiniomorphids and CH4 emissions. A reduction in CH4 emissions was observed, averaging 7.96 and 4.25 g/d, per log unit reduction in TRP and isotrichid concentrations, respectively. Total RP and isotrichids were important variables in predicting CH4 emissions from ruminants. Isotrichid CH4 prediction model was more robust than the TRP, evidenciated by lower predicted sigma hat study (%), and error (%), and with higher concordance correlation coefficient. Both TRP and isotrichid models can accurately predict CH4 emissions across different ruminant types, as shown by the low square root of the mean square prediction error, with 6.59 and 4.08% of the mean of root of the mean square prediction error in the TRP and isotrichid models, respectively. Our results confirm that isotrichids are more important than entodiniomorphids in methanogenesis. Distinguishing these 2 populations yielded a more robust CH4 prediction model than combining them as total protozoa.

ABSTRACT: 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.

ABSTRACT: Numerous empirical and mechanistic models predicting methane (CH4) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH4 production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH4 production were not included in the data set. The relationships between observed and predicted CH4 (pCH4) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH4 (g/d) = 0.94 (±0.022) × pCH4 (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH4 (g/d) = CH4 (g/d) = 0.98 (±0.019) × pCH4 (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RMSPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, respectively. The residuals of CH4 predictions were more strongly related to factors associated with CH4 production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH4 yield with the Molly model. It was concluded that both models could be used for prediction of CH4 production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

This study investigated the effect of forage type (grass or red clover) and harvesting time (primary growth or regrowth) of silage on energy and N utilisation by sheep fed at maintenance level. Specifically, the assumption of constant loss of energy of digestible organic matter from energy losses in urine and CH4 applied in evaluation of silage metabolisable energy (ME) was investigated. Urinary excretion of high-energy phenolic compounds related to solubilisation of lignin was assumed to affect urinary energy (UE) losses from sheep fed highly digestible grass silage (GS). A total of 25 primary growth and regrowth silages of timothy (Phleum pratense) and meadow fescue (Festuca pratensis) grass mixtures and red clover (Trifolium pratense) samples collected in digestibility trials with sheep, including faecal and urine samples, were used for energy and N determinations. Urinary concentration of monophenolic compounds and CH4 emissions in vitro were also analysed. Daily faecal N output, CH4 yield (MJ/kg DM intake), proportion of CH4 energy in digestible energy (DE) and proportion of UE in DE were greater (P ≤ 0.03) in sheep fed red clover silage (RCS) than GS. Furthermore, less (P = 0.01) energy was lost as UE of DE in sheep fed primary growth GS compared with the other treatments. The relationship between UE and silage N intake or urinary N output for both silage types (i.e. grass v. red clover) was strong, but the fit of the regressions was better for GS than RCS. The CH4/DE ratio decreased (P < 0.05) and the UE/DE ratio increased (P < 0.05) with increasing organic matter digestibility in RCS. These relationships were not significant (P < 0.05) for the GS diets. The regression coefficient was higher (P < 0.05) for GS than RCS when regressing ME concentration on digestible organic matter. The results of this study imply that ME/DE ratio is not constant across first-cut GS of different maturities. The ME production response may be smaller from highly digestible first-cut GS but could not be clearly related to urinary excretion of monophenols derived from solubilisation of lignin. Furthermore, energy lost in urine was not clearly defined for RCS and was much more predictable for GS from silage N concentration.

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

It may be possible for dairy farms to improve profitability and reduce environmental impacts by selecting for higher feed efficiency and lower methane (CH4) emission traits. It remains to be clarified how CH4 emission and feed efficiency traits are related to each other, which will require direct and accurate measurements of both of these traits in large numbers of animals under the conditions in which they are expected to perform. The ranking of animals for feed efficiency and CH4 emission traits can differ depending upon the type and duration of measurement used, the trait definitions and calculations used, the period in lactation examined and the production system, as well as interactions among these factors. Because the correlation values obtained between feed efficiency and CH4 emission data are likely to be biased when either or both are expressed as ratios, therefore researchers would be well advised to maintain weighted components of the ratios in the selection index. Nutrition studies indicate that selecting low emitting animals may result in reduced efficiency of cell wall digestion, that is NDF, a key ruminant characteristic in human food production. Moreover, many interacting biological factors that are not measured directly, including digestion rate, passage rate, the rumen microbiome and rumen fermentation, may influence feed efficiency and CH4 emission. Elucidating these mechanisms may improve dairy farmers ability to select for feed efficiency and reduced CH4 emission.

The objective of this meta-analysis was to develop and validate empirical equations predicting BW gain (BWG) and carcass traits of growing cattle from intake and diet composition variables. The modelling was based on treatment mean data from feeding trials in growing cattle, in which the nutrient supply was manipulated by wide ranges of forage and concentrate factors. The final dataset comprised 527 diets in 116 studies. The diets were mainly based on grass silage or grass silage partly or completely replaced by whole-crop silages, hay or straw. The concentrate feeds consisted of cereal grains, fibrous by-products and protein supplements. Mixed model regression analysis with a random study effect was used to develop prediction equations for BWG and carcass traits. The best-fit models included linear and quadratic effects of metabolisable energy (ME) intake per metabolic BW (BW0.75), linear effects of BW0.75, and dietary concentrations of NDF, fat and feed metabolisable protein (MP) as significant variables. Although diet variables had significant effects on BWG, their contribution to improve the model predictions compared with ME intake models was small. Feed MP rather than total MP was included in the final model, since it is less correlated to dietary ME concentration than total MP. None of the quadratic terms of feed variables was significant (P>0.10) when included in the final models. Further, additional feed variables (e.g. silage fermentation products, forage digestibility) did not have significant effects on BWG. For carcass traits, increased ME intake (ME/BW0.75) improved both dressing proportion (P0.10) effect on dressing proportion or carcass conformation score, but it increased (P

The objective of this meta-analysis was to develop empirical equations predicting growth responses of growing cattle to protein intake. Overall, the data set comprised 199 diets in 80 studies. The diets were mainly based on grass silage or grass silage partly or completely replaced by whole-crop silages or straw. The concentrate feeds consisted of cereal grains, fibrous by-products and protein supplements. The analyses were conducted both comprehensively for all studies and also separately for studies in which soybean meal (SBM; n=71 diets/28 studies), fish meal (FM; 27/12) and rapeseed meal (RSM; 74/35) were used as a protein supplement. Increasing dietary CP concentration increased (P0.10) with increasing proportion of concentrate in the diet. All protein supplements had a significant effect on BWG, but the effects were greater for RSM (P

An empirical regression model for the prediction of total dry matter intake (DMI) of dairy cows was developed and compared with four published intake models. The model was constructed to include both animal and dietary factors, which are known to affect DMI. For model development, a data set based on individual cow data from 10 change-over and four continuous milk production studies was collected (n = 1554). Relevant animal (live weight (LW), days in milk (DIM), parity and breed) and dietary (total and concentrate DMI, concentrate composition, forage digestibility and fermentation quality) data were collected. The model factors were limited to those that are available before the diets are fed to animals, that is, standardized energy corrected milk (sECM) yield, LW, DIM and diet quality (total diet DMI index (TDMI index)). As observed ECM yield is a function of both the production potential of the cow and diet quality, ECM yield standardized for DIM, TDMI index and metabolizable protein concentration was used in modelling. In the individual data set, correlation coefficients between sECM and TDMI index or DIM were much weaker (0.16 and 0.03) than corresponding coefficients with observed ECM (0.65 and 0.46), respectively. The model was constructed with a mixed model regression analysis using cow within trial as a random factor. The following mixed model was estimated for DMI prediction: DMI (kg DM/day) = −2.9 (±0.56)+0.258 (±0.011) × sECM (kg/day) + 0.0148 (±0.0009) × LW (kg) −0.0175 (±0.001) × DIM −5.85 (±0.41) × exp (−0.03 × DIM) + 0.09 (±0.002) × TDMI index. The mixed DMI model was evaluated with a treatment mean data set (207 studies, 992 diets), and the following relationship was found: Observed DMI (kg DM/day) = −0.10 (±0.33) + 1.004 (±0.019) × Predicted DMI (kg DM/day) with an adjusted residual mean square error of 0.362 kg/day. Evaluation of the residuals did not result in a significant mean bias or linear slope bias, and random error accounted for proportionally >0.99 of the error. In conclusion, the DMI model developed is considered robust because of low mean prediction error, accurate and precise validation, and numerically small differences in the parameter values of model variables when estimated with mixed or simple regression models. The Cornell Net Carbohydrate and Protein System was the most accurate of the four other published DMI models evaluated using individual or treatment mean data, but in most cases mean and linear slope biases were relatively high, and, interestingly, there were large differences in both mean and linear slope biases between the two data sets.

The aim of this work was to develop an index describing the relative intake of the total diet by dairy cows, and hence the ability to predict intake responses to changes in both forage and concentrate variables. An evaluation of concentrate factors affecting silage dry matter (DM) intake of dairy cows was conducted based on dietary treatment means from milk production experiments. The data were divided into four subsets according to concentrate treatments used within the experiments: the amount of concentrate supplementation (n = 217), protein supplementation (n = 336), carbohydrate composition (n = 114) and fat concentration of the concentrate (n = 29). The data were subjected to mixed-model regression analysis. Increased concentrate DM intake (CDMI) decreased silage DM intake (SDMI) quadratically. The substitution rate (substitution of silage DM for concentrate DM) increased with improved silage intake potential. SDMI increased quadratically with concentrate protein intake, the response being negatively related to the effective protein degradability (EPD) of concentrates. Replacement of starchy concentrate ingredients with fibrous supplements had a small positive effect on silage intake, whereas increased concentrate fat concentration slightly decreased SDMI. The outcome of concentrate factors influencing total DM intake (TDMI) was used to create a relative CDMI index as follows: CDMI index = 100 + 10 × [(CDMI − 0.1629 × CDMI − 0.01882 × CDMI2 − 5.49) + ((0.9474 × CCPI − 0.4965 × CCPI2) − 2.017 × (CEPD − 0.74)) + 0.00225 × (CNDF − 250) − 0.0103 × (40 − Cfat) − 0.00058 × (CDMI − 8.0) × (SDMI index − 100)], where CDMI = concentrate DM intake (kg/day), CCPI = supplementary concentrate CP intake (kg/day; CP>170 g/kg DM), CEPD = concentrate EPD (g/g), CNDF = concentrate NDF concentration (g/kg DM), Cfat = concentrate fat concentration (g/kg DM) and SDMI index is the relative intake potential of silage (Huhtanen, Rinne and Nousiainen 2007. Animal 1, 758–770). TDMI index was calculated as SDMI index + CDMI index − 100 to describe the relative intake potential of the total diet. For the whole data set (n = 943), one TDMI index unit was equivalent to 0.095 kg/day DM intake, i.e. close to the default value of 0.100 kg. The CDMI index explained proportionally 0.88 of the variation in TDMI within a study with a 0.27 kg/day residual mean-square error (n = 616). The corresponding values for the TDMI index were 0.81 and 0.37 kg/day (n = 943), respectively. The residual mean-square errors in cross-validation were marginally higher. The developed TDMI index can be used to estimate the intake responses to diet changes. It provides an improved basis for practical dairy cow ration formulation and economic evaluation.

An evaluation of the factors affecting silage dry-matter intake (SDMI) of dairy cows was conducted based on dietary treatment means. The data were divided into six subsets based on the silage treatments used in the experiments: concentration of digestible organic matter in dry matter (D-value) influenced by the maturity of grass ensiled (n = 81), fermentation quality influenced by silage additives (n = 240), dry matter (DM) concentration influenced by wilting of grass prior to ensiling (W; n = 85), comparison of silages made from primary growth or regrowth of grass (n = 46), and replacement of grass silage with legume (L; n = 53) or fermented whole-crop cereal (WC; n = 37) silages. The data were subjected to the mixed model regression analysis. Both silage D-value and fermentation quality significantly affected SDMI. The average effects of D-value and total acid (TA) concentration were 17.0 g and − 12.8 per 1 g/kg DM, respectively. At a given D-value, silage neutral-detergent fibre (NDF) concentration tended to decrease SDMI. Silage TA concentration was the best fermentation parameter predicting SDMI. Adding other parameters into the multivariate models did not improve the fit and the slopes of the other parameters remained insignificant. Total NDF intake was curvilinearly related to silage D-value the maximum intake being reached at a D-value of 640 g/kg DM. Results imply that physical fill is not limiting SDMI of highly digestible grass silages and that both physical and metabolic factors constrain total DM intake in an interactive manner. Silage DM concentration had an independent curvilinear effect on SDMI. Replacing primary growth silage with regrowth, L or WC silages affected SDMI significantly, the response to regrowth silage being linearly decreasing and to L and WC quadratically increasing. The outcome of factors affecting SDMI was used to update the relative SDMI index as follows: SDMI index = 100+10 × [(D-value − 680) × 0.0170 − (TA − 80) × 0.0128+(0.0198 × (DM − 250) − 0.00002364 × (DM2 − 250 2)) − 0.44 × a+4.13 × b − 2.58 × b2+5.90 × c − 6.14 × c2 − 0.0023 × (550 − NDF)], where a, b and c represent the proportions (0–1) of regrowth, L or WC silages from total silage DM. For the whole data set, one index unit corresponded to the default value of 0.10 kg in SDMI. The SDMI index explained proportionally 0.852 of the variation in SDMI with 0.34 kg DM per day residual. The updated SDMI index provides improved basis for the practical dairy cow ration formulation and economic evaluation.

Field data from 16 051 Finnish milk recorded herds including milk yield (MY), feed consumption, feed analyses, and the herd effect for milk yield (HMILK) obtained from the national breeding value estimation program, were analysed to detect the influence of concentrate feeding on milk production. HMILKs are deviations from the average national level with mean of 45 kg and SD of 722 kg. Mean MY was 6917 kg and mean dry matter intake (DMI) 5679 kg per cow per year. The effect of concentrate feeding on HMILK and MY was studied by using quantitative [amount of energy (FUI) and concentrates (CI) in the diet] and qualitative [proportion of grain (Gc) or compound feed (Mc) in concentrates or CP content (CPc) of concentrates] diet characteristics as dependent variables in multivariate regression analysis. The general linear effect of CI was 1.18 kg MY/kg CI. Production response of CI decreased with increasing CI as indicated by significant interactions between CI and CI classes. Gc showed a negative relationship with HMILK, but CPc proved to be a more important factor affecting HMILK. Feeding grain instead of compound feed was connected with too low protein content in concentrates. Mc was positively correlated with CP content of concentrates. However, the use of compound feed appeared to give a slight increase in HMILK even after accounting for the effect of CP. ;

Twenty-eight bulls were used in a 3 × 2 factorial design to study the effects of two by-products from the integrated starch-ethanol process, barley fibre and distillers' solubles, as supplements for grass silage. The animals were divided into five blocks and slaughtered when the average live weight (LW) of each block reached 500 kg. The three energy supplements were barley (B), a mixture (1:1 on a dry matter (DM) basis) of barley and barley fibre (BF), and barley fibre (F), fed without (DS-) or with (DS+) wet distillers' solubles (200 g kg-1 concentrate on DM basis). Concentrates were given at the rate of 95 g DM kg-1 LW0.6. Including barley fibre in the diet did not affect feed intake, but distillers' solubles tended to increase both silage and total DM intakes as well as amino acids absorbed in the intestine and energy intake. The protein balance in the rumen increased with the inclusion of barley fibre (P

Four ruminally fistulated Finnish Ayrshire cows were used to study the effects of different protein supplements on concentration and flow of soluble non-ammonia N (SNAN)into the omasum.The treatments in a 4 × 4 Latin square design were a basal diet of grass silage and barley and the basal diet supplemented with fishmeal, soybean meal and maize gluten meal. Protein supplements significantly increased concentrations of peptide N (P=0.009)and total SNAN (P =0.03)fractions in omasal digesta.Peptide constituted the largest proportion of SNAN flow into the omasum indicating that hydrolysis of peptides to amino acids is the most limiting step in rumen proteolysis.The microbial contribution to SNAN was on an average 0.64 indicating that a large proportion of SNAN flow leaving the rumen was of microbial origin.The estimated SNAN flow per kg dry matter intake from the basal diet and protein supplemented diets indicated that approximately 49,22 and 37 g kg-1 of fishmeal,soybean meal and maize gluten meal protein, respectively, escaped from ruminal degradation as SNAN.;

Twelve Ayrshire cows were used to study the effects of and interactions between extent of silage fermentation and level of protein supplementation on silage intake and milk production. Experimental design was a cyclic change-over with six dietary treatments, with two replicate blocks of six cows and four 3-week periods. Dietary treatments in a 2 x 3 factorial arrangement consisted of two wilted silages treated with either a formic acid-based (FA) 5 l/t or a bacterial inoculant additive (I) and three levels of protein supplementation obtained by fish meal (FM) inclusion (0, 60 and 120 g/kg concentrate). Grass silage was given ad libitum and concentrates were offered at a rate of 10 kg/d. Both silages were well- preserved but I-silage was more extensively fermented than FA-silage. Feeding FA-silage decreased diet organic matter digestibility, particularly that of neutral detergent fibre, compared with I-silage. Inclusion of FM increased the digestibility of all dietary constituents. Dry matter (DM) intake of I-silage was lower than that of FA-silage but milk yield was not significantly affected. Restricting silage fermentation increased milk fat content, fat yield and energy corrected milk yield. FM inclusion increased silage DM intake, milk yield, milk protein content and yield. FM120 inclusion increased milk fat yield more with FA- silage than with I-silage, whereas the response in milk protein yield was greater with I-silage. Plasma glucose, plasma urea and milk urea concentrations were lower and blood -hydroxybutyrate higher in cows given FA-diets than those given I-diets. ;

Twenty-four Ayrshire cows were used to study the effects of physical treatment of barley, rapeseed meal (RSM) supplementation and heat-moisture treatment of RSM on silage intake and milk production. Experimental design was a cyclic change-over with six dietary treatments. The treatments in a 2 x 3 factorial arrangement consisted of either untreated (UB) or heat-moisture treated barley (TB), given without protein supplementation (control) or with untreated or heat-moisture treated RSM. Grass silage was given ad libitum and the concentrates at a rate of 10 kg/d. For the RSM diets, 2 kg/d of the basal concentrate was replaced with either untreated or treated RSM. Treatment of barley decreased silage intake, the effect being greater when the supplement did not contain RSM. There was no effect on milk yield, but due to the lower milk fat content, energy corrected milk yield was lower in cows given TB than in those given UB. Feeding the TB diets was also associated with lower milk urea content, and with increased milk protein content but not protein yield. Faster initial rate of gas production in vitro suggested that the treatment of barley increased the rate of fermentation. Compared with the control diets, RSM supplementation significantly increased silage intake, milk yield, milk protein content and yields of all milk constituents. Heat-moisture treatment of RSM did not produce any further production response.;

A dataset of grasses and respective silages was collected by systematically varying the harvesting time in primary growth (n = 27) and in regrowth (n = 25). The swards were mixtures of timothy and meadow fescue. The grasses were ensiled unwilted with formic acid. Fixed or mixed regression procedure of SAS was used to investigate the relationships between composition of grasses and respective silages and to develop regression equations for predicting silage in vivo organic matter digestibility (OMD) from herbage pepsincellulase organic matter solubility (OMS). The silages were well preserved showing only limited amounts of secondary fermentation products. The silage dry matter (DM), crude protein and neutral detergent fibre contents could be estimated relatively accurately from grass variables as judged by relatively small prediction errors (RMSEmixed = 3.6, 8.1 and 18 g (kg DM)-1, respectively). The average OMS of grasses was significantly higher than that of respective silages (779 vs. 756 g (kg DM)-1, P < 0.001). However, silage OMD was equally accurately predicted from grass and silage OMS (RMSEmixed = 15.1 and 15.8 g (kg DM)-1, respectively). When predicting silage OMD from OMS, specific equations should be used for primary growth and regrowth silages, because the slopes and intercepts of correction equations were numerically though not statistically significantly different. It is concluded that silage composition and digestibility can be reliably predicted from herbage characteristics provided that silages are well preserved with moderate ensiling losses.;

Third harvest samples from a pot experiment were analysed to study the effects of sodium (Na) (0, 200 and 400 mg dm-3 of soil in a single application as Na2SO4 . 10H2O) and potassium (K) application (0, 100 and 200 mg dm-3 applied at each harvest as KCl) on the nutritive value of timothy grown on three different soil types (clay, loam and organogenic soil). The effects of fertilization on concentrations of crude protein, neutral detergent fibre (NDF) and non-structural carbohydrates, although statistically significant, were relatively minor in absolute terms. Na applications increased and K applications decreased sulphur and phosphorus concentrations, the magnitude of which was dependent on soil type. The increase in sulphur concentration can be attributed to sulphate in Na-fertilizer. The effects of fertilizers on in vitro organic matter digestibility and the potential extent of dry matter (DM) and NDF digestibility were small. Digestion kinetic parameters estimated from fermentative gas production measured using a fully automated system were used in a rumen simulation model to estimate digestibility. Total gas volume and the rate of gas production from the rapidly digestible fraction were negatively correlated with timothy S and N concentrations. Na application had no effect, but K application increased true rumen DM digestibility, the effect being most profound on organogenic soil. The results suggest that Na application does not elicit substantial positive effects on the nutritive value of timothy which has often been reported for perennial ryegrass, but K application can improve the nutritive value of timothy grown on K deficient soil.;

The present re-evaluation of a dataset of systematically collected laboratory analyses and in vivo digestibility information for several types of silages gives convincing evidence of the biological weaknesses of feed characterisation based on the proximate feed analysis. The problems include intrinsic failures of the analysis in describing cause-response relationships between forage composition and digestibility, and heavy dependency of the equations on forage specific and environmental factors. It is concluded that proximate analysis is not suitable for characterisation of neither forages nor concentrate feedstuffs. In vitro pepsin-cellulase solubility of organic matter (OMS) and concentration of indigestible neutral detergent fibre (iNDF) predicted forage organic matter digestibility (OMD) with an acceptable accuracy for practical feed evaluation purposes provided that forage type dependent correction equations were employed. The revised detergent system dividing forage dry matter (DM) into almost completely available neutral detergent solubles (NDS), and insoluble residue (neutral detergent fibre, NDF) shows potential for future development. The combined use of long-term in situ ruminal incubation and NDF fractionation can be used to divide forage DM into three biologically meaningful fractions: NDS, iNDF and potentially digestible NDF (pdNDF). The summative models can then be used to predict forage D-value, i.e. apparently digestible organic matter in forage (g kg-1 DM). The models sum digestible NDS, which can be determined by Lucas equation, and digestible NDF (dNDF), which is the amount of pdNDF that is actually digested during any specific fermentation or retention time. Forage type specific summative models were as good as regression equations based on OMS or iNDF in predicting forage D-value and general summative models gave better results than general equations based on iNDF and especially OMS. If the goal is to reduce prediction error of D-value below 15 g kg-1 DM, forage type specific prediction equations should be used regardless of whether they are based on OMS, iNDF or summative models. Another option in the future may be dynamic models, which can incorporate simultaneously the two important dynamic processes constraining feed digestion in ruminants: the rates of NDF passage and degradation (kd). However, a vital prerequisite to employ dynamic models in practical feed evaluation is that iNDF and kd can be easily and reliably determined from on-farm forages. Although a NIRS prediction equation for iNDF will be adopted in practical use in the near future in Finland, the methodology for estimating kd warrants further research.;

The objective of this paper is to review research which has evaluated the feeding of dairy cows with diets containing large proportions of grass silage. In Finland, milk production systems evolved are based on the use of restrictively fermented silages. Higher potential yields, smaller production risks than with cereal grains, short grazing period and high digestibility of grasses grown in northern latitudes have facilitated this development. Factors affecting nutrient supply from these diets are discussed. Digestibility is determined mainly by the stage of maturity at harvesting and it is not markedly affected by the level of energy and protein supplementation. Intake of grass silage is influenced both by digestibility and fermentation characteristics. Efficiency of microbial synthesis is high in animals given diets based on restrictively fermented silage but rumen fermentation pattern is characterised by low molar proportions of propionate. Production responses to additional concentrate are relatively small, especially when the amount of concentrate exceeds 10 kg day-1. High substitution of silage dry matter (DM), negative associative effects on digestion and partitioning of energy towards body tissues account for small production responses. Protein supplementation has consistently increased milk protein yield but responses do not appear to be related to the level of milk production, silage crude protein content, amount of concentrate or stage of lactation. The new protein evaluation system provides an accurate prediction of protein yield with the typical Finnish dairy cow diets. The high slopes (ca. 0.5) between protein supply and milk protein yield within experiments suggest that protein supply is suboptimal and protein supplements are used with a high efficiency.;

Enteric methane (CH4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Purpose: eduroam[TM] has already been proved to be a scalable, secure and feasible way for universities and research institutions to connect their wireless networks into a WLAN roaming community, but the advantages of eduroam[TM] have not yet been fully discovered in the wireless community networks aimed at regular consumers. This aim of this paper is to describe how eduroam[TM] architecture and technologies can be utilised in building these kinds of wireless community networks and to present the experiences gathered in building the Wireless Tampere community network. Design/methodology/approach: The eduroam[TM] architecture and technologies were chosen as the basis of Wireless Tampere community network architecture because of their scalability and security. Deploying eduroam[TM] technologies and architecture to a wider user base both confirmed old and revealed new issues and solutions in improving the usability and the deployment effort of eduroam[TM] and similar technologies. Findings: The eduroam[TM] technology and architecture can be utilised to build wireless community networks, but additional effort must be allocated in improving the usability and the ease of deployment when consumers, company and other regular users are involved. The improvements achieved would not have been developed if the concept and architecture had not been exposed to consumer and company users. The development of both the eduroam[TM] and Wireless Tampere concept requires deploying them to a wider audience and improving them iteratively utilising the existing solutions as the basis for new improvements. Originality/value: The paper presents the issues and problems, which were confronted when applying eduroam[TM] technologies in building the Wireless Tampere community network. The solutions found and deployment experiences presented can be utilised in improving eduroam[TM] as well as a basis for developing new, open and inter-connected wireless community networks. (Contains 2 figures and 9 notes.)

The Huhtanens are a military family who are rising to the challenges of their son's recent autism diagnosis. They have been trying to stay afloat, facing the steep learning curve of interpreting and understanding TRICARE regulations, Applied Behavior Analysis (ABA), and biomedical options. In this article, the authors share their experience and certain things they wanted parents to know if they think their child could have severe delays or have autism. Parents should: (1) understand the milestones of their child's development; (2) be proactive in seeking medical care for their child; (3) know what to do if they receive a diagnosis of autism; and (4) take care of each other.

It is a pre-requisite of radiographer education globally that students learn to apply theoretical knowledge during clinical placements. This is essential for the development of competence and confidence in the emerging graduate. The number of undergraduate radiographers in training poses challenges to placement capacity in many countries, which in turn impacts the quality of the clinical education experience. Within constrained placement periods, teaching and learning can tend to focus on the technical aspects of an examination. Solutions are needed to ensure that teaching and learning remain patient-centred and address all elements of the patient imaging pathway. FORCE (Framework for Online Radiographer Clinical Education) is a web-based Virtual Learning Environment (VLE) containing simulated clinical cases that encourage learner interaction and problem solving. Each case includes content pertaining to patient care; performance of the examination or therapy treatment; patient safety; image interpretation and radiographer responsibilities. A key feature of each case is the complex patient story that invites the learner to consider the comprehensive care of the whole person. Under approval of University College Dublin's Ethics Committee, multi-national users (Radiographers and students) of the FORCE VLE were invited to give feedback on the cases in an online questionnaire containing Likert scale questions and opportunity for open text additions. Feedback on the interactive video-based format of the case simulations was very positive. One theme drawn from the open comments was the value of the learning interactions related to patient care and communication. Participants offered that the patient perspective materials were insightful into real practice and a great asset in learning how to interact with patients. Interactive clinical simulation can be a useful tool for teaching patient-centred care across all stages of the patient imaging or therapy pathway. [ABSTRACT FROM AUTHOR]