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Production of animal feed pellets with uniform, predictable, and good technical pellet quality is challenging. The objective of this work was to investigate water adsorption in dry ingredients and in mixture of dry ingredients commonly used in animal feed. Rehydration at 20 and 80°C and water adsorption in moist air at 80°C were studied. Ingredients studied were soybean meal, wheat, barley, dehulled oats, rapeseed cake, sugar beet pulp, maize, and wheat bran. The ingredients were milled fine (< 0.5 mm) or coarse (> 0.5 mm). Results show that the chemical composition of ingredients and the physical state of water affect water adsorption. A combination of steam and water should be used to optimize production of pelletized feed. The combination of a fine degree of milling, high temperature, and a long residence time favor the water adsorption process for most of the dry ingredients. For mixtures of dry ingredients, the individual chemical composition of each ingredient could be as important as physical factors such as particle size, temperature, and residence time in water. [ABSTRACT FROM AUTHOR]

A new biokinetic model for iodine in dairy cows was developed utilizing data from a 4 × 4 factorial feeding experiment with rumen-cannulated cows that were fed rapeseed cake containing substances known to influence iodine metabolism, and soybean meal without such effects. 131 I was administered both intravenously and intra-ruminally to document metabolism pathways. The new model included compartments such as saliva and thyroid gland, often ignored in other models, and in contrast to previously available biokinetic models, it fitted well to our experimental data. Compared to cows fed soybean meal diet, rapeseed cake diet resulted in lower retention of 131 I in body compartments, reduced 131 I transfer to milk, and increased urinary excretion of 131 I. Over time, low retention of iodine from diets containing goitrogenic substances like rapeseed would increase the risk of iodine deficiency, in both cattle and humans. Based on sensitivity analysis, milk, blood and liver pathways contributed the most to the model uncertainty. The proposed model should be relevant in elucidating mechanisms governing iodine uptake and metabolism in dairy cows, to predict how feeding can be used to optimize iodine concentrations in milk, and to reduce transfer of radioiodine through dairy products in case of fallout from nuclear events., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Brit Salbu reports financial support was provided by Research Council of Norway (RCN). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Four formulations of concentrate feeds, three contrasting qualities of grass silages, and mixtures of the silages (55%) and concentrates (45%, dry weight) were tested for in vitro fermentation kinetics, in vitro dry matter degradation (IVDMD), utilizable crude protein (uCP), and metabolizable energy (ME) values. The concentrates were pelleted control concentrate for dairy cows (CONT-P); pelleted alkaline concentrate with ammoniated cereal grains (ALKA-P); mash form concentrate with ALKA-P main ingredients but with feed-grade urea and barley replacing ammoniated cereal grain (UREA-M); and mash form of ALKA-P ingredients prior to alkalization (ALKA-M). The grass silages were early cut, late cut, and a mixture (1:1) of early and late cut. The objectives were to test if the feeds differed in the tested parameters within each feed category and assess the modulatory effect of concentrate feeds on the grass silage fermentation characteristics in the mixed diets. No interaction effects of the concentrate feeds by silage quality were observed for the tested parameters in the mixed diets. For concentrates, the pelleted diets were higher ( p < 0.05) in IVDMD and molar proportion of propionate but lower in butyrate. The ALKA-P produced the highest estimated uCP ( p < 0.01). For silages, uCP, ME, total short-chain fatty acids (VFAs), and molar proportions of propionate and branched-chain VFAs decreased ( p < 0.05) with increasing stage of maturity. In conclusion, the ALKA-P could match the CONT-P in uCP and ME values and fermentation characteristics. Results for silages and their mixtures with concentrates highlight the importance of silage quality in dietary energy and protein supply for ruminants.

Dynamics of starch digestion in dairy cows fed extruded pellets differing in physical functional properties were investigated by measuring starch digestibility, post-prandial rumen fermentation patterns, and post-prandial duodenal starch appearance. Additionally, starch digestion effects on neutral detergent fiber (NDF) digestibility and methane (CH 4 ) emission were studied. Pure barley was extruded to produce three treatments having pellets of either low-density (LD), medium-density (MD) or high-density (HD). The experiment was conducted in a 3 × 3 Latin square design using three lactating Danish Holstein cows fitted with ruminal, duodenal and ileal cannulas. After the allocation of experimental concentrate directly into the rumen through the rumen cannula, cows were fed a basal diet low in starch. Eight samples were collected on equal time intervals (9 h) from duodenal digesta, ileal digesta and feces (grab sample) to determine digestibility. For post-prandial rumen fermentation patterns, four sample sets of rumen dorsal, medial and ventral fluid were taken from each cow, whereas for post-prandial duodenal starch appearance, 14 samples of duodenal chyme were obtained from each cow relative to morning feeding of experimental concentrate at 07:00 h. Ruminal, small intestinal, hindgut and total tract digestibility of starch did not differ among treatments. Similarly, NDF digestibility and CH 4 emission also remained unaffected by treatments. However, compared with the LD and MD treatments, the HD treatment showed higher acetate: propionate ratio at all positions in the rumen and a higher post-prandial duodenal starch appearance. This indicates lower ruminal starch degradation (RSD) and higher starch flow into the small intestine for HD treatment. In conclusion, the current study indicates that pellets' physical properties can manipulate RSD, where pellets with high density and fluid stability can partly shift starch digestion from the rumen to the small intestine. Indeed, further investigations are needed., (© 2022 The Authors. Journal of Animal Physiology and Animal Nutrition published by Wiley-VCH GmbH.)

The current study assessed the effects of red macroalgae Asparagopsis taxiformis (AT)-included as an enteric methane inhibitor-in dairy cow diets on feed intake and eating-rumination behaviour. Fifteen early lactating Norwegian Red dairy cows were offered ad libitum access to drinking water and a total mixed ration (TMR) composed of 35% concentrate feed and 65% grass silage on a dry matter (DM) basis. The experiment lasted for 74 days with the first 22 days on a common diet used as the covariate period. At the end of the covariate period, the cows were randomly allocated into one of three dietary treatments: namely, 0% AT (control), 0.125% AT and 0.25% AT in the TMR. The TMR was offered in individual feed troughs with AT blended in a 400 g ( w / w ) water-molasses mixture. Eating-rumination behaviour was recorded for 11 days using RumiWatchSystem after feeding the experimental diets for 30 days. The 0.25% AT inclusion significantly reduced the DM intake (DMI). Time (min/d) spent on eating and eating in a head-down position increased with the increasing AT level in the diet, whereas rumination time was not affected. The greater time spent on eating head-down with the 0.25% AT group resulted in a significantly higher chewing index (min/kg DMI). Estimated saliva production per unit DMI (L/kg DMI, SE) increased from 10.9 (0.4) in the control to 11.3 (0.3) and 13.0 (0.3) in the 0.125% and 0.25% AT groups, respectively. This aligned with the measured ruminal fluid pH (6.09, 6.14, and 6.37 in the control, 0.125% AT and 0.25% AT groups, respectively). In conclusion, either the level of the water-molasses mixture used was not sufficient to mask the taste of AT, or the cows used it as a cue to sort out the AT. Studies with relatively larger numbers of animals and longer adaptation periods than what we used here, with varied modes of delivery of the seaweed may provide novel strategies for administering the additive in ruminant diets.

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows' lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database ( n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH 4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH 4 emissions than extant models. In order to propose country-specific values for the CH 4 conversion factor Y m (% of gross energy intake partitioned into CH 4 ) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Y m over a wide range of feeding situations. A simulated operational database containing CH 4 production (predicted by the basic model), feed intake and composition, Y m and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Y m were updated based on the results from the basic models. The predicted Y m ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH 4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH 4 in Norway.

The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.

We assessed the interactive effects of gross feed use efficiency (FUE, milk yield/kg DMI) background ("high" = HEFF vs. "low" = LEFF) and graded levels of dietary CP (130, 145, 160, and 175 g/kg DM) on milk production, enteric methane (CH4) emission, and apparent nitrogen use efficiency (NUE, g milk protein nitrogen/g nitrogen intake) with Norwegian Red (NRF) dairy cows. Eight early- to mid-lactation cows were used in a 4 × 4 Latin square design experiment (2 efficiency backgrounds, 4 dietary treatments, and 4 periods each lasting 28 d). The diets were designed to be identical in physical nature and energy density, except for the planned changes in CP, which was a contribution of slight changes in other dietary constituents. We hypothesized that HEFF cows would partition more dietary energy and nitrogen into milk components and, as such, partition less energy in the form of methane and excrete less nitrogen in urine and feces compared with their LEFF contemporaries. We observed no interactions between dietary CP level and efficiency background on DMI, other nutrient intake, NUE, CH4 emission, and its intensity (g CH4/kg milk). Gradually decreasing dietary CP from 175 to 130 g/kg DM did not affect DMI, milk and energy-corrected milk yield, and milk component yields and daily CH4 emission. However, decreasing dietary CP increased NUE and reduced urinary nitrogen (UN) excretion both in quantitative terms and as proportion of nitrogen intake. The HEFF cows showed improved NUE and decreased CH4 emission intensity compared with the LEFF cows. In the absence of interaction effects between efficiency background and dietary CP level, our results suggest that CH4 emission intensity and UN excretions can be reduced by selecting dairy cows with higher FUE and reducing dietary CP level, respectively, independent of one another. Furthermore, UN excretion predictions based on milk urea nitrogen (MUN) and cow BW for NRF cows produced very close estimates to recorded values promising an inexpensive and useful tool for estimating UN excretion under the Nordic conditions where ordinary milk analysis comes with MUN estimates., (© The Author(s) 2018. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Background: Dairy products account for approximately 60% of the iodine intake in the Norwegian population. The iodine concentration in cow's milk varies considerably, depending on feeding practices, season, and amount of iodine and rapeseed products in cow fodder. The variation in iodine in milk affects the risk of iodine deficiency or excess in the population., Objective: The first goal of this study was to develop a model to predict the iodine concentration in milk based on the concentration of iodine and rapeseed or glucosinolate in feed, as a tool to securing stable iodine concentration in milk. A second aim was to estimate the impact of different iodine levels in milk on iodine nutrition in the Norwegian population., Design: Two models were developed on the basis of results from eight published and two unpublished studies from the past 20 years. The models were based on different iodine concentrations in the fodder combined with either glucosinolate (Model 1) or rapeseed cake/meal (Model 2). To illustrate the impact of different iodine concentrations in milk on iodine intake, we simulated the iodine contribution from dairy products in different population groups based on food intake data in the most recent dietary surveys in Norway., Results: The models developed could predict iodine concentration in milk. Cross-validation showed good fit and confirmed the explanatory power of the models. Our calculations showed that dairy products with current iodine level in milk (200 µg/kg) cover 68, 49, 108 and 56% of the daily iodine requirements for men, women, 2-year-old children, and pregnant women, respectively., Conclusions: Securing a stable level of iodine in milk by adjusting iodine concentration in different cow feeds is thus important for preventing excess intake in small children and iodine deficiency in pregnant and non-pregnant women.

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.