Evaluation of five models predicting feed intake by dairy cows fed total mixed rations

Kidney function, but not nitrogen excretion differs between Brown Swiss and Holstein dairy cows

Brown Swiss (BS) cows have greater urea concentrations in milk and blood compared with Holstein (HO) cows. We tested the hypothesis that BS and HO cows differ in kidney function and nitrogen excretion. Blood, saliva, urine, and feces were sampled in 31 multiparous BS and 46 HO cows kept under identical feeding and management conditions. Samples were collected at different lactational stages after the monthly DHIA control test-day. To test the glomerular filtration rate (GFR) and urea excretion, concentrations of creatinine and urea were measured in serum, urine, and saliva. As an additional marker to estimate GFR, we determined symmetric dimethylarginine (SDMA) in serum. Feces were analyzed for dry matter content and nitrogen concentration. Data on milk urea and protein concentrations, and daily milk yield were obtained from the monthly DHIA test-day records. The effects of breed, time, and parity number on blood, saliva, urine, feces, and milk parameters were evaluated with the GLM procedure with breed, time, and parity number as fixed effects. Differences between BS and HO were assessed by the Tukey-corrected t-test at P < 0.05. Concentrations of urea, creatinine, and SDMA in serum, were greater in BS than in HO cows (P < 0.01): 5.46 ± 0.19 vs 4.72 ± 0.13 mmol/L (urea), 105.96 ± 2.23 vs 93.07 ± 1.50 mmol/l (creatinine), and 16.78 ± 0.69 vs 13.39 ± 0.44 µg/dL (SDMA). We observed a greater urea concentration in BS cows (25.8 ± 0.7 vs 21.8 ± 0.7 mg/dL) and protein content in milk (3.70 ± 0.08 vs 3.45 ± 0.07%) than in HO cows (P < 0.01). Urea and creatinine concentrations in urine and saliva did not differ among breeds. No differences between BS and HO were observed for milk yield, fecal DM, and fecal nitrogen content. Dry matter intake and body weight were similar in BS and HO cows (P > 0.05). Despite greater urea, creatinine, and SDMA concentrations in blood as well as a higher milk urea content in BS compared with HO, respective concentrations in urine did not differ between breeds. In conclusion, our results demonstrate a lower renal GFR in BS compared with HO cows, thereby contributing to the greater plasma urea concentration in BS cows. However, estimation of nitrogen excretion via milk, urine, and feces does not entirely reflect nitrogen turnover within the animal.

Effect of Metabolizable Protein Supply on Milk Performance, Ruminal Fermentation, Apparent Total-Tract Digestibility, Energy and Nitrogen Utilization, and Enteric Methane Production of Ayrshire and Holstein Cows

In North America, the nutrient requirements of dairy cattle are predicted using the Cornell Net Carbohydrate and Protein System (CNCPS) or the National Research Council (NRC). As Holstein is the most predominant dairy cattle breed, these models were developed based on the phenotypic, physiological, and genetic characteristics of this breed. However, these models may not be appropriate to predict the nutrient requirements of other breeds, such as Ayrshire, that are phenotypically and genetically different from Holstein. The objective of this study was to evaluate the effects of increasing the metabolizable protein (MP) supply using CNCPS on milk performance, ruminal fermentation, apparent total-tract digestibility, energy and N utilization, and enteric methane production in Ayrshire vs. Holstein lactating dairy cows. Eighteen (nine Ayrshire; nine Holstein) lactating cows were used in a replicated 3 × 3 Latin square design (35-d periods) and fed diets formulated to meet 85%, 100%, or 115% of MP daily requirement. Except for milk production, no breed × MP supply interaction was observed for the response variables. Dry matter intake (DMI) and the yields of energy-corrected milk (ECM), fat, and protein were less (p < 0.01) in Ayrshire vs. Holstein cows. However, feed efficiency and N use efficiency for milk production did not differ between the two breeds, averaging 1.75 kg ECM/kg DMI and 33.7 g milk N/100 g N intake, respectively. Methane yield and intensity and urinary N also did not differ between the two breeds, averaging 18.8 g CH₄ /kg DMI, 10.8 g CH₄ /kg ECM, and 27.6 g N/100 g N intake, respectively. Yields of ECM and milk protein increased (p ≤ 0.01) with increasing MP supply from 85% to 100% but no or small increases occurred when MP supply increased from 100 to 115%. Feed efficiency increased linearly with an increasing MP supply. Nitrogen use efficiency (g N milk/100g N intake) decreased linearly (by up to 5.4 percentage units, (p < 0.01) whereas urinary N excretion (g/d or g/100 g N intake) increased linearly (p < 0.01) with an increasing MP supply. Methane yield and emission intensity were not affected by MP supply. This study shows that feed efficiency, N use efficiency, CH₄ (yield and intensity), and urinary N losses did not differ between Ayrshire and Holstein cows. Energy-corrected milk yield and feed efficiency increased, but N use efficiency decreased and urinary N losses increased with increasing dietary MP supply regardless of breed. Ayrshire and Holstein breeds responded similarly to increasing MP levels in the diet.

Mathematical model to predict the dry matter intake of dairy cows on pasture

In pasture-based dairy systems, there is a close relationship between milk production and dry matter intake (DMI), hence the importance of measuring these variables, although obtaining this information implies high labour and costs. The objective of this study was to design a mathematical model to predict DMI for grazing dairy cows. This model was based on the basic principle of the fill-unit system. In this scheme, cows and feedstuffs were described in terms of feed intake capacity (FIC) and fill (unit/amount of feed), respectively. The FIC was determined by the animal's ability to regulate feed intake which depends on factors such as body size, age and lactation status. The "fill" was determined by the nutritional properties of the feedstuff such as its dry matter (DM) digestibility and crude protein (CP) content, among others. In the design of the model, ad lib. feed consumption was assumed. Parity, state of lactation and gestation were considered to estimate the cow ingestion capacity. Satiety values (SV) were determined for Festuca arundinacea and Lolium multiflorum and these values were incorporated into the model, including DM, CP, neutral detergent fibre (NDF) and in vitro digestible organic matter (dOM). The fixed parameters of the model were determined by adjusting a polynomial regression to the data from three experiments with lactating Holstein cows from Baja California, Mexico (n=30).The model allows predicting DMI, using as inputs, easily measured data and does not require knowing daily milk yield (MY) or body weight (BW), so the model is practical and consistent. The results obtained from the model were satisfactory because they were similar to those attained experimentally. Average DMI was 21.68 kg/d in one group and 23.44 kg/d in the other; when applying the model, we obtained an estimate of 22.82 kg/d for a cow with characteristics similar to those of the cows under study.

Accuracy of enteric methane emission models for cattle in sub-Saharan Africa: status quo and the way forward

Cattle emit over 65% of enteric methane (CH4) in sub-Saharan Africa (SSA), making them the focus of many mitigation strategies targeting livestock emissions. Since measured feed intake data are sparse, emission factors for enteric CH4 (EFCH4) are mainly estimated indirectly from gross energy intake (GEI) using the net energy (NE) requirements for different metabolic processes in cattle. However, all NE requirement systems commonly used for cattle in SSA were developed for cattle in temperate regions. Therefore, we assessed the suitability of different enteric CH4 models for estimating the GEI of cattle in SSA. The Intergovernmental Panel on Climate Change (IPCC) and South African models were identified as the main tier 2-based methods used to estimate enteric CH4 emissions from cattle in SSA. In the IPCC model, EFCH4 was estimated as (GEI * [Ym/100])/55.65, where Ym is the conversion factor (%) of gross energy in feed to CH4 and 55.65 the energy content of CH4 (MJ/kg). The GEI was estimated based on NE requirements for different metabolic processes in cattle as per the American National Research Council. In the South African model, EFCH4 was estimated as (Y/100 * GEI/55.22), where Y is the CH4 yield and 55.22 is the energy content of CH4; Y was calculated from the dry matter (DM) digestibility while GEI was calculated by predicting DM intake and multiplying it by 18.4 MJ (gross energy per kilogram DM). Also, the suitability of the British and German NE requirement systems was assessed as alternatives used for cattle nutrition in SSA. These NE systems were implemented in the IPCC model to yield the "AFRC" and "GfE" models, respectively. The four models were then evaluated using an evaluation dataset summarizing feed quality and DM intake results from 21 studies conducted in SSA, with 125 dietary treatments, and 822 cattle observations. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used to evaluate the models' accuracy. Only the South African model estimated the GEI in dairy cattle with an acceptable RPE (18.9%) and highest CCC (0.87), while the other three models yielded estimates with RPE > 20%. None of the four models we assessed estimated GEI for other cattle (i.e., nondairy) with an RPE < 20% or CCC > 0.30. The inaccuracy in GEI estimates suggests an error of the same magnitude in EFCH4 estimates. Therefore, a concerted effort is needed to improve the accuracy of enteric CH4 estimation models for cattle in SSA.

Balancing Trade-Offs in Milk Production by Making Use of Animal Individual Energy Balancing

Traditionally, the energy supply of dairy cows is based on the average performance of the herd. Because this contradicts the great variation in requirements between individual animals, the objective of the present study was to quantify both the extent and consequences of variation in the relevant sub-variables used to calculate the energy balance (EB) on an individual animal basis. Total energy supply (TES) and requirements (TER) of 28 multiparous German Holstein dairy cows fed TMR with 7.0 MJ NEL were studied between the 2nd and 15th week after calving. TES, mainly influenced by DMI, increased from 100.1 (week 2) to 152.1 MJ NEL/d (week 15; p < 0.01). Weekly coefficients of variation (CV) ranged between 0.10 and 0.16 and were similar to the CV of DMI (0.09 to 0.17). TER, as the sum of energy requirement for maintenance (body weight) and production (milk yield), decreased from 174.8 (week 2) to 164.5 MJ NEL/d (week 15; p < 0.01) and CV varied between 0.16 (week 2) and 0.07 (week 11). EB increased from −74.8 (week 2) to −12.4 MJ NEL/d (week 15; p < 0.01) and CV varied from 0.32 (week 3) to 1.01 (week 10). The results indicate that calculating EB on an individual animal basis is a prerequisite to identify animals with an increased risk of failing to cope with their energy situation, which cause failure costs that drain the profit of affected cows.

Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times per Day

Simple Summary

It is difficult to obtain feed intake of dairy cows in experiments since all cows are raised in a free-stall barn in commercial dairy farms nowadays. Therefore, it is necessary to develop a simple, accurate, and reliable feed intake prediction model to replace direct measurement. In this study, we generated a forecasting model to predict daily dry matter intake (DMI) of dairy cows in mid and late lactation based on the feed intake of first 2 h after feeding (DMI-2h). The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × Body weight (kg/day) (R² = 0.46). Compared with NRC model (2001), our model shows higher accuracy and precision on predicting daily DMI of dairy cows with fed ration three times per day in mid and late lactation period. This prediction model could be used as an alternative approach for researchers who have difficulty in measuring DMI in dairy cows’ experiments.

Abstract

The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R² = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R² (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day.

Evaluation of the Modified LIVestock SIMulator for Stall-Fed Dairy Cattle in the Tropics

Simple Summary

Models can play an important role in identifying and filling knowledge gaps related to sustainable resource use in (sub-)tropical livestock production systems. Yet, most simulation models used to study cattle production systems in the (Sub-)Tropics were developed using data that quantify and characterize biological processes of cattle kept in temperate regions, which may reduce the accuracy of predictions. Therefore, we adopted some published data that quantify and characterize biological processes of cattle kept in (sub-)tropical production systems to modify an existing ruminant livestock herd model. Then, the accuracy of predictions of feed intake and productive performance from the original and modified models were evaluated using meta data from (sub-)tropical stall-fed cattle. The modified model predicted voluntary dry matter intake and productive performance more accurately than the original model. Consequently, adopting relevant data that correctly describe the biological processes in (sub-)tropical cattle production systems is the way forward for improving simulation models for these systems.

Abstract

Ruminant livestock systems in the (Sub-)Tropics differ from those in temperate areas. Yet, simulation models used to study resource use and productive performance in (sub-)tropical cattle production systems were mostly developed using data that quantify and characterize biological processes and their outcomes in cattle kept in temperate regions. Ergo, we selected the LIVestock SIMulator (LIVSIM) model, modified its cattle growth and lactation modules, adjusted the estimation of the animals’ metabolizable energy and protein requirements, and adopted a semi-mechanistic feed intake prediction model developed for (sub-)tropical stall-fed cattle. The original and modified LIVSIM were evaluated using a meta-dataset from stall-fed dairy cattle in Ethiopia, and the mean bias error (MBE), the root mean squared error of prediction (RMSEP), and the relative prediction error (RPE) were used to assess their accuracy. The modified LIVSIM provided more accurate predictions of voluntary dry matter intake, final body weights 140 days postpartum, and daily milk yields than the original LIVSIM, as shown by a lower MBE, RMSEP, and RPE. Therefore, using data that quantify and characterize biological processes from (sub-)tropical cattle production systems in simulation models used in the (Sub-)Tropics can considerably improve their accuracy.

Effect of parity number on the dry matter intake of dairy cows during the first week after calving

We investigated the effect of parity number on the dry matter intake (DMI) of cows during the first week after calving. Eighty-three cows were evaluated from 14 days before to 7 days after calving. DMI and milk yield were measured for 7 days after calving, and the calving score was measured. Blood samples were collected throughout the experiment. The average DMI during the first week after calving was reduced in the first-lactation heifers and high-parity number cows. A quadratic relationship between the parity number and the DMI was observed. The first-lactation heifers had lower prepartum serum total protein (TP) concentration and milk yield, higher prepartum serum nonesterified fatty acid (NEFA) concentration and calving score than the multiparous cows. The recovery rate of serum calcium (Ca) after calving was slow in the cows in the parity 6. The DMI was positively affected by the serum Ca concentration after calving, milk yield, and prepartum serum TP concentration and was negatively affected by the calving score and prepartum serum NEFA concentration. We conclude that the DMI immediately after calving tends to be lower in first-lactation heifers and high-parity number cows, but factors that reduce the DMI differ according to parity number.

Predicting dry matter intake using conceptual models for cattle kept under tropical and subtropical conditions1

Using empirical models to predict voluntary dry matter intake (VDMI) of cattle across production systems in the (Sub-)Tropics often yields VDMI estimates with low adequacy (i.e., accuracy and precision). Thus, we investigated whether semimechanistic conceptual mathematical models (CMM) developed for cattle in temperate areas could be adopted and adjusted to adequately predict VDMI of stall-fed cattle in the (Sub-)Tropics. The CMM of Conrad et al. (1964) (C1) and Mertens (1987) (M1) were identified and adopted for their simplicity in reflecting physicophysiological VDMI regulation. Both CMM use 2 equations that estimate the physiologically and physically regulated VDMI and retain the lower VDMI prediction as actual VDMI. Furthermore, C1 was modified by increasing the daily average fecal dry matter output from 0.0107 to 0.0116 kg/kg body weight, yielding the modified model C2. For M1, the daily neutral detergent fiber intake capacity was increased from 0.012 to 0.0135 kg/kg body weight and the daily metabolizable energy requirements for maintenance from 0.419 to 0.631 MJ/kg0.75 body weight, whereas the metabolizable energy requirements for gain was reduced from 32.5 to 24.3 MJ/kg body weight gain, yielding the modified model M2. Last, also the mean of the physically and physiologically regulated VDMI rather than the lower of both estimates was retained as actual VDMI to generate the models C3 (from C1), C4 (from C2), M3 (from M1), and M4 (from M2). The 8 CMM were then evaluated using a data set summarizing results from 52 studies conducted under (sub)tropical conditions. The mean bias, root mean square error of prediction (RMSEP) and concordance correlation coefficient (CCC) were used to evaluate adequacy and robustness of all CMM. The M4, C2, and C1 were the most adequate CMM [i.e., lowest mean biases (0.07, -0.22, and 0.14 kg/animal and day, respectively), RMSEP (1.62, 1.93, and 2.0 kg/animal and day, respectively), and CCC (0.91, 0.86, and 0.85, respectively)] and robust of the 8 CMM. Hence, CMM can adequately predict VDMI across diverse stall-fed cattle systems in the (Sub-)Tropics. Adjusting CMM to reflect the differences in feed quality and animal physiology under typical husbandry conditions in the (Sub-)Tropics and those in temperate areas improves the adequacy of their VDMI predictions.

Analysis of lactating cows on commercial Austrian dairy farms: the influence of genotype and body weight on efficiency parameters

The aim of this study was twofold: first, to evaluate the influence of body weight on the efficiency of dairy cows, and second, to analyze the current state of dairy cattle populations as part of the Austrian Cattle Breeding Association's Efficient Cow project.

Data of Fleckvieh (FV, dual-purpose Simmental), Fleckvieh×Red Holstein (FV×RH), Holstein (HF) and Brown Swiss (BS) dairy cows (161 farms, 6098 cows) were collected at each performance recording during the year 2014.

In addition to routinely recorded data (e.g., milk yield, fertility), body weight, body measurements, body condition score (BCS) and individual feed information were also collected. The following efficiency traits were considered: body weight efficiency as the ratio of energy-corrected milk (ECM) to metabolic body weight, feed efficiency (kilogram ECM per kilogram dry-matter intake) and energy efficiency expressed as the ratio of energy in milk to energy intake.

The relationship of milk yield to body weight was shown to be nonlinear. Milk yield decreased in cows above the 750 kg body weight class for HF, BS and FV×RH with 68 % RH genes, but less dramatically and later for FV at 800 kg. This resulted in an optimum body weight for feed and energy efficiency. BS and HF had the highest efficiency in a narrower and lighter body weight range (550–700 kg) due to a stronger curvature of the parabolic curve. Contrary to this, the efficiency of FV did not change as much as it did in the dairy breeds with increasing body weight, meaning that FV had a similar feed and energy efficiency in a range of 500–750 kg. The breed differences disappeared when body weight ranged between 750 and 800 kg.

The average body weight of the breeds studied (FV 722 kg, BS 649 and HF 662 kg) was in the optimum range. FV was located at the upper end of the decreasing segment.

In conclusion, an optimum body weight range for efficiency does exist, due to the nonlinear relationship of milk yield and body weight. Specialized dairy breeds seem to respond more intensively to body weight range than dual-purpose breeds, due to the stronger curvature. Cows with medium weights within a population are the most efficient. Heavy cows (>750 kg) produce even less milk. A further increase in dairy cows' body weights should therefore be avoided.

Updating predictions of dry matter intake of lactating dairy cows

Our objective was to model dry matter intake (DMI) by Holstein dairy cows based on milk energy (MilkE), body weight (BW), change in BW (ΔBW), body condition score (BCS), height, days in milk (DIM), and parity (primiparous and multiparous). Our database included 31,631 weekly observations on 2,791 cows enrolled in 52 studies from 8 states of the United States, mostly in the Upper Midwest. The means ± standard deviations of these variables were 24 ± 5 kg of DMI, 30 ± 6 Mcal of MilkE/d, 624 ± 83 kg of BW, 0.24 ± 1.50 kg of ΔBW/d, 3.0 ± 0.5 BCS, 149 ± 6 cm height, and 102 ± 45 DIM. Data analysis was performed using a mixed-effects model containing location, study within location, diet within study, and location and cow within study as random effects, whereas the fixed effects included the linear effects of the covariates described previously and all possible 2-way interactions between parity and the other covariates. A nonlinear (NLIN) mixed model analysis was developed using a 2-step approach for computational tractability. In the first step, we used a linear (LIN) model component of the NLIN model to predict DMI using only data from mid-lactation dairy cows (76-175 DIM) without including information on DIM. In the second step, a nonlinear adjustment for DIM using all data from 0 to 368 DIM was estimated. Additionally, this NLIN model was compared with an LIN model containing a fourth-order polynomial for DIM using data throughout the entire lactation (0-368 DIM) to assess the utility of an NLIN model for the prediction of DMI. In summary, a total of 8 candidate models were evaluated as follows: 4 ways to express energy required for maintenance (BW, BW^0.75, BW adjusted for a BCS of 3, and BW^0.75 adjusted for a BCS of 3) × 2 modeling strategies (LIN vs. NLIN). The candidate models were compared using a 5-fold across-studies cross-validation approach repeated 20 times with the best-fitting model chosen as the proposed model. The metrics used for evaluation were the mean bias, slope bias, concordance correlation coefficient (CCC), and root mean squared error of prediction (RMSEP). The proposed prediction equation was DMI (kg/d) = [(3.7 + parity × 5.7) + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (-0.689 + parity × -1.87) × BCS] × [1 - (0.212 + parity × 0.136) × exp^{(-0.053 × DIM)}] (mean bias = 0.021 kg, slope bias = 0.059, CCC = 0.72, and RMSEP = 2.89 kg), where parity is equal to 1 if the animal is multiparous and 0 otherwise. Finally, the proposed model was compared against the Nutrient Requirements of Dairy Cattle (2001) prediction equation for DMI using an independent data set of 9,050 weekly observations on 1,804 Holstein cows. The proposed model had smaller mean bias and RMSEP and higher CCC than the Nutrient Requirements of Dairy Cattle equation to predict DMI and has potential to improve diet formulation for lactating dairy cows.

Grasslands: A Source of Secondary Metabolites for Livestock Health

The need for environmentally friendly practices in animal husbandry, in conjunction with the reduction of the use of synthetic chemicals, leads us to reconsider our agricultural production systems. In that context, grassland secondary metabolites (GSMs) could offer an alternative way to support to livestock health. In fact, grasslands, especially those with high dicotyledonous plant species, present a large, pharmacologically active reservoir of secondary metabolites (e.g., phenolic compounds, alkaloids, saponins, terpenoids, carotenoids, and quinones). These molecules have activities that could improve or deteriorate health and production. This Review presents the main families of GSMs and uses examples to describe their known impact on animal health in husbandry. Techniques involved for their study are also described. A particular focus is put on anti-oxidant activities of GSMs. In fact, numerous husbandry pathologies, such as inflammation, are linked to oxidative stress and can be managed by a diet rich in anti-oxidants. The different approaches and techniques used to evaluate grassland quality for livestock health highlight the lack of efficient and reliable technics to study the activities of this complex phytococktail. Better knowledge and management of this animal health resource constitute a new multidisciplinary research field and a challenge to maintain and valorize grasslands.

Biological implications of longevity in dairy cows: 1. Changes in feed intake, feeding behavior, and digestion with age

Milk production strategies focusing on longevity and limited use of concentrate are receiving increasing attention. To evaluate such strategies, knowledge of the development with age of animal characteristics, particularly digestion, is indispensable. We therefore investigated the development of feed intake, chewing activity, and digestion in 30 lactating Brown Swiss cows (876-3,648 d old) and 12 heifers (199-778 d old). We also studied whether age effects were exhibited differently in animals selected from herds subjected for 11 yr either to a forage-only or to a forage-concentrate feeding regimen. Forages consisted of grass hay (the only feed for heifers), corn silage, and grass pellets. Measurements lasted for 8 d, where amounts and composition of feeds, feces, and milk were recorded and analyzed. Ruminal pH data and eating and rumination activity were assessed by pH sensors put into the rumen and halter-mounted noseband sensors. The mean retention time of feed particles was assessed using Cr-mordanted fiber and data were used to calculate dry matter gut fill. Data were subjected to regression analyses with age and feeding regimen as explanatory variables, and body weight, milk yield, and proportion of hay in forage as covariates. This allowed separating age-related changes of body weight and milk yield from independent age effects and correcting for differences in preference for individual forages. In cows, organic matter intake increased with age (from slightly below to above 20kg/d), as did mean retention time and gut fill. Digestibility of organic matter did not show a clear age dependency, but fiber digestibility had a maximum in cows of around 4 to 6 yr of age. Ruminal pH and absolute eating and rumination times did not vary with cow age. Young and old cows chewed regurgitated boluses more intensively (60-70 times) than middle-aged cows (about 50 times). Effects of feeding regimen were small, except for fiber intake and rumination time per unit of intake, owing to the different fiber content of the diets. No significant interactions between age and feeding regimen were found. Heifers spent more time eating and ruminating per unit of feed than cows, which resulted in a high fiber digestibility. Irrespective of the feeding regimen tested, older cows maintained intake and digestion efficiency with longer retention times and chewing rumination boluses more intensively. The results support efforts to extend the length of productive life in dairy cows.