Models of voluntary food intake in cattle

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.

Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d-1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Breeding goal traits accounting for feed intake capacity and roughage or concentrate intake separately

Current breeding tools aiming to improve feed efficiency use definitions based on total dry matter intake (DMI); for example, residual feed intake or feed saved. This research aimed to define alternative traits using existing data that differentiate between feed intake capacity and roughage or concentrate intake, and to investigate the phenotypic and genetic relationships among these traits. The data set contained 39,017 weekly milk yield, live weight, and DMI records of 3,164 cows. The 4 defined traits were as follows: (1) Feed intake capacity (FIC), defined as the difference between how much a cow ate and how much she was expected to eat based on diet satiety value and status of the cow (parity and lactation stage); (2) feed saved (FS), defined as the difference between the measured and the predicted DMI, based on the regression of DMI on milk components within experiment; (3) residual roughage intake (RRI), defined as the difference between the measured and the predicted roughage intake, based on the regression of roughage intake on milk components and concentrate intake within experiment; and (4) residual concentrate intake (RCI), defined as the difference between the measured and the predicted concentrate intake, based on the regression of concentrate intake on milk components and roughage intake within experiment. The phenotypic correlations were -0.72 between FIC and FS, -0.84 between FS and RRI, and -0.53 between FS and RCI. Heritability of FIC, FS, RRI, and RCI were estimated to be 0.21, 0.12, 0.15, and 0.03, respectively. The genetic correlations were -0.81 between FS and FIC, -0.96 between FS and RRI, and -0.25 between FS and RCI. Concentrate intake and RCI had low heritability. Genetic correlation between DMI and FIC was 0.98. Although the defined traits had moderate phenotypic correlations, the genetic correlations between DMI, FS, FIC, and RRI were above 0.79 (in absolute terms), suggesting that these traits are genetically similar. Therefore, selecting for FIC is expected to simply increase DMI and RRI, and there seems to be little advantage in separating concentrate and roughage intake in the genetic evaluation, because measured concentrate intake was determined by the feeding system in our data and not by the genetics of the cow.

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.

Precision finishing of South African lambs in feedlots: a review

In the intensification of sheep production systems, feedlot finishing plays a fundamental role in preparing lambs for slaughter, as well as relieving the grazing pressure on pasture. The profit margins in feedlot operations are often narrow and require the economics of scale to generate a sufficient income. In order to minimise expenses, intensive management and precision rearing of lambs to an ideal slaughter weight is needed to obtain premium carcass prices. The South African sheep industry is made up of wool, dual-purpose as well as meat type breeds, which also vary in terms of maturity. In order to implement precision finishing of South African lamb, a complete understanding of the growth, intake and fat deposition trends of growing lambs of different breed types is needed. This review outlines feedlot lamb production within the Southern African context for the major commercial breeds, while also providing insight in the considerations necessary to develop a decision support system for lamb rearing. Integrating such a decision support system into a lamb feedlot operation can then be used for precision finishing of lambs by predicting the optimal length of the feeding period and ideal slaughter weights of lambs.

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.

Equation to predict feed intake response by lactating cows to factors related to the filling effect of rations

Our objective was to predict the dry matter intake (DMI) response during ration formulation to factors related to the filling effect of rations and their interaction with milk yield (MY) by lactating cows past peak lactation. A data set was developed consisting of 134 treatment means from 34 experiments reported in 32 peer-reviewed articles published from 1990 through 2015. The data set included data for cows ranging from 60 to 309 d postpartum with mean DMI ranging from 17.6 to 30.6 kg/d and MY ranging from 20.3 to 51.1 kg/d. Ration composition among treatments ranged from 12.7 to 21.8% of dry matter (DM) for crude protein, 11.5 to 31.0% of DM for acid detergent fiber (ADF), 25.5 to 48.2% of DM for neutral detergent fiber (NDF), 9.9 to 39.3% of DM for forage NDF (FNDF), and 0.45 to 0.84 for the ratio of ADF% to NDF% (ADF/NDF). Laboratory measures of digestibility of NDF (in vitro or in situ, FNDFD) for the sole or major forage ranged from 24.1 to 72.7%. The model included the random effect of study to account for various experiment-specific effects including different methods of measurement of NDF and FNDFD among studies. The full model also included linear and quadratic effects of crude protein, ADF, NDF, FNDF, ADF/NDF, and FNDFD, as well as their linear and quadratic interactions, and mean MY for each study and its interaction with ration factors. The proposed prediction equation is DMI (kg/d) = 12.0 - 0.107 × FNDF + 8.17 × ADF/NDF + 0.0253 × FNDFD - 0.328 × (ADF/NDF - 0.602) × (FNDFD - 48.3) + 0.225 × MY + 0.00390 × (FNDFD - 48.3) × (MY - 33.1) with mean bias = 0.00 kg/d, root mean square error = 1.55 kg/d, and concordance correlation coefficient = 0.827. Dry matter intake was positively related to MY and ADF/NDF and negatively related to FNDF, and FNDFD was positively related to DMI for cows with high MY but negatively related to MY for cows with low MY. In addition, DMI was positively related to FNDFD for low ADF/NDF but negatively related to FNDFD for high ADF/NDF. The ADF/NDF was included to represent differences in forage fragility between grasses and legumes. The proposed model was compared with the equation recommended by the National Research Council (2001) that was developed using only animal factors by fitting each equation to a subset of the data set that included the required inputs for both. The National Research Council (2001) equation without diet factors had a higher root mean square error and over-predicted DMI at high DMI and under-predicted DMI at low DMI. Our proposed equation should be useful to predict DMI response to factors related to the filling effects of rations during ration formulation.

Food intake regulation in late pregnancy and early lactation

The dip in food intake, which starts in late pregnancy and continues into early lactation, has traditionally been interpreted as a depression in intake due to physical constraints. However, the rôle of physical constraints on intake has been overemphasized, particularly in early lactation. There is mounting evidence that the presence and mobilization of body reserves in early lactation play an important rôle in regulating intake at this time.

Conceptually, the dip in intake in early lactation observed when cows have access to non-limiting foods can be accounted for by assuming that the cow has a desired level of body reserves. When the cow is not compromised, the changes with time in body reserves and the dip in intake represent the normal case and provide the basis against which to assess true depressions in intake which may occur when the cow is compromised by limiting nutrition or environment.

The regulation of body reserves and intake in the periparturient cow is orchestrated through nervous and hormonal signals. Likely factors that are involved in intake regulation are reproductive hormones, neuropeptides, adrenergic signals, insulin and insulin resistance and leptin. Furthermore, oxidation of NEFA in the liver may result in feedback signals that reduce intake. The relative importance of these is discussed. A better understanding of the physiological signals involved in intake regulation and their interrelations with body weight regulation may provide important indicators of the degree of compromise that periparturient cows may experience.

In situ degradability of dry matter and neutral-detergent fibre of thorn scrubland forage consumed by goats in the semi-arid region of north Mexico

Three goats provided with oesophageal and ruminal cannulae were used to determine variations in dry matter (DM) and neutral-detergent fibre (NDF) degradability of the forage consumed when grazing thorn scrubland in the semi-arid region of north Mexico, during two consecutive dry and wet periods. Ingesta samples were incubated intraruminally, the data were fitted to the exponential equation P = a + b (l - e-ct) and statistically analysed using a randomized-block design. Organic matter and crude protein (CP) contents were higher (P < 0.05) in the wet seasons. Values of NDF were similar in dry and wet season of both years whereas higher numerical values of acid-detergent fibre (ADF), lignin and cellulose were registered in the dry seasons. DM and NDF degradabilities after 24 and 48 h of ruminal incubation were higher (P < 0.05) in the wet seasons. Higher values (P < 0.05) in DM and NDF bag losses at zero time (A fraction) were registered in the two wet seasons. The insoluble but fermentable DM and NDF (B fractions) were higher (P < 0.05) in the 1999 wet season and variable in the rest of the studied period. Numerically higher values of DM and NDF c fraction were found in wet periods, whereas DM and NDF potential degradabilities were higher (P < 0.05) in the wet season in 1999 and similar across seasons in 2000. Lowest (P < 0.05) contents of CP in grazed forage, DM and NDF degradabilities after 48 h of ruminal incubation, and A, and B, and c fractions were observed in the dry seasons. Thus, these results may be related to both the lower feeding value of forage consumed by the animals and lower performance of livestock during this period. Then, the DM and NDF degradability after 48 h, together with the insoluble but fermentable matter and the c fraction permit the nutritive value of the forage consumed by grazing goats to be accurately described.

Effects of metabolizable protein on intake and milk production of dairy cows independent of effects on ruminal digestion

The rôle of protein in food intake regulation is complex in ruminants. Previous research has shown that a deficiency in degradable nitrogen (N) could affect microbial activity and decrease intake. On the other hand, an increase in metabolizable protein content of the diet seems to stimulate food intake in lactating dairy cows. The aim of this experiment was to determine whether metabolizable protein supply plays a direct rôle in the stimulation of food intake. Treatments comprised two infusions of soya protein isolate (800 g/day) either into the rumen (RP) or into the duodenum (DP), which were compared with two iso-energy infusions of glucose (880 g/day) either into the rumen (RG) or into the duodenum (DG). Four ruminally and duodenally cannulated cows producing 36·5 kg/day of milk were assigned to a 4 ✕ 4 Latin-square design with periods of 4 weeks. Duodenal infusions of protein (DP) significantly increased (P < 0·05) dry-matter intake (DMI) ( +1·9 kg/day), rate of intake ( + 8·2 g DMI per min), milk yield ( + 4 kg/day), protein content ( + 2·3 g/kg) and protein yield ( +191 g/day) compared with the glucose infusion in the duodenum (DG). No significant effect was observed with ruminal infusion of protein (RP) compared with the glucose infusion in the rumen (RG). The protein infusions had no effect (P > 0·05) on the apparent digestibility of dry matter, organic matter, neutral-detergent fibre or acid-detergent fibre and also no or only small effects on ruminal fermentation variables. Plasma concentrations of most of the essential amino acids increased significantly with the duodenal infusion of protein, whereas ruminal infusion of protein had no significant effect. It is concluded that direct supply of metabolizable protein stimulates intake independently of ruminal digestion effects.

Effect of total mixed ration composition and daily grazing pattern on milk production, composition and fatty acids profile of dairy cows

The possibilities of using high quality pastures in conjunction with total mixed ration (TMR) during the grazing season have been examined. An experiment with sixteen Holstein cows blocked and randomly assigned to four treatments in a factorial arrangement was conducted in order to evaluate the influence of grazing time of day (day or night) and type of silage (maize or Italian ryegrass) included in the TMR of dairy cows grazing 12 h daily on milk yield, composition and fatty acid profile. The silage type had no effect on the dry matter intake, milk yield and fat and protein proportions. However, cows grazing during the night ate more grass than cows grazing during the day (8·53 vs. 5·65 kg DM/d; P<0·05). No differences were seen between grazing-time with respect to milk production, fat and protein contents. However, the proportion of polyunsaturated fatty acid was higher in milk of dairy cows grazing at night-time than grazing at day-time, especially 18:2n-6 (2·37 vs. 2·12 g/100 g FA respectively, P<0·05) and 18:2cis9trans11 (2·08 vs. 1·74 g/100 g FA respectively, P<0·05).

Adaptation and evaluation of the GrazeIn model of grass dry matter intake and milk yield prediction for grazing dairy cows

The prediction of grass dry matter intake (GDMI) and milk yield (MY) are important to aid sward and grazing management decision making. Previous evaluations of the GrazeIn model identified weaknesses in the prediction of GDMI and MY for grazing dairy cows. To increase the accuracy of GDMI and MY prediction, GrazeIn was adapted, and then re-evaluated, using a data set of 3960 individual cow measurements. The adaptation process was completed in four additive steps with different components of the model reparameterised or altered. These components were: (1) intake capacity (IC) that was increased by 5% to reduce a general GDMI underprediction. This resulted in a correction of the GDMI mean and a lower relative prediction error (RPE) for the total data set, and at all stages of lactation, compared with the original model; (2) body fat reserve (BFR) deposition from 84 days in milk to next calving that was included in the model. This partitioned some energy to BFR deposition after body condition score nadir had been reached. This reduced total energy available for milk production, reducing the overprediction of MY and reducing RPE for MY in mid and late lactation, compared with the previous step. There was no effect on predicted GDMI; (3) The potential milk curve was reparameterised by optimising the rate of decrease in the theoretical hormone related to secretory cell differentiation and the basal rate of secretory cell death to achieve the lowest possible mean prediction error (MPE) for MY. This resulted in a reduction in the RPE for MY and an increase in the RPE for GDMI in all stages of lactation compared with the previous step; and (4) finally, IC was optimised, for GDMI, to achieve the lowest possible MPE. This resulted in an IC correction coefficient of 1.11. This increased the RPE for MY but decreased the RPE for GDMI compared with the previous step. Compared with the original model, modifying this combination of four model components improved the prediction accuracy of MY, particularly in late lactation with a decrease in RPE from 27.8% in the original model to 22.1% in the adapted model. However, testing of the adapted model using an independent data set would be beneficial and necessary to make definitive conclusions on improved predictions.

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

The prediction of the control of nutrient partitioning, particularly energy, is a major issue in modelling dairy cattle performance. The proportions of energy channelled to physiological functions (growth, maintenance, gestation and lactation) change as the animal ages and reproduces, and according to its genotype and nutritional environment. This is the first of two papers describing a teleonomic model of individual performance during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. The conceptual framework is based on the coupling of a regulating sub-model providing teleonomic drives to govern the work of an operating sub-model scaled with genetic parameters. The regulating sub-model describes the dynamic partitioning of a mammal female's priority between life functions targeted to growth (G), ageing (A), balance of body reserves (R) and nutrient supply of the unborn (U), newborn (N) and suckling (S) calf. The so-called GARUNS dynamic pattern defines a trajectory of relative priorities, goal directed towards the survival of the individual for the continuation of the specie. The operating sub-model describes changes in body weight (BW) and composition, foetal growth, milk yield and composition and food intake in dairy cows throughout their lifespan, that is, during growth, over successive reproductive cycles and through ageing. This dynamic pattern of performance defines a reference trajectory of a cow under normal husbandry conditions and feed regimen. Genetic parameters are incorporated in the model to scale individual performance and simulate differences within and between breeds. The model was calibrated for dairy cows with literature data. The model was evaluated by comparison with simulations of previously published empirical equations of BW, body condition score, milk yield and composition and feed intake. This evaluation showed that the model adequately simulates these production variables throughout the lifespan, and across a range of dairy cattle genotypes.

Milk casein and fatty acid fractions in early lactation are affected by nutritional regulation of body condition score at the beginning of the transition period in primiparous and multiparous cows under grazing conditions

The objective was to evaluate the effect of body condition score (BCS) at 30 days before calving (-30 days) induced by a differential nutritional management, parity and week of lactation (WOL) on milk yield and composition, and milk casein and fatty acid composition. Primiparous and multiparous Holstein cows with high BCS (PH, n = 13; MH, n = 9) and low BCS (PL, n = 9; ML = 8) under grazing conditions were sampled at WOL 2 and 8 (before and after peak of lactation). Milk yield was greater in multiparous than in primiparous cows and tended to decrease from WOL 2 to 8 only in ML cows. Milk protein, fat and casein yields were greater in multiparous than in primiparous cows and decreased from WOL 2 to 8. Milk casein concentration in milk protein was greater in MH cows than in ML, PH and PL cows at WOL 2. Milk κ-casein was greater, and β-casein was less in multiparous than in primiparous cows. As lactation progressed, proportion of casein fractions were not altered. Only κ-casein fraction was affected by BCS at -30 days as PL showed a higher concentration than PH. The de novo (4:0-15:1) and mixed-origin fatty acids (16:0-16:1) in milk fat increased, whereas preformed fatty acids (≥17:0) decreased from WOL 2 to 8. Saturated (SAT) fatty acids tended to be greater and monounsaturated fatty acids (MUFA) were less in multiparous than in primiparous cows. High-BCS cows had greater concentrations of polyunsaturated (PUFA), conjugated linoleic acid (CLA) as well as n-6 and n-3 fatty acids in milk fat than low-BCS cows. The results indicate that casein and fatty acid fractions in milk were affected by parity and may be modified by a differential nutritional management during the pre-calving period (BCS at -30 days) in cows under grazing conditions.

Can Iberian red deer (Cervus elaphus hispanicus) discriminate among essential minerals in their diet?

Optimal foraging predicts that animals should be able to assess the content of important nutrients in food. Ungulates discriminate salt and P, but discrimination of other minerals is controversial even though they are also essential and often limiting. Animal scientists have explained this taste through palatability, which predicts the same pattern of discrimination for calves and hinds and greater consumption by the latter. Social learning may also be involved, predicting a correlation between mother and calf and less consumption by the latter. The present study examines the consumption behaviour of free-choice supplemented minerals by hinds and calves of Iberian red deer (Cervus elaphus hispanicus) to discern between these hypotheses. Behavioural indices of intake correlated with actual mineral consumption (P < 0.001). Mother and calf behavioural indices correlated only for salt-mixed minerals. Calves showed overall behavioural indices of consumption greater than hinds (P < 0.01 and P < 0.001), and also for all single supplements except NaCl, as expected from growth needs and in contrast to the palatability hypothesis. Calves showed a greater consumption of CuSO(4) and lower of Na(2)SeO(3) than pure salt. Hinds showed a different pattern, ingesting lower amounts of all minerals except CuSO(4) and salt. Additional analyses also showed discrimination between minerals unmixed with salt, such as CaHPO(4) and CaCO(3) (P = 0.012 and P = 0.020). The greater intake of growing calves and the different consumption patterns for hinds and calves suggest that deer can discriminate among minerals, and that they do not consume minerals for their palatability or driven by social learning. Therefore, deer may be selecting minerals according to nutritional requirements.

Prediction of Dry Matter Intake Throughout Lactation in a Dynamic Model of Dairy Cow Performance

In the dynamic modeling of dairy cow performance over a full lactation, the difference between net energy intake and net energy used for maintenance, growth, and output in milk accumulates in body reserves. A simple dynamic model of net energy balance was constructed to select, out of some common dry matter intake (DMI) prediction equations, the one that resulted in a minimum cumulative bias in body energy deposition. Dry matter intake was predicted using the Cornell Net Carbohydrate and Protein System, Agricultural Research Council, or National Research Council (NRC) DMI equations from body weight (BW) and predicted fat-corrected milk yield. The instantaneous BW of cows at progressive weeks of lactation was simulated as the numerical integral of the BW change obtained from the predicted net energy balance. Predicted DMI and BW from each DMI equation, using either of 2 equations to describe maintenance energy expenditures, were compared statistically against observed data from 21 herd average published full lactation data sets. All DMI equations underpredicted BW and DMI, but the NRC DMI equation resulted in the minimum cumulative error in predicted BW and DMI. As a general solution to prevent predicted BW from deviating substantially over time from the observed BW, a lipostatic feedback mechanism was integrated into the NRC DMI equation as a 2-parameter linear function of the relative size of simulated body reserves and week of lactation. Residual sum of squares was reduced on average by 52% for BW predictions and by 41% for DMI predictions by inclusion of the negative feedback with parameters taken from the average of all 21 least squares fits. Similarly, root mean square prediction error (%) was reduced by 30% on average for BW predictions and by 23% for DMI predictions. Inclusion of a feedback of energy reserves onto predicted DMI, simulating lipostatic regulation of BW, solved the problem of final BW deviation within a dynamic model and improved its DMI prediction to a satisfactory level.

Development and Evaluation of Models to Predict the Feed Intake of Dairy Cows in Early Lactation

Inaccurate prediction of dry matter intake (DMI) limits the ability of current models to anticipate the technical and economic consequences of adopting different strategies for production management on individual dairy farms. The objective of the present study was to develop an accurate, robust, and broadly applicable prediction model and to compare it with the current NRC model for dairy cows in early lactation. Among various functions, an exponential model was selected for its best fit to DMI data of dairy cows in early lactation. Daily DMI data (n = 8,547) for 3 groups of Holstein cows (at Illinois, New Hampshire, and Pennsylvania) were used in this study. Cows at Illinois and New Hampshire were fed totally mixed diets for the first 70 d of lactation. At Pennsylvania, data were for the first 63 d postpartum. Data from Illinois cows were used as the developmental dataset, and the other 2 datasets were used for model evaluation and validation. Data for BW, milk yield, and milk composition were only available for Illinois and New Hampshire cows; therefore, only these 2 datasets were used for model comparisons. The exponential model, fitted to the individual cow daily DMI data, explained an average of 74% of the total variation in daily DMI for Illinois data, 49% of the variation for New Hampshire data, 67% of the variation for Pennsylvania data, and 64% of the variation overall. Based on all model selection criteria used in this study, the exponential model for prediction of weekly DMI of individual cows was superior to the current NRC equation. The exponential model explained 85% of the variation in weekly mean DMI compared with 42% for the NRC equation. Compared with the relative prediction error of 6% for the exponential model, that associated with prediction using the NRC equation was 14%. The overall mean square prediction error value for individual cows was 5-fold higher for the NRC equation than for the exponential model (10.4 vs. 2.0 kg2/d2). The consistently accurate and robust prediction of DMI by the exponential model for all data-sets suggested that it could safely be used for predicting DMI in many circumstances.

Efeito da suplementação e do intervalo de pastejo sobre a qualidade da forragem e consumo voluntário de vacas Holandês × Zebu em lactação em pastagem de capim-elefante

O trabalho foi realizado entre fevereiro e setembro, com o objetivo de avaliar a qualidade e o consumo de forragem de capim-elefante manejado sob pastejo rotativo, com período de ocupação dos piquetes de três dias e variação do intervalo de pastejo de: 30 dias (com e sem o uso de concentrado ao longo do ano) e 36 e 45 dias (sem o uso de concentrado). Foram usadas 24 vacas Holandês × Zebu em lactação. De fevereiro a maio o pasto foi o único volumoso. De junho a setembro, as vacas receberam cana mais 1% de uréia. A composição química de extrusas de capim-elefante foi, de modo geral, semelhante (P>0,05) entre intervalos de pastejo (6,1 a 18,2% para PB; 63,5 a 81,4% para FDN; 32,7 a 47,6% para FDA e 43,7 a 72,9% para digestibilidade in vitro da MS). Houve decréscimo no teor de PB do capim-elefante e aumento nos de FDN e FDA para cada dia adicional de ocupação do piquete (P<0,0001). Na estação chuvosa, o consumo diário de capim-elefante foi, de modo geral, semelhante (P>0,05) nos diferentes intervalos de pastejo, variando de 1,7 a 3,6%PV para MS e de 1,1 a 2,7%PV para FDN. Na estação seca, nas pastagens sem concentrado, o consumo de MS do capim-elefante variou de 0,39 a 2,2%PV e sua contribuição no consumo total da dieta decresceu de 52% no primeiro para 43% no terceiro dia de ocupação do piquete. O consumo suplementar de cana mais uréia foi efetivo em minimizar a redução no consumo total de MS.

A stochastic model simulating milk fever in a dairy herd

A simulation model was developed to evaluate the long-term effect of control strategies against milk fever (MF); here, we present the base model and sensitivity analyses. The representation of the within-herd dynamics was based on the existing SimHerd II model. Because of the relationships between MF and other diseases, the new model (called "SimHerd III") includes diseases common in a dairy herd. The cow level risk factors modelled were: base risk in the herd, parity, milk-yield potential, lactational disease recurrence, disease interrelationships, body condition and season. The diseases include clinical cases of MF, dystocia, downer-cow syndrome, retained placenta, metritis, displaced abomasum, ketosis and mastitis. The effects of diseases were represented by daily milk yield, daily body weight, daily feed intake, risk of stillbirth, conception probability, decision on culling, death and immediate removal. Simulated technical results showed that the herd effects of reduced risk of MF differed according to the reproductive efficiency in the herd. These interactions between reproduction efficiency and the effect of reduced base risk of MF were related to differences in how the simulated herds reacted to the reduction in replacements caused by MF. In the sensitivity analysis, eight potential key parameters were changed to their lowest and highest expected values retrieved from the literature. When measuring the sensitivity on milk production in the herd (as the economically most important technical effect), the model seemed most sensitive to the uncertainty of effect of MF on death risk and MF-recurrence risk.

Effect of genetic merit and concentrate supplementation on grass intake and milk production with Holstein Friesian dairy cows

A total of 48 high genetic merit (HM) and 48 medium merit (MM) cows, each given a low (LC), medium (MC), or high (HC) level of concentrate supplementation, were used in a split-plot design experiment, which was run in three consecutive years, to evaluate animal production responses. Individual cow intakes were estimated twice each year while at pasture; measurement period 1 (MP1) was in May/June, and measurement period 2 (MP2) was in early September, corresponding on average to d 110 and 200 of lactation, respectively. In MP1, cows were offered 0 (LC), 3 (MC), and 6 kg (HC), whereas in MP2 the levels were 0 (LC), 0 (MC), and 4 kg (HC) of concentrate daily. Genotype had a significant effect on all milk production parameters in MP1 and MP2. The HM cows had the highest yield of milk, fat, protein, and lactose, whereas the MM cows had the highest milk fat, protein, and lactose concentrations. The HM cows had significantly higher grass dry matter intake (GDMI) estimates. In MP1, the average responses, per kg concentrate dry matter, was +1.10 kg of milk, +0.038 kg of protein, +0.032 kg of fat. The corresponding values in MP2 were +0.94 kg of milk, +0.037 kg of protein, and +0.025 kg of fat. The response to concentrate was linear and independent of preexperimental milk yield. In MP1, the partial regression coefficients relating daily GDMI to an increase in 1 kg of preexperimental milk yield (PMY), preexperimental BW (PBW), and concentrate intake (CI) were 0.123, 0.006, and -0.54, respectively, whereas the corresponding values in MP2 were 0.190,0.007, and-0.444, respectively. This study indicates that with high yielding dairy cows, on gras only GDMI of 17 kg of supporting milk yield of 30-kg/d is achievable. In this scenario, concentrate supplementation will result in lower substitution rates, and higher milk yield response than previously published with lower yielding cows.

Predicting average feed intake of lactating Holstein cows fed totally mixed rations

A dry matter intake (DMI) prediction equation was estimated by using a data file that contained 124 treatment means collected from published studies. Animal factors considered for inclusion in the prediction model were body weight (BW) and its natural logarithm, BW(0.75), milk yield (MY) and its natural logarithm, milk fat and protein yields, month of lactation and its square, as well as its natural logarithm. The dietary factors considered were the percentages of neutral detergent fiber, acid detergent fiber, crude protein and hemicellulose in the ration dry matter together with the square of all these predictors. The multiple regression model selected by using the maximum R2 method include both animal and dietary factors as independent variables. The accuracy of this DMI prediction equation was evaluated and compared with that of five other equations previously published by using three independent datasets also containing treatment means collected from literature. Even though the latest NRC equation was slightly more accurate than the equation proposed in this study with the three evaluation datasets, the latter can be used for some applications for which the NRC equation is not appropriate.

Feed intake of young double-muscled bulls fed on grass and supplemented with sugar-beet pulp

Daily dry matter intake in young growing double-muscled bulls, fed indoors on grass, was estimated based on forty-four intake data from 28 animals, ageing at least five months and weighing up to 400 kg live weight. Intake was measured during five consecutive days using one of eighteen cuts of grass. Fresh meadow grass (mainly Lolium perenne) was fed ad libitum and two kg dried sugar-beet pulp was offered per animal and per day. Animal live weight averaged 278 +/- 82 kg and mean total daily dry matter intake amounted to 5.05 +/- 1.59 kg or 73.6 +/- 13.7 g per kg metabolic weight, while pulp dry matter intake amounted to 1.49 +/- 0.50 kg per day. Regression analysis showed that animal as well as feed characteristics could explain up to approximately 90% of the variation in daily dry matter intake. The supplementation resulted in an extra daily dry matter intake of 0.68 g per g pulp dry matter. Intake of double-muscled animals was considerably lower than previously reported for non-double-muscled cattle. An extra supplementation of young grazing double-muscled animals could be advised from these findings, while extra protein should also be considered.

Theoretical developments in the study and prediction of food intake

The purpose of the present paper is to review recent theoretical developments in food intake modelling applied to animal science and ecology. The models are divided into those that have been developed for intensive agricultural systems, and those which consider more extensive systems and natural systems. For the most part the present paper discusses models that predict the food intake of herbivores. The mechanisms of each model are discussed, along with a brief mention of the experimental support for the most popular models. We include a discussion of models that approach the study of food intake behaviour from an evolutionary perspective, and suggest that lifetime models are especially useful when food intake carries an intrinsic cost. These long timescale evolutionary models contrast with the more common food intake models, whose timescale is usually much shorter. We conclude that the 'eating to requirements' model highlights an important food intake mechanism that provides an accurate predictive tool for intensive agricultural systems. The mechanisms of food intake regulation in extensive systems are less certain, and closer links between the ideas of animal science and ecology will be helpful for improving our understanding of food intake regulation.