A flexible,two-component feeding system derived from Blaxter's three-component,metabolisable-energy system

A flexible, energy-system is described based on Blaxter's three-component metabolisable-energy (ME) system, in which animal energy requirements are expressed as net energy (NE), the value of feeds is given as ME, and there is a third component comprising a set of experimentally derived rules for converting the ME values of different feeds to NE. The proposed system retains NE as the method of defining animal energy requirements and combines the ME values for different feeds with conversion of ME to NE for maintenance, growth and lactation using the most appropriate conversion for the particular feeding situation.The system retains all the essential features of Blaxter's three-component system, but allows rations for both dairy and beef production to be calculated by the same procedure. Four examples are given for rationing lactating cows and beef cattle.It is suggested that this system should permit easier calculation of rations and the rapid incorporation of new information and should lead to a better understanding by producers, advisers, students and scientists of the principles and limitations of energy rationing.     

Comparison of energy evaluation systems and a mechanistic model for milk production by dairy cattle offered fresh grass-based diets

Grass-based diets are of increasing social-economic importance in dairy cattle farming, but their low supply of glucogenic nutrients may limit the production of milk. Current evaluation systems that assess the energy supply and requirements are based on metabolisable energy (ME) or net energy (NE). These systems do not consider the characteristics of the energy delivering nutrients. In contrast, mechanistic models take into account the site of digestion, the type of nutrient absorbed and the type of nutrient required for production of milk constituents, and may therefore give a better prediction of supply and requirement of nutrients. The objective of the present study is to compare the ability of three energy evaluation systems, viz. the Dutch NE system, the agricultural and food research council (AFRC) ME system, and the feed into milk (FIM) ME system, and of a mechanistic model based on Dijkstra et al. [Simulation of digestion in cattle fed sugar cane: prediction of nutrient supply for milk production with locally available supplements. J. Agric. Sci., Cambridge 127, 247–60] and Mills et al. [A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation and application. J. Anim. Sci. 79, 1584–97] to predict the feed value of grass-based diets for milk production. The dataset for evaluation consists of 41 treatments of grass-based diets (at least 0.75 g ryegrass/g diet on DM basis). For each model, the predicted energy or nutrient supply, based on observed intake, was compared with predicted requirement based on observed performance. Assessment of the error of energy or nutrient supply relative to requirement is made by calculation of mean square prediction error (MSPE) and by concordance correlation coefficient (CCC). All energy evaluation systems predicted energy requirement to be lower (6–11%) than energy supply. The root MSPE (expressed as a proportion of the supply) was lowest for the mechanistic model (0.061), followed by the Dutch NE system (0.082), FIM ME system (0.097) and AFRC ME system (0.118). For the energy evaluation systems, the error due to overall bias of prediction dominated the MSPE, whereas for the mechanistic model, proportionally 0.76 of MSPE was due to random variation. CCC analysis confirmed the higher accuracy and precision of the mechanistic model compared with energy evaluation systems. The error of prediction was positively related to grass protein content for the Dutch NE system, and was also positively related to grass DMI level for all models. In conclusion, current energy evaluation systems overestimate energy supply relative to energy requirement on grass-based diets for dairy cattle. The mechanistic model predicted glucogenic nutrients to limit performance of dairy cattle on grass-based diets, and proved to be more accurate and precise than the energy systems. The mechanistic model could be improved by allowing glucose maintenance and utilization requirements parameters to be variable.     

Efficiency of energy utilisation and voluntary feed intake in ruminants

Energy requirements of animals are most readily expressed in terms of net energy (NE), while the energy yield of feed is, at least initially, expressed in terms of metabolisable energy (ME). Energy evaluation systems 'translate' NE requirements into ME requirements (ME systems) or assign NE values to feeds (NE systems). Efficiency of ME utilisation is higher for maintenance than for production and the NE yield of a feed varies, therefore, with ME intake. In addition, energetic efficiency for maintenance and production is thought to be different for lactating and non-lactating animals and to be affected by diet quality. As a result, there are currently many national energy evaluation systems that are complex, differ in their approach and are, as a result, difficult to compare. As ruminants in most production systems are fed ad libitum, this is also the most appropriate intake level at which to estimate energetic efficiency. Analyses of older as well as more recent data suggest that ad libitum feeding (i) abolishes the effects of diet quality on energetic efficiency (almost) completely, (ii) abolishes the differences between lactating and non-lactating animals (almost) entirely and (iii) results in overall energetic efficiencies that are always close to 0.6. The paper argues that there is now sufficient information to develop an international energy evaluation system for ad libitum fed ruminants. Such a system should (i) unify ME and NE systems, (ii) avoid the systematic bias and large errors that can be associated with current systems (iii) be simpler than current systems and (iv) have as a starting point a constant efficiency of ME utilisation, with a value of around 0.6. The remarkably constant efficiency of ME utilisation in ad libitum fed ruminants could be the result of energetic efficiency as well as feed intake regulation being affected by the same variables or of a direct role of energetic efficiency in feed intake regulation. Models to predict intake on the basis of the latter hypothesis are already available for non-reproducing ruminants but remain to be developed for reproducing animals.     

Genome‐wide association analysis and gene set enrichment analysis with SNP data identify genes associated with 305‐day milk yield in Holstein dairy cows

Milk production traits, such as 305‐day milk yield (305MY), have been under direct selection to improve production in dairy cows. Over the past 50 years, the average milk yield has nearly doubled, and over 56% of the increase is attributable to genetic improvement. As such, additional improvements in milk yield are still possible as new loci are identified. The objectives of this study were to detect SNPs and gene sets associated with 305MY in order to identify new candidate genes contributing to variation in milk production. A population of 781 primiparous Holstein cows from six central Washington dairies with records of 305MY and energy corrected milk were used to perform a genome‐wide association analysis (GWAA) using the Illumina BovineHD BeadChip (777 962 SNPs) to identify QTL associated with 305MY (P < 1.0 × 10^?5). A gene set enrichment analysis with SNP data (GSEA‐SNP) was performed to identify gene sets (normalized enrichment score > 3.0) and leading edge genes (LEGs) influencing 305MY. The GWAA identified three QTL comprising 34 SNPs and 30 positional candidate genes. In the GSEA‐SNP, five gene sets with 58 unique and 24 shared LEGs contributed to 305MY. Identification of QTL and LEGs associated with 305MY can provide additional targets for genomic selection to continue to improve 305MY in dairy cattle.     

Estimation of metabolisable energy and net energy of rice straw and wheat straw for beef cattle by indirect calorimetry

Two experiments were conducted to estimate the metabolisable energy (ME) and net energy (NE) of rice straw and wheat straw for beef cattle. In each experiment, 16 Wandong bulls (Chinese indigenous yellow cattle) were assigned to 4 dietary treatments in a completely randomised design. Four dietary treatments included one corn silage-concentrate basal diet and three test diets in which the basal diet was partly substituted by rice straw (Exp. 1) or wheat straw (Exp. 2) at 100, 300 and 600 g/kg. Total collection of faeces and urine was conducted for 5 consecutive days after a 2-week adaption period, followed by a 4-d period where gas exchange measurements were measured by an open-circuit respiratory cage. Linear regression equations of rice straw- or wheat straw-associated ME and NE contribution in test diets against rice straw or wheat straw substitution amount were developed to predict the ME and NE values of rice straw and wheat straw. These regression equations resulted in ME and NE values (dry matter basis) of 6.76 and 3.42 MJ/kg for rice straw and 6.43 and 3.28 MJ/kg for wheat straw, respectively. The NE and ME requirement for maintenance of Wandong cattle fed a straw-based diet were 357 and 562 kJ·kg^?0.75·d^?1, respectively. The regression-derived ME and NE have lower standard errors and coefficients of variation than those estimated by any single substitution ratio. Our study found that the regression method based on multiple point substitution is more reliable than the substitution method for energy evaluation of feedstuffs for beef cattle.     

Animal board invited review: Specialising and intensifying cattle production for better efficiency and less global warming: contrasting results for milk and meat co-production at different scales

Cattle are the world’s largest consumers of plant biomass. Digestion of this biomass by ruminants generates high methane emissions that affect global warming. In the last decades, the specialisation of cattle breeds and livestock systems towards either milk or meat has increased the milk production of dairy cows and the carcass weight of slaughtered cattle. At the animal level and farm level, improved animal performance decreases feed use and greenhouse gas emissions per kg of milk or carcass weight, mainly through a dilution of maintenance requirements per unit of product. However, increasing milk production per dairy cow reduces meat production from the dairy sector, as there are fewer dairy cows. More beef cows are then required if one wants to maintain the same meat production level at country scale. Meat produced from the dairy herd has a better feed efficiency (less feed required per kg of carcass weight) and emits less methane than the meat produced by the cow-calf systems, because the intake of lactating cows is largely for milk production and marginally for meat, whereas the intake of beef cows is entirely for meat. Consequently, the benefits of breed specialisation assessed at the animal level and farm level may not hold when milk and meat productions are considered together. Any change in the milk-to-meat production ratio at the country level affects the numbers of beef cows required to produce meat. At the world scale, a broad diversity in feed efficiencies of cattle products is observed. Where both productions of milk per dairy cow and meat per head of cattle are low, the relationship between milk and meat efficiencies is positive. Improved management practices (feed, reproduction, health) increase the feed efficiency of both products. Where milk and meat productivities are high, a trade-off between feed efficiencies of milk and meat can be observed in relation to the share of meat produced in either the dairy sector or the beef sector. As a result, in developing countries, increasing productivities of both dairy and beef cattle herds will increase milk and meat efficiencies, reduce land use and decrease methane emissions. In other regions of the world, increasing meat production from young animals produced by dairy cows is probably a better option to reduce feed use for an unchanged milk-to-meat production ratio.     

Evidence for increasing digestive and metabolic efficiency of energy utilization with age of dairy cattle as determined in two feeding regimes

The changes taking place with age in energy turnover of dairy cattle are largely unknown. It is unclear whether the efficiency of energy utilization in digestion (characterized by faecal and methane energy losses) and in metabolism (characterized by urine and heat energy losses) is altered with age. In the present study, energy balance data were obtained from 30 lactating Brown Swiss dairy cows aged between 2 and 10 years, and 12 heifers from 0.5 to 2 years of age. In order to evaluate a possible dependence of age effects on diet type, half of the cattle each originated from two herds kept at the same farm, which were fed either on a forage-only diet or on the same forage diet but complemented with 5 kg/day of concentrate since their first calving. During 2 days, the gaseous exchange of the animals was quantified in open-circuit respiration chambers, followed by an 8-day period of feed, faeces, urine and milk collection. Daily amounts and energy contents were used to calculate complete energy balances. Age and feeding regime effects were analysed by parametric regression analysis where BW, milk yield and hay proportion in forage as consumed were considered as covariates. Relative to intake of gross energy, the availability of metabolizable energy (ME) increased with age. This was not the result of an increasing energy digestibility, but of proportionately lower energy losses with methane (following a curvilinear relationship with the greatest losses in middle-aged cows) and urine (continuously declining). The efficiency of utilization of ME for milk production (k_l) increased with age. Potential reasons include an increase in the propionate-to-acetate ratio in the rumen because of a shift away from fibre degradation and methane formation as well as lower urine energy losses. The greater k_l allowed older cows to accrete more energy reserves in the body. As expected, offering concentrate enhanced digestibility, metabolizability and metabolic utilization of energy. Age and feeding regime did not interact significantly. In conclusion, older cows seem to have digestive and metabolic strategies to use dietary energy to a certain degree more efficiently than younger cows.     

A system for predicting energy and protein requirements of wild ruminants

Wild ruminants require energy and protein for the normal function. I developed a system for predicting these energy and protein requirements across ruminant species and life stages. This system defines requirements on the basis of net energy (NE), net protein (NP), and ruminally degraded protein (RDP). Total NE and NP requirements are calculated as the sum of NE and NP required for several functions (maintenance, activity, thermoregulation, gain, lactation, and gestation). To estimate the requirements for each function, I collected data predominantly for wild species and then formulated allometric and other equations that predict requirements across species. I estimated RDP requirements using an equation for cattle. I then related NE, NP, and RDP to quantities more practical for diet formulation (e.g. dry matter intake). I tabulated requirements over a range of body mass and life stages (neonate, juvenile, nonproductive adult, lactating adult, and gestating adult). Tabulated requirements suggest that adults at peak lactation require greatest quantities of energy and neonates generally require greatest quantities of protein, agreeing with suggestions that lactation is energetically expensive and protein is most limiting during growth. Equations used in this system were precise (allometric equations had R² generally ≥0.89 and coefficient of variation <31.1%) and expected to reliably predict requirements across species. Results showed that a system for beef cattle would overestimate NE and either over‐ or underestimate NP for gain when applied to wild ruminants, showing that systems for wild ruminants should not extrapolate from requirements for domestic ruminants. One prominent system for wild ruminants predicted at times vastly different protein requirements from those predicted by the proposed system. The proposed system should be further evaluated and expanded to include other nutrients. Zoo Biol 30:165–188, 2011. © 2010 Wiley‐Liss, Inc.     

Evaluation of energy systems in determining the energy cost of gain of growing-finishing pigs fed diets containing different levels of dietary fat

Two experiments were conducted to evaluate the impact of different energy systems in predicting the energy cost of gain of growing-finishing pigs fed diets containing different levels of dietary fat. The diets in both experiments were based on wheat, corn and soybean meal and supplemented with 0, 1.75, 3.50 and 5.25% tallow. In Experiment 1, 24 crossbred barrows (Duroc x Landrace x Yorkshire) were randomly allocated to one of the four dietary treatments to determine the digestible (DE) and metabolizable (ME) energy content of the diets and net energy (NE) was calculated from DE and ME values. In Experiment 2, 96 crossbred barrows (Duroc x Landrace x Yorkshire) were used to evaluate the effectiveness of the energy systems in predicting the energy cost of gain for growing-finishing pigs fed ad libitum. There were six pens per treatment and four pigs per pen. The results obtained in trial 1 were used for calculating the energy cost of gain in trial 2. During the growing period, there was a linear decrease (p < 0.05) in the DE and ME cost of gain, while the NE cost of gain was not influenced by level of fat. During the finishing period, neither DE, ME or NE cost of gain were influenced by the dietary fat level. For the total experiment, the DE and ME cost of gain decreased (linear effect; p = 0.001), but there was no significant decrease in the NE cost of gain. It is concluded that the NE system can predict the performance of growing-finishing pigs more precisely for diets differing in fat content than can the DE and ME systems.     

Genomic selection for feed efficiency in dairy cattle

Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.     

A model predicting the seasonal dynamics of intake and production for suckler cows and their calves fed indoors or at pasture

To investigate the dynamics of animal intake and production in grassland-based suckler systems, we constructed a model for suckling cows with their calves. The model calculates on a day-to-day basis the selective intake at pasture and the animal production (weight, condition, milk production) in response to energy intake. The model dynamically applies the feed evaluation systems developed by the INRA: the “cattle fill unit” system to predict forage intake, and the “feed unit” system to predict net energy requirements and supply. To predict intake at pasture, we adapted the cattle fill unit system by adding effects of herbage availability and sward structural composition on the amount and quality of intake.At pasture, the grazeable herbage is divided into structural components characterized by their biomass and digestibility. The model predicts the composition of the diet, assuming that the most digestible and abundant components of herbage are preferred. The amount of herbage ingested depends on the animal profile, the digestibility of the diet and the amount of herbage available. Sward depletion by animal intake at pasture has feedback effects on herbage growth and quality, which can be calculated by a vegetation model. Animal production is calculated based on net energy balance, which is the difference between net energy intake and net energy requirements for maintenance (for cow and calf), gestation and lactation (for the cow). The net energy balance determines weight and condition gain or loss, and – after 3 months of lactation – influences milk production the following day. Changes in weight and condition have feedback effects on energy requirements and intake capacity.Sensitivity analysis on the input values highlighted the importance of forage digestibility for the production of cows and calves. Calf growth was also driven over 3 months old by calf live weight, and under 3 months old by the milk production of the cow. The model's response to stocking rate during the grazing down of a paddock was consistent with current knowledge. The model was validated against experimental data for cows fed indoors or at pasture, at different feed allowances. Model predictions were precise for the digestibility of intake and for live weight (error represents 2–3% of the average observed value), satisfactory for dry matter intake, body condition score and milk production at the beginning of lactation (error represents 10% of the average observed value), and very imprecise for milk production after the third month of lactation (error represents 23% of the average observed value), but the latter had small consequences on calf live weight.     

Suitability of feeding and chewing time for estimation of feed intake in dairy cows

Monitoring of feeding and rumination behaviour can provide useful information for dairy herd management. The feeding behaviour of dairy cows can be recorded by different techniques, such as video cameras, weighing troughs or chewing sensors. Among feeding characteristics, individual feed intake of cows is of utmost interest, but as weighing troughs have high space and cost requirements they are used primarily in research studies. The objective of the present study was to evaluate whether records on feeding time or chewing activity or a combination of both contain enough information to estimate feed intake with sufficient accuracy. Feed intake and feeding time per cow were recorded by means of weighing troughs. Concurrently, chewing activity of seven cows was recorded by MSR-ART pressure sensors during five to eight measuring days per cow. Feeding and chewing behaviour were evaluated in time slots (1 min) and additionally assigned to feeding bouts for further analysis. The 1 min time slots were classified into feeding/no feeding or chewing/no chewing by the two systems, and agreement was found in 92.2% of the records. On average, cows spent 270±39 min/day at the feeding troughs and chewed 262±48 min/day. The average fresh matter intake (FMI) was 49.6±5.1 kg/day. Feed intake was divided into 9.7 bouts/day during which cows fed in average 27.8±21.7 min/bout and chewed 27.0±23.1 min/bout. The correlation between FMI and feeding time was r=0.891 and between FMI and chewing time r=0.780 overall cows. Hence, both systems delivered suitable information for estimating feed intake.     

Cornell Net Carbohydrate and Protein System: A model for precision feeding of dairy cattle

The Cornell Net Carbohydrate and Protein System (CNCPS) predicts cattle requirements and nutrient supply for site-specific situations. This paper describes the CNCPS version 6 (CNCPSv6), which represents a re-engineering and updating of CNCPS version 5 with the following objectives: (1) improve the organization of the model and user interface to improve speed and accuracy in formulating diets for a herd of dairy cattle, (2) expand the carbohydrate pools to include sugars, soluble fibers, and organic and volatile fatty acids, (3) integrate a fat model to account for ruminal lypolization and biohydrogenation, and absorption of fatty acids in the small intestine, and (4) update the computational sub-models with new information. The CNCPSv6 model was re-designed using object-oriented programming in which physiological functions (e.g. growth, lactation, pregnancy) and anatomical compartments (e.g. rumen, intestines) were programmed as objects. The interface uses farm, location, and group flow, which decreases the number of inputs required per cattle group and allows for more rapid evaluation of diets, feed requirements, and nutrient excretion by location, group, and herd. The revised implementation of the body reserves sub-model allows accounting for fluxes in energy reserves when formulating diets. Updated equations and coefficients include the prediction of rumen ammonia balance and feed passage rates, indigestible DM, MP lactation efficiency, and DMI. The CNCPSv6 was evaluated with data from individually fed lactating dairy cows from three independent studies. As implemented, CNCPSv6 accounted for a similar proportion of the variation (86%) in first limiting (ME or MP) milk production as CNCPSv5 but with a lower bias (1% versus 4%, respectively). We concluded the re-designing and updating of the CNCPS improved its ability to formulate and evaluate a feeding program for a herd of dairy cattle with greater accuracy and efficiency.     

Genetic relationship of discrete-time survival with fertility and production in dairy cattle using bivariate models

Bivariate analyses of functional longevity in dairy cattle measured as survival to next lactation (SURV) with milk yield and fertility traits were carried out. A sequential threshold-linear censored model was implemented for the analyses of SURV. Records on 96 642 lactations from 41 170 cows were used to estimate genetic parameters, using animal models, for longevity, 305 d-standardized milk production (MY305), days open (DO) and number of inseminations to conception (INS) in the Spanish Holstein population; 31% and 30% of lactations were censored for DO and INS, respectively. Heritability estimates for SURV and MY305 were 0.11 and 0.27 respectively; while heritability estimates for fertility traits were lower (0.07 for DO and 0.03 for INS). Antagonist genetic correlations were estimated between SURV and fertility (-0.78 and -0.54 for DO and INS, respectively) or production (-0.53 for MY305), suggesting reduced functional longevity with impaired fertility and increased milk production. Longer days open seems to affect survival more than increased INS. Also, high productive cows were more problematic, less functional and more liable to being culled. The results suggest that the sequential threshold model is a method that might be considered at evaluating genetic relationship between discrete-time survival and other traits, due to its flexibility.     

The development of a model to predict BW gain of growing cattle fed grass silage-based diets

The objective of this meta-analysis was to develop and validate empirical equations predicting BW gain (BWG) and carcass traits of growing cattle from intake and diet composition variables. The modelling was based on treatment mean data from feeding trials in growing cattle, in which the nutrient supply was manipulated by wide ranges of forage and concentrate factors. The final dataset comprised 527 diets in 116 studies. The diets were mainly based on grass silage or grass silage partly or completely replaced by whole-crop silages, hay or straw. The concentrate feeds consisted of cereal grains, fibrous by-products and protein supplements. Mixed model regression analysis with a random study effect was used to develop prediction equations for BWG and carcass traits. The best-fit models included linear and quadratic effects of metabolisable energy (ME) intake per metabolic BW (BW^0.75), linear effects of BW^0.75, and dietary concentrations of NDF, fat and feed metabolisable protein (MP) as significant variables. Although diet variables had significant effects on BWG, their contribution to improve the model predictions compared with ME intake models was small. Feed MP rather than total MP was included in the final model, since it is less correlated to dietary ME concentration than total MP. None of the quadratic terms of feed variables was significant (P>0.10) when included in the final models. Further, additional feed variables (e.g. silage fermentation products, forage digestibility) did not have significant effects on BWG. For carcass traits, increased ME intake (ME/BW^0.75) improved both dressing proportion (P<0.01) and carcass conformation (P<0.001) and increased (P<0.001) carcass fat score. Increased dietary CP concentration had no significant (P>0.10) effect on dressing proportion or carcass conformation score, but it increased (P<0.01) carcass fat score. The current study demonstrated that ME intake per BW^0.75 was clearly the most important variable explaining the BWG response in growing cattle. The effect of increased ME supply displayed diminishing responses that could be associated with increased energy concentration of BWG, reduced diet metabolisability (proportion of ME of gross energy) and/or decreased efficiency of ME utilisation for growth with increased intake. Negative effects of increased dietary NDF concentration on BWG were smaller compared to responses that energy evaluation systems predict for energy retention. The present results showed only marginal effects of protein supply on BWG in growing cattle.     

Exploration of robustness indicators using adaptive responses to short-term feed restriction in suckling primiparous beef cows

Animal robustness is a complex trait of importance for livestock production systems and genetic selection. Phenotyping is essential for evaluation of the adaptation of different genotypes to changing environments. This study tested an experimental framework to induce marked deviations in the adaptive responses of suckling beef cows and to identify relevant indicators of responses to characterise individual differences in the robustness of cows. The production and metabolic responses of primiparous suckling Charolais cows to two periods of feed restriction (FR, 50% of their net energy requirements) of different durations were monitored. After calving, 13 cows (aged 39 ± 2 months, BW of 680 ± 42 kg at calving) had ad libitum access to a diet composed of hay and supplemented with concentrate to meet their energy and protein requirements. Starting at 54 ± 6 days postcalving, the cows underwent two periods of FR: 4 days of FR (FR4), which was followed by 17 days of ad libitum intake to study the recovery from FR4, and 10 days of FR (FR10), which was followed by 18 days of ad libitum intake to study the recovery from FR10. The milk yield (MY), BW, body condition score and plasma non-esterified fatty acid (NEFA), β-hydroxybutyrate, glucose and urea concentrations were measured before, during and after each FR. Among all measured variables, the MY and NEFA concentrations showed the most significant changes in response to FR. A functional data analysis approach was applied to the MY and NEFA data to model the adaptive responses and extract quantifiable indicators of deviation and recovery. Linear correlations (P < 0.03–0.07) between FR4 and FR10 were found for some indicators describing MY and NEFA levels before and after FR. The overall repeatability of MY and NEFA responses between both FR accounted for 46% based on quartile analysis performed on average responses. Moreover, the variance in both the MY and NEFA variables did not differ significantly between FR4 and FR10, despite a trend for higher variances in FR10. Altogether, (1) the calculated variables derived from the functional data analysis of the time patterns of the MY and NEFA accounted for the differences in the cow responses to FR, and (2) the animal responses appeared to show concordance between FR4 and FR10. In conclusion, short-term FR is a relevant framework for studying productive and metabolic adaptive responses in suckling cows and allows the identification of potential robustness indicators.     

Production costs,economic viability,and risks associated with compost bedded pack,freestall, and drylot systems in dairy farms

The adoption of intensive production systems, such as compost bedded pack (CB) and freestall (FS), has increased recently in tropical regions, mainly replacing the drylot system (DL). Thus, our objectives were to compare production costs, economic outcomes, and risk of dairy operations in CB, FS, and DL systems. We collected data from 2 181 Brazilian farms over 120 consecutive months; 960 farms (144 CB, 133 FS, and 683 DL) met our selection criteria. All costs were modeled for two animal production categories: milking cows and non-milking animals. We used a regression model that included linear and quadratic parameters, and we added the production system as a fixed variable for all parameters tested with this model. Consultant, year, herd, and herd × system interaction were included in the model as random variables. Further, we simulated annual technical and economic indexes per farm. In addition, we developed a risk analysis to measure the probability of negative profit of the farms based on a 14-year historical series of milk prices. All production costs were affected by the system. Feed, medicine, sundry, and labor costs per farm per year were greater in DL farms when milk yield (MY) was greater than 3 500 L/day. The variables such as milk yield, assets per liter, asset turnover rate, return on assets, operational profit, profit per cow, and per liter of milk variables were greater in CB and FS with high MY (>3 000 L/day). Nonetheless, DL had the greatest economic indexes with a lower MY (<3 000 L/day), lower operating costs, and greater economic outcomes. The risk analysis indicated that the probability of negative profit (risk) was reduced for CB and FS as MY increased, but DL had the lowest risk with low MY levels. In conclusion, we suggest DL as the most attractive system for farms with MY between 150 and 3 000 L of milk/day as the DL had the lowest risk and the greatest profit in this production scale. Despite similar outcomes for CB and FS in most of the farms, the profit per cow ($/year), assets turnover rate (%), risk (%) and expected profit ($/L) analysis indicated that CB could be recommended for farms with MY greater than 3 200 L of milk/day, whereas based on risk (%) and expected profit ($/L), FS would be the most profitable system in dairies producing more than 8 000 L of milk/day per farm.     

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.     

The energetic feed evaluation in Germany

The paper describes the energetic feed evaluation systems for ruminants, pigs, poultry and horses presently used in Germany. During the last ten years the “Ausschuß für Bedarfsnormen”; (AfB; Committee of Nutrient Requirements) of the “Gesellschaft für Ernährungsphysiologie”; (GfE, Society of Nutritional Physiology) introduced new recommendations for energy requirements of domestic animals including national and international references.

The energetic requirements were factorially deduced (demand for maintenance and various performances) under consideration of partial efficiency of utilized metabolizable energy (ME) for various performances (k‐values). Except for lactating cows (Net Energy Lactation; NEL) the energy requirements of other ruminants as well as of pigs and poultry are given in ME. At present, the energy requirement of horses is still expressed as digestible energy (DE).

Besides several energy requirements reported there are equations to determine and to calculate the energy content of feeds (GfE, 1986–1998) and corresponding tables which include the nutritive values of feedstuffs for various species. The energy content of mixed feeds may be estimated by specific equations.     

Genome‐wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population

Bovine leukosis virus is an oncogenic virus that infects B cells, causing bovine leukosis disease. This disease is known to have a negative impact on dairy cattle production and, because no treatment or vaccine is available, finding a possible genetic solution is important. Our objective was to perform a comprehensive genetic analysis of leukosis incidence in dairy cattle. Data on leukosis occurrence, pedigree and molecular information were combined into multitrait GBLUP models with milk yield (MY) and somatic cell score (SCS) to estimate genetic parameters and to perform whole‐genome scans and pathway analysis. Leukosis data were available for 11 554 Holsteins daughters of 3002 sires from 112 herds in 16 US states. Genotypes from a 60K SNP panel were available for 961 of those bulls as well as for 2039 additional bulls. Heritability for leukosis incidence was estimated at about 8%, and the genetic correlations of leukosis disease incidence with MY and SCS were moderate at 0.18 and 0.20 respectively. The genome‐wide scan indicated that leukosis is a complex trait, possibly modulated by many genes. The gene set analysis identified many functional terms that showed significant enrichment of genes associated with leukosis. Many of these terms, such as G‐Protein Coupled Receptor Signaling Pathway, Regulation of Nucleotide Metabolic Process and different calcium‐related processes, are known to be related to retrovirus infection. Overall, our findings contribute to a better understanding of the genetic architecture of this complex disease. The functional categories associated with leukosis may be useful in future studies on fine mapping of genes and development of dairy cattle breeding strategies.