Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example

[The lipidosis in the liver of the dairy cow: Part 2 Genetic predisposition and prophylaxis]

Hepatic lipidosis in dairy cows is the result of a disturbed balance between the uptake of non-esterified fatty acids (NEFA), their metabolism in the hepatocytes, and the limited efflux of TG as very-low-density lipoprotein (VLDL). Lipidosis and the associated risk for ketosis represents a consequence of selecting dairy cows primarily for milk production without considering the basic physiological mechanisms of this trait. The overall risk for lipidosis and ketosis possesses a genetic background and the recently released new breeding value of the German Holstein Friesian cows now sets the path for correction of this risk and in that confirms the assumed genetic threat. Ectopic fat deposition in the liver is the result of various steps including lipolysis, uptake of fat by the liver cell, its metabolism, and finally release as very-low-density lipoprotein (VLDL). These reactions may be modulated directly or indirectly and hence, serve as basis for prophylactic measures. The pertaining methods are described in order to support an improved understanding of the pathogenesis of lipidosis and ketosis. They consist of feeding a glucogenic diet, restricted feeding during the close-up time as well as supplementation with choline, niacin, carnitine, or the reduction of milking frequency. Prophylactic measures for the prevention of ketosis are also included in this discussion.

Dairy Cows Are Limited in Their Ability to Increase Glucose Availability for Immune Function during Disease

Shortages of energy and glucose have been hypothesized to play a key role in the development of and responses to production diseases in dairy cows during early lactation. Given the importance of glucose for immune functions, we used a recently established method for the estimation of glucose balance (GB) to evaluate glucose availability during disease phases. A dataset comprising ration analyses as well as individual daily milk yields (MY), dry matter intake (DMI), body weights, and health records of 417 lactations (298 cows) was used to calculate individual daily GB and energy balance (EB). The magnitude and dynamics of MY, DMI, GB, and EB were evaluated in the weeks before, at, and after diagnoses of inflammatory diseases in different stages of early lactation from week in milk 1 to 15. Diagnoses were categorized as mastitis, claw and leg diseases, and other inflammatory diseases. Mixed linear models with a random intercept and slope term for each lactation were used to evaluate the effect of diagnosis on MY, DMI, GB, and EB while accounting for the background effects of week in milk, parity, season, and year. When unaffected by disease, in general, the GB of cows was close to zero in the first weeks of lactation and increased as lactation progressed. Weekly means of EB were negative throughout all lactation stages investigated. Disease decreased both the input of glucose precursors due to a reduced DMI as well as the output of glucose via milk due to a reduced MY. On average, the decrease in DMI was −1.5 (−1.9 to −1.1) kg and was proportionally higher than the decrease in MY, which averaged −1.0 (−1.4 to −0.6) kg. Mastitis reduced yield less than claw and leg disease or other diseases. On average, GB and EB were reduced by −3.8 (−5.6 to −2.1) mol C and −7.5 (−10.2 to −4.9) MJ in the week of diagnosis. This indicates the need to investigate strategies to increase the availability of glucogenic carbon for immune function during disease in dairy cows.

Nitrogen isotopic discrimination as a biomarker of between-cow variation in the efficiency of nitrogen utilization for milk production: A meta-analysis

Estimating the efficiency of N utilization for milk production (MNE) of individual cows at a large scale is difficult, particularly because of the cost of measuring feed intake. Nitrogen isotopic discrimination (Δ¹⁵N) between the animal (milk, plasma, or tissues) and its diet has been proposed as a biomarker of the efficiency of N utilization in a range of production systems and ruminant species. The aim of this study was to assess the ability of Δ¹⁵N to predict the between-animal variability in MNE in dairy cows using an extensive database. For this, 20 independent experiments conducted as either changeover (n = 14) or continuous (n = 6) trials were available and comprised an initial data set of 1,300 observations. Between-animal variability was defined as the variation observed among cows sharing the same contemporary group (CG; individuals from the same experimental site, sampling period, and dietary treatment). Milk N efficiency was calculated as the ratio between mean milk N (grams of N in milk per day) and mean N intake (grams of N intake per day) obtained from each sampling period, which lasted 9.0 ± 9.9 d (mean ± SD). Samples of milk (n = 604) or plasma (n = 696) and feeds (74 dietary treatments) were analyzed for natural ¹⁵N abundance (δ¹⁵N), and then the N isotopic discrimination between the animal and the dietary treatment was calculated (Δ¹⁵n = δ¹⁵N_animal - δ¹⁵N_diet). Data were analyzed through mixed-effect regression models considering the experiment, sampling period, and dietary treatment as random effects. In addition, repeatability estimates were calculated for each experiment to test the hypothesis of improved predictions when MNE and Δ¹⁵N measurements errors were lower. The considerable protein mobilization in early lactation artificially increased both MNE and Δ¹⁵N, leading to a positive rather than negative relationship, and this limited the implementation of this biomarker in early lactating cows. When the experimental errors of Δ¹⁵N and MNE decreased in a particular experiment (i.e., higher repeatability values), we observed a greater ability of Δ¹⁵N to predict MNE at the individual level. The predominant negative and significant correlation between Δ¹⁵N and MNE in mid- and late lactation demonstrated that on average Δ¹⁵N reflects MNE variations both across dietary treatments and between animals. The root mean squared prediction error as a percentage of average observed value was 6.8%, indicating that the model only allowed differentiation between 2 cows in terms of MNE within a CG if they differed by at least 0.112 g/g of MNE (95% confidence level), and this could represent a limitation in predicting MNE at the individual level. However, the one-way ANOVA performed to test the ability of Δ¹⁵N to differentiate within-CG the top 25% from the lowest 25% individuals in terms of MNE was significant, indicating that it is possible to distinguish extreme animals in terms of MNE from their N isotopic signature, which could be useful to group animals for precision feeding.

Body Condition Score Change throughout Lactation Utilizing an Automated BCS System: A Descriptive Study

Simple Summary

The aim of this study was to implement a commercially available automated body condition scoring (ABCS) camera system to collect data for developing a predictive equation of body condition dynamics throughout the lactation period. The body condition score can vary depending on many factors relative to a specific cow. Lactation number, DIM, disease status, and 305d-predicted-milk-yield (305PMY) were significant factors to create a multivariate prediction model for automatic body condition scores throughout lactation.

Abstract

Body condition scoring (BCS) is a traditional visual technique often using a five-point scale to non-invasively assess fat reserves in cattle. However, recent studies have highlighted the potential in automating body condition scoring using imaging technology. Therefore, the objective was to implement a commercially available automated body condition scoring (ABCS) camera system to collect data for developing a predictive equation of body condition dynamics throughout the lactation period. Holstein cows (n = 2343, parity = 2.1 ± 1.1, calving BCS = 3.42 ± 0.24), up to 300 days in milk (DIM), were scored daily using two ABCS cameras mounted on sort-gates at the milk parlor exits. Scores were reported on a 1 to 5 scale in 0.1 increments. Lactation number, DIM, disease status, and 305d-predicted-milk-yield (305PMY) were used to create a multivariate prediction model for body condition scores throughout lactation. The equation derived from the model was: ABCS_ijk = 1.4838 − 0.00452 × DIM_i − 0.03851 × Lactation number_j + 0.5970 × Calving ABCS_k + 0.02998 × Disease Status(neg)_l − 1.52 × 10⁻⁶ × 305PMY_m + e_ijklm. We identified factors which are significant for predicting the BCS curve during lactation. These could be used to monitor deviations or benchmark ABCS in lactating dairy cows. The advantage of BCS automation is that it may provide objective, frequent, and accurate BCS with a higher degree of sensitivity compared with more sporadic and subjective manual BCS. Applying ABCS technology in future studies on commercial dairies may assist in providing improved dairy management protocols based on more available BCS.

How much energetic trade-offs limit selection? Insights from livestock and related laboratory model species

Trade-offs between life history traits are expected to occur due to the limited amount of resources that organisms can obtain and share among biological functions, but are of least concern for selection responses in nutrient-rich or benign environments. In domestic animals, selection limits have not yet been reached despite strong selection for higher meat, milk or egg yields. Yet, negative genetic correlations between productivity traits and health or fertility traits have often been reported, supporting the view that trade-offs do occur in the context of nonlimiting resources. The importance of allocation mechanisms in limiting genetic changes can thus be questioned when animals are mostly constrained by their time to acquire and process energy rather than by feed availability. Selection for high productivity traits early in life should promote a fast metabolism with less energy allocated to self-maintenance (contributing to soma preservation and repair). Consequently, the capacity to breed shortly after an intensive period of production or to remain healthy should be compromised. We assessed those predictions in mammalian and avian livestock and related laboratory model species. First, we surveyed studies that compared energy allocation to maintenance between breeds or lines of contrasting productivity but found little support for the occurrence of an energy allocation trade-off. Second, selection experiments for lower feed intake per unit of product (i.e. higher feed efficiency) generally resulted in reduced allocation to maintenance, but this did not entail fitness costs in terms of survival or future reproduction. These findings indicate that the consequences of a particular selection in domestic animals are much more difficult to predict than one could anticipate from the energy allocation framework alone. Future developments to predict the contribution of time constraints and trade-offs to selection limits will be insightful to breed livestock in increasingly challenging environments.

Can Nitrogen Excretion of Dairy Cows Be Reduced by Genetic Selection for Low Milk Urea Nitrogen Concentration?

The objectives of this study were two-fold. Firstly, to estimate the likely correlated responses in milk urea nitrogen (MUN) concentration, lactation yields of milk (MY), fat (FY) and crude protein (CPY) and mature cow liveweight (LWT) under three selection scenarios which varied in relative emphasis for MUN; 0% relative emphasis (MUN_0%: equivalent to current New Zealand breeding worth index), and sign of the economic value; 20% relative emphasis positive selection (MUN_+20%), and 20% relative emphasis negative selection (MUN_-20%). Secondly, to estimate for these three scenarios the likely change in urinary nitrogen (UN) excretion under pasture based grazing conditions. The predicted genetic responses per cow per year for the current index were 16.4 kg MY, 2.0 kg FY, 1.4 kg CPY, -0.4 kg LWT and -0.05 mg/dL MUN. Positive selection on MUN in the index resulted in annual responses of 23.7 kg MY, 2.0 kg FY, 1.4 kg CPY, 0.6 kg LWT and 0.10 mg/dL MUN, while negative selection on MUN in the index resulted in annual responses of 5.4 kg MY, 1.6 kg FY, 1.0 kg CPY, -1.1 kg LWT and -0.17 mg/dL MUN. The MUN_-20% reduced both MUN and cow productivity, whereas the MUN_+20% increased MUN, milk production and LWT per cow. Per cow dry matter intake (DMI) was increased in all three scenarios as milk production increased compared to base year, therefore stocking rate (SR) was adjusted to control pasture cover. Paradoxically, ten years of selection with SR adjusted to maintain annual feed demand under the MUN_+20% actually reduced per ha UN excretion by 3.54 kg, along with increases of 63 kg MY, 26 kg FY and 16 kg CPY compared to the base year. Ten years of selection on the MUN_0% index generated a greater reductions of 10.45 kg UN and 30 kg MY, and increases of 32 kg FY and 21 kg CPY per ha, whereas the MUN_-20% index reduced 14.06 kg UN and 136 kg MY with increases of 32 kg FY and 18 kg CPY compared to base year. All three scenarios increased partitioning of nitrogen excreted as feces. The selection index that excluded MUN was economically beneficial in the current economic circumstances over selection indices including MUN regardless of whether selection was either for or against MUN. There was no substantial benefit from an environmental point of view from including MUN in the Breeding Worth index, because N leaching is more a function of SR rather than of individual cow UN excretion. This study demonstrates that attention needs to be paid to the whole system consequences of selection for environmental outcomes in pastoral grazing circumstances.

Agroecological practices to support tropical livestock farming systems: a Caribbean and Latin American perspective

With global climate changes currently occurring, and particularly given the severe energy and food shortages occurring throughout tropical regions, agroecological (AE) systems are drawing renewed attention as an efficient alternative to intensive models of production, particularly unsuitable in regions of the world such as the Caribbean or Latin America. There is a pressing need to focus on livestock farming systems (LFS) and characterize their potential contributions to global sustainability. A multidisciplinary approach is needed to address these multiple and complex problems. Traditionally, LFS have shown their sustainability high potential. The purpose of this work is to highlight solutions to minimize inputs, by describing some success and diverse case studies through the Caribbean. These systems were analyzed at different levels: animal/function, farm/family, and territory/society. This produced a set of guidelines that help to increase the efficiency of tropical systems, particularly those concerned with (1) choosing the best-suited genotypes, while enhancing population biodiversity; (2) matching the farming system to the available resources, feed, and by-products; (3) steering the whole farming system through reproduction management with no hormonal treatment, while facilitating system reproducibility and increasing performances; (4) controlling health constraints instead of annihilating risks and implement an integrated management design to reduce chemical treatments or increase the use of nutraceuticals; and (5) mitigating climate constraints by using soft techniques. In the second part of this work, some integrated systems are described. The Tosoly farm of Colombia, for instance, is conceived as a totally integrated crop-livestock system, allowing the additional production of energy at the farm level. Thus, it epitomizes the positive impact that livestock can exert upon the environment. The case study of Haiti indicates how AE practices can help in designing a pro-poor sustainable milk production system. It concerns an entire dairy sector built all over the country on the basis of micro-units of milk production and processing.

Pasture-Based Dairy Systems in Temperate Lowlands: Challenges and Opportunities for the Future

Improved efficiency in dairy systems is a significant challenge for the future, to meet increased food demand while competing for inputs, adapting to climate change, and delivering ecosystem services. Future grazing systems can play a major role to supply healthier foods within systems with a reduced reliance on fossil fuels and chemical inputs, while also delivering environmental, biodiversity, and animal welfare benefits. Can we design lower-input systems that deliver efficient levels of output in a positive environmental context? Lower-input systems will have a lower reliance on concentrates and inorganic fertilizers, and an increased reliance on extended grazing seasons and high quality forage. Multiple strategies will be needed to maximize nitrogen use efficiency, including a strong reliance on legume-based swards that displace inorganic nitrogen fertilizer. Expected environmental benefits include a reduction in GHG emissions and nitrate leaching, an increase in C sequestration and a reduced reliance on the use of herbicides and pesticides. In comparison with confinement feeding systems, the relatively low energy density and high climate sensitivity of grazing diets requires both effective pasture management and robust and adaptive animals. The appropriate cow for grazing systems must be able to harvest pasture efficiently by re-calving every 365 days to efficiently utilize peak pasture supply, achieve large intakes of forage relative to their genetic potential for milk production (i.e., aggressive grazers) and be adaptable to fluctuations in feed supply. Legume-based multi-species grassland mixtures can maximize the use of symbiotically-fixed nitrogen, and displace the use of inorganic N fertilizer. There is a need for system-scale experiments that use legume-based mixtures within paddocks, and in grassland leys within crop rotations. Moreover, lower-input systems will need a combined focus on research and knowledge transfer for rapid testing and implementation. New opportunities and requirements will arise as policy, society, and the markets demand a higher level of environmental sustainability from food systems and products. This raises the possibility of public-private partnerships for the demand and reward of provision of environmental benefits. To deliver these benefits, future food systems will need to be redesigned to incorporate the enhanced supply of a range of ecosystem goods and services, which should be better incentivized through the market price returned to producers.

Mismatch of Glucose Allocation between Different Life Functions in the Transition Period of Dairy Cows

Immune cell functions such as phagocytosis and synthesis of immunometabolites, as well as immune cell survival, proliferation and differentiation, largely depend on an adequate availability of glucose by immune cells. During inflammation, the glucose demands of the immune system may increase to amounts similar to those required for high milk yields. Similar metabolic pathways are involved in the adaptation to both lactation and inflammation, including changes in the somatotropic axis and glucocorticoid response, as well as adipokine and cytokine release. They affect (i) cell growth, proliferation and activation, which determines the metabolic activity and thus the glucose demand of the respective cells; (ii) the overall availability of glucose through intake, mobilization and gluconeogenesis; and (iii) glucose uptake and utilization by different tissues. Metabolic adaptation to inflammation and milk synthesis is interconnected. An increased demand of one life function has an impact on the supply and utilization of glucose by competing life functions, including glucose receptor expression, blood flow and oxidation characteristics. In cows with high genetic merits for milk production, changes in the somatotropic axis affecting carbohydrate and lipid metabolism as well as immune functions are profound. The ability to cut down milk synthesis during periods when whole-body demand exceeds the supply is limited. Excessive mobilization and allocation of glucose to the mammary gland are likely to contribute considerably to peripartal immune dysfunction.

Relationship between the fatty acid profile of hair and energy availability of lactating primiparous cows

We hypothesised that a relationship would exist between hair fatty acids, especially C12:0, C14:0 and C16:0, and parameters of energy metabolism such as energy intake, energy mobilisation, and energy requirement for maintenance and milk performance. For this study, 11 primiparous German Holstein cows were available from which hair samples at weeks 6 and 8 of lactation were analysed. The average body weight of these animals was 558 ± 27 kg at calving and milk yield at 100-days in milk was 3,537 ± 529 kg. Feed intake and milk yield were measured daily. Body weight and back fat thickness were measured at calving and in weeks 2, 4, and 8 of lactation. Energy balance and energy utilisation were calculated until week 6 of lactation. Spearman's correlation coefficients were found to be significantly positive for the relationship between the percentage of C12:0 and C14:0 fatty acids in the hair in lactation week 8 and energy intake in weeks 5 and 6 (0.62 < r < 0.65, P < 0.05). If the animals are grouped according to their energy utilisation between weeks 1 and 6 into two groups higher (n = 6) or lower (n = 5) than the median, animals of the high energy utilising group had a higher energy intake. These animals had also higher percentages of the C12:0 fatty acid in their hair fat (week 6: 4.9% vs. 3.1%, P < 0.05; week 8: 4.3% vs. 2.9%, P = 0.05). Our hypothesis is supported, and this study justifies further investigation of the content of medium-chain fatty acids in hair samples as biomarkers for the metabolic status of a cow during early lactation.

Invited review: resource allocation mismatch as pathway to disproportionate growth in farm animals - prerequisite for a disturbed health

The availability of resources including energy, nutrients and (developmental) time has a crucial impact on productivity of farm animals. Availability of energy and nutrients depends on voluntary feed intake and intestinal digestive and absorptive capacity at optimal feeding conditions. Availability of time is provided by the management in animal production. According to the resource allocation theory, resources have to be allocated between maintenance, ontogenic growth, production and reproduction during lifetime. Priorities for these processes are mainly determined by the genetic background, the rearing system and the feeding regimen. Aim of this review was to re-discuss the impact of a proper resource allocation for a long and healthy life span in farm animals. Using the barrel model of resource allocation, resource fluxes were explained and were implemented to specific productive life conditions of different farm animal species, dairy cows, sows and poultry. Hypothetically, resource allocation mismatch neglecting maintenance is a central process, which might be associated with morphological constraints of extracellular matrix components; evidence for that was found in the literature. A potential consequence of this limitation is a phenomenon called disproportionate growth, which counteracts the genetically determined scaling rules for body and organ proportions and could have a strong impact on farm animal health and production.

Disentangling the relative roles of resource acquisition and allocation on animal feed efficiency: insights from a dairy cow model

Background

Feed efficiency of farm animals has greatly improved through genetic selection for production. Today, we are faced with the limits of our ability to predict the effect of selection on feed efficiency, partly because the relative importance of the components of this complex phenotype changes across environments. Thus, we developed a dairy cow model that incorporates the dynamic interplay between life functions and evaluated its behaviour with a global sensitivity analysis on two definitions of feed efficiency. A key model feature is to consider feed efficiency as the result of two processes, acquisition and allocation of resources. Acquisition encapsulates intake and digestion, and allocation encapsulates partitioning rules between physiological functions. The model generates genetically-driven trajectories of energy acquisition and allocation, with four genetic-scaling parameters controlling these processes. Model sensitivity to these parameters was assessed with a complete factorial design.

Results

Acquisition and allocation had contrasting effects on feed efficiency (ratio between energy in milk and energy acquired from the environment). When measured over a lactation period, feed efficiency was increased by increasing allocation to lactation. However, at the lifetime level, efficiency was increased by decreasing allocation to growth and increasing lactation acquisition. While there is a strong linear increase in feed efficiency with more allocation to lactation within a lactation cycle, our results suggest that there is an optimal level of allocation to lactation beyond which increasing allocation to lactation negatively affects lifetime feed efficiency.

Conclusions

We developed a model to predict lactation and lifetime feed efficiency and show that breaking-down feed conversion into acquisition and allocation, and introducing genetically-driven trajectories that control these mechanisms, permitted quantification of their relative roles on feed efficiency. The life stage at which feed efficiency is evaluated appears to be a key aspect for selection. In this model, body reserves are also a key component in the prediction of lifetime feed efficiency since they integrate the feedback of acquisition and allocation on survival and reproduction. This modelling approach provided new insights into the processes that underpin lifetime feed efficiency in dairy cows.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0251-8) contains supplementary material, which is available to authorized users.

Genotype by environment interaction and breeding for robustness in livestock

The increasing size of the human population is projected to result in an increase in meat consumption. However, at the same time, the dominant position of meat as the center of meals is on the decline. Modern objections to the consumption of meat include public concerns with animal welfare in livestock production systems. Animal breeding practices have become part of the debate since it became recognized that animals in a population that have been selected for high production efficiency are more at risk for behavioral, physiological and immunological problems. As a solution, animal breeding practices need to include selection for robustness traits, which can be implemented through the use of reaction norms analysis, or though the direct inclusion of robustness traits in the breeding objective and in the selection index. This review gives an overview of genotype × environment interactions (the influence of the environment, reaction norms, phenotypic plasticity, canalization, and genetic homeostasis), reaction norms analysis in livestock production, options for selection for increased levels of production and against environmental sensitivity, and direct inclusion of robustness traits in the selection index. Ethical considerations of breeding for improved animal welfare are discussed. The discussion on animal breeding practices has been initiated and is very alive today. This positive trend is part of the sustainable food production movement that aims at feeding 9.15 billion people not just in the near future but also beyond.

Metabolic Disorders in the Transition Period Indicate that the Dairy Cows' Ability to Adapt is Overstressed

Simple Summary

Metabolic disorders are a key problem in the transition period of dairy cows and often appear before the onset of further health problems. Problems derive from difficulties animals have to adapt to large variations and disturbances occurring both outside and inside the organism. A lack of success in solving these issues may be due to predominant approaches in farm management and agricultural science, dealing with such disorders as merely negative side effects. Instead, a successful adaptation of animals to their living conditions should be seen as an important end in itself. Both farm management and agricultural sciences should support animals in their ability to cope with nutritional and metabolic challenges by employing a functional and result-driven approach.

Abstract

Metabolic disorders are a key problem in the transition period of dairy cows and often appear before the onset of further health problems. They mainly derive from difficulties the animals have in adapting to changes and disturbances occurring both outside and inside the organisms and due to varying gaps between nutrient supply and demand. Adaptation is a functional and target-oriented process involving the whole organism and thus cannot be narrowed down to single factors. Most problems which challenge the organisms can be solved in a number of different ways. To understand the mechanisms of adaptation, the interconnectedness of variables and the nutrient flow within a metabolic network need to be considered. Metabolic disorders indicate an overstressed ability to balance input, partitioning and output variables. Dairy cows will more easily succeed in adapting and in avoiding dysfunctional processes in the transition period when the gap between nutrient and energy demands and their supply is restricted. Dairy farms vary widely in relation to the living conditions of the animals. The complexity of nutritional and metabolic processes and their large variations on various scales contradict any attempts to predict the outcome of animals’ adaptation in a farm specific situation. Any attempts to reduce the prevalence of metabolic disorders and associated production diseases should rely on continuous and comprehensive monitoring with appropriate indicators on the farm level. Furthermore, low levels of disorders and diseases should be seen as a further significant goal which carries weight in addition to productivity goals. In the long run, low disease levels can only be expected when farmers realize that they can gain a competitive advantage over competitors with higher levels of disease.

Biological efficiency profiles over the lactation period in multiparous high-producing dairy cows under divergent production systems

Abstract. The study examined variation in energetic-efficiency profiles among production systems and cow parities. Further, the correlation between cows' body condition score (BCS) and energetic efficiency over the lactation period was determined. Biological efficiency was defined using four measures of production efficiency and two measures of energetic efficiency. The following were measures of energetic efficiency: the net energy intake required to produce 1 kg milk solids (NEin / MS) and the proportion of net energy utilized for milk production after accounting for maintenance (NElact / (NEin- NEm)). Seven years of data were gathered from a total of 595 Holstein-Friesian cows in a long-term genetics × feeding–management interaction project. Two feeding regimes – High forage (HF) and Low forage (LF) – were applied to each of two genetic lines (Control (C) and Select (S)), giving four dairy production systems: Low Forage Control (LFC), Low Forage Select (LFS), High Forage Control (HFC) and High Forage Select (HFS). LFS was the most efficient system using all measures. Variation in the rate and scale of change at which the cows' energetic efficiency declined over lactation was significantly different (P < 0.001) amongst different dairy production systems and parities. Loss of efficiency over the lactation period was lower in Select cows than in Control cows and increased with parity. The trajectory of energetic-efficiency profiles was influenced by cow genetic line, and yet the level of the efficiency profile was influenced by the feeding regime. There was a strong relationship between BCS and energetic efficiency. Continued in situ monitoring of cows' biological efficiency may enable optimal management of dairy systems.

Milk production and nutrient partitioning as measured by (13)C enrichment of milk components during C3 and C4 plant feeding in purebred Holstein and in Charolais × Holstein F2 crossbred cows

Nutrient partitioning was investigated in cows with different genetic merits for milk production by measuring (13)C/(12)C ratios (reported by delta values δ(13)C) in milk components in response to C3 (grass silage) and C4 diets (corn silage). We hypothesised that changes of δ(13)C in milk differ between Holstein (HOL; high milk production) and Charolais × Holstein cows with medium (CHM) and low (CHL) milk production. Changes of δ(13)C (Δδ(13)C) in milk components were estimated by calculating differences of δ(13)C due to switch from C3 to C4 feeding. After switch to C4 feeding, Δδ(13)C of lactose was greater in HOL than in CHL. Immediate Δδ(13)C of milk fat was the lowest in CHL. The maximal Δδ(13)C of casein was the lowest in HOL. The proportion of carbon in milk derived from diet increased with milk yield, indicating the main impact of the milk production level, but minor impact of breed, on nutrient partitioning towards the mammary gland.

Plasma parameters related to energy and lipid metabolism in periparturient Modenese and Italian Friesian cows

The aim of this work was to compare energy and lipid metabolism during the peripartum period between Modenese (MO) and Italian Friesian (IF) cows. The study was carried out on 33 pluriparous pregnant cows, 19 IF and 14 MO, reared together in the same herd and kept under equal conditions of management and nutrition. Blood was sampled from jugular vein starting 4 week before expected calving date until 4 week post-calving, once weekly. Plasma was analysed for glucose, NEFA, BHBA, triglycerides and cholesterol concentrations. Body condition score (BCS) was assessed weekly after blood sampling. Data from antepartum (a.p.) and post-partum (p.p.) periods were separately analysed as repeated measures by a linear mixed models with the effect of breed, time and their interaction as main factors and random cow within breed. The energy status differed between the two breeds during the peripartum period. We observed higher BCS a.p. and p.p. and lower BCS variations p.p. in MO compared to IF group. Modenese cows showed lower glucose and cholesterol concentrations (p < 0.001), but higher NEFA values, NEFA to cholesterol and NEFA to albumin ratios (p < 0.001) during a.p.; on the contrary, IF cows had higher (p < 0.05) cholesterol, NEFA, BHBA levels and NEFA to albumin ratio than MO ones during p.p. The differences observed between the two breeds suggest how MO cows are subjected to lipid mobilization during late gestation; on the contrary, IF cows are predisposed to mobilize their lipid reserves at the beginning of lactation to support high production. The results indicate a diverse ability to cope with metabolic stress and suggest the hypothesis that the differences in concentrations of plasma parameters and their variation amplitude around the calving period might depend on the different genetic merit for milk production between the two breeds.

e-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems

A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype × environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were <10% for all variables, and concordance correlation coefficients over 0.80 for annual pasture utilisation, yields of milk and milk solids (MS; fat plus protein), and of 0.69 and 0.48 for LW and BCS, respectively. A simulation of two contrasting dairy systems is presented to show the practical use of the model. The model can be used to explore the effects of feeding level and genetic merit and their interactions for grazing dairy systems, evaluating the trade-offs between profit and the associated risk.

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.

Developing breeding schemes to assist mitigation of greenhouse gas emissions

Genetic improvement of livestock is a particularly effective technology, producing permanent and cumulative changes in performance. This paper highlights some of the options for including mitigation in livestock breeding schemes, focusing on ruminant species, and details three routes through which genetic improvement can help to reduce emissions per kg product via: (i) improving productivity and efficiency, (ii) reducing wastage in the farming system and (iii) directly selecting on emissions, if or when these are measurable. Selecting on traits that improve the efficiency of the system (e.g. residual feed intake, longevity) will have a favourable effect on the overall emissions from the system. Specific examples of how genetic selection will have a favourable effect on emissions for UK dairy systems are described. The development of breeding schemes that incorporate environmental concerns is both desirable and possible. An example of how economic valuation of public good outcomes can be incorporated into UK dairy selection indices is given. This paper focuses on genetic selection tools using, on the whole, currently available traits and tools. However, new direct and indirect measurement techniques for emissions will improve the potential to reduce emissions by genetic selection. The complexities of global forces on defining selection objectives are also highlighted.

An individual-based model simulating goat response variability and long-term herd performance

Finding ways of increasing the efficiency of production systems is a key issue of sustainability. System efficiency is based on long-term individual efficiency, which is highly variable and management driven. To study the effects of management on herd and individual efficiency, we developed the model simulation of goat herd management (SIGHMA). This dynamic model is individual-based and represents the interactions between technical operations (relative to replacement, reproduction and feeding) and individual biological processes (performance dynamics based on energy partitioning and production potential). It simulates outputs at both herd and goat levels over 20 years. A farmer's production project (i.e. a targeted milk production pattern) is represented by configuring the herd into female groups reflecting the organisation of kidding periods. Each group is managed by discrete events applying decision rules to simulate the carrying out of technical operations. The animal level is represented by a set of individual goat models. Each model simulates a goat's biological dynamics through its productive life. It integrates the variability of biological responses driven by genetic scaling parameters (milk production potential and mature body weight), by the regulations of energy partitioning among physiological functions and by responses to diet energy defined by the feeding strategy. A sensitivity analysis shows that herd efficiency was mainly affected by feeding management and to a lesser extent by the herd production potential. The same effects were observed on herd milk feed costs with an even lower difference between production potential and feeding management. SIGHMA was used in a virtual experiment to observe the effects of feeding strategies on herd and individual performances. We found that overfeeding led to a herd production increase and a feed cost decrease. However, this apparent increase in efficiency at the herd level (as feed cost decreased) was related to goats that had directed energy towards body reserves. Such a process is not efficient as far as feed conversion is concerned. The underfeeding strategy led to production decrease and to a slight feed cost decrease. This apparent increase in efficiency was related to goats that had mobilised their reserves to sustain production. Our results highlight the interest of using SIGHMA to study the underlying processes affecting herd performance and analyse the role of individual variability regarding herd response to management. It opens perspectives to further quantify the link between individual variability, herd performance and management and thus further our understanding of livestock farming systems.

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.

Tissue-specific mRNA expression patterns reveal a coordinated metabolic response associated with genetic selection for milk production in cows

The molecular mechanisms regulating the physiological adaptation of tissues important for nutrient partitioning and metabolism in lactating cows are still not completely understood. The aim of our study was to identify tissue-specific regulatory mechanisms necessary to accommodate metabolic changes associated with different genetic potential for milk performance. For this purpose, we analyzed mRNA expression of genes involved in energy metabolism of segregating F(2) beef type cows with a combined genetic dairy and beef background (Charolais × German Holstein cross, CH×GH) in contrast to purebred German Holstein (GH) dairy cows. Three groups of cows differing in milk performance were examined using quantitative real-time PCR in liver, mammary gland, and skeletal muscle. Our results describe substantial tissue-specific differences in mRNA transcription profiles between cow groups in relation to their genetic potential for milk performance and highlight genes exhibiting specific, partially yet-unknown functions in dairy and beef type cows, e.g., upregulation of PCK2 transcripts in the mammary gland and FBP2 transcripts in skeletal muscle of dairy cows. Noticeably, PCCA and PPARGC1A mRNA abundance varied significantly across experimental groups in all three tissues, pointing to potential key gene functions in the metabolic adaptation relative to divergent milk production performance. Correlations of mRNA expression levels to milk performance traits indicate that gene transcriptional processes may play a regulatory role in liver, mammary gland, and skeletal muscle to enable cows with different genetic potential for milk performance to cope with metabolic lactation-associated challenges.

Unravelling the relationship between animal growth and immune response during micro-parasitic infections

Background

Both host genetic potentials for growth and disease resistance, as well as nutrition are known to affect responses of individuals challenged with micro-parasites, but their interactive effects are difficult to predict from experimental studies alone.

Methodology/Principal Findings

Here, a mathematical model is proposed to explore the hypothesis that a host's response to pathogen challenge largely depends on the interaction between a host's genetic capacities for growth or disease resistance and the nutritional environment. As might be expected, the model predicts that if nutritional availability is high, hosts with higher growth capacities will also grow faster under micro-parasitic challenge, and more resistant animals will exhibit a more effective immune response. Growth capacity has little effect on immune response and resistance capacity has little effect on achieved growth. However, the influence of host genetics on phenotypic performance changes drastically if nutrient availability is scarce. In this case achieved growth and immune response depend simultaneously on both capacities for growth and disease resistance. A higher growth capacity (achieved e.g. through genetic selection) would be detrimental for the animal's ability to cope with pathogens and greater resistance may reduce growth in the short-term.

Significance

Our model can thus explain contradicting outcomes of genetic selection observed in experimental studies and provides the necessary biological background for understanding the influence of selection and/or changes in the nutritional environment on phenotypic growth and immune response.

Reconstruction of metabolic pathways for the cattle genome

Background

Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement.

Results

An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly.

Conclusion

CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology.