Nutritional sub-fertility in the dairy cow: towards improved reproductive management through a better biological understanding

There are numerous recent studies highlighting sustainability problems for the development of ruminant production systems (RPS) while facing increasing human food necessities and global climate change. Despite the complexity of the context, in our view the main objectives of the ruminants' physiologist should be convergent for both industrialized (IC) and developing countries (DC) in a common and global strategy of advancing knowledge. In DC, this means improving the efficiency of RPS, taking into account the unique possibility of using rangelands. For IC settings, RPS should be revisited in terms of autonomy and environment-friendly feeding and managing practices. Assuming that competition for feed/food use is still a crucial criterion, future ruminant feeding systems (FeSyst) should preferably focus on lignocellulosic sources. According to biome distributions, and the recent increases in volumes of crop residues and their by-products, the annually renewed volumes of these biomasses are considerable. Therefore, we need to redesign our strategies for their efficient utilization at the local level. For this purpose, digestion processes and rumen functioning need to be better understood. The renewed vision of ruminal digestion through the reduction of greenhouse gas emissions is also a key aspect as it is an environmental demand that cannot be ignored. With regard to other ruminants' physiological functions, accumulated knowledge could be mobilized into an integrative approach that puts forward the adaptive capacities of animals to face variability in quantity and quality of supplied feeds. Basically, the reduction of inputs that were traditionally used to ensure FeSyst will need more flexible animals. In that sense, the concepts of homeostasis and teleophorhesis need to be updated and adapted to domestic species and breeds that were until now largely excluded from the dominant productive systems. In conclusion, a more holistic approach of research targets is required in which physiological functions and farmers' practices must converge and respond to each particular situation in an integral, dynamic and flexible conceptual perspective. From a scientific point of view, both for ICs and DCs, a broader range of experimental scenarios should be explored in order to arrive at innovative practices and solutions that respect environmental, ethical and economical issues. The clear challenge is to in evaluate the sustainability of RPSs. This includes, in our opinion, a strong interaction with other disciplines (multi-and trans-disciplinary conception), thus structuring new relevant indicators for the evaluation sustainability.

Section: Effects Of Feeding Practices In Producing Animalsmentioning

confidence: 99%

Section: Effects Of Feeding Practices In Producing Animalsmentioning

confidence: 99%

Sustainability of ruminant agriculture in the new context: feeding strategies and features of animal adaptability into the necessary holistic approach

Bocquier

García

2010

“…The finding that selecting cows for a specific production trait, that is, creating specialist animals, decreases their robustness is supported by studies in other species (Reznick et al, 2000;Theilgaard et al, 2007) and also by simulation studies (van der Waaij, 2004). Thus, and contrary to some reports (Collier et al, 2005), continued selection for increased milk production is not without cost (see also Friggens et al, 2010).…”

Section: Introductionmentioning

confidence: 95%

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Friggens

Brun-Lafleur

Faverdin

et al. 2013

Self Cite

In recent years, it has become increasingly clear that understanding nutrient partitioning is central to a much broader range of issues than just being able to predict productive outputs. The extent to which nutrients are partitioned to other functions such as health and reproduction is clearly important, as are the efficiency consequences of nutrient partitioning. Further, with increasing environmental variability, there is a greater need to be able to predict the ability of an animal to respond to the nutritional limitations that arise from the environment in which it is placed. How the animal partitions its nutrients when resources are limited, or imbalanced, is a major component of its ability to cope, that is, its robustness. There is mounting evidence that reliance on body reserves is increased and that robustness of dairy cows is reduced by selection for increased milk production. A key element for predicting the partition of nutrients in this wider context is to incorporate the priorities of the animal, that is, an explicit recognition of the role of both the cow's genotype (genetic make-up), and the expression of this genotype through time on nutrient partitioning. Accordingly, there has been a growing recognition of the need to incorporate in nutritional models these innate driving forces that alter nutrient partitioning according to physiological state, the genetically driven trajectories. This paper summarizes some of the work carried out to extend nutritional models to incorporate these trajectories, the genetic effects on them, as well as how these factors affect the homeostatic capacity of the animal. At present, there are models capable of predicting the partition of nutrients throughout lactation for cows of differing milk production potentials. Information concerning genotype and stage of lactation effects on homeostatic capacity has not yet been explicitly included in metabolic models that predict nutrient partition, although recent results suggest that this is achievable. These developments have greatly extended the generality of nutrient partitioning models with respect to the type of animal and its physiological state. However, these models remain very largely focussed on predicting partition between productive outputs and body reserves and, for the most part, remain research models, although substantial progress has been made towards developing models that can be applied in the field. The challenge of linking prediction of nutrient partitioning to its consequences on health, reproduction and longevity, although widely recognized, is only now beginning to be addressed. This is an important perspective for future work on nutrient partitioning.

“…The occurrence of GHP, that is, metritis, placental retention or vaginal infection, at or following calving was included in the model for its impact on the calving-to-first ovulation (CFO) interval. Although a decrease in BCS increases the risk of GHP (Friggens et al, 2010), we applied a unique and fixed probability of GHP occurrence to every cow, equal to 0.28 for each calving. This value was the observed frequency of GHP in the original database (n 5 98 lactations).…”

Section: Description Of the Modelmentioning

confidence: 99%

“…Both of these factors are known to affect reproduction (Roche et al, 2009;Friggens et al, 2010), a proportion of these effects being directly attributable to genetic selection (Boichard et al, 2002;Berry et al, 2003). More precisely, each step of the reproductive process appears to be affected either by MY level and dynamics or by the level of energy balance and body reserves or both (Friggens et al, 2010;Cutullic et al, 2011). Recently, these effects were quantified for each step of the reproductive process (Cutullic et al, 2012) but not for the overall process.…”

Section: Introductionmentioning

confidence: 99%

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows

Brun-Lafleur

Cutullic

Faverdin

et al. 2013

Self Cite

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.Keywords: dairy cow, reproduction, model, milk yield, body condition score Implications This paper describes an up-to-date stochastic reproduction model of Holstein dairy cows. This model is sensitive to both milk production and body reserves and takes into account the recent evolutions of reproductive performance in dairy cows. This model is able to well simulate the overall reproductive performance observed in commercial farms. Therefore, it can be used in dynamic individual-based herd simulation models to anticipate the consequences of management strategies (in terms of reproduction or feeding) on dairy herd demography and global performance.