Prediction of the lifetime productive and reproductive performance of Holstein cows managed for different lactation durations, using a model of lifetime nutrient partitioning.

The GARUNS model is a lifetime performance model taking into account the changing physiological priorities of an animal during its life and through repeated reproduction cycles. This dynamic and stochastic model has been previously used to predict the productive and reproductive performance of various genotypes of cows across feeding systems. In the present paper, we used this model to predict the lifetime productive and reproductive performance of Holstein cows for different lactation durations, with the aim of determining the lifetime scenario that optimizes cows' performance defined by lifetime efficiency (ratio of total milk energy yield to total energy intake) and pregnancy rate. To evaluate the model, data from a 16-mo extended lactation experiment on Holstein cows were used. Generally, the model could consistently fit body weight, milk yield, and milk components of these cows, whereas the reproductive performance was overestimated. Cows managed for repeated 12-, 14-, or 16-mo lactation all their life were simulated and had the highest lifetime efficiency compared with shorter (repeated 10-mo lactations: scenario N-N) or longer lactations (repeated 18-, 20-, or 22-mo lactations). The pregnancy rates increased slightly from a 10-mo to a 16-mo lactation but not significantly. Cows managed for a 16-mo lactation during their first lactation, followed by 10-mo lactations for the rest of their lives (EL-N scenario), had a similar lifetime efficiency as cows managed for 16-mo lactation all of their lives (EL-EL scenario). Cows managed for a 10-mo lactation during their first lactation, followed by 16-mo lactations for the rest of their lives (N-EL scenario), had a similar lifetime efficiency as that of the N-N scenario. The pregnancy rates of these 4 scenarios (N-N, EL-EL, N-EL, and EL-N) were similar to one another. To conclude, the GARUNS model was able to fit and simulate the extended lactation of Holstein cows. The simulated outputs indicate that managing the primiparous cows with a 16-mo extended lactation, followed by 10-mo lactations, allows their lifetime efficiency to increase and become similar to cows managed for 16-mo lactation during their entire lives. Further work should include health incidence (i.e., diseases) in the prediction model to have more accurate and realistic predictions of lifetime efficiency.

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows.

Reproductive success is a key component of lifetime efficiency - which is the ratio of energy in milk (MJ) to energy intake (MJ) over the lifespan, of cows. At the animal level, breeding and feeding management can substantially impact milk yield, body condition and energy balance of cows, which are known as major contributors to reproductive failure in dairy cattle. This study extended an existing lifetime performance model to incorporate the impacts that performance changes due to changing breeding and feeding strategies have on the probability of reproducing and thereby on the productive lifespan, and thus allow the prediction of a cow's lifetime efficiency. The model is dynamic and stochastic, with an individual cow being the unit modelled and one day being the unit of time. To evaluate the model, data from a French study including Holstein and Normande cows fed high-concentrate diets and data from a Scottish study including Holstein cows selected for high and average genetic merit for fat plus protein that were fed high- v. low-concentrate diets were used. Generally, the model consistently simulated productive and reproductive performance of various genotypes of cows across feeding systems. In the French data, the model adequately simulated the reproductive performance of Holsteins but significantly under-predicted that of Normande cows. In the Scottish data, conception to first service was comparably simulated, whereas interval traits were slightly under-predicted. Selection for greater milk production impaired the reproductive performance and lifespan but not lifetime efficiency. The definition of lifetime efficiency used in this model did not include associated costs or herd-level effects. Further works should include such economic indicators to allow more accurate simulation of lifetime profitability in different production scenarios.

Deriving estimates of individual variability in genetic potentials of performance traits for 3 dairy breeds, using a model of lifetime nutrient partitioning.

This study explored the ability of an existing lifetime nutrient partitioning model for simulating individual variability in genetic potentials of dairy cows. Generally, the model assumes a universal trajectory of dynamic partitioning of priority between life functions and genetic scaling parameters are then incorporated to simulate individual difference in performance. Data of 102 cows including 180 lactations of 3 breeds: Danish Red, Danish Holstein, and Jersey, which were completely independent from those used previously for model development, were used. Individual cow performance records through sequential lactations were used to derive genetic scaling parameters for each animal by calibrating the model to achieve best fit, cow by cow. The model was able to fit individual curves of body weight, and milk fat, milk protein, and milk lactose concentrations with a high degree of accuracy. Daily milk yield and dry matter intake were satisfactorily predicted in early and mid lactation, but underpredictions were found in late lactation. Breeds and parities did not significantly affect the prediction accuracy. The means of genetic scaling parameters between Danish Red and Danish Holstein were similar but significantly different from those of Jersey. The extent of correlations between the genetic scaling parameters was consistent with that reported in the literature. In conclusion, this model is of value as a tool to derive estimates of genetic potentials of milk yield, milk composition, body reserve usage, and growth for different genotypes of cow. Moreover, it can be used to separate genetic variability in performance between individual cows from environmental noise. The model enables simulation of the effects of a genetic selection strategy on lifetime efficiency of individual cows, which has a main advantage of including the rearing costs, and thus, can be used to explore the impact of future selection on animal performance and efficiency.

Factors affecting energy and nitrogen efficiency of dairy cows: a meta-analysis.

A meta-analysis was performed to explore the correlation between energy and nitrogen efficiency of dairy cows, and to study nutritional and animal factors that influence these efficiencies, as well as their relationship. Treatment mean values were extracted from 68 peer-reviewed studies, including 306 feeding trials. The main criterion for inclusion of a study in the meta-analysis was that it reported, or permitted calculation of, energy efficiency (Eeff; energy in milk/digestible energy intake) and nitrogen efficiency (Neff; nitrogen in milk/digestible nitrogen intake) at the digestible level (digestible energy or digestible protein). The effect of nutritional and animal variables, including neutral detergent fiber, acid detergent fiber (ADF), digestible energy, digestible protein, proportion of concentrate (PCO), dry matter intake, milk yield, days in milk, and body weight, on Eeff, Neff, and the Neff:Eeff ratio was analyzed using mixed models. The interstudy correlation between Eeff and Neff was 0.62, whereas the intrastudy correlation was 0.30. The higher interstudy correlation was partly due to milk yield and dry matter intake being present in both Eeff and Neff. We, therefore, also explored the Neff:Eeff ratio. Energy efficiency was negatively associated with ADF and PCO, whereas Neff was negatively associated with ADF and digestible energy. The Neff:Eeff ratio was affected by ADF and PCO only. In conclusion, the results indicate a possibility to maximize feed efficiency in terms of both energy and nitrogen at the same time. In other words, an improvement in Eeff would also mean an improvement in Neff. The current study also shows that these types of transverse data are not sufficient to study the effect of animal factors, such as days in milk, on feed efficiency. Longitudinal measurements per animal would probably be more appropriate.

Effect of fungal treatments of fibrous agricultural by-products on chemical composition and in vitro rumen fermentation and methane production.

Maize stover, rice straw, oil palm fronds and sugarcane bagasse were treated with the white-rot fungi Ceriporiopsis subvermispora, Lentinula edodes, Pleurotus eryngii, or Pleurotus ostreatus at 24 °C for 0-6 weeks. The fungi increased total gas production from oil palm fronds by 68-132%, but none of the fungi improved the in vitro rumen fermentability of maize stover. C. subvermispora and L. edodes increased total gas production of sugarcane bagasse by 65-71%, but P. eryngii and P. ostreatus decreased it by 22-50%. There was a linear relationship (P<0.05) between the proportion of lignin in the original substrate and the increase in in vitro gas production observed for C. subvermispora and L. edodes treatments (R2=0.92 and 0.96, respectively). It is concluded that C. subvermispora and L. edodes have a particularly high potential to improve the nutritive value of highly lignified ruminant feeds.