Cow-level factors associated with nitrogen utilization in grazing dairy cows using a cross-sectional analysis of a large database

Reducing nitrogen pollution while maintaining milk production is a major challenge of dairy production. One of the keys to delivering on this challenge is to improve the efficiency of how dairy cows use nitrogen. Thus, estimating the nitrogen utilization of lactating grazing dairy cows and exploring the association between animal factors and productivity with nitrogen utilization are the first steps to understanding the nitrogen utilization complex in dairy cows. Nitrogen utilization metrics were derived from milk and body weight records from 1,291 grazing dairy cows of multiple breeds and crossbreeds; all cows had sporadic information on nitrogen intake concurrent with information on nitrogen sinks (and other nitrogen sources, such as body tissue mobilization). Several nitrogen utilization metrics were investigated, including nitrogen use efficiency (nitrogen output as products such as milk and meat divided by nitrogen intake) and nitrogen excreted (nitrogen intake less the nitrogen output as products such as milk and meat). In the present study, a primiparous Holstein-Friesian used, on average, 20.6% of the nitrogen it ate, excreting the surplus as feces and urine, representing 402 g of nitrogen per day. Intercow variability existed, with a between-cow standard deviation of 0.0094 for nitrogen use efficiency and 24 g of nitrogen per day for nitrogen excretion. As lactation progressed, nitrogen use efficiency declined and nitrogen excretion increased. Nevertheless, nitrogen use efficiency improved (i.e., decreased) from first to second parity, even though it did not improve from second to third parity or greater. Furthermore, nitrogen excretion continued to increase from first to third parity or greater. Nitrogen use efficiency and nitrogen excretion were negatively correlated (-0.56 to -0.40), signifying that dairy cows who partition more of the ingested nitrogen into products such as milk and meat, on average, also excrete less nitrogen. Milk urea nitrogen was, at best, weakly correlated with nitrogen use efficiency and nitrogen excretion; the correlations were between -0.01 and 0.06. In conclusion, several cow-level factors such as parity, stage of lactation, and breed were associated with the range of different nitrogen efficiency metrics investigated; moreover, even after accounting for such effects, 4.8% to 6.3% of the remaining variation in the nitrogen use efficiency and nitrogen balance metrics were attributable to intercow differences.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

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.

Evaluation of predicted ration nutritional values by NRC (2001) and INRA (2018) feed evaluation systems, and implications for the prediction of milk response

Net energy and protein systems (hereafter called feed evaluation systems) offer the possibility to formulate rations by matching feed values (e.g., net energy and metabolizable protein) with animal requirements. The accuracy and precision of this approach relies heavily on the quantification of various animal digestive and metabolic responses to dietary changes. Therefore, the aims of the current study were, first, to evaluate the predicted responses to dietary changes of total-tract digestibility (including organic matter, crude protein, and neutral detergent fiber) and nitrogen (N) flows at the duodenum (including microbial N and undigested feed N together with endogenous N) against measurements from published studies by 2 different feed evaluation systems. These feed evaluation systems were the recently updated Institut National de la Recherche Agronomique (INRA, 2018) and the older, yet widely used, National Research Council (NRC, 2001) system. The second objective was to estimate the accuracy and precision of predicting milk yield responses based on values of net energy (NE_L) and metabolizable protein (MP) supply predicted by the 2 feed evaluation systems. For this, published studies, with experimentally induced changes in either NE_L or MP content, were used to calibrate the relationship of NE_L and MP supply, with milk component yields. Based on the slope, root mean square prediction error, and concordance correlation coefficient (CCC), the results obtained show that total nonammonia nitrogen flow at the duodenum was predicted with similar accuracy and precision, but considerably better prediction was achieved when the INRA model was used to predict organic matter and neutral detergent fiber digestibility responses. The average NE_L and MP content predicted by both models was similar, but NE_L and MP content of individual diets differed substantially between both models as indicated by determination coefficients of 0.45 (NE_L content) and 0.50 (MP content). Despite these differences, this work shows that when response equations are calibrated with NE_L and MP values either from the INRA model or from the NRC model, the accuracy and precision (slope, root mean square prediction error, and CCC) of the predicted milk component yields responses is similar between the models. The lowest accuracy and precision were observed for milk fat yield response, with CCC values in the range of 0.37 to 0.40, compared with milk lactose and protein yields responses for which CCC values were in the range of 0.75 to 0.81.

Efficiency of Crude Protein Utilisation in Grazing Dairy Cows: A Case Study Comparing Two Production Systems Differing in Intensification Level in New Zealand

Simple Summary

Improving the dietary crude protein utilisation in dairy cows is a key aspect of agronomically and environmentally sustainable production systems. The intensification process of grazing dairy systems identified with the increase of milking cows linked with the addition of supplementary feed along with the increasing use, and particularly inefficient use, of nitrogen fertiliser, has led to increasing pressure on the environment. However, feeding solely on pasture could result in an excess of crude protein intake relative to nutritional requirements, and this could reduce the dietary crude protein utilisation. In this study, we modelled the dietary crude protein utilisation, along with nitrogen excreta partitioning of milking cows, of two contrasting spring-calving pasture-based herds differing in intensification level in New Zealand. We found that feeding diets with higher fresh pasture proportions, such as those employed in low-intensification dairy systems, led to an excess of crude protein intake with greater nitrogen partitioned towards urine, which is sensitive in terms of body water eutrophication. In the high-intensity production system, the inclusion of low-crude protein supplements resulted in better dietary crude protein utilisation, along with less urinary nitrogen losses.

Abstract

In this study, we modelled and compared lactation curves of efficiency of crude protein utilisation (ECPU) and the nitrogen (N) excreta partitioning of milking cows of two contrasting spring-calving pasture-based herds to test some aspects of farming intensification practices on cow performance and N partition. In the low-intensity production system (LIPS), 257 cows were milked once-daily and fed diets comprised of pasture with low supplementary feed inclusion during lactation (304 kg pasture silage/cow). In the high-intensity production system (HIPS), 207 cows were milked twice-daily and fed pasture with higher supplementary feed inclusion (429 kg pasture silage and 1695 kg concentrate/cow). The dietary crude protein (CP) utilisation was calculated for each cow at every herd test date as the ECPU as a proportion of protein yield (PY) from the CP intake (CPI) derived from intake assessments based on metabolisable energy requirements, and the CP balance (CPB) calculated as the difference between CPI and PY. Total N excreta partitioned to faeces (FN) and urine (UN) was estimated by back-calculating UN from FN, considering dietary N, and from N retained in body tissues, taking into account live weight change during the lactation. The higher CPI (2.7 vs. 2.5 kg CP/day), along with the reduced milk yield (1100 kg milk/cow less), of the LIPS cows led to a lower ECPU (23% vs. 31%) and to a higher CPB (2.1 vs. 1.8 kg CP/day) when compared to the HIPS cows. Mean N excreta, and particularly UN, was significantly higher in LIPS cows, and this was explained by higher dietary CP and by the reduced PY when compared to the HIPS cows. Reducing the low-CP supplementation in the “de-intensified” herd lessened the ECPU, resulting in higher UN, which is sensitive in terms of body water eutrophication.

Symposium review: Decomposing efficiency of milk production and maximizing profit

The dairy industry has focused on maximizing milk yield, as it is believed that this maximizes profit mainly through dilution of maintenance costs. Efficiency of milk production has received, until recently, considerably less attention. The most common method to determine biological efficiency of milk production is feed efficiency (FE), which is defined as the amount of milk produced relative to the amount of nutrients consumed. Economic efficiency is best measured as income over feed cost or gross margin obtained from feed investments. Feed efficiency is affected by a myriad of factors, but overall they could be clustered as follows: (1) physiological status of the cow (e.g., age, state of lactation, health, level of production, environmental conditions), (2) digestive function (e.g., feeding behavior, passage rate, rumen fermentation, rumen and hindgut microbiome), (3) metabolic partitioning (e.g., homeorhesis, insulin sensitivity, hormonal profile), (4) genetics (ultimately dictating the 2 previous aspects), and (5) nutrition (e.g., ration formulation, nutrient balance). Over the years, energy requirements for maintenance seem to have progressively increased, but efficiency of overall nutrient use for milk production has also increased due to dilution of nutrient requirements for maintenance. However, empirical evidence from the literature suggests that marginal increases in milk require progressively greater marginal increases in nutrient supply. Thus, the dilution of maintenance requirements associated with increases in production is partially overcome by a progressive diminishing marginal biological response to incremental energy and protein supplies. Because FE follows the law of diminishing returns, and because marginal feed costs increase progressively with milk production, profits associated with improving milk yield might, in some cases, be considerably lower than expected.

Modeling homeorhetic trajectories of milk component yields, body composition and dry-matter intake in dairy cows: Influence of parity, milk production potential and breed

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.