Development of an improved representation of rumen digesta outflow in a mechanistic and dynamic model of a dairy cow, Molly

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

Numerous empirical and mechanistic models predicting methane (CH₄) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH₄ production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH₄ production were not included in the data set. The relationships between observed and predicted CH₄ (pCH₄) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH₄ (g/d) = 0.94 (±0.022) × pCH₄ (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH₄ (g/d) = CH₄ (g/d) = 0.98 (±0.019) × pCH₄ (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RMSPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, respectively. The residuals of CH₄ predictions were more strongly related to factors associated with CH₄ production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH₄ yield with the Molly model. It was concluded that both models could be used for prediction of CH₄ production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

Evaluation of Molly model predictions of ruminal fermentation, nutrient digestion, and performance by dairy cows consuming ryegrass-based diets

Several studies have been conducted to improve grazing management and supplementation in pasture-based systems. However, it is necessary to develop tools that integrate the available information linking the representation of biological processes with animal performance for use in decision making. The objective of this study was to evaluate the precision and accuracy of the Molly cow model predictions of ruminal fermentation, nutrient digestion, and animal performance by cows consuming pasture-based diets to identify model strengths and weaknesses, and to derive new digestive parameters when relevant. Model modifications for adipose tissue, protein synthesis in lean body mass and viscera representation were included. Data used for model evaluations were collected from 25 publications containing 115 treatment means sourced from studies conducted with lactating dairy cattle. The inclusion criteria were that diets contained ≥45% perennial ryegrass (Lolium perenne L.), and that dry matter intake, dietary ingredient composition, and nutrient digestion observations were reported. Animal performance and N excretion variables were also included if they were reported. Model performance was assessed before and after model reparameterization of selected digestive parameters, global sensitivity analysis was conducted after reparameterization, and a 5-fold cross evaluation was performed. Although rumen fermentation predictions were not significantly improved, rumen volatile fatty acids absorption rates were recalculated, which improved the concordance correlation coefficient (CCC) for rumen propionate and ammonia concentration predictions but decreased CCC for acetate predictions. Similar degradation rates of crude protein were observed for grass and total mixed ration diets, but rumen-undegradable protein predictions seemed to be affected by the solubility of the protein source as was the intestinal digestibility coefficient. Ruminal fiber degradation was greater after reparameterization, driven primarily by hemicellulose degradation. Predictions of ruminal and fecal outflow of neutral detergent fiber and acid detergent fiber, as well as total fecal output predictions, improved significantly after reparameterization. Blood urea N and urinary N excretion predictions resulted in similar accuracy using both sets of model parameters, whereas fecal N excretion predictions were significantly improved after reparameterization. Body weight and body condition score predictions were greatly improved after model modifications and reparameterization. Before reparameterization, yield predictions for daily milk, milk fat, milk protein, and milk lactose were greatly overestimated (mean bias of 61.0, 58.7, 73.7, and 64.6% of mean squared error, respectively). Although this problem was partially addressed by model modifications and reparameterization (mean bias of 3.2, 1.1, 1.7, and 0.4% of mean squared error, respectively), CCC values were still small. The ability of the model to predict grass digestion and animal performance in dairy cows consuming pasture-based diets was improved, demonstrating the applicability of this model to these productive systems. However, the failure to predict grass digestion based on standard model inputs without reparameterization indicates there are still fundamental challenges in characterizing feeds for this model.

Metatranscriptomic analyses reveal ruminal pH regulates fiber degradation and fermentation by shifting the microbial community and gene expression of carbohydrate-active enzymes

Background

Volatile fatty acids (VFA) generated from ruminal fermentation by microorganisms provide up to 75% of total metabolizable energy in ruminants. Ruminal pH is an important factor affecting the profile and production of VFA by shifting the microbial community. However, how ruminal pH affects the microbial community and its relationship with expression of genes encoding carbohydrate-active enzyme (CAZyme) for fiber degradation and fermentation are not well investigated. To fill in this knowledge gap, six cannulated Holstein heifers were subjected to a continuous 10-day intraruminal infusion of distilled water or a dilute blend of hydrochloric and phosphoric acids to achieve a pH reduction of 0.5 units in a cross-over design. RNA-seq based transcriptome profiling was performed using total RNA extracted from ruminal liquid and solid fractions collected on day 9 of each period, respectively.

Results

Metatranscriptomic analyses identified 19 bacterial phyla with 156 genera, 3 archaeal genera, 11 protozoal genera, and 97 CAZyme transcripts in sampled ruminal contents. Within these, 4 bacteria phyla (Proteobacteria, Firmicutes, Bacteroidetes, and Spirochaetes), 2 archaeal genera (Candidatus methanomethylophilus and Methanobrevibacter), and 5 protozoal genera (Entodinium, Polyplastron, Isotricha, Eudiplodinium, and Eremoplastron) were considered as the core active microbes, and genes encoding for cellulase, endo-1,4-beta- xylanase, amylase, and alpha-N-arabinofuranosidase were the most abundant CAZyme transcripts distributed in the rumen. Rumen microbiota is not equally distributed throughout the liquid and solid phases of rumen contents, and ruminal pH significantly affect microbial ecosystem, especially for the liquid fraction. In total, 21 bacterial genera, 4 protozoal genera, and 6 genes encoding CAZyme were regulated by ruminal pH. Metabolic pathways participated in glycolysis, pyruvate fermentation to acetate, lactate, and propanoate were downregulated by low pH in the liquid fraction.

Conclusions

The ruminal microbiome changed the expression of transcripts for biochemical pathways of fiber degradation and VFA production in response to reduced pH, and at least a portion of the shifts in transcripts was associated with altered microbial community structure.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00092-6.

A revised representation of ruminal pH and digestive reparameterization of the Molly cow model

Ruminal pH is a critical factor to regulate nutrient degradation and fermentation. However, it has been poorly predicted in the Molly cow model, and recent improvements in the representation of nitrogen cycling across the rumen wall altered some of the modeled responses to feed nutrients, resulting in some model bias. The objectives of this study were to further improve the representation of pH and to refit parameters related to ruminal metabolism and nutrient digestion in the model to resolve this bias, and to use the improved model to estimate nitrogen and energy fluxes with varying rumen-degradable protein (RDP; 40 vs. 60%) and ruminally degraded starch (RDSt; 50 vs. 75%). A meta data set containing 284 peer reviewed studies with 1,223 treatment means was used to derive parameter estimates for ruminal metabolism and nutrient digestions. Refitting the parameters significantly improved the accuracy and precision of the model predictions for ruminal nutrient outflow [acid detergent fiber (ADF), neutral detergent fiber (NDF), total N, microbial N, nonammonia N, and nonammonia nonmicrobial N], ammonia and blood urea concentrations, and fecal nutrient outflow (protein, ADF, and NDF). The prediction error for body weight was decreased from 19.3 to 6.2% with decreased mean bias (from 76.0 to 11.5%) and slope bias (from 17.2 to 7.7%), primarily due to improved representations of ruminal dry matter and liquid pool size. Adding ammonia concentration as a driver to the pH equation increased the precision of predicted ruminal pH and, thereby, the precision of predicted volatile fatty acid (VFA) concentrations, due to improved representation of pH regulation of VFA production rates. Although minor mean and slope bias were observed for ruminal pH and VFA concentrations, the concordance correlation coefficients indicated that much of the observed variation in these variables remains unexplained. Overall, the biological functions of nutrient degradation and digestion appear to be represented without bias. Simulated results indicated that decreasing RDP and RDSt proportions in an isonitrogenous and isocaloric diet can slightly improve N efficiency, and increasing RDSt proportions can increase energy efficiency.

Rumen Virus Populations: Technological Advances Enhancing Current Understanding

The rumen contains a multi-kingdom, commensal microbiome, including protozoa, bacteria, archaea, fungi and viruses, which enables ruminant herbivores to ferment and utilize plant feedstuffs that would be otherwise indigestible. Within the rumen, virus populations are diverse and highly abundant, often out-numbering the microbial populations that they both predate on and co-exist with. To date the research effort devoted to understanding rumen-associated viral populations has been considerably less than that given to the other microbial populations, yet their contribution to maintaining microbial population balance, intra-ruminal microbial lysis, fiber breakdown, nutrient cycling and genetic transfer may be highly significant. This review follows the technological advances which have contributed to our current understanding of rumen viruses and drawing on knowledge from other environmental and animal-associated microbiomes, describes the known and potential roles and impacts viruses have on rumen function and speculates on the future directions of rumen viral research.

Molly reborn in C++ and R

The dairy cow model 'Molly' is a mixed discrete event-continuous system model that simulates feeding, metabolism and lactation of dairy cows. Decades of model development have resulted in a valuable tool in dairy science. Due to the deprecation of the ACSL (Advanced Continuous Simulation Language) programming language, Molly has been translated into C++. This paper describes the translation process and discusses the advantages of the new implementation, one of which is the ability to run Molly within RStudio, a popular integrated development environment (IDE) for data science.

Use of Mechanistic Nutrition Models to Identify Sustainable Food Animal Production

To feed people in the coming decades, an increase in sustainable animal food production is required. The efficiency of the global food production system is dependent on the knowledge and improvement of its submodels, such as food animal production. Scientists use statistical models to interpret their data, but models are also used to understand systems and to integrate their components. However, empirical models cannot explain systems. Mechanistic models yield insight into the mechanism and provide guidance regarding the exploration of the system. This review offers an overview of models, from simple empirical to more mechanistic models. We demonstrate their applications to amino acid transport, mass balance, whole-tissue metabolism, digestion and absorption, growth curves, lactation, and nutrient excretion. These mechanistic models need to be integrated into a full model using big data from sensors, which represents a new challenge. Soon, training in quantitative and computer science skills will be required to develop, test, and maintain advanced food system models.

Theoretical turnover rate of the rumen liquid fraction in dairy cows and its relationship to feed intake, rumen fermentation, and milk production

Ruminant animals are able to convert plant materials (grain and the human-indigestible portion of carbohydrates) to milk and meat. In this conversion, most of the plant materials are digested by rumen fermentation and are changed to short-chain fatty acids, microbial cells, and methane, which is released into the atmosphere. The relationships among feed, rumen fermentation, and milk production are poorly understood. Here we report a novel indicator of characteristics of rumen fermentation, theoretical turnover rate (TTOR) of the rumen liquid fraction. The TTOR was calculated from the presumed rumen volume (PRV) which is estimated by dividing the methane yield by the methane concentration of rumen fluid. The formula for the TTOR is: TTOR = PRV/body weight^0.75 . Our present analyses confirm that the TTOR as an indicator is capable of connecting feed, rumen fermentation, and milk production, because dry matter intake/TTOR showed a strong correlation with milk yield/TTOR. In addition, the TTOR may be related to ruminal pH, as we observed that the ruminal pH decreased as the TTOR increased. We propose that the TTOR is a factor characterizing rumen fermentation and a good indicator of the productivity of ruminants and dysbiosis of the rumen microbiome.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

The assessment of supplementation requirements of grazing ruminants using nutrition models

This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.

A revised representation of urea and ammonia nitrogen recycling and use in the Molly cow model

Accurately predicting nitrogen (N) digestion, absorption, and metabolism will allow formulation of diets that more closely match true animal needs from a broad range of feeds, thereby allowing efficiency of N utilization and profit to be maximized. The objectives of this study were to advance representations of N recycling between blood and the gut and urinary N excretion in the Molly cow model. The current work includes enhancements (1) representing ammonia passage to the small intestine; (2) deriving parameters defining urea synthesis and ruminal urea entry rates; (3) adding representations of intestinal urea entry, microbial protein synthesis in the hindgut, and fecal urea-N excretion; and (4) altering existing urinary N excretion equations to scale with body weight and adding purine derivatives as a component of urinary N excretion. After the modifications, prediction errors for ruminal outflows of total N, microbial N, and nonammonia, nonmicrobial N were 29.8, 32.3, and 26.2% of the respective observed mean values. Prediction errors of each were approximately 7 percentage units lower than the corresponding values before model modifications and fitting due primarily to decreased slope bias. The revised model predicted ruminal ammonia and blood urea concentrations with substantially decreased overall error and reductions in slope and mean bias. Prediction errors for gut urea-N entry were decreased from 70.5 to 26.7%, which was also a substantial improvement. Adding purine derivatives to urinary N predictions improved the accuracy of predictions of urinary N output. However, urinary urea-N excretion remains poorly predicted with 69.0% prediction errors, due mostly to overestimated urea-N entry rates. Adding representations of undigested microbial nucleic acids, microbial protein synthesized in the hindgut, and urea-N excretion in feces decreased prediction errors for fecal N excretion from 21.1 to 17.1%. The revised model predicts that urea-N entry into blood accounts for approximately 64% of dietary N intake, of which 64% is recycled to the gut lumen. Between 48 and 67% of the urea recycled to the gut flows into the rumen largely depending on diet, which accounts for 29 to 54% of total ruminal ammonia production, and 65 to 76% of this ammonia-N is captured in microbial protein, which represents 17% of N intake. Based on model simulations, feeding a diet with moderately low crude protein and high rumen-undegradable protein could increase apparent ruminal N efficiency by 20%.

Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in “omic” data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent “omics” approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

The performance of an efficient dairy system using a combination of nitrogen leaching mitigation strategies in a variable climate

An efficient dairy system, that implemented a combination of nitrogen (N) leaching mitigation strategies including lower N fertilizer input, standing cows off pasture for part of the day in autumn and winter (stand-off), and importing limited amounts of low protein supplements was evaluated over four consecutive years of a farmlet study. This efficient system consistently demonstrated a lower measured annual N leaching of 40 to 50% compared with a baseline system representing current practice with no mitigations. To maximize return from this system fewer cows but of higher genetic merit were used resulting in an average decrease in milk production of 2% and operating profit by 5% compared with the baseline system. The magnitude of the N leaching reduction from mitigation strategies was predicted in pre-trial modelling. Using similar mechanistic models in a post-trial study, we were able to satisfactorily predict the trends in the observed N leaching data over the four years. This enabled us to use the calibrated models to explore the contributions of the different mitigation strategies to the overall leaching reduction in the efficient system. In one of the years half of the leaching reduction was achieved by the 'input' component of the strategy (less feed N flowing through the herd from lower fertilizer use, less grass grown, and low-protein supplement use), while the other half was achieved by the stand-off strategy. However, these contributions are determined by the weather of a particular year. We estimate that on average stand-off would contribute 60% and 'input' 40% to the reduction. The implication is that farmers facing nutrient loss limitations have some current and some future technologies available to them for meeting these limitations. A shift towards the mitigations described here can result in a downward trend in their own N-loss metrics. The challenge will be to negate any reductions in production and profit, and remain competitive.

The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction

Several attempts have been made to quantify microbial protein flow from the rumen; however, few studies have evaluated tradeoffs between empirical equations (microbial N as a function of diet composition) and more mechanistic equations (microbial N as a function of ruminal carbohydrate digestibility). Although more mechanistic approaches have been touted because they represent more of the biology and thus might behave more appropriately in extreme scenarios, their precision is difficult to evaluate. The objective of this study was to derive equations describing starch, neutral detergent fiber (NDF), and organic matter total-tract and ruminal digestibilities; use these equations as inputs to equations predicting microbial N (MicN) production; and evaluate the implications of the different calculation methods in terms of their precision and accuracy. Models were evaluated based on root estimated variance σˆe and concordance correlation coefficients (CCC). Ruminal digestibility of NDF was positively associated with DMI and concentrations of NDF and CP and was negatively associated with concentration of starch and the ratio of acid detergent fiber to NDF (CCC=0.946). Apparent ruminal starch digestibility was increased by omasal sampling (compared with duodenal sampling), was positively associated with forage NDF and starch concentrations, and was negatively associated with wet forage DMI and total dietary DMI (CCC=0.908). Models were further evaluated by calculating fit statistics from a common data set, using stochastic simulation, and extreme scenario testing. In the stochastic simulation, variance in input variables were drawn from a multi-variate random normal distribution reflective of input measurement errors and predicting MicN while accounting for the measurement errors. Extreme scenario testing evaluated each MicN model against a data subset. When compared against an identical data set, predicting MicN empirically had the lowest prediction error, though differences were slight (σˆe 23.3% vs. 23.7 or 24.3%), and highest concordance (0.52 vs. 0.48 or 0.44) of any approach. Minimal differences were observed between empirical MicN prediction (σˆe 25.3%; CCC 0.530) and MicN prediction (σˆe 25.3%; CCC 0.532) from rumen carbohydrate digestibility in the stochastic analysis or extreme scenario testing. Despite the hypothesized benefits of a more mechanistic prediction approach, few differences between the calculation approaches were identified.

Screening for diets that reduce urinary nitrogen excretion and methane emissions while maintaining or increasing production by dairy cows

Farmers face complex decisions at the time to feed animals, trying to achieve production goals while contemplating social and environmental constraints. Our purpose was to facilitate such decision making for pastoral dairy farmers, aiming to reduce urinary N (UN) and methane emissions (CH4), while maintaining or increasing milk production (MP). There is a number of feeds the farmers can choose from and combine. We used 50 feeds (forages and grains) combined systematically in different proportions producing 11,526 binary diets. Diets were screened, using an a posteriori approach and a Pareto front (PF) analysis of model (Molly) outputs. The objective was to identify combinations with the best possible compromise (i.e. frontier) between UN, CH4, and MP. Using high MP and low UN as objective functions, PF included 10, 14, 12 and 50 diets, for non-lactating, early-, mid- and late-lactation periods, with cereals and beets featuring strongly. Using the same objective functions, but including ryegrass as dietary base PF included 2, 4, 8 and 4 diets for those periods. Therefore, from a wide range of diets, farmers could choose from few feeds combined into binary diets to reduce UN while maintaining or increasing MP. If the intention is maintaining pasture-based systems, there are fewer suitable options. Reducing UN will simply require dilution of N supplied by pasture by supplementing low N conserved forages. The results also evidence the risk of pollution swapping, reaching the frontier means arriving at a point where trade-off decisions need to be made. Any further reduction in UN implies an increment in CH4, or reduction in CH4 emissions increases UN. There is no perfect diet to optimize all objectives simultaneously; but if the current diet is not in the frontier some options can offset pollution swapping. The choice is with the farmers and conditioned by their context.