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Abstract Background The frequent exchange of genetic material among prokaryotes means that extracting a majority or plurality phylogenetic signal from many gene families, and the identification of gene families that are in significant conflict with the plurality signal is a frequent task in comparative genomics, and especially in phylogenomic analyses. Decomposition of gene trees into embedded quartets (unrooted trees each with four taxa) is a convenient and statistically powerful technique to address this challenging problem. This approach was shown to be useful in several studies of completely sequenced microbial genomes. Results We present here a web server that takes a collection of gene phylogenies, decomposes them into quartets, generates a Quartet Spectrum, and draws a split network. Users are also provided with various data download options for further analyses. Each gene phylogeny is to be represented by an assessment of phylogenetic information content, such as sets of trees reconstructed from bootstrap replicates or sampled from a posterior distribution. The Quartet Decomposition server is accessible at http://quartets.uga.edu. Conclusions The Quartet Decomposition server presented here provides a convenient means to perform Quartet Decomposition analyses and will empower users to find statistically supported phylogenetic conflicts.

Abstract Phylogenetic reconstruction using DNA and protein sequences has allowed the reconstruction of evolutionary histories encompassing all life. We present and discuss a means to incorporate much of this rich narrative into a single model that acknowledges the discrete evolutionary units that constitute the organism. Briefly, this Rooted Net of Life genome phylogeny is constructed around an initial, well resolved and rooted tree scaffold inferred from a supermatrix of combined ribosomal genes. Extant sampled ribosomes form the leaves of the tree scaffold. These leaves, but not necessarily the deeper parts of the scaffold, can be considered to represent a genome or pan-genome, and to be associated with members of other gene families within that sequenced (pan)genome. Unrooted phylogenies of gene families containing four or more members are reconstructed and superimposed over the scaffold. Initially, reticulations are formed where incongruities between topologies exist. Given sufficient evidence, edges may then be differentiated as those representing vertical lines of inheritance within lineages and those representing horizontal genetic transfers or endosymbioses between lineages. Reviewers W. Ford Doolittle, Eric Bapteste and Robert Beiko.

Abstract Background Genome analysis of three Frankia sp. strains has revealed a high number of transposable elements in two of the strains. Twelve out of the 20 major families of bacterial Insertion Sequence (IS) elements are represented in the 148 annotated transposases of Frankia strain HFPCcI3 (CcI3) comprising 3% of its total coding sequences (CDS). EAN1pec (EAN) has 183 transposase ORFs from 13 IS families comprising 2.2% of its CDS. Strain ACN14a (ACN) differs significantly from the other strains with only 33 transposase ORFs (0.5% of the total CDS) from 9 IS families. Results Insertion sequences in the Frankia genomes were analyzed using BLAST searches, PHYML phylogenies and the IRF (Inverted Repeat Finder) algorithms. To identify putative or decaying IS elements, a PSI-TBLASTN search was performed on all three genomes, identifying 36%, 39% and 12% additional putative transposase ORFs than originally annotated in strains CcI3, EAN and ACN, respectively. The distribution of transposase ORFs in each strain was then analysed using a sliding window, revealing significant clustering of elements in regions of the EAN and CcI3 genomes. Lastly the three genomes were aligned with the MAUVE multiple genome alignment tool, revealing several Large Chromosome Rearrangement (LCR) events; many of which correlate to transposase clusters. Conclusion Analysis of transposase ORFs in Frankia sp. revealed low inter-strain diversity of transposases, suggesting that the majority of transposase proliferation occurred without recent horizontal transfer of novel mobile elements from outside the genus. Exceptions to this include representatives from the IS3 family in strain EAN and seven IS4 transposases in all three strains that have a lower G+C content, suggesting recent horizontal transfer. The clustering of transposase ORFs near LCRs revealed a tendency for IS elements to be associated with regions of chromosome instability in the three strains. The results of this study suggest that IS elements may help drive chromosome differences in different Frankia sp. strains as they have adapted to a variety of hosts and environments.

Abstract Background The mal genes that encode maltose transporters have undergone extensive lateral transfer among ancestors of the archaea Thermococcus litoralis and Pyrococcus furiosus. Bacterial hyperthermophiles of the order Thermotogales live among these archaea and so may have shared in these transfers. The genome sequence of Thermotoga maritima bears evidence of extensive acquisition of archaeal genes, so its ancestors clearly had the capacity to do so. We examined deep phylogenetic relationships among the mal genes of these hyperthermophiles and their close relatives to look for evidence of shared ancestry. Results We demonstrate that the two maltose ATP binding cassette (ABC) transporter operons now found in Tc. litoralis and P. furiosus (termed mal and mdx genes, respectively) are not closely related to one another. The Tc. litoralis and P. furiosus mal genes are most closely related to bacterial mal genes while their respective mdx genes are archaeal. The genes of the two mal operons in Tt. maritima are not related to genes in either of these archaeal operons. They are highly similar to one another and belong to a phylogenetic lineage that includes mal genes from the enteric bacteria. A unique domain of the enteric MalF membrane spanning proteins found also in these Thermotogales MalF homologs supports their relatively close relationship with these enteric proteins. Analyses of genome sequence data from other Thermotogales species, Fervidobacterium nodosum, Thermosipho melanesiensis, Thermotoga petrophila, Thermotoga lettingae, and Thermotoga neapolitana, revealed a third apparent mal operon, absent from the published genome sequence of Tt. maritima strain MSB8. This third operon, mal3, is more closely related to the Thermococcales' bacteria-derived mal genes than are mal1 and mal2. F. nodosum, Ts. melanesiensis, and Tt. lettingae have only one of the mal1-mal2 paralogs. The mal2 operon from an unknown species of Thermotoga appears to have been horizontally acquired by a Thermotoga species that had only mal1. Conclusion These data demonstrate that the Tc. litoralis and P. furiosus mdx maltodextrin transporter operons arose in the Archaea while their mal maltose transporter operons arose in a bacterial lineage, but not the same lineage as the two maltose transporter operons found in the published Tt. maritima genome sequence. These Tt. maritima maltose transporters are phylogenetically and structurally similar to those found in enteric bacteria and the mal2 operon was horizontally transferred within the Thermotoga lineage. Other Thermotogales species have a third mal operon that is more closely related to the bacterial Thermococcales mal operons, but the data do not support a recent horizontal sharing of that operon between these groups.

Adequate prediction of postruminal outflows of essential AA (EAA) is the starting point of balancing rations for EAA in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System version 6.5.5 [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict EAA outflows (Trp was not tested). The data set included a total of 354 treatment means from 70 duodenal and 24 omasal studies. To avoid Type I error, mean and linear biases were considered of concern if statistically significant and representing >5.0% of the observed mean. Analyses were conducted on raw observed values and on observations adjusted for the random effect of study. The analysis on raw data indicates the ability of the feed evaluation system to predict absolute values whereas the analysis on adjusted values indicates its ability to predict responses of EAA outflows to dietary changes. For the prediction of absolute values (based on raw data), NRC underpredicted outflows of all EAA, from 5.3% to 8.6% of the observed mean (% obs.mean ) except for Leu, Lys, and Val; NASEM overpredicted Lys (10.8% obs.mean ); and CNCPS overpredicted Arg, His, Lys, Met, and Val (5.2 to 26.0% obs.mean ). No EAA had a linear bias of concern with NASEM, followed by NRC for His (6.8% obs.mean ), and CNCPS for all EAA (5.6 to 12.2% obs.mean ) except Leu, Phe, and Thr. In contrast, for the prediction of responses to dietary changes (based on adjusted data), NRC had 2 EAA presenting a linear bias of concern, followed by NASEM and CNCPS with 4 and 6 EAA, respectively. Predictions of His showed a linear bias of concern (5.3 to 9.6% obs.mean ) with the 3 feed evaluation systems. Measured chemistry of crude protein and EAA were reported for 1 or more feed ingredients of the ration in 36% of the studies, and resulted in decreased linear biases in the 3 feed evaluation systems. The difference in mean biases of Met outflows was systematically positive when comparing omasal versus duodenal studies. Predictions of Met outflows with NRC had a higher concordance correlation coefficient in duodenal (used to develop NRC equations) versus omasal studies, whereas the opposite was observed with CNCPS, the latter showing the lowest mean bias for Met in omasal sampling studies. The 30% difference in Met mean biases between sampling sites appeared related to a similar difference found for observed Met versus nonammonia nitrogen outflows between duodenal and omasal studies, which is independent of predictions. In conclusion, NRC and NASEM yielded accurate predictions of EAA outflows, with a small superiority of NASEM to predict absolute values, and slight superiority of NRC to predict the responses to dietary changes. In comparison, CNCPS may present mean and linear biases of concern for many EAA. Moreover, it remains to determine which sampling site is more representative of the true supply of EAA to the cows., (Copyright © 2024 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Quantification of potassium (K) excretion in dairy cattle is important to understand the environmental impact of dairy farming. To improve and monitor the environmental impact of dairy cows, there is a need for a simple, inexpensive, and less laborious method to quantify K excretion on dairy farms. The adoption of empirical mathematical models has been shown to be a promising tool to address this issue. Thus, the current study aimed to develop empirical predictive models for K excretion in dairy cattle from urine and feces that can help evaluate efficiency and monitor the environmental impact of milk production. To develop urine K (K Ur , g/d) and fecal K (K Fa , g/d) excretion prediction models, published literature that involved 45 and 54 treatment means from 10 and 14 studies, respectively, were used. Some studies reported either urinary or fecal K excretion or both, but in total, treatment means used to develop the models were from 17 studies. The linear mixed models were fitted with the fixed effect of K intake, DMI, dietary K content, urine volume, milk yield, and water intake, and the random effect of study weighted according to the number of observations. Leave-one-study out cross-validation was used to evaluate the performance of the proposed models and the best model was based on the lowest root mean square prediction error as a percentage of the observed mean values (RMSPE%) and highest concordance correlation coefficient (CCC). As expected, most daily K excretion was through urine (202.5 ± 92.1 g/d) than through feces (43.5 ± 21.0 g/d), and among the proposed models, the model including dietary K concentration showed poor predictive ability for both K Ur and K Fa with the lowest CCC values (-0.15 and -0.02, respectively) and systematic bias. The model developed using DMI to predict K Fa excretion showed reasonable accuracy, as indicated by RMSPE, CCC, and R 2 marginal of 46.6%, 0.42, and 48%, respectively. Among the proposed models for K Ur and K Fa , the model with K intake demonstrated better predictive performance, showing minimal systematic bias and random errors due to data variability of >92%. While these proposed models suggested that reducing K intake can lead to a decrease in K excretion, it is important to ensure that dairy cows receive adequate amounts of this nutrient to maintain optimal health and productivity, especially during periods of heat stress., (© 2024.)

Adequate prediction of postruminal outflow of protein fractions is the starting point for the determination of metabolizable protein supply in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict outflows (g/d) of nonammonia nitrogren (NAN), microbial N (MiN), and nonammonia nonmicrobial N (NANMN). Predictions of rumen degradabilities (% of nutrient) of protein (RDP), NDF, and starch were also evaluated. The data set included 1,294 treatment means from 312 digesta flow studies. The 3 feed evaluation systems were compared using the concordance correlation coefficient (CCC), the ratio of root mean square prediction error (RMSPE) on standard deviation of observed values (RSR), and the slope between observed and predicted values. Mean and linear biases were deemed biologically relevant and are discussed if higher than a threshold of 5% of the mean of observed values. The comparisons were done on observed values adjusted or not for the study effect; the adjustment had a small effect on the mean bias but the linear bias reflected a response to a dietary change rather than absolute predictions. For the absolute predictions of NAN and MiN, CNCPS had the best-fit statistics (8% greater CCC; 6% lower RMSPE) without any bias; NRC and NASEM underpredicted NAN and MiN, and NASEM had an additional linear bias indicating that the underprediction of MiN increased at increased predictions. For NANMN, fit statistics were similar among the 3 feed evaluation systems with no mean bias; however, the linear bias with NRC and CNCPS indicated underprediction at low predictions and overprediction at elevated predictions. On average, the CCC were smaller and RSR ratios were greater for MiN versus NAN indicating increased prediction errors for MiN. For NAN responses to a dietary change, CNCPS also had the best predictions, although the mean bias with NASEM was not biologically relevant and the 3 feed evaluation systems did not present a linear bias. However, CNCPS, but not the 2 other feed evaluation systems, presented a linear bias for MiN, with responses being overpredicted at increased predictions. For NANMN, responses were overpredicted at increased predictions for the 3 feed evaluation systems, but to a lesser extent with NASEM. The site of sampling had an effect on the mean bias of MiN and NANMN in the 3 feed evaluation systems. The mean bias of MiN was higher in omasal than duodenal studies in the 3 feed evaluation systems (from 55 to 61 g/d) and this mean bias was twice as large when 15 N labeling was used as a microbial marker compared with purines. Such a difference was not observed for duodenal studies. The reasons underlying these systematic differences are not clear as the type of measurements used in the current meta-analysis does not allow to delineate if one site or one microbial marker is yielding the "true" postruminal N outflows. Rumen degradabilities of protein was underpredicted with CNCPS, and RDP responses to a dietary change was underpredicted by the 3 feed evaluation systems with increased RDP predictions. Rumen degradability of NDF was underpredicted and had poor fit statistics for NASEM compared with CNCPS. Fit statistics were similar between CNCPS and NASEM for rumen degradability of starch, but with an underprediction of the response with NASEM and absolute values being overpredicted with CNCPS. Multivariate regression analyses showed that diet characteristics were correlated with prediction errors of N outflows in each feed evaluation system. Globally, compared with NAN and NANMN, residuals of MiN were correlated with several moderators in the 3 feed evaluation systems reflecting the complexity to measure and model this outflow. In addition, residuals of NANMN were correlated positively with RDP suggesting an overestimation of this parameter. In conclusion, although progress is still to be made to improve equations predicting postruminal N outflows, the current feed evaluation systems provide sufficient precision and accuracy to predict postruminal outflows of N fractions., (Crown Copyright © 2023. Published by Elsevier B.V. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the Open Government Licence (https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/).)

Dairy cattle excreta are a valuable source of orthophosphate (Ortho-P), an inorganic form of phosphorus (P) that is readily available for microorganisms, plant growth, and development. There is, however, a growing environmental concern about the potential negative environmental impact of excessive amounts of Ortho-P excretion, which can lead to the eutrophication of water bodies. As a result, the development of mathematical equations to quantify and manage Ortho-P excretion on dairy farms could prove valuable for environmental sustainability. This study aimed to use literature data to develop empirical predictions for Ortho-P (g/kg dry matter [DM]) excretion using total P (TP [g/kg DM]) content of dairy cattle feces (Ortho-P f ) and manure (Ortho-P m ). Data sets from studies that evaluated and characterized the different forms of P in feces and manure from dairy cattle were compiled. After outlier exclusion, the final retained database for feces included 37 treatment means from 4 published papers while the manure comprised 23 treatment means from 7 published papers. A linear-mixed model was used to develop the predictive equations, incorporating the random effect of the study. A leave-one-out cross-validation procedure was used to evaluate the predictive ability of the developed models, whereby studies were regarded as folds. The fecal equation was determined as Ortho-P f (g/kg DM) = -2.447 (0.572) + 0.966 (0.083) × TP (g/kg DM) (R 2 = 0.79) and resulted in a root mean square prediction error as a percentage of mean observed value (RMSPE, %) of 32.8% and error due to random sources of 97.6%. Additionally, the manure equation was determined as Ortho-P m (g/kg) = -0.204 (0.446) + 0.590 (0.065) × TP (g/kg) (R 2 = 0.77) and had an RMSPE of 43.3% with a random error of 93.9%. Both models revealed minimal mean and slope biases on feces and manure data. Findings suggest that these sets of equations can be used to estimate excreted Ortho-P from total excreted P, helping nutritionists and farmers to understand the impact of dietary P changes on the environment. Further, these equations can be incorporated into extant models such as the Cornell Net Carbohydrate and Protein System (CNCPS) to aid in understanding and mitigating P and Ortho-P excretion from dairy cattle and to clarify the portion of P that migrates more rapidly into watersheds., (© 2023.)

Hypocalcemia induced by immune activation is a conserved response among mammals. Early postpartum cows will experience decreased circulating Ca concentrations following acute immune activation; however, the cause for decreased Ca concentration is unknown. Our objectives were to (1) describe Ca dynamics following an intravenous (IV) LPS challenge in early postpartum cows, and (2) compare inflammatory-induced changes in Ca dynamics between IV Ca-treated cows and control cows. Cows (n = 14, 8 ± 1 d in milk) were enrolled in a matched-pair randomized controlled design to receive IV Ca (IVCa) in a eucalcemic clamp for 12 h, or 0.9% NaCl (CTRL) following an IV LPS infusion (0.040 or 0.045 µg of LPS/kg of body weight over 1 h). During the 24 h following LPS infusion, circulating concentrations of parathyroid hormone and serotonin were measured, serum and urine samples were collected to calculate urinary fractional excretion of Ca (FECa), and fecal samples were collected to calculate Ca apparent digestibility (ADCa) using amylase-treated and ash-corrected undigested neutral detergent fiber after 240 h (uNDFom 240 ) as an internal marker. Changes in Ca intake and milk Ca secretion were also quantified and compared with baseline values. Cows were fasted during challenge and dry matter intake was 20 ± 5% less than baseline values on the day of challenge and did not differ between groups. On the day of challenge, milk Ca concentration increased, but milk yield decreased such that total Ca secreted in milk did not change from baseline. Urine FECa was low overall, but an interaction of treatment and time was identified such that FECa increased in IVCa but decreased in CTRL. Concentrations of parathyroid hormone increased and serotonin decreased following challenge. Fecal dry matter decreased from baseline, but did not differ between 6, 12, and 24 h, and did not differ between groups. An interaction of treatment and time was identified for ADCa and apparent digestibility of dry matter such that digestibility was decreased in CTRL but not IVCa at 6 h. Acute immune activation induced hypocalcemia in CTRL, and although urinary Ca excretion was not a primary cause, it is unclear to what degree hypocalcemia was due to altered ADCa. Eucalcemia appeared to alter adaptations in Ca homeostasis during immune activation as FECa was increased in IVCa animals., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Greenhouse gas emissions, such as enteric methane (CH 4 ) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH 4 management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH 4 emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH 4 production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH 4 production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH 4 production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper's inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH 4 emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH 4 are needed. In order to explore CH 4 predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH 4 production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH 4 production. All databases observed an improvement in prediction abilities in CH 4 production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH 4 yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH 4 emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH 4 production prediction in dairy cattle.

Improving the ability of diet formulation models to more accurately predict AA supply while appropriately describing requirements for lactating dairy cattle provides an opportunity to improve animal productivity, reduce feed costs, and reduce N intake. The goal of this study was to evaluate the sensitivity of a new version of the Cornell Net Carbohydrate and Protein System (CNCPS) to formulate diets for rumen N, Met, and all essential AA (EAA). Sixty-four high-producing dairy cattle were randomly assigned to 1 of the 4 following diets in a 14-wk longitudinal study: (1) limited metabolizable protein (MP), Met, and rumen N (Base), (2) adequate Met but limited MP and rumen N (Base + M), (3) adequate Met and rumen N, but limited MP (Base + MU), and (4) adequate MP, rumen N, and balanced for all EAA (Positive). All diets were balanced to exceed requirements for ME relative to maintenance and production, assuming a nonpregnant, 650-kg animal producing 40 kg of milk at 3.05% true protein and 4.0% fat. Dietary MP was 97.2, 97.5, 102.3, and 114.1 g/kg of dry matter intake for the Base, Base + M, Base + MU, and Positive diets, respectively. Differences were observed for dry matter intake and milk yield (24.1 to 24.7 and 39.4 to 41.1 kg/d, among treatments). Energy corrected milk, fat, and true protein yield were greater (2.9, 0.13, and 0.08 kg/d, respectively) in cows fed the Positive compared with the Base diet. Using the updated CNCPS, cattle fed the Base, Base + M, and Base + MU diets were predicted to have a negative MP balance (-231, -310, and -142 g/d, respectively), whereas cattle fed the Positive diet consumed 33 g of MP/d excess to ME supply. Bacterial growth was predicted to be depressed by 16 and 17% relative to adequate N supply for the Base and Base + M diets, respectively, which corresponded with the measured lower apparent total-tract NDF degradation. The study demonstrates that improvements in lactation performances can be achieved when rumen N and Met are properly supplied and further improved when EAA supply are balanced relative to requirements. Formulation using the revised CNCPS provided predictions for these diets, which were sensitive to changes in rumen N, Met, all EAA, and by extension MP supply., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objective of this study was to evaluate the effect of rolled barley grain (RB) supplementation on rumen metabolism, omasal flow of nutrients, and microbial dynamics in lactating dairy cows fed fresh perennial ryegrass (Lolium perenne L.; PRG)-based diets. Ten ruminally cannulated Holstein cows averaging (mean ± standard deviation) 49 ± 23 d in milk and 513 ± 36 kg of body weight were assigned to 1 of 2 treatments in a switchback design. The treatment diets were PRG only (G) or PRG plus 3.5 kg of dry matter RB (G+RB). The study consisted of three 29-d periods where each period consisted of 21 d of diet adaptation and 8 d of data and sample collection. A double marker system was used to quantify nutrient flow entering the omasal canal along with labeled 15 N-ammonium sulfate to measure bacterial, protozoal, and nonmicrobial N flow. Rumen evacuation techniques were used to determine nutrient and microbial pool size, allowing the calculation of fractional rates of digestion and microbial growth. There was no difference in daily milk yield or energy-corrected milk yield between treatments. Milk fat concentration and milk urea N decreased, whereas milk protein concentration increased in cows fed the G+RB diet. During the omasal sampling phase, dry matter intake was higher in cows fed the G+RB diet. Ruminal and total-tract neutral detergent fiber digestibility was lower in G+RB cows; however, no difference was observed in reticulorumen pH. The rumen pool size of fermentable carbohydrate was increased in cows fed the G+RB diet; however, the fractional rate of digestion was decreased. Flow of nonammonia N and bacterial N at the omasal canal increased in cows fed the G+RB diet compared with the G diet. Protozoa N flow was not different between diets; however, protozoa appeared to supply a much larger amount of microbial N and exhibited shorter generation time than previously considered. Feed N ruminal digestibility, corrected for microbial contribution, was similar for both treatments (88.4 and 89.0% for G and G+RB, respectively). In conclusion, RB supplementation did not benefit overall animal performance; however, it reduced ruminal neutral detergent fiber digestibility and increased bacterial N flow. The results demonstrate the large dependence of cows consuming PRG-based diets on microbial N as the main source of nonammonia N supply. Additional quantitative research is required to further describe the supply of nutrients and microbial dynamics in cows consuming PRG-based diets in an effort to determine most limiting nutrients., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Strategies that can improve health and maximize growth in the preweaning period should improve the subsequent production and longevity of replacement animals. Few data are available that quantify feed and water consumption, as well as growth, in healthy versus non-healthy calves-the objective of this study. A database of Holstein calves (<1 wk of age; n = 313) was developed to compare calves that developed diarrhea in the first 21 d after arrival from commercial farms to the research facility versus calves that remained healthy. Individual calf data from 4 experiments included daily intake of milk replacer, free water, electrolyte solution, and starter grain, as well as weekly body weight (BW) and frame measures for 21 d after arrival. Calves with a fecal score of >2 for ≥3 consecutive days over the first 21 d of each experiment were retrospectively classified as diarrheic (DIA; n = 96); the remainder were classified as healthy (HEA; n = 217). Other health issues were minimal. The likelihood of elevated fecal score occurrence and the cumulative number of days with an elevated score were greater for DIA calves than for HEA calves. The initial total protein concentration in blood did not differ between classifications. Cumulative milk replacer dry matter intake (DMI) and water consumed from milk replacer were significantly less for DIA calves than for HEA calves, because DIA calves were more likely to refuse milk replacer. Cumulative starter DMI was decreased for DIA versus HEA calves. As a result, cumulative total DMI was significantly less for DIA calves than for HEA calves. Cumulative free water intake did not differ between classifications. The DIA calves were more likely to receive electrolyte solution and have more days given electrolyte solution than HEA calves. As a result, total cumulative intake of electrolyte solution was greater in DIA calves than in HEA calves. Cumulative total water intake did not differ between classifications. Initial BW did not differ between classifications; however, a classification × time interaction for BW indicated that HEA calves were heavier than DIA calves and had greater ADG. Significant classification × time interactions for hip height and heart girth revealed that HEA calves had a larger frame size. Gain-feed ratios for both milk replacer intake and total DMI differed between classifications: DIA calves were less feed-efficient than HEA calves. In conclusion, diarrhea in young calves decreases DMI, BW gain, and feed efficiency relative to HEA calves within 21 d of arrival., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)