Mathematical models describing disappearance of Lucerne hay in the rumen using the nylon bag technique

It is essential to study the dynamics of rumen degradation of feeds before their potential use in formulating diets for ruminants. Various mathematical models have been developed to describe this degradation. The non-lagged exponential model (Model I), the lagged exponential model (Model II), the Gompertz model (Model III), and the generalized Mitscherlich model (Model IV) were examined using two alternative software (SAS and MATLAB) to determine their efficacy in accounting for variation in ruminal disappearance of dry matter (DM) and crude protein (CP) of lucerne hay from three cuttings. All models described DM degradability well (R2 >0.98). Only Models I and II converged when fitted to CP degradability data (R2 >0.98). It was concluded that any of these models could be used to describe the degradation of DM, whereas only Models I and II could be used to describe the degradation of CP from three cuttings of Lucerne hay. All the models that were fitted to the DM degradation data performed reasonably well, with only minor differences in goodness of fit. However, these models differed in values of the parameter estimates. Additionally, SAS failed to converge in the analyses of CP with Models III and IV, and MATLAB converged to nonsensical values with Model III. Model I might be recommended because it fitted the data well and required estimates of the fewest parameters Keywords: alfalfa hay, in situ digestion, model selection, nonlinear regressio

Energy utilization and milk fat responses to rapeseed oil when fed to lactating dairy cows receiving different dietary forage to concentrate ratio

We evaluated energy and N utilization, performance, and milk fatty acid (FA) profile using grass silage-based diets when rapeseed oil (RO) was included in high- or low-forage diets. Four multiparous Nordic Red cows averaging 101 ± 16 days in milk at the beginning of the study were randomly assigned to a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments. Each 21-d period consisted of a 14-d diet adaptation period and 7-d collection period. Cows were fed the following diets comprised total mixed rations based on grass silage with forage to concentrate (FC) ratio of 35:65 and 65:35 containing 0 or 50 g/kg of RO. Significant FC × RO interactions were observed for milk yield, milk protein and lactose yields, milk fat concentration, and milk proportions of trans-11 18:1, trans-10 18:1, trans-10, cis-12 18:2, and saturated FA. Feeding low-forage diet was effective in increasing milk yield compared with the high-forage diet, and the RO supplementation increased it further (P ≤ 0.01). A similar pattern was observed for the yields of milk protein and lactose. Supplementing the low-forage diet with RO reduced milk fat concentration by 19% relative to other diets without affecting milk fat yield. The proportion of N intake lost as urine decreased (P ≤ 0.05) with the RO supplementation of low-forage diet without affecting energy and N balances. Nutrient intakes were greater (P ≤ 0.01) in cows fed low-forage diet, whereas RO decreased (P < 0.05) protein, starch, and fiber intakes. Methane production, expressed as a proportion of energy intake, decreased with low-forage compared with high-forage diets and this variable declined similarly by RO supplementation of both diets (P < 0.01). The milk proportions of trans-10 18:1 and trans-10, cis-12 CLA increased (P ≤ 0.01) by RO supplementation of the low-forage but not high-forage diet. However, RO supplementation of both high- and low-forage diets increased (P < 0.01) total trans FA and decreased saturated FA proportions, even though the changes were more profound in low-forage diet (P ≤ 0.01). In addition, RO increased (P < 0.01) cis monounsaturated FA in milk for both high- and low-forage diets. Overall, the low-forage diets had lower methane emissions and RO increased partitioning of N towards milk secretion (P ≤ 0.01) without influencing energy or N balances. According to the results, RO supplementation did not compromise intake of nutrients with low-forage diets containing 150 g/kg starch, and oil could be preferentially used to improve milk production and milk fat quality accompanied by a reduction in methane energy loss

The effects of energy metabolism variables on feed efficiency in respiration chamber studies with lactating dairy cows

The objective of the present study was to investigate factors related to variation in feed efficiency (FE) among cows. Data included 841 cow/period observations from 31 energy metabolism studies assembled across 3 research stations. The cows were categorized into low-, medium-, and high-FE groups according to residual feed intake (RFI), residual energy-corrected milk (RECM), and feed conversion efficiency (FCE). Mixed model regression was conducted to identify differences among the efficiency groups in animal and energy metabolism traits. Partial regression coefficients of both RFI and RECM agreed with published energy requirements more closely than cofficients derived from production experiments. Within RFI groups, efficient (Low-RFI) cows ate less, had a higher digestibility, produced less methane (CH4) and heat, and had a higher efficiency of metabolizable energy (ME) utilization for milk production. High-RECM (most efficient) cows produced 6.0 kg/d more of energy-corrected milk (ECM) than their Low-RECM (least efficient) contemporaries at the same feed intake. They had a higher digestibility, produced less CH4 and heat, and had a higher efficiency of ME utilization for milk production. The contributions of improved digestibility, reduced CH4, and reduced urinary energy losses to increased ME intake at the same feed intake were 84, 12, and 4%, respectively. For both RFI and RECM analysis, increased metabolizability contributed to approximately 35% improved FE, with the remaining 65% attributed to the greater efficiency of utilization of ME. The analysis within RECM groups suggested that the difference in ME utilization was mainly due to the higher maintenance requirement of Low-RECM cows compared with Medium- and High-RECM cows, whereas the difference between Medium- and High-RECM cows resulted mainly from the higher efficiency of ME utilization for milk production in High-RECM cows. The main difference within FCE (ECM/DMI) categories was a greater (8.2 kg/d) ECM yield at the expense of mobilization in High-FCE cows compared with Low-FCE cows. Methane intensity (CH4/ECM) was lower for efficient cows than for inefficient cows. The results indicated that RFI and RECM are different traits. We concluded that there is considerable variation in FE among cows that is not related to dilution of maintenance requirement or nutrient partitioning. Improving FE is a sustainable approach to reduce CH4 production per unit of product, and at the same time improve the economics of milk production.202

Between-cow variation in the components of feed efficiency

A meta-analysis based on an individual-cow data set was conducted to investigate between-cow variations in the components and measurements of feed efficiency (FE) and to explore the associations among these components. Data were taken from 31 chamber studies, consisting of a total of 841 cow/period observations. The experimental diets were based on grass or corn silages, fresh grass, or a mixture of fresh grass and straw, with cereal grains or by-products as energy supplements, and soybean or canola meal as protein supplements. The average forage-to-concentrate ratio across all diets on a dry matter basis was 56:44. Variance component and repeatability estimates of FE measurements and components were determined using diet, period, and cow within experiment as random effects in mixed procedures of SAS (SAS Institute Inc., Cary, NC). The between-cow coefficient of variation (CV) in gross energy intake (GE; CV = 0.10) and milk energy (El) output as a proportion of GE (El/GE; CV = 0.084) were the largest among all component traits. Similarly, the highest repeatability estimates (≥0.50) were observed for these 2 components. However, the between-cow CV in digestibility (DE/GE), metabolizability [metabolizable energy (ME)/GE], methane yield (CH4E/GE), proportional urinary energy output (UE/GE), and heat production (HP/GE), as well as the efficiency of ME use for lactation (kl), were rather small. The least repeatable component of FE was UE/GE. For FE measurements, the between-cow CV in residual energy-corrected milk (RECM) was larger than for residual feed intake (RFI), suggesting a greater possibility for genetic gain in RECM than in RFI. A high DE/GE was associated with increased CH4E/GE (r = 0.24), HP/GE (r = 0.12), ME/GE (r = 0. 91), energy balance as a proportion of GE (EB/GE; r = 0.35), and kl (r = 0.10). However, no correlation between DE/GE and GE intake or UE/GE was observed. Increased proportional milk energy adjusted to zero energy balance (El(0)/GE) was associated with increases in DE/GE, ME/GE, EB/GE, and kl but decreases in UE/GE, CH4E/GE, and HP/GE, with no effect on GE intake. In conclusion, several mechanisms are involved in the observed differences in FE among dairy cows, and reducing CH4E yield (CH4E/GE) may inadvertently result in reduced GE digestibility. However, the selection of dairy cows with improved energy utilization efficiencies offers an effective approach to lower enteric CH4 emissions.202

Performance enhancement of safety message communication via designing dynamic power control mechanisms in vehicular ad hoc networks

In vehicular ad hoc networks (VANETs), transmission power is a key factor in several performance measures, such as throughput, delay, and energy efficiency. Vehicle mobility in VANETs creates a highly dynamic topology that leads to a nontrivial task of maintaining connectivity due to rapid topology changes. Therefore, using fixed transmission power adversely affects VANET connectivity and leads to network performance degradation. New cross-layer power control algorithms called (BL-TPC 802.11MAC and DTPC 802.11 MAC) are designed, modeled, and evaluated in this paper. The designed algorithms can be deployed in smart cities, highway, and urban city roads. The designed algorithms improve VANET performance by adapting transmission power dynamically to improve network connectivity. The power adaptation is based on inspecting some network parameters, such as node density, network load, and media access control (MAC) queue state, and then deciding on the required power level. Obtained results indicate that the designed power control algorithm outperforms the traditional 802.11p MAC considering the number of received safety messages, network connectivity, network throughput, and the number of dropped safety messages. Consequently, improving network performance means enhancing the safety of vehicle drivers in smart cities, highway, and urban city. © 2020 Wiley Periodicals LLC

The effect of dietary rumen-protected trans-10,cis-12 conjugated linoleic acid or a milk fat-depressing diet on energy metabolism, inflammation, and oxidative stress of dairy cows in early lactation

The objective of this study was to determine the effects of milk fat depression induced by supplementing conjugated linoleic acid (CLA; trans-10,cis-12 and cis-9,trans-11 CLA) or feeding a higher starch and oil-containing diet (HSO) on metabolic changes in dairy cows after calving. The main hypothesis was that the 2 strategies to decrease milk fat yield could have different effects on performance, energy balance (EB), and inflammatory status in early lactation. Thirty-three Nordic Red dairy cows were used in a randomized block design from 1 to 112 d of lactation and fed one of the following treatments: control (CON), CLA-supplemented diet, or HSO diet. Dry matter intake and milk yield were measured daily whereas milk composition was measured weekly throughout the experiment. Nutrient digestibility, EB, and plasma hormones and metabolites were measured at 3, 7, 11, and 15 wk of lactation in respiration chambers. The HSO diet led to lower intakes of dry matter, neutral detergent fiber, and gross energy compared with CON and CLA diets. The CLA diet and especially the HSO diet resulted in lower energy-corrected milk yield during the first 7 wk of lactation than those fed CON. The EB was numerically higher for HSO and CLA diets compared with CON at wk 3 and 7. Plasma glucose concentration was higher by the CLA diet at wk 3 and by the HSO diet from wk 3 to 15 compared with CON. Plasma nonesterified fatty acids were higher at wk 3 in the CON group (indicating more lipid mobilization) but decreased thereafter to similar levels with the other groups. The HSO-fed cows had higher plasma ceruloplasmin, paraoxonase, and total bilirubin concentrations in the entire experiment and showed the highest levels of reactive oxygen metabolites. These results suggest an increased inflammatory and oxidative stress state in the HSO cows and probably different regulation of the innate immune system. This study provides evidence that milk fat depression induced by feeding HSO (as well as CLA) decreased milk fat secretion and improved EB compared with CON in early lactation. The increase in plasma glucose and paraoxonase levels with the HSO diet may imply a better ability of the liver to cope with the metabolic demand after parturition. However, the negative effect of HSO on feed intake, and the indication of increased inflammatory and oxidative stress warrant further studies before the HSO feeding strategy could be supported as an alternative to improve EB in early lactation

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH production (g CH/animal·d, ANIM-B models) and CH yield (g CH/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH emissions from sheep, providing valuable insights for future research and mitigation strategies.Te authors gratefully acknowledge funding for this project from the USDA National Institute of Food and Agriculture (Award number: 2014-67003-21979). Te animal and microbial data originated from a study funded by the Pastoral Greenhouse Gas Research Consortium (www.pggrc.co.nz)

Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050

To meet the 1.5 °C target, methane (CH) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH per unit meat or milk) and absolute (ABS) enteric CH emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies—namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—decreased CH per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies—namely CH inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH emissions.We thank the GLOBAL NETWORK project for generating part of the database. The GLOBAL NETWORK project (https://globalresearchalliance.org/research/livestock/collaborative-activities/global-research-project/; accessed 20 June 2020) was a multinational initiative funded by the Joint Programming Initiative on Food Security, Agriculture, and Climate Change and was coordinated by the Feed and Nutrition Network (https://globalresearchalliance.org/research/livestock/networks/feed-nutrition-network/; accessed 20 June 2020) within the Livestock Research Group of the Global Research Alliance on Agricultural GHG (https://globalresearchalliance.org; accessed 20 June 2020). We thank MitiGate, which was part of the Animal Change project funded by the EU under Grant Agreement FP7-266018 for sharing their database with us (http://mitigate.ibers.aber.ac.uk/, accessed 1 July 2017). Part of C.A., A.N.H., and S.C.M.’s time in the early stages of this project was funded by the Kravis Scientific Research Fund (New York) and a gift from Sue and Steve Mandel to the Environmental Defense Fund. Another part of C.A.’s work on this project was supported by the National Program for Scientific Research and Advanced Studies - PROCIENCIA within the framework of the "Project for the Improvement and Expansion of the Services of the National System of Science, Technology and Technological Innovation" (Contract No. 016-2019) and by the German Federal Ministry for Economic Cooperation and Development (issued through Deutsche Gesellschaft für Internationale Zusammenarbei) through the research “Programme of Climate Smart Livestock” (Programme 2017.0119.2). Part of A.N.H.’s work was funded by the US Department of Agriculture (Washington, DC) National Institute of Food and Agriculture Federal Appropriations under Project PEN 04539 and Accession no. 1000803. E.K. was supported by the Sesnon Endowed Chair Fund of the University of California, Davis

A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions

A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.R. John Wallace, Goor Sasson, Philip C. Garnsworthy, Ilma Tapio, Emma Gregson ... John L. Williams ... et al