Diurnal Concentration of Urinary Nitrogen and Rumen Ammonia Are Modified by Timing and Mass of Herbage Allocation

New insights in improving sustainability in meat production: opportunities and challenges

Treating livestock as senseless production machines has led to rampant depletion of natural resources, enhanced greenhouse gas emissions, gross animal welfare violations, and other ethical issues. It has essentially instigated constant scrutiny of conventional meat production by various experts and scientists. Sustainably in the meat sector is a big challenge which requires a multifaced and holistic approach. Novel tools like digitalization of the farming system and livestock market, precision livestock farming, application of remote sensing and artificial intelligence to manage production and environmental impact/GHG emission, can help in attaining sustainability in this sector. Further, improving nutrient use efficiency and recycling in feed and animal production through integration with agroecology and industrial ecology, improving individual animal and herd health by ensuring proper biosecurity measures and selective breeding, and welfare by mitigating animal stress during production are also key elements in achieving sustainability in meat production. In addition, sustainability bears a direct relationship with various social dimensions of meat production efficiency such as non-market attributes, balance between demand and consumption, market and policy failures. The present review critically examines the various aspects that significantly impact the efficiency and sustainability of meat production.

Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows

Dairy cows' urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin's concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (p > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems.

Does the timing of pasture allocation affect rumen and plasma metabolites and ghrelin, insulin and cortisol profile in dairy ewes?

A study was undertaken to assess the impact of the timing of grazing on rumen and plasma metabolites and some metabolic hormones in lactating dairy sheep allocated to an Italian ryegrass (Lolium multiflorum Lam) pasture in spring for 4 h/d. Twenty-four mid lactation Sarda ewes stratified for milk yield, body weight, and body condition score, were divided into four homogeneous groups randomly allocated to the treatments (2 replicate groups per treatment). Treatments were morning (AM, from 08:00 to 12:00) and afternoon pasture allocation (PM, from 15:30 to 19:30). Samples of rumen liquor (day 39) and blood plasma (days 17 and 34 of the experimental period) were collected before and after the grazing sessions. Moreover, on days 11 and 35, grazing time was assessed by direct observation and herbage intake measured by the double weighing procedure. Grazing time was longer in PM than AM ewes (P < 0.001) but herbage intake was undifferentiated between groups. The intake of water-soluble carbohydrates at pasture was higher in PM than AM ewes (P < 0.05). The post-grazing propionic and butyric acid concentration, as measured on day 39, were higher in PM than AM ewes (P < 0.05). The basal level of glucose on day 34 and insulin (on both sampling days) were higher in PM than AM (P < 0.05). The opposite trend was detected for non-esterified fatty acids (P < 0.05, day 34) and urea (both days). Pasture allocation in the afternoon rather than in the morning decreased plasma concentration of ghrelin (P < 0.001) and cortisol (P < 0.001), with a smoothed trend on day 34 in the latter variable. To conclude, postponing the pasture allocation to afternoon increased the intake of WSC, favoring a glucogenic pattern of rumen fermentation and a rise of glucose and insulin levels in blood, although these effects were not consistent across the whole experimental period. Moreover, the afternoon grazing decreased the level of cortisol and ghrelin, suggesting a higher satiation-relaxing effect.

The Replacement of Ground Corn with Sugar Beet in the Diet of Pasture-Fed Lactating Dairy Cows and Its Effect on Productive Performance and Rumen Metabolism

(1) Background: Sugars have a potential to provide great amounts of fermentable energy in the rumen. Feeding fresh sugar beet (SB) to dairy cattle to replace a portion of the grain in the ration has not received sufficient attention. This study determined dry matter intake (DMI), feeding behavior, rumen fermentation and milk production responses when replacing corn grain with increasing levels of SB in pasture-fed lactating dairy cow diets. (2) Methods: A total of 12 early-lactation cows were used in a replicated (n = 4) 3 × 3 Latin square design. The control diet consisted of 21 kg dry matter (DM) composed of 6.3 kg DM green chopped perennial ryegrass, 7 kg DM grass silage, 2 kg DM of concentrate, 1 kg DM soybean meal and 4.5 kg DM of ground corn. The other treatments replaced 50% or 100% of the ground corn with SB roots. (3) Results: The replacement of ground corn with sugar beet reduced DMI and milk yield (p < 0.05), but it increased milk fat concentration (p = 0.045), reduced feeding costs and increased margin over feed costs (p < 0.01). Urinary nitrogen was linearly reduced with SB supplementation (p = 0.026). (4) Conclusions: Using SB roots as energetic supplement can be a suitable alternative to ground corn in pasture-fed lactating dairy cows.

Review: New feeds and new feeding systems in intensive and semi-intensive forage-fed ruminant livestock systems

The contributions that ruminant livestock make to greenhouse gas and other pollutant emissions are well documented and of considerable policy and public concern. At the same time, livestock production continues to play an important role in providing nutrient-rich foodstuffs for many people, particularly in less developed countries. They also offer a means by which plants that cannot be digested by humans, e.g. grass, can be converted into human-edible protein. In this review, we consider opportunities to improve nutrient capture by ruminant livestock through new feeds and feeding systems concentrating on intensive and semi-intensive systems, which we define as those in which animals are given diets that are designed and managed to be used as efficiently as possible. We consider alternative metrics for quantifying efficiency, taking into account resource use at a range of scales. Mechanisms for improving the performance and efficiencies of both individual animals and production systems are highlighted. We then go on to map these to potential changes in feeds and feeding systems. Particular attention is given to improving nitrogen use efficiency and reducing enteric methane production. There is significant potential for the use of home-grown crops or novel feedstuffs such as insects and macroalgae to act as alternative sources of key amino acids and reduce reliance on unsustainably grown soybeans. We conclude by highlighting the extent to which climate change could impact forage-based livestock production and the need to begin work on developing appropriate adaptation strategies.

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.