The assessment of supplementation requirements of grazing ruminants using nutrition models

Development and application of a novel extraction protocol for the monitoring of microplastic contamination in widely consumed ruminant feeds

Plastics and, in particular, microplastics (MPs) (< 5 mm) are emerging environmental pollutants responsible for interconnected risks to environmental, human, and animal health. The livestock sector is highly affected by these contaminants, with 50-60 % of the foreign bodies found in slaughtered domestic cattle being recognized as plastic-based materials. Additionally, microplastics were recently detected inside ruminant bodies and in their feces. MPs presence in ruminants could be explained by the intensive usage of plastic materials on farms, in particular to store feeds (i.e. to cover horizontal silos and to wrap hay bales). Although feed could be one of the main sources of plastics, especially of microplastics, a specific protocol to detect them in ruminant feeds is not actually present. Hence, the aim of this study was to optimize a specific protocol for the extraction, quantification, and identification of five microplastic polymers (high-density polyethylene, low-density polyethylene, polyamide fibers/particles, polyethylene terephthalate and polystyrene) from feeds typically used in ruminant diets (corn silage, hay, high protein feedstuff and total mixed ration). Several combinations of Fenton reactions and KOH digestion were tested. The final extraction protocol involved a KOH digestion (60 °C for 24 h), followed by two/three cycles of Fenton reactions. The extraction recoveries were of 100 % for high-density, low-density polyethylene, polyamide particles, and polystyrene and higher than 85 % for polyethylene terephthalate and polyamide fibers. Finally, the optimized protocol was successfully applied in the extraction of microplastics from real feed samples. All the feeds contained microplastics, particularly polyethylene, thus confirming the exposure of ruminants to MPs.

Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

The Effects of Harvest Maturity of Eragrostis tef 'Moxie' Hay and Supplemental Energy Source on Forage Utilization in Beef Heifers

The phenological stage of maturity of grasses and supplementation program can impact forage utilization in grazing beef cattle. However, the potential interaction between harvest maturity of Eragrostis tef (teff) hay and energy supplement source was yet to be fully evaluated. Therefore, our objective was to determine the effects of harvest maturity of teff hay and supplemental energy sources on nutrient intake, apparent total-tract nutrient digestion, nitrogen (N) utilization, and ruminal fermentation characteristics in beef heifers. A split-plot design with teff hay harvest maturity as the whole plot and supplemental energy source as the subplot was administered in a three-period (21 d), three × three Latin square design. Six crossbred beef heifers (804 ± 53.6 kg of body weight; BW) were allocated to two harvest maturities (early- (EH]) or late-heading (LH)) and to two supplemental energy sources (no supplement (CON), or rolled corn grain or beet pulp pellet fed at 0.5% of BW). Data were analyzed using SAS. There was no harvest maturity × energy supplement interaction. Although harvest maturity had no impact on total dry matter intake (DMI), crude protein (CP) intake was greater (p < 0.01) for EH than LH heifers. Total intakes of dry (DM) and organic matter (OM) were also greater (p < 0.01) for supplemented than CON heifers, whereas acid detergent fiber (ADF) intake was greater for beet pulp heifers compared to heifers fed the CON diet and supplemental corn grain. Harvest maturity had no impact on ruminal pH. However, mean ruminal pH was lower (p = 0.04), duration pH < 6.2, and molar proportions of butyrate and branched-chain fatty acids were greater (p ≤ 0.049) for heifers fed corn grain compared to CON and beet pulp diets. Heifers fed EH hay had greater (p ≤ 0.02) apparent total-tract DM, OM, CP, NDF, and ADF digestibility than heifers fed LH hay. Although there was no supplemental energy effect on microbial nitrogen (N) flow, it was greater (p < 0.01) for EH than LH heifers. Apparent N retention, which did not differ, was negative across all diets. In summary, delaying the harvest of teff hay from the EH to LH stage of maturity compromised nutrient supply, which was not attenuated by feeding supplemental corn grain and beet pulp at 0.5% of diet DM. Because N retention was negative across harvest maturity, there might be a need to provide both energy and protein supplements to improve growth performance when feeding teff hay to beef cattle.

Detoxified castor seed meal replaces soybean meal in the supplement for Holstein-Zebu crossbred steers finished on tropical pasture during the rainy season

The objective of this study was to examine the effect of replacing soybean meal (SBM) with detoxified castor seed meal (DCM) on the intake, digestibility, feeding behavior, and performance of pasture-finished (rainy season) steers supplemented with concentrate at 0.4% of their body weight. Forty ½ Holstein + ½ Zebu steers (initial weight: 283.3 ± 36.3 kg) were allocated to four treatments in a completely randomized experimental design. Treatments consisted of diets in which DCM replaced 0, 30, 60, and 90% of SBM in the supplement dry matter (DM). The steers were finished on an Urochloa brizantha pasture and the experiment lasted 112 days. Replacing SBM with DCM did not influence (P > 0.05) the intake or apparent digestibility of DM, crude protein, or neutral detergent insoluble fiber of the animals. Grazing time increased (P < 005), whereas the intake and rumination efficiencies of the steers did not change (P > 0.05) with the substitution. The replacement of SBM with DCM in the supplement fed to the steers also did not influence (P > 0.05) their final weight, average daily gain, or feed conversion (P > 0.05). We recommend replacing up to 90% (DM basis) of SBM with DCM in the concentrate supplement of steers grazing on Urochloa brizantha pasture during rainy season while supplemented with concentrate at 0.4% of their body weight.

Review: Harnessing extant energy and protein requirement modeling for sustainable beef production

Numerous mathematical nutrition models have been developed in the last sixty years to predict the dietary supply and requirement of farm animals' energy and protein. Although these models, usually developed by different groups, share similar concepts and data, their calculation routines (i.e., submodels) have rarely been combined into generalized models. This lack of mixing submodels is partly because different models have different attributes, including paradigms, structural decisions, inputs/outputs, and parameterization processes that could render them incompatible for merging. Another reason is that predictability might increase due to offsetting errors that cannot be thoroughly studied. Alternatively, combining concepts might be more accessible and safer than combining models' calculation routines because concepts can be incorporated into existing models without changing the modeling structure and calculation logic, though additional inputs might be needed. Instead of developing new models, improving the merging of extant models' concepts might curtail the time and effort needed to develop models capable of evaluating aspects of sustainability. Two areas of beef production research that are needed to ensure adequate diet formulation include accurate energy requirements of grazing animals (decrease methane emissions) and efficiency of energy use (reduce carcass waste and resource use) by growing cattle. A revised model for energy expenditure of grazing animals was proposed to incorporate the energy needed for physical activity, as the British feeding system recommended, and eating and rumination (HjEer) into the total energy requirement. Unfortunately, the proposed equation can only be solved iteratively through optimization because HjEer requires metabolizable energy (ME) intake. The other revised model expanded an existing model to estimate the partial efficiency of using ME for growth (kg) from protein proportion in the retained energy by including an animal degree of maturity and average daily gain (ADG) as used in the Australian feeding system. The revised kg model uses carcass composition, and it is less dependent on dietary ME content, but still requires an accurate assessment of the degree of maturity and ADG, which in turn depends on the kg. Therefore, it needs to be solved iteratively or using one-step delayed continuous calculation (i.e., use the previous day's ADG to compute the current day's kg). We believe that generalized models developed by merging different models' concepts might improve our understanding of the relationships of existing variables that were known for their importance but not included in extant models because of the lack of proper information or confidence at that time.

On-farm evaluation of models to predict herbage intake of dairy cows grazing temperate semi-natural grasslands

The objective of the present on-farm study was to evaluate the adequacy of existing models in predicting the pasture herbage DM intake (PDMI) of lactating dairy cows grazing semi-natural grasslands. The prediction adequacy of 13 empirical and semi-mechanistic models, which were predominantly developed to represent stall-fed cows or cows grazing high-quality pastures, were evaluated using the mean bias, relative prediction error (RPE), and partitioning of mean square error of prediction, where models with an RPE ≤ 20% were considered adequate. The reference dataset comprised n = 233 individual animal observations from nine commercial farms in South Germany with a mean milk production, DM intake, and PDMI (arithmetic means ± one SD) of 24 kg/d, (±5.6), 21 kg/d (±3.2), and 12 kg/d (±5.1), respectively. Despite their adaptation to grazing conditions, the behaviour-based and semi-mechanistic grazing-based models had the lowest prediction adequacy among the evaluated models. Their underlying empirical equations likely did not fit the grazing and production conditions of low-input farms using semi-natural grasslands for grazing. The semi-mechanistic stall-based model Mertens II with slight modifications achieved the highest and a satisfactory modelling performance (RPE = 13.4%) when evaluated based on the mean observed PDMI, i.e., averaged across animals per farm and period (n = 28). It also allowed for the adequate prediction of PDMI on individual cows (RPE = 18.5%) that were fed < 4.8 kg DM of supplement feed per day. Nevertheless, when used to predict PDMI of individual animals receiving a high supplementation level, the model Mertens II also did not meet the threshold for an acceptable adequacy (RPE = 24.7%). It was concluded that this lack of prediction adequacy for animals receiving greater levels of supplementation was due to a lack of modelling precision, which mainly could be related to inter-animal and methodological limitations such as the lack of individually measured supplement feed intake for some cows. The latter limitation is a trade-off of the on-farm research approach of the present study, which was chosen to represent the range in feed intake of dairy cows across the diverse low-input farming systems using semi-natural grasslands for grazing.

Effects of the Level and Composition of Concentrate Supplements before Breeding and in Early Gestation on Production of Different Hair Sheep Breeds

Female hair sheep, 27 Dorper (DOR), 41 Katahdin (KAT), and 39 St. Croix (STC), were used to determine influences of the nutritional plane before breeding and in early gestation on feed intake, body weight, body condition score, body mass indexes, blood constituent concentrations, and reproductive performance. There were 35 multiparous and 72 primiparous sheep, with initial ages of 5.6 ± 0.25 years and 1.5 ± 0.01 years, respectively (average overall initial age of 2.8 ± 0.20 years). Wheat straw (4% crude protein; dry matter [DM] basis) was consumed ad libitum and supplemented with approximately 0.15% initial body weight (BW) of soybean meal (LS) or a 1:3 mixture of soybean meal and rolled corn at 1% BW (HS; DM). The supplementation period was 162 days, with the breeding of animals in two sets sequentially, with the pre-breeding period 84 and 97 days, and that after breeding began at 78 and 65 days, respectively. Wheat straw DM intake (1.75, 1.30, 1.57, 1.15, 1.80, and 1.38% BW; SEM = 0.112) was lower (p < 0.05), but average daily gain (-46, 42, -44, 70, -47, and 51 g for DOR-LS, DOR-HS, KAT-LS, KAT-HS, STC-LS, and STC-HS, respectively; SEM = 7.3) was greater (p < 0.05) for HS than LS treatment during the supplementation period. Additionally, changes in body condition score during the supplementation period (-0.61, 0.36, -0.53, 0.27, -0.39, and -0.18; SEM = 0.058), and changes in body mass index based on height at the withers and body length from the point of the shoulder to the pin bone (BW/[height × length], g/cm²) from 7 days before supplementation (day -7) to day 162 were -1.99, 0.07, -2.19, -0.55, -2.39, and 0.17 for DOR-LS, DOR-HS, KAT-LS, KAT-HS, STC-LS, and STC-HS, respectively; (SEM = 0.297) were affected by supplement treatment. All blood constituent concentrations and characteristics addressed varied with the day of sampling (-7, 14, 49, 73, and 162) as well as the interaction between the supplement treatment and the day (p < 0.05), with few effects of interactions involving breed. Birth rate (66.7, 93.5, 84.6, 95.5, 82.8, and 100.0; SEM = 9.83) and individual lamb birth weight (4.50, 4.61, 4.28, 3.98, 3.73, and 3.88 kg; SEM = 0.201) were not affected by supplement treatment (p = 0.063 and 0.787, respectively), although litter size (0.92, 1.21, 1.17, 1.86, 1.12, and 1.82; SEM = 0.221) and total litter birth weight (5.84, 5.74, 5.92, 7.52, 5.04, and 6.78 kg for DOR-LS, DOR-HS, KAT-LS, KAT-HS, STC-LS, and STC-HS, respectively; SEM = 0.529) were greater (p < 0.05) for HS than for LS. In conclusion, although there was some compensation in wheat straw intake for the different levels of supplementation, soybean meal given alone rather than with cereal grain adversely affected BW, BCS, BMI, and reproductive performance, the latter primarily through litter size but also via a trend for an effect on the birth rate. Hence, the supplementation of low-protein and high-fiber forage such as wheat straw should include a consideration of the inclusion of a feedstuff(s) high in energy in addition to nitrogen.

Feeding behavior of post-weaned crossbred steers supplemented in the dry season of the year

The objective of this study was to evaluate the feeding behavior of grazing steers supplemented in the dry season of the year. Thirty-two castrated crossbred (½ Holstein-Zebu) steers with an average initial weight of 378 ± 7.54 kg, aged 14 months, were distributed into four treatment groups in a completely randomized design with eight replicates. The animals were managed in an area covered with a pasture of Brachiaria brizantha cv. Marandu, in a rotational grazing system. The supplement was formulated so that the animals in the different treatment groups would ingest the same amount of crude protein (CP) daily. Thus, the treatments consisted of increasing levels of supplementation, based on the animals' body weight (BW), as the protein content of the supplement was reduced. The following treatments (dry matter basis) were tested: T2, supplement at 0.2% BW, with 50% CP; T4, supplement at 0.4% BW, with 25% CP; T6, supplement at 0.6% BW, with 16.67% CP; and T8, supplement at 0.8% BW, with 12.5% CP. Forage dry matter intake decreased linearly (P < 0.05), characterizing a substitution effect. The increasing supplementation levels influenced the animals' feeding behavior, especially grazing time, total feeding time, number of grazing bouts, and number of bites per day, which showed a positive quadratic response (P < 0.05), and rumination time, number of rumination bouts, number of cuds ruminated per day, and number of chews per ruminated cud, which exhibited a negative quadratic behavior (P < 0.05). Dry matter and neutral detergent fiber (NDF) feed efficiencies and dry matter and NDF rumination efficiencies responded quadratically (P < 0.05). In conclusion, concentrate supplementation at up to 0.8% BW improves the feeding behavior of grazing steers in terms of the intake of concentrate supplement and forage as well as the feed and rumination efficiencies.

Quantification of methane emitted by ruminants: A review of methods

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH₄), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH₄ emissions. This review documents existing methods and methodologies to measure and estimate CH₄ emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH₄ have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH₄ for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH₄ from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH₄ emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

GHG Emissions from Dairy Small Ruminants in Castilla-La Mancha (Spain), Using the ManleCO2 Simulation Model

Simple Summary

Greenhouse gas emissions from ruminants contribute to global warming. “ManleCO₂” is an empirical model that simulates different management aspects in dairy sheep and goat farming, linking milk production to farming and environmental health. The carbon footprint of 1 L of fat- and protein-corrected milk varied from 2.01 to 5.62 kg CO₂e. Simulation scenarios showed a higher reduction in GHG emissions associated with animal feeding strategies and a lower reduction associated with farming management strategies. ManleCO₂ may provide useful information for planning and developing different strategies that might support the reduction of GHG emissions at the dairy sheep and goat farm level.

Abstract

The first goal of this work was the description of a model addressed to quantify the carbon footprint in Spanish autochthonous dairy sheep farms (Manchega group), foreign dairy sheep farms (foreigners group: Lacaune and Assaf breeds), and Spanish autochthonous dairy goat farms (Florida group). The second objective was to analyze the GHG emission mitigation potential of 17 different livestock farming practices that were implemented by 36 different livestock farms, in terms of CO₂e per hectare (ha), CO₂e per livestock unit (LU), and CO₂e per liter of fat- and protein-corrected milk (FPCM). The study showed the following results: 1.655 kg CO₂e per ha, 6.397 kg CO₂e per LU, and 3.78 kg CO₂e per liter of FPCM in the Manchega group; 12.634 kg CO₂e per ha, 7.810 CO₂e kg per LU, and 2.77 kg CO₂e per liter of FPCM in the Foreigners group and 1.198 kg CO₂e per ha, 6.507 kg CO₂e per LU, and 3.06 kg CO₂e per liter of FPCM in Florida group. In summary, purchasing off-farm animal feed would increase emissions by up to 3.86%. Conversely, forage management, livestock inventory, electrical supply, and animal genetic improvement would reduce emissions by up to 6.29%, 4.3%, 3.52%, and 0.8%, respectively; finally, an average rise of 2 °C in room temperature would increase emissions by up to 0.62%.

Effect of energy supplementation on intake, digestibility of diets and performance of grazing lambs during the rainy season

This study aimed to evaluate different energy sources in multiple supplements on performance, intake, and digestibility of Santa Ines sheep grazing urochloa grass (Urochloa mosambicensis) during the rainy season. The experimental area was divided into seven paddocks of 4 ha each, with an average of dry matter (DM) availability of 3.21 tn/ha. A completely randomized design was carried out, in which there were four treatments, and each treatment was repeated six times. Twenty-four intact lambs (average: 32.0 kg of body weight) were supplemented with a mineral mixture, the control group (MM), mesquite pod meal (MPM), wheat bran (WB), or sorghum grain (SG) as energy sources. The digestibility of DM and crude protein (CP) in MPM and WB is higher than that in MM and SG groups. Neutral detergent fiber (NDF) digestibility was similar between supplemented lambs, and it was higher than the MM. The supplementation promoted higher weight gain than in the control group (0.126 vs. 0.061 g/day, respectively; P < 0.001). The supplementation increased the DM, and CP intake. The NDF intake only increased in the WB group. The CP digestibility was higher for the MPM and WB groups than that for MM and SG ones (P < 0.001). Sheep supplementation in the rainy season increased the average daily gain (ADG). Any supplement tested in the present study can be used during the rainy season. The choice for the supplement will depend on the availability and costs of the mesquite pod meal, sorghum grain, or wheat bran.

Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d-1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Production and Health Management from Grazing to Confinement Systems of Largest Dairy Bovine Farms in Azores: A Farmers' Perspective

Simple Summary

This study aimed to evaluate differences and critical factors in production and health management between dairy cattle farms with fixed milk parlours (FMP), and mobile milk systems (MMS) from Azorean grasslands. According to the farmers’ perspective, calf diarrhea, calf pneumonia, infertility/poor reproductive management, and mastitis were the main problems that farms faced in 2020. FMP was associated with more advanced and mechanized production systems, with a higher adherence to preventive and biosecurity control programs, than traditional MMS farms. MMS farms also showed a greater vocation for dual-purpose farming (beef and milk), smaller herd sizes and more grazing time for cows. In conclusion, inherent and non-inherent differences in production and health management between FMP and MMS were quantified by authors. These results indicate that a greater adoption of preventive veterinary medicine and biosecurity measures should be taken, especially among MMS farms. The education of farmers should also be improved and stimulated.

Abstract

The intensification of bovine milk production in the Azores has led farmers to increase farm size and specialization in grasslands, implementing confined and semi-confined production systems. Fixed milking parlours (FMP) have progressively gained more popularity, at the expense of conventional mobile milking systems (MMS). The present study aimed to evaluate the associations between production and health management in dairy cattle farms, with FMP or MMS, in grasslands (São Miguel, Azores), according to the farmers’ perspective. A total of 102 questions about production and health management were surveyed in 105 farms with >30 dairy cows each. Farms with FMP were associated (p ≤ 0.05) with larger herd size, better facilities, and specialized management, however, the adoption of preventive and biosecurity measures should be improved by these farmers. MMS farms implemented a lower level of disease prevention or control programs, less frequent transhumance, and showed a wider vocation to dual-purpose (milk and cross beef) than FMP farms. In conclusion, MMS and FMP farms tried to optimize yield and economic viability in different ways using grasslands. Several biosecurity and health prevention constraints were identified for improvement.

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.

Does grazing management provide opportunities to mitigate methane emissions by ruminants in pastoral ecosystems?

Agriculture, and livestock production in particular, is criticized for being a contributor to global environmental change, including emissions of greenhouse gases (GHG). Methane (CH₄) from grazing ruminants accounts for most of livestock's carbon footprint because a large share of them are reared under suboptimal grazing conditions, usually resulting in both low herbage intake and animal performance. Consequently, the CH₄ quota attributed to animal maintenance is spread across few or no animal outputs, increasing the CH₄ intensity [g CH₄/kg live weight (LW) gain or g CH₄/kg milk yield]. In this review, the generalized idea relating tropical pastures with low quality and intrinsically higher CH₄ intensity is challenged by showing evidence that emissions from animals grazing tropical pastures can equal those of temperate grasses. We demonstrate the medium-to-high mitigation potential of some grazing management strategies to mitigate CH₄ emissions from grazing ruminants and stress the predominant role that sward canopy structure (e.g., height) has over animal behavioral responses (e.g., intake rate), daily forage intake and resulting CH₄ emissions. From this ecological perspective, we identify a grazing management concept aiming to offer the best sward structure that allows animals to optimize their daily herbage intake, creating opportunities to reduce CH₄ intensity. We show the trade-off between animal performance and CH₄ intensity, stressing that mitigation is substantial when grazing management is conducted under light-to-moderate intensities and optimize herbage intake and animal performance. We conclude that optimizing LW gain of grazing sheep and cattle to a threshold of 0.14 and 0.7 kg/day, respectively, would dramatically reduce CH₄ intensity to approximately 0.2 kg CH₄/kg LW gain, as observed in some intensive feeding systems. This could represent a mitigation potential of around 55% for livestock commodities in pasture-based systems. Our results offer new insights to the debate concerning mitigation of environmental impacts of pastoral ecosystems.

Environmental Impacts of Beef as Corrected for the Provision of Ecosystem Services

We aimed to assess whether the environmental impacts in terms of global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), and land occupation (LO) of beef can be decreased when ecosystem and cultural/provisioning services are included in the evaluation. We used four Italian production systems: Fat, with beef imported calves kept in confinement; CoCaI, with beef cows and calves kept in confinement; SpEx, with beef cows and calves kept on pasture and finishing conducted in confinement; and Pod, with Podolian cows and calves kept on pasture and finishing conducted in confinement. After the economic allocation, the GWP of system Pod decreased considerably and showed values lower than those computed for systems CoCaI and SpEx (P < 0.05 and P < 0.001, respectively). System Pod showed the lowest AP and EP as compared with all the other systems (P < 0.01). Systems Fat and CoCaI showed the smallest LO, with values lower than systems Pod (P < 0.05) and SpEx (P < 0.001). We conclude that the environmental impacts of extensive and local beef production systems in terms of GWP, AP, and EP was markedly reduced when the provision of accessory services was included in the calculation. Conversely, LO did not markedly change due to the high absolute values needed to allow pasture-based feeding. The estimation of additional positive aspects linked to the use of natural pastures, such as removal of carbon dioxide, increased biodiversity, and exploitation of feeds nonedible by humans, may allow a further reduction of LO.

Cattle adapted to tropical and subtropical environments: social, nutritional, and carcass quality considerations

Beef production needs to increase from 60 million to 130 million tons by 2050 to feed a growing world population, and 70% of this production increase is expected from beef industries located in subtropical and tropical regions of the world. Bos indicus-influenced cattle predominate in these regions but are often managed using practices developed for Bos taurus breeds reared in temperate climates. Hence, a fundamental step to meet the increasing global demand for beef is to develop specific management for B. indicus-influenced cattle in tropical or subtropical environments. Bos taurus and B. indicus are different subspecies, and diverge in social and biological functions due to selection pressure caused by complex evolutionary and domestication processes. Bos indicus cattle display different social responses compared with B. taurus counterparts, which must be taken into account by management planning as these traits directly impact cattle performance and welfare. In tropical and subtropical regions, warm-season perennial C4 grasses are the dominant forages, and their availability has a significant influence on the productivity of beef cattle systems. The resilience of C4 grasses under adverse conditions is one of their most important characteristics, even though these forages have reduced nutritive value compared with forages from temperate climates. Accordingly, nutritional planning in tropical and subtropical conditions must include management to optimize the quantity and quality of C4 forages. Nutritional requirements of cattle raised within these conditions also require special attention, including inherent metabolic compromises to cope with environmental constraints and altered energy requirements due to body composition and heat tolerance. Nutritional interventions to enhance beef production need to be specifically tailored and validated in B. indicus-influenced cattle. As an example, supplementation programs during gestation or early life to elicit fetal programming or metabolic imprinting effects, respectively, yield discrepant outcomes between subspecies. Bos indicus-influenced cattle produce carcasses with less marbling than B. taurus cattle, despite recent genetic and management advances. This outcome is mostly related to reduced intramuscular adipocyte volume in B. indicus breeds, suggesting a lesser need for energy stored intramuscularly as a mechanism to improve thermotolerance in tropical and subtropical climates.

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.