Search Results (16)

Effective greenhouse gas mitigation strategies in the agricultural sector are crucial for reducing emissions. Methane (CH₄) emissions associated with agriculture are predominantly the result of enteric fermentation from ruminant production systems. Accurate measurement of these emissions is essential for assessing environmental impacts and developing effective mitigation strategies. The eddy covariance (EC) method is widely used to measure trace gas and energy fluxes and has since also been adapted to measure enteric CH₄ emissions from grazing ruminants effectively. This study combined EC measurements of CH₄ emissions from pasture-based Jersey cows with milk production, feed intake data and CH₄ prediction equations during four measurement campaigns between September and November 2022 in northern Germany. Cows’ distance relative to the EC station was controlled by a specialized fencing system and its effect on the measured CH₄ fluxes was adjusted by means of footprint (FP) flux allocation based on a two-dimensional FP model. The EC method presented very low daily emissions of 205 g CH₄ cow⁻¹ day⁻¹, below the estimations based on the Intergovernmental Panel on Climate Change (IPCC) Tier 2 default values and other equations based on feed intake and feed quality parameters. The results of this study indicated that the EC method, in combination with a specialized fencing design, is an appropriate method to measure enteric CH₄ emissions of dairy cows in pasture-based systems. Moreover, this study showed that a comprehensive dataset of animal-related data is a practical tool to contextualize the results. Full article

An essential component required for calculating stocking rates for livestock grazing extensive rangeland is dry matter intake (DMI). Animal unit months are used to simplify this calculation for rangeland systems to determine the rate of forage consumption and the cattle grazing duration. However, there is an opportunity to leverage precision technology deployed on rangeland systems to account for the individual animal variation of DMI and subsequent impacts on herd-level decisions regarding stocking rate. Therefore, the objectives of this study were, first, to build a precision system model (PSM) to predict total DMI (kg) and required pasture area (ha) using precision body weight (BW), and second, to evaluate differences in PSM-predicted stocking rates compared to the traditional herd-level method using initial or estimated mid-season BW. A deterministic model was constructed in both Vensim (version 10.1.2) and Program R (version 4.2.3) to incorporate individual precision BW data into a commonly used rangeland equation using %BW to estimate individual DMI, daily herd DMI, and area (ha) required to meet animal DMI requirements throughout specific grazing periods. Using the PSM, differences in outputs were evaluated using three scenarios: (1) initial BW (business as usual); (2) average mid-season BW; and (3) individual precision BW using data from two precision rangeland experiments conducted at the South Dakota State University Cottonwood Field Station. The data from the two experiments were used to develop PSM case studies. The trial data were collected using precision weight data (SmartScale™) collected from replacement heifers (Case study 1, n = 60) and steers (Case study 2, n = 254) grazing native rangeland. In Case study 1 (heifers), Scenario 1 versus Scenario 3 resulted in an additional 73.41 ha required. Results from Case study 2 indicated an average additional 4.4 ha required per pasture when comparing Scenario 3 versus Scenario 1. Sensitivity analyses resulted in a difference between maximum and minimum simulated values of 27,995 and 4265 kg forage consumed, and 122 and 8.9 pasture ha required for Case studies 1 and 2, respectively. Thus, results from the scenarios indicate an opportunity to identify both under- and over-stocking situations using precision DMI estimates, which helps to identify high-leverage precision tools that have practical applications for enhancing animal and plant productivity and environmental sustainability on extensive rangelands. Full article

This study evaluated 5 annual and 11 perennial Indian pasture legumes species for their nutritive value, dry matter and mineral contents and in vitro fermentation parameters. Legume species differed significantly (p < 0.05) in various nutritional aspects such as organic matter, crude protein (CP), ether extract, fibres and protein fractions. Perennial Clitoria ternateaa had higher (p < 0.05) buffer soluble protein (477), while neutral detergent soluble protein was highest in annually grown Lablab purpureus (420 g/kg CP). Atylosia scarabaeoides (AS) had higher levels of nonstructural carbohydrates (NSCs) (392 g/kg dry matter (DM)) than structural carbohydrates (SC) (367 g/kg DM). Its rapidly degradable fraction (51.7 g/kg (total carbohydrate) tCHO) was lower (p < 0.05) than other fractions of carbohydrates. Total digestible nutrients, digestible energy and metabolisable energy varied, with Desmodium virgatus (DV) having higher values and Stylosanthas seabrana (SSe) having the lowest. Predicted dry matter intake, digestible dry matter and relative feed value also showed significant differences (p < 0.05). Annual grasses such as Dolichos biflorus, Macroptilium atropurpureum, Rhynchosia minima (RM) were found to be better balanced with micro minerals. In vitro dry matter degradability, partition factor, short-chain fatty acids and microbial protein production of legumes varied significantly (p < 0.05). Gas and CH₄ production (mL/g and mL/g (digestible DM) DDM) also varied, with Clitoria ternatea-blue having the highest gas production and C. ternatea -white (CT-w) and AS having lower CH₄ production. Methane in total gas was low for DV, RM and CT-w (8.99%, 9.72% and 9.51%). Loss of DE and ME as CH₄ varied (p < 0.05) among the legumes. Each legume offers unique benefits, potentially allowing for tailored combinations of annual and perennial legumes to optimize rumen feed efficiency. Full article

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. Full article

In this work, untargeted metabolomics was used to shed light on the impact of different pasture-based diets on the chemical profile of Sarda sheep milk. The study considered 11 dairy sheep farms located in Sardinia, and milk samples were collected in 4 different periods, namely January, March, May, and July 2019, when all sheep had 58, 98, 138, and 178 days in milk, respectively. The animal diet composition was based on the intake of grazed herbage in natural pasture, hay, and concentrate. Overall, the combination of two comprehensive databases on food, namely the Milk Composition Database and Phenol-Explorer, allowed the putative identification of 406 metabolites, with a significant (p < 0.01) enrichment of several metabolite classes, namely amino acids and peptides, monosaccharides, fatty acids, phenylacetic acids, benzoic acids, cinnamic acids, and flavonoids. The multivariate statistical approach based on supervised orthogonal projections to latent structures (OPLS-DA) allowed us to predict the chemical profile of sheep milk samples as a function of the high vs no fresh herbage intake, while the prediction model was not significant when considering both hay and concentrate intake. Among the discriminant markers of the herbage intake, we found five phenolic metabolites (such as hippuric and coumaric acids), together with lutein and cresol (belonging to carotenoids and their metabolites). Additionally, a high discriminant power was outlined for lipid derivatives followed by sugars, amino acids, and peptides. Finally, a pathway analysis revealed that the herbage intake affected mainly five biochemical pathways in milk, namely galactose metabolism, phenylalanine metabolism, alpha-linolenic acid metabolism, linoleic acid metabolism, and aromatic amino acids involved in protein synthesis (namely tyrosine, phenylalanine, and tryptophan). Full article

The botanical and chemical composition of diets consumed by ruminants is different from the composition of plant species available in the rangeland or pastures on which they graze. Exploring alternative and improving existing methods of estimating botanical composition (diet selection) is imperative in advancing sustainable feeding practices in extensive production systems. The ability to predict the intake and digestibility of the diet consumed is important in designing grazing management for different feeding systems as well as supplementation strategies. This facilitates the efficient use of feed resources for optimal animal performance. This review assesses the merits, limitations, and potential advancements in techniques used to estimate botanical composition, forage intake, and digestibility in ruminants. Supplements containing sufficient quantity and identifiable n-alkanes can be used to determine the total forage intake in grazing ruminants without dosing the animals with synthetic even-numbered n-alkanes. When the botanical composition, intake, and digestibility of diet are estimated using internal markers, the results should be validated with those of faecal near-infrared reflectance spectroscopy (NIRS) or plant cuticular compounds to enhance the prediction accuracy. This should be done to determine the degree of error in the use of internal markers. Conclusively, the use of internal markers with automated solver routine software is a prudent approach to predicting botanical composition due to the analytical ease of the markers involved and the associated model assumptions. Full article

Feed chemical composition is associated with methane (CH₄) formation in the rumen, and thus CH₄ yields (Y_m; CH₄ emitted from per unit of dry matter intake) could be predicted using near-infrared reflectance spectroscopy (NIRS) of feeds fed to ruminants. Two databases of NIRS data were compiled from feeds used in experiments in which CH₄ yields had been quantified in respiration chambers. Each record in the databases represented a batch of feed offered to a group of experimental animals and the mean CH₄ yield for the group. A near-infrared reflectance spectrum was obtained from each feed, and these spectra were used to generate a predictive equation for Y_m. The predictive model generated from brassica crops and pasture fed at a similar feeding level (n = 40 records) explained 53% of the variation in Y_m and had a reasonably good agreement (concordance correlation coefficient of 0.77). The predictive ability of the NIRS calibration could be useful for screening purposes, particularly for predicting the potential Y_m of multiple feeds or feed samples, rather than measuring Y_m in animal experiments at high expenses. It is recommended that the databases for NIRS calibrations are expanded by collecting feed information from future experiments in which methane emissions are measured, using alternative algorithms and combining other techniques, such as terahertz time-domain spectroscopy. Full article

Predicting feed intake in suckling lambs consuming both milk and pasture can be challenging, and thus intake values are often derived from solely milk or solid feed consumption. The present study investigated if dry matter (DM), organic matter (OM), and metabolisable energy (ME) intakes of lambs given a combination of milk and pellets under controlled conditions could be predicted with enough precision using dietary and faecal chemical composition. A total of 34 pre-weaned lambs bottle-fed milk replacer with or without access to pellets and kept in metabolic cages for four days were used. To develop the prediction equations, 54 faecal samples with detailed information on their chemical compositions, and the feed consumed by the lambs, were used. Pellet DMI was predicted from neutral detergent fibre concentration in faeces and pellets, pellets %DM, and live weight (LW) of lambs. Milk DMI was predicted from faecal Nitrogen concentration and LW. Milk and pellet DMI and their ME content were combined to predict DMI/d and ME intake/d. The equations developed were validated against 40 spot faecal samples randomly selected from the lambs. DM, OM, and ME intakes were predicted with high accuracy and precision. The results showed that the developed equations can be used with enough accuracy to predict ME, OM, and DM intakes in pre-weaned lambs ingesting milk and pellets concurrently, thus the results revealed that the established equations may be used to predict ME, OM, and DM intakes in pre-weaned lambs drinking milk and pellets at the same time, allowing feeding regimens for young lambs to be developed. Full article

This experiment examined the effect of breeding heavier ewe lambs on lamb production and their efficiency over their first three breeding seasons. Two groups of ewe lambs were bred at seven months of age at an average pre-breeding live weight of either 47.9 ± 0.36 kg (heavy; n = 135) or 44.9 ± 0.49 kg (control; n = 135). Ewe live weight, number of lambs born and weaned, and lamb live weight were recorded until 39 months of age, and efficiency was calculated for each ewe. Although the number and lamb weaning weight did not differ between treatments over three years, when data were pooled, heavier ewe lambs at breeding weaned a greater number of lambs over the three-year period. The total lamb weaning weight over the three-year period increased by 2% for each additional kilogram at ewe lamb breeding. Breeding heavier ewe lambs had no effect on efficiency. These results suggest that although breeding heavier ewe lambs had a positive effect on lamb production over the three-year period, it had no effect on efficiency. Before final recommendations can be made, lifetime performance and longevity to five years of age of heavier ewe lambs at breeding are required. Full article

This study aimed to identify suitable predictors of nitrogen (N) use efficiency (NUE; milk N/N intake) for cows that differed in breeds and were fed with ryegrass pasture, using existing data from the scientific literature. Data from 16 studies were used to develop models based on the relationships between NUE and dietary and animal-based factors. Data from a further 10 studies were used for model validation. Milk urea N (MUN) and dietary water-soluble carbohydrate-to-crudeprotein ratio (WSC/CP) were the best and most practical animal- and diet-based proxies to predict NUE. The results indicate that it might be necessary to adopt separate models for different breeds when using WSC/CP to predict NUE but not when using MUN. Full article

Feed is the largest variable cost for dairy farms in Australia, and dairy farmers are faced with the challenge of profitably feeding their cows in situations where there is significant variation in input costs and milk price. In theory, the addition of 5.2 MJ of metabolisable energy to a lactating cow’s diet should be capable of supporting an increase in milk production of one litre of milk of 4.0% fat, 3.2% protein and 4.9% lactose. However, this is almost never seen in practice, due to competition for energy from other processes (e.g., body tissue gain), forage substitution, associative effects and imbalances in rumen fermentation. Pasture species, stage of maturity, pasture mass, allowance and intake, stage of lactation, cow body condition and type of supplement can all affect the milk protein plus fat production response to additional feed consumed by grazing dairy cows. We developed a model to predict marginal milk protein plus fat response/kg DM intake when lactating dairy cows consume concentrates and pasture + forages. Data from peer reviewed published experiments undertaken in Australia were collated into a database. Meta-analysis techniques were applied to the data and a two-variable quadratic polynomial production function was developed. Production economic theory was used to estimate the level of output for given quantities of input, the marginal physical productivity of each input, the isoquants for any specified level of output and the optimal input combination for given costs and prices of inputs and output. The application of the model and economic overlay was demonstrated using four scenarios based on a farm in Gippsland, Victoria. Given that feed accounts for the largest input cost in dairying, allocation of pasture and supplements that are based on better estimates of marginal milk responses to supplements should deliver increased profit from either savings in feed costs, or in some cases, increased output to approach the point where marginal revenue equals marginal costs. Such data are critical if the industry is to take advantage of the opportunities to use supplements to improve both productivity and profitability. Full article

In cattle, greenhouse gas (GHG) emissions and nutrient balance are influenced by factors such as diet composition, intake, and digestibility. This study evaluated CH₄ emissions and surpluses of crude protein, using five simulated scenarios of supplementation in small-scale dairy systems (SSDS). In addition, two pasture managements (cut-and-carry versus grazing) and two varieties of legumes (red clover vs. white clover) were considered. The diets were tested considering similar milk yield and chemical composition; CH₄ emission was estimated using Tier-2 methodology from the Intergovernmental Panel on Climate Change (IPCC), and the data were analyzed in a completely randomized 5 × 2 × 2 factorial design. Differences (p < 0.05) were found in predicted CH₄ emissions per kg of milk produced (g kg⁻¹ FCM 3.5%). The lowest predicted CH₄ emissions were found for S3 and S4 as well as for pastures containing white clover. Lower dietary surpluses of CP (p < 0.05) were observed for the control diet (1320 g CP/d), followed by S5 (1793 g CP/d), compared with S2 (2175 g CP/d), as well as in cut-and-carry management with red clover. A significant correlation (p < 0.001) was observed between dry matter intake and CH₄ emissions (g⁻¹ and per kg of milk produced). It is concluded that the environmental impact of formulating diets from local inputs (S3 and S4) can be reduced by making them more efficient in terms of methane kg⁻¹ of milk in SSDS. Full article

The objectives of this study were two-fold. Firstly, to estimate the likely correlated responses in milk urea nitrogen (MUN) concentration, lactation yields of milk (MY), fat (FY) and crude protein (CPY) and mature cow liveweight (LWT) under three selection scenarios which varied in relative emphasis for MUN; 0% relative emphasis (MUN_0%: equivalent to current New Zealand breeding worth index), and sign of the economic value; 20% relative emphasis positive selection (MUN_+20%), and 20% relative emphasis negative selection (MUN_−20%). Secondly, to estimate for these three scenarios the likely change in urinary nitrogen (UN) excretion under pasture based grazing conditions. The predicted genetic responses per cow per year for the current index were 16.4 kg MY, 2.0 kg FY, 1.4 kg CPY, −0.4 kg LWT and −0.05 mg/dL MUN. Positive selection on MUN in the index resulted in annual responses of 23.7 kg MY, 2.0 kg FY, 1.4 kg CPY, 0.6 kg LWT and 0.10 mg/dL MUN, while negative selection on MUN in the index resulted in annual responses of 5.4 kg MY, 1.6 kg FY, 1.0 kg CPY, −1.1 kg LWT and −0.17 mg/dL MUN. The MUN_−20% reduced both MUN and cow productivity, whereas the MUN_+20% increased MUN, milk production and LWT per cow. Per cow dry matter intake (DMI) was increased in all three scenarios as milk production increased compared to base year, therefore stocking rate (SR) was adjusted to control pasture cover. Paradoxically, ten years of selection with SR adjusted to maintain annual feed demand under the MUN_+20% actually reduced per ha UN excretion by 3.54 kg, along with increases of 63 kg MY, 26 kg FY and 16 kg CPY compared to the base year. Ten years of selection on the MUN_0% index generated a greater reductions of 10.45 kg UN and 30 kg MY, and increases of 32 kg FY and 21 kg CPY per ha, whereas the MUN_−20% index reduced 14.06 kg UN and 136 kg MY with increases of 32 kg FY and 18 kg CPY compared to base year. All three scenarios increased partitioning of nitrogen excreted as feces. The selection index that excluded MUN was economically beneficial in the current economic circumstances over selection indices including MUN regardless of whether selection was either for or against MUN. There was no substantial benefit from an environmental point of view from including MUN in the Breeding Worth index, because N leaching is more a function of SR rather than of individual cow UN excretion. This study demonstrates that attention needs to be paid to the whole system consequences of selection for environmental outcomes in pastoral grazing circumstances. Full article

The production of milk must be balanced with the sustainable consumption of water resources to ensure the future sustainability of the global dairy industry. Thus, this review article aimed to collate and summarize the literature in the dairy water-usage domain. While green water use (e.g., rainfall) was found to be largest category of water use on both stall and pasture-based dairy farms, on-farm blue water (i.e., freshwater) may be much more susceptible to local water shortages due to the nature of its localized supply through rivers, lakes, or groundwater aquifers. Research related to freshwater use on dairy farms has focused on monitoring, modeling, and analyzing the parlor water use and free water intake of dairy cows. Parlor water use depends upon factors related to milk precooling, farm size, milking systems, farming systems, and washing practices. Dry matter intake is a prominent variable in explaining free water intake variability; however, due to the unavailability of accurate data, some studies have reported moving away from dry matter intake at the expense of prediction accuracy. Machine-learning algorithms have been shown to improve dairy water-prediction accuracy by 23%, which may allow for coarse model inputs without reducing accuracy. Accurate models of on-farm water use allow for an increased number of dairy farms to be used in water footprinting studies, as the need for physical metering equipment is mitigated. Full article

Under the combined effect of stocking rate and grazing season, it is very significant to ascertain whether there is a quantitative relationship between plant community characteristics, chemical composition of forage, and grazing behaviors of Tibetan sheep to better utilize native pasture in the northeast region of the Qinghai–Tibetan Plateau (QTP). The two consecutive year observation experiments on Tibetan sheep’s grazing behavior were conducted to evaluate the above-stated relationships between stocking rates of 8 sheep/ha and 16 sheep/ha stocking rates in the both the warm and cold seasons. The results demonstrated that at 8 sheep/ha or in the warm season, due to better forage quality, Tibetan sheep had higher herbage mass, forage crude protein (CP) concentration, CP intake, dry matter intake (DMI), and interval between feed boluses and total number of steps, as well as lower fiber concentration than that at 16 sheep/ha or in the cold season. Diurnal intake rate and walking velocity while intaking increased as both average daylight ambient temperature and relative humidity rose. Using the CP concentration, acid detergent fiber (ADF) concentration, neutral detergent fiber (NDF) concentration, and forage metabolic energy (ME) to predict grazing behavior yielded the best fit equation for Tibetan sheep. For local herdsmen to sustainably use the alpine meadow, 8 sheep/ha in the warm season should be considered as the better grazing condition for preventing grassland degradation. Full article