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Perennial ryegrass (Lolium perenne L.) is a commonly grown pasture species in temperate agriculture, mainly serving as a primary energy source for dairy cows. However, its limited persistence often leads to missed production potential and early resowing, especially in countries that experience summer drought, e.g., Australia and New Zealand. Therefore, understanding the factors influencing perennial ryegrass pasture persistence is crucial for sustainable land management and climate resilience in pasture-based animal production systems. Significant gaps in knowledge exist regarding the factors influencing pasture persistence, as the number of conducted studies in this area remains limited. This study aimed to investigate the factors influencing the expression of persistence in perennial ryegrass populations using airborne and ground-based sensors. A field experiment was conducted in the southwest region of Victoria, Australia, involving ten commercial perennial ryegrass cultivar–endophyte combinations in two different populations. Persistence was evaluated using sensor-based and conventional pasture measurements over two consecutive autumns. The results revealed significant fixed effects of cultivar, endophyte, and environment and their interactions on persistence traits of perennial ryegrass. Cultivars Alto, Samson, and One50 exhibited high levels of persistence when infected with novel endophyte strains. Furthermore, prolonged environmental stresses were found to drive directional selection within pasture populations. The findings emphasise the importance of selecting appropriate cultivar–endophyte combinations and early detection of signs of poor persistence to optimise sward longevity and financial returns from pasture-based animal production systems. This study fills a knowledge gap regarding the factors influencing pasture persistence and provides valuable insights for sustainable pasture management strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Mitigating methane emissions from ruminants requires strategies that are sustainable and acceptable to both consumers and producers. Direct-fed microbials could meet these requirements. Forty Holstein-Friesian cows were randomly allocated one of two treatments, a control or control plus a direct-fed microbial (MYLO®—a mixture of Lactobacillus species; Terragen Biotech Pty Ltd., Coolum Beach, Queensland, Australia). Adding the direct-fed microbial had no significant effect on feed intake, milk yield, feed conversion efficiency, or methane parameters. While these results are contrary to our expectations, all were numerically in a favorable direction. Given there are reports that diet and dose rate may impact the size of any effect, we recommend a dose–response study be undertaken using a basal diet that is commonly used in pasture-based dairy systems. Using direct-fed microbials to mitigate enteric methane emissions could be sustainable and acceptable to both consumers and producers. Forty lactating, multiparous, Holstein-Friesian cows were randomly allocated one of two treatments: (1) a base of ad libitum vetch (Vicia sativa) hay and 7.0 kg DM/d of a grain mix, or (2) the basal diet plus 10 mL of MYLO® (Terragen Biotech Pty Ltd., Coolum Beach, Queensland, Australia) delivering 4.17 × 108 cfu of Lactobacillus per mL. Neither feed intake (25.4 kg/d vs. 24.8 kg/d) nor milk yield (29.9 vs. 30.3 kg/d) were affected by treatment. Feed conversion efficiency was not affected by treatment when expressed on an energy-corrected milk basis (1.15 vs. 1.18 kg/kg DMI). Neither methane yield (31.6 vs. 31.1 g/kg DMI) nor methane intensity (27.1 vs. 25.2 g/kg energy corrected milk) were affected by treatments. While these results are contrary to our expectations and not significant, all were numerically in a favorable direction. Given there are reports that diet and dose rate may impact the size of any effect, we recommend a dose–response study be undertaken using a basal diet that is commonly used in pasture-based dairy systems. [ABSTRACT FROM AUTHOR]

Short-chain fatty acids (SCFA) and lactate in ruminal fluid are products resulting from the microbial fermentation of substrates and can be used to reflect the composition and activity of the ruminal microbiome. Determination of SCFA and D-/L-lactate in ruminal fluid currently requires two separate protocols, which is time-consuming and costly. In this study, we have optimised and validated a simple and unified 3-nitrophenylhydrazine (3-NPH) derivatisation protocol and a 20 min chiral-LC-MS method for the simultaneous quantification of all SCFA and D- and L-lactate in ruminal fluid. This method, which requires no sample pretreatment or purification shows adequate sensitivity (limit of detection (LOD): 0.01 µg/mL), satisfactory accuracy (recovery: 88–103%), and excellent reproducibility (relative standard deviation (RSD) for repeated analyses < 3% for most analytes). The application of this method to a cohort of 24 animals allowed us to reveal a large inter-cow variation in ruminal SCFA and lactate level, the concentration range for each species, the widespread correlation between different SCFA, and the strong correlation between D- and L-lactate. [ABSTRACT FROM AUTHOR]

We investigated the association of muscle iron concentration, in addition to ultimate pH (pHU), on dark meat formation in sheep of different breeds fed forage-based diets. At 1 h simulated display, redness of meat (a*-value) increased (P <.0001) by about 3 units as the iron concentration increased from 10 to 22 mg/kg of meat, whereas the a*-value decreased by 2 units as pHU increased from 5.5 to 6.2 in fresh meat (P <.0001). After 90 days storage the corresponding responses were about 2 units increase for iron concentration and about 1 unit decrease for pHU, respectively. The results clearly show that increased muscle iron concentration was strongly associated with reduced dark cutting in fresh and stored meat evaluated at 1 h simulated display. We conclude that it may be desirable to measure iron concentration, along with pHU, for evaluation of the potential for carcasses to produce dark cutting meat, and for the meat to turn brown during display. • The association of muscle iron concentration and ultimate pH on redness of sheep meat at retail display was investigated • Increased muscle iron concentration was strongly associated with increased redness at 1 h retail display • The muscle iron effect on redness occurred both in fresh and in stored (aged) meat • The effect of muscle iron concentration was additive to the accepted effect of ultimate pH at 1 h retail display • There is a necessity to measure both iron concentration and ultimate pH when evaluating carcasses for dark cutting meat [ABSTRACT FROM AUTHOR]

Simple Summary: Grape marc comprises the skins, seeds and stems of grapes remaining after grapes are pressed to make wine. Globally, about nine million tonnes of grape marc are produced annually. However, little is known about the comparative nutritional value of grape marc from red and white grapes and their effects on milk production and methane emissions when fed to dairy cows. Our experiment assessed the potential role of grape marc as a feed source for the grazing based, Australian dairy industry. We fed diets based on harvested perennial ryegrass to lactating dairy cows and compared milk production and methane emissions when grape marc from either red or white grapes was substituted for some of the perennial ryegrass. Diets containing grape marc from either red or white grapes equally decreased milk yields by approximately 10% and methane emissions by 15%. When fed to dairy cows, grape marc reduces methane emissions but at the cost of decreased milk production. The effects on methane emissions were mainly mediated by the high concentrations of lignin and fat in grape marc while decreased milk production was due to decreased intake of metabolizable energy. Globally, annual production of grape marc (GM), the residue of skins, seeds and stems remaining after making wine, has been estimated to be approximately nine million tonnes. No previous studies have compared effects on milk production and methane emissions when GM from either red or white grapes was fed to dairy cows. This experiment examines the effects of partial replacement of a perennial ryegrass (Lolium perenne L.) based diet with GM from either red or white grapes on yield and composition of milk and methane emissions. Thirty-two Holstein dairy cows in early lactation were offered either a control diet containing 15.0 kg dry matter (DM) of freshly harvested perennial ryegrass and 5.2 kg of a concentrate mix, or a diet similar to the control diet but with 5 kg DM of ryegrass replaced with 5 kg DM of GM from red grapes (RGM), or a diet similar to the RGM diet except the GM was from white grapes (WGM). Individual cow feed intakes, milk yields, and methane emissions were measured. Both diets containing GM decreased milk yields by approximately 10% and methane emissions by 15%. When fed to dairy cows, GM reduces methane emissions but at the cost of decreased milk production. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne L.) is considered the most important pasture species in temperate agriculture, with over six million hectares of sown area in Australia alone. However, perennial ryegrass has poor persistence in some environments because of low tolerance to a range of both abiotic and biotic stresses. To breed perennial ryegrass, cultivars with greater persistence and productivity may require evaluation of genotypes over a number of years. Persistence assessment in pasture breeding depends on manual ground cover estimation or counting the number of surviving plants or tillers in a known area. These methods are subjective and labour intensive, which may limit data collection in large‐scale breeding programs. With the rapid development of sensors and image processing algorithms, image‐based high‐throughput phenotyping (HTP) is becoming commonplace in the breeding of major food crops. Image‐based HTP approaches consist of the deployment of a wide range of sensors on ground‐based or airborne platforms and data analysed through image processing pipelines. Image‐based HTP shows high potential for use in pasture phenotyping in breeding programs and may be able to reduce timeframes for releasing new cultivars. Moreover, existing image‐based HTP approaches could be further developed to include precise tools for phenotyping pasture persistence traits such as pasture senescence, botanical composition, pathogen and pest resistance. In this study, we reviewed existing image‐based HTP approaches in precision agriculture and discussed their feasibility for perennial ryegrass persistence estimation in pasture breeding. Although the paper focuses on application in perennial ryegrass, the principles equally apply to other perennial forage species. [ABSTRACT FROM AUTHOR]

A foreword to the "Animal Production Science" journal is presented.

BACKGROUND There are several methods for estimating methane production (MP) from feedstuffs in vented in vitro systems. One method (A; 'gold standard') measures methane proportions in the incubation bottle's headspace (HS) and in the vented gas collected in gas bags. Four other methods (B, C, D and E) measure methane proportion in a single gas sample from the HS. Method B assumes the same methane proportion in the vented gas as in the HS, method C assumes constant methane to carbon dioxide ratio, method D has been developed based on empirical data, and method E assumes constant individual venting volumes. This study aimed to compare the MP predictions from these methods to that of the gold standard method under different incubation scenarios, to validate these methods based on their concordance with a gold‐standard method. RESULTS: Methods C, D and E had greater concordance (0.85, 0.88 and 0.81), lower root‐mean‐square error (RMSE; 0.80, 0.72 and 0.85) and lower mean bias (0.20, 0.35, −0.35) with the gold standard than did method B (concordance 0.67, RMSE 1.49 and mean bias 1.26). Methods D and E were simpler to perform than method C, and method D was slightly more accurate than method E. CONCLUSION: Based on precision, accuracy and simplicity of implementation, it is recommended that, when method A cannot be used, methods D and E are preferred to estimate MP from vented in vitro systems. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including challenges and opportunities for dairying in Australia and New Zealand, advances in biotechnology and animal welfare and well-being.

The nutrition of grazing ruminants can be optimized by allocating pasture according to its nutritive characteristics, provided that nutritive concentrations are determined in near-real time. Current proximal spectrometers can provide accurate predictive results but are bulky and expensive. This study compared an industry standard, 'control', proximal spectrometer, often used for scientific estimation of pasture nutrient concentrations in situ (350–2500 nm spectral range), with three lower-cost, 'next-generation', handheld spectrometers. The candidate sensors included a hyperspectral camera (397–1004 nm), and two handheld spectrometers (908–1676 nm and 1345–2555 nm respectively). Pasture samples (n = 145) collected from two paddocks on a working Australian dairy farm, over three timepoints, were scanned in situ by each instrument and then analysed for eight nutritive parameters. Chemometric models were then developed for each nutrient using data from each sensor (split into 80:20 calibration and validation sets). According to Lin's Concordance Correlation Coefficient (LCCC) from independent validation (n = 29), the hyperspectral camera was the best candidate instrument (LCCC from 0.31 to 0.85, and 0.67 on average), rivalling the control sensor (LCCC from 0.41 to 0.84, and 0.67 on average). Consideration was given to whether the hyperspectral camera's success was due to spectral range or data type/capture method. It was found that the 400–920 nm (trimmed) spectral region was slightly less sensitive in principle to nutrient concentrations than higher spectral ranges. Therefore, the predictive performance of the camera was attributed to the advantage of gathering data as hyperspectral images as opposed to single spectra. [ABSTRACT FROM AUTHOR]

Simple Summary: A common nutritional strategy to reduce heat stress on dairy cows is to provide a more slowly degradable starch source that reduces the amount of heat generated during digestion. The aim of this experiment was to investigate the responses of late lactation dairy cows to cereal grain-based diets in a short-term heat challenge. Cows were offered a diet of alfalfa hay supplemented with either wheat grain (fast rumen degradable) or corn grain (slow rumen degradable). Individual cow measurements of feed intake, milk yield and composition, respiration rate, and body temperature were taken daily before, during and after a 4-day heat challenge, during which the cows were in individual controlled-climate chambers and exposed to air temperature up to 33 °C with 50% relative humidity. While exposed to the heat challenge during late lactation, cows that were offered corn grain had greater feed intake and tended to produce more energy-corrected milk but had lower respiration rates and similar body temperature to the cows offered wheat grain. The economic impact of feeding corn in place of wheat grain needs to be assessed before any comparative value of feeding corn grain or wheat grain can be determined. Cereal grains that differ in the rate and extent of ruminal fermentation differ in heat increment and may be used to improve thermoregulation during heat stress. This experiment investigated the responses of dairy cows in late lactation to a heat challenge when offered wheat-grain or corn-grain. Eighteen lactating cows, 220 ± 94 (mean ± standard deviation) days in milk, 3.7 ± 0.17 years of age and 558 ± 37 kg bodyweight, were allocated treatments containing 6 kg dry matter (DM)/day of wheat grain or 6 kg DM/day corn grain (9 per treatment) plus 14 kg DM/day of alfalfa hay. Measurements were made during a 7-day pre-challenge period at ambient conditions in individual stalls, during a 4-day heat challenge (temperature humidity index of 74 to 84) in individual controlled-climate chambers, then during a 7-day recovery period at ambient conditions in individual stalls. During the heat challenge, cows offered corn had lower respiration rates (p = 0.017) and greater feed intake (p = 0.021) but energy-corrected milk (p = 0.097) was not different to that of cows offered wheat. Feeding corn grain to dairy cows during a heat challenge reduced some of the negative impacts of heat stress, enabling the cows to consume more forage compared with supplementing with wheat grain. [ABSTRACT FROM AUTHOR]

The gastrointestinal tract of humans is a complex microbial ecosystem known as gut microbiota. The microbiota is involved in several critical physiological processes such as digestion, absorption, and related physiological functions and plays a crucial role in determining the host's health. The habitual consumption of specific dietary components can impact beyond their nutritional benefits, altering gut microbiota diversity and function and could manipulate health. Phytochemicals are non-nutrient biologically active plant components that can modify the composition of gut microflora through selective stimulation of proliferation or inhibition of certain microbial communities in the intestine. Plants secrete these components, and they accumulate in the cell wall and cell sap compartments (body) for their development and survival. These compounds have low bioavailability and long time-retention in the intestine due to their poor absorption, resulting in beneficial impacts on gut microbiota population. Feeding diets containing phytochemicals to humans and animals may offer a path to improve the gut microbiome resulting in improved performance and/or health and wellbeing. This review discusses the effects of phytochemicals on the modulation of the gut microbiota environment and the resultant benefits to humans; however, the effect of phytochemicals on the gut microbiota of animals is also covered, in brief. [ABSTRACT FROM AUTHOR]

The majority of dairy farmers and processors in Australia and New Zealand are considered world class due to their ability to produce dairy products at a cost that is competitive on the world market without requirement for subsidy. International and domestic forces beyond the farm influence the international competitiveness of Antipodean dairy systems, as much or more than, the within-farm characteristics of the systems. Critical external forces include: world population growth, protein demand from increasingly wealthy developing countries, dairy supply from domestic and international producers, international dairy prices and exchange rate volatility. Within farm, the keys to persistent profitability, business survival, and growth will continue to be management ability and labour skill as well as the relationship between milksolids (milk fat + milk protein) produced per system and total production costs. Domestic forces will include competition for resources such as land, water, quality labour and capital, and public expectation that farms will meet the costs of community environmental and welfare objectives. Public and industry investment in research, development and extension in innovations that increase productivity is essential if dairying is to remain competitive. The operation of the comparative advantage principle determines which industries thrive, or decline, in an economy. New Zealand dairying has a strong comparative advantage over alternative pastoral industries which will continue. In Australia, the comparative advantage of dairy farming over alternative activities is less clear-cut. History shows that the best farmers and processors handle risks such as market and climate volatility and other challenges better than others, and their prospects are positive. However, world class performers in the future dairy industry will certainly not be all, or even the majority, of the current population of dairy farmers. Australian and New Zealand dairy farm businesses are world class, but there is no guarantee that their comparative advantage over alternative land uses will continue. An analysis of key physical and financial characteristics of dairy farming shows that there is wide variation in financial performance for farm businesses no matter what level of feed inputs are used. The best performers in both countries are poised to take advantage of the increased global demand for milk products. [ABSTRACT FROM AUTHOR]

Genotype-by-environment interaction (G×E) is commonly observed in perennial ryegrass species and significantly impacts dry matter yield (DMY) performance across environments. Variation in DMY of perennial ryegrass cultivars and their distinct responses to different environments directly influence the profitability of the Australian dairy industry. In this study, we implemented a separate two-way factor analytic strategy to fit multi-environment-multi-harvest data collected from 18 trials spanning 14 years to account for the G×E effects across complex pasture environments in south-eastern Australia to achieve accurate DMY predictions. Three mega-environments were identified, and the seasonal and environmental DMYs of 126 cultivars or breeding lines (defined by a combination of varieties and endophytes) were predicted. Statistical differences in DMY performance and crossover G×E effects in cultivar rankings were observed among the three mega-environments. Several high-yielding cultivars specific to each mega-environment were also identified. The analysis demonstrated the importance of accounting for G×E effects when predicting the DMY of perennial ryegrass and highlighted the potential for identifying high-yielding cultivars specific to mega-environments. The research rationalizes implementing an appropriate independent pasture trialling system to generate necessary data for the dairy industry and could further facilitate dairy farmers to select suitable cultivars based on their specific environments and perennial ryegrass breeders when accounting for the genetic relationship of cultivars. • G×E impacts perennial ryegrass cultivar rankings and breeding strategies. • Factor analytic model is a robust tool for analysing G×E. • Analysing G×E provides accurate DMY prediction and facilitates identifying high-yielding cultivars. • Identified 3 mega-environments and predicted DMYs for 126 cultivars. • Appling an independent trialling system is necessary to identify environmental-specific cultivars. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of Australia and New Zealand. However, it can have poor persistence due to a low tolerance to both abiotic and biotic stresses. A major challenge in measuring persistence in pasture breeding is that the assessment of pasture survival depends on ranking populations based on manual ground cover estimation. Ground cover measurements may include senescent and living tissues and can be measured as percentages or fractional units. The amount of senescent pasture present in a sward may indicate changes in plant growth, development, and resistance to abiotic and biotic stresses. The existing tools to estimate perennial ryegrass ground cover are not sensitive enough to discriminate senescent ryegrass from soil. This study aimed to develop a more precise sensor-based phenomic method to discriminate senescent pasture from soil. Ground-based RGB images, airborne multispectral images, ground-based hyperspectral data, and ground truth samples were taken from 54 perennial ryegrass plots three years after sowing. Software packages and machine learning scripts were used to develop a pipeline for high-throughput data extraction from sensor-based platforms. Estimates from the high-throughput pipeline were positively correlated with the ground truth data (p < 0.05). Based on the findings of this study, we conclude that the RGB-based high-throughput approach offers a precision tool to assess perennial ryegrass persistence in pasture breeding programs. Improvements in the spatial resolution of hyperspectral and multispectral techniques would then be used for persistence estimation in mixed swards and other monocultures. [ABSTRACT FROM AUTHOR]

• The value of extra pasture DM for livestock production can be estimated using several different methods. • The two-price market value method can be an efficient way to estimate the value of extra seasonal dry matter yield of pasture grown and used. • To demonstrate its application, the method is used to value the seasonal yield of 19 perennial ryegrass cultivars grown in temperate Australia. • The value of extra pasture estimated using the two-price market value method can be easily generalised across a range of farming systems. The challenges of estimating the value of growing and using extra pasture to the farm business often necessitates using complex systems modelling of individual farms. In this paper, a straightforward market value method is outlined, where technical substitutes for which there is a defined market price are used as proxies to estimate an equivalent value for extra pasture used in a farm system. The method can be useful for all grazed livestock production systems where substitutes for pasture are purchased and sold. The two-price approach presented advances on previous applications of using market values by accounting for differences in the value of extra pasture DM used on farm in different seasons. The value per extra kg DM harvested in seasons when pasture typically limits livestock production, for example during summer/autumn in temperate environments, and used on farm equates to the market price of the replacement cost as purchased supplementary feed. This is the maximum the extra pasture DM could be worth when used in the farm system. Conversely, the value per extra kg DM harvested in spring when extra pasture is likely to be in surplus of livestock requirements, equates to its market price if sold, as standing hay for example, termed the salvage value. This two-price approach was applied to value the previously published seasonal DM yield differences of 19 perennial ryegrass cultivars, compared to an industry standard cultivar, grown in three regions of Victoria, Australia. Results were then compared to the value of extra pasture DM previously estimated using system simulation analyses of three farms and applied to the same perennial ryegrass data set. The difference in the value of extra pasture DM estimated using the two methods ranged from $0.01/kg DM to $0.24/kg DM. Overall, the two-price method estimated an extra $24 to $200/ha.year benefit over the industry standard cultivar for the 19 cultivars across the three regions, similar to the extra $0 to ∼ $180/ha.year estimated by the previous system simulation analysis. The two-price market value method can be an efficient way to estimate the value of extra seasonal DM yield of pasture, with results easily generalised across a range of farming systems. [ABSTRACT FROM AUTHOR]

Simple Summary: Methane emissions from cattle are difficult to measure, and some proxies used to estimate them have required information that is not always available. An example is predicting methane from the fatty-acid profile of milk, but this strategy is not suitable for non-lactating animals. We propose equations to predict the methane emitted per unit of feed eaten (methane yield) based on the volatile fatty acids within the rumen of the animals. Three of the seven equations we investigated were equally good at predicting the methane yield of dairy cows. Validation of these equations using previously published results indicated that the equations should also work for beef cattle. Being able to predict the methane yield for all classes of cattle means that a single strategy can be used, eliminating differences because of the use of different methods for different animal classes. Further work is necessary, but our strategy should be able to be adapted for use in cattle production environments. Being able to predict the methane of production animals will enable accurate estimates of the methane emissions from those animals, and assessment of strategies to reduce those emissions. The dry matter intake (DMI) of forage-fed cattle can be used to predict their methane emissions. However, many cattle are fed concentrate-rich diets that decrease their methane yield. A range of equations predicting methane yield exist, but most use information that is generally unavailable when animals are fed in groups or grazing. The aim of this research was to develop equations based on proportions of ruminal volatile-fatty-acids to predict methane yield of dairy cows fed forage-dominant as well as concentrate-rich diets. Data were collated from seven experiments with a total of 24 treatments, from 215 cows. Forage in the diets ranged from 440 to 1000 g/kg. Methane was measured either by open-circuit respiration chambers or a sulfur hexafluoride (SF6) technique. In all experiments, ruminal fluid was collected via the mouth approximately four hours after the start of feeding. Seven prediction equations were tested. Methane yield (MY) was equally best predicted by the following equations: MY = 4.08 × (acetate/propionate) + 7.05; MY = 3.28 × (acetate + butyrate)/propionate + 7.6; MY = 316/propionate + 4.4. These equations were validated against independent published data from both dairy and beef cattle consuming a wide range of diets. A concordance of 0.62 suggests these equations may be applicable for predicting methane yield from all cattle and not just dairy cows, with root mean-square error of prediction of 3.0 g CH4/kg dry matter intake. [ABSTRACT FROM AUTHOR]

Background: A pelleted diet containing camelina hay (CAMH) or camelina meal (CAMM) as a supplement along with a control pellet (CONT) diet formulated with commonly available feeds during summer was used to investigate an alternative pathway for sustainable meat production. Sustainable meat production was based on a simple estimation of income from meat produced versus feed costs if animals were fed for an extended period over summer compared to early slaughter at the beginning of summer. Eighty maternal composite wether lambs (Composite) based on Coopworth genetics and 80 pure Merino wether yearlings were divided into 10 groups within breed (n = 8) using stratified randomisation based on liveweights. Following 1 week of adaptation to experimental diets, animals were fed experimental diets for up to10 weeks. Results: Animals were slaughtered after either 8, 9 or 10 weeks of full feeding when the average liveweight of diet/genetic combination reached a weight appropriate for either 'heavy lamb' or 'heavy hogget' production, which occurred between 8 and 10 weeks of full feeding. There was no diet × breed interactions except for dressing percentage (DP), where Composite lambs fed the CAMH diet had the greatest DP (48.1 ± 0.35) and the Merino yearlings fed the CAMM diet the lowest DP (45.8 ± 0.33). Composite lambs gained 17.6–20.3 kg and Merino yearlings gained 10.7–12.9 kg liveweight. Based on their DP, this resulted in the production of approximately 8.3–9.5 kg additional carcass weight in Composites and 4.9–5.7 kg in Merinos, which in turn produced greater profit per Composite lamb and a small profit per Merino yearling. Conclusions: Composite lambs fed CAMM and CAMH had 5% greater carcass weights at slaughter compared to the CONT group, but dietary treatments did not change carcass weight of Merino yearlings at slaughter. The extended feeding approach offered the producer an estimated economic gain of AUD $20.00 to $25.00 when yearly average prices were used (Method 1) and AUD $40.00 to $47.70 when pre- and post-summer average prices were used (Method 2) per Composite lambs, but extra carcass gain did not result in the same profit per Merino yearling. Among the Composites, the profit for animals fed the CAMH and CAMM were AUD $2.75 to $4.50 greater than CONT group when full year average prices were applied while AUD $3.50 to $5.50 greater than CONT group when pre- and post-summer average prices were applied. However, we acknowledge a combination approach of extended feeding for a portion of animals already on ground and selling the remaining animals pre-summer with joining of additional ewes is the most likely strategy. Considering the scenario of extended feeding for 3 weeks, based on the growth rates observed for Composite lambs, if an additional 2 kg carcass weight per animal can be gained for 50% of the 22 million lambs slaughtered in Australia (= 11 million animals), it would potentially supply an additional 22 million kg of lamb carcasses produced per annum. This is equivalent to producing an extra 1 million lamb carcasses per annum weighing 22 kg each. Feeding Composite lambs for an extended period and selling Merino yearlings pre-summer may be a good option due to faster growth rate of Composites that may help quick turn-over to market. [ABSTRACT FROM AUTHOR]

BACKGROUND Supplementation of ruminant diets with wheat and corn grains influences ruminal fermentation. In vitro fermentation is a methodology that can be used to screen feeds for their potential to produce enteric methane. However, there is evidence that the diet of the donor cows could impact the results of in vitro analysis. This research investigated the in vitro fermentation of wheat and corn grain when incubated in ruminal fluid from cows fed different grain types and different forage‐to‐grain ratios. RESULTS: The type of grain fed to the donor cows, as well as forage‐to‐grain ratio, affected the outcome of fermentation of wheat and corn grain. Differences in methane production (MP) between grains were only observed when incubated with ruminal fluid adapted to each specific grain type. Increasing proportions of wheat but not of corn decreased in vitro MP in a linear manner compared with MP produced from forage only. CONCLUSIONS: Wheat grain has a greater in vitro antimethanogenic effect than corn. However, to detect the different fermentations between wheat and corn, grains should be incubated in ruminal fluid from cows adapted to that specific grain type. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Abstract Agricultural industries are faced with the challenge to reduce their emissions of greenhouse gases (GHG) such as nitrous oxide (N 2 O), carbon dioxide (CO 2) and methane (CH 4). Approximately 27% of the GHG emissions generated by agriculture and 80% of the GHG emissions generated by livestock are in the form of CH 4 from enteric fermentation and manure management. Two feed additives that have been shown to decrease enteric CH 4 emissions are 3-nitrooxypropanol (3-NOP) and nitrate. However, data are lacking on the net impact of these CH 4 mitigating additives on whole farm GHG emissions across different production systems. The objectives of this partial life cycle assessment (LCA) were: 1) to analyse the effect of 3-NOP and nitrate on whole farm GHG emissions of Australian and Canadian beef and dairy farms, and 2) to analyse the effect of different strategies of 3-NOP and nitrate feeding on whole farm GHG emissions. Two Australian dairy farms (ADF), one Australian beef farm (ABF), one Canadian dairy farm (CDF) and one Canadian beef farm (CBF) were modelled over the productive lifespans of dairy and beef herds. Australian farm GHG emissions were estimated based on the Australian National Greenhouse Gas Inventory method using the Greenhouse Accounting Framework calculators for beef and dairy. For the Canadian farms, total GHG emissions were estimated using Holos 3.0.3, a whole-farm software model. For all farms, the system boundary of the partial LCA was defined as the farm gate, including emissions associated with on-farm activities and emissions associated with production and transport of major production inputs. Both additives decreased whole farm GHG emissions; however, 3-NOP had a greater effect than nitrate. Feeding 3-NOP but not nitrate could be economical for both beef and dairy farms, depending on the cost of 3-NOP. It is concluded that 3-NOP can make an important contribution to reducing whole farm GHG emissions; however, a carbon offset method would have to be in place to incentivize its use in the livestock industries. Highlights • Feeding 3-NOP decreased whole farm greenhouse gas emissions. • Feeding nitrate decreased whole farm greenhouse gas emissions. • Feeding 3-NOP had greater effect than nitrate on whole farm greenhouse gas emissions. • With current carbon prices, feeding 3-NOP could be economical for use on farm. [ABSTRACT FROM AUTHOR]

Eighty-four crossbred wether and ewe lambs were allocated to four finishing diets. The diets were: Lucerne pasture (n = 24), Annual ryegrass with sub clover pasture (n = 18), Standard commercial feedlot pellets (n = 24) and Annual ryegrass based pasture and commercial feedlot pellets (500 g/day/head) (n = 18). After 8 weeks of feeding the lambs were slaughtered and the m. longissimus (LL) and m. semimembranosus (SM) were vacuum packaged and held chilled for 5 (fresh) or 60 (long aged) days, after which samples of each were displayed for 4 days under simulated retail conditions. Irrespective of muscle type the long aged samples exhibited a rapid reduction in redness (a*-values) and R630/580 nm ratio values such that consumer acceptable thresholds for both traits were quickly exceeded providing limited shelf life. Long ageing also lead to high TBARS levels measured as MDA mg/kg muscle suggestive of a product likely to exhibit rancidity and off flavours from lipid oxidation. [ABSTRACT FROM AUTHOR]

This review examines research aimed at reducing enteric methane emissions from the Australian dairy industry. Calorimeter measurements of 220 forage-fed cows indicate an average methane yield of 21.1 g methane (CH4)/kg dry matter intake. Adoption of this empirical methane yield, rather than the equation currently used in the Australian greenhouse gas inventory, would reduce the methane emissions attributed to the Australian dairy industry by ~10%. Research also indicates that dietary lipid supplements and feeding high amounts of wheat substantially reduce methane emissions. It is estimated that, in 1980, the Australian dairy industry produced ~185 000 t of enteric methane and total enteric methane intensity was ~33.6 g CH4/kg milk. In 2010, the estimated production of enteric methane was 182 000 t, but total enteric methane intensity had declined ~40% to 19.9 g CH4/kg milk. This remarkable decline in methane intensity and the resultant improvement in the carbon footprint of Australian milk production was mainly achieved by increased per-cow milk yield, brought about by the on-farm adoption of research findings related to the feeding and breeding of dairy cows. Options currently available to further reduce the carbon footprint of Australian milk production include the feeding of lipid-rich supplements such as cottonseed, brewers grains, cold-pressed canola, hominy meal and grape marc, as well as feeding of higher rates of wheat. Future technologies for further reducing methane emissions include genetic selection of cows for improved feed conversion to milk or low methane intensity, vaccines to reduce ruminal methanogens and chemical inhibitors of methanogenesis. [ABSTRACT FROM AUTHOR]

The effect of feeding flaxseed or algae supplements to lambs on muscle antioxidant potential (vitamin E), major fatty acid groups, lipid oxidation and retail colour was investigated. Lambs (n = 120) were randomly allocated to one of 4 dietary treatments according to liveweight and fed the following diets for eight weeks: Annual ryegrass hay [60%] + subterranean clover hay [40%] pellets = Basal diet; Basal diet with flaxseed (10.7%) = Flax; Basal diet with algae (1.8%) = Algae; Basal diet with flaxseed (10.7%) and algae (1.8%) = FlaxAlgae. Flaxseed or algae supplementation significantly affected major fatty acid groups in muscle. The addition of algae (average of Algae and FlaxAlgae) resulted in lower vitamin E concentration in muscle ( P < 0.003; 1.0 vs 1.3 mg/kg of muscle) compared with lambs fed a diet without algae (average of Basal and Flax). Increasing muscle EPA + DHA by algae supplementation significantly increased lipid oxidation, but retail display colour of fresh meat was not affected. [ABSTRACT FROM AUTHOR]

For Australian and New Zealand dairy farms, the primary source of home-grown feed comes from grazed perennial pastures. The high utilisation of perennial pasture is a key factor in the low cost of production of Australian and New Zealand dairy systems and, hence, in their ability to maintain international competiveness. The major pasture species used are perennial ryegrass (Lolium perenne L.) and white clover (Trifolium repens L.), normally grown in a simple binary mixture. As pasture production has been further driven by increasing use of nitrogen fertiliser and irrigation, farms are getting closer to their economic optimum level of pasture utilisation. Increasing inputs and intensification have also increased scrutiny on the environmental footprint of dairy production. Increasing the diversity of pasture species within dairy swards presents opportunities to further increase pasture utilisation through additional forage production, extending the growing season, improving forage nutritive characteristics and, ultimately, increasing milk production per cow and/or per hectare. Diverse pastures also present an opportunity to mitigate some of the environmental consequences associated with intensive pasture-based dairy systems. A consistent finding of experiments investigating diverse pastures is that their benefits are due to the attributes of the additional species, rather than increasing the number of species per se. Therefore, the species that are best suited for inclusion into dairy pastures will be situation specific. Furthermore, the presence of additional species will generally require modification to the management of dairy pastures, particularly around nitrogen fertiliser and grazing, to ensure that the additional species remain productive and persistent. Diverse pastures offer benefits to pasture-based dairy systems in terms of increased forage and animal production, improved resilience and the potential to reduce nitrate leaching. The management required to maintain the diversity in the pastures may limit their application. However, the benefits of diverse pastures can be archived with only three species making their management and application simpler. [ABSTRACT FROM AUTHOR]

Simple Summary: Hot weather is associated with reduced milk yield of dairy cows. Supplementing the diet of lactating cows with ingredients that increase dietary energy density or that reduce internal heat production, may reduce some of the negative impacts of hot weather on milk yield. We used controlled-climate chambers to simulate a short hot-weather event and measured changes in milk yield, feed intake, and body temperature of cows fed either a fat supplement, betaine or a combination of both. Feeding cows fat resulted in improved milk production but also increased body temperature and caused a decrease in feed intake. Feeding betaine did not affect milk yield but did reduce cow body temperature at times. Contrary to our expectations, the combination of fat and betaine supplements did not result in a clear benefit in terms of milk production or body temperature. Further work is warranted to understand the interactions between dietary fat type and betaine supplements when offered to cows during periods of hot weather. Supplementing the diet of lactating cows with ingredients that increase energy density, or reduce internal heat production, may reduce some of the negative impacts of hot weather on milk yield. Thirty-two dairy cows were assigned either: (1) basal diet only, (2) basal diet plus canola oil, (3) basal diet plus betaine, or (4) basal diet plus canola oil and betaine. The basal diet was lucerne hay, pasture silage, and grain. Cows were exposed to a four-day heat challenge (temperature-humidity index 74 to 84) in controlled-environment chambers. Canola oil supplementation increased milk production (22.0 vs. 18.7 kg/d) across all periods of our experiment and increased body temperature (39.6 vs. 39.0 °C) during the heat challenge. Betaine supplementation reduced maximum body temperature during the pre-challenge period (39.2 vs. 39.6 °C) but not during the heat challenge (40.3 °C). Cows fed canola oil had greater declines in dry matter intake (5.4 vs 2.7 kg DM) and energy corrected milk (1.3 vs. 1.0 kg) from the pre-challenge to the heat challenge than other cows. Contrary to our expectations, the combination of fat and betaine supplements did not result in a clear benefit in terms of milk production or body temperature. Further work is warranted to understand the interactions between diet and hot weather. [ABSTRACT FROM AUTHOR]

Abstract: The relationship between muscle vitamin E, forms of iron, polyunsaturated fatty acids (PUFA) and the redness of meat (retail display) at days 3 to 4 post slaughter from lambs offered 2 different diets was examined. Meat redness was positively related to vitamin E and heme iron and negatively related to total n-3, total n-6 and total PUFA content. However, after adjusting for the effects of vitamin E and heme iron content, there was no indication of any residual relationship between redness at days 3–4 of retail display and total n-3, total n-6 or total PUFA. This indicates that the relationship between PUFA and redness in meat is mediated through the effects of heme iron and vitamin E in the muscle. It appears that the level of highly oxidisable PUFAs in muscle tissues do not play a major role in maintenance of redness at days 3–4 of retail display, but the level of vitamin E and heme iron content are important. [Copyright &y& Elsevier]

• Ten mathematical models were created to predict pasture yield and nutritive value • Model inputs were field spectrophotometer readings (with or without added covariates) • The models can be applied in situ without destructive sampling to analyse pasture • The pasture yield, crude protein, and energy fraction models predicted most accurately • Models should not be applied where there is substantial bare ground exposure To optimise grazing livestock nutrition, it is necessary to know both the available dry matter yield and the nutritive characteristics of pasture at the farm-scale in near real time. Previous studies have shown the potential of using field spectrophotometers that measure the reflectance of light across the visible to near infrared spectrums to gather information on pasture dry matter yield (DMY) and nutritive characteristics. This study sought to calibrate and validate new mathematical models for ten parameters including dry matter yield and nine nutritive characteristics of relevance to ruminant nutrition. As a part of the analysis process, two innovative approaches were tested: the use of a hybrid modelling approach where partial least squares regression (PLSR) outputs were used as support vector regression (SVR) inputs; and, the inclusion of covariate data. These approaches were compared with traditional stand-alone PLSR and SVR modelling approaches without covariates. The study was undertaken in six predominantly perennial ryegrass pastures on a single farm in the temperate zone of South-Eastern Australia. A total of 204 pasture samples were scanned with a field spectrophotometer over several spring growth stages in late 2019 and subsequently analysed by wet chemistry to obtain reference nutritive values. The raw reflectance spectra were initially pre-processed using a variety of techniques and then used to test the four kinds of chemometric models. In cross validation, hybrid models showed a superior fit for all variates in comparison to the other model types tested. However, the differential was reduced in independent validation where, out of 10 best-performing models for dry matter yield and key nutrient properties, six were produced by the hybrid modelling, three from SVR and one from PLSR. For every hybrid model that was built, adding covariate(s) consistently improved model performance but the increase was small (a reduction in normalised root mean square error (RMSE) of -0.36 % on average for all properties considered). The best performing models were comparable with other published literature with normalised RMSE of prediction ranging from 1.7 – 23.1 % (a mean of 9.7%). Well-predicted variates included metabolisable energy, digestible energy, DMY, and crude protein. Fibre fractions, ash and dry matter were less well-predicted but still had acceptable normalised RMSE values (< 10 %) while carbohydrate fractions were the poorest predicted variates. It was concluded that hybrid modelling in chemometric analyses can modestly improve accuracy and shows promise as an alternative to more traditional approaches. Using covariates also improved accuracy, but the additional time and effort to gather such information outweighed the minor benefits of inclusion. [ABSTRACT FROM AUTHOR]

This study aimed to develop empirical pasture dry matter (DM) yield prediction models using an unmanned aerial vehicle (UAV)-borne sensor at four flying altitudes. Three empirical models were developed using features generated from the multispectral sensor: Structure from Motion only (SfM), vegetation indices only (VI), and in combination (SfM+VI) within a machine learning modelling framework. Four flying altitudes were tested (25 m, 50 m, 75 m and 100 m) and based on independent model validation, combining features from SfM+VI outperformed the other models at all heights. However, the importance of SfM-based features changed with altitude, with limited importance at 25 m but at all higher altitudes SfM-based features were included in the top 10 features in a variable importance plot. Based on the independent validation results, data generated at 25 m flying altitude reported the best model performances with model accuracy of 328 kg DM/ha. In contrast, at 100 m flying altitude, the model reported an accuracy of 402 kg DM/ha which demonstrates the potential of scaling up this technology at farm scale. The spatial-temporal maps provide valuable information on pasture DM yield and DM accumulation of herbage mass over the time, supporting on-farm management decisions. [ABSTRACT FROM AUTHOR]

• Accurate seasonal predictions of ryegrass cultivar DM yields using LMMs. • Base AR 37 and Bealy NEA2 were the best performing cultivars based on annual ranking. • Seasonal variability was found to be larger than genotypic variability. • An antedependence of order 3 was the best residual covariance structure for harvests. With over 60 commercial perennial ryegrass cultivars available on the market in Australia, selecting cultivars with high dry matter (DM) yield and economic profitability requires accurate estimates of their DM yield across target environments. This study, using data from multi-environment multi-harvest (MEMH) trials conducted in south-eastern Australia, derived accurate seasonal predictions of DM yield of these cultivars using linear mixed models (LMM). Base AR37 and Bealy NEA2 were found to be the best performing cultivars in most of the seasons in the target south-eastern Australian environments. Seasonal variability was found to be larger than genotypic variability as usually is the case in multi-environment trials. We have provided details of the LMM methodology used, along with ASReml-R code, to enable others to apply it in similar studies with appropriate changes as required for dataset used. The statistical theory underlying this methodology has also been briefly described in an Appendix for interested readers. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale. [ABSTRACT FROM AUTHOR]