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There is a need to identify key existing and emerging issues relevant to digitalisation in agricultural production that would benefit from a stronger evidence base and help steer policy formulation. To address this, a prioritisation exercise was undertaken to identify priority research questions concerning digital agriculture in the UK, but with a view to also informing international contexts. The prioritisation exercise uses an established and effective participatory methodology for capturing and ordering a wide range of views. The method involves identifying a large number of participants and eliciting an initial long list of research questions which is reduced and refined in subsequent voting stages to select the top priorities by theme. Participants were selected using purposive sampling and snowballing to represent a number of sectors, organisations, companies and disciplines across the UK. They were each invited to submit up to 10 questions according to certain criteria, and this resulted in 195 questions from a range of 40 participants (largely from England with some representation from Scotland and Wales). Preliminary analysis and clustering of these questions through iterative analysis identified seven themes as follows: data governance; data management; enabling use of data and technologies; understanding benefits and uptake of data and technologies; optimising data and technologies for performance; impacts of digital agriculture; and new collaborative arrangements. Subsequent stages of voting, using an online ranking exercise and a participant workshop for in-depth discussion, refined the questions to a total of 27 priority research questions categorised into 15 gold, 7 silver and 5 bronze, across the 7 themes. The questions significantly enrich and extend previous clustering and agenda setting using literature sources, and provide a range of new perspectives. The analysis highlights the interconnectedness of themes and questions, and proposes two nexus f

The species-area relationship is among the most important patterns in ecology, and is an important tool in conservation. However, research into the species-area and minimum area dynamics of Mediterranean vegetation remains scarce, and this is particularly concerning since the heterogeneity of Mediterranean mosaic systems is being threatened by widespread land abandonment. This study investigates the woody vegetation at three semi-abandoned olive groves in the Sierra Morena de Córdoba, Southern Spain. We determined the mathematical nature of the species-area curves, and obtained minimum area values as a threshold patch size to target in terms of efficiently maximising species richness conservation. Power models proposed by Arrhenius could represent the species-area relationship of all three sites, yet Gleason’s exponential model performed better at one of the groves. Surrounding habitat quality and vegetation history impacted the nature of the species-area relationship, and this was reflected in significant differences among the minimum area values. This study provides one the first descriptions of woody plant communities at partially and intermittently managed groves in the Mediterranean region, and gives insight into the potential impacts of partial abandonment on landscape conservation. Enlarging patches up to the minimum area threshold could promote plant diversity whilst maintaining an open landscape, although researchers should give special care into adequately modelling the species-area relationship, in order to obtain appropriate minimum area values. &nbsp

Aim of study: The existence of distinct vegetation habitats within semiabandoned olive groves provides a unique chance to study the plant community within semiabandoned Mediterranean landscapes. We investigated changes in community composition of woody plants across a gradient of patch sizes by providing an example of a novel statistical technique. We also aimed to determine if commonness, life form and dispersal mechanisms of woody plants are key factors influencing species presence at vegetation patches of different sizes. Area of study: Three traditional, partially managed mountain olive groves (La Soledad, Las Niñas and Piquín) were selected within Sierra Morena de Córdoba, in Central Southern Spain. Material and methods: The woody vegetation within patches at the three groves was sampled in July 2020 following a stratified random approach. All woody plants were identified and recorded. Variation in community composition across patch area was examined using generalised linear mixed models (GLMMs) with a binomial distribution. Main results: There were significant changes in the community composition of woody plants as patch area increased. There was turnover of species with increasing area, characterised by the gain of species. This was observed both as a general and site-specific trend. Patterns in presence across area showed clear among species variation. Including dispersal strategies and life form variables improved model fit, revealing these are important factors influencing the community composition within the patches. Research highlights: The GLMM analysis demonstrated that patches of larger areas support higher richness without incurring in any loss of species. Thus, maintaining large patches is important for woody plant conservation within semi-abandoned groves.

Nerve growth factor (NGF) is a neurotrophin that mediates pain sensitization in pathologic states, including osteoarthritis. In clinical trials, antibodies to NGF reduce pain and improve physical function due to osteoarthritis of the knee or hip and have a long duration of action. Rapidly progressive osteoarthritis is a dose-dependent adverse event with these agents, and additional joint safety signals, such as subchondral insufficiency fractures and increased rates of total joint replacement, are reported. The effects on pain and potential mechanisms behind these joint events both are of considerable importance in the consideration of future use of anti-NGF therapies for osteoarthritis.

The New Zealand Superannuation Fund (NZSF) is one of New Zealand’s largest publicly-owned financial assets. Its primary purpose is to act as an inter-generational tax smoothing vehicle, in order to assist future taxpayers cover the cost of providing the public pension, New Zealand Superannuation (NZS). New Zealand’s ageing population structure will, in the absence of any changes to pension settings, lead to a significant lift in NZS expenditure as a percentage of gross domestic product (GDP) over the next few decades. The paper includes an explanation of the reasons behind why this demographic change is occurring and will continue to unfold over the foreseeable future. The paper contributes a comprehensive analysis of the NZSF’s role in New Zealand’s public finances. This includes descriptions and modelling of NZS and the NZSF, explanations of the mathematical relationships behind the NZSF’s legislated contribution rate formula, and a brief history of the NZSF. Future outcomes for the NZSF’s size and role in helping to fund NZS, depending on scenarios that vary the evolution of NZS relative to GDP, are illustrated and explained. How and why projections related to the NZSF have changed over time is also analysed and explained, including what factors have had the most influence on these changes. The paper does not comment on the merits of policy decisions that have been made in regard to either NZS or the NZSF. That is not the paper’s purpose, outside of covering any aspects of these in regard to how the NZSF has evolved or may evolve in future, or how its logic operates.

The aim of this review was to report on the imaging modalities used to assess morphological and architectural properties of the m. triceps surae muscle in typically developing children, and the available reliability analyses. Scopus and MEDLINE (Pubmed) were searched systematically for all original articles published up to September 2020 measuring morphological and architectural properties of the m. triceps surae in typically developing children (18 years or under). Thirty eligible studies were included in this analysis, measuring fibre bundle length (FBL) (n = 11), pennation angle (PA) (n = 10), muscle volume (MV) (n = 16) and physiological cross-sectional area (PCSA) (n = 4). Three primary imaging modalities were utilised to assess these architectural parameters in vivo: two-dimensional ultrasound (2DUS; n = 12), three-dimensional ultrasound (3DUS; n = 9) and magnetic resonance imaging (MRI; n = 6). The mean age of participants ranged from 1.4 years to 18 years old. There was an apparent increase in m. gastrocnemius medialis MV and pCSA with age; however, no trend was evident with FBL or PA. Analysis of correlations of muscle variables with age was limited by a lack of longitudinal data and methodological variations between studies affecting outcomes. Only five studies evaluated the reliability of the methods. Imaging methodologies such as MRI and US may provide valuable insight into the development of skeletal muscle from childhood to adulthood; however, variations in methodological approaches can significantly influence outcomes. Researchers wishing to develop a model of typical muscle development should carry out longitudinal architectural assessment of all muscles comprising the m. triceps surae utilising a consistent approach that minimises confounding errors.

Colin Rowe is remembered having said that, aside from buildings, there are several scales of architecture, which includes both “micro-architecture like furniture and silverware, and macro-architecture like London and Paris… well maybe more Paris than London.” Space & Anti-Space: The Fabric of Place, City and Architecture, by Steven Peterson and Barbara Littenberg, is devoted to what Rowe called the “macro” scale, and this book adds an immense amount to understanding how to design at the scale of the city. Steven Peterson and Barbara Littenberg. Space & Anti-Space: The Fabric of Place, City and Architecture. ORO Editions, 2020.

Partners in Expert Working Group WG2 of the COST Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF6) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. The aim of the current study was to review and compare the suitability of methods for large-scale measurements of methane output by individual animals, which may be combined with other databases for genetic evaluations. Accuracy, precision and correlation between methods were assessed. Accuracy and precision are important, but data from different sources can be weighted or adjusted when combined if they are suitably correlated with the ‘true’ value. All methods showed high correlations with respiration chambers. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite higher numbers of animals and in most cases simultaneous repeated measures per cow per method. Lower correlations could be due to increased variability and imprecision of alternative methods, or maybe different aspects of methane emission are captured using different methods. Results confirm that there is sufficient correlation between methods for measurements from all methods to be combined for international genetic studies and provide a much-needed framework for comparing genetic correlations between methods should these become available.

Frederick, Maryland is a unique and beautiful city, rich in history and steeped in historic architecture. Situated at the foothills of the Catoctin Mountains, the City is a robust employment center. Its location within forty miles of Baltimore and Washington D.C., allows Frederick residents to commute to jobs in these cities and their outer suburbs. Founded in 1745, Frederick possesses a rich texture of historic residential, commercial and civic architecture. The city is principally defined by Market Street, the main commercial spine of the city, and by Carroll Creek. Carroll Creek crosses Market Street at a low point, passes through Baker Park and residential neighborhoods on the City’s west side, and terminates in the light industrial areas on the east. In the 1970’s, Carroll Creek flooded twice, devastating the commercial enterprises in the downtown and causing great hardship to the regional economy. In an effort to reduce the risk to downtown Frederick and restore economic vitality to the historic commercial district, the City built Carroll Creek Park. This project provides flood control and protects the downtown while offering new public outdoor space and civic amenities. Today, more than $150 million in private investments are underway or planned in new construction, infill development or historic renovation in the park area. The first phase of the park improvements total nearly $11 million in construction. New elements to the park include brick pedestrian paths, water features, shade trees and plantings, pedestrian bridges, and a 350-seat amphitheater for outdoor performances. The Carroll Creek project has been a tremendous success for the City as a rebirth of the downtown is underway.

Improvements in production efficiencies and profitability of products from cattle are of great interest to farmers. Furthermore, improvements in production efficiencies associated with feed utilization and fitness traits have also been shown to reduce the environmental impact of cattle systems, which is of great importance to society. The aim of this paper was to discuss selected novel monitoring systems to measure dairy cattle phenotypic traits that are considered to bring more sustainable production with increased productivity and reduced environmental impact through reduced greenhouse gas emissions. With resource constraints and high or fluctuating commodity prices the agricultural industry has seen a growing need by producers for efficiency savings (and innovation) to reduce waste and costs associated with production. New data obtained using fast, in some cases real-time, and affordable objective measures are becoming more readily available to aid farm level monitoring, awareness, and decision making. These objective measures may additionally provide an accurate and repeatable method for improving animal health and welfare, and phenotypes for selecting animals. Such new data sources include image analysis and further data-driven technologies (e.g., infrared spectra, gas analysis), which bring non-invasive methods to obtain animal phenotypes (e.g., enteric methane, feed utilization, health, fertility, and behavioral traits) on commercial farms; this information may have been costly or not possible to obtain previously. Productivity and efficiency gains often move largely in parallel and thus bringing more sustainable systems.

Differences in performance among the areas of England, Scotland, Wales and Northern Ireland can provide some insight into the resilience of UK milk supplies from forage-based dairy herds. This study used a Markov Chain approach to model the average herd in each region between the years 2010 to 2015. The effect of a single unit change in milk production (milk volume, fat yield and protein yield), fitness (survival, somatic cell count, mastitis and calving interval) and efficiency (methane) traits on the economic value and GHG emissions intensity (expressed as carbon dioxide equivalents per cow and per kg milk solids) were assessed. Production data were obtained from a total of about half a million milk recorded dairy cows in the UK and the Farm Business Surveys for each region. Across the UK improving the health (SCC and mastitis), fertility (calving intervals) and survival of cows will increase profitability and reduce emissions intensity of milk production. In Scotland, herds had higher milk yields but poorer survival, which potentially could be due to poor fertility indicated by a longer calving interval compared to other regions. Herds in Northern Ireland had the shortest average calving interval but the highest somatic cell counts (SCC), and thus greater estimated mastitis incidence and wasted milk. Notably, England had considerably higher economic values (between 10 and 30%) and emission intensity values (between 11 and 37%) for SCC and mastitis incidence than other regions, due to lost milk production and the higher gross margin. This study provides a framework that can be customised for individual herds to allow assessment of resilience and resource efficiency of milk production not only in the UK but for comparison with international dairy systems.

Methane (CH4) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH4 emissions can be substantial. The extent of variation in CH4 emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH4 emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH4 during milking on commercial dairy farms. Enteric CH4 emissions from 1,964 individual cows across 21 farms were measured for at least 7 days per cow using CH4 analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH4 emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH4 emissions varied among farms. Considerable variation in CH4 emissions was observed among cows after adjusting for fixed and random effects, with the coefficient of variation ranging from 22 to 67% within farms. This study confirms that enteric CH4 emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.

The aims of this study were to compare methods for examining measurements of CH4 and CO2 emissions of dairy cows during milking and to assess repeatability and variation of CH4 emissions among individual dairy cows. Measurements of CH4 and CO2 emissions from 36 cows were collected in 3 consecutive feeding periods. In the first period, cows were fed a commercial partial mixed ration (PMR) containing 69% forage. In the second and third periods, the same 36 cows were fed a high-forage PMR ration containing 75% forage, with either a high grass silage or high maize silage content. Emissions of CH4 during each milking were examined using 2 methods. First, peaks in CH4 concentration due to eructations during milking were quantified. Second, ratios of CH4 and CO2 average concentrations during milking were calculated. A linear mixed model was used to assess differences between PMR. Variation in CH4 emissions was observed among cows after adjusting for effects of lactation number, week of lactation, diet, individual cow, and feeding period, with coefficients of variation estimated from variance components ranging from 11 to 14% across diets and methods of quantifying emissions. No significant difference was detected between the 3 PMR in CH4 emissions estimated by either method. Emissions of CH4 calculated from eructation peaks or as CH4 to CO2 ratio were positively associated with forage dry matter intake. Ranking of cows according to CH4 emissions on different diets was correlated for both methods, although rank correlations and repeatability were greater for CH4 concentration from eructation peaks than for CH4-to-CO2 ratio. We conclude that quantifying enteric CH4 emissions either using eructation peaks in concentration or as CH4-to-CO2 ratio can provide highly repeatable phenotypes for ranking cows on CH4 output.

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms.

Enteric methane (CH4) is a by-product from fermentation of feed consumed by ruminants, which represents a nutritional loss and is also considered a contributor to climate change. The aim of this research was to use individual animal data from 17 published experiments that included sheep (n = 288), beef cattle (n = 71) and dairy cows (n = 284) to develop an empirical model to describe enteric CH4 emissions from both cattle and sheep, and then evaluate the model alongside equations from the literature. Data were obtained from studies in the United Kingdom (UK) and Australia, which measured enteric CH4 emissions from individual animals in calorimeters. Animals were either fed solely forage or a mixed ration of forage with a compound feed. The feed intake of sheep was restricted to a maintenance amount of 875 g of DM per day (maintenance level), whereas beef cattle and dairy cows were fed to meet their metabolizable energy (ME) requirement (i.e., production level). A linear mixed model approach was used to develop a multiple linear regression model to predict an individual animal’s CH4 yield (g CH4/kg dry matter intake) from the composition of its diet. The diet components that had significant effects on CH4 yield were digestible organic matter (DOMD), ether extract (EE) (both g/kg DM) and feeding level above maintenance intake: CH4 (g/kg DM intake) = 0.046 (_0.001) X DOMD

Improvements in production efficiencies and profitability of products from cattle are of great interest to farmers. Furthermore, improvements in production efficiencies associated with feed utilization and fitness traits have also been shown to reduce the environmental impact of cattle systems, which is of great importance to society. The aim of this paper was to discuss selected novel monitoring systems to measure dairy cattle phenotypic traits that are considered to bring more sustainable production with increased productivity and reduced environmental impact through reduced greenhouse gas emissions. With resource constraints and high or fluctuating commodity prices the agricultural industry has seen a growing need by producers for efficiency savings (and innovation) to reduce waste and costs associated with production. New data obtained using fast, in some cases real-time, and affordable objective measures are becoming more readily available to aid farm level monitoring, awareness, and decision making. These objective measures may additionally provide an accurate and repeatable method for improving animal health and welfare, and phenotypes for selecting animals. Such new data sources include image analysis and further data-driven technologies (e.g., infrared spectra, gas analysis), which bring non-invasive methods to obtain animal phenotypes (e.g., enteric methane, feed utilization, health, fertility, and behavioral traits) on commercial farms; this information may have been costly or not possible to obtain previously. Productivity and efficiency gains often move largely in parallel and thus bringing more sustainable systems.

Differences in performance among the areas of England, Scotland, Wales and Northern Ireland can provide some insight into the resilience of UK milk supplies from forage-based dairy herds. This study used a Markov Chain approach to model the average herd in each region between the years 2010 to 2015. The effect of a single unit change in milk production (milk volume, fat yield and protein yield), fitness (survival, somatic cell count, mastitis and calving interval) and efficiency (methane) traits on the economic value and GHG emissions intensity (expressed as carbon dioxide equivalents per cow and per kg milk solids) were assessed. Production data were obtained from a total of about half a million milk recorded dairy cows in the UK and the Farm Business Surveys for each region. Across the UK improving the health (SCC and mastitis), fertility (calving intervals) and survival of cows will increase profitability and reduce emissions intensity of milk production. In Scotland, herds had higher milk yields but poorer survival, which potentially could be due to poor fertility indicated by a longer calving interval compared to other regions. Herds in Northern Ireland had the shortest average calving interval but the highest somatic cell counts (SCC), and thus greater estimated mastitis incidence and wasted milk. Notably, England had considerably higher economic values (between 10 and 30%) and emission intensity values (between 11 and 37%) for SCC and mastitis incidence than other regions, due to lost milk production and the higher gross margin. This study provides a framework that can be customised for individual herds to allow assessment of resilience and resource efficiency of milk production not only in the UK but for comparison with international dairy systems.

Methane (CH4) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH4 emissions can be substantial. The extent of variation in CH4 emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH4 emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH4 during milking on commercial dairy farms. Enteric CH4 emissions from 1,964 individual cows across 21 farms were measured for at least 7 days per cow using CH4 analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH4 emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH4 emissions varied among farms. Considerable variation in CH4 emissions was observed among cows after adjusting for fixed and random effects, with the coefficient of variation ranging from 22 to 67% within farms. This study confirms that enteric CH4 emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.

The aims of this study were to compare methods for examining measurements of CH4 and CO2 emissions of dairy cows during milking and to assess repeatability and variation of CH4 emissions among individual dairy cows. Measurements of CH4 and CO2 emissions from 36 cows were collected in 3 consecutive feeding periods. In the first period, cows were fed a commercial partial mixed ration (PMR) containing 69% forage. In the second and third periods, the same 36 cows were fed a high-forage PMR ration containing 75% forage, with either a high grass silage or high maize silage content. Emissions of CH4 during each milking were examined using 2 methods. First, peaks in CH4 concentration due to eructations during milking were quantified. Second, ratios of CH4 and CO2 average concentrations during milking were calculated. A linear mixed model was used to assess differences between PMR. Variation in CH4 emissions was observed among cows after adjusting for effects of lactation number, week of lactation, diet, individual cow, and feeding period, with coefficients of variation estimated from variance components ranging from 11 to 14% across diets and methods of quantifying emissions. No significant difference was detected between the 3 PMR in CH4 emissions estimated by either method. Emissions of CH4 calculated from eructation peaks or as CH4 to CO2 ratio were positively associated with forage dry matter intake. Ranking of cows according to CH4 emissions on different diets was correlated for both methods, although rank correlations and repeatability were greater for CH4 concentration from eructation peaks than for CH4-to-CO2 ratio. We conclude that quantifying enteric CH4 emissions either using eructation peaks in concentration or as CH4-to-CO2 ratio can provide highly repeatable phenotypes for ranking cows on CH4 output.

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over

Enteric methane (CH4) is a by-product from fermentation of feed consumed by ruminants, which represents a nutritional loss and is also considered a contributor to climate change. The aim of this research was to use individual animal data from 17 published experiments that included sheep (n = 288), beef cattle (n = 71) and dairy cows (n = 284) to develop an empirical model to describe enteric CH4 emissions from both cattle and sheep, and then evaluate the model alongside equations from the literature. Data were obtained from studies in the United Kingdom (UK) and Australia, which measured enteric CH4 emissions from individual animals in calorimeters. Animals were either fed solely forage or a mixed ration of forage with a compound feed. The feed intake of sheep was restricted to a maintenance amount of 875 g of DM per day (maintenance level), whereas beef cattle and dairy cows were fed to meet their metabolizable energy (ME) requirement (i.e., production level). A linear mixed model approach was used to develop a multiple linear regression model to predict an individual animal’s CH4 yield (g CH4/kg dry matter intake) from the composition of its diet. The diet components that had significant effects on CH4 yield were digestible organic matter (DOMD), ether extract (EE) (both g/kg DM) and feeding level above maintenance intake: CH4 (g/kg DM intake) = 0.046 (_0.001) X DOMD

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms.

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms.

MDT Project Number – 9832-766, State departments of transportation, including the Montana Department of Transportation (MDT) focus on eliminating fatalities and serious injuries on our roadways. To accomplish this, MDT employed several strategies including an emphasis on improving traffic safety culture. Traffic safety culture strategies, including public awareness campaigns and traffic safety videos, are an important way to educate the public and possibly change their beliefs and influence their behaviors related to traffic safety. This project aimed to improve traffic safety culture through displaying traffic safety videos in waiting areas at motor vehicle division driver license stations and county treasurer offices. The project had three phases: 1) pre-deployment planning, 2) deployment, and 3) evaluation and support. The videos reached a broad demographic including those that are the focus of MDTs media campaigns. The location of the television significantly influenced whether or not the videos were viewed; the location behind staff entering data is preferred.

MSU Project Number 4W8305,MDT Project Number 9831-765, The Federal Highway Administration has established measures for state departments of transportation to develop an asset management plan that includes deterioration forecasting to improve or preserve bridge conditions. The primary objective of this research was to support Montana’s bridge management system by developing state-specific deterioration curves for bridges owned by the Montana Department of Transportation (MDT). The deterioration analysis used the National Bridge Inventory (NBI) data in combination with maintenance targets set by MDT. The deterioration curves developed for this research were designed to be compatible with AASHTOWare Bridge Management software (BrM).

Improvements in production efficiencies and profitability of products from cattle are of great interest to farmers. Furthermore, improvements in production efficiencies associated with feed utilization and fitness traits have also been shown to reduce the environmental impact of cattle systems, which is of great importance to society. The aim of this paper was to discuss selected novel monitoring systems to measure dairy cattle phenotypic traits that are considered to bring more sustainable production with increased productivity and reduced environmental impact through reduced greenhouse gas emissions. With resource constraints and high or fluctuating commodity prices the agricultural industry has seen a growing need by producers for efficiency savings (and innovation) to reduce waste and costs associated with production. New data obtained using fast, in some cases real-time, and affordable objective measures are becoming more readily available to aid farm level monitoring, awareness, and decision making. These objective measures may additionally provide an accurate and repeatable method for improving animal health and welfare, and phenotypes for selecting animals. Such new data sources include image analysis and further data-driven technologies (e.g., infrared spectra, gas analysis), which bring non-invasive methods to obtain animal phenotypes (e.g., enteric methane, feed utilization, health, fertility, and behavioral traits) on commercial farms; this information may have been costly or not possible to obtain previously. Productivity and efficiency gains often move largely in parallel and thus bringing more sustainable systems.

Differences in performance among the areas of England, Scotland, Wales and Northern Ireland can provide some insight into the resilience of UK milk supplies from forage-based dairy herds. This study used a Markov Chain approach to model the average herd in each region between the years 2010 to 2015. The effect of a single unit change in milk production (milk volume, fat yield and protein yield), fitness (survival, somatic cell count, mastitis and calving interval) and efficiency (methane) traits on the economic value and GHG emissions intensity (expressed as carbon dioxide equivalents per cow and per kg milk solids) were assessed. Production data were obtained from a total of about half a million milk recorded dairy cows in the UK and the Farm Business Surveys for each region. Across the UK improving the health (SCC and mastitis), fertility (calving intervals) and survival of cows will increase profitability and reduce emissions intensity of milk production. In Scotland, herds had higher milk yields but poorer survival, which potentially could be due to poor fertility indicated by a longer calving interval compared to other regions. Herds in Northern Ireland had the shortest average calving interval but the highest somatic cell counts (SCC), and thus greater estimated mastitis incidence and wasted milk. Notably, England had considerably higher economic values (between 10 and 30%) and emission intensity values (between 11 and 37%) for SCC and mastitis incidence than other regions, due to lost milk production and the higher gross margin. This study provides a framework that can be customised for individual herds to allow assessment of resilience and resource efficiency of milk production not only in the UK but for comparison with international dairy systems.

The aims of this study were to compare methods for examining measurements of CH4 and CO2 emissions of dairy cows during milking and to assess repeatability and variation of CH4 emissions among individual dairy cows. Measurements of CH4 and CO2 emissions from 36 cows were collected in 3 consecutive feeding periods. In the first period, cows were fed a commercial partial mixed ration (PMR) containing 69% forage. In the second and third periods, the same 36 cows were fed a high-forage PMR ration containing 75% forage, with either a high grass silage or high maize silage content. Emissions of CH4 during each milking were examined using 2 methods. First, peaks in CH4 concentration due to eructations during milking were quantified. Second, ratios of CH4 and CO2 average concentrations during milking were calculated. A linear mixed model was used to assess differences between PMR. Variation in CH4 emissions was observed among cows after adjusting for effects of lactation number, week of lactation, diet, individual cow, and feeding period, with coefficients of variation estimated from variance components ranging from 11 to 14% across diets and methods of quantifying emissions. No significant difference was detected between the 3 PMR in CH4 emissions estimated by either method. Emissions of CH4 calculated from eructation peaks or as CH4 to CO2 ratio were positively associated with forage dry matter intake. Ranking of cows according to CH4 emissions on different diets was correlated for both methods, although rank correlations and repeatability were greater for CH4 concentration from eructation peaks than for CH4-to-CO2 ratio. We conclude that quantifying enteric CH4 emissions either using eructation peaks in concentration or as CH4-to-CO2 ratio can provide highly repeatable phenotypes for ranking cows on CH4 output.

Methane (CH4) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH4 emissions can be substantial. The extent of variation in CH4 emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH4 emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH4 during milking on commercial dairy farms. Enteric CH4 emissions from 1,964 individual cows across 21 farms were measured for at least 7 days per cow using CH4 analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH4 emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH4 emissions varied among farms. Considerable variation in CH4 emissions was observed among cows after adjusting for fixed and random effects, with the coefficient of variation ranging from 22 to 67% within farms. This study confirms that enteric CH4 emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms.

Enteric methane (CH4) is a by-product from fermentation of feed consumed by ruminants, which represents a nutritional loss and is also considered a contributor to climate change. The aim of this research was to use individual animal data from 17 published experiments that included sheep (n = 288), beef cattle (n = 71) and dairy cows (n = 284) to develop an empirical model to describe enteric CH4 emissions from both cattle and sheep, and then evaluate the model alongside equations from the literature. Data were obtained from studies in the United Kingdom (UK) and Australia, which measured enteric CH4 emissions from individual animals in calorimeters. Animals were either fed solely forage or a mixed ration of forage with a compound feed. The feed intake of sheep was restricted to a maintenance amount of 875 g of DM per day (maintenance level), whereas beef cattle and dairy cows were fed to meet their metabolizable energy (ME) requirement (i.e., production level). A linear mixed model approach was used to develop a multiple linear regression model to predict an individual animal’s CH4 yield (g CH4/kg dry matter intake) from the composition of its diet. The diet components that had significant effects on CH4 yield were digestible organic matter (DOMD), ether extract (EE) (both g/kg DM) and feeding level above maintenance intake: CH4 (g/kg DM intake) = 0.046 (_0.001) X DOMD

319.03, The objective of this project is to assist Colorado Department of Transportation (CDOT) in the identification of regionally sourced agricultural co- or by-product deicer additives that will enhance corrosion protection and provide added deicing performance and longevity to road surfaces. A literature review provides a summary of agriculturally derived additives (agbased) that have been used in deicing products, the benefits of incorporating these ag-based additives into deicing products, and potential sources local to Colorado for these products. Based on information gained from literature reviewed, the agricultural products with the greatest potential to serve as locally sourced additives for deicing are sugar beets, corn, and apples. Colorado based industries with the most promising co-products or by-products include the dairy industry, and beer, wine, and distilled alcohol production. Products investigated for further testing included waste cheese brine, apple pomace, and by-products from regional beer and cider production. Cheese brine was the only ag-based product in which further testing was performed. Laboratory analysis of the Leprino cheese brine (LCB) determined it functions as a deicer, because it is a sodium chloride based product, that provides some corrosion protection. A beneficial use permit was granted by CDPHE to Leprino Foods for the use of LCB by CDOT as a pre-wetting agent for field trial applications. LCB must only be applied at 8 gal/ton to pre-wet salt or salt sand and can only be applied in the defined field trail locations in Fort Morgan and Greeley, CO. Initial field trail results suggest training before the 2022-2023 winter season is needed to support use of LCB in winter operations. Future work will consider improvements to LCB to reduce the phosphorus concentration, support data collection and analysis from field trials, and investigate other regionally sourced ag-based additives that could be used by CDOT in deicing operations.

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms.

Improvements in production efficiencies and profitability of products from cattle are of great interest to farmers. Furthermore, improvements in production efficiencies associated with feed utilization and fitness traits have also been shown to reduce the environmental impact of cattle systems, which is of great importance to society. The aim of this paper was to discuss selected novel monitoring systems to measure dairy cattle phenotypic traits that are considered to bring more sustainable production with increased productivity and reduced environmental impact through reduced greenhouse gas emissions. With resource constraints and high or fluctuating commodity prices the agricultural industry has seen a growing need by producers for efficiency savings (and innovation) to reduce waste and costs associated with production. New data obtained using fast, in some cases real-time, and affordable objective measures are becoming more readily available to aid farm level monitoring, awareness, and decision making. These objective measures may additionally provide an accurate and repeatable method for improving animal health and welfare, and phenotypes for selecting animals. Such new data sources include image analysis and further data-driven technologies (e.g., infrared spectra, gas analysis), which bring non-invasive methods to obtain animal phenotypes (e.g., enteric methane, feed utilization, health, fertility, and behavioral traits) on commercial farms; this information may have been costly or not possible to obtain previously. Productivity and efficiency gains often move largely in parallel and thus bringing more sustainable systems.

Differences in performance among the areas of England, Scotland, Wales and Northern Ireland can provide some insight into the resilience of UK milk supplies from forage-based dairy herds. This study used a Markov Chain approach to model the average herd in each region between the years 2010 to 2015. The effect of a single unit change in milk production (milk volume, fat yield and protein yield), fitness (survival, somatic cell count, mastitis and calving interval) and efficiency (methane) traits on the economic value and GHG emissions intensity (expressed as carbon dioxide equivalents per cow and per kg milk solids) were assessed. Production data were obtained from a total of about half a million milk recorded dairy cows in the UK and the Farm Business Surveys for each region. Across the UK improving the health (SCC and mastitis), fertility (calving intervals) and survival of cows will increase profitability and reduce emissions intensity of milk production. In Scotland, herds had higher milk yields but poorer survival, which potentially could be due to poor fertility indicated by a longer calving interval compared to other regions. Herds in Northern Ireland had the shortest average calving interval but the highest somatic cell counts (SCC), and thus greater estimated mastitis incidence and wasted milk. Notably, England had considerably higher economic values (between 10 and 30%) and emission intensity values (between 11 and 37%) for SCC and mastitis incidence than other regions, due to lost milk production and the higher gross margin. This study provides a framework that can be customised for individual herds to allow assessment of resilience and resource efficiency of milk production not only in the UK but for comparison with international dairy systems.

Methane (CH4) emissions by dairy cows vary with feed intake and diet composition. Even when fed on the same diet at the same intake, however, variation between cows in CH4 emissions can be substantial. The extent of variation in CH4 emissions among dairy cows on commercial farms is unknown, but developments in methodology now permit quantification of CH4 emissions by individual cows under commercial conditions. The aim of this research was to assess variation among cows in emissions of eructed CH4 during milking on commercial dairy farms. Enteric CH4 emissions from 1,964 individual cows across 21 farms were measured for at least 7 days per cow using CH4 analysers at robotic milking stations. Cows were predominantly of Holstein Friesian breed and remained on the same feeding systems during sampling. Effects of explanatory variables on average CH4 emissions per individual cow were assessed by fitting a linear mixed model. Significant effects were found for week of lactation, daily milk yield and farm. The effect of milk yield on CH4 emissions varied among farms. Considerable variation in CH4 emissions was observed among cows after adjusting for fixed and random effects, with the coefficient of variation ranging from 22 to 67% within farms. This study confirms that enteric CH4 emissions vary among cows on commercial farms, suggesting that there is considerable scope for selecting individual cows and management systems with reduced emissions.

The aims of this study were to compare methods for examining measurements of CH4 and CO2 emissions of dairy cows during milking and to assess repeatability and variation of CH4 emissions among individual dairy cows. Measurements of CH4 and CO2 emissions from 36 cows were collected in 3 consecutive feeding periods. In the first period, cows were fed a commercial partial mixed ration (PMR) containing 69% forage. In the second and third periods, the same 36 cows were fed a high-forage PMR ration containing 75% forage, with either a high grass silage or high maize silage content. Emissions of CH4 during each milking were examined using 2 methods. First, peaks in CH4 concentration due to eructations during milking were quantified. Second, ratios of CH4 and CO2 average concentrations during milking were calculated. A linear mixed model was used to assess differences between PMR. Variation in CH4 emissions was observed among cows after adjusting for effects of lactation number, week of lactation, diet, individual cow, and feeding period, with coefficients of variation estimated from variance components ranging from 11 to 14% across diets and methods of quantifying emissions. No significant difference was detected between the 3 PMR in CH4 emissions estimated by either method. Emissions of CH4 calculated from eructation peaks or as CH4 to CO2 ratio were positively associated with forage dry matter intake. Ranking of cows according to CH4 emissions on different diets was correlated for both methods, although rank correlations and repeatability were greater for CH4 concentration from eructation peaks than for CH4-to-CO2 ratio. We conclude that quantifying enteric CH4 emissions either using eructation peaks in concentration or as CH4-to-CO2 ratio can provide highly repeatable phenotypes for ranking cows on CH4 output.