Selecting the ‘Sustainable’ Cow Using a Customized Breeding Index: Case Study on a Commercial UK Dairy Herd

The aim of the current study was to investigate using a customized profit and carbon total merit index to identify sustainable milking cows and herd replacements within a commercial dairy herd. Balancing the economic, social and environmental aspects of milk production has gained interest given the increasing global demand for milk products. Furthermore, a farm-level customized breeding index with farm-derived weightings for biological traits would incorporate the effect of the farm environment. This study used a Markov chain approach to model a commercial dairy herd in the UK between the years 2017 and 2022. Production, financial, genetic and nutritional data for the herd were used as input data. The model derived the economic (GBP per unit) and carbon values (kilograms CO2-eq. emissions per unit) for a single phenotypic increase in milk volume, milk fat yield, milk protein yield, somatic cell count, calving interval and lifespan, which were used in a profit and carbon index. The study proposed a methodology for selecting individual milking cows and herd replacements based on their potential to increase herd profitability and reduce carbon emissions as a means to identify more sustainable animals for a given farm environment. Of the 370 cows and herd replacements studied, 76% were classified as sustainable with a desirable increase in profit and reduction in carbon emissions. Customized breeding indices with trait weightings derived from the farm environment and selecting individual animals on economic and carbon metrics will bring permanent and cumulative improvements to the sustainability of milk production with appropriate nutrition and management. The approach used can be applied to any commercial farm to select animals that are more sustainable

Detection of Methane Eructation Peaks in Dairy Cows at a Robotic Milking Station Using Signal Processing

SIMPLE SUMMARY: The objective of this study was to investigate the use of signal processing to detect eructation peaks in methane (CH(4)) released by dairy cows during robotic milking using three gas analysers. This study showed that signal processing can be used to detect CH(4) eructations and extract spot measurements from individual cows whilst being milked. There was a reasonable correlation between the gas analysers studied. Measurement of eructations using a signal processing approach can provide a repeatable and accurate measurement of enteric CH(4) emissions from cows with different gas analysers. ABSTRACT: The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH(4) released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH(4) in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH(4) amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH(4) spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH(4) concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH(4) concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH(4) eructations from animals when using different gas analysers

Effect of herbage density, height and age on nutrient and invertebrate generalist predator abundance in permanent and temporary pastures

© 2020 by the authors. The aim of this research was to assess differences in the quantity and quality of herbage and invertebrate generalist predator abundance among permanent and temporary pastures. Two permanent pastures and four temporary ley pastures (either one year or two years since being sown) were monitored weekly for 10 weeks in the spring. Permanent pastures included a diverse range of native UK grass species, and temporary ley pastures were predominantly perennial ryegrass (Lolium perenne) with or without white clover (Trifolium repens). Weekly measurements of herbage height (in centimeters), herbage cover (fresh and dry matter in kg per hectare) and herbage density (fresh and dry matter in kg per hectare per centimeter) were obtained for each field, along with lycosid spider and carabid beetle abundance. Weekly pasture samples were used to obtain nutrient concentrations of dry matter, crude protein, neutral detergent fibre (NDF), acid detergent fibre (ADF), ash, oil, sugars, digestible organic matter in the dry matter (DOMD) and metabolisable energy (ME) in the herbage as a measure of forage quality for grazing or harvesting. A linear mixed model was used to assess the effect of sward age, herbage density and height on herbage production, nutrient concentrations and invertebrate abundance. Although this study showed that permanent pastures were associated with lower nutrient concentrations of crude protein, ash, oil and ME compared to younger and predominantly perennial ryegrass pastures, the older pastures were associated with higher carabid numbers. Furthermore, permanent pastures had a higher density of dry matter herbage compared to younger pastures, and more dense and taller swards were associated with higher lycosid numbers. The study suggests that within pastures of 3 to 20 cm height, increasing the height and density of swards increases both ME and oil concentrations in herbage, therefore enhancing forage nutrient quality. Older and more permanent pastures can be beneficial for plant and invertebrate generalist predator populations, and still provide a useful source of nutrients for forage production

Changes in Dairy Cow Behavior with and without Assistance at Calving

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition

Modified approach to estimating daily methane emissions of dairy cows by measuring filtered eructations during milking

The aim of this study was to compare metrics for quantifying enteric methane (CH4) emissions from individual cows during milking using frequent spot measurements and peak analysis methods. An infrared gas analyser was used to measure the CH4 emitted by cows, and eructation peaks were identified using a Signal Processing Toolbox provided by Matlab. CH4 emissions were quantified by gas peak height, peak amplitude and average concentration, and were expressed in grams per day and CH4 yield (grams per kilogram of dry matter intake (DMI)). Peak analysis measurements of CH4 were obtained from 36 cows during 2,474 milkings, during which cows were fed a ration containing between 39 and 70 % forage. Spot measurements of CH4 were compared to a separate dataset of 196 chamber CH4 records from another group of 105 cows, which were fed a ration containing between 25 and 80 % forage. The results showed that the metrics of CH4 peak height and CH4 peak amplitude demonstrated similar positive relationships between daily CH4 emissions and DMI (both r = 0.37), and a negative relationship between CH4 yield and DMI (r = -0.43 and -0.38 respectively) as observed in the chamber measurements (r = 0.57 for daily emissions and r = -0.40 for CH4 yield). The CH4 metrics of peak height and peak amplitude were highly repeatable (ranging from 0.76 to 0.81), comparable to the high repeatability of production traits (ranging from 0.63 to 0.99) and were more repeatable than chamber CH4 measurements (0.31 for daily emissions and 0.03 for CH4 yield). This study recommends quantifying CH4 emissions from the maximum amplitude of an eructation

The use of mobile near-infrared spectroscopy for real-time pasture management

Changes in pasture nutrients over the growing season are typically not monitored but doing so may help farmers improve how effectively they utilize forage. The aim of this research was to assess the use of real-time near-infrared spectroscopy (NIRS) for monitoring seasonal changes in nutrient concentrations of different pasture types used for grazing and silage production. Three permanent pastures and three temporary ley pastures (3 years old) grazed by cattle or sheep and/or used for silage production were monitored weekly for 20 weeks from April to August 2017 in the UK. Five pasture samples per field were obtained per week for NIRS analysis and estimation of fresh and dry matter herbage cover (both kg per hectare). Herbage height was also measured each week. Permanent pastures included a diverse range of native UK grass species, and temporary ley pastures were predominantly perennial ryegrass (Lolium perenne) with either white (Trifolium repens) or red clover (Trifolium pretense). Effects of pasture type (permanent or temporary), phase of production (grazed or rested for regrowth) and month of year (April to August) on pasture nutrients [dry matter, crude protein, acid detergent fiber (ADF), neutral detergent fiber (NDF), water soluble carbohydrate (WSC), ash, digestible organic matter (DOMD), and dry matter digestibility (DMD)] were assessed by fitting a linear mixed model. Considerable variation was observed in pasture production and in the concentrations of drymatter, crude protein and WSC in pastures. This study suggests that grazing pastures to a mean height of below 7 cm results in a significantly reduced concentration of crude protein, DOMD, and DMD, which may be detrimental to the grass intake and protein intake of the grazing animal. The DOMD and DMD of pasture were positively correlated with herbage height and herbage cover crude protein concentration. An approach of real-time nutrient monitoring will facilitate more timely adaptive pasture management than currently feasible for farmers. This should lead to productivity gain

Detecting Dairy Cow Behavior Using Vision Technology

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management

Physical Conditions of Accreting Gas in T Tauri Star Systems

We present results from a low resolution (R~300) near-infrared spectroscopic variability survey of actively accreting T Tauri stars (TTS) in the Taurus-Auriga star forming region. Paschen and Brackett series H I recombination lines were detected in 73 spectra of 15 classical T Tauri systems. The values of the Pan/PaB, Brn/BrG, and BrG/Pan H I line ratios for all observations exhibit a scatter of < 20% about the weighted mean, not only from source to source, but also for epoch-to-epoch variations in the same source. A representative or `global' value was determined for each ratio in both the Paschen and Brackett series as well as the BrG/Pan line ratios. A comparison of observed line ratio values was made to those predicted by the temperature and electron density dependent models of Case B hydrogen recombination line theory. The measured line ratios are statistically well-fit by a tightly constrained range of temperatures (T < 2000 K) and electron densities 1e9 < n_e < 1e10 cm^-3. A comparison of the observed line ratio values to the values predicted by the optically thick and thin local thermodynamic equilibrium cases rules out these conditions for the emitting H I gas. Therefore, the emission is consistent with having an origin in a non-LTE recombining gas. While the range of electron densities is consistent with the gas densities predicted by existing magnetospheric accretion models, the temperature range constrained by the Case B comparison is considerably lower than that expected for accreting gas. The cooler gas temperatures will require a non-thermal excitation process (e.g., coronal/accretion-related X-rays and UV photons) to power the observed line emission.Comment: 12 pages, emulateapj format, Accepted for publication in Ap

Comparison of Methods for Monitoring the Body Condition of Dairy Cows

Dairy cows are known to mobilize body fat to achieve their genetic potential for milk production, which can have a detrimental impact on the health, fertility and survival of the cow. Better monitoring of cows with poor body condition (low or high body fat) will lead to improvements in production efficiencies and less wasted resources when producing milk from dairy cows. The aim of this study was to compare different methods for monitoring the body condition (body fat) of dairy cows. The methods used to measure body condition were: ultrasound scanner, manual observation, and a still digital image of the cow. For comparison, each measure was expressed as a body condition score (BCS) on a scale of extremely thin (1) to very fat (5) in quarter intervals. A total of 209 cows at various stages of lactation were assessed. Lin's concordance correlation coefficient (CCC) and the root mean square prediction error (RMSPE) were used to compare the accuracy of methods. The average BCS across cows was 2.10 for ultrasound, 2.76 for manual and 2.41 for digital methods. The study found that both manual (r = 0.790) and digital (r = 0.819) approaches for monitoring cow body condition were highly correlated with ultrasound BCS measurements. After adjusting correlation coefficients for prediction bias relative to a 45° line through the origin, the digital BCS had a higher CCC of 0.789 when compared to the ultrasound BCS than the manual BCS with a CCC of 0.592. The digital BCS also had a lower prediction error (RMSPE = 28.3%) when compared with ultrasound BCS than the manual BCS (RMSPE = 42.7%). The prediction error for digital and manual BCS methods were similar for cows with a BCS of 2.5 or more (RMSPE = 20.5 and 19.0%, respectively) but digital BCS was more accurate for cows of &lt; 2.5 BCS (RMSPE = 35.5 and 63.8%, respectively). Digital BCS can provide a more accurate assessment of cow body fat than manual BCS observations, with the added benefit of more automated and frequent monitoring potentially improving the welfare and sustainability of high production systems

Modelling the Interactions of Soils, Climate, and Management for Grass Production in England and Wales

This study examines the effectiveness of a model called LINGRA-N-Plus to simulate the interaction of climate, soil and management on the green leaf and total dry matter yields of ryegrass in England and Wales. The LINGRA-N-Plus model includes modifications of the LINGRA-N model such as temperature- and moisture-dependent soil nitrogen mineralization and differential partitioning to leaves and stems with thermal time from the last harvest. The resulting model was calibrated against the green leaf and total grass yields from a harvest interval x nitrogen application experiment described by Wilman et al. (1976). When the LINGRA-N-Plus model was validated against total grass yields from nitrogen experiments at ten sites described by Morrison et al. (1980), its modelling efficiency improved greatly compared to the original LINGRA-N. High predicted yields, at zero nitrogen application, were related to soils with a high initial nitrogen content. The lowest predicted yields occurred at sites with low rainfall and shallow rooting depth; mitigating the effect of drought at such sites increased yields by up to 4 t ha−1. The results highlight the usefulness of grass models, such as LINGRA-N-Plus, to explore the combined effects of climate, soil, and management, like nitrogen application, and harvest intervals on grass productivity