The time elapsed between assessments of blood metabolome and live weight affects associations between the abundance of metabolites and growth rate in beef cattle.

INTRODUCTION: This study aimed to assess the associations between the relative abundance (RA) of blood metabolites and growth rate (i.e., live weight change, LWC) calculated using different intervals of time between live weight (LW) measurements from the metabolome assessment. METHODS: Grazing beef cattle were raised for 56 days and blood samples from each animal were taken on day 57. Live weight was continuously measured using an automatic in-paddock weighing scale. The RA of plasma metabolites were determined using proton nuclear magnetic resonance (NMR). Live weight data were filtered for outliers and one LW record was selected every 1, 7, 14, 21, 28, 35, 42, 49 and 56 days before the metabolome assessment (LWC1 to LWC56, respectively). Live weight change was then re-calculated for each interval between LW data selected. RESULTS: Associations between LWC calculations and the RA of metabolites were greatly affected by the interval of time between LW data selected. Thus, the number of significant associations decreased from 9 for LWC1 to 5 for LWC35 whereas no significant associations were found for LWC56 (P > 0.05). There were 7 metabolites negatively associated with LWC1 including leucine, 2-hydroxybutyrate, valine, creatinine, creatine, phenylalanine and methylhistidine; however, correlations were positive for 2 lipids. The strength of the correlation coefficients decreased as the length of the interval between LW measures increased although this reduction was greater for some metabolites such as leucine compared to others such as lipids. Our findings suggest that the time frame in which a particular response variable, such as LWC, is measured and metabolomic samples are taken could largely impact associations and thus conclusions drawn. CONCLUSIONS: Depending on the variable to be explored, rapid changes in cattle metabolome may not be reflected in correlations if they are not assessed close in time. Our findings suggest that LWC should be measured for a period shorter than 28 days before the metabolome assessment as the number of significant associations decreases when LWC is measured for longer periods.

Optimising profitability and productivity of pasture-based dairy farms with automatic milking systems.

There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to âˆ¼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.

Suitability of milk lactate dehydrogenase and serum albumin for pathogen-specific mastitis detection in automatic milking systems.

In response to intramammary infection (IMI), blood-derived leukocytes are transferred into milk, which can be measured as an increase of somatic cell count (SCC). Additionally, pathogen-dependent IgG increases in milk following infection. The IgG transfer into milk is associated with the opening of the blood-milk barrier, which is much more pronounced during gram-negative than gram-positive IMI. Thus, milk IgG concentration may help to predict the pathogen type causing IMI. Likewise, lactate dehydrogenase (LDH) and serum albumin (SA) cross the blood-milk barrier with IgG if its integrity is reduced. Because exact IgG analysis is complicated and difficult to automate, LDH activity and SA concentration aid as markers to predict the IgG transfer into milk in automatic milking systems (AMS). This study was conducted to test the hypothesis that LDH and SA in milk correlate with the IgG transfer, and in combination with SCC these factors allow the differentiation between gram-positive and gram-negative IMI or even more precisely the infection-causing pathogen. Further, the expression of these parameters in foremilk before (BME) and after (AME) milk ejection was tested. In the AMS, quarter milk samples (n = 686) from 48 Holstein-Friesian cows were collected manually BME and AME, followed by an aseptic sample for bacteriological culture. Mixed models were used to (1) predict the concentration of IgG transmitted from blood into milk based on LDH and SA; (2) use principal component analysis to evaluate joint patterns of SCC (cells/mL), IgG (mg/mL), LDH (U/L), and SA (mg/mL) and use the principal component scores to compare gram-positive, gram-negative, and control IMI types and BME versus AME samples; and (3) predict gram-positive and gram-negative IMI by inclusion of combined SCC-LDH and SCC-SA as predictors in the model. Overall, the SA and LDH had similar ability to predict IgG transmission from blood into milk. Comparing the areas under the curve (AUC) of the receiver operator characteristic curves, the SCC-LDH versus SCC-SA had lower gram-positive (AUC = 0.984 vs. 0.986) but similar gram-negative (AUC = 0.995 vs. 0.998) IMI prediction ability. The SCC, IgG, LDH, and SA were greater in gram-negative than in gram-positive IMI (BME and AME) in early lactation. All measured factors had higher values in milk samples taken BME than AME. In conclusion, LDH and SA could be used as replacement markers to indicate the presence of IgG transfer from blood into milk; in combination with SCC, both SA and LDH are suitable for differentiating IMI type, and BME is better for mastitis detection in AMS.

Using estimated nutrient intake from pasture to formulate supplementary concentrate mixes for grazing dairy cows.

In pasture-based dairy systems, feeding a complex concentrate mix in the parlor during milking that contains cereal grains and protein supplements has been shown to have milk production advantages over feeding straight cereal grain. This experiment had the aim of testing whether further milk production advantages could be elicited by adjusting the composition of the concentrate mix in an attempt to match the expected nutrient intake from pasture during late spring. The experiment used 96 lactating dairy cows, grazing perennial ryegrass pasture offered at a target allowance of 30 kg of dry matter/cow per day (to ground level) during late spring (mid October to November) in southeastern Australia. Cows were allocated into 3 replicates of 4 treatment groups, with 24 cows in each treatment. Each treatment group was offered 1 of 4 dietary treatments in the parlor at milking: control consisting of crushed wheat and barley grains; formulated grain mix (FGM) consisting of crushed wheat, barley, and corn grains and canola meal; designer grain mix 1 (DGM1) consisting of the same ingredients as the FGM grain mix but formulated using the CPM Dairy nutrition model to take into account the expected nutrient intake from pasture; and designer grain mix 2 (DGM2) consisting of the same ingredients as DGM1 but with canola meal replaced by urea and a fat supplement (Megalac, Volac Wilmar, Gresik, Indonesia). Concentrate mixes were offered at 8.0 kg of dry matter/cow per day, except for DGM2 cows, which were offered 7.5 kg of dry matter/cow per day. The experiment ran for a total of 28 d; after a 14-d adaptation period, nutrient intake, milk production, and body weight were measured over a 14-d measurement period. Milk yield (kg) of cows fed the FGM diet was greater than that of the control cows but was not different from that of the DGM1 and DGM2 cows. However, milk fat and protein yields (kg) were greater for cows fed the FGM diet than for all other diets. There was no difference in estimated daily pasture or total dry matter intakes between the 4 treatment groups, despite cows fed the DGM2 treatment consuming less of the concentrate mix (average 6.5 kg of dry matter/cow per day when offered 7.5 kg of dry matter/cow per day). This research has demonstrated the potential for using a nutrition model to take into account the expected nutrient intake from pasture to formulate a concentrate mix (DGM1) to achieve similar milk yields, but also highlighted the need for near real-time analyses of the pasture to be grazed so as to also capture benefits in terms of milk fat and protein yield.

Physical and economic comparison of pasture-based automatic and conventional milking systems.

Automatic milking systems (AMS) have the potential to increase dairy farm productivity and profitability; however, adoption rates, particularly in pasture-based systems, have been lower than expected. The objectives of this study were to compare the physical and economic performance of pasture-based AMS with conventional milking systems (CMS) and to identify gaps for improving AMS productivity and profitability. We used data from 14 AMS and 100 CMS located in the main Australian dairy regions and collected over 3 yr (2015-2016, 2016-2017, 2017-2018). Farms within similar regions and herd sizes were compared. Results showed that all the main physical performance indicators evaluated such as milk production per cow, milk production per hectare, pasture grazed per hectare, or milk solids per full-time equivalent were similar between systems. The AMS farms had higher overhead costs such as depreciation and repairs and maintenance; however, no differences in total labor costs were observed between systems. Profitability, measured as earnings before interest and tax, operating profit margin, and return on total assets, was not significantly different between AMS and CMS. Opportunities for improving pasture utilization, labor efficiency, and robot utilization in AMS farms were identified. Improving efficiency in these areas could improve productivity and profitability of these systems, and therefore increase the interest of this technology.

Modeling variability of the lactation curves of cows in automated milking systems.

Historically, cow selection criteria were developed for conventional milking systems that have regular milking intervals (MI). However, in automatic milking systems (AMS), there is variability in MI within and between cows. These sources of variability provide an opportunity to identify cows with high daily milk yield (DY) and long MI. An extended MI (longer than 16 h in pasture-based systems) has a negative effect on DY. Cows that tolerate extended MI and maintain high DY can be considered more efficient than cows with low DY and long MI, or with high DY but short MI, thereby improving robotic system use. Knowledge of the behavior and parameters of lactation curves of cows in AMS could help farmers to identify cows with a specific lactational phenotype. The objective of this study was to identify individual cows with high DY and long MI within herds, which could reflect increased tolerance to milk accumulation under AMS. A database containing records for 773,483 milking events for one year (July 2016-June 2017) from 4 pasture-based AMS farms was used. Lactation curves within each herd were fitted using several mixed models including fixed effects for the parameters of the lactation curve and random cow effects. Predicted curves of average DY according to parity (multiparous and primiparous) were obtained. The best linear unbiased prediction of the random cow effect allowed us to categorize lactations as having either high or low milk production. The median MI of each lactation was then used to categorize cows as having either short or long MI. Daily yield at the peak of lactation, days to peak and 305-d cumulative milk production were used to compare the effect of DY and MI categories, as well as the DY × MI interaction. Milk production by multiparous and primiparous cows with high DY and long MI was between 35 and 45% higher than that of the low DY and short MI. From all lactations analyzed, the incidence of animals with high DY and long MI across farms was 7.5%. We have identified and quantified a new, AMS-specific, phenotype (the combination of a relatively higher DY with relatively longer MI) with potential to increase use of AMS units. Identifying more efficient animals should help generate new approaches for differential management and for selecting cows in AMS.

Transitioning from conventional to automatic milking: Effects on the human-animal relationship.

There are several differences in how an automatic milking system (AMS; milking equipment not requiring human intervention for the milk harvesting process) and a conventional milking system (CMS) are managed, where the effect of milking system type on the human-animal relationship has remained unexplored. A survey and observations from 5 Australian dairy farms transitioning from CMS to AMS were taken twice, 1 yr apart, before and after transition to an AMS. The farmers completed a survey and had all farmer-cow interactions documented for 3 d. In addition, a random selection of lactating cows had their avoidance distance (the distance at which they move away from an approaching person) recorded and were involved in a handling test during both visits. The survey findings indicated that basic management practices remained mostly unchanged, whereas records of farmer-cow interactions showed 4 out of 5 farms had less interaction time after AMS transition. This was caused by a reduction in milk harvesting tasks, where a small portion of this time was re-invested into time that farmers spent around the cows without directly interacting with them and into tasks involving close cow contact. Overall, an approximate 27% decline was observed in avoidance distances of cows from an AMS compared with the CMS. A handling test was performed on 4 of the 5 farms before and after AMS transition, where the farmers were asked to move a selection of cows through a gate one at a time. In the AMS more vocal effort was required to move the cows, and the cows responded with a reduced occurrence of running past the farmer and reduced occurrence of slipping in an attempt to avoid the farmers compared with the CMS. Overall, results show that farmers spent less time interacting with cows in the AMS, and that cows were less fearful around people as seen by reduced avoidance distances and reduced stress responses to close handling.

Suitability of somatic cell count, electrical conductivity, and lactate dehydrogenase activity in foremilk before versus after alveolar milk ejection for mastitis detection.

Mastitis is responsible for substantial economic loss and significant animal welfare concerns for the dairy industry. Sensors that measure electrical conductivity (EC) and enzyme concentrations of lactate dehydrogenase (LDH) are presently used for automatic detection of mastitis. However, EC is not sensitive enough to detect mastitis, and the ability of LDH activity to identify mastitis caused by different pathogens is a potential option that needs to be investigated. This study was conducted to test the following hypotheses: (a) strict foremilk before milk ejection is more informative in detecting mastitis, in general, than foremilk removed after cows were stimulated for milk ejection; and (b) the value of LDH activity as a mastitis indicator depends on the type of pathogen associated with the infection. Milk samples (before afternoon milking) from 48 Holstein-Friesian cows at the University of Sydney's dairy farm (Camden, New South Wales, Australia) with EC > 7.5 mS/cm in any of the 4 quarters were collected over a period of 2 mo. Quarter milk samples (n = 343) from 48 cows were collected manually in the automatic milking rotary in 3 steps: foremilk before (strict foremilk) and after milk ejection, followed by an aseptic sample for bacteriological culture. The EC (mS), LDH (U/L), SCC (cells/mL), and milk protein and fat content (%) of foremilk in both sampling times were compared and used as predictors for gram-positive and gram-negative mastitis. Quarter (n = 515) observations from 44 cows were analyzed using a logistic mixed or linear mixed model, with cow and quarter nested within cow as random effects. Milk from both sampling times was also assessed by producing a receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) to determine ability to detect mastitis. Overall, EC and LDH were greater and milk protein (%) was lower in strict foremilk than in milk fractions obtained after milk ejection. Data from strict foremilk samples had slightly higher AUC values (0.98 to 0.99 vs. 0.97 to 0.98, respectively) than did the after-ejection milk samples. Although gram-negative coliform mastitis had significantly higher LDH activity than did gram-positive mastitis (6.19 vs. 5.34 log10 U/L), the robustness of this result is questionable due to limited sample size. We concluded that milk samples taken before ejection can influence major mastitis indicators, suggesting that automatic milking system sensors could be modified to monitor milk before ejection for more efficient mastitis detection.

The effect of temporal variation in feed quality and quantity on the diurnal feeding behaviour of dairy cows.

The diurnal feeding patterns of dairy cows affects the 24 h robot utilisation of pasture-based automatic milking systems (AMS). A decline in robot utilisation between 2400 and 0600 h currently occurs in pasture-based AMS, as cow feeding activity is greatly reduced during this time. Here, we investigate the effect of a temporal variation in feed quality and quantity on cow feeding behaviour between 2400 and 0600 h as a potential tool to increase voluntary cow trafficking in an AMS at night. The day was allocated into four equal feeding periods (0600 to 1200, 1200 to 1800, 1800 to 2400 and 2400 to 0600 h). Lucerne hay cubes (CP = 19.1%, water soluble carbohydrate = 3.8%) and oat, ryegrass and clover hay cubes with 20% molasses (CP = 11.8%, water soluble carbohydrate = 10.7%) were offered as the 'standard' and 'preferred' (preference determined previously) feed types, respectively. The four treatments were (1) standard feed offered ad libitum (AL) throughout 24 h; (2) as per AL, with preferred feed replacing standard feed between 2400 and 0600 h (AL + P); (3) standard feed offered at a restricted rate, with quantity varying between each feeding period (20:10:30:60%, respectively) as a proportion of the (previously) measured daily ad libitum intake (VA); (4) as per VA, with preferred feed replacing standard feed between 2400 and 0600 h (VA + P). Eight non-lactating dairy cows were used in a 4 × 4 Latin square design. During each experimental period, treatment cows were fed for 7 days, including 3 days habituation and 4 days data collection. Total daily intake was approximately 8% greater (P < 0.001) for the AL and AL + P treatments (23.1 and 22.9 kg DM/cow) as compared with the VA and VA + P treatments (21.6 and 20.9 kg DM/cow). The AL + P and VA treatments had 21% and 90% greater (P < 0.001) dry matter intake (DMI) between 2400 and 0600 h, respectively, compared with the AL treatment. In contrast, the VA + P treatment had similar DMI to the VA treatment. Our experiment shows ability to increase cow feeding activity at night by varying feed type and quantity, though it is possible that a penalty to total DMI may occur using VA. Further research is required to determine if the implementation of variable feed allocation on pasture-based AMS farms is likely to improve milking robot utilisation by increasing cow feeding activity at night.

The effect of pasture quantity temporal variation on milking robot utilization.

In pasture-based automatic milking systems (AMS), a decrease in robot utilization (RU) often occurs in the early morning hours. Novel feeding strategies that encourage voluntary cow traffic throughout 24 h could help mitigate this problem. We determined the effect of 3 distinct pasture allocation methods on RU patterns throughout a 24-h period. The experiment was conducted at the University of Melbourne's Dookie research farm in northern Victoria, Australia. Three Lely Astronaut A3 robotic milking units (Lely, Maassluis, the Netherlands) milked 133 cows, grazing pasture, with concentrate offered at milking in the robots. The farm operated a system of 3-way grazing, with active access to each pasture allocation: 2030-0400 h (allocation A), 0400-1330 h (allocation B), and 1330-2030 h (allocation C). Treatments varied in the quantity of feed offered per hour of active access to each of the 3 pasture allocations. The control treatment offered the same proportion of feed (corrected for active access time) in all 3 pasture allocations (allocation A = 31.3%, B = 39.6%, and C = 29.2%). The day treatment offered the largest proportion of feed during the day (allocation A = 20%, B = 40%, and C = 40%), following the cows' diurnal pattern of feeding activity. The night treatment offered the largest proportion of feed at night (allocation A = 42%, B = 40%, and C = 18%). Due to the nature of pasture-based AMS, treatments could not be applied simultaneously. Therefore, treatments were applied to the entire herd and repeated twice over 42 d, lasting 7 d/treatment, with the first 3 d for habituation, followed by 4 d of data collection. Robot utilization (milkings/h) varied throughout 24 h between treatments, with the night treatment recording greater RU at 0800, 1800, and 1900 h and lower RU between 2100 to 0100 h, compared with the day treatment. The proportion of the herd milking between 0000 and 0600 h was greater for the control (43.3%) and day (45.3%) treatments compared with the night treatment (25.8%). Herd-average daily pasture intake was similar (10.5 kg of dry matter) for all treatments. This experiment is the first to demonstrate the manipulation of RU by varying the quantity of pasture offered. However, the use of variable allocation alone did not eliminate the decrease in RU between 0000 and 0600 h, with the timing of allocation also likely to play a role. We recommend a further research focus on combining both timing and quantity of pasture allocated to improve RU in pasture-based AMS.

Robot utilisation of pasture-based dairy cows with varying levels of milking frequency.

Achieving a consistent level of robot utilisation throughout 24 h maximises automatic milking system (AMS) utilisation. However, levels of robot utilisation in the early morning hours are typically low, caused by the diurnal feeding behaviour of cows, limiting the inherent capacity and total production of pasture-based AMS. Our objective was to determine robot utilisation throughout 24 h by dairy cows, based on milking frequency (MF; milking events per animal per day) in a pasture-based AMS. Milking data were collected from January and February 2013 across 56 days, from a single herd of 186 animals (Bos taurus) utilising three Lely A3 robotic milking units, located in Tasmania, Australia. The dairy herd was categorised into three equal sized groups (n=62 per group) according to the cow's mean daily MF over the duration of the study. Robot utilisation was characterised by an interaction (P< 0.001) between the three MF groups and time of day, with peak milking time for high MF cows within one h of a fresh pasture allocation becoming available, followed by the medium MF and low MF cows 2 and 4 h later, respectively. Cows in the high MF group also presented for milking between 2400 and 0600 h more frequently (77% of nights), compared to the medium MF group (57%) and low MF group (50%). This study has shown the formation of three distinct groups of cows within a herd, based on their MF levels. Further work is required to determine if this finding is replicated across other pasture-based AMS farms.

Development of a new clinical mastitis detection method for automatic milking systems.

This study investigated the potential for accurate detection of clinical mastitis (CM) in an automatic milking system (AMS) using electronic data from the support software. Data from cows were used to develop the model, which was then tested on 2 independent data sets, 1 with 311 cows (same farm but from a different year) and 1 with 568 cows (from a different farm). In addition, the model was used to test how well it could predict CM 1 to 3 d before actual clinical diagnosis. Logistic mixed models were used for the analysis. Twelve measurements were included in the initial model before a backward elimination, which resulted in the following 6 measurements being included in the final model: quarter-level milk yield (MY; kg), electrical conductivity (EC; mS/cm), average milk flow rate (MF; kg/min), occurrence of incompletely milked quarters in each milking session (IM; yes or no), MY per hour (MYH; kg/h), and EC per hour (ECH; mS/cm/h) between successive milking sessions. The other 6 measurements tested but not included in the final model were peak milk flow rate (kg/min), kick-offs (yes or no) in each milking session, lactation number, days in milk (d), blood in milk (yes or no), and a calculated mastitis detection index used by DeLaval (DelPro software; DeLaval International AB, Tumba, Sweden). All measurements were assessed to determine their ability to detect CM as both individual variables and combinations of the 12 above-mentioned variables. These were assessed by producing a receiver operating characteristic curve and calculating the area under the curve (AUC) for each model. Overall, 9 measurements (i.e., EC, ECH, MY, MYH, MF, IM, peak flow rate, lactation number, and mastitis detection index) had significant mastitis detection ability as separate predictors. The best mastitis prediction was possible by incorporating 6 measurements (i.e., EC, ECH, MY, MYH, MF, and IM) as well as the random cow and quarter effects in the model, resulting in 90% sensitivity and 91% specificity with excellent AUC (0.96). Assessment of the model was found to produce robust results (AUC >0.9) in different data sets and could detect CM with reductions in sensitivity and specificity with increasing days before actual diagnosis. This study demonstrated that improved mastitis status prediction can be achieved by using multiple measurements, and new indexes based on that are expected to result in improved accuracy of mastitis alerts, thereby improving the detection ability and utility on farm.

The effect of temperate or tropical pasture grazing state and grain-based concentrate allocation on dairy cattle production and behavior.

Grain-based concentrate (GBC) supplement is of high cost to dairy farmers as a feed source as opposed to grazed pasture. Milk production response to GBC is affected by the composition and nutritive value of the remainder of the diet, animal factors, and interactions between forage type and level of GBC. In grazing systems, dairy cattle encounter contrasting pasture states, primarily because the social structure of the herd affects the timing of when each animal accesses a paddock after milking as a result of a relatively consistent cow milking order. However, the effect of feed management, namely pasture state and GBC allocation, on dairy cattle production and behavior is unknown. We examined the effect of varying GBC allocation for dairy cattle grazing differing states of kikuyu grass (Pennisetum clandestinum, a tropical pasture species; experiment 1) and annual ryegrass (Lolium multiflorum L., a temperate pasture species; experiment 2) on dry matter intake, milk production and composition, and grazing behavior. For each experiment, 90 lactating dairy cattle were randomly allocated to 2 consistent (fresh-fresh and depleted-depleted) and 2 inconsistent (fresh-depleted and depleted-fresh pasture state treatments (defined as sequences of pasture state allocation for the morning and afternoon grazing events) and 3 GBC treatments [2.7, 5.4, and 8.1 kg of dry matter (DM)/cow per day], giving 12 treatment combinations for each experiment. The duration of each experiment was 14 d, with the first 7 d used as adaptation to treatment. In each experiment, 3 cattle were selected from each of the 12 pasture type × GBC treatment groups within the experimental herd to determine herbage intake and total DM digestibility using the n-alkanes method (n = 36). There was no interaction between kikuyu grass or ryegrass pasture state and GBC level for intake, digestibility, or milk yield or components. Dairy cattle offered fresh-fresh and depleted-fresh ryegrass produced 9% more milk yield, in line with greater pasture intakes, compared with fresh-depleted and depleted-depleted pasture states. Dairy cattle offered fresh-fresh kikuyu grass had 8% more milk yield and 14% more milk protein yield than other pastures states, but there was no effect of pasture state on milk composition. Milk yield increased with GBC level for both pasture species (∼0.7-0.8 kg of milk/kg of DM GBC) as GBC level increased from 2.5 to 5.4 kg of DM/cow per day. There was a poor response (0.3 kg of milk/kg of DM GBC), and no response, when GBC levels increased from 5.4 to 8.1 kg of DM/cow per day for kikuyu grass and ryegrass, respectively, in line with pasture DMD. Time spent grazing, lying, and ruminating were not associated with kikuyu grass pasture state, GBC, or their interaction. Despite this, there was a linear increase in grazing time in the afternoon coinciding with a linear decrease in lying and rumination time for both kikuyu grass and ryegrass pasture. Together these findings reveal the effect of pasture state and GBC allocation on dairy cattle production and behavior. Tailoring GBC allocation to the state of pasture accessed by cattle appears unwarranted, but there is an opportunity to alter the timing of pasture access to increase herd-level milk production efficiency.

Dairy farmers with larger herd sizes adopt more precision dairy technologies.

An increase in the average herd size on Australian dairy farms has also increased the labor and animal management pressure on farmers, thus potentially encouraging the adoption of precision technologies for enhanced management control. A survey was undertaken in 2015 in Australia to identify the relationship between herd size, current precision technology adoption, and perception of the future of precision technologies. Additionally, differences between farmers and service providers in relation to perception of future precision technology adoption were also investigated. Responses from 199 dairy farmers, and 102 service providers, were collected between May and August 2015 via an anonymous Internet-based questionnaire. Of the 199 dairy farmer responses, 10.4% corresponded to farms that had fewer than 150 cows, 37.7% had 151 to 300 cows, 35.5% had 301 to 500 cows; 6.0% had 501 to 700 cows, and 10.4% had more than 701 cows. The results showed that farmers with more than 500 cows adopted between 2 and 5 times more specific precision technologies, such as automatic cup removers, automatic milk plant wash systems, electronic cow identification systems and herd management software, when compared with smaller farms. Only minor differences were detected in perception of the future of precision technologies between either herd size or farmers and service providers. In particular, service providers expected a higher adoption of automatic milking and walk over weighing systems than farmers. Currently, the adoption of precision technology has mostly been of the type that reduces labor needs; however, respondents indicated that by 2025 adoption of data capturing technology for monitoring farm system parameters would be increased.

Short communication: The diurnal intake and behavior of dairy cows when access to a feed of consistent nutritive value is restricted.

The diurnal variation in pasture nutritive value adds a confounding factor to studies elucidating the effect of time of day on behavior. Our work separates the effect of time of day on both feeding and lying patterns for cows outdoors to enable the alignment of feeding behavior with feed management. We determined the diurnal intake patterns and behavior of dairy cows when the nutritive value of feed remained constant throughout 24 h in an outdoor environment, and when feed access was restricted. Nine nonlactating Holstein-Friesian cows (live weight 626 ± 53 kg, age 96 ± 33 mo; mean ± SD) were split into 3 groups of 3 and offered lucerne hay cubes (cube volume 32 mm3) ad libitum according to 3 treatments: full access (FA, feed access 24 h), day access (DA, feed access between 0600 and 1800 h), and night access (NA, feed access between 1800 and 0600 h). Treatments were applied to individual cows in a crossover design with 7-d periods. During the last 4 d of each period, data were collected on feed intake, as well as feeding and lying behaviors. Total daily intake was greater for cows on the FA treatment (3.5% of BW) compared with the DA and NA treatments at 3.1 and 2.9% of BW, respectively. The cows with FA consumed 69% of their total intake during the day (0600-1800 h), with the greatest intake (39%) occurring during 1200 to 1800 h and only 12% of intake occurring during 2400 to 0600 h. Cows with DA consumed 56% of feed during 0600 to 1200 h and 44% during 1200 to 1800 h. In contrast, NA cows consumed more feed (74%) during the first 6 h period (1800-2400 h), thus maximizing lying time between 2400 and 0600 h. The time spent lying throughout daylight periods varied between treatments; however, total daily lying time was similar across the 3 treatments. This experiment shows the feeding and lying behaviors of cows when feed quality remains constant throughout 24 h, which will assist the formulation of variable feed allocation strategies for future testing in both robotic and conventional milking systems. Varying the quantity of feed offered throughout 24 h may benefit robot utilization at night in automatic milking systems through increased feeding activity, and as we observed, is likely to have little effect on lying time or DMI, with cows readily adapting to changes in feed management. Conversely, aligning feed on offer with preferred feeding time in conventional milking systems may increase the intake of high quality pasture.

Demographics, farm and reproductive management strategies used in Australian automatic milking systems compared with regionally proximal conventional milking systems.

OBJECTIVE: To determine the management practices utilised in automatic milking systems (AMS) that affect reproductive management and performance and how these compare with the management practices used in regionally proximal conventional milking systems (CMS). METHODS: This study examined demographic and management data from AMS and CMS dairy farms through a survey, with a specific focus on reproductive management procedures. RESULTS: Overall, responses from AMS and CMS dairy farms showed little difference in terms of respondent demographics, farm size, herd structure and most farm management strategies. AMS dairies were more likely to use activity meters or other electronic oestrus detection aids than CMS dairies (P < 0.001) and were also more likely to have changed to electronic recording systems (P = 0.007). Although many respondents indicated that they used key monitoring parameters to assess reproductive performance (e.g. days in milk, conception vs pregnancy rate etc.), the format of responses varied significantly, indicating a relatively widespread (among the respondents) lack of knowledge regarding the meaning and usage of some of these common parameters/terminology. CONCLUSIONS: Ultimately, reproductive management practices of AMS dairies were largely similar to those of CMS dairies, indicating that such practices can be implemented in a practical sense, even though the resultant reproductive performance is not yet understood. Understanding that the key reproductive management strategies do not need to change vastly is important to ensure that new adoptees are well informed. Further work is needed to objectively measure AMS performance to increase the knowledge base and generate the confidence that will facilitate further adoption of this innovation.

Synthesis, Photocatalytic, and Antifungal Properties of MgO, ZnO and Zn/Mg Oxide Nanoparticles for the Protection of Calcareous Stone Heritage.

More recently, the biological colonization of stone heritage and consequently its biodeterioration has become the focus of numerous studies. Among all microorganisms, fungi are considered to be one of the most important colonizers and biodegraders on stone materials. This is why the development of new antifungal materials requires immediate action. ZnMgO nanoparticles (NPs) have several exciting applications in different areas, highlighting as an efficient antimicrobial agent for medical application. In this research, the application of Zn-doped MgO (Mg1-xZnxO, x = 0.096) NPs obtained by sol-gel method as antifungal coatings on dolomitic and calcitic stones has been explored as a means to develop effective protective coatings for stone heritage. Moreover, the photocatalytic and antifungal activity of Mg1-xZnxO NPs were comparatively studied with single ZnO and MgO NPs. Thus, compared to the MgO and ZnO nanomaterials, the Mg1-xZnxO NPs exhibited an enhanced photocatalytic activity. After UV irradiation for 60 min, 87% methylene blue was degraded over Zn-doped MgO NPs, whereas only 58% and 38% of MB was degraded over ZnO and MgO NPs, respectively. These nanoparticles also displayed a better antifungal activity than that of single pure MgO or ZnO NPs, inhibiting the growth of fungi Aspergillus niger, Penicillium oxalicum, Paraconiothyrium sp., and Pestalotiopsis maculans, which are especially active in the bioweathering of stone. The improved photocatalytic and antifungal properties detected in the Mg1-xZnxO NPs was attributed to the formation of crystal defects by the incorporation of Zn into MgO. The application of the MgO- and Zn-doped MgO NPs as protective coatings on calcareous stones showed important antifungal properties, inhibiting successfully the epilithic and endolithic colonization of A. niger and P. oxalicum in both lithotypes, and indicating a greater antifungal effectiveness on Zn-doped MgO NPs. The use of Zn-doped MgO NPs may thus represent a highly efficient antifungal protection for calcareous stone heritage.

Interference of single walled carbon nanotubes (SWCNT) in the measurement of lipid peroxidation in aquatic organisms through TBARS assay.

Nanomaterials (NM) exhibit unique properties due their size and relative area, but the mechanisms and effects in the living organisms are yet to be unfold in their totality. Potential toxicity mechanisms concerning NM as carbon nanotubes include oxidative stress generation. Several fluorimetric and colorimetric methods have been systematically used to measure NM toxicity, and controversial results have been reported. One of the problems can be related to the interference effects induced by NM, leading to artifacts that can lead to misleading conclusions. In present study, it was performed in vitro assays with two aquatic species: the zebrafish Danio rerio and the polychaete Laeonereis acuta to evaluate the potential interference capacity of single-wall carbon nanotubes (SWCNT) in a fluorometric method (TBARS assay) to measure lipid peroxidation. Obtained results indicated that gills and brain of zebrafish presented a lowered fluorescence only at extremely high concentrations (50 and 500mg/L). Determinations in anterior, middle, and posterior body regions of L. acuta showed a quite different pattern: high fluorescence at low SWCNT concentrations (0.5mg/L) and lowering at the highest (500mg/L). To eliminate matrix effect of biological samples, tests employing the standard for TBARS assay, 1,3,3-tetramethoxipropane, were run and the results showed again higher fluorescence values at low concentrations (0.5-5mg SWCNT/L), a technique artifact that could lead to misleading conclusions since higher fluorescence values implicate higher TBARS concentration, implying oxidative stress. Using the colorimetric FOX assay with cumene hydroperoxide as standard presented remarkable better results since no artifacts were observed in the same SWCNT concentration range that employed with the TBARS technique.

Review: Milking robot utilization, a successful precision livestock farming evolution.

Automatic milking systems (AMS), one of the earliest precision livestock farming developments, have revolutionized dairy farming around the world. While robots control the milking process, there have also been numerous changes to how the whole farm system is managed. Milking is no longer performed in defined sessions; rather, the cow can now choose when to be milked in AMS, allowing milking to be distributed throughout a 24 h period. Despite this ability, there has been little attention given to milking robot utilization across 24 h. In order to formulate relevant research questions and improve farm AMS management there is a need to determine the current knowledge gaps regarding the distribution of robot utilization. Feed, animal and management factors and their interplay on levels of milking robot utilization across 24 h for both indoor and pasture-based systems are here reviewed. The impact of the timing, type and quantity of feed offered and their interaction with the distance of feed from the parlour; herd social dynamics, climate and various other management factors on robot utilization through 24 h are provided. This novel review draws together both the opportunities and challenges that exist for farm management to use these factors to improved system efficiency and those that exist for further research.