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.
Summary
Lumpy skin disease, sheeppox and goatpox are high‐impact diseases of domestic ruminants with a devastating effect on cattle, sheep and goat farming industries in endemic regions. In this ...article, we review the current geographical distribution, economic impact of an outbreak, epidemiology, transmission and immunity of capripoxvirus. The special focus of the article is to scrutinize the use of currently available vaccines to investigate the resource needs and challenges that will have to be overcome to improve disease control and eradication, and progress on the development of safer and more effective vaccines. In addition, field evaluation of the efficacy of the vaccines and the genomic database available for poxviruses are discussed.
This paper reviews developments in our understanding of the state of the Antarctic and Southern Ocean climate and its relation to the global climate system over the last few millennia. Climate over ...this and earlier periods has not been stable, as evidenced by the occurrence of abrupt changes in atmospheric circulation and temperature recorded in Antarctic ice core proxies for past climate. Two of the most prominent abrupt climate change events are characterized by intensification of the circumpolar westerlies (also known as the Southern Annular Mode) between ∼6000 and 5000 years ago and since 1200–1000 years ago. Following the last of these is a period of major trans‐Antarctic reorganization of atmospheric circulation and temperature between A.D. 1700 and 1850. The two earlier Antarctic abrupt climate change events appear linked to but predate by several centuries even more abrupt climate change in the North Atlantic, and the end of the more recent event is coincident with reorganization of atmospheric circulation in the North Pacific. Improved understanding of such events and of the associations between abrupt climate change events recorded in both hemispheres is critical to predicting the impact and timing of future abrupt climate change events potentially forced by anthropogenic changes in greenhouse gases and aerosols. Special attention is given to the climate of the past 200 years, which was recorded by a network of recently available shallow firn cores, and to that of the past 50 years, which was monitored by the continuous instrumental record. Significant regional climate changes have taken place in the Antarctic during the past 50 years. Atmospheric temperatures have increased markedly over the Antarctic Peninsula, linked to nearby ocean warming and intensification of the circumpolar westerlies. Glaciers are retreating on the peninsula, in Patagonia, on the sub‐Antarctic islands, and in West Antarctica adjacent to the peninsula. The penetration of marine air masses has become more pronounced over parts of West Antarctica. Above the surface, the Antarctic troposphere has warmed during winter while the stratosphere has cooled year‐round. The upper kilometer of the circumpolar Southern Ocean has warmed, Antarctic Bottom Water across a wide sector off East Antarctica has freshened, and the densest bottom water in the Weddell Sea has warmed. In contrast to these regional climate changes, over most of Antarctica, near‐surface temperature and snowfall have not increased significantly during at least the past 50 years, and proxy data suggest that the atmospheric circulation over the interior has remained in a similar state for at least the past 200 years. Furthermore, the total sea ice cover around Antarctica has exhibited no significant overall change since reliable satellite monitoring began in the late 1970s, despite large but compensating regional changes. The inhomogeneity of Antarctic climate in space and time implies that recent Antarctic climate changes are due on the one hand to a combination of strong multidecadal variability and anthropogenic effects and, as demonstrated by the paleoclimate record, on the other hand to multidecadal to millennial scale and longer natural variability forced through changes in orbital insolation, greenhouse gases, solar variability, ice dynamics, and aerosols. Model projections suggest that over the 21st century the Antarctic interior will warm by 3.4° ± 1°C, and sea ice extent will decrease by ∼30%. Ice sheet models are not yet adequate enough to answer pressing questions about the effect of projected warming on mass balance and sea level. Considering the potentially major impacts of a warming climate on Antarctica, vigorous efforts are needed to better understand all aspects of the highly coupled Antarctic climate system as well as its influence on the Earth's climate and oceans.
Fluid transit time is understood to be an important control on the shape of concentration‐discharge (C‐q) relationships, yet empirical evidence supporting this linkage is limited. We investigated C‐q ...relationships for weathering‐derived solutes across seven Antarctic glacial meltwater streams. We hypothesized that (H1) solute fluxes in McMurdo Dry Valley streams are reaction limited so that C‐q relationships are characterized by dilution and that (H2) transit time explains between‐stream variability in the degree of C‐q dilution. Results show that C‐q relationships are chemostatic because solute equilibrium times are shorter than stream corridor fluid transit times. Between‐stream variability in the efficiency of solute production is positively correlated with transit time, suggesting that transit time is an important control on the solute export regime. These results provide empirical evidence for the controls on weathering‐derived C‐q relationships and have important implications for Antarctic ecosystems and solute export regimes of watersheds worldwide.
Plain Language Summary
Relationships between solute concentration and stream discharge (i.e., streamflow) rates in rivers contain important information regarding how water moves through a watershed. In this research, we use a combination of observations and mathematical modeling to show that the shape of concentration‐discharge relationships is related to hydrologic transit time, the duration of time a parcel of water spends moving through the watershed. Because the time scales of chemical reactions are shorter than hydrologic transit times, solute concentration is invariant with discharge. Results of this work empirically corroborate previous theoretical work regarding the physical controls on concentration‐discharge relationships, and have important implications for understanding chemical concentration patterns in rivers worldwide.
Key Points
Antarctic streams exhibit chemostatic concentration‐discharge relationships for weathering‐derived solutes
Hydrologic transit times are short (less than four months), yet solute export is predominantly transport limited because equilibrium time scales are also short (<13 days)
Hydrologic transit time explains between‐stream variability in the shape of concentration‐discharge relationships
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.
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 Australian dairy herd size has doubled over the last 20 years substantially increasing the time that farmers require for individual animal attention to monitor and intervene with events such as ...calving. Technology will help focus this limited labour resource on individual cows that require assistance. The objective of this experiment was to first determine the profiles of rumination duration and level of activity as determined by sensors between, and within, days around calving and second to use these data to predict the day of calving for pasture-based dairy cows. After 2 weeks from the expected calving date, 27 cows were fitted with SCR HR LD Tags, located in 40×90 m² paddock and offered ad libitum oaten hay and 2 kg grain-based concentrate/cow per day until calving. Hourly activity and rumination data for each cow, as determined by the SCR tags, were fitted with linear mixed models and all parameters were estimated using restricted maximum likelihood. Rumination duration decreased by 33% over the day prior and the day of calving, with the decline in rumination duration starting the day prepartum. Activity levels were maintained prepartum but increased in the days postpartum. The day of calving was recorded and used to determine the gold standard positive (the day before calving) and negative (all other) dates. A threshold rumination level of 0.9 (decline in rumination duration of 10%) gave the optimal combination of 70% sensitivity and 70% specificity. This experiment shows the potential to use rumination duration to predict the day of calving and the opportunity to use sensor data to monitor animal health.
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 ∼ 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.
Display omitted
•First decision-support system developed for pasture-based automatic milking systems.•Uses data from 37 farms across Australia, Ireland, New Zealand, Chile and Argentina.•Differences ...between actual and predicted physical performance ranged from 2 to 14%.•Farmers agreed that the decision-support system is useful and easy to use.•Applications include investment planning and system performance optimisation.
There is a significant opportunity to improve profitability and productivity in pasture-based automatic milking systems (AMS). A decision-support system (DSS) is required for AMS that can integrate key mechanics of dynamic biological processes with farm economics. Here we developed and evaluated a web-based DSS named the Integrated Management Model (IMM) designed for assisting AMS farmers and their advisors to evaluate and improve the physical and economic performance of their businesses (https://bit.ly/MilkingEdgeAMSTool). The IMM comprises a series of empirically determined predictive equations derived from the main drivers of productivity and profitability in AMS, together with stochastic simulation and optimisation modelling. The equations and models in the IMM were developed using two data sources available through the Milking Edge project: (a) an annual physical and economic dataset collected from 14 AMS farms across Australia (from years 2015 to 2018), and (b) a monthly physical dataset from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile (years 2015 to 2019). Model predictions were evaluated using two similar datasets: (a) an annual physical and economic dataset from 11 Australian AMS farms (2018 to 2020) and a monthly physical dataset from 20 AMS from Australia, New Zealand, Ireland, and Argentina (2019 to 2020). The DSS was tested by 11 AMS farmers that provided feedback using the technology acceptance model framework. Results from the model evaluation showed the accuracy of the equations and simulations to predict physical variables such as milk harvested (kg milk/robot.d) or milking frequency (milkings/cow.d) was reasonably good (2–14% differences between observed and predicted values). The economic equations, which predicted operating profit margin (OP) (%) and return on total assets (ROTA) (%), could determine the relative changes and direction of profitability when physical variables change. However, the accuracy of these equations to estimate absolute values was low (ROTA: R2 0.26 and MAE 2.0%; OP: R2 0.36 and MAE 13.5%), probably due to the empirical nature of the equations and the relatively small number of farms involved in their development. Overall, farmers agreed that the IMM was a useful and easy-to-use DSS that can be used confidently to simulate physical scenarios and optimise the system performance. Whilst this DSS was designed for AMS farmers and farmers considering investing in AMS, future applications could include research, training, extension, teaching, consultancy or even innovation and development of new technology.
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.
PDF
