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.

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.

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.

Posttraumatic stress disorder in breast cancer patients following autologous bone marrow transplantation or conventional cancer treatments.

We assessed 17 women who had undergone autologous bone marrow transplants (BMT) for their breast cancer and 20 other women who had been treated for breast cancer (but not with BMT) by structured clinical interviews examining each stage of the breast cancer experience (e.g. initial diagnosis, initial treatment, recurrence of cancer (if applicable) and BMT (if applicable)) and at follow-up points; 3, 6 and 12 months (if applicable) posttreatment. The two groups did not differ on incidence of posttraumatic stress disorder (PTSD), major depressive disorder (MDD) or generalized anxiety disorder at any stage. We found a high rate of PTSD over the cancer experience, 35% for the combined sample, with cancer diagnosis being the most likely point for developing PTSD, 27% for the combined sample. None of the 17 women who had undergone BMT developed PTSD as a result of the treatment.

Validity of self-report measures of girls' pubertal status.

To understand the meaning of somatic changes to the adolescent and their possible effects on behavior, developmentalists have begun to examine maturational timing and status in detail. Efforts have been hampered by the necessity of obtaining somatic growth data by physician examination. In the present study, 3 self-report methods for rating secondary sexual characteristic growth were compared to physician ratings; the accuracy of self-reported height and weight also was assessed. Specifically, 151 11-, 12-, and 13-year-old girls rated their breast and pubic hair development using schematics of the 5 Tanner stages and filled out the Pubertal Development Scale (PDS); their mothers also rated their daughters' development using the Tanner stage schematics. The correlation with physician Tanner ratings was .82 for self-ratings, .85 for ratings by the mother, and between .61 and .67 for self-reports on the PDS. The correlations for self- and actual reports of weight and height were .98 and .75, respectively. The usefulness of the PDS, Tanner ratings, and self-reported height and weight as adequate estimates of pubertal development is discussed.

The Effect of Cycle Phase on the Adolescent Swimmers.

In brief: Performance times of six postmen-archeal adolescents who competed on junior and senior national levels were recorded during a 12-week period. The fastest times for the 100-yd freestyle and 100-yd best event occurred during the menstrual phase and the slowest times during the premenstruum. Symptoms were rated biweekly with the Moos Menstrual Distress Questionnaire. Subjects reported feeling better during menstruation than premenstruation, and none reported dysmenorrhea. Based on basal body temperature, five of ten cycles were clearly biphasic, two were monophasic, and three were possibly biphasic with very short luteal phases. These results, albeit from a small sample, suggest that menstrual cycle phase may affect athletic performance as measured by practice times.