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.

There is a substantial opportunity to lift feed utilization and profitability on pasture-based dairy systems through both increased pasture monitoring accuracy and frequency. The first objective of this experiment was to determine the impact of the number of electronic rising plate meter (RPM) readings and walking pattern on the accuracy of the RPM to determine pasture biomass. The second objective was to evaluate current satellite technology (i.e., small CubeSats and traditional large satellites) in combination with the electronic RPM as an accurate tool for systematic pasture monitoring. The experiment was conducted from October to December 2019 at Camden, Australia. Two experimental paddocks, each of 1.1 ha, were sown with annual ryegrass and monitored with an electronic RPM integrated with Global Navigation Satellite System and with two different satellites (Planet CubeSats and Sentinel-2 satellite). Here we show that 70 RPM readings achieve a ± 5% error in the pasture biomass estimations (kg DM/ha), with no effect of the walking pattern on accuracy. The normalized difference vegetation index (NDVI) derived from satellites showed a good correlation with pasture biomass estimated using the electronic RPM (R2 0.74–0.94). Satellite pasture biomass and growth rate estimations were similar to RPM in one regrowth period but underestimated by ≈20% in the other. Our results also reveal that the accuracy of uncalibrated satellites (i.e., biomass estimated using NDVI to kg DM/ha standard equations) is low (R2 0.61, RMSE566–1307 kg DM/ha). However, satellites calibrated with a RPM showed greater accuracy in the estimations (R2 0.72, RMSE255 kg DM/ha). Current satellite technology, when used with the electronic RPM, has the potential to not only reduce the time required to monitor pasture biomass manually but provide finer scale measurements of pasture biomass within paddocks. Further work is required to test this hypothesis, both spatially and temporally.

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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