In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

Agriculture is being increasingly transformed into a technological industry and calls for a greater need for digitally literate employees. To ensure school students are best placed for this requirement, the development of teacher digital literacy, self-efficacy, and the awareness of agricultural technology is essential. The current study explores the digital literacy and self-efficacy of Australian Technology Mandatory teachers who were participants in a one-day workshop (n = 185). The workshop introduced participants to the GPS Cows module, a complete teaching resource specifically designed to cover agricultural aspects of the Technology Mandatory syllabus. Data were collected by way of classroom ‘clickers’ during the workshop and by a post-workshop survey. Teachers were found to have reasonable basic digital literacy but lacked the confidence to conduct more detailed analytics. There was also some evidence that a teacher’s own digital literacy may also impact their perception of their students’ skills. Professional development workshops, such as the GPS Cows workshop, can improve teacher digital literacy and self-efficacy through hands-on learning in a collaborative, team environment.

Context: On-animal sensing systems are being promoted as a solution to the increased demand for monitoring livestock for health and welfare. One key sensor platform, global navigation satellite system (GNSS) positioning, provides information on the location and movement of sheep. This information could be used to detect partition in sheep, a key period of time when both ewes and lambs are at risk. The development of algorithms based on key behavioural features could provide alerts to sheep managers to enable intervention when problems arise. Aims: To investigate the use of GNSS monitoring as a method for detecting behavioural changes in sheep in the period around parturition. Methods: GNSS collars were attached to 40 late gestation ewes grazing a 3.09 ha paddock in New Zealand. Several metrics were derived: (i) mean daily speed, (ii) maximum daily speed, (iii) minimum daily speed, (iv) mean daily distance to peers, and (v) spatial paddock utilisation by 95% minimum convex polygon. Speed metrics and distance to peers were also evaluated at an hourly scale for the 12 h before and 12 h after lambing. Key results: Minimum daily speed peaked on the day of parturition (P < 0.001), suggesting animals may have been expressing more agitation and did not settle. Isolation was also evident during this time, with postpartum ewes located further from their peers than pre-partum ewes (P < 0.001). Day of lambing was also evident by reduced spatial paddock utilisation (P < 0.001). Conclusions: This study demonstrates that GNSS technology can be used to detect parturition-related behaviours in sheep at a day scale; however, detection at the hour scale using GNSS is not possible. Implications: This research highlights the opportunity to develop predictive models that autonomously detect behavioural changes in ewes at parturition using GNSS. This could then be extended to identify ewes experiencing prolonged parturition, for example dystocic birth enabling intervention which would improve both production and welfare outcomes for the sheep industry.

The future of work is influenced by the digital transformation of industries, including agriculture. The current study aimed to understand the social drivers of automated technology acceptance and adoption in Australian cotton farms. The study employed a mixed-methods approach to compare those who were (a) currently using automated technology, (b) not currently using automated technology but considering adoption, and (c) not currently using automated technology and no intention to adopt. The research found that social factors and workforce considerations influence growers’ motivation to adopt automated technology on farms. Furthermore, differences on appraisals of perceived usefulness were observed when comparing growers with no intention to adopt automated technology with those considering adoption or who have adopted automated technology. Both perceived usefulness and ease of use barriers are challenges for those considering adoption of automated technology. Support that improves ease of use for those who have adopted automated technology is important for continued appraisals of perceived usefulness of automated technology. Further research to understand antecedents to appraisals of perceived usefulness and ease of use, and how these interact to influence acceptance and automated technology, is required to inform strategic workforce interventions that support the digital transformation of cotton farms.

This systematic review explores the emergence and utilisation of agricultural technology (“AgTech”) in secondary schools globally. A total of 14 studies published between 2000 and 2020, inclusive, were reviewed, each exploring the use of agricultural technologies in secondary school classrooms and barriers to adoption. For all reviewed studies, each had aimed to address one of three major objectives: (a) to determine or increase teacher knowledge of AgTech; (b) to evaluate the effectiveness of AgTech professional development (PD); or (c) to evaluate the effectiveness of AgTech classroom activities. Requirements for future AgTech PD or classroom activities were also identified, including the use of improved pre-service learnings and in-service PD. This study highlights the importance of improving the opportunities of teachers for AgTech learning, including both an introduction to the technologies and support for classroom implementation. Applications of these findings could be used by teachers, schools, industry organisations, universities and policymakers to ensure sufficient teacher knowledge and skills for effective student learning. By increasing the knowledge and skills of the next-generation agricultural workforce, it is anticipated that AgTech adoption on farms will increase.