An assessment of producer precision dairy farming technology use, prepurchase considerations, and usefulness

Abstract

An online survey to identify producer precision dairy farming technology perception was distributed in March 2013 through web links sent to dairy producers through written publications and e-mail. Responses were collected in May 2013 and 109 surveys were used in statistical analysis. Producers were asked to select parameters monitored by technologies on their farm from a predetermined list and 68.8% of respondents indicated technology use on their dairies (31.2% of producers not using technologies). Daily milk yield (52.3%), cow activity (41.3%), and mastitis (25.7%) were selected most frequently. Producers were also asked to score the same list of parameters on usefulness using a 5-point scale (1=not useful and 5=useful). Producers indicated (mean ± SE) mastitis (4.77±0.47), standing estrus (4.75±0.55), and daily milk yield (4.72±0.62) to be most useful. Producers were asked to score considerations taken before deciding to purchase a precision dairy farming technology from a predetermined list (1=not important and 5=important). Producers indicated benefit-to-cost ratio (4.57±0.66), total investment cost (4.28±0.83), and simplicity and ease of use (4.26±0.75) to be most important when deciding whether to implement a technology. Producers were categorized based on technology use (using technology vs. not using technology) and differed significantly across technology usefulness scores, daily milk yield (using technologies: 4.83±0.07 vs. not using technologies: 4.50±0.10), and standing estrus (using technologies: 4.68±0.06 vs. not using technologies: 4.91±0.09). The same categories were used to evaluate technology use effect on prepurchase technology selection criteria and availability of local support (using technologies: 4.25±0.11 vs. not using technologies: 3.82±0.16) differed significantly. Producer perception of technology remains relatively unknown to manufacturers. Using this data, technology manufacturers may better design and market technologies to producer need.

Managing Socio-Ethical Challenges in the Development of Smart Farming: From a Fragmented to a Comprehensive Approach for Responsible Research and Innovation

“…Labour-saving devices (e.g. automated cup removers, automated feeding) have proven most popular among dairy farmers internationally (Borchers and Bewley 2015;Eastwood et al 2016), and this is mirrored in NZ (Edwards et al 2015). In NZ, the uptake of the more complex (and expensive) technologies, such as robotic milking, has not proved popular because of lower costs of conventional milking and issues of scale, with only approximately 20 farms using milking robots by 2016.…”

A review of determinants for dairy farmer decision making on manure management strategies in high-income countries

“…This includes data and information about the characteristics of farms that apply manure at varying rates (Powell et al 2007), as well as equipment and infrastructure development that could enable farmers to haul manure longer distances, which could prevent over application in fields closest to storage facilities. The development of farmer-friendly software that could assist farmers in field by field data collection and record keeping could also assist in adoption of better MMS (Tao et al 2014), although emerging precision agriculture technologies are already making significant strides in this area and dairy farmer adoption of these technologies is growing (Carolyn 2014, Borchers andBewley 2018).…”

Field validation of protocols developed to evaluate in-line mastitis detection systems

“…gies include the perception that current commercially available technologies are unproven, unreliable, and have an uncertain return on investment (Russell and Bewley, 2013;Borchers and Bewley, 2015;Steeneveld and Hogeveen, 2015).…”

“…The systems need to identify cows with clinical mastitis (CM) and identify cows with a high SCC to manage BMSCC. Adoption rates of in-line mastitis-detection systems are reported to be ~26% among surveyed dairy farmers internationally who have also invested in other sensor technologies (Borchers and Bewley, 2015). In the Netherlands, adoption rates of 35% with conventional milking systems and 93% with automatic milking systems have been reported (Steeneveld and Hogeveen, 2015) for mastitis-detection systems based on electrical conductivity (EC).…”

“…Edwards et al (2015) reported a much lower adoption rate of 6% for in-line mastitis-detection systems in a representative survey of New Zealand dairy farmers. Farmers without an in-line mastitis-detection system ranked such systems in the top 3 of potentially useful technologies for their farm (Borchers and Bewley, 2015) or expressed an interest in investing in them . Reasons for dairy farmers not to invest in decision support technolo-…”