Factors influencing intention to apply spatial approaches to on-farm experimentation: insights from the Australian winegrape sector.
Xinxin SongKatherine J EvansRobert G V BramleySaideepa KumarPublished in: Agronomy for sustainable development (2022)
Grape growers are often constrained by available time and labor to conduct trials that deliver informative results. Spatially distributed trial designs coupled with data collection using sensing technologies can introduce efficiencies and also account for the impact of land variability on trial results. Various spatial approaches have been proposed, yet how farmers perceive them is largely unknown. We collaborated with four wine businesses in Australia to explore how grape growers and viticultural consultants perceive a simplified spatial approach to experimentation involving one or more vineyard rows or "strips." In each case, the simplified strip approach was applied alongside growers' or consultants' own methods to compare the perceived value of different methods. The Theory of Planned Behavior was used as an analytical framework to identify factors influencing participants' intentions towards adopting the strip approach. Our findings show that growers and consultants perceived several advantages of the strip approach over their own methods. Key factors impeding uptake were resource constraints for collecting trial data and lack of skills and knowledge to use and analyze spatial data to position the trial and interpret results. These constraints highlight the need to support growers and consultants who see value in this approach by developing automated and affordable measurements for viticultural variables beyond yield, and by providing training on how to analyze and interpret spatial and response data. This study provides novel insights for private and public sectors on where to focus efforts to facilitate adoption of spatial approaches to On-Farm Experimentation by specific target audiences.
Keyphrases
- electronic health record
- phase iii
- study protocol
- healthcare
- clinical trial
- phase ii
- big data
- mental health
- physical activity
- depressive symptoms
- randomized controlled trial
- social support
- machine learning
- artificial intelligence
- deep learning
- data analysis
- health insurance
- quality improvement
- adverse drug
- medical students
- double blind
- water quality