Jakku, Emma (author), Taylor, Bruce (author), Fleming, Aysha (author), Mason, Claire (author), Fielke, Simon (author), Sounness, Chris (author), and Thorburn, Peter (author)
Format:
Journal article
Publication Date:
2019-12
Published:
Netherlands: Elsevier
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 203 Document Number: D12272
13 pages, Advances in Smart Farming and Big Data applications have the potential to help agricultural industries meet productivity and sustainability challenges. However, these benefits are unlikely to be realised if the social implications of these technological innovations are not adequately considered by those who promote them. Big Data applications are intrinsically socio-technical; their development and deployment are a product of social interactions between people, institutional and regulatory settings, as well as the technology itself. This paper explores the socio-technical factors and conditions that influence the development of Smart Farming and Big Data applications, using a multi-level perspective on transitions combined with social practice theory. We conducted semi-structured interviews with 26 Australian grain farmers and industry stakeholders to elicit their perspectives on benefits and risks of these changes. The analysis shows that issues related to trust are central concerns for many participants. These include procedural concerns about transparency and distributional concerns about who will benefit from access to and use of "farmers' data". These concerns create scepticism about the value of `smart' technologies amongst some industry stakeholders, especially farmers. It also points to a divergence of expectations and norms between actors and institutions at the regime and niche levels in the emerging transition towards Smart Farming. Bridging this divide will require niche level interventions to enhance the agency of farmers and their local networks in these transactions, and, the cooperative design of new institutions at regime level to facilitate the fair and transparent allocation of risk and benefit in farming data information chains.
29 pages, Agent-based models are important tools for simulating farmers’ behaviour in response to changing environmental, economic or institutional conditions and policies. This article introduces an agent-based modelling approach that combines behavioural factors with standard bio-economic modelling of agricultural production. More specifically, our framework integrates the cumulative prospect theory and social interactions with constrained optimisation decisions in agricultural production. We apply our modelling approach to an exemplary bio-economic model on the assessment of weed control decisions. Results show the effects of heterogeneous farm decision-making and social networks on mechanical weed control and herbicide use. This framework provides a generic and conceptually sound approach to improve the scope for representing farmers’ decision-making and allows the simulation of their decisions and recent advances in behavioural economics to be aligned with existing bio-economic models of agricultural systems.
12 pages. Located on pages 17-28 of pdf., The study assessed the level of awareness and determined the level of participation of fish farmers in Agricultural Insurance Scheme (AIS) with a view to improving on the level of awareness and consequently participation. The study adopted the survey method of research. The study population comprised all the 1,728 registered fish farmers in Ondo State. Only 295 respondents were sampled from the population using the Raosoft sample size calculator. Multi-stage sampling procedure was adopted to distribute the sample population among the Local Government Areas (LGAs). Two Local Governments Areas (LGAs) were purposively selected from each of the four zones based on the prominence in fish farming. Second stage involved random selection of two communities each from the selected LGAs. At the last stage, fish farmers register was used to proportionately distribute the farmers to LGAs. The results showed the mean age of fish farmers to be 44.6±10.1years and majority (83.4) were married. The mean household size was 5±2 and about 96% was able to read and write. The mean years of fish farming experience was 13.54±11.9 and all of them were smallholders. About 70.5% were aware of AIS but only 15% were under fish policy cover for the last five years. Majority (82.3%) had moderate participation level with only 4.4% with high level of participation. There was strong correlation (R = 0.759) between the variables investigated and level of participation. Also three variables age, contact with extension and awareness regressed positively while number of information sources and household size regressed negatively with level of participation. It was concluded that despite the high level of awareness, level of participation was low.
24 pages, We present a systematic review of the extensive body of research on farmer risk preference measurement across Europe. We capture the methodological developments over time and discuss remaining challenges and potential areas for further research. Given the constantly evolving policy environment in Europe, and increasing climate-change related risks and uncertainties, there is large value to be gained from enhancing our understanding of this fundamental aspect of farmers’ decision-making processes and consequent actions.
