Restoring sense out of disorder? Farmers' changing social identities under big data and algorithms.

Advances in precision agriculture (PA), driven by big data technologies and machine learning algorithms can transform agriculture by enhancing crop and livestock productivity and supporting faster and more accurate on and off-farm decision making. However, little is known about how PA can influence farmers' sense of self, their skills and competencies, and the meanings that farmers ascribe to farming. This study is animated by scholarly commitment to social identity research, and draws from socio-cyber-physical systems research, domestication theory, and activity theory. This conceptualization of PA within these theoretical perspectives helps to render visible how big agricultural data and machine learning algorithms can affect meaning, doing, and being for US farmers. Through analysis of data from six focus group discussions and follow-up surveys with stakeholders across the PA value chain, this paper shows that PA tools can necessitate farmers to learn and develop new competencies such as flying drones and interpreting yield maps. At the same time, PA can shape new meaning of farm work and new expectation about a 'good farmer', changing what it means to be a 'successful' farmer from someone who is not only a data observer or data gatherer but also validators of PA models by using their local knowledge of agronomic and environmental phenomenon. We conclude that PA can alter social expectations about farming by reorienting the role of farmers. Policymakers and agriculture extension and outreach programmers can develop more socially relevant PA knowledge and innovation if they can attend to both new and traditional 'good farmer' identities. [ABSTRACT FROM AUTHOR]