What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers

As the sustainability of agricultural citizen science projects depends on volunteer farmers who contribute their time, energy and skills, understanding their motivation is important to attract and retain participants in citizen science projects. The objectives of this study were to assess 1) farmers’ motivations to participate as citizen scientists and 2) farmers’ mobile telephone usage. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. The questionnaire was applied in three communities of farmers, in countries from different continents, participating as citizen scientists. We used statistical tests to compare motivational factors within and among the three countries. In addition, the relations between motivational factors and farmers characteristics were assessed. Lastly, Principal Component Analysis (PCA) was used to group farmers based on their motivations. Although there was an overlap between the types of motivations, for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. While fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Two major groups of farmers were distinguished: one motivated by sharing information (egoistic intrinsic), helping (altruism) and contribute to scientific research (collectivistic) and one motivated by egoistic extrinsic factors (expectation, expert interaction and community interaction). Country and education level were the two most important farmers’ characteristics that explain around 20% of the variation in farmers motivations. For educated farmers, contributing to scientific research was a more important motivation to participate as citizen scientists compared to less educated farmers. We conclude that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. Citizen science does have high potential, but easy to use mechanisms are needed. Moreover, gamification may increase the egoistic intrinsic motivation of farmers.

[1]  Luciano Messori The Theory of Incentives I: The Principal-Agent Model , 2013 .

[2]  Zachary Fitz-Walter,et al.  Orientation Passport: using gamification to engage university students , 2011, OZCHI.

[3]  C. Vogl,et al.  Explaining the resurgent popularity of the wild: motivations for wild plant gathering in the Biosphere Reserve Grosses Walsertal, Austria , 2015, Journal of Ethnobiology and Ethnomedicine.

[4]  W. Rice ANALYZING TABLES OF STATISTICAL TESTS , 1989, Evolution; international journal of organic evolution.

[5]  V. Strezov,et al.  An Analysis of Citizen Science Based Research: Usage and Publication Patterns , 2015, PloS one.

[6]  Thomas G. Dietterich,et al.  The eBird enterprise: An integrated approach to development and application of citizen science , 2014 .

[7]  John Hornbuckle,et al.  Using a mobile phone Short Messaging Service (SMS) for irrigation scheduling in Australia - Farmers' participation and utility evaluation , 2012 .

[8]  R. Bhagat Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations , 2002 .

[9]  Derek L. Hansen,et al.  Motivations Affecting Initial and Long-Term Participation in Citizen Science Projects in Three Countries , 2014 .

[10]  C. Batson,et al.  Four Motives for Community Involvement , 2002 .

[11]  K. Shepherd,et al.  The global Land-Potential Knowledge System (LandPKS): Supporting evidence-based, site-specific land use and management through cloud computing, mobile applications, and crowdsourcing , 2013, Journal of Soil and Water Conservation.

[12]  E. Deci,et al.  Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.

[13]  Krista G. Hilchey,et al.  A review of citizen science and community-based environmental monitoring: issues and opportunities , 2011, Environmental monitoring and assessment.

[14]  Kevin Crowston,et al.  The future of citizen science: emerging technologies and shifting paradigms , 2012, Frontiers in Ecology and the Environment.

[15]  E. Hand,et al.  Citizen science: People power , 2010, Nature.

[16]  J. Lepš,et al.  Multivariate Analysis of Ecological Data using Canoco 5 , 2014 .

[17]  Viola Krebs Motivations of Cybervolunteers in an Applied Distributed Computing Environment: MalariaControl.net as an Example , 2010, First Monday.

[18]  James Sumberg,et al.  Agricultural research in the face of diversity, local knowledge and the participation imperative: theoretical considerations , 2003 .

[19]  Sotiris Karetsos,et al.  Web and mobile technologies in a prototype DSS for major field crops , 2010 .

[20]  Kenton O'Hara,et al.  Gamification. using game-design elements in non-gaming contexts , 2011, CHI Extended Abstracts.

[21]  Brian L. Sullivan,et al.  eBird: Engaging Birders in Science and Conservation , 2011, PLoS biology.

[22]  David R. Millen,et al.  Removing gamification from an enterprise SNS , 2012, CSCW.

[23]  Göran Ericsson,et al.  Tackling the motivation to monitor: success and sustainability of a participatory monitoring program , 2014 .

[24]  Jacob van Etten,et al.  Crowdsourcing Crop Improvement in Sub‐Saharan Africa: A Proposal for a Scalable and Inclusive Approach to Food Security , 2011 .

[25]  Anurag Garg,et al.  Collaboration Online: The Example of Distributed Computing , 2005, J. Comput. Mediat. Commun..

[26]  Brian L. Sullivan,et al.  eBird: A citizen-based bird observation network in the biological sciences , 2009 .

[27]  Oded Nov,et al.  Dusting for science: motivation and participation of digital citizen science volunteers , 2011, iConference.

[28]  C. Lintott,et al.  Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers. , 2009, 0909.2925.

[29]  Daren C. Brabham Moving the crowd at iStockphoto: The composition of the crowd and motivations for participation in a crowdsourcing application , 2008, First Monday.

[30]  Vickie Curtis,et al.  Motivation to Participate in an Online Citizen Science Game , 2015 .

[31]  Lammert Kooistra,et al.  Review of yield gap explaining factors and opportunities for alternative data collection approaches , 2017 .

[32]  David R. Flatla,et al.  Calibration games: making calibration tasks enjoyable by adding motivating game elements , 2011, UIST.

[33]  Rick Bonney,et al.  The current state of citizen science as a tool for ecological research and public engagement , 2012 .

[34]  Oded Nov,et al.  Technology-Mediated Citizen Science Participation: A Motivational Model , 2011, ICWSM.

[35]  Anne M. Land-Zandstra,et al.  Motivation and learning impact of Dutch flu-trackers , 2016 .

[36]  R. Ankaiah,et al.  A framework of information technology-based agriculture information dissemination system to improve crop productivity , 2005 .

[37]  S. Ceccarelli,et al.  Decentralized-participatory plant breeding: an example of demand driven research , 2007, Euphytica.

[38]  G. Villamor,et al.  Tree-cover transition in Northern Vietnam from a gender-specific land-use preferences perspective , 2017 .

[39]  J. Laffont,et al.  The Theory of Incentives: The Principal-Agent Model , 2001 .

[40]  T. Bernet,et al.  Tailoring agricultural extension to different production contexts: a user-friendly farm-household model to improve decision-making for participatory research , 2001 .

[41]  Anne M. Land-Zandstra,et al.  Citizen science on a smartphone: Participants’ motivations and learning , 2016, Public understanding of science.

[42]  Cher Ping,et al.  Motivating students to learn , 2005, Br. J. Educ. Technol..

[43]  G. Hofstede,et al.  Culture′s Consequences: International Differences in Work-Related Values , 1980 .

[44]  Eddie J. B. van Etten,et al.  Multivariate Analysis of Ecological Data Using canoco , 2005 .

[45]  Jennifer Preece,et al.  Dynamic changes in motivation in collaborative citizen-science projects , 2012, CSCW.

[46]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[47]  E. Deci,et al.  Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. , 2000, The American psychologist.

[48]  Mauricio R. Bellon,et al.  PARTICIPATORY RESEARCH METHODS FOR TECHNOLOGY EVALUATION: A MANUAL FOR SCIENTISTS WORKING WITH FARMERS , 2001 .

[49]  J. Hellin,et al.  INCREASING THE IMPACTS OF PARTICIPATORY RESEARCH , 2008, Experimental Agriculture.

[50]  Daren C. Brabham MOVING THE CROWD AT THREADLESS , 2010 .

[51]  M. Misiko Dilemma in participatory selection of varieties , 2013 .

[52]  Krithi K. Karanth,et al.  Network environmentalism: Citizen scientists as agents for environmental advocacy , 2014 .

[53]  Adrien Treuille,et al.  Predicting protein structures with a multiplayer online game , 2010, Nature.

[54]  Andreas Neef,et al.  Stakeholder participation in agricultural research projects: a conceptual framework for reflection and decision-making , 2011 .

[55]  Sebastian Deterding,et al.  Situated motivational affordances of game elements: A conceptual model , 2011 .

[56]  B. Lundman,et al.  Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. , 2004, Nurse education today.

[57]  Jacob van Etten,et al.  FIRST EXPERIENCES WITH A NOVEL FARMER CITIZEN SCIENCE APPROACH: CROWDSOURCING PARTICIPATORY VARIETY SELECTION THROUGH ON-FARM TRIADIC COMPARISONS OF TECHNOLOGIES (TRICOT) , 2016, Experimental Agriculture.

[58]  T. Muhr ATLAS/ti — A prototype for the support of text interpretation , 1991 .