Playing to Adapt: Crowdsourcing Historical Climate Data with Gamification to Improve Farmer's Risk Management Instruments - Hernández Aguilera, J. Nicolas; Mauerman, Max; Osgood, Daniel (Elsevier, 2020)
Ground-truthed community-based information over time and space can improve the design of climate risk instruments, reducing the mismatch between farmers’ reported events and remote sensing datasets. However, increasing constraints on direct interaction and a lack of incentives for rural communities’ participation can compromise crowdsourced verification. To address these issues, we designed a game, KON, that uses “gamified” incentives and behavioral elements to gather accurate historical climate data by priming memory through the pairwise comparisons of years and incentivizing accuracy through a points-reward matching system. Our preliminary results suggest that pairwise comparison can facilitate historical bad years recalling, and there is a high correspondence between farmers reporting and satellite sources. Moreover, farmers’ reporting clarifies the story when satellite sources disagree. In addition, the number of responses to the online prototype of the game and the level of participant engagement demonstrated that our game can be easily adapted to different types of weather events and facilitate the collection of a large amount of data in a short amount of time. To adapt and generalize the impact of gamification in diverse agricultural settings, future stages in this project include improve and expand game versions and interphases (i.e., smartphone, SMS), and perform an RCT evaluation for additional hypothesis testing.