Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers

Abstract

Big data in crop agriculture is information collected by sophisticated machinery at the farm level, as well as externally generated data, such as field satellite imagery. Although some of this data is useful to individual farmers, much of it has little value to the farmer that collects it. Capturing the true value of big data comes when it is aggregated over many farms, allowing researchers to find underlying bio-physical and economical relationships. We conduct a hypothetical choice experiment to analyze farmers’ willingness to share data by asking farmers in Saskatchewan whether they would participate in a big data sharing program. The choice tasks varied the type of organization that operated the big data program and included financial and non-financial incentives. Heteroscedastic and random effects probit models are presented using the data from the survey. The results are consistent across models and find that farmers are most willing to share their data with university researchers, followed by crop input suppliers or grower associations, and financial institutions or equipment manufacturers. Farmers are least willing to share their data with government. Farmers are more willing to share data in the presence of a financial incentive or non-financial incentive such as comparative benchmark statistics or prescription maps generated from the data submitted. Checks for robustness and heterogeneity indicate there is no self-selection bias into the survey, and no heterogeneity in the results for financial incentive and farm revenue. A latent class logit model determines the farmer population may be heterogenous in their willingness to participate in a big data sharing program, but homogenous in their ordering of preferences for organization, financial incentive, and non-financial incentive. In addition, demographic variables are not related to class membership.