While conservation practices promote soil health and reduce the negative environmental effects from agricultural production, their adoption rates are generally low. To facilitate farmer adoption, we carried out a survey to identify potential challenges faced by farmers regarding conservation tillage and cover crop adoption in the western margin of the US Corn Belt. We found farmers' top two concerns regarding conservation tillage were delayed planting, caused by slow soil warming in spring, and increased dependence on herbicide and fungicides. Narrow planting window and lack of time/labor were perceived by farmers as the two primary challenges for cover crop adoption. Some sense of place factors, including the commonly included dimensions of attachment, identity and dependence, played a role in farmers' perceived challenges. For example, respondents more economically dependent on farming perceived greater challenges. We found that farmers' challenge perceptions regarding reduced yield and lack of time/labor significantly decreased as years of usage increased, implying that time and experience could dilute some challenges faced by farmers. Our findings indicate that social network use, technical guidance and economic subsidies are likely to address the concerns of farmers and facilitate their adoption of conservation practices.
The use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers’ concerns about DSS. Factor loadings indicated that farmers’ concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States.
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