Growers' perceptions and acceptance of soil quality indices

Soil quality (SQ) assessment tools may facilitate adaptive management decisions that promote sustainable agricultural practices. However, without input from the target audience, these decision tools' potential for adoption remains unknown. In an effort to consider the end-user in SQ index development, we examined fanner reactions to index outcomes and uses for soil quality information. We calculated SQ indices for side-by-side comparisons of alternative (organic amendment) and conventional practices in the San Joaquin Valley, CA. The indices integrated chemical, biological, and physical data collected over 3 years in a participatory, on-farm demonstration project. In a focus group format, we asked the participating farmers about their perceptions of SQ in the study fields. We then asked the farmers to compare their perceptions with the calculated SQ indices by rating the amount of agreement between the two on a scale from 1 to 10, with 10 being excellent agreement. The survey results showed a mean of 8 and standard deviation = 1 (N = 12). When we presented all participants with a variety of output options for soil quality indicator information, they were asked to rate each for usefulness and understandability. The apparent disparity among their preferences (high ratings for the most and the least integrated data) was explained by the suggestion of several farmers that it would be most useful to have access to several forms of the information. Participants also discussed how they would most likely use the tools and what information would be needed for them to change a management practice. A demonstrated link between soil quality and economics was the most discussed need. In response to the farmers' emphasis on economic outcomes, we compared our SQ index with yield results (as one component of net revenue) for participating fields. Pearson correlation coefficients showed statistically significant correlations between yield and SQ index outcomes. Correlations were stronger within subsets of the data grouped by crop or soil suborder.