Solar Grazing Perceptions Study
This dataset includes the data used to conduct a quantitative and qualitative analysis of a survey of sheep producers conducted by the Bock Agricultural Law & Policy Program at the University of Illinois Urbana-Champaign between January 2023 and May 2023. The data was utilized in a research paper assessing American sheep producer's perceptions of solar grazing. The quantitative data contains general demographic information on the survey respondent as well as their familiarity with solar grazing indicated by a 1-5 Likert scale where 1 is "Not Familiar" and 5 is "Very Familiar" and their perceptions of solar grazing's impact on the environment, economy, and their community's perception of solar energy also indicated on a 1-5 Likert scale where 1 is "Very Negative" and 5 is "Very Positive". The qualitative data is an Atlas.TI project file which contains the qualitative responses of each respondent elaborating on their general opinions of solar grazing and explaining why or why not they would choose to solar graze given the same rate of return as their current operation. This file also contains the codebook used to conduct the qualitative analysis.
Steps to reproduce
For the quantitative data, the research team conducted three one-way ANOVA tests assessing the individual impacts of region, farm composition, and state-level solar energy generation on familiarity with and perceptions of solar grazing. Farm compositions were gathered by determining whether the farm was above or below the average farm acreage in the state and whether the farm generated more than 50% of its income from sheep. Farms with more than 50% income were considered "Primary" and farms with less than 50% of their income were considered "supplementary". The state-level solar energy generation data was gathered by examining the total utility-scale solar energy generation for each state in 2022 and organizing it into quintiles for the ANOVA Analysis. The data was analyzed in RStudio. The qualitative analysis was performed using Thematic analysis. The codebook was generated by analyzing the results of the data.
National Renewable Energy Laboratory