Predicting Homeowner Satisfaction and Long- Term Use of Smart Irrigation Controllers

Transcription

1 Predicting Homeowner Satisfaction and Long- Term Use of Smart Irrigation Controllers Maria C. Morera, Ph.D. Paul F. Monaghan, Ph.D. Michael D. Dukes, Ph.D. P.E. Eliza Breder, B.S. Department of Agricultural Education and Communication Department of Agricultural and Biological Engineering University of Florida Institute of Food and Agricultural Sciences November 17, 2016

2 Background Orange County Smart Irrigation Pilot Project Assessed the water conservation potential of soil moisture sensor (SMS)- and evapotranspiration (ET)-based smart controllers under residential conditions 167 single-family homes were installed with automatic meter reading devices (AMRs) and received either a smart controller or monitoring only Irrigation data was collected from January 2012 to December 2013 Survey data was collected from January to March 2014

3 Earlier Findings Evaluation of Homeowner Response to Smart Irrigation Controllers (Morera et al., 2015) Most survey participants were satisfied with their controllers and planned to continue using them Both types of controllers were consistently praised for saving money and irrigating efficiently Long-term use was best predicted by levels of knowledge regarding the workings of the devices and whether any challenges were experienced operating them

4 Current Study Further explored factors impacting homeowners satisfaction with smart irrigation controllers and their long-term use of the devices Compared irrigation data to survey data Analyzed relationships between water savings, conservation attitudes, demographics, and satisfaction with smart controllers Investigated whether water savings is a better predictor of long-term adoption Sought broader understanding of homeowners preferences to enhance promotion efforts

5 Analysis Pre-installation of smart controller Post-installation of smart controller Estimated irrigation divided by Theoretical gross irrigation requirement Irrigation ratio (IR) Actual irrigation divided by Theoretical gross irrigation requirement Monthly average Monthly average Jan 2007 Dec 2008 Jan 2012 Dec 2013 Relative change in irrigation = Post mean IR Pre mean IR Pre mean IR

6 Analysis Irrigation system automatic settings adjustments Familiarity with landscape system Controller settings adjustments Challenges Demographics Relative change in irrigation Contractor license/certification Technical knowledge UF/IFAS programming Routine inspections Controller settings updates reflecting changes in the landscape

7 Analysis Perceived water savings effectiveness of controller Relative change in irrigation Level of comfort operating controller Challenges Demographics Satisfaction with smart controller Tutorial Technical knowledge Conservation attitude index Satisfaction with irrigation practices Satisfaction with amount of irrigation county permitting Technology Satisfaction with appearance of the lawn/landscape

8 Analysis Substitute calculated relative change in monthly irrigation 2014 Model predicting likelihood of continuing to use smart controller For perceived water savings effectiveness of controller

9 Results +UF/IFAS programming Relative change In irrigation

10 Results UF/IFAS programming of the controller was significantly correlated with survey participants relative decrease in irrigation (r =.29, p =.006) No other analyzed variables were significantly correlated with the relative decrease in irrigation

11 Results +Perceived water savings effectiveness of controller Satisfaction with smart controller +Satisfaction with the appearance of the lawn/landscape

12 Results Substitute calculated relative change in monthly irrigation Predictive power of the model decreases slightly 2014 Model predicting likelihood of continuing to use smart controller For perceived water savings effectiveness of controller Technical knowledge levels and the experience of any challenges remain significant

13 Results Results of logistic regression analysis performed to evaluate the effects of homeowners perceptions, experiences, knowledge, age, and water savings on the likelihood of continuing to use their installed controllers after the completion of the Orange County, FL smart irrigation pilot project, based on (n = 87) valid responses to survey questions regarding the regression model s dependent and independent variables. Predictor B SE Wald χ 2 Odds Ratio (Constant) Satisfied with irrigation practices Satisfied with the appearance of lawn/landscape Average monthly water savings Feels own conservation of water affects overall water supply Received tutorial Experienced challenge(s) with the controller * 0.13 Technical knowledge test score * 3.91 Age Note: R 2 =.39 (Nagelkerke, 1991) * p<.05.

14 Results +Perceived water savings effectiveness of controller +Satisfaction with the appearance of the lawn/landscape Satisfaction with smart controller -Challenges +Technical knowledge Likelihood of continuing to use smart controller

15 Conclusions Because the relative change in mean monthly irrigation only correlated with the initial programming of the controller, results suggest that implementation techniques are critical to the controllers contribution to water conservation and promotion efforts should include training for contractors Substituting the perceived water-saving effectiveness of the controller with the calculated relative change in mean monthly irrigation reduced the predictive power of the logistic regression models, suggesting perceptions are more relevant than controller performance to homeowner decision making Satisfaction with the performance of the devices is necessary but not sufficient for their continued acceptance; their ongoing use rests with the knowledge and experiences of the homeowners

16 Acknowledgements This research was generously supported by: Orange County Utilities Water Research Foundation St. John s River Water Management District South Florida Water Management District UF/IFAS Center for Landscape Conservation and Ecology