1. Managing Vineyard Variability with Variable Rate Irrigation: A Proof-of-Concept Prototype for Perennial Crops Brent Sams – March 5, 2015

2. Managing Vineyard Variability with Variable Rate Irrigation (VRI) Outline Characterizing vineyard variability Economic impact of vineyard variability Variable rate irrigation (VRI) prototype collaboration with IBM Vineyard response to VRI Summary and next steps PHOTO 1

3. 2 Geo-spatial vineyard analytics characterize vineyard variability Cabernet Sauvignon Mean yield = 9.2 tons per acre

4. 3 Mean yield per acre = 9.2 tons

5. 4 40% of vineyard below mean block yield Block improvement opportunity = 30% yield increase Mean yield per acre = 9.2 tons Mean average annual increased revenue = $856/acre Cost avoidance per acre Land $15,000 Establishment $25,000

6. Plant available water in soil Vegetation Index (NDVI) Yield Fruit Quality 5 Integrated vineyard analytics -

7. Significant Correlations with Yield per Acre ParameterCorrelation (r 2 ) Subsurface K + Soil rooting depth Subsurface pH Subsurface P Subsurface organic matter Subsurface K/Mg ratio 0.903 0.774 – 0.805 – 0.882 – 0.890 Significant Correlations with Grape Quality ParameterCorrelation (r 2 ) Soil rooting depth Surface CA Subsurface CA / Mg ratio Surface CEC – 0.673 – 0.506 – 0.510 – 0.554 6 Modeling Yield and Fruit Quality Data with Soil Parameters

8. Vineyard water use is variable 7 17 gallons per vine per week Row direction 28 gallons per vine per week

9. Vineyard water use is variable 8 Row direction 28 gallons per vine per week Modular system can be used to maximize water use efficiency Or used to manage based on canopy vigor

10. Objective Work in collaboration with IBM to develop a proof-of-concept variable rate irrigation (VRI) system prototype and validate its economic impact on vineyard performance Decreased vineyard variability Optimized fruit yield and quality Increased water use efficiency Variable Rate Irrigation (VRI) Photo of vineyard here

11. Experiment location Colony Ranch, Wilton CA

12. Variable Rate Irrigation Prototype Conventional drip irrigation 11 Block selected for study based on characterized yield variability Block size ~30 acres; 10 acres placed under VRI VRI block divided into 140, 50 vine irrigation zones VRI timing, frequency and amount controlled separately in each 50 vine zone Adjacent portion of block run with standard drip irrigation for comparison Experimental layout

13. General layout

14. System design 4” Check valve 2” Solenoid valve Power loc adapter Power loc tee Tubing, 0.69”ID emitters

15. System design

16. Irrigation zone control Computer network with single master coordinating operation Master-slave messaging protocol based on MODBUS High speed over the 3,000+ feet cable PC and master control are accessed remotely through cell link to load irrigation schedules

17. METRIC (Mapping evapotranspiration at high resolution and internalized calibration) ET residual of surface energy balance Rn + LE + G + H = 0 Inputs –Landsat (visible & infrared) –CIMIS weather data Outputs –ETc –Kc (f/NDVI) Watering of each zone: ETc = ETref * Kc * Km Irrigation scheduling

18. Precision Irrigation July 2012 July 2013 Changes in canopy vigor (NDVI) Colony 2A Cabernet Sauvignon 2012 yield map 17

19. Precision Irrigation 2012 Block Yield 8.9 t/ac 2014 Block Yield 10.2 t/ac

20. 2013 Water Use Efficiency

21. Summary and Next Steps Successfully implemented the first variable rate irrigation (VRI) system in a commercial vineyard Cleary demonstrated the impact of VRI –VRI increased canopy size uniformity –VRI increased water use efficiency –VRI Increased yield per acre Immediate impact on established vineyard –Can efficacy be further improved by initiating VRI at vineyard planting? Next Steps - Commercialization –Phase II commercial prototype for vineyards –Other perennial crops

22. Acknowledgments E&J Gallo Winery –Viticulture Lab: Luis Sanchez, Maegan Salinas, Erin Troxell, Shijian Zhuang, Nona Ebisuda –Chemistry: Hui Chong, Bruce Pan, Natalia Loscos –Research Winery: David Santino, Bianca Wiens, Steven Kukesh –GIS-CE: Martin Mendez, Andrew Morgan IBM (TJ Watson Lab & Data Center Services) –TJ Watson Lab, NY: Levente Klein, Nigel Hinds, Hendrik Hamann –Data Services, CA: Alan Claassen, David Lew James Taylor, New Castle University, UK Ernie and Jeff Dosio, Pacific Agrilands Scott Britten and Associates, Bennett & Bennett