Vineyard Tech Advancements for Viticultural DecisionMaking Vineyard Tech

Vineyard Tech Advancements for Viticultural Decision-Making

Vineyard Tech Advancements for Viticultural Decision. Making Daniel Bosch

• Any sensors we use or data we collect should be part of a larger management decision making process. • Ideally we would use optimization models to improve and hasten decisions. • Data and models should reduce the complexity of the decision.

VSIM Model Lars Pierce lpierce@csumb. edu California State University, Monterey Bay Funding: CA Department of Water Resources

Modeling Irrigation Decisions VSIM Vineyard Soil-Irrigation Model • Automated Irrigation Scheduler • Forecasts Weekly Irrigation Needs • Evaluates Irrigation Schedules

VSIM Software System Automatically: Creates Farm Database Extracts By Block Vine Kc Soils Slope Aspect

Runs the VSIM Model Canopy Kc Model + Soil Water Budget Model

VSIM Calculates Daily: • • • Plant Water Use Soil Moisture Deep Drainage Leaf Water Potential Irrigation

Emails Summary Forecast to Grower

Emails Block Summaries to Grower

Emails Graphs to Grower

Benefits: VSIM uses farm shapefile to set up farm input database (easy set up for new farms). VSIM runs whenever VSIM server receives email with updated grower irrigation logs.

Benefits: VSIM irrigation forecasts are automatically generated and emailed to the grower. VSIM reports provide “early warning system” regarding vine growth and water stress.

Benefits: VSIM reports assist grower in maintaining target LWPs during wet and dry years. Helps grower to document efficient irrigation water use over growing season.

Initial VSIM Irrigation Trial Results (2014): • • • No significant differences in yield Water savings Improved juice color Earlier ripening Reduced Time Managing Irrigation

2015 Expand number of users & set up VSIM for use in other fruit/nut crops. Lars Pierce – lpierce@csumb. edu

Ideas to Enhance Irrigation Models Using Imagery • Higher Resolution Images to measure vine size versus cover crops • Precision farming • Heat Sensors to monitor vine stress • Greater Frequency of Images

Surface Renewal Tom Shapland Tule Technologies info@tuletechnologies. com

Heat and water vapor the wind carries away from the canopy

Tom Shapland’s Ph. D

Matches lysimeter ETc at Kearney

Gallons per vine use per day Irrigation amounts

Plant Response Index kc = ETc / ETo

/2 5/ 01 20 4 /2 5/ 01 27 4 /2 0 6/ 14 3/ 2 6/ 01 10 4 /2 6/ 01 17 4 /2 6/ 01 24 4 /2 0 7/ 14 1/ 20 7/ 14 8/ 2 7/ 01 15 4 /2 7/ 01 22 4 /2 7/ 01 29 4 /2 0 8/ 14 5/ 2 8/ 01 12 4 /2 8/ 01 19 4 /2 8/ 01 26 4 /2 0 9/ 14 2/ 20 9/ 14 9/ 2 9/ 01 16 4 /2 9/ 01 23 4 /2 9/ 01 30 4 /2 10 01 /7 4 10 /20 /1 14 4 10 /2 14 1 10 /2 14 8/ 2 11 01 /4 4 /2 01 4 5/ 13 ETo versus ETc 2. 5 2 3. 5 eta eto Stress beginning 1. 5 1 0 3 2. 5 2 1. 5 1 0. 5 0

5/ 13 / 5/ 201 20 4 / 5/ 201 27 4 /2 6/ 014 3/ 6/ 201 10 4 / 6/ 201 17 4 / 6/ 201 24 4 /2 7/ 014 1/ 2 7/ 014 8/ 7/ 201 15 4 / 7/ 201 22 4 / 7/ 201 29 4 /2 8/ 014 5/ 8/ 201 12 4 / 8/ 201 19 4 / 8/ 201 26 4 /2 9/ 014 2/ 2 9/ 014 9/ 9/ 201 16 4 / 9/ 201 23 4 / 9/ 201 30 4 / 10 201 /7 4 10 /20 /1 14 4 10 /2 14 1 10 /2 14 8/ 11 201 /4 4 /2 01 4 Max kc = % shaded area? ETc /ETo= kc 1. 2 1 0. 8 0. 6 ETc /Eto= kc 0. 4 0. 2 0

0. 2 2 0 0 11/4/2014 10/28/2014 10/21/2014 10/14/2014 10/7/2014 9/30/2014 9/23/2014 9/16/2014 9/9/2014 9/2/2014 8/26/2014 8/19/2014 8/12/2014 8/5/2014 7/29/2014 7/22/2014 7/15/2014 7/8/2014 7/1/2014 6/24/2014 6/17/2014 6/10/2014 6/3/2014 5/27/2014 1. 2 5/20/2014 % 5/13/2014 Stress beginning 55% ETc - Surface Renewal 45% ETc - UC Williams 1. 6 14 1. 4 12 Max kc 10 1 8 0. 6 6 0. 4 4 Gal/ vine

Amount of Water to Apply

Needed Technology for Irrigation • Less expensive water meters • Less expensive soil moisture sensors

Farm Management Databases / Spreadsheets / Maps Budgets Monitor: Budget versus daily actuals Crop Estimation – random rows and vines Maps – work orders, precision farming Scheduling

Experimental Designs and Evaluation Simple Experimental Design setup and evaluation – inexpensive software Develop methods for quick evaluation, yield monitors, photo of bin, etc. Test and Retest

Drones Will imagery have zero cost or lower cost? Imagery is less important than the value of the data derived from imagery Will a drone be part of a management strategy or is it equipment?

Drones Size of drone will determine function Largest drones - Spraying, Seeding Smaller drones – imagery, place traps, survey for virus, broken emitters, count missing vines, monitor spray drift, collect data from ground sensors

Computer description of an image in 10 years?

And The Green Grass Grew All Around Well, the feather on the wing And the wing on the bird And the bird on the egg And the egg in the nest And the nest on the twig And the twig on the branch And the branch on the tree And the tree on the root And the root in the hole And the hole in the ground And the green grass grew all around, all around William Jerome, 1912