1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials

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1 Project Overview The overall goal of this project is to deliver the tools, techniques, and information for spatial data driven variable rate management in commercial vineyards. Identified 2016 Needs: 1. Continuing the development and validation of mobile sensors 2. Streamlining spatial data processing 3. Identifying and establishing variable rate management field trials 4. Collecting baseline economic information 5. Developing an educational platform for industry stakeholders

2 The Efficient Vineyard project is broken into five working themes: Precision Vineyard Sensing (Steve Nuske) Spatial Data Processing and Decision Support System (James Taylor) Differential Vineyard Management and Testing (Kaan Kurtural) Economic Evaluation and assessment (Julian Alston) Technology Adoption and Outreach (Tim Weigle)

3 The overall objectives for each theme in 2016 are: Precision Vineyard Sensing Generate maps of vineyard soil, canopy and crop data and to develop and validate different sensors/methods to measure the characteristics. Spatial Data Processing and Decision Support System (SSDS) Establish a clearinghouse for project spatial data to ensure that the data is correctly processed and passed into the SDSS Differential Vineyard Management and Testing Collect, process, and analyze spatial soil, canopy, and crop information from commercial vineyards and determine five locations to establish and test variable rate management. Economic Evaluation and Assessment Develop a more complete qualitative understanding of the range of current practices with respect to precision viticulture across the relevant industry segments and devise appropriate models to develop a quantitative understanding of the consequences of adoption of various practices and technologies to be developed by the project. Technology Adoption and Outreach Determine preferred method(s) of learning of grape growers and industry members and develop a portal of learning and PR through a public face of the project

4 Precision Vineyard Sensing Generate maps of vineyard soil, canopy and crop data and to develop and validate different sensors/methods to measure the characteristics. 2 New CMU Imaging Units designed, fabricated, and delivered to UC Davis and Gallo 50% lighter and easier to fabricate and mount. Daylight Operation. Faster processing and higher capacity storage. 400 times greater data collection than human. Greater than three times the image acquisition potential for year 2 of the project.

5 Precision Vineyard Sensing Generate maps of vineyard soil, canopy and crop data and to develop and validate different sensors/methods to measure the characteristics Image acquisition in table, wine, and juice grape vineyards and developed algorithms for berry count, berry size, berry color, shoot counts, and leaf area. Over 400,000 images processed with each field scan. Improved berry algorithms from pilot study. Berry count and volume measurements used to predict yield ~ 6% error. New algorithms for canopy imaging. Multi-function image analysis platform for future crop and canopy measurements.

6 Precision Vineyard Sensing Generate maps of vineyard soil, canopy and crop data and to develop and validate different sensors/methods to measure the characteristics. New harvester-mounted grape yield monitor tested at CLEREL Constructed from off-the-shelf components. Side-by-side comparison on ~100 Concord acres. Less expensive and potentially more robust than the ATV unit. Two options for destructive grape yield monitoring.

7 Spatial Data Processing and Decision Support System (SSDS) Establish a clearinghouse for project spatial data to ensure that the data is correctly processed and passed into the SDSS #1 A prototype Spatial Decision Support System (SDSS) for the US viticulture industry with open source Web technologies. Prototype is modular with a presentation layer, a business layer, a GIS layer, and a data/analysis layer as independent components. Software components may be replaced as the project proceeds A prototype open source spatial data platform for easy management of vineyard information.

8 Spatial Data Processing and Decision Support System (SSDS) Establish a clearinghouse for project spatial data to ensure that the data is correctly processed and passed into the SDSS #2 Baseline information on grower interface with SDSS Grower survey on how they would like to interact with spatial data. Sampling protocols in commercial vineyards to validate spatial data. Literature and research review for SDSS input and output components. Understanding of grower preferences to viewing, adding, and interacting with spatial vineyard data.

9 Spatial Data Processing and Decision Support System (SSDS) Establish a clearinghouse for project spatial data to ensure that the data is correctly processed and passed into the SDSS Central shared location for project research spatial data. Project data clearinghouse maintained at CLEREL Organized file naming and storage system Organized clearinghouse for project data that can be used by all themes in the project.

10 Differential Vineyard Management and Testing Collect, process, and analyze spatial soil, canopy, and crop information from commercial vineyards and determine five locations to establish and test variable rate management. Commercial table, wine, and juice grape vineyard soil and canopy scanned and analyzed for potential variable rate management. 3 table grape, 8 wine grape, and 6 juice grape vineyards scanned in 2016 Spatial statistics (variogram) modeling used to characterize potential in the vineyards for VRM. Criteria for evaluating potential cooperating vineyards established. Protocol for initial scanning and evaluation of commercial vineyards for potential variable rate management.

11 Differential Vineyard Management and Testing Collect, process, and analyze spatial soil, canopy, and crop information from commercial vineyards and determine five locations to establish and test variable rate management. Five viticulture field trials established in California based on spatial data to determine future variable rate management treatments. 2 Table grape and 3 wine grape vineyards including the north central valley, south central valley, central coast, and north coast regions. Commercially relevant treatments in irrigation, leaf pulling, shoot thinning, and differential harvest. Canopy, crop, and fruit quality measurements collected. Research based spatial viticulture information for the project SDSS, economic analysis, and VRM.

12 Differential Vineyard Management and Testing Collect, process, and analyze spatial soil, canopy, and crop information from commercial vineyards and determine five locations to establish and test variable rate management. Variable rate mechanical shoot thinning and fruit thinning based on spatial vineyard data collection and processing. Spatial vineyard prescription map generated from spatial data collection, layering, and analysis. Commercial mechanical vineyard equipment (shoot thinner and harvester) made variable rate through implement hydraulics. Off-the-shelf precision agriculture hardware/software used to translate prescription map information into variable rate action on-the-fly. Demonstrated on-the-fly mechanical crop load management in commercial vineyards.

13 Economic Evaluation and Assessment Develop a more complete qualitative understanding of the range of current practices with respect to precision viticulture across the relevant industry segments and devise appropriate models to develop a quantitative understanding of the consequences of adoption of various practices and technologies to be developed by the project. Assembled and updated the existing budgets for representative grape growers of wine and table grapes. Partial budget models show grower-level operating costs and benefits for a specific variety in a specific location First step of quantitative analysis of economic impact of adoption of precision agriculture technology Can be extended to show the economic impact of technology on a specific sector of the industry Impact statement. Budget models are an effective way to demonstrate the economic impact of new technology to industry participants and individual growers to facilitate adoption.

14 Economic Evaluation and Assessment Develop a more complete qualitative understanding of the range of current practices with respect to precision viticulture across the relevant industry segments and devise appropriate models to develop a quantitative understanding of the consequences of adoption of various practices and technologies to be developed by the project. Developed a general framework for evaluating economic benefits to growers from 1) ability to control harvest timing, and 2) increase in the quality of the fruit. Market price premium for being early to the market for early ripening varieties of table grapes. Market price premium for table grapes of higher quality Price/quality distributions of winegrapes within each crush district to establish a possible quality premium Impact statement. Benefits from harvest timing and changes in the quality of the fruit are outcomes of the project that are difficult to quantify and may vary widely among individual growers. We established a range of possible outcomes using general market data.

15 Economic Evaluation and Assessment Develop a more complete qualitative understanding of the range of current practices with respect to precision viticulture across the relevant industry segments and devise appropriate models to develop a quantitative understanding of the consequences of adoption of various practices and technologies to be developed by the project. Spatial vineyard economic maps based on yield and fruit quality information and processor payment models. Spatial maps on yield and fruit quality. Economic modeling based on current processor or winery payments Crop value set against production costs to give a profit/loss map. Yield Brix Payment Scale y = x x x y = x x x Payment Scale y = x x x Estimated Average Low Brix Poly. (2016) Poly. (Estimated Average) Poly. (Low Brix) Brix Visual representation of vineyard economics based on spatial vine data, processor payments, and grower production costs. Profit/Loss

16 Technology Adoption and Outreach Determine preferred method(s) of learning of grape growers and industry members and develop a portal of learning and PR through a public face of the project Pre-project stakeholder survey on current use of sensor technology in U. S. vineyards. Survey disseminated in CA, NY, and PA and on-line Taken by 16 advisory committee members and 67 stakeholders. Baseline stakeholder information on current precision viticulture practices and the potential for project impact.

17 Technology Adoption and Outreach Determine preferred method(s) of learning of grape growers and industry members and develop a portal of learning and PR through a public face of the project Project Web Site development: One central location for disseminating project progress Open access section for the general industry/public and a restricted access section for advisory committee members One mechanism for stakeholder feedback to the project team. Easy and fast mechanism for extending current project information to stakeholders.

18 Technology Adoption and Outreach Determine preferred method(s) of learning of grape growers and industry members and develop a portal of learning and PR through a public face of the project Outreach content regularly updated on the project web site. Open access to research updates, outreach articles, interviews, videos, and blog posts. Restricted advisory group access to the annual work plan and progress reports. Immediate access to current project information.

19 Precision Vineyard Sensing Greater than three times the image acquisition potential for year 2 of the project. Multi-function image analysis platform for future crop and canopy measurements. Two options for destructive grape yield monitoring. Spatial Data Processing and Decision Support System (SSDS) A prototype open source spatial data platform for easy management of vineyard information. Understanding of grower preferences to viewing, adding, and interacting with spatial vineyard data. Organized clearinghouse for project data that can be used by all themes in the project. Differential Vineyard Management and Testing Protocol for initial scanning and evaluation of commercial vineyards for potential variable rate management. Research based spatial viticulture information for the project SDSS, economic analysis, and VRM. Demonstrated on-the-fly mechanical crop load management in commercial vineyards. Economic Evaluation and Assessment Budget models are an effective way to demonstrate the economic impact of new technology to industry participants and individual growers to facilitate adoption. Benefits from harvest timing and changes in the quality of the fruit are outcomes of the project that are difficult to quantify and may vary widely among individual growers. We established a range of possible outcomes using general market data. Visual representation of vineyard economics based on spatial vine data, processor payments, and grower production costs. Technology Adoption and Outreach Baseline stakeholder information on current precision viticulture practices and the potential for project impact. Easy and fast mechanism for extending current project information to stakeholders. Immediate access to current project information.

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