Using small/medium sized vineyard operations in the northeast US as a target audience, the goal of this project is to increase the adoption of precision viticulture by (A) completing the development of a web-based spatial data platform for growers, (B) demonstrating the value of precision viticulture through research field trials, and (C) providing experiential learning activities for producers to implement in their own operations. Our model for this project is to provide the process (software tools), content (research-based information), and experience (hands-on learning activities) for transformational education and improved adoption of spatial-data-driven vineyard management.Objective 1: Develop an accessible, intuitive, and affordable spatial data management software platform for small and medium-sized farms. One outcome from a previous USDA-SCRI project on precision viticulture was a free web-based spatial data processing platform for grape growers called MyEfficientVineyard or MyEV to upload, process, and visualize spatial vineyard data. We propose to complete MyEV as an easy-to-use spatial data processing platform by developing additional functions to expand types of data import, assist users with data validation and translation tools, and generate spatial prescription maps for variable-rate management applications. Objective 2: Evaluate strategic and efficient deployment of spatial data validation protocols and spatial decision support for efficient variable-rate management. Using Cornell research vineyards, we propose to use multi-layer prescription mapping to study the impact of spatial-data-driven decision support at three levels of complexity. The research trials will target real production/business management issues, such as variable-rate pesticide applications to reduce production costs or differential crop load management to improve yield and fruit quality. The field research plots will be used to test data translation sampling protocols, generate different prescription maps, and evaluate the economic impact of variable-rate management. Success in this objective will provide producers with research-based information on how to efficiently conduct in-field sampling for spatial data translation and then generate variable-rate prescription maps which positively impact their management practices.Objective 3: Engage producers to use personalized digital agriculture solutions in their own operations. We propose to follow a transformational education model to engage growers and drive the way precision viticulture is used in the industry. The transformational education model in extension addresses issues in process, content, and high impact activities to drive a change of behavior in a community of interest. We will leverage industry early adopters already using the MyEV tool to identify their business need for spatial data management, establish on-farm precision viticulture trials, and measure the change in their operations.