Microclimate Prediction for Willamette Valley Vineyards

Microclimate Prediction for Willamette Valley Vineyards
Willamette Valley Microclimate
Team Organizations	Oregon State University
Team Leaders	Shawn Irvine
Participating Municipalities	Independence OR
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Document	None

Description

Leveraging regional weather data and weather stations at individual vineyards to develop a regional prediction for when bud break and bloom will happen as well as highly specific predictions of those same dates for individual vineyards. Additional opportunities to predict and develop alerts for freezes, powdery mildew, and other events targeted at specific vineyards.

Challenges

Weather patterns are inherently unpredictable, and agriculture is highly dependent on weather conditions. 100 growing degree days (bud break) and 500 growing degree days (bloom) are the critical points that drive all vineyard management during the growing season. Accurate prediction of these dates will enable better planning and more efficient management of vineyard operations. Additionally, late season freezes can damage buds, and diseases like powdery mildew can reduce quality and yield. Early prediction of problems will enable grower to deploy proven countermeasures and preserve their crop.

Solutions

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Major Requirements

• Collect existing/past weather data – regional and from existing weather stations – as well as key milestones like bud break/bloom dates from previous years.
• Deploy additional weather stations as needed
• Clean/organize the data
• Develop a weather model for the Polk County region
• Use localized weather data from individual vineyards to create specific microclimate predictions

Performance Targets

Key Performance Indicators (KPIs)	Measurement Methods
Prediction of when bud break and bloom will occur regionally within a two week window. Prediction of bud break and bloom within a three day window for individual vineyards Accurate prediction of overnight spring freezes for individual vineyards	Data/KPIs will be tracked through existing and new weather stations, as well as communication with growers.

Standards, Replicability, Scalability, and Sustainability

While the weather model will be unique to the region, the method for deploying weather stations and collecting and analyzing the data could be replicated anywhere. The KPIs were derived from grower conversations, so there is likely to be a willingness to pay for a commercial solution that can reliably deliver the KPIs.

Cybersecurity and Privacy

The data collection network will use commercially-available encryption to secure transmissions. For the pilot, we will be working with data that growers are already comfortable sharing publicly. Future phases will explore the opportunity to create an anonomized network of weather stations and data sources which could gather data and generate predictions without disclosing where the data was specifically gathered.

Impacts

Prediction of weather-related KPIs will make farm operations more efficient and increase yields and quality. Accurate predictions of potential issues will enable growers to apply countermeasures most effectively, minimizing input costs and environmental impacts. All of these results will make area wineries more profitable and enable additional economic growth.

Demonstration/Deployment

This will largely be a data analysis project so we could develop a slide deck describing the project with pictures from participating wineries, etc. The project will launch summer 2019 so we likely will not have results in time to report out at GCTC.