Microclimate Prediction for Willamette Valley Vineyards - Revision history

Pinfold at 01:56, January 25, 2023

2023-01-25T01:56:27Z

← Older revision		Revision as of 01:56, January 25, 2023
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	\|imagecaption=Willamette Valley Microclimate		\|imagecaption=Willamette Valley Microclimate
	\|municipalities=Independence OR		\|municipalities=Independence OR
	\|status=~~Investigation~~		\|status=Master Planning
	\|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.		\|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.		\|challenges=Weather patterns are inherently unpredictable, and agriculture is highly dependent on weather conditions.

Pinfold at 01:55, January 25, 2023

2023-01-25T01:55:13Z

← Older revision		Revision as of 01:55, January 25, 2023
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	\|imagecaption=Willamette Valley Microclimate		\|imagecaption=Willamette Valley Microclimate
	\|municipalities=Independence OR		\|municipalities=Independence OR
			\|status=Investigation
	\|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.		\|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.		\|challenges=Weather patterns are inherently unpredictable, and agriculture is highly dependent on weather conditions.

Pinfold at 01:54, January 25, 2023

2023-01-25T01:54:57Z

← Older revision		Revision as of 01:54, January 25, 2023
Line 7:		Line 7:
	\|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.		\|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.		\|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.		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.
			\|solutions=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.
	\|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.		\|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		* Deploy additional weather stations as needed

Pinfold at 01:53, January 25, 2023

2023-01-25T01:53:41Z

← Older revision		Revision as of 01:53, January 25, 2023
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	{{ActionCluster		{{ActionCluster
			\|image=micoclimates-internal.jpg
	\| ~~title~~ = ~~Microclimate Prediction for Willamette Valley Vineyards~~		\|team=Oregon State University
			\|leader=Shawn Irvine
	\| team = Oregon State University		\|imagecaption=Willamette Valley Microclimate
			\|municipalities=Independence OR
	\| leader = Shawn Irvine		\|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.
	~~\| email = sirvine@ci.independence.or.us~~		\|challenges=Weather patterns are inherently unpredictable, and agriculture is highly dependent on weather conditions.
	~~\| image = micoclimates-internal.jpg~~
	\| imagecaption = Willamette Valley Microclimate
	\| municipalities = Independence OR


	~~\| status =~~
	~~\| website =~~
	~~\| download =~~

	\| 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.		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.
			\|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.


	~~\| solutions =~~

	\| 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		* Deploy additional weather stations as needed
	* Clean/organize the data		* Clean/organize the data
	* Develop a weather model for the Polk County region		* Develop a weather model for the Polk County region
	* Use localized weather data from individual vineyards to create specific microclimate predictions		* Use localized weather data from individual vineyards to create specific microclimate predictions
			\|kpi=* Prediction of when bud break and bloom will occur regionally within a two week window.



	\| kpi =
	* 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		* Prediction of bud break and bloom within a three day window for individual vineyards
	* Accurate prediction of overnight spring freezes for individual vineyards		* Accurate prediction of overnight spring freezes for individual vineyards
			\|measurement=Data/KPIs will be tracked through existing and new weather stations, as well as communication with growers.
			\|standards=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=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.
	\| measurement =		\|demonstration=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.
	Data/KPIs will be tracked through existing and new weather stations, as well as communication with growers.		\|chapter=Predictive Modeling
			\|supercluster=Rural
			\|year=2019
			\|title=Microclimate Prediction for Willamette Valley Vineyards
			\|email=sirvine@ci.independence.or.us
	\| standards =
	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 =
	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 =
	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.



	\| supercluster = Rural
	\| year = 2019

	}}		}}

Pinfold at 19:56, January 20, 2022

2022-01-20T19:56:19Z

← Older revision		Revision as of 19:56, January 20, 2022
Line 3:		Line 3:
	\| title = Microclimate Prediction for Willamette Valley Vineyards		\| title = Microclimate Prediction for Willamette Valley Vineyards

	\| team = Oregon State University~~, Oregon State University Extension~~		\| team = Oregon State University

	\| leader = Shawn Irvine		\| leader = Shawn Irvine

Pinfold at 06:34, January 11, 2022

2022-01-11T06:34:20Z

← Older revision		Revision as of 06:34, January 11, 2022
Line 3:		Line 3:
	\| title = Microclimate Prediction for Willamette Valley Vineyards		\| title = Microclimate Prediction for Willamette Valley Vineyards

	\| team = Oregon State University ~~and~~ Oregon State University Extension		\| team = Oregon State University, Oregon State University Extension

	\| leader = Shawn Irvine		\| leader = Shawn Irvine
Line 9:		Line 9:
	\| image = micoclimates-internal.jpg		\| image = micoclimates-internal.jpg
	\| imagecaption = Willamette Valley Microclimate		\| imagecaption = Willamette Valley Microclimate
	\| municipalities = Independence~~, Oregon~~		\| municipalities = Independence OR

Pinfold at 07:22, November 11, 2021

2021-11-11T07:22:04Z

← Older revision		Revision as of 07:22, November 11, 2021
Line 7:		Line 7:
	\| leader = Shawn Irvine		\| leader = Shawn Irvine
	\| email = sirvine@ci.independence.or.us		\| email = sirvine@ci.independence.or.us
	\| image =		\| image = micoclimates-internal.jpg
	\| imagecaption =		\| imagecaption = Willamette Valley Microclimate
	\| municipalities = Independence, Oregon		\| municipalities = Independence, Oregon

Pinfold: 1 revision imported

2021-10-31T20:45:02Z

1 revision imported

← Older revision	Revision as of 20:45, October 31, 2021
(No difference)

en>Ocadmin at 05:28, March 23, 2020

2020-03-23T05:28:11Z

New page

{{ActionCluster

| title = Microclimate Prediction for Willamette Valley Vineyards

| team = Oregon State University and Oregon State University Extension

| leader = Shawn Irvine
| email = sirvine@ci.independence.or.us
| image =
| imagecaption =
| municipalities = Independence, Oregon

| status =
| website =
| download =

| 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 =

| 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

| kpi =
* 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

| measurement =
Data/KPIs will be tracked through existing and new weather stations, as well as communication with growers.

| standards =
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 =
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 =
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.

| supercluster = Rural
| year = 2019

}}