PREDICTING KEY PHENOLOGICAL STAGES FOR GRAPEVINES. A SIMPLE BUT SCIENTIFIC APPROACH FOR MANAGEMENT AND SITE SELECTION

• Sponsoring Institution: State Agricultural Experiment Station
• Project Status: COMPLETE
• Funding Source: STATE
• Reporting Frequency: Annual
• Accession No.: 1003531
• Project Start Date: Jul 2, 2014
• Project End Date: Jun 30, 2019
• Program Code: [(N/A)]- (N/A)

Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001

Performing Department
Agricultural Weather Network (AWN)

Non Technical Summary
It is important and useful for growers to have access to models and a decision support system that not only provides them with access to real-time weather information, but also provides an advisory about the requirements and the critical growing stages linked to local weather data, such as those collected by AgWeatherNet, a statewide weather network of automated weather stations in Washington state. Through relevant media and information and communication technologies, including the internet and cell phones, timely information can be provided for estimates of the development stage of grapevines. This will then allow for appropriate planning and marketing that could potentially lead to an improvement in quality and a reduction in costs based on informed decision making. The ultimate goal of this proposal is to develop a robust model for the prediction of the phenological stages for grapevine Vitis sp. that can be used and applied for a wide range of varieties and environments across the US.

Animal Health Component

60%

Research Effort Categories

Basic

10%

Applied

60%

Developmental

30%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
132	1131	2070	25%
132	1131	2080	25%
205	1131	2070	25%
205	1131	2080	25%

Knowledge Area
205 - Plant Management Systems; 132 - Weather and Climate;

Subject Of Investigation
1131 - Wine grapes;

Field Of Science
2070 - Meteorology and climatology; 2080 - Mathematics and computer sciences;

Keywords

grapes

weather

climate

climate variabilitty

climate change

modeling

simulation

phenology

development

decision support

Goals / Objectives
To develop a model to predict the key phenological stages for grapevine, including budbreak, bloom, and veraison for grapevine.To determine the base temperature and duration in terms of thermal time for budbreak, bloom, and veraison for three grapevine varieties.To identify an appropriate model for estimating cold and heat requirements for grapevine varieties under local weather conditions.To analyze the sensitivity of the model to changes in model parameter values and the variability of weather variables and evaluate the model with expanded data representing a wide range of environmental conditions.To develop an information and decision aid tool for the prediction of the key phenological stages for grapevine for application by growers, fieldmen, and enologists.

Project Methods
Task 1. Develop a model to predict budbreak, bloom, and veraison for grapevine. A large database currently exists that includes the key phenological stages collected at WSU's Irrigated Agriculture and Extension Center (IAREC). Rather than conducting new experiments and collecting new data, we are planning to use this existing database for the initial development of a model that can predict the key phenological states for grapevine, including budbreak, bloom and veraison. During the first phase of the project, an individual model will be developed for three representative varieties, including Cabernet Sauvignon, Chardonnay and Merlot, using the locally collected data and the weather data from AgWeatherNet. During the second phase, this model will be generalized in order to develop a model that is independent of local conditions. The simulation will be conducted through the daily integration of phenological progression as a function of thermal time. The ultimate goal is to develop a model that can be used for any environment once the base temperatures and the duration of the critical development stages are known. Model performance will be evaluated using the root mean square error (RMSE) and the Willmott d-index statistic.Task 2. Determine the base temperature and duration for budbreak, bloom, and veraison for three grapevine varieties. Phenological observations will be collected during the growing season for three different varieties. The data will be collected during all three years of the project in three climatically distinct environments. Buds of selected shoots of five individual grape vines for three varieties will be labeled and carefully monitored. At the same time the dates of initial budbreak, bloom, veraison and harvest will also be recorded. The phenological observations will be obtained in the vicinity of a meteorological station, while the detailed weather data will be collected by AgWeatherNet. The first step is to estimate the base temperature for the critical phenological stages. An empirical linear or exponential model will be adjusted to the observed rate of appearance of the critical stages related with the daily accumulated temperature where the base temperature or Tb, one of the key input parameters for the model, will be estimated. The duration of these stages will be established in terms of thermal time (GDD), using the accumulation of the average daily temperature above the base temperature Tb. A simple thermal-time (TT) model based on historical observations of phenological stages for local conditions will be development.Task 3. Identify an appropriate model for estimating heat requirements for grapevine varieties under local weather conditions. To identify an appropriate model for the estimation of heat requirements for grapevine, the concept of heat units, measured in Growing Degree Days (GDD) and Growing Degree Hours (GDHs), will be considered. Hourly temperature records from AgWeatherNet will be used for estimating heat requirements for breaking bud dormancy for the selected varieties. The model will be validated using historical data for a wide range of environmental conditions. Based on this evaluation, the heat unit requirements for the beginning of the season, bloom, and veraison can be determined.Task 4. Evaluate the model with expanded data from different environmental conditions. Long-term historical weather data will be obtained from both AgWeatherNet and the Cooperative Observer Program of the National Weather Service for the model evalatuaton. During the final year of the project the phenological model will be evaluated for at least three sites that differ in local weather and environmental conditions. The sites should be located in the vicinity of one of the automated weather stations of AgWeatherNet. The simulated data will be compared with observed data and various descriptive statistics similar to those described under objective one will be conducted to determine the accuracy of the model.Task 5. Develop an information and decision aid tool for the prediction of budbreak, bloom, and veraison grapevine for application by growers, fieldmen, and enologists. The final model will be coupled to the weather data base and decision support aids of AgWeatherNet (weather.wsu.edu). A web-based decision support tool will be developed that will provide grape growers and fieldmen with the progression of phenological stages of grapevines and expected progression of these stages in the near future, using both current weather conditions from AgWeatherNet and weather predictions. The information will be displayed in the form of tables, graphs, and maps. A warning and alert system will also be integrated into this system to provide warnings and messages directly to a grower's or fieldman's cell or smart phone. The development of this phenological model will greatly enhance growers' and fieldmen's abilities to plan management practices in relation to the critical developmental events that occur during the production season. The overall approach is to develop a web portal that will provide a guideline and advisory for growers and fieldmen who are monitoring individual vineyards in terms of weather conditions and weather predictions. This will ultimately allow for better planning to improve grape quality and increase yield and use resources more efficiently. The web portal will be available through both the AgWeatherNet Program (weather.wsu.edu) and the Viticulture and Enology Program (wine.wsu.edu)

Progress 07/02/14 to 06/30/19

Outputs
Target Audience: Growers were able to monitor their individual vineyards for key phenological developments for 17 different grapevine cultivars under different climatic conditions of the Pacific Northwest and use the AWN portal for advice andguide that allowed better planning for crop management practices. Changes/Problems:Project Director was changed. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?The results have been disseminated in theacademic and industry community throughconferences inthe Annual meetings of theWashington Association for Wine Grape Growers as a poster and oral presentations,and peer-review publications. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Phenological data collected for 17 wine grape cultivars from 1990 to 2013 by the Viticulture Program at Washington State University, Prosser, Washington were used in this study. The red cultivars that were selected included Cabernet Franc, Cabernet Sauvignon, Lemberger, Malbec, Merlot, Pinot Meunier, Pinot Noir, Syrah and Zinfandel, while the white cultivars that were selected included Chardonnay, Chenin Blanc, Gewürztraminer, Muscat Blanc, Pinot Gris, Sauvignon Blanc, Sémillon and Riesling. The phenological data that were collected corresponded to the day of year (DOY) when budbreak, bloom, and veraison occurred, these phases were chosen due to their importance, as they match with the periods where major changes in phenology occurred. Budbreak was defined as the stage when green leaf tissue is visible on 50% of the previously dormant buds. Bloom was considered when 50% of the flower caps have dropped, while veraison corresponded to the beginning of ripening (softening or color change on 50% of the berries). The weather data that were used in this study included the minimum (Tmin) and maximum (Tmax) daily air temperature and were recorded by two automated weather stations located at approximately 750-868 m from the vineyard (46.3°N; 119.7°W; 260-365 m. above sea level). The weather stations were located close each other and had similar conditions. The data were downloaded from the AgWeatherNet Portal (www.weather.wsu.edu ). The daily mean air temperature (Ti) for successive phenological stages and the DOY when a stage was observed for each cultivar. DD requirement for reaching the stages was calculated as the accumulation of daily DD throughout the duration among successive stages (n days) using the base temperature method (Arnold 1959) as showed below. Ti=(Tmax+Tmin)/2 DD=Ti-tb Base temperatures were estimated individually for the three phenological stages and for each individual cultivar and estimating the temperature that minimizes the standard deviation for DD between the different years . Using the variation between years a lapse of DDmin and DDmax where the occurrence of the stage is more likely was calculated. The values obtained for the Tb varied among cultivars and phenological stages. The lowest Tb value was found for budbreak, with an increase for both bloom and veraison. Also, the variation of the estimated Tb's for each stage increased from budbreak through veraison .

Publications

• Type: Journal Articles Status: Published Year Published: 2015 Citation: Zapata D.M., *Salazar M., Chaves B., Keller M., and Hoogenboom G. 2015. Estimation of the base temperature and growth phase duration in terms of thermal time for four grapevine cultivars. Int. Jour. Biometeorol. 59: 1771
• Type: Journal Articles Status: Published Year Published: 2017 Citation: Zapata D.M., Salazar M., Chaves B., Keller M., and Hoogenboom G. 2017. Predicting Key Phenological Stages for 17 Grapevine Cultivars (Vitis vinifera L.). Am. J. Enol. Vitic. 68 (1): 60-68.

Progress 10/01/17 to 09/30/18

Outputs
Target Audience:Scientists, Extension personnel, growers, managers, and field men Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Sponsor Intern: Adriana Cifuentes who collaborate with data collection. How have the results been disseminated to communities of interest?Preliminary website has been expanded for grapevine phenology with information from this season, growing degree-days tool have been upgraded using data from year 2018 and its improvement will be a continuous activity for the following year of the project. An information delivery system and media tool has been updated and will be fully developed in collaboration with the growers and the industry, to present the observed data on the web as a Decision Aid Tool. We still need to identify collaboration with industry members as beta testers. Data and Preliminary models have been incorporated into the AgWeatherNet portal at: www.weather.wsu.edu. What do you plan to do during the next reporting period to accomplish the goals?Dr. Melba Salazar took over the activities associated with this project.

Impacts
What was accomplished under these goals? This activity is in progress; analysis has been conducted and will continue during 2019 season. We are analyzing long-term historical phenology and weather data obtained from AgWeatherNet and phenology records available at the V&E Program to adjust the duration of the phenological stages based on thresholds temperatures most suited for the season. The development of scientific models for the prediction of critical growth stages of grape vines.

Publications

Progress 10/01/16 to 09/30/17

Outputs
Target Audience:Project Director has left Washington State University. No data to report. Changes/Problems:Project Director has left Washington State University. No data to report. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Project Director has left Washington State University. No data to report.

Publications

Progress 10/01/15 to 09/30/16

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Project Director has left Washington State University. No information to report.

Publications

Progress 10/01/14 to 09/30/15

Outputs
Target Audience:Scientists, Extension personnel, growers, managers, and field men. Changes/Problems:Project Director has left Washington State University.Tentatively, Dr. Melba Salazarhas taken over the activities associated with this project. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?Dataand models were incorporated into the AgWeatherNet portal at: www.weather.wsu.edu. What do you plan to do during the next reporting period to accomplish the goals?Project Director has left Washington State University. It is yet undetermined if this project will be continue with current members.Tentatively, Dr. Melba Salazar will take over the activities associated with this project.

Impacts
What was accomplished under these goals? The development of scientific models for the prediction of critical growth stages of grape vines.

Publications

• Type: Journal Articles Status: Published Year Published: 2015 Citation: Li, Z.T., J.Y. Yang, C.F. Drury, and G. Hoogenboom. 2015. Evaluation of the DSSAT-CSM for simulating yield and soil organic C and N of a long-term maize and wheat rotation experiment in the Loess Plateau of Northwestern China. Agricultural Systems 135(1):90-104.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: McNider, R.T., C. Handyside, K. Doty, W.L. Ellenburg, J.F. Cruise, J.R. Christy, D. Moss, V. Sharda, and G. Hoogenboom. 2015. An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands. Environmental Modeling & Software 72(1):314-355.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: White, J.W., G. Alagarswamy, M.J. Ottman, C.H. Porter, U. Singh, and G. Hoogenboom. 2015. An overview of CERES-Sorghum as implemented in the Cropping Systems Model Version 4.5. Agronomy Journal 107(6):1987-2002.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Woli, P., B.V. Ortiz, J.W. Johnson, and G. Hoogenboom. 2015. El Ni�o-Southern Oscillation effects on winter wheat yield in the southeastern USA. Agronomy Journal 107(6):2193-2204.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Zapata, D., M. Salazar, B. Chaves, M. Keller, and G. Hoogenboom. 2015. Estimation of the base temperature and growth phase duration in terms of thermal time for four grapevine cultivars. International Journal of Biometeorology 59:1771-1781.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Amaral, T.A., C. de L.T. Andrade, G. Hoogenboom, D. de Silva, A. Garcia y Garcia, and M. Noce. 2015. Nitrogen management strategies for smallholder maize production systems: Experimental data and crop modeling. International Journal of Plant Production 9(1):51-74
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Amaral, T.A., C. de L.T. Andrade, J.O. Duarte, J.C. Garcia, A. Garcia y Garcia, D.F. Silva, and G. Hoogenboom. 2015. Nitrogen management strategies for smallholder maize production systems: Yield and profitability variability. International Journal of Plant Production 9(1): 75-98.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Andarzian, B., G. Hoogenboom, M. Bannayan, M. Shirali, and B. Andarzian. 2015. Determining optimum sowing date of wheat using CSM CERES Wheat model. Journal of the Saudi Society of Agricultural Sciences 14:189-199.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Araya, A., G. Hoogenboom, E. Luedeling, K.M. Hadgu, I. Kisekka, and L.G. Martorano. 2015. Assessment of maize growth and yield using crop models under present and future clinate in southwestern Ethiopia. Agricultural and Forest Meteorology 214-215:252-265.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Asseng, S., F. Ewert, P. Martre, R. R�tter, D. Lobell, D. Cammarano, B. Kimball, M. Ottman, G. Wall, J.W. White, M. Reynolds, P. Alderman, P. Prasad, P. Aggarwal, J. Anothai, B. Basso, C. Biernath, A. Challinor, G. De Sanctis, J. Doltra, E. Fereres, M. Garcia-Vila, S. Gayler, G. Hoogenboom, L. Hunt, R. Izaurralde, M. Jabloun, C. Jones, K. Kersebaum, A. Koehler, C. M�ller, S.N. Kumar, C. Nendel, G. O'Leary, J. Olesen, T. Palosuo, E. Priesack, E. Rezaei, A. Ruane, M. Semenov, I. Shcherbak, C. St�ckle, P. Stratonovitch, T. Streck, I. Supit, F. Tao, P. Thorburn, K. Waha, E. Wang, D. Wallach, J. Wolf, Z. Zhao, and Y. Zhu. 2015. Rising temperatures reduce global wheat production. Nature Climate Change 5(2):143-147.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Bao, Y., G. Hoogenboom, R.W. McClendon, and P. Urich. 2015. Soybean production in 2025 and 2050 in the southeastern USA based on the SimCLIM and the CSM-CROPGRO-Soybean models. Climate Research 63(1):73-89.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Bao, Y., G. Hoogenboom, R.W. McClendon, and J.O. Paz. 2015. Potential adaptation strategies for rainfed soybean production in the southeastern USA under climate change based on the CSM-CROPGRO-Soybean model. The Journal of Agricultural Science 153(5):798-824.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Chen, Y., L. Shuangwei, G. Hoogenboom, G. Yan, L. Baoguo, and M. Yuntao. 2015. Simulation of maize kernel growth using source-sink approach with priority function. Transactions of the Chinese Society of Agricultural Engineering 31 (Supp. 2):152-158.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Devkota, K.P, G. Hoogenboom, K. J. Boote, J.P.A. Lamers, M. K. Devkota, and P.L.G. Vlek. 2015. Simulating the impact of water saving irrigation and conservation agriculture practices for rice-wheat systems in the irrigated arid drylands of Central Asia. Agricultural and Forest Meteorology 214215:266280.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Athanasiadis, I.A., S. Jansen, D. Holzworth, P. Thorburn, M. Donatelli, V.Snow, G. Hoogenboom, and J.W. White. Thematic issue on agricultural systems modeling and software  Part II. Environmental Modeling & Software 72(1):274-275.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Badr, G., G. Hoogenboom, J. Davenport, and J. Smithyman. 2015. Estimating growing season length using vegetation indices based on remote sensing: A case study for vineyards in Washington. Transactions of the ASABE 58(3):551-564.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Bannayan, M., and G. Hoogenboom. 2015. Quantification of agricultural drought occurrence as an estimate for insurance programs. Theoretical and Applied Climatology 122(3):799-808.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Etkin, D., P. Kirshen, D. Watkins, C. Roncoli, M. Sanon, L. Some, Y. Dembele, J. Sanfo, J. Zoungrana, and G. Hoogenboom. 2015. Programming for improved multiuse reservoir operation in Burkina Faso, West Africa. Journal of Water Resources Planning and Management 141(3) 04014056 (2015).
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Gheysari, M., H.W. Loescher, S.H. Sadeghi, S.M. Mirlatifi, M.J. Zareian, and G. Hoogenboom. 2015. Water-yield relations and water use efficiency of maize under nitrogen fertigation for semiarid environments: Experiment and synthesis. Advances in Agronomy 130:175-229.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Hammad, H.M., A. Ahmad, F. Abbas, W. Farhad, B.C. Chaves, and G. Hoogenboom. 2015. Water and nitrogen productivity of maize under semiarid environments. Crop Science 55(2):877-888.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Holzworth, D.P., V. Snow, S. Janssen, I.N. Athanasiadis, M. Donatelli, G. Hoogenboom, J.W. White, and P. Thorburn. 2015. Agricultural production systems modelling and software: current status and future prospects. Environmental Modeling & Software 72(1):276-286.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Ishikawa, D., G. Hoogenboom, S. Hakoyama, Y. Ozaki, and E. Ishiguro. 2015. A potential of the growth stage estimation for paddy rice by using chlorophyll absorption band in the 400 - 1100 nm region. Journal of Agricultural Meteorology 71(1):1-8.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: Kersebaum, K.C., K.J. Boote, J.S. Jorgenson, C. Nendel, M. Bindi, C. Fr�hauf; T. Gaiser, G. Hoogenboom, C. Kollas, J.E. Olesen, R.P. R�tter, F. Ruget, P. Thorburn, M. Trnka, and M. Wegehenkel. 2015. Analysis and classification of data sets for calibration and validation of agro ecosystem models. Environmental Modeling & Software 72(1):402-417

Progress 07/02/14 to 09/30/14

Outputs
Target Audience: Scientists; Extension personnel; Growers; Managers and Field men Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Presentations at the annual meetings of the Washington Association of Wine Grape Growers and the Washington State Grape Society What do you plan to do during the next reporting period to accomplish the goals? We are planning to summarize the outcomes of the research in scientific papers and implement the models on the AgWeatherNet portal (www.weather.wsu.edu).

Impacts
What was accomplished under these goals? The development of scientific models for the prediction of critical growth stages of grape vine.

Publications

• Type: Journal Articles Status: Under Review Year Published: 2015 Citation: Zapata, D., M. Salazar, B. Chaves, M. Keller, and G. Hoogenboom 2015. Estimation of the base temperatures and duration in terms of thermal time for four grapevine cultivars. International Journal of Biometeorology. (Submitted for publication).
• Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Zapata D.M., Salazar M., Chaves B., Keller M., and Hoogenboom G. 2014. Determination of cultivar-specific threshold temperatures and heat requirements for prediction of budbreak, full bloom, and veraison in wine grape. Poster presentation at the 2014 Annual Conference of the American Society for Horticultural Science (ASHS) that was held on July 28th  August 1st in Orlando (FL).
• Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Zapata D.M., Salazar M., Chaves B., Keller M., and Hoogenboom G. 2014. Dormancy Stage and Budbreak in Wine Grapes. Poster presentation at the Washington State University Academic Showcase, March 28th, Pullman (WA).