By: Golnaz Badr
Concord (Vitis labruscana Bailey) grape is the most important grape variety for the New York (NY) grape industry. Hence, the future advancement of NY grape industry relies on in-depth knowledge of factors that affect the growth and development of Concord grapes. These factors, subsequently control Concord yield, concord juice quality, and hence, indirectly impact economic profits for stakeholders and growers.
Concord grape yield is affected by several factors that could be categorized into two major groups: 1) a region’s physical environment such as soil, topography, and weather parameters; 2) vineyard management practices such as shoot thinning, pruning, shoot positioning, etc. Previous studies have indicated that yield for a single Concord vine or a Concord vineyard can be potentially estimated by using various information on key known factors in Concord vine reproduction stages. This information is recognized as “Yield Components”. The yield components include: number of buds per vine, number of berries per cluster, number of clusters per shoot, number of shoots per bud, and berry weight.
Although the yield components are clearly defined, still there is a lack of available data on the yield components which makes the precise estimation of Concord yield hard to achieve. The common practice for Concord yield calculation usually involves sampling several rows or post-lengths in a vineyard to obtain an accurate estimate of the yield components. The method that has been used by Dr. Bates team here at CLEREL focuses on historical berry weight curves. Berry weight around harvest is then estimated using berry weight at 30 days after bloom. This method, however, needs to be revised due to its high dependency to a specific growing season for a certain vineyard. Therefore, there is a need to develop state of the art methodology to predict Concord fresh berry weight that has the ability to precisely predict Concord berry weight across multiple vineyards and growing seasons. In order to do so we are utilizing several weather parameters and historical phenological observations in the Lake Erie grape production region to develop a mathematical algorithm. Once the best algorithm is developed we are going to evaluate the performance of the algorithm across various vineyards and multiple growing seasons. The main purpose of evaluation stage is to quantify the bias in predictions and report the confidence associated in the results. Finally, we are going to make sure that growers, decision makes and extension educators get access to such a berry weight prediction tool.
The Efficient Vineyard project aims at the advancement of digital viticulture by developing tools, sensors, and mathematical algorithms. Once fully established, this technology has the potential to help growers better monitor their crop and are able to produce a rather healthy and uniform crop. In addition, decision makers will also benefit from such a technology especially when it comes to vineyard management tasks such as cluster thinning.