The response of yield, number of clusters, and cluster weight to meteorological factors and irrigation practices in grapevines: A multi-experiment study

Knowledge of the yield components of wine grapevines is essential to achieve target production and effectively design vineyard management. Therefore, it is necessary to map and analyze the factors that control the yield components to optimize wine production and profitability. The objective of this study was to identify the ranked effects of environmental and agrotechnical factors (e.g., meteorological, physiological, vegetative, and irrigation) on grapevine yield, number of clusters, and cluster weight, and to quantify their patterns. To achieve this objective, a multi-experiment database was used, assembling data collected during four long-term irrigation experiments including different varieties and diverse climatic regions across Israel. First, a paired analysis of the yield components was conducted to define the relationships among them. Then, prediction models for the three variables (i.e., yield components) were designed using the support vector machine (SVM) algorithm to explore the ranked effects and patterns of the controlling factors. Validation was conducted using a test set, consisting of 20 % of the dataset. The relationships between yield and cluster weight (0 < r < 0.73) and between yield and the number of clusters were always positive (0.67 < r < 0.77), while negative relationships were found between cluster weight and number of clusters (-0.61 < r < 0). The accuracy of the prediction models was high, with mean absolute errors (normalized to the range) ranging between 4.1 % and 8.11 %. The most dominant factors affecting yield components were cumulative leaf area index, accumulated growing degree days (GDD), cumulative irrigation, and evapotranspiration. Leaf area showed an optimum point, below and above which the values of the yield components decrease; GDD negatively affected yield and cluster weight but had a positive effect on the number of clusters; irrigation was positively associated with yield components but leveled out above 348–395 mm per season. Reference evapotranspiration had a negative effect on yield and cluster weight, and a generally positive effect on the number of clusters. Utilizing data from multiple experiments and applying multivariate analysis, allow to define general patterns of relationships and interactions between controlling factors and yield components. Extracting this knowledge from the data and managing the growing amounts of information can be achieved through complex modeling and the implementation of standard protocols for data collection and monitoring.