# Spatial Variability of Grape Berry Maturation Program at the Molecular Level
###### Abstract  
The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield, and quality parameters and determine spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways contributing to different grape quality traits behave in zones diverging on any of these parameters. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated through a Normalized Difference Vegetation Index (NDVI) map calculated from a multispectral image and detailed ground-truthing (e.g., vegetative, yield, and berry ripening compositional parameters). The relationships between NDVI and ground measurements were explored by correlation analysis. Moreover, berries were investigated by microarray gene expression analysis performed at five time points from fruit set to full ripening. Comparison between the transcriptomes of samples taken from locations with the highest and lowest NDVI values identified 968 differentially expressed genes. Spatial variability maps of the expression level of key berry ripening genes showed consistent patterns aligned with the vineyard vigor map. These insights indicate that berries from different vigor zones present distinct molecular maturation programs and suggest that transcriptome analysis may be a valuable tool for the management of vineyard variability. B 2024 10 238 2024 202  
vineyard management and assist decision-making [9]. One option is to minimize the observed variation between the zones by the application of site-specific interventions [10; 11], and another one is to maintain the vineyard variability, allocating grapes from different areas to differentiated enological targets [12]. However, the informative potential of sensors is highly dependent on the nature of the approaches adopted in the field. The sensor performance is affected by many factors, including climate conditions [13], vineyard trellis systems [14], and the distance of the sensor from the canopy [15]. To overcome such obstacles, the use of sensors in a new vineyard ecosystem is usually accompanied by detailed ground truthing. This practice provides empirical evidence of the phenotypic variation within the vineyard by contrasting remotely sensed data with classic agronomical measurements [16]. However, establishing the relationship between these two data sources is rather complicated. Although cases with stable intra-vineyard variability between growing seasons have been reported [17; 18], the correlations between the sensor data and the agronomic parameters can change within the season, and the timing at which these correlations are the strongest can vary, as well [19]. Examples of this inconsistency are reported for the relationship between spectral vegetation indices and berry phenolic content, which has been reported to have a peak of negative correlation either at veraison [20] or at harvest [21], and even to correlate positively [22]. Hence, additional insightful research is needed to improve the sensor's ability to predict the final fruit quality by its application during early vegetative phases. Over the past decade, the generation and analysis of large-scale transcriptomic datasets have played a crucial role in elucidating the molecular basis of grapevine bery development. Numerous investigations into gene expression patterns have established connections between molecular changes, primary and secondary metabolic pathways, and biological processes, together providing a comprehensive understanding of the transcriptomic program governing berry maturation [23]. However, little is known about the molecular differences during the ripening program associated with intra-vineyard variability of agronomic parameters. Our study aimed at better characterizing distinct zones within the vineyard by combining standard NDVI detection with molecular approaches.