Learning from remote sensing data: a case study in the Trentino region 

Climate plays a predominant role in vines’ growth and productivity and several environmental variables are already known to pose challenges to grapevine production and the horticultural industry as a whole. In this context, a number of extreme weather events already occurring and expected to occur in the next decades even more frequently and with higher magnitude results from current climate change scenario. The aim of this study was to examine the physiological responses of roots, leaves, and berries of Vitis vinifera cv. Sauvignon blanc to consecutive and combined stressors simulated in a semi-controlled environment.

This study focuses on investigating the effects of climate change on the plant physiology and berries of Vitis vinifera cv “Monastrell” in a commercial vineyard managed organically in Southeastern Spain (Jumilla, Murcia). For this purpose, open top chambers and rainout shelters were employed to simulate warming (~2-7 ºC, W) and rainfall reduction (~30%, RR) respectively. Additionally, a combination of both treatments (W+RR) was employed. Vines without either top chambers or rainout shelters were considered as control (C). The experiment was established in February of 2023. Predawn leaf water potential (measured using a pressure chamber), stomatal conductance (assessed with a porometer at mid-morning) and leaf chlorophyll and flavonoid content (measured using the Dualex® leaf clip sensor) were analyzed at veraison (5 months after the installation of structures).

Three-dimensional functional-structural root architecture models, which decompose the root system architecture (RSA) into elementary developmental processes such as root emission, axial growth, branching patterns and tropism have become useful tools for (i) reconstructing in silico the spatial and temporal dynamics of root systems in a soil volume, (ii) analyzing their genotypic diversity and plasticity to the environment, and (iii) overcoming the bottleneck associated with their visualization and measurement in situ. Here, we present an original work on RSA phenotyping and modelling in grapevine. First, we developed 2D image-based analysis pipelines to quantify morphological and architectural traits in young grafts. Second, we parametrized and validated the 3D root model Archisimple on two rootstock genotypes (RGM, 1103P) grafted with V. vinifera Cabernet-Sauvignon and grown in different controlled conditions (rhizotrons, pots, tubes).

AIM: Vitis vinifera L. cv. Moscato includes different varieties, mainly white grapes with a medium-sized berry, spheroidal or slightly flattened in shape, yellow greenish color which becomes golden yellow or amber when exposed to the sun. Moscato varieties are mainly used for the production of sweet aromatic wines

The past three decades of terrestrial remote sensing research have delivered unprecedented insights into our fundamental ability to detect, quantify, and differentiate plant disease (Gold 2021). However, much of our fundamental knowledge in this domain has come from studies in non-agricultural systems and until recently, most agricultural studies, when extant, have focused on tree crops where canopy closure and large plot and plant size facilitate stress detection at low spatial resolution. Recent engineering innovations and advancements in constellation architecture design have refined the accuracy and scalability of airborne and spaceborne sensing platforms, enabling us to monitor diverse specialty crops, including grapevine, planted in smaller, spatially varied fields.