Thermal risk assessment for viticulture using monthly temperature data

Protected Designation of Origin “Jumilla” (PDO Jumilla) is located in the Spanish provinces of Albacete and Murcia, in the South-eastern part of the Iberian Peninsula, where most of the models predict a severe impact of climate change in next decades. PDO Jumilla covers an area of 247,054 hectares, of which more than 22,000 hectares

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

There is an urgent need in viticulture to adopt alternative herbicide-free soil management strategies to mitigate climate change, increase biodiversity, reduce plant protection products and improve soil quality while minimizing detrimental effects on grapevine’s stress tolerance and fruit quality. To propose sustainable solutions, adapted to different pedoclimatic conditions in Switzerland, we developed a multidisciplinary 4-year project, started in 2020. Objectives of the project are to a) evaluate the impact of green covers (spontaneous flora, winter cover crop and permanent ground cover) on environmental and agronomic parameters and b) develop subsequently innovative strategies for different viticultural contexts of Switzerland. The project is divided into 3 phases: 1) diagnosis, 2) on-farm and 3) on-station experiments. Phase 1) consisted in an assessment of 30 commercial vineyards all over Switzerland, where growers already use different herbicide-free soil management strategies. The most promising practices identified in this exploratory phase will be replicated in commercial vineyards across Switzerland (“on-farm”) as well as in a classical randomized block design in an experimental plot (“on-station”). For phase 1), measurements consisted in evaluation of soil status (compaction, structure, roots development), soil microbial diversity (metagenomics), plant diversity and biomass, vine physiology (water stress, vigor, leaf nitrogen) and berry quality (acidity, sugar, available nitrogen). Interestingly, the permanent ground cover resulted in a higher Shannon index thus a higher biodiversity as compared to the other itineraries. The winter cover crop increased vine nitrogen and vigor while deteriorating soil quality, leaving the soil more exposed and compacted likely due to more frequent tillage. The spontaneous flora led to higher berry sugar accumulation, less nitrogen and higher malic acid concentration putatively due to a higher water retention of the flora in a particularly wet vintage. Phases 2) and 3) are required to confirm those tendencies, over the 3 next vintages and different climatic conditions.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65