Response of the plant: a chief element for the characterisation of wine-growing “terroirs”

The objective of the work described here is the elaboration of a map of the different types of vineyard soils that to guide the famers in the choice of the most productive vine rootstocks and varieties. 90 vineyard soils profiles were analysed in the entire territory of the Origen Denominations of Valdepeñas. The sampling was carried out in 2018 (June to October) by making a sampling grid, followed by photointerpretation and control in the field. The studied soils can be grouped into 9 different soil types (according to FAO 2006 classification): Leptosols, Regosols, Fluvisols, Gleysols, Cambisols, Calcisols, Luvisols and Anthrosols. A map showing the soil distribution with different type of soils has been made with the ArcGIS program. Regarding to the choice of rootstock, Calcisoles are soils with a high active limestone content, so the rootstocks used in these soils must be resistant to this parameter; Luvisols are deep soils with high clay content, so they will support vigorous rootstocks. Because the cartographic units are composed of two or more subgroups, with are associated in variable proportions, 9 different soil associations have been established; Unit 1: Leptosols, Cambisols and Luvisols (80%, 15% and 5% respectively); Unit 2: Cambisols with Regosols and Luvisols (40%, 30% and 30% respectively); Unit 3: Cambisols and Gleysols with Regosols (40%, 40% and 20% respectively); Unit 4: Regosols with Cambisols, Leptosols and Calcisols (40%, 30%, 15% and 15% respectively); Unit 5: Cambisols, Leptosols, Calcisols and Regosols (25% each of them); Unit 6: Luvisols with Cambisol and Calcisols (80%, 10% and 10% respectively); Unit 7: Luvisols and Calcisols with Cambisols (40%, 40% and 20% respectively); Unit 8: Calcisols with, Cambisols and Luvisols (80%, 10% and 10% respectively); Unit 9: Anthrosols. These study allow to elaborate the first map of vineyard soils of this Protected Designation of Origin in Castilla-La Mancha.

Globalisation and climate change have radically transformed world wine production upsetting the established order of wine ecologies. Ecological risks and the future of traditional agricultural systems are widely debated in anthropology, but very little is understood of the particular challenges posed by climate change to viticulture which is seen by many as the canary in the coalmine of global agriculture. Moreover, wine as a globalised embedded commodity provides a particularly telling example for the study of climate change having already attracted early scientific attention. Studies of climate change in viticulture have focused primarily on the production of systematic models of adaptation and vulnerability, while the human and cultural factors, which are key to adaptation and sustainable futures, are largely missing. Climate experts have been unanimous in recognising the urgent need for a better understanding of the complex dynamics that shape how climate change is experienced and responded to by human systems. Yet this call has not yet been addressed. Climate ethnography, coined by the anthropologist Susan Crate (2011), aims to bridge this growing disjuncture between climate science and everyday life through the exploration of the social meaning of climate change. It seeks to investigate the confrontation of its social salience in different locations and under different environmental guises (Goodman 2018: 340). By understanding how wine producers make sense of the world (and the environment) and act in it, it proposes to focus on the co-production of interdisciplinary knowledge by identifying and foreshadowing problems (Goodman 2018: 342; Goodman & Marshall 2018). It seeks to offer an original, transformative and contrasted perspective to climate change scenarios by investigating human agency -individual or collective- in all its social, political and cultural diversity. An anthropological approach founded on detailed ethnographies of wine production is ideally placed to address economic, social and cultural disruptions caused by the emergence of these new environmental challenges. Indeed, the community of experts in environmental change have recently called for research that will encompass the human dimension and for more broad-based, integrated through interdisciplinarity, useful knowledge (Castree & al 2014). My paper seeks to engage with climate ethnography and discuss what it brings to the study of wine environmental futures while exploring the limitations of the anthropological environmental approach.

Elevated temperature during the grape maturation period is a major threat for grape quality and thus wine quality. Therefore, characterizing the grape composition response to temperature at a larger scale would represent a crucial step towards adaptation to climate change. In response to changes in temperature, various physiological mechanisms regulate grape composition. Primary and secondary metabolisms are both involved in this response, with well-known effects, for example on anthocyanins, and lesser known effects, for example on aromas or aroma precursors. At the field scale or at the regional scale, however, numerous environmental or plant-specific factors intervene to make the effects of temperature difficult to distinguish from overall variability. In this study, it was attempted to overcome this difficulty by selecting well-characterized situations with differing temperatures.
A long-term study of air temperature variability across several Merlot vineyards in the Saint-Emilion and Pomerol wine producing area found significant temperature differences and gradients at various time scales linked to environmental factors. From this study area, a few sites were selected with similar age, soil and training system conditions, and with repeated and contrasted temperature differences during the maturation period. The average temperature difference during the maturation period was about 2°C between cooler and warmer sites, a difference similar to that expected under future climate change scenarios. In close vicinity to the temperature sensors at each site, grape berries were sampled at different times until full maturity during 2019 and 2020. Also, berries from bunches on either side of the row were analyzed separately, allowing an investigation of bunch exposure effect associated with the coupling of berry temperature and solar radiation. Four replicates of pooled berries for each time – site – bunch exposure combination were obtained and analyzed for biochemical composition. Analyses of variance of the biochemical composition data collected at different sampling times reveal significant effects associated with temperature, site, and bunch azimuth. For instance, anthocyanins in grape skins are clearly influenced by temperature and solar radiation exposure, with up to 30% reduction in warmer conditions.

“Flavescence dorée” (FD) is a grapevine quarantine disease associated with phytoplasmas and transmitted to healthy plants by insect vectors, mainly Scaphoideus titanus. Infected plants usually develop symptoms of stunted growth, unripe cane wood, leaf rolling, leaf yellowing or reddening, and shrivelled berries. Since plants can remain symptomless up to four years, they may act as reservoirs of FD contributing to the spread of the disease. So far, conventional management strategies rely mainly on the insecticide treatments, uprooting of infected plants and use of phytoplasma-free propagation material. However, these strategies are costly and could have undesirable environmental impacts. Thus, the development of sustainable and noninvasive approaches for early detection of FD and its management are of great importance to reduce disease spread and select the best cultural practices and treatments. The present study aimed to evaluate if multispectral/hyperspectral technologies can be used to detect FD before the appearance of the first symptoms and if infected grapevines display a spectral imaging fingerprint. To that end, physiological parameters (leaf area, chlorophyll content and photosynthetic rate) were collected in concomitance to the measurements of plant reflectance (using both a portable apparatus and a remote sensing drone). Measurements were performed in two leaves of 8 healthy and 8 FD-infected grapevines, at four timepoints: before the development of disease symptoms (21st June); and after symptoms appearance (ii) at veraison (2nd August); at post-veraison (11th September); and at harvest (25th September). At all timepoints, FD infected plants revealed a significant decrease in the studied physiological parameters, with a positive correlation with drone imaging data and portable apparatus analyses. Moreover, spectra of either drone imaging and portable apparatus showed clear differences between healthy and FD-infected grapevines, validating multispectral/ hyperspectral technology as a potential tool for the early detection of FD or other grapevine-associated diseases.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.