Terroir 2020 banner
IVES 9 IVES Conference Series 9 International Terroir Conferences 9 Terroir 2020 9 History and innovation of terroir 9 Within-vineyard variability in grape composition at the estate scale can be assessed through machine-learning modeling of plant water status in space and time. A case study from the hills of Adelaida District AVA, Paso Robles, CA, USA

Within-vineyard variability in grape composition at the estate scale can be assessed through machine-learning modeling of plant water status in space and time. A case study from the hills of Adelaida District AVA, Paso Robles, CA, USA

Abstract

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.

Methods and Results: Work was conducted during two consecutive seasons in a ~40ha (100ac) premium wine estate located in the Adelaida District AVA of Paso Robles, CA, USA. The vineyard topography was very diverse, with a large variation in slope grade (0-30%) and exposure (0-359). One hundred experimental units were identified by a maximum dissimilarity sampling algorithm based on environmental attributes derived from a digital elevation model and a soil map. Reflecting the estate varietal distribution, ~70% were Cabernet-Sauvignon units, 20% Cabernet-Franc, and 10% Petit-Verdot units grafted on 1103P or 420A (~50-50%). Grapevine water status was monitored by weekly measurements of stem water potentials, Ψstem, and analysis of carbon isotope discrimination of grape musts, δ13C, at harvest. The grape composition during ripening was assessed by measuring total soluble solids, titratable acidity, and pH of musts and by a comprehensive assessment of skin phenolic composition with HPLC-DAD. Additional field measurements included shoot-count and yield assessment. Vegetation indexes were derived from canopy reflectance obtained from ~3m resolution CubeSat satellites. Irrigation amounts were provided by the grower, and weather data were obtained from three on-site stations. 

Grapevine Ψstem was modelled from weather data (temperature, relative humidity, rainfall), irrigation amounts, vegetation indexes, topographic attributes, soil type using a gradient-boosting-machine algorithm. The model was able to predict plant water status with <0.1 MPa of error (estimated as root mean squared error in a cross-validation procedure). Significant differences in water status were observed between rootstocks and main environmental drivers were slope grade and aspect (i.e. exposure). External validation of the model was carried out by correlating predictions with δ13C. The model allowed obtaining high-resolution daily mapping of Ψstem at the estate scale. Time-series of grapevine Ψstem were significantly correlated with the content of total soluble solids of musts, grape anthocyanin amounts, and the ratio of tri-hydroxylated to di-hydroxylated compounds at harvest and mapped. Spatial-clustering of grape anthocyanin composition was obtained from Ψstem model-estimates and used to guide harvest selectively. 

Conclusion: 

Grapevine water status confirmed to be an important driver in the variability of grape composition, even though the vineyard was irrigated. Variability in water status was related to environmental attributes (slope, aspect, incoming radiation) and the machine-learning approach proved to be useful to predict and understand plant-environment interactions and effects on grape composition in a varied and large dataset.

Significance and Impact of the Study: Vineyards are often located on slopes and accurate modelling of grapevine water status in hillslope conditions is a challenging task. This research demonstrates for the first time that it is possible to obtain daily estimates of grapevine water status at the estate scale by re-elaborating routine measurements with machine-learning technologies. This information can be used to drive selective harvest decisions and clustering within-vineyard variability at the estate scale to easily implement selective harvest decisions.

DOI:

Publication date: March 19, 2021

Issue: Terroir 2020

Type: Video

Authors

Luca Brillante

California State University Fresno, Fresno, United States

Contact the author

Keywords

Grapevine water status, machine learning, phenolic composition

Tags

IVES Conference Series | Terroir 2020

Citation

Related articles…

Modeling island and coastal vineyards potential in the context of climate change

Climate change impacts regional and local climates, which in turn affects the world’s wine regions. In the short term, these modifications rises issues about maintaining quality and style of wine, and in a longer term about the suitability of grape varieties and the sustainability of traditional wine regions. Thus, adaptation to climate change represents a major challenge for viticulture. In this context, island and coastal vineyards could become coveted areas due to their specific climatic conditions. In regions subject to warming, the proximity of the sea can moderate extremes temperatures, which could be an advantage for wine. However, coastal and island areas are particular prized spaces and subject to multiple pressures that make the establishment or extension of viticulture complex.
In this perspective, it seems relevant to assess the potentialities of coastal and island areas for viticulture. This contribution will present a spatial optimization model that tends to characterize most suitable agroclimatic patterns in historical or emerging vineyards according to different scenarios. Thanks to an in-depth bibliography a global inventory of coastal and insular vineyards on a worldwide scale has been realized. Relevant criteria have been identified to describe the specificities of these vineyards. They are used as input data in the optimization process, which will optimize some objectives and spatial aspects. According to a predefined scenario, the objectives are set in three main categories associated with climatic characteristics, vineyards characteristics and management strategies. At the end of this optimization process, a series of maps presents the different spatial configurations that maximize the scenario objectives.

Green berries on Gewürztraminer (Vitis vinifera L.) in South Tyrol (Italy)

The grape variety Gewürztraminer is known to be affected by two physiological disorders namely berry shrivel and bunch stem necrosis. During the season 2014 we noticed a new symptomatology type of ripening disorder on the variety. The new symptom showed not all berries fallowing the normal maturation stages, but single berries remaining at a soft but green stage till harvest. The broad distribution of these so called “green berries” symptoms in different production sites of our region, caused huge damage due to the difficulty of eliminating single berries per bunch before harvesting. Therefore, the Research Centre Laimburg began to investigate the reasons and origins of this new symptom. This work shows the results of first attempts to find causes for the symptom as well as the resulting approach to mitigate symptoms. Applications of magnesium leaf fertilizer showed first promising results against this putative disorder. To study the causal effect of the green berries 30 symptomatic vineyards in 2014 have been selected for a monitoring during the season 2016. To evaluate the foliar nutrient treatment two vineyards have been selected for application of magnesium sulfate and magnesium chloride. Leaf and berry nutrient analysis, as well as the main quality parameters during ripening have been performed. As soon as “green berries” symptoms appeared, incidence and severity have been evaluated. Most of the symptomatic vineyards of the 2016 monitoring showed light to clear magnesium deficit symptoms on their foliage. Only during the seasons 2020 and 2021 “green berries” symptoms could be found in the leaf fertilizer treatment vineyards. Both seasons showed a significant effect of the magnesium treatments to reduce the incidence and severity of the symptom. It seems that the appearance of the “green berries” symptom on Gewürztraminer is correlated to a disturbed uptake of magnesium of the vines.

Analysis of Cabernet Sauvignon and Aglianico winegrape (V. vinifera L.) responses to different pedo-climatic environments in southern Italy

Water deficit is one of the most important effects of climate change able to affect agricultural sectors. In general, it determines a reduction in biomass production, and for some plants, as in the case of grapevine, it can endorse fruit quality. The monitoring and management of plant water stress in the vineyard

Rapid damage assessment and grapevine recovery after fire

There is increasing scientific consensus that climate changeis the underlying cause of the prolonged dry and hot conditions that have increased the risk of extreme fire weather in many countries around the world. In December 2019, a bushfire event occurred in the Adelaide Hills, South Australia where 25,000 hectares were burnt and in vineyards and surrounding areas various degrees of scorching and infrastructure damage occurred. The ability to coordinate and plan recovery after a fire event relies on robust and timely data. The current practice for measuring the scale and distribution of fire damage is to walk or drive the vineyard and score individual vines based on visual observation. The process is time consuming, subjective, or semi-quantitative at best. After the December 2019 fires, it took many months to access properties and estimate the area of vineyard damaged. This study compares the rapid assessment and mapping of fire damage using high-resolution satellite imagery with more traditional ground based measures. Satellite imagery tracking vineyard recovery in the season following the bushfire is being correlated to field assessments of vineyard productivity such as canopy health and development, fertility and carbohydrate storage. Canopy health in the seasons following the fires correlated to the severity of the initial fire damage. Severely damaged vines had reduced canopy growth, were infertile or had very low fertility as well as lower carbohydrate levels in buds and canes during dormancy, which reduced productivity in the seasons following the bushfire event. In contrast, vines that received minor damage were able to recover within 1-2 years. Tools that rapidly and affordably capture the extent and severity of damage over large vineyard area will allow producers, government and industry bodies to manage decisions in relation to fire recovery planning, coordination and delivery, improving the efficiency and effectiveness of their response.

Better understand the soil wet bulb formation with subsurface or aerial drip irrigation in viticulture

The gradual change in rainfall patterns experienced in the south of France vineyards, especially around the Mediterranean sea, means that the vines are increasingly subject to summer drought. The winegrowers developped the use of irrigation techniques to ensure the maintenance of competitive yields in the production of wines under Protected Geographical Indication label. In practice, drip irrigation pipes can be installed above the ground or buried into the soil as well as at different distances from the vine row. The objective of this study was to examine the profiles of the wet bulbs of the soil obtained from two drip irrigation systems : aerial drip located under the vine row and subsurface drip placed in the middle of the inter-row. This experiment took place over two consecutive seasons (2020-2021) on a 3.4 ha Viognier plot in the Mediterranean region (PGI Oc, France) on sandy clay soil. The annual rainfalls were less than 400 mm. Soil water content probes were installed at different depths (20 – 40 – 60 – 80 cm) and at different lateralities from the vine row (30 – 60 – 90 – 120 cm) to control the formation of the soil wet bulb during irrigation. The mapping and the analysis of the data allowed a better understanding and differentiation of the water percolation when irrigating with subsurface or aerial drip. For the same amount of water and without differences of vine water status, it is shown that in a subsurface drip irrigation situation, the size of the wet bulb formed is larger than in aerial drip irrigation system.