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…

Downscaling of remote sensing time series: thermal zone classification approach in Gironde region

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Local ancient grapevine cultivars to face future viticulture

Among the different strategies to cope with the negative impacts of climate change on viticulture, the exploitation of genetic diversity is one of the most promising to adapt to new conditions and maintain wine production and quality. One of the biggest concerns in the context of climate change is to improve water use efficiency (WUE). In this way, the use of genotypes that present a better response to drought and high WUE is a key issue. In this work, physiological performance analysis was conducted to compare the water deficit stress (WDS) responses of local and widespread grapevines cultivars. Leaf gas exchange, water use efficiency (WUE) at different levels (leaf and long-term WUE (∆13C)), leaf osmotic adjustment and other water relations parameters were determined in plants under well-watered and WDS conditions alongside assessment of the levels of foliar hormones concentrations. Results denote that local cultivars displayed better physiological performance under WDS as compared to the widely-distributed ones. he results corroborate the hypothesis that better stomatal control allows increasing leaf WUE under drought as occurred in the local Callet cv.; but the minority local cultivar Escursac cv. showed high WUE under both treatments. In this case, high WUE can be related to maintaining higher photosynthetic activity under drought. The different mechanisms underlying the better performance under WDS and high WUE of minority local cultivars are discussed.

Towards adaptation to climate change in Rioja: Quality evaluation of wines obtained from Grenache x Tempranillo selections

The wine sector is of great relevance and tradition in Mediterranean countries, however, it may be most susceptible to climate change. In recent years, wine production is facing changes worldwide, both at environmental as well as commercial levels, due to global warming and the shift in consumers’ preferences. Wine growers and wine makers are in search of solutions that allow to face these new challenges. One of the most promising initiatives in the long term is the introduction of new plant materials, specifically intraspecific hybridizations between premium varieties that may improve traditional germplasm in its adaptation to climate change. These inter-varietal crosses have the potential to generate quality wines, whilst maintaining the regional typicity, and constitute an attractive alternative for the consumer due to their sensory attributes. In this study, we have evaluated wines from 29 intraspecific Garnacha x Tempranillo hybrids in two different locations, with the aim to assess their oenological potential and sensory attributes. Thirteen of the selections were white and 16 were red. Microvinifications were conducted with two or three replications depending on grape availability. Conventional oenological parameters were determined for all wines. The sensory evaluation and hedonic scores were given by five experts. Red selections obtained higher quality scores than white ones. Among the white selections with higher quality scores, GT-41 Varea and GT-159 Varea outstand, due to their high total acidity and high malic acid content. Regarding red selections, GT-57 Varea and GT-57 UR were perceived as higher in quality, highlighted for their moderate alcoholic and high anthocyanin content. Our results indicate that intraspecific hybridization may be a powerful tool for adapting traditional cultivars to climate change in Rioja.

1H-NMR-based Metabolomics to assess the impact of soil type on the chemical composition of Mediterranean red wines

The aim of this study was to evaluate the effects of different soil types on the chemical composition of Mediterranean red wines, through untargeted and targeted 1H-NMR metabolomics. One milliliter of raw wine was analyzed by means of a Bruker Avance II 400 spectrometer operating at 400.15 MHz. The spectra were recorded by applying the NOESYGPPS1D pulse sequency, to achieve water and ethanol signals suppression. No modification of the pH was performed to avoid any chemical alteration of the matrix. The generation of input variables for untargeted analysis was done via bucketing the spectra. The resulting dataset was preprocessed prior to perform unsupervised PCA, by means of MetaboAnalyst web-based tool suite. The identification of compounds for the targeted analysis was performed by comparison to pure compounds spectra by means of SMA plug-in of MNova 14.2.3 software. The dataset containing the concentrations (%) of identified compounds was subjected to one-way analysis of variance (ANOVA) to highlight significant differences among the wines. The untargeted analysis, carried out through the PCA, revealed a clear differentiation among the wines. The fragments of the spectra contributing mostly to the separation were attributed to flavonoids, aroma compounds and amino acids. The targeted analysis leaded to the identification of 68 compounds, whose concentrations were significant different among the wines. The results were related to soils physical-chemical analysis and showed that: 1) high concentrations of flavan-3-ols and flavonols are correlated with high clay content in soils; 2) high concentrations of anthocyanins, amino acids, and aroma compounds are correlated with neutral and moderately alkaline soil pH; 3) low concentrations of flavonoids and aroma compounds are correlated with high soil organic matter content and acidic pH. The 1H-NMR metabolomic analysis proved to be an excellent tool to discriminate between wines originating from grapes grown on different soil types and revealed that soils in the Mediterranean area exert a strong impact on the chemical composition of the wines.

The combined effects of climate, soils, and deficit irrigation on yield and quality of Touriga Nacional under high atmospheric demand in the Douro Region

Global warming is one of the biggest environmental, social and economic threats in several viticultural regions. In the Douro Valley, changes are expected in the coming years, namely an increase in temperature and a decrease in precipitation. These changes are likely to have consequences for the production and quality of wine.
The aim of this study was to explore the effects of different soil characteristics combined with several deficit irrigation strategies, managed throughout ETc references and predawn leaf water potentials thresholds, on physiology, yield, and qualitative attributes on the Touriga Nacional variety under years of mild to severe water and heat stress.
The studies were conducted over seven years (2015 to 2021) in two plots of a commercial vineyard located at Quinta do Ataíde (Symington Family Estates) planted in 2011 and 2014 at 170 meters elevation, growing under three water regimes: non-irrigated (NI) and two deficit irrigation strategies (30% and 60% ETc) assessed weekly by Ψpd. The site has an annual rainfall below 500 mm, with high atmospheric demand. Climate data was collected from a weather station, located on site. Berry ripening was followed weekly for fruit analysis. At harvest, yield, vigour and pruning weight per vine were determined from 90 vines by treatment. Each season at veraison the NDVI Index was accessed by a drone. The soils physic-chemistry in the experimental blocs were analysed and grouped by SWHC. Delta C-13 analyses were also performed per treatment in two years.Irrigation had a positive effect on yield per vine, mostly due to an increase in berry and cluster weight, and fertility index through the years. A significant increase in sugar content, colour and phenols was observed with deficit irrigation in some years, but vine vigour related to soil characteristics had by far the greatest impact on quality.