GiESCO 2019 banner
IVES 9 IVES Conference Series 9 GiESCO 9 GiESCO 2019 9 Data mining approaches for time series data analysis in viticulture. Potential of the bliss (Bayesian functional linear regression with sparse step functions) method to identify temperature effects on yield potential

Data mining approaches for time series data analysis in viticulture. Potential of the bliss (Bayesian functional linear regression with sparse step functions) method to identify temperature effects on yield potential

Abstract

Context and purpose of the study – Vine development, and hence management, depends on dynamic factors (climate, soil moisture, cultural practices etc.) whose impact can vary depending upon their temporal modalities (timing, duration, threshold, eventually trajectory and memory effects). Therefore, understanding the effect of the temporal variation of these factors on grapevine physiology would be of strategic benefit in viticulture, for example in establishing yield potential. Today many estates own data that can support temporal analyses, while the emergence of precision viticulture allows management at higher spatial and temporal resolutions. These data are a great opportunity to advance knowledge about the dynamics of grapevine physiology and production, and promote an improved precision of vineyard practices. The exploitation of these data needs analytical methods that fully explore time series data. However, current methods tend to only focus on a few key phenological stages or time steps. Such approaches do not fully address the potential information captured by continuous temporal measurements because they introduce limitations : i) they rely on choices of variables and timing, ii) they often require suppressing data or analysing only parts of a time series and iii) data correlation over time is not taken into account. A new approach is explored in this paper, using a Bayesian functional Linear regression with Sparse Steps functions (BLiSS method). The BLiSS method overcomes the mentioned limitations and leads to a more complete and objective analysis of time series data. Based on the identification of climatic periods affecting yield, the objective of the study is to evaluate the potential of the BLiSS method.

Materials and method ‐ Minimum and maximum daily temperatures during the year preceding the harvest year were regressed against the number of clusters per vine using the BLiSS method on one block of a commercial vineyard in the Bordeaux region over 11 years. The reliability and pertinence of the BLiSS method to reveal already reported, ignored or underestimated temperature effects on the number of clusters per vine are tested by comparison with literature results.

Results ‐ The BLiSS method allowed the detection of periods when temperature influenced the number of clusters per vine during the year preceding the harvest year. Some of the detected periods of influence had already been reported in literature. However, the BLiSS outcomes suggested that some of those known periods may have a different duration or several effects, thus challenging actual knowledge. Finally, some new periods of influence were identified by the BLiSS method. These results confirmed the potential of the BLiSS method to undertake a fuller exploration of time series data in the case of climate influence on grape yield.

DOI:

Publication date: June 22, 2020

Issue: GiESCO 2019

Type: Article

Authors

Cécile LAURENT (1,2,3), Meïli BARAGATTI (4), James TAYLOR (1), Bruno TISSEYRE (1), Aurélie METAY (2), Thibaut SCHOLASCH (3)

(1) ITAP, Univ. Montpellier, Montpellier SupAgro, Irstea, France
(2) SYSTEM, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRA, Montpellier SupAgro, France
(3) Fruition Sciences, Montpellier, France
(4) MISTEA, Univ Montpellier Montpellier SupAgro, INRA, France

Contact the author

Keywords

climate, functional analysis, temporal variability, cluster number

Tags

GiESCO 2019 | IVES Conference Series

Citation

Related articles…

Effect of the commercial inoculum of arbuscular mycorrhiza in the establishment of a commercial vineyard of the cultivar “Manto negro

The favorable effect of symbiosis with arbuscular mycorrhizal fungi (AMF) has been known and studied since the 60s. Nowadays, many companies took the chance to start promoting and selling commercial inoculants of AMF, in order to be used as biofertilizers and encourage sustainable biological agriculture. However, the positive effect of these commercial biofertilizers on plant growth is not always demonstrated, especially under field conditions. In this study, we used a commercial inoculum on newly planted grapevines of a local cultivar grafted on a common rootstock R110. We followed the physiological status of vines, growth and productivity and functional biodiversity of soil bacteria during the first and second years of 20 inoculated with commercial inoculum bases on Rhizophagus irregularis and Funeliformis mosseaeAMF at field planting time and 20 non-inoculated control plants. All the parameters measured showed a neutral to negative effect on plant growth and production. The inoculated plants always presented lower values of photosynthesis, growth and grape production, although in some cases the differences did not reach statistical significance. On the contrary, the inoculation supposed an increase of the bacterial functional diversity, although the differences were not statistically significant either. Several studies show that the effect of inoculation with AMF is context-dependent. The non-favorable effects are probably due to inoculation ineffectiveness under complex field conditions and/or that, under certain conditions, AMF presence may be a parasitic association. This puts into question the effectiveness of its application in the field. Therefore, it is recommended to only resort to this type of biofertilizer when the cultivation conditions require it (e.g., very low previous microbial diversity, foreseeable stress due to drought, salinity, or lack of nutrients) and not as a general fertilization practice.

Diagnosis of soil quality and evaluation of the impact of viticultural practices on soil biodiversity in a vineyard in southwestern France

Viticulture is facing two major changes – climate change and agroecological transition. In both cases, soil quality is seen as a lever to move towards a more sustainable viticulture. However, soil biological quality is little considered in the implementation of viticultural practices. Gascogn’Innov (2017-2022) is an Operational Group funded by the European Innovation Partnership for Agriculture. As such, it brings together winegrowers from the south-west of France, scientists, advisors and technicians, around a project focused on viticultural soil biological functioning and the design of technical routes more respectful toward soil heritage. To achieve this, the project aims to acquire references on the impact of viticultural practices on soil biology from a dynamic way, and to test a methodology to integrate information provided by the soil bioindicators to manage farming systems. A set of indicators of soil biological quality are evaluated in the project: microorganisms (bacteria and fungi abundance and diversity), fauna (abundance and diversity of nematodes and earthworms), physico-chemical characteristics, soil structure assessment and degradation rate of organic matter. Based on a network of 13 plots that have been subject to an initial diagnosis in 2017, several agronomical practices to restore soil fertility are experimented to redesign the cropping system (for instance plant cover, organic matter inputs, reduction of herbicides, mineral fertilizers). System redesign was made in collaboration by winegrowers and an interdisciplinary group of experts (agronomists, biologists). Several indicators are measured on vine and soil at each vintage to assess vine health and productivity. At the end of the project (2021), a final diagnosis was carried out. Gascogn’Innov allowed to create a regional database on the quality of wine-growing soils, which permitted to evaluate the effect of practices according to soil types. Especially, decreasing the intensity of tillage and increasing the duration and diversity of grass coverage tends to increase the abundance of all the organisms studied. This project confirmed the value of soil biological quality indicators to drive the sustainability of practices, but also highlighted the key-role of expertise, in both agronomy and soil biology, to help winegrowers understand and appropriate their soil quality diagnoses.

Grapevine sugar concentration model in the Douro Superior, Portugal

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

Effect of multi-level and multi-scale spectral data source on vineyard state assessment

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.

Second pruning as a strategy to delay maturation in cv. ‘Touriga nacional’ in the Portuguese Douro region

The advance in maturation of wine grapes is an important climate change risk related effect that could affect warm regions like Portuguese Douro Wine Region. Indeed, the climate analysis over the past years registered a decrease in the precipitation, significant higher average temperatures, and a more frequent occurrence of extreme weather events, including heat waves. In these conditions the length from anthesis until maturation is shortened and the uncoupling of technical and phenolic maturity results in berries with higher sugar concentration (and lower acidity), but lower anthocyanins, tannins, and total phenolic concentration, which produce unbalanced wines.
In this work, an innovative strategy of crop forcing, based on forcing vine regrowth after a second pruning of green shoots, was tested, aimed at delaying ripening until the temperature becomes lower and, therefore, preventing acidity loss and increasing anthocyanin-to-sugar ratio. The experiments were conducted in 2019 and 2020 in a commercial vineyard of ‘Touriga Nacional’ located in the Douro Region. Crop forcing was conducted 15 (CF1) to 30 (CF2) days after fruit set. Vines pruned with conventional methods were used as control (CF0). Results confirmed that fruit ripening was shifted from the hot season (August/September), until a cooler period (October through early-November). At harvest, grapevine berries from CF1 and CF2 presented lower pH and higher acidity, than control, with no significant differences in colour intensity and phenolic levels composition. Sugar content was lower in CF2-treated vines in both seasons. However, in CF-treated vines the number and size of clusters were significantly lower (up to 88% reduction) than in control plants. A metabolomics analysis of mature berries from CF-treated vines and control is underway. Crop forcing was indeed effective in producing a more balance berry composition but severely reduced grapevine yield,