Climate Change and Grape Wine Quality: A GIS Approach to Analysing New Zealand Wine Regions

The influences of seasonal climate variability on the phenological dynamics of certain terrestrial communities observed mostly since the mid‐20th century are seen as leading to unprecedented consequences (Richard, et al., 2009). The potential impacts of the phenomenon on the phenological development and in turn on the species composition of certain specific plant, insect, aquatic, bird and animal communities evolved in parallel over millions of years to form the existing “make‐up” of what is referred to as the “biodiversity” or “endemic species” of these natural habitats, are depicted as significant (Peñuelas and Estiarte, 2010). Scientific research results have revealed that the recent rapid climate change effects on these systems, more specifically during the last few decades, have resulted in presently being seen “temporal mismatch in interacting species”. Such ecological observations are even described as early vital signs of imminent “regime shifts” in the current base climate of these regions or latitudes (Schweiger, Settele, Kudrna, & Klotz, 2008: Saino, et al., 2009). On the other hand, climatologists portray the major cause for such rapid “climate regime shifts” and the consequent impacts on the survival of so called co‐evolved species, as anthropogenic (Anderson, Kelly, Ladley, Molloy, & Terry, 2011). For this reason, research relating to climate change impacts on vegetation spread over landscapes, phenological development and population dynamics of susceptible communities, in some cases even with potential threat for total extinction of “endangered species” under future climate change, has in recent years gained enormous momentum. In fact, this unprecedented attention has also drawn greater scrutiny and controversies at never seen before proportions in a way hindering any form of formal research on the phenomenon (Shanmuganathan & Sallis, 2010)

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

Barriguinha, A., Jardim, B., De Castro Neto, M., & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. International Journal of Applied Earth Observation and Geoinformation, 114(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: The authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Portuguˆes do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)Estimating vineyard yield in advance is essential for planning and regulatory purposes at the regional level, with growing importance in a long-term scenario of perceived climate change. With few tools available, the current study aimed to develop a yield estimation model based on remote sensing and climate data with a machine-learning approach. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The study was conducted in the Douro Demarcated Region in Portugal over the period 2016–2021 using yield data from 169 administrative areas that cover 250,000 ha, in which 43,000 ha of the vineyard are in production. The optimal combination of input features, with an Mean Absolute Error (MAE) of 672.55 kg/ha and an Mean Squared Error (MSE) of 81.30 kg/ha, included the NDVI, Temperature, Relative Humidity, Precipitation, and Wind Intensity. The model was tested for each year, using it as the test set, while all other years were used as input to train the model. Two different moments in time, corresponding to FLO (flowering) and VER (veraison), were considered to estimate in advance wine grape yield. The best prediction was made for 2020 at VER, with the model overestimating the yield per hectare by 8 %, with the average absolute error for the entire period being 17 %. The results show that with this approach, it is possible to estimate wine grape yield accurately in advance at different scales.publishersversionpublishe

Development of a plant-based strategy for water status monitoring and stress detection in grapevine

Water shortage has become a major problem, leading to a growing interest for efficient and precise irrigation scheduling even in areas that were completely rain-fed so far. Appropriate irrigation for grapevines (Vitis vinifera L.) is not exclusively a story of fulfilling water demand, but rather of defining the optimum level and timing and having a good knowledge of the grapevine water status. Specific levels of soil water deficit at specific times in the growing season are known to play a key role in the production of high quality grapes and resulting wines, but both severe and no drought stress are not desired as they negatively influence the grape’s and wine’s potential. Innovative techniques for monitoring the plant water status and for applying an adequate irrigation scheduling are required to achieve this crucial water balance for a grapevine. It is internationally recognised that such tools should rely on plant measurements, as they provide information on the actual plant water status, rather than be based on soil or microclimatic measurements. The aim of this thesis was to develop and evaluate a strategy for water status monitoring and stress detection in grapevine based on automated plant measurements. To this end, both experimental and modelling work was carried out on potted grapevines that were subjected to conditions ranging from fully irrigated to severe drought. Two different plant-based monitoring approaches were tested and compared. In a first approach, an accurate monitoring of the grapevine water status and a fast detection of drought stress (i.e. several days before the first clear visible symptoms appeared) were accomplished using two data-driven models: Unfold Principle Component Analysis (UPCA) and Functional Unfold Principle Component Analysis (FUPCA). These models were originally developed for statistical process monitoring of multivariate data sets where accurate mechanistic knowledge is lacking or difficult to achieve. In this study, the multivariate data set consisted of measured microclimatic variables and a plant measurement that served as indicator for plant water status, either sap flow rate or stem diameter variations. Using a large amount of data from well-watered conditions, the models extracted the information and patterns underlying these measured variables and made a profile of normal, expected data behaviour under sufficient water availability. Monitoring new data then implied checking these data against this pattern. When a discrepancy between new data and this normal pattern was observed, the models indicated abnormality, which was in this study related to a deviating water status or drought stress. Unlike the data-driven approach in which a priori information on underlying plant mechanisms was not crucial, the second approach focused on developing a comprehensive mechanistic water transport and storage model for grapevine. This mechanistic model mathematically describes the axial and radial water transport and stem diameter dynamics of grapevine. The basic principles originated from an existing tree water transport and storage model, which enabled among others accurate simulations of the stem water potential (Ψstem) under well-watered conditions, which is one of the best indicators for plant water status. To obtain better drought response simulations with the model, the constant hydraulic plant resistances were replaced by equations in this PhD study. Both the integrated hydraulic resistance experienced during upward water transport through the soil-to-stem segment (RX) and the hydraulic resistance encountered during radial water transport between xylem and elastic living tissues (RS) were dependent on soil water potential. Modelled and measured data were compared to verify the implemented mechanisms. The mechanistic model was applied twofold. First, the model contributed to our understanding of grapevine functioning during drought conditions, as it revealed new insights. Despite the generally assumed constant RX and RS behaviour in several other plant models, the improved model demonstrated that both RX and RS showed daily fluctuations and, superimposed on these fluctuations, exponentially increased when drought progressed. Furthermore, it was shown that mean turgor in the elastic storage tissues rapidly decreased with drought. Finally, an in situ soil-to-stem vulnerability curve that integrated the hydraulic conductance in soil and plant (KX = 1/RX) was generated using the model. Such a curve depicts the loss in KX as a function of declining Ψstem and is often applied in the literature to assess vulnerability of species to drought. Second, the mechanistic model was elaborated as a tool to monitor grapevine water status in real-time. Except under most severe drought stress conditions, which are not favourable for grape and wine quality and should be avoided in practice, the model simulated Ψstem well and kept a tight supervision over the grapevine water status, as Ψstem could be continuously compared against expected plant behaviour defined under well-watered conditions. Simulated Ψstem, representing the actual water status of the grapevine, were then compared with a dynamic threshold beyond which the grapevine is considered to experience drought stress. In this study, the uncertainty band on the dynamic threshold estimation was used to represent the range within which Ψstem was expected to occur under well-watered conditions. Two different dynamic Ψstem thresholds were tested: an approach using vapour pressure deficit (VPD) as input, and a more elaborate approach using potential evapotranspiration (λEp). The latter includes VPD and radiation, both known as key driving variables for plant transpiration. The use of both the VPD- or the λEp-based dynamic threshold and uncertainty band allowed a fast detection of drought stress and tight supervision over the plant water status during a drought experiment on grapevines. To conclude, both the data-driven and the mechanistic modelling approach were judged promising as plant-based strategy for monitoring the grapevine water status. To apply these strategies for optimising grape and wine quality in practice, some challenges remain. As all experiments in this study were conducted on potted grapevines, future experiments should test the performance of the models under field conditions. In addition, the exact impact on the grape berries of different drought levels at specific times during the growing season should be investigated, in order to be able to steer grape and wine quality in the future

Knowledge description for the suitability requirements of different geographical regions for growing wine

The production of wine has progressed on every main continent. The knowledge modeling can support the sharing of expertise, methods and good practice concerning international grape vine growing and wine production while maintaining a high level of quality. Our research focuses specifically on the development of a support system for knowledge formalization. We describe some procedural rules to represent experienced knowledge in the viticulture domain and plant pathology. We use a graphical software for rules management. The visual representation is a step toward the improvements of interaction between Artificial Intelligence methods and domain experts to make interpretable learning models for concrete decisions. This implementation enables us to make valuable visual reasoning to search whether the Chinese regions are capable of receiving a production of French vineyards. In particular, one outcome is that two Chinese regions appear more favorable and consistent for the development of wine from the Bordeaux region

Impact of climate dynamics on cyclical properties of wine production in Douro region using time-frequency approach

Abctract. In this paper we model the impact of climate dynamics on wine production temporal cycles for the period 1933 to 2013 in the Douro wine region. We identify the cyclical properties of wine production and which cycles are determined by spring temperature and soil water levels during summer. We achieve that by applying a time-frequency approach, which is based on Kalman filter regressions in the time domain. The time-varying autoregressive model can explain 79% of the variability of wine production in Douro region. We then transfer the results in the frequency domain and can show that wine production is characterized by two cycles of 5.7 and 2.5 years around the long run trend. The in-season spring temperature as well as the temperatures of two and three years ago could explain about 65% of the variability of wine production. When the soil water level in summer is incorporated, the R2 increases to 83% and the Akaike criterion value is lower. The effects of soil water in wine production are depending on the timing. The in-season effect of an increase in soil water is negative, whilst soil water from two and three years ago have a positive effect on wine production. There is a stable but not constant link between production and the spring temperature. The temperature is responsible for two long-medium cycles of 5.8 year and 4.2 years as well as a short one of 2.4 years that began since the 80s. The soil water level can explained 60%of the 7 years cycles of wine production as well as a short one of 2.3 years cycle which has been happening since the 90s. We can recognise a shift of the relative importance away from temperature to soil water. Despite using a new an extended dataset, our results largely confirm the results of the impact of climate on the wine production in Douro region in our previous research. Modelling the impact of climate on the wine production can be an important instrument contributing for mitigation strategies facing the projected climate conditions in order to remain competitive in the market.Keywords. Climate variability, Wine production, Time-varying spectra, Kalman filter, Douro region.JEL. L52, B52, F63

Space-time variability of soil salinity in irrigated vineyards of South Africa

Salts present in the soil and surface waters of the Western Cape Province of South Africa represent a limitation to farming activities. Therefore the management of salinity in the landscape, which includes measuring, mapping and monitoring of its behaviour on a regional scale, is the subject of this investigation. Four sites were actively investigated in this study. These are the Robertson experimental farm, Goedemoed farm near Robertson, Broodkraal farm near Piketberg and the Glenrosa farm near Paarl. Regular point measurements and soil samples were taken to study the behaviour of salinity in irrigated soils over several irrigation seasons. The quality of the irrigation water was adjusted to six different levels of salinity, between 30 mS m-1 and 500 mS m-1. At the Broodkraal and Glenrosa farms, large scale investigations were conducted to estimate salinity over larger areas. Broodkraal farm was a large newly established table grape enterprise offering the opportunity to study the initial change in soil salinity when irrigated with saline water. At the Glenrosa farm, there was an opportunity to characterize the soil even before irrigated vineyards were established. Suction cup measurements were taken with self-designed and patented lysimeters and used to follow the seasonal soil water salinity changes through the root zone under different salinity regimes. These results were used to characterize an average salt depth trend, which was found to be best represented by a linear function, and its evolution over time. A method was proposed to reduce the number of samples necessary to determine this salt depth trend and to estimate the quality of the soil water that drains below the rooting zone. One of the important findings was that an ECe threshold for vines of 100 mS m-1 was more suitable than the conventional 150 mS m-1 and that the sensitivity of the vines to levels beyond this threshold increased with the number of years of exposure. The detailed surveys at the Broodkraal and Glenrosa Farms helped the modelling of the regional salinity behaviour. This study allowed gaining a comprehensive understanding of the soil salinity dynamics in an irrigated landscape using saline water

Understanding Spatiotemporal Patterns of Chemical Attributes in ‘Vitis vinifera’ L. cv. Cabernet Sauvignon Vineyards in Central California as a Basis for Predicting Fruit Composition

Spatial variability of vine productivity in winegrapes is important to characterise as both yield and quality are relevant for the production of different wine styles and products. Few studies have analysed spatial variability of individual fruit compositional attributes, and even fewer in Vitis vinifera L. cv. Cabernet Sauvignon in California, USA. Previous studies have focused on basic chemistry (pH, TA, TSS), groups of attributes (total phenolics), or fruit colour, and few have reported maps of spatial variability of individual aroma precursors or specific phenolic compounds related to mouthfeel in the resulting wines. The overall objectives of the research presented in this thesis were to understand how patterns of variability of Cabernet Sauvignon fruit composition changed over time and space, how these patterns could be characterised with proximal and remote measurements, and how spatial patterns of the variation in specific fruit compositional attributes can aid in improving management decisions. Prior to the 2017 vintage, 125 data vines were distributed across each of four vineyards in the Lodi American Viticultural Area (AVA) of central California. Each data vine was sampled at commercial harvest in 2017, 2018, and 2019. Yield components and fruit composition were measured at harvest for each data vine, and maps of yield and fruit composition were produced for eight ‘objective measures of fruit quality’: total anthocyanins, polymeric tannins, quercetin glycosides, malic acid, yeast assimilable nitrogen, β-damascenone, C6 alcohols and aldehydes, and 3-isobutyl-2-methoxypyrazine. Maps were produced for each compound in each vineyard to assess the temporal stability of their patterns of spatial variability, and to identify which compounds were most useful in describing overall fruit compositional variability. Of all the compounds analysed, patterns of variation in anthocyanins and phenolic compounds were found to be most stable over time. Given this relative stability, management decisions focussed on fruit quality could be based on zonal descriptions of anthocyanins or phenolics to increase profitability in some vineyards. In addition to the yield and fruit composition measurements in each season, dormant season pruning weights and soil cores were collected at each location. In each vineyard, elevation and soil apparent electrical conductivity surveys were completed, and remotely sensed imagery was captured by fixed wing aircraft and two satellite platforms at major phenological stages. The data collected were used to develop relationships among biophysical data, soil, imagery, and fruit composition. Remote sensing measures provided similar patterns of variability to those obtained by ground measures. Characterisation of patterns of spatial variability is difficult because of the cost associated with large sampling numbers and densities required to produce geostatistically rigorous maps. The standardised and aggregated samples from four vineyards over three seasons were included in the estimation of ‘common variograms’ to assess how this technique could aid growers in producing geostatistically rigorous maps of fruit composition variability without cumbersome, single season sampling efforts. Overall, the characterisation of spatial variability of multiple fruit composition parameters is important for the development of prescriptive farming practices aimed at the enhancement of wine quality.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 202