Wine grape cultivar influence on the performance of models that predict the lower threshold canopy temperature of a water stress index

The calculation of a thermal based Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions. The objective of this study was to assess the influence of different wine grape cultivars on the performance of models that predict canopy temperature non-water stressed wine vines. The canopy temperature of the wine grape cultivars Malbec, Syrah, Chardonnay and Cabernet franc were measured under well-watered conditions over multiple years and modeled as a function of climatic parameters solar radiation, air temperature, relative humidity and wind speed using multiple linear regression and neural network modeling. Despite differences among cultivars in non-water stressed canopy temperature, both models provided good prediction results when all cultivars were collectively modeled. All predictive models had an uncertainty of plus or minus 0.1 in calculation of the CWSI despite significantly different prediction error variance between models

Deficit Irrigation in Mediterranean Fruit Trees and Grapevines: Water Stress Indicators and Crop Responses

In regions with Mediterranean climate, water is the major environmental resource that limits growth and production of plants, experiencing a long period of water scarcity during summer. Despite the fact that most plants developed morphological, anatomical, physiological, and biochemical mechanisms that allow to cope with such environments, these harsh summer conditions reduce growth, yield, and fruit quality. Irrigation is implemented to overcome such effects. Conditions of mild water deficit imposed by deficit irrigation strategies, with minimal effects on yield, are particularly suitable for such regions. Efficient irrigation strategies and scheduling techniques require the quantification of crop water requirements but also the identification of pertinent water stress indicators and their threshold. This chapter reviews the scientific information about deficit irrigation recommendations and thresholds concerning water stress indicators on peach trees, olive trees, and grapevines, as case studies

Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

Precision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring leaf temperature under identical environmental conditions. In this study, we calculated a daily CWSI for the wine grape cultivars Syrah and Malbec (Vitis vinifera L.) by estimating well-watered leaf temperature with an artificial neural network (NN) model and non-transpiring leaf temperature based on the cumulative probability of the measured difference between ambient air and deficit-irrigated grapevine leaf temperature. We evaluated the reliability of this methodology by comparing the calculated CWSI to midday leaf water potential and irrigation amount in replicated plots of above ground, drip-irrigated vines provided with 30, 70 or 100% of their estimated evapotranspiration demand under warm, semiarid field conditions in southwestern Idaho USA. Infrared and environmental sensors were used to monitor leaf temperature and weather conditions throughout berry development. The input variables for the NN model with lowest error were 15-minute average values for air temperature, relative humidity, solar radiation and wind speed collected between 13:00 and 15:00 MDT. A feed-forward perceptron NN model was developed for each cultivar because of their difference in well-watered leaf temperature. Predicted and measured well-watered leaf temperature had correlation coefficients of 0.91 and 0.86 for ‘Syrah’ and ‘Malbec’, respectively. Non-transpiring leaf temperature for both cultivars was air temperature plus 15 degrees Celsius. The daily CWSI consistently differentiated between deficit irrigation amounts, irrigation events, and rainfall and explained between 51 and 70% of the variability in midday leaf water potential. The methodology used to calculate a daily CWSI for wine grape in this study provided a real-time indicator of vine water status that could be automated for use as a decision-support tool in a precision irrigation system

Development of a new non-invasive vineyard yield estimation method based on image analysis

Doutoramento em Engenharia Agronómica / Instituto Superior de Agronomia. Universidade de LisboaPredicting vineyard yield with accuracy can provide several advantages to the whole vine and wine industry. Today this is majorly done using manual and sometimes destructive methods, based on bunch samples. Yield estimation using computer vision and image analysis can potentially perform this task extensively, automatically, and non-invasively. In the present work this approach is explored in three main steps: image collection, occluded fruit estimation and image traits conversion to mass. On the first step, grapevine images were collected in field conditions along some of the main grapevine phenological stages. Visible yield components were identified in the image and compared to ground truth. When analyzing inflorescences and bunches, more than 50% were occluded by leaves or other plant organs, on three cultivars. No significant differences were observed on bunch visibility after fruit set. Visible bunch projected area explained an average of 49% of vine yield variation, between veraison and harvest. On the second step, vine images were collected, in field conditions, with different levels of defoliation intensity at bunch zone. A regression model was computed combining canopy porosity and visible bunch area, obtained via image analysis, which explained 70-84% of bunch exposure variation. This approach allowed for an estimation of the occluded fraction of bunches with average errors below |10|%. No significant differences were found between the model’s output at veraison and harvest. On the last step, the conversion of bunch image traits into mass was explored in laboratory and field conditions. In both cases, cultivar differences related to bunch architecture were found to affect weight estimation. A combination of derived variables which included visible bunch area, estimated total bunch area, visible bunch perimeter, visible berry number and bunch compactness was used to estimate yield on undisturbed grapevines. The final model achieved a R2 = 0.86 between actual and estimated yield (n = 213). If performed automatically, the final approach suggested in this work has the potential to provide a non-invasive method that can be performed accurately across whole vineyards.N/

A exposição das vinhas mediterrâneas ao ozono troposférico: uma abordagem de modelação

The main objective of this Thesis is to develop and evaluate a modeling system capable of simulating in detail the exposure and uptake of ambient ozone (O₃) by vineyards in Mediterranean environments. It also aims to contribute to the state of the art on the influence of climate in viticulture in the context of climate change. The selected study area was the Douro Demarcated Region (DDR) in Portugal. The assessment on climate change potential impact was based on WRF downscaled ERA-Interim and MPI-ESM-LR global simulations forced with a RCP8.5 GHG emission scenario, for recent-past (1986–2005) and future periods (2046–2065, 2081–2100). For the evaluation of phytotoxic risk due to ozone, a validation of a simulation with the WRF-CHIMERE system was carried out. This simulation covered a grapevine growing season from April to September 2017. In the same period, a field campaign was carried out, including measurements of ambient ozone, phenology, and leaf gas-exchange and water relations for the grapevine along representative vineyards of the study area. The field campaign indicated that the phytotoxicity threshold for ambient O₃ (40 ppb) was reached in all stages of grapevine development, including a sensitive period such as flowering. Regarding ambient O₃ exposure standards, the measured May-Jun AOT40, 8 ppm-h, exceeded the long-term objective for the protection of vegetation, 3 ppm-h, and was close to that established as a general annual standard by the 2002/03 and 2008/50 European Directives, 9 ppm-h. The validated ambient ozone simulations by the WRF-CHIMERE system also indicated that grapevine-specific thresholds for Jun-Sep AOT40 could be exceeded, mainly in the drier, warmer Douro Superior eastern subregion. On the other hand, the standard based on the phytotoxic ozone dose introduced into the plant, POD, also indicated risk of phytotoxicity, this time mostly located in the Baixo and Cima Corgo western DDR subregions. The POD risk had a lesser extension when adjusted to the physiological behavior of local grapevine varieties, mainly due to the inclusion of the plant water stress effect throughout the region. It has also been possible to relate the WRF-ERA recent-past climate simulations with vintage yield and quality in the DDR. The mid-term and long term WRF-MPI climate scenarios revealed shifts to warmer and drier conditions not remaining within the ranges for quality and production. Important conclusions of this work are the relevance of including phenological and physiological parametrizations of local grapevine varieties to refine standards related with ozone phytotoxic risk and climate change. A current limitation is the lack of valid O₃ exposure and dose-effect relationships for the grapevine.O principal objectivo desta Tese consiste no desenvolvimento e validação de um sistema de modelação capaz de simular em detalhe a exposição e a absorção do ozono (O₃) ambiental pelas vinhas em ambientes mediterrânicos. Visa também contribuir para o estado da arte sobre a influência do clima na viticultura no contexto das alterações climáticas. A área de estudo seleccionada foi a Região Demarcada do Douro (DDR), em Portugal. A avaliação do impacto potencial das alterações climáticas baseou-se no refinamento da resolução das simulações globais de ERA-Interim e MPI-ESMLR, forçadas com um cenário de emissão de GEE RCP8.5 para períodos recentes (1986-2005) e futuros (2046-2065, 2081-2100), recorrendo ao modelo WRF. Para a avaliação do risco fitotóxico devido ao ozono, foi efetuada uma validação de uma simulação do sistema WRF-CHIMERE. Esta simulação abrangeu um período de crescimento para as vinhas entre Abril e Setembro de 2017. No mesmo período, foi realizada uma campanha experimental, incluindo medições do ozono ambiente, fenologia, troca de gases foliares e relações de água, ao longo de vinhas representativas da área de estudo. A campanha indicou que o limiar de fitotoxicidade para O₃ ambiental (40 ppb) foi atingido em todas as fases de desenvolvimento das vinhas, incluindo um período sensível como a floração. Em relação aos padrões de exposição ambiental ao O₃, o indicador AOT40 entre Maio-Junho, 8 ppm-h, excedeu o objetivo a longo prazo para a proteção da vegetação, 3 ppm-h, e aproximou-se do estabelecido como norma geral anual pelas Diretivas Europeias 2002/03 e 2008/50, 9 ppm-h. As simulações de ozono ambiente, validadas pelo sistema WRF-CHIMERE, também indicaram que os limiares específicos para as vinhas, para AOT40 entre Junho-Setembro, podiam ser excedidos, principalmente na sub-região mais seca e quente mais oriental da DDR, o Douro Superior. Por outro lado, o padrão baseado na dose de ozono fitotóxico introduzida na planta, POD, indicou também risco de fitotoxicidade, principalmente nas sub-regiões ocidentais da DDR, o Baixo Corgo e o Cima Corgo. O risco indicado pelo POD teve uma menor extensão quando ajustado ao comportamento fisiológico das castas de videira locais, sobretudo devido à inclusão do efeito de stress hídrico das plantas em toda a região. Os resultados das simulações climáticas WRFERA para o período recente revelaram também uma relação coerente com o rendimento e qualidade da vinha na DDR em clima presente. Os cenários climáticos de médio e longo prazo do WRF-MPI indicaram uma tendência para condições mais quentes e secas, que propiciarão valores de produção e de qualidade inferiores aos recomendados. Conclusões importantes deste trabalho são a relevância de incluir parametrizações fenológicas e fisiológicas das castas de videira locais para refinar as normas relacionadas com o risco fitotóxico do ozono e as alterações climáticas. Uma limitação atual é a falta de relações exposição ou dose-efeito válidas para o O₃ e as vinhas.Programa Doutoral em Ciências e Engenharia do Ambient

Evaluation of neural network modeing to calculate well-watered leaf temperature of wine grape

Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality, but precision irrigation management is hindered by the lack of a reliable method to easily quantify and monitor vine water status. The crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring leaf temperature under identical environmental conditions. In this study, a daily CWSI for the wine grape cultivar Syrah was calculated by estimating well-watered leaf temperature with an artificial neural network (NN) model and non-transpiring leaf temperature based on the cumulative probability of the measured difference between ambient air and deficit-irrigated grapevine leaf temperature. The reliability of this methodology was evaluated by comparing the calculated CWSI with irrigation amounts in replicated plots of vines provided with 30, 70 or 100% of their estimated evapotranspiration demand. The input variables for the NN model were 15-minute average values for air temperature, relative humidity, solar radiation and wind speed collected between 13:00 and 15:00 MDT. Model efficiency of predicted well-watered leaf temperature was 0.91 in 2013 and 0.78 in 2014. Daily CWSI consistently differentiated between deficit irrigation amounts and irrigation events. The methodology used to calculate a daily CWSI for wine grape in this study provided a real-time indicator of vine water status that could potentially be automated for use as a decision-support tool in a precision irrigation system

Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region

Hyperspectral data collected through a handheld spectroradiometer (400-1010 nm) were tested for assessing the grapevine predawn leaf water potential (ѱpd) measured by a Scholander chamber in two test sites of Douro wine region. The study was implemented in 2017, being a year with very hot and dry summer, conditions prone to severe water shortage. Three grapevine cultivars, 'Touriga Nacional', 'Touriga Franca' and 'Tinta Barroca' were sampled both in rainfed and irrigated vineyards, with a total of 325 plants assessed in four post-flowering dates. A large set of vegetation indices computed with the hyperspectral data and optimized for the ѱpd values, as well as structural variables, were used as predictors in the model. From a total of 631 possible predictors, four variables were selected based on a stepwise forward procedure and the Wald statistics: irrigation treatment, test site, Anthocyanin Reflectance Index Optimized (ARIopt_656,647) and Normalized Ratio Index (NRI711,700). An ordinal logistic regression model was calibrated using 70 % of the dataset randomly selected and the 30 of the remaining observations where used in model validation. The overall model accuracy obtained with the validation dataset was 73.2 %, with the class of ѱpd corresponding to the high-water deficit presenting a positive prediction value of 79.3 %. The accuracy and operability of this predictive model indicates good perspectives for its use in the monitoring of grapevine water status, and to support the irrigation tasks

Data-driven models for canopy temperature-based irrigation scheduling

Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adopted in production agriculture. Development of generalized crop-specific upper and lower reference temperature is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data driven models for predicting reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed using measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over 5 years under full and severe deficit irrigation. Lower reference temperatures were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1 degree Celsius. The relationship between well-watered canopy temperature minus ambient temperature and vapor pressure deficit was represented by a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures nearly double values reported in previous studies. Daily CWSI calculated as the average of 15-min values determined between 13:00 and 16:00 MDT for sugarbeet and 13:00 and 15:00 local time for wine grape was well correlated with irrigation events and amounts. A quadratic relationship between daily CWSI and midday leaf water potential of Malbec and Syrah wine grape was significant (p<0.001) with an R2 of 0.67. The data driven models developed in this study to estimate reference temperatures permit automated calculation of CWSI for effective assessment of crop water stress, however, wet canopy conditions or solar radiation < 200 W m-2 can result in irrational values of CWSI. Automated calculation of CWSI using the methodology of this study would need to check for wet canopy or low solar radiation conditions and omit calculation of CWSI if determined to be probable

Comparison of different methodologies to estimate bunch compactness

Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoBunch compactness (BC) is a key target for wine sector because it affects disease susceptibility, berry ripening among other grapes characteristics. The most common method to estimate BC is the O.I.V. descriptor n°204: manual and subjective. Objective and automated methods are based on indices, using different relations between bunch traits, some obtained manually and other automatically through image analysis (as example: BW – weight; BV – volume; ML – maximum length; A – projected area; MVO – morphological volume; V3 – derived volume; BN – berries number). All the variables were significantly and positively correlated between each other: the highest Pearson correlation coefficient was between BW and BV (r = 0.99) followed by BW and A (r = 0.95). Fourteen compactness indices (CI) were tested (9 published and 5 created) on 61 Syrah bunches. These indices were then correlated with the mode of the O.I.V. descriptor n°204, where 11 were positively correlated and three were negatively correlated (CI-3, CI-3a, CSF). The index CI-10a, which relates bunch weight and maximum length, was the most suitable one to define BC (r = 0.78). In the frame of the EU VINBOT project, to improve BW estimation finding the best explanatory variables, a stepwise regression analysis between BW and the variables considered easy to extract by automated image analysis (A1 – projected area, V3 – volume 3, BN – berries number and CI-10a as index) was performed. The variable which explained best BW was A1 (partial R2 = 0.905), followed by CI-10a and V3 with a much smaller contribution (partial R2 <0.06 and partial R2<0.007, respectively). The variable BN was not selected by the model. We concluded that BC can be estimated in an objective and automatic way using image analysis. Furthermore, such estimations can enhance BW prediction by using BC as one of the explanatory variables which can improve automatic yield estimation methodologiesN/