Protocol for soil functionality assessment in vineyards

Protocols used by Resolve partners during the project, to assess soil functionality on degraded aeras and evaluate soil restoration after applying recovering practices

Protocol for soil functionality assessment in vineyards

Protocols used by Resolve partners during the project, to assess soil functionality on degraded aeras and evaluate soil restoration after applying recovering practices

Protocols for soil functionality assessment in vineyards

The purpose of this guideline is to describe the methods used during ReSolVe project for soil functionality assessment, so they can be implemented in similar studies. A brief introduction first underlines what are the main functions of soil and why maintaining an optimal soil functionality is particularly of major interest in viticulture. Then the different protocols selected for ReSolVe project and this guideline are presented according to the following classification: - Part I: assessment of soil physical and chemical features; - Part II: assessment of soil biological features (ecosystem service provision and providers); - Part III: assessment of rhizosphere biological features; - Part IV: assessment of grapevine quantitative and qualitative indicators reflecting soil functionality. In each part, global objectives of the monitoring are explained (what is it used for, in which cases…) and the parameters to evaluate are listed with their corresponding methodological sheet. In these sheets, instructions and information are given about: - Materials needed to perform the sampling and the measurement - Sampling procedure - Analysis procedure - Possible interpretations and conclusions that can be drawn (value and meaning of the results, indication of reference values when existing, potential limit of the protocol) - Bibliographic references related to the method described - Additional helpful information where appropriate (ex: template of sampling sheet

Assessing berry number for grapevine yield estimation by image analysis: case study with the white variety “Arinto”

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation in recent years is identified as one of more important topics in viticulture because it can lead to more efficiently managed vineyards producing wines of highly quality. Recently, to improve the efficiency of yield estimation, image analysis is becoming an important tool to collect detailed information from the vines regarding the yield. New technologies were developed for yield estimation using a new ground platform, such as VINBOT, using image analysis. This work was done in a vineyard of the “Instituto Superior de Agronomia”, with the aim to estimate the final yield, during the growing cycle 2019 of the variety “Arinto”, using images collected in three different modality: laboratory condition (1), field condition (2) and VINBOT robot. In the every condition, the images were captured with the RGB-D camera. For (1) and (2) the photos were acquired manually through the use of a digital camera placed on a tripod but in the (3) the RGB-D camera was fixed on the VINBOT robot. In this work, the correlation of yield components between field data and images data was evaluated. In addition, throught MATLAB, it was evaluate the number of visible berries in the images and the percentage of visible berries not occluded by leaves and by other berries. Througt the laboratory results was calculate a growth factor of bunches on the periods pea-size and veraison. On the VINBOT analysis the efficacy to estimate the total yield from the number of berries was higher at maturation with a 10% error ratio. The relationship between canopy porosity and exposed berries showed for all the stages high and significant R2 indicating that we can use it to estimate berries occlusion through image analysis. This accuracy makes the proposed methodology ideal for early yield prediction as a very helpful tool for the grape and wine industryN/

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide

Perspectives on Tannins

Tannins are a family of versatile, natural phenolic biomolecules whose key role is to protect plants against insects and fungi. They are also valuable in use for humans. We show tannins' antioxidant and antibacterial properties, in addition to their potential application in the food industry. We prove the accessibility of condensed tannins to a wide range of potential applications, including NH3 neutralizer, the building block of numerous porous materials, such as foams, organic, and carbon gels. Finally, they are known as wood adhesives, heavy metal scavengers, and corrosion inhibitors. With this book, we want to present the most promising perspectives of tannin

Application of multidimensional and conventional fluorescence techniques for classification of beverages originating from various berry fruit

The objectives of this study were to characterize fluorescence of beverages from berry fruit, including chokeberry, blackcurrant, raspberry and strawberry, and to develop classification models based on different types of fluorescence spectra to identify beverages depending on the fruit species. Total fluorescence spectra (excitation-emission matrices, EEMs) and total synchronous fluorescence spectra (TSFS) were recorded for a series of commercial berry fruit beverages. An analysis of EEMs using parallel factor analysis (PARAFAC) revealed four components characterized by the excitation/emission maxima at 275/326, 319/410, 414/600, and 360/460 nm, respectively. Based on the spectral profiles, these components were assigned to various groups of phenolic compounds. Partial least squares discriminant analysis was used to develop the classification models. The analysis was performed on PARAFAC scores, unfolded EEMs (uEEMs), unfolded TSFS (uTSFS), and additionally on conventional emission spectra (EMS) measured at particular excitation wavelengths and single synchronous fluorescence spectra (SFS). The classification models with the same average classification error of 4.86% were obtained for the analysis of both the entire uEEMs and uTSFS. Among models based on the individual spectra, the lowest error of 4.42% was obtained for SFS measured at Delta lambda = 40 nm, and an error of 7.64% was obtained for EMS measured at the excitation wavelength of 360 nm. The classification model based on the PARAFAC scores had the highest error of 15.27%. The present results show good potential of fluorescence as rapid and reagent-free tool for authenticity evaluation of berry beverages.National Science Centre, PolandNational Science Centre, Poland [2016/23/B/NZ9/03591]info:eu-repo/semantics/publishedVersio

Grapevine yield estimation using image analysis for the variety Syrah

Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation in recent years is identified as one of more important topics in viticulture because it can lead to more efficiently managed vineyards producing wines of highly quality. Recently, to improve the efficiency of yield estimation, image analysis is becoming an important tool to collect detailed information from the vines regarding the yield. New technologies were developed for yield estimation using a new ground platform, such as VINBOT, using image analysis. This work was done in a vineyard of the “Instituto Superior de Agronomia”, with the aim to estimate the final yield, during the growing cycle 2019 of the variety “Syrah”, using images collected by the VINBOT robot. The images were captured with the RGB-D camera placed on the VINBOT robot in the vineyard and in addition, we obtained laboratory images using an RGB-D manual camera. In this work, the correlation of yield components between ground truth data and images data was evaluated. In addition, it was evaluate the projected bunches area in the images and the percentage of visible bunches not occluded by leaves and by other bunches. It was found a growth factor of bunches on the periods from pea-size to harvest. The efficacy to estimate bunch weight from the projected area was higher at maturation. The relationship between canopy porosity and exposed bunches showed for all the stages high and significant R2 indicating that we can use it to estimate bunches covered by leaves through image analysis. The percentage of visible bunches without the leaves occlusion and bunch occlusion was 29% at pea-size, 21% at veraison and 45% at maturation. It was estimated the final yield at pea-size, with an MA%E of 54%, at veraison and maturation were observed values of MA%E of 7% and 5%, respectively. Our results enable to conclude that the image analysis is an alternative to the traditional way to estimate the yieldN/