Leaf water content estimation by functional linear regression of field spectroscopy data

11 p.Grapevine water status is critical as it affects fruit quality and yield. We assessed the po-tential of field hyperspectral data in estimating leaf water content (Cw) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencı´a, Cabernet Sauvignon, Merlot and Tempranillo). Two regression models were evaluated and compared: ordinary least squares regression (OLSR) and functional linear regression (FLR). OLSR was used to fit Cw and vegetation indices, whereas FLR considered reflectance in four spectral ranges centred at the 960, 1190, 1465 and 2035 nm wavelengths. The best parameters for the FLR model were determined using cross-validation. Both models were compared using the coefficient of determination (R2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable Cw models than the vegetation indices, considering both the fit and degree of adjustment and the interpretation of the model. The best model was obtained using FLR in the range centred at 1465 nm (R2 ¼ 0.70 and %RMSE ¼ 8.485). The results depended on grape variety but also suggested that leaf Cw can be predicted on the basis of spectral signature.S

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

Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards.

Weeds have negative impacts on crop production but also play a role in sustaining biodiversity in agricultural landscapes. This trade‐off raises the question of whether it is possible to promote weed communities with low competitive potential but high value to biodiversity. Here, we explored how weed communities respond to different vineyard management practices in South Africa's Western Cape, aiming to identify whether any specific practices are associated with more beneficial weed communities. Eight weed community characteristics representative of abundance, diversity and functional composition were used as indicators of competitive potential and biodiversity value. We explored how these responded to farm management strategy (organic, low input or conventional) and weed management practices (herbicides, tillage, mowing or combinations of these) using ordination and mixed models. Mown sites were associated with weed communities of high biodiversity value, with higher weed cover in both winter and summer, higher diversity and more native weeds. Mowing also promoted shorter weeds than either tillage or herbicides, considered to be less competitive with grapevines. However, high summer weed cover may be problematic where competition for water is critical, in which case tillage offers a method to limit summer weed cover that did not adversely affect diversity or native weeds. In contrast, herbicide‐treated sites had characteristics indicative of a lower biodiversity value and higher potential for competitiveness with few native weeds, lower diversity and relatively tall, small‐seeded weeds. Mowing in winter combined with tillage in spring may thus optimise the biodiversity benefits and production costs of Western Cape vineyard weeds

Functional statistical techniques applies to vine leaf water content

P. 1116-1122A statistical analysis of functional data, obtained as reflectance values measured using a hyperspectral sensor, was used to determine water content in vine leaves. Our study was conducted using a sample of 80 vine leaves whose water content was determined by calculating the weight difference between leaves before and after drying in an oven.S

Determining optimum wavelengths for leaf water content estimation from re ectance: a distance correlation approach

P. 1-10This paper proposes a method to estimate leaf water content from reflectance in four commercial vineyard varieties by estimating the local maxima of a distance correlation function. First, it applies four different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Finally, it compares the results to those obtained by means of two different methods: a nonparametric kernel smoothing for variable selection in functional data and a wavelet-based weighted LASSO functional linear regression. Our approach proved to have some advantages over these two testing approaches, mainly in terms of the computing time and the lack of assumption of an underlying model. Finally the paper concludes that estimating water content from a few wavelengths is almost equivalent to doing so using larger wavelength intervalsS

Determining optimum wavelengths for leaf water content estimation from reflectance: A distance correlation approach

This paper proposes a method to estimate leaf water content from reflectance in four commercial vineyard varieties by estimating the local maxima of a distance correlation function. First, it applies four different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Finally, it compares the results to those obtained by means of two different methods: a nonparametric kernel smoothing for variable selection in functional data and a wavelet-based weighted LASSO functional linear regression. Our approach proved to have some advantages over these two testing approaches, mainly in terms of the computing time and the lack of assumption of an underlying model. Finally, the paper concludes that estimating water content from a few wavelengths is almost equivalent to doing so using larger wavelength intervalsThis study was made possible withfinancial funding from: a) FC-15-GRUPIN14-033 of the Fundaci on para el Fomento en Asturias de la Investigación Científica Aplicada y la Tecnología (FICYT) (Spain), with FEDER support included, b) Ministry of Economy and Competitiveness (MTM2016-76969P) and European Regional Development Fund, b) Spanish Ministry of Economy and Competitiveness (Grant numbers MTM2013-41383-P and MTM2016-76969-P) and European Regional Development Fund (ERDF). c) Grupo de Referencia Competitiva,2016–2019 (ED431C 2016/040),financiado pola Consellería de Cultura,Educación e Ordenación Universitaria, Xunta de GaliciaS