Abstract
Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5-6 (4-5) days with a negligible mean bias and moderately good R-2 (0.5-0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important.

Abstract
n-Alkanes and long-chain alcohols (LCOH) have been used as faecal markers to assess the feeding behaviour of both wild and domestic herbivore species. However, their chemical analysis is time-consuming and expensive, making it necessary to develop more expeditious methodologies to evaluate concentrations of these markers. This work aimed to evaluate the use of Fourier Transform Infrared Spectroscopy (FTIR) technology in the near infrared (NIR) and mid infrared (MIR) intervals, for the determination of n-alkane and LCOH concentrations of different plant species and faecal samples of domestic herbivores. Spectra of 33 feed samples, namely L. perenne, T. repens, U. gallii, short heathers (mixture of Erica spp. and Calluna vulgaris), improved pasture grasses (mixture of L. perenne and A. capillaris), heath grasses (mixture of P. longifolium and A. curtissii), improved pasture species (mixture of L. perenne, T. repens and A. capillaris) and herbaceous species (mixture of all herbaceous species found in the plot)) and 181 faecal samples (cattle and horses) were recorded. In order to develop calibrations for the prediction of n-alkanes and LCOH concentrations, partial least squares (PLS) regression was used. Regarding the models developed for plant species, the best results were observed for the calibrations using NIR. The best external validation coefficients of determination (R(2)v) obtained were 0.90 and 0.79 for LCOH and n-alkanes, respectively. For faecal samples, in the NIR interval, results indicate similar external validation predictions (R(2)v) for both animal species (0.64). On the contrary, in the MIR interval, differences between cattle (0.70) and horses (0.57) faecal samples in R(2)v were observed. Regarding the models created for both animal species faeces, LCOH (C-26-OH and C-30-OH concentrations ranging from 713.3 to 4451.9 mg/kg DM, respectively; R(2)v values ranging from 0.72 to 0.95) and n-alkanes (C31 and C33 concentrations ranging from 112.8 to 643.2 mg/kg DM, respectively; R(2)v values ranging from 0.19 to 0.90) present in higher concentrations tended to be those with better estimates. Results obtained suggest that the selection of the technique to be used may depend on the type of matrix, being the homogeneity of the matrices one of the most important factors for its success. In order to improve the accuracy and robustness of the models created for the estimation of the concentrations of these markers using these methodologies, the database (greater variability) used for the calibrations of these models must be increased.

Abstract
Kaolin-particle film has been considered a low-cost technology to mitigate the adverse effects of high light and temperature, and drought in several crops. However, the underlying excess energy absorption and dissipation mechanisms, and related components associated with kaolin photoprotective effects in grapevines are poorly explored. This study aims to understand the interactions between kaolin foliar treatment and photosynthetic pigments accumulation, carotenoids metabolism, xanthophyll cycle regulation, and its putative role on the non photochemical quenching (NPQ) processes in Touriga-Franca (TF) and Touriga-Nacional (TN) varieties. The experiments were conducted during the 2017 summer season in a commercial vineyard, and measurements were performed at pre-dawn and midday in each sampling date (EL35 - veraison; EL38 - full mature). Overall, TF variety showed higher accumulation of chlorophylls, xanthophylls, and de-epoxidation state (DPS) than TN. Kaolin treatment enhanced TN chlorophyll accumulation up to 114 % at EL35 (veraison) and 123 % at EL38 (full mature), highlighting its protective role on chlorophyll degradation, while no changes were found in TF, which might indicate a lower need for particle-film technology in this variety under the current environmental conditions. Individual carotenoids were mainly higher in the treated leaves of both varieties, as well as the xanthophyll cycle pigments zeaxanthin (Z(x)) and violaxanthin (V-x). Simultaneously, the DPS and NPQ values were lower in TN and TF treated leaves (1.92 - 2.36) compared to untreated vines (3.19 - 3.24), suggesting that there might be other components influencing NPQ levels beyond Z(x), with an indirect role in long-lasting NPQ processes. In addition, in the TF kaolin-treated leaves, violaxanthin de-epoxidase (VvVDE1) and zeaxanthin epoxidase (VvZEP1) gene expression were respectively 3-fold and 4-fold upregulated at stage EL35, while VvZEP1 gene expression decreased at stage EL38 in TN kaolin-treated leaves, indicating an optimised regulation of the xanthophyll cycle. These findings suggest that kaolin treatment promoted a fine-tuning of grapevine summer stress responses under sustained summer stress factors, by managing xanthophyll cycle dynamics, and pigments accumulation.

Abstract
BACKGROUND The application of kaolin particle film is considered a short-term strategy against several environmental stresses in areas with a Mediterranean-like climate. However, it is known that temperature fluctuations and water availability over the season can jeopardize kaolin efficiency in many Mediterranean crops. Hence, this study aims to evaluate the effects of kaolin foliar application on berry phytohormones, antioxidant defence, and oenological parameters at veraison and harvest stages of Touriga-Franca (TF) and Touriga-Nacional (TN) grapevines in two growing seasons (2017 and 2018). The 2017 growing season was considered the driest (-147.1 dryness index) and the warmest (2705 degrees C growing degree days) of the study. RESULTS In 2017, TF kaolin-treated berries showed lower salicylic acid (-26.6% compared with unsprayed vines) and abscisic acid (ABA) (-10.5%) accumulation at veraison, whereas salicylic acid increased up to 28.8% at harvest. In a less hot season, TN and TF kaolin-treated grapevines showed a twofold in ABA content and a threefold increase in the indole-3-acetic acid content at veraison and lower ABA levels (83.8%) compared with unsprayed vines at harvest. Treated berries showed a decreased sugar content, without compromising malic and tartaric acid levels, and reactive oxygen species accumulation throughout berry ripening. CONCLUSION The results suggest kaolin exerts a delaying effect in triggering ripening-related processes under severe summer stress conditions. Treated berries responded with improved antioxidant defence and phytohormone balance, showing significant interactions between kaolin treatment, variety, and developmental stage in both assessed years. (c) 2021 Society of Chemical Industry.

Abstract
Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety-training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of -2 to 4 days for phenology and -232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.