Influence of leaf area density and trellis/training system on the light microclimate within grapevine canopies

The influence of leaf area density and canopy configuration on the light microclimate within 6 wine grape trellis/ training systems commonly used in California (single curtain, double curtain, vertically shoot positioned, lyre, Smart-Henry and Smart-Dyson) was examined in two experimental vineyards (Oakville and Parlier). Mean canopy leaf area density varied considerably among the systems, ranging from approximately 2.8 m2m-3 for the Wye to 10.1 m2m-3 for the VSP. Non-positioned systems were characterized by a layer of relatively high leaf area density in their outer envelope and lower leaf area densities in their interior. In contrast, leaf area density in positioned systems increased from the top of the canopy moving downward to the fruit zone. Mean leaf area density within the fruit zone ranged from near 6 m2m-3 in the DC to over 12 m2m-3 in the VSP and LYR. The pattern of light attenuation within the canopy was generally similar among the systems, with PPF reaching its lowest level in or near the fruit zone. Fruit zone PPF was >10 % of ambient sunlight in low density canopies and <5 % in high density canopies. A gradual decline in fruit zone PPF was found as leaf area density increased in positioned systems. PPF decreased sharply in the fruit zone of non-positioned systems as leaf area density increased from 2 to 4 m2m-3, then leveled as leaf area density exceeded 6 m2m-3. Fruit zone PPF decreased as the leaf area density of divided systems increased from 2 to 4 m2m-3, then declined gradually as leaf area density approached 6 m2m-3. Fruit zone PPF in non-divided systems was initially lower, and declined more gradually as leaf area density increased, compared to divided systems. Compared to positioned systems, leaf layer number in the fruit zone rose more sharply in non-positioned systems as leaf area density increased. Leaf layer number was greater in nondivided systems compared to divided systems, but declined at similar rates in both systems as leaf area density increased. Shoot-positioned systems achieved well-exposed fruit zones at higher leaf area densities, but lower leaf layer numbers, compared to non-positioned canopies

The Effect of Early and Late Defoliation on Phenolic Composition and Antioxidant Properties of Prokupac Variety Grape Berries (Vitis vinifera L.)

The influence of leaf area and various variants of manual defoliation on the phenolic profile of the Prokupac variety grape berry were investigated in the agroecological conditions of southern Serbia. The following four trial variants of manual defoliation were assessed: Early defoliation-variant I (flowering stage, 50% open flowers); early defoliation-variant II (grape size 3-5 mm); late defoliation-variant III (onset of grape ripening, veraison); and control (no defoliation). The first six leaves of each primary shoot were removed from all defoliated vines. The greatest assimilation area of primary and lateral shoots during the study was observed in the control, i.e., the trial variant with no defoliation. Defoliation significantly decreased the grape yield of the all three defoliated variants in regard to the control. The phenolic profile of the three variants and control was established by analyzing the grape seeds and skin. Based on the collected results for the Prokupac variety, significant differences between the trial variants were established regarding the content of phenols and total polyphenols, as well as radical scavenging activity. Defoliation variants showed a significant effect on the total phenols content of grape skin. In all defoliation variants, as well as in the control, high amounts of ellagic acid were measured. Resveratrol was identified only in grape skin samples of the control variant. The removal of leaves increased the concentration of phenolic compounds in variants where early defoliation was applied. The highest total anthocyanins content was found in 2015 in variant I, where leaves were removed during the full flowering stage

Impact of Temperature, Ethanol and Cell Wall Material Composition on Cell Wall-Anthocyanin Interactions.

The effects of temperature and ethanol concentration on the kinetics of anthocyanin adsorption and desorption interactions with five cell wall materials (CWM) of different composition were investigated. Using temperatures of 15 °C and 30 °C and model wine with ethanol concentrations of 0% and 15% (v/v) over 120 min, the adsorption and desorption rates of five anthocyanin-glucosides were recorded in triplicate. Small-scale experiments were conducted using a benchtop incubator to mimic a single berry fermentation. Results indicate that more than 90% of the adsorption occurs within the first 60 min of the addition of anthocyanins to CWM. However, desorption appears to occur much faster, with maximum desorption being reached after 30 min. The extent of both adsorption and desorption was clearly dependent not only on temperature and ethanol concentration but also on the CWM composition

MODELING SEASONAL WINE GRAPE DEVELOPMENT USING A MIXTURE TECHNIQUE

Biological growth data typically display an increasing sigmoidal pattern over time. Grape development is no exception and shows a similar general trend. A detailed examination of the growth process in grapes, however, reveals a few systematic deviations from this pattern. Specifically, grape development is often characterized by localized areas of growth plateaus leading to an overall growth pattern referred to as a double sigmoidal curve. Capturing and characterizing these local changes in growth is important as they represent important phases in grape development such as veraison. This paper utilizes a model adapted from the technique of mixture models to estimate the growth curve of grapes. The resulting model provides a more accurate description of the growth process and has parameter estimates directly related to the various phases of grape development. The model is demonstrated using data collected from an experimental trellis tension monitoring system in the Chardonnay grape varietie

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data

Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High-Resolution Imagery in Commercial Vineyards

Estimation of surface energy fluxes using thermal remote sensing–based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in Ts and Tc estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%

Estimation of Evapotranspiration and Energy Fluxes Using a Deep-Learning-Based High-Resolution Emissivity Model and the Two-Source Energy Balance Model with sUAS Information

Surface temperature is necessary for the estimation of energy fluxes and evapotranspiration from satellites and airborne data sources. For example, the Two-Source Energy Balance (TSEB) model uses thermal information to quantify canopy and soil temperatures as well as their respective energy balance components. While surface (also called kinematic) temperature is desirable for energy balance analysis, obtaining this temperature is not straightforward due to a lack of spatially estimated narrowband (sensor-specific) and broadband emissivities of vegetation and soil, further complicated by spectral characteristics of the UAV thermal camera. This study presents an effort to spatially model narrowband and broadband emissivities for a microbolometer thermal camera at UAV information resolution (~0.15 m) based on Landsat and NASA HyTES information using a deep learning (DL) model. The DL model is calibrated using equivalent optical Landsat / UAV spectral information to spatially estimate narrowband emissivity values of vegetation and soil in the 7–14- nm range at UAV resolution. The resulting DL narrowband emissivity values were then used to estimate broadband emissivity based on a developed narrowband-broadband emissivity relationship using the MODIS UCSB Emissivity Library database. The narrowband and broadband emissivities were incorporated into the TSEB model to determine their impact on the estimation of instantaneous energy balance components against ground measurements. The proposed effort was applied to information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) over a vineyard located in Lodi, California. A comparison of resulting energy balance component estimates, with and without the inclusion of high-resolution narrowband and broadband emissivities, against eddy covariance (EC) measurements under different scenarios are presented and discussed

To What Extent Does the Eddy Covariance Footprint Cutoff Influence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards?

Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (ustar), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management,with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown.The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient.From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University Aggie Air TM sUAS program over a commercial vineyard located near Lodi, California.This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2,14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (HandLE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity (u∗)that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications

Potential Fertilization Capacity of Two Grapevine Varieties: Effects on Agricultural Production in Designation of Origin Areas in the Northwestern Iberian Peninsula

In the present study, we analyzed the main parameters related with the potential fertilization ability of two grapevine varieties, Godello and Mencía, during the years 2017 and 2018. The research was carried out in two vineyards of the Galician winegrowing Designation of Origin areas of Ribeiro and Ribeira Sacra. Ten vines of each variety were selected for bunch and flower counting, pollen calculations, pollen viability studies by means of aceto-carmine (AC) stain and 2, 3, 5-triphenyl tetrazolium chloride (TTC) methods, and the determination of their germination rate. In all vineyards the 50% fruitset was reached, except for Godello in Cenlle during 2017. The mean coulure value was higher for Godello (40.5%) than for Mencía (31%). Analyzing the pollen production per plant and airborne pollen levels, we observed important discordances between them, which can be due to the influence of weather conditions and be related with self-pollination processes. We found important differences on pollen viability depending on the applied method and variety, with higher values for the AC method than the TTC for both varieties in all study plots, and higher values for Mencía variety than Godello. Regarding germination rates, we observed a marked reduction in 2017 with respect to 2018, in all study sites and for both varieties. The analyzed parameters were useful to explain the different productive abilities of Godello and Mencía varieties in the two studied bioclimatic regions of Ribeiro and Ribeira SacraThis research was funded by the Xunta de Galicia (Consellería de Educación, Universidade e Formación Profesional) through the recognition as Grupo de Referencia Competitivo de Investigación (GRC GI-1809 BIOAPLIC “Biodiversidad y Botánica Aplicada”, ED431C 2019/07), the Agrupación Estratégica de Investigación BioReDes (ED431E 2018/09) and the BV1 Reference Competitive Research Groups ED431C 2017/62 (Xunta de Galicia, Spain). This work was partially funded by Xunta de Galicia CITACA “Cluster de Investigación y Transferencia Agroalimentaria de Campus del Agua” Strategic Partnership (Reference: ED431E 2018/07) and the AGL2014-60412-R Economy and Competence Ministry of Spain Government project. Fernández-González M. was supported by FCT “Fundação para a Ciência e a Tecnologia” (SFRH/BPD/125686/2016) through the HCOP-Human Capital Operational Program, financed by “Fundo Social Europeu” and “Fundos Nacionais do MCTES (Ministério da Ciência, Tecnologia e Ensino Superior). González-Fernández E. was supported by the Ministry of Sciences, Innovation and Universities (FPU “Ayudas para la Formación de Profesorado Universitario” grant FPU15/03343)S