gallo jan05

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Viticulture Soil Characterization using Geophysical Methods and USDA/Gallo Field Plan

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Mapping Soil Property Variability using Geophysical Methods Robert Mondavi Winery Dehlinger Winery USDA/Gallo Field Plan OUTLINE

Soil Variations and Mapping Techniques: 

Soil Variations and Mapping Techniques Water availability influences winegrape quality; Soil texture controls water holding capacity;

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Due to natural geologic processes, soil textures and associated moisture vary spatially, often over very short length scales; It is difficult to map these variations using conventional borehole approaches. Soil Property Spatial Variability Moisture Variation at the same soil depth as a function of distance Moisture variation at the same location as a function of time and depth

Use of Surface Geophysical Methods for high resolution, non-invasive imaging of Soil Variabilities: 

Use of Surface Geophysical Methods for high resolution, non-invasive imaging of Soil Variabilities Ground Penetrating Radar (GPR) The velocity of the GPR waves is very sensitive to soil moisture and thus can be used to estimate soil water content By using different GPR arrivals at different frequencies, can sample different soil layers

GPR Groundwave Techniques: 

GPR Groundwave Techniques GPR groundwave compared favorably with conventional ‘point’ sample measurement techniques; Accuracy: 0.01 m3/m3, Depth of Penetration 900 Mhz: ~10-15 cm; Non-invasive method and DENSE data: block below contains 20,000 measurements over 3 acres. Volumetric Water Content

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10 40 PERCENT SAND Clay-rich Sand-rich Spatial Persistant Soil Moisture Patterns Moisture controlled by Soil Texture

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Synthetic Precision Viticulture Example at Mondavi Block If preferentially irrigated following requirements rather than uniformly water to satisfy most ‘thirsty’ areas, water savings of 25% could be realized; Alternatively, variable spacing could be developed to meet target vegetation criteria using uniform irrigation approach.

Qualitative comparison of Soil and Canopy/Fruit* Properties vs. Fruit Quality (2003 Harvest): 

Qualitative comparison of Soil and Canopy/Fruit* Properties vs. Fruit Quality (2003 Harvest) High Sand Low Moisture High Vigor (NDVI) Low MAD, Low K, High TA: “Soft Tannins, Mix of Green and Cooked Flavors” Low Sand Content, High Moisture Low Vigor High MAD, High K, Low TA “Smaller berries, more sunburn, un-ripe Tannins, cooked and green flavors” Moderate Sand Moderate Moisture Moderate Vigor (NDVI) “Semi-lignified, ripe flavors, ripe tannins” * Fruit quality notes provided by Thibaut Scholasch MOVE BEYOND ‘CORRELATIONS’ TO PREDICTONS USING COMPUTATIONAL FRAMEWORK

Use of High Resolution Geophysical Estimates for Precision Viticulture: 

Use of High Resolution Geophysical Estimates for Precision Viticulture Develop optimal farming strategies and vineyard remediation approaches (such as precision irrigation) Optimal vineyard development (or redevelopment) based on soil heterogeneities – Work in progress Delineating blocks; Choosing grape varieties and rootstock best suited to the soil conditions within the subblocks; Choosing and developing cover crops; Developing flexible row and vine spacing; Developing subblock farming strategies (irrigation schemes) best matched to the particular matched to the particular environment. Strive to create a uniform expression of plant and fruit expression across the block. facilitates farming (such as harvesting), as all grapes can be processed at the same time. facilitates winemaking.

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2. USDA Field Plan Work within existing Gallo Vineyards One Microclimate Two varieties = Two blocks Four experimental stations in each block One precision trial transect in each block Example: Block 1 Cabernet Sauvignon Block 2 Merlot CIMIS Station Note: color variations within blocks schematically indicate variations in soil and canopy properties

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Data Acquisition NDVI, reconnaissance surface geophysical data, and conventional plant/soils point measurements will be used to map block canopy/soils variability and to choose Experimental Station and Transect Locations within each block; Each Experimental Station ~12 vines large; Each Transect ~6 rows wide; Block/Station/Transect: Climate, Plant, Soils, and Fruit Data Collection……

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Block Micrometeorological measurements to assess climate and fluxes. solar radiation air temperature, vapor pressure, wind speed, resultant wind wind direction precipitation, hourly ET0, relative humidity, dew point, Penman-Monteith ET Incident and diffused photosynthetic radiation (PAR) will be collected using a photosynthetic active radiometer. Light meter measurements From CIMIS or Eddy Covariance Tower

Station/Transect Data Acquisition: Plant DataPlant measurements will be taken to assess canopy vegetation density, plant stomatal conductance, assimilation, transpiration, and to gain an understanding of where the plant is getting soil water over time. : 

Station/Transect Data Acquisition: Plant Data Plant measurements will be taken to assess canopy vegetation density, plant stomatal conductance, assimilation, transpiration, and to gain an understanding of where the plant is getting soil water over time. Leaf area index Canopy density based: percent shade and NDVI; Stem water potentials Leaf-scale photosynthesis and respiration will be measured using a Li-Cor 6400. C, O, and H isotope measurements will be made using stem, leaf, shallow soil water, deeper soil water, irrigation water, and fruit samples at one station within each block; Canopy height and geometry will be measured at each experimental site using visual examination Plant-scale measurements will be taken at least once a week during the growing season prior and subsequent to irrigation and at three different times during the day (10:30AM, 1:30 PM, and 3:30 PM).

Station/Transect Data Acquisition: SoilsSoil data will be collected to assess soil texture, thickness, and compaction, depth to water table, and soil water distribution over time : 

Station/Transect Data Acquisition: Soils Soil data will be collected to assess soil texture, thickness, and compaction, depth to water table, and soil water distribution over time Soil samples for texture and thickness; Soil density using gamma ray attenuation meter; Soil water content using TDR, neutron probe, gravimetric, GPR; Soil respiration – SR chamber Soil temp- thermocouples At one location within each block, use time-lapse, crosshole GPR to ‘image’ water infiltration: GPR moisture distribution ‘imaging’, Williams et al., 2000

Station/Transect Data Acquisition: Fruit/CropCollect fruit/crop parameters that may be important for wine quality and that also may relate to the parameters that are predicted by the computational framework : 

Station/Transect Data Acquisition: Fruit/Crop Collect fruit/crop parameters that may be important for wine quality and that also may relate to the parameters that are predicted by the computational framework Brix tartaric acid L-malic acid pH, titratable acidity ammonia (NH3) alpha amino compound potassium, and anthocyanins Color measurement (using the CASV Puissant Leon test or HPLC methods). liquid chromatography analysis for measuring a wide range of compounds (such as polymeric anthocyanins, tannins, catechine/epicatechine quercitosine, glycerol, butane diol, enositol, and methoxy pyrazine) at limited sites; Phenolic profile of the fruit (and of the wine at early stages) to assess caffeic acid, caftaric acid, gallic acid, malvidin glucoside monomeric and polymeric glucoside, polymeric anthocyanins, polymeric phenols, quercetin aglycone, quercetin glycosides and total anthocyanins at limited sites; Sampling: 20 clusters that have been randomly sampled form each station once a week (from three weeks prior to harvest through harvest).

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Computational Framework: Incorporate soil, plant, and climatic Variabilities to predict responses that can be used to guide precision viticulture Modified from Bramley et al., 2002