Elvis A.Takow1, Edward W. Hellman2, Andrew G. Birt1, Maria D. Tchakerian1,

Robert N. Coulson1

Modeling Viticultural Landscapes: An Environmental Viticulture Information System

1Knowledge Engineering Laboratory, Department of Entomology, Texas A&M University, College Station, TX 77843 USA 2Texas A&M University, AgriLife Research and Extension Center, 1102 East FM 1294, Lubbock, TX 79403 USA; Department of Plant and Soil Science, Texas Tech University

Rationale

• Growing US and Texas wine industry.

• Increased demand for quality grapes and wine.

• Limited knowledge base of varietal suitability in US and Texas in particular.

• Match appropriate grape varieties to existing environmental conditions.

Assumptions

• Grape varieties in established European winegrowing regions are ‘optimal’ for the prevailing climatic and edaphic conditions.

• Relationships between environmental conditions and varieties in the “Old World” can be used as reliable predictor of grape variety selection in new regions.

Goal • Understand the environmental factors that

drive grape variety selection and use this knowledge in the establishment of vineyards in the “New World”.

Objectives

1. Develop a spatial database of environmental information.

2. Develop statistical models that relate environmental conditions to selection of appropriate grape varieties.

3. Deliver a web-based technology for further analysis and interpretation of models towards site selection – Decision Support

Data Sources

• National Climatic Data Center (NCDC)

• World Meteorological Organization (WMO).

• Soil Survey Geographic (SSURGO) Database

• Harmonized World Soil Database (HWSD)

• Topography-US Geological Survey

Raw

• Mean temperature (.1 Fahrenheit)

• Mean dew point (.1 Fahrenheit)

• Mean wind speed (.1 knots)

• Maximum temperature (.1 Fahrenheit)

• Minimum temperature (.1 Fahrenheit)

• Precipitation amount (.01 inches)

Climate Data

• Organic Carbon • pH • Available Water Capacity • Soil Depth • Cation Exchange Capacity • Salinity • Soil Texture Class • Elevation • Slope

Soil/Topography Data

Data

Grape Varieties

• Cabernet Sauvignon

• Chardonnay

• Pinot Noir

• Riesling

• Sangiovese

• Tempranillo

Methodology

• Identify relevant indices of climate, soil and topography suitable to grapevine growth.

• Use quantitative (data driven) statistical methods to analyze location based environmental factors important for grapevine growth.

Statistical Methods Multiple Logistic Regression

• Predict or estimate the probability that variety (Y) can be grown at a particular location.

• Understand the functional relationships between variety and environmental conditions.

• Determine which conditions might be causing the variation in the variety choice.

GSAT Tvar Srad ET FF LF Variety

55.43 8.42 20.98 23.47 16.00 111.30 NoCab

57.73 14.32 21.45 26.00 44.64 69.15 NoCab

57.94 9.83 20.72 25.41 26.58 107.86 NoCab

53.69 10.81 20.72 23.85 45.73 65.96 NoCab

57.73 14.32 21.45 26.00 44.64 69.15 NoCab

65.26 15.31 21.74 30.00 21.59 111.65 NoCab

61.36 10.52 21.31 27.69 17.00 113.00 NoCab

57.73 14.32 21.45 26.00 44.64 69.15 NoCab

53.69 10.81 20.72 23.85 45.73 65.96 NoCab

57.89 8.20 20.99 26.29 38.78 70.22 NoCab

49.63 7.38 18.16 17.55 57.95 56.95 Cab

45.00 5.51 19.03 16.05 56.95 63.25 Cab

50.72 4.05 18.28 18.26 62.40 76.60 Cab

46.94 5.54 18.32 0.00 61.20 59.56 Cab

49.81 6.59 19.03 17.94 43.97 73.71 Cab

60.83 11.80 21.00 23.24 22.25 110.68 Cab

55.29 9.24 19.79 20.85 41.28 79.76 Cab

53.36 3.55 19.83 19.95 41.38 103.88 Cab

36.05 5.18 19.43 13.28 106.72 30.84 Cab

57.19 5.08 20.42 21.99 7.29 117.36 Cab

Sample Data

Var Prob (Y)= α0 + α1Prcp + α2MaxT + α3MinT+ α4Tvar + α5Srad+ α6ET + α7FF+ α8LF

Statistical Methods Discriminant Analysis

• Predict variety membership based on a linear combination of environmental variables.

• Observations of conditions at selected locations are used to best separate varieties based on average of the conditions under which that variety is grown.

• A Likelihood function expresses the probability of the observed data as a function of the unknown parameters.

Modeling Approach

Climate Soils

Topography

Environmental Database

Geoprocessing

Climate

Soils

Topography

Site Selection

Web Page (Name of APP)

Online Mapping Define Area of Interest

View Tabular Data View Graphical Data

Download Data

Server Technologies Data analysis, Web Services

Raw Data Weather, Soils, Topography

Explore Retrieve raw and interpreted data

Compare Compare data for two or

more areas of interest

Analyze Model output relates data

to production &varietal selection

Environmental Viticulture Information System

Incre

ased

Inte

rpretatio

n an

d U

tility

System Architecture

Describe a Location

Bordeaux, France Napa Valley, California

McLaren Vale, Australia Santiago, Chile

Compare Locations

Analyze a Location

Case Study Soil Series Horizon Depth(cm) Organic

Matter (%)

Available Water

Capacity(cm)

pH(1:1 H20)

Texture

Dev-River wash complex H1 24 4 0.09 7.9 Loam Dev-River wash complex H2 70 4 0.09 7.9 Sandy

Loam River wash complex H1 203 0.5 0.03 8.2 —

Region Analogous Regions Existing Vars Recommeded Vars

Leakey,TX Malaga, Esp Shiraz Tempranillo

Jerez, Esp Sangiovese

Muscat