ARI Project Final Paper (version date: 29 April 2008) Assessing Wine Quality Relative to Soil Types: A Study of Terroir T.J. Rice, J. Montecalvo and A. Schaffner ABSTRACT The relationships between soil type and wine quality, as measured by the inorganic and organic constituents in wines (post-fermentation, pre-barrel stage) and grape must obtained from Cabernet Sauvignon grapes grown on their own roots, were investigated. The two contrasting soil types are Calodo clay loam, a shallow Calcic Haploxeroll, and Zaca clay, a deep Vertic Haploxeroll. The vineyard site is Carmody McKnight Estate Wines, located about seven miles west of Paso Robles, CA. Grape must and the pre-barrel wine samples were obtained over three harvest years, 2004, 2005 and 2006. Statistical analyses demonstrate significantly higher levels of calcium (Ca), potassium (K), pH, and reducing sugars in the grape must and wine samples grown on the Calodo clay loam. Ammonia levels were higher in the wine samples grown on the Zaca clay. In 2004, color intensity measurements of the must and pre-barrel wine samples were higher for the Zaca clay samples. These data indicate that the shallow Calodo soils with lower water supplying capacities resulted in higher reducing sugar levels in the grapes. This relates to higher alcohol levels in the wines. There appears to be a relationship between the inorganic Ca and K levels in the Calodo clay loam and these same levels in the grape must and wine. The cause is not entirely clear, since there is no difference in these same nutrient levels between the two soil types. A higher K value in the grapes corresponds with higher pH levels in the grapes grown on the Calodo soils. The relationship between K and grape pH has been demonstrated by past researchers. Future research on soil type and grape quality relationships should incorporate cane and fruit yield data along with atmospheric and soil climate data. INTRODUCTION The relationships between soil type and wine quality, as measured by the inorganic and organic constituents in wines (post-fermentation, pre-barrel stage) and grape must obtained from Cabernet Sauvignon grapes grown on their own roots, were investigated. Grape must and the pre-barrel wine samples were obtained over three harvest years, 2004, 2005 and 2006. The two contrasting soil types are Calodo clay loam, a shallow Calcic Haploxeroll, and Zaca clay, a deep Vertic Haploxeroll. The vineyard site is Carmody McKnight Estate Wines, located about seven miles west of Paso Robles, CA. METHODS Faculty and students of California Polytechnic State University (Cal Poly), San Luis Obispo, CA began projects in 1996 to inventory the soils and document the soil chemical and physical

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properties on chemically diverse soils at Carmody McKnight Vineyards. These past projects began with a comprehensive soils study of the Carmody McKnight Vineyards in the Adelaida Hills region of San Luis Obispo County, CA, about seven miles northwest of Paso Robles. The primary objectives were to inventory and map the major vineyard soils and to provide laboratory characterizations of the important chemical and physical soil properties related to vineyard management. There are two Cal Poly senior projects that resulted in a detailed soil survey and map of the Carmody McKnight Vineyards (Sloan and Westerling, 1996) and a comprehensive soil chemical property characterization of 19 soil profiles in the Carmody McKnight Vineyards (Roberts and Stubler, 1996). Building upon these past Cal Poly soils studies, Carmody McKnight Vineyards was chosen to be the subject of long term testing and research with the Soil Information System (SIS) from Earth Information Technologies Corporation (EarthIT). This is a joint effort by John Deere Global Ag. Services, and Earth IT and will usher in a new age in earth science technologies applied to agricultural enterprises. In 2003, EarthIT employees under contract with Deere & Company became involved in this soil mapping project in order to introduce state-of-the-art digital technology into these soil inventory and vineyard management efforts. Based on these several soils studies, the grape sampling sites for this study were chosen. The primary sampling sites will be within Block 3 of Carmody McKnight Vineyards, which is planted to Cabernet Sauvignon grapes. The Cabernet Sauvignon vines are planted on their own roots and are irrigated similarly throughout the total block length. The entire Cabernet Sauvignon block is managed similarly relative to pest management and other viticulture considerations. The Cabernet Sauvignon block is planted in a north-south orientation and soils vary from a shallow Calodo clay loam soil (loamy, mixed, superactive, thermic, shallow Calcic Haploxerolls) in the northernmost portion of the block and changes downslope to a deep Zaca clay loam soil (fine, smectitic, thermic Vertic Haploxerolls) in the southernmost portion of the block. Based on the existing soil maps, backhoe pits at least two meters long will be dug in the Calodo and Zaca soils to weathered bedrock. The soils were described according to standard USDA nomenclature (Soil Survey Staff, 1994). The described soils will be classified according to soil taxonomy (Soil Survey Staff, 1997) and correlated to the series level using USDA official series descriptions. When the soils descriptions are completed, about two (2) kilograms of soil was sampled to one meter depth or to bedrock and placed in plastic or paper bags. These soil samples were sent to A&L Western Agricultural Laboratories in Modesto, CA for characterization of the inorganic and organic soil components (Table 1). Five (5) composite soil samples were collected for laboratory analyses within each of the two soil types in August, 2004 and in August 2007.

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Table 1: Soil Chemical Properties and Analysis Methods

Soil Chemical Property Analysis Method pH Saturated paste measured with a pH meter

equipped with a calomel/glass electrode. Organic matter content Walkley-Black method on dry sample Nitrate-Nitrogen (NO3

+-N). Saturated paste extraction with a potentiometer with nitrate electrode.

Calcium (Ca2+) and Magnesium (Mg2+) Extracted with 1 M (1 molar) ammonium acetate solution at pH 7.0. Measured by atomic absorption spectroscopy (AAS).

Sodium (Na+) and Potassium (K+) Extracted with 1 M (1 molar) ammonium acetate solution at pH 7.0. Measured by flame photometry.

Copper (Cu2+), Iron (Fe2+); Manganese (Mn2+) and Zinc (Zn2+)

Extracted with diethylenetriaminepentaacetic acid (DTPA) solution. Measured by atomic absorption spectroscopy (AAS).

Phosphorus (P) Extracted with sodium bicarbonate at pH 8.2. Measured by colorimetric method using a spectrometer.

Sulfate-sulfur (SO42--S) Extracted with deionized water. Extract

mixed with barium chloride. Spectrometry. Electrical conductivity (ECe; in dS/m) Extracted with deionized water from

saturated paste. Measured with conductivity meter.

Cabernet Sauvignon grapes were sampled separately within each soil type (Calodo vs. Zaca) at time of harvest. Samples of the grapes from each site were frozen and stored for future analyses. The grape samples to be fermented into wine will then be crushed and placed separately in plastic fermenting tanks. The fermented, pre-barrel wines from each site were characterized by a variety of organic and inorganic properties (Table 2). Ten total grape and wine samples were collected for analyses with five replications of each grape sample and each fermented, pre-barrel wine from each of the two soil areas. Table 2: Wine Chemical Properties and Analysis Methods Wine Chemical Property Analysis Method Tartaric Acid Metavandate Spectrophotometric Analysis And

Titratable Acidity Malic, Citric Lactic Enzymatic UV-VIS Spectrophotometric Analysis Color Spectrophotometric Determination Of Hue (Tint) And

Intensity (Density) Total Reducing Sugars Spectrophotometric UV Analysis Sucrose (Non-Reducing) Enzymatic UV Spectrophotometric Method BRIX Abbe Refractometry

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Ammonia Ion Selective Electrode Method Amino Nitrogen Spectrophotometric CBB-G-250 Visible Analysis Titratable Acidity Std. Sodium Hydroxide Micrometric Procedure Minerals Calcium, Copper, Iron, Potassium

Atomic Absorption Spectroscopy

Sulfur as SO2 Total Aeration-Oxidation Method Grape and wine samples were analyzed in the Cal Poly Wine Chemistry and Analysis Laboratory in the FSN Department (for some year-2004 properties) and at Vinquiry, Inc. in Santa Maria, CA (for some year-2004 properties and all the year-2005 and year-2006 grape and wine properties). All methods used for wine analysis are AOAC (American Association of Official Analytical Chemists) and TTB (Alcohol, Tobacco and Firearms; Tax and Trade Bureau) are used in the Wine Chemistry and Analysis Laboratory in the Cal Poly FSN Department and at Vinquiry, Inc. in Santa Maria, CA. Statistical Evaluation; Sampling of grapes and wine Cabernet Sauvignon wine grapes were randomly sampled at five (5) locations within each of the two soil types. A one kilogram (fresh weight) sample of wine grapes was obtained within the five locations from each of the two soil areas. These samples were frozen and stored for potential future analytical laboratory analyses. Five (5) separate sample lots of wine grapes within each soil area were harvested separately and five corresponding wine samples were separately fermented in fifty (50) gallon plastic fermenting tanks. Random liquid samples of each of the five wine lots will be obtained post-fermentation and delivered to the analytical laboratory for analyses. In order to test for correlation and resulting significance between soil mineral analysis and wine mineral analysis, results were tabulated in the form of means, standard deviation and coefficient of variation for all individual analysis (Nielson, 2003). Both regression analysis including linear regression, correlation coefficients and test among sample populations of analysis were conducted by Dr. Andrew Schaffner, Cal Poly Statistics Department. To assess differences in soil chemistry associated with soil types a third order mixed effects analysis variance model was used with soil type (Calodo or Zaca) and sample source (grape must or wine) as fixed effects and year (2004, 2005, 2006) as a random effect. When comparing soil compositions across the soil types a second order mixed effects analysis of variance was used with soil type (Calodo or Zaca) as fixed effects and year (2004 or 2007) as a mixed effect. RESULTS and DISCUSSION Results from the grape must and the pre-barrel wine samples Grape must and the pre-barrel wine samples were obtained over three harvest years, 2004, 2005 and 2006. Statistical analyses demonstrate significantly higher levels of calcium (Ca), potassium

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(K), pH, and reducing sugars in the grape must and wine samples collected from the Calodo clay loam as compared with those from the Zaca clay. Ammonia levels were higher in the wine samples grown on the Zaca clay. In 2004, color intensity measurements of the must and pre-barrel wine samples were higher for the Zaca clay samples.

Calcium (Ca) statistical analyses

Must WineYear Zaca Calodo Zaca Calodo

2004 Mean 172.60 173.60 82.20 86.80SD 7.47 13.26 7.36 12.93

2005 Mean 122.20 128.20 77.00 79.20SD 9.36 12.36 3.54 5.81

2006 Mean 112.00 114.00 69.80 78.20SD 5.10 9.27 3.90 1.92

Figure Ca.

Mean calcium (mg/L) levels in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (F1,48=33.81, p=0.028) (Figure Ca). In addition, the magnitude of the differences between the mean calcium (mg/L) levels in the grape must and wine varied significantly over the course of the three year study (F2,2=101.97, p=0.010).

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Potassium (K) statistical analyses

Must WineYear Zaca Calodo Zaca Calodo

2004 Mean 1068.4 1341.8 584.0 868.2SD 53.6 47.7 112.6 247.4

2005 Mean 1477.8 1630.8 686.6 888.6SD 223.1 227.8 301.3 79.2

2006 Mean 1843.6 1961.8 1198.6 1420.8SD 185.4 152.2 172.5 254.3

Figure K.

Mean potassium (mg/L) levels in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (F1,48=35.51, p=0.027). (Figure K). In addition, the magnitude of the differences between the mean potassium (mg/L) levels in the grape must and wine varied significantly over the course of the three year study (F2,2=38.25, p=0.025).

pH statistical analyses

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Must WineYear Zaca Calodo Zaca Calodo

2004 Mean 3.2264 3.5050 3.1110 3.4410SD 0.0566 0.1143 0.0249 0.1391

2005 Mean 3.4560 3.5300 3.3520 3.4780SD 0.0623 0.0745 0.0981 0.1519

2006 Mean 3.4500 3.5420 3.4840 3.6080SD 0.0339 0.0683 0.0503 0.1184

Mean pH levels in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) and the magnitude of these differences varied over the course of the study (F2,2=413.72, p=0.002) and depended on whether or not the sample was wine or must (F1,2=47.21, p=0.021). (Figure pH).

Reducing Sugars statistical analyses

Must WineYear Zaca Calodo Zaca Calodo

2004 Mean 25.88 30.27 0.17 0.56SD 1.74 2.13 0.07 0.31

2005 Mean 23.41 25.21 0.47 1.10

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SD 1.00 1.11 0.15 1.01

2006 Mean 21.90 24.16 0.37 0.73SD 1.66 0.70 0.23 0.56

Figure RS.

There is significant evidence (F(1,48)=18.79, p=0.049) that mean reducing sugar differs between the top and bottom slope.

Reducing sugars (g/100 ml) in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (F1,48=18.79, p=0.049) (Figure RS).

Grape and Wine Color (as Absorbance at 520) statistical analyses

Must WineYear Zaca Calodo Zaca Calodo

2004 Mean 0.1797 0.1144 0.6578 0.7135SD 0.0756 0.0399 0.1153 0.1630

2005 Mean 0.1876 0.1186 0.6384 0.5462SD 0.0919 0.0183 0.1589 0.2148

2006 Mean 0.0502 0.0466 0.4468 0.3498SD 0.0025 0.0025 0.0495 0.0971

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Figure Ab520. Mean grape must and wine color (measured as Absorbance at 520) in the grape must and wine samples collected from the Calodo clay loam (top) were significantly lower when compared with those from the Zaca clay (bottom) (F1,48=35.82, p=0.027) (Figure Ab520).

Ammonia (NH3) statistical analyses

Must WineYear Zaca Calodo Zaca Calodo

2004 Mean 64.40 40.00 1.40 1.00SD 15.32 3.39 0.55 0.00

2005 Mean 57.20 32.80 1.00 1.00SD 9.47 6.06 0.71 0.00

2006 Mean 100.20 77.20 27.00 16.80SD 19.38 11.71 6.78 2.39

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Figure Am. Mean ammonia (mg/L) in the grape must and wine samples collected from the Calodo clay loam (top) were significantly lower when compared with those from the Zaca clay (bottom) (F1,48=102.93, p=0.010). The magnitude of the differences varied significantly across the course of the study and between grape must and wine (F2,48=5.06, p=0.010) (Figure Am).

Results from Soils Data

NOTE: Show some relevant graphs and data that show soil differences between Calodo clay loam (top) and Zaca clay (bottom). All of the soil plots (shown below) have bars that represent one SE. The captions for these plots should state this.

1.) Soil water holding capacity (WHC)

Because of the nature of these WHC measurements, no formal statistical analysis could be performed. Empirically, the WHC in the Zaca clay (bottom) was found to be higher than the Calodo clay loam (top).

Year Zaca Calodo

2004 Mean 17.1 8.5SD 0.0 0.0

2007 Mean 17.1 8.5SD 0.0 0.0

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Figure soil WHC: 2.) Soil potassium (K)

Mean potassium in the Calodo clay loam (top) was found to be significantly higher when compared with Zaca clay (bottom) and the magnitude of this difference varied by year (F1,16=21.32, p<0.0005).

Figure soil K:

Year Zaca Calodo

2004 Mean 93.6 144.2SD 9.2 58.2

2007 Mean 182.0 445.0

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SD 22.2 81.4

3.) Soil copper (Cu)

Mean copper in the Calodo clay loam (top) was found to be marginally significantly higher when compared with Zaca clay (bottom) (F1,1=112.89, p<0.060). Copper was also marginally significantly higher in 2007 compared to 2004 (F1,1=101.25, p<0.063).

Figure soil Cu:

Year Zaca Calodo

2004 Mean 1.760 4.840SD 1.144 0.503

2007 Mean 4.660 8.380SD 0.358 0.259

4.) Soil calcium (Ca)

Mean soil calcium was not significantly different between the soil types or across the years (F1,1=0.52, p=0.602 and F1,1=1.17, p=0.475).

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Figure soil Ca:

Year Zaca Calodo

2004 Mean 6547 5405SD 437 1034

2007 Mean 8203 6422SD 604 121

5.) Soil Zn

Mean zinc in the Calodo clay loam (top) was found to be significantly higher when compared with Zaca clay (bottom) the magnitude of the difference varies by soil type (F1,16=14.01, p=0.002).

Year Zaca Calodo

2004 Mean 0.560 1.180SD 0.230 0.409

2007 Mean 1.500 4.060SD 0.274 1.024

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slope20072004

topbottomtopbottom

5

4

3

2

1

0

Zinc

bottomtop

slope

Figure soil Zn.

NOTE: Most of the soil chemical property values (pH, ECe, CEC, Mg, Na, NO3-N, PO4-P, S, Mn, Fe, Cu) are not significantly different (p>0.05) between Calodo and Zaca soils. For example, the soils data for Fe and S are shown below.

1.) Soil iron (Fe)

Figure soil Fe.

Year Zaca Calodo

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2004 Mean 3.400 3.400SD 1.517 0.548

2007 Mean 7.400 8.600SD 1.517 0.894

2.) Soil sulfur (S)

Figure soil S.

Year Zaca Calodo

2004 Mean 63.60 49.40SD 3.65 9.76

2007 Mean 18.60 17.00SD 6.47 9.17

Summary discussion of soils data results 1.) Main differences between the two soil areas over the complete period of this study (2004 to 2007): Soil water holding capacity (WHC), K, Ca, Cu, and Zn. The soil AWHC is higher in the Zaca clay (bottom). The K and Zn levels are higher in the Calodo clay loam (bottom). The higher AWHC in the Zaca clay means that the grapes also have higher water contents and lower reducing sugar levels at time of harvest.

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The higher K levels in the must and grapes results in lower pH levels in the grape must and wine, but has no affect on soil pH levels. This relationship between K and pH in grapes has been noted by past researchers (reference). Copper is relatively high in these Monterey Formation calcareous rocks (shale, mudstone and limestone). The shallow Calodo soils have more Cu since these calcareous rocks contribute more to Cu to this soil as compared to the deeper Zaca soils. 2.) Similarities between the two soil areas over the complete period of this study (2004 to 2007): pH, ECe, CEC, Mg, Na, NO3-N, PO4-P, S, Mn, and Fe. Due to the close proximity of these two soil areas and their common parent materials (calcareous mudstone, shale and limestone), they have many similar properties. 3.) Soil properties lower in 2007 (higher in 2004): pH, Ca (% of CEC), Na (% of CEC), and S. The reduction in soil pH is likely related to the addition of S, which is used as an organic-certified fungicide. As S dust is added to the grapevines, some S falls onto the soil surface. The added S mixes with water in the soil and produces sulfuric acid (H2SO4), resulting in a reduction in soil pH. The addition of K in foliar fertilizer would result in a relative reduction in Ca and Na in the soils. Some K from the fertilizer spray will fall onto the soil surface and be incorporated into the soil. 4.) Soil properties higher in 2007 (lower in 2004): ECe, CEC, OM, K (% of CEC), NO3-N, PO4-P, K, Mg, Ca, Zn, Mn, Fe, and Cu. 5.) Soil properties same (similar) in 2004 and 2007: Mg (% of CEC) and Ca:Mg ratio. Summary of grape and wine must data 1.) Calcium (mg/L) levels in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (Figure Ca).

2.) Potassium (mg/L) levels in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (Figure K).

3.) pH levels in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (Figure K).

4.) Reducing sugars (g/100 ml) in the grape must and wine samples collected from the Calodo clay loam (top) were significantly higher when compared with those from the Zaca clay (bottom) (Figure RS).

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5.) Grape and wine color (measured as Absorbance at 520) in the grape must and wine samples collected from the Calodo clay loam (top) were significantly different when compared with those from the Zaca clay (bottom) (Figure Ab520).

BIBLIOGRAPHY Boulton, R. B., Singleton V. L., Bisson, L. F. and R. E. Kunkee. 1999. Principles and Practices of Winemaking. Aspen Publishers, Gaithersburg, Maryland. pp. 13-60 Coombe, B.G. and P.R. Day. (eds.). 1988. Viticulture: Vol. 1 Resources. Hyde Park Press, Adelaide, So. Australia. Coombe, B.G. and P.R. Day. (eds.). 1988. Viticulture: Vol. 2 Practices. Hyde Park Press, Adelaide, So. Australia. Durham, D.L. 1968. Geologic map of the Adelaida Quadrangle, San Luis Obispo county, California. U. S. Geological Survey Map GQ-768. Miller, R.W., and D.T. Gardiner. 1998. Soils in our environment. 8th edition. Prentice Hall Publ., Englewood Cliffs, NJ. Mottana, A., R. Crespi, and G. Liborio. 1978. Guide to rocks and minerals. Simon and Schuster, Inc. New York, NY. Nielsen, S. S. Food Analysis, Third Ed. (2003) Kluwer Academic Publishers, New York, NY. pp. 51-65. Pacific and Western. 1993. Aerial photo of the Carmody McKnight (Silver Canyon) Vineyard. (PWSLO 7-4-7). Santa Barbara, CA. Renner, R. 1990. Does the secret lie in the soil? New Scientist 22:62-64. Roberts, D.R., and C.P. Stubler. 1996. Chemical analysis of the soils of the Carmody McKnight (Silver Canyon) Vineyard. Cal. Poly. Senior Project. Soil Sci. Dept. Cal. Poly., San Luis Obispo, CA. Robinson, J. (editor). 1997. The Oxford companion to wine. Oxford Univ. Press, New York, NY. Sloan, R., and W. Westerling. 1996. Order 2 soil survey of the Carmody McKnight (Silver Canyon) Vineyard. . Cal. Poly. Senior Project. Soil Sci. Dept. Cal. Poly., San Luis Obispo, CA. Soil Survey Staff. 1993. Soil survey manual. USDA Handbook No. 18. U.S. Gov. Print. Office, Washington, D.C. Soil Survey Staff. 1997. Keys to soil taxonomy, 7th ed. SMSS Technical Monograph no. 19.

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U. S. Govt. Print. Office, Washington, D.C. Tisdale, S.L., W.L. Nelson, J.D. Beaton, and J.L. Havlin. 1993. Soil fertility and fertilizers. 5th edition. Macmillan Publ. Co., New York, NY. USDA-Soil Conservation Service (USDA-SCS). 1980. Soil survey of San Luis Obispo county; Paso Robles Area; California. U. S. Govt. Print. Office, Washington, D.C. APPENDICES Add all the other raw data and statistical graphs and analyses in the Appendices.

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Soil Characteristic means and standard deviations (in italics) for all years and locations, n = 5. Zaca Calado

pH2004

8.000 7.9600.158 0.114

20077.840 7.6400.055 0.114

CEC2004

35.58 29.362.83 6.97

200744.62 36.403.45 1.04

K%2004

0.680 1.2400.110 0.351

20071.020 3.1200.045 0.466

Ca%2004

91.900 92.5002.036 3.977

200791.720 88.0001.110 1.872

Ca.Mg2004

14.66 20.367.13 10.28

200713.54 10.422.34 2.22

PO42004

14.40 13.001.52 3.94

200730.60 34.403.51 16.59

Mg2004

310.8 237.2106.9 219.1

2007378.8 386.877.5 82.0

S2004

63.60 49.403.65 9.76

200718.60 17.006.47 9.17

Mn2004

2.400 1.6000.894 0.548

200713.600 13.0001.673 3.082

Cu2004

1.760 4.8401.144 0.503

20074.660 8.3800.358 0.259

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Zaca Calado

EC2004 0.3000 0.2800

0.0707 0.0447

2007 0.5600 0.54000.0548 0.0894

OM2004 3.540 3.560

0.297 0.627

2007 4.500 5.2600.255 0.477

Mg%2004 7.08 5.96

2.11 3.84

2007 6.94 8.721.15 1.71

Na%2004 0.460 0.280

0.251 0.045

2007 0.280 0.2000.084 0.000

N032004 9.00 10.40

2.24 1.67

2007 18.60 20.204.10 12.70

K2004 93.6 144.2

9.2 58.2

2007 182.0 445.022.2 81.4

Ca2004 6547 5405

437 1034

2007 8203 6422604 121

Zn2004 0.560 1.180

0.230 0.409

2007 1.500 4.0600.274 1.024

Fe2004 3.400 3.400

1.517 0.548

2007 7.400 8.6001.517 0.894

WHC2004 17.1 8.5

0.0 0.0

2007 17.1 8.50.0 0.0

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Wine ANOVA Summaries

Analysis of Variance for RedSug, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 61.67 61.67 30.83 0.82 0.553 x Source 1 9056.01 9056.01 9056.01 236.68 0.004 Slope 1 40.23 40.23 40.23 18.79 0.049 Yr*Source 2 76.53 76.53 38.26 14.24 0.066 Yr*Slope 2 4.28 4.28 2.14 0.80 0.557 Source*Slope 1 20.92 20.92 20.92 7.79 0.108 Yr*Source*Slope 2 5.37 5.37 2.69 2.21 0.121 Error 48 58.35 58.35 1.22 Total 59 9323.36 x Not an exact F-test. S = 1.10258 R-Sq = 99.37% R-Sq(adj) = 99.23%

Analysis of Variance for Sucr, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 663177 663177 331588 1.02 0.656 x Source 1 8251019 8251019 8251019 26.07 0.036 Slope 1 913 913 913 0.00 0.966 Yr*Source 2 632970 632970 316485 0.84 0.545 Yr*Slope 2 778082 778082 389041 1.03 0.493 Source*Slope 1 1500 1500 1500 0.00 0.956 Yr*Source*Slope 2 757694 757694 378847 2.89 0.065 Error 48 6292104 6292104 131085 Total 59 17377459 x Not an exact F-test. S = 362.057 R-Sq = 63.79% R-Sq(adj) = 55.49%

Analysis of Variance for logNH3, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 5.6554 5.6554 2.8277 3.04 0.260 x Source 1 18.4577 18.4577 18.4577 19.13 0.048 Slope 1 0.2508 0.2508 0.2508 102.93 0.010 Yr*Source 2 1.9295 1.9295 0.9647 26.13 0.037 Yr*Slope 2 0.0049 0.0049 0.0024 0.07 0.938 Source*Slope 1 0.0390 0.0390 0.0390 1.06 0.412 Yr*Source*Slope 2 0.0739 0.0739 0.0369 5.06 0.010 Error 48 0.3501 0.3501 0.0073 Total 59 26.7613 x Not an exact F-test. S = 0.0854069 R-Sq = 98.69% R-Sq(adj) = 98.39%

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Analysis of Variance for AAN, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 5366.1 5366.1 2683.0 12.17 0.212 x Source 1 27093.8 27093.8 27093.8 120.34 0.008 Slope 1 252.1 252.1 252.1 1.58 0.336 Yr*Source 2 450.3 450.3 225.2 1.37 0.422 Yr*Slope 2 318.9 318.9 159.5 0.97 0.507 Source*Slope 1 1.4 1.4 1.4 0.01 0.936 Yr*Source*Slope 2 328.3 328.3 164.2 1.19 0.313 Error 48 6614.0 6614.0 137.8 Total 59 40424.9 x Not an exact F-test. S = 11.7385 R-Sq = 83.64% R-Sq(adj) = 79.89%

Analysis of Variance for Cu, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.046043 0.046043 0.023022 5.36 0.195 x Source 1 0.013802 0.013802 0.013802 3.83 0.189 Slope 1 0.003375 0.003375 0.003375 1.32 0.370 Yr*Source 2 0.007203 0.007203 0.003602 1.92 0.342 Yr*Slope 2 0.005130 0.005130 0.002565 1.37 0.422 Source*Slope 1 0.001402 0.001402 0.001402 0.75 0.478 Yr*Source*Slope 2 0.003743 0.003743 0.001872 1.52 0.229 Error 48 0.059120 0.059120 0.001232 Total 59 0.139818 x Not an exact F-test. S = 0.0350951 R-Sq = 57.72% R-Sq(adj) = 48.03%

Analysis of Variance for Fe, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.194560 0.194560 0.097280 0.55 0.636 x Source 1 0.133482 0.133482 0.133482 0.82 0.461 Slope 1 0.103335 0.103335 0.103335 6.72 0.122 Yr*Source 2 0.325173 0.325173 0.162587 86.18 0.011 Yr*Slope 2 0.030760 0.030760 0.015380 8.15 0.109 Source*Slope 1 0.007482 0.007482 0.007482 3.97 0.185 Yr*Source*Slope 2 0.003773 0.003773 0.001887 0.42 0.660 Error 48 0.216400 0.216400 0.004508 Total 59 1.014965 x Not an exact F-test. S = 0.0671441 R-Sq = 78.68% R-Sq(adj) = 73.79%

23

Analysis of Variance for K, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 4279864 4279864 2139932 17.74 0.032 x Source 1 5634696 5634696 5634696 53.69 0.018 Slope 1 654170 654170 654170 35.51 0.027 Yr*Source 2 209898 209898 104949 38.25 0.025 Yr*Slope 2 36848 36848 18424 6.71 0.130 Source*Slope 1 11179 11179 11179 4.07 0.181 Yr*Source*Slope 2 5488 5488 2744 0.08 0.926 Error 48 1719055 1719055 35814 Total 59 12551199 x Not an exact F-test. S = 189.245 R-Sq = 86.30% R-Sq(adj) = 83.16%

Analysis of Variance for Ca, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 13664.2 13664.2 6832.1 1.95 0.342 x Source 1 50866.8 50866.8 50866.8 14.37 0.063 Slope 1 244.0 244.0 244.0 33.81 0.028 Yr*Source 2 7080.0 7080.0 3540.0 101.97 0.010 Yr*Slope 2 14.4 14.4 7.2 0.21 0.828 Source*Slope 1 16.0 16.0 16.0 0.46 0.567 Yr*Source*Slope 2 69.4 69.4 34.7 0.48 0.623 Error 48 3482.0 3482.0 72.5 Total 59 75437.0 x Not an exact F-test. S = 8.51714 R-Sq = 95.38% R-Sq(adj) = 94.33%

Analysis of Variance for logAceA, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.0006628 0.0006628 0.0003314 0.27 0.784 x Source 1 0.0077976 0.0077976 0.0077976 6.75 0.122 Slope 1 0.0002993 0.0002993 0.0002993 4.95 0.156 Yr*Source 2 0.0023088 0.0023088 0.0011544 ** Yr*Slope 2 0.0001209 0.0001209 0.0000605 ** Source*Slope 1 0.0000683 0.0000683 0.0000683 ** Yr*Source*Slope 2 0.0000009 0.0000009 0.0000005 0.04 0.956 Error 48 0.0005020 0.0005020 0.0000105 Total 59 0.0117606 x Not an exact F-test. ** Denominator of F-test is zero. S = 0.00323393 R-Sq = 95.73% R-Sq(adj) = 94.75%

24

Analysis of Variance for AceA, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.0042843 0.0042843 0.0021421 0.30 0.769 x Source 1 0.0446628 0.0446628 0.0446628 6.48 0.126 Slope 1 0.0016960 0.0016960 0.0016960 5.31 0.148 Yr*Source 2 0.0137766 0.0137766 0.0068883 3791.73 0.000 Yr*Slope 2 0.0006390 0.0006390 0.0003195 175.88 0.006 Source*Slope 1 0.0004004 0.0004004 0.0004004 220.41 0.005 Yr*Source*Slope 2 0.0000036 0.0000036 0.0000018 0.03 0.971 Error 48 0.0029828 0.0029828 0.0000621 Total 59 0.0684457 x Not an exact F-test. S = 0.00788300 R-Sq = 95.64% R-Sq(adj) = 94.64%

Analysis of Variance for MalA, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.007718 0.007718 0.003859 0.74 0.549 x Source 1 0.000968 0.000968 0.000968 0.56 0.532 Slope 1 0.003856 0.003856 0.003856 0.99 0.424 Yr*Source 2 0.003459 0.003459 0.001729 4.46 0.183 Yr*Slope 2 0.007779 0.007779 0.003890 10.03 0.091 Source*Slope 1 0.000075 0.000075 0.000075 0.19 0.703 Yr*Source*Slope 2 0.000775 0.000775 0.000388 0.37 0.694 Error 48 0.050456 0.050456 0.001051 Total 59 0.075085 x Not an exact F-test. S = 0.0324216 R-Sq = 32.80% R-Sq(adj) = 17.40%

Analysis of Variance for TarA, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 1.01312 1.01312 0.50656 3.95 0.140 x Source 1 1.83400 1.83400 1.83400 18.71 0.050 Slope 1 0.03156 0.03156 0.03156 1.02 0.419 Yr*Source 2 0.19609 0.19609 0.09805 122.66 0.008 Yr*Slope 2 0.06202 0.06202 0.03101 38.80 0.025 Source*Slope 1 0.00015 0.00015 0.00015 0.18 0.710 Yr*Source*Slope 2 0.00160 0.00160 0.00080 0.09 0.913 Error 48 0.42077 0.42077 0.00877 Total 59 3.55931 x Not an exact F-test. S = 0.0936277 R-Sq = 88.18% R-Sq(adj) = 85.47%

25

Analysis of Variance for logTotA, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.261841 0.261841 0.130920 2.26 0.240 x Source 1 0.330042 0.330042 0.330042 8.02 0.105 Slope 1 0.002208 0.002208 0.002208 0.12 0.760 Yr*Source 2 0.082323 0.082323 0.041162 33.64 0.029 Yr*Slope 2 0.035979 0.035979 0.017990 14.70 0.064 Source*Slope 1 0.000459 0.000459 0.000459 0.38 0.602 Yr*Source*Slope 2 0.002447 0.002447 0.001224 0.39 0.677 Error 48 0.149438 0.149438 0.003113 Total 59 0.864738 x Not an exact F-test. S = 0.0557970 R-Sq = 82.72% R-Sq(adj) = 78.76%

Analysis of Variance for pH, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.415186 0.415186 0.207593 2.14 0.247 x Source 1 0.023089 0.023089 0.023089 0.77 0.473 Slope 1 0.437419 0.437419 0.437419 6.53 0.125 Yr*Source 2 0.060062 0.060062 0.030031 185.59 0.005 Yr*Slope 2 0.133894 0.133894 0.066947 413.72 0.002 Source*Slope 1 0.007639 0.007639 0.007639 47.21 0.021 Yr*Source*Slope 2 0.000324 0.000324 0.000162 0.02 0.981 Error 48 0.402879 0.402879 0.008393 Total 59 1.480491 x Not an exact F-test. S = 0.0916150 R-Sq = 72.79% R-Sq(adj) = 66.55%

Analysis of Variance for TitA, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.213502 0.213502 0.106751 2.42 0.284 x Source 1 0.492411 0.492411 0.492411 11.50 0.077 Slope 1 0.010231 0.010231 0.010231 4.47 0.169 Yr*Source 2 0.085624 0.085624 0.042812 45.02 0.022 Yr*Slope 2 0.004573 0.004573 0.002286 2.40 0.294 Source*Slope 1 0.009413 0.009413 0.009413 9.90 0.088 Yr*Source*Slope 2 0.001902 0.001902 0.000951 0.26 0.773 Error 48 0.176187 0.176187 0.003671 Total 59 0.993842 x Not an exact F-test. S = 0.0605851 R-Sq = 82.27% R-Sq(adj) = 78.21%

26

Analysis of Variance for logCol420, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 0.79847 0.79847 0.39923 5.46 0.108 x Source 1 5.00669 5.00669 5.00669 86.53 0.011 Slope 1 0.00223 0.00223 0.00223 0.11 0.774 Yr*Source 2 0.11572 0.11572 0.05786 10.68 0.086 Yr*Slope 2 0.04145 0.04145 0.02072 3.83 0.207 Source*Slope 1 0.00034 0.00034 0.00034 0.06 0.825 Yr*Source*Slope 2 0.01083 0.01083 0.00542 0.59 0.556 Error 48 0.43708 0.43708 0.00911 Total 59 6.41280 x Not an exact F-test. S = 0.0954240 R-Sq = 93.18% R-Sq(adj) = 91.62%

Analysis of Variance for logCol520, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Yr 2 1.43898 1.43898 0.71949 7.66 0.209 x Source 1 8.13662 8.13662 8.13662 68.74 0.014 Slope 1 0.12199 0.12199 0.12199 35.82 0.027 Yr*Source 2 0.23674 0.23674 0.11837 4.24 0.191 Yr*Slope 2 0.00681 0.00681 0.00341 0.12 0.891 Source*Slope 1 0.01680 0.01680 0.01680 0.60 0.519 Yr*Source*Slope 2 0.05578 0.05578 0.02789 1.70 0.194 Error 48 0.78800 0.78800 0.01642 Total 59 10.80172 x Not an exact F-test. S = 0.128127 R-Sq = 92.70% R-Sq(adj) = 91.03%

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Soil ANOVA Summaries

Analysis of Variance for pH, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 0.07200 0.07200 0.07200 2.25 0.374 year 1 0.28800 0.28800 0.28800 9.00 0.205 slope*year 1 0.03200 0.03200 0.03200 2.37 0.143 Error 16 0.21600 0.21600 0.01350 Total 19 0.60800 S = 0.116190 R-Sq = 64.47% R-Sq(adj) = 57.81%

Analysis of Variance for EC, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 0.00200 0.00200 0.00200 ** year 1 0.33800 0.33800 0.33800 ** slope*year 1 0.00000 0.00000 0.00000 0.00 1.000 Error 16 0.07200 0.07200 0.00450 Total 19 0.41200 ** Denominator of F-test is zero. S = 0.0670820 R-Sq = 82.52% R-Sq(adj) = 79.25%

Analysis of Variance for CEC, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 260.64 260.64 260.64 52.13 0.088 year 1 323.21 323.21 323.21 64.64 0.079 slope*year 1 5.00 5.00 5.00 0.29 0.599 Error 16 278.03 278.03 17.38 Total 19 866.88 S = 4.16854 R-Sq = 67.93% R-Sq(adj) = 61.91%

Analysis of Variance for OM, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 0.7605 0.7605 0.7605 1.11 0.483 year 1 8.8445 8.8445 8.8445 12.92 0.173 slope*year 1 0.6845 0.6845 0.6845 3.54 0.078 Error 16 3.0960 3.0960 0.1935 Total 19 13.3855 S = 0.439886 R-Sq = 76.87% R-Sq(adj) = 72.53%

Analysis of Variance for K%, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 8.8445 8.8445 8.8445 2.98 0.334 year 1 6.1605 6.1605 6.1605 2.08 0.386 slope*year 1 2.9645 2.9645 2.9645 33.50 0.000 Error 16 1.4160 1.4160 0.0885 Total 19 19.3855

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S = 0.297489 R-Sq = 92.70% R-Sq(adj) = 91.33%

Analysis of Variance for Mg%, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 0.544 0.545 0.545 0.05 0.858 year 1 8.581 8.581 8.581 0.82 0.532 slope*year 1 10.513 10.513 10.513 1.79 0.199 Error 16 93.740 93.740 5.859 Total 19 113.378 S = 2.42049 R-Sq = 17.32% R-Sq(adj) = 1.82%

Analysis of Variance for Ca%, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 12.168 12.168 12.168 0.52 0.602 year 1 27.378 27.378 27.378 1.17 0.475 slope*year 1 23.328 23.328 23.328 3.78 0.070 Error 16 98.808 98.808 6.176 Total 19 161.682 S = 2.48506 R-Sq = 38.89% R-Sq(adj) = 27.43%

Analysis of Variance for Na%, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 0.08450 0.08450 0.08450 6.76 0.234 year 1 0.08450 0.08450 0.08450 6.76 0.234 slope*year 1 0.01250 0.01250 0.01250 0.69 0.417 Error 16 0.28800 0.28800 0.01800 Total 19 0.46950 S = 0.134164 R-Sq = 38.66% R-Sq(adj) = 27.16%

Analysis of Variance for Ca.Mg, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 8.32 8.32 8.32 0.09 0.819 year 1 152.90 152.90 152.90 1.57 0.429 slope*year 1 97.24 97.24 97.24 2.33 0.147 Error 16 668.20 668.20 41.76 Total 19 926.67 S = 6.46241 R-Sq = 27.89% R-Sq(adj) = 14.37%

Analysis of Variance for N03, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 11.25 11.25 11.25 225.00 0.042 year 1 470.45 470.45 470.45 9409.00 0.007 slope*year 1 0.05 0.05 0.05 0.00 0.974 Error 16 743.20 743.20 46.45 Total 19 1224.95

29

S = 6.81542 R-Sq = 39.33% R-Sq(adj) = 27.95%

Analysis of Variance for PO4, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 7.20 7.20 7.20 0.21 0.725 year 1 1767.20 1767.20 1767.20 52.28 0.087 slope*year 1 33.80 33.80 33.80 0.44 0.515 Error 16 1221.60 1221.60 76.35 Total 19 3029.80 S = 8.73785 R-Sq = 59.68% R-Sq(adj) = 52.12%

Analysis of Variance for K, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 122931 122931 122931 2.18 0.379 year 1 189346 189346 189346 3.36 0.318 slope*year 1 56392 56392 56392 21.32 0.000 Error 16 42326 42326 2645 Total 19 410995 S = 51.4332 R-Sq = 89.70% R-Sq(adj) = 87.77%

Analysis of Variance for Mg, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 5379 5379 5379 0.65 0.569 year 1 59187 59187 59187 7.11 0.228 slope*year 1 8323 8323 8323 0.46 0.507 Error 16 288745 288745 18047 Total 19 361635 S = 134.338 R-Sq = 20.16% R-Sq(adj) = 5.18%

Analysis of Variance for Ca, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 10676988 10676988 10676988 20.92 0.137 year 1 8931161 8931161 8931161 17.50 0.149 slope*year 1 510401 510401 510401 1.24 0.281 Error 16 6562202 6562202 410138 Total 19 26680753 S = 640.420 R-Sq = 75.40% R-Sq(adj) = 70.79%

Analysis of Variance for S, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 312.1 312.1 312.1 1.57 0.429 year 1 7488.5 7488.5 7488.5 37.73 0.103 slope*year 1 198.5 198.5 198.5 3.39 0.084 Error 16 937.6 937.6 58.6 Total 19 8936.6

30

S = 7.65506 R-Sq = 89.51% R-Sq(adj) = 87.54%

Analysis of Variance for Zn, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 12.640 12.641 12.641 2.69 0.349 year 1 18.240 18.240 18.240 3.88 0.299 slope*year 1 4.704 4.704 4.704 14.01 0.002 Error 16 5.372 5.372 0.336 Total 19 40.957 S = 0.579439 R-Sq = 86.88% R-Sq(adj) = 84.42%

Analysis of Variance for Mn, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 2.45 2.45 2.45 49.00 0.090 year 1 638.45 638.45 638.45 12769.00 0.006 slope*year 1 0.05 0.05 0.05 0.01 0.904 Error 16 53.60 53.60 3.35 Total 19 694.55 S = 1.83030 R-Sq = 92.28% R-Sq(adj) = 90.84%

Analysis of Variance for Fe, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 1.800 1.800 1.800 1.00 0.500 year 1 105.800 105.800 105.800 58.78 0.083 slope*year 1 1.800 1.800 1.800 1.26 0.278 Error 16 22.800 22.800 1.425 Total 19 132.200 S = 1.19373 R-Sq = 82.75% R-Sq(adj) = 79.52%

Analysis of Variance for Cu, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 57.800 57.800 57.800 112.89 0.060 year 1 51.842 51.842 51.842 101.25 0.063 slope*year 1 0.512 0.512 0.512 1.17 0.296 Error 16 7.024 7.024 0.439 Total 19 117.178 S = 0.662571 R-Sq = 94.01% R-Sq(adj) = 92.88%

Analysis of Variance for WHC, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P slope 1 369.80 369.80 369.80 ** year 1 0.00 0.00 0.00 ** slope*year 1 0.00 0.00 0.00 ** Error 16 0.00 0.00 0.00 Total 19 369.80 ** Denominator of F-test is zero.

31

S = 1.166018E-15 R-Sq = 100.00% R-Sq(adj) = 100.00%