Impact of annual weather fluctuations on output, quality and profits of wine producers in GermanyAAWE Meeting - Mendoza 2015

Britta Niklas

Impact of annual weather fluctuations on output, quality and profits of wine producers in GermanyAAWE Meeting - Mendoza 2015

Britta Niklas

3

1. Introduction

2. Literature Review

3. Theory and model applied

4. Data

5. Fixed effects Regression and results

6. Estimation of weather impacts

7. Next steps

Mendoza 2015

Impact of annual weather fluctuations on output, quality and profits of wine producers in Germany

4Mendoza 2015

13 German Wine Regions

Land: ca. 102 000 ha (2013)

No. of producers: 18700 (2010) with more than 5 ha land

Yield: (2013) 8,3 million hectoliters

Export: 3,9 million hectoliters

Share white/red/rosé (2013):59,6% / 30,2% / 10.,2%

Important grapes:Riesling: 22,7%, Mueller-Thurgau: 12,6%,Grauburgunder: 5,2%, Silvaner: 5,0%

1. Introduction – Wine Production in Germany

5Mendoza 2015

- German Wines categorized by degree of ripeness, measured in natural grape sugar upon harvest (degree Oechsle/Brix).- The higher the sugar content of the grapes used for the wine, the higher up the wine will be categorized.

2013

Quality Wine >51°Oe

Kabinett>70°Oe

Spätlese>76°Oe

Auslese>83°Oe

BA/Eisw./TBA>110/150

Rest Total

Baden 967000 82000 24000 1000 0 0 1074000

Mosel 505000 53000 56000 10000 0 1000 625000

Pfalz 1708000 71000 51000 9000 2000 0 1841000

Yield per Quality level in hl - 2013

1. Introduction – Quality labels and Oechsle degree (Brix)

6Mendoza 2015

•German wine regions are situated near the northern boundary of commercial grape growing.•Regions depend on favourable weather conditions.•Yields, quality and profits depend on weather and vary widely from year to year.

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AhrBadenFrankenHess-BergMittelrheinMoselNahePfalzRheingauRheinhessenSaale-UnstrutSachsenWürttemberg

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ha

1. Introduction – Yields, quality and profits

7Mendoza 2015

1. Introduction – Yields, quality and profits

•German wine regions are situated near the northern boundary of commercial grape growing.•Regions depend on favourable weather conditions.•Yields, quality and profits depend on weather and vary widely from year to year.

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OechsleAhrBadenFankenHess-BergMittelrheinMoselNahePfalzRheingauRheinhessenSaale-UnstrutSachsenWürttemberg

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1. Introduction – Yields, quality and profits

8Mendoza 2015

81

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4(m

ean

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ture

1995 2000 2005 2010 2015years

Ahr BadenFranken Hess-BergMittelrhein MoselNahe Pfalz

Rheingau RheinhessenSaale-Unstrut SachsenWürttemberg

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Ahr BadenFranken Hess-BergMittelrhein MoselNahe Pfalz

Rheingau RheinhessenSaale-Unstrut SachsenWürttemberg

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1995 2000 2005 2010 2015year

Ahr BadenFranken Hess-BergMittelrhein MoselNahe Pfalz

Rheingau RheinhessenSaale-Unstrut SachsenWürttemberg

1. Introduction – Weather fluctuations in german wine regions

9Mendoza 2015

Weather and Yield:Adams et al.(2003) and Lobell et al.(2006):

Results differ for California – increase of yields/stable yields

Weather and Quality:Jones et al.(2005) and Storchmann(2005) and Alston et al.(2011):

Rising temperatures lead to better quality in Germany and higher sugar levels (Brix/Oechsle) in California

Weather and Profits: almost no studies that analyze profits as a function of climate variables Webb(2006) and Ashenfelter/Storchmann(2010) and Antoy et al.(2010):

Losses for Australia/positve relationship for Mosel wines, net value added per ha in different grape growing regions of Europe.

2. Literature Review

10Mendoza 2015

Extended version of Ricardian approach is applied.(developed by Mendelsohn/Nordhaus/Shaw (1994), extended by Schlenker/Hanemann/ Fisher (2005, 2006) and Deschenes/Greenstone (2006))

Yit Yield/Oechsle/Profit/Quality/Share r/wi = 1-13 (RegionID), t = 1-20 (year)k=1-10 Weather variables W (temp, percip., sun …)β = parameters to estimate

δ = regional fixed effect, to absorb unobserved region-specific time invariant heterogenityu = idiosyncretic error term

Model: Yit = β0+ βk Wkit + δi + uit,

3. Theory and model applied

•Fixed effects Regression:Analysis of impact of variables, that vary over time (weather)

•Assumption:Specific time-invariant characteristics of German Wine regions,which can have an impact/can bias the predictor or the outcome variables.

•Fixed effects Regression removes the effect of those time-invariant characteristics.

11Mendoza 2015

Weather Data:13 different weather stations of DWD in the 13 wine regions – daily data 1994-2013

Average temperature, temp-max, temp-min, soil-temp-min (in degree Celsius).Sum of days of frost

March – October------------------------------Sum of precipitation (in mm), hours of sun.

Winter before harvest: December to February (only for precipitation)Growing period: March – 15 SeptemberHarvest: 16 September – October

Annual quantities (hl) per quality level for 13 wine region - "Deutsche Weinstatistik ", published by "Deutsches Weininstitut “ – Years 2003 – 2013

4. Data

12Mendoza 2015

Federal Ministry of Food and Agriculture - 5 regions:

Profits in € ha/land: Years 1997 – 2010 (14 years)

Limitation: no information about subsidies …

Federal Ministry of Food and Agriculture - 13 wine regions:

Average Oechsle degree: Years 1996 – 2013 (18 years)Yields in hl/ha: Years 1994 – 2013 (20 years)Share red/white in %: Years 2003 – 2013 (11 years)

4. Data

13Mendoza 2015

BATBAEiswein 143 .1098881 .1778773 0 .9644703 Auslese 143 .9900206 1.089417 0 5.000927 Spätlese 143 5.967954 3.75231 0 18.90088 Kabinett 143 8.561725 6.827894 0 31.23324 QualityWine 143 83.93433 8.778068 59.95936 99.72299 ShareRed 143 32.20979 22.12744 8.3 88.2 ShareWhite 143 67.8042 22.1373 11.8 91.7 profithaLF 70 3656.486 1188.632 1144 7316 Oechsle 234 80.6047 5.421577 61 95 yieldhahl 260 80.49269 21.78002 15 145.3 Variable Obs Mean Std. Dev. Min Max

Descriptive Statistics of dependent variables

4. Data

14Mendoza 2015

Descriptive Statistics of exogenous variables

frost 260 28.12692 8.499105 12 52 sun_harvest 260 187.8079 47.92692 65.9 313.4 sun_growing 260 1257.005 129.5187 745.7 1602.8precip_har~t 260 79.20154 41.7142 19.1 248.5precip_gro~g 260 391.5296 123.0239 160.9 938.3precip_win~r 260 147.665 57.53587 48.6 396.8temp_soil_~n 260 6.163224 .8015668 4.150916 8.074627 temp_min 260 8.022431 .7181976 5.781752 9.513186 temp_max 260 17.71472 1.16689 13.45839 20.78461 temperature 260 12.65278 .8336075 9.409854 14.51465 Variable Obs Mean Std. Dev. Min Max

4. Data

15Mendoza 2015

Impact on yield

* p<0.05, ** p<0.01, *** p<0.001t statistics in parentheses N 260 260 260 260 (-1.70) (-0.45) (0.13) (1.55) _cons -32.99 -8.613 2.492 31.19

(-4.75) frost -0.571***

(2.66) temp_soil_min 4.274**

(-5.16) (-4.07) (-2.65) sun_growing -0.0408*** -0.0336*** -0.0225**

(-0.15) (-2.23) (-2.95) (-2.43) precip_growing -0.00157 -0.0252* -0.0345** -0.0263*

(0.31) (0.10) (0.12) (0.28) precip_winter 0.00652 0.00193 0.00238 0.00531

(5.93) (7.69) (4.26) (4.88) temperature 8.942*** 11.86*** 8.464*** 8.155*** yieldhlha yieldhlha yieldhlha yieldhlha (1) (2) (3) (4)

5. Fixed effects regression and results

Temperature 1 degree higher***:+ 8,155 hl/ha yield

Precep. Growing 1 mm more*:- 0.026 hl/ha yield

Sun Growing 1 hour more**:- 0.0225 hl/ha yield

1 days of frost more***:- 0,571 hl/ha yield

16Mendoza 2015

Impact on Oechsle degree

* p<0.05, ** p<0.01, *** p<0.001t statistics in parentheses N 234 234 234 234 (6.96) (5.71) (4.80) (3.34) _cons 43.40*** 32.51*** 25.10*** 19.92***

(5.01) frost 0.181***

(-7.22) temp_soil_min -3.496***

(7.83) (5.45) (4.91) sun_growing 0.0184*** 0.0124*** 0.0124***

(-5.31) (-2.47) (-0.73) (-2.52) precip_growing -0.0192*** -0.00858* -0.00236 -0.00831*

(0.03) (0.31) (-0.97) (0.25) precip_winter 0.000269 0.00224 -0.00639 0.00171

(7.32) (4.82) (8.84) (6.85) temperature 3.525*** 2.207*** 4.984*** 3.392*** Oechsle Oechsle Oechsle Oechsle (1) (2) (3) (4)

5. Fixed effects regression and results

Temperature 1 degree higher***:+ 3,392 Oechsle degree

Precep. Growing 1 mm more*:- 0.00831 Oechsle degree

Sun Growing 1 hour more***:+ 0.0124 Oechsle degree

1 days of frost more***:+ 0,181 Oechsle degree

17Mendoza 2015

Impact on Profits

* p<0.05, ** p<0.01, *** p<0.001t statistics in parentheses N 70 70 70 70 (0.98) (0.86) (0.39) (0.37) _cons 3697.7 3268.5 1450.4 1513.3

(1.15) frost 20.84

(-2.66) temp_soil_min -621.4**

(-1.12) (-2.30) (-1.61) sun_growing -1.282 -2.844* -2.432

(-0.16) (-0.48) (0.05) (-0.50) precip_growing -0.244 -0.759 0.0835 -0.785

(-1.09) (-1.16) (-0.99) (-1.10) precip_winter -3.541 -3.792 -3.095 -3.590

(0.15) (0.69) (2.11) (1.16) temperature 43.23 226.6 811.2* 438.8 profithaLF profithaLF profithaLF profithaLF (1) (2) (3) (4)

5. Fixed effects regression and results

Temperature 1 degree higher*:+ 811,22 €/ha profit

Sun Growing 1 hour more*:- 2,84 €/ha profit

1 degree minimum soil temperature more**:- 621,36 €/ha profit

… of course correlation of average air temp. And soil temp., by 0,4063 …

18Mendoza 2015

Impact on Profits – with Trend

5. Fixed effects regression and results

.

F test that all u_i=0: F(4, 59) = 17.75 Prob > F = 0.0000 rho .67814871 (fraction of variance due to u_i) sigma_e 816.33777 sigma_u 1184.9628 _cons 238.1412 3789.051 0.06 0.950 -7343.732 7820.015 Trend 41.80943 33.76606 1.24 0.221 -25.75631 109.3752 temp_soil_min -445.9397 272.2162 -1.64 0.107 -990.6431 98.76361 sun_growing -3.003967 1.237417 -2.43 0.018 -5.480032 -.5279022precip_growing -1.095192 1.794528 -0.61 0.544 -4.686035 2.495651 precip_winter -2.044841 3.218223 -0.64 0.528 -8.484491 4.394808 temperature 817.1904 382.3096 2.14 0.037 52.19069 1582.19 profithaLF Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.5329 Prob > F = 0.0884 F(6,59) = 1.94

overall = 0.0192 max = 14 between = 0.0002 avg = 14.0R-sq: within = 0.1651 Obs per group: min = 14

Group variable: RegionID Number of groups = 5Fixed-effects (within) regression Number of obs = 70

19Mendoza 2015

Impact on Share red/white

* p<0.05, ** p<0.01, *** p<0.001t statistics in parentheses N 143 143 (6.74) (19.16) _cons 26.52*** 73.96***

(1.22) (-1.32) sun_harvest 0.00436 -0.00460

(-1.73) (1.78) sun_growing -0.00256 0.00259

(2.86) (-3.06) precip_growing 0.00505** -0.00530**

(-0.71) (0.66) precip_winter -0.00269 0.00243

(-1.30) (1.31) temp_max -0.618 0.614

(2.17) (-2.24) temperature 1.392* -1.412* ShareRed ShareWhite (1) (2)

Temperature 1 degree higher*:Share of Red wine+ 1,392%Share of White wine- 1,412%(rosé was not included in the analysis)

Precipitation growing one 1 mm more**:Share of Red wine+ 0,00505%Share of White wine- 0,0053%

5. Fixed effects regression and results

20Mendoza 2015

Impact on Quality

* p<0.05, ** p<0.01, *** p<0.001t statistics in parentheses N 143 143 143 143 143 (6.56) (1.27) (3.19) (0.01) (-2.74) _cons 74.05*** 6.474 17.14** 0.0258 -0.764**

(0.78) (0.42) (-0.51) (-2.29) (-0.66) frost 0.0420 0.0103 -0.0131 -0.0254* -0.000889

(2.52) (-1.42) (-1.88) (-2.29) (-1.22) sun_harvest 0.0232* -0.00593 -0.00823 -0.00430* -0.000278

(-2.22) (0.28) (0.44) (2.70) (-0.96) sun_growing -0.00945* 0.000547 0.000899 0.00234** -0.000101

(1.17) (0.71) (-2.39) (-0.00) (-0.43) precip_growing 0.00529 0.00144 -0.00513* -6.24e-08 -0.0000479

(-0.64) (0.95) (-0.35) (1.04) (-0.12) precip_winter -0.00615 0.00415 -0.00163 0.00204 -0.0000277

(1.25) (0.21) (-1.41) (-0.28) (3.75) temperature 1.179 0.0902 -0.635 -0.0549 0.0877*** QualityWine Kabinett Spätlese Auslese BATBAEiswein (1) (2) (3) (4) (5)

Difficult to interpret, as changes are caused by movements from both directions …

5. Fixed effects regression and results

21Mendoza 2015

Climate Change Estimation: average temperature: +2 degrees

Average yields: + 20,3%Average Oechsle: + 6,8 degree (8,4%)Average profit: + 44,4%

Additional assumptions: +1 degree min soil_temp., -5 days of frost gr., +40 mm precip. winter, -40 mm precip. gr., +40 hours sun. gr.Effect on average yields: +27 % (total model) and + 24% (significant model)Effect on Oechsel degrees: +6,8 degree (total) and +6,7 degree (significant)Effect on average profits: + 20,8 % (total) and + 24% (significant)

Slight shift to red varietals is assumed …

6. Estimation of weather impacts

22Mendoza 2015

•Include interactions in the analysis

•Get price data, turnover data etc.

•Find out if only one weather stations leads to similar results

•Act on any suggestion/recommendation you might have

7. Next steps

23Mendoza 2015

Thank you for listening!!!