Impact of annual weather fluctuations on output, quality and profits of wine producers in Germany AAWE Meeting - Mendoza 2015 Britta Niklas

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

4 Mendoza German Wine Regions Land: ca ha (2013) No. of producers: (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

5 Mendoza 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 Quality Wine >51°Oe Kabinett >70°Oe Spätlese >76°Oe Auslese >83°Oe BA/Eisw./ TBA >110/150 RestTotal Baden Mosel Pfalz Yield per Quality level in hl Introduction – Quality labels and Oechsle degree (Brix)

6 Mendoza 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. 1.Introduction – Yields, quality and profits

7 Mendoza 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. 1.Introduction – Yields, quality and profits

8 Mendoza Introduction – Weather fluctuations in german wine regions

9 Mendoza 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

10 Mendoza 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)) Y it Yield/Oechsle/Profit/Quality/Share r/w i = 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 heterogenity u = idiosyncretic error term Model: Y it = β 0 + β k W kit + δ i + u it, 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.

11 Mendoza 2015 Weather Data: 13 different weather stations of DWD in the 13 wine regions – daily data 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 September Harvest:16 September – October Annual quantities (hl) per quality level for 13 wine region - "Deutsche Weinstatistik ", published by "Deutsches Weininstitut “ – Years 2003 – Data

12 Mendoza 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

13 Mendoza 2015 Descriptive Statistics of dependent variables 4.Data

14 Mendoza 2015 Descriptive Statistics of exogenous variables 4.Data

15 Mendoza 2015 Impact on yield 5.Fixed effects regression and results Temperature 1 degree higher***: + 8,155 hl/ha yield Precep. Growing 1 mm more*: hl/ha yield Sun Growing 1 hour more**: hl/ha yield 1 days of frost more***: - 0,571 hl/ha yield

16 Mendoza 2015 Impact on Oechsle degree 5.Fixed effects regression and results Temperature 1 degree higher***: + 3,392 Oechsle degree Precep. Growing 1 mm more*: Oechsle degree Sun Growing 1 hour more***: Oechsle degree 1 days of frost more***: + 0,181 Oechsle degree

17 Mendoza 2015 Impact on Profits 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 …

18 Mendoza 2015 Impact on Profits – with Trend 5.Fixed effects regression and results

19 Mendoza 2015 Impact on Share red/white 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

20 Mendoza 2015 Impact on Quality Difficult to interpret, as changes are caused by movements from both directions … 5.Fixed effects regression and results

21 Mendoza 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

22 Mendoza 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

23 Mendoza 2015 Thank you for listening!!!