Regional diversity of grape varieties is important for climate change

There was a recent paper published in the Proceedings of the National Academy of Sciences of the USA that discussed the possible effects of global climate change on the grape-growing regions of the world. The wine media focussed almost entirely on one aspect of that paper: the expected reduction in suitability of many of the current prime locations (eg. Wine regions wiped out by projected climate change, disaster for some).

However, this was not actually the main point of that paper, as is indicated by its title:

Thus, the paper makes the important point that genetic diversity of grape varieties within each grape-growing region is the key to resisting climate change. This is a basic principle of biology, that genetic variation is a Good Thing, because even if one genotype is affected by a change in conditions, others will probably not be. (For a discussion of genetics and grape varieties, see Grape clones and varieties are not always what they seem.)

The idea can be made clear by thinking about a family. One way of ensuring the long-term continuation of the family is to have lots of children, on the principle that at least some of them will survive. However, this is true only up to a point. If the children all have very similar genes (as they probably will if they all have the same two parents), then they will probably all be affected by the same sorts of problems, such as lack of resistance to a particular disease. This limitation can be addressed by having children who have lots of different genes (ie. different genotypes) — it will then take lots of different problems to affect all of them.

Biologists have long understood this principle of genetic diversity, because it applies throughout the biological world, including agriculture — putting all of your eggs in one basket is not a good long-term idea. Farmers know this perfectly well, and it was the purpose of the PNAS paper to analyze the grape-growing situation in detail, to make some predictions about what might be done in response to climate change. The bit about reduction in the suitability of many current areas was simply scene-setting. These predictions not all bad, of course — New Zealand and Germany were identified as places expected not only to survive but to gain in output, for example.

So, if grape-growers are willing to continue doing what their predecessors did, which was to grow whatever varieties did best in each local area (see Palaeogenomic insights into the origins of French grapevine diversity), then many (if not most) vineyards could continue to exist, even under quite serious effects of climate change. However, some areas will no longer be suitable for any of the currently known varieties, and may require us to develop new late-ripening varieties for the hottest areas. Also, many unplanted areas will become suitable for an increasingly broad range of the currently known varieties (see last week’s post on Climate change and the most northerly vineyards in Europe).

Genetic diversity is an important issue, because observers have sometimes expressed concern about the diversity of wine-grapes narrowing down to a few so-called “international” varieties, often presented as mono-varietal wines. That is, trying to maximize economic returns has resulted in growers specializing in a few varieties that they believe will sell well when turned into wine. So, the recent trend has been to reduce regional genetic diversity, not maintain or increase it, because the economic pressures contradict this idea. Putting all of your eggs in one basket may make short-term economic sense, but not long-term biological sense.

Current varietal diversity

This leads me to wonder which grape-growing areas currently have the most (and least) diversity of grape varieties. Surprisingly, the PNAS paper does not tell me this. So, I thought that I should work it out for myself.

I have used the data from the Database of Regional, National and Global Winegrape Bearing Areas by Variety. This compendium has vineyard area data (in hectares) from 2010, for 1,446 named grape varieties in 642 defined grape-growing regions. Some of these regions are whole countries, if their wine industry is quite small; and countries with very small vineyard areas are excluded entirely. However, most of the data refer to local regions within each country.

You will, of course, never have heard of the vast majority of the varieties! There have probably been at least 10,000 of them recorded at some time in history; and many of them are not very genetically different from each other (see: How many grape clones are there per variety?).

I have put the complete results at the bottom of this post, along with an explanation of the various ways of mathematically measuring diversity. Here, I will simply summarize the main patterns in the data.

We can start with a simple count of the number of varieties (called Richness), which turns out to vary from 1 to 258, which is a lot of grape varieties for any one location — in this case, in Verona, in Italy. Indeed, there 12 regions with > 200 varieties, as shown in the graph, all of them in Italy.

Half of the regions have 15—45 varieties, which may still be more than you were expecting. On the other hand, there are 10 regions with only one recorded grape variety, 5 of them in the USA (Arizona, Arkansas, Georgia, North Carolina, Orange), 4 in China, and 1 in Turkey.

However, for our purposes we need to look at Diversity, which takes into account what is called Evenness. Evenness, and thus diversity, is at its maximum when all of the varieties are equally abundant (measured in hectares, in this case). This means an area dominated by only a few varieties is not very diverse, even if there are lots of rare varieties in the same region. This is actually the case for those regions with the largest numbers of varieties — most of those varieties are very rare, and are thus not making a big contribution to diversity.

So, let's look at the average regional diversity for the top 20 wine-producing countries of 2019 (as listed by the International Organisation of Vine and Wine). This is shown in the table, with two mathematical measures of diversity (as explained at the bottom of the post). Diversity represents the “effective” number of varieties — the amount of diversity that would exist if all of the varieties were equally abundant.

Country Italy France Spain United States Argentina Australia Chile South Africa China Germany Portugal Russia Romania Hungary New Zealand Brazil Austria Greece Georgia Switzerland

Number of regions 110 72 36 89 28 94 9 9 10 13 9 2 8 22 11 1 4 13 1 18

Richness 83.5 30.5 31.5 17 43.5 19 29 18 2 42 102 34.5 21.5 53 9 103 35.5 20 23 36

Shannon Diversity 9.91 6.71 5.19 8.04 12.92 7.00 7.29 10.36 1.30 10.45 13.78 17.25 5.60 14.63 4.26 10.78 13.59 6.88 4.87 5.56

Simpson Diversity 5.63 4.72 3.34 5.89 7.33 4.98 4.54 8.28 1.19 6.26 8.07 11.91 3.69 9.29 3.35 5.49 8.97 4.82 3.07 2.80

So, in Italy’s case there is an average of 83 grape varieties per region (median = 83.5), which seems like a lot, but most of the varieties are very rare, so there is only as much diversity as there would be for an average of 10 varieties per region if they were equally abundant (median Shannon Diversity = 9.91). A similar situation exists for Spain, Portugal, Brazil and Switzerland — the large number of varieties does not represent a lot of diversity.

On the other hand, places like the USA, South Africa and New Zealand have far fewer varieties, but those varieties are more equally abundant, so that diversity is still fairly high.

This means that, ultimately, it is Hungary that does best in terms of grape-vine diversity, at the regional level. No matter how you measure diversity, the top local regions are in Hungary (7 regions), Italy (2 regions), Czechia (1 region), and the USA (Kentucky). Obviously, in the latter case there is not a big area of grape-vines, but there are 25 recorded varieties and these are fairly evenly abundant — this is the essence of biological diversity.

At this stage, of course, we have little idea which, if any, of these grape varieties will be suitable for wine-making in any particular region. The point, though, is that we do currently, have a lot of diversity available, if we have the incentive to do something practical with it. All it takes is a willingness to move the current varieties to new regions, with a suitable climate, and use other varieties in the current regions, when they are happy there. We can also develop new varieties from the ones we have, although it does seem like it would be easier to trial the currently available varieties first.

What the wine industry and the wine-drinking public will think about all of this is another matter. Some of those Hungarian varieties have names that you may have trouble working out how to pronounce!

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Grape variety diversity

There are two components of diversity, called richness and evenness. Richness, in this case, is simply a count of the number of varieties in the region. Evenness, on the other hand, refers to how much difference there is in abundance among the varieties (measured in hectares, in this case). Diversity will be at its maximum when all of the varieties have equal abundance, and it reduces as the varietal abundances become more uneven. Obviously, varieties are never in equal abundance in any given region — indeed, most grape varieties are very rare.

So, attempts to measure true diversity take into account both richness and evenness, Sadly, there are many mathematical ways to do this. They differ in how much mathematical weight they give to the rarer varieties, varying from equal weight down to very little. Three measures of diversity are listed for each region in the table below.

If we treat all varieties as equal (weighting using the harmonic mean) then diversity = richness. If we give a fair bit of weight (weighting using the geometric mean) then we call this the Shannon Diversity. If we apply even more weight (weighting using the arithmetic mean) then we call this the Simpson Diversity.

All three measures contain relevant (but somewhat different) information; and so it is quite usual to list all three of them.

Country Algeria Argentina Armenia Australia Austria Brazil Bulgaria Canada Chile China Croatia Cyprus Czechia Ethiopia France Georgia Germany Greece Hungary Israel Italy Japan Kazakhstan Luxembourg Mexico Moldova Morocco Myanmar New Zealand Peru Portugal Romania Russia Serbia Slovakia Slovenia South Africa Spain Switzerland Taiwan Thailand Tunisia Turkey Ukraine United Kingdom United States Uruguay

Region of planting 25 de Mayo 9 de Julio Albardon Angaco Catamarca Caucete Junin La Rioja Lavalle Lujan de Cuyo Maipu Neuquen Other-Argentina Other-Mendoza Other-San Juan Pocito Rawson Rio Negro Rivadavia Salta San Martin San Martin S San Rafael Santa Rosa Sarmiento Tunuyan Tupungato Ullum Adelaide Hills Adelaide Plains Alpine Valleys Australian Capital Territory Barossa - other Barossa Valley Beechworth Bendigo Big Rivers - other Blackwood Valley Canberra District (ACT) Canberra District (NSW) Central Ranges - other Central Victoria - other Central Western Australia Clare Valley Coonawarra Cowra Currency Creek Eastern Plains, Inland and North of WA Eden Valley Far North - Other Fleurieu - other Geelong Geographe Gippsland Glenrowan Goulburn Valley Grampians Granite Belt Great Southern Greater Perth - other Gundagai Hastings River Heathcote Henty Hilltops Hunter Hunter Valley - other Kangaroo Island King Valley Langhorne Creek Limestone Coast - other Lower Murray - other Macedon Ranges Manjimup Margaret River McLaren Vale Mornington Peninsula Mount Benson Mount Lofty Ranges - other Mudgee Murray Darling - NSW Murray Darling - VIC New England Australia North East Victoria - other North West Victoria - other Northern Rivers - other Northern Slopes - other Orange Padthaway Peel Pemberton Perricoota Perth Hills Port Phillip - other Pyrenees Queensland - other Riverina Riverland Robe Rutherglen Shoalhaven Coast South Burnett South Coast - other South West Australia - other Southern Fleurieu Southern Flinders Ranges Southern Highlands Southern NSW - other Strathbogie Ranges Sunbury Swan District Swan Hill (NSW) Swan Hill (VIC) Tasmania The Peninsulas Tumbarumba Upper Goulburn Western Australian South East Coastal Western Plains - other Western Victoria - other Wrattonbully Yarra Valley Burgenland Niederosterreich Steiermark Wien and other Bundeslander Severen tsentralen Severoiztochen Severozapaden Yugoiztochen Yugozapaden Yuzhen tsentralen British Colombia Ontario Araucania Atacama Coquimbo De Los Lagos Del Bio Bio Del Maule Metropolitana O'Higgins Valparaiso Beijing Gansu Ningxia other region Shandong ShanXi Sichuan Tianjin Xinjiang Yantai Dalmatinska Zagora Hrvatsko Primorje Istra Moslavina Other Regions Plesivica Podunavlje Pokuplje Prigorje - Bilogora Sjeverna Dalmacija Slavonija Srednja Juzna Dalmacija Zagorje-Medimurje Cechy Morava Ain Aisne Allier Alpes-de-Haute-Provence Alpes-Maritimes Ardeche Ariege Aube Aude Aveyron Bas-Rhin Bouches-du-Rhone Cantal Charente Charente-Maritime Cher Correze Corse-du-Sud Cote-d'Or Deux-Sevres Dordogne Doubs Drome Eure-et-Loire Gard Gers Gironde Haute-Corse Haute-Garonne Haute-Loire Haute-Marne Hautes-Alpes Haute-Saone Haute-Savoie Hautes-Pyrenees Haut-Rhin Herault Indre Indre-et-Loire Isere Jura Landes Loire Loire-Atlantique Loiret Loir-et-Cher Lot Lot-et-Garonne Lozere Maine-et-Loire Marne Mayenne Meurthe-et-Moselle Meuse Moselle Nievre Puy-de-Dome Pyrenees-Atlantiques Pyrenees-Orientales Rhone Saone-et-Loire Sarthe Savoie Seine-et-Marne Tarn Tarn-et-Garonne Var Vaucluse Vendee Vienne Vosges Yonne Ahr Baden Franken Hessische Bergstrass Mittelrhein Mosel-Saar-Ruwer Nahe Rheingau Rheinhesse Rhein-Pfalz Saale-Unstrut Sachsen Wurttemberg Anatoliki Makedonia, Thraki Attiki Dytiki Ellada Dytiki Makedonia Ionia Nisia Ipeiros Kentriki Makedonia Kriti Notio Aigaio Peloponissos Sterea Ellada Thessalia Vorreio Aigaio Badacsony Balatonboglar Balatonfelvidek Balatonfured-Csopak Bukk Csongrad Eger Etyek-Budai Hajos-bajai Kunsag Matra Mor Nagy-Somlo Neszmely Pannonhalma Pecs Sopron Szekszard Tokaj Tolna Villany Zala Agrigento Alessandria Ancona Arezzo Ascoli Piceno Asti Avellino Bari Barletta-Andria-Trani Belluno Benevento Bergamo Biella Bologna Bolzano-Bozen Brescia Brindisi Cagliari Caltanissetta Campobasso Carbonia-Iglesias Caserta Catania Catanzaro Chieti Como Cosenza Cremona Crotone Cuneo Enna Fermo Ferrara Firenze Foggia Forlì-Cesena Frosinone Genova Gorizia Grosseto Imperia Isernia La Spezia L'Aquila Latina Lecce Lecco Livorno Lodi Lucca Macerata Mantova Massa-Carrara Matera Medio Campidano Messina Milano Modena Monza e della Brianza Napoli Novara Nuoro Ogliastra Olbia-Tempio Oristano Padova Palermo Parma Pavia Perugia Pesaro e Urbino Pescara Piacenza Pisa Pistoia Pordenone Potenza Prato Ragusa Ravenna Reggio di Calabria Reggio nell'Emilia Rieti Rimini Roma Rovigo Salerno Sassari Savona Siena Siracusa Sondrio Taranto Teramo Terni Torino Trapani Trento Treviso Trieste Udine Valle d'Aosta Varese Venezia Verbano-Cusio-Ossola Vercelli Verona Vibo Valentia Vicenza Viterbo Hokkaido Nagano other region Yamagata Yamanashi Almaty East - Kazakhstan Other-region South - Kazakhstan West - Kazakhstan Zhambyl Aguascalientes Sonora Suma Baja California Suma Coahuila Zacatecas Auckland Canterbury Gisborne Hawkes Bay Marlborough Nelson Otago Other Regions Waikato Waipara Wairarapa Arequipa Lima Moquegua Tacna Alentejo Algarve Alto Tras-os-Montes Beira Interior Beira Litoral Entre Douro e Minho Regiao Autonoma da Madeira (PT) Regiao Autonoma dos Acores Ribatejo e Oeste Bucuresti - Ilfov Centru Nord-Est Nord-Vest Sud - Muntenia Sud-Est Sud-Vest Oltenia Vest Krasnodar Krai Rostov Oblast Juznoslovenska Malokarpatska Nitrianska Stredoslovenska Tokajska Vychodoslovenska Outside wine-growing districts Podravje - Prekmurje Podravje - Stajerska Slovenija Posavje - Bela krajina Posavje - Bizeljsko Sremic Posavje - Dolenjska Primorje - Goriska brda (Brda) Primorje - Kras Primorje - Slovenska Istra Primorje - Vipavska dolina (Vipava) Breedekloof Little Karoo Malmesbury Olifants River Orange River Paarl Robertson Stellenbosch Worcester South Korea Total Alava Albacete Alicante Almeria, Granada, Jaen, Sevilla Avila, Palencia, Salamanca, Segovia, Soria Badajoz Barcelona Burgos Caceres Cadiz Canarias Cantabria Castellon Ciudad Real Comunidad de Madrid Comunidad Foral de Navarra Cordoba Cuenca Galicia Girona, Lleida Guadalajara Guipuzcoa, Vizcaya Huelva Huesca, Teruel Illes Balears La Rioja Leon Malaga Principado de Asturias Region de Murcia Tarragona Toledo Valencia Valladolid Zamora Zaragoza Aargau Basel-Landschaft Bern Fribourg Geneva Graub_nden Jura Lucerne Neuchytel other region Schaffhausen Schwyz St. Gallen Thurgau Ticino Valais Vaud Zurich Akdeniz Ege Guney Dogu Marmara Orta Dogu Orta Guney Orta Kuzey Alameda Amador Arizona Arkansas Benton Co. Butte Calaveras Chautauqua-Erie Colorado Columbia Gorge Columbia River Columbia Valley Colusa Contra Costa Douglas Co. El Dorado Finger Lakes Fresno Georgia Glenn Horse Heaven Hills Humboldt Illinois Indiana Iowa Jackson Co. Josephine Co. Kentucky Kern Kings Lake Lake Chelan Lane Co. Los Angeles Madera Marin Marion Co. Mariposa Mendocino Merced Michigan Minnesota Missouri Monterey Napa Nevada North Carolina Ohio Orange Other New York Other W. Valley Pennsylvania Placer Polk Co. Puget Sound Rattlesnake Hills Red Mountain Riverside Sacramento San Benito San Bernardino San Diego San Joaquin San Luis Obispo San Mateo Santa Barbara Santa Clara Santa Cruz Shasta Siskiyou Snipes Mountain Solano Sonoma Stanislaus Sutter Tehama Texas Trinity Tulare Tuolumne Ventura Virginia Wahluke Slope Walla Walla Valley Washington Co. Yakima Valley Yamhill Co. Yolo Yuba

Richness 8 46 36 30 34 28 43 47 45 46 46 45 31 45 49 44 36 30 48 48 38 50 41 47 47 41 36 37 25 7 32 27 35 6 9 39 18 24 18 16 15 25 19 12 15 38 25 20 19 13 28 3 9 30 26 20 16 34 19 31 25 15 12 13 25 11 22 31 6 15 30 34 19 12 18 12 36 38 26 8 22 30 29 34 15 15 12 14 14 27 24 22 18 18 28 17 22 32 38 42 12 35 19 17 24 11 28 12 20 14 15 18 30 19 32 23 11 13 18 10 19 14 19 30 36 37 33 35 103 13 13 14 13 15 14 67 30 5 17 23 3 29 52 29 35 29 1 7 16 2 1 6 1 1 4 2 22 21 21 22 2 22 22 22 22 22 22 22 22 17 31 34 6 29 5 22 38 37 80 30 8 105 47 22 94 19 42 45 16 23 29 22 38 44 31 75 2 97 71 49 41 67 10 8 33 12 26 37 19 116 31 35 26 20 52 25 42 27 41 57 69 8 36 6 3 16 11 20 16 21 53 59 28 19 26 29 5 58 62 75 110 44 44 11 30 23 23 42 49 25 23 46 50 28 67 60 31 24 45 25 18 20 15 15 13 30 29 19 30 22 28 12 61 51 54 66 36 43 63 62 54 89 69 32 43 51 37 52 50 57 30 52 54 63 12 91 82 133 201 172 79 93 220 216 50 111 58 35 92 67 106 193 62 62 208 45 114 74 66 107 32 142 40 50 82 45 141 60 190 236 87 107 59 49 221 40 140 62 34 91 157 32 117 23 186 152 83 193 70 59 65 37 94 7 73 39 57 45 43 84 217 78 79 61 161 118 42 85 170 115 69 117 60 32 93 88 100 84 80 119 124 161 65 44 201 36 38 207 60 121 103 99 128 236 37 61 54 37 163 18 28 258 50 210 111 8 4 14 3 4 17 17 17 17 16 17 12 3 4 11 5 3 39 10 11 9 18 9 9 9 9 8 8 23 9 9 17 11 19 19 102 58 182 166 195 102 9 11 143 2 18 27 19 24 27 24 17 49 20 6 37 34 36 35 22 33 8 8 11 9 8 6 10 5 8 11 18 18 18 18 16 18 18 18 18 5 23 61 42 37 36 59 35 21 37 13 32 13 28 47 25 23 19 61 50 34 25 14 15 50 21 23 22 14 8 28 38 46 57 31 32 51 38 36 40 32 50 33 11 30 15 36 36 27 37 33 46 51 34 44 4 13 10 10 27 1 14 3 9 4 22 44 29 36 1 1 9 8 33 11 12 10 10 22 11 25 11 38 31 42 1 8 20 14 22 12 30 11 11 44 25 12 34 14 9 15 46 5 9 11 43 25 20 18 13 41 48 19 1 19 1 36 9 44 18 10 5 19 14 39 36 26 14 26 52 52 6 41 34 16 11 10 7 29 52 32 5 13 19 10 31 5 12 24 19 13 10 26 10 32 14 42

Shannon Diversity 6.32 15.26 10.89 5.13 8.01 6.41 13.88 13.97 10.36 15.93 7.84 15.22 6.96 14.04 12.01 11.28 8.50 6.53 15.84 14.82 6.46 13.83 15.26 15.21 16.35 14.13 8.25 9.52 14.18 5.27 10.56 10.10 11.79 5.66 2.42 5.91 8.45 5.32 6.93 6.55 8.25 8.51 5.69 5.36 7.85 6.96 4.10 4.84 5.84 9.60 7.13 2.06 3.44 7.20 8.72 6.79 4.86 10.18 3.93 13.31 8.08 6.57 3.76 9.00 5.69 5.99 5.66 6.92 2.99 5.52 12.75 6.15 6.27 5.11 6.61 7.45 8.90 6.04 5.58 5.03 6.09 7.06 9.42 9.11 5.50 7.12 8.30 5.60 7.15 7.87 7.63 8.28 7.36 6.50 9.55 6.04 6.03 11.79 11.86 9.68 5.79 8.70 12.37 7.79 13.68 7.58 8.07 2.14 10.44 7.42 8.56 8.08 12.18 9.46 12.39 5.43 5.55 4.79 8.25 4.88 6.55 6.93 5.02 7.04 13.60 9.22 13.57 14.56 10.78 8.46 10.99 9.77 10.39 5.99 7.47 18.80 15.34 4.44 12.08 8.80 3.00 10.30 8.91 5.66 7.29 5.61 1.00 3.20 2.34 1.54 1.00 4.55 1.00 1.00 2.72 1.05 11.64 6.13 5.55 10.24 1.90 14.02 5.21 9.14 9.32 14.32 5.75 7.92 11.62 7.00 16.57 20.82 3.19 8.30 2.18 3.72 13.56 11.14 12.60 9.10 1.77 14.23 8.96 7.92 13.26 7.87 1.42 1.73 2.32 4.76 6.80 2.53 10.11 6.95 8.47 5.88 1.82 11.37 10.20 3.76 11.33 11.06 3.11 2.81 10.56 5.38 5.40 8.08 7.49 16.04 9.51 4.13 6.80 5.17 12.14 4.81 3.38 8.87 7.31 4.19 9.66 4.81 6.20 2.97 2.65 6.42 6.63 8.61 2.61 3.33 6.01 10.57 1.35 3.31 5.93 6.28 2.83 12.69 11.91 9.82 7.51 18.19 13.40 2.20 2.20 4.87 4.90 8.72 10.46 6.94 4.14 4.64 13.40 2.27 16.84 15.79 15.80 13.20 10.45 11.17 1.89 5.38 4.15 6.88 4.10 14.75 7.43 9.16 9.90 3.79 7.69 2.64 9.62 20.85 7.34 13.13 17.84 14.54 21.88 22.27 16.95 23.02 24.32 11.61 7.70 17.97 14.72 21.78 6.45 12.32 2.83 20.49 12.83 5.67 10.06 14.89 7.78 8.88 6.18 10.16 6.93 5.36 19.25 14.05 13.53 11.09 9.27 7.79 10.65 17.12 11.94 10.47 11.72 4.48 9.78 3.76 20.04 6.16 12.95 5.32 11.71 16.06 14.81 2.54 6.97 9.89 12.62 7.88 4.23 17.20 4.44 17.08 17.38 13.28 9.39 5.44 11.42 9.62 2.62 13.76 4.81 10.90 10.72 11.67 16.78 17.98 17.61 26.42 17.85 10.76 10.00 12.45 7.22 2.90 9.93 6.73 2.97 1.63 5.81 16.90 12.76 9.86 18.39 8.73 14.94 7.32 3.09 9.09 8.35 5.53 10.83 5.53 7.59 6.66 4.22 14.34 6.86 16.59 4.66 12.54 10.54 14.64 8.87 5.71 4.37 2.63 1.53 10.87 3.54 19.06 9.27 9.27 11.52 7.30 9.88 15.13 15.25 16.81 13.50 6.73 3.88 10.81 9.26 14.00 19.02 4.62 2.79 7.53 2.32 3.46 5.05 8.11 8.55 5.71 5.98 2.21 7.15 2.74 2.25 8.98 4.50 2.40 12.41 6.29 5.65 7.31 5.61 4.15 7.18 2.50 5.23 2.32 2.06 17.17 4.26 4.03 4.93 4.34 4.26 3.58 16.71 10.77 20.77 19.65 13.78 9.00 5.60 3.31 19.57 1.68 8.06 5.18 3.54 5.62 11.50 5.57 12.62 20.37 14.13 4.08 15.94 16.32 17.00 15.29 3.76 14.85 6.39 5.90 9.07 7.65 6.67 4.54 7.91 2.37 4.52 9.60 11.79 9.60 10.36 8.86 4.50 11.41 11.32 10.09 11.76 3.55 4.14 11.84 5.54 16.32 7.10 5.76 8.07 1.36 15.13 1.58 5.39 9.22 10.85 2.99 4.16 4.99 1.91 6.61 11.65 12.71 4.07 3.58 1.32 11.65 12.48 2.75 4.51 2.68 4.14 2.42 11.22 4.05 5.53 3.91 4.53 5.98 5.28 5.83 6.28 5.30 11.00 2.93 6.84 11.79 3.66 7.79 3.65 8.94 4.77 4.44 2.81 9.29 4.19 5.87 2.86 6.47 5.46 9.24 10.33 1.00 6.31 2.22 2.72 2.70 12.76 15.88 8.73 5.54 1.00 1.00 2.26 3.91 13.37 1.79 10.59 7.67 7.87 8.40 4.54 9.48 5.22 15.27 16.28 14.26 1.00 6.34 7.87 8.10 15.92 10.01 17.74 8.33 6.42 21.51 11.27 8.14 7.81 10.39 2.82 7.59 13.43 2.01 3.05 7.28 10.01 10.39 13.39 10.55 10.96 7.33 6.91 9.69 1.00 5.34 1.00 15.49 3.88 11.90 10.10 2.26 3.50 7.97 4.95 15.82 9.74 8.39 2.52 12.87 9.27 10.42 3.46 7.51 10.59 4.55 4.45 9.25 6.66 10.31 8.16 13.33 4.02 3.86 14.12 7.22 15.39 3.34 3.73 16.08 7.66 6.35 3.16 8.90 2.23 8.47 8.10 11.30

Simpson Diversity 5.41 8.57 5.60 2.70 4.21 4.36 7.57 9.83 6.22 11.19 3.72 9.31 4.99 9.26 7.09 5.67 3.91 3.12 10.27 10.66 4.29 9.07 9.35 11.86 11.41 8.77 4.68 6.11 10.71 4.14 7.80 6.31 8.42 5.33 1.66 3.15 6.28 3.01 5.16 5.61 6.81 5.69 4.51 3.15 5.09 4.67 2.64 3.12 4.01 7.57 5.02 1.98 2.91 4.65 6.80 4.67 3.44 6.41 2.31 9.24 6.58 4.21 2.75 6.96 2.79 4.40 3.85 4.88 2.13 3.69 10.14 4.11 4.49 3.86 4.43 6.49 6.81 3.25 3.69 4.76 3.29 4.94 6.70 6.05 3.70 5.41 6.74 3.79 5.56 5.70 5.25 5.51 5.33 4.56 6.63 4.27 3.86 7.18 7.42 5.99 4.75 4.42 9.87 5.63 9.04 6.51 4.84 1.49 8.93 5.65 7.17 5.66 9.29 6.68 7.48 3.85 4.18 3.69 6.81 3.73 4.05 5.38 3.58 5.14 8.62 4.51 10.86 9.31 5.49 7.02 10.11 8.17 9.18 4.12 5.69 12.75 10.50 4.12 8.56 6.42 3.00 7.25 5.34 3.24 4.54 3.99 1.00 2.20 1.63 1.36 1.00 3.86 1.00 1.00 2.16 1.02 6.61 2.82 3.00 7.06 1.82 9.76 2.61 5.86 5.59 10.83 2.67 3.78 7.92 4.13 12.19 17.27 2.55 5.38 1.78 2.80 8.83 8.26 7.89 5.49 1.40 9.28 5.80 6.91 7.12 6.04 1.13 1.23 1.79 3.03 4.87 1.94 6.24 5.14 5.65 3.47 1.69 6.96 6.57 2.65 8.40 5.86 2.10 1.97 6.99 4.81 3.29 4.77 6.34 10.69 7.19 2.75 4.27 4.04 9.26 3.33 1.85 7.98 4.41 2.28 5.68 4.44 3.95 2.93 2.37 4.98 5.44 6.42 1.63 2.26 4.66 7.86 1.13 2.77 3.83 4.26 2.61 10.41 6.07 6.90 3.58 13.60 11.09 1.96 1.55 3.07 2.55 5.22 6.26 3.64 2.17 2.56 8.18 1.51 11.37 9.64 11.84 10.19 7.38 8.41 1.27 3.64 2.75 5.00 3.17 11.05 5.66 7.58 6.42 2.04 4.82 1.81 4.40 16.88 3.35 5.67 11.86 9.15 14.43 16.19 9.95 15.06 18.39 8.67 5.34 13.05 8.71 15.26 2.86 7.49 1.99 15.00 9.43 2.35 8.79 8.18 5.12 5.09 2.36 5.19 3.74 2.98 9.23 6.04 6.54 7.05 5.47 5.34 6.42 13.58 5.78 3.98 7.71 2.11 3.55 2.02 14.05 2.74 6.17 3.10 7.35 7.51 9.83 1.49 5.14 5.55 6.69 3.74 1.89 8.30 2.62 10.84 10.36 9.39 3.35 4.20 6.42 6.01 1.53 8.37 1.93 6.96 5.95 7.79 7.10 10.59 11.54 14.66 10.83 6.26 6.24 8.00 4.48 2.21 6.62 4.06 1.61 1.18 2.84 13.06 5.74 4.47 11.56 6.11 8.64 3.42 1.77 5.59 3.33 2.57 6.04 2.47 3.74 3.53 2.16 7.69 3.85 11.55 2.30 6.95 4.33 8.23 5.67 3.15 1.86 1.52 1.16 3.65 1.98 12.17 6.02 4.56 6.75 3.13 6.89 10.40 9.85 10.88 7.48 4.37 2.10 5.30 4.91 7.10 11.03 3.73 2.32 5.87 2.18 3.25 2.83 5.79 7.24 3.99 3.79 2.01 6.14 2.59 1.77 7.47 4.27 2.21 10.00 4.65 4.21 6.65 4.29 2.93 6.29 1.69 4.09 1.59 1.65 14.00 3.35 2.80 3.23 3.81 3.06 2.49 10.38 6.49 12.07 10.68 8.07 6.64 3.80 2.05 9.77 1.51 6.68 2.87 2.08 3.70 8.38 3.69 11.10 13.02 10.81 3.22 11.15 10.88 11.61 11.74 2.74 9.41 5.37 4.50 7.61 6.96 6.00 3.82 6.73 1.68 3.21 8.57 9.21 6.73 8.28 6.15 2.90 8.99 9.30 8.10 8.86 2.95 2.52 8.27 2.99 11.52 4.90 3.35 5.67 1.12 10.70 1.20 3.80 7.33 7.70 1.91 3.12 3.32 1.30 4.23 9.02 10.13 2.67 2.15 1.10 7.51 9.95 1.87 3.42 1.71 3.39 1.46 7.97 2.37 2.84 2.77 2.68 3.65 2.67 2.71 3.65 3.14 6.66 1.66 6.02 6.62 2.63 4.09 1.97 4.54 2.23 2.47 1.55 5.48 2.43 2.89 2.53 4.14 3.76 8.76 7.43 1.00 4.75 2.07 2.32 2.14 8.95 9.70 5.97 2.54 1.00 1.00 1.67 3.04 9.71 1.32 9.58 6.82 6.77 6.34 3.13 6.42 3.22 10.00 10.29 9.21 1.00 5.82 5.56 6.26 13.17 8.87 12.33 7.33 4.16 14.80 8.73 6.60 4.77 8.73 2.34 4.70 9.59 1.47 2.45 5.90 7.04 7.37 9.88 7.76 9.52 4.57 4.26 7.28 1.00 2.54 1.00 10.35 2.42 5.01 7.32 1.57 3.00 5.63 3.08 9.82 7.19 4.97 1.66 8.62 5.89 6.49 2.87 4.30 6.94 3.17 3.11 8.70 6.36 6.60 6.00 10.20 3.78 2.22 10.96 5.82 11.37 2.60 2.04 12.25 5.55 4.41 2.09 6.48 1.54 4.41 6.65 7.58

Climate change and the most northerly vineyards in Europe

As I sit here in my workroom writing this, it would normally be the depths of winter outside; instead, the hazel is now flowering.

SMHI, the Swedish weather bureau, officially defines the start of spring as seven consecutive days with an average temperature > 0°C. This normally occurs here during the last third of March; this year it occurred back in January. Indeed, SMHI has just announced that much of southern Sweden did not actually have winter (based on their official definition), but was recorded as going straight from autumn to spring. Also, new Swedish records have been set for the highest daily average temperature in January, with the towns of Oskarshamn and Gladhammar averaging 10.6 °C and 10.1 °C, respectively, across the month.

This example may be extreme, but it has become increasingly obvious to the wine industry that the world’s modern climate is not going to allow grape-growing to continue in its current form in its current locations. The climatic requirements of the common grape varieties no longer match where they have traditionally been grown.

This means that we need to either: (i) change the varieties in the current regions, or (ii) move the regions further away from the equator or from sea level. A quick glance at a globe will show you that it is difficult to move the grape-growing regions of the southern hemisphere any further south. However, we could move the grape-growing regions of the northern hemisphere further north.

In the Americas, the current wine-growing regions of Canada are not actually any further north than the vineyards of Washington and Oregon in the USA. These are actually at the same latitude as northern France and the middle of Germany; so there is plenty of scope in Canada, as well as in Alaska.

In Europe itself, there are already plenty of vineyards in the Nordic countries (Denmark, Finland, Norway, Sweden), as I discussed in a previous blog post (Swedish wineries — who'd have thought it?). These are much further north than anywhere else on the planet; and there is also plenty of land even further north.

This leads obviously to the question: just how far north do the Nordic vineyards currently extend?

The answer is shown in the next map, where the pointers mark the northerly vineyards of Norway, Sweden and Finland (left to right). Note that these are at the latitude of the northern-most British islands (in Europe) and southern Alaska (in North America).

The leftmost vineyard shown in Norway was recently discussed in a range of wine media, because it harvested its first decent crop last year — see Keller’s most northerly Riesling project, and Norway’s first riesling (the latter has a video of the harvest).

The Riesling vines were planted in 2008, in a former potato field overlooking the sea, so there has been no hurry to get any grapes. The fact that the vines are Riesling is notable, because in the north most vines are hardy varieties, like Solaris (white) and Rondo (red), rather than the classic European varieties. Apparently, a protective wall was built to help matters, along with the collaborative expertise of grape-growers from Alsace.

The more northerly Norwegian vineyard (west of Oslo) is far more typical of the Nordic situation. It is part of a farm, Egge Gård, that grows other fruits (eg. apples and strawberries), and mainly makes fruit-juice, ciders, spirits, etc. The first media report of note about the grapes is Norway’s first sparkling wine, from 2015, which seems to have been otherwise ignored.

Only one wine is mentioned in the report, a champagne-method sparkler made from Solaris grapes (in the report there is a video of the farm and wine-making). The vine-stocks (1,000) were planted in 2010, and the first harvest was in 2014. The grapes were pressed in the farm’s apple press. This produced 500 bottles of wine, called ONE, with 350 of them going to Poland! The vines were planted on the warmest part of the farm, on the south-facing slope of an old moraine ridge, where there is sun all day.

There are plans to expand the vineyard with 10,000 more plants, intended to produce 20 tonnes of fruit and 12,000 bottles of wine; see Ready for vintage in Norway’s Tuscany.

The above mention of day-length is currently the crucial thing about Nordic vineyards. There are longer days than most European countries, with up to 25% more hours of daily sunlight during the summer compared to, say, France. Day-length makes up for warmth.

Since it extends further south, there are currently many more vineyards in southern Sweden than in Norway, as discussed in my post Swedish wineries — who'd have thought it? Also, the vineyards are mostly around the coast, making them considerably warmer than the inland areas of Europe (and North America). You can see a current location map by visiting Föreningen Svenskt Vin.

The most northerly of the commercial Swedish vineyards is actually inland, at Blaxsta Gård, as shown on the map above. In the following satellite photo, the property is the farm irregularly outlined at the bottom-left, with the vine rows clearly visible.

There are c. 3.5 hectares of vines, with 5,500 vine-stocks, the first being planted in 2000. The vines are planted along a south-facings slope near a lake, which helps moderate the climate. The varieties include Merlot, Chardonnay, Cabernet Franc, and Vidal Blanc, with the latter taking up 90% of the area.

These vines produce 7,500 liters of wine, all of which is commercially available. The most famous (and expensive) wine is the Vidal Ice Wine — it retails for $US70 for a one-third bottle. The Merlot and Chardonnay make table wine, while the Cabernet Franc also makes Ice Wine, as also do apples and strawberries (does this sound familiar?).

Finally, this brings us to Finland, whose fruit-based products almost all come from the Åland islands, in the Baltic Sea. There are, as you might expect by now, farms making alcohol products from apples and other fruits. The largest appears to be Tjudö Vingård, which specializes in apples (10,000 trees) and cherries (2,500 trees).

However, of interest to us here is Mattas Vingård, which is shown on the map above. This farm has Solaris and Rondo grape-vines, and appears to be the only one to have grapes. The 600 vine-stocks were planted in 2009, with Pinot Noir also being (unsuccessfully) tried.

Sadly, the grapes are mostly used to make jelly, which is the farm’s advertised specialty (they recommend it with cheese). However, starting in 2014 they also tried making a sparkling wine, Trädgårdslindan Rosé Brut. In 2015 and 2017 it won medals at the Indy International Wine Competition, in the USA. Only 150-200 bottles get made, which are not commercially available. You can read about the vineyard at Slotte odlar prisbelönt vin på Åland, if you happen to read Swedish.

So, it seems to me that Norway currently has Europe’s most northerly commercial vineyard, with Sweden and Finland not far behind. This can be expected to change in the not-too-distant future, unless we can reverse climate change.

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Postscript

As pointed out in the Comments below, there are now vineyards along the Sognefjord, which is even further north-west in Norway.

The one that seems to have garnered media attention (eg. New York Times and Wall Street Journal) is Slinde vingård, established in 2014 by Bjørn Bergum and Halldis Nedrebø. The vines are at the top of a steep south-facing slope above the fjord, along with the usual Nordic fruit trees (visible at the upper-right of the photo below). Wine has been made since 2018, from Solaris (white) and Léon Milot (red), as well as some Pinot noir. Apparently, Lydia (Solaris) has already one a medal. The first commercial wines are expected this year; and vineyard tours are already available.

Are wine scores from different reviewers correlated with each other?

I recently noted that Quality scores changed the wine industry, and created confusion. The main source of confusion is that the scores are numbers but they do not have any coherent mathematical properties. This issue does not occur for word evaluations of wine quality, of course.

One possible reaction to this situation has been to deride the scores, or even reject the very concept of scores being useful in the wine industry. For example, back in 2005 Elin McCoy (The Emperor of Wine: The Rise of Robert Parker, Jr. and the Reign of American Taste) expressed the view of many people:

I find scoring wine with numbers a joke in scientific terms and misleading in thinking about either the quality or pleasure of wine, something that turns wine into a contest instead of an experience.

We can thus ask ourselves whether wine-quality scores will play as big a part in the future, after Parker’s retirement, as they have over the past three decades (see After Parker: wine in the ‘Age of Re-Discovery’).

However, let us suppose for a moment that they will. If we take this road, then we need to evaluate the wine scores themselves, to try to understand how scores from different critics relate to each other. If we are going to use numbers, then those numbers need to be interpretable, preferably without us having to know about the unique facets of each and every wine critic.

That is, we need to ask: Is there some common basis to wine scores? That is, is there a shared scale (see Denton Marks, A critique of wine ratings as psychophysical scaling)? We need this to be so, beyond the trivial knowledge that a higher number is better than a lower number. In particular, it would be good in situations where multiple critics are employed, such as widely published wine magazines. Surely we can do better than the old fall-back, of saying: “Find a critic you like and follow their advice”. After all, we can do that without any numbers at all.

The basic issue when trying to compare critics is finding circumstances under which a direct comparison between them would be fair. Speaking as a professional scientist, it has long been established that, for a comparison to be valid, all of the circumstances need to be identical except for the one characteristic that you are studying. In the case of wine scores, this means that the critics should ideally be tasting the same wines, at the same time and the same place.

This does not usually happen. Either different critics are tasting different wines, or they are doing so at very different times (months or years apart), as well as very different places (even on different continents). I do know, however, of one situation where the exact same wines do get tasted at almost the same time and place. This is worth looking at.

I have noted before (Wine monopolies, and the availability of wine) that the Swedish wine chain Systembolaget, along with its general assortment of wines, releases small quantities of wines 20 times per year (c. 60-90 products per release). These wines are tasted by various media commentators shortly before their release. So, while these critics are not actually in the same room doing the tasting, this situation may be as close as we can expect to find in practice.

So, in order to address the question posed in this blog post’s title, I will compare the data from 2019 for two of these media sources. I am well aware that comparing only two critics is rather limited, especially as most of you have never heard of either of them. After all, in 2018 Morten Scholer (Coffee and Wine: Two Worlds Compared) listed 44 different sources of 100-point schemes and 18 different 20-point schemes, plus 16 others, none of which were from the modern social media; and neither of the ones discussed here was included.

Both score sources use a 20-point scale. The first source is one I have used before, from Jack Jakobsson at BKWine Magazine. I deleted the data for beer, cider, saki, fortified wines, and spirits, leaving the reds, whites and rosés. The points are provided in 0.5-point increments.

The second source is from Johan Edström at Vinbanken. The scores are reported separately for reds and whites, with rosés included with the whites. The points are usually provided in 0.5-point increments, although occasionally finer divisions appear.

There were 1,034 wines scored by both sources during 2019, with another 20 solely by Vinbanken and 15 solely by BKWine. This makes for a healthy sample size. The direct comparison between them is shown in the first graph. Each point represents one or more wines (depending on how many wines got the same scores), with the Vinbanken score shown horizontally and the BKWine score shown vertically.

The dashed line is the line of equal point scores for the wines. Clearly, the BKWine scores are below this line quite a lot, indicating that they are often less than the Vinbanken score for the same wine. Indeed, the average difference is 0.57 points — this is shown by the solid line, which clearly runs through the center of the points distribution.

Another way of seeing this same pattern of difference is shown in the next graph, which displays the counts of the difference in points for each wine — Vinbanken score minus BKWine score. It shows that the Vinbanken score varies from 2.5 points less than the equivalent BKWine score to 4 points greater, However, most of the wine-quality scores (71%) are either equal or the Vinbanken score is ≤ 1 point greater. So, the evaluations of wine quality are in broad agreement.

However, the amount of information shared by the two sets of scores is c. 55% (ie. R² = 0.55) and the other 45% is unique to one set of scores or the other — quite literally, the glass is both half full and half empty. In one sense this is quite good, because R² values this high are relatively rare for subjective (hedonic) judgements. On the other hand, I suspect that most wine drinkers are expecting better than this. If critics only half agree, then the consumer may not be much better off with them than without them.

Note, also, that the differences in points are more pronounced for smaller point scores — that is, there is more variation in points at the left of the first graph. Indeed, the biggest variation is at 15 Vinbanken points. So, it seems that there is more agreement for the better-quality wines than for the lower qualities.

Finally, it is worth considering the relationship between the assessed quality scores and the prices of the wines (see my prior post The relationship of price to wine-quality scores). Based on the exponential relationship used in my previous posts, the BKWine scores correlate slightly better (54%) with the prices than do the Vinbanken scores (51%).

However, this is still the same situation as above (a glass both half full and half empty). Wine prices are only partly associated with wine quality, which means that there are both good-value wines and complete rip-offs. Nevertheless, one-third of the wines have, based on their assessed quality using each of the two score systems, an “expected” value within $US3 of each other, so that either set of scores could be used to identify wines that are selling for below their assessed worth.

Are you ready for the cyclical downturn in the US wine market?

There has been much discussion recently about the effects of import tariffs in depressing sales of wine in the USA, as well as discussions about a current glut of domestic wine grapes (eg. The perilous state of the US wine industry?). However, to put these data in context we need an “expectation” for what US wine sales might look like in the short-term future, independent of such influences.

By this, I mean that discussions of the current situation in any wine market need to be based on more than just reports of the current situation compared to, say, last year. We need some insight into what has happened in the past and how it might relate to the near future. What we have at the moment is apparently contradictory reports (and opinions) about the current status of the US wine market. We need some sort of formal “expectation” of where we are at, in order to put things into context.

Such a thing can be produced using mathematical forecasts, in the same manner as a weather bureau produces forecasts for tomorrow’s weather. We take our knowledge of the current situation, and formally add to it our knowledge of what has happened in the past under similar circumstances. This often works remarkably well, until something comes along to disturb the current situation. For example, James Lawrence recently re-visited his wine-industry forecasts from last year, and discovered a number of disrupting factors that nullified those forecasts (The folly of wine prophecy).

The US wine market

As far as US wine sales are concerned, the bw166 group has recently released some long-term data for the U.S. Beverage Alcohol Market, based on information from The Alcohol and Tobacco Tax and Trade Bureau. Their summary of the US Beer Wine & Spirits January 2020 provides a graph of the number of wine cases entering commerce from 1960 to 2019, as shown here.

The data are interpreted by the author(s) as showing that “wine experienced strong growth for 25 years but that growth has slowed.” It seems to me that we can turn this into a quantitative forecast for the next few years.

We need to ignore the dotted polynomial provided by bw166 in their graph, as it is of no use for forecasting. When I look at the graph, I see two super-imposed patterns: a long-term linear trend (market growth) and a repeated cycle (ups and downs in the size of the market). So, to make the mathematical forecast, we first need to separate these two patterns. The next graph shows the linear trend as a straight line.

If we now subtract this straight line from the data, we are left with a clear view of the cyclical pattern, as shown in the next graph.

It is this cyclical pattern of ups and downs in the US wine market that is of most interest for our forecast. It shows (at the left) a dip in the market until the mid-1960s, followed by an uptrend until the mid-1980s, then a sharp downturn until the mid 1990s, and followed by another uptrend until the mid-2010s. The regularity of these cycles provides a clear forecast for the near future.

The current growth of the US wine market has covered the past c. 20 years, as it also did in the previous cycle (mid-60s to mid-80s). That previous growth was followed by a c. 10-year decline (mid-80s to mid-90s). My forecast as to what comes next should now be obvious — a repeat of the previous cycle.

That is, what looks like a current “market slow-down” in the original bw166 data (graph 1) looks very much like the beginning of a decline, to me. This is actually what we would (sadly) expect to happen about now, based on the previous cycle (graph 3). The decline is obscured in the original graph (graph 1) because it is masked by the long-term linear growth pattern (ie. linear growth + cyclical decline = apparent slow-down) — it is only when the two patterns are separated that they become clear.

Conclusion

This forecast helps explain the supposed contradiction between the bw166 data analysis, which reports an annual 1.1% increase in the size of the US market, and the recent data analysis from the International Wines and Spirits Record (IWSR), which reports a 0.9% decrease (US wine consumption falls for first time in 25 years). Neither of these reports is intrinsically “better” than the other — the former simply reflects the linear trend (graph 2) and the latter reflects the cyclical pattern (graph 3).

Anyway, there is my (unfortunate) forecast. As with all mathematical forecasts, its limitations are that it assumes that: (i) the current data are accurate and relevant; (ii) there are sufficient past relevant data to recognize the pertinent long-term patterns; and (iii) nothing new comes along to disrupt the current situation. The recent wrangle about tariffs is a relevant example of a potentially disruptive influence, as would be increasing effects of climate change.

However, if the forecast is accurate, at least in the short-term, then US wineries may soon need to place more reliance on their export markets (California export boosters aim for 37% more wine abroad by 2030). The alternative, I guess, is to reduce their vineyard area (eg. Wine slump threatens California vineyard cull).

PS. You may like to try the same analysis on the bw166 graph for the size of the US spirits market, which shows remarkably similar cycles.