No one expects the Spanish Inquisition to help the tomato

The latest episode of Eat This Podcast explores why the tomato, first recorded in England in the 1590s, took more than a century to become an important food. The explanation offered was that it took a combination of factors: a somewhat warmer climate, the movement of people and culinary traditions caused by the Spanish Inquisition, and its connection with another New World crop, the chile pepper. Do listen to the episode, it’s a fascinating story.

What struck me most about it was how little of the tomato’s eventual success depended on technology. Sure, glasshouses and fermenting horse dung helped, but so did luck and recipes.

Today, discussions about agricultural diversification often emphasize research, breeding, seed systems and value chains. The recent paper on the Vision for Adapted Crops and Soils (VACS), for example, lays out an ambitious roadmap to transform Africa’s Cinderella “opportunity crops” through investment in breeding, seed delivery, agronomy, markets and policy support.

There is much to admire in that vision. Many neglected crops undoubtedly suffer from decades of underinvestment. Better varieties, better seed systems and better market access could surely make a substantial difference.

Yet the tomato’s history offers an interesting counterpoint.

The tomato did not become a success in England back in the early 1700s because somebody developed an improved variety. It did not require a major breeding programme. It was not the product of a coordinated development initiative. Rather, its rise seems to have depended largely on changes in climate, cuisine and culture. People learned how to use it. They incorporated it into recipes. It found a place within evolving food traditions.

In other words, the tomato became important because food systems adapted to it, not because the crop itself was somehow transformed.

This is not an argument against VACS. Rather, it is a reminder that technological interventions are only part of the reason why crops become successful. History suggests something else is needed too.

The tomato spread because it became embedded in dishes that people wanted to eat. The chile pepper may have played a role in that process, helping to create new flavour combinations and culinary traditions in which tomatoes made sense.

For some of Africa’s opportunity crops, the principal constraint may well be genetic improvement. For others, however, the limiting factor may lie elsewhere. Middle-class consumers may not know how to prepare them. Urban markets may not value them. Food processors may not see commercial opportunities in them. In such cases, the most effective intervention may not be a breeding programme but a chef, an entrepreneur, a recipe book or a social media campaign.

The VACS paper rightly argues that there should be “no romance” about opportunity crops. But perhaps there should also be no assumption that technological tweaking is always the decisive factor.

The history of the tomato suggests that crops can sometimes become important without being substantially “improved” at all. What matters is whether societies discover compelling reasons to grow, sell, cook and eat them.

That is a useful reminder that agricultural diversification is ultimately as much a cultural process as a technological one. Though we could probably do without the Spanish Inquisition.

Brainfood: Unusual data edition

Himalayan maize: The saga continues

I decided to dig a little deeper into the climatic adaptation of Himalayan maize. You may remember from my last post on this that Genesys has 96 maize accessions from over 2000 masl in the Himalayas, collected at some 50-odd unique localities. When I ran these accessions through the Subsetting Tool in Genesys, I got the following histogram.

What struck me — and surprised me — was the spike of sites way at the left hand of the precipitation plot. So I took a closer look at the results of the subsetting analysis. And the clustering algorithm it uses to look for similar sites did in fact identify two climatically quite different groups of locations: 45 of the unique high altitude maize collecting sites (the blue ones) are indeed drier than the other 7 (in orange).

Much drier. (And also colder actually, but that’s another story.)

They’re the ones mainly collected in Pakistan and Afghanistan.

Now, I don’t know whether these areas really get 135 mm of annual precipitation, which seems really low, and in any case the agriculture there is clearly irrigated.

But those maize samples, mainly now conserved at CGN in the Netherlands incidentally, the results of something called the 1976 Netherlands-Pakistan Expedition by the Stichting voor Plantenveredeling, do seem to have some very unique adaptations.

Maize location, location location…

A quick search on Genesys revealed 302 maize accessions from above 1500 masl in the Himalayas, and 62 above 2500 masl. Of course, there are many more maize accessions from high altitudes in Central and South America, but their photoperiod adaptation (among other things) is likely to be quite different.

That’s from a post I put up here a few days ago. Some people said I should back up that “among other things,” so here goes.

I extracted from Genesys lat/longs for 2,388 maize landrace accessions collected above 2000 masl in the Andes, and for 96 in the Himalayas. I then asked ChatGPT to calculate separate averages for the two sets of accession collecting localities for two climate variables, i.e. mean annual temperature and precipitation. It asked me to supply it with the WorldClim data as a zip file, which I duly did.

It told me the Andean sites had a mean annual temperature of about 12°C and the Himalayan ones of about 6°C. Mean annual precipitation was around 750mm and 640mm, respectively. So there could well be some significant overall differences in adaptation between the two sets of germplasm.

But…

I used the coarsest WorldClim dataset, which is probably not a great idea in mountain areas. And many accessions were collected at the same sites: those 96 Himalayan maizes for example come from only 52 distinct places. I should probably have only used unique collecting localities to make the calculations. The “Subsetting Tool” in Genesys does do that, and displays nice histograms, but it doesn’t give you average values for the whole subset. Incidentally, when I looked at the histogram for total precipitation for the Himalayan material, there was a suspiciously big spike way at the dry end. Really not sure what’s happening there.

Maybe some climatologists or geographers or GIS jockeys can explain. And do a better analysis. And come up with a really easy way of extracting climate data for a long list of localities.