Nibbles: Crop mapping, Climate change impacts, Rice cheese, Andean blueberry, Rare apples, Hungarian genebank, Old seed collection

  1. AI doesn’t recognize tropical agriculture very well.
  2. So presumably it can’t easily be used in assessing climate change impacts in agricultural heritage systems? FAO has some ideas on how to do it.
  3. Maybe rice heritage systems can be used to make cheese.
  4. I bet Andean blueberry (Vaccinium floribundum) goes great with rice cheese.
  5. But if not, heritage apples will probably do.
  6. The Hungarian genebank is hoping to inject heritage grains into non-heritage agricultural systems. AI and FAO unavailable for comment.
  7. Maybe AI can help with the mystery of this old seed collection at the Natural History Museum, London.

Brainfood: History edition

Nibbles: Agricultural expansion maps, Brassica diversity, Not against the grain, South African seedbanks, Safer peanuts, Diné seedbank

  1. Agriculture is bad for natural ecosystems. But great for maps, you have to admit.
  2. Greens are good for you. And this is a great roundup of the latest scholarship on brassica evolution, domestication and diversity. You’ll find most of the paper quoted in past Brainfoods.
  3. Grains are great. Especially with greens.
  4. Thank goodness for household seed banking. Especially in conjunction with the formal kind.
  5. All so we can breed a better peanut. And cut down more natural ecosystem?
  6. No, there’s community genebanks for that too…

Brainfood: Genetic erosion edition

Another chance for Bambara groundnut

Yesterday’s Nibble on the annoyingly always-on-the-verge-of-breaking-through Bambara groundnut had me rummaging through the blog’s archives. Among dozens of references, I came across a post from almost 15 years ago that included some maps — of genebank accession localities and the distribution of the crop. On a whim, I downloaded the Genesys data and fed it into the maw of ChatGPT, asking it to identify gaps in the world’s ex situ holdings. For each of the top 10 priority collecting regions, I then asked for a best-bet locality for exploration. ChatGPT obliged with a KML file, which I then looked at in Google Earth, together with the accession localities.

This is the result.

And here’s close-up on West Africa, because that’s where accessions are densest, and the suggested “gaps” a little more difficult to understand.

Asked for a justification, this is what the LLM came up with.

Does it make any sense? Well, it’s not exactly where I would have plumped for, just eyeballing the data. But it is not complete nonsense. Maybe it was the prompt? Any ideas what that should look like to get the best results?

Not that any of this is going to help Bambara groundnut much, I suspect.