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: Rice breeding, Cowpea diversity, Sorghum pangenome, Faba bean genome, Banana wild relative, Cassava breeding, Seed laws, Microbiome double

Brainfood: Restoration edition

Crowdsourcing crop diversity, and information

A couple of crowd-sourcing initiatives caught my eye.

First, the good people at the COUSIN project want to expand genebank collections of wild relatives of wheat, barley, lettuce, brassica, and peas in Europe. And they have a pretty good idea where the collecting needs to be done. Think you can help? Check out the call for proposals.

And from a bit further south comes a plea on LinkedIn from Chris Jones of the ILRI genebank. He needs help getting stuff out of the genebank rather than into it.

As part of the ‘low-methane forages’ project, funded by the Gates Foundation and the Bezos Earth Fund, we have been screening the methane emission intensity of a range of forage accessions, in vitro, from the International Livestock Research Institute (ILRI) genebank. The aim is to screen approximately 10% of the accessions held in our genebank and, to date, we have assessed 155 herbaceous legumes towards this goal, including several of our lablab accessions. From these, we have identified two accessions of interest. The methane emission intensity of accession #14447 was 27.7 ml/g total digestible dry matter (TDDM), 43% lower than the highest ten legumes measured so far, and methane emission intensity of accession #14458 was 33.8 ml/g TDDM, 30% lower. So, assuming that similar differences in methane emission intensity are realised in vivo (and that is no guarantee), the preferred candidate seems obvious. However, in our field plots #14458 produced 60% more biomass than #14447, which was an ‘average’ yielder. This higher level of production should be attractive to farmers who currently struggle to incorporate much in the way of legumes in their feed rations. So, which one would you prioritise?

I’ve added the links to the Genesys entries for the accessions in questions for people who want a bit more data to base their decision on. You can provide your input on Chris’ post, or right here and I promise to pass it on.

Brainfood: Genetic erosion edition