Check out Jeremy’s latest Eat This Newsletter for his pithy takes on recent articles on fonio beer and the Vegetable Lamb of Tartary. Talk about opportunity crops.
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.
Brainfood: Diversity of Oats, Cotton, Sugarcane, Rice, Amaranthus, Vegetables, Agroforestry, Value chains
- Genome-wide comparative diversity uncovers population structure, global distribution, and targets of selection in hexaploid oat. A worldwide survey reveals how oat diversity is structured, spread, and shaped by breeding, helping pinpoint untapped genetic resources for future improvement.
- Genomic diversity and the domestication history of cotton (Gossypium hirsutum). Its genome traces cotton’s journey from its wild origins in Mesoamerica while documenting the genetic narrowing that accompanied domestication.
- Genetic architecture of sugarcane traits in a polyploid genomics framework. New genomic tools finally begin to untangle the diversity of one of agriculture’s most genetically complex crops, exposing the basis of traits breeders have long selected largely in the dark.
- Projected warming will exceed the long-term thermal limits of rice cultivation. Rice has historically thrived within remarkably stable climatic boundaries. Those boundaries are now on course to be crossed across major growing regions, with profound implications for global food security. Diversity to the rescue?
- An inter-specific Amaranthus pangenome captures genetic variation potentially underlying key leafy vegetable traits in this underutilised crop. A rich reservoir of previously hidden diversity emerges from across multiple cultivated amaranths, offering breeders new options for improving a neglected but nutritious vegetable.
- Impact of gardening and nutrition support provided to women in refugee camps in Cox’s Bazar, Bangladesh. Even in one of the world’s most challenging humanitarian settings, greater interspecific crop diversity translated into better diets, improved food security, and enhanced wellbeing.
- Designing perennial crop-based agroforestry systems: specificities, challenges, and opportunities. Diversification does not stop at the field edge: how perennial crops can be combined with trees to deliver productive, resilient, and biodiversity-friendly farming systems.
- Towards Nature Positive supply chains: From biodiversity commitments to organisational action. What would it take to move biodiversity from corporate promises to business practice? Maybe the above examples can help turn aspiration into measurable action.
The wild bunch
Never rains but it pours. Along very similar lines as the previous post on a fun effort to document people’s favourite breadfruit varieties, here comes the FruitDev project’s Wild Fruit Population of the Month.
Each month, the series highlights one (or more) populations identified by a FRUITDIV partner, illustrating how field exploration, local knowledge, and cross-partner collaboration contribute to a better understanding of wild fruit genetic resources.
By focusing on individual populations, the series aims to make visible the often-overlooked genetic diversity found in natural and semi-natural landscapes, many of which are shaped by environmental pressures such as drought, poor soils, or past disturbances. These populations represent valuable reservoirs of adaptive traits that are increasingly relevant for resilience, conservation, and future breeding strategies.
This month’s featured population is a dwarf almond from North Macedonia. Nice idea.
A tale of many breadfruits
I can’t find anything online about the results of the regional Breadfruit Biocultural Conservation Knowledge Exchange Workshop organized by the Pacific Island Farmers Organisation Network (PFO) at the Tutu Rural Training Centre in Fiji from 27–29 April 2026. Beyond social media posts, that is. But I really like the idea of participants sharing the breadfruit varieties that are special to them.
