- The Broad Spectrum Species: Plant Use and Processing as Deep Time Adaptations. Hundreds of plant species, many now forgotten, show up in archaeological assemblages stretching back tens of thousands of years. Exploiting an astonishing diversity of plants was a fundamental human adaptation long before agriculture. And the data was kinda always there.
- Evaluating cultivars for pollinator gardens. Some ornamental cultivars attract more pollinators than the wild plants they were bred from. The relationship between genetic modification through breeding and ecological function is not always straightforward. And I now want to see the descriptor “pollinator attractiveness” in evaluation datasets.
- Chemotypic Diversity and Integrated Metabolic Profiling of Myrtle (Myrtus communis L.) from Mediterranean Turkey. Dozens of different chemical compounds vary dramatically among individual myrtle plants that look much the same to the naked eye.
- Essential oil composition and ethnobotanical survey of male and female Juniperus seravschanica Kom. (Cupressaceae) in Iran. Traditional knowledge and chemical profiling show that juniper male shoots, female shoots and cones each produce distinct blends of essential oils, exposing a surprising layer of sex-linked diversity within a single species.
- Earth Metabolome and Digital Botanical Gardens Initiatives: Chemodiversity Knowledge for Biodiversity Conservation. Millions of plant-produced molecules remain undocumented, forming an invisible dimension of biodiversity. We need global digital infrastructures to catalogue this vast reservoir of chemodiversity before it disappears. Of course we do.
- Herbaria Provide a Valuable Resource for Obtaining Informative mRNA. Decades-old herbarium specimens still contain usable messenger RNA, opening the door to studying historical patterns of gene expression from preserved plant collections.
- The Politics of Open Infrastructures: Power, Governance, and Justice in Digital Knowledge Practices. Data infrastructures may be open, but control over them often is not. And that probably goes even more for the unusual sorts of data represented by the above papers than for the crop diversity data we normally deal with here.
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.
Opportunity crops in sheep’s clothing
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.