- The Cayman Islands bets on a genebank of coconut diversity.
- The Alliance of Bioversity & CIAT’s genebank bets on growth cabinets to save picky wild bean.
- IITA bets on stakeholders to build a better Bambara groundnut. And its genebank, presumably.
- The Australian Seed Bank Partnership bets on, well, seeds.
- The UAE bets on a PGRFA law.
- Ethiopia bet on a national genebank 50 years ago.
- People have been betting on the chagra in the Amazon for 4,500 years.
Brainfood: Seeds through time
- Natufian sickle blades (ca. 15,000–11,700 cal. BP) reveal cereal cultivation ca. 4.5 millennia before domestication. Archaeological evidence from ancient sickle blades suggests that people were harvesting cereal seeds thousands of years before domesticated forms emerged, blurring the line between foraging and farming, and offering fresh insight into the long co-evolution of humans and crops.
- Teosinte alleles enhance nitrogen assimilation and seed protein in maize. Wild relatives continue to provide valuable genetic resources for the improvement of crop seeds.
- Modeling seed germination data to meet biodiversity conservation needs in the Mediterranean. Robust germination models can improve both restoration planning and ex situ conservation by predicting when and how seeds are most likely to establish.
- To grow or not to grow: questioning seed dormancy and thermal germination responses along elevational gradients in four plant taxa. Seed dormancy does not always follow predictable patterns across environmental gradients.
- Delayed Seed Germination as a Strategy to Cope With Environmental Stress and Disturbance. Seed dormancy follows a (fairly) predictable pattern when you look at stress and disturbance.
- Reconsidering how to dry orthodox seeds for improved ex situ conservation outcomes. Conventional wisdom about drying orthodox seeds before storage may deserve re-evaluation, and refining drying protocols could enhance long-term viability and strengthen the effectiveness of seed banks.
- Short periods dominate mast seeding across diverse tree species. A broad analysis of mast seeding reveals that many tree species synchronize seed production over relatively short recurring intervals rather than highly irregular cycles.
Nibbles: NSW genebank, Ghana genebank, Community seed bank standards, Kenya legislation, Valuing diversity, BBC on potato, Ube yams in Philippines, Strawberry anatomy and history
- Another genebank in Australia. Unclear how it relates to the existing ones.
- Ghana’s genebank in funding trouble.
- How to run a community seed bank, according to the Bureau of Indian Standards. Apparently includes things like its relationship with other genebanks and funding.
- How to change legislation in Kenya to be more supportive of genebanks.
- Why we need genebanks in the first place.
- Otherwise decent podcast on the potato manages not to mention genebanks.
- Otherwise decent article on ube (Dioscorea alata) manages not to mention genebanks.
- Otherwise excellent dissection of the strawberry manages not to mention genebanks.
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.