- We need to diversify the food system.
- Start with collard greens maybe?
- Continue with pawpaws.
- And do something about meat.
- Finally, open a molecular lab.
- Wait, what?
Nibbles: Oz genebanks, Turkish heirlooms, Indian heirlooms, Peruvian cryobank, Chinese cryobank, Seeds for farmers, Gamma gardens, Bees, DSI, Seville cathedral, Food & climate change
- Australian politicians promise genebank. World holds its breath.
- Meanwhile, in Turkey…
- … and India…
- …and Peru…
- …and China.
- Another way of getting seeds to farmers.
- What, even irradiated ones?
- But don’t forget the bees.
- Hopefully DIS won’t scupper all this sharing.
- Because although our foods are not set in stone…
- …we’ll need more than changes in habits to adapt agriculture to climate change.
Brainfood: Ultra-processed food, Micronutrients, Wheat breeding, Bambara groundnut breeding, Seed longevity, Sheep diversity, Switchgrass ploidy, Seed services, Jersey in Africa, Wine history, Austronesian dispersal
- Ultra-processed foods should be central to global food systems dialogue and action on biodiversity. Should.
- Global plant diversity as a reservoir of micronutrients for humanity. As.
- Mainstreaming grain zinc and iron concentrations in CIMMYT wheat germplasm. Eventually.
- Utilization of Plant Genetic Resources of Bambara Groundnut Conserved Ex Situ and Genetic Diversification of Its Primary Genepool for Semi-Arid Production. Insufficient.
- More on seed longevity phenotyping. p50.
- Gender dynamics around introduction of improved forages in Kenya and Ethiopia. Training.
- Whole-Genome Resequencing of Worldwide Wild and Domestic Sheep Elucidates Genetic Diversity, Introgression, and Agronomically Important Loci. Hairy.
- Jersey cattle in Africa: From the Breed’s Documented Past to a Profit IndexLinked Future. Promising?
- A generalist–specialist trade-off between switchgrass cytotypes impacts climate adaptation and geographic range. Polyploidy!
- Decentralized Seed Services in Africa. Quality.
- The Rise of Wine among Ancient Civilizations across the Mediterranean Basin. Ritual.
- Genome Analysis Traces Regional Dispersal of Rice in Taiwan and Southeast Asia. Ok, I give up, I can’t do this. Austronesian speakers did not spread out of Taiwan because of rice. If you can summarize that in a word, I’m impressed, so let me know.
Brainfood: Digitizing collections, Bean core, Livestock diversity, Maya & maize, Fish stocks & CC, Save the weed, Flax CWR, Italian agrobiodiversity
- Cross-validation of a semantic segmentation network for natural history collection specimens. Computers can distinguish the herbarium label from the actual specimen and other stuff on the sheet, helping with the whole automatic digitization thing, but it takes some really fancy math.
- The landscapes of livestock diversity: grazing local breeds as a proxy for domesticated species adaptation to the environment. Medium fancy math used to map breed diversity in the Iberian Peninsula for different livestock species and relate it to environmental factors.
- South-to-north migration preceded the advent of intensive farming in the Maya region. Sort of like tomato, but in the other direction. Plenty of math involved, but behind the scenes, thankfully.
- Timing and magnitude of climate-driven range shifts in transboundary fish stocks challenge their management. Huge amount of data and very fancy math shows fish are in trouble.
- A Core Set of Snap Bean Genotypes Established by Phenotyping a Large Panel Collected in Europe. Ok, even I can follow the math on this one.
- Assessment of biogeographic variation in traits of Lewis flax (Linum lewisii) for use in restoration and agriculture. Very approachable math shows which populations of a CWR can best be used for restoration, and where; and also for domestication and breeding.
- An updated checklist of plant agrobiodiversity of northern Italy. Very useful use of very basic maths. Key number: only 43% of the PGR on the list are conserved ex situ.
- Cannabis, the multibillion dollar plant that no genebank wanted. No math needed to figure out weed needs a genebank.
Brainfood: NPGS use, Descriptor clustering, Fast phenotyping, Flax duplicates, Photosynthesis variation, Brassica breeding, Robusta & CC, Seaweed domestication, Fighting fish domestication, Hotspots & diets, Cotton & wildlife
- Developing country demand for crop germplasm conserved by the U.S. National Plant Germplasm System. 5 years, 10 crops, 100,000 samples.
- cacGMS: An Algorithm Cluster Germplasm based on Categorical Genetic Traits. Build a better cluster algorithm for categorical descriptors and the world will beat a path to your genebank. If it isn’t already.
- Deep learning: as the new frontier in high-throughput plant phenotyping. A really fancy way of scoring those descriptors.
- Selection of duplicates of flax accessions – an important task in the management of collection of genetic resources of Linum usitatissimum L. But you can do a lot with passport data.
- Mining for allelic gold: finding genetic variation in photosynthetic traits in crops and wild relatives. Let the gene editing begin!
- Expanding the genetic variation of Brassica juncea by introgression of the Brassica rapa genome. AABB gets a shot of AA.
- Adaptive potential of Coffea canephora from Uganda in response to climate change. Some populations are going to do better than others under climate change. Ah, but are they the best populations for other traits?
- Pre-domestication bottlenecks of the cultivated seaweed Gracilaria chilensis. Founder effect and over-exploitation mean that more diversity from New Zealand might be needed.
- Genomic consequences of domestication of the Siamese fighting fish. You don’t need huge genetic diversity to get huge phenotypic diversity, even with strong selection. But will new diversity be needed eventually? From Siam?
- Food versus wildlife: Will biodiversity hotspots benefit from healthier diets? Some hotspots will actually do worse if people eat better, so we will have to look at better agriculture too. Including seaweed, for all I know.
- Commodity crops in biodiversity-rich production landscapes: Friends or foes? The example of cotton in the Mid Zambezi Valley, Zimbabwe. Possibly an example of the above. Cotton was better for wildlife than what came after prices dropped.