Category: Information

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The plants that statistics forgot

FAO has just put out new guidance on capturing wild foods and neglected and underutilized species (NUS) in dietary surveys. It’s very much worth a look, even if your interest runs more to grain landraces on the farm than to greens gathered from the forest. The methodology is built to overcome a very valid problem: standard dietary assessment tools are generally designed with the main staples in mind, so anything outside that narrow frame (think foraged, seasonal, localized, thinly documented) tends to fall straight through the cracks.

FAO’s fix is a set of very sensible, practical steps: engage local knowledge holders to compile inventories under their own names for things, survey markets to see what’s actually being sold and eaten, map harvest calendars against agroecological zones and seasons, and build simple identification tools (photobooks, reference databases) that let enumerators and communities work from a shared understanding of what they’re counting.

None of that machinery is specific to wild foods though. The same toolkit could easily be adapted to survey the diversity hiding in plain sight on farms: crops and landraces known only by local names, grown in a handful of villages, marginalized, on their way to be forgotten. And invisible to national crop statistics that only track a handful of named improved varieties of the main staples, if that. A market survey designed to catch wild greens sold at the roadside works just as well for catching a bean landrace in the same market. A harvest calendar built to track when forest foods peak works just as well for tracking when fonio gets planted or harvested, and why farmers still bother with it. Great for quantifying the opportunity in “opportunity crops.”

And there’s a useful downstream application: surveys built this way could help flag where crop diversity is thinning out on the ground, or where it’s abundant but under-represented in genebank holdings. In other words, the kind of gap analysis that ought to be steering germplasm collecting missions.

FAO’s framing kind of gestures at this already: wild, managed and cultivated aren’t three separate boxes but points on a continuum. A methodology built to navigate that blurriness for wild foods is also a methodology that ought to work for navigating the blurriness at the cultivated end. It would be a shame if this toolkit stayed confined to the wild-food side of the spectrum when the conceptual heavy lifting behind it applies just as well to neglected cultivated diversity.

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Putting names and faces to organic seed diversity

LIVESEEDING is a project…

…to foster the growth of the organic sector and transition towards more sustainable local food systems by delivering high quality organic seed of diverse cultivars adjusted to organic farming for a wide range of crops.

It is funded by Horizon Europe (Innovation Action), the Swiss State Secretariat for Education, Research and Innovation and UK Research and Innovation, and gathers 37 partners from 16 countries.

The project is producing some interesting resources, but the ones I like best are actually a little difficult to find on their website. So difficult, in fact, that I have to resort to linking to them via a search result on Organic Farm Knowledge.

They are plant genetic cards, each providing information on a specific cultivar, “supporting its identification, conservation, cultivation and use by farmers, seed practitioners, breeders, researchers and other stakeholders of the seed system.”

Here’s a little piece of one such card from Greece.

There’s also something called the EU Organic Seed Database “to create more transparency for the EU member states and for plant reproductive material suppliers regarding available offers of organic plant reproductive materials and to increase the supply of organic plant reproductive material in the EU member states and Switzerland.”

To be honest, I haven’t really played around with it enough to form a definitive judgement, but it does seem a little complicated to navigate at first blush. If anyone has a go, and has an opinion, please let me know in the comments. However, the plant genetic cards do have links to possible sources of seeds.

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Towards a digital workflow for forest restoration

I missed the “From Seeds to Success: Digital Tools for Planning and Managing Forest Restoration” webinar a few weeks ago, jointly organized by the Alliance of Bioversity International and CIAT, the Millennium Seed Bank at the Royal Botanic Gardens, Kew, and the Forest Restoration Research Unit (FORRU), Chiang Mai University. Too busy moving to another continent. But fortunately there are now a handy summary and even a recording online. And there will be a re-run.

To remind everyone what the webinar was about:

The programme featured live demonstrations of five innovative digital tools, developed to support restoration planning, seed sourcing and project management, followed by an interactive discussion, during which participants explored their applications, strengths and future development.

With these five tools, therefore, we have the beginnings of a digital seed-to-restoration pipeline. Which is very exciting to me.

Let’s go through it step by step, and tool by tool.

1. Define the restoration objective and choose species: Diversity for Restoration (D4R)

Picture yourself looking at a site you want to restore. This tool answers the question: What should we plant here?

D4R is the front-end decision-support tool. It combines information on:

  • restoration objectives
  • environmental conditions
  • species distributions
  • functional traits
  • seed zones
  • future climate scenarios

    • to recommend suitable species and seed sources.

    2. Find the seed: SeedPOD

    Then we need to know: Where can we actually obtain suitable seed?

    Once D4R has identified desirable species and potentially appropriate provenances, SeedPOD steps up as the seed-sourcing layer, providing information from different organizations worldwide on the storage, availability, and germination of their seed collections. It is scheduled for launch in September 2026.

    3. Decide how to handle and store the seed: Wyse–Dickie Seed Storage-Behaviour Predictor

    But you won’t necessarily be able to get such information on all the seeds you might need. So how should we handle those seeds?

    This tool predicts whether seeds are likely to be:

  • orthodox — tolerant of drying and suitable for conventional storage
  • recalcitrant — sensitive to drying and therefore requiring different handling

    • using species characteristics, taxonomy and environmental variables. That means you can work out how to process and store them.

    4. Germinate and produce seedlings: Germination Experiment Assistant (GEA)

    Having the seeds is great, but how do we turn them into viable seedlings?

    Where there is little or no published germination information, you will need to generate it yourself. GEA uses experimental data and predictive modelling to produce species-specific germination protocols, including practical procedures for nurseries.

    5. Track the material through the restoration operation: MyFarmTrees

    Finally, once the work has started, you’ll need to monitor how it’s going: What happened to each seed lot, seedling and planting?

    MyFarmTrees is the operational backbone of the five tools. It tracks restoration activities from seed collection to nursery production to field establishment using QR-coded seed lots and mobile technology. This gives you traceability. It also creates the possibility of connecting restoration activities to monitoring and ultimately payment-for-ecosystem-services systems and the like.

    Is anything missing?

    Having these five tools is great, but they don’t quite constitute the entire restoration workflow. They cover the biological-material pipeline extremely well: selection → sourcing → handling → propagation → deployment → monitoring. But the webinar participants did identify restoration approach selection, as well as planning collecting programmes, as gaps in the current digital toolkit. Along with, inevitably, seamless integration, or at least interoperability, of all the different tools.

    So there should maybe be an initial stage to the above sequence:

    0. Assess the site and restoration strategy

    Because before unleashing D4R, you do need to know a bunch of stuff, for example:

  • What is the state of the site?
  • Is active planting actually necessary?
  • What natural regeneration is occurring?
  • What ecosystem are you trying to recover?
  • What functions are missing?
  • What species are already present?
  • What are the local threats?
  • What restoration approach is appropriate?

    • And there should also maybe be a stage parallel to what I labelled 2. Find the seed above. Call it…:

    2b. Collect the seed

    Because if SeedPOD says suitable seed is available, all well and good. But if it isn’t, it would be nice to have a “seed collection planner” to generate a collecting programme for you, factoring in the distribution, ecology, mating system, and phenology of the target species, the accessibility of potential collecting sites, the budget available, you get the idea.

    So maybe eventually the complete architecture of the system will be something along these lines:

  • Site assessment: What needs restoring? Gap
  • Restoration design: What restoration approach is appropriate? Gap
  • Species & provenance selection: What should we plant? D4R
  • Seed sourcing: Where can we obtain seeds? SeedPOD/Gap (collecting)
  • Seed handling/storage: How should we handle the seeds? Wyse–Dickie
  • Germination/propagation: How do we produce seedlings? GEA
  • Traceability & deployment: Where did every seed/seedling go, and what happened to them? MyFarmTrees
  • Monitoring: Did restoration succeed? Gap/MyFarmTrees

    • Interested in seeing where all this goes, as I am? Start by registering for the re-run of the webinar on 30 September.

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    The revenge of the sweetpotato

    Speaking of digital imagery and its uses in genebanks, get a load of the recently published catalogues of the Cuban sweet potato collection at the Research Institute of Tropical Roots and Tuber Crops (INIVIT), and of Peruvian cacaos. Beautiful. And do yourself a favour and don’t skip the foreword, preface and introduction to the Cuban volume. I found them very eloquent — and moving. Maybe a touch overwrought, admittedly, but it does take some gumption to say, of sweetpotato, in a genebank catalogue of all places, that: “Today, history grants it its revenge.” I just hope it’s true.

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    A picture is worth a thousand descriptors

    For decades, germplasm characterization has relied on people looking at plants, seeds and fruits and recording what they see; first on paper forms, more recently admittedly on tablets and the like. I’ve done that myself, and let me tell you, recording the colour of taro stems on bits of damp paper in the middle of a forest clearing in Vanuatu is no fun.

    Lately, thankfully, the camera has been taking over.

    A recent overview of new tools for plant genebanks highlights digital photography as a way of capturing standardized information on the colour, size and morphology of seeds and other plant parts, alongside more sophisticated technologies such as hyperspectral imaging and mobile field sensors. The attraction is obvious: instead of recording a handful of descriptors by eye, images can capture a much richer set of characteristics that can subsequently be measured and analysed at your leisure.

    The potential is particularly striking for fruit crops. A new study of heritage apples used controlled multi-view imaging to characterize about 350 accessions over three years and two locations. Fancy maths achieved 95% accuracy in distinguishing 38 cultivars, rising to 99% when images of three fruits were combined.

    And the technology does not necessarily require sophisticated equipment. In a recent demonstration with beans with complex colour patterns, Miguel Angel Acosta Chinchilla used ordinary photographs, image pre-processing and clustering algorithms to extract dominant palettes and colour distributions.

    We’re moving from a limited number of human-defined descriptors to infinitely explorable machine-readable phenotypes. An image can preserve information that nobody thought to score at the time, and algorithms can return to it later to measure traits that were not originally part of the characterization protocol. For genebanks, that could be transformative. A photograph taken today may become a source of data for questions that don’t actually arise until tomorrow.

    I wish I had a digital camera with me in that taro patch twenty-odd years ago. Goodness knows what people could be finding in those photos now.