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What seventy years of adoption research can teach genebanks

There’s a paper out that’s worth getting to grips with if you are interested in genebanks having an impact, even though it never actually mentions genebanks. David Pannell’s Reflections on Adoption of New Agricultural Practices distils more than 70 years and 5,000 studies of why farmers take up, adapt, abandon or reject innovations, be they new practices or technologies. It’s not specifically about plant genetic resources, but think of bringing back lost landraces, releasing a new variety or giving a neglected crop a second chance as an innovation and some of Pannell’s conclusions will seem very relevant to genebanks and their users. I’ll just pull out what I think are the top lessons.

Perhaps the main point Pannell makes is that testing something out isn’t adoption.

That sounds pretty obvious, but agricultural projects routinely give farmers something to try and then apparently count the resulting trial as adoption. Pannell points out that what is in fact being measured may be nothing more than farmers trying to figure out whether the innovation is good enough to keep using once the project support disappears.

The farmer planting that accession from the genebank or that new variety from the breeder is evidence of interest, not adoption. The more meaningful result is whether she is still using it, without being paid or encouraged to do so, several seasons later.

And speaking of trying things out, how easy that is to do matters for adoption, and it varies enormously among innovations.

Pannell notes that a new crop variety has an important advantage over other agricultural innovations: farmers probably already grow the crop. They can try the new variety right alongside the older ones without redesigning their whole farming system. In contrast, innovations that require systemic change can take decades to reach widespread adoption.

So, a package of sorghum landraces, or a new disease-resistant sorghum variety, being presented to sorghum farmers is one type of adoption problem. Promoting an opportunity crop that most farmers have forgotten how to grow is quite another. That may require new knowledge, equipment, markets and consumers, as well as a convincing reason to grow it. It’s bound to take longer.

Next, and perhaps more surprisingly, extension cannot compensate for a weak advantage.

Pannell is pretty clear: extension services can speed up awareness and evaluation early on, but they cannot manufacture adoption if and when farmers discover that an innovation isn’t sufficiently good.

That puts the emphasis back on the thing being offered. Does the new variety really outperform what farmers already have? Does the old landrace being revived have drawbacks? Does the neglected but really nutritious opportunity crop provide a return that compensates for the risks and additional work involved? If the answer is no, the best communications operation in the world won’t fix it.

Finally, every innovation is unique.

Pannell shows that different innovations being considered by the same farmers, and the same innovation being considered by different farmers, can have radically different drivers of adoption. Farmers aren’t a uniform population, and neither are innovations.

That is a useful reality check. The story of one neglected crop becoming an agricultural opportunity will never be repeated exactly. Each innovation has its own combination of advantages, constraints and players. There is no generic path to impact, though there may be recurring questions.

So what does this mean for genebanks trying to have — and measure — impact?

I’ll go with four fairly simple things. First, don’t treat germplasm distribution or a farmer field day as the end point: follow up and find out what breeders actually used, and farmers actually kept using. Second, design projects tightly around the particular innovation rather than assuming that the same adoption machinery will work across crops, countries, ecologies, whatever. Third, spend at least as much effort understanding why farmers might want something as on getting it in front of them. If an advantage isn’t there, no amount of factsheets and posters and field days will rescue it. Finally, and maybe most importantly, remember that non-adoption isn’t always failure. Genebanks exist in large part for option value: material nobody wants now may be exactly what everyone wants in 2060. Plan the project so that even a failure to adopt teaches you something.

I’ve written plenty of posts here celebrating some interesting new diversity getting into farmers’ hands. I’ve written far fewer checking back to see what happened next. That’s the more important story to tell.

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The NPGS after Beltsville

The USDA has decided to close its iconic flagship site, the Beltsville Agricultural Research Center (BARC) in Maryland. Everyone in plant genetic resources knows what “Beltsville” means. Cost is apparently to blame: the agency says modernizing just 11 of BARC’s 400-plus buildings and carrying out deferred maintenance would require a $500 million one-time investment plus $40 million a year ongoing, which it calls financially unsustainable. That’s about 3 hours of US defence spending.

What does this mean for the National Plant Germplasm System (NPGS)?

The NPGS’s actual seed and other collections are already distributed across the country, and USDA is using the closure to invest in new infrastructure elsewhere, including a centralized germplasm distribution centre in Ames, Iowa. Which is good.

But what happens to the National Germplasm Resources Laboratory, a key denizen of BARC? The NGRL is not a genebank. It’s a team of people who, from Beltsville, “facilitates the acquisition, exchange, and documentation of crop genetic resources important to world food security.” In other words, it provides some of the connective tissue that turns a bunch of genebanks spread around the country into a national system.

The USDA announcement is not clear about the NGRL. It does says that research projects at Beltsville will be redistributed among other USDA locations, but not what will happen to NGRL staff and its system-wide functions. Ames will gain an important new distribution facility, but that’s not the same as the NGRL as a whole.

It will be interesting to see whether USDA moves the NGRL’s expertise and functions intact, or divides them among different locations: dispersion runs the danger of weakening the institutional knowledge of how acquisition, documentation, quarantine, distribution and international exchange fit together. For the NPGS, that may ultimately be the most important consequence of the Beltsville reorganization. Stay tuned.

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Brainfood: Opportunity crops, meet your constraints

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The case for a case manager

In an earlier post I argued that the crop diversity conservation and use system has a missing middle: someone whose job it is to get the whole way from an agricultural problem to a diversity-based solution.

That would address the perceived problem of “underuse” of genebanks collections better, I think, than making more data on collections available, or easier to search. I know we now have cheap genome sequencing and high-throughput phenotyping and powerful databases and AI on our phones, for pity’s sake. But we’ve been counting on the latest technological leap for quite a while now, and I figured it was time to look elsewhere for a solution.

I compared this missing role or function to that of an insurance broker.

I still think there’s a missing middle; but that was the wrong analogy. Insurance brokers navigate a liquid market of interchangeable, price-comparable products with a client who already knows they want insurance. That’s not like working with genebanks, if you think it through better than I originally did. I think I was seduced by the notion that crop diversity is a form of insurance.

There’s a better fit: medical case manager.

That’s the person who follows the patient through the maze of the health system. They don’t do the surgery or prescribe the drugs. They make sure the referrals happens, the test results gets chased, the specialists talk to each other, and nobody quietly drops the ball because, well, their particular bit of the system is working perfectly well.

Sound familiar?

A breeding programme can have a perfectly good reason for aiming for a particular combination of traits. A genebank can have exactly the right diversity to test. The accessions can be sequenced, well characterized, searchable through a beautifully designed database, and readily available. The breeders might be excellent breeders.

And still nothing might happen. Not because anyone screwed up necessarily, but because nobody owned the process as a whole, and thus put the entire package together.

That, I think, is the missing middle.

And this better analogy points to something else that the insurance-broker metaphor rather conveniently (for me) glossed over: independence.

Independence as a structural necessity. Someone sitting inside Genebank A, however well-intentioned, is going to have a harder time saying “actually, the stuff you need is in Genebank B,” because their salary, mandate and incentives all live inside Genebank A.

A case manager, on the other hand, is supposed to follow the patient, not defend the department.

The starting point should be the agricultural problem. The answer might be in a genebank. It might be in another genebank. It might be on a farm, in a community seed bank, in a breeding programme, or nowhere in the genetic-resources system at all. The case manager needs to be positioned so that they have no stake in which collection, programme or database ends up being the answer.

There is another useful thing about the case-manager analogy: it is not hypothetical. Health systems have been paying people to do this for decades. And they have had to work out some fairly prosaic questions that we tend to skip when talking about “unlocking” genebank diversity.

Who pays? What counts as success? How do you stop the navigator becoming a glorified administrator? What happens when whoever pays the salary starts deciding where the patients should go?

These are not trivial details. Getting them right is what turns a nice metaphor into an institution that might just work.

Start with the problem. Find the relevant expertise and diversity, wherever it happens to be. Get the handoffs made. Notice when things stall. Keep going until there is an outcome, including, if necessary, the conclusion that crop diversity wasn’t the answer.

We’ve gotten better at opening the door to genebanks over the years. We have lots of data, fancy databases and AI. What we’re still missing is the person who walks through that door with you, the user, and is knowledgeable and independent enough to help you figure out whether the answer lies inside, elsewhere in the system, or somewhere else altogether. Call it a broker or a case manager, or whatever else you like, but that’s what I believe we need.

Again, this is just me thinking out loud here. Maybe I’m the patient who has been sent home with a stack of test results, a list of specialists and a phone number for the hospital switchboard, and is trying to work out what to do next. Help me out.

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Listening for genetic erosion

I came across a study that uses a particularly clever form of longitudinal visual ethnography. Researchers returned, after about 15 years, to the same Australian livestock producers and the same locations that had been photographed in the original study. They recreated the photographs and used the old and new images as prompts for interviews, asking nine graziers to explain what had changed, what had stayed the same, and how they now understood the landscape.

But what particularly caught my attention was what they did with the interviews. They constructed “I-Poems”: by extracting a farmer’s first-person statements from the transcript (I see…”, “I remember…”, “I notice…”, “I feel…”) and arranging them as a poem, they could crystallize how the farmer’s own sense of self and relationship with the landscape had changed over time. It struck me as a remarkably simple but powerful way of turning a mass of interview transcripts into something that vividly captures how people actually experience change.

I wonder whether something similar could add a new dimension to monitoring crop diversity on-farm. There is plenty of work documenting what landraces farmers grow, of course, and much research has explored why some are retained, abandoned or acquired. But imagine revisiting the same farmers and communities every five or ten years, recording their crop diversity alongside interviews and then constructing I-Poems from their own words.

Rather than simply showing that a landrace has disappeared, the poems might reveal how its role and meaning have changed: “I used to grow it, but now…”, “My mother really loved it because…”, “Nobody asks me for it now…”, “I still hang on to a few seeds…”, “I don’t know who has the seed anymore…” Read alongside inventories and photographs from successive visits, these could provide a very different kind of longitudinal record, one that explores not just changes in the amount of crop diversity, but in how farmers value, remember, use and make decisions about it. It could help us understand genetic erosion as the social and cultural process it is.

This wouldn’t be entirely without precedent. In Nepal, the “Gramin Kabita Yatra”, or Rural Poetry Journey, brought poets into farming communities to learn about local landraces and turn farmers’ knowledge and appreciation of them into poems and songs. The difference is that the Nepali poems were primarily a means of communicating the value of crop diversity, whereas I-Poems like the ones in the Australian study could be used as an analytical tool: capturing farmers’ own words to reveal how their relationships with particular crops and varieties change over time, and indeed space. An interesting possibility would be to bring these two traditions together, and use poetry not just to celebrate crop diversity, but to listen for the social processes through which it is maintained, transformed and unfortunately sometimes lost.