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.

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.

Missing use, or missing middle?

Genebank data seems to be in the air. Just in the past few days, I pontificated about descriptors, Mike Jackson has reminisced about the early days of rice molecular markers and CGIAR announced a big new initiative.

More data is always good to have, I’m sure. But, but, but… Genebanks are underused and more data will solve the problem? We’ve been saying something much like this for a long time now. Leave aside for the moment the question of whether CGIAR genebanks really are “underused.” Underused by whom, according to what metric, compared to what? But let’s just assume for the sake of argument that they are, indeed, underused. What if… Hear me out: what if that problem cannot be solved just by making genebank databases bigger, deeper and easier to search?

Ok, that will need some unpacking. But remember as you read this that I’m not saying that genebanks working with others to generate and share more data on nutritional quality or disease resistance is a bad thing. Just that it’s not enough. Never has been. Probably never will be.

Continue reading “Missing use, or missing middle?”

Do crop descriptors really rule the world?

Helen Anne Curry and Sabina Leonelli’s chapter in Agricultural Science as International Development: Historical Perspectives on the CGIAR Era, entitled “Crop Descriptors and the Forging of ‘System-Wide’ Research in CGIAR,” advances a provocative argument: IBPGR’s famous, even iconic, standardized descriptor lists — the shared controlled vocabularies used to record traits like plant height, fruit shape and disease resistance in many genebanks around the world — were never just a neutral book-keeping exercise. By 1997, the authors note, something like 80% of genebank curators were using them. Those that didn’t risked being marginalized and excluded from other activities.

The implication is that what seemed like rather “boring” infrastructure quietly decided which traits got noticed, valued and studied, and thus whose priorities counted in the process of conservation and use of plant genetic resources. But not just conservation and use of plant genetic resources. No, descriptors in effect helped drive CGIAR’s whole research agenda for 30 years:

We argue that the project of producing descriptors both defined and embodied CGIAR institutional identity and objectives as these evolved from the 1970s to the 2000s.

I have some sympathy with some of this, not gonna lie. Standards are never innocent or neutral, and it’s a useful corrective to point out that coding conventions, qualitative state labels and data formats can carry as much institutional power as more seemingly consequential, not to say exciting, technical interventions.

But I think the authors overstate their case. Here’s why.

Continue reading “Do crop descriptors really rule the world?”

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