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 farmers 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?”

Brainfood: Tree conservation from assisted migration to cryo

Data is not the destination

Mike Jackson’s account of the early molecular work at IRRI’s International Rice Genebank is a nice reminder that the idea of the “genomic genebank” (as he calls it) is not as new as it may sounds. In the 1990s, RAPD and AFLP markers were already being used to identify duplicates, reveal genetic structure and, more ambitiously, to predict which accessions might possess useful traits. It’s the continuation of the trajectory I traced in my recent post on descriptors: from names and human-scored traits to photographs, digital phenotypes, molecular markers and now genomic information, each adding a layer of information that makes the collection more searchable and usable. The question keeps shifting from “what do we have?” to “which of what we have might be useful?”

Jackson’s story is also a key piece of the argument I tried to make in another recent post: preserving options is only the beginning. More, and better, information makes options easier to discover, but discovering an option is not the same as exercising it. The harder question is what happens next: how do we turn knowledge about what’s in a collection into actual selection, testing, breeding, adoption and impact? Data can open the door to better use of genebank collections. It cannot walk through it for us.

And there is a danger here. As our information about genebank collections becomes ever more layered, richer and more precise, it can start to look as though we’re solving the problem of use. We are not. We are solving the problem of finding possibilities. That’s only one part of the journey. A genomic prediction is not a breeding line; a photograph is not a phenotype under farmers’ conditions. The distance between knowing an option exists and actually exercising it still has to be travelled. That is a social and institutional process as much as a biological and technological one, and it starts only when the search is over.