Reporting threats to agrobiodiversity: A modest proposal

Yesterday Hannes, à propos of something else, reminded me of a post I did a few months back about ProMED which asked the question “Why do we still not have an early warning system for genetic erosion?” Today I read about pestMapper — “[an] internet-based software tool for reporting and mapping biological invasions and other geographical and temporal events.” Whose objectives is basically to make a more participatory, Web 2.0-like ProMED. Coincidence? Maybe. Anyway, this is exactly the kind of thing we’ve been thinking here a “global genetic erosion threat reporting and monitoring portal” might look like. Any thoughts? An idea worth pursuing?

pest map

Nibbles: Chicory symbolism, Watermelon disease, Olive documentation, Camassia quamash, Pig maps

Nibbles: Pluots, Village chickens, Axolotl, Artisanal fishing, Fruit and climate change, Stamps, Hornless cattle, Artemisin for malaria, Aquatic agroecosystems characterization, Speciation and ploidy

Snorkel rice

ResearchBlogging.orgYoko Hattori and colleagues report in Nature ((Hattori, Y., Nagai, K., Furukawa, S., Song, X., Kawano, R., Sakakibara, H., Wu, J., Matsumoto, T., Yoshimura, A., Kitano, H., Matsuoka, M., Mori, H., & Ashikari, M. (2009). The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water Nature, 460 (7258), 1026-1030 DOI: 10.1038/nature08258)) that they have identified two genes involved in the awesome elongation of deep water rice; the type of rice that can grow in several meters deep water. The genes, baptized SNORKEL1 and SNORKEL2, can now be identified with molecular markers and crossed into popular rice varieties. The BBC has a nice video comparing — I assume — genetically otherwise nearly identical rice varieties with and without the genes.

The avid reader will remember the runner-up entry in The Competion about the sub-1 gene ((

Kenong Xu, Xia Xu, Takeshi Fukao, Patrick Canlas, Reycel Maghirang-Rodriguez, Sigrid Heuer, Abdelbagi M. Ismail, Julia Bailey-Serres, Pamela C. Ronald & David J. Mackill, 2006. Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature 442: 705-708. doi:10.1038/nature04920
)), that is used by IRRI to make rice
flood-proof. Some of these new sub-1 varieties, such as Swarna-sub1 are already grown by farmers in India and Bangladesh.

Interestingly, sub-1 does the very opposite of SNORKEL. Sub-1 shuts the plant off to stop elongation, so that it saves its energy, and can recover later. This works great with flash floods if the water recedes after a week or two. But if the water stays for longer than that, the crop dies. With stagnant deep water, a variety with the SNORKEL gene could be a better bet.

If farmers know beforehand that the water is going to be very deep (because it happens most years), they probably already plant deep water varieties (or plant later or do some other smart thing). Deep water rice is somewhat in decline, because of low yield, but it is grown on a very large area, probably about 3.5 million ha worldwide, mostly in India, Bangladesh, Myanmar, Thailand, Indonesia, Vietnam and Cambodia.

However, if flooding is rare it could be more profitable, though risky, to plant other than deep-water varieties. For their earliness, yield, quality, or what not. Adding either the sub-1 or the SNORKEL gene ((The combination of the two would make for an interesting experiment.)) to those varieties would be an insurance policy for flood years. But which gene to choose? And in what variety? And where to grow it? Not an easy question, but we have been trying to answer it.

Mapping Argentinian pests and diseases

The Instituto Nacional de Tecnología Agropecuaria in Argentina has a new online pest and disease atlas out. It’s in Spanish, but pretty intuitive to use. You can search by host or by pest/disease organism. And you get a bunch of references, descriptive notes, photos and a map, albeit a static one — not sure how updating will be done. Below is the distribution of Setosphaeria turcica, the causal agent of Northern Leaf Blight in corn, for example. Would be interesting to mash this kind of thing up with the distribution of tolerant material. Must look into that. And of course one could do some climate change prediction modeling too. Thanks to IIALD.

argentina