- Cynthia gives us her personal history with apple diversity, and the history of Tarte Tatin; yum!
- Farmers in Koraput, India, recognized as Globally Important Agricultural Heritage System (GIAHS).
- Ugandan farmers who select one of four new groundnut varieties increase incomes. Good to know. What happens to the old varieties?
Predictions for 2012: maize will be affected by climate
I predict that we (using the term in its widest possible sense) will understand the links between growing conditions and yield much more deeply. In particular, how changes in a plant’s environment will affect its output.
This is based on Heat, Humidity and Crop Yields, a post by Michael J Roberts, who has an economist’s ability to build complex models that explain some things rather well. The latest looks at how much better predictions of yield are when vapour pressure deficit is added to the model. As Roberts explains:
Vapor pressure deficit, a close cousin to relative humidity, has a linear relationship with evaporation, and is a key input in many crop models.
The point is that VPD is not that easy to measure, but that if you manage to do so, it is very handy.
There are two interesting things about the VPD measure we construct. First, average VPD for July and August is closely associated with our best-fitting extreme heat measure, at least in Illinois. Second, adding VPD for the season and VPD for July and August to our standard regression greatly improves prediction. Using just five variables, these two plus growing degree days (degree days between 10C and 29C), extreme heat degree days (degree days above 29C) and precipitation, we can explain over 70 percent of the variance of Illinois yields, excluding the upward trend. That’s better than USDA’s August and September forecasts, which are based on field-level samples and farmer interviews. The model can explain almost half the difference between the August forecast and the final yield for Illinois.
OK, so it is only in Illinois. And only for maize. But it might yet end up improving crop prediction models more generally.
This would appear to be post No. 5000. That it deals not with agrobiodiversity but with the effects of climate change on a single species, and probably very few varieties of that species, seems rather fitting.
Leftovers: Coconuts, Genebank, Vegetables, Famine, Danish, Bissap, Brazil nuts, Dates, Papas y mas, Fruit, Rice, Everything
We found these nibbles at the back of the fridge, and they’re not too mouldy, so lets fry them up before we get anything fresh.
- Boss of India’s agricultural research exhorts international coconut genebank to do more and be heard.
- And, first out of the gate for 2012, Nepal says it will create a new genebank for plants “on the verge of extinction”.
- Immigrant urban agriculture — in Cleveland, Ohio.
- Aid man Edward Carr interviewed: “drought does not equal famine”
- Meetings on “biodiversity” in Europe, under the Danish presidency. Indigestible?
- Hibiscus tea, what a tonic.
- Resources Research goes crazed for book about brazil nuts, and other Amazonian agrobiodiversity.
- A cure for Bayoud disease of dates? And it’s based on medicinal plants!
- Pueblos andinos reciben ejemplares de tubérculos nativos. Otra vez?
- Guerilla grafting? Now there’s an idea for “covert agriculture”. Wonder what the graftees think.
- “The giant panda of the botanical world”? Blimey. A new reserve for real wild rice.
- Huge Satoyama-style paper from Bioversity on THE USE OF AGROBIODIVERSITY BY INDIGENOUS AND TRADITIONAL AGRICULTURAL COMMUNITIES IN: ADAPTING TO CLIMATE CHANGE And they’re the ones doing the shouting.
Nibbles: Rice genes, Wheat flour
- “[R]ice plants in hotter and drier parts of Australia tend to be more genetically diverse“. Which means, natch, that they’re “a bulwark against climate change”.
- USDA tests wholewheat flour from 14 different varieties for their value in baking. Which varieties? They aren’t saying.
Taking Climate Analogues for a drive around the block
I’ve been holding off taking a proper test drive of CCAFS’s new dream machine, Climate Analogues, despite all the media attention, because I heard that the boys and girls in the pits at CIAT were still tightening the cylinder head bolts and optimizing the valve timing. But now it seems they’re done fiddling, at least for now, and I’m going to take it out for a spin.
It’s a simple idea. If you want to help a farming community adapt to climate change, you need to have some idea of what their climate is going to look like down the line. In terms of agrobiodiversity, for example, it would be nice to know of places which right now have a climate like your site will have in 2030, or whatever date, because that’s where you’d look for crop varieties with the climatic adaptations they’re going to need. That is the guts of Climate Analogues. You can of course fiddle with emission scenarios, climate models, length of growing season, whether to deal with temperature and/or rainfall, and dissimilarity thresholds (how dissimilar do two places have to be to really matter?), and the manual takes you through all those options in detail, but what it basically does is compare the climate of a reference site, now and in the future, to the climate of all other places on earth, now and in the future. 1
Easy enough to say, and extremely worthy, but clearly technically complex. That hasn’t stopped these guys before, though. Alas, the implementation in this case is not perhaps as elegant as one has come to expect. It’s still in beta, of course, so things are hopefully going to improve, but I’m sorry to have to report that I did not have an altogether smooth user experience.
Let’s get the little things out of the way first. Like if you don’t know that the default base map is called “Streets” it is a somewhat annoying process to get back to it. Like if you produce a pdf of your results you can’t then get back to your interactive map where you left it, unless you remember to open the said pdf in a separate tab. Like in the results pdf the legend of the map is totally different to the one you’ve just struggled to get to grips with in the online version. Like the fact that in that online map legend red means high dissimilarity, where really what I for one would want to highlight is low dissimilarity, or high similarity, between sites. Like the fact that the place where you change probably the most crucial thing, unless you’re a total climate geek, which is the direction of comparison (now with now, or future with now, or now with future), is buried in a menu called “Additional parameters.” Like the fact that it’s not entirely clear what future year we’re talking about anyway.
Forget all that, I’m probably just a pernickety user who hasn’t read the manual attentively enough and these smallish details will anyway be dealt with in time, no doubt. What I can’t really excuse is that you’re not really enabled to directly use the maps you get, to do anything else with them once you get them. Not unless you download the results and import them into your GIS and fiddle with them there. This seems to me CGIAR GIS geeks producing a tool for other GIS geeks. The blurb talks about facilitating farmer-to-farmer exchange of information. As things stand, the only way that’s going to happen is if there’s a person with a GIS mediating the exchange. Good for GIS people, not so good for your average researcher or policy maker. Whether good or bad for the farmer is moot, I would say.
Let me give you an example. Here’s a screen grab (because that’s the only way I could think of to export a bit of the map) of the results, using all the defaults for simplicity, for the now-to-now comparison of the climate of my mother-in-law’s farm in the Limuru highlands (X=36.679110, Y=-1.077666).
Let’s say we want to give my mother-in-law beans adapted to her current climate. Remember that what we’re looking for is high similarity, which means low dissimilarity, which means green, according to this legend (as I said, it’s a different legend in the “Results” tab). Phew. Anyway, we should look for the beans in Ethiopia, shouldn’t we. Result! Then we say to grandma, well you also need to look ahead, so here’s a map of places which right now look like your place will look like in 2030. You need beans from there too, madam.
Aha! Grandma needs beans from a bit further north in Kenya and the Great Lakes region as well as some bits of Ethiopia. 2 Yes, but wouldn’t it be nice at this stage to import a dataset of bean accessions worldwide and see if any of them come from green squares in either map? Can’t do it here, that I can see. Need to call the guy with the GIS, I suppose. Or wouldn’t it be nice to print out a nice map of Kenya that grandma can use to drive to the green squares and find bean farmers and swap some germplasm? Can’t do it here, that I can see. Need to call that GIS guy again, it seems. 3
So, in summary, a great idea, a significant technical achievement, and a potentially really useful tool for climate change adaptation. But it seems to me that if the greasemonkeys at CIAT really want this baby driven around at full speed by people other than other mechanics, they need to get back under the hood and do a thorough tune-up. Or tell me I’m wrong. I’d love to hear from you. Seriously. This is important.