- Livingstone potato (Plectranthus) on the menu in Burundi. Yeah but what does it taste like?
- The world’s roads mapped. About time too.
- The world’s convenience food made better. Maybe.
- Dog cooking pot from ancient China. Woof. Via.
- Hybrid rice backfires. Via.
- Mapping the impacts of climate change. Only country level though.
- Native lawns better. But are they greener?
- JSTOR does a cassava roundup despite hating tapioca.
- Biodiversity monitoring? There’s an app for that.
- Wild rice (not a wild relative of rice, mind, but sacred to the local Native Americans) vs the copper-nickel mining industry.
- Slideshow on rice (the real thing) in Vietnam.
Mapping aid
Thanks to CIAT’s Meike for news that
InterAction has just launched an interactive US Food Security Aid Map that provides detailed project-level information on food security and agriculture work being done by their member NGOs. The site can be browsed by location, sector, organization or project.
Here’s the map of agriculture projects: 1
As coincidence would have it, one of the projects is the orange-fleshed sweet potato work we mentioned in a recent post.
Searching on “agrobiodiversity” yielded nothing, but there were a few hits with “diversification.” Well worth exploring in a bit more detail. If only to identify places where some pre-emptive germplasm collecting might be in order.
Mapping drought risk
Just a quick follow-up to the rhyming couplet on water-related stresses in the just-published Brainfood. The Center for Hazards and Risks Research (CHRR) at Columbia University, which we have mentioned here before in connection with tsunami risk, also has data on Global Drought Hazard Distribution.
With a little R-related effort by Robert 2 you can get a Google Earth file, which looks like this for Asia. 3 I’ve also added MODIS fire hotspots for the past 24 hours, merely because I can. That would be the little fire icons.
And that means you can mash up drought risk with germplasm origin (from Genesys, say), in this case from Chad as an example.
Which is a great thing to be able to do because as we have just had reconfirmed by our friend Dag Endresen, the origin of germplasm allows you to make some predictions about its performance.
The climate–demography vulnerability index of my mother-in-law
Another dispatch from the outer reaches of GISland. Yesterday’s post on the likely consequences of climate change around my mother-in-law’s farm in Kenya got me thinking that it would be nice to see where that locality fits in the global vulnerability scene. One can actually do that thanks to a recent paper in Global Ecology and Biogeography. 4
The authors start by calculating something they call Global Climate Vulnerability Index (CVI)
…by combining climate change forecasts with current relationships between human density and climate. We further refined the CVI by contrasting predicted vulnerabilities with demographic growth rates to create a climate–demography vulnerability index (CDVI) reflecting the spatial disparities between demographic trends and climate-consistent population growth.
The global map of CDVI is Fig. 5 in the paper. But how to get that into Google Earth? Thanks to the raw GIS files from one of the authors, and some R magic from friend and occasional contributor Robert, I now have a kmz file of CDVI, on top of which I can easily plot the location of the mother-in-law’s spread at Gataka near Limuru. In the map below, dark blue is bad, light blue less so.
Gataka turns out to have a slightly positive CDVI.
Highly negative values [of CDVI] … represent low-vulnerability situations where current demographic growth is much lower than climate-consistent population growth, while highly positive values … represent high-vulnerability situations where current demographic growth vastly exceeds climate-consistent population growth.
So, bad news for the mother-in-law, but not actually as bad as I feared. I wonder if I can persuade her to diversify. Perhaps into indigenous leafy greens. And sorghum, as maize seems to be heading for trouble. SPAM says sorghum should be the main crop here anyway. It may yet turn out to be right.
More messing about with Droppr
I continued my exploration of IFPRI’s wonderful Droppr software by looking at its future climate tool. You click on a spot on the Earth and it tells you how total precipitation and average temperature will change, for each month of the year. Again, I did it for the mother-in-law’s farm, and this is the result:
Looks pretty bad, at least for temperature. Although of course, for maize at least, which is the main food crop in that area, what you really want to know is peak, rather than average, temperatures. That’s according to a study by David Lobell and Marianne Bänziger we nibbled a few days back, and which recently got a big write up in The Economist:
Days above 30°C are particularly damaging. In otherwise normal conditions, every day the temperature is over this threshold diminishes yields by at least 1%. Moreover, days where the temperature exceeds 32°C do twice the harm of those at 31°C. And during a drought, things are worse still. Then, yields take a hit of 1.7% per day over 30°C.