Nibbles: Sake, Wine, Kew, Climate change, Canada, Banana processing

The climate–demography vulnerability index of my mother-in-law

ResearchBlogging.orgAnother 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. ((Samson, J., Berteaux, D., McGill, B., & Humphries, M. (2011). Geographic disparities and moral hazards in the predicted impacts of climate change on human populations Global Ecology and Biogeography DOI: 10.1111/j.1466-8238.2010.00632.x))

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.

Nibbles: ABS in ITPGRFA, Wheat Yield Consortium, Plasticity and climate change, Sustainable intensification, Early agriculture

Nibbles: Mead, Treaty, Zoonoses, Flowery margins, Post-doc, Sacred Groves, Posters, Maize in Africa.