Category: GIS

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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: ((478 of 776 projects))

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.

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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 ((Ok, ok, I’m installing it!)) you can get a Google Earth file, which looks like this for Asia. ((I know, the colours are a bit funny. But basically, orange and cream are bad. Working on that.)) 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.

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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.

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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.

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Ground-truthing SPAM

Jeff Horwich has an interesting post over at HarvestChoice Labs looking at the effect of the tsunami on agriculture in northern Japan. He used the Droppr tool, which combines Google Maps with lots of other data, in this particular case the world-wide crop distribution data from the Spatial Production Allocation Model (SPAM). In SPAM

…tabular crop production statistics are blended judiciously with an array of other secondary data to assess the production of specific crops within individual ‘pixels’ – typically 25–100 square kilometers in size. The information utilized includes crop production statistics, farming system characteristics, satellite-derived land cover data, biophysical crop suitability assessments, and population density.

Intrigued, I decided to do a little lighthearted ground-truthing of the SPAM data. I only looked at one location, I admit, but what I found was a bit disappointing. I zoomed in on the location of the mother-in-law’s farm in the Limuru highlands (-1° 4′ 39.60″, +36° 40′ 44.80″). Here’s what the place looks like.

This is the view from space, courtesy of Google Earth. ((The blue dot is directly over the house you see in the middle of the photograph above, which was taken from the S.))

Now, according to the SPAM methodology that’s a cropping intensity of 0.59%, with the main crops being sorghum, sweet potato/yam, groundnut, banana/plantain, potato, coffee and sugarcane.

Jeff very niftily embedded both a map and a spreadsheet of the production data for the main crops in his post, but I wasn’t able to work out how to do that. So you’ll have to make do with this wholly inadequate screengrab, I’m afraid.

What my mother-in-law and her neighbours actually grow is maize, beans, potato and tea, tea and tea. A pretty different set of agrobiodiversity to what SPAM thinks. And I think she would be surprised at the low value of cropping intensity. This is a very high-potential area.

Anyway, that’s only one data point. It would be interesting to know from the SPAM guys if there’s a more systematic attempt going on to check on, and refine, the results of the model.