Category: GIS

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Rapid agrobiodiversity surveys

This SciDevNet piece led me to this Nature article on the theory and practice of the Rapid Biological Inventory, “a quick, intensive taxonomic expedition designed to identify areas of particular biological, geological and cultural significance before development and exploitation take hold.”

Using satellite images, maps and other data, biologists target promising areas and then work with local scientists and students to walk existing and newly cut trails, recording the species they encounter. (…) In parallel with these are social inventories — surveys of the organisational structure of local communities and how they use the forest. The teams work with indigenous groups, government and local conservation organisations to deepen their understanding of the value of the surveyed areas.

I think the concept was pioneered by Conservation International, under the name Rapid Assessment Program, or RAP, but as far as I can see it hasn’t been applied to agricultural biodiversity, at least not explicitly. Seems to me one could come up with a pretty good “rapid agrodiversity assessment” methodology based on standard crop descriptors combined with traditional knowledge, wrapped up in a participatory rural appraisal (PRA) approach. Maybe someone already has?

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Prioritizing African protected areas

This EU-funded project has looked at all the national parks and reserves in Africa and assessed the contribution each makes to conserving biodiversity as part of the overall system of protected areas. Really an incredible job. Mainly dealing with animals, however, so I wonder if something similar could be done with things like wild crop relatives or something. Also, could these techniques be applied to in situ crop conservation?

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EU conserves sheep and goats

Not sure what to make of this. A European Research Headline piece of news gives some information about a project to use molecular genetics, socio-economics and geostatistics to decide which populations of sheep and goats are worth conserving. But the article doesn’t actually say anything about the project’s conclusions. And when I looked earlier today the project web site had not been updated since Agusut 2006. That’s annoying because the results could well be interesting and I’d really like to know how they analyzed the information and how they used it to advise policymakers.

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Trade information by mobile

Between the Common Catalogue on one side and regulations on the entry of new agricultural products on the other, it does sometimes seem like the EU just doesn’t want farmers to grow diverse crops, either within its borders or indeed anywhere else on Earth. Anyway, on the latter issue, maybe one of the answers is for developing world farmers to trade more among themselves. One of the bottlenecks to that, of course, is the availability of price and other information. So it was really interesting to read in The Economist about tradenet, an internet application developed by a software company out of Ghana that enables users to exchange market information, including by SMS text messages. Mobile telephony is of course expanding at tremendous speed in Africa. Tradenet is basically a sort of eBay for agricultural products, where you can put in your bid by cell phone. And more. Listen to this: “we will incorporate the ability to generate digital maps of your country with overlays of pricing for commodities, as well as include key markets in neighboring countries, where you can zoom or pan around vector maps.” Cool or what?

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Identifying trees from the air

We received the following request from Carlos E. Gonzalez of the Department of Geography, King’s College London in response to an earlier posting on botanical keys. I hope readers will be able to help him out.

As part of my PhD I have been developing an online taxonomic key for tree identification (higher taxa) on the basis on aerial photography. The taxonomic key uses some aerial photography over the Tiputini Biodiversity Station, and the user answers a series of questions on each crown in order to come to an identification. I am now testing this key in order to understand better (a) its success rate in the identification of trees using a large number of different observers and (b) the patterns of correct and erroneous identifications and implications for the key and for how different observers visualise and separate crown features in imagery. I would be very grateful if you would take a short time to identify 20 trees for me. You can find instructions and access to the key and imagery here. Your computer needs to have a copy of Google Earth version 4, available here. I cannot identify which users have given particular answers but will be able to provide some general feedback to the group of users as a whole. PLEASE ALSO FORWARD TO OTHERS WHO MIGHT CONTRIBUTE. Many thanks! Carlos