- South Africans produce virus-resistant cassava.
- Success stories in agricultural development documented. Some agrobiodiversity in there.
- Fisheries and food security. And more. And more: why not eat “weedy”fish?
- A crop modeler speaks.
- African agriculture is on youtube, but just barely.
- Forage grasses for beginners. Straight from the grazier’s mouth.
More graphics on the global effects of climate change on crops
The Science Museum in London has launched an interactive map. The data are from the Met Office’s Hadley Centre, as analyzed by the Walker Institute. I like the Hadley Centre’s one-page summary.
Niche modeling and common sense
We have blogged a few times about niche modeling and how to improve it. Below Mohamed Fawzy Farag Nawar briefly highlights what will become a useful resource in this field, Lifemapper (the data for the modeling comes from GBIF), but points out some limitations.
Lifemapper.org is an initiative to implement online some sort of generic model to predict where a species might exists based on where it was collected, or where it was otherwise documented that it lived. Fine. But here is the results of the model on the species Clivipollia pulcher. This is a marine mollusk that was found along the coasts of eastern Australian, Papua New Guinea and the Philippines. You will see on the map of predicted distribution that the model suggests it might be found in various places inland in central Africa and Latin America. That is what you get when essential prior knowledge is not introduced to the model. Something like telling the developer of the model that marine species should be modeled to a different set of environmental data than Worldclim, which should only be used for terrestrial fauna and flora. Agricultural species are included in Lifemapper, though again the predicted distributions will have to be looked at fairly carefully before use.
Andy Jarvis in the limelight
Our friend, colleague and occasional contributor Andy Jarvis received GIBF‘s Ebbe Nielsen Prize for innovative bioinformatics research last night in Copenhagen. Andy has been doing his trademark work on the spatial analysis of crop wild relative distributions ((The maps below are an example. They show the modeled distribution of species richness in the genus Phaseolus now, and then in 2050.)) at CIAT, just outside Cali in Colombia, jointly with Bioversity International. He used the occasion to highlight the contribution made to this effort by his numerous Colombian colleagues. Congratulations to all of them.
LATER: Here’s Andy’s talk.
Mapping livelihoods diversity in East Africa
As the world discusses desertification and worries about the drought in East Africa, it’s as well to remember that it is livestock keepers that bear the brunt of these problems. A recent paper in Agriculture, Ecosystems and Environment helps to quantify the size of the challenge. ((Cecchi, G., Wint, W., Shaw, A., Marletta, A., Mattioli, R., & Robinson, T. (2009). Geographic distribution and environmental characterization of livestock production systems in Eastern Africa. Agriculture, Ecosystems & Environment. DOI: 10.1016/j.agee.2009.08.011.))
It uses environmental and livelihoods data to map the geographic distribution of different livestock-keeping strategies in East Africa. The authors — a team lead by FAO — conclude that:
…nearly 40% of all livestock in the IGAD region are kept in mixed farming areas, where they contribute to rural livelihoods in diverse ways, not least by enhancing crop production through manure and draught power and by providing additional indirect inputs to livelihoods that are seldom properly accounted for. Moreover, an estimated 50 million rural people in Eastern Africa — over a third of the rural population — live in areas where livestock predominate over crops as a source of income. Investment statistics would suggest that this fact often fails to be appreciated fully by governments, donors and policy makers.
The map itself will hopefully prove useful in guiding policy in the future, ((Though I am bound to say I wont be holding my breath on that one.)) but I want to concentrate here on some of the analysis that having all these data in a GIS allowed. In particular, look at graphs of the prevalence of different livelihoods strategies plotted against human population density, and then length of growing period. ((A pastoral production system is where total household income from livestock (L) is 4 or more times greater than total household income from crops (C). An agro-pastoral system has a L/C ratio of 1-4. And in a mixed farming production system the income from crops exceeds that from livestock (L/C<1).))
It looks like areas with a human population density of 20-30 people per square kilometer and a growing season of about 150 days are the most diverse in terms of production systems. It would be interesting to know whether they are also most diverse at the species and genetic levels, for either crops or livestock. I suspect the necessary data weren’t collected in the livelihoods surveys that formed the basis of this study. Will no enterprising student go in and test the hypothesis?