Category: GIS

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Earth Engine and crop wild relatives

The other recent Google Earth innovation is Earth Engine, where you can check out a bunch of interesting visualizations of environmental data. Spurred by something Julian said, I downloaded the MODIS VCF global tree cover change dataset (2000-2005). And then I went to Genesys and downloaded data on wild beans (Phaseolus spp). It was not very difficult to put the two together in Google Earth. In the map below, which just looks at central Mexico, orange means high deforestation, and green afforestation. Is it me, or do germplasm accession seem to be concentrated in areas of high deforestation? Anyway, with a little work, this could be a cheap and cheerful way to identify particularly threatened areas for germplasm collecting.

mexico

Who wants to be the first to put crop wild relatives data in Earth Engine?

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3D trees in Google Earth

The latest version of Google Earth has 3D trees! Just a few cities’ parks, a couple of wild sites (rainforest, mangroves…) and a reforestation project for now, but surely more to come.

I look forward to seeing the world’s great field genebanks in 3D in due course, such as the coconut genebank in Ivory Coast or the Breadfruit Institute’s collection in Hawaii. And maybe eventually even smaller ones, such as this fruit collection I visited last week in Tajikistan.

But maybe we could start with Pavlovsk?

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Nibbles: Horticulture, Phylogeny, Wheat stripe, Chaffey, Shrubs, AnGR, Spirulina, Capparis, Cricetus, Biofortification

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Gap-filling may be harder than we thought

Future PGRFA collections will focus on filling gaps in existing collections, collection of certain regional, minor and subsistence crops and collection from particular countries where collection has not taken place or been very limited.

That’s from the Global Plan of Action for the Conservation and Sustainable Utilization of Plant Genetic Resources for Food and Agriculture (GPA). In fact, “gap-filling” is often mentioned as a way to be more strategic and cost-efficient in germplasm collecting. This approach relies on knowing where a crop (or, more correctly, landraces of a crop) is grown, and comparing that with the distribution of germplasm accessions in genebanks (which could in fact be done in various different ways, depending on how you define “gap”, but forget that for a minute).

We all know about the problems associated with data on germplasm accessions (lack or inaccuracy of georeferences in passport data, for example). But in fact there are issues with the crop distribution data too, as we’ve recently discussed here and here. This shows the range of answers you get when you ask the seemingly simple question: where do bananas grow in Africa? Click on the image to see it better, or go to the previous posts.

So, is “gap-filling” a forlorn hope, at least at the continental and global levels? Looking forward to your thoughts…