Category: GIS

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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…

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Yes, we have more banana distribution data

More today on where bananas grow. Or where people think they may grow, anyway. First, let’s tie up some loose ends. Yesterday I mentioned Chad Monfreda‘s dataset from the Land Use and Global Environmental Change project, but said that I hadn’t been able to get an actual map. Thanks to Julian and Andy, I now have. To the left is the result for banana.

And to the right for plantain.

Andy also pointed me to another dataset, called MIRCA2000: “Global data set of monthly irrigated and rainfed crop areas around the year 2000.” Here’s Andy’s pragmatic take on this proliferation of datasets:

You do have information on quality and uncertainties and there is enough info — if you are interested — to work out which to use. I don’t believe that there is a right or wrong answer here, they all try to make the best of what is often very cruddy and incomplete data.

In short – you’ll have to sort it out yourself, along with the rest of us. I just take the average of these when I need to make a rice area map – though the average of a bunch of wrong datasets is still a wrong dataset.

It’s a choice between modelled crop distributions or statistical data or a combination of both. Neither is wholly accurate and like most global data sets, it is best not to look too closely at the results.

4 data sets are better than none right?

Glenn also chimed in with his work for the Generation Challenge Programme, which uses the SPAM dataset I mentioned yesterday. It’s still in beta, so you can’t play around with it yet, but, again, this is what banana in Africa looks like:

Julian sums it up very well in his very comprehensive comment on yesterday’s post, and I leave the final word to him:

This is what you’d normally expect when there’s no coordination or data sharing among institutions: you end up with several institutions, each one re-inventing the wheel. The public then does not know which source to use.
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So, you end up with two sources of data: (1) FAO country level statistics, and (2) expert knowledge; two methods: Monfreda et al.’s and HarvestChoice’s; and one visualization tool. Anyone interested in comparing or merging?