Category: GIS

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Getting to grips with rice in Europe

A little more on that RiceAtlas that I blogged about a couple of days ago. I managed to download the shapefile of rice growing areas, and open it in Google Earth. I then imported the Genesys rice dataset, and zoomed in on Europe. Here’s what those two things together look like.

Definitely a few issues. I’m not too worried about the accessions in northern Europe. Those are probably just wrong passport data. But what about all those in Turkey which are also outside putative rice growing areas? Are those passport data mistakes too, or is RiceAtlas missing something?

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Who moved my rice?

In reply to our plea for a definitive crop distribution dataset, Andy Nelson, who used to work at IRRI and is now at the University of Twente, had this to say in a comment.

Well, this may be one way to go.

Compiling the best expert information with subnational statistics as a first cut and then using that to guide further detailed mapping with remote sensing/big data.

I’d like to see more efforts in crowdsourcing for crop mapping as well.

“This” is “RiceAtlas, a spatial database of global rice calendars and production.” ((Laborte, A. G. et al. RiceAtlas, a spatial database of global rice calendars and production. Sci. Data 4:170074 doi: 10.1038/sdata.2017.74 (2017).))

Because of the need to develop a spatially explicit global database of rice calendars that includes detailed information on rice areas with more than one rice crop in a year, we compiled the most detailed available datasets of rice planting and harvesting dates by growing season in all rice-producing countries, and linked the database to subnational production data. ‘RiceAtlas’ provides a spatial and seasonal distribution of the world’s rice production. RiceAtlas contributes to the GEOGLAM (Group on Earth Observations Global Agricultural Monitoring) initiative and regional partnerships, such as the Asian Rice Crop Estimation and Monitoring initiative (Asia-RiCE), by providing information for agricultural monitoring requirements, satellite data acquisition plans, and global crop outlook.

Here’s what it looks like.

With regards to crowdsourcing, there are various initiatives out there that could be relevant, including Geo-Wiki‘s Field Size Campaign.

Anyway, no doubt the RiceAtlas will eventually end up on the website of the CGIAR’s Consortium for Spatial Information. One bit of this, the SRTM 90m Digital Elevation Data, made a recent list of Top 15 Free GIS Data Sources.

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Mapping crops: Are we there yet?

I ran across a bunch of nice crop maps on the internet ((Though there are some strange things about a couple of them. Check out the shea one, for example.)), so I made a GIF for you (click on it to get it cycling).

You’re welcome.

I’m reliably informed the source is Monfreda et al. (2008). You can download the data in multiple formats, but I don’t think I’ve ever seen the headline maps displayed all together as The Decolonial Atlas has done, albeit without attribution, which is naughty.

Anyway, people have obviously taken the trouble to download and play around with the data. For example, they have been ably mashed up by Bioversity to get a global crop diversity map.

Which, in turn, it is instructive to compare with the one from the Lancet Planetary Health map we blogged about a few days ago.

But which dataset to use to do this kind of stuff? Monfreda’s is only one of many.

I see that we now, after a long wait, have WordClim 2, thanks to the work of our friend Robert Hijmans and his colleagues. ((Not to mention climate surfaces going back 20,000 years.)) Is it too much to hope for that he’ll now turn his hand to producing the definitive crop distribution dataset? ((Yes, we’ve blogged about this before. More than once.)) Maybe something for the CGIAR’s Big Data Platform, just launched, to think about organizing, convening, and/or inspiring.

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Brainfood: Cannabis roundup, Citrus genomes, Mapping Africa, Maize diversity, Qat diversity, Language diversity, Apple taste, Coconut diversity, Napier grass review, Rangeland management, Chinese goats, Arabica evaluation, Bangladeshi chickens, Seed endophytes

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Targeting germplasm in an age of climate change

Let me expand a little on yesterdays’s teaser about the Seedlot Selection Tool.

Say you have an accession of maize, for example, collected 20 years ago, for example, somewhere in the middle the NW zone of Mexico defined by Orozco-Ramírez, Perales & Hijmans (2017), for example.

Say that you’d like to know where you could grow that material 20 years from now.

Say you have a few minutes to learn how to use the USDA’s Seedlot Selection Tool.

This is what you would get, more or less, depending on the details.

The blue dot is your collecting site, the red bits are the places where that material will be adapted in 2040. And you can run the thing the other way around too. That is, given that you want to grow something in 2040 where that blue dot is now, where would you have to have collected it in the past?

The Seedlot Selection Tool seems to be aimed primarily at forest and landscape managers, but I see no reason why it couldn’t be used in agricultural applications too, as above, subject to the same provisos.

The Seedlot Selection Tool (SST) is a web-based mapping application designed to help natural resource managers match seedlots with planting sites based on climatic information. The SST can be used to map current climates or future climates based on selected climate change scenarios. It is tailored for matching seedlots and planting sites, but can be used by anyone interested in mapping climates defined by temperature and water availability. The SST is most valuable as a planning and educational tool because of the uncertainty associated with climate interpolation models and climate change projections. The SST allows the user to control many input parameters, and can be customized for the management practices, climate change assumptions, and risk tolerance of the user.

Would love to get my hands on a global version.