GIS – Page 46 – Agricultural Biodiversity Weblog

May 17, 2017March 4, 2018

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.

May 8, 2017May 8, 2017

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

Cannabis Domestication, Breeding History, Present-day Genetic Diversity, and Future Prospects. The weed is in trouble, man. Just one of the papers in a very special issue of Critical Reviews in Plant Sciences. And for a historical perspective…
Genomic analyses of primitive, wild and cultivated citrus provide insights into asexual reproduction. Apomixis is down to a “miniature inverted-repeat transposable element insertion in the promoter region of CitRWP.”
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. Croplands increased by 1 Mha/yr between 2003 and 2014.
Genetic diversity and population structure of native maize populations in Latin America and the Caribbean. Three groups: Mexico + southern S. America, lowland Mesoamerica + Caribbean, Andes.
Phylogeography of the wild and cultivated stimulant plant qat (Catha edulis, Celastraceae) in areas of historical cultivation. Centres of origin in Kenya and Ethiopia, with limited movement between the two, but some hybrids in N. Kenya; the Yemeni stuff came from Ethiopia.
Process-based modelling shows how climate and demography shape language diversity. If you assume human groups split after reaching a certain population size, and rainfall limits population density, you can predict language diversity in Australia.
Volatile Compound Profiles of Malus baccata and Malus prunifolia Wild Apple Fruit. Look at the esters.
Genetic diversity, population structure and association analysis in coconut (Cocos nucifera L.) germplasm using SSR markers. I can’t see much new here. Please, coconut experts, tell me what I’m missing.
Opportunities for Napier Grass (Pennisetum purpureum) Improvement Using Molecular Genetics. Could do better.
Heterogeneity as the Basis for Rangeland Management. Gotta mix it up.
Analysis of genetic diversity of Chinese dairy goats via microsatellite markers. The locally developed breeds are all derived from a single European breed.
Variation in bean morphology and biochemical composition measured in different genetic groups of arabica coffee (Coffea arabica L.). You can’t use morphology to predict taste.
Breeding for the improvement of indigenous chickens of Bangladesh: evaluation of performance of first generation of indigenous chicken. I don’t understand this much, but I liked the names of the genotypes: Naked Neck, Hilly and Non-descript Desi.
Persistence of endophytic fungi in cultivars of Lolium perenne grown from seeds stored for 22 years. It’s a record!

May 7, 2017May 3, 2017

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.

May 6, 2017May 4, 2017

Deconstructing restoration

Just to remind ourselves that conserved seeds are not just there to be used in breeding, let us “deconstruct symbolic promises of fertility and rebirth carried by domesticated seeds and look at the reality of the seeds that have never been at our service,” think as holistically as we can, and consider taking the MOOC on “Landscape Restoration for Sustainable Development: a Business Approach.” And, if we live in the western bit of North America, let us play around with the USDA’s Seedlot Selection Tool too.

May 2, 2017May 2, 2017

More Mexican maize mayhem

It didn’t take long for my prediction to come true that the Mexican maize dataset I blogged about a couple of weeks back would get some more attention. The lead author of that previous paper, Hugo Perales, has teamed up with Quetzalcóatl Orozco-Ramírez and our old friend Robert Hijmans to do a deep dive into the database of 18,176 georeferenced observations of about 60 maize races. Some key findings:

Both at national and state level, there are a few very common races, and many races with very few observations.
10% of the races account for 54% of the records.
Over half of the races account for 10% of the records.
The maximum distance between two records of the same race was just over 1000 km on average, the maximum about 2600 km, and lower than 200 km for 7 races.
There was a positive association between the number of observations and the number of races in both 50 km and 100 km square cell.

I particularly liked the new map of “maize communities,” that is, regions where more or less similar assemblages of races are found.

Although the previous paper had a similar map of “biogeographic regions,” this is more detailed and robust. Intriguingly, the hotspots of highest diversity tend to occur where distinct maize communities meet.

I’ll see if I can get Robert so say a few words about this work here.