- Opportunities and Threats to Harmonisation of Plant Breeders’ Rights in Africa: ARIPO and SADC. While the intention of ARIPO and SADC is to create a single internal market for protected varieties in Africa, “the end result may look quite differently.”
- Evaluation of Resistance to Ralstonia solanacearum in Tomato Genetic Resources at Seedling Stage. Out of 285 varieties from 21 countries in the Korean genebank, 4 may be resistant to bacterial wilt.
- Following the Open-Source Trail Outside the Digital World: The Case of Open-Source Seeds. “…by not rejecting the idea of property, including intellectual property, but rather attempting to manage it differently, it creates its own enclosures.”
- Grain protein concentration and harvestable protein under future climate conditions. A study of 108 spring barley accessions. Higher CO2 and temperatures lead to higher protein concentrations but lower yields, so lower harvestable protein. The good news is that there’s variation in the response of varieties.
- How can we harness quantitative genetic variation in crop root systems for agricultural improvement? Apparently we still don’t have a mechanistic understanding of root growth, and we’ll need it if we’re going to improve function.
- Bambara Groundnut for Food Security in the Changing African Climate. It’s nutritious, it’s drought tolerant, and it can be intercropped. What’s not to like?
- Neutral and functional marker based genetic diversity in kodo millet (Paspalum scrobiculatum L.). Indian material falls into 4 groups, with Bihar being very diverse. African genepool and wild species should be useful in broadening base in India.
- Origin, Dispersal, and Current Global Distribution of Cacao Genetic Diversity. We’ve come to the limit of the usefulness of the Pound Collection.
- Exploring the role of local heirloom germplasm in expanding western Washington dry bean production. 24 bean varieties have been grown in the area for 20–130 years, representing a useful starting point for participatory plant breeding.
- Growing Cassava (Manihot esculenta) in Mato Grosso, Brazil: Genetic Diversity Conservation in Small–Scale Agriculture. Lots of diversity within communities, and differences among communities. Varieties with same name not necessarily genetically similar.
- Establishment of an in vitro propagation and transformation system of Balanites aegyptiaca. So?
- Mapping cropland-use intensity across Europe using MODIS NDVI time series. Four indicators show highest cropping intensity in Germany, Poland, and the eastern European Black Earth regions, and lowest in eastern Europe outside the Black Earth region. Interesting to mash this up with agricultural biodiversity? Like earthworms?
Vulnerability of crop wild relatives kinda sorta mapped
There’s a nice paper in Nature on how sensitive vegetation around the world is expected to be to climate change. Here’s the money map.
Cries to be mashed up with crop wild relative distributions and gap analysis.
Nibbles: Cover crops, Viet coconut, Water maps, Mao’s mango, Tudor bread, Belgian gardening, IRRI fingerprints, Stay green barley, Miniature donkey
- Uncovering cover crops, the NY Times way.
- Uncovering coconut cultivation in Vietnam, the Roland Bourdeix way.
- Where to expect water shortages, and irrigation. Crying for a mashup.
- When a mango is not just a mango.
- Bread, and much else, according to the Tudors.
- A Belgian plantsman is revolutionizing gardening. No, really.
- How genomics will revolutionize rice breeding. No, really.
- How to get deeper barley roots for drought tolerance? Look to sorghum.
- And today’s miniature livestock is…a donkey.
On mammal diversity and vegetation biomass
Two global maps coincidentally turned up almost side-by-side on Twitter this weekend. Interesting in their own right individually, they threw up a question for me when I was forced to look at them together in my feed. A paper in Diversity and Distributions mapped what the diversity of large mammals would look like if not for what humans have wrought. Here it is.
And a paper in Global Change Biology mapped above-ground vegetation biomass across the tropics.
So my question is this: why does high-biomass vegetation support a relatively large diversity of mammals in SE Asia, but not in tropical Africa or South America?
Well, since we’re calling for paradigm shifts…
There’s a lot that’s both nice and deliciously ironic about IFPRI’s recent blog post “Granular socioeconomic data are increasingly becoming available in agricultural research.” This summarizes a letter to Nature Climate Change from HarvestChoice scientists which adds some nuance to a previous commentary in that journal calling on socioeconomists to up their data game. The point is a good one, and it’s stated right up front in the post, quoting the letter:
Spatially explicit, harmonized socio-economic data products are increasingly available to the public, such as population and poverty grids, microdata derived from national household surveys, and rasterized sociodemographic indicators. While these products are often overlooked in the economic literature, they are well suited to the study of climate’s impact on human geography across scales.
But, then comes the irony.
First, both the letter and the original article, helpfully linked to in the blog post, are of course behind paywalls. Second, the map included in the post is provided with an incorrect caption. There’s a screen grab here on the left. As you can see, the caption suggests that the map illustrates that childhood wasting is more prevalent in the drier areas of sub-Saharan Africa. But the map shows no such thing, as a glance at the legend, or indeed the map in the original letter, will prove. What the map caption should actually be is “Subnational Demographic and Health Surveys (DHS) data showing centroids of DHS clusters overlaid on Agro-ecological Zones.” Not quite so catchy. The map showing the relationship between wasting and agroecological zones is this one, and it’s in the Supplementary Materials to the letter. ((Which, bizarrely, seem to be freely available.)) I hope I don’t get into trouble for reproducing it here, but it is pretty cool.
And thirdly, and most importantly, frankly neither the socioeconomic datasets nor the agroecological map which the HarvestChoice researchers cleverly mashed up to make their point about data availability are exactly easy for the average non-GIS geek to use, let alone to combine. Try it and see.
The original paper calls for a “new paradigm in data gathering.” The blog post echoes the follow-up letter in saying “the paradigm shift is alive and kicking already.” Oh good. But I’d like to be able to look at the distribution of stunting and other nutritional indicators together with the distribution of different crops and varieties without having to beg a GIS person to do it for me, or spending half a day putzing around trying to understand what this means
Data layers are available in comma-separated values format (.csv) suitable for MSExcel, in ESRI ASCII Raster (.asc) and GeoTIFF formats (.tif) suitable for any desktop GIS tool. To view ASCII or GeoTIFF rasters in ArcMap or QuantumGIS simply drag and drop the downloaded files onto the layer pane.
Sure, have your paradigm shift in data gathering. But can I also have one in usability, please?