Featured: Data, data everywhere…

Cindy Cox of HarvestChoice sort of, kind of, maybe agrees with some parts of what we say in a recent post about the usability of data.

Although a new generation of development practitioners and analysts are increasingly taking advantage of so-called alternative data sources like we used in our paper, such resources are still underutilized in socio-economics. And without a story, data are meaningless.

Sure, and who has those stories? I suspect it’s not the people who can do the analysis.

Nibbles: Solutions edition

  • No new salinity tolerance in cereals? You need to look at the right thing.
  • No new crops? Focus on plants’ sex lives.
  • No hope for drylands? Look to biodiversity.
  • No new agricultural land? No problem.
  • No data on neglected Himalayan crops? Got you covered.
  • No way you’re drinking coffee from civet droppings? Chemistry to the rescue.
  • No place for the offspring of F1 hybrids in your agriculture? Go apomictic.
  • No new fruits left to try? Hang in there.
  • No diversity in your Aragonese homegarden? There’s a genebank for that.
  • No impact for your agricultural research. Try clusters.
  • No agroecological patterning to your crop’s genetic diversity? It’s the culture, stupid.

Brainfood: Animal genomics, Konjac diversity, New wild cassava, New wild cowpeas, Saline breeding, Land sparing, Sorghum diversity

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.

Screen Shot 2016-02-01 at 11.33.52 AMFirst, 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. 1 I hope I don’t get into trouble for reproducing it here, but it is pretty cool.

Screen Shot 2016-02-01 at 11.37.11 AM

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?

The status of wheat landraces in Tajikistan, Turkey and Uzbekistan

Six years in the making, FAO announced today the publication of surveys of wheat landraces in farmers’ fields in Tajikistan, Turkey and Uzbekistan. The work was done in collaboration with CIMMYT, ICARDA and national researchers. Although, perhaps surprisingly, dozens of landraces were still found — 162 distinct names in Turkey — they are certainly under threat:

Local landraces today account for less than 1 percent of total wheat production. Over the past 75 years, according to field surveys, the number of wheat landraces fell from 37 to 7 in Balikesir, a western province of Turkey.

Here’s the distribution of the landrace Zerun, according to the Turkey survey.

Screen Shot 2016-01-29 at 1.43.15 PM

There are 14 accessions with this name listed in Genesys, and they do indeed come from the region in question, though mainly from close to roads (click to see better):

turkey

It was not clear to me from the report of the work in Turkey whether samples were taken for ex situ conservation, or at least genotypic comparison with existing accessions. But if this one landrace is anything to go by, there might still be scope for some gap-filling collecting.