- An informational view of accession rarity and allele specificity in germplasm banks for management and conservation. Basically a better way of making cores.
- Multi-indicator sustainability assessment of global food systems. Thankfully includes both “Shannon Diversity of Food Supply” and “Food Production Diversity”. No sign of the Agrobiodiversity Index, though, alas.
- Cytoplasmic Diversity Studies in Sunflower (Helianthus annuus L.): A Review. Have the wild relatives to thank for it.
- Mosaic of Traditional and Modern Agriculture Systems for Enhancing Resilience. Refers specifically to rice irrigation systems, but could be generalizable, why not?
- Post-disaster agricultural transitions in Nepal. To cardamon, mainly.
- Simulating the Impacts of Climate Variability and Change on Crop Varietal Diversity in Mali (West-Africa) Using Agent-Based Modeling Approach. Less favourable and unstable climatic conditions lead to loss of diversity.
- Genetic Diversity in Argentine Andean Potatoes by Means of Functional Markers. There’s a small group of weird, interesting ones.
- Single Nucleotide Polymorphism (SNP) markers associated with high folate content in wild potato species. Ten-fold variation in content in in F2 population derived from cross between high folate diploid clone of wild Solanum boliviense and low/medium folate diploid S. tuberosum. Nice.
- Identification of new sources of resistance for pearl millet downy mildew disease under field conditions. 20 really good ones out of 101. Could have been worse.
- Assay of Genetic Architecture for Identification of Waterlogging Tolerant Pigeonpea Germplasm. 38 out of 128 survived. People are lucky this week.
- Phenotypic evaluation of a diversity panel selected from the world collection of sugarcane (Saccharum spp) and related grasses. Out of 300, 27 were higher than commercial standards in dry or fresh mass. On a roll here.
- Genotyping by Sequencing and Genome–Environment Associations in Wild Common Bean Predict Widespread Divergent Adaptation to Drought. Two genes identified. Let’s quit while we’re ahead. No, come on, let’s do another one.
- Tree genetic resources at risk in South America: A spatial threat assessment to prioritize populations for conservation. 7 of 80 socieconomically important trees threatened across their range. Damn.
Spatial data everywhere, but is that enough?
Last week saw something of a Big Spatial Data blitz, and not just Kofi Annan’s Nature piece in which he pithily set out why data — both big and small — is important:
Data gaps undermine our ability to target resources, develop policies and track accountability. Without good data, we’re flying blind. If you can’t see it, you can’t solve it.
The occasion for the aphorism was a monumental study in the same journal on “Mapping child growth failure in Africa between 2000 and 2015,” which plotted various child heath and education variables over the entire African continent at the unbelievable resolution of 5×5 kilometres. Interestingly, other spatial data, this time on agricultural production and nutrient diversity (which we have blogged about), was used to explain patterns in child growth stunting. There was also a call in the correspondence section of Nature to “democratise” smallholders’ access to such data.
But that wasn’t all.
A study in the American Journal of Agricultural Economics on “Food Abundance and Violent Conflict in Africa” used a huge spatial dataset of population, agricultural production and conflict locations. It found that, contrary to expectation, “[a]lthough droughts can lead to violence, such as in urban areas; this was … not … the case for rural areas, where the majority of armed conflicts occurred where food crops were abundant.”
And, finally, there was “Winners and losers of national and global efforts to reconcile agricultural intensification and biodiversity conservation” in Global Change Biology. Unhelpfully titled, the more interesting finding of this study was that the “uneven spatial distribution of both yield gaps and [vertebrate] biodiversity provides opportunities for reconciling agricultural intensification and biodiversity conservation through spatially optimized intensification.”
Will all these pretty maps be used? Perhaps Lawrence Haddad said it best (not for the first time) in a tweet referring to the malnutrition study:
My comment on the paper is the same @l_haddad – https://t.co/40pvXz8uKO
— Dr. Purnima Menon (@PMenonIFPRI) March 1, 2018
I’d add one thing. It’s probably too much to ask for “the powerful” to learn some GIS, but researchers could get better at helping them to bring together and explore disparate datasets such as these three in powerful, easy-to-use visualisations.
LATER: I forgot one: there’s also a new global dataset on evaporative stress index.
Nibbles: Ruby chocolate, Wild Cicer, Lost rices, Breeding beans, Pawpaw, Pink pineapple, Indigenous livestock, Aquaculture, Coffee Atlas, Egyptian beer, Tequila shortage, Crop diversity
Spatial data everywhere
Looks like mapping is in the air. Hardly had I finished messing around with European trees maps that I ran across this random dump of Brazilian crop distribution data. The source is given as the Brazilian Institute of Geography and Statistics (IBGE), but I was not able to find the original maps there. I still wanted to do a mashup with Genesys, though, of course, which meant a little more messing around.
In the end, it turned out to be fairly easy, though not as easy as with those EUFGIS shapefiles. You have to hack the map off that first website as a screenshot, then add the JPG as an image layer in Google Earth and tweak the corners until it more or less fits on top of the borders of Brazil, which is the bit that takes time. Once you’re happy with the fit, you can download an appropriate KML file from Genesys and plonk it on top. Here’s the result for cassava (click on the image to see it better).
The green splodges mean cassava cultivation according to IBGE, and the red dots are cassava landrace accessions from Genesys. That would be a pretty good way to identify gross geographic gaps in ex situ holdings, but for the fact that, crucially, there’s no data from the national collection at Cenargen in Genesys. Yet. We’re working on it. Stay tuned.
Brainfood: Marginal breeds, Biodiversity vs C, Cassava bread, Biodiversity & function, High throughput genomics, Speed breeding, Spiderplant breeding, Agronomy & breeding, Accessibility, PA threats, Diversification, Self-medicating apes, Rusty wheat
- Signatures of positive selection in African Butana and Kenana dairy zebu cattle. Cattle breeds from marginal environments show signs of selection in genome regions associated with adaptation to marginal environments.
- The extent and predictability of the biodiversity–carbon correlation. Co-benefits in about 20% of tropical regions.
- Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Losing biodiversity has different effects on individual functions across ecosystems, but consistent effects on the overall impact on functionality. If you see what I mean.
- Cassava bread in Nigeria: the potential of ‘orphan crop’ innovation for building more resilient food systems. The end of the value chain is the important bit.
- Scaling up: A guide to high throughput genomic approaches for biodiversity analysis. Will probably need to be revised next year.
- Speed breeding is a powerful tool to accelerate crop research and breeding. Shuttle breeding on steroids.
- A roadmap for breeding orphan leafy vegetable species: a case study of Gynandropsis gynandra (Cleomaceae). Could do with some high-throughput speed breeding focused on the end of the value chain. How’s that for a coincidence (see 3 entries above)?
- Impact of Crop Diversification on Rural Poverty in Nepal. Growing high-value vegetables can help. Is Cleome high enough value, I wonder, not for the first time?
- Planning for food security in a changing climate. Actually it starts with envisioning new crop management systems, then comes breeding (see entry above).
- A global map of travel time to cities to assess inequalities in accessibility in 2015. Over 10 years in the making, I’m told. Let the mashups begin.
- An assessment of threats to terrestrial protected areas. Number of threats increases with accessibility. Somebody mention mashups?
- Self-medication by orang-utans (Pongo pygmaeus) using bioactive properties of Dracaena cantleyi. External application as anti-inflammatory done by both orangutans and local indigenous human populations.
- Yield effects of rust-resistant wheat varieties in Ethiopia. Improved resistant varieties are better, except under abiotic stress, which is why farmers are going back to traditional varieties. But are they comparing apples and oranges (as it were)?