- Human diets drive range expansion of megafauna-dispersed fruit species. Megafauna dropped the ball (or the fruit), humans picked it up and ran with it.
- Experimental evidence on payments for forest commons conservation. Maybe we should have paid the megafauna.
- Vegetable genetic resources in China. 3 genebanks, 36,000 accessions, 120 species, about 1000 distributions per year (to research units).
- A cost-effective ground pollination system for hybridization in tall coconut palms. I have seen the future of coconut pollination.
- Determinants of pastoral and agro-pastoral households’ participation in fodder production in Makueni and Kajiado Counties, Kenya. Household heads who are female, have access to extension services, or are members of social groups are more likely to go in for fodder production.
- Taxonomy based on science is necessary for global conservation. Incredible to me that needs to be said.
- Development of next-generation sequencing (NGS)-based SSRs in African nightshades: Tools for analyzing genetic diversity for conservation and breeding. Solanum scabrum and S. villosum separate nicely, and show much diversity.
- A natural adaptive syndrome as a model for the origins of cereal agriculture. Large seed, awns and monodominance.
- Development and Examination of Sweet Potato Flour Fortified with Indigenous Underutilized Seasonal Vegetables. Ticks all the boxes, lets call it divortification.
Brainfood: Core collections, Food system sustainability, Sunflower breeding, Modern/traditional mosaic, Nepal earthquake response, Modelling erosion, Folate in potato, Argentinian andigena, Millet evaluation, Pigeonpea evaluation, Sugarcane evaluation, Bean drought genes, Threatened trees
- 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 found not to be the case for rural areas, where the majority of armed conflicts occurred where food crops were abundant. Food scarcity can actually have a pacifying effect.”
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? I’ll just say that 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 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.