Brainfood: Makapuno, Middle Eastern dogs, Date palm origins, Speedy NUS, Red apples, Apple characterization, Phenotyping double, Assisted migration & pathology, Soya diversity, Sustainable intensification, Seed research, Cucurbita history, Potato value chains, Livestock ES

Brainfood: Climate resilient crops, Food system limits, Phenotyping double, Sweet sorghum, Melon history, Paying4data, Beercalypse, Village chickens, Breeding 4.0, European maize, Brachiaria ROI

Brainfood: Emmer & drought, European legumes, Sainfoin, Alpine nomads, American domestication, Cereal domestication, Tree plantations, Garlic breeding, African veggies, African staple breeding, Sugarcane genome

Counting your chickens

A new version is out of the FAO global livestock density datasets, which we have blogged about before. This is the third iteration (GLW3), and it has a reference year of 2010. There’s a paper to give you all the details. ((Gilbert. M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data. 5:180227 doi: 10.1038/sdata.2018.227 (2018).))

But there’s more to come:

Future versions of GLW will differentiate stocks according to production systems for ruminant (meat vs. dairy) and monogastric species (intensive vs. extensive, meat vs. egg production). Higher resolution models ((The current global dataset has a resolution of 0.083333 decimal degrees, or 10 km at the equator.)) for individual countries where the census data can support such predictions will also be produced.

The datasets are also available for download from Harvard Dataverse.