GIS – Page 38 – Agricultural Biodiversity Weblog

November 22, 2018November 22, 2018

With Gabe on the trail of the wild banana

From the Facebook page of Banana Natural Biodiversity Mapping – Citizen Science, which I think we have blogged about before:

CIRAD and its partners are currently collecting Musa material in Vietnam, within the frame of the “BSV for Banana Diversity” project. As an expert to the project, Gabe Sachter-Smith has begun to post his observations to iNaturalist, including Musa balbisiana and many other endemic species. He then added the observations to our Banana Natural Diversity Mapping Project (http://bit.ly/MusaDivMapping). #BananaDivMapping #CitizenScience #iNaturalist #AddYours #ShareTheNews

Follow Gabe along as he explores banana diversity. He’s also on Instagram.

Almost like being there. Almost.

November 22, 2018

Nibbles: Protected areas, Waley dance, Edible cricket, Plant & people, Open-pollinated seed, Apple origins, Horticultural tree genomics

Protected Planet report: The Website.
In praise of cows.
New edible insect turns up.
Telling the stories of humanity’s relationship with plants.
The beauty of open pollination.
You’d have thought everyone would know where the apple comes from by now.
The avocado, on the other hand…

November 12, 2018November 12, 2018

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

Are agricultural researchers working on the right crops to enable food and nutrition security under future climates? No. Well, kinda.
Options for keeping the food system within environmental limits. You need to pursue several. Including those discussed above, presumably.
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives. 3D CNN architectures applied to hyperspectral imaging is the future, apparently.
Dealing with multi‐source and multi‐scale information in plant phenomics: the ontology‐driven Phenotyping Hybrid Information System. You’re going to need a fancy system to keep track to the data from the above.
Sweet Sorghum Originated through Selection of Dry, a Plant-specific NAC Transcription Factor Gene. Could be applied to other cereals?
Repeated domestication of melon (Cucumis melo) in Africa and Asia and a new close relative from India. Once was not enough.
Paying for Digital Information: Assessing Farmers Willingness to Pay for a Digital Agriculture and Nutrition Service in Ghana. Cheaper is better. There’s a shocker. Oh, and women are more careful with their money.
Decreases in global beer supply due to extreme drought and heat. 32% decrease in consumption in Argentina, 193% increase in price in Ireland. But clearly the authors have never heard of sorghum beer.
The role of local adaptation in sustainable production of village chickens. Location, location, location.
On the Road to Breeding 4.0: Unraveling the Good, the Bad, and the Boring of Crop Quantitative Genomics. Beyond breeding on predicted phenotypes, it’s sort of breeding for predicted environments.
Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. You don’t even need Breeding 1.0, if you deploy existing diversity optimally.
Got forages? Understanding potential returns on investment in Brachiaria spp. for dairy producers in Eastern Africa. Definitely worth a flutter.

October 31, 2018October 31, 2018

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.

October 25, 2018

Mapping crops, field by field

That’s what agriculture looks like around here, according to the Belarus-based ag-tech startup, OneSoil. According to the press release:

Today, OneSoil, a precision farming startup, announced the launch of a new map, the OneSoil Map, which it has developed to allow everyone in the agriculture industry to explore and compare fields and crops in Europe and the United States – 44 countries in total. The key feature of the map is that it allows users to see how these fields have changed over the past three years (2016 – 2018). To accomplish this, OneSoil combined public data from the European Copernicus Programme and Mapbox GL JS, the latter of which helped in visualizing large volumes of ag data. The metrics included on the map are hectarage, the crop, and country crop rating.

Still processing the enormity of it all.