- The genomic footprints of wild Saccharum species trace domestication, diversification, and modern breeding of sugarcane. The genome of modern sugarcane is a mosaic of wild introgressions, including one from an unknown source.
- Evolutionary histories of functional mutations during the domestication and spread of japonica rice in Asia. Selection by biotic stresses acted differently on standing variation in rice across geographic regions. Colour me surprised.
- Ancient DNA from lentils (Lens culinaris) illuminates human-plant-culture interactions in the Canary Islands. Local lentils trace back a thousand years in the Canaries.
- An olive parentage atlas: founder cultivars, regional diversification, and implications for breeding programs. Modern cultivars derive from a surprisingly small set of founding genotypes…
- Intraspecific variation and phenotypic plasticity of olive varieties in response to contrasting environmental conditions. …but cultivated olives maintain high within-species variation and plasticity, enabling adaptation across Mediterranean environments.
- Deciphering the Origins of Commercial Sweetpotato Genotypes Using International Genebank Data. One Brazilian sweetpotato traced back to a CIP accession with a different name, but others did not match anything in the genebank.
- Exploring genetic diversity and selective signatures, a journey through Colombian cassava’s landscape. Colombia’s farmers and environments have shaped its cassava diversity. No word on whether any of it traces back to the CIAT genebank.
- Novel germplasm of tepary and other Phaseolus bean wild relatives from dry areas of southwestern USA. The available genepool for bean breeding gets a welcome boost.
- Insight into root system architecture of buckwheat through genome-wide association mapping-first study. Want drought-resilient, high-yielding buckwheat varieties? Here are the genes — and genotypes — to play with. So the available genepool doesn’t need a boost?
- Non-destructive prediction of nitrogen, iron and zinc content in diverse common bean seeds from a genebank using near-infrared spectroscopy. High-throughput, non-destructive phenotyping methods capture nutritional trait variation across a bean core collection. Wild teparies unavailable for comment.
- Germplasm exploration and digital phenotyping reveal indigenous diversity and farmer preferences in pigeon pea (Cajanus cajan (L.) Millsp.) for climate-smart breeding. Not all phenotyping can be high-throughput, but that doesn’t mean it’s not useful, at least in pigeon peas.
- Agricultural landscape genomics to increase crop resilience. Could have been applied to all of the above, I guess.
Brainfood: Crop (species) diversity edition
- Small farms contribute a third of the food consumed in high-income nations. And those small farms are disproportionately diverse…
- The Global Spatial Co-Variation Between Crop Diversity and Landscape Heterogeneity. …and crop diversity on farms goes with landscape diversity.
- Beyond Crop Hotspots: Why Overlooked Marginal Agricultural Lands Deserve Urgent Attention. I’m willing to bet landscape diversity is often associated with marginality, but that’s not the end of the world.
- Food Biodiversity and its Association with Diet Quality and Health Outcomes-A Scoping Review. Why should we care about diverse farms? Because diversity in your food is associated with nutritional adequacy, a reduced risk of mortality, or a reduced risk of gastrointestinal cancers. Ok, I know, I missed a step there. There was nothing in the past few weeks in the literature specifically linking farm diversity and food diversity, but you know the link is there. At least sometimes.
- Long-term agricultural diversification increases financial profitability, biodiversity, and ecosystem services: a second-order meta-analysis. Diversity on farms is not just good for (ok, maybe) diets.
- Global evidence that plant diversity suppresses pests and promotes plant performance and crop production. Another way farm diversity is useful is via pest control. Well, actually, this could count as an ecosystem service, and so an example of the above.
- Ecological drivers of intercropping performance for enhanced global crop production. Ah, that explains how those farm ecosystem services actually works.
- Crop rotations synergize yield, nutrition, and revenue: a meta-analysis. Rotations are diversification too, and good for you too.
- Revitalizing orphan crops to combat food insecurity. But of course the diversification strategy de jour is opportunity crops.
- Value chain research and development: The quest for impact. And for that revolution to happen, we’ll need a better grip on value chains.
- Cultural innovation can increase and maintain biodiversity: A case study from medieval Europe. Yes, agricultural revolution can lead to increased biodiversity.
- Household vegetable agro-biodiversity in northern Vietnam requires diversity in seed sources. Any revolution is going to need good sources of good seeds though.
Brainfood: Biodiversity intactness, Landuse change, Drought stress, Crop suitability, Yield variance, Phenotypic data
- Consistent global dataset on biodiversity intactness footprint of agricultural production from 2000 to 2020. Spatial dataset shows how global consumption drives ecological degradation.
- Rapid monitoring of global land change. Spatial dataset shows how in 2023 direct human action and fires caused land use conversion globally over an area the size of California.
- Remote monitoring of plant drought stress with the apparent heat capacity. Spatial dataset can provide early warning of drought. Early warning system for genetic erosion, anyone?
- CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment. Spatial dataset shows where 48 crops will have the best yields.
- Climate change increases the interannual variance of summer crop yields globally through changes in temperature and water supply. Spatial dataset shows that climate change impacts not just yields but variation in yield from year to year for maize, soybean and sorghum.
- Reassessing data management in increasingly complex phenotypic datasets. Datasets need to be properly managed to be widely used.
Brainfood: Breeding edition
- Unlocking the potential of wild rice to bring missing nutrition to elite grains. A solution for better nutrition.
- Characterization of Oryza glaberrima derived genetic resources for stagnant flooding tolerance in interspecific rice pre-breeding populations. A solution for too much water.
- Strengthening Global Rice Germplasm Sharing: Insights from the INGER Platform. A solution for getting the above solutions out to those who need them.
- Comprehensive nutritional and antinutritional characterization of pigeonpea (Cajanus cajan): Insights into genotypic diversity and protein quality. A solution for better protein.
- Exploring the agro-morphological performance of mini core collection of finger millet [Eleusine coracana (L.) Gaertn] germplasm under sodic condition. A solution for high sodium in soils.
- A Public Private Partnership in Plant Breeding — The Case of Irish Malting Barley. A solution for Irish malt.
- The business case for grasspea in Ethiopia: An action plan to provide Ethiopian farmers with a safe, nutritious and climate-smart protein source. A solution for ODAP. Which will still need to be sold, though.
- Harnessing historical genebank data to accelerate pea breeding. A solution for cold, and more.
- Genetic basis of phenotypic diversity in C. stenophylla: a stepping stone for climate-adapted coffee cultivar development. A solution for heat.
- A phylogenetic approach to prioritising crop wild relatives in Brassiceae (Brassicaceae) for breeding applications. A solution for finding solutions.
Brainfood: Taxonomic identification, Niche mapping, Harvest tracking, Drones, Phenomics, Yield analysis
- Review of herbarium plant identification of crop wild relatives using convolutional neural network models. Cool tech helps you figure out which species is which. Now you can map them properly I guess.
- Habitat prediction mapping for prioritizing germplasm collection areas of cowpea (Vigna unguiculata (L.) Walp) in India using BioClim model. Having mapped them, another cool tech helps you figure out where to collect them.
- Harvest Date Monitoring in Cereal Fields at Large Scale Using Dense Stacks of Sentinel-2 Imagery Validated by Real Time Kinematic Positioning Data. And when.
- Drone methods and educational resources for plant science and agriculture. In the field, cool tech could help you find and collect them. And not just that…
- Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm. Having collected them, more cool tech helps you evaluate them.
- Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping. Finally, having evaluated them over many years, cool tech helps you figure out what’s going on.