- Guidelines and best practices for the scientific use of global iNaturalist data. Citizen scientists have generated an enormous photographic record of biodiversity, but turning those observations into reliable scientific data requires some care. Here’s some practical guidance on accessing and using iNaturalist records and images, addressing taxonomic uncertainty, geoprivacy, observer bias, attribution and reproducibility.
- Mapping the spatial extent of agroforestry lands across Africa by characterizing tree spatial arrangements. Moving from individual biodiversity observations to the landscape, this study uses remote sensing to identify agroforestry across Africa by looking at the spatial arrangement of trees.
- AI tool for mapping smallholder crops proves itself in Senegal. Once we know where trees and agricultural landscapes are, the next question is what is actually being grown. Here researchers use Tessera, an open-source AI model trained on satellite imagery, to identify different crops in smallholder systems, achieving 84% accuracy while requiring considerably less computing power and labelled training data than other approaches.
- National Mapping of Monoculture and Intercropped Maize via Phenology-Assisted Sampling: Application in Kenya. Mapping crops is one thing; distinguishing how they are grown is another. This study distinguished intercropped from monocropped maize in smallholder farming systems, using satellite time series and phenological information to generate training samples for machine-learning classification.
- Smartphone-based proximal sensing as an accessible platform for improved physiological phenotyping. From mapping whole agricultural landscapes we move to the research station, and from satellites to smartphones. Using a smartphone to capture repeated images of plants in cowpea plots generated high-resolution time series, from which could be extracted information on vegetative growth, flowering and pod production.
- Determination of dry bean physiological maturity from UAS RGB time series using a stacking ensemble of color-derived spectral features. The cowpea study shows what can be extracted from repeated close-range images of individual plants. Here the emphasis shifts to using drone-mounted cameras to estimate a specific breeding trait: physiological maturity in dry beans. Combining colour-based indicators with time-series features and machine learning achieved a prediction error of only 2.68 days.
- Multi-scale spatial-temporal remote sensing fusion for phenology identification in rice germplasm resources. If maturity can be estimated from drone imagery in dry beans, can similar approaches monitor hundreds of different rice genotypes in a breeding collection? Apparently yes, by combining frequent medium-resolution images with less frequent high-resolution UAV imagery.
- Digitalization of the genotypic and phenotypic information retained within the FEM grapevine germplasm bank. We end where phenotyping meets conservation. The Fondazione Edmund Mach grapevine collection brings together more than 3,000 accessions, with genetic identity assessed using microsatellite markers and phenotypic characterisation covering vine development, ampelographic traits and responses to biotic stresses. The paper describes the digitisation of this information in FEMVitisDB, a FAIR-compliant database with links to international data infrastructures. And not a drone in sight. Yet
Having a go at Crops·To·Go
The International Potato Centre (CIP) seems to have a new, AI-powered (of course) interface for requesting material from its genebank. They call it Crops·To·Go.
You have to register even if all you want to do is browse the collection. It didn’t take me very long to do so, but still: why? Requiring users to create an account before they can explore what is available seems an unnecessary barrier.
I was also not entirely convinced that all the search filters were working as expected. In particular, as I added evaluation-data filters, the number of accessions that purported to match them remained unchanged. It wasn’t clear whether the filters had been applied, whether they had returned no additional matches, or whether the matching count was simply not updating. Some more visible feedback on the effect of applying filters would make the experience a bit more reassuring.
Overall, this looks like a promising step towards making CIP’s germplasm more accessible, but the browsing experience and the transparency of the filtering process could do with some attention.
Brainfood: WorldCuP, Crop origins, Abrahamic plants, UK landraces double, Ex situ change, Santorini vineyards, Apulian olives
- WorldCuP: A database of checklists documenting the World’s Cultivated Plants and their distributions. Cool new database records 1,444,325 occurrences representing 91,358 accepted cultivated plant species across 2,718 regions in 156 countries. Geographic coverage is extensive but uneven, with notable gaps in parts of Africa, southwest and central Asia, and Oceania. Most of the taxa recorded are not in existing major crop and useful-plant databases.
- Integrating multiple disciplines to locate the origins of domestication of crops. Combining evidence from botany, biogeography, genetics, archaeology, linguistics, and history overturns the traditional Mesoamerican hypothesis for cacao origins, supporting domestication in western Amazonia, probably involving multiple events. Peach palm’s domestication is best placed in southwestern Amazonia. Cupuaçu remains unresolved. Now for the other 91,355 species.
- Plant Species Mentioned in Abrahamic Scriptures: Implications for Biodiversity and Traditional Agriculture and Horticulture. Olive, grapevine and fig, together with cereals such as wheat and barley, occur prominently across the three scriptural traditions examined, while date palm is particularly prominent in the Qur’an. Not clear whether scriptures qualify as checklists.
- Crop landrace conservation, status, challenges and motivations in Great Britain. A national inventory (checklist even) identifies 566 landrace populations maintained on farms by 197 growers, alongside 1,876 British-origin genebank accessions, representing a total of 1,201 distinct landraces. Cereal and vegetable landrace cultivation has gone up since 2004, but forage diversity is increasingly threatened. Climate change, labour shortages and lower yields are the main challenges reported by growers.
- An inventory, threat and conservation assessment of UK wheat landrace diversity. The UK currently has 27 wheat landrace varieties growing on more than 700 hectares, an increase in both variety numbers and cultivated area since 2009. Interviews with 50 farmers identify insufficient market demand and limited seed availability as key constraints. So, not climate change, labour shortages and lower yields.
- Strong differentiation between source and ex situ populations of rare plant species revealed by next-generation sequencing. Across four rare plant species, ex situ populations showed significant genetic differentiation from their wild sources despite only one generation in cultivation, while overall genetic diversity and inbreeding did not differ significantly. Unrepresentative seed collection, unconscious selection during propagation or genetic drift could explain the differences. Something to test on all those British landraces, labour permitting?
- Rethinking the vineyard of Santorini in a changing climate and preserving an historic wine region. Recommends a staged transition from vineyard recovery to ecological regeneration, prioritising soil organic matter, drought management and indigenous grapevine diversity. Sounds like lots of labour will be needed.
- Conservation dilemmas: Xylella fastidiosa and the future(s) of olive farming. In Puglia, disease management is pushing olive farming towards competing futures: intensive, mechanised production vs the preservation of ancient trees as cultural landscapes. The fate of traditional groves depends less on technical disease solutions than on whether markets, subsidies and labour arrangements make their continued care economically viable. Well that doesn’t sound promising. Maybe try prayer?
Weed all about it
I think I vaguely knew that USDA had a hemp collection, but I would definitely not have been able to tell you that it was located in Geneva, NY, that it may be the largest in the world, or that it only started in 2021. Fortunately, though, the researchers responsible for the collection organized a USDA Hemp Stakeholder Event at Cornell AgriTech on September 10, and the good people at the International Hemp Podcast are summarizing the proceedings in a series of four episodes. Episode 1 is out, and it includes those tidbits I began with, as well as lots of other interesting information, about hemp in particular but also more generally about how the US genebank system works. Well worth a listen.
But does USDA in fact have the largest hemp collection in the world? Well, the podcast says it has over 800 accessions, and GRIN does indeed return 871 (714 active, 92 available) hits. However, Genesys, which gets its USDA data from GRIN, says 569, which is at least closer to the “N > 600 accessions” on the webpage of the USDA Hemp Germplasm Laboratory. As it’s a fairly new collection, this all probably just means that its numbers go up fairly frequently and USDA just hasn’t updated either its website or its data on Genesys lately. Anyway, that would still give it more accessions than at VIR (491), though fewer than in Ukraine (610). That Ukraine number is split over two institutes, however. So it does look like USDA has managed to put together the world’s largest hemp collection in just 5 years. Pretty impressive. Too bad the podcast doesn’t go into how they did it.
Give farmers room to grow
Farmers conserve and improve the crop diversity around them. They have been doing it for millennia. Their livelihoods depend on it. But how much more might they be able to do?
Consider the coconut. Farmers know which palms have characteristics they value, but with ordinary open pollination they can choose the mother but not the father of future coconuts. A recent paper proposes relatively simple techniques that would allow farmers to control pollination and thus choose both parents and produce more predictable seednuts. It won’t turn them into breeders, but it could help them produce — and maybe even sell — better planting material.
Tamarind presents a different version of the same problem. Much of the African crop still comes from scattered, semi-wild trees. Harvesters know which trees are best, but turning a particularly good tree into a reliable source of planting material is hard. Seedlings don’t necessarily reproduce the parent’s characteristics, and tamarind takes years to mature. Grafting and other forms of vegetative propagation exist, but have not been widely adopted, according to a recent review.
There’s a nice example from southern Africa that gives a glimpse of what might be. Researchers are identifying exceptionally productive wild fruit trees in the miombo woodlands and cloning them through grafting and air-layering. A tree that might otherwise take around a dozen years to fruit can produce within four or five when propagated this way.
But it doesn’t have to be a researcher doing that. A farmer or harvester who knows a particularly productive tree, or one with especially good fruit, could propagate it through cloning rather than relying on whatever seedlings happen to arise.
Coconut and tamarind aren’t the same, and neither are the technologies needed. But the principle is similar. We sometimes fall into the error of thinking that conserving and improving crop diversity are things done for farmers, whether in a genebank, a breeding programme or a research station. But they already do these things. Sometimes they just need a little room to try doing them a little bit differently.