Agricultural Biodiversity Weblog

November 2, 2026November 2, 2026

A busy October, but what changed for opportunity crops?

The newsletter of the Community of Practice on Africa Forgotten and Underutilized Food Commodities just recycled a digest of posts from the blog of PAEPARD (Platform for African – European Partnership in Agricultural Research for Development) which included a plethora of events about opportunity crops that took place this October.

AgrEcoNUS+ Launch Conference. The EU-funded project “Enhancing value chains and markets of neglected and underutilized crop species to contribute to agroecological transition in Africa” was launched on 1 October.
African Union Inaugural Africa Seed Summit. Held on 5–7 October 2026 in Eswatini under the theme “Resilient Seed Systems for a Food Secure Africa.” We have mentioned it before.
World Food Map: An Interactive Atlas of Edible Biodiversity. Launched online on 20 October 2026, it “brings together the foods people eat today with opportunity species that could diversify them, showing how diets can be improved with locally available foods that are culturally relevant, climate resilient, and nutritionally rich.”
Advancing Underutilized Legumes for Food Security, Nutrition and Climate Resilience in Africa. The Society for Underutilized Legumes organised its 9th International Scientific Conference on 21–22 October, in Zaria, Nigeria.
Bringing Forgotten Foods Back to the Table: Wageningen Symposium on Neglected and Underutilized Species. Wageningen University & Research brought together scientists, chefs and food-system specialists on 26 October to explore how forgotten foods can contribute to nutrition, biodiversity and culinary innovation.
16th Indigenous and Traditional Seed and Food Festival. Organised by PELUM Uganda, on 29–30 October, in Kampala, Uganda, the festival celebrated indigenous seeds, traditional foods, farmer-managed seed systems and agroecological knowledge for climate resilience.

Clearly a busy month for the NUS (neglected and underutilized species, aka opportunity crops) community. But what does it all mean? What do we know on 1 November that we didn’t know on 30 September that will help farmers to grow NUS? I just hope someone is keeping score of the results of all these meetings, and will let the rest of us know.

November 2, 2026November 2, 2026

Brainfood: iNaturalist, Agroforestry mapping, Crop mapping, Intercrop mapping, Smartphone phenotyping, Bean phenology, Rice phenology, Grape database

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

October 30, 2026October 30, 2026

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.

October 26, 2026October 26, 2026

Wild silk found in the archives

Readers will be familiar with the domesticated silkworm, Bombyx mori, munching mulberry leaves, and only mulberry leaves, and obligingly spinning the fine thread that has dressed the rich for millennia. But how many of you know about alternative, wild sources of silk? Quite a few, perhaps, if you’ve been following us for a while and your memory is better than mine.

Because, as it turns out, though I’d totally forgotten about it, we’ve blogged about this a few times before. I linked to silk-making in the drylands of Mwingi in Kenya back in 2007, which I followed up the following year. And Jeremy also chipped in, twice in fact.¹ Wild silk was one of our most popular topics at the time, if comments are anything to go by.

Anyway, this trip into the archives was prompted by a recent piece in The Hindu about how the Central Sericultural Germplasm Resource Centre (CSGRC) in Hosur has been teaching students at the Forest College and Research Institute of Tamil Nadu Agricultural University all about non-mulberry sericulture. Apparently as part of the “exploration of opportunities for integrating forest-based silk production into forestry ecosystems.”

The best introduction to the beauty and diversity of wild silks I’ve found is the website of the Florida Museum’s wonderful collection. But no doubt there are other useful resources out there on this fascinating subject.

October 26, 2026October 24, 2026

Brainfood: Ancient Madagascar cattle, American bison, Tibetan yaks, Korean silkworms, European cattle genomic selection, Slovenian cattle footprint, Nigerian ponds, African swine fever in Asia

Past rapid spread of introduced livestock coincided with vegetation clearance on Madagascar. Ancient sheep, goat and cattle bones show livestock racing across the island about 1,200 years ago, in step with forest clearance and the loss of elephant birds and pygmy hippos. People had arrived long before, so hunting alone is an insufficient culprit. Also I can’t imagine pygmy hippos being all that nice to eat.
Paleogenomic insight into the collapse, recovery, and management of American bison. 115 ancient and 45 modern genomes show that past bison populations were widely connected, which means that today’s herds, while more fragmented, retain more diversity than was feared. Also, many modern bison lack the cattle ancestry long assumed to be ubiquitous, but wood bison do carry plains bison ancestry from 1920s translocations. So, swings and roundabouts.
Whole-genome resequencing reveals genetic diversity, population structure, and selection signatures in tibetan yak. Three recently recognised Xizang populations (Chawula, Jila and Niangya) prove genetically richer than many others and cluster with Yushu and wild yak. Selection signals turn up at coat colour genes such as MC1R and at candidates for meat production. Has anyone done selection signals in bison?
Selection of a phenotypic and functional core set for South Korean silkworm genetic resources, Bombyx mori (Lepidoptera: Bombycidae). A core set is distilled from a larger collection on multiple morphological, physiological and quality criteria, a manageable shortlist so the genebank’s holdings can be more easily used rather than merely admired. What would a core collection for the bison look like, I wonder?
Impact of genomic selection on genetic diversity in 5 local European cattle breeds. Genomic selection uses DNA information from across an animal’s genome to predict its breeding value, allowing breeders to select animals likely to pass desirable traits to their offspring earlier and more accurately than relying on observed characteristics or pedigree alone. But can it be bad for genetic diversity if applied to a small local breed? Well, it shortened sire generation intervals and evened out top-sire contributions, but inbreeding rose in two breeds and fell in three. Genetic management mattered more than genomic selection, so the lesson is to monitor, and adjust as needed. Possibly relevant to the bison?
Adoption of support tools for biodiversity conservation: a mixed-methods analysis of ecosystem services awareness among Slovenian cattle farmers. Livestock farmers who are younger and with more positive attitudes to biodiversity and the European Green Deal are more likely to adopt biodiversity-supporting practices. Knowing this could have saved many pigmy hippos 1,200 years ago in Madagascar.
A national fish pond dataset for Nigeria generated using automated detection and segmentation. A segmentation model run over imagery, with a Random Forest classifier filtering the candidates, yields an openly released map of aquaculture production that was previously hard to see, let alone manage.
Pathways to recover Asian wild pigs from African swine fever. Since 2018 the virus has caused huge mortality in naive wild pig populations, and the authors set out eight priority actions, with early-warning surveillance, captive breeding and emergency financing the ones that can’t wait. Free-ranging domestic pigs and thin biosecurity at the wildlife-livestock interface keep kind of bring us back to where we began on Madagascar.