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Integrating ethnolinguistic and archaeobotanical data to uncover the origin and dispersal of cultivated sorghum in Africa: a genomic perspective
No full textData and code availability: The raw GBS data were available in the NCBI Sequence Read Archive (from references Brenton et al. 2016 ; Lasky et al., 2015 ; Yu et al., 2016 ). The vcf file containing the filtered SNPs and the passport data of the accessions analyzed in the study are publicly available on the CIRAD Dataverse repository, under the doi identifier: 10.18167/DVN1/SAAMUT v2 (Gilabert et al. 2025 ). The R scripts for the analyses performed in the study are available on the CIRAD gitlab plateform (https://gitlab.cirad.fr/agap/sorgho/africrop_sorghum). Supplementary material is available online in the CIRAD Dataverse repository (https://doi.org/10.18167/DVN1/SAAMUT v2; Gilabert et al. 2025 ).Acknowledgment: We acknowledge the following bioinformatic facilities for providing HPC resources and support: the Core Cluster of the Institut Français de Bioinformatique, the MESO@LR-Platform at the University of Montpellier and the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10.15454/1.5572390655343293E12).International audienceArchaeobotanical evidence suggests that the beginning of cultivation and emergence of domesticated sorghum was located in eastern Sudan during the fourth millennium BCE. Here, we used a genomic approach, together with archaeobotanical and ethnolinguistic data, to refine the spatial and temporal origin and the spread of cultivated sorghum in Africa. We built a probability map of the origin of sorghum domestication in Eastern Africa using genomic data and spatial Bayesian models. The origin was located in Eastern Sudan and Western Ethiopia, in perfect concordance with recent archaeobotanical evidence. Calibrated on archaeological remains, our genomic-based model suggests that the beginning of the expansion of sorghum agriculture took place around 4,600 years ago. Spread of sorghum cultivation led to a sorghum population structure fitting ethnolinguistic groups at different scales, suggesting that human social groups and sorghum populations co-diffused. Consequently, ethnolinguistic barriers and social preferences, as well as adaptation to specific climate zones, have contributed to structuring domesticated sorghum diversity during its diffusion
Uncertain times require new thinking for agri-food science to ensure food security and nutrition for all
No full textSource Agritrop Cirad (https://agritrop.cirad.fr/614676/)International audienceFor over six decades, international policy has enabled agricultural products to move relatively easily across national borders. Currently, however, the landscape is changing. Deglobalization and the erosion of multilateral principles threaten international food supply chains while climate change is increasingly undermining production. In addition, today's food systems contribute to major environmental and human health problems. The global agri-food research agenda must adapt quickly to these realities. Here we propose that a new research agenda be established based on three principles to help respond to challenging times, promote human rights, sustain gains made in the past, and support greater positive impacts in the future. Principle one – a strengthened commitment to community engagement. Principle two – better supporting interdisciplinary systems thinking. Principles three – combatting misinformation by enabling enhanced public communication. We believe that today's crises present an opportunity to establish the foundations of a food system transformation that is more equitable, transparent, sustainable, and democratic
Multiyear drought strengthens positive and negative functional diversity effects on tree growth response
No full textSource Agritrop Cirad (https://agritrop.cirad.fr/614641/)International audienceMixed-species forests are proposed to enhance tree resistance and resilience to drought. However, growing evidence shows that tree species richness does not consistently improve tree growth responses to drought. The underlying mechanisms remain uncertain, especially under unprecedented multiyear droughts. We used a network of planted tree diversity experiments to investigate how neighborhood tree diversity and species' functional traits influence individual tree responses to drought. We analyzed tree cores (948 trees across 16 species) from nine young experiments across Europe featuring tree species richness gradients (1–6 species), which experienced recent severe droughts. Radial growth response to drought was quantified as tree-ring biomass increment using X-ray computed tomography. We applied hydraulic trait-based growth models to analyze single-year drought responses across all sites and site-specific responses during consecutive drought years. Growth responses to a single-year drought were partially explained by the focal species' hydraulic safety margin (representing species' drought tolerance) and drought intensity, but were independent of neighborhood species richness. The effects of neighborhood functional diversity on growth responses shifted from positive to negative with increasing drought duration during a single growing season. Tree diversity effects on growth responses strengthened during consecutive drought years and were site-specific with contrasting directions (both positive and negative). This indicates opposing diversity effects pathways under consecutive drought events, possibly resulting from competitive release or greater water consumption in diverse mixtures. We conclude that tree diversity effects on growth under single-year droughts may differ considerably from responses to consecutive drought years. Our study highlights the need to consider trait-based approaches (specifically, hydraulic traits) and neighborhood scale processes to understand the multifaceted responses of tree mixtures under prolonged drought stress. This experimental approach provides a robust framework to test biodiversity-ecosystem functioning (BEF) relationships relevant for young, planted forests under increased drought stress
Contribution of multiblock methods for predicting the severity of COVID-19 from clinical, biochemical and metabolomic data
No full textInternational audienceDuring the first wave of the COVID pandemic, elderly men with metabolic diseases were found to be at higher risk of severe forms. In order to improve the early detection of patients who will require intensive care, 62 patients were included in our study. Phenotypic data (age, sex, obesity) and blood biochemistry measurements were collected within the first 2 days of hospitalisation. In addition, a multiplatform approach, consisting in untargeted metabolomics (HILIC and C18) and complementary targeted lipidomics LC-MS/MS analyses, was carried out. The patients were classified at the end of their follow-up as “moderate” or “severe”, according to the severity of the developed COVID. Exploratory (PCA, consensus-PCA) and supervised (PLS-DA, multiblock-PLS-DA (MB-PLS-DA), sequential and orthogonalised-PLS-DA (SO-PLS-DA) to predict the COVID severity) data analyses were then performed, using the R package rchemo, to identify early and predictive biomarkers of evolution towards severe forms of COVID.PCA on separate blocks failed to visualise a variability in the datasets related to the COVID severity on the first axes. PLS-DA models, were valid (based on permutation tests) only for biochemistry and HILIC data, with cross-validated error rates (5-fold repeated 30 times) of 26.56±0.92% and 31.08±2.54%, respectively. Consensus-PCA on the 7 datasets, revealed only a very subtle effect in the data related to the COVID severity. In contrast, the MB-PLS-DA and SO-PLS-DA models were valid, with cross-validated error rates of 25.32±2.40% and 23.33±3.08%, respectively. The most important variables in the MB-PLS-DA model were age, sex and markers of inflammatory response from the different biochemistry and metabolomic datasets. The SO-PLS-DA model used only one latent variable from the biochemistry block and one from a lipidomics dataset. The significant variables were inflammatory markers and metabolites associated with altered metabolic status. In conclusion, MB-PLS-DA may be an interesting first step to highlight and explain slight effects in the datasets related to the outcome. SO-PLS-DA may be used to deepen the analysis and bring out complementary information. It may also be used to select blocks of data, an advantage for biomarker validation purposes
Estimating the severity of coffee leaf rust using deep learning and image processing
No full textSource Agritrop Cirad (https://agritrop.cirad.fr/614550/)International audienceThe global coffee industry faces significant challenges from crop diseases, of which coffee leaf rust (CLR) caused by the fungus Hemileia vastatrix, stands out as one of the most damaging. Accurate assessment of disease severity is essential for applying effective control strategies. In response to this need, this study introduces a modern approach using deep learning and image processing techniques to identify and quantify CLR injury automatically. We developed thirteen models using convolutional neural networks, to classify lesions into different degrees of severity. It offers a promising alternative to conventional methods, especially under data-limited conditions, although some limitations remain in robustness across datasets. Manual rust detection requires close visual inspection of leaves, a laborious and error-prone process, especially in large cultivation areas. This challenge makes it harder to apply timely and effective disease management strategies
Les déterminants de l’innovation variétale des coopératives et unions de coopératives vinicoles françaises
No full textCette communication a obtenu le prix de la meilleure publication scientifique étudiante lors de la Conférence.International audienc
Quelle sobriété en eau ?
No full textInternational audienceCe chapitre clarifie le concept de sobriété en eau et donne des pistes concrètes. En quoi se distingue-t-elle de l’efficacité ? Quels leviers politiques, réglementaires, économiques peuvent être mobilisés pour l’accroître ? Quelles stratégies et techniques permettent plus de sobriété en eau ?Alors que s’accroît la menace du manque d’eau, la sobriété en eau est souvent citée comme un objectif à viser. Ce chapitre clarifie ce concept, en le distinguant de l’efficacité (dont relèvent les systèmes d’irrigation économes en eau) et de la substitution (comme la réutilisation des eaux usées traitées), avec laquelle elle est souvent confondue. La sobriété, qui consiste à produire et consommer moins de biens et de services, implique des changements profonds, contrairement à l’efficacité, qui ne se concrétise souvent que par une simple amélioration technique. Ainsi, en agriculture, principal consommateur d’eau, il s’agit de mobiliser de manière systémique un ensemble de stratégies agroécologiques à différentes échelles pour gagner en sobriété. Quant aux collectivités, elles peuvent privilégier la collecte séparative des urines, et les toilettes sèches en milieu rural. Rechercher la sobriété en eau implique idéalement de prendre appui simultanément sur plusieurs politiques sectorielles. Au sein de la politique de l’eau, les leviers mobilisables sont la limitation des prélèvements, le partage de la ressource par la concertation, les incitations économiques comme la tarification ou les subventions, et la formation ainsi que la sensibilisation
Spatiotemporal relationships between rainfall indices and crop yields in the Sudano-Sahelian zone of Cameroon
No full textSource Agritrop Cirad (https://agritrop.cirad.fr/614417/) * Autres projets (id;sigle;titre): ;INNOVACC;(EU) Innovation pour l’Adaptation au Changement Climatique// ;DeSIRA;(EU) Development Smart Innovation through Research in Agriculture//International audienceIn the Sudano-Sahelian zone of Cameroon, agriculture — predominantly rainfed — constitutes a major economic sector, underpinning food security but remaining vulnerable to rainfall variability. This study explores statistical relationships between 25 rainfall indices and the yields of maize (Zea mays L.) from 1999 to 2021 and cotton (Gossypium hirsutum L.) from 1991 to 2010, employing Pearson correlation tests across temporal and spatial scales. At the temporal scale, results indicate that maize and cotton yields respond similarly to two indices: the end of the rainy season (EOS) and cumulative dry spells (CDS15). A longer rainy season is associated with higher yields for both crops, whereas dry spells exert a negative influence across the entire study area. However, spatial analyses reveal significant local variations in crop responses. Specifically, maize yields exhibit positive correlations with indices such as rainfall amount (PRCPSEAS), rainy days (R1mm), wet days (R20mm), season length (SL) in the northern and southwestern parts of the study area, reflecting the importance of consistent moisture availability for optimal growth. Conversely, cotton yields are strongly negatively correlated with these same indices in the northern, northwestern, central, and southeastern parts, likely reflecting the crop's lower tolerance for excessively humid conditions. The findings highlight the need for crop-specific adaptation strategies to rainfall variability, including the selection of appropriate crop varieties, adjustments to planting calendars, improved water management practices, particularly in the context of increasing rainfall trends. Policymakers could invest in localized agro-climatic forecasting systems and improve the integration of climate data into agricultural advisory services