Biology subjects

Dumont, B.

Publications and source records attributed to Dumont, B..

12 recordsLinked to original sources

bioRxiv2025

Reconnecting food production and consumption through redesigning food systems to support healthy diets

Balancing the social and environmental costs of food production with the needs of future populations in the face of climate change is the greatest challenge that agriculture is facing in the 21st century. The modification of eating habits towards more environmentally friendly and healthy diets is a key lever to meet this challenge. In 2019, the EAT-Lancet commission defined a universal guideline diet that should allow 9 billion people across the globe to eat healthily while respecting planetary boundaries. In an attempt to reconnect cropping systems to such diets, we developed an innovative approach and designed a decision-making model to assist in the planning and optimization of cropping systems. This model evaluates their ability to supply a specific food system in accordance with the diet recommended by the EAT-Lancet Commission, while comparing vegan, ovo-lacto vegetarian, and omnivorous diets and minimizing the import and export of commodities. Results show that longer and more diverse crop rotations are more likely to comply with the EAT-Lancet dietary requirements, especially if grazing animals are integrated within rotations. Integrated crop-livestock systems including temporary pasture and forage cover crops minimize the excess and deficit of all commodities (food and feed supplies), while reaching the required amount of daily calories and meeting the requirements in each food category. Crop rotations typically need to include rapeseed to provide oil for human consumption and oilseed meals for livestock, as well as a legume crop for pulses and several cereals. The model allows testing a wider range of dietary recommendations or assessing the impact of specific agro-ecological practices. It supports the design of multi-objective crop rotations aligned with dietary guidelines. By reconnecting food production and consumption following a healthy diet, the model provides practical solutions to sustain all three pillars of sustainability for future food systems.

plant biology↗

bioRxiv2025

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Computer vision is increasingly used in farmers fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimetre ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although todays AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of wheat organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90%. However, the precision for stems with 54% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.

plant biology↗

bioRxiv2025

A Multi-Model Ensemble Reveals Soil Carbon Gains from Regenerative Practices in the U.S. Midwest Cropland

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4- km2 to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 {+/-} 0.12 Mg ha-1 yr-1 aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives.

ecology↗

bioRxiv2024

MYC shapes ER-mitochondria calcium transfer by directly targeting ITPR1: implications for MYC-induced safeguard mechanisms and cancer

The MYC and NMYC transcription factors (TFs) play a key role in cell proliferation and are overexpressed in most cancer cells. However, in normal cells their overexpression triggers safeguard mechanisms promoting cell death and cellular senescence, which are bypassed in cancer cells. The mechanisms of action of this TF family are only partially understood. Here, we reveal that in normal cells MYC binds to the Inositol 1,4,5-Trisphosphate Receptor type 1 (ITPR1) gene and upregulates its expression, triggering an ER-mitochondria calcium (Ca2+) transfer, which is involved in MYC-induced cell death and senescence. Supporting a tumor suppressive role of MYC/ITPR1 axis, ITPR1 expression is generally decreased in cancer and reactivation of this pathway induces cancer cell death. Nevertheless, some cancer cells, generally expressing high levels of MYCN and/or MYC, also express high level of ITPR1, which correlates with high expression of BCL2, encoding an inhibitor of ITPR1. Strikingly, in high-risk MYCN-amplified neuroblastoma, ITPR1 expression is controlled by NMYC and its level correlates with worse patient survival. In these cells, blocking the interaction between BCL2 and ITPR1 induces mitochondrial Ca2+ accumulation and cell death, and decreases tumor size. Collectively these data highlight a new function of MYC factors by controlling Ca2+ signaling, which could constitute an unsuspected vulnerability for some cancer cells, including high-risk MYCN-amplified neuroblastoma cells.

cancer biology↗

bioRxiv2023

A calibration protocol for soil-crop models aimed at reducing prediction error and inter-model variability

Process-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations.

plant biology↗

bioRxiv2023

Uncertainty in crop phenology simulations is driven primarily by parameter variability

It has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated sing random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.

plant biology↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2021

Survey data on European organic multi-species livestock farms

While there is increasing evidence of the sustainability benefits of diversified systems in the organic cropping sector, this has been much less investigated with organic livestock farming. To fill this knowledge gap, we surveyed a sample of 128 European organic multi-species livestock farms located across seven countries - Austria, Belgium, France, Germany, Italy, Sweden and Switzerland - and covering a large range of livestock species combinations. We recorded 1574 variables as raw data out of which we calculated 107 indicators describing farm structure, management and several sustainability dimensions: resource use efficiency and conservation, animal, land and work productivities, animal and human welfare. After technical validation of the data, we withdrew 26 farms and the database covers 102 farms. This database is well suited to unveil relationships between various dimensions of organic multi-species livestock farm sustainability and their structure and management. It can help reveal sustainable strategies for organic multi-species livestock farming systems and understand levers or barriers to their development.

ecology↗

bioRxiv2020

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

plant biology↗

bioRxiv2020

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

plant biology↗

bioRxiv2020

Sexual dimorphism in the meiotic requirement for PRDM9: a mammalian evolutionary safeguard

In many mammals, genomic sites for recombination are determined by histone methyltransferase PRMD9. Mice lacking PRDM9 are infertile, but instances of fertility or semi-fertility in the absence of PRDM9 have been reported in mice, canines and a human female. Such findings raise the question of how the loss of PRDM9 is circumvented to maintain reproductive fitness. We show that genetic background and sex-specific modifiers can obviate the requirement for PRDM9 in mice. Specifically, the meiotic DNA damage checkpoint protein CHK2 acts as a modifier allowing female-specific fertility in the absence of PRDM9. We also report that in the absence of PRDM9, a PRDM9-independent recombination system is compatible with female meiosis and fertility, suggesting sex-specific regulation of meiotic recombination, a finding with implications for speciation. One Sentence SummarySex-specific modulation of a meiotic DNA damage checkpoint limits the requirement for PRDM9 in mammalian fertility.

genetics↗

bioRxiv2019

How well do crop models predict phenology, with emphasis on the effect of calibration?

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

plant biology↗