Biology subjects

Fossati, D.

Publications and source records attributed to Fossati, D..

5 recordsLinked to original sources

bioRxiv2025

Cultivar mixtures stabilize wheat baking quality rather than improve it

Integrating diversity into agricultural systems represent a promising way to increase the resilience of crop production. In particular, cultivar mixtures are gaining attention in Europe because they are a practical way to stabilize wheat yields. However, the impact of these mixtures on wheat baking quality remains unclear. In this study, we examined the effects of cultivar mixtures on grain and flour quality. The experiment involved eight Swiss wheat cultivars grown in pure stands, in every possible 2-cultivar mixture, and in the 8-cultivar mixture. The experiment was repeated in eight year-by-site environments, allowing to evaluate the stability of baking quality in mixtures and pure stands. The results showed that the effects of the mixtures were negative for most flour quality parameters. Furthermore, these effects were not due to changes in cultivar proportions within the mixtures, but rather to cultivar-specific alterations in response to the mixture environment. Finally, mixtures significantly increased the stability of flour quality, by buffering the effects of fluctuating weather conditions. This study is the first to extensively investigate flour quality in eight contrasting environments. It demonstrates the potential of cultivar mixtures to mitigate the effects of changing abiotic conditions and ensure stable flour quality.

plant biology↗

bioRxiv2025

The plant-time-bender model: predicting yield through wheat's perception of time

To address challenges in food security, a better understanding of crop performance under varying and changing environmental conditions is required. Plant Time Warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield. PTW leverages image time series, genetic markers, and environmental covariates to learn genotype-specific physiological responses to temperature and vapor pressure deficit. Compared to mere genomic prediction models, PTW demonstrates superior performance when predicting yield in unseen environments across 48 year-locations in Europe. The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability. Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapor pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence. The learned response pattern enable retrospective and prospective yield predictions, providing a foundation for location-specific variety recommendations and targeted breeding strategies. The integration of phenomic, genomic, and enviromic data has the potential to substantially advance research in climate adaptation strategies for crop production by addressing generalization challenges of predictions to novel environmental conditions. HighlightWe present a novel deep learning model that seamlessly combines high-throughput image data, genomic data, and weather data, enabling better crop predictions for future climates.

genomics↗

bioRxiv2025

Plasticity in ear density drives complementarity effects and yield benefits in wheat variety mixtures

Variety mixtures represent a promising option to sustainably increase the productivity of grain cropping systems, but the underlying processes driving potential yield benefits remain poorly understood. Notably, the role of variety-specific phenotypic changes in mixtures - defined here as plasticity - and their effects on plant-plant interactions has scarcely been evaluated. Here, we examined the trait responses of 8 Swiss wheat varieties when grown in mixtures, and how these plastic changes contributed to overyielding, complementarity and selection effects. For this, we conducted an outdoor field experiment in 3 years and 3 sites, where wheat varieties were grown in 2-way mixtures and in pure stands. We used a visual criterion (awns) to differentiate individuals of the different varieties in mixtures. We found significant plastic changes in response to mixing for several traits in 7 varieties. Furthermore, mixture-induced plasticity in ear density was the main driver of overyielding, itself largely dominated by complementarity effects. An additional experiment allowed us to positively link plasticity in ear density to the speed of tillering onset under shading. This study improves our understanding of the plastic processes fostering overyielding in variety mixtures, and provides a key criterion - tillering onset under shade - as a potential breeding target of cultivars for mixtures.

ecology↗

bioRxiv2024

Multi trait assessment of wheat variety mixtures performance and stability: mixtures for the win!

In the current quest for a more sustainable, environment-friendly agriculture, variety mixtures are often suggested as a practical option to increase the stability of food production systems. Their effects on yield have been extensively researched, yet clear conclusions remain elusive, notably in terms of mechanistic processes and optimal variety combinations. Furthermore, in the case of wheat, yield is not the only component in the equation: grain quality is crucial for the bread value chain, yet the effects of variety mixtures on wheat quality and its stability have rarely been investigated. To that end, we conducted a multi-year, multi-site wheat variety mixture experiment investigating the role of variety mixtures on the performance and stability of five traits linked to grain yield and quality, and the mechanisms underlying these effects. Eight varieties were grown in pure stands and 2-variety mixtures, following a full diallel design. We considered the responses of grain yield, protein content, thousand kernel weight, hectoliter weight, and Zeleny sedimentation value. Results showed that mixtures generally outperformed pure stands in terms of global performance and stability for the 5 parameters. We particularly noticed an increase in quality stability and in Zeleny sedimentation value in mixtures, showing the potential of mixtures to improve crop quality. Moreover, we highlighted the important role of light interception for increased mixtures benefits. A more detailed investigation into individual mixture performances led us to some practical rules for optimal variety combinations: we advise combining varieties with similar heights and phenologies but different tillering abilities and yield potential. This study thus shows that variety mixtures represent a promising solution to sustainably increase the stability of wheat yield and quality. With practical recommendations, our results could benefit farmers but also processors and bakers, and promote the adoption of wheat variety mixtures.

plant biology↗

bioRxiv2022

Image-based phenomic prediction can provide valuable decision support in wheat breeding

Traditionally, breeders selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders ratings with such information became attractive. In this research, it is hypothesized that GxE interactions of secondary traits (i.e., growth dynamics traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at five year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate GxE interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points, and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little GxE interactions. In contrast, the modeling of GxE for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content. Key messageGenotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials.

physiology↗