Biology subjects

Casadebaig, P.

Publications and source records attributed to Casadebaig, P..

5 recordsLinked to original sources

bioRxiv2024

Differences in growth features between species are driving cereal-legume intercrop yield: a statistical learning approach based on aggregated dataset

Increasing crop diversification is crucial for developing more sustainable agricultural systems, and cereal-legume intercropping is a promising strategy. This study investigates the factors influencing the yield of cereal-legume intercrops using data from six field experiments in southwestern France, where durum wheat was intercropped with either faba bean or pea. We assessed how differences in plant traits between the associated species (e.g., height or biomass growth rates) are related to the intercrop productivity. Additionally, we developed a novel modeling approach, combining machine learning and mixed-effects models, to identify the key traits driving intercrop performance based on variable importance. Our results show that interspecific differences in plant traits, particularly in biomass accumu-lation rate, maximum leaf area index, and elongation rate, were the most important factors explaining intercrop yield. These traits and their differences mainly suggest that compet-itive processes shape the outcome of a mixture and highlight the importance of dynamic measurements in agronomic experiments. The relationship between species yield and trait differences was symmetric for both intercropped species. Furthermore, these relationships were scale-dependent, with trends observed at the aggregate level not always consistent at the level of individual experiments. Our study highlights the importance of considering trade-offs when designing intercropping systems for practical applications and demonstrates the value of combining machine learning with ecological knowledge to gain insights into complex agricultural systems from aggregated datasets. HighlightsO_LIJoint analysis of experimental datasets provided new insights into crop mixtures func-tioning. C_LIO_LIDifferences in growth traits between species in the mixture predicted their perfor-mance. C_LIO_LIThe strength of the correlation between performance and trait distance was similar for both species. C_LIO_LIFarmers and researchers need to consider trade-offs when designing intercropping sys-tems. C_LI

ecology↗

bioRxiv2021

Interspecific interactions regulate plant reproductive allometry in cereal-legume intercropping systems

O_LICalls for ecological principles in agriculture have gained momentum. Intercropping systems have long been designed by growing two, or more, annual crop species in the same field, aiming for a better resource use efficiency. However, assembly rules for their design are lacking. Notably, it is still unknown whether species performances are maximized during both the vegetative and reproductive phases given the sensitivity of reproductive allocation rules to resource limitation. Interestingly, ecological theory provides expectations regarding putative invariance of plant reproductive allometry (PRA) under non-limiting conditions for plant growth. Here we examined whether and how PRA changes in response to plant-plant interactions in intercropping systems, which can inform both ecological theory and the understanding of the functioning of intercropping systems. C_LIO_LIWe analyzed a dataset of 28 field cereal-legume intercropping trials from various climatic and management conditions across Western Europe. PRA was quantified in both mixing and single-species situations. C_LIO_LIPRA was positively impacted in specific management conditions, leading to a greater increase in yield for a given increase in plant size. Variations in PRA were more beneficial for legumes grown in unfertilized mixture, which explains their use as a key component in actual intercrop systems. The response for cereals was similar but less pronounced in magnitude, and was greater under limiting resource conditions. Focusing on intercropping conditions, hierarchical competition (indicated by biomass difference between intercropped species) appears as a strong driver of the reproductive output of a given species. C_LIO_LISynthesis and applications. Plant reproductive allometry behaves in crop species in the same way as it does in wild species. However, contrary to theoretical expectations about an overall invariance of PRA, we highlighted taxon-specific and context-dependent effects of plant-plant interactions on PRA. This systematic deviation to PRA expectations could be leveraged to cultivate each species up to its reproductive optimum while accounting for the performance of the other, whether farmers objective is to favor one species or to reach an equilibrium in seed production. Sowing density and cultivar choice could regulate the biomass of each component, with specific targets derived from allometric relationships, aiming for an optimal reproductive allocation in mixtures. C_LI

ecology↗

bioRxiv2020

Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale

Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim is to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype x environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars x environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions. Key messageCrop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time.

plant biology↗

bioRxiv2019

A new approach to crop model calibration: phenotyping plus post-processing.

Crop models contain a number of genotype-dependent parameters, which need to be estimated for each genotype. This is a major difficulty in crop modeling. We propose a hybrid method for adapting a crop model to new genotypes. The genotype-dependent parameters of the model could be obtained by phenotyping (or gene-based modeling). Then field data for example from variety trials could be used to provide a simple empirical correction to the model, of the form a+b times an environmental variable. This approach combines the advantages of phenotyping, namely that the genotype-specific parameters have a clear meaning and are comparable between genotypes, and the advantages of fitting the model to field data, namely that the corrected model is adapted to a specific target population. It has the advantage of being very simple to apply, and furthermore gives useful information as to which environmental variables are not fully accounted for in the initial model. In this study, this empirical correction is applied to the SUNFLO crop model for sunflower, using field data from a multi-environment trial network. The empirical correction reduced mean squared error (MSE) on the average by 54% for prediction of yield and by 26% for prediction of oil content, compared to the initial model. Most of the improvement came from eliminating bias, with some further improvement from the environmental term in the regression.

plant biology↗

bioRxiv2018

Heliaphen, an outdoor high-throughput phenotyping platform designed to integrate genetics and crop modeling

Heliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.

plant biology↗