Philippe Debaeke

Model-assisted phenotyping and ideotype design

By formalizing traits as the result of genotypic and environmental effects and the relations among traits, ecophysiological models (or process-based models) provide a platform for integrative analyses of trait impacts on whole-plant and crop phenotypes. Over the past two decades, model development has been increasingly driven by the need to account for genotypic differences across environments, and improvements in this area have developed around well-defined traits such as leaf elongation, early vigor and flowering time. Process-based models are now increasingly used to define and characterize crop environments at various scales and help breeding programs take advantage of G × E interactions. Ecophysiological models are also used to assist plant phenotyping and could provide necessary links between controlled-conditions phenotyping and plant performance in the field. The integration of genetic controls in ecophysiological models has allowed analysis of the genetic control of phenotypic plasticity across wide ranges of environments, and the G × E × M space is now explored using efficient algorithms to find ideotypes optimizing many antagonist criteria. This later approach lies in finding combinations of values of the genetic and agronomic parameters that best satisfy the pre-defined objectives, but it is currently limited by the lack of quantitative relationships between genes and model parameters. Considerable efforts are still needed to develop robust links between genetic controls, physiological determinants and traits relevant to breeders.

A biomarker based on gene expression indicates plant water status in controlled and natural environments

Plant or soil water status is required in many scientific fields to understand plant responses to drought. Because the transcriptomic response to abiotic conditions, such as water deficit, reflects plant water status, genomic tools could be used to develop a new type of molecular biomarker. Using the sunflower (Helianthus annuus L.) as a model species to study the transcriptomic response to water deficit both in greenhouse and field conditions, we specifically identified three genes that showed an expression pattern highly correlated to plant water status as estimated by the pre-dawn leaf water potential, fraction of transpirable soil water, soil water content or fraction of total soil water in controlled conditions. We developed a generalized linear model to estimate these classical water status indicators from the expression levels of the three selected genes under controlled conditions. This estimation was independent of the four tested genotypes and the stage (pre- or post-flowering) of the plant. We further validated this gene expression biomarker under field conditions for four genotypes in three different trials, over a large range of water status, and we were able to correct their expression values for a large diurnal sampling period.

A model-based approach to assist variety evaluation in sunflower crop

Abstract Assessing the performance and the characteristics (e.g. yield, quality, disease resistance, abiotic stress tolerance) of new varieties is a key component of crop performance improvement. However, the variety testing process is presently exclusively based on experimental field approaches which inherently reduces the number and the diversity of experienced combinations of varietiesxa0×xa0environmental conditions in regard of the multiplicity of growing conditions within the cultivation area. Our aim is to make a greater and faster use of the information issuing from these trials using crop modeling and simulation to amplify the environmental and agronomic conditions in which the new varieties are tested. In this study, we present a model-based approach to assist variety testing and implement this approach on sunflower crop, using the SUNFLO simulation model and a subset of 80 trials from a large multi-environment trial (MET) conducted each year by agricultural extension services to compare newly released sunflower hybrids. After estimating parameter values (using plant phenotyping) to account for new genetic material, we independently evaluated the model prediction capacity on the MET (relative RMSE for oil yield was 16.4%; model accuracy was 54.4%) and its capacity to rank commercial hybrids for performance level (relative RMSE was 11%; Kendalls τ xa0=xa00.41, P

Effects of plant growth stage and leaf aging on the response of transpiration and photosynthesis to water deficit in sunflower

Water deficit influences leaf transpiration rate and photosynthetic activity. The genotype-dependent response of the latter has not been assessed in sunflower (Helianthus annuus L.), particularly during the reproductive period when grain filling and lipogenesis depend greatly on photosynthate availability. To evaluate genotypic responses to water deficit before and after flowering, two greenhouse experiments were performed. Four genotypes-two inbred lines (PSC8, XRQ) and two cultivars (Inedi, Melody)-were subjected to progressive water deficit. Non-linear regression was used to calculate the soil water deficit threshold (FTSWt) at which processes (transpiration and photosynthetic activity) were affected by water deficit. In the vegetative growth stage, photosynthetic activity was affected at a lower mean value of FTSWt (0.39) than transpiration (0.55). However, in the reproductive stage, photosynthetic activity was more sensitive to soil water deficit (FTSWt=0.45). We found a significant (P=0.02) effect of plant growth stage on the difference between photosynthesis and transpiration rate thresholds and, a significant (P=0.03) effect of leaf age on transpiration. Such results will improve phenotyping methods and provide paths for integrating genotypic variability into crop models.

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIMs modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIMs modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiencyu200a=u200a0.51, Root Mean Square Error of Predictionu200a=u200a24%; biasu200a=u200a5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Effects of plant morphological traits on phoma black stem in sunflower

Despite the importance of Phoma black stem of sunflower in France, no specific management tools are currently deployed to control this disease. The deployment of host plant resistance could be a cost-effective and sustainable way to manage the disease. Relationships between plant morphological traits and disease intensity may provide guidance towards the identification of sunflower morphological ideotypes associated with reduced disease intensity and therefore partial resistance. Such relationships were quantified in field experiments conducted over 2xa0years with a set of 21 sunflower genotypes, where several morphological attributes and several disease intensity variables were measured. Plant morphology was assessed prior to epidemic onset. Disease intensity was assessed at different scales of crop and plant hierarchy, using a nested sampling design, and implementing the concept of conditional disease intensity. The various analyses performed indicated that experimental plots grouped according to morphological attributes of sunflower at the flowering stage were associated with experimental plots grouped according to disease intensity variables, therefore indicating an association between morphological traits and disease intensity. Low disease intensity was associated with a morphological ideotype with large number of green leaves and tall stature. A sunflower plant morphological ideotype with more leaves and taller stature may represent an operational target in sunflower breeding when considering resistance to Phoma black stem.