Pierre Casadebaig

Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review

The diversity of growing conditions and the development of new outlets for agricultural products favour a diversity of crop management systems requiring various cultivars, with specific characteristics. Genotype performance is usually assessed through multi-environment trials comparing a variable number of genotypes grown in a wide range of soils, climatic conditions and cropping systems. Field experiments show empirical evidence for the interactions between genotype, environment and cropping system. However, such interactions are rarely taken into account to design ideotypes or for cultivar assessment, or in the definition of crop management plans adapted to cultivars. Agronomic models, built to simulate the dynamic response of crops to their environment, and thus to techniques which modify it, appear to be appropriate tools to evaluate and predict these interactions. This paper reviews the three main uses of model-based predictions of the interactions between genotype, environment and cropping system: definition of breeding targets, characterisation of the environments in cultivar experiments and support for the choice of the best cultivar to grow in a given situation. Models specifically allow understanding the influence of one or a combination of specific traits on performances and long-term ecological impacts. We show that a diversity of models is required, from physiologically based crop models to agroecology-based cropping system or landscape models, able to account well for farmers’ practices. A way of taking cultivars into account in crop models is proposed, based on three main steps: the choice of the model, the identification and estimation of its cultivar parameters, and testing the model for decision support. Finally, the analysis of the limitations for wider use of crop models in variety breeding and assessment addresses some major questions for future research.

Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis

Present work focussed on improving the description of organogenesis, morphogenesis and metabolism in a biophysical plant model (SUNFLO) applied to sunflower (Helianthus annuus L.). This first version of the model is designed for potential growth conditions without any abiotic or biotic stresses. A greenhouse experiment was conducted to identify and estimate the phenotypic traits involved in plant productivity variability of 26 sunflower genotypes. The ability of SUNFLO to discriminate the genotypes was tested on previous results of a field survey aimed at evaluating the genetic progress since 1960. Plants were phenotyped in four directions; phenology, architecture, photosynthesis and biomass allocation. Twelve genotypic parameters were chosen to account for the phenotypic variability. SUNFLO was built to evaluate their respective contribution to the variability of yield potential. A large phenotypic variability was found for all genotypic parameters. SUNFLO was able to account for 80% of observed variability in yield potential and to analyse the phenotypic variability of complex plant traits such as light interception efficiency or seed yield. It suggested that several ways are possible to reach high yields in sunflower. Unlike classical statistical analysis, this modelling approach highlights some efficient parameter combinations used by the most productive genotypes. The next steps will be to evaluate the genetic determinisms of the genotypic parameters.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

A Generic Model to Simulate Air-Borne Diseases as a Function of Crop Architecture

In a context of pesticide use reduction, alternatives to chemical-based crop protection strategies are needed to control diseases. Crop and plant architectures can be viewed as levers to control disease outbreaks by affecting microclimate within the canopy or pathogen transmission between plants. Modeling and simulation is a key approach to help analyze the behaviour of such systems where direct observations are difficult and tedious. Modeling permits the joining of concepts from ecophysiology and epidemiology to define structures and functions generic enough to describe a wide range of epidemiological dynamics. Additionally, this conception should minimize computing time by both limiting the complexity and setting an efficient software implementation. In this paper, our aim was to present a model that suited these constraints so it could first be used as a research and teaching tool to promote discussions about epidemic management in cropping systems. The system was modelled as a combination of individual hosts (population of plants or organs) and infectious agents (pathogens) whose contacts are restricted through a network of connections. The system dynamics were described at an individual scale. Additional attention was given to the identification of generic properties of host-pathogen systems to widen the models applicability domain. Two specific pathosystems with contrasted crop architectures were considered: ascochyta blight on pea (homogeneously layered canopy) and potato late blight (lattice of individualized plants). The model behavior was assessed by simulation and sensitivity analysis and these results were discussed against the model ability to discriminate between the defined types of epidemics. Crop traits related to disease avoidance resulting in a low exposure, a slow dispersal or a de-synchronization of plant and pathogen cycles were shown to strongly impact the disease severity at the crop scale.

Using numerical plant models and phenotypic correlation space to design achievable ideotypes: Phenotypic correlations in ideotype design

Simulation models can be used to predict the outcome of plant traits modifications resulting from the genetic variation (and its interaction with the environment) on plant performance, hence gaining momentum in plant breeding process. Optimization methods complement those models in finding ideal values of a set of plant traits, maximizing a defined criteria (e.g. crop yield, light interception). However, using such methods carelessly may lead to misleading solutions, missing the appropriate traits or phenotypes. Therefore, we propose to use domains of potential phenotypes for the search of an optimum, taking into account correlations between traits to ground numerical experiments in biological reality. In addition, we propose a multi-objective optimization formulation using a metric of performance returned by numerical model and a metric of feasibility based on field observations. This can be solved with standard optimization algorithms without any model modification. We applied our approach to two contrasted simulation models: a process-based crop model of sunflower and a structural-functional plant model of apple tree. On both cases, we were able to characterize key plant traits and a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides a proof of concept for this approach and shows that it could improve trait-based breeding methods with paths describing desirable trait modifications both in direction and intensity.Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, that is, ideal values of a set of plant traits, resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of performance criteria (e.g. yield and light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modelling approach, which identified paths for desirable trait modification, including direction and intensity.

Optimization of black-box models with uncertain climatic inputs—Application to sunflower ideotype design

Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most “off-the-shelf” optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies.

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.

Genetic Analysis of the Transpiration Control in Sunflower (Helianthus Annuus L) Subjected to Drought

Stomatal control of transpiration was implied as the major strategies by which plants cope with water stress. Here we did investigate the genetic control of this process using the following trait: Fraction of Transpirable Soil Water threshold (FTSWt) representing the threshold of soil water content at which the stomatal control of transpiration started. We conducted a progressive water deficit experiment using recombinant inbred lines (RILs) of sunflower and we analyzed the variation of FTSWt. Quantitative trait loci (QTL) mapping was then performed to determine the loci involved and to identify the genetic control. This work has shown, for the first time, QTL mapping for FTSWt in crops. In this work QTL mapping was made in sunflower.

Data describing the eco-physiological responses of twenty-four sunflower genotypes to water deficit

This article presents experimental data describing the physiology and morphology of sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France). Here, we provide data including specific leaf area, osmotic potential and adjustment, carbon isotope discrimination, leaf transpiration, plant architecture: plant height, leaf number, stem diameter. We also provide leaf areas of individual organs through time and growth rate during the stress period, environmental data such as temperatures, wind and radiation during the experiment. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the physiological basis of heterosis. It is available on the following repository: https://doi.org/10.25794/phenotype/er6lPW7V