Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

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 x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (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 x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation 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.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

Analyse et modélisation de l'interaction génotype - environnement - conduite de culture : application au tournesol (Helianthus annuus L.)

La stagnation actuelle des rendements de la culture de tournesol résulte d'une surprenante compensation entre les progrès génétiques (+ 1.3 % de productivité par an) et la dégradation des conditions de cultures du tournesol : l'arrêt de l'irrigation, son déplacement vers des terres présentant un moindre potentiel et une augmentation des contraintes liées au développement de maladies. Indépendamment du progrès génétique réalisé, la performance d'un génotype est très variable selon les conditions pédoclimatiques dans lequel il est cultivé et l'itinéraire technique auquel il est soumis : on parle d'interactions entre la variété, l'environnement (sol, climat, bioagresseurs) et la conduite de culture. Ainsi, la brusque évolution des prix de l'huile végétale dans un contexte de production jusqu'à présent tourné vers l'extensification a toutes les chances de déboucher sur des choix génotype-milieu-conduite originaux maximisant la marge brute. L'objectif principal de cette étude est le développement et l'évaluation d'un modèle de culture, capable d'analyser et de prévoir le comportement de différentes variétés dans des environnements contrastés (eau et azote). Cet outil a été ensuite utilisé pour répondre à deux questions : (i) peut-on identifier une date de semis optimale dans le Sud-Ouest? et (ii) peut-on définir un idéotype différent dans les grandes zones de production de tournesol? En perspective, l'avancement actuel du projet, constitué par le modèle et les méthodes pour étendre sa prise en compte de nouvelles variétés, permet d'envisager une première application de cet outil dans l'optique d'une évaluation variétale assistée par modèle. ABSTRACT : In sunflower crop, the actual seed yield stagnation results from a singular compensation between genetic progress (+1.3% potential yield/year) and degradation of cultural environments and crop management. Independently of this progress, Genotype by environment (GxE) interactions lead to highly variable cultivar performance regarding pedo-climatic and management crop conditions. The sudden surge of vegetable oils price in a crop context generally aiming towards extensification may result in original cultivar- nvironment-management choices. Such context requires to rapidly forecast the ability of each new variety to valorise cropping systems for various conditions. The main goal of this study was to develop a crop model accounting for GxE interactions. Each genotype was represented by a set of measured parameters that are valid under a wide range of conditions. Two virtual studies were conducted to assess the model adequacy in identifying interesting crop management and to a further point, in assisting crop breedin

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

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, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, 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 modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop

Identifying target traits for breeding stable and high-yielded cultivars simultaneously is difficult due to limited knowledge of physiological mechanisms behind yield stability. Besides, there is no consensus about the adequacy of a stability index (SI) and the minimal number of environments and genotypes required for evaluating yield stability. We studied this question using the crop model APSIM-Wheat to simulate 9100 virtual genotypes grown under 9000 environments. By analysing the simulated data, we showed that the shape of phenotype distributions affected the correlation between SI and mean yield and the genotypic superiority measure (Pi) was least affected among 11 SI. Pi was used as index to demonstrate that more than 150 environments were required to estimate yield stability of a genotype convincingly and more than 1000 genotypes were necessary to evaluate the contribution of a physiological parameter to yield stability. Network analyses suggested that a physiological parameter contributed preferentially to yield or Pi. For example, soil water absorption efficiency and potential grain filling rate explained better the variations in yield than in Pi; while light extinction coefficient and radiation use efficiency were more correlated with Pi than with yield. The high number of genotypes and environments required for studying Pi highlight the necessity and potential of in silico experiments to better understand the mechanisms behind yield stability

Prediction of sunflower grain oil concentration as a function ofvariety, crop management and environment using statistical models

Sunflower (Helianthus annuus L.) raises as a competitive oilseed crop in the current environmentallyfriendly context. To help targeting adequate management strategies, we explored statistical models astools to understand and predict sunflower oil concentration. A trials database was built upon experi-ments carried out on a total of 61 varieties over the 2000–2011 period, grown in different locations inFrance under contrasting management conditions (nitrogen fertilization, water regime, plant density).25 literature-based predictors of seed oil concentration were used to build 3 statistical models (multiplelinear regression, generalized additive model (GAM), regression tree (RT)) and compared to the refer-ence simple one of Pereyra-Irujo and Aguirrezábal (2007) based on 3 variables. Performance of modelswas assessed by means of statistical indicators, including root mean squared error of prediction (RMSEP)and model efficiency (EF). GAM-based model performed best (RMSEP = 1.95%; EF = 0.71) while the simplemodel led to poor results in our database (RMSEP = 3.33%; EF = 0.09). We computed hierarchical contribu-tion of predictors in each model by means of R2and concluded to the leading determination of potentialoil concentration (OC), followed by post-flowering canopy functioning indicators (LAD2 and MRUE2),plant nitrogen and water status and high temperatures effect. Diagnosis of error in the 4 statistical mod-els and their domains of applicability are discussed. An improved statistical model (GAM-based) wasproposed for sunflower oil prediction on a large panel of genotypes grown in contrasting environments

Prediction of sunflower leaf area at vegetative stage by image analysis and application to the estimation of water stress response parameters in post-registration varieties

The automatic measurement of developmental and physiological responses of sunflowers to water stress represents an applied challenge for a better knowledge of the varieties available to growers, but also a fundamental one for identifying the biological, genetic and molecular bases of plant response to their environment.On INRAE Toulouse's Heliaphen high-throughput phenotyping platform, we set up two experiments, each with 8 varieties (2*96 plants), and acquired images of plants subjected or not to water stress, using a light barrier on a daily basis. At the same time, we manually measured the leaf surfaces of these plants every other day for the duration of the stress, which lasted around ten days. The images were analyzed to extract morphological characteristics of the segmented plants and different models were evaluated to estimate total plant leaf areas using these data.A linear model with a posteriori smoothing was used to estimate total leaf area with a relative squared error of 11% and an efficiency of 93%. Leaf areas estimated conventionally or with the developed model were used to calculate the leaf expansion and transpiration responses (LER and TR) used in the SUNFLO crop model for 8 sunflower varieties studied. Correlation coefficients of 0.61 and 0.81 for LER and TR respectively validate the use of image-based leaf area estimation. However, the estimated values for LER are lower than for the manual method on Heliaphen, but closer overall to the manual method on greenhouse-grown plants, potentially suggesting an overestimation of stress sensitivity.It can be concluded that the LE and TR parameter estimates can be used for simulations. The low cost of this method (compared with manual measurements), the possibility of parallelizing and repeating measurements on the Heliaphen platform, and of benefiting from the Heliaphen platform's data management, are major improvements for valorizing the SUNFLO model and characterizing the drought sensitivity of cultivated varieties.Comment: in French languag

Increased genetic diversity improves crop yield stability under climate variability: a computational study on sunflower

A crop can be represented as a biotechnical system in which components are either chosen (cultivar, management) or given (soil, climate) and whose combination generates highly variable stress patterns and yield responses. Here, we used modeling and simulation to predict the crop phenotypic plasticity resulting from the interaction of plant traits (G), climatic variability (E) and management actions (M). We designed two in silico experiments that compared existing and virtual sunflower cultivars (Helianthus annuus L.) in a target population of cropping environments by simulating a range of indicators of crop performance. Optimization methods were then used to search for GEM combinations that matched desired crop specifications. Computational experiments showed that the fit of particular cultivars in specific environments is gradually increasing with the knowledge of pedo-climatic conditions. At the regional scale, tuning the choice of cultivar impacted crop performance the same magnitude as the effect of yearly genetic progress made by breeding. When considering virtual genetic material, designed by recombining plant traits, cultivar choice had a greater positive impact on crop performance and stability. Results suggested that breeding for key traits conferring plant plasticity improved cultivar global adaptation capacity whereas increasing genetic diversity allowed to choose cultivars with distinctive traits that were more adapted to specific conditions. Consequently, breeding genetic material that is both plastic and diverse may improve yield stability of agricultural systems exposed to climatic variability. We argue that process-based modeling could help enhancing spatial management of cultivated genetic diversity and could be integrated in functional breeding approaches

Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modeling and genome-wide association

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modeling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65,534 tested SNP, we identified nine QTL controlling oil yield plasticity to cold stress. Associated SNP are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, LTP, cystatin, alternative oxidase, or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.Comment: 12 pages, 5 figures, Plant, Cell and Environmen