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 varieties x environmental 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 (model accuracy was 54.4 %) and its capacity to rank commercial hybrids for performance level (Kendall's $\tau$ = 0.41, P < 0.01). We then designed a numerical experiment by combining the previously tested genetic and new cropping conditions (2100 virtual trials) to determine the best varieties and related management in representative French production regions. We suggest that this approach could find operational outcomes to recommend varieties according to environment types. Such spatial management of genetic resources could potentially improve crop performance by reducing the genotype-phenotype mismatch in farming environments.Comment: 25 pages, 10 figure

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

“…This would be a key step to extend classical variety evaluation, and work on recommendation zones based on environment-types identified with simulation studies. Although the variety recommendation method performed in this study was illustrated for abiotic stress tolerance and cannot yet constitute an operational choosing method for growers (as genotypes can greatly differ for their potential yield), this is an important step to link plant phenotyping platforms and model-based recommendation of varieties as performed previously (Casadebaig, Mestries, et al, 2016). In addition, model-based environmental characterization was also showed powerful for genetic study of yield response to combined abiotic stresses .…”

“…In the model, a genotype is represented by a combination of eight genotype-dependent parameters whose values are assumed to be constant among environments, thus trying to mimic genetic information (Boote et al, 2003;Jeuffroy et al, 2013). All parameter, except two, were measured directly on field trials (methods in Casadebaig, Mestries, et al, 2016). The two remaining parameters, the responses of leaf expansion and stomatal conductance to water deficit, necessitate controlled-condition experiments.…”

“…The SUNFLO simulation model was evaluated both on specific research trials (40 trials, 110 plots) and on agricultural extension trials that were representative of its targeted use (96 trials, 888 plots). Over these two datasets, the model was able to simulate significant genotypes-environment interaction and rank genotypes Casadebaig, Mestries, et al, 2016). The prediction error for grain yield was 15.7% when estimated over all data (9% -30% in individual trials).…”

“…Accordingly, simulation can be used as a tool to predict trait × trait and trait × environment interactions and help to assess traits value when scaling up from the individual plant to the plant population (one field) and finally up to the population of fields in the cropping area. Simulation successfully allowed to cluster ressource availability time-series in stress scenarios at a continental scale (Chenu et al, 2013), to search for adapted traits for specific targeted environment types (Casadebaig, Mestries, et al, 2016;Chapman et al, 2002 ), and to infer trait values beyond field experiments and available genetic diversity (Casadebaig, Zheng, et al, 2016;Martre et al, 2015 ).…”

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

“…The SUNFLO was evaluated on both specific research trials (40 trials, 110 plots) and agricultural extension trials that were representative of its targeted use (96 trials, 888 plots). Over these two datasets, the model was able to simulate significant G × G interaction and rank genotypes (Casadebaig & Debaeke ; Casadebaig et al ). The prediction error for grain yield was 15.7% when estimated over all data (9–30% in individual trials).…”