Using a crop model to evaluate and design combinations of genotypes x
management x environments that improve sunflower crop performance.
Pierre Casadebaig1 and Philippe Debaeke1
1
INRA, UMR 1248 “Agrosystèmes et Agricultures, Gestion de ressources, Innovations et Ruralités”
(AGIR), BP 52627 - F 31326 Castanet Tolosan, France (pierre.casadebaig@toulouse.inra.fr and
philippe.debaeke@toulouse.inra.fr)
ABSTRACT
● Crop modelling might help at the different steps of genotype design and assessment, by completing
and widening the factorial experiments. A model of the sunflower crop (SUNFLO) where complex traits
(yield and oil content) are sensitive to main abiotic stresses and to genotype-specific traits has been
developed in France. The model parameters representing the genetic variability were measured as
phenotypic traits.
The aim of this study is to exploit the integrative properties of crop modelling to identify genotype x
environment x management (GEM) combinations that are interesting for crop yield performance or
stability.
● The space defined by all the possible GEM combinations can be referred as the fitness landscape
(Hammer and Jordan, 2007). This landscape is partly sampled during the breeding steps, when multienvironment trials (MET) and various models of variance analysis are used mainly to identify the most
performing genotypes and, additionally, to check that GxE interactions are not too excessive. This study
focus on the inclusion of management actions and target environments in the otherwise genotype-centered
view to crop improvement.
● A virtual factorial plan was built from seven genotypic traits (phenology, architectural and response
traits), two management actions (sowing date and nitrogen fertilization) and an environmental variability
representative of the soils and cultivation area. These GEM combinations were simulated and variance
analysis methods were used to identify the pertinent combinations. Simulation results were analyzed from
two viewpoints: (i) by identifying favourable links between phenotypic trait combinations ("virtual
genotypes"), target environments and management actions and (ii) by assessing the climatic variability
impact on these favorable combinations.
● Some traits impact on performance were trivial (precocity), others were foreseeable but has been
quantified (plant leaf area). Some traits whom impact is difficult to assess in field trials (shape of leaf
profile, stomatal regulation) showed positive properties on drought-prone conditions. It was also shown
that annual variations in climate could greatly affect the apparent benefit of a yield-increasing trait.
● The same way that a conciliation of genetics and crop physiology is necessary to bridge the
genotype-phenotype gap, the inclusion of computer science seems essential to navigate into this
additional layer of complexity created by modeling approaches.
Key words: crop modeling, phenotypic plasticity, ideotype design, sunflower
Introduction
In France, newly bred sunflower genotypes are evaluated for 2 years on a multi-environment trial
(MET) for their agronomic value before their release on the market. Then, technical institutes
(intermediate structures between breeders and farmers) seek to improve the knowledge on this new
genetic material by evaluating the genotypes for 2 additional years and thus provide management advices
to farmers. These additional experiments or surveys allow to extend the breeders' evaluation in relation to
the main production regions. As the results of these evaluations are used for different goals, the
information over the 4 years is only partly addressed so the whole process is subject to experienced
climatic variability over the 2 years trials.
Sunflower physiological knowledge (Connor and Hall, 1997) is well advanced and it was integrated
into a few models (Chapman et al., 1993; Villalobos et al. 1996; Pereyra-Irujo and Aguirrezabal, 2007)
that use different biophysical frameworks. Depending on the level of elucidation of the genetic
determinism for a considered phenotypic trait, its representation in a crop model can range from
parameters whose value is algorithmically estimated on genotypic-dependant datas (low determinism) to
parameters representing QTLs or allelic compositions (strong determinism). For sunflower crop, the
genetic component in actual models was more crude than the environmental one (limiting factors and
plant response) and we identified a need to investigate this question by developing a new model for this
crop. Our goal at this time was to help the technical institutes with the assessment of genotype,
environment and management (GEM) combinations so its updatability for yearly cultivar releases by the
breeding process was a major concern and was a constraint on the number of specific parameters used to
describe a genotype. We finally proposed an approach where each genotype was represented by a set of
measured phenotypic traits whose values were directly genotypic-dependant parameters in the model.
These genotypic parameters are, despite their name, under uncertain genetic control (Slafer, 2003).
The space defined by all the possible GEM combinations can be referred as the fitness landscape
(Hammer and Jordan, 2007). This landscape is partly sampled during the breeding steps, when multienvironment trials and various models of variance analysis are used mainly to identify the most
performing genotypes and, additionally, to check that GxE interactions are not too excessive. Modeling
and simulation is here view as a tool to extend the potentialities of field experimentation by adding
another step to improve the otherwise resource-limited exploration of the fitness landscape (Messina et
al., 2006).
The aim of this study is to exploit the integrative properties of crop modeling to explore factorial
plans similar to field MET but including management actions and a much wider genetic and climatic
variability. The approach focus on a descriptive analysis as first step to a more elaborated optimizationbased methodology.
Material and methods
Modelling framework
Although the model algorithm and its evaluation were published recently (Casadebaig et al., 2011,
Lecoeur et al., 2011) synthetics elements from the model development and evaluation are recalled here.
The modelling framework was those proposed by Monteith (1977) and shared by numerous crop models
as most of its terms are easily measurable on the field. Broad scale processes of this framework (mainly
radiation use efficiency and light interception) were split into finer level (e.g leaf expansion, senescence)
to reveal genotypic specificity and to allow the emergence of interactions. In cropping condition the
performances are limited by numerous physical or biotic factors but only the supposed main limiting
abiotic factors (temperature, light, water and nitrogen) were included in the model as a depreciative and
multiplicative effect of the potential processes (expansion, transpiration, photosynthesis).
The model was evaluated on both specific research trials and on trials that were representative of its
practical utilization (small scale MET, 16 sites x 20 genotypes). It was found that the model could not
discriminate between close-performing genotypes ( < 0.2 t/ha) but in spite of uncertainty on inputs for the
MET, a variance analysis of real and simulated networks indicated that genotype x environment
interactions were also significant in the simulated network, but with a much lower amplitude (mean
square for GxE interactions was 9 fold weaker that in the real network).
Genotypic variability
In the model, the genotypic variability is represented by 13 genotype-dependent parameters whose
distribution was phenotyped on a panel of 20 genotypes that were representative of thirty years of genetic
selection in France (Vear et al., 2003). In order to decorrelate the traits' association in the phenotyped
material we defined 128 virtual genotypes from the factorial combination of 2 levels (min and max) by 7
traits :length of maturity phase, leaf number, shape of leaf profile, plant leaf area, light extinction
coefficient, leaf expansion and stomatal conductance response to water stress.
Experimental design and simulation
Another factorial plan was built from the combination of the virtual genotypes (128 G levels),
environments (90 E levels) : 30 years on one geographical site (Auzeville-Tolosane, France), 3 soil water
capacity (100, 150, 250 mm) and management actions (6 M levels) : 3 sowing dates (1st, 15th and 30th
april) and 2 nitrogen fertilizations (0, 40 kg/ha 10 days after sowing) ; giving a total of 69120
combinations.
Modelling and simulation was done on the VLE-RECORD open-source platform (Quesnel et al.,
2009). Data analysis and visualization were performed with R software and used to identify the pertinent
combinations. Each GEM combination was characterized by crop yield and two scales of abiotic stress :
water stress intensity was based one the fraction of transpirable soil water (FTSW) (sum of 1-FTSW on
the crop cycle) and nitrogen stress intensity computation was similar but based on the nitrogen nutrition
index (NNI). The sensitivity (S) of an output variable (Y) to a variation in each phenotypic trait was
computed as S = (mean(YT) – mean(Yt)) / mean(Y) with subscripts T and t indicating the maximum and
minimum value for the trait.
Results and discussion
Simulation of the phenotypic plasticity on virtual genetic material
Each virtual genotype was defined by a unique combination of input traits that generated different
behaviors in relation to the experienced environment. Figure 1 (left section) illustrates the observed
variability in input traits from which the virtual genotypes were constructed. The right section of the
figure 1 shows how the environment is experienced differently by the genotypes. The aim was simply to
illustrate the repartition of individual simulation in a space of abiotic stress. The density is not so
homogeneous : while the majority of simulations is centered around usual cropping conditions, a much
larger space is being explored, corresponding to fully watered conditions (water stress < 20, mean
ETR/ETM ~ 0.9) to harsh water stress (stress > 95, mean ETR/ETM ~ 0.2). The combination of a strong
water stress with a good fertilization is less represented because the main nitrogen uptake process was
modeled as function of the transpirational stream (mass flow).
Figure 1 : Phenotypic variability in input traits of the model and its consequence on the simulated fitness
landscape. The left panel shows the variability of 13 phenotypic traits measured on 20 contrasted
genotypes. The right panel represent abiotic stress intensities experienced by individual combinations
from a large factorial plan of genotypes, environments and management actions.
Assessment of the impact of input traits on crop performance.
In a second step, we evaluated the contribution of input traits to the simulated yield variability. From
the 13 initial traits, we did not considered those who had an obvious effect on performance whether it was
caused by a direct multiplicative effect (maximum photosynthesis, harvest index) or a modeling bias (late
flowering time correlated to a higher radiation use efficiency). On the remaining traits (figure 2, left
panel) only the precocity at maturity (longer maturity phase), the leaf profile (higher flag leaf) and
conductance (early stomatal closure under water stress) had a significant effect (p < 0.001) on crop yield.
; a variation in the plant leaf area had a null mean effect but wide deviation.
Figure 2 : Impact of genotypic input traits on crop performance. The left panel shows boxplots for the
variability of the traits' impact on yield (n = 540). The right panel represents the estimated density of
traits' impact in relation to sowing dates and soil water content (mm).
The right panel of the figure 2 details the variability hidden by boxplots for the 3 main traits
impacting yield in relation to management (sowing dates) and environment (soil depth). Mainly, the
impact of traits is fluctuating with EM combinations : in the most opposed practices the distribution of
precocity and conductance impact could partly overlap (late sowing in shallow soils) or be clearly distinct
(early sowing in deep soil). The simulation also reveals that the sowing date modify the shape of the
distribution of impacts : the impact of precocity widens with late sowing (mainly in deep soils).
This descriptive analysis could be used to improve knowledge on how to link genotypic traits and
management : for example if a genotype shows an early stomatic regulation capacity, its performance is
more reliable with early sowings on drought-prone environments.
Figure 3 : Distribution of crop yield in relation to environmental variability
As we explored only two levels per input trait, the computed distribution for traits' impact are
multimodal. Figure 3 presents distributions of crop yield conditioned by the level of the trait with either
taking account the full climatic variability (“climatic”) or ignoring it by averaging by management levels
(“management”). The point in this figure is to show that multi-modality is related to the experimental
design : the two main modes of the distribution are matched by the levels of precocity. Furthermore, it is
interesting to point that these modes, which are clearly separated when dealing with mean values
(“management” panels in fig. 3) are closer (early sowing, deep soils) and nearly confounded (late sowing,
shallow soils) when considering individual years (“climatic” panels).
Annual variations in climate could greatly affect the apparent benefit of a yield-increasing trait. For
that reason 2 or 3 years of field evaluation as it is done are very helpful but insufficient to explore the
advantages and limits of a new variety. To reach the margin of improvement for crop performance
glimpsed by the modeling approach, technical institutes and advisers could be associated to modeling and
simulation research actions. Two objectives could thus be pursued in this model-assisted genotypic
assessment : (i) the promotion of peculiar genotypes viewed as a specific combination of traits in defined
cropping areas and (ii) the adaptation of field MET based on abiotic stress evaluation to better match the
target population of environments in cropping areas.
When looking backwards to the breeding process, that is essentially increasing the number of
genotypes to work with, high throughput phenotyping methods become central but can be difficult to
adapt to some physiological traits (e.g stomatal conductance, photosynthetic efficiency). However, it is
not clear if breeding directly for interesting physiological traits would be less time consuming than
integrating stronger genetic determinant in crop models, given that the two methods require a comparable
amount of phenotyping.
Modelling and in silico experimentations generates a large amount of data. Efficient software and
mathematical methods to simulate (parallelization, vectorisation), to explore models (global sensitivity
analysis, meta-modelisation) or to identify optimal solutions are needed. This study focused on a
descriptive analysis of the crop yield performance but others criteria should have been considered (yield
stability, water use efficiency) to take full advantage of the possibilities opened by dynamic models. The
same way that a conciliation of genetics and crop physiology is necessary to bridge the genotypephenotype gap, the inclusion of computer science seems essential to navigate into this additional layer of
complexity created by modeling approaches.
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