Breeding in winter wheat (Triticum aestivum L.) can be further progressed by targeting previously neglected competitive traits

Abstract

Breeders work to adapt winter wheat genotypes for high planting densities to pursue sustainable intensification and maximize canopy productivity. Although the effects of plant-plant competition at high planting density have been extensively reported, the quantitative relationship between competitiveness and plant performance remains unclear. In this study, we introduced a shoot competitiveness index (SCI) to quantify the competitiveness of genotypes and examined the dynamics of nine competitiveness-related traits in 200 winter wheat genotypes grown in heterogeneous canopies at two planting densities. Higher planting densities increased shoot length but reduced biomass, tiller numbers, and leaf mass per area (LMA), with trait plasticity showing at least 41% variation between genotypes. Surprisingly, genotypes with higher LMA at low density exhibited greater decreases under high density, challenging expectations from game theory. Regression analysis identified tiller number, LMA, and shoot length as key traits influencing performance under high density. Contrary to our hypothesis, early competitiveness did not guarantee sustained performance, revealing the dynamic nature of plant-plant competition. Our evaluation of breeding progress across the panel revealed a declining trend in SCI (R² = 0.61), aligning with the breeding objective of reducing plant height to reduce individual competitiveness and increase the plant-plant cooperation. The absence of historical trends in functional traits and their plasticities, such as tiller number and LMA, suggests their potential for designing ideal trait-plasticity for plant-plant cooperation and further crop improvement.

Full text

Breeding in winter wheat
(Triticum aestivum L.)
can be further progressed
by targeting previously
neglected competitive traits
Annette Manntschke
1
†
, Lina Hempel
1
†
, Andries Temme
1,2
,
Marcin Reumann
1
and Tsu-Wei Chen
1
*
1
Group of Intensive Plant Food Systems, Albrecht Daniel Thaer-Institute, Faculty of Life Sciences,
Humboldt Universität zu Berlin, Berlin, Germany,
2
Plant Breeding, Wageningen University & Research,
Wageningen, Netherlands
Breeders work to adapt winter wheat genotypes for high planting densities to
pursue sustainable intensification and maximize canopy productivity. Although
the effects of plant-plant competition at high planting density have been
extensively reported, the quantitative relationship between competitiveness
and plant performance remains unclear. In this study, we introduced a shoot
competitiveness index (SCI) to quantify the competitiveness of genotypes and
examined the dynamics of nine competitiveness-related traits in 200 winter
wheat genotypes grown in heterogeneous canopies at two planting densities.
Higher planting densities increased shoot length but reduced biomass, tiller
numbers, and leaf mass per area (LMA), with trait plasticity showing at least 41%
variation between genotypes. Surprisingly, genotypes with higher LMA at low
density exhibited greater decreases under high density, challenging expectations
from game theory. Regression analysis identified tiller number, LMA, and shoot
length as key traits influencing performance under high density. Contrary to our
hypothesis, early competitiveness did not guarantee sustained performance,
revealing the dynamic nature of plant-plant competition. Our evaluation of
breeding progress across the panel revealed a declining trend in SCI (R² =
0.61), aligning with the breeding objective of reducing plant height to reduce
individual competitiveness and increase the plant-plant cooperation. The
absence of historical trends in functional traits and their plasticities, such as
tiller number and LMA, suggests their potential for designing ideal trait-plasticity
for plant-plant cooperation and further crop improvement.
KEYWORDS
plant-plant interaction, phenotypic plasticity, canopy productivity, breeding progress,
plant-plant competition, intergenotypic competition in plant, planting density
Frontiers in Plant Science frontiersin.org01
OPEN ACCESS
EDITED BY
Sergio Tombesi,
Catholic University of the Sacred Heart, Italy
REVIEWED BY
Guy Golan,
Leibniz Institute of Plant Genetics and Crop
Plant Research (IPK), Germany
Franca Bongers,
Wageningen University and Research,
Netherlands
*CORRESPONDENCE
Tsu-Wei Chen
tsu-wei.chen@hu-berlin.de
†
These authors have contributed equally to
this work
RECEIVED 03 September 2024
ACCEPTED 20 February 2025
PUBLISHED 19 March 2025
CITATION
Manntschke A, Hempel L, Temme A,
Reumann M and Chen T-W (2025)
Breeding in winter wheat (Triticum aestivum
L.) can be further progressed by targeting
previously neglected competitive traits.
Front. Plant Sci. 16:1490483.
doi: 10.3389/fpls.2025.1490483
COPYRIGHT
© 2025 Manntschke, Hempel, Temme,
Reumann and Chen. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 19 March 2025
DOI 10.3389/fpls.2025.1490483

1 Introduction
A central objective in agronomy and breeding is the sustainable
intensification of cereal crop production to enhance yield per unit
area. This has driven the adaptation of maize and winter wheat
varieties to high planting densities (Lollato et al., 2019;Perez et al.,
2019;Bastos et al., 2020;Lacasa et al., 2022;Tombeur et al., 2022).
High planting densities can optimize resource use, improve nutrient
efficiency, promote early canopy closure (Perez et al., 2019;Pao
et al., 2023), suppress weeds, and boost seed production (Weiner
et al., 2010;Postma et al., 2021;Wheeldon et al., 2021). However,
increased density also raises challenges, such as higher disease
pressure, greater insect susceptibility, lodging, and elevated soil
water use, which can negatively impact yield, grain quality
(Reynolds et al., 2009;Weiner et al., 2017;Lollato et al., 2019)
and importantly, increased plant-plant interactions (Weiner, 1990;
Subrahmaniam et al., 2018). This results in the “constant final yield”
concept (Weiner, 1990), which indicates that while biomass
increases with density, it eventually plateaus, rendering extremely
high densities impractical due to diminishing returns and increased
costs (Bastos et al., 2020;Postma et al., 2021).
In the context of plant-plant interactions, increased planting
density would be beneficial if the plants maximize plant-plant
cooperation (commensal or reciprocal helping) and minimize plant-
plant competition (competitive, selfish or altruism). Plant-plant
competition is undesirable in cropping systems since competing
individuals suffer from investment costs for resource capture and
therefore reduce their potential productivity (Subrahmaniam et al.,
2018). Using principles from game theory, theoretical ecologists have
used conceptual models to study the physiological strategy of plants
under inter- and intra-specificcompetitions(Schieving and Poorter,
1999;Vermeulen, 2015;Bongers et al., 2019). Game theory predicts
that a homogenous canopy with one genotype having thicker leaves
(high leaf mass per area, LMA) can be invaded by a second genotype
which develops “cheap”leaves (low LMA, low construction costs) for
light interception (Schieving and Poorter, 1999). Therefore, selection
tends to favor individuals with low LMA, implying that an
evolutionarily stable strategy results in a suboptimal canopy carbon
gain. In other words, selection pressure in a competitive environment,
e.g. high plant density in the agricultural systems, might favour a
genotype using strategies suboptimal for its potential performance and
productivity under monoculture condition (Reynolds et al., 2009;
Fischer and Rebetzke, 2018). It is important to note that LMA is
influenced not only by the plant’sgenotypicstrategyforplant-plant
interactions but also by environmental conditions. For example, in
resource-rich environments with ample light and CO
2
,plantstendto
develophigherLMA.However,theirresponse varies under different
stress conditions—for instance, increased LMA under low
temperatures or low water availability (Poorter et al., 2010). This
adds another layer of complexity to the environmental effects on
plant-plant interactions. The discrepancy in advantageous strategies
in competitive, heterogeneous conditions implies that a breeder might
select, especially in early generations (e.g. F2 to F4) where each
individual plant is genetically different, genotypes having advantages
in inter-genotypic competition for resource acquisition rather than
genotypes with high potential in homogeneous canopies (Fasoula,
1990;Fischer and Rebetzke, 2018). For example, it has been
demonstrated that high-yielding genotype exhibited lower
competitiveness for radiation but compensated with greater radiation
use efficiency (Cossani and Sadras, 2021). Plant height, an obvious trait
related to plant-plant competition, has been intensively considered for
in the context of plant breeding (Voss-Fels et al., 2019;Snowdon et al.,
2021;Lacasa et al., 2022). Conversely, less obvious traits related to
plant-plant competition, e.g. low LMA, might have been neglected by
plant breeders and genotypes with cooperative behaviors in
homogeneous canopies might have been discarded during the
breeding process due to their low competitiveness with other
genotypes. This implies that plant breeders, who select intensively in
their fields for high individual yield, might be in the wrong direction
and obtain the cultivars suboptimal for homogeneous population
(Weiner, 2019;Maurer and Pillen, 2021). Therefore, it is rational to
hypothesize that less obvious traits related to plant-plant interactions,
such as LMA, have not yet been considered in the breeding history.
Additionally, it is also important to notice the difference between plant
vigor and competitiveness. Plant vigor refers to robust growth and
health under ideal conditions, characterized by rapid growth and high
biomass, e.g. the growth behavior under low planting density.
Competitiveness, however, is a plant’s ability to secure resources and
thrive among neighbor plants, often involving resource acquisition and
interference with neighbors.
Many functional traits of plants exhibit plasticity in response to
planting density, which significantly influences the degree of plant-
plant interactions. This phenotypic plasticity is a reaction for
increasing individual competitiveness for total light capture per
plant (Kiaer et al., 2013;Weiner, 2019;Zhu et al., 2019;Postma
et al., 2021). While numerous studies have documented systematic
changes in plant traits throughout breeding history in recent years
(Perez et al., 2019;Voss-Fels et al., 2019;Lichthardt et al., 2020;
Welcker et al., 2022), there has been less focus on variations in
phenotypic plasticity in response to environmental factors and
planting density (Nimmo et al., 2023;Matesanz and Milla, 2018).
Phenotypic plasticity enables a genotype to adapt to varying
environmental conditions without genetic changes and there is
increasing evidence for the importance of phenotypic plasticity in
resource capture (Grogan et al., 2016;Nielsen and Papaj, 2022).
Recent approaches suggest considering plasticity in multiple traits
to gain a comprehensive understanding of a genotype’s adaptive
capacity (Kikuchi et al., 2017;Nielsen and Papaj, 2022). Therefore,
it is important to study multiple traits related to plant-plant
competition and their plasticity in response to planting density.
In this study, we aim to unravel the complex relationships
between functional traits, their phenotypic plasticity, and their
effects on plant-plant competition within canopies of high
planting density. Utilizing a panel of 200 winter wheat genotypes
from Germany, registered between 1966 and 2016, we examined
nine functional traits and their plasticity in response to varying
planting densities. We hypothesize that: 1) there are significant
genotypic variations in functional traits related to plant-plant
competition; 2) there are genotypic differences in the plasticity of
these traits; 3) plant vigor and competitiveness are distinct traits; 4)
Manntschke et al. 10.3389/fpls.2025.1490483
Frontiers in Plant Science frontiersin.org02

competitiveness is positively correlated with performance under
heterogeneous canopy with high planting density and can be used
to predict shoot biomass in the later developmental stage; and 5)
traits related to plant-plant competition have changed over
breeding history. Our analysis centered on how these traits
exhibit plasticity under different planting densities, with the goal
of elucidating their implications for wheat breeding and production.
2 Materials and methods
2.1 Plant material and experimental design
To investigate cultivar-specific responses to intergenotypic
shoot competition, we grew 200 winter wheat genotypes in two
density treatments in a greenhouse experiment at Humboldt
Universität zu Berlin, Germany. The genotypes used are a curated
collection of historical and modern German winter wheat cultivars
with registration dates between 1966 and 2016, as well as a few other
European and exotic cultivars to increase diversity (Supplementary
Table S1). This panel was extensively studied in field trials, so that
yield-related data is available (Voss-Fels et al., 2019;Lichthardt
et al., 2020;Sabir et al., 2023;Wang et al., 2025).
In January 2022, seeds were germinated in seedling trays and grown
for four weeks. Each seedling was then transplanted into a customized
plastic cuboid pot (22 cm x 7 cm x 3 cm) containing 235 g of substrate
(Klasmann-Deilmann, Geeste, Germany). These pots were organized
into heterogeneous multi-genotype canopies (Supplementary Figure
S1). Two treatments were established: a low planting density canopy
(T1, 83 plants/m²) and a high planting density canopy (T2, 333 plants
m
-2
), with one plant per genotype. In typical winter wheat fields in
Germany, a planting density of 320–380 seeds/m² is common, so the
competitive pressure among shoots in T2 is similar to field conditions,
while T1 plants experience minimal plant-plant interactions. The
position of each cultivar within a canopy was randomized, and
borderplantswereaddedinT2(Supplementary Figure S1)toreduce
lateral light interceptions that reduce competition pressure (Chen et al.,
2019). Both treatments were replicated four times in a split-plot design.
Additionally, T2 was repeated with five more replicates to perform
destructive measurements at two developmental stages. In total, 2600
individual plants were grown and measured. Temperatures in the
greenhouse were recorded and appropriate for vernalization during
seedling stages (Supplementary Figure S2). Sufficient amounts of
nutrients and water were available in the substrate and no additional
fertilizer was applied during the experiment. Plants were watered when
needed, as was the application of pest controls.
2.2 Data collection
To quantify shoot competitiveness index (SCI) and understand
how traits are associated with SCI, destructive measurements were
conducted in T1 and T2 approximately 16 weeks after sowing at the
booting stage (referred to as H1). To test whether the obtained SCI can
be used to predict biomass in T2 at a later developmental stage, the
second set of T2 was harvested four weeks later at anthesis (referred to
as H2). In H1 and H2, nine functional traits were measured: 1) shoot
length, 2) stage of development, 3) number of living tiller, 4) number of
dead tillers, 5) number of leaves on main stem, 6) percentage of
senescence of leaves on main stem, 7) total dry shoot biomass, 8) leaf
area for the youngest fully developed leaves on the main stem and their
9) dry weigh. As these traits influence the performance of plants by
affecting growth, reproduction and survival, they were considered as
surrogates for competitiveness.
Shoot length was measured as the maximum length of the plant
from the base of stem at soil height to the tip of the most extended leaf. It
is important to note that shoot length, which includes both stem and leaf
length, was used as a measure here because it was infeasible to measure
the traditional plant height (from the base to the highest point) of
individual plants in a dense, heterogeneous canopy. Developmental stage
was determined by the BBCH scale, which allows a categorization of
plant developmental stages based on observable characteristics (Meier,
2001). Living and dead tillers were counted, whereas dead tillers included
the aborted and aborting tillers that were expected to die within the next
few days. The sum of living and dead tillers resulted in the number of
total tillers. The total number of leaves consisted of all leaves on the main
stem of the plant, including both living and dead leaves. To assess leaf
senescence on the main stem, all leaves from the bottom that were not
fully green were counted. If the last wilting or yellowing leaf was still
partially green, we noted its degree of wilting by adjusting the decimal
point. The number of living leaves was then determined by subtracting
the number of senescent leaves from the total leaf count. Leaf area was
measured on the youngest fully developed leaf of the main stem using a
portable leaf area meter (CI-203, CID-Bioscience, USA) and individual
leaves were oven dried at 60°C for 48 hours and weighed to determine
the dry mass and leaf mass per area (LMA, g m
-2
). Total shoot biomass
was weighed after oven drying at 60°C for 48 hours and refers to the dry
weight of the entire aboveground plant material.
2.3 Data analyses
For each trait, a linear mixed-effects model to account for the
split-plot design was fitted by lmer function in lme4 R-package
(Bates et al., 2015):
yijk =m+ai+bj+gij +bk+eijk
where the trait value yof the i
th
cultivar in the j
th
treatment is
modeled with the overall mean value mand the fixed effects of
genotype (a
i
) and treatment (b
j
), as well as their interaction g
ij
. The
random factor bconsidered the k
th
treatment-genotype replication.
e
ijk
was the residual errors.
Based on the model, marginal means (EMMs) of each trait was
calculated, genotype and treatment and post-hoc pairwise
comparisons using emmeans function in emmeans R-package
(Searle et al., 1980;Lenth, 2017). The significance of fixed effects
and their interactions were assessed using the Wald`s c
2
test with
the anova function from the car package (Fox et al., 2019). Two-way
ANOVA for each of the analyzed traits was performed. All further
analyses were performed with EMMs of traits.
Manntschke et al. 10.3389/fpls.2025.1490483
Frontiers in Plant Science frontiersin.org03

To quantify the proportional change in trait values due to the
density treatments, we defined plasticity (P) of a functional trait X
as:
PX,i=XT2,i−XT1,i
XT1,i
where for each genotype i, the difference of EMMs between
treatments T1 and T2 is normalized to the trait EMM in T1. This
standardized measure is widely used to compare the magnitudes of
plasticity across genotypes and traits (Kikuchi et al., 2017;Arnold
et al., 2019;Laitinen and Nikoloski, 2019).
By comparing the ability for resource capture of a plant with its
neighbor in a multi-genotype canopy, the competitiveness of the
plant can be quantified (Chen et al., 2019). This concept is adopted
here to calculate a shoot competitiveness index (SCI):
SCIi=SBMT2,i
SBMT2
−
SBMT1,i
SBMT1
whereSBMistheshootdrybiomassofi
th
genotype and SBMisthe
average shoot dry biomass per plant of the whole canopy in the
treatments T1 and T2, respectively. Normalization to the average
response in each treatment compare the ability for resource capture
of a genotype with the whole canopy in both planting density. Positive
SCI values indicate above-average competitiveness in T2, while
negative SCI values signify a disadvantage in dry biomass production
under high planting density. Please note that reduced competitiveness
does not necessarily imply cooperation. Later, we discuss cooperation
in the context of plants adjusting their traits to be less selfish, such as
reducing shade-avoidance responsesormodifyingLMA.To investigate
the associations between SCI and functional traits, simple linear
regression and different models of multiple linear regressions were
conducted and the ability of each regression model to explain the
variationinthedependentvariables were compared.
To investigated how breeding history affected the functional traits,
plasticity and SCI estimated in this study, breeding progress was
estimated using a sliding-widow approach (Lichthardt et al., 2020).
In short, ten genotypes were averaged in each sliding window, starting
from the oldest to the most recent, and progressed with a step size of
two. The breeding progress was determined by performing a linear
regression on the resulting means, using the slope’s inclination. For this
analysis, only genotypes registered for conventional agriculture in
Germany were included (see Voss-Fels et al., 2019 and
Supplementary Table S1), while others were excluded.
3 Results
3.1 Planting density affects investment
trade-off for light harvesting and
photosynthetic capacity at early
plant development
Analysis of variance (ANOVA) revealed significant effects of density
treatments (low density: T1 and high density: T2) on leaf area (LA), leaf
mass per area (LMA), shoot length (SL), shoot dry mass (SDM), total
tiller number (TT), and the number of dead tillers (DT) at the booting
stage (Table 1). For example, shoot dry mass in T2 was reduced by 20%
to 80% compared to T1 (Figure 1A), and leaf mass per area decreased by
6% to 53% among genotypes (Figure 1M). Two genotypes showed
reduced shoot length (SL) under T2 (Figure 1D), indicating their
incapability to grow under increased competitive pressure.
High planting density led to a median plasticity increase of 51% in
LA and 25% in SL (Table 1,Figures 1B,C,E,F). Both traits are
considered ‘selfish’because they enhance the plant’s ability to shade
neighboring plants and increase competitiveness for light interception.
Incontrast,LMA,TT,andDTshowed negative responses to high
planting density, indicating a trade-off in resource allocation by
reducing the canopy size (TT, Figures 1H,I) and photosynthetic
capacity per leaf area (LMA, Figures 1N,O). ANOVA results for all
12 traits showed significant genotypic differences and genotype-
treatment interactions (Table 1). This highlights the varied plastic
responses among traits and among genotypes, suggesting genotype-
specific mechanisms that optimize the cost-benefittrade-offcausedby
shoot competition. Understanding the performance relationships of
individual genotypes in T1 and T2 is therefore crucial.
In treatment T1, where plant-plant interactions were minimal,
SDM reflected the vigor of a genotype. Although one might expect
more vigorous plants to show higher SDM in high density, only 32% of
the variation in SDM in T2 could be explained by vigor alone, as shown
by a linear regression model (Figure 1B). Remarkably, plasticity of
SDM was the only trait analyzed that did not correlate performance in
T1 (Figure 1C), suggesting a physiological independence between plant
vigor and competitiveness under high density. Total tiller number
(TT), which was strongly correlated with SDM (Figure 2), also
decreased with planting density. However, 23% of the variation in
plasticity for TT could be explained by TT values in T1. Genotypes with
a low number of tillers in T1 either showed only a slight reduction in
tillernumberorevenanincreaseinT2(Figure 1I).
The number of green leaves on the main stem (GL) represent
the outcome after the trade-off between density-induced leaf
senescence and light harvesting. Interestingly, this trade-off,
compared with LMA, is less affected by the competition pressure
(Figures 1J,M). Under competitive conditions, maintaining leaf
greenness and having a lower leaf LMA are physiologically different
strategies to optimizing light capture and photosynthesis at plant
level. It is interesting to observe low correlation between GL in T1
and T2 (R
2
= 0.08, Figure 1K) and no correlation between LMA in
the two treatments (Figure 1N), but moderately correlation between
GL in T1 and its plasticity (R
2
= 0.28, Figure 1L) and between LMA
in T1 and its plasticity (R
2
= 0.19, Figure 1O). This underscores
another interplaying trade-off between the resource capture and
resource allocation under plant-plant competition.
3.2 Competitive genotypes are more
plastic in traits-related to plant-
plant competition
To quantify the competitiveness of a genotype, the relative
performance of the genotype in the whole panel between low and
Manntschke et al. 10.3389/fpls.2025.1490483
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high planting density was compared. Although there is a high
coefficient of determination between the competitiveness index (SCI)
and relative plasticity of shoot dry mass in response to planting density
(R
2
=0.97,Supplementary Figure S3), as SCI values deviate further
from zero, the residuals to this relationship increases, indicating that
SCI would correct the biased of extreme plastic responses of shoot
dry mass.
It is important to emphasize that plant vigor, measured as shoot
dry mass under low planting density, did not correlate with SCI
(Figure 2A). To investigate how functional traits influenced plant
vigor, shoot dry mass, and SCI, we visualized correlation matrices
between these measures and among the functional traits (Figure 2).
This visualization suggested that total tiller number (TT), leaf mass
per area (LMA), and shoot length (SL) were most strongly
associated with SCI and shoot biomass. Therefore, they were
further used in multiple regression analysis to provide a
comprehensive overview of their contributions to SCI.
Plant vigor was poorly explained by TT, LMA and PL, with R²
values ranging from 0.01 to 0.15. In contrast, genotypic performance in
shoot dry mass under high planting density was 62% explained by the
positive effects of TT, LMA, and PL, similar to the results for dry
biomass plasticity (49%) and SCI (56%, Table 2). Interestingly, while
PL was consistently significant across models, its plasticity was
significant only in explaining the SCI, asserting the differences
between SCI and the plasticity of shoot biomass. SCI was slightly
influenced by traits values under low-density (R² = 0.18) but
significantly by high-density trait values (R² = 0.56) and plasticity (R²
=0.45).Allcoefficients were significant and positive, indicating greater
tiller numbers, longer shoot length, and surprisingly higher leaf mass
per area, enhanced competitiveness.
Interestingly, SCI was not associated with the growth in shoot
dry mass between the booting stage and anthesis (Figure 2) and
accounted for only 30% of the variation in shoot dry mass at
anthesis (Figure 3). This finding suggests a distinction between
competitiveness and vigor or indicates that SCI may change
dynamically across developmental stages. Therefore, we further
examined how well the traits measured at the booting stage
explained plant growth between booting and anthesis, as well as
the total shoot biomass at anthesis by multiple linear regression.
Using shoot biomass in high density provided a higher explanatory
power (R² = 0.50) than that in low density (R² = 0.32, Table 3).
Including SCI slightly increased the explanatory power (R² = 0.54),
but unexpectedly, its negative coefficient suggests that higher
competitiveness at the booting stage led to lower shoot biomass.
This result is biologically not meaningful and likely reflects the poor
explanatory power of the measured traits for growth between
booting and anthesis (R²< 0.10, Table 3;Figure 2). Ultimately,
shoot biomass at booting, total tiller number, and shoot length were
the best predictors of shoot biomass at anthesis (R² = 0.55).
3.3 Shoot competitiveness is reduced in
the breeding history, but not all
competitiveness-related traits
were improved
To evaluate the influence of breeding history on competitiveness,
traits and plasticity, we conducted a sliding-window analysis. Notably,
the shoot competition index (SCI), which indicates genotypes’ability to
compete in high-density environments, showed a significant decline
TABLE 1 Effects of planting density on winter wheat at booting stage. Median trait values are shown for low denisty (T1) and high density (T2)
treatments with ranges in parentheses.
Trait (unit) T1 T2 T G T x G Plasticity
Leaf area (cm
2
) 20.19 (11.60, 33.25) 29.92 (16.05, 55.63) *** *** *** 51% (-0.08, 1.30)
Senescence (leaf) 1.43 (0.55, 2.60) 1.84 (0.67, 2.88) n.s. ** ** 28% (-0.53, 2.82)
Shoot length (cm
2
) 48.12 (38.00, 72.00) 61.12 (47.50, 85.62) *** *** ** 25% (-0.03, 0.50)
Total leaves (leaf) 5.75 (4.00, 8.00) 5.75 (3.00, 7.25) n.s. *** * 0% (-0.31, 0.58)
BBCH stage (-) 34.50 (30.25, 58.50) 33.75 (30.00, 55.50) n.s. *** *** -2% (-0.31, 0.11)
Leaf mass (g leaf
-1
) 0.13 (0.07, 0.20) 0.12 (0.08, 0.28) n.s. *** *** -3% (-0.42, 0.73)
Green leaves (leaf) 4.40 (3.02, 5.65) 3.90 (1.90, 5.20) n.s. *** * -10% (-0.47, 0.36)
LMA (g m
-2
) 63.12 (48.28, 80.01) 41.62 (29.66, 58.39) *** *** *** -34% (-0.53, -0.06)
Tiller alive (tiller) 4.75 (2.50, 15.25) 3.00 (1.50, 7.00) n.s. *** *** -38% (-0.75, 0.57)
Total tiller (tiller) 10.50 (4.00, 18.00) 5.75 (2.50, 10.50) *** *** *** -44% (-0.67, 0.50)
Tiller dead (tiller) 5.50 (0.00, 11.00) 3.00 (0.00, 5.50) * *** *** -49% (-0.87, 5e+13)
Shoot dry mass (g) 4.94 (2.35, 7.75) 2.12 (0.89, 5.55) *** *** *** -56% (-0.80, -0.20)
Results of a two-way ANOVA are displayed for genotype (G), treatment (T) and their interaction (T x G) with asterisks (*p< 0.05; **p< 0.01; ***p< 0.001; n.s., not significant). The median
plasticity as percentual deviation of T2 values from T1 is shown with ranges in parentheses.
Manntschke et al. 10.3389/fpls.2025.1490483
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FIGURE 1
Effects of planting density on functional traits in winter wheat. Each data point represents the estimated marginal means of a genotype that were
grown in low (T1) and high (T2) planting density at the booting stage. Plasticity was calculated as the percentage deviation of T2 trait values from T1.
Traits shown include shoot biomass (A–C), shoot length (D–F), total tiller number (G–I), total green leaves on the main stem (J–L), and leaf mass
per area [LMA, (M–O)]. Boxplots of trait distributions in T1 and T2, with solid lines showing genotype responses (A, D, G, J, M) and scatter plots of
trait values in T1 vs. T2, with fitted linear regression lines, formulas, adjusted R², and p-values, with dashed lines indicating 1:1 relationship (B, E, H, K,
N). Comparison between plasticity and T1 trait values, with fitted linear regression lines, formulas, adjusted R², and p-values, with dashed lines
marking zero plasticity (C, F, I, L, O).
Manntschke et al. 10.3389/fpls.2025.1490483
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over the years (Figure 4). The strong trend, with an R² value of 0.61,
highlights the substantial reduction in competitiveness, as evidenced by
the regression line crossing the zero line.
To further assess the breeding progress and associated changes
in our wheat genotypes, we analyzed the plasticity and trait values of
key functional traits identified in the multiple-linear regression
analyses. This revealed a more negative plasticity of shoot biomass
(R² = 0.61) over the years (Figure 4A), due to the findings that shoot
biomass showed no distinct trend at low planting density and
contrastingly a consistent decline at high planting density (slope
= -0.0144 g per year; Figure 4B). The plasticity of shoot length and
total tiller number exhibited considerable fluctuations, with
minimal overall changes (R²< 0.01, Figures 4C,E), suggesting that
plasticity in these traits has not been systematically selected for in
breeding. In contrast, reduced plasticity of LMA (R² = 0.17,
Figure 4G) suggests a slight trend towards a decrease in high
planting density in more recently released genotypes, an
indication of plant-plant cooperation. While shoot length
decreased slightly over the years at booting stage (Figure 4D),
genotypic values for total tiller number fluctuated, irrespective of
treatment or harvest date, indicating a lack of consistent breeding
impact on this trait (Figure 4E), similar to that in LMA (Figure 4G).
4 Discussion
Recent approaches emphasize considering plasticity in multiple
traits for a comprehensive understanding of plant responses to
density (Kikuchi et al., 2017;Nielsen and Papaj, 2022). We use this
concept here to explore the complex relationships between nine
functional traits, phenotypic plasticity, and plant-plant competition
at high planting density. Using 200 winter wheat genotypes from
Germany, we further examined the breeding history of phenotypic
plasticity and its implications for breeding wheat. We would like to
FIGURE 2
Pearson correlation coefficients between functional traits and shoot competitiveness index (SCI). (A) Trait values at low density at booting stage;
(B) trait values at high density at booting stage; (C) plasticity of traits at booting stage; and (D) trait values at high density at anthesis. The correlation
coefficient was calculated between pairs of traits in the 200 studied genotypes. The absolute growth was defined as the difference in shoot dry mass
between booting stage and anthesis. X indicates insignificant correlation.
Manntschke et al. 10.3389/fpls.2025.1490483
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highlight two key aspects of this study. First, because the root
system of each plant was isolated, plant-plant interactions were
limited to the shoots. Effects of root interactions will be an
interesting topic for future studies. Second, since neighboring
plants in our experiments belonged to different genotypes, the
canopies were heterogeneous, and all plant-plant interactions
were intergenotypic, with some interpretations extended to
monoculture performance.
4.1 Difference between inter- and intra-
genotypic competitions
In conventional monocultures, plant-plant interaction is
symmetrical, with evenly spaced plants of the same genotype and
developmental stage (Fischer and Rebetzke, 2018;Lollato et al., 2019;
Postma et al., 2021). In contrast, heterogeneous canopies—found in
varietal mixtures, intercropping, organic farming using genetic
populations, high-throughput phenotyping platforms, and the
current study—present an asymmetrical scenario of plant-plant
interaction (Chen et al., 2019;Gaudio et al., 2019;Tombeur et al.,
2022;Dubs et al., 2023). While the effects of planting density on
monocultures are well-documented, its impact on competitiveness and
performance in heterogeneous canopies remains less understood
(Litrico and Violle, 2015;Subrahmaniam et al., 2018;Beaugendre
et al., 2022). Therefore, predicting the best-performing genotypes in
such heterogeneous canopy is well-known challenging, even though
some correlations between phenotypic traits and mixing effects have
been reported. However, we are still unable to generalize robust and
mechanistic rules for predicting productivity in heterogeneous canopy,
e.g. varietal mixtures (Montazeaud et al., 2022;Dubs et al., 2023). This
is reflected in our results, where the functional traits with the highest
significance (total tiller number and shoot length) could not accurately
predict absolute growth, regardless of density treatments (Table 3).
Additionally, while differences in developmental stage could influence
competitiveness, they were not significant in our study. These suggest
that growth could be a highly state-specifictrait,asrecentliteraturehas
shown for Arabidopsis, where temporal variation in marker-trait
associations for relative growth rates was detected within a growth
period of only seven days (Meyer et al., 2021). Similar findings were
previously made regarding wheat tiller expression at different
quantitative trait loci during growth stages (Ren et al., 2018).
TABLE 2 Multiple regression models for predicting shoot biomass under low (T1) and high (T2) planting density (SBM
T1
and SBM
T2
, respectively),
plasticity of shoot biomass (SBM
Plast
), and shoot competitiveness index (SCI) using tiller number (TT), leaf mass per area (LMA) and shoot length (SL)
measured at booting stage.
Model abgR²
SBMT1=a∗TTPlast +b∗LMAPlast +g∗SLPlast -0.443 n.s. 1.024 * -0.342 n.s. 0.01
SBMT1=a∗TTT1+b∗LMAT1+g∗SLT1-0.003 n.s. 0.026 n* 0.047 n** 0.14
SBMT1=a∗TTT2+b∗LMAT2+g∗SLT2-0.017 n.s. 0.014 n 0.042 n** 0.15
SBMT2=a∗TTPlast +b∗LMAPlast +g∗SLPlast 0.877 n* 4.111 n** 0.479 n.s. 0.48
SBMT2=a∗TTT1+b∗LMAT1+g∗SLT1-0.006 n.s. -0.002 n.s. 0.069 n** 0.2
SBMT2=a∗TTT2+b∗LMAT2+g∗SLT20.083 n** 0.058 n** 0.062 n** 0.62
SBMPlast =a∗TTPlast +b∗LMAPlast +g∗SLPlast 0.26 n** 0.714 n** 0.138 n.s. 0.56
SBMPlast =a∗TTT1+b∗LMAT1+g∗SLT1-0.002 n.s. -0.003 n.s. 0.008 n** 0.1
SBMPlast =a∗TTT2+b∗LMAT2+g∗SLT20.017 n** 0.01 n** 0.007 n** 0.49
SCI =a∗TTPlast +b∗LMAPlast +g∗SLPlast 0.626 n** 1.02 n** 0.549 n** 0.45
SCI =a∗TTT1+b∗LMAT1+g∗SLT1-0.002 n.s. -0.007 n 0.02 n** 0.18
SCI =a∗TTT2+b∗LMAT2+g∗SLT20.052 n** 0.011 n** 0.028 n** 0.56
The coefficients and their significance are shown (*p< 0.05; **p< 0.01; ***p< 0.001; n.s., not significant), together with the adjusted R² of each model.
FIGURE 3
Relationship between shoot biomass at anthesis (H2) and shoot
competitiveness index (SCI) at booting. Each point represents
marginal mean of a genotype. The regression is shown as solid blue
line. A SCI greater than 0 indicates higher competitiveness
compared to neighboring plants.
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It has been proposed that competitiveness of a plant in a
heterogenous canopy can be quantified by comparing its resource
capture capacity to that of neighboring plants (Chen et al., 2019).
However, this method requires integrating high-throughput
phenotyping platforms with computational pipelines and scientific
workflow systems (Pradal et al., 2017;Artzet et al., 2019), which are
not available in every lab. Therefore, we adapted this concept and
proposed the shoot competitiveness index (SCI), which can be
quantified in any lab and is specifically suited for heterogeneous
canopies. Using this index, we show clear evidence that shoot
competitiveness has decreased over breeding history (Figure 5), while
plant vigor remains unchanged (Figure 4B). This aligns with the broader
breeding objective to reduce plant height, minimize individual
competitiveness (Colombo et al., 2022) and modify canopy
architecture to optimize light distribution suitable for homogeneous
canopy (Perez et al., 2019;Lacasa et al., 2022). Additionally, this panel of
genotypes has been extensively studied under homogeneous canopy and
field conditions, showing that modern cultivars are more productive
than the older ones (Voss-Fels et al., 2019). The poorer performance of
modern cultivars in heterogeneous canopies (Figure 4B) clearly
indicates that old cultivar excelling in multi-genotypic canopies may
not be optimal for monoculture conditions, emphasizing the
importance of considering inter-genotypic interactions in breeding
strategies (Weiner et al., 2017;Chen et al., 2019) and indicating the
more cooperative responses of modern cultivars.
4.2 Importance of phenotypic plasticity in
plant-plant interactions
In general, the observed phenotypic plasticity in response to planting
density largely in accordance with the literature (Kikuchi et al., 2017;
Bongers et al., 2018;Subrahmaniam et al., 2018;Poorter et al., 2019;
Postma et al., 2021;Tombeur et al., 2022). We observed significant
genotypic differences in the phenotypic plasticity of traits related to plant-
plant competition (Table 1). Interestingly, the lack of strong correlations
among the plasticity of different functional traits (Figure 2C)suggestsa
decoupling of their physiological regulation, offering potential to
optimize combination of plasticity in response to planting density and
enhance plant-plant cooperation under competition pressure.
Although Chen et al. (2019) advanced the quantification of plant-
plant competition, their morpho-physiological explanation of
competitiveness remains less clear. Furthermore, in their analyses,
highly competitive genotypes have higher biomass production than
but similar leaf area to the less competitive ones, implying thicker leaves
or stem of the highly competitive genotypes (higher in LMA and in stem
mass per length). This contradictsthepredictionofgametheory
suggesting that competitive genotypes should produce thinner leaves
(Schieving and Poorter, 1999) but in accordance with our results
(Table 2;Figure 2B). Given that LMA, among 12 environmental
factors, exhibits the highest plasticity in response to light quantity
(Poorter et al., 2010), our findings propose an alternative hypothesis: a
highly competitive genotype may initially produce cheaper leaves with
low LMA and subsequently exhibit greater plasticity in adjusting LMA
to the light environment encountered post-developmentally (Figure 2C).
This could explain the observed low plasticity in LMA (less negative
plasticity) within the competitive genotype (Figure 2C). Since LMA and
leaf area were measured from a single leaf, they may not fully reflect
whole-plant variation. It will be interesting, although labor-intensive, to
exam the response of LMA to competition at the whole-plant level.
The high correlation between LMA and photosynthetic capacity
suggests that plasticity in LMA can be explained by the strategy of
developmental and post-developmental photosynthetic acclimation
(Pao et al., 2019b), which can be explained by the concept of
photosynthetic protein turnover (Pao et al., 2019a). Recently, it has
been demonstrated that sensitivity of photosynthetic protein synthesis
rate to light (referred to as photosynthetic acclimation strategies, PAS),
is especially crucial for the leaf plasticity in response to light
environments (Pao et al., 2023). This suggests complex feedbacks
between PAS, light gradient resulted from the canopy architecture
and canopy productivity. Therefore, optimizing canopy productivity
requires coordination between PAS and dynamics in canopy
architecture. This coordination becomes more complex and more
difficult to be achieved in a heterogeneous canopy, where PAS and
architecture of each individual plant are different. The clear association
between the plasticity of LMA and SCI in this study (Table 2) could be
an indirect evidence supporting the importance of this coordination.
To our knowledge, this type of plant-plant interactions has not yet been
investigated and appears as a missing link towards a full understanding
of the productivity of heterogeneous canopies.
TABLE 3 Multiple regression models for predicting shoot biomass at anthesis (SBM
H2T2
) and absolute growth between booting and anthesis (AG)
under high planting density (T2) using shoot dry biomass (SBM) shoot competitiveness index (SCI) and tiller number (TT) and shoot length (SL).
Model abgR²
SBMH2T2=a∗SBMH1T2+b∗SCI 17.445 *** -19.397 *** 0.54
SBMH2T2=a∗SBMH1T2107.233 *** 0.50
SBMH2T2=a∗SBMH1T10.886 *** 0.32
AG =a∗SCI -0.183 n.s. 0.00
AG =a∗TTH1T1+b∗SLH1T1-0.058 * 0.013 n.s. 0.05
AG =a∗TTH1T2+b∗SLH1T2-0.125 ** 0.0253 * 0.09
SBMH2, T2=a∗SBMH1T2+b∗TTH1T2+g∗SLH1T20.935 *** -0.116 ** 0.032 *0.55
The subscripts denote measurement from low (T1) or high (T2) planting density at booting stage (H1) or anthesis (H2). The coefficients and their significance are shown (*p< 0.05; **p< 0.01;
***p< 0.001; n.s., not significant), together with the adjusted R² of each model.
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4.3 Breeding progress in winter wheat can
be further optimized by targeting
previously neglected competitive traits
Yield stagnation in European wheat production since the mid-
nineties (Brisson et al., 2010;Bönecke et al., 2020) highlights the need
to identify traits that could further increase yield. Targeting traits
related to plant-plant competition is particularly important, as
competition for resources in dense planting environments reduces
canopy performance (Weiner et al., 2010;Kiaer et al., 2013;
Subrahmaniam et al., 2018). Reducing the negative effects of light
competition at high planting densities could enhance future genetic
FIGURE 4
Breeding progress of shoot biomass (B), shoot length (D), total tiller (F), leaf mass per area (LMA, H)andtheirplasticity(A,C,E,G)betweenlow(T1)andhigh
(T2) planting density. Using a sliding-window approach, each data point represents the mean values of a subset group of 10 genotypes, with the shaded area
indicating the standard deviation. The black line represents the linear regression with the formula and the adjusted R² reflecting absolute breeding progress.
Manntschke et al. 10.3389/fpls.2025.1490483
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progress (Perez et al., 2019;Colombo et al., 2022). In this study, we
investigated the breeding progress of plant-plant competition by
using a shoot competitiveness index and analyzing their
competitiveness-related traits and their phenotypic plasticity in a
set of 200 genotypes used by other researchers in our field (Lichthardt
et al., 2020;Sabir et al., 2023). Our results suggest that modern
genotypes exhibit more cooperative behavior, possibly a hidden factor
in breeding success. Further study is needed to identify which traits
contribute to plant-plant cooperation. Potential candidates include
architectural traits or their plasticity, as breeding progress in maize
shoot architecture has been documented (Mantilla-Perez and Salas
Fernandez, 2017;Perez et al., 2019;Lacasa et al., 2022). Although it
was infeasible to study architectural traits in a heterogeneous canopy
of current study, it will be interesting to explore these traits and their
plasticity using high-throughput phenotyping platforms in the future.
As wheat breeding programs progress, integrating insights into
trait plasticity and competitiveness is crucial. Our study highlights how
plasticity of functional traits affects competitiveness, yet its diverse
aspects have not been targeted by breeders (Figure 4). Designing ideal
combinations of plasticity to increase plant-plant cooperation could
sustain breeding progress. Future studies in this direction are
promising, as they will further unravel the intricate relationships
between trait plasticity, competitiveness, and performance in high-
density conditions within heterogeneous canopies, laying a foundation
for continued refinement in wheat breeding programs.
Data availability statement
The original contributions presented in the study are included
in the article/Supplementary Material. Further inquiries can be
directed to the corresponding author.
Author contributions
AM: Data curation, Formal analysis, Investigation, Visualization,
Writing –original draft. LH: Data curation, Formal analysis,
Investigation, Visualization, Writing –original draft. AT:
Investigation, Methodology, Supervision, Writing –review &
editing. MR: Data curation, Investigation, Writing –original draft.
T-WC: Conceptualization, Funding acquisition, Methodology,
Writing –review & editing.
Funding
The author(s) declare that financial support was received for
the research, authorship, and/or publication of this article. This
work was funded by Emmy Noether Program of Deutsche
Forschungsgemeinschaft (German Research Foundation) to T-
WC under project no. 442020478.
Acknowledgments
We extend our gratitude to our technical assistant, Constantin
Schmidt, for his invaluable supervision of the gardeners and student
helpers, ensuring meticulous plant care and accurate measurements.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
The reviewer FB declared a shared affiliation with the author AT
to the handling editor at the time of review.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1490483/
full#supplementary-material
FIGURE 5
Breeding progress of shoot competitiveness index (SCI). Using a
sliding-window approach, each data point represents the mean SCI
of a subset group of 10 genotypes, with the shaded area indicating
the standard deviation. The black line represents the linear
regression with the formula and the adjusted R² reflecting absolute
breeding progress.
Manntschke et al. 10.3389/fpls.2025.1490483
Frontiers in Plant Science frontiersin.org11

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