Introduction

Intercropping, i.e. the mixed cultivation of crop species, is recognized as a pathway towards more sustainable agriculture (Brooker et al. 2015; Li et al. 2020; Lithourgidis et al. 2011; Maitra et al. 2021; Malézieux et al. 2009), because it has the potential to outperform monocropping in several aspects. In particular, intercropping has been found to improve resource use efficiency (Hauggaard-Nielsen et al. 2008, 2011), increase above ground biomasses (Cardinale et al. 2007; Rauber et al. 2001; Trydeman Knudsen et al. 2004), provide favorable habitat for beneficial organisms (Potts et al. 2003), reduce weeds, diseases and insect pests (Aziz et al. 2015; Bedoussac et al. 2015; Lithourgidis et al. 2011; Lopes et al. 2016), and assist farmers to deal with adverse and unpredictable weather conditions due to climate change (van Zonneveld et al. 2020; Waha et al. 2018). Moreover, cereal legume mixtures are known for complementary use of nitrogen (N) sources due to symbiotic N2 fixation (SNF) from the atmosphere by legumes (Peoples et al. 2009). Most studies on mixtures have concentrated on final (grain) yield which integrates growth dynamics between species over the whole growing period. In contrast, only a few studies have investigated the degree of interaction between intercrops that may already be detected at a very early growing stage (Bellostas et al. 2003; Benincasa et al. 2012; Elsalahy et al. 2021), such as at crop emergence and the carry-on effects of mixing from early to later growth stages (Luo et al. 2021).

Crop emergence, i.e. the emergence of the shoot from a germinated seed through soil, is the first essential stage in the life cycle of plants because this process often affects components of plant fitness (Verdú and Traveset 2005) and determines the future crop performance both at the individual (plant) and the population (crop stand) level. Presumably the final yield benefit of cereal legume crop mixture compared to their monocrops could not appear without earlier below- and aboveground competition and/or facilitation processes. In barley and pea intercropping, for example, the reduction in yield of pea has been found likely to be induced in early growth stages (Tofinga et al. 1993). However, the exact proportion and direction of the mixture effects on that early growing stage are currently largely unknown.

Seed germination and seedling emergence are influenced by the combination of different biotic and abiotic factors. Different studies show that germination and early crop establishment are affected by a range of factors, including temperature and soil water availability (T. Luo et al. 2018; Tribouillois et al. 2016), soil characteristics such as soil bulk density and the presence of soil crusts (Briggs and Morgan 2011; Soureshjani et al. 2019), as well as the identity and density of neighboring seeds and seedlings (Fenesi et al. 2020; Leverett et al. 2016, 2018; Tielbörger and Prasse 2009). Some studies demonstrate that the acceleration of germination is most likely influenced by the presence of a competitive neighborhood (Dyer et al. 2008; Orrock and Christopher 2010). The soil chemistry related to early root and shoot growth could change due to volatile organic compound emission from the germinating seed coat (Fincheira et al. 2017; Tielbörger and Prasse 2009) and thus affect their neighbors. However, the growth dynamics in a crop mixture depend on the balance between competitive (Renne et al. 2014; Tielbörger and Prasse 2009) and facilitative interactions (Orrock and Christopher 2010; Schiffers and Tielborger 2006) between seedlings. Indeed, facilitation was observed to enhance seedling survival, especially in extreme weather events such as harsh winters where low temperatures constrain alpine tree regeneration and growth (Batllori et al. 2009). By accelerating or reducing the emergence rate, crop mixtures might have a decisive role on plant biomass in the early growth stage, and thereby also on the balance of competition among partners at later growth stages.

Crop mixtures of cereals and grain legumes have a high potential of increasing the system productivity compared to the respective sole crops (Xiao et al. 2018). In general, in such crop mixtures, cereals have been shown to gain a higher relative yield than grain legumes (Xiao et al. 2018; Yu et al. 2016). However, estimating the final yield as an overall growth dynamic is obviously insufficient for drawing conclusions about lifetime dominancy over the whole growing stage of the cereal over the legume. It is currently unknown when the domination (i.e. relative higher proportion of biomass in the mixture) of the cereal over the legume starts, in particular, whether this domination is evident already at an early growing stage. Further, it is not known if there is a legacy of early dominance (if any) on later performance of the crops. It is important to understand the competitive balance between the two intercropping partners at an early growing stage because this may play a major role in determining productivity in mixtures. To understand the complexity of crop mixtures and predict their productivity and the relative performance of the partners, it is vital to study the entire life cycle of the interacting plants from crop emergence. Our goal was therefore to look at the early dynamic of cereal legume interaction in the mixture and its effect on the subsequent crop developmental stage.

We therefore conducted an experiment to test the mixture effect of spring wheat/ faba bean crop mixtures of emerging seedlings in three different environments. In particular, we tested the following four hypotheses: (H1) There is a positive mixture effect of spring wheat/faba bean crop mixture at the early growing stage (emergence); a positive mixture effect, i.e. higher crop emergence in mixture than in the respective monocultures, may occur due to higher relative emergence of SW or FB or both partners in the mixture. (H2) The early mixture effect depends on factors such as SW cultivar, FB cultivar, environment, and sowing density; (H3) Domination of SW in the mixture can be observed already at crop emergence; here, by domination we mean that the relative proportion of the dominating partner in the mixture is higher compared to the other partner; (H4) There is a legacy of early mixture effects on later growth stages as measured by crop biomass, i.e. the strength of domination of one partner over the other at crop emergence is carried over to later stages.

Material and methods

Experimental site

The field experiment was carried out in three environments, namely in one year (2020) at Campus Klein-Altendorf (CKA) and in two years (2020 and 2021) at Wiesengut (WG), both research stations of the University of Bonn, Germany. These three environments are referred to as CKA2020, WG2020, and WG2021. Campus Klein-Altendorf is a conventionally managed research station located at 50°36'North, 6°59'East, and at an altitude of 186 m above sea level (a.s.l.) in Rheinbach, about 40 km south of Cologne. The site is characterized by a fertile Haplic Luvisol with a loamy silt texture. Wiesengut, an organically managed research station, is located at 50°47'North, 7°15'East at an altitude of 65 m a.s.l. in the lowland of the river Sieg near Hennef. The soil type is a Haplic Fluvisol with a silt loam texture on gravel layers, and fluctuating groundwater level. Compared to CKA, total organic carbon (Ct) and total N (Nt) concentrations in the soil were higher at WG. Mineral N (Nmin) concentrations in 2020 were higher at CKA than at WG, and, at the latter site, higher in 2021 than in 2020 (Table 1).

Table 1 Total soil organic carbon (Ct), total nitrogen (Nt), and mineral N concentrations in three soil layers (0-30 cm, 30-60 cm, 60-90 cm) at sowing at Campus Klein-Altendorf (CKA) and Wiesengut (WG). Mean and standard deviation (s.d.) are provided
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The average annual temperature and annual rainfall at the experimental sites was 10.3 °C and 669 mm at CKA, and 10.7 °C and 732.8 mm at WG between 1991 and 2020, respectively. In April and May, the most relevant months for early crop development of the spring sown trial crops, precipitation was relatively low in 2020, but higher in 2021, with an average temperature mean at both sites (Fig. S1). Soil temperature at 15 cm soil depth during April and May was higher in 2020 (15.52 °C at WG and 12.28 °C in CKA) compared to 2021 (10.14 °C in WG).

Experimental design

Each of the three field experiments was performed as a randomized complete block design with four replicates and four experimental factors. Factor A compared the mixture of spring wheat (SW) and faba bean (FB) with the respective monocultures. In the mixtures, both crop species were mixed in a substitutive equiproportional mixture (i.e. replacement design) within the row during sowing. Factor B was the FB cultivar with two levels (cv. Mallory and cv. Fanfare). Factor C had 12 levels of different SW entries, using ten spring SW cultivars (Table 2) and two 5-component equiproportional mixtures of these SW cultivars. To increase the level of functional diversity, two groups of cultivars mixtures were created by dividing the SW cultivars according to their plant height scores. Scores of SW cultivars were obtained from official national variety lists provided by the German Federal Plant Variety Office (Bundessortenamt 2019). Group 1 contained five cultivars with diverse plant height scores (standard deviation (s.d.) = 2.4), whereas Group 2 contained five cultivars with more similar plant height scores (s.d. = 0.8) (Table 2). The required amounts of seeds for each cultivar within the group were calculated separately according to their seed weight (thousand kernel weight, TKW), germination percentage (GP) (Table S1), plot area, and sowing density (Equation S3). Finally seeds of the five cultivars were mixed homogeneously before sowing. Although the two groups of 5-component equiproportional cultivars mixtures are mentioned here as SW entries, no significant effect of those groups on crop emergence were observed and will not be discussed further in this paper.

Table 2 Ten spring wheat cultivars and two mixed groups of spring wheat cultivars with their characteristics scores based on a 1–9 scale according to German Federal Plant Variety Office (Bundessortenamt 2019). High and low figures (score 9 and score 1, respectively) indicate that the cultivar shows the character to a high, or low degree, respectively
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Factor D varied the sowing density and had two levels, 120% and 80% of the recommended sole crop densities (%RD) of 400 seed m−2 for SW and 45 seed m−2 for FB (Table 3). Spring wheat and faba bean were mixed in a 1:1 ratio; that means for the high density in the mixture 50% of the high density of SW monoculture was mixed with 50% of the high density used FB monoculture, and low density mixture consisted of 50% of the low density used for SW monoculture and 50% of the low density used for FB. In total, the experiment included 76 treatments per block and both crops, SW and FB. The monocultures of FB were doubly replicated to stabilize comparisons with the numerous mixtures. This resulted in 320 plots for each of the three field trials.

Table 3 Faba bean and spring wheat seed densities in the monocultures and the mixtures for two levels of sowing density
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Management practices

After harvesting the pre-crop (Table 4), the soil was ploughed once (30 cm depth, with a moldboard plough) in WG and twice (7 and 10 cm depth, with a chisel plough) in CKA, and the seedbed was prepared with a rotary harrow at both sites. The single plot size was 1.5 m × 10 m with 6 rows and 21 cm row to row distance. Faba bean was sown first, at 6 cm depth; to maintain the appropriate FB sowing density. A direct seeding machine type Hege 95 B was used and optimized by rotation setting according to sowing density (Table S2). Thereafter, SW was sown directly over FB with a Hege 80 seeder at 3 cm soil depth. No seed treatment was performed on FB or SW before sowing. The trials were run with no use of chemical fertilizers, herbicides, or pesticides. In both years, mechanical weeding was carried out twice (3 and 5 weeks after sowing) using a hoe, and once four weeks after sowing with a harrow.

Table 4 The three environments, pre-crops, sowing dates, and measurements of faba bean and spring wheat in the intercropping experiment
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Measurements

In order to monitor the emergence and early growth of the crop, crop emergence (plant m−2) was measured by counting plants on defined areas (Fig. 1) per plot about 23 days after sowing (DAS), and biomass measurements (dry matter, DM, in t ha−1) were performed twice about 52 and 82 DAS, respectively; the exact times in days after sowing (DAS) are reported in Table 4. For all measurements, 1 m long sections from the 3rd and 4th rows of the plots were chosen after discarding 1 m for biomass and 3 m from the 3rd rows and 4 m from the 4th rows for crop emergence from the edge to avoid potential edge effects. Counting of emerged seedlings and the first biomass cut was performed from one side of the plot whereas a second biomass cut was performed from another side of the plot (Fig. 1).

Fig. 1
figure 1

Schematic illustration of a single plot showing the locations where the individual measurements were taken

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During the first biomass cut, 12 selected treatments (all FB monocultures, FB mixtures with SW cultivars Lennox and SU Ahab, and SW monocultures with cultivars Lennox and SU Ahab; which makes 48 plots in total out of 320 plots per site) were taken to manage work load of the large field trial. During both biomass cuts, both crop and weed aboveground biomasses were taken at the base. Fresh biomass samples from the sole crops were manually separated into weeds and crops; samples from the intercrops were separated into weeds, SW, and FB before further processing. Biomass dry weight (t ha−1) of the samples was obtained after oven-drying at 105 °C until weight constancy.

Data processing and calculations

To quantify the effect of the mixtures in comparison to their respective monocultures, land equivalent ratio (LER), and partial LER (PLER) were calculated for both the first and second crop biomass cut. The LER of a mixture measures the relative land area that is required for the crop monocultures to produce the same biomass as observed in the mixture; it was calculated as the sum of the PLERs of the two species (Eqs. 1 and 2) in the mixture by using Eq. (3) (Willey and Rao 1980).

$${\mathrm{PLER}}_{\mathrm{SW}}={\mathrm{Wheat}}_{\mathrm{mix}}/{\mathrm{Wheat}}_{\mathrm{mono}}$$
(1)
$${\mathrm{PLER}}_{\mathrm{FB}}={\mathrm{Bean}}_{\mathrm{mix}}/{\mathrm{Bean}}_{\mathrm{mono}}$$
(2)
$$\mathrm{LER}={\mathrm{PLER}}_{\mathrm{SW}}+{\mathrm{PLER}}_{\mathrm{FB}}$$
(3)

where Wheatmono and Beanmono are the biomass of SW and FB in monoculture and Wheatmix and Beanmix are the biomass of each crop in the mixture. An LER value > 1 indicates an advantage of intercropping over monocropping, while LER < 1 indicates that intercropping reduces the biomass of the components in comparison to the monocultures (Dariush et al. 2006).

We also applied the same concepts to crop emergence data to quantify the effect of mixture on emergence in comparison to the monocultures. In particular, we calculated partial emergence equivalent ratio (PEER) of SW and FB by dividing the number of emerged crop plants in the mixture by the number of emerged crop plants in monocultures (Eqs. 4 and 5). Emergence equivalent ratio (EER) was calculated as the sum of the PEERs of the two species by using Eq. (6).

$${\mathrm{PEER}}_{\mathrm{SW}}=\mathrm{number}\;\mathrm{of}\;\mathrm{wheat}\;\mathrm{plants}\;\mathrm{in}\;\mathrm{mix}/\mathrm{number}\;\mathrm{of}\;\mathrm{wheat}\;\mathrm{plants}\;\mathrm{in}\;\mathrm{monoculture}$$
(4)
$${\mathrm{PEER}}_{\mathrm{FB}}=\mathrm{number}\;\mathrm{of}\;\mathrm{bean}\;\mathrm{plants}\;\mathrm{in}\;\mathrm{mix}/\mathrm{number}\;\mathrm{of}\;\mathrm{bean}\;\mathrm{plants}\;\mathrm{in}\;\mathrm{monoculture}$$
(5)
$$\mathrm{EER}={\mathrm{PEER}}_{\mathrm{SW}}+{\mathrm{PEER}}_{\mathrm{FB}}$$
(6)

The emergence equivalent ratio is the analogous to the germination ratio (GR) used in Elsalahy et al. (2021), but refers to emergence rather than germination.

To measure the competitive ability of intercrops, to identify the dominant partner producing relative higher proportion of biomass in the mixture than the other partner and to indicate the degree of dominance, a simple competitive ratio (CR) is considered. CR refers to the ratio of the PLERs of the two partners in the intercropping (Willey and Rao 1980), and, by analogy, to the ratio of the PEERs. Competitive ratio (CR) of biomass was only calculated for the second crop biomass cut.

$${\mathrm{CR}}_{\mathrm{SW}}\;\mathrm{on}\;\mathrm{emergence}={\mathrm{PEER}}_{\mathrm{SW}}/{\mathrm{PEER}}_{\mathrm{FB}}$$
(7)
$${\mathrm{CR}}_{\mathrm{SW}}\;\mathrm{on}\;\mathrm{biomass}={\mathrm{PLER}}_{\mathrm{SW}}/{\mathrm{PLER}}_{\mathrm{FB}}$$
(8)

Statistical analyses

Partial land equivalent ratio (PLER) and Partial emergence equivalent ratio (PEER) were non-normally distributed according to the Shapiro–Wilk test and the variances between the groups were homogeneous according to Fisher's F test which was complemented by graphical assessments. As we observed the data were non-normally distributed, non-parametric tests were performed in most of the cases. In particular, we examined the mixture effects at the early growth stage by using a two-sided Wilcoxon signed-rank test against PEER above 0.5. In a substitutive mixture, a PEER values > 0.5 for individual partners (SW and/or FB) means a positive mixture effect on crop emergence on that respective partner. Dominance of one partner over the other at emergence as well as at later biomass was assessed by comparing the two partners using a two-sided Wilcoxon test of PLER and PEER. For multiple comparisons between different factors, non-parametric Kruskal–Wallis test was used followed by Dunn test with Bonferroni correction.

In addition, to detect interactions and dependencies of mixture effects from other trial factors, the multifactorial trial was subject to analysis of variance (ANOVA), although most of the data significantly deviated from normal distribution as the robustness of ANOVA is proven under application of non-normally distributed data (Schmider et al. 2010). When the sample size is reasonably large and equal, the ANOVA’s F test is robust enough to deal with moderate departures from normality (Sainani 2012; Winer et al. 1991). Even with extreme deviations from normality, a sample size of approximately 80 is sufficient to run a parametric test t-test (Lumley et al. 2002). Here, we conducted ANOVA, to test the effects of the mixture on cultivar spring wheat, cultivar faba bean, environment, and sowing density. In the first step, a model was prepared considering all possible interactions between those four factors. Non-significant factors or interactions were iteratively removed from the model to improve model performance according to Akaike’s Information Criterion (AIC) (Burnham et al. 2011). The final model included two independent variables spring wheat cultivar and environment, and their interaction.

We assumed a legacy of early mixture effects on later biomass. To verify this, we tested for a correlation (Pearson) and linear regression model between the competitive ratio (CR) at emergence and CR at later crop biomass. All statistical analyses were conducted in RStudio version 1.4.1106 (R Core Team 2020).

Results

Mixture effects at the early growth stage

Although the expected crop emergence (100% of sown plants, based on densities shown in Table 3) was the same in the three environments, the observed emergence was highly different between the three trials (Table 5). At CKA2020, the observed SW emergence was much lower for both densities compared to the other two environments (WG2020 and WG2021, Table 5 and Fig. 2). Compared to the expected crop emergence, the observed SW emergence at CKA2020 in monoculture was 46.1% at low density and 46.2% at high density. In some plots the counted number of crops was higher than the calculated number of sown seeds (Fig. 2), probably due to slight technical inaccuracies of the sowing machine in placing the seeds within the row.

Table 5 Mean values for observed crop emergence (number m−2) of faba bean and spring wheat (monocultures and mixtures) for the three environments depending on the sowing density (low density: LD and high density: HD). Within each column, environments with different letters are significantly different according to the Dunn test with Bonferroni correction
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Fig. 2
figure 2

Crop emergence (plant m−2, each symbol representing one plot) of spring wheat (SW, panels A, C, and E, green symbols) and faba bean (FB, panels B, D, and F, blue symbols) in monocultures (mono) and mixtures (mix) for high (filled circles) and low (open triangles) sowing densities in the environments CKA2020 (A, B), WG2020 (C, D) and WG2021 (E, F). Points above the black diagonal (PEER = 0.5) indicate higher crop emergence in the mixture than in monoculture. Sown high and low densities are represented by solid and dashed lines, respectively

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In mixture, the observed SW emergence at CKA compared to the expected SW emergence was comparatively higher than in monoculture. We observed a high variation of SW emergence at CKA2020 compared to the other two environments (WG2020 and WG2021; Fig. 2A). At the environment WG2021, a relatively low FB emergence was noticed compared to the expected emergence (in monoculture 83.3% and 81.4% for high density (HD) and low density (LD), respectively; and in mixture 86.2% and 94.7% in LD and HD, respectively; Table 5 and Fig. 2F).

Depending on the trial, we marked positive mixture effects already at crop emergence (PEER > 0.5) about 23 days after sowing (DAS) (Table 6). In particular, the mixture effect was consistently positive for SW at both densities at the environment CKA2020 where the observed SW emergence was low (Table 5). At WG2021, the mixture effect was positive for both species (FB and SW) and both densities (LD and HD) where the observed FB emergence was low. That means although the observed crop emergence in general was lower in both cropping systems (mixture and monoculture) than what was sown, the observed emergence was much lower in monoculture than mixture. Neither species nor densities showed any positive effect on PEER at WG2020 (Table 6).

Table 6 Mean values for partial emergence equivalent ratio (PEER) of faba bean and spring wheat for three environments depending on sowing density; differences from 0.5 according to Wilcoxon test at p < 0.01 (**) and p < 0.001 (***) or non-significant (ns)
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Dependence of mixture effects on other experimental factors

The mixture effect at the crop emergence stage may depend on different other factors, such as SW cultivar, FB cultivar, sowing density, environment, and their interactions. To find out which of these had a significant effect on potential mixture effects, a full model ANOVA was conducted considering all possible interactions between those four factors (Table S3). Step by step all non-significant factors and interactions between those factors were removed from the model to improve model performance according to Akaike’s Information Criterion (AIC). The final model included two independent variables SW cultivar and environment, and their interaction (Table 7).

Table 7 The AIC-selected final ANOVA model for emergence equivalent ratio (EER) with spring wheat (SW) cultivar and environment, and their interaction; df: degrees of freedom
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Comparatively higher divergence of SW emergence equivalent ratios (EER) was observed at CKA2020 than at WG2020 and WG2021 (Figs. 2 and 3). At CKA2020, SW cultivar Lennox showed significantly higher crop emergence in the mixtures than monocultures, across both bean cultivars. On the other hand, the SW cultivars Anabel and Quintus showed significantly lower crop emergence in the mixture than monocultures. At WG2021, SW cultivar SU Ahab showed significantly higher crop emergence than SW cultivar Jasmund. No significant effect of SW cultivar was observed at WG2020.

Fig. 3
figure 3

Mean of emergence equivalent ratio (EER) of 12 levels of different spring wheat entries (ten cultivars and two 5-component mixtures of these wheat cultivars) in three different environments. Within each environment, SW cultivars with different letters are significantly different according to Tukey’s HSD test

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Spring wheat dominance at the early stage

At the environment CKA2020, domination of SW over FB was already observed at crop emergence (Fig. 4A), as the mean partial emergence equivalent ratio (PEER) of SW was 0.11 higher than FB mean PEER (Wilcoxon test; P < 0.001). This level of domination of SW over FB at CKA2020 increased over time (Fig. 4B and C). The PLER mean difference between SW and FB at CKA2020 increased to 0.4 (Wilcoxon test; P < 0.001) at about 52 DAS and to 0.50 (Wilcoxon; P < 0.001) at about 82 DAS.

Fig. 4
figure 4

Comparison between faba bean (blue boxes) and spring wheat (green boxes) partial emergence equivalent ratio (PEER; A) about 23 days after sowing (DAS) and the partial land equivalent ratio about 52 DAS (PLER, B) and about 82 DAS (C) in the three environments. Significant differences were indicated according to Wilcoxon test at P < 0.05 (*) and P < 0.001 (***) or non-significant (ns). PEER was calculated by Eq. (4) and Eq. (5); PLER was calculated by Eq. (1) and Eq. (2)

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Although no significant PEER mean differences between SW and FB were observed during the early developmental stage at WG2020 and WG2021, the SW domination over FB changed over time for these environments (Fig. 4). At WG2021, a significant mean difference between SW and FB was first observed about 52 DAS (PLER mean difference 0.11; P < 0.05) which increased by about 82 DAS to a PLER mean difference of 0.31 P < 0.001; Fig. 4B and C). On the other hand, at WG2020, a highly significant mean difference between SW and FB PLER was only observed after about 82 DAS (PLER mean difference of 0.25; P < 0.001; Fig. 4C).

Legacy of early mixture effects on later biomass

In cereal legume crop mixtures, the cereal is known to be a dominant partner by achieving a higher relative yield than grain legumes. However, it is not known if the degree of early cereal domination affects its domination at later biomass production, i.e. if there is any legacy of early mixture effects on later biomass. To estimate the legacy effect, SW competitive ratio (CRSW) was calculated by Eq. (7) and the relationship between CRSW at emergence vs. later CRSW was tested with linear regression. There was a strong linear relationship between the two CRSW values at CKA2020 for both densities (P < 0.001, Fig. 5 and Table 8), i.e. the higher the proportion of SW early on in SW-FB mixtures, the higher was its dominance later in the season. In addition, dominance, as measured by the CRSW, increased over time. In the two other environments (WG2020 and WG2021) and two densities (high and low), only a weak significant correlation was observed, which at WG2020 for high density was significant at p < 0.05 (Table 8).

Fig. 5
figure 5

Spring wheat competitive ratio (CRSW) at PLER (about 82 DAS) against CRSW at PEER (about 23 DAS) for high (filled circles) and low (open triangles) sowing densities in three environments CKA2020 (A), WG2020 (B) and WG2021 (C). The regression lines of high and low densities are represented by red and green lines, respectively. The regression line was only added for significant linear relationships according to the linear regression model (Table 8). SW competitive ratio (CRSW) was calculated by Eq. (7) and Eq. (8). At CR = 1 (dashed lines), neither of the two partners dominates the mixture

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Table 8 Summary of the linear regression model of spring wheat competitive ratio (CRSW) at PLER (about 82 days after sowing) against CRSW at PEER (about 23 days after sowing) for three environments depending on sowing density
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Discussion

Mixture effects at the early development stage

Because of the small size of the young plants and the short time of coexistence for the two species it is reasonable to expect that at the early crop developmental stage there is a lack of interference and therefore a neutral mixture effect. In contrast to this expectation, however, this study showed clear evidence of positive mixture effects at this stage in some of the trial environments. In two out of three environments, we observed higher crop emergence in spring wheat/faba bean mixtures compared to their respective monocultures. The strongest mixtures effects were found in those environment-crop combinations where absolute crop emergence was low (Fig. 2A;Tables 5, 6), but we were unable to identify the exact reasons for spring wheat emergence being low at CKA2020. Although in this environment SW emergence was low (Table 5), its PEER was high (Table 6), i.e. higher relative SW emergence in mixtures compared to monocultures. It may be assumed that the higher SW emergence in mixtures was due to the temporal complementarity effect in resource use between SW and FB (Li et al. 2016; Xiao et al. 2018) in the very early stage of plant development (Elsalahy et al. 2021). In our field experiment, we observed that FB was slower to germinate than SW, potentially because of the (necessarily) deeper seed depth of FB (6 cm) than of SW (3 cm). In the mixture, this asynchronous germination between SW and FB may allow SW to temporarily access more resources and induce higher germination than in monoculture (Elsalahy et al. 2021).

Results also showed the same pattern of high mixture effects and low absolute emergence in the case of WG2021 where a lower FB emergence at high density and a positive mixture effect for both species was observed. These phenomena may be explainable by compensation effects in the mixture (Elsalahy et al. 2021). The mechanism of compensation means that if one of the partners in a mixture fails, the other partner takes its place; it has been shown to lead to high-yield stability in crop mixtures in response to different environmental stresses (Creissen et al. 2013). More specifically, with regard to emergence, a locally acting mortality factor that only affects one partner will reduce competition for the remaining partner and finally may induce higher emergence or survival in the mixture. A positive mixture effect was also observed at CKA2020 for FB emergence at low density.

Our results provide evidence of compensation by one species; this may be particularly important for the low emergence of species in case of climate extremes and increasing environmental stresses. However, further experiments with different combinations of other species are required to test the generality of our results.

Dependence of mixture effects on cultivar identity, environment and density

The mixture effect is an outcome from a complex combination of different factors and the interactions between them. In this study, the SW cultivars, environments and their interactions are the influencing factors of mixture effects on crop emergence. Higher differences among SW emergence were observed at the low input conventional site (CKA2020) than at the organic sites (WG2020 and WG2021). At CKA2020, the SW cultivars Lennox and Anabel and at WG2021, the SW cultivar SU Ahab showed significantly higher crop emergence in mixture than in monoculture. While the existence of mixing potential in some SW cultivars may been indicated by this finding, the identity of cultivars and the direction of their differences was not consistent across environments. Furthermore, the effect of mixture completely disappeared for all SW cultivars in WG2020.

Although a wide range of SW cultivars are currently available for monocropping, there is lack of cultivars suitable for intercropping (Bančič et al. 2021; Bourke et al. 2021; Louarn et al. 2020). Given this present lack of genotypes suitable for mixing, the experiment of SW-FB crop mixture must rely on the SW cultivars available in the market which are selected for monocultures. Yet, it has been reported that the performance in sole crop could not be considered as an index of wheat performance in mixed cropping (Kammoun et al. 2021; Tavoletti and Merletti 2022). The final performance of intercropping depends on the combination of competition, complementarity, cooperation, and compensation between the species which has been named as “The 4C approach” (Demie et al. 2022; Justes et al. 2021). Therefore, the best cultivars for monocropping are likely not the best ones for intercropping. Furthermore, the assessment of the mixing ability of SW cultivars will depend on many further criteria beyond crop emergence, and may change in other environments. Specific breeding for intercropping tends to be particularly important at the targeted growing conditions (Annicchiarico et al. 2019), and this is likely to affect selection of SW cultivars for SW-FB mixed cropping.

Wheat dominates the species mixture (even) at the early stage

Although cereals are considered strong competitors in cereal-legume mixtures (Agegnehu et al. 2008; Hauggaard-Nielsen et al. 2001; Yu et al. 2016), it is not clear if the cereal domination over legumes already starts as early as crop emergence. The individual cereal plant, at this stage, is smaller than the legume, and up to this point in time, there is only short duration of coexistence for the two species in which competition could occur (Bellostas et al. 2003; Benincasa et al. 2012). In fact, however, we did observe early domination of SW in the mixture at CKA when counting emerged plants about 23 DAS (Fig. 4A). On the other hand, dominance does not necessarily mean suppression of the other partner. The relative emergence of SW was 16.4% higher than FB (Table 6) at that stage, indicating dominance, but the PEER value of FB (0.51 at LD and 0.50 at HD; Table 6) showed no reduction of FB emergence in the mixture compared to FB monoculture. The higher emergence of SW in the mixture than in the SW monoculture was observed presumably due to the complementarity effect (Xiao et al. 2018), and could be linked to asynchronous germination between the two species (Elsalahy et al. 2021). At the same time, the observed dominance of SW did not lead to the suppression of FB emergence in the mixture possibly due to the small size of the cereal, and the short time of co-growth between the two species.

At WG2021, the domination of SW started about 52 DAS with a slight suppression of FB biomass (PLER of 0.47; Fig. 4B) where we also noticed low FB emergence in both the mixture and the monoculture, especially at high seed density (Fig. 2F). One mechanism to explain this result could be that crop failure of FB may have relaxed competition and thereby enhanced the domination of SW in the mixture. In line with this, a study with grassland species, in which Asteraceae were sown together with different neighboring species, reported the dominance of these companion species to be particularly strong after a severe drought disturbance (Fenesi et al. 2020), which was linked to the failure of the weaker species. Finally, the SW dominated the mixture about 82 DAS in all three environments by suppressing FB biomass by 38%, 16%, and 18% at CKA2020, WG2020, and WG2021, respectively, compared to monoculture (Fig. 4C); observed differences between the sites are potentially due to their different fertility levels. Characteristics that allowed rapid access of SW cultivars to environmental resources are likely to be the determining factors leading to the dominance at a later stage. A rapid growth because of deeper root distribution may help the cereal to access extra nutrients, especially N, and greater shoot length of the cereal may cause shading of the legume and thereby reduce its growth in crop mixture (Hauggaard-Nielsen et al. 2001; Xiao et al. 2018; Yu et al. 2016). These findings may assist farmers to manage competition in the mixture, e.g. by reducing relative sowing densities of cereals, to enhance complementarity and improve total productivity under different environmental conditions. Grain legume breeding for intercropping with higher vigour at the early growth stages may reduce the early, and thereby also the later domination of the cereal. However, further research is recommended especially under the different environmental conditions to understand the dynamics between cereal and legumes and the (early) domination of cereals over legumes.

Legacy of early mixture effects on later biomass

Despite the fact that the SW domination was detected at all three environments at about 82 DAS, the degree of domination was different among those three environments (Fig. 4C). The higher degree of SW domination (PLER 62% higher than monoculture; Fig. 4C) and a higher degree of FB suppression (PLER 38% lower than monoculture; Fig. 4C) was observed at CKA2020 where we also recorded an early dominating effect of SW in the mixture about 23 DAS. That means the larger the proportion of SW seedlings early on in the SW-FB mixture, the higher SW domination and FB suppression later in the season. The domination intensity of SW for both densities exhibited a consistent expanding trend through the overall growth period at CKA2020 (Fig. 5A). It has been reported that earlier emerging seedlings of dicotyledonous sand dune annual plants tended to become larger adults (Turkington et al. 2005). In line with this, also other studies found that the strength of superiority that started at a very early developmental stage expanded as plants grew (Benincasa et al. 2012; Mangla et al. 2011; Schiffers and Tielborger 2006). The increasing intensity of SW domination over FB was also observed at WG2020 for low density (Fig. 5B).

The early proportion between partners during crop establishment determines future development patterns in the mixture (Mangla et al. 2011). But measuring the strength and direction of interactions at multiple life history stages between cereal and legume may reveal valuable information to assess the importance of competition on community composition later on in the season. A further step would be to integrate this finding into a decision support tool to design cereal/legume intercrops by fitting the early competitive balance with future intercrop productivity.

Conclusions

Our study highlights that positive mixture effects may be detected at the early developmental stage but these mixture effects depend on the environmental conditions and selected SW cultivars. The results also suggest that SW and FB crop mixtures may also improve the ability of crop emergence to buffer environmental stresses, especially during the partial crop failure of one partner. Spring wheat domination in the mixture was noticed in all the environments. Cereals-grain legumes crop mixtures have a high potential to bring stability in productivity compared to the respective sole crops. The findings of this paper highlight the importance of early competition between intercropping partners, and its potential legacy effects on the performance of the mixture. Early competition could be managed in three different ways, namely through crop management decisions before sowing, or after sowing, or through long-term decisions via plant breeding.

With regard to decisions taken before sowing early competition could be managed, e.g. by reducing the relative density of the dominant partner or increasing the relative density of the suppressed partner, by adjusting the sowing time and sowing depth of partners, or by selecting appropriate cultivars so as to favor the weaker partner. Further, arranging the intercropping partners in alternating rows (not in mixed within row) would facilitate the management of individual partners. After sowing, it may be difficult to take crop management actions with a view to correct early dominancy. However, if the partners are known to respond differentially to within-season management of the crop, such as irrigation or mechanical weeding, the targeted application of these practices may help to balance competition between the partners. Finally, targeted breeding for intercropping is recommended as a longer-term tool to manage early competition. Grain legumes with higher vigour at the early growth stages or/and SW cultivars with lower early growth may reduce the early domination and thereby the later domination of cereal.

More generally, a better understanding of the mechanisms of early domination and its intensity changing over time may help to improve the development and management of diversified cropping systems towards sustainable agriculture.