Performance and Evolutionary Adaptation of Bulk Populations of Wheat

doi:10.21203/rs.3.rs-964499/v1

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Performance and Evolutionary Adaptation of Bulk Populations of Wheat

https://doi.org/10.21203/rs.3.rs-964499/v1

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The aim of this study was to investigate the potential of highly heterogeneous bulk populations ("Composite CrossPopulations", CCP) to increase sustainability and resilience of wheat cropping, a so far largely neglected approach.

For this purpose, 10 CCP, generated in 2001, based on the same set of 20 older varieties, but evolved under different environmental conditions were compared with 10 recent cultivars and 10 inbred lines extracted randomly from the populations. The trials were performed over 2 years at 4 locations.

Despite the older germplasm, the yields of the CCPs were similar to those of recent commercial varieties with comparable protein content, and considerably higher than that of the inbred lines.

CCPs that had evolved in different environments differed in some morphological and phenological traits, indicating that adaptation to environmental conditions had taken place through natural selection.

The yield stability and health of CCPs was on average higher than that of commercial varieties and considerably higher than that of inbred lines, the latter indicating that the higher stability was due to their high genetic diversity, not to their genetic background.

Higher crop densities and a stronger soil cover in the juvenile stage indicate that niche complementarity among the genotypes occurs, which may contribute to an improved use of the available resources.

We conclude, that the cultivation of CCP or similar heterogeneous populations represents an important opportunity to increase the resilience of wheat cultivation, which should be given greater consideration in future research and breeding.

Agronomy

General Biochemistry

Molecular Biology

Wheat

Breeding

Composite Cross Populations

Evolutionary Breeding

With climate change increasingly affecting yield stability, in Germany yield increase patterns have changed considerably since 2000. While until 2000, yield increases have been steady, the standard deviation post-2000 on average has more than doubled and increased significantly and the yield gap between practical (official on-farm measurements) and experimental conditions (official variety trials) is continuously increasing (Figure 1). This supports the claim that, due to a reduced “response diversity” of current wheat varieties, the overall climate resilience of wheat in Europe has decreased (Kahiluoto et al., 2019a) although there has been a debate about this observation ( Kahiluoto et al. 2019B, Piepho, 2019, Snowdon et al., 2019;).

The more frequent occurrence of drought stress or other extreme weather conditions cannot all be compensated by improved varieties (Fischer et al., 2014; Allen et al., 2018). In addition, considerable changes in the distribution and occurrence of many pests and pathogens are being observed (Juroszek and Tiedemann, 2013a, b). Thus, breeding has to take into account increasingly different, and in part opposed characteristics, such as tolerance to heat, cold, drought stress and water logging, as well as disease and pest resistances (Heisey and Rubinstein, 2015). In addition, there is a need for an improved ability to acquire nutrients from the soil despite low water content, to use water and nutrient reserves from deeper layers of the soil, or to escape summer drought through early ripening (Mohammadi, 2018; Mwadzingeni et al., 2016). The form of nitrogen (derived through the organic cycle versus soluble mineral fertilizer) and the interactions between water availability and mineralization are also relevant (Tegeder and Masclaux-Daubresse 2018).

Since the early 20th century, most wheat breeding has relied on line breeding and produced highly uniform varieties. Due to the sensitivity of pure lines to changing or extreme environmental conditions, especially pests and pathogens, diversification strategies such as the use of multilines, cultivar mixtures, and evolutionary breeding have been advocated since the 1940s (Finckh and Wolfe, 2015). In the USA, evolutionary breeding based on the use of multiparental diallelic or hierarchical crossing systems (composite cross populations, CCPs) is being used since the 1910s (Harlan and Martini, 1929). Pure lines are routinely selected out of CCPs (e. g. Jackson, 2011, a, b). Under the (extensive) conditions in the USA, populations not only showed stable productivity, but often yields increased over time by adapting to the respective environmental conditions (Suneson, 1956; Allard, 1961; Jain and Qualset, 1975; McDonald et al., 1989; Allard, 1990; Danquah and Barrett, 2002). In response to the multiplicity of requirements needed for successful varieties, interest in CCPs was on the one hand rekindled by the organic sector (Murphy et al., 2005; Wolfe et al., 2006; Dawson et al., 2008; Döring et al., 2011; Murphy et al., 2013, Phillips 2005) and, on the other hand, by breeders interested in creating stable and adaptable materials (Goldringer et al., 1998; Bonnin et al., 2014; Tsujimoto et al., 2015).

One outcome of this interest was the creation of a half diallel cross of 20 wheat varieties released between 1934 and 2000 performed in 2001 by the Organic Research Centre, Elm Farm and the John Innes Institute in the UK as described in detail by Döring et al. (2015). Parents were selected for good performance under low-input conditions in the UK in 2001 (Wolfe et al., 2006; Jones et al., 2010). In addition, crosses of the parents with naturally male sterile lines were added to enhance outcrossing in early generations (Döring et al., 2015; Brumlop et al., 2019). Three CCPs composed of the crosses of either only high yielding (Y) or high baking quality (Q) or the intercross of the high yielding and high quality parents (the CCYQ) were derived.

Despite wheat largely being an inbreeder, genetic variability in these CCPs is preserved provided that a sufficient effective population size is maintained to avoid genetic drift (Brumlop et al., 2019). Nevertheless, populations maintained under the same conditions may still change over time in different ways (Weedon and Finckh, 2019). Early vigor, as measured through root system development and seedling growth is, on the one hand related to the genetic make-up of the CCPs. On the other hand, it has been shown to increase under organic management compared to conventional management (Bertholdsson et al., 2016; Vijaya Bhaskar et al., 2019b) pointing to specific adaptive processes within CCPs to the growing system. Under organic conditions, yields and baking quality correspond to or exceed that of the crossing parents even after many years (Döring et al., 2015; Brumlop et al., 2017).

The CCYQ has been shown to be highly weed suppressive (Finckh et al., 2018) and quite adaptable to ecological intensification (Baltazar et al., 2019). Preliminary data suggest that when sown broadcast under organic conditions and managed without further mechanical weed control the CCYQ may be pre-adapted to minimum tillage conditions (Schmidt et al., 2018). The F₁₈ of the CCYQ that has been in Germany since 2005 under organic conditions has been registered as an open-source heterogeneous variety under the name ‘EQuality’ in 2020. Yield stability is of particular interest in heterogeneous populations as it is expected to be enhanced by increased diversity. Numerous parameters have been developed to characterize yield stability (Annichiarico, 2002). Two basic cases can be distinguished here, each requiring the use of different parameters:

Minimisation of the risk in the event of unforeseeable environmental influences (e. g. years with different weather conditions or occurrence of pathogens): In this case, ideally similar (high) yields are achieved in the individual years unless this has to be payed with lower average yields. Suitable for this purpose are the so-called "static" measures of stability, i. e. the variance across environments and parameters derived from it, such as the coefficient of variation as well as the parameters "POLAR" and "aCV" proposed by Döring et al. (2015). The regression coefficient according to Finlay and Wilkinson (1963), though also classified “static” stability parameter (Annicchiarico 2002, is less suitable, since a flat regression line is possible even with strongly fluctuating yields.

Equal distribution of the yield risk to all locations in the area where the genotype is to be cultivated. As high static stability favors locations with lower yield potential,. "dynamic" stability measures that account for the productivity level of each environment are more suitable for this purpose. These include the variance of relative yields (Annichiarico, 2002), as well as the parameters proposed by Eberhard and Russel (1966) and Shukla (1972). In addition, several indices, mainly for estimating the dynamic (Type 2) stability have been proposed and applied to populations and are described in detail elsewhere (Weedon and Finckh, 2019).

The ranking of genotypes with regard to their stability is often, but not always similar when comparing different indices of stability.

If the factors that cause yield fluctuations or differences are the same respectively (e. g. water availability, temperatures or diseases), it is not possible to optimize static and dynamic stability at the same time, because high static stability leads (but is not equivalent) to low dynamic stability, while high dynamic stability results in medium static stability. Thus, static parameters are relevant for risk minimization over years, while dynamic parameters are more suitable to identify genotypes adapted to a wide range of environments. However, dynamic parameters may also be relevant for risk minimization over years, if the possibilities to obtain similar yields in all environments are limited, if yields are highly divergent and if there is little correlation of individual yields with the mean yield of each environment.

First insights into the yield stability of the wheat CCPs described above could be gained by analyzing the data of organically and conventionally maintained CCPs from ten generations with the performance of one or two reference varieties in the respective systems at one site (Weedon and Finckh, 2019). Across generations, yields were comparable to the reference varieties under organic conditions, but inferior under conventional conditions. In contrast, yield stability (both in the dynamic and static sense) of the populations was higher than that of reference variety ‘Capo’ in both management systems (Weedon and Finckh, 2019). For a more thorough comparison of yield and yield stability and in order to determine the effects of population history on performance, populations with variable histories have to be compared with a larger set of pure line varieties across a set of environments, preferably including conventional and organic systems, as well as variable nutrient inputs.

In the present paper, we have studied a set of 10 Populatione, derived from the basic population described above, but with varying population histories for yield and yield stability. Conventionally and organically bred baking quality varieties were used for comparison. In addition, the role of diversity for yield and stability was assessed by including 10 lines derived from the CCYQ population in the F₇. Since hybrids aim at a maximum of heterozygosity, they present an alternative way to exploit genetic diversity in wheat, albeit to a lesser degree than populations. Since no baking quality hybrid wheat was available, a feed hybrid wheat and a line variety with similar characteristics were included in the trials. Trials were conducted for two seasons in two organic and two conventional sites and with two fertilizer input levels per site. The following questions were addressed:

How do the populations and commercial varieties of different breeding origin compare in their performance for yield, protein content and stability across environments (organic vs. conventional farming system and fertilization level)?

What are the effects of within population diversity on performance and stability? For this, the populations were compared to the randomly extracted lines for overall performance and stability.

What are the effects of evolutionary history on agronomic traits of the populations and do these interact with the farming system and input levels?

Plant Material

Populations

All populations examined in this study are based on the CCYQ population. In brief it is based on crosses of eight high yielding (Y) with nine high baking quality (Q) parents and all of these with ‘Bezostaya’ performed in 2001 by The Organic Research Centre (ORC), Elm Farm and the John Innes Institute in the UK. In addition, crosses of the parents with naturally male sterile lines were added to enhance outcrossing in early generations (Döring et al., 2015; Brumlop et al., 2019). Parental varieties were released between 1934 and 2000 and selected for good performance under low-input conditions in the UK (Wolfe et al., 2006; Jones et al., 2010). In 2005, seed of the F₄ grown at various UK sites was bulked and a portion was kept in the UK, one sent to the University of Kassel and one to the Hungarian Academy of Sciences in Martonvasar where the CCPs have been maintained since.

At the University of Kassel, the CCP was split and increased under both organic and conventional conditions as described in Vijaya Bhaskar et al. (2019a) for one year. In 2006, each of the organic and the conventional population were divided into two parallel sub-populations, which were maintained at that site without mixing from then on (Brumlop et al. 2019). Genetic drift was effectively avoided by using plot sizes of ≥150 m² (Brumlop et al. 2019). Plots were separated from other wheat by at least 3m. In 2008, two further organic subpopulations were created that were sown broadcast every year and maintained without weed control under organic management.

Also in 2008, eight partners in seven European countries (UK, Netherlands, Denmark, Germany, Switzerland, France, and Hungary) received a portion either of the population maintained in the UK, Hungary or at Uni Kassel. The populations were grown from the F₈ – F₁₂ by each partner for one year and then passed on to the next in a distinct cycling scheme with the aim to expose the populations to as variable environmental conditions as possible over time (Weedon et al., 2016; Weedon, 2018). One each of these cycling populations originating from the three different countries were included in this study. In summary, the CCPs had the following variable histories (see also Table S3):

Organic vs. conventional farming systems at University of Kassel
Changing environments across sites for 5 years

Random Lines

In 2007, 100 spikes were extracted randomly from the original population (CCYQ, F₆ in the UK, grown under organic conditions) and inbred lines were derived from them and maintained at the Technical University of Munich (TUM) under conventional conditions. Ten randomly selected apparently homogeneous lines were included in the trial in order to assess the genetic potential of the populations while excluding effects due to the diversity of the populations.

Commercial varieties

The commercial varieties included in the trials were (a) four baking quality varieties bred for conventional agriculture, (b) four baking quality varieties bred for organic agriculture and (c) two high-yielding conventionally bred varieties, one fodder line variety and one hybrid (Table S2). According to breeding goals for organic conditions most organically bred varieties are characterized by particularly high baking quality and grain protein content and tend to be taller and more competitive against weeds than most conventionally bred varieties (van Bueren and Myers, 2012).

Field trial locations, management and experimental design

The field trials took place at four locations: Viehhausen, Dürnast, Neu-Eichenberg, and Quedlinburg (Table S1). The experimental station Viehhausen (VH) of TUM has been managed organically since 1995. Soils are cambisols based on sandy loam with a water holding capacity of approx. 180mm. No mineral fertilizers, fungicides, insecticides or herbicides were applied. Weeds were controlled mechanically through harrowing at tillering. The pre-crop was a grass-clover ley in both years.

Environmental and soil conditions at the conventional research station Dürnast (DN) of TUM, situated close to Viehhausen, are very similar to VH. The pre-crop was maize in both years. Weather data for both sites were provided by the Bayerische Landesanstalt für Landwirtschaft (Station Nr. 8, Freising), which is situated approx. 7 km from VH and 3 km from DN. The research station of the University of Kassel in Neu-Eichenberg (NE) has been managed organically since 1984; no mineral fertilizers, fungicides, insecticides or herbicides were applied. Weeds were controlled mechanically through hoeing at tillering stage. The soil is a fine loamy loess soil (deep Haplic Luvisol) with a usable field capacity of approx. 180 mm. In order to provide a very low nitrogen site, the pre-crop was triticale in both years.

The soil at the conventionally managed experimental station of the Julius-Kühn-Institute at Quedlinburg (QB) is a chernozem with a usable field capacity of approx. 220 mm. Pre-crop was oilseed rape in both years. Weather data were provided by on-station meteorological stations in both sites.

All Experiments were set up as split-plot experiments with N-fertilisation as the main factor and genotypes assigned to the subplots. Plot size varied depending on the equipment available at the respective experiment stations.

Assessments

Ground cover of weeds and wheat (%) was determined visually at the end of tillering (BBCH 27-29). Heading date was determined visually and expressed in number of days from 1 January. Leaf diseases were determined visually as percentage of non-green leaf area per plot as described by Brumlop et al. (2017) at the time of ଂowering. Moreover, the predominant diseases were noted. Plant height was measured manually after flowering. Based on manual cuts number of spikes per m², number of grains per ear and the average ear weight were determined before combine harvest. Yields were corrected to 14% moisture content and TGW determined. Grain protein content was determined by near-infrared spectrometry using commercial standard devices and calibrations.

Data analysis

The statistical analyses were performed using R Version 3. 4. 3 (R Core Team, 2020). Linear mixed models were built using the R package “Lme4” (Bates et al., 2015); multiple comparison of means was successively performed based on the significance of pairwise comparisons of adjusted means, using the packages “emmeans” (Lenth, 2018) and “lmerTest” (Kuznetsova et al., 2017).

The percentage of leaf area affected by diseases at flowering were not included in the overall ANOVA, but analyzed for each single experiment, as different diseases were present in the single environments and the data were therefore not comparable.

For the group comparisons (i. e. populations, the different groups of commercial varieties (Table S2) and the randomly extracted lines), the group (G), the farming system, i. e. organic vs. conventional (S), the input level (I), as well as the interactions G x S, G x I and G x S x I were considered fixed factors, while trial, i. e. location x year (T), and Genotype, i. e. cultivar, line or population (G) were considered random factors.

Response variables were ground cover, heading date, plant height, yield, yield components, protein content, and protein yield. This analysis was performed with two different subsets: (a) only commercial varieties and populations for assessing the potential of populations and (b) populations and random lines for assessing the impact of within-population diversity on yield and other characteristics of the populations.

For comparison of populations evolved under different conditions, the fixed factors were Genotype (G), farming system in the evaluation trial (S), Nitrogen input level in the evaluation trial (F), as well as their interactions G x S, G x F and G x S x F. The single Experiment (T), replications and Nitrogen fertilization blocks were considered random factors. Response variables were as above. Among the ten CCPs compared, there were three pairs. The high and low input CCPs had evolved without mixing as parallel populations in Neu-Eichenberg for 10 years while the two broadcast populations had evolved in parallel for 7 years. The three cycling populations had started from the same bag in 2005 but grew in different places from 2005-2013 (Table S3). Linear contrasts were used to compare among pairs that had evolved in parallel and other aspects of differing evolutionary pathways

Stability was assessed both for grain yield and protein content based on the variance of absolute and relative yields or protein contents over the 16 environments (2 year * 4 locations * 2 fertilization levels). The variance of absolute yields describes the “static” or “Type 1” stability (Annicchiarico, 2002), while the variance of the yields relative to the overall mean in each environment is a measure for the “dynamic” or “Type 2” stability (Annicchiarico, 2002). In order to avoid eventual biases, due to the application of only one stability index, we calculated in addition the “Joint Regression” coefficient according to Finlay and Wilkinson (1963), i. e. the slope of the regression of genotype (or population) yields on the environmental mean, which may be both interpreted as static (considering the deviation from 0) and dynamic stability (considering the deviation from 1), and the variance of the residuals (Mean square error, MSE)of the joint regression (Finlay and Wilkinson, 1963) according to Eberhard and Russel (1966), a measure of dynamic stability (type 2). Confidence intervals were calculated for the stability indices and they were compared using Ekbom's Test (Ekbohm 1981).

General observations

In both years, the annual mean temperature was similar to the long-term mean from 2003 - 2017. There was an overall water deficit in 2015/16 in Quedlinburg and in 2016/17 in Neu-Eichenberg (Figure 2). In addition, the year 2014/15 had been exceptionally dry in Neu-Eichenberg resulting in a carry-over water deficit for 2015/16 at that site (Weedon and Finckh, 2021). Thus, although overall yields were considerably higher under conventional than under organic conditions, differences due to the fertilization level were only about 5 % in 2016 and 10- 20 % in 2017, reflecting the drought effects in Quedlinburg and Neu-Eichenberg in 2015/16. In 2016, differences due to growing system were much higher than in 2017, because organic yields were particularly low in 2016 while conventional yields were lower in 2017 (Figure 3). The prevailing foliar diseases in 2016 were yellow rust (caused by Puccinia striiformis) in the two organic sites and DTR leaf spots (Drechslera tritici-repentis) and Septoria (Zymoseptoria tritici) in the conventional sites. In 2017, in addition, brown rust (Puccinia triticina) played a role in the organic sites. Disease pressure was generally low (< 14 % non-green leaf area on average, no disease in the conventional site Dürnast in both years) with the exception of Neu-Eichenberg in 2016 where yellow rust pressure was rather high (approx. 22 %) (Figure 4).

Comparison of populations with commercial cultivars

Genotype group (G), fertilization level (I) and farming system (S) affected plant development (ground cover, heading, height), yield, most yield components, protein yield and grain protein content with signiﬁcant G x S interactions in most cases. While no G x I interactions occurred, there were three-way G x I x S interactions for ground cover and grain yield. For yield, this was due to differing degrees of lodging depending on fertilizer level and is not considered in detail (Table 1).

Compared to commercial baking quality varieties, the populations were characterized by a slightly higher ground cover under organic but not under conventional management, high tillering capacity, later heading, smaller spikes and lower thousand grain weights (Table 2).

As expected, the yield of the two high yielding varieties was higher than that of all other entries while grain protein contents were lowest. They also yielded the most protein ha^-1, had the highest number of seeds ear^-1 and usually ranked very high in the number of ears m². For the baking quality entries, under organic farming conditions, conventionally and organically bred varieties and the populations did not differ significantly in yield. In contrast, under conventional conditions, the conventionally bred varieties outyielded the populations and the organically bred populations (Table 2).

In both farming systems, grain protein content was highest in the varieties bred for organic farming, the populations were similar to the conventionally bred baking varieties. Under conventional management, all groups achieved an average grain protein content above the threshold of 11 % which is commonly regarded as the minimum required for bread production. Under organic management, however, only the organically bred varieties and the random lines achieved an average grain protein content above the threshold of 11 %. This was compensated by lower yields.

Where diseases occurred, the mean disease levels of the CCPs were similar to the mean of the commercial cultivars. In contrast, the random lines were significantly more diseased in the organic environments where rust diseases were prevailing. In Neu-Eichenberg and Viehhausen 2016, the severity of yellow rust on the commercial varieties varied considerably with “Kerubino” being particularly susceptible (Figure 4, Table 4).

Comparison of populations with randomly extracted inbred lines

Yields of the inbred lines extracted from the populations were significantly lower than those of the populations. The difference was greater under organic than under conventional management. In contrast, grain protein content was slightly higher in the inbred lines than in the populations (Table 2).

There was a marked difference in yield structure between populations and randomly extracted lines: high tillering capacity, but smaller spikes and grain weight were observed in the populations, however, not in the randomly extracted lines. Moreover, compared to inbred lines, the populations were characterized by a higher ground cover at tillering and, earlier heading date. Plant height was similar in populations and random lines.

Effects of evolutionary history on the performance of the populations

There were signiﬁcant effects of the evolutionary history (E), on plant height, yield, grain protein content and most yield parameters (Table 3). Heading date was affected by history under conventional but not under organic conditions, with all populations heading earlier under conventional conditions (Table 3). Otherwise, the evolutionary history did not interact with the test system (S) and fertilization (I). No significant effect of the evolutionary environment on foliar diseases could be detected (Data not shown).

The tallest populations evolved under HI conditions, these populations were significantly taller than the LI or broadcast CCPS that had evolved at the same site. The highest yield was achieved by the cycling population that originated from the UK. This populations also produced the highest number of ears/m², while the highest TKG was produced by the cycling populations that had stayed three years in HU. The highest protein content was achieved by the cycling control, which was a mixture of the two LI CCPs.

The cycling populations differed in height, number of ears/m² and TKW and for heading date under organic conditions. In contrast, the population that had stayed three extra years in UK from 2005-2008 was significantly shorter than the other two cycling populations. The population that had been in HU for three extra years produced significantly less head bearing tillers and higher TKW than the other two.

Stability

The yields relative to the site mean of the entries across all 16 environments varied considerably with the lowest variation seen in the cycling and non-cycling CCYQs followed by the reference varieties and then the extracted lines (Figure 5).

The static stability as expressed by low variance of absolute yields across sites was highest for the organically bred varieties, albeit at a low yield level. In contrast, the variance of the CCYQs was higher but also at a higher yield level. The conventionally bred baking varieties were similar to the CCYQs in variance except for the yellow rust susceptible variety ‘Kerubino’ (Figure 6A). The dynamic stability, i. e. the variance of the yields relative to the site mean was best (lowest variance) for the CCYQs followed by the baking quality varieties and the high yielding varieties (Figure 6A). Both for static and dynamic stability parameters, there was a negative correlation between yield and stability among commercial varieties (Figure 6A), the higher the yield, the lower the stability. In contrast, the extracted lines had very high variances despite low yields indicating low static and dynamic stability.

The regression coefficient according to Finlay and Wilkinson (1963) (Figure 6A) allows conclusions to be drawn on the suitability for high or low input systems. According to this, the two high-yielding varieties were distinct HI varieties, while the varieties bred for organic farming were suitable for LI systems, followed by the populations, which, like the other groups, were in the middle field. However, in both cases the statistical dispersion within the groups was very large (Figure 6A), so that in most cases they could not be clearly distinguished.

No differences concerning yield stability and stability of protein contents among populations with different evolutionary history could be detected (Data not shown).

Similar trends were observed for protein content stability, which is not surprising as protein content and yield are closely correlated. (Figure 6B)

Performance and Potential of Populations

The yields of the conventional trials were in the range of the mean yields in Germany during 2016 and 2017 (Fig 1) while organic yields were considerably lower, especially in 2016. Overall, the composite cross populations performed similar in yield to the organically and conventionally bred baking varieties under organic conditions and in between those two types under conventional conditions. They had the same protein contents than the conventionally bred varieties but one percentage point less than the organically bred ones. In addition, the CCPs performed considerably better than the inbred lines extracted from them.

The poor performance of the organic trials in 2016 was in part due to the season 2014/15 being excessively dry and a water deficit carry-over that also led to the low overall effects of the fertilization. These were even less under organic conditions as organic crops depend on optimal conditions with respect to soil water and temperature for nutrient mineralization. The similarity of the yields of the populations to the organic and conventionally bred varieties is remarkable, as the populations were generated from genetic materials from 1934-2000 that had been chosen for adaptation to low-input environments (Jones et al. 2010) and comprises many parents bred in the UK, which are not fully adapted to the environmental conditions of Germany. The populations included in these trials had been grown in Neu-Eichenberg for 10 generations and were in the F₁₅ (2015/16) and F₁₆ (2016/17). During these years and up to the F₁₈, under organic conditions yields were close to that of the conventionally bred varieties Capo and Achat but under conventional conditions, Capo outyielded the populations (Weedon and Finckh, 2019, 2021) and highly stable (Weedon and Finckh, 2021). This is also in accordance with older results on other populations, where yields were found to be stable or tended to increase over generations (Suneson, 1956; Qualset, 1968, Allard and Adams, 1969; Jain and Qualset, 1976; Danquah and Barrett, 2002b). Compared to their parents or parental mixtures the CCPs outyielded them when tested (Brumlop, 2017; Döring et al. , 2015; Finckh et al. , 2009; Weedon and Finckh, 2021). Similar results with other wheat populations were obtained by Goldringer et al. (2001). This suggests that populations can be considered an as alternative to inbred varieties, both in organic and conventional agriculture. However, our experiments are not representative for environments with very favorable growth conditions typical for oceanic climates in northern Germany, where yield of more than 10 t/ha are possible.

Crop density (i. e. the number of spikes/m²) was highest in populations both in comparison with commercial varieties and the inbred lines extracted from the populations. Corresponding to this, grain and spike weights were lower. The same could be observed for ground cover during tillering, where the difference between populations and inbred lines was particularly notable. Early development in the field and ground cover correlate well with root development where the CCPs have been shown to outperform most pure lines (Vijaya Bhaskar et al 2019a) and organically evolved CCPs outperformed conventionally evolved ones (Bertholdsson et al. , 2016; Vijaya Bhaskar et al. , 2019b). Through this adaptation and the adaptability combinations of different genotypes the CCPs appear to be better able to use the available space and resources than homogeneous stands of single lines. Possible benefits are higher wheat biomass and consequently yield, improved organic matter supply to the soil (Simon and Reents, 2019) as well as improved weed suppression (Finckh et al. , 2018).

Role of diversity for performance

The superiority of the CCPs over the inbred lines extracted from them was more evident in the organic than in the conventional trials where fungicides were used to control rust diseases. Under organic conditions when no fungicides were used, the CCPs always were considerably healthier presumably due to their resistance diversity. However, even in the trials with low leaf infestation and where the populations were similarly or even more affected by foliar diseases, yields of populations were higher. Thus, greater resistance to foliar pathogens is not the only beneﬁt gained from diversity, but other factors are also of importance.

Morphological characteristics are considered a main factor for a high yield potential of wheat varieties. Besides harvest index, particular importance is given to competitive ability, which in wheat as a “community plant” should be kept low. The resulting ideotype in pure line breeding is therefore low in stature, scarcely tillering and possesses short, erect leaves (Donald, 1968). Plant types with higher competitive ability are considered less suitable, at least for high-input-conditions, because all plants compete exactly for the same resources and may thus suppress each other.

In the case of heterogeneous populations, with a range of morphological and phenological characteristics, each individual occupies a different niche, leading to a certain degree of complementary. This would allow for denser canopies and more complete ground cover, which actually was observed in our ﬁeld experiments. Therefore, the negative effect of more competitive ideotypes is alleviated, if a certain degree of diversity is present in the canopies. Diversiﬁcation may therefore be considered an alternative or supplementary way to achieve higher crop densities, in addition (or alternatively to) to ideotypes with low competitive ability. As the overall competitive ability of such canopies is higher, this results in the observed improved weed suppressiveness (Finckh et al. , 2018).

Several new yellow rust races have emerged after 2010 in Europe affecting many of the current varieties (Hovmøller et al. , 2016) including ‘Kerubino’ in our trials. Some of the parents of the CCPs carried resistances against these new races just like most of the pure line varieties but also against the brown rust races present during their evolution in Neu-Eichenberg (Weedon and Finckh, 2021). This diversity in resistances protected the CCPs well. The pure lines extracted from the CCPs in 2007 before the new yellow rust races arrived apparently did not carry the relevant yellow rust resistances and were as susceptible as ‘Kerubino’ on average. Thus, highly diverse populations are a valuable and durable alternative to resistance breeding based on single resistance genes.

Under conventional conditions, where Septoria nodorum and Drechslera tritici-repentis were the prevailing pathogens the populations performed similar to all other entries with at most 4. 5 percentage points higher infestation (15. 4% DLA in the CCPs versus 10. 9% in the references) that was biologically not significant. Although canopy architecture diversity generally decreases splash dispersed diseases (Vidal et al. 2017, 2018), the higher crop density of the populations may have led to higher canopy moisture and to higher disease conduciveness.

Role of Diversity for Stability and Adaptation

While the commercial varieties with the highest yields were able to exploit their potential primarily in environments with high yield potential the varieties bred for organic farming proved to be distinctly low-input varieties. The fact that the populations ranged in between with a high dynamic stability points to their wider adaptation to different site conditions. In contrast to the varieties bred for organic farming, the adaptation of the populations was not limited to low-input conditions or organic farming, but they were likewise adapted to the conventionally managed environments. However, our study lacks environments, where extremely high yields of more than 9 t/ha are possible; consequently, we don’t know, whether the populations are also adapted to this type of environments.

The stability of the CCPs was in contrast to the isolated inbred lines, which were clearly better adapted to HI-conditions, but highly spreading and with low overall yields. The marked difference in yield stability of populations and isolated inbred lines shows that the higher stability of the populations is due to their diversity, not their genetic background. The findings are in accordance with results of Döring et al (2015) and Weedon and Finckh, (2019). We can therefore conclude, that higher diversity is an appropriate way to improve overall adaptation to different site conditions.

The comparison with the hybrid variety ‘Hybery’, and the high-yielding variety ‘Elixer‘ should be considered with care. Stability parameters are strongly inﬂuenced by the speciﬁc environments, where the genotypes have been tested, and in the case of parameters of “dynamic“ stability also on the other genotypes tested. Thus, other studies have reported particularly high stability of wheat hybrids (Mühleisen et al. 2014). ‘Hybery’ has a superior root system during early development compared to ‘Elixer’ (Vijaya Bhaskar et al. , 2019) which could have led to its higher static stability. More than one hybrid variety of the same quality class should be included in future comparisons.

In our studies, the differences among environments were mainly caused by local factors and different crop management (fertilization levels and farming system). The results are therefore primarily valid for the adaptation to local conditions and cultivation measures. Nevertheless, high resilience of the CCPs with respect to water availability, abiotic stresses and disease pressure in one site has been documented over 13 generations (Weedon and Finckh, 2021).

We conclude, that using genetically more diverse material can be considered an alternative to varieties consisting of single inbred lines and contribute to improve yield stability. Moreover, we found, that the use of populations is not limited to low-input or Organic Farming systems, but, in contrast to the varieties bred for organic farming, they show a wider adaptation do a broad range of environments.

Evidence for evolution and adaptation to speciﬁc environments

We could show that populations having evolved under different conditions differed for several agronomic traits. Since population sizes were large enough to avoid genetic drift (Brumlop et al. , 2019), and since two parallel populations s (With exception of the populations Cycl_HU,Cycl_DEand Cycl_{UKCYQ LM} and land CCYQ LM. ctr, see Table S3), these differences have to be considered as adaptation to the speciﬁc environments. We could however not directly show, speciﬁc adaptations to either organic or conventional environments or either high-or low-N-fertilization were evident in the field trials.

No speciﬁc adaptations to either organic or conventional environments or either high-or low-N-fertilization were evident in the field trials. Additional unpublished data, where populations in the F₁₇ were compared with stored seed of earlier generations show, that evolutionary changes did not coincide with yield losses.

However, under controlled conditions changes in early root and shoot development of the HI and LI CCPs have been demonstrated as well as their translation into early soil cover (Vijaya Bhaskar et al, 2019). An earlier comparison for yield effects among populations with different histories also was inconclusive (Brumlop et al. , 2017). It has also been documented that in the ﬁrst generations when the populations were still growing int the UK only, genotypes carrying rht-genes for reduced height had been strongly reduced without negative yield effects, however (Knapp et al. , 2019). Similar observations were reported by Goldringer et al (2001) in other wheat populations. For conventional environments with particularly high yield potential (more than 8 t/ha), this might increase the risk of lodging and require higher doses of growth regulators.

A concern that has been raised with respect to the use of populations in dynamic management, is that favouring of more competitive plant types should lead to yield depression due to natural selection (Denison, 2012). Natural selection favours genotypes with higher seed production of individual plants. Evolution would therefore favour more competitive genotypes, lead to more uniform populations and to lower yields. If all genotypes compete for exactly the same resources, this would result in the reduction of productivity of other, competing genotypes. This could occur, if there were little diversity within the populations concerning specific resource efﬁciency, if all available resources were already exploited, i. e. the genotypes were perfectly adapted to the respective environments and if no outcrossing occurred.

This is in contrast with the high yield levels in the CCYQs . Also, the CCPs have maintained high diversity as outcrossing in wheat is regularly occurring (Brumlop et al. 2019). Thus, most likely, the population consists of members differing in their speciﬁc resource efﬁciency and adaptation which allows for the resilience in variable environmental conditions among years. An additional aspect is, that environmental conditions change from year to year, and different genotypes will be more competitive in different years. In the long-term, this would lead to an equilibrium between differently adapted genotypes, potentially increasing yield stability over time and, on average also grain yields.

If there are unexploited niches and if genotypes differ in their response to specific resources, production per plant coincides with better exploitation of non- or underexploited resources/niches, without reducing seed production of plants belonging to other genotypes. This should lead to differentiation within the population, and might lead to higher total yields. Such a development would ideally occur, if there is a high genetic variability within the population and if there are many unexploited niches.

The CCPs have not been evaluated for yield increases or decreases over time. This would require growing many generations side by side. Compared to reference varieties, however, no increases or decreases have been found (Weedon and Finckh, 2021). Thus, it seems, that niche differentiation is more relevant. Our results on crop density and ground cover support this hypothesis: in populations a higher plant density, and consequently higher ground cover was realized, compared to isolated lines from the same populations.

From our results, in conjunction with the literature reviewed, we conclude, that populations have an interesting, currently neglected potential for improving resilience of agricultural systems:

Populations may be considered an alternative to inbred lines under organic and low to moderately high input conditions providing protection from diseases thus help reducing pesticide inputs.
Populations are also less affected by the loss of resistance due to the apperance of new, virulent strains of pathogens and may therefore be much more long-lived than inbred varieties.
Though there may be strong initial selection for single traits strongly influenced by major genes, such as plant height or phenology, populations remain stable in the long term; a selection towards types with a higher number of kernels per plant does not necessarily result in yield decrease; a possible explanation is niche differentiation, resulting in higher overall crop densities.
Yield stability and stability of protein contents tend to be higher in populations than in pure lines. In addition, the considerable effort required for maintenance breeding in inbred varieties is avoided.
Crop density and consequently, weed suppression, ground cover density and biomass during tillering are higher in populations than in inbred lines resulting in reduced need for herbicides.

Our recommendation based on the results of these trials would be to introduce populations into practical agriculture as stipulated by the new EU organic regulation 2018/1519 particularly for use in organic and low-input systems. Additionally, this should be accompanied by multisite studies in order to further assess yield stability of a range of populations with differing genetic background and the socio-economic impact relating to the introduction of heterogeneous populations. Research into the optimization of parental variety choice for the establishment of bulk populations as well as population improvement through admixing new materials in order to optimize yield and yield stability is essential.

Acknowledgements

This research project has been supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the project “INSUSFAR” (Innovative approaches to optimize genetic diversity for sustainable farming systems of the future)

Disclosure of conﬂicts of interest

The authors declare have no competing interests, that are directly or indirectly related to the work submitted for publication concerning the present publication.

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Table 1: Effects of Group (Populations. inbred lines E- C- and organic varieties). Farming System. N fertilization and their interactions on Grain Yield and other parameters. Results of the ANOVA. p-values.

Factor	DF	Protein Yield	Yield	Height	TKW	Protein	Crop density	Seeds /ear	Heading	Ground cover
Group (G)	4	0. 000	0. 000	0. 000	0. 003	0. 000	0. 000	0. 000	0. 000	0. 012
Input (I)	1	0. 054	0. 008	0. 168	0. 838	0. 379	0. 007	0. 165	0. 272	0. 446
System (S)	1	0. 000	0. 002	0. 241	0. 768	0. 016	0. 143	0. 054	0. 784	0. 406
G x I	4	0. 043	0. 119	0. 471	0. 909	0. 914	1. 000	0. 348	0. 674	0. 123
G x S	4	0. 032	0. 023	0. 015	0. 112	0. 474	0. 017	0. 015	0. 000	0. 000
I x S	1	0. 499	0. 521	1. 000	0. 028	0. 918	0. 438	0. 809	0. 153	0. 213
G x I x S	4	1. 000	0. 005	0. 646	0. 067	0. 472	1. 000	0. 073	0. 743	0. 045

Table 2: Grain Yield and other parameters per group. Comparison of Least-squares means of populations and commercial cultivars. Different letters indicate significant differences among groups.

A. Organic environments
	Commercial cultivars
	High yielding		Quality		Organic breeding		Populations		Lines
Yield (q/ha)	55. 03	c	45. 98	b	42. 62	b	43. 34	b	37. 94	a
Grain weight (mg)	42. 24	ab	42. 69	ab	44. 18	ab	41. 52	a	43. 76	b
Grain Protein (%)	9. 57	a	10. 63	b	11. 75	c	10. 74	b	11. 12	b
Ears/m2	360. 31	bc	353. 89	bc	330. 39	b	374. 92	c	302. 81	a
Seeds Ear-1	37. 88	c	31. 43	b	30. 39	ab	28. 54	a	30. 78	b
Heading (days of year)	153. 33	abc	152	ab	153. 96	bc	151. 69	a	155. 07	c
Ground cover (%)	23. 8	a	24. 11	ab	23. 84	ab	26. 47	b	22. 43	a
Protein yield (kg ha-1)	517. 2	c	486. 1	bc	497. 8	bc	463. 9	b	416. 6	a
B. Conventional environments
	Commercial cultivars
	High yielding		Quality		Organic breeding		Populations		Lines
Yield (q/ha)	87. 78	d	77. 11	c	69. 44	ab	72. 2	a	69. 09	a
Grain weight (mg)	40. 59	ab	41. 47	ab	43. 86	b	41. 03	a	44. 03	b
Grain Protein (%)	12. 13	a	13. 09	b	14. 42	c	13. 35	b	13. 98	c
Ears/m2	450. 03	bc	453. 57	c	416. 19	ab	443. 34	bc	407	a
Seeds Ear-1	42. 72	b	35. 9	a	33. 46	a	34. 73	a	34. 09	a
Heading (days of year)	151. 7	a	151. 23	a	152. 99	a	150. 49	a	156. 18	b
Ground cover (%)	36. 84	a	36. 25	a	36. 12	a	36. 36	a	37. 75	a
Protein yield (kg ha-1)	1056	b	1005	ab	1000	ab	962	a	964	a

Table 3: Grain yield and other parameters by Genotype and evolutionary environment. mean values and results of the ANOVA and contrast analysis. For heading date. means of organic and conventional testing environments are reported separately. since a significant interaction of farming system (S) and Evolutionary environment could be detected.

	yield (dt/ha)	TKW (g)	Ears/m²	kernels/Ear	Protein content (%)	Protein yield (kg/ha)	Ground cover	Heading (day of Year)	height (cm)	Heading Org	Heading Conv
Average Values per Group
Broadcast (BC)	56. 4	41. 4	406. 4	31. 2	11. 94	693. 0	30. 8	151. 33	101. 6	151. 8	151. 1
Cycling Control (CC)	57. 6	41. 8	406. 9	31. 5	12. 21	755. 6	31. 3	150. 53	101. 6	151. 7	149. 9
High-Input (HI)	57. 3	41. 5	411. 5	31. 5	12. 05	711. 8	31. 4	150. 92	104. 7	151. 7	150. 5
Low-Input (LI)	57. 1	41. 6	407. 1	31. 2	12. 02	707. 9	31. 4	150. 66	102. 5	151. 5	150. 2
Cycl_UK	58. 7	40. 4	427. 0	31. 1	11. 77	712. 9	31. 2	150. 47	98. 6	152. 0	150. 2
Cyc_lDE	56. 9	41. 0	419. 4	30. 8	11. 93	702. 1	31. 3	150. 94	101. 3	152. 4	150. 3
Cycl_HU	57. 0	42. 0	396. 1	31. 9	11. 79	688. 8	31. 7	150. 78	102. 6	151. 3	149. 8
Results of ANOVA (p-values)
Genotype (G)	0. 002	0. 006	0. 016	0. 485	0. 045	0. 012	0. 897	0. 002	0. 000	0. 363	0. 000
Farming System (S)	0. 004	0. 820	0. 285	0. 028	0. 011	0. 001	0. 495	0. 778	0. 227	-	-
Fertilization (F)	0. 000	0. 598	0. 001	0. 015	0. 003	0. 000	0. 032	0. 860	0. 001	0. 912	0. 888
G x S	0. 895	0. 073	0. 506	0. 163	0. 085	0. 811	0. 853	0. 000	0. 276	-	-
G x F	0. 662	0. 980	0. 840	0. 996	0. 781	0. 818	0. 108	0. 484	0. 320	0. 944	0. 232
S x F	0. 026	0. 000	0. 207	0. 413	0. 966	0. 027	0. 392	0. 993	0. 420	-	-
G x S x F	0. 407	0. 844	0. 467	0. 540	0. 695	0. 734	0. 920	0. 809	0. 705	-	-
Contrasts (p-values)
LI vs HI	0. 761	0. 730	0. 477	0. 566	0. 658	0. 630	0. 960	0. 076	0. 009	0. 282	0. 147
LI vs BC	0. 137	0. 457	0. 752	0. 840	0. 256	0. 061	0. 201	0. 000	0. 240	0. 502	0. 000
HI vs BC	0. 075	0. 689	0. 308	0. 440	0. 115	0. 021	0. 219	0. 073	0. 000	0. 687	0. 001
Cycl_UK vs Cyc_lDE	0. 070	0. 104	0. 472	0. 620	0. 118	0. 469	0. 867	0. 016	0. 021	0. 014	0. 455
Cycl_UK vs Cycl_HU	0. 052	0. 000	0. 001	0. 294	0. 881	0. 045	0. 496	0. 121	0. 001	0. 023	0. 709
Cycl_DE vs Cycl_HU	0. 896	0. 028	0. 007	0. 124	0. 158	0. 190	0. 607	0. 387	0. 226	0. 849	0. 263
Cycl_DE vs CC	0. 851	0. 461	0. 664	0. 653	0. 848	0. 773	0. 807	0. 001	0. 792	0. 569	0. 000

Table 4: Leaf diseases of populations and isolated lines: Major diseases in the respective trial and infected leaf area in percent. P-values refer to the difference between groups. Check table S1 for details on the locations.

Year	Location		Management	Major diseases	Lines	Populations	Ratio (%)	p
2016	Neu-Eichenberg	(NE)	Organic	Yellow Rust. DTR	31. 7	16. 7	190	0. 00
	Viehhausen	(VH)	Organic	Yellow Rust	13. 9	8. 1	172	0. 06
	Quedlinburg	(QB)	Conventional	DTR. Septoria	10. 9	15. 4	71	0. 00
	Dürnast	(DN)	Conventional	DTR. Septoria	3. 9	4. 5	87	0. 14
2017	Neu-Eichenberg	(NE)	Organic	Yellow and Brown Rust	14. 3	11. 3	127	0. 02
	Viehhausen	(VH)	Organic	Yellow and Brown Rust	12. 8	7. 3	175	0. 00
	Quedlinburg	(QB)	Conventional	DTR. Septoria	7. 4	9. 7	76	0. 00
	Dürnast	(DN)	Conventional	DTR. Septoria	5. 8	5. 7	102	0. 68

Table 5 Stability parameters for grain yield of commercial varieties. populations and inbred lines with 95% confidence intervals

Groups

Geno-type

Variance, Absolute Yield

Variance, Relative Yield

B (Finlay & Wilkinson)

MSE (Eberhard & Russel

Lower Limit

Esti-mate

Upper Limit

Lower Limit

Esti-mate

Upper Limit

Lower Limit

Esti-mate

Upper Limit

Lower Limit

Esti-mate

Upper Limit

High-yielding (class C)

Elixer

203

385

1000

0, 53

1, 00

2, 60

0, 99

1, 16

1, 34

11, 1

21, 2

54, 9

Hybery

179

340

882

0, 92

1, 75

4, 55

0, 84

1, 07

1, 29

17, 6

33, 5

86, 9

High Quality (Class E)

Achat

126

244

666

0, 31

0, 59

1, 62

0, 77

0, 90

1, 04

6, 4

12, 4

33, 8

Capo

164

318

867

0, 24

0, 47

1, 29

0, 91

1, 04

1, 17

5, 7

11, 1

30, 2

Genius

141

268

697

0, 45

0, 86

2, 23

0, 76

0, 95

1, 14

13, 1

24, 9

64, 7

Kerubino

209

416

1199

0, 17

0, 34

0, 98

1, 04

1, 16

1, 28

4, 4

8, 8

25, 2

„Organic“ Varieties

Butaro

108

206

536

0, 15

0, 28

0, 74

0, 71

0, 85

0, 98

6, 8

13, 0

33, 8

Poesie

122

232

602

0, 26

0, 50

1, 31

0, 75

0, 90

1, 04

7, 6

14, 4

37, 5

Tobias

126

245

667

0, 16

0, 31

0, 83

0, 81

0, 92

1, 02

3, 7

7, 1

19, 4

Wiwa

105

199

516

0, 46

0, 88

2, 29

0, 53

0, 77

1, 01

20, 1

38, 3

99, 3

Inbred Lines

180

342

888

0, 74

1, 40

3, 64

0, 90

1, 09

1, 27

12, 4

23, 6

61, 4

151

287

744

0, 19

0, 37

0, 95

0, 90

1, 02

1, 13

4, 4

8, 4

21, 9

184

358

975

0, 18

0, 34

0, 93

1, 00

1, 11

1, 22

4, 1

7, 9

21, 6

141

268

696

0, 41

0, 77

2, 01

0, 79

0, 96

1, 13

10, 4

19, 8

51, 4

161

313

854

0, 39

0, 76

2, 06

0, 85

1, 02

1, 19

9, 3

18, 1

49, 2

154

307

885

0, 52

1, 04

3, 01

0, 77

0, 98

1, 19

13, 2

26, 2

75, 6

216

411

1066

0, 42

0, 80

2, 07

1, 05

1, 21

1, 37

9, 1

17, 3

44, 8

131

248

644

0, 43

0, 82

2, 12

0, 72

0, 91

1, 10

12, 9

24, 5

63, 7

199

378

980

0, 24

0, 46

1, 20

1, 06

1, 17

1, 28

4, 1

7, 8

20, 1

L10

160

311

847

0, 46

0, 89

2, 43

0, 79

1, 00

1, 21

14, 0

27, 2

74, 1

Popu-lations

CCYQ-Cycl. Control

136

258

671

0, 07

0, 14

0, 35

0, 89

0, 97

1, 05

2, 3

4, 4

11, 3

CyclUK

146

277

719

0, 16

0, 30

0, 78

0, 89

1, 00

1, 11

4, 4

8, 3

21, 6

CyclDE

147

286

778

0, 09

0, 17

0, 45

0, 89

0, 99

1, 08

3, 0

5, 9

16, 1

CyclHU

159

303

787

0, 18

0, 34

0, 89

0, 90

1, 04

1, 17

6, 5

12, 4

32, 3

CCYQ. LI. brdc. 1,

135

263

717

0, 12

0, 24

0, 65

0, 86

0, 95

1, 05

2, 8

5, 4

14, 7

CCYQ LI. brdc. 2

118

224

583

0, 13

0, 24

0, 62

0, 80

0, 90

1, 00

3, 6

6, 9

17, 8

CCYQ HI. 1,

163

310

803

0, 11

0, 21

0, 55

0, 95

1, 06

1, 16

3, 9

7, 4

19, 3

CCYQ HI. 2

128

244

633

0, 17

0, 32

0, 84

0, 81

0, 93

1, 05

5, 0

9, 4

24, 5

CCYQ LI. 1,

155

294

763

0, 10

0, 19

0, 49

0, 92

1, 03

1, 14

4, 2

8, 0

20, 8

CCYQ LI. 2

146

278

721

0, 17

0, 33

0, 85

0, 89

1, 00

1, 11

4, 3

8, 2

21, 2

Table 6 Stability parameters for grain protein content of commercial varieties. populations and inbred lines with 95% confidence intervals

Groups	Genotype	Variance, Protein content			V. , relative protein content
		Lower Limit	Estimate	Upper Limit	Lower Limit	Estimate	Upper Limit
High-yielding (class C)	Elixer	2, 41	4, 80	13, 84	0, 05	0, 11	0, 30
High-yielding (class C)	Hybery	2, 96	5, 91	17, 03	0, 08	0, 15	0, 44
High Quality (Class E)	Achat	3, 75	7, 47	21, 53	0, 13	0, 26	0, 75
	Capo	2, 70	5, 39	15, 53	0, 07	0, 14	0, 41
	Genius	1, 65	3, 28	9, 46	0, 20	0, 40	1, 15
	Kerubino	2, 67	5, 33	15, 35	0, 08	0, 17	0, 48
„Organic“ Varieties	Butaro	3, 48	6, 93	19, 97	0, 09	0, 17	0, 49
	Poesie	2, 46	5, 21	17, 36	0, 07	0, 16	0, 53
	Tobias	2, 72	5, 42	15, 61	0, 13	0, 26	0, 74
	Wiwa	2, 07	4, 13	11, 91	0, 18	0, 36	1, 03
Inbred Lines	L1	2, 84	5, 65	16, 30	0, 02	0, 05	0, 14
	L2	2, 87	5, 73	16, 51	0, 05	0, 10	0, 28
	L3	4, 20	8, 36	24, 11	0, 19	0, 38	1, 10
	L4	2, 94	5, 86	16, 89	0, 16	0, 32	0, 91
	L5	3, 18	6, 34	18, 27	0, 04	0, 08	0, 23
	L6	3, 76	7, 49	21, 59	0, 09	0, 17	0, 50
	L7	3, 20	6, 38	18, 38	0, 08	0, 15	0, 44
	L8	2, 60	5, 17	14, 91	0, 04	0, 07	0, 21
	L9	2, 99	5, 95	17, 17	0, 05	0, 09	0, 27
	L10	2, 76	5, 49	15, 84	0, 17	0, 33	0, 96
Populations	CCYQ-Cycl. Control	2, 60	5, 18	14, 95	0, 03	0, 05	0, 15
	CyclUK	2, 54	5, 07	14, 61	0, 05	0, 10	0, 28
	CyclDE	2, 53	5, 04	14, 53	0, 02	0, 05	0, 14
	CyclHU	2, 35	4, 69	13, 51	0, 02	0, 04	0, 12
	CCYQ. LI. brdc. 1,	2, 56	5, 10	14, 71	0, 01	0, 03	0, 07
	CCYQ LI. brdc. 2	2, 87	5, 72	16, 48	0, 05	0, 11	0, 31
	CCYQ HI. 1,	2, 26	4, 49	12, 96	0, 02	0, 04	0, 12
	CCYQ HI. 2	2, 62	5, 23	15, 07	0, 02	0, 05	0, 14
	CCYQ LI. 1,	2, 21	4, 41	12, 70	0, 03	0, 05	0, 16

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First submitted to journal
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Performance and Evolutionary Adaptation of Bulk Populations of Wheat

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Version 1

Abstract

Figures

Introduction

Materials And Methods

Plant Material

Field trial locations, management and experimental design

Assessments

Data analysis

Results

General observations

Comparison of populations with commercial cultivars

Comparison of populations with randomly extracted inbred lines

Effects of evolutionary history on the performance of the populations

Stability

Discussion

Performance and Potential of Populations

Role of diversity for performance

Role of Diversity for Stability and Adaptation

Evidence for evolution and adaptation to speciﬁc environments

Conclusions

Declarations

Acknowledgements

Disclosure of conﬂicts of interest

References

Tables

Supplementary Files

Status:

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