Comparative analysis of performance and stability among composite cross populations, variety mixtures and pure lines of winter wheat in organic and conventional cropping systems

Field Crops Research 183 (2015) 235–245

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Comparative analysis of performance and stability among composite cross populations, variety mixtures and pure lines of winter wheat in organic and conventional cropping systems Thomas F. Döring a,∗,1 , Paolo Annicchiarico b , Sarah Clarke c , Zoë Haigh a,d , Hannah E. Jones e , Helen Pearce a,d , John Snape f , Jiasui Zhan g,2 , Martin S. Wolfe a,d a

The Organic Research Centre – Elm Farm, Hamstead Marshall, Newbury, RG20 0HR Berkshire, UK Consiglio per la Ricerca e la sperimentazione in Agricoltura (CRA), Centro di Ricerca per le Produzioni Foraggere e Lattiero-Casearie, viale Piacenza, 29, 26900 Lodi, Italy c ADAS UK Ltd., Gleadthorpe, Meden Vale, Mansfield, Nottinghamshire NG20 9PF, UK d The Organic Research Centre, Wakelyns Agroforestry, Fressingfield, Eye, IP21 5SD Suffolk, UK e School of Agriculture, Policy and Development, University of Reading, Earley Gate, PO Box 237, Reading RG6 6AR, UK f John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK g Institute of Integrative Biology, ETH Zurich, Zurich CH-8092, Switzerland b

a r t i c l e

i n f o

Article history: Received 21 October 2014 Received in revised form 6 August 2015 Accepted 7 August 2015 Available online 27 August 2015 Keywords: Triticum aestivum Stability Composite cross population Evolutionary breeding

a b s t r a c t This study investigated the effects of increased genetic diversity in winter wheat (Triticum aestivum L.), either from hybridization across genotypes or from physical mixing of lines, on grain yield, grain quality, and yield stability in different cropping environments. Sets of pure lines (no diversity), chosen for high yielding ability or high quality, were compared with line mixtures (intermediate level of diversity), and lines crossed with each other in composite cross populations (CCPn , high diversity). Additional populations containing male sterility genes (CCPms ) to increase outcrossing rates were also tested. Grain yield, grain protein content, and protein yield were measured at four sites (two organically-managed and two conventionally-managed) over three years, using seed harvested locally in each preceding year. CCPn and mixtures out-yielded the mean of the parents by 2.4% and 3.6%, respectively. These yield differences were consistent across genetic backgrounds but partly inconsistent across cropping environments and years. Yield stability measured by environmental variance was higher in CCPn and CCPms than the mean of the parents. An index of yield reliability tended to be higher in CCPn , CCPms and mixtures than the mean of the parents. Lin and Binns’ superiority values of yield and protein yield were consistently and significantly lower (i.e. better) in the CCPs than in the mean of the parents, but not different between CCPs and mixtures. However, CCPs showed greater early ground cover and plant height than mixtures. When compared with the (locally non-predictable) best-yielding pure line, CCPs and mixtures exhibited lower mean yield and somewhat lower yield reliability but comparable superiority values. Thus, establishing CCPs from smaller sets of high-performing parent lines might optimize their yielding ability. On the whole, the results demonstrate that using increased within-crop genetic diversity can produce wheat crops with improved yield stability and good yield reliability across variable and unpredictable cropping environments. © 2015 Elsevier B.V. All rights reserved.

1. Introduction

∗ Corresponding author. E-mail address: [email protected] (T.F. Döring). 1 Present address: Humboldt University Berlin, Faculty of Life Sciences, Department of Agronomy and Crop Science, Albrecht-Thaer-Weg 5, 14195 Berlin, Germany. 2 Present address: Institute of Plant Virology, Fujian Agricultural and Forestry University, Jinshan, Fuzhou, Fujian 350002, PR China. http://dx.doi.org/10.1016/j.fcr.2015.08.009 0378-4290/© 2015 Elsevier B.V. All rights reserved.

Future cropping systems are expected to be strongly affected by climate change (Pretty et al., 2010; Chakraborty and Newton, 2011; Pautasso et al., 2012; Murphy et al., 2013). In particular, as weather variability is predicted to increase (Arnell, 2003; Schär et al., 2004; Coumou et al., 2013), approaches that can stabilize agronomic performance across variable and fluctuating environments are urgently needed. Further reasons why systems with

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more stable yields need to be developed originate from the necessity to reduce inputs into cropping systems. Traditional plant breeding programmes rely on selecting genotypes in tightly controlled growing conditions, i.e., in the (near) absence of weeds, with comparatively good nutrient availability, and often under low pest and disease pressure (Newton et al., 2010; Lammerts van Bueren and Myers, 2012). This breeding approach has produced many successful pedigree line varieties for high-input production systems. However, low input and organic agriculture has suffered from a lack of varieties adapted to the environmental variability on farms (Wolfe et al., 2008; Lammerts van Bueren et al., 2010; Chable et al., 2014). In organic or low input conditions, the variability of the environment often has a more pronounced influence on crop yield than in conventional conditions (Wolfe et al., 2008; Newton et al., 2010), leading to a lack of stability in crop performance (Soliman and Allard, 1991). Consequently, approaches need to be developed for organic or low input cropping systems that can stabilize the crop performance across, and buffer against, environmental fluctuations. Biological diversity has long been recognized as a possibility to increase stability of plant productivity (Allard, 1961; Tracy and Sanderson, 2004; Tilman et al., 2006; Ives and Carpenter, 2007). Because of compensation, complementation and facilitation effects in diverse plant material (Creissen et al., 2013), genetically heterogeneous crops are expected to be more stable-yielding over time and across different locations than genetically homogeneous pedigree lines, i.e. than most commercially traded varieties (Soliman and Allard, 1991; Wolfe, 2001; Döring et al., 2011; Murphy et al., 2013). For cereals as for many other crops, there are various methods to increase genetic diversity. One is to create physical mixtures of genetically uniform cultivars (Finckh and Mundt, 1992; Walsh and Noonan, 1998). Such cultivar mixtures have been shown to provide an improved ability of a crop to buffer variation in soil, climate and weed pressures (Wolfe, 2001; Didon and Rodriguez, 2006). The advantage of diversity in cereal variety mixtures has been demonstrated especially in terms of containment of fungal diseases (McDonald et al., 1988; Finckh et al., 2000; Zhu et al., 2000; Finckh and Wolfe, 2006) and plant virus diseases (Power, 1991). A second way to increase genetic diversity is to create segregating composite cross populations (CCPs), in which varieties are crossed with each other (Suneson, 1956) rather than being physically mixed. After crossing the parent plants and a cycle of seed multiplication from each cross, the seeds are mixed to produce the first CCP generation. A proportion of the harvested seed is saved for sowing without active selection of individual genotypes. The grain of the genetically highly diverse CCPs can then be used as food or feed as any pure line variety, or it can provide input into plant breeding programmes (Döring et al., 2011). CCPs follow an evolutionary plant breeding approach which uses natural selection acting on genetically diverse crop populations with the aim to improve crop performance over time (Allard and Jain, 1962; Allard, 1988). Over the last decade there has been renewed interest in the application of this approach to cereals such as wheat and barley, in particular for organic and low input farming systems (Goldringer et al., 2001; Phillips and Wolfe, 2005; Döring et al., 2011; Dawson and Goldringer, 2012; Thomas et al., 2012). Mixtures and CCPs differ in a number of respects. One of them is legality in that mixtures can be traded freely in Europe whereas trading genetically diverse crop populations is illegal (Wolfe et al., 2013). From 2014, however, ‘experimental’ (i.e. preliminary) European legislation permits CCPs to be traded under restricted conditions for a limited test period. For physical mixtures, there are practical limits to the number of genotypes that can be used, usually no more than three or four, which could limit the potential for buffering against

environmental variability. As CCPs exhibit higher genetic diversity than corresponding mixtures, they can be hypothesized to confer greater stability to crop performance. On the other hand, CCPs are likely to contain a larger proportion of genotypes with relatively low performance. Therefore, the potentially higher stability of CCPs may come at the cost of reduced average yield. At present, however, these hypotheses on the relative agronomic merits of CCPs vs mixtures have not been tested. In particular, the yield performance and yield stability of evolving wheat CCPs, in comparison to mixtures and to pure lines, is currently unclear, as no direct measurements of yield or yield stability have been conducted involving wheat CCPs, their corresponding mixtures and their parent lines. Further uncertainty about the relative merits of CCPs and mixtures arises from the variable and unknown rates of outcrossing in different kinds of wheat stand. Previous estimates indicate that out-crossing rates in wheat range from 6% (Hucl, 1996) to 32% in stress conditions (Demotes-Mainard et al., 1996). The out-crossing rate may also be a factor affecting the ability of wheat CCPs to evolve. This study therefore included a subset of CCPs with male sterility, denoted here as CCPms , particularly since these were predicted to have a greater capacity to segregate over the duration of the trial. These CCPms are genetically different from the nonmale sterile CCPs (denoted as CCPn ) and from the mixes because of their different parentage; however, the inclusion of the CCPms in the experiments allowed us to increase the genetic diversity above that of the CCPn . The main objectives of this study were therefore (1) to test various CCPs against their corresponding mixtures and component parents in terms of agronomic performance and stability across contrasting environments; and (2) to compare CCPs with and without added male sterility in terms of agronomic performance and stability. 2. Material and methods 2.1. Selection of parents Six composite cross populations of winter wheat were created in 2002 by John Innes Centre (JIC, Norwich, UK). The selection of the parental cultivars was based on data from both published and unpublished studies. Key criteria for selection included a diverse genetic base and the potential for robust performance under low input agronomic conditions in Europe. Target production traits included high grain yield, and good bread making quality as indicated by high grain protein content and, to a lesser extent, high Hagberg Falling Number. The parent cultivars were selected in summer 2002 in two categories, namely: high yielding (Y) varieties (Bezostaya, Buchan, Claire, Deben, High Tillering Line, Norman, Option, Tanker, Wembley), and high bread making quality (Q) varieties (Bezostaya, Cadenza, Hereward, Maris Widgeon, Mercia, Monopol, Pastiche, Renan, Renesansa, Soissons, Spark, Thatcher). Bezostaya was included in both categories, as it was known as both high yielding and high quality in Russia, where it was grown successfully over many years. The time of release for the selected varieties ranges from 1934 to 2000. The performance of individual parents was comprehensively analysed in a previous study (Jones et al., 2010). 2.2. Creation of CCPs All 20 parents were crossed together in a complete half-diallel to produce all 190 F1 cross combinations. The F1 seeds were harvested, germinated and grown to maturity in a glasshouse. All ears were bagged to ensure self-pollination, and the F2 seeds from each of the individual F1 plants were harvested and bulked for each cross.

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Three separate ‘foundation’ composite cross populations (CCPs) were started by bulking F2 seeds from the individual crosses. The first CCP (YCCPn ) was synthesized from the 36 crosses for varieties identified as having high yield potential (Y); the second (QCCPn ), from the 66 crosses among varieties with good milling potential (Q); and the third (YQCCPn ), from the all 190 crosses among Y and Q parents (YQ). These (non-male sterile) CCPs were collectively denoted as CCPn . In addition, male sterile CCP populations (CCPms ) were generated by artificially hybridizing all the above parents to characterized genetic males sterile lines (as females) obtained from two sources, RAGT Seeds (Shango derivatives: JB Plant 1, JB Plant 2, F2/F3 Sterile Bulk Population 2/77, JB NWH 65) and CIMMYT (F1TOPDMSO102 7 TURACO DMS, F1TOPDMSO102 10 GALVEZ S 87 DMS, F1TOPDMSO102 12 CUMPAS T88 DMS and F1TOPDMSO102 14 NING8201 DMS). The F2 s of these crosses were bulked as above to create YCCPms , QCCPms and YQCCPms populations. Hence, three CCPn and three CCPms were available as starting material for field evaluation. 2.3. Creation of mixtures In order to compare the performance of mixtures of homozygous lines with that of CCPs, parental seed of equal proportions was also mixed in the same categories as those used to create the CCPs. Thus, physical mixtures of seed resulted in three mixtures, namely a Yield Mixture, a Quality Mixture and a Yield-Quality Mixture, hereafter termed as YMix, QMix and YQMix. The number of component lines was the same as for the creation of the CCPs, i.e. 9 for the Y set, 12 for the Q set and 20 for the YQ set. 2.4. Seed bulking The six CCPs, three mixtures, and all parental varieties were hand broadcast in single replicate plots of varying size depending on seed availability (CCPs: mean 19 m2 , range 7.8–32.5 m2 ; all other entries: mean 3.8 m2 , range 1.3–4.0 m2 ) at four locations in October 2003 (see description of sites below). There was enough seed available in autumn 2004 to begin standard replicated field trials. Field trials were also performed in the following three years, using in each year CCP and mixture seed that had been harvested in the preceding cropping year, thereby allowing for potential evolutionary adaptation of the genetically heterogeneous materials to site-specific cropping conditions. 2.5. Diversity levels In this study, wheat diversity in the tested material was treated as an ordinal variable with three levels (low in parents, medium in mixtures and high in the CCPs). In principle this diversity could be measured by the number of genotypes within each plot. For the monocultures, this value is equal to 1 for all sets. For the physical mixtures, the number of genotypes per plot is equal to the number of parents used to create the mixture (9 for the YMix, 12 for the QMix and 20 for the YQMix). In the CCPs, the number of genotypes per plot cannot be quantified with certainty because of the unknown degree of outcrossing vs selfing. However, it is clear that the number of genotypes, at least in the early generations, was close to the number of plants sown per plot (i.e. several hundreds). 2.6. Field trial site description and experimental design Field trials were carried out in two conventionally managed sites and two organically managed sites located in the South and East of England. The conventional sites were located at Metfield Hall

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Farm (MET) (52◦ 41 N, 1◦ 29 E), and Morley Farm (MOR), an experimental farm in Norfolk managed by The Arable Group (52◦ 56 N, 1◦ 10 E). The two organic sites were Wakelyns Agroforestry (WAF) in Suffolk (52◦ 39 N, 1◦ 17 E), directly adjacent to MET in Suffolk, and Sheepdrove Organic Farm (SOF) in Berkshire (51◦ 41 N, 1◦ 52 W). All sites were characterized by a maritime climate, with relatively even distribution of precipitation across months, as well as mild winters and moderate summer temperatures. Mean January and July temperatures over the three study years were 5.1 ◦ C and 17.2 ◦ C, respectively. Detailed monthly precipitation and air temperature for the test years are reported elsewhere (Jones et al., 2010). Experiments were established in autumn 2004, 2005 and 2006 at each site in different fields, for a total of 12 test environments (location–year combinations). Soil texture, pH and soil organic matter (SOM) content were different among the four study sites (WAF: clay content 23–26% by volume, pH 7.4–8.0, SOM 23–28 g kg−1 ; SOF: c. 40% clay, pH 7.8, SOM 35–49 g kg−1 ; MET: c. 16% clay, pH 7.6–7.9, SOM c. 20 g kg−1 ; MOR: c. 21% clay, pH 7.5–7.7, SOM 17–20 g kg−1 ). The preceding crop was grass-clover or grass-vetchclover ley at WAF; grass-clover at SOF; winter wheat at MET; and winter oil seed rape at MOR. At both conventional sites, growth regulators were used; mineral nitrogen fertilizer was applied at an average rate of 197 kg N ha−1 year−1 (range 161–236); insecticides, fungicides and herbicides were applied dependent on pest, disease and weed conditions according to local commercial practice. Further details on soil texture, and nitrogen applications at the conventional sites are described in a previous study (Jones et al., 2010). Plots were drilled in October in all experiments, with the exception of the experiment at SOF in 2004 which was drilled in early November. The experimental design of each trial was a randomized complete block design with three replications. Plot size was 20 m × 1.45 m at SOF, and 20 m × 1.2 m at the other three sites. Seed rate was 200 kg ha−1 for the two organic sites and 170 kg ha−1 for the non-organic sites. Seed rate calculations were based on an average target plant density of 425 plants m−2 (see Jones et al. (2010) for further details on seed rates). 2.7. Recorded data Assessments were carried out on a plot basis on each trial entry (CCPs, mixtures and pure lines). Growth stages (GS) were determined across the growth cycle on 10 randomly chosen plants using the Zadoks scale (Zadoks et al., 1974). In addition to grain yield (t ha−1 at 15% moisture content), the following yield components were measured: crop emergence (number of seedlings m−2 , GS10-12 in autumn), establishment (number of seedlings m−2 , GS25 in spring), tillering (number of stems per 0.5 m along a single coulter, GS80-90), head density (number of heads m−2 , GS90), and thousand grain weight (g at 15% moisture). Further assessments included leaf area index (LAI, measured using the Sunscan Canopy Analysis system, GS31, 39 and 55), crop and weed cover (% cover assessed from above, GS31, 55), and straw height (from 10 randomly chosen plants per plot, cm, GS60). Foliar infections with fungal pathogens were assessed as percentage of infected leaf area on flag leaves for Septoria blotch (Septoria tritici and Septoria nodorum), powdery mildew (Blumeria graminis f. sp. tritici), yellow rust (Puccinia striiformis f. sp. tritici) and brown rust (Puccinia recondita f. sp. tritici), at GSs 37–93 (May–August). Lodging of the plants was assessed at GS90, by estimating the proportion of plot area for which the plot was upright (0◦ ), partially lodged (1–30◦ ), lodged (31–60◦ ) or fallen (90◦ ). A lodging index was calculated for each plot as the average of the degrees deviating from upright weighted by the % area. The harvest index (HI) was determined as the ratio of grain mass to total

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above-ground biomass, based on the measurement of straw mass and grain mass on 50 cm length of two adjacent rows per plot, excluding outer rows. Grain protein content (%) was measured in the ground grains, using an Inframatic Flour Analyzer from Perten Instruments (Hägersten, Sweden). Protein yield was calculated as the product of grain yield and protein content. 2.8. Statistical analysis Each trait underwent an analysis of variance (ANOVA, here denoted as A1 ) including the following fixed factors: (i) diversity level, with four levels represented by mean value of the parent lines, physical mixtures of the parent lines, CCPs from ordinary lines and CCPs from male-sterile lines; (ii) parent set, with three levels represented by Y, Q, and YQ sets; (iii) cropping year (2004–05, 2005–06, and 2006–07); (iv) site (WAF, SOF, MET, and MOR). Year was considered as a fixed factor because of the evolutionary nature of the trial (i.e., since seed was used locally at each trial site from the previous harvest). This ANOVA A1 included randomized block within site and year as an additional, random factor (which acted as the error term for year, site, and their interaction). Thus, values of parent lines belonging to each parent set were previously averaged within each block of each environment for ANOVA A1 , which included 432 observations as the product of 4 diversity levels × 3 parent sets × 3 years × 4 sites × 3 blocks. The pooled experimental error for ANOVA A1 was provided by a second ANOVA, denoted as A2 , which was performed on plot values of all material, i.e., 20 parents plus 3 mixtures and 6 CCPs. This latter ANOVA A2 , which included 1044 observations as the product of 29 entries × 3 years × 4 sites × 3 blocks (featuring 672 degrees of freedom for the pooled experimental error), was performed to avoid the possible underestimation of the pooled experimental error in the former ANOVA A1 derived by averaging plot values of relevant parents within each block. The ANOVA variation for diversity level × year interaction and diversity level × site interaction was partitioned into sets of non-orthogonal contrasts aimed to test specific hypotheses, e.g., to verify whether diversity is more beneficial for a specific cropping system (organic vs non-organic), or whether it increases its advantage from the first to the third cropping year as a result of site-specific evolutionary adaptation. Mean values of diversity levels or parent sets across environments or in specific sets of environments were compared using Duncan’s test. These statistical analyses were performed using SAS (Statistical Analysis System) software. In many studies on yield stability the main aim is comparing stability of genotypes across environments. Here, because CCPs and mixtures consist of several genotypes, we do not use the term genotype but denote the individual CCPs, mixtures and parent lines as ‘entries’. Stability of grain yield, grain protein content and protein yield was estimated for individual parent lines, mixtures and CCPs in terms of environmental variance (EV) of their grain yield values across the 12 test environments (see below). Indicating by Rij the yield response of the entry i in the environment j, and by mi the entry mean yield, the EV of the entry across e environments is equal to: EVi =

 (Rij − mi )2 e−1

.

A stable entry has low EV values and tends to maintain a constant yield across environments, according to a static (or homeostatic) concept of stability which offers the following advantages over dynamic stability measures: (i) somewhat higher repeatability, (ii) estimation independent from the set of tested entries (which allows for a broader generalization), (iii) more straightforward agronomic interpretation, and (iv) greater relevance for agricultural income

(Annicchiarico, 2002). The number of environments required for a reliable assessment of yield stability measures is considered to be eight or more (Kang, 1998). Since any subsets of environments would all have numbered fewer than eight, stability could not be assessed for such subsets. Paired comparisons between entries were based on Ekbohm’s (1981) test as described in Annicchiarico (2002). High stability may be associated with low mean yield (or vice versa). A yield reliability measure proposed by Kataoka (1963) allows for combining mean yield and EV-based yield stability into an index of performance which estimates the lowest entry yield that is expected for a probability P which is fixed according to the level of farmers’ risk aversion (Annicchiarico, 2002). Here, P = 0.75 was adopted, i.e., the lowest yield expected in 75% cases, which is reasonable for modern agriculture in climatically-favourable regions (Eskridge, 1990). With reference to previous notations, the index for the entry i is: Ii = mi − Z(P)



EVi

where Z(P) (percentile from the standard normal distribution for which the cumulative distribution function reaches the value P) equals 0.675. Finally, CCPs, mixtures and parents were compared (for various attributes such as yield and quality) by the cultivar-superiority measure Si proposed by Lin and Binns (1988), using the ‘GEstability’ package of the software GENSTAT version 10, 2007 (VSN International Ltd., Hemel Hempstead, UK). For each entry, this is the sum of the squares of the differences between its yield response Rij in each environment j and the yield response Mj of the best entry in that environment, divided by twice the number of environments: Si =

 (Rij − Mj )2 2e

.

Entries with smaller Si values are ‘superior’, e.g. have greater yield superiority according to the index. Thus, a ‘superior’ entry in this sense is one with a performance nearest to the maximum performance in multiple test environments. 3. Results 3.1. Means of yield, protein content and protein yield The three sets of parents which were selected for high grain yield (Y), high protein content (Q) or both (YQ) characteristics differed for mean values of these traits across environments in a manner consistent with their selection purpose (Y top-ranking for grain yield; Q top-ranking for protein content; YQ ranking intermediate for both traits) (Tables 1 and 2). However, their differences for grain yield were affected by the test site (parent set × site interaction significant at P < 0.01: Table 1), mainly because these differences tended to be lower in organic than in conventional management. For example, the set Y out-yielded Q by 11.5% across conventionallymanaged sites and by 5.7% across organically-managed locations. Parent sets did not differ for mean protein yield across locations, since high yield is commonly found to dilute total protein, and vice versa (Tables 1 and 2). On average, grain yield differed among cropping years (with lowest yields occurring in the third year: Table 3) and among locations, where organically-managed sites produced 41–44% less than conventionally-managed sites (Table 4). On average, organicallymanaged sites also showed lower grain protein content than conventionally-managed sites (data not shown). However, differences for grain yield, protein content and protein yield were detected also between sites within cropping system (Table 1).

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Table 1 Analysis of variance F test results for grain yield at 15% grain moisture, protein content and dry protein yield for four diversity levels [mean of parent lines; physical mixture of the parent lines; composite cross population (CCPn ) from ordinary lines; CCPms from male-sterile lines] across three years at four sites (two sites within conventional cropping system and two within organic system), for three parent sets used for the creating CCPs and mixtures a Source of variation

Degrees of freedom

Yieldb

Protein contentb

Protein yieldb

Diversity level Parent set Year Site – System – Within system (organic vs conv.) Diversity level × parent set Diversity level × year – (CCPn vs parent) × yearc – (CCPms vs parent) × yearc – (Mixture vs Parent) × yearc – (CCPn vs mixture) × yearc – (CCPms vs mixture) × yearc – (CCPn vs CCPms ) × yearc Diversity level × site – (CCPn vs Parent) × system – (CCPms vs Parent) × system – (Mixture vs parent) × system – (CCPn vs mixture) × system – (CCPms vs mixture) × system – (CCPn vs CCPms ) × system Parent set × year Parent set × site Year × site Diversity level × parent set × year Diversity level × parent set × site Diversity level × year × site Parent set × year × site Diversity level × parent set × year × site

3 2 2 3 1 1 6 6 1 1 1 1 1 1 9 1 1 1 1 1 1 4 6 6 12 18 18 12 36

** ** ** ** ** ** NS + NS * NS NS ** NS ** NS + NS NS * NS NS ** ** NS NS NS NS NS

NS ** ** ** ** ** NS NS NS NS + NS NS NS NS NS NS + NS NS NS ** NS ** * NS NS * NS

* NS ** ** ** ** NS + NS NS NS NS + NS NS NS NS NS NS NS NS NS + ** NS NS NS NS NS

a Sets of parent germplasm selected for high grain yield (9 parent lines), high protein content (12 parents), and both traits (20 parents); CCP and mixture material resown in each site from seed harvested in the previous cropping year, starting with F4 material for CCP and equal parent seed amount for mixture in the first year. b +, P < 0.10; *, P < 0.05; **, P < 0.01; NS = not significant. c Interaction of diversity level with the linear component of the time factor.

Table 2 Mean grain yield at 15% grain moisture, protein content and dry protein yield of material for three sets of parent germplasm.a Parent set

Yield (t ha−1 )b

Protein content (%)b

Protein yield (t ha−1 )b

High yield (Y) High protein content (Q) High yield and protein content (YQ) Standard errorc

7.854 a 7.172 c 7.505 b 0.0467

11.83 c 12.46 a 12.16 b 0.047

0.790 a 0.760 a 0.775 a 0.0060

a Averaged across three years, four sites, and values for composite cross populations from ordinary (CCPn ) and male-sterile lines (CCPms ), physical mixture and mean of their parent germplasm. b Column means with same letter do not differ at P < 0.05 according to Duncan’s test. c Degrees of freedom = 672.

Levels of genetic diversity showed differences with regard to mean yield but not for mean protein content across environments, so that differences for mean protein yield (i.e. the product of the two variables) arose largely from those for grain yield (Tables 1 and 3). In

particular, both the CCPs and the mixtures significantly out-yielded the mean of their respective parents across years, and also in year two specifically (Table 3). In relative terms, the yield advantage over the monocultures across years averaged 2.4% and 3.6% for the

Table 3 Grain yield at 15% seed moisture in each year and across years, and protein content and dry protein yield across years, for composite cross population (CCP) from ordinary and male-sterile lines (CCPn and CCPms , respectively), physical mixture and mean of their parent germplasm.a Material

Mean of parents Mixture CCPn Male-sterile CCPms Standard errorc Meand

Yield (t ha−1 )

Protein content (%)

Protein yield (t ha−1 )

Year 1b

Year 2b

Year 3b

Meanb

Meanb

Meanb

7.674 a 7.783 a 7.876 a 7.927 a 0.0979 7.815 B

7.862 c 8.325 a 8.105 ab 8.048 bc 0.0804 8.085 A

6.586 ab 6.814 a 6.662 ab 6.463 b 0.0938 6.631 C

7.374 c 7.641 a 7.548 ab 7.480 bc 0.0526

12.18 a 12.09 a 12.16 a 12.17 a 0.055

0.763 b 0.784 a 0.779 ab 0.773 ab 0.0067

a Averaged across four sites and three sets of parent germplasm selected for high grain yield (9 parent lines), high protein content (12 parents) and both traits (20 parents), respectively; CCP and mixture material resown in each site from seed harvested in the previous year, starting with F4 material for CCP and equal parent seed amount for mixture in the first year. b Column means with same small letter do not differ at P < 0.05 according to Duncan’s test. c Degrees of freedom are 224 for yearly values and 672 for mean values. d Year means with same capital letter do not differ at P < 0.05 according to Duncan’s test.

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Table 4 Grain yield at 15% seed moisture in two conventionally-managed and two organically-managed sites, for composite cross population (CCP) from ordinary and male-sterile lines (CCPn and CCPms , respectively), physical mixture and mean of their parent germplasm.a Material

Mean of parents Mixture CCPn Male-sterile CCPms Standard errorc Meand

Conventionally-managed sites

Organically-managed sites

Metfieldb

Morleyb

Mean

Sheepdroveb

Wakelynsb

Mean

9.869 b 10.125 a 10.052 ab 9.983 ab 0.0759 10.007 A

9.013 bc 9.490 a 9.169 b 8.856 c 0.1058 9.132 B

9.441 bc 9.807 a 9.610 b 9.420 c 0.0654

5.376 a 5.208 a 5.483 a 5.538 a 0.1112 5.401 C

5.238 b 5.739 a 5.487 ab 5.539 ab 0.1224 5.501 C

5.307 a 5.474 a 5.485 a 5.538 a 0.0829

a Averaged across three years and three sets of parent germplasm selected for high grain yield (9 parent lines), high protein content (12 parents) and both traits (20 parents), respectively; CCP and mixture material resown in each site from seed harvested in the previous year, starting with F4 material for CCP and equal parent seed amount for mixture in the first year. b Column means with same small letter do not differ at P < 0.05 according to Duncan’s test. c Degrees of freedom are 168 for site-specific values, and 336 for mean values. d Site means with same capital letter do not differ at P < 0.05 according to Duncan’s test.

CCPn and the mixtures, respectively. The CCPms did not differ significantly either from the mean of the parents or from the CCPn , while being lower-yielding than mixtures from the second year onwards and across years (Table 3). The mixtures were top-ranking for mean protein yield, although they showed a significant (P < 0.05) advantage only with respect to mean parent value (+2.8%; Table 3). All differences among diversity levels were consistent across the three sets of parents (no diversity level × parents set interaction: Table 1). However, the differences for grain yield among diversity levels depended on the cropping system and the site within organic systems. The CCPms performed relatively worse in conventionally-managed sites than in organically-managed ones, particularly with respect to mixtures (relevant diversity level × site interaction contrast significant at P < 0.05: Table 1), to which they were inferior only in conventional systems (Table 4). Line mixture represented the top-yielding diversity level, and out-yielded significantly the mean value of its component parents, in both conventionally-managed sites but only one of the organicallymanaged sites (Wakelyns) (Table 4). The comparison across locations for mixture, non-male sterile CCPn and male-sterile CCPms with the best-performing pure line within the relevant set of parents is reported in Table 5 for each target trait. Within the parent set selected for high grain yield (Y), the top-yielding pure line Deben out-yielded the mixture and each CCP for grain yield, while no difference was found for protein yield (owing to low protein content of this line). Within the parent set selected for protein content (Q), the top-yielding pure line Thatcher outperformed the mixture and each CCP for protein content while being out-yielded for protein yield (owing to its low grain yield). Finally, in the YQ parent set the top-yielding pure line Spark outyielded each CCP while not differing from the mixture, for both grain yield and protein yield (Table 5).

3.2. Stability and superiority of grain yield and protein yield Within material selected only for yield (Y) or for both yield and quality (YQ), the two CCPs tended to show greater stability of grain yield or protein yield – as expressed by lower environmental variance (EV) across locations – than the top-yielding pure lines, or the mean value of the pure lines, of the respective parent set (Table 5). The strongest effect in relation to the parents was observed in the YCCPms , where the EV was 18% lower than the mean of the parents, and in the YQCCPn for which the EV was 20% lower than the parental mean. Within the same sets (Y and YQ), two CCPs exhibited yield reliability consistently higher than the mean of the parents and comparable with that of the mixtures (Table 5). However, the topyielding pure line within Y (Deben) tended to have a higher grain

Fig. 1. Relative superiority (Lin and Binns, 1988) of grain yield, protein content and protein yield in winter wheat entries over 12 environments, comparing the mean of parents (black bars, set to 100%) with the mixture (dark grey bars), a composite cross population (CCPn , light grey bars) and a CCPms with added male sterility genes (white bars). Lower values indicate higher superiority. Mixtures and CCPs were derived from the same parent lines (see methods). Means with same letter within each of the three response variables do not differ at P < 0.05 according to Duncan’s test.

yield reliability and similar protein yield reliability in comparison with both CCPs and the mixture, owing to its high mean yield (Table 5). The top-yielding line within the YQ set (Spark) exhibited grain and protein yield reliability comparable with the two CCPs and the mixture. Superiority values of yield and protein yield were consistently and significantly lower (i.e. better) in the diverse material than in the mean of the parents (Fig. 1). Qualitatively, grain yield superiority values were best (lowest values) in the mixture and the CCPn , followed by the CCPms . However, within each of the three parent sets (Y, Q, and YQ), differences of grain yield superiority values between the mean of parents, the CCPn , the CCPms and the mixture were not significant (P > 0.10). Also, there were no significant differences for yield superiority between the best-yielding variety (Deben), and any of the corresponding diverse materials (YCCPn , the YCCPms and the Y mixture); similarly, superiority indices with regard to protein yield were not significantly different between Spark, the YQCCPs and the YQ mixture. 3.3. Yield components and other agronomic traits Diversity level had significant effects on several agronomic traits of the wheat plants, such as early ground cover, LAI, seed weight per stem, individual seed weight, straw height, biomass wet weight,

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Table 5 Mean value, stability expressed as environmental variance (EV), and index of reliability (IR) as a function of mean and stability values, for grain yield at 15% seed moisture, protein content and dry protein yield across 12 environments of composite cross population (CCP) from ordinary and male-sterile lines (CCPn and CCPms , respectively), physical mixture, top-ranking and mean parent germplasm, for each of three sets of parent germplasm.a Yield (t ha−1 )

Material

Protein yield (t ha−1 )

Protein content (%)

Meanb

EVc

IRd

Meanb

EVc

IRd

Meanb

EVc

IRd

7.721 7.902 bc 7.886 c 7.909 bc 8.827 a 8.196 b 8.181 bc

7.626 8.265 ab 6.967 bc 6.250 c 8.519 ab 9.497 a 7.561 abc

5.857 5.961 6.104 6.222 6.855 6.116 6.325

11.80 11.70 ab 11.90 a 11.91 a 11.00 c 11.41 b 11.42 b

2.47 2.43 a 2.13 a 2.33 a 2.13 a 2.08 a 2.74 a

10.74 10.65 10.92 10.88 10.01 10.43 10.30

0.774 0.786 a 0.798 a 0.801 a 0.825 a 0.795 a 0.794 a

0.102 0.105 ab 0.099 ab 0.088 b 0.100 ab 0.121a 0.103 ab

0.559 0.568 0.585 0.600 0.612 0.560 0.577

Parent set for high protein content 7.030 Mean of parents 7.373 a Mixture 7.192 a CCPn 7.092 a CCPms 5.563 b Thatcher (1st ranking)

5.922 5.553 a 5.715 a 4.727 a 3.475 b

5.387 5.782 5.578 5.624 4.305

12.54 12.38 b 12.53 b 12.39 b 13.50 a

2.93 3.36 a 3.69 a 4.18 a 3.59 a

11.38 11.15 11.23 11.01 12.22

0.749 0.776 a 0.766 a 0.747 a 0.638 b

0.091 0.084 ab 0.099 a 0.080 ab 0.068 b

0.546 0.580 0.553 0.556 0.463

Parent set for high yield and protein content 7.370 Mean of parents 7.648 ab Mixture CCPn 7.565 b 7.438 b CCPms 7.909 a Spark (1st ranking)

6.706 6.473 ab 5.383 b 6.125 ab 7.437 a

5.622 5.931 5.999 5.767 6.068

12.21 12.16 a 12.04 a 12.21 a 12.27 a

2.78 2.48 b 2.42 b 1.95 b 4.08 a

11.09 11.10 10.99 11.27 10.91

0.765 0.791 ab 0.774 b 0.772 b 0.825 a

0.097 0.099 ab 0.082 b 0.096 ab 0.118 a

0.555 0.579 0.581 0.563 0.594

Average of parent sets Mean of parents Mixture CCPn CCPms

6.751 6.764 6.022 5.701

5.622 5.891 5.894 5.871

12.18 12.09 12.16 12.17

2.73 2.76 2.75 2.82

11.07 10.97 11.05 11.05

0.763 0.784 0.779 0.773

0.097 0.096 0.093 0.088

0.553 0.575 0.573 0.573

Parent set for high yield Mean of parents Mixture CCPn CCPms Deben (1st ranking) Option (2nd ranking) Claire (3rd ranking)

7.374 7.641 7.548 7.480

a Environments are combinations of four sites by three cropping years; sets of parent germplasm selected for high grain yield (9 parent lines), high protein content (12 parents), and both traits (20 parents); CCP and mixture material resown in each site from seed harvested in the previous year, starting with F4 material for CCP and equal parent seed amount for mixture in the first year. b Entry means with same letter within each parent set do not differ at P < 0.05 according to Duncan’s test; standard errors are 0.1023 for grain yield, 0.102 for protein content, and 0.0129 for protein yield. c Entry environmental variance values with same letter within each parent set do not differ at P < 0.05 according to Ekbohm’s (1981) variance ratio test. d As lowest yield across environments expected in three cases out of four, estimated according to Kataoka (1963).

Table 6 Analysis of variance F test results for agronomic traits of composite cross populations from ordinary and male-sterile lines, physical mixture and mean of their parent germplasm, limited to main effects of material and its first-order interaction with set of parent germplasm, year and site factors.a Trait c

Early ground cover Weed cover Leaf area index Septoria spp.d Lodging index Head density Seed weight per stem Individual seed weight Straw height Biomass wet weighte Harvest index

Diversity levelb

Diversity level × parent setb

Diversity level × yearb

Diversity level × siteb

** NS * NS NS NS * ** ** ** **

NS NS NS NS NS * NS NS NS NS NS

NS NS NS – NS NS NS NS ** * NS

NS NS NS NS NS ** NS NS NS * NS

a For three years, four sites, and three sets of parent germplasm selected for high grain yield (9 parent lines), high protein content (12 parents) and both traits (20 parents), respectively; CCP and mixture material resown in each site from seed harvested in the previous year, starting with F4 material for CCP and equal parent seed amount for mixture in the first year. b *, P < 0.05; **, P < 0.01; NS = not significant. c At growth stage 31. d Assessed only in the third year. e At harvest.

and harvest index (Table 6). Interactions between diversity levels and other factors were mostly non-significant. Notably, effects of diversity level on foliar disease infection with Septoria (assessed in year 3 of the study) were not significant. Septoria levels were highest at Wakelyns, with infected leaf area ranging from 11.7 to 82% (mean ± SD 42.8 ± 17.3%), while the other three sites showed consistently lower Septoria levels (10.1 ± 4%, 9.3 ± 5.8%, and 7.9 ± 3.5% at Metfield, Morley and Sheepdrove, respectively). Within the conventional sites,

significant negative correlations were found between the percentage of Septoria infested leaf area and grain yield (Metfield: r = −0.51, P < 0.01; Morley, r = −0.57, P < 0.01; analysis on arcsin-square root transformed data). In contrast, the correlation between disease and grain yield was not significant within the two organically managed sites (P > 0.10). Leaf area index and biomass fresh weight were significantly higher in the two CCPs and mixtures than in the mean of the parents (Table 7). Harvest index was significantly lower in the CCPs

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Table 7 Agronomic traits across environments (AE), in specific years or in conventional (CS) and organic (OS) cropping systems, for composite cross population (CCP) from ordinary (CCPn ) and male-sterile lines (CCPms ), physical mixture and mean of their parent germplasm.a Traitb

Mean of parents

Mixture

CCPn

CCPms

Standard error

Early ground cover, AE (%) Leaf area index, AE Seed weight per stem, AE (mg) Individual seed weight, AE (mg) Biomass wet weight, AE (t ha−1 ) Harvest index, AE Straw height, year 1 (cm) Straw height, year 2 (cm) Straw height, year 3 (cm) Head density, CS (number m−2 ) Head density, OS (number m−2 )

33.9 b 4.14 b 940.4 b 43.19 c 14.51 b 0.515 a 74.5 b 75.5 c 78.8 c 558.9 ab 556.3 b

34.5 b 4.31 a 955.1 b 43.57 bc 15.41 a 0.504 b 73.8 b 82.7 b 86.2 b 571.8 a 601.1 a

36.9 a 4.35 a 970.8 ab 44.19 a 15.35 a 0.498 b 77.6 a 86.0 a 88.6 a 567.7 a 574.6 ab

34.7 b 4.33 a 998.6 a 44.17 ab 15.20 a 0.501 b 78.9 a 85.5 a 88.4 a 534.0 b 612.9 a

0.66 0.055 14.23 0.209 0.137 0.0030 1.02 0.71 0.57 11.51 14.02

a For three years, two conventionally-managed and two organically-managed sites, and three sets of parent germplasm selected for high grain yield (9 parent lines), high protein content (12 parents) and both traits (20 parents), respectively; CCP and mixture material resown in each site from seed harvested in the previous year, starting with F4 material for CCP and equal parent seed amount for mixture in the first year. b Row means with same small letter do not differ at P < 0.05 according to Duncan’s test.

and mixtures than in the mean of the parents. In all three years straw was taller in the CCPs than in the mixture (Table 7). In the third year, the CCPs were nearly 10 cm taller than the average of the parents.

4. Discussion 4.1. Effects of genetic diversity on yield, grain quality and stability In this study, mixtures and CCPn significantly out-yielded the mean of their parents. However, the size of this yield advantage of material with greater genetic diversity was not large, and showed some inconsistency across years and locations. On the other hand, the diversity effects were consistent across the different genetic backgrounds (i.e., no interaction between diversity level and parent set) for the three traits grain yield, protein content and protein yield. Causes underlying these observations remain speculative at present since individual genotypes within the mixtures and CCPs were not characterized separately. However, when comparisons are made between more diverse material (mixtures and CCPs) against the mean of the components, our study adds further evidence to the general finding that increased diversity in plants is often linked to higher productivity (Cardinale et al., 2011). With the exception of yield stability of the YMix, the advantage of greater diversity over the mean response of component parent lines for stability and yield reliability were consistent across parent sets. Also, CCPn and mixtures showed a distinct advantage over the mean of their component parent lines in terms of Lin and Binn’s superiority of yield and protein yield. Early research on mixtures and CCPs has occasionally found inconclusive results with regard to the effects of genetic diversity on yield stability (Hockett et al., 1983), or even lower yield stability in mixtures than in monocultures (Clay and Allard, 1969). However, most studies found a yield-stabilizing effect of cultivar mixtures (Frey and Maldonado, 1967; Wolfe, 1985; Dubin and Wolfe, 1994; Mundt, 2002) and composite cross populations (Soliman and Allard, 1991) in cereal crops, in agreement with our results. The partly contradictory results obtained in earlier research are likely to be a consequence of the range of environments in which the mixtures and monocultures were tested, as well as the specific adaption of individual genotypes to these environments. Further, the degree of genetic relatedness between the components of different mixtures, and the consequent differences in functional diversity among the genotypes may also influence the effect of diversity. In this study, genetic relatedness among the parents was not quantified. However, for the comparison of the three different diversity levels (monoculture – physical seed mixture – CCP), this

is not necessary because the three diversity levels are extremely different, and the sets of parents used were the same at all three levels of diversity. There are several mechanisms that can lead to increased stability of genetically diverse material, including compensation, complementation and facilitation. The currently relevant mechanisms are unknown, since we could not record the performance of individual genotypes within the diverse mixtures and populations. In a study on genotype mixtures of the annual weed species Arabidopsis thaliana it was found that seed yield stability, measured as standard deviation across growing environments, resulted from compensation among genotypes. As the fittest genotype overyielded in genetic mixtures, it compensated for the lower seed yield of genotypes with smaller fitness (Creissen et al., 2013). Interestingly, this effect was amplified when Arabidopsis plants were under abiotic stress. This is consistent with findings on winter wheat, where the yield advantage of diverse material over monocultures was stronger under low-yield conditions (Döring et al., 2010b). Superiority indices were comparable between best lines and diverse material. Also, when compared with best-performing pure lines, CCPs and mixtures showed advantages in some cases (protein content in the Y parent set; mean yield in the Q parent set) but not in others (grain yield in the Y parent set; protein yield in the YQ parent set), with a trend towards higher yield stability emerging mainly for production traits of CCP material. Thus, with the general observation in mind that grain yield and protein content in wheat are often inversely related (Kibite and Evans, 1984; Bogard et al., 2010), the results indicate that both CCPs and mixtures tend to behave in a complementary way, showing higher protein content than those pure line varieties that are specialized for high yield performance and vice versa. At the same time, in their respective areas of specialization the best individual varieties outperformed both the CCPs and mixtures, as indicated by the higher mean yield and yield reliability of Deben (top-yielding for grain yield) relative to CCPs and mixtures. While this pattern is not surprising, given the high number and heterogeneity of parents used to create the mixtures and CCPs (Jones et al., 2010), it raises the question whether mixtures and CCPs can in fact offer an advantage over the best pure line varieties. For three reasons, we think that this might be the case or, that pure lines and diverse material could at least be comparable. First, although the means of CCPs and mixtures are significantly lower than the top-performing varieties in a number of cases, our results also show that in terms of Lin and Binn’s superiority index CCPs and mixtures were generally not significantly different from the best pure lines; on the other hand, as a single parameter, the superiority index does of course not provide the full picture necessary for the assessment of CCPs and mixtures, and it therefore

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needs to be complemented by other stability and performance indicators. Second, and particularly in farming practice, it is difficult to predict which variety will be best-performing in a given environment, and appropriate information might not be available at the time of choosing a variety. In this study, this information was gained a posteriori and, not surprisingly, it was found that different varieties proved to be best for grain yield, grain protein content and the combination of the two parameters. This issue becomes more important as the predictability of environmental conditions decreases (Schär et al., 2004; Urban et al., 2012). In particular, the advantage of CCPs and mixtures over the mean of the monocultures increases with increasing environmental fluctuations (i.e., as the ecological differences among sites increases) and with the increasing uncertainty about the performance of individual varieties in a given, but unpredictable, environment (Döring et al., 2010b). In this respect, a range of more challenging test environments (with regard to biotic and abiotic stresses) may have resulted in greater benefits of high genetic diversity. An example illustrating this potential is a preliminary study from Germany that used the same winter wheat CCPs as in the research presented here. When Finckh and Brumlop (2013) compared the grain yield performance of the CCPs to the component parent monocultures under German conditions in 2011/12, a severe winter resulted in complete kill of 16 out of the 20 parent varieties, while the CCPs recovered and produced low but acceptable yields. The poor survival of many of the parent monocultures is likely associated with their genetic background, since many of them were bred in and for a maritime climate. As a consequence, the German result may overestimate the value of the CCPs, and a possibly more appropriate comparison would be against the lines that did survive. However, the case illustrates the ability of the diverse material to perform under extreme conditions. In the end, the relative merit of CCPs in comparison to ‘best lines’ will depend on the (unknown) degree to which it can be determined in advance which line will in fact be the best one in a previously untested environment. A third argument is that the varieties used to create the CCPs and mixtures included some relatively old varieties with comparatively low yield potential (Jones et al., 2010). The parents were chosen to represent a large genetic background. When parents with more similar yield potential are chosen, it is likely that differences among monocultures are more difficult to detect; consequently, the identification of ‘best’ lines will become less reliable. On the other hand, in future studies, developing CCPs and/or mixtures from a smaller subset of higher performing varieties than here might be crucial for enhancing the agronomic value this material relative to top-performing pure lines. Finally, the main purpose of the current study was to explore the mechanisms that govern the behaviour of CCPs. In this case, the null-hypothesis states that genetic diversity has no effect when the genetic material is the same, and accordingly, the fairest comparison is against the mean of the parents. A subsequent question of more practical nature is about the potential commercial value and viability of CCPs. Therefore, after the completion of the study reported here, further field trials have been undertaken to compare the CCPs and mixes in a larger range of environments against a set of commercially relevant and widespread benchmark varieties in the UK. Results of this study which will be published in a future paper will help to clarify the agronomic acceptability of CCPs and cultivar mixes. In any case, the creation of CCPs could also be viewed as a step to create pure line varieties more effectively and/or inexpensively. This is particularly true for low-input agriculture (Danquah and Barrett, 2002). A further strategy for exploiting CCPs is mixing them with elite pure lines. Differences between CCPn and the corresponding mixtures for all traits were small and inconsistent in their direction. Also, while

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the stability advantage of greater genetic diversity was more distinct for CCPs than for the mixtures, the differences in superiority values between CCPn and mixtures were not significant. This study does therefore not offer any evidence that the greater genetic diversity of CCPn provides a yield, quality or stability advantage over the less diverse variety mixtures. We speculate that diversity effects in the test cropping systems already saturated at the level of the mixtures, because of the large number of genotypes used to create the mixes. This would be consistent with published work on the effects of plant biodiversity at species level, showing that gains in biomass production from increased diversity decrease with increasing producer richness (Cardinale et al., 2011). 4.2. Effects of diversity on agronomic traits Interestingly, there were some differences between CCPn and mixtures with respect to growth parameters. In particular, early ground cover and plant height were significantly higher in the CCPn than in the mixtures, suggesting higher competitiveness against weeds in the CCPn . More generally, leaf area index, biomass fresh weight and harvest index were all significantly higher in the diverse material (mixtures, CCPs, and male sterile CCPs) than in the monocultures, indicating higher investment of the plants in vegetative growth when grown in diverse populations or mixtures. This might be an advantage from an ecological point of view, as it implies higher shading of the soil and higher competitiveness against weeds. Creissen et al. (2013) suggested that highly competitive genotypes may not perform well in monoculture but over-yield in mixture because of their yield potential and their ability to utilize limited resources. However, investment in vegetative growth can also be regarded as a disadvantage when allocation of resources to grain biomass is the main concern (Denison, 2012), and also because of the greater risk of lodging. Especially for plant height, there is a concern that it might increase to unacceptable levels over the years, because of competition for light among plants within an (evolving) CCP or complex mixture Indeed, the differences between CCPs and parents tended to be higher in year 3 than in year 1. Further studies, ideally over a longer time frame and more environments would be needed to verify whether this trend poses a problem. However, CCPs and mixture could also be composed entirely from semi-dwarf material, which would restrict height increase over time. The level of genetic diversity had no significant effects on foliar diseases of the wheat plants. This is in contrast with other studies, where mixing generally showed a disease reducing effect (Wolfe, 1985; Finckh, 1992). While conclusions must be drawn with caution because disease data were obtained only in one year, the results are in line with the observation that more generalist pathogens (such as Septoria ssp.) are less amenable to the effect of increasing crop genetic diversity than specialist pathogens, where differences in susceptibility are greater (Wolfe, 1985). In terms of disease restriction through genetic diversity, true effects might have been underestimated by this trial because of relatively small plot size; especially in terms of plant disease epidemiology, effects are likely to be more pronounced with larger plots. For the organic sites, no correlation was found between disease and grain yield, which is in line with a previous study with the same YQCCPn and two pure line varieties (Hereward and Aristos) conducted at the organic sites (Döring et al., 2010a). 4.3. Outcrossing and male sterility Because of notable differences in the numbers, origins and ages of parents used (Jones et al., 2010), it is likely that the potential number of genotypes generated in the CCPs was, probably by some orders of magnitude, greater than the numbers of seeds

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produced, i.e. in the early generations tested here, each individual plant within a CCP is a different genotype. By contrast, the diversity of the mixtures was limited to the original numbers of parents physically mixed into each, unless there was out-crossing in subsequent generations. Despite the initial increase of genetic diversity in the segregating populations, they were expected to return to homozygosity within about ten generations, assuming no out-crossing. It was not possible to quantify the effect of heterozygosity in the CCPs in this study. A small degree of out-crossing is not uncommon in wheat (Martin, 1990; Rieben et al., 2011), and outcrossing levels may be high especially under stress (Lukac et al., 2012). Such out-crossing would maintain some heterozygosity and segregation permanently in the populations as well as in the mixtures. Possible contributing reasons to the current inability of CCPms material to out-yield the mean of its parent lines might be the yield penalty associated with a lack of pollination of male sterile florets, or lower genetic value conferred by male-sterile donors. 5. Conclusions As resources become scarcer and climates less predictable, large-scale genetically-uniform monocultures are likely to become less valuable than more diverse crops. Effective, inexpensive and readily applicable solutions are represented by high-yielding variety mixtures and composite cross populations. The latter are somewhat more complex to implement because of the initial crossing phase, but offer greater potential for rapid and dynamic responses to change. Both approaches, however, require careful selection of elite parent germplasm, when the aim is to generate material at least as productive as the (current) best-yielding pure lines. The integration of this material into farming systems that make greater use of plant diversity also in other respects can enhance the importance and maintenance of biodiversity as a whole. This in turn will improve ecosystem services (Hajjar et al., 2008; Costanzo and Bàrberi, 2014) and so help to ensure a sustainable future for a wide range of organisms, including humans. Acknowledgement This work was funded by the UK Government, Department for Environment, Food and Rural Affairs, and formed part of the work carried out in project AR0914. References Allard, R.W., 1961. Relationship between genetic diversity and consistency of performance in different environments. Crop Sci. 1, 127–133. Allard, R.W., 1988. Genetic changes associated with the evolution of adaptedness in cultivated plants and their wild progenitors. J. Hered. 79, 225–238. Allard, R.W., Jain, S.K., 1962. Population studies in predominantly self-pollinated species. II. Analysis of quantitative genetic changes in a bulk-hybrid population of barley. Evolution 16, 90–101. Annicchiarico, P., 2002. Genotype × Environment Interactions: Challenges and Opportunities for Plant Breeding and Cultivar Recommendations, vol. 174. Food & Agriculture Organization, Rome. Arnell, N.W., 2003. Relative effects of multi-decadal climatic variability and changes in the mean and variability of climate due to global warming: future streamflows in Britain. J. Hydrol. 270, 195–213. Bogard, M., Allard, V., Brancourt-Hulmel, M., Heumez, E., Machet, J.-M., Jeuffroy, M.-H., Gate, P., Martre, P., Le Gouis, J., 2010. Deviation from the grain protein concentration-grain yield negative relationship is highly correlated to post-anthesis N uptake in winter wheat. J. Exp. Bot. 61, 4303–4312. Cardinale, B.J., Matulich, K.L., Hooper, D.U., Byrnes, J.E., Duffy, E., Gamfeldt, L., Balvanera, P., O’Connor, M.I., Gonzalez, A., 2011. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592. Chable, V., Dawson, J., Bocci, R., Goldringer, I., 2014. Seeds for Organic Agriculture: Development of Participatory Plant Breeding and Farmers’ Networks in France. Organic Farming, Prototype for Sustainable Agricultures. Springer, pp. 383–400. Chakraborty, S., Newton, A.C., 2011. Climate change, plant diseases and food security: an overview. Plant Pathol. 60, 2–14. Clay, R.E., Allard, R.W., 1969. A comparison of the performance of homogenous and heterogenous barley populations. Crop Sci. 9, 407–412.

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