Relationships between yield and agronomic traits among geographic groups of barley landraces grown under high yielding conditions

doi:10.21203/rs.3.rs-568936/v2

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Relationships between yield and agronomic traits among geographic groups of barley landraces grown under high yielding conditions

https://doi.org/10.21203/rs.3.rs-568936/v2

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Future crop production needs to deliver sustained yield increase in important crops such as barley. Although significant progress has been made over the last century in improving yields, in the last few decades yield improvement has stagnated. Morphological traits related to yield from a wide genetic base can be explored to identify novel variation to support future plant breeding. This includes exploitation of traits and yield association for developing varieties with more consistent yield.

We investigated the strength of the association between yield (grain yield, thousand grain weight) and related agronomic traits (seed number, height, ear number, flag leaf width and length, ear length). This was done among groups of European barley landrace from different geographic origins and Harlan composite cross populations compared to modern cultivars grown under a common high yield test protocol. Relationship of agronomic traits and yield were assessed for each group across environments and years. There were significant relationships between yield and morphological traits among landrace and other groups and between 2 and 6 rowed ear habit.

Traits most strongly associated with grain yield were number of ears, plant height, flag leaf width and seeds per ear.

Grain size as thousand grain weight was correlated with number of ears, ear length, seeds per ear, flag leaf width and plant height.

We conclude that phenotypic relationships between yield and agronomic traits among barley landraces could be exploited for enhancing yield and sustainable production in future barley breeding.

barley

landraces

agronomic traits

association

yield

Increasing crop production is one of the most important challenges in this century (Powell et al., 2012). An understanding of the relationship between yield, yield components and agronomic traits is essential to overcome crop yield-limitations. Most arable crop production is dominated by monocultures of modern elite cultivars that have been bred and subsequently grown under high inputs of artificial fertilizers and pesticides. In barley (Hordeum vulgare), which is the fourth most planted crop in the world by area (FAO, 2004), the most common genetic structure is the pedigree-bred pure line (elite cultivar). However, barley is also grown in different parts of Europe and world-wide as landraces and other traditional material. Genetically diverse lines, such as traditional landraces and composite cross populations (CCPs) have wider variation in agronomic traits compared to elite cultivars (Nandety et al., 2014). Furthermore, traditional barley material has wider variation in its developmental growth habits to establish their reproductive structures and grain yield. Most traditional or diverse barley landraces have been locally adapted, often under low input farming, and with high consistency in yield.

In high input systems, the expectation is that yield of elite cultivars will outperform traditional landraces. Although landraces and CCPs may have inferior yield under high agronomic inputs, they demonstrate increased resilience under reduced inputs, or more stressful conditions (Ceccarelli, 1994). Different genetic backgrounds of barley landrace material have resulted in variation in yield establishment. This is mainly because of phenotypic trait association. In European elite 2 row material spikes per plant (and grain number per unit area) is the main association with yield whereas in 6 row elite varieties it is seeds per spike (Peltonen-Sainio et al. 2007; Slafer et al., 2014). Traditional material is much more varied and has varying genetic architecture underlying the traits. Identifying variation in yield related traits in traditional material would indicate opportunities for useful variation in pre-breeding barley programs.

An approach to exploiting wider genetic resources, including landraces, is to quantify variation in and relationships between agronomic traits under a common field protocol. Such approaches can be used to compare landraces from different geographic and climatic backgrounds for agronomic and pre-breeding value. This would include a practical guide to phenotypic variance in agronomic traits such as leaf morphology, shoot production (tillering), grain number per ear and specific grain weight (thousand grain weight) that are being associated with improved yield performance. Different studies (Toker & Cagirgan, 2004; Slafer et al., 2014; Oladosu et al., 2018) show a relationship between agronomic traits during plant development and yield; however, there is not much known about variation in these trait correlations between material with a wider genetic background.

Traditional landraces tend to originate from low input conditions under local environmental conditions where genetic by environmental (GxE) effects are best exploited under local conditions (Gage et al, 2019). This contrasts with high inputs systems, or elite cultivars, in which high GxE is controlled using high agronomic inputs. There are very few studies that have compared genetically diverse landraces from different geographic backgrounds in relation to phenotypic expression and their relationship to final grain yield against elite cultivars when grown under common protocol for achieving high yield. Using pre-adapted genetic resources such as landraces and CCPs provides an opportunity to discover untapped agronomic value, as a large proportion of traditional barley material has remained stored in gene banks with very little information on its agronomic performance.

In order to exploit the potential benefits of wider genetic variation for future crop breeding, we tested the hypothesis that traditional landraces and a CCP (Harlan & Martini, 1929), show different, and exploitable, relationships between agronomic traits and yield than modern elite cultivars. We tested this hypothesis by comparing five geo-climatic landrace groups comprised of two or six rowed ear habit along with a group of Harlan CCPs and a group of elite cultivars grown under a common high-yielding field evaluation protocol over several years and sites. We used European geographic region of origin (North, East, South and West; Metzger, 2005; Peel et al., 2007) based on gene bank information. Agronomic traits of interest were plant height, final ear number, number of seeds per ear, ear length, thousand grain weight (TGW), and flag leaf dimensions (length & width). Variation in relationships between agronomic traits and yield across sites were examined among the nine groups, with main comparisons being between landraces, CCPs and cultivars, and between the 2 or 6 rowed ear habit.

2.1 Plant material

Seed of landraces (also referred to as ‘traditional lines’ or ‘heritage varieties’) were obtained from IPK Gatersleben, Nordic Gene Bank, CGN Wageningen, GRC-INRA, NGBA and cultivars from several UK breeding companies. Bulked CCP material from the original composite-cross Harlan populations was sourced from Prof. Quelset at UC Davis, California via Michigan State University and represented several generations of composite cross populations differing in their crossing designs (Harlan & Martini, 1929; Online resource 1).

Landraces were divided in four geographical groups; north, west, east and south Europe (Metzger et al., 2005; Peel et al., 2007; Olesen et al., 2011). The north (N) group included lines from Norway, Sweden, Finland and Denmark; west (W) included Ireland, UK, the Netherlands, Belgium, France, Switzerland and Germany; east (E) was Poland, Lithuania, Estonia, Czech, Slovakia, Austria, Romania, Serbia, whilst south (S) was Spain, Italy, Greece and Malta. Apart from S, which consisted only of 6 row material, each geographical group was divided into two (2) and six (6) rowed ear habit. This resulted in 7 landrace groups (E2, E6, N2, N6, W6, W2, S6) and the number of lines in each group were: E2 33, E6 29, N2 12, N6 10, W2 52, W6 13, and S6 4. There were 20 two-rowed elite cultivars commercially grown in the UK (Cultivar) and 41 6 row Harlan CCP lines (Harlan), developed in the USA.

All seed stocks were stored at 4°C for two weeks prior to sowing. The experimental design was a fully randomised. Lines were hand sown as a mini plot (0.5 m²) in 2011 and 2012 and drilled as a mini plot (1 m²) in 2013 at three SRUC trials centres: (i) Humbie in East Lothian (55°50'45"N 2°52'29"W), (ii) Bush Estate, Mid-Lothian (55°52'26"N 3°12'10"W) and (iii) Drumalbin, Lanarkshire (55°37'41"N 3°45'07"W) which represented a range of soil and climatic conditions (Figure S1) that where associated with high crop productivity. The mini plots were drilled with a Wintersteiger Rowseed S drill (Trials Equipment UK Ltd). All plots were treated with standard 120 kg N ha^− 1 ammonium nitrate fertilizer applied in a 50:50 split to the seed bed and at leaf 3, and a comprehensive fungicide treatment to keep all foliar diseases to a minimum during the growing season.

2.2 Measurement of plant traits and yield components

Seven agronomic traits and yield were measured. Three replicate plants per plot measured for each trait; the replicates were meaned prior to analysis. Plant height (PH) was measured after flowering, from base of the main to the base of the ear in three plants per plot. In the same plants, the length (FLL) and width (FLW) of the flag (final) leaf were measured as a proxy of leaf area. At harvest ripe, the population of fertile ears per plot (NE) was counted, and ears hand harvested. Mean grain number per ear (SE) and ear length (EL) were measured in each ear, in the laboratory. Ears were threshed in a Wintersteiger Grain Thresher and grain collected to measure thousand grain weight (TGW). Grain yield (GY) was estimated as grain weight from the whole plot at harvest ripeness, adjusted to 15% moisture content.

2.3 Data analysis

Each of the nine barley groups were treated as experimental units for statistical analysis of differences and variation in yield and agronomic traits. Thus, the lines for each of the barley groups was used in each stage of the analysis. As the Initially, ANOVA was used to test the differences between combined geographic region and ear type for the traits. The blocking term was year site and differences were considered significant for P < 0.05. The geographic region (N, S, E and W) and ear habit (2 or 6) combinations were the fixed effect. Significant differences were tested at the 5% level. Correlation between measured agronomic and yield trait means per group was calculated using the Pearson method. The last stage of the analysis used a stepwise regression approach to identify which combination of traits for each of the barley groups was the best predictor of the grain yield and the thousand grain weight. The best model was chosen by minimising RMSE. Due to the number of observations, S6 was excluded from the stepwise regression. All analysis was conducted in R Studio (R Core Team, 2023) and utilised the tidyverse (Wickham et al., 2019), corrr (Kuhn et al, 2022), caret (Kuhn, 2008) and leaps (Lumley, 2020) packages.

Differences between landrace groups for agronomic traits and yield components

We found significant differences between groups for several agronomic and yield related traits. Both Harlan and modern cultivars groups had significantly shorter leaves than the landraces from either the W and N geographical regions. The W2 type were also significantly longer than the E type (Online Resource 2).

Flag leaf width was explained by row type, where 6 rows, except for Harlan, showed significantly wider leaves than 2 rowed groups. N6 had significantly wider leaves than all other groups, whilst Harlan had significantly narrower leaves than all other 6-rowed types, except S6.

Modern cultivars were significantly shorter than all groups except S6. Harlan and E6 were significantly smaller than W2 and N2.

Harlan, W6 and N6 had significantly lower NE than either N2 or W2, in contrast, there was no difference in NE between the modern cultivars and any of the landrace regions or ear types. Modern cultivars and 2 row landraces had a significantly longer EL than the 6 row groups, The Harlan group had significantly shorter EL than W6, E6 or N6.

As expected, SE were significantly higher in 6-rowed than in 2-rowed habit for all geographic groups. Within the 6 rows, both N6 and W6 had significantly higher SE than other groups.

There were no significant differences in GY between groups.

Modern cultivars had a significantly higher TGW than all landrace groups. Harlan had a significantly higher TGW than E6. With the exception of E6, the TGW for the 6 row type was significantly lower than 2 row type from the same geographical region.

Association between agronomic traits and yield components

The correlations between GY and the measured plant attributes are similar for all 9 barley groupings. Nevertheless, the level of significance of the correlation differed between the groups (Fig. 1). Thus, although GY showed the strongest correlation coefficient with NE for all groups, the correlation was not significant for N2 (Fig. 1).

PH and FLW also showed a significant but moderate showed significant correlations in groups N2, E6, N6, W6, Harlan whereas FLL was significant in groups Harlan, W2, N6, N2, Cultivar, E2, S6. Although the correlation of EL and FLL with GY was low, they were significant for E6 and W6, and Harlan and W2 respectively. The correlation between SE and GY was significant for the 6 row types and Harlan.

Similarly to GY, the pattern of correlation between the TGW and the plant traits showed a similar pattern regardless of the barley groupings (Fig. 2). TGW showed the strongest correlation with NE for all groups (Fig. 2). However, this was only significantly for the modern cultivars, E6, N2, N6, W2 and W6. PH, EL and FLW also showed strong correlations for most groups. the correlation between PH and TGW was ~ 0.2 for all grouping, although it was only significant for E2, E6 and W2. A similar level of correlation was observed for EL and TGW, although this was significant for the modern cultivars, E2, N2 and W2. FLW was significant in groups Cultivars, E6, Harlan, W2 and W6. In contrast to the other plant traits, FLL showed a negative but non significant weak correlation with TGW. SE showed a weak, although significant, correlation in groups Cultivar, Harlan, and W2.

Linking traits to GY and TGW

Using a stepwise regression model, we found that differences in GY and TGW were explained by different agronomic traits for each group (Tables 1 & 2).

For GY (Table 1) we found that NE was included for all groups, except N2. where FLW was included in all 2 row groups, Harlan and N6. PH was only relevant for E6, Harlan and N2, while SE was included in the regression equation for E2. With the exception of N2, the R² were all greater than 0.5. In general, the R² were lower for TGW than for the GY (Table 2). Additionally, more of the plant attributes were involved in explaining the TGW. For W2 and Harlan, all plant traits measured were significant. In the case of the modern cultivars, all plant traits were significant except EL wile for N2, the only trait that was not significant was NE. For N6 and W6, the traits that explained the TGW were NE and FLW, while for E2 it was NE and FLL.

Table 1

*Grain yield explained by different agronomic traits for each group using stepwise regression, the coefficient of determination (R*²*) and the number of observations included in the analysis.*
Group	No of obs	R²	NE	FLW	SE	PH
Cultivar	39	0.51	x
E2	84	0.51	x	x	x
E6	81	0.50	x			x
Harlan	138	0.53	x	x		x
N2	39	0.37		x		x
N6	29	0.78	x	x
W2	140	0.38	x	x
W6	24	0.76	x

Table 2

*TGW explained by different agronomic traits for each group using stepwise regression, the coefficient of determination (R*²*) and the number of observations included in the analysis.*
Group	No of obs	R²	NE	FLW	SE	FLL	PH	EL
Cultivar	45	0.48	x	x	x	x	x
E2	84	0.37	x			x		x
E6	86	0.19	x	x		x
Harlan	158	0.19	x	x	x	x	x	x
N2	42	0.44		x	x	x	x	x
N6	30	0.61	x	x
W2	145	0.44	x	x	x	x	x	x
W6	26	0.78	x	x

Geographic structuring among landraces for variation in key traits was only significant for leaf length (FLL) whereas variation in leaf width (FLW), height (PH) and tillering (NE) was explained by ear-row type. Furthermore, we identified several agronomic traits in different geographic regions or landraces groups that were associated with grain yield, which could be used in yield selection in (pre-) breeding programs.

Agronomic trait differences between landrace groups

We found a number of agronomic traits showing significant differences between groups based on origin and row type.

One of the agronomic traits that indicated a strong geographic influence was FLL where northern and western European varieties (N2, N6, W2 and W6) had longer leaves compared to other regions. This was confirmed in an earlier study on light interception in European barley landraces, which included a subset of the current study material, although there was no difference between specific leaf areas (Florence et al., 2019).

FLW was mostly determined by row type where 6 rows were wider. A study on leaf width and grain number explained a relationship through a genetic component Vrs1 which is also related to row type (Digel et al., 2016). The 6 row Harlan group (H6) though, showed the narrowest leaves but also relatively low seed numbers compared to other 6 rows and this could be related to their underlying genetic Vrs1 variant.

Modern varieties (CU2) in our study showed the shortest plants compared to the heritage varieties which have been developed using dwarfing genes in breeding programmes (Mickelson & Rasmusson, 1994). This has led to a smaller stature including several correlated plant traits including leaf size (Niu et al., 2022). Within the heritage varieties, six row types were shorter than 2 row barley which was not supported by Alqudah et al. (2016) which only found significant differences in height between wild and cultivated barley types.

TGW was highest for modern varieties, indicating the breeding programs optimising this for malting varieties. Harlan varieties showed the highest TGW for 6 rows which would be explained by the trade-off of their relatively low number of SE (Gambín & Borrás, 2010).

EN was higher for 2 rows compared to 6 row barleys, which would be explained by higher tillering capacity. Tillering has a strong environmental component (Shaaf et al., 2019) which is more expressed in 2 row barley compared to 6 row barley. In our study there was not much difference between different groups overall which was unexpected for the modern varieties (Liller et al., 2015). This would also explain the lack of differences between groups for GY although using small sized plots might have accentuated this.

Association between agronomic traits and yield

We found significant correlations between NE and GY for all groups but N2. All groups showing a significant and relatively high correlation (r²) coefficient, with an r² of around 0.45 but this was lower than another study using modern varieties under similar growing conditions (Tanaka & Nakano, 2019, r = 0.67).

SE were significantly correlated to GY for 6 row groups which is similar for former studies (Arisnabarreta & Miralles, 2006). Environmental responses differ between these two barley types where two rows maintain seed production through variation in tiller number and seeds per ear whereas in six rows the variation is in the seeds per ear and TGW and the tiller number is stable (Arisnabarreta & Miralles, 2008; Liller et al., 2015). Identifying 2 and 6 row lines that show plasticity in both yield components would improve yield stability under varying environmental conditions.

We also found that PH showed a significant correlation with grain yield in 6 row barley groups but also N2, which was unexpected. Only few studies found height as a factor related to grain yield (Jui et al., 1997; Costa & Boller, 2001) and this was mainly in six row barley types. The strong correlation between PH and GY in six rows was unexpected as the opposite would be expected with dwarfing genes increasing yield by optimising the harvest index (Xu et al., 2017). Nevertheless, this correlation may be explained by the underlying genetic interactions between genes involved in ear development (Vrs1) and height which seem to interact together with heading time and tillering (Thirulogachandar et al., 2017).

There was a correlation between FLW and SE in some 2 and 6 rowed barleys which was consistent with the work of Thirulogachandar et al. (2017) who found a positive relationship between leaf width and grain number. This association may be explained by the fact that the Vrs1 gene, which underlies row type in barley, is also linked to the development of flag leaf width and longitudinal vein number through rapid growth of leaf primordial cells increasing the size of leaf primordia (Thirulogachandar et al., 2017).

Contrary to our study, a study by Du et al. (2019), using a cross between a 2 and 6 row modern line, found a negative correlation between seeds per plant and flag leaf length but had not included FLW. Further studies would look at yield prediction for both grain and biomass in breeding programmes to use early agronomic traits to predict yield in varieties.

Association between agronomic traits and TGW

TGW showed a positive relationship with almost all traits for both 2 and 6 row barleys where EL only showed a relationship with 2 row barleys and FLL showed no significant relationship. Our results showed a non significant negative correlation between TGW and FLL, which is contrary to Du et al (2019) who found a positive correlation between TGW and FLL.

The relationship of TGW with SE and EL seems like a trade-off and sink limited and another study using diverse material (Sharma et al., 2018) also found a very small relation between TGW and SE. Contrary to our study to our study, Hadjichristodoulou (1990) found a negative relationship for 2 row barley between TGW and number of ears per plant. The same study found that in 6-row barley, TGW was positively correlated with PH, which was confirmed by our study, but dissimilar to our results, they found a negative relation with NE and SE. Interestingly, in our study there is a positive correlation between TGW and SE for all groups, although only significant for two rows, where a negative trade-off between these traits would be expected due to resource limitation. Another study, using modern material, also found that TGW seems related to number of fertile shoots in early growth and final ear number (Bingham et al., 2007). Six row barley are less sink limited than two row barley, due to the higher number of seeds per ear, but for E6, W6 and N6 there is a relationship between TGW and ear number indicating that there might be variation between 6 row backgrounds for sink limitation. In 2 row barley number of grains per ear is relatively stable in modern varieties where yield is set early on by tiller number. In six row barleys there is a similar effect as wheat where florets are compensated by grain size across the ear (Kennedy et al., 2016). A study using row type mutants (Liller et al., 2015), found genotypic variation in ability to produce tillering and inflorescence numbers suggesting trade off relations between different traits.

Linking traits to grain yield and TGW

The stepwise regression analysis for GY per group (Table 1) showed that GY is mostly influenced by one or two traits for modern and NW European material (CU2, N6, W2, W6) whereas E European, Harlan and N2 groups are influenced by at least two traits (E2, N2, E6, H6). In terms of the coefficient of determination (R²), particularly N6 and W6 explained a high proportion of the variance (R² = 0.78 and 0.76) respectively by the indicated variables (NE and FLW). Other studies had found that yield in modern 2 row barley varieties had particularly improved yield through height reduction, TGW and tillering (Nadolska-Orczyk et al., 2017) but also spike morphology and flowering time (Sharma et al., 2018).

Most groups, both 2 and 6 row barley, were influenced by NE in terms of GY although FLW was also influential. Other studies (Liller et al., 2015; Schaaf et al., 2019) found that grain yield in 2 row varieties is mostly determined by tillering and grains per ear for 6 row in barley. This study showed a much larger effect of NE on GY in 6 row barleys. FLW plays an important role in most groups as well in relation to GY. The coefficient of determination (R²) for the modern cultivars (CU2, R² = 0.51) was lower than a study using similar material (Kennedy et al., 2016, R² = 0.62) in their model to predict yield, although in our study some of the other groups showed similar or higher R² values. These latter groups from our study similarly showed ear number, but also FLW or SE, as a factor to predict GY.

The stepwise regression analysis approach showed that multiple traits influenced TGW in all groups. TGW in 2 row barleys seem to be mostly influenced by a combination of SE/EL, FLL/FLW and PH whereas the 6 rows by NE and FLW/FLL. The higher amount of traits influencing 2 row compared to 6 row barley groups is surprising given the higher uniformity and stability of 2 row barley compared to 6 row seeds in other studies (Schwarz & Li, 2010; McKim et al., 2018). Further research into the size of each effect would be useful to improve understanding of stability of TGW in barley for end use.

Applications

Landraces and CCPs housed in genebanks are a rich source of genetic material for crop improvement where yield related correlations between agronomic traits and yield components can be exploited under high yielding growing conditions (Dwidevi et al., 2016; 2017). However, these resources are largely untapped. Identification of novel variation and the selection of specific landraces (genotypes) for relationships between desirable traits and yield components under appropriate testing systems as described herein is a key next step to support development of novel crop cultivars. Large field screenings of genebank material over different locations and years can be used to identify useful variation for pre-breeding applications. Our results indicate that barley resources from different geographic regions may have value in pre-breeding of barley for specific growing conditions. Our findings showed that landraces among different geographic groups had novel relationships between agronomic traits and yield components even compared to elite cultivars. Novel correlations include correlations between GY and FLW but also PH in certain European 2 and 6 rows groups, strong correlations between NE, PH and SE for some 6 rows group and NE and TGW for most 2 and 6 row groups A small number of individual lines from different groups showed novel trait relationships and will be subject of further research.

This work was supported by the Scottish Government Strategic Research Programme, Work Package 5.1. The authors declare no conflict of interest.

The study conception and design were done by Peter Hoebe, Rodrigo Alegria-Terrazas and Steve Hoad. Material preparation and data collection were performed by Peter Hoebe and Rodrigo Alegria-Terrazas. Data analysis was done by Kairsty Topp. The first draft of the manuscript was written by Peter Hoebe and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The datasets generated during and/or analysed during the current study are not publicly available due to lack of funding but are available from the corresponding author on reasonable request.

Author Contribution

The study conception and design were done by PH, RA and SH. Material preparation and data collection were performed by PH and RA. Data analysis was done by KT. The first draft of the manuscript was written by PH and all authors commented on previous versions of the manuscript.

Acknowledgements:

We would like to thank the technical team from SRUCs Crop & Soil Systems group for their help with field work and sample processing during the project.

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Relationships between yield and agronomic traits among geographic groups of barley landraces grown under high yielding conditions

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1 Introduction

2 Materials & Methods

2.1 Plant material

2.2 Measurement of plant traits and yield components

2.3 Data analysis

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