The potential contribution of wild barley (Hordeum vulgare ssp. spontaneum) germplasm to drought tolerance of cultivated barley (H. vulgare ssp. vulgare)

Field Crops Research 120 (2011) 161–168

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The potential contribution of wild barley (Hordeum vulgare ssp. spontaneum) germplasm to drought tolerance of cultivated barley (H. vulgare ssp. vulgare) B. Lakew a , J. Eglinton b , R.J. Henry c,1 , M. Baum d , S. Grando d , S. Ceccarelli d,∗ a

Holetta Agricultural Research Centre, Ethiopian Institute of Agricultural Research, P.O. Box 2003, Addis Ababa, Ethiopia School of Agriculture, Food and Wine, Faculty of Sciences, Waite Campus, University of Adelaide, PMB1 Glen Osmond, SA 5064, Australia Centre for Plant Conservation Genetics, Southern Cross University, P.O. Box 157, Lisq1Vmore, NSW 2480, Australia d Biodiversity and Integrated Gene Management Program, ICARDA, P.O. Box 5466, Aleppo, Syria b c

a r t i c l e

i n f o

Article history: Received 12 June 2010 Received in revised form 20 September 2010 Accepted 21 September 2010 Keywords: Drought Wild relatives Climate changes Specific adaptation Abiotic stresses

a b s t r a c t Improving drought tolerance has always been an important objective in many crop improvement programs and is becoming more important as one way of adapting crops to climate changes. However, due to its complexity, the genetic mechanisms underlying the expression of drought tolerance in plants are poorly understood and this trait is difficult to characterize and quantify. This study assessed the importance of the wild progenitor of cultivated barley, Hordeum spontaneum C. Koch, in contributing developmental and yield-related traits associated with drought tolerance and therefore its usefulness in breeding for improved adaptation to drought stress conditions. Fifty-seven fixed barley lines derived from crosses with two H. spontaneum lines (41-1 and 41-5) were evaluated in Mediterranean low rainfall environments with 10 improved varieties and three landraces for grain yield, developmental and agronomic traits. The study was conducted for three years (2004–2006) in a total of nine environments (location–year combinations), eight in Syria and one in Jordan, which were eventually reduced to seven due to a large error variance in two of them. There was significant genetic variation among the genotypes for most of the traits measured, as well as differential responses of genotypes across environments. Traits such as peduncle length, peduncle extrusion and plant height were positively correlated with grain yield in the dry environments. Differences in phenology were small and not significantly correlated with differences in grain yield under stress. Performances at the three highest yielding environments were much more closely correlated than those at the four stress environments. The GGE biplot analysis allowed identification of genotypes consistently best adapted to the lowest yielding environments and confirmed the existence of unique environments for identifying better adapted genotypes in the low rainfall environments of Syria. The top yielding lines in the driest of the seven environments derived mostly from crosses with H. spontaneum 41-1, while most of the improved varieties showed a positive genotype by environment (GE) interaction with the highest yielding environments. The results of the field experiments indicated that there was variation for grain yield under drought stress among barley genotypes, and that some of the lines derived from H. spontaneum had consistently superior specific adaptation to the range of severe stress conditions used in this study. The usefulness of H. spontaneum in breeding programs for stress conditions is likely to increase in view of the predicted increase in the occurrence of high temperatures and droughts. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Among the abiotic stresses, drought is by far the most complex and devastating on a global scale (Ceccarelli, 2010; Pennisi, 2008) and its frequency is expected to increase as a consequence

∗ Corresponding author. E-mail address: [email protected] (S. Ceccarelli). 1 Present address: Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072, Australia. 0378-4290/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2010.09.011

of climate changes (Ceccarelli et al., 2010). Breeding crops with improved drought tolerance is one approach to alleviating the effect of drought on crop production. However, progress towards this goal has been slow because of the polygenic nature of the inheritance of drought tolerance; the high level of environmental variation often involved in terms of intensity and timing of drought stress and its interaction with other environmental factors, mostly temperature extremes; and the difficulty in quantifying drought tolerance. It has been suggested (Passioura, 2006) that the use of water productivity, being quantifiable with units of crop yield per volume of water supplied or used, should be preferred over the

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B. Lakew et al. / Field Crops Research 120 (2011) 161–168

Table 1 Level of introgression with H. spontaneum of 57 barley lines used in this study together with 13 checks. Group no. 1 2 3 4 5 6 7

No. of Lines 7 15 6 5 12 6 6

Level of introgression

Example

1st generation H. spontaneum 41-1 2nd generation H. spontaneum 41-1 H. spontaneum 41-1/Tadmor crosses withwide elite background 1st generation of H. spontaneum 41-5 2nd generation of H. spontaneum 41-5 3rd generation of H. spontaneum 41-1 Double introgression of H. spontaneum 41-5

H. spont.41-1/Tadmor Hml//H. spont.41-1/Tadmor H. spont.41-1/Tadmor/4/Gloria’S “/Copal’S”//Abn/3/Shyri SLB39-39/H. spont.41-5 Zanbaka//H. spont.41-5/Tadmor Moroc9-75//H. spont.41-1/Tadmor/ 3/Moroc9-75/ArabiAswad Clipper/3/JLB37-74/H. spont.41-5//JLB37-74/H. spont.41-5

Entry no.

Name

Description

58 59 60 61 62 63 64 65 66 67 68 69 70

Harmal WI 2291 Tadmor Zanbaka Arta Sara Keel Saesea Shege Atsa Arabi Abiad Arabi Aswad Barque

Improved cultivar from Syria Improved cultivar from Australia Improved cultivar from Syria selected from A. Aswad Improved cultivar from Syria selected from A. Aswad Improved cultivar from Syria selected from A. Abiad Improved cultivar from Syria Improved cultivar from Australia Landrace from Ethiopia Improved variety from Ethiopia obtained from a landraces Landrace from Eritrea Syrian white seeded landrace Syrian black seeded landrace Improved cultivar from Australia

less quantifiable terms ‘drought tolerance’ or ‘drought resistance’. Notwithstanding its advantages, the use of water productivity in field experiments, particularly in Multi Environment Trials (MET) requires accurate measurements of available moisture, including residual moisture, which in our case were not available. In addition we believe that the concept of water productivity does not account for the importance of rainfall distribution, particularly in marginal environments. The genetic and physiological bases of yield in water-limited conditions is far from well understood (Boyer, 1996; Ceccarelli and Grando, 1996; Passioura, 1996, 2002) even though single genes, such as those controlling flowering time, plant height, ear type and osmotic adjustment, may have important roles in adaptation to some specific drought–prone environments (Cattivelli et al., 2011). Barley is one of the cereal crops best adapted to several abiotic stresses including drought. Barley also provides a simple genetic model for evaluating mechanisms of drought tolerance (Baum et al., 2004, 2007; Ceccarelli, 1987) and therefore has often been used in studies conducted to understand the genetic basis of drought tolerance. However, the majority of these studies have used simulated drought conditions with a remarkable increase in precision but a questionable relevance to actual drought conditions. Two key issues in breeding for drought tolerance are the choices of selection environment and of germplasm (Ceccarelli, 1996; Cleveland, 2001). In the case of barley, breeding programs for tolerance to drought-specific traits may be introgressed from wild barley and landraces in backcrossing programs (Nevo, 1992). Wild barley is a rich source of genetic diversity and its use has been considered potentially beneficial for the improvement of drought tolerance (Baum et al., 2003; Ceccarelli et al., 2004; Forster et al., 1997; Grando et al., 2001; Ivandic et al., 2000). Breeders have long recognized the inherent value of wild species for the improvement of important agronomic, disease and quality traits, and therefore introgression of germplasm from wild barley could be useful to increase the genetic variation for characters that contribute to drought tolerance in barley. The objective of this study was to assess the importance of wild barley in increasing barley yields in marginal environments characterized by severe moisture stress. Such areas predominate in the barley production systems of most farmers in South Europe, Australia, North Africa, the Horn of Africa and the Near East and they are expected to be particularly affected by climate changes.

2. Materials and methods 2.1. Plant materials The International Center for Agricultural Research in the Dry Areas (ICARDA) barley breeding program has evaluated several hundred wild barley accessions and identified two wild barley lines, namely H. spontaneum 41-1 and 41-5 as having superior adaptation to drought stress (Grando et al., 2001). These two lines have been used extensively as parents in the ICARDA barley breeding program and the progenies have gone through several cycles of selection and recombination, and have generated breeding lines with improved adaptation to drought stress under Mediterranean low rainfall environments. In this study, 70 advanced breeding lines, of which 57 carried various levels of introgression from H. spontaneum 41-1 and 41-5 (introgression lines), and 13 varieties and landraces from ICARDA, Australia and Ethiopia/Eritrea (Table 1) were examined. The 57 lines were classified in groups based on the level of introgression with H. spontaneum and on the H. spontaneum parent. Three of the varieties and landraces (Barque, Atsa and Shege) have a high yield potential in their area of adaptation, two (Arabi Abiad and Arabi Aswad) are the two most widely grown landraces in Syria, and the other seven have various degrees of drought tolerance. The introgression lines were not genetically balanced in the sense that the vulgare parent was not the same at the different levels of introgression. For two of the introgression lines we did not have enough seed at the end of the first year and so they were replaced by Harmal and WI2291, two improved lines adapted to the area where the experiment was conducted. 2.2. Site description and experimental layout The 70 lines were field tested during 2003–2004 (04), 2004–2005 (05), and 2005–2006 (06) at two locations in Syria, Tel Hadya (TH: 36◦ 01 N, 36◦ 56 E, elevation 284 m asl) and Breda (BR: 35◦ 56 N, 37◦ 10 E, elevation 300 m asl) with a long-term average rainfall of 343 mm (30 seasons) and 275 mm (25 seasons), respectively, and hence representing, in the case of barley, a favorable and a stress environment, respectively. The field layouts were rectangular and comprised 14 rows and 10 columns in all locations. In 2005, three more environments were added to represent a higher

B. Lakew et al. / Field Crops Research 120 (2011) 161–168

level of drought stress by using a late planting at Breda (BRL05) and by adding two additional dry locations, namely Khanasser in Syria (KH: 30◦ 14 N, 28◦ 55 E, elevation 200 m asl) and Khanasri in Jordan (KHJ: 32◦ 24 N, 36◦ 03 E, elevation 800 m asl), known from previous experimental work as two locations providing severe stress – with long-term average rainfall of 221 and 150 mm, respectively. Therefore, the lines were tested in a non-orthogonal set of nine environments (location × year combinations). Growing season rainfall varied from a maximum of 381.3 mm in TH04 to a minimum of 74.8 at BRL05 with KHJ and KH being also very dry (138 and 175 mm, respectively). The experimental design was an alpha lattice with two replications and seven incomplete blocks per replication; a plot consisted of eight rows, spaced 0.2 m apart and the row length was 2.5 m, with 3.0 m2 harvested from six central rows for yield determination. Seeds were sown using a plot drill with a seeding rate of 125 kg/ha. Plots were kept free from weeds, diseases and insect pests by a combination of chemical and hand weeding. Data were collected on the following 12 developmental and yield related traits, from which one additional trait was derived. 1. Growth habit (GH) scored visually at 5–6-leaf stages (1 = erect; 5 = prostrate) in all environments, 2. Early growth vigor (GV) scored visually at 5–6-leaf stages (1 = good vigor, 5 = poor vigor) in all environments, 3. Days to heading (DH) as number of days from emergence to 50% of the ears completely emerged from the leaf sheaths, measured in all environments, 4. Plant height (PH in cm) measured from the soil surface up to the bottom of the spike in all environments except KHJ, 5. Spike length (SL in cm) measured from the base of the spike to the top of the spike excluding awns in all environments except KHJ, 6. Peduncle length (PED in cm) measured from the last node to the base of the spike in all environments except KHJ, 7. Peduncle extrusion (PEDEX in cm) measured from the ligule of the flag leaf to the base of the spike in all environments except KHJ, 8. Number of seeds per spike (NGS) as average number of seeds counted from five ears collected randomly at maturity in all environments except KHJ, 9. 1000 grain weight (TKW in g) measured on a sample of 250 seeds, only in the normal planting in TH and BR, and in KH (seven environments), 10. Grain yield (GY in kg/ha) as transformed plot yield measured after harvesting in all environments, 11. Biological yield (BY in kg/ha) as aboveground biomass collected from 1 m2 at maturity, measured only in BRL05 and KHJ, 12. Harvest index (HI) derived as: GY/(GY + BY), measured only in BRL05 and KHJ. 2.3. Data analysis Genotypic variability in each individual environment was based on the two-stage procedure developed by Singh et al. (2003) – first identifying the spatial pattern best suited for the specific environment using a selection criteria, and then fitting such a spatial pattern to estimate the genotype effects. Singh et al. (2003) screened the best spatial pattern out of 18 combinations of block structures, local fertility trends and spatially correlated plot errors, and the selection of the model was done using Akaike information criteria. The restricted maximum likelihood method (REML) was used to estimate the parameters of such a mixed model formulation. Statistical significance of genotypic variability was assessed using a chi-square based Wald-test when the genotypes’ effects

163

were assumed fixed and the inference is specific to the genotypes used. Further, the genotypes were considered to be a random sample from a population of infinite size and therefore the data was modeled with the above spatial pattern and with genotype effects assumed random. We used such a model to estimate broad-sense heritability (h2 ), and obtain the best linear unbiased predictor (BLUP) estimates of the genotypes for each environment, along with their standard errors. The genotype × environment interaction was assessed using a weighted analysis of variance by analyzing the BLUP estimates over the environments. All the statistical analyzes were carried out in GenStat Release 9.1 environment (Payne et al., 2006). Two locations (BRL05 and KHJ05) had coefficients of variation (CV) > 30% and were therefore excluded from subsequent analyzes. The adaptation of the genotypes to the remaining seven environments was investigated by GGE-biplot software (Yan et al., 2000) which visualizes genotypic main effects (G) and genotype by environment interaction (GE) effects. GGE biplot software was also used to analyze the ‘which won where’ patterns (Yan and Kang, 2003) – a polygon was drawn connecting the entries which lay farthest from the origin such that all other entries were contained within the polygon; then a set of lines perpendicular to each of the sides of the polygon were drawn that divided the biplot into sectors. The winning, i.e. the best performing for the trait being analyzed, genotypes in one or more locations were those falling within the same sector as those locations. The biplot analysis was also used to graphically represent the relationships between the traits at the level of the groups listed in Table 1. Provided that the biplot explained a sufficient amount of the total variation (>60%), the correlation coefficient between any two traits is approximated by the cosine of the angle between their vectors (Yan and Rajcan, 2002). Thus, r = cos 180◦ = −1, r = cos 0◦ = 1, and r = cos 90◦ = 0. The relationships were analyzed independently for the non-stress and the stress environments as defined below. The drought susceptibility index (DSI) was calculated according to Fischer and Maurer (1978) as DSI = [1−(YD/YP)]/D; where YD is the mean yield in the stress environment, YP is the mean yield in the non-stress environment (potential yield), and D is the environment stress intensity, calculated as 1 − (mean YD of all genotypes/mean YP of all genotypes). Genotypes with lower DSI values are considered drought tolerant. The environments used to estimate YP (TH04, TH05 and TH06) and those used to estimate YD (BR04, BR05, BR06 and KH05) were determined by the pattern of GE interactions in the GGE biplot. In TH04 and BR04, the data of Harmal and WI2291 used to replace the two introgression lines in the second and third year were considered as missing data.

3. Results Grain yield varied widely between locations and years (Table 2) and in each of the three years, as expected, the highest average yields (3312–4532 kg/ha) were obtained in TH, the location with the highest rainfall. BR was intermediate, with yields ranging from 1243 (BR04) to 2145 kg/ha (BR06), while the three environments added in 2005 – to represent severe stress conditions – had the lowest yields ranging from as little as 38 in KHJ to 306 kg/ha in the late planting at BR05, to 771 kg/ha in KH: these were also the three environments with the lowest amount of rainfall. GH and GV were not affected by rainfall, with a marked change only in KHJ05, where the extreme drought hastened crop development and plants were more erect and less vigorous than in other environments.

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Table 2 Growing season rainfall (mm), grain yield (GY in kg/ha), days to heading (DH in days from emergence), plant height (PH in cm), spike length (SL in cm), peduncle length (PED in cm), peduncle extrusion (PEDEX in cm), 1000 kernel weight (TKW in g) and number of grains per spike (NGS) on the 70 barley genotypes in the nine location × year combinations. Trait

TH04

TH05

TH06

BR04

BR05

BR06

KH05

BRL05

KHJ05

Mean

GY GH GV DH PH SL PED PEDEX TKW NGS Rainfall

3312 2.84 2.40 100.3 90.2 8.1 25.9 5.4 43,3 23.8 381.3

4532 2.11 2.22 93.3 108.9 9.2 26.5 6.7 42.0 30.8 303.3

3452 2.04 1.95 85.9 100.6 9.1 30.5 9.0 35.9 20.9 262.0

1243 2.33 2.32 100.2 55.1 7.1 18.8 −0.78 43.0 18.7 260.4

1912 2.21 2.62 85.7 63.2 8.8 16.6 −3.8 39.7 23.3 264.8

2145 2.30 2.39 89.3 69.3 8.3 22.7 1.3 37.9 23.4 202.9

771 2.42 2.54 112.8 43.2 5.8 14.3 −2.0 35.6 17.5 175.0

306 2.68 2.66 90.3 39.9 6.7 8.9 −9.3 n.a. n.a. 74.8

38 1.44 4.19 88.0 n.a. n.a. n.a. n.a. n.a. n.a. 138.0

1972 2.26 2.59 94.0 71.3 10.1 18.1 0.80 39.2 22.3 229.2

n.a. = not available.

DH varied from about 113 d from emergence in KH05 to about 86–90 d in TH06, BR06, BR06, BRL05 and in KHJ05, with no relation with rainfall. Plant height was greatly affected by the decreasing rainfall, even though there were differences between years within locations, and was reduced from nearly 100 cm (average of the three years in TH), to about 40 cm in the two driest locations (KH05 and BR05L) in which this trait was measured. Similarly, spike length, peduncle length and number of seeds per spike were reduced by 30, 58 and 19%, respectively, from TH to the two driest locations (KH05 and BR05L) in which traits were measured. Peduncle extrusion went from positive in TH (spikes fully emerged above the collar of the flag leaf) to negative in BR (except in 2006 – the highest yielding year in that location) and KH (spikes partially inside the boot). In BR05L the entire spike remained within the boot as shown by the absolute value of PEDEX > SL. The 1000-kernel weight went from 40.4 g (average of three years in TH) to 40.2 g (average of three years in BR) to 35.6 g in KH05, with a reduction of only 9%. Individual environment spatial-analysis of data for grain yield and drought-related traits in nine environments (location–years) showed that with few exceptions (e.g. SL at TH04, GH at KH05, and HI at BRL05), there were significant differences amongst the genotypes for all the traits measured (data not shown). The CVs for most of the traits measured were relatively low (<30%) except for PEDEX and GY in BRL05, and for traits measured at KHJ05. The phenotypic correlation coefficients between the means of the 70 genotypes in the nine environments (Table 3) and rainfall showed that rainfall significantly reduced GY (r = 0.801, P < 0.01), PH (r = 0.737, P < 0.05), PED (r = 0.756, P < 0.05) and PEDEX (r = 0.745, P < 0.05). Grain yield was independent of phenology, and positively and significantly correlated with PH, PED and PEDEX (P < 0.01) and with NGS (P < 0.05). Good early growth vigor (low GV score) was positively correlated with PH (P < 0.05) and with PED and PEDEX

(P < 0.01). As expected, there were strong positive correlation coefficients between PH and SL, and between PED and PEDEX. The estimates of h2 for most of the traits computed across the nine environments were high, indicating that a high percentage of the observed difference was due to genetic effects (data not shown). In the case of GY, h2 varied from a minimum of 0.31 in BR04 to a maximum of 0.81 in TH06. The overall average grain yield value for the 57 introgression lines across the seven environments with the lowest CV was 2463 kg/ha (range 1930–3044) while the mean grain yield of the improved lines and landraces was 2639 kg/ha (range 1896–3048; Table 4). The 10 entries with the largest mean yield across the seven environments included five improved varieties (WI2291 – the highest yielding entry overall, Harmal, Arta, Keel and Barque) and five introgression lines: one belonging to group 1 (line 2 = H. spont.411/Tadmor), one to group 2 (line 10 = PI386540/ArabiAbiad//H. spont.41-1/Tadmor), one to group 4 (line 29 = SLB39-39/H. spont.41-5) and two to group 5 (line 42 = ArabiAbiad/Arar//H. spont.41-5/Tadmor, the fourth-highest yielding entry overall; and line 34 = Arta//H. spont.41-5/Tadmor). Among the introgression lines, group 3 was the lowest yielding (2097 kg/ha) across the seven environments and groups 6, 7 and 4 (2642, 2572 and 2568 kg/ha, respectively) the highest yielding. The GGE biplot (Fig. 1) accounted for 70.5% of the variation due to G and GE and showed three main clusters of environments, the first comprising the three highest yielding environments (TH04, TH05 and TH06), the second comprising the two highest yielding years in Breda (BR05 and BR06), and the third comprising the two lowest yielding environments (BR04 and KH05). However, while the first cluster clearly corresponds to one mega-environment with little genotype × year (GY) interaction (the ranking of genotypes is similar in the three years as indicated by the narrow angle between

Table 3 Correlation coefficients (r) according to Pearson between 7 agronomic traits and growing season rainfall (R = rainfall, GY = grain yield, DH = days to heading, PH = plant height, SL = spike length, PED = peduncle length, PEDEX = peduncle extrusion, KW = 1000 kernel weight, NGS = number of grains per spike). Values in bold significantly different from zero at P < 0.01, values in italics significantly different from zero at P < 0.05 (df = 68).

GY GH GV DH PH SL PED PEDEX TKW NGS Rainfall

GY

GH

GV

DH

PH

SL

PED

PEDEX

TKW

NGS

1.000 0.142 −0.652 −0.136 0.990 0.832 0.899 0.888 0.290 0.843 0.801

1.000 −0.600 0.439 −0.394 −0.541 −0.409 −0.393 0.409 −0.187 0.238

1.000 −0.164 −0.773 −0.573 −0.878 −0.886 −0.112 −0.165 −0.499

1.000 −0.345 −0.741 −0.243 −0.078 0.028 −0.444 0.116

1.000 0.843 0.928 0.912 0.230 0.747 0.737

1.000 0.750 0.643 0.185 0.727 0.582

1.000 0.451 −0.227 −0.455 0.426

1.000 0.190 0.574 0.790

1.000 0.474 0.736

1.000 0.578

B. Lakew et al. / Field Crops Research 120 (2011) 161–168

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Table 4 Best linear unbiased predictors (BLUPs) of grain yield (kg/ha) of the 57 barley lines and the 13 checks in seven combinations of years and locations, mean grain yield (rank in parenthesis) and drought susceptibility index (DSI). No.

Group

TH04

TH05

TH06

BR04

BR05

BR06

KH05

Mean

DSI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 Tadmor Zanbaka Arta Sara Keel Saesea Shege Atsa Arabi Abiad Arabi Aswad Barque Harmal WI2991 LSD0.05

2302 4325 3713 2610 2642 2929 3067 3707 3200 4270 3223 2532 3160 3225 3307 3016 3586 2973 3817 3203 3047 3220 2951 3273 3057 3026 2799 2915 3719 3339 3718 2993 3231 3756 3291 3148 3126 2829 3521 2386 3806 4318 3013 3240 3067 3412 3750 3930 3158 3119 3212 3811 3214 3559 3263 3466 3766 3069 3295 3819 2655 4502 3564 3480 2567 3219 3244 4570

3656 5010 5266 3429 4669 2290 5086 5284 4402 5478 3863 3640 3745 4023 4239 3673 3839 4173 5421 4651 3946 4026 3244 4363 4149 4146 4082 3616 5984 5053 5380 3274 4654 5269 5045 3889 3531 4401 4175 3252 4732 6026 4687 4945 4736 5121 4967 5092 4921 5158 5098 4958 4371 4753 4586 4866 5365 4181 3883 5221 4312 5516 6127 4451 3883 4837 4867 5180 5774 5729 815

2817 4030 3548 2871 2932 2372 3427 3816 3477 4079 2982 3395 2477 3292 3391 3245 3254 3038 3860 3380 2772 3571 3008 3106 2793 2869 2679 2309 4051 4008 3823 3055 3079 4515 3930 2805 3189 3509 3474 2412 3453 4600 3713 3551 3518 3862 3691 3604 3654 3518 3711 3415 3870 3768 3634 3809 4185 2319 3121 3962 3525 4645 3636 3134 2067 4414 3531 3975 4512 4575 974

1086 1348 1322 1206 1247 1163 1319 1362 1297 1214 1086 1117 1446 1266 1215 1507 1327 1305 1383 1364 1331 1432 1258 1430 1273 1152 1352 1325 1298 1126 1014 1159 1358 1387 1313 1135 1459 1325 1419 1056 1577 1393 1254 1087 960 1389 1370 1354 1208 1135 1140 1167 1310 1209 1097 1105 1134 1178 1234 1606 1111 1276 855 747 823 1240 1070 1318 * * 929

1580 2263 2491 1593 2041 1824 2066 1293 1827 2338 2073 1967 2003 1939 2022 1960 1992 2200 1866 1656 1877 2443 1083 1076 1314 1338 993 1391 2202 1615 1998 2000 2082 2322 2016 2206 1905 2162 2089 1653 1813 1809 2028 1809 2066 2448 1890 2259 1947 2210 2068 1904 1540 1686 1538 1640 2089 2176 1486 2841 1775 2116 2519 968 1757 1760 1764 1734 2700 2733 276

1925 2417 2341 1817 2366 2063 2628 2210 2039 2277 2180 1973 2212 2261 2206 2267 2069 2045 1858 1988 2156 2358 1585 1661 1883 1733 1545 1731 2344 2260 2268 1819 2063 2355 2453 1987 2036 2413 2251 1942 2134 2448 2055 2264 2051 2355 2439 2074 1972 1988 2223 2102 2161 2506 2368 2161 2225 2152 2243 3049 2225 2486 2082 876 1445 2331 2370 2300 2385 2679 886

738 830 863 806 1061 871 838 826 762 594 780 706 941 838 808 895 747 781 795 833 754 927 772 824 790 730 707 733 851 810 807 695 761 805 781 865 808 790 761 868 739 715 706 762 671 741 818 799 671 803 681 779 767 733 698 673 783 701 778 836 694 628 589 488 730 805 756 910 626 760 460

2015 (65) 2889 (9) 2792 (12) 2048 (62) 2422 (44) 1930 (69) 2633 (22) 2643 (21) 2429 (43) 2893 (8) 2313 (50) 2190 (58) 2283 (54) 2406 (45) 2455 (39) 2366 (47) 2402 (46) 2359 (48) 2715 (17) 2439 (41) 2269 (55) 2568 (28) 1986 (67) 2248 (57) 2180 (59) 2142 (61) 2022 (63) 2003 (66) 2921 (6) 2602 (25) 2716 (16) 2142 (60) 2461 (38) 2915 (7) 2690 (19) 2291 (53) 2293 (51) 2490 (36) 2527 (31) 1938 (68) 2608 (23) 3044 (4) 2494 (35) 2523 (32) 2439 (42) 2761 (14) 2703 (18) 2730 (15) 2504 (34) 2562 (29) 2590 (27) 2591 (26) 2462 (37) 2602 (24) 2455 (40) 2532 (30) 2793 (11) 2254 (56) 2292 (52) 3048 (3) 2328 (49) 3024 (5) 2767 (13) 2021 (64) 1896 (70) 2658 (20) 2514 (33) 2855 (10) 3200 (2) 3295 (1) 386

0.908 1.025 0.966 0.906 0.847 0.692 0.927 1.111 0.998 1.086 0.907 0.914 0.787 0.919 0.952 0.833 0.949 0.890 1.103 1.017 0.884 0.839 1.029 1.086 1.009 1.050 1.066 0.934 1.058 1.081 1.078 0.906 0.953 1.033 0.998 0.880 0.879 0.888 0.937 0.810 1.014 1.134 1.005 1.036 1.032 0.967 1.010 1.025 1.049 1.016 1.031 1.056 1.036 1.032 1.046 1.092 1.082 0.856 0.970 0.866 0.975 1.112 1.100 1.319 0.969 1.052 1.027 1.096 1.050 1.001

a

Missing data.

a a

851

166

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Fig. 3. GGE biplot for grain yield data in seven year × location combinations of 8 groups of barley genotypes including the 7 groups of lines introgressed with H. spontaneum described in Table 1.

Fig. 1. GGE biplot for grain yield data in seven year × location combinations of 70 barley genotypes including the 7 groups of lines introgressed with H. spontaneum described in Table 1. Not all genotypes are shown. The exact position of the genotypes is the beginning of the label.

the three vectors), it seems more realistic to consider the three years of BR as a second mega-environment (moderate stress) and KH a representative of a severe moisture stress environment. This was tested by analyzing the ‘which won where’ patterns in the GGE biplot (Fig. 2), which confirmed the grouping of the locations and years in three sectors: one comprising TH04, TH05 and TH06; the second comprising BR04, BR05 and BR06; and the third comprising only KH05, the lowest yielding location.

Fig. 2. The which-won-where pattern of the GGE biplot shown in Fig. 1 for grain yield data in seven year × location combinations of 70 barley genotypes including the 7 groups of lines introgressed with H. spontaneum described in Table 1.

The two mega-environments including TH and BR differed clearly in the amount of GY interaction (as suggested by the spreading of the vectors), with a higher GY in the mega-environment comprising BR. The best performing genotypes in the highest yielding mega-environment were Keel, Harmal, and genotype no. 42 (ArabiAbiad/Arar//H.spont. 41-5/Tadmor, from group 5). The best performing genotypes in the moderately stressed megaenvironment were Arta, Barque and WI2291 among the improved varieties, three genotypes from group 1 (genotype no. 3 = H.spont. 41-1/Tadmor; genotype no. 7 = SLB05-96/H.spont. 41-1; and genotype no. 2 = H.spont. 41-1/Tadmor), one from group 2 (genotype no. 22 = Sara/3/H.spont. 41-1//ER/Apm) and one from group 5 (genotype no. 34 = Arta//H.spont. 41-5/Tadmor). The best performing genotypes in the location with the highest level of moisture stress were one from group 1 (line 5 = H.spont. 41-1/Tadmor) and two from group 2 (line 13 = Tadmor//H.spont. 41-1/SLB39-39 and line 16 = Zanbaka//H.spont. 41-1/SLB39-39). The cultivars derived from the Syrian landraces (Tadmor, Zanbaka and Sara) were located near the origin, showing an average performance in most of the environments, while, as expected, the varieties from Ethiopia and Eritrea (Saesea, Shege and Atsa) were poorly adapted to all locations. The GGE biplot of the average yields of the eight groups of genotypes (Fig. 3) confirms that group 1 (1st generation H. spontaneum 41-1) was, on average, the best adapted to the lowest yielding environment (KH05) followed by group 2 (2nd generation H. spontaneum 41-1) and by group 5 (2nd generation of H. spontaneum 41-5). These were the only three groups having a higher than average adaptation to all the four stress environments. Other groups, such as group 3 (H. spontaneum 41-1/Tadmor crosses with elite background), although specifically adapted to one of the stress environments (BR04), were poorly adapted to the other environments. Groups 4, 5, 7 and 8 had a higher than average adaptation to TH (all three years) and also to BR05 and BR06, but were poorly adapted to BR04 and KH05. The two mega-environments indicated by the GGE biplot were used to estimate YD and YP in the calculation of the DSI. YD was estimated as the mean grain yield of BR04, BR05, BR06 and KH05, and YP was estimated as the mean of TH04, TH05 and TH06. The drought intensity, D = 1 − (mean YD/mean YP), was 0.60. The DSI ranged from 0.692 (line 6 = SLB05-96/H. spont.41-1) to 1.319 (the variety Shege) (Table 4). The 25 entries with the lowest DSI (higher drought tolerance) were all introgression lines (15 of which belonged to either group 1 or 2) except Tadmor and Arta (known for their drought tolerance) which ranked 7th and 8th,

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167

Table 5 Days to heading (DH in days from emergence), plant height (PH in cm), 1000 kernel weight (TKW in g) peducle length (PED in cm), peduncle extrusion (PEDEX in cm), spike length (SL in cm), and number of grains per spike (NGS) on the 8 groups in the nine location × year combinations. Group

1 2 3 4 5 6 7 8 LSD0.05

DH

PH

KW

PED

PEDEX

SL

NGS

NS

S

NS

S

NS

S

NS

S

NS

S

NS

S

NS

S

90.9 e 94.4 a 92.9 c 90.7 e 93.9 b 93.6 b 91.7 d 93.8 b

94.5 d 97.8 b 96.0 c 94.5 d 97.7 b 98.7 a 95.7 c 98.1 b

102.3 a 102.4 a 100.2 a 99.0 b 100.8 ab 100.7 ab 100.2 ab 94.6 c

59.4 ab 59.8 ab 61.2 a 54.1 d 57.3 c 58.3 b 59.8 abc 53.4 d

38.5 c 40.8 c 37.8 e 44.1 a 39.3 d 39.3 d 40.6 c 42.1 b

37.6 c 39.4 b 38.0 c 40.4 a 37.9 c 38.0 c 39.7 b 40.5 a

29.1 a 28.4 a 27.9 a 28.0 a 27.2 a 27.7 a 28.6 a 25.6 b

19.7 a 18.6 ab 19.2 a 17.0 cd 17.6 bc 19.0 a 19.5 a 16.1 d

9.03 a 7.25 b 7.19 b 7.35 b 7.41 b 6.58 bc 7.02 b 5.38 c

0.46 a −1.34 bc −0.39 ab −2.26 cd −0.95 b −0.88 b −1.00 b −3.09 d

8.7 9.0 8.8 8.7 8.9 8.8 8.8 8.6 NS

7.4 7.8 7.5 7.5 7.4 7.5 7.3 7.3 NS

25.4 ab 25.9 a 24.4 b 25.0 ab 25.5 ab 25.2 ab 24.5 b 24.7 b

19.7 c 21.0 a 19.6 c 19.9 c 20.5 ab 19.6 c 19.2 c 20.0 bc

NS = non-stressed.; S = stressed environments.

respectively. However, as DSI is a relative measure, not all the low DSI lines had good adaptation to the stress environments; in fact the first 25 entries for YD included only seven of the lines with the lowest DSI, five of which belong to either group 1 or 2. Among the improved varieties, Tadmor and Arta, two varieties adopted by farmers in the rainfed areas of Syria had the lowest DSI (0.856 and 0.866, respectively). The other improved varieties such as Barque, Shege, Saesea and Keel were among those with the highest DSI (1.096, 1.100, 1.112 and 1.319, respectively). Interestingly, all selections from the two Syrian landraces had a lower DSI than the respective original landrace. The lines of the first group of introgression lines had the lowest average DSI (0.896), confirming previous indications that this is likely to be, on average, the most drought tolerant group. The average DSI of the groups of introgression lines derived from crosses with H. spontaneum 41-1 (0.896 for group 1, 0.946 for group 2, 1.029 for group 3, and 1.016 for group 6) seemed to indicate that as the contribution of the spontaneum parent decreased, the drought susceptibility increased. However, this was not the case for the lines derived from crosses with H. spontaneum 41-5 (1.015 for group 4, 0.970 for group 5, and 1.057 for group 7). The comparisons above show that the lines derived from crosses with H. spontaneum 41-5 at the same level of introgression always had a value of DSI higher than the corresponding crosses with H. spontaneum 41-1. In the case of the 1st generation introgression lines, those derived from H. spontaneum 41-1 had a DSI of 0.896 and those derived from H. spontaneum 41-5 had a DSI of 1.015. In the case of the 2nd generation introgression lines, those derived from H. spontaneum 41-1 had a DSI of 0.946 and those derived from H. spontaneum 41-5 had a DSI of 0.970. The 3rd generation introgression lines derived from H. spontaneum 41-1 had a DSI of 1.016, while the double introgression of H. spontaneum 41-5 had a DSI of 1.057. The largest difference in phenology (measured as days to heading) was <3 d in non-stress and <4 d in stress environments (Table 5), with group 1 always the earliest or among the earliest heading. With the exception of group 4 in the stress environments, all the seven groups of introgression lines were significantly taller, had significantly smaller kernels, had a significantly longer peduncles, and longer peduncle extrusion than the average of the improved varieties. There were no differences in spike length, while the introgression lines had a similar number of seeds per spike as the improved varieties. 4. Discussion A field experiment was conducted in nine contrasting environments (non-orthogonal combinations of three locations and three years) with a grain yield range of 38–4532 kg/ha. The two lowest yielding environments (BRL05 with 306 kg/ha and 74.8 mm rain-

fall, and KHJ05 with 38 kg/ha and 138 mm rainfall) represented a simulated (by late planting in a low rainfall site) and a real highmoisture-stress production environment, respectively. The inverse relationship between grain yield and rainfall is likely due to the presence of residual moisture in BRL05 (planting was in January after >100 mm rainfall). Such production levels are increasingly common in the barley growing areas of the Fertile Crescent and, depending on the prices of grain and straw, farmers decide when it is convenient to harvest the grain or when the crop is to be grazed. In this study we could not use the data from these two environments (as CV was very high); however, the use of such environments in the breeding program increases the probability of exposing the breeding materials to high levels of stress such as those expected to become more frequent due to climate changes. The study confirmed that stress environments represent a much more heterogeneous population of target environments than nonstress environments (Ceccarelli et al., 2007). This is probably due to the variability in the frequency, timing and severity of various climatic stresses, and particularly to the differences between years, not only in the amount of rainfall but in its distribution, and in the interaction between rainfall and temperature (particularly winter temperatures). Hence, this leads to the difficulty and lack of progress in selection for grain yield in a single location that receives low and variable amounts of rain. It is not surprising that the examples of successful breeding programs for dry environments based on an analytical approach come from areas with mild winters and stored moisture (Richards et al., 2002), and that it is easier to find consistency of performance when breeding material is tested in non-stress, as opposed to stress, conditions. Due to the large variability in the stress macro-environment defined by the GGE biplot, different entries performed well in specific year × location combinations within the macro-environment represented by the three years in BR and KH05. It was therefore interesting that some introgression lines – particularly single crosses with H. spontaneum 41-1 (group 1 in Table 1), and to a lesser extent the top crosses of group 1 with an improved cultivar (group 2) and single crosses with H. spontaneum 41-5 (group 5) – had a positive interaction with all the four lowest yielding (i.e. most stressed) locations and years. A note of caution in attributing the positive effect on grain yield in stress environments to the H. spontaneum 41-1 parent is necessary because the material used in this study was not genetically balanced (the better vulgare parent, namely Tadmor, was in fact used more frequently with H. spontaneum 41-1 than with H. spontaneum 41-5) and therefore a role of the vulgare parent cannot be ruled out. It is interesting that Tadmor and Zanbaka, two pure lines selected from the landrace Arabi Aswad and widely adopted by farmers in the dry areas of Syria, comprised a significant proportion of the parentage of the materials, and yet on average the wild barley

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derivatives performed better than these key parents. This supports the view that the wild barley contributed favorable alleles. The ‘which won where’ patterns of the GGE biplot analysis identified three distinct mega-environments. The first was represented by Tel Hadya 04, 05 and 06, where improved varieties such as Harmal and Keel but also some introgression lines had the highest mean yields; the second contained locations Breda 04, 05 and 06, where improved varieties such as WI2291, Arta, Barque and Keel and a number of introgression lines had the highest mean yields; and the third mega-environment included the lowest yielding environment, namely Khanaser 05, where only introgression lines were the best yielding genotypes. The results show an interesting aspect of the relationship between stress and non-stress environments. Figs. 1 and 2 show that, because of the heterogeneity of stress environments, in the case of genotype × environment interaction studies based on a limited number of location/year combinations, it is always possible to find cases of absence of correlation (e.g. BR05 and TH05; Fig. 1) or of positive correlation (e.g. the three years in TH, and also BR05 and BR06). This may also be the case in studies based on simulated drought that artificially remove the characteristic unpredictability of stress environments and real drought conditions. Because of the expected large GY interaction in stress environments, studies on the relationships between genotypic performance in stress and nonstress environments, as well as studies on drought tolerance, should be conducted in a sufficiently large sample of real (non-simulated) environments representing such variability in order to be relevant. Simulated drought conditions can be used as an addition to, but not as a substitute for, real drought conditions. Overall, the barley lines exhibited high variation for yield, developmental and agronomic traits across the various drought-stress environments, which suggests the existence of genetic variability for drought tolerance. Therefore, the performance of the wildbarley-derived lines indicated that H. spontaneum could be a useful source of genes for improved drought tolerance, particularly in extreme conditions. Ongoing research will clarify whether the two lines of H. spontaneum harbor novel genes, different from those of the best H. vulgare lines. If this is the case, then with careful selection of H. vulgare parents and introgression of useful wild barley adaptive traits, together with the application of an appropriate selection method in the segregating populations, it could be possible to obtain lines with higher levels of field drought tolerance and able to cope with climate changes. References Baum, M., Grando, S., Backes, G., Jahoor, A., Ceccarelli, S., 2004. Localization of quantitative trait loci (QTL) for dryland characters in barley by linkage mapping. In: Rao, S.C., Ryan, J. (Eds.), Challenges and Strategies for Dryland Agriculture, 32. CSSA Spec. Publication, ASA and CSSA, Madison, WI, pp. 191–202. Baum, M., Grando, S., Backes, G., Jahoor, A., Sabbagh, A., Ceccarelli, S., 2003. QTLs for agronomic traits in the Mediterranean environment identified in recombinant inbred lines of the cross ‘Arta’ × H. spontaneum 41 1. Theor. Appl. Genet. 107, 1215–1225.

Baum, M., van Korff, M., Guo, P., Lakew, B., Udupa, S.M., Sayed, H., Choumane, W., Grando, S., Ceccarelli, S., 2007. Molecular approaches and breeding strategies for drought tolerance in barley. In: Varshney, R., Tuberosa, R. (Eds.), Genomic Assisted Crop Improvement, Vol. 2. Genomics Applications in Crops, Springer, Dordrecht, the Netherlands, pp. 51–79. Boyer, J., 1996. Advances in drought tolerance in plants. Adv. Agron. 56, 187– 218. Cattivelli, L., Ceccarelli, S., Romagosa, I., Stanca, M., 2011. Abiotic stresses in barley: problems and solutions. In: Ullrich, S.E. (Ed.), Barley: Improvement, Production, and Uses. John Wiley and Sons Inc, New York. Ceccarelli, S., 1987. Yield potential and drought tolerance of segregating populations of barley in contrasting environments. Euphytica 36, 265–273. Ceccarelli, S., 1996. Adaptation to low/high input cultivation. Euphytica 92, 203–214. Ceccarelli, S., 2010. Drought and Drought Resistance. Encyclopedia of Biotechnology in Agriculture and Food 1, 205–207. Ceccarelli, S., Grando, S., 1996. Drought as a challenge for the plant breeder. Plant Growth Regul. 20, 149–155. Ceccarelli, S., Grando, S., Baum, M., 2007. Participatory plant breeding in waterlimited environments. Exp. Agr. 43, 411–435. Ceccarelli, S., Grando, S., Baum, M., Udupa, S., 2004. Breeding for drought resistance in a changing climate. In: Roberts, C.A. (Ed.), Challenges and Strategies for Dry Land Agriculture. Crop Science Society of America Inc and American Society of Agronomy Inc., Madison, WI, pp. 167–190. Ceccarelli, S., Grando, S., Maatougui, M., Michael, M., Slash, M., Haghparast, R., Rahmanian, M., Taheri, A., Al-Yassin, A., Benbelkacem, A., Labdi, M., Mimoun, H., Nachit, M., 2010. Plant breeding and climate changes. J. Agr. Sci. 148, 1–11. Cleveland, D.A., 2001. Is plant breeding science objective truth or social construction? The case of yield stability. Agr. Hum. Values 18, 251–270. Fischer, R.A., Maurer, R., 1978. Drought resistance in spring wheat cultivars I. Grain yield responses. Aust. J. Agric. Res. 29, 897–912. Forster, B.P., Russell, J.R., Ellis, R.P., Handley, L.L., Robinson, D., Hackett, C.A., Nevo, E., Waugh, R., Gordon, D.C., Keith, R., Powell, W., 1997. Locating genotypes and genes for abiotic stress tolerance in barley: a strategy using maps, markers and the wild species. New Phytol. 137, 141–147. Grando, S., von Bothmer, R., Ceccarelli, S., 2001. Genetic diversity of barley: use of locally adapted germplasm to enhance yield and yield stability of barley in dry areas. In: Cooper, H.D., Spillane, C., Hodgink, T. (Eds.), Broadening the Genetic Base of Crop Production. CABI/FAO/IPRI, New York, USA/Rome, Italy/Rome,Italy, pp. 351–372. Ivandic, V., Hackett, C.A., Zhang, Z.J., Staub, J.E., Nevo, D., Thomas, W.T.B., Forster, B.P., 2000. Phenotypic responses of wild barley to experimentally imposed water stress. J. Exp. Bot. 51, 2021–2029. Nevo, E., 1992. Origin, evolution, population genetics and resources for breeding of wild barley, Hordeum spontaneum, in the Fertile Crescent. In: Shewry, R.P. (Ed.), Barley: Genetics, Biochemistry, Molecular Biology and Biotechnology. CAB International Wallingford, UK, pp. 19–43. Passioura, J.B., 1996. Drought and drought tolerance. Plant Growth Regul. 20, 79–83. Passioura, J.B., 2002. Soil conditions and plant growth. Plant Cell Environ. 25, 311–318. Passioura, J.B., 2006. Increasing crop productivity when water is scarce—from breeding to field management. Agr. Water Manage. 80, 176–196. Payne, R.W., Harding, S.A., Murray, D.A., Soutar, D.M., Baird, D.B., Welham, S.J., Kane, A.F., Gilmour, A.R., Thompson, R., Webster, R., Tunnicliffe Wilson, G., 2006. GenStat for Windows (9th Edition) Introduction. VSN International, Hemel Hempstead. Pennisi, E., 2008. The blue revolution, drop by drop, gene by gene. Science 320, 171–173. Richards, R.A., Rebetzke, G.J., Condon, A.G., van Herwaarden, A.F., 2002. Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Sci. 42, 111–121. Singh, M., Malhotra, R.S., Ceccarelli, S., Sarker, A., Grando, S., Erskine, W., 2003. Spatial variability models to improve dry land field trials. Exp. Agr. 39, 1–10. Yan, W., Hunt, L.A., Sheng, Q., Szlavnics, Z., 2000. Cultivar evaluation and megaenvironment investigation based on the GGE biplot. Crop Sci. 40, 597–605. Yan, W., Kang, M.S., 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton, FL. Yan, W., Rajcan, I., 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 42, 11–20.