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Developmentandcharacterizationofthe4thCSISA-spotblotchnurseryofbreadwheat

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Development and characterization of the 4th CSISA-spotblotch nursery of bread wheat

Pawan K. Singh & Yong Zhang & Xinyao He & Ravi P. Singh & Ramesh Chand &

Vinod K. Mishra & Paritosh K. Malaker & Mostofa A. Reza & Mokhlesur M. Rahman &

Rabiul Islam & Apurba K. Chowdhury & Prateek M. Bhattacharya &

Ishwar K. Kalappanavar & José Crossa & Arun K. Joshi

Accepted: 10 July 2015# Koninklijke Nederlandse Planteziektenkundige Vereniging 2015

Abstract Spot blotch (SB) caused by Cochliobolussativus is a serious biotic stress to wheat in warm andhumid areas, particularly South Asia (SA). In order tosupport South Asian farmers to combat SB,International Maize and Wheat Improvement Center(CIMMYT) established an efficient SB screening sys-tem at Agua Fria, Mexico and developed a nurseryunder the project - Cereal Systems Initiative for SouthAsia (CSISA). The materials used to form CSISA-SBnursery were selected from advanced breeding linesfrom different wheat breeding programs at CIMMYT.Seed of CSISA-SB nursery was produced at disease-free plots at El Batan and Mexicali, and distributed to

SA after rigorous seed health checks. The 4th CSISA-SB, made available in 2012, comprised 50 entries in-cluding two resistant and two susceptible checks. Thenursery was evaluated in seven locations in Mexico,India, and Bangladesh in the 2012–13 cropping season.The results indicated that although few lines exhibitedstable resistance across locations due to strong G × Einteraction, promising lines with SB resistance and goodagronomy can still be identified in each location. Thetwo most promising lines showing consistent spotblotch resistance across the regions were CHUKUI#1(CIMMYT germplasm bank identification number, GID6178575) and VAYI#1 (GID 6279248). These lines

Eur J Plant PatholDOI 10.1007/s10658-015-0712-x

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10658-015-0712-x) contains supplementarymaterial, which is available to authorized users.

P. K. Singh (*) :Y. Zhang :X. He :R. P. Singh : J. CrossaInternational Maize and Wheat Improvemnet Center(CIMMYT), Apdo. Postal 6-641, 06600 Mexico,DF, Mexicoe-mail: pk.singh@cgiar.org

Y. ZhangLixiahe Region Institute of Agricultural Sciences of JiangsuProvince, Yangzhou 225007, China

R. Chand :V. K. Mishra :A. K. JoshiDepartment of Genetics and Plant Breeding, Institute ofAgricultural Sciences, Banaras Hindu University,Varanasi, India

P. K. Malaker :M. A. RezaWheat Research Centre, Bangladesh Agricultural ResearchInstitute, NashipurDinajpur, Bangladesh

M. M. RahmanRegional Agricultural Research Station, BangladeshAgricultural Research Institute, Jamalpur, Bangladesh

R. IslamRegional Agricultural Research Station, BangladeshAgricultural Research Institute, Jessore, Bangladesh

A. K. Chowdhury : P. M. BhattacharyaDepartment of Plant Pathology, Uttar Banga Krishi VishwaVidyalaya, Coochbehar, W. Bengal, Indiax

I. K. KalappanavarDr. Sanjaya Rajaram Wheat Laboratory, University ofAgricultural Sciences, Dharwad, Karnataka, India

A. K. JoshiCIMMYT South Asia Regional Office, Kathmandu, Nepal

could be promoted as sources of SB resistance or direct-ly released as cultivars in SA.

Keywords Disease screening . Resistance .

Cochliobolus sativus . Triticum aestivum

Introduction

Spot blotch (SB), also known as Helminthosporium leafblight or foliar blight in South Asia (SA), is caused byCochliobolus sativus (Ito and Kurib.) Drechsler exDastur (anamorph Bipolaris sorokiniana (Sacc.)Shoem.) (Dastur 1942; Maraite 1998). As an importantbiotic stress to growing wheat in humid and warm areas,this foliar disease has long been recognized (Saari andWilcoxson 1974; Dubin et al. 1991; Dubin and Rajaram1996; Duveiller 2004; Joshi et al. 2007a; Eisa et al.2013). C. sativus is a non-specific hemi-biotrophic fun-gus with worldwide distribution and is found mainly onwheat, triticale and barley, causing seedling blight, SBon leaves, black point on grains, and common root roton the sub-crown internodes.

No host immunity to SB has been found, and evenresistant wheat varieties can suffer from a significantyield reduction. Therefore, an integrated approach in-cluding host resistance, good agronomy and reasonablechemical control is needed to reduce the losses (Dubinand Duveiller 2011). Genetic improvement is an envi-ronmentally friendly and cost-effective way to achieve asustainable control of SB in all epidemic areas and hasbeen demonstrated to be achievable while maintainingyield superiority (Joshi et al. 2007b).

Early researches on SB involved screening for newresistant germplasm from Zambia, Brazil and theYangtze River Valley in China, and many lines withsatisfactory levels of resistance were identified, whichwere then widely used in the CIMMYT’s wheat breed-ing programs and were tested in international nurseriesin epidemic regions (Dubin and Rajaram 1996; Mehtaet al. 1996; Dubin et al. 1998; van Ginkel and Rajaram1998). Resistance sources were also sought in widecross derivatives made at CIMMYT, and a number oflines with good resistance to SB were identified(Mujeeb-Kazi et al. 1996). Based on extensive evalua-tions in subsequent years, several wheat varieties withgood resistance to SB were recommended by Dubinet al. (1998). Additional resistance sources were report-ed in SA (Singh et al. 1998; Joshi et al. 2004, 2007a, b;

Sharma et al. 2004b; Sharma and Duveiller 2007).These resistant materials were extensively used andmore elite varieties with higher levels of resistance wereselected (Sharma et al. 2004a; Siddique et al. 2006;Joshi et al. 2007b). Duveiller and Sharma (2009a) pub-lished a comprehensive list of SB resistant varieties; butsources with a high level of resistance seem to belimited.

During the past two decades, genetic control of SBresistance has been extensively investigated, and the re-sults indicate quantitative genetic controls of resistance(Joshi et al. 2004; Duveiller and Sharma 2009, 2012).Moderate (0.58-0.62) to high (0.71-0.72) heritability es-timates of SB resistance have been reported, indicatingthe effectiveness of selection for this trait in segregatingpopulations generated from parents with different resis-tance levels (Sharma and Duveiller 2003; Joshi et al.2004; Kumar et al. 2009, 2010). It was also suggestedthat selection for this trait should be done in advancedgenerations in order to accumulate minor resistance genes(Dubin and Rajaram 1996; Joshi et al. 2004).

Application of molecular markers in breeding prac-tices could accelerate the improvement of SB resistance.However, the related research has been limited and thusno marker assisted selection (MAS) has been done inthis regard, due to a lack of closely linked markers(Sharma et al. 1997; Das et al. 2002; Kumar et al.2005, 2009, 2010; Makandar and Prabhu 2009).Nevertheless, a recent report indicated that two rustresistant genes, Lr34 and Lr46, confer also SB resis-tance in a CIMMYT breeding line Saar (Lillemo et al.2013), indicating the potential value of these two genesalong with their associated morphological marker (leaftip necrosis, LTN) in MAS.

In 2009, the Cereal Systems Initiative for South Asia(CSISA) project was launched, aiming at the improve-ment of food and income security for resource-poorfarm families in SA. Considering the importance of SBin this region, CIMMYT developed a special nursery,CSISA-SB, in 2009. Since then, the CSISA-SB nurser-ies have been released every year, comprising the mostpromising SB resistant lines developed at CIMMYT.The nursery has not only been sent to South Asiancountries, but also to Zambia, Brazil, Paraguay,Bolivia etc., where SB is of major concern. The aimsof this research were to show the strategy of CSISA-SBdevelopment at CIMMYT, taking the 4th CSISA-SB asan example, and to identify new sources of resistance toSB that form the CSISA-SB.

Eur J Plant Pathol

Materials and methods

Plant materials and their screening for SB resistance

The materials used to form CSISA-SB were from ad-vanced breeding lines of different wheat breeding pro-grams at CIMMYT. Generally all resistant lines evalu-ated in the SB screening nursery are potential candidatesfor CSISA-SB. The CIMMYT SB screening station islocated in Agua Fria, a location with hot and rainyclimate similar to SA (Table 1). The selection of the4th CSISA-SB started with 909 advanced breeding linestested in the 2010–11 cycle (November to March) inAgua Fria. The promising lines were selected for fieldevaluation in the 2011–12 cropping season, and a finallist of the materials for the 4th CSISA-SB nursery wasmade. To provide suitable references, a resistant checkChirya 3, a moderately resistant check Francolin #1 andtwo susceptible checks (CIANO T79 and Sonalika)were included in the experiments. More detailed infor-mation on screening protocols and strategies at AguaFria, Mexico is given below.

Disease screening protocols

The virulent local isolates of B. sorokiniana previouslyidentified and stored in −20 °C freezers were reactivatedand cultured on V8 medium for 5–7 days, with 12 h lightand 12 h darkness at 22–25 °C for mycelia increase.Subsequently, the isolates were multiplied on sorghumseeds that had previously been soaked and autoclaved.The flasks containing B. sorokiniana inoculated sorghumgrains were incubated for approximately 6 weeks at room

temperature with frequent shaking to mix the grains andto promote good coverage of the fungus. For field inoc-ulation, B. sorokiniana colonized sorghum grains werescattered at the base of plants, in the middle of thedouble row. Four to five weeks after inoculation,disease severity was visually scored for each plot,using the double-digit scale (00–99) developed asa modification of Saari and Prescott’s severityscale for assessing wheat foliar diseases (Saariand Prescott 1975). The first digit (D1) indicatesdisease progress in canopy height from the groundlevel and the second digit (D2) refers to severitymeasured based on diseased leaf area. Both D1and D2 were scored on a scale of 1 to 9.Disease evaluation was repeated three to fourtimes at 7–10 day intervals. For each evaluation,percentage disease severity was estimated based onthe following formula:

% severity ¼ D1=9ð Þ � D2=9ð Þ � 100

Area under disease progress curve (AUDPC) wascalculated from the three or four disease evaluations,using the formula:

AUDPC ¼Xn

i¼1

Y i þ Y iþ1ð Þ� �

=2� �� t iþ1ð Þ−ti

� �� �

Where Yi=SB severity at time ti, t(i+1) - ti=time interval(days) between two disease scores, n=number of timeswhen SB was recorded. AUDPC was then divided bythe number of days between the first and last scoring toproduce AUDPC/day, a standardized disease parameterfor further analyses.

Table 1 Geographical information of the experimental stations used in this study

Name State/Division Country Latitude Longitude Altitude (m)

Agua Fria Puebla Mexico 20.5°N 97.6°W 100

El Batana Mexico Mexico 19.5°N 98.8°W 2240

Mexicalia Baja California Mexico 32.29°N 115.3°W 8

Dharwad Karnataka India 15.5°N 75.1°E 678

Varanasi Uttar Pradesh India 25.3°N 83.0°E 76

Coochbehar West Bengal India 26.3°N 89.5°E 43

Jamalpur Dhaka Bangladesh 25.0°N 90.0°E 16

Jessore Khulna Bangladesh 23.2°N 89.2°E 7

Dinajpur Rangpur Bangladesh 25.7°N 88.7°E 40

a These locations were used only for seed increase, not for disease screening experiment

Eur J Plant Pathol

Strategy for developing a CSISA-SB nursery

The general workflow for developing a CSISA-SB ispresented below and summarized in Fig. 1.

1. The candidate lines obtained from Global WheatProgram (GWP) of CIMMYT were tested for the1st year screening in the winter cycle (November-March) at Agua Fria. Due to a large number ofentries, planting was done in 1-m double rows with-out replications. Field evaluation was done for SB,heading, and height, and lines with low disease,earliness (a few late lines with very good resistancealso selected), and good agronomy were harvested.Black point incidence and thousand kernel weight(TKW) were tested in the laboratory to retain mostthe promising lines. During the selection process,selection index (SI), (Sharma and Duveiller 2003)that adds up ranks of disease (the lower the better),heading (the earlier the better) and TKW (the higherthe better) was used to facilitate selection. Based onpedigree information limited number of sister lineswere retained to ensure genetic diversity.

2. Since the amount of initial seed was normally lim-ited and the seed derived from the screening plotswas of inferior quality, seed increase for the selectedentries from the 1st year screening was done in thefungicide protected Pre-Mexicali block in the sum-mer cycle (May-September) at El Batan (Table 1),where CIMMYT’s headquarters is located.

3. The seed produced in Pre-Mexicali was then sent to:i) Agua Fria for the 2nd year evaluation wherein theentries were planted in two replications, and ii)Mexicali for seed increase (December-June).

Mexicali station is a disease free seed productionbase in the north of Mexico.

4. After 2nd year screening at Agua Fria, CSISA-SBwas made based on multiple trait evaluations. Seedproduced at Mexicali was sent to the SeedInspection and Distribution Unit for seed healthchecks and then distributed globally as CSISA-SBinternational nursery.

Evaluation of the 4th CSISA-SB in different locations

In the 2012–13 cycle, the nursery was evaluated inmultiple environments but data for seven locations,i.e., Agua Fria in Mexico, three in India (BanarasHindu University, Varanasi; University of AgriculturalSciences (UAS), Dharwad; and Uttar Banga KrishiVishwa Vidyalaya (UBKV), Coochbehar), and three(Dinajpur, Jamalpur, and Jessore) in Bangladesh(Table 1) was obtained and analysed.

Statistical analyses

The phenotypic data were analysed by the SPSS programver. 16.0 (SPSS Inc. 2007). Analysis of variance(ANOVA) was carried out with the General LinearModel procedure in SPSS, and the information in theANOVA table was used to calculate the broad senseheritability for AUDPC/day in each location, using theformula h2 = /(+/r) for single environments and h2 = /(+/y+/ry) for multiple years or environments; in which standsfor genetic variance, for genotype-by-environment (GxE)interaction, for error variance, y for the number of envi-ronments, and r for the number of replications (Lillemo

Mat

eria

l fro

m G

loba

l Whe

at P

rogr

am,

CIM

MY

T

Winter cycle

Year 1

Summer cycle Winter cycle

Year 2 Year 3

Summer cycle

El Batan(Pre-Mexicali)

Agua Fria

Mexicali CSISA-SB international nursery

Agua Fria

Agua Fria

Winter cycle

Fig. 1 Workflow for the development of the CSISA-spot blotch screening nurseries (CSISA-SB) at Mexico

Eur J Plant Pathol

et al. 2013). A mixed model with covariance betweenrelatives incorporating GxE interaction was used to de-rive the adjusted AUDPC/day means, following themethodology by Crossa et al. (2004) and Crossa et al.(2006), with detailed information presented in ElectronicSupplementary Material (Electronic supplementary ma-terial) 1. AUDPC/day values in the 2012–13 cycle acrossenvironments were normalized with the PROCSTANDARD function in the SAS program ver. 9.2 priorto the principal component analysis (PCA) using thePAST software ver. 3.01 (Hammer et al. 2001).

Results and discussion

Development of the 4th CSISA-SB at Agua Fria

In the 2010–11 cropping season, AUDPC/day values ofthe 909 lines ranged from 14.0 to 67.1, with the resistantcheck Chirya 3 and the susceptible Sonalika havingvalues of 17.3 and 44.3, respectively (the other twochecks Francolin #1 and CIANO T79 were not includedin this experiment). Based on field data, 300 lines withdisease values lower than 30.0 and with good agronom-ic performance were harvested. Subsequently, 205 lineswith low black point scores and high TKW were select-ed for further screening, aided by pedigree information.

In the 2011–12 cropping season, disease was higherthan the previous season (Fig. 2), although a similar rangeof AUDPC/day was recorded (from 20.9 to 63.5). Thetwo susceptible checks Sonalika and CIANO T79 exhib-ited high disease values of 76.1 and 50.1, respectively,

while the resistant check Chirya 3 showed a value of26.2. However, the moderately resistant check Francolin#1 had an unexpected high value of 47.6. Based on fielddata, 103 lines were harvested for black point and TKWtests, and 46 lines were finally selected as members of the4th CSISA-SB nursery (Table 2).

Performance of the 4th CSISA-SB at different locations

Across the locations, a significant ‘entry’ effect wasfound, although its mean square (MS) was lower thanthat of ‘environment’; also significant was the ‘entry xenvironment’ effect (Table 3). When data for each loca-tion were analyzed independently, the ‘entry’ effect wasthe most prominent in five locations (Agua Fria,Dharwad, Varanasi, Jamalpur, and Dinajpur), and theheritability estimates ranged from 0.84 to 0.95 (Table 3).In Coochbehar and Jessore, however, ‘replication’showed bigger MS than ‘entry’, although the latterwas still significant at P<0.01; consequently, the heri-tability estimates for these two locations (both being0.59) were much lower than in other locations(Table 3). The ANOVA data indicated the predominantinfluence of environment on SB level, which was inaccordance with previous reports (Sharma et al. 2004a,b; Joshi et al. 2007a, b; Kumar et al. 2009). In a singleenvironment, however, ‘entry’ effect often was the mostimportant, and the moderate to high heritability valuesguaranteed the effectiveness of phenotypic selection.However, the disease rank of entries across the environ-ments varied due to the significant ‘entry’ by ‘environ-ment’ interaction, which implies that the resistant lines

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

<20 30 40 50 >50

Pro

po

rtio

n o

f en

trie

s (%

)

AUDPC/day

2010-11

2011-12

2012-13

Fig. 2 Frequency distribution of AUDPC/day for CSISA-SB materials evaluated in Agua Fria, Mexico, over three cropping seasons. Note:In the 2010–11 cropping season, 909 lines were evaluated; in 2011–12, 205 lines; and in 2012–13, 46 lines excluding checks were tested

Eur J Plant Pathol

Table 2 Entries of CIMMYT’s 4th CSISA-spot blotch nursery and their adjusted AUDPC/daymeans for the 2012–2013 cropping season inseven locations of Mexico and South Asia

SN GID Cross name AguaFria

Varanasi Dharwad Coochbehar Jessore Jamalpur Dinajpur Acrosslocation

1 6176487 KACHU #1/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/5/KACHU

15.34 28.87 11.84 30.68 13.05 4.49 11.46 16.71

2 6279248 VAYI#1 15.96 29.74 6.08 32.12 12.87 5.07 13.56 16.98

3 6179322 KAUZ/PASTOR//PBW343/3/HAR311/5/OASIS/SKAUZ//4*BCN/3/PASTOR/4/KAUZ*3/YACO

15.86 29.18 7.81 35.07 12.79 5.04 13.48 17.40

4 6176480 KACHU #1/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/5/KACHU

17.10 38.56 7.15 28.19 14.50 5.03 13.01 17.57

5 6175667 ALTAR 84/AE.SQUARROSA (221)//3*BORL95/3/URES/JUN//KAUZ/4/WBLL1/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA(213)//PGO/4/HUITES

16.81 32.84 6.50 31.64 13.15 5.56 15.26 17.87

6 6178575 CHUKUI#1 16.30 30.61 7.97 35.92 12.88 5.32 14.48 18.00

7 6175697 MURGA/KRONSTAD F2004 15.75 30.14 19.92 31.53 13.12 4.77 12.45 18.16

8 6279970 ISENGRAIN/3/CROC_1/AE.SQUARROSA (205)//KAUZ/4/WORRAKATTA/2*PASTOR

17.89 42.91 6.61 27.32 15.14 5.28 13.71 18.17

9 6280230 BABAX/LR39//BABAX/3/VORB/4/SUNCO/2*PASTOR

16.92 28.56 1.19 40.31 11.91 6.33 18.35 18.65

10 6278942 VORB/SOKOLL 17.27 31.30 2.63 37.51 12.42 6.32 18.19 18.79

11 6178541 SAUAL/KIRITATI//SAUAL 17.35 33.94 2.99 39.44 13.08 6.01 16.91 18.98

12 6278937 VORB/4/KRICHAUFF/FINSI/3/URES/PRL//BAV92

16.82 30.75 6.70 40.27 12.57 5.89 16.60 19.00

13 6176600 BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU

17.37 34.63 6.70 37.24 13.25 5.93 16.54 19.12

14 6278940 VORB/SOKOLL 18.64 45.82 10.81 25.03 15.43 5.69 15.10 19.16

15 6280233 BABAX/LR39//BABAX/3/VORB/4/SUNCO/2*PASTOR

17.61 36.55 16.51 27.73 13.61 5.91 16.41 19.35

16 6178243 UP2338*2/KKTS*2//YANAC 17.34 39.41 26.58 24.14 14.56 5.18 13.50 19.54

17 6175708 MURGA/KRONSTAD F2004 16.77 30.42 17.40 36.04 12.52 5.88 16.57 19.67

18 6176410 ATTILA*2/PBW65*2//W485/HD29 17.45 32.12 8.54 38.80 12.52 6.40 18.43 19.77

19 6178201 TUKURU//BAV92/RAYON*2/3/PVN 16.65 28.09 11.73 42.99 11.97 6.09 17.50 19.81

20 6177765 BL2064//SW89-5124*2/FASAN/3/TILHI/5/KAUZ//ALTAR 84/AOS/3/KAUZ/4/SW94.15464

17.60 32.25 3.63 42.53 12.46 6.54 18.98 19.82

21 6278782 CHIR3/4/SIREN//ALTAR 84/AE.SQUARROSA (205)/3/3*BUC/5/PFAU/WEAVER/6/SOKOLL

18.65 35.41 2.40 34.71 12.61 7.26 21.51 19.97

22 6279000 VORB/3/T.DICOCCON PI94625/AE.SQUARROSA (372)//3*PASTOR

18.20 37.97 3.65 40.87 13.60 6.37 18.05 20.06

23 6175979 ATTILA*2/PBW65*2/4/CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN

17.61 32.55 8.95 39.45 12.53 6.51 18.85 20.07

24 6175413 WAXWING*2/HEILO 17.33 30.80 8.13 44.45 12.25 6.46 18.72 20.28

25 6278812 D67.2/PARANA 66.270//AE.SQUARROSA (320)/3/CUNNINGHAM/4/VORB

18.96 36.32 1.30 35.95 12.65 7.48 22.26 20.33

Eur J Plant Pathol

Table 2 (continued)

SN GID Cross name AguaFria

Varanasi Dharwad Coochbehar Jessore Jamalpur Dinajpur Acrosslocation

26 6177627 PBW343*2/KHVAKI//JUCHI 17.13 35.73 27.10 31.45 13.71 5.50 14.88 20.38

27 6278943 VORB/SOKOLL 19.30 45.47 3.58 38.00 14.89 6.49 18.13 20.68

28 6280385 VORB/4/D67.2/PARANA 66.270//AE.SQUARROSA (320)/3/CUNNINGHAM/5/D67.2/PARANA 66.270//AE.SQUARROSA (320)/3/CUNNINGHAM

16.58 27.59 25.51 40.64 11.88 6.09 17.51 21.02

29 6280064 EGA BONNIE ROCK/6/CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA (208)/5/2*WESTONIA

17.49 33.27 19.53 39.80 12.80 6.27 17.90 21.08

30 6176841 PBW343*2/KHVAKI*2/3/ACHTAR*3//KANZ/KS85-8-5

18.36 39.04 17.64 37.13 13.79 6.39 18.05 21.33

31 6279118 CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA(TAUS)/4/WEAVER/5/PICUS/6/2*PBW65/2*PASTOR

16.98 28.05 23.53 41.77 11.73 6.48 18.95 21.42

32 6178012 MUU/4/BAV92//IRENA/KAUZ/3/HUITES/5/BAV92//IRENA/KAUZ/3/HUITES

17.07 32.41 25.78 41.60 12.85 5.92 16.63 21.48

33 6174949 QUAIU #3//MILAN/AMSEL 19.95 46.57 3.15 40.93 14.76 7.06 20.19 21.72

34 6280583 CHIH95.7.4//INQALAB 91*2/KUKUNA

18.54 34.58 10.16 44.62 12.46 7.27 21.56 21.85

35 6280189 SKAUZ*2/FCT‘S’//VORB 16.73 31.54 39.67 36.91 12.84 5.67 15.73 22.12

36 6278914 CAL/NH//H567.71*2/3/SERI/4/2*KAUZ/5/WH576/6/WH 542/7/VORB

19.59 42.34 8.16 42.16 13.86 7.29 21.27 22.22

37 6278844 WESTONIA/4/KRICHAUFF/FINSI/3/URES/PRL//BAV92

20.09 44.83 4.80 42.29 14.20 7.48 21.86 22.35

38 6280231 BABAX/LR39//BABAX/3/VORB/4/SUNCO/2*PASTOR

15.40 28.81 72.08 22.12 12.99 4.58 11.77 22.54

39 6280576 CHIH95.7.4//INQALAB 91*2/KUKUNA

19.16 37.35 13.40 45.70 12.79 7.56 22.51 22.96

40 6278971 PARUS/PASTOR/4/KRICHAUFF/FINSI/3/URES/PRL//BAV92

19.71 47.39 23.93 35.43 15.14 6.66 18.67 23.06

41 6280600 2401 PI2401//PBW343*2/TUKURU/3/PBW343*2/KUKUNA

18.11 34.99 30.40 41.34 12.85 6.72 19.50 23.16

42 6176425 BAV92//IRENA/KAUZ/3/HUITES*2/4/CROC_1/AE.SQUARROSA(224)//KULIN/3/WESTONIA

17.48 32.76 40.30 45.57 12.68 6.33 18.16 24.05

43 6279589 QT6581/4/PASTOR//SITE/MO/3/CHEN/AEGILOPS SQUARROSA(TAUS)//BCN/5/CROC_1/AE.SQUARROSA (224)//OPATA

21.49 57.16 11.40 42.87 16.60 7.21 20.24 24.17

44 6424867 VORB*2/3/PFAU/WEAVER//KIRITATI

22.14 56.82 6.60 41.47 16.07 8.00 23.22 24.35

45 6278825 FRAME//MILAN/KAUZ/3/PASTOR/4/SOKOLL

21.80 51.09 5.72 45.46 14.75 8.48 25.30 24.62

46 6424868 VORB*2/3/PFAU/WEAVER//KIRITATI

23.87 57.47 28.48 52.09 15.09 9.87 30.20 30.17

LSD (5%)=3.13. The Jamalpur data was disease severity due to only one disease score available. Raw SB data, phonological and agronomictraits for this nursery is available in Table S1

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identified in one environment may not necessarily beeffective in other environments. Therefore, multipleyear/location screening is justified for the identificationof lines being resistant across environments.

In Agua Fria, the disease level was the lowest in2012–13 among the three cycles (Fig. 2), demonstratingthe effectiveness of screening strategies to retain onlythe best lines. When only the 4th CSISA-SB memberswere considered, grand mean disease values of 2010–11and 2012–13 cycles were quite similar, whereas that of2011–12 cycle was higher (Table S1), reflecting differ-ent disease pressures across years. At this location,Sonalika and CIANO T79 were the worst two in termsof disease level while Chirya 3 was the best; but thesupposed moderately resistant check Francolin #1 wasonly next to the worst, in consistence with the previouscycle (Table S1). Thus, Francolin #1 may not be

considered a good moderately resistant check at AguaFria but serves as a reference for minimum level ofresistance required in SA.

At the six locations in SA, disease levels variedmarkedly, from high at Varanasi and Coochbehar (bothwith grand mean AUDPC/day of around 37.5) to low inDharwad, Jamalpur, Dinajpur, and Jessore (from 8.6 to18.8) (Table S1). Unlike in Agua Fria, the checks per-formed inconsistently or sometimes contradictorily.Chirya 3 was among the best in most places exceptDinajpur and Varanasi, particularly for the latter whereit ranked 39th among the 50 entries. CIANO T79 wasamong the worst in most cases, except at Coochbehar,where it was not that bad and at Jessore it ranked even inthe third place. Sonalika was more consistent thanCIANO T79, with the only exception at Coochbehar,where it ranked 24th. The most contradictory results

Table 3 Analysis of variance (ANOVA) of AUDPC/day and heritability in the 4th CSISA-SB tested in nine environments of Mexico andsouth Asia

Location Source of variation df Mean square F value Pr> F Heritabilitys

Agua Fria Entry 49 128.30 10.88 <0.0001 0.91

Rep 1 50.37 4.27 0.0441

Error 49 11.79

Dhawad Entry 49 477.25 9.93 <0.0001 0.90

Rep 1 205.90 4.28 0.0438

Error 49 48.08

Varanasi Entry 49 285.84 6.31 <0.0001 0.84

Rep 1 94.11 2.08 0.1560

Error 49 45.33

Coochbehar Entry 49 175.34 2.45 0.0010 0.59

Rep 1 253.18 3.54 0.0657

Error 49 71.43

Jessore Entry 49 34.71 2.44 0.0011 0.59

Rep 1 39.90 2.81 0.1002

Error 49 14.21

Jamalpur Entry 49 273.81 20.24 <0.0001 0.95

Rep 1 97.55 7.21 0.0099

Error 49 13.53

Dinajpur Entry 49 103.82 10.42 <0.0001 0.90

Rep 1 23.41 2.35 0.1319

Error 49 9.97

All environments Entry 49 502.36 16.64 <0.0001 0.68 s

Rep(Environment) 7 109.20 3.57 0.0010

Environment 6 13319.99 435.01 <0.0001

Entry*Environment 294 162.78 5.32 <0.0001

Error 343 30.62

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came from Francolin #1, which was among the best atVaranasi and Jessore, but the worst in all other locations.The resistance levels of the 4th CSISA-SB membersvaried greatly across locations, as reflected in the gen-erally low correlations among the locations (Fig. 3).

Similarities of the seven environments can be observedin the biplot based on the angles between vectors. An acuteangle indicates positive correlation, e.g., Agua Fria vs.Jamalpur; a right angle shows an absence of correlation,e.g., Dharwad vs. Varanasi; and an obtuse angle stands fornegative correlation, e.g., Dharwad vs. Jessore (Fig. 3).Generally, low similarities were observed among the loca-tions, and being from a same country did not guaranteehigh correlations. This demonstrated the high environmen-tal variation in SA, which implied also the representative-ness of the locations, i.e., no redundant experimental siteswere used in this study. It is noticeable that Agua Friashowed positive correlations with all other six South Asianlocations (Fig. 3), justifying its role as a qualified screeningsite for SB resistance, although located outside SA. Thisconclusion was supported also by the fact that promisinglines could be observed in all the locations, in terms of bothSB resistance and SI (Table S1).

The influences of heading and height on SB

Reports have shown that there was a general trend in SAthat late and tall varieties tended to have lower SBdisease (Dubin et al. 1998; Duveiller et al. 1998). But

further studies demonstrated that the two traits were notclosely associated with SB resistance, and can be select-ed independently (Joshi et al. 2002; Sharma andDuveiller 2003; Duveiller and Sharma 2009). In thecurrent study, there were no consistent correlations ofSB resistance with heading and height. For heading,negative correlations were found at Agua Fria (2010–11 cycle, r=−0.52, P<0.01), Jamalpur (r=−0.46,P<0.01), and Dinajpur (r=−0.53, P<0.01); and a pos-itive correlation was found in Jessore with r=0.55 atP<0.01. For height, a positive correlation was found atVaranasi (r=0.41, P<0.01) whereas it was negative atCoochbehar (r=−0.31, P<0.05). This was in accor-dance with the conclusion that heading and height werenot the determinant factors for SB resistance, and it ispossible to select resistant lines that are early and short.

Implication for disease resistance breeding

Although there were significant environmental influ-ences as shown by ANOVA and disease data, linesperformed consistently well across environments, andcan still be identified in this nursery, which was facili-tated by the adoption of kinship and GxE adjustedAUDPC/day values (Table 2). This strategy wasclaimed to allow accurate prediction of breeding valuesby using covariance structures that consider correlationsbetween locations and plots in the field, as well asgenetic associations between relatives (Crossa et al.

Fig. 3 Biplot of the 4th CSISA-spot blotch nursery based onprincipal component analysis (PCA) on SB across seven environ-ments in the 2012–13 cropping season. Note: Cosine of the angle

between vectors indicates correlation between variables in thedimension of the first two principal components (PCs). Refer toTable 2 for the identification of each serial number

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2006). It is predicted that the resistance of a line be morestable if a low ‘across location’ value is observed inTable 2. In the biplot, the first principal component (PC1)was for SB resistance, with the resistant lines on the leftand the susceptible ones on the right; whereas PC2 for thestability of resistance across environments, with the stablelines close to and the unstable ones far from the X axis(Fig. 3). To interpret the dataset from different aspects, thetwo methods reached a similar conclusion for the mostresistant and the most susceptible lines, although the ranksfor intermediate lines differed markedly. Taking into ac-count also of SI, it is evident that CHUKUI#1 (GID6178575, SN # 2 in Table 2 and Fig. 3) and VAYI#1(GID 6279248, SN # 6 in Table 2 and Fig. 3) were themost promising ones. In fact, they were among the bestlines that performed consistently, in terms of both diseaseresistance and SI (Table S1). These two lines could be usedas resistance donors or could be tested in national trials fortheir direct release given their earliness and high TKW.Additionally, a few more promising lines like KACHU#1/4/CROC_1 /AE.SQUARROSA(205)//KAUZ/3/SASIA/5/KACHU (GID 6176487) and BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU(GID6176600) could alsobe used as resistance sources since they had low disease inmultiple environments despite the often high SI. Severalother lines with low SI and moderate disease resistancecould also be tested for direct release as moderately resis-tant cultivars (Table S1).

Considering the high environmental influence, asdemonstrated at Agua Fria in three cropping seasons, itis strongly recommended that multiple evaluations to bedone for the 4th CSISA-SB nursery in a certain locationto identify genotypes with stable resistance and to in-corporate them into breeding programs or to release ascultivars.

Acknowledgments The helpful assistance of Javier Segura andFrancisco Lopez with field trials and Nerida Lozano for her effortsin inoculum preparation is highly acknowledged. Dr. Yong Zhangis grateful to Jiangsu Provincial Department of Education, Chinafor providing the financial support as ‘Jiangsu Government Schol-arship for Overseas Studies’. Financial support from the Bill andMelinda Gates Foundation and USAID through the CSISA projectis gratefully acknowledged.

References

Crossa, J., Yang, R.-C., & Cornelius, P. L. (2004). Studyingcrossover genotype × environment interaction using linear-

bilinear models and mixed models. Journal of Agricultural,Biological, and Environmental Statistics, 9, 362–380.

Crossa, J., Burgueño, J., Cornelius, P. L., McLaren, G.,Trethowan, R., & Krishnamachari, A. (2006). Modelinggenotype × environment interaction using additive geneticcovariances of relatives for predicting breeding values ofwheat genotypes. Crop Science, 46, 1722–1733.

Das, S., Aggarwal, R., & Singh, D. V. (2002). Molecular differentia-tion of spot blotch resistant and susceptible genotypes of wheatusing RAPD markers. Indian Phytopathol, 55, 488–491.

Dastur, J. (1942). Notes on some fungi isolated from ‘black point’affected wheat kernels in the Central Provinces. IndianJournal of Agricultural Science, 12, 731–742.

Dubin, H. J., & Duveiller, E. (2011). Fungal, bacterial and nema-tode diseases of wheat: Breeding for resistance and othercontrol measures. In W. Angus, A. Bonjean, & M. vanGinkel (Eds.), The world wheat book: A history of wheatbreeding (Vol. 2, pp. 1131–1191). Paris: Lavoisier.

Dubin, H., & Rajaram, S. (1996). Breeding disease-resistantwheats for tropical highlands and lowlands. Annual Reviewof Phytopathology, 34, 503–526.

Dubin, H., van Ginkel, M., & Saunders, D. (1991). The status ofwheat diseases and disease research in warmer areas. Paperpresented at the Wheat for the nontraditional warm areas: aproceedings of the international conference Foz do Iguaçu,Brazil, July 29-August 3, 1990.

Dubin, H., Arun, B., Begum, S., Bhatta, M., Dhari, R., Goel, L.,Joshi, A., Khanna, B., Malaker, P., & Pokhrel, D. (1998).Results of the South Asia regional Helminthosporium leafblight and yield experiments, 1993–94. In E. Duveiller, H. J.Dubin, J. Reeves, & A. McNab (Eds.), Helminthosporiumblights of wheat: Spot blotch and tan spot (pp. 182–187). ElBatan: CIMMYT.

Duveiller, E. (2004). Controlling foliar blights of wheat in the rice-wheat systems of Asia. Plant Disease, 88, 552–556.

Duveiller, E., & Sharma, R. C. (2009). Genetic improvement and cropmanagement strategies to minimize yield losses in warm non‐traditional wheat growing areas due to spot blotch pathogenCochliobolus sativus. Journal of Phytopathology, 157, 521–534.

Duveiller, E., & Sharma, R. C. (2012). Wheat resistance to spotblotch or foliar blight. In I. Sharma (Ed.), Disease resistancein wheat (pp. 120–135). Wallingford: CABI.

Duveiller, E., Garcia, I., Franco, J., Toledo, J., Crossa, J., & Lopez,F. (1998). Evaluating spot blotch resistance of wheat:Improving disease assessment under controlled conditionsand in the field. In E. Duveiller, H. J. Dubin, J. Reeves, &A. McNab (Eds.), Helminthosporium blights of wheat: Spotblotch and tan spot (pp. 171–181). El Batan: CIMMYT.

Eisa, M., Chand, R., & Joshi, A. K. (2013). Biochemical andhistochemical traits: a promising way to screen resistanceagainst spot blotch (Bipolaris sorokiniana) of wheat.European Journal of Plant Pathology, 137, 805–820.

Hammer, Ø., Harper, D., & Ryan, P. (2001). PAST: paleontolog-ical statistics software package for education and data analy-sis. Palaeontologia Electronica, 4, 1–9.

Joshi, A. K., Chand, R., & Arun, B. (2002). Relationship of plantheight and days to maturity with resistance to spot blotch inwheat. Euphytica, 123, 221–228.

Joshi, A. K., Kumar, S., Chand, R., & Ortiz-Ferrara, G. (2004).Inheritance of resistance to spot blotch caused by Bipolarissorokiniana in spring wheat. Plant Breeding, 123, 213–219.

Eur J Plant Pathol

Joshi, A. K., Ortiz-Ferrara, G., Crossa, J., Singh, G., Alvarado, G.,Bhatta, M. R., Duveiller, E., Sharma, R. C., Pandit, D. B.,Siddique, A. B., Das, S. Y., Sharma, R. N., & Chand, R.(2007a). Associations of environments in South Asia basedon spot blotch disease of wheat caused by Cochliobolussativus. Crop Science, 47, 1071–1081.

Joshi, A. K., Ferrara, O., Crossa, J., Singh, G., Sharma, R. C.,Chand, R., & Parsad, R. (2007b). Combining superior agro-nomic performance and terminal heat tolerance with resis-tance to spot blotch (Bipolaris sorokiniana) in the warmhumid Gangetic plains of south Asia. Field CropsResearch, 103, 53–61.

Kumar, U., Kumar, S., Tyagi, K., Chand, R., & Joshi, A. K.(2005). Microsatellite markers for resistance to spot blotchin spring wheat. Communications in Agricultural andApplied Biological Sciences, 70, 59–60.

Kumar, U., Joshi, A. K., Kumar, S., Chand, R., & Röder, M. S.(2009). Mapping of resistance to spot blotch disease causedby Bipolaris sorokiniana in spring wheat. Theoretical andApplied Genetics, 118, 783–792.

Kumar, U., Joshi, A. K., Kumar, S., Chand, R., & Röder, M. S.(2010). Quantitative trait loci for resistance to spot blotchcaused by Bipolaris sorokiniana in wheat (T. aestivum L.)lines ‘Ning 8201’ and ‘Chirya 3’. Molecular Breeding, 26,477–491.

Lillemo, M., Joshi, A. K., Prasad, R., Chand, R., & Singh, R. P.(2013). QTL for spot blotch resistance in bread wheat lineSaar co-locate to the biotrophic disease resistance loci Lr34and Lr46. Theoretical and Applied Genetics, 126, 711–719.

Makandar, R., & Prabhu, K. (2009). Identification of RAPDmarkers associated with Helminthosporium leaf blight(HLB) disease resistance in wheat. Indian J Genet PlBreed, 69, 171–177.

Maraite, H. (1998). Evolution of the nomenclature used forHelminthosporium spp. causing leaf blight of wheat. In E.Duveiller, H. J. Dubin, J. Reeves, & A. McNab (Eds.),Helminthosporium blights of wheat: Spot blotch and tan spot(pp. 6–9). El Batán: CIMMYT.

Mehta, Y., Campos, L., & Guzman, E. (1996). Genetic resistanceof wheat cultivars to Bipolaris sorokiniana. FitopatologiaBrasileira, 21, 455–459.

Mujeeb-Kazi, A., Villareal, R., Gilchrist, L., & Rajaram, S. (1996).Registration of five wheat germplasm lines resistant toHelminthosporium leaf blight. Crop Science, 36, 216–217.

Saari, E. E., & Prescott, J. M. (1975). A scale for appraising thefoliar intensity of wheat disease. Plant Disease Report, 59,377–380.

Saari, E. E., & Wilcoxson, R. D. (1974). Plant disease situation ofhigh-yielding dwarf wheats in Asia and Africa. AnnualReview of Phytopathology, 12, 49–68.

Sharma, R. C., & Duveiller, E. (2003). Selection index for im-proving Helminthosporium leaf blight resistance, maturity,and kernel weight in spring wheat. Crop Science, 43, 2031–2036.

Sharma, R. C., & Duveiller, E. (2007). Advancement toward newspot blotch resistant wheats in South Asia. Crop Science, 47,961–968.

Sharma, R. C., Dubin, H. J., Devkota, R. N., & Bhatta, M. R.(1997). Heritability estimates of field resistance to spot blotchin four spring wheat crosses. Plant Breeding, 116, 64–68.

Sharma, R. C., Duveiller, E., Gyawali, S., Shrestha, S., Chaudhary,N., & Bhatta, M. (2004a). Resistance to Helminthosporiumleaf blight and agronomic performance of spring wheat ge-notypes of diverse origins. Euphytica, 139, 33–44.

Sharma, R. C., Duveiller, E., Ahmed, F., Arun, B., Bhandari, D.,Bhatta, M. R., Chand, R., Chaurasiya, P. C. P., Gharti, D. B.,Hossain, M. H., Joshi, A. K., Mahto, B. N., Malaker, P. K.,Reza, M. A., Rahman, M., Samad, M. A., Shaheed, M. A.,Siddique, A. B., Singh, A. K., Singh, K. P., Singh, R. N., &Singh, S. P. (2004b). Helminthosporium leaf blight resistanceand agronomic performance of wheat genotypes across warmregions of South Asia. Plant Breeding, 123, 520–524.

Siddique, A. B., Hossain, M. H., Duveiller, E., & Sharma, R. C.(2006). Progress in wheat resistance to spot blotch inBangladesh. Journal of Phytopathology, 154, 16–22.

Singh, D., Singh, R. V., Singh, A. K., & Singh, B. N. (1998).Identification and inheritance of resistance to foliar blight ofwheat. In E. Duveiller, H. J. Dubin, J. Reeves, & A. McNab(Eds.), Helminthosporium blights of wheat: Spot blotch andtan spot (pp. 259–262). El Batan: CIMMYT.

SPSS Inc. Released 2007. SPSS for Windows, Version 16.0.Chicago, SPSS Inc.

van Ginkel, M., & Rajaram, S. (1998). Breeding for resistance tospot blotch in wheat: Global perspective. In E. Duveiller, H.J. Dubin, J. Reeves, & A. McNab (Eds.), Helminthosporiumblights of wheat: Spot blotch and tan spot (pp. 162–170). ElBatan: CIMMYT.

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