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Field Crops Research 121 (2011) 186–194

Contents lists available at ScienceDirect

Field Crops Research

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ultivar evaluation and trait analysis of tropical early maturing maize undertriga-infested and Striga-free environments

. Badu-Apraku ∗, R.O. Akinwalenternational Institute of Tropical Agriculture (UK) Limited, Carolyn House, 26 Dingwall Road, Croydon CR9 3EE, UK

r t i c l e i n f o

rticle history:eceived 2 August 2010eceived in revised form 7 December 2010ccepted 10 December 2010

eywords:ase indexGE biplot

ndirect selection

a b s t r a c t

Striga hermonthica is a major constraint to maize (Zea mays L.) production in sub-Saharan Africa. Theuse of secondary traits that have high heritability and genetic correlation with grain yield can improvethe precision with which Striga resistant genotypes are identified. Fifteen early cultivars were evaluatedunder Striga-infested and Striga-free conditions for 2 years at Mokwa and Abuja, Nigeria. The objectivewas to examine their performance based on multiple traits under stress and non-stress conditions andanalyze the interrelationship among traits using genotype-by-trait (GT) biplot so as to assess the valueof traits used in the base index for selection for Striga resistance and improved grain yield (YLD). TZE-WDT STR C4 had the best performance based on multiple traits while TZE-W DT STR C4, TZE-Y DT STR C4,

ultiple traits Multicob Early DT, and TZE-W DT STR QPM C0 were the closest to the ideal cultivar when Striga infested.Ears per plant, Striga damage at 8 and 10 weeks after planting, and ear aspect (EASP) were the most reliabletraits for selecting for resistant genotypes. Striga emergence count at 8 and 10 weeks after planting werenot among the reliable traits identified for selection for improved grain yield and their inclusion in thebase index needs to be further verified. EASP had high correlation with grain yield and was one of themost reliable traits for selection for increased grain yield under Striga infestation and should be included

in the index.

. Introduction

Striga hermonthica (Del.) Benth. severely constrains maize pro-uction in the sub-Saharan Africa (SSA). Yield loss due to Strigaamage ranges from 20 to 80%; complete yield loss also occurs.any farmers who suffer complete crop failure have been com-

elled to abandon their fields. Striga infests an estimated 20 to 40illion hectares of farmland cultivated by resource-poor farmers

hroughout SSA. Annual yield losses to Striga account for an esti-ated US$7 billion in SSA and affect the welfare and livelihood

f over 100 million people. Maize is most vulnerable to Striga inoils of low fertility. Increased population in the savannas has ledo reduced fallow periods and greater production of monocroppedereals to meet farmers’ needs for food and cash. As land use inten-ifies, soil fertility declines, Striga seed-bank in the soil increasesnd Striga infestation becomes severe, greatly reducing crop yields.

Several control measures have been developed to combathe Striga menace. Of these, host plant resistance or tolerances considered the most affordable and environmentally friendlyor resource-poor farmers. Genetic resistance to Striga has been

∗ Corresponding author. Tel.: +44 234 2241 2626.E-mail address: b.badu-apraku@cgiar.org (B. Badu-Apraku).

378-4290/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.fcr.2010.12.011

© 2010 Elsevier B.V. All rights reserved.

reported in several cereal crops including rice (Oryza sativa;Bennetzen et al., 2000; Gurney et al., 2006), sorghum (Sorghumbicolor; Maiti et al., 1984; Hess et al., 1992; Vogler et al., 1996;Haussmann et al., 2004), and maize (Adetimirin et al., 2000b; Gethiand Smith, 2004; Menkir, 2006). Resistance to Striga refers to theability of the host plant to stimulate the germination of Strigaseeds but prevent the attachment of the parasite to its roots, or killthe attached parasite. When Striga-infested, the resistant genotypesupports significantly fewer Striga plants and produces a higheryield than a susceptible (converse of resistance) genotype (Doggett,1988; Ejeta et al., 1992; Haussmann et al., 2000; Rodenburg et al.,2006). On the other hand, a Striga tolerant genotype germinatesand supports as many Striga plants as the intolerant or as proposedby DeVries (2000) sensitive genotype but produces more grain andstover, and shows fewer damage symptoms (Kim, 1994). Accordingto Amusan et al. (2008) Striga on the susceptible maize genotypeusually penetrates the xylem and shows substantial internal haus-torial development. On the other hand, the haustorial ingress onthe resistant inbred is often stopped at the endoderm.

In maize research, Striga damage rating is used as the index of

tolerance while the number of emerged Striga plants is used asthe index of resistance. Tolerance to Striga is quantified by a hostdamage rating score on a scale of 1–9, where 1 = most tolerant and9 = highly intolerant or sensitive. Different measures of tolerancehave been suggested, ranging from host plant damage scores to

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B. Badu-Apraku, R.O. Akinwale / Fi

igh yield, yield loss, or relative yield loss under Striga infestationKim, 1994; Adetimirin et al., 2000b; Gurney et al., 2006). However,one of these measures account for the difference in resistancemong genotypes and therefore they fail to recognize the fact thathe observed damage is due to both Striga infection level (resis-ance) and the extent to which the specific genotype endures thesenfections (tolerance). Therefore, differences among genotypes inevel of yield reduction cannot be attributed to only tolerance. A

aize genotype that combines superior levels of resistance andolerance is a promising breeding strategy and has been proposedor Striga resistance breeding in many studies (Kim, 1991; DeVries,000; Kling et al., 2000; Haussmann et al., 2001; Pierce et al., 2003;odenburg et al., 2006).

The ultimate goal of the IITA Maize Breeding Program for bioticnd abiotic stress tolerance is improved grain yield under threepecific stress factors; that is, low soil nitrogen, drought and S. her-onthica infestation. Maize cultivars that combine improved grain

ield with Striga resistance are desirable in SSA to ensure foodecurity. Breeding for high yielding maize varieties with effectiveesistance and/or tolerance to the hemi-parasitic weed S. hermon-hica requires suitable selection measures for both characteristics.n breeding maize for tolerance or resistance to Striga, appropriateolerance or resistance indicator-traits can improve the precisionith which resistant genotypes are identified. For the stress tol-

rance aspect of its research activities, the IITA Maize Programocuses on four maize maturity groups; late, intermediate, early andxtra-early. At the initial stages of the stress-tolerance/resistanceesearch work, the Program concentrated on late and intermediateaturity groups and used a base index, which combines grain yield

nder Striga infestation, Striga damage rating, Striga emergence,nd ears per plant (EPP) to select for high grain yield measurednder Striga-infested and non-infested conditions (MIP, 1996;enkir and Kling, 2007). At the time research on improvement

f the breeding populations in the extra-early and early matu-ity groups started in 1994, the early-maturity component of theaize Program adopted the base index used for the improvement

f the late/intermediate maturity groups but found inconsistentesults, depending on traits used and type of germplasm subjectedo selection. A few examples will suffice. The primary traits of inter-st in selecting for tolerance or resistance and high grain yieldnder Striga infestation are host-plant damage rating (Striga dam-ge) and Striga emergence count (number of emerged Striga plants).here are contradictory reports on the importance of Striga emer-ence count as a reliable trait for selecting for Striga resistance andmproved grain yield under artificial Striga infestation. For instance,ontrary to the results obtained for the late and intermediate matu-ity groups (Kim and Adetimirin, 1995; Gethi and Smith, 2004;enkir and Kling, 2007; Yallou et al., 2009), Badu-Apraku et al.

2005, 2006, 2007), reported weak phenotypic and genotypic cor-elations between grain yield and Striga emergence count in earlyermplasm. In contrast, Badu-Apraku (2010) studied the relativehanges in genetic variances, heritabilities, and genetic correlationsollowing four cycles of S1 family selection in the extra-early whiteopulation and reported that under Striga infestation, yield was notorrelated with other traits at C0, but was significantly correlatedith ears per plant, Striga damage and emerged Striga plants in

dvanced cycles. Therefore, the value of the traits that are used inhe base index by IITA Maize Program for selecting for Striga toler-nt and resistant genotypes requires assessment and confirmationn order to determine whether or not they are appropriate for theelection of resistant/tolerant early and extra-early maize.

The present strategy of the IITA Maize Program is to breed forombined resistance with tolerance in individual genotypes ando select simultaneously for low Striga emergence and high grainield. This strategy has also been proposed for Striga resistancereeding in maize (DeVries, 2000) and sorghum (Haussmann et al.,

ps Research 121 (2011) 186–194 187

2001). Maize genotypes which combine low Striga damage syn-drome ratings and few emerged Striga plants have been identifiedin our program. But also, genotypes combining low emerged Strigaplants and severe Striga damage syndrome ratings have equallybeen identified in our program. Host damage rating score is pos-itively correlated with number of emerged Striga plants, and thetwo traits are negatively correlated with yield; that is, the lowertheir values, the higher the grain yield under Striga infestation.Also, Badu-Apraku et al. (2007) in a study of the genetic variancesand correlations in an early tropical white maize population foundthat grain yield had a large positive additive genetic correlationwith EPP, and moderately large negative genetic correlations withflowering traits. Similar results were reported by earlier workers(Kim and Adetimirin, 1995; Akanvou et al., 1997; Menkir and Kling,2007). However, the genotypic correlation between host damagerating and emerged Striga plants have been found to be low suggest-ing that different genes control the two traits (Kim, 1994; Akanvouet al., 1997; Badu-Apraku et al., 2007).

From the foregoing, the traits to use in selecting for S. her-monthica resistance or tolerance in early and extra-early maizepopulations need to be clearly identified. Such traits would have tobe combined with grain yield in a base index to maximise yield per-formance of selected genotypes. There are several statistical toolsthat could be used for this purpose, including correlation, multipleregression, path analysis (Wright, 1921), and multivariate models.All of these methods have the common disadvantage of not beingcapable of identifying genotypes with specific desirable traits thatcould be used in a selection program. Correlation measures themutual association between a pair of variable independently of allother variables across all genotypes. Regression analysis, includingstepwise multiple regression and path analysis, which is a specialcase of partial multiple regression analysis as well as multivariatetechnique examines the association among traits measured on aset of genotypes without identifying individual genotypes superiorfor specific traits.

The Genotype-by-trait (GT) biplot proposed by Yan and Kang(2003) is a powerful statistical tool for evaluating cultivars basedon multiple traits and for identifying those that are superior in cer-tain traits and hence could be candidates for use as parents in abreeding program or directly released for commercial production.GT analysis allows visualization of the genetic correlation amongtraits (Yan and Rajcan, 2002; Lee et al., 2003) and also evaluation ofgenotypes on the basis of multiple traits (Yan and Rajcan, 2002; Yanand Kang, 2003; Morris et al., 2004; Ober et al., 2005). It also pro-vides information on the usefulness of cultivars for production aswell as information that helps to detect less important (redundant)traits, and identify those that are appropriate for indirect selectionfor a target trait.

The objectives of the present study were to (i) examine the per-formance of early maturing cultivars based on multiple traits toidentify superior genotypes for release for commercial productionin WCA (ii) analyze the interrelationship between grain yield andother traits using GT biplot with a view to identifying traits that aremost appropriate for indirect selection for improved grain yieldunder Striga-infested and Striga-free environments.

2. Materials and methods

2.1. Field layout, experimental design and trial management

A study was conducted in 2008 and 2009 to evaluate the perfor-mance of fifteen selected Striga resistant/tolerant and susceptibleearly maturing cultivars developed in the IITA Maize Breeding Pro-gram and at the Institute of Agricultural Research, Zaria, Nigeria(Table 1), under artificial Striga infestation at Mokwa and Abuja,

188 B. Badu-Apraku, R.O. Akinwale / Field Cr

Table 1Characteristics of early-maturing maize cultivars evaluated under Striga-infestedand Striga-free environments in Nigeria, in 2008 and 2009.

Cultivars CODE Origin S. hermonthica

TZE-W DT STR C4 TW4 IITA ResistantTZE-Y DT STR C4 TY4 IITA TolerantTZE Comp 3 DT C2F2 CP2 IITA SusceptibleTZE Comp 3 DT C1F2 CP1 IITA SusceptibleTZE-W DT STR QPM C0 TWQ IITA ResistantBG97 TZE Comp 3 × 4 BG97 IITA SusceptibleEV DT-Y 2000 STR QPM

C0

EYQ IITA Resistant

Tillering Early DT TDT IAR Moderately resistantEV DT-Y 2000 STR C0 EY IITA ResistantAC 90 Pool 16 DT STR P16 IITA/CIMMYT TolerantPool 18-SR/AK94-

DMRESR-Y/AK94-DMRESR-Y

P18 IITA Susceptible

Multicob Early DT MDT IAR SusceptibleTZE-Y DT STR QPM C0 TYQ IITA ResistantEV DT-W 99 STR QPM EWQ IITA Resistant

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PC2, g and g are the PC1 and PC2 eigenvectors, respectively, for

C0

WARI BASE WB Burkina Faso Susceptible

oth in the southern Guinea savanna (SGS) agro-ecological zonef Nigeria where Striga is endemic. A randomized complete blockesign with four replications was used in the study. A plot con-isted of 2 rows, 5 m long, spaced 0.75 m apart with 0.40 m spacingetween plants within the row. Three seeds were planted per hill.wo rows of each entry were infested with seeds of S. hermonthicahile the other two rows were Striga non-infested. The two Striga-

nfested rows of each entry were arranged in such a way that theyere directly opposite the two non-infested rows of the same entry,

eparated by a 1.5 m alley. The plots were arranged in a serpentineashion so that the Striga-infested rows were back-to-back in stripscross the field, and alternated with Striga-free strips so that therst range, for example, was non-infested, range 2 and 3 infested,hile ranges 4 and 5 were non-infested. This arrangement mini-ized the movement of Striga seeds into the non-infested plots.

he Striga infestation method developed by IITA Maize ProgramKim, 1991; Kim and Winslow, 1991) was used. The Striga seedssed were collected from fields of sorghum [Sorghum bicolor (L.)oench] at the end of the growing season and mixed with finely

ieved sand in the ratio of 1:99 by weight. About 5000 germinableeeds were used in each hill for infestation. About 2 weeks beforelanting and Striga infestation, the soil was fumigated with ethy-

ene gas to stimulate suicidal germination of existing Striga seedsn the soil at both sites. Except for Striga seed infestation, all man-gement practices for both Striga-infested and non-infested plotsere the same. The maize plants were thinned to two per hill about

wo weeks after emergence to give a final plant population densityf 66,000 plants ha−1. Fertilization of the artificially Striga-infestednd Striga-free maize plots at Mokwa and Abuja was carried out atbout 30 days after planting when 30 kg ha−1 each of N, P and K waspplied as 15–15–15 NPK. Weeds other than Striga were controlledanually.

.2. Measured agronomic traits

Data were recorded on both Striga-infested and Striga-free plotsor days to 50% silking (DYS), and days to anthesis (DYA) as the num-er of days from planting to when 50% of the plants had emerged

ilks and had shed pollen, respectively. The anthesis–silking inter-al (ASI) was calculated as the difference between days to 50%ilking and 50% anthesis. Plant (PLHT) and ear (EHT) heights wereeasured as the distance from the base of the plant to the height of

he first tassel branch and the node bearing the upper ear, respec-

ops Research 121 (2011) 186–194

tively. Root lodging (RL) (percentage of plants leaning more than30◦ from the vertical), and stalk lodging (SLG) (percentage bro-ken at or below the highest ear node), and number of rotten ears(EROT), were also recorded. The number of ears per plant (EPP) wasobtained by dividing the total number of ears per plot by the num-ber of plants harvested. Plant aspect (PASP) was recorded on a scaleof 1 to 9 based on overall plant type, where 1 = excellent plant typeand 9 = poor plant type. Husk cover (HUSK) was rated on a scaleof 1–5, where 1 = husks tightly arranged and extended beyond theear tip and 5 = ear tips exposed. Ear aspect (EASP) was based on ascale of 1–9, where 1 = clean, uniform, large, and well-filled ears and9 = ears with undesirable features. Host plant damage syndromeratings (STRA1 and STRA2) (Kim, 1991) and emerged Striga counts(STC1 and STC2) were made at 8 and 10 WAP (56 and 70 days afterplanting) in the Striga-infested plots at Mokwa and Abuja. Strigadamage syndrome was scored per plot using the modified scaleof 1–9 (Kim, 1991) where 1 = no damage, indicating normal plantgrowth and high level of tolerance, and 9 = complete collapse ordeath of the maize plant; i.e., highly sensitive/intolerant.

2.3. Statistical analyses

2.3.1. Analysis of varianceAnalyses of variance (ANOVA), combined across environments

were performed on plot means for grain yield and other measuredtraits with PROC GLM in SAS using a RANDOM statement withthe TEST option (SAS Institute, 2001). The ANOVA were conductedseparately for data collected from Striga-infested and non-infestedenvironments for the selected traits of the cultivars. The variance ofStriga counts has been found to increase with the mean, thereforea log transformation {log(counts + 1)} was used to reduce the het-erogeneity of variance. In the combined ANOVA, the location-yearcombinations and replicates of each experiment were consideredas random factors while entries were considered as fixed effects.The LSD was used for mean separation.

2.3.2. Genotype-by-trait analysisMean values generated for each trait from the ANOVA were

subjected to GT biplot analysis (Yan, 2001; Yan and Kang, 2003).The GT biplot was used to obtain information on traits of thecultivars that were most appropriate for selection for Striga resis-tance/tolerance and improved grain yield under Striga-infestedand Striga-free environments. The analyses were done usingGGE biplot software, a Windows application that fully auto-mates biplot analysis (Yan, 2001). The program is available atwww.ggebiplot.com (verified 1 December 2009). The data werenot transformed (‘Transform = 0’), standard deviation-standardized(‘Scale = 1’), trait-centered (‘Centering = 2’) and is therefore appro-priate for visualizing the relationships among genotypes and traits.The GGE biplot model equation is:

Yij − � − ˇj

dj= �1gi1e1j + �2gi2e2j + εij

where Yij is the genetic value of the combination between inbred iand trait j; � is the mean of all combinations involving trait j; ˇj isthe main effect of trait j; �1and �2 are the singular values for PC1 and

i1 i2inbred i e1j and e2j are the PC1 and PC2 eigenvectors, respectively,for trait j; dj is the phenotypic standard deviation; and εij is theresidual of the model associated with the combination of inbred iand trait j.

B. Badu-Apraku, R.O. Akinwale / Field Crops Research 121 (2011) 186–194 189

Fig. 1. A “Which is best/worst for what” or “Which wins where” of genotype × traitsbiplot of all traits of 15 early maturing maize cultivars evaluated under artificialStriga infestation at Mokwa and Abuja, Nigeria in 2008 and 2009. The biplot wasba1

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ased on genotype-focused singular value partitioning (‘SVP = 2) and is thereforeppropriate for visualizing the relationships among traits. Principal component (PC)and PC 2 for model 2 explained 59.8% of the variation among traits.

. Results and discussion

.1. Cultivar grain yields under Striga-infested and Striga-freenvironments

Combined analysis of variance across locations and yearshowed that under Striga infestation, mean squares of all traitsere significant for genotypes (G), environments (E) and geno-

ype × environment (G × E) interactions except the mean squaresf G for ASI and SLG; and G × E for ASI and PLHT (Table 2). On thether hand, under Striga-free conditions, mean squares for G, E and× E were significant for all traits with the exception of SLG, PASP,

PP, and RL of G and HUSK, RL, and EROT of G × E.Mean grain yield of the cultivars ranged from 600 to

520 kg ha−1 under Striga infestation; and 2530 to 4160 kg ha−1

nder Striga-free conditions. Average grain yield of cultivars undertriga infestation was 35% of that under Striga-free conditionsTable 2). The observed large loss in grain yield, high host plantamage syndrome rating, and large number of emerged Strigalants recorded under Striga infestation were clear indications ofhe severe parasite pressure achieved during the evaluation of theultivars at Mokwa and Abuja.

.2. Genotype-by-trait biplot of cultivars under artificial Striganfestation

Presented in Figs. 1 and 2 are the “Which is best/worst for what”r “Which wins where” view of GT biplots showing cultivars thatere superior in terms of certain traits under artificial Striga infes-

ation. The cultivar at the vertex of the polygon in each sectors considered the best/worst for the traits within the sector. Theiplots explained 59.8% of the total variation (39.9% for Principal

omponent (PC) 1 and 19.9% for PC2) among the measured traitsf the cultivars. Fig. 1 shows that TY4, was the vertex cultivar forhe sector containing YLD, EPP, STC1, STC2, and PLHT; WB for DYAnd DYS; P18 for STRA1, STRA2, and EASP; BG97 for SLG and ASI. Ta

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190 B. Badu-Apraku, R.O. Akinwale / Field Crops Research 121 (2011) 186–194

Fig. 2. A reverse of a “Which is best/worst for what” or “Which wins where” of geno-type × traits biplot of all traits of 15 early maturing maize cultivars evaluated underawf(

CctSchSSvhacmFmPBshhacspebaflpteoab

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Fig. 3. An entry/tester genotype × trait biplot of six selected traits of 15 early matur-ing maize cultivars evaluated under artificial Striga infestation at Mokwa and Abuja,Nigeria in 2008 and 2009. The biplot was based on trait-focused singular valuepartitioning (‘SVP = 1) and is therefore appropriate for visualizing the relationships

rtificial Striga infestation at Mokwa and Abuja, Nigeria in 2008 and 2009. The biplotas based on genotype-focused singular value partitioning (‘SVP = 2) and is there-

ore appropriate for visualizing the relationships among traits. Principal componentPC) 1 and PC 2 for model 2 explained 59.8% of the variation among traits.

ultivars TW4 and TY4 had high values for YLD and EPP and wereonsidered superior in performance in terms of these traits. Despitehe high grain yield, TY4 and TW4 also had high values for STC1,TC2, and PLHT. The high STC1 and STC2 values imply that the twoultivars supported large number of Striga plants and yet producedigh grain yield, suggesting that that they were tolerant/resistant totriga. The cultivars with high values for DYA, DYS, ASI, LDTH, EASP,LG, STRA1, STRA2, or PASP were considered inferior because highalues for these traits were undesirable. For example, cultivar P18ad high values for STRA1 and STRA2 and was therefore classifieds Striga intolerant or sensitive while WB, which was the vertexultivar in the sector where DYA and DYS fell, was the latest inaturity. The polygon view in Fig. 2 shows the transformed view of

ig. 1 and displays the cultivars that had the lowest values for one orore traits. P18 had the lowest values for YLD, EPP, STC1, STC2, and

LHT while BG97 had the lowest values for DYA. This indicates thatG97 was the earliest maturing cultivar in the study. P18 was out-tanding in terms of emerged Striga counts and low plant height butad several barren plants resulting in poor yield. The low EPP andigh Striga damage might have contributed to the low yield of P18nd indicates that it was Striga susceptible. Low Striga emergenceounts that are often recorded on susceptible maize plants are pre-umably due to the inability of the host to support undergroundarasitic seedlings to emergence or poor adaptation to the growingnvironment. Reduction in parasite emergence counts of suscepti-le genotypes has also been reported by Kim and Adetimirin (1995)nd Badu-Apraku et al. (2007). TY4 and TW4 had the lowest scoresor STRA1, STRA2, and EASP. Since the two cultivars also supportedarge number of Striga plants (Fig. 1), it may be concluded that theyossessed Striga tolerant genes. It was therefore not surprising thathey were the highest-yielding cultivars (Fig. 1). TDT had the low-st values for ASI and SLG, indicating that it was superior in termsf these traits. The cultivars with superior traits such as TY4, TW4,nd TDT could be used as germplasm sources for improving the

reeding populations of national maize programs in WCA.

The entry/tester GT biplot view in Fig. 3 was constructed usinghe six traits for computing the base index for selecting for Strigaolerant/resistant genotypes in the IITA Maize Program. The traitsSTRA1 and STRA2), which had significant negative correlation with

among genotypes. Principal component (PC)1 and PC 2 for model 2 explained 85.3%of the variation among traits.

yield were rescaled such that all the six selected traits fell on oneside of the average-trait-vector and large values for the traits weremore desirable. In addition, all selected traits were assigned equalweights (viewed as equally important). This biplot view shows thatTW4 was the top-ranking cultivar while P18 was the poorest inperformance based on multiple traits under Striga infestation. Eightout of the fifteen cultivars, TW4, TY4, MDT, TWQ, EWQ, TYQ, TDT,and CP1 were above average in performance. An ideal genotype, onthe basis of multiple traits, can be described as the genotype withcombination of highest number of desirable qualities/characters inits genetic make-up. It should have the highest mean performanceacross traits (i.e. longest projection onto the average tester axis(ATC abscissa) and shortest entry-vector) thus, it should be close tothe ideal genotype represented by the innermost concentric circlewith an arrow pointing to it (Yan and Kang, 2003). On this basis,TW4, TY4, MDT, and TWQ were the closest to the ideal cultivar(Fig. 4).

The GT biplot in Fig. 5 shows the interrelationships among mea-sured traits of the cultivars under artificial Striga infestation. In thebiplot display, the rays connecting the traits to the biplot originare described as trait vectors. The cosine of the angle between thevectors of two traits measures the similarity between them rel-ative to their effects on yield. Thus, EPP, PLHT, STC1, and STC2had acute angles (<90◦) with YLD, suggesting that they had pos-itive genetic correlation with it. However, PLHT, STC1, and STC2,had short trait vectors, indicating weak or non-significant positivegenetic correlations with yield. On the other hand, DYA, DYS, ASI,SLG, EASP, STRA1, and STRA2 had angles greater that 90◦, indicat-ing that they were negatively correlated with YLD. Highly positivecorrelations existed among the trait pairs SLG and ASI; and STRA1and STRA2; suggesting that they were closely related. The weakor non-significant positive correlation observed between YLD andSTC1 and STC2 is in agreement with the findings of Badu-Apraku etal. (2005, 2006) and Badu-Apraku (2007). However, this result is in

disagreement with those reported for the extra-early white pop-ulation by Badu-Apraku (2010) as well as those obtained for thelate and intermediate maturity groups (Kim and Adetimirin, 1995;Gethi and Smith, 2004; Menkir and Kling, 2007; Yallou et al., 2009).

B. Badu-Apraku, R.O. Akinwale / Field Crops Research 121 (2011) 186–194 191

Fig. 4. A vector view of genotype-by-trait biplot showing the ranking of maizecultivars under artificial Striga infestation on the basis of their mean performanceaigv

ttteStFsaSe

Faigff

Fig. 6. A vector view of the genotype-by-trait biplot displaying most reliable traitsfor indirect selection for yield (inside box) under artificial Striga infestation at P < 0.01and R2 value of ≥43. 61%. The biplot was based on genotype-focused singular value

cross selected traits. The biplot was based on trait-focused singular value partition-ng (‘SVP = 1) and is therefore appropriate for visualizing the relationships amongenotype. Principal component (PC) 1 and PC 2 for model 2 explained 85.3% of theariation among traits.

An important advantage of the GT biplot is that it can be usedo identify redundant traits in an effort to reduce cost in measuringraits in field experiments without sacrificing precision. Therefore,he high positive correlation between SLG and ASI suggests thatither of the parameters will be sufficient as a selection criterion.imilarly, the high correlation between STRA1 and STRA2 suggestshat either of the two traits will suffice as a selection criterion.ig. 6 provides information on the reliability of the traits for indirect

election for improved grain yield under artificial Striga infestationt P < 0.01 and R-square value of ≥43.61%. Based on this biplot, EPP,TRA1, STRA2, and EASP were identified as the most reliable of theleven measured traits for selecting for Striga resistance.

ig. 5. A vector view of the genotype-by-trait biplot showing interrelationshipsmong all traits of 15 early maturing maize cultivars evaluated under artificial Striganfestation at Mokwa and Abuja, Nigeria in 2008 and 2009. The biplot was based onenotype-focused singular value partitioning (‘SVP = 2) and is therefore appropriateor visualizing the relationships among traits. Principal component (PC) 1 and PC 2or model 2 explained 59.8% of the variation among traits.

partitioning (‘SVP = 2) and is therefore appropriate for visualizing the relationshipsamong traits. Principal component (PC)1 and PC2 for model 2 explained 88.8% of thevariation among traits.

3.3. Genotype-by-trait biplot of cultivars under Striga-freeenvironment

Farmers in West Africa have the perception that Striga-resistantcultivars are lower yielding in Striga-free environments and thatcultivars selected under artificial Striga infestation might be specif-ically adapted to Striga-infested environments and that a yieldpenalty occurs in Striga-free environments (Badu-Apraku, 2010).Therefore, cultivars with high yield potential in both Striga-infestedand Striga-free environments are desirable. Because of this, theearly maturing cultivars were also evaluated under Striga-free con-ditions. Results of the GT biplot analysis (figure not shown) showedthat TDT was the tallest cultivar, had high ear placement and wasvery productive (highest EPP) while EWQ, was the highest-yieldingcultivar but had severe lodging. WB was latest in maturity (DYAand DYS) and there was a long delay in the interval betweenanthesis and silking and poor plant aspect while EY and TYQ werethe poorest in terms of HUSK and EASP. EWQ was superior in termsof EASP, ASI, PASP, DYA, and DYS while TDT was superior in termsof reduced number of rotten ears, root lodging, and good HUSK(Figure not shown). Furthermore, WB was the most resistant tostalk lodging while EY and TYQ were the lowest yielding cultivars.The GT biplot in Fig. 7 ranked the cultivars in the followingorder based on the mean performance across selected traits:EWQ ≈ CP2 > TDT > CP1 ≈ TW4 ≈ TY4 ≈ EYQ > MDT ≈ BG97 ≈ P16 ≈TWQ > TYQ ≈ P18 ≈ EY ≈ WB. Cultivar CP2 was the closest to theideal cultivar and also had superior performance across traits andwas therefore identified as the ideal cultivar under Striga-freeconditions. It is striking to note that EWQ, a Quality Protein Maize(QPM) cultivar with drought tolerance and Striga resistance, wascomparable not only in grain yield performance to the best droughttolerant normal endosperm cultivar, CP2, but was also superiorin terms of EASP, PASP, ASI, DYA, and DYS (Figure not shown).This confirms the earlier reports of Badu-Apraku and Lum (2010)

that QPM cultivars with comparable performance to the normalendosperm cultivars are now available in WCA. This cultivar hasbeen released in Ghana for production by farmers. It is also slatedfor release in Nigeria.

192 B. Badu-Apraku, R.O. Akinwale / Field Crops Research 121 (2011) 186–194

Fig. 7. An entry/tester genotype × trait biplot of five selected traits of 15 earlymaturing maize cultivars evaluated under Striga-free conditions at Mokwa andAbuja, Nigeria in 2008 and 2009. The data were not transformed (‘Transform = 0’),sbpa

cmcwtiR

Fafn(itv

Fig. 9. A vector view of the genotype-by-trait biplot displaying most reliable traitsfor indirect selection for yield (inside box) under Striga-free conditions, at P < 0.01and R2 value of ≥25.54%. The data were not transformed (‘Transform = 0’), standard-ized (‘Scale = 1), and were trait-centered (‘Centering = 2’). The biplot was based ongenotype-focused singular value partitioning (‘SVP = 2) and is therefore appropriate

tandardized (‘Scale = 1), and were trait-centered (‘Centering = 2’). The biplot wasased on trait-focused singular value partitioning (‘SVP = 1) and is therefore appro-riate for visualizing the relationships among genotypes. Principal component (PC)1nd PC 2 for model 2 explained 75.9% of the variation among traits.

To examine the interrelationship among traits under Striga-freeonditions, a biplot was generated to display the association amongeasured traits (Fig. 8). Results showed that YLD was positively

orrelated with PLHT, EHT, EPP, and SLG but negatively correlated

ith EASP, ASI, PASP, DYA, DYS, HUSK, EROT, and RL. Fig. 9 reveals

hat EASP and ASI were the most reliable traits for selecting formproved grain yield under Striga-free conditions at P < 0.01 and-square value of 25.54%.

ig. 8. A vector view of the genotype-by-trait biplot showing interrelationshipsmong all traits of 15 early maturing maize varieties evaluated under Striga-ree conditions at Mokwa and Abuja, Nigeria in 2008 and 2009. The data wereot transformed (‘Transform=0’), standardized (‘Scale = 1), and were trait-centered‘Centering = 2’). The biplot was based on genotype-focused singular value partition-ng (‘SVP = 2) and is therefore appropriate for visualizing the relationships amongraits. Principal component (PC) 1 and PC 2 for model 2 explained 48.6% of theariation among traits.

for visualizing the relationships among traits. Principal component (PC)1 and PC 2for model 2 explained 96.2% of the variation among traits.

3.4. Comparison of performance of cultivars and traitinterrelationships under Striga-infested and Striga-freeenvironments

Comparison of the cultivar performance based on multiple traitsusing the GT biplots generated separately for Striga-infested andStriga-free conditions revealed several interesting trends. Culti-vars TY4 and TW4 were the most outstanding based on YLD andEASP under Striga infestation (Figs. 1 and 2) while EWQ was thebest in terms of these two traits under Striga-free conditions (Fig-ures not shown). EPP had higher positive correlation with grainyield under Striga infestation than under Striga-free conditionsthus justifying its inclusion in the list of traits identified as mostreliable for selecting for tolerance/resistance to Striga (Fig. 6). ASIhad higher negative correlation with YLD when Striga-free thanwhen Striga-infested, suggesting that it may not be a reliable traitfor selection for improved grain yield under Striga infestation, buta very important indicator of improved grain yield under Striga-free environments. This result is consistent with the findings ofBadu-Apraku (2007). EASP was the most reliable trait identifiedfor indirect selection for improved grain yield under both researchconditions. In addition to this trait, STRA1, STRA2 and EPP were themost reliable for selecting for improved grain yield under Strigainfestation while ASI was the most reliable yield predictor underStriga-free conditions.

3.5. Assessment of agronomic traits used in the base index forselection for improved grain yield and Striga resistance

A base index which combines grain yield under Striga infesta-tion, Striga damage rating, Striga emergence, PASP and EPP is usedfor selecting for high grain yield measured under Striga-infestedand non-infested conditions (MIP, 1996; Menkir and Kling, 2007;

Badu-Apraku et al., 2010). Therefore, an important objective of thepresent study was to assess the appropriateness of these traits inthe base index. Based on the GT biplot, EPP, STRA1, STRA2, andEASP were identified as the most reliable of the eleven measured

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B. Badu-Apraku, R.O. Akinwale / Fi

raits for selection for Striga resistance thus justifying the use ofPP, STRA1, and STRA2 in the base index. It is not surprising thatPP was identified as one of the most reliable traits for selectionor Striga resistance. Badu-Apraku et al. (2008) reported EPP to be

major component of the increased grain yield associated withecurrent selection programs under drought stress and Striga infes-ation. Similar results were also reported under drought stress byolanos and Edmeades (1993), Chapman and Edmeades (1999),nd Monneveux et al. (2006). This result therefore justifies thenclusion of EPP in the selection index for yield improvement intriga prone environments as earlier reported by Adetimirin et al.2000a), Badu-Apraku (2007), and Badu-Apraku et al. (2008). Simi-arly, Badu-Apraku et al. (2007) reported high negative genetic andhenotypic correlations between grain yield and host plant damageating and concluded that Striga damage rating is an appropriaterait for the assessment of tolerance under Striga infestation (Kimnd Adetimirin, 1995). In contrast, STC1 and STC2 were among theraits that had weak correlation with yield, suggesting that theyo not qualify to be included in the base index. This finding isupported by Badu-Apraku et al. (2007) who reported weak phe-otypic and genotypic correlations between grain yield and Strigamergence count, indicating that it is not a reliable trait for detect-ng Striga resistance. However, it was argued that the result couldlso mean that grain yield and Striga emergence count were genet-cally independent (no linkage, pleiotropy) and can be effectivelyelected for simultaneously using an appropriate index. Contrary tohis result, Badu-Apraku (2007) in a study of the genetic variancesnd correlations in an early white maize population, reported aoderately large negative genetic correlation (rg = −0.56) between

rain yield and Striga emergence count at 10 WAP. This result isurther supported by Badu-Apraku (2010) who reported that undertriga infestation, yield was not correlated with other traits at C0,ut was strongly correlated with ears per plant, Striga damagend emerged Striga plants in advanced cycles of the extra-earlyhite population. Similar results were also reported by Menkir andling (2007) for Striga emergence count at 8 (rp = −0.78) and 10

rp = −0.72) WAP and EASP (rp = −0.97) under Striga infestation forlate maturing tropical maize population. In this study, further

nalysis using the stepwise multiple regression analysis revealedhat Striga emergence count at 8 WAP is among the five traits iden-ified as important yield determinant (data not shown). Similarly,ased on a study using the Path Coefficient Analysis, Badu-Aprakut al. (2010, unpublished data), found a high direct effect of STC1nd STC2 on grain yield, indicating that the two traits are usefulelection criteria for selecting for improved grain yield under artifi-ial Striga infestation and thus justifying their inclusion in the basendex. EASP had a consistently high correlation with grain yieldnder Striga infestation suggesting the need for its inclusion in thease index. This result is consistent with the findings of a similartudy with early maturing inbred lines by Badu-Apraku et al. (2010,npublished data).

. Summary

EPP, STRA2, EASP and STRA1 were identified as the most reliableraits for selecting Striga tolerant genotypes. TW4, TY4, MDT, andWQ were identified to be closest to the ideal cultivar when Striganfested while CP2 had superior performance across the measuredraits and was the closest to the ideal cultivar when Striga free. TheT biplot analysis revealed that STC1, and STC2 had weak corre-

ation with grain yield, implying that their inclusion in the basendex for selecting for Striga resistant genotypes is not justified.ASP had a consistently high correlation with grain yield undertriga infestation, suggesting that it should be included in the basendex.

ps Research 121 (2011) 186–194 193

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