Research ArticleAnalysis of Direct and Indirect Selection and Indices inBread Wheat (Triticum aestivum L) Segregating Progeny
Zine El Abidine Fellahi 1 Abderrahmane Hannachi2 and Hamenna Bouzerzour3
1Department of Agronomy Faculty of Natural Life and Earth Sciences and the UniverseUniversity of Mohamed El Bachir El Ibrahimi 34034 Bordj Bou Arreridj Algeria2National Agronomic Research Institute of Algeria (INRAA) Unit of Setif 19000 Setif Algeria3Department of Ecology and Plant Biology Valorization of Natural Biological Resources LaboratoryFaculty of Natural and Life Sciences University of Ferhat Abbas Setif-1 19000 Setif Algeria
Correspondence should be addressed to Zine El Abidine Fellahi zinouagrogmailcom
Received 27 December 2017 Accepted 5 March 2018 Published 19 April 2018
Academic Editor Iskender Tiryaki
Copyright copy 2018 Zine El Abidine Fellahi et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited
Three selectionmethods including direct and indirect selection along with selection index based on the phenotypic values of eleventraits of agronomic interest were assessed for their application in F4 bread wheat progenies Significant genetic variation existedamong parents and crosses for the traits measured The following were the most efficient indices for simultaneous selection ofsuperior lines for yield and its components base index of Williams followed by the sum of ranks index of Smith and Hazel Theselection-based index provided the highest grain yield gains as compared to the other selection criteria except for flag leaf areaindicating that the direct and indirectmonotrait selectionwere not appropriate in the situation analyzed in thiswork PCA identifiedAin Abid times Mahon-Demias Ain Abid times Rmada and Ain Abid times El-Wifak as the most promising populations At 5 selectionintensity the top 30 lines selected were distinguished in comparison with the standard check Hidhab by significant improvementsin yield and yield components
1 Introduction
In Algeria most of wheat producing areas are located inthe High Plateaus which are characterized by cold wintersinsufficient and erratic rainfall frequent spring frosts andlate-season sirocco occurrence [1 2] In addition to these cli-matic stresses there are some other technical constraints thatessentially arise from the use of unproductive varieties andoften bad agronomic practices Selection for a better adapta-tion to environmental stresses is therefore more promisingoutcome in the field of wheat breeding Breeders are contin-ually seeking to improve the selection methods in order todevelop superior wheat varieties with high grain yield goodend-use quality and tolerance to biotic and abiotic stresses
Direct selection based on grain yield is mainly practicedinwheat breeding programswithout considering the adaptivetraits that are crucial production regulators under variableenvironments [3ndash5] In these environments the presence of
genotype times environment interactions reduces the efficiencyof using grain yield as the sole selection criterion and thuscomplicates the efforts of selection [6 7] In addition to theenvironmental effects other factors such as polygenic naturelow heritability of grain yield linkage and nonadditive geneaction may make the selection less efficient mainly in earlysegregating generations
In order to overcome these difficulties breeders are focus-ing on other traits that can be used in parallel or indepen-dently of yield in a multitraits approach Indirect selectionuses some yield components that are more heritable thanyield itself andmore stable in relation to genetic and environ-mental factors affecting them When these components aremeasured without error and expressed in appropriate unitstheir product is yield This has created new opportunities forplant breeders to use certain morphological physiologicaland biochemical traits during selection for grain yield In theliterature several authors have reported the use of many of
HindawiInternational Journal of AgronomyVolume 2018 Article ID 8312857 11 pageshttpsdoiorg10115520188312857
2 International Journal of Agronomy
these traits to improve grain yield in diverse environments[8ndash10]
Selection-based index is another approach certainlycomplex but can avoid the limits of the single-trait selec-tion particularly the undesirable between-trait relationsthat present an additional nuisance in breedersrsquo work [11]Selection-based index approach targets the simultaneousimprovement of several traits at the same time includingthe grain yield [12 13] The indices allow the use of a singlevalue in the selection process since the analysis is carriedout by means of linear combinations of phenotypic dataof different traits of agronomic interest with the geneticproperties of a population [14] The objective is to guaranteethe improvement of the population genotypic values and con-sequently the efficiency of the selection process maximizingthe expected genetic gain In this purpose many selectionindices have been used as an effective selection criterion inplant breeding programs on different crops [15] Howeverthe conditions determining the usefulness of an appropriateselection index may vary with individual plant breeder Theobjective of this research paper was to evaluate the efficiencyand applicability of different selection criteria based on theestimation of genetic gain in F4 segregating populations ofbread wheat evaluated under semiarid conditions
2 Materials and Methods
21 Plant Material and Experimental Design This experi-ment had 609 genotypes comprising 20 F4-derived familiesand their 9 parents The history of these families is thatafter the initial crosses in 201011 [18] 30 F2 lines wereselected in each family by using pedigree method and wereevaluated during the 201213 cropping season [19] A totalof 600 lines were planted and harvested in bulk during twoconsecutive growing seasons 201314 and 201415 The F4lines along with their parents were planted in Setif ResearchUnit (36∘151015840N 5∘871015840E 1081 masl) of the Algerian NationalInstitute of Agronomic Research (INRAA) in a modifiedrandomized complete block design with three replicationsThe experimental unit consisted of a single row plot of 2mlength with rows spaced 02m apartThe plots were fertilizedwith 100 kg haminus1 of 46 superphosphate before sowing and75 kg haminus1 of 35 urea at tillering stage Weed control wasperformed chemically using 12 g haminus1 of the Granstar [tribe-nuron methyl] herbicide According to Chennafi et al [20]the climate of the region is a semiarid type continental withcold winter and hot and dry summer Total annual rainfallis around 350mm The soil used was classified as a silty claywith high CaCO3 content [20]
22Measurements The following traits weremeasured as perplot basis Chlorophyll content (CHL Spad) was determinedat heading stage using the SPAD-502 chlorophyll meter(Minolta Camera Co Osaka Japan) Flag leaf area (FLAcm2) was obtained using the formula described by Spagno-letti Zeuli and Qualset [21] FLA (cm2) = 119871 (cm) times 119897 (cm)where 119871 is the flag leaf length and 119897 refers to the flag leafwidth Heading date (HD days) was recorded as the number
Table 1 The skeleton of the analysis of variance
Source of variation df MS 119865-testBlock 119887 minus 1 M1 M1M6Genotype 119892 minus 1 M2 M2M6
Parents 119875 minus 1 M3 M3M6F4 119899 minus 1 M4 M4M6Parents vs F4 1 M5 M5M6
Residual (119892 minus 1)(119887 minus 1) M6 - -Total 119887119892 minus 1 - - - -
of calendar days from January first to the date when 50of the spikes were half way out from flag leaf Plant height(PH cm) was measured at maturity from the soil surface tothe top of the spike awns excluded Above ground biomass(BIO gmminus2) was estimated from a harvested area of 05mlong times 020m interrow spacing which served also to obtainthe grain yield (GY gmminus2) number of spikes (SN mminus2)and spikes weight (SW gmminus2) 1000-kernel weight (TKW g)was obtained from the count and weight of 250-kernel Thenumber of grains per spike (GN) was derived from estimatedvalues of grain yield number of spikes and 1000-kernelweight Harvest index (HI ) was estimated by the ratio be-tween grain yield and above ground biomass
23 Data Analysis Data collected were subjected to analysisof variance following the procedures described by McIntosh[22] the skeleton of the analysis of variance is shown inTable 1 The statistical model used considered the completerandomized block design as 119884119894119895 = 120583 + 119892119894 + 119887119894 + 120576119894119895 where 119884119894119895is the observation of the 119894th genotype evaluated in the 119895threplicate 120583 is the overall mean of the experiment 119892119894 is theeffect of the 119894th genotype 119887119895 is the effect of the 119895th block and120576119894119895 is the effect of the 119894119895th plot
In case 119865-test was significant standard error and criticaldifferences were calculated by using the least significantdifference test at 005 probability level (LSD005) accordingto Steel and Torrie [23] LSD005 = 119905005radic21205902119890 119887 where 119905005is the tabulated value of the 119905-test at 005 probability level for(119892 minus 1)(119887 minus 1) residual degrees of freedom 1205902119890 is the residualvariance and 119887 refers to the number of replications or blocks
The genotypic variance (1205902119892) and residual variance (1205902119890)were calculated and served to determine the following geneticand nongenetic parameters for each trait The coefficient ofexperimental variation (CV) was calculated by CV () =radic1205902119890 119883 where1205902119890 is the residual variance and119883 is the generalmean of the trait The coefficient of genetic variation (CV119892)was calculated by the following equation CV119892 () = radic1205902119892119883where1205902119892 is the genotypic variance and119883 is the generalmeanof the trait The variation index was determined as the ratioof CV119892CV119890 Broad-sense heritability of average progenies(ℎ2bs) was estimated by the expression [24] ℎ2bs () = 100 times[1205902119892(1205902119892+1205902119890 )]Narrow-sense heritability of individualswithinfamilies (ℎ2ns) was determined based on the parent-offspring
International Journal of Agronomy 3
regression To do so two methods were used the first wasby the linear regression of F4 on the parental F3 individualvalues [16] while the second approach was performed withstandardized data of offspring (F4) versus standardized of thecorresponding parent (F3) according to Frey andHorner [17]
The selection gains were estimated among families basedon three selection criteria direct selection indirect selectionand selection-based index considering the selection intensityof 5 of top families The expected gains by direct selectionfor each trait evaluated were estimated by the expression [25]Δ119866119894 = ℎ2119894times119878119894 = ℎ2119894times(119883119904119894minus1198830119894) whereΔ119866119894 is the gainwith thedirect selection carried for the 119894th trait ℎ2119894 is the heritability ofthe 119894th trait 119878119894 refers to the differential selection of the 119894th trait119883119904119894 is themean of the 119894th trait for the selected individuals and1198830119894 is the mean of the 119894th trait in the base population Theexpected gain of direct selection expressed as a percentage ofthe population mean is given by Δ119866119894119894 = (Δ119866119894 times 100)1198830119894
Gains from indirect response to selection were calculatedusing the following expression [25] GS119895(119894) = ℎ2119895 times (119883119894119895 minus1198830119895) = ℎ2119895 times DS119895(119894) where GS119895(119894) is the gain on the 119895th traitwith selection based on the 119894th trait 119883119904119895 is the mean of the119895th trait for the selected individuals based on the 119894th trait1198830119895 is the mean of the 119895th trait ℎ2119895 is the heritability ofthe 119895th trait and DS119895(119894) refers to the differential selection ofthe 119895th trait in which the selected lines presented the bestperformance for the 119894th trait The expected gain of indirectselection expressed as a percentage of the population meanis given by GS119895(119894) = (GS119895(119894) times 100)1198830119894
For the selection-based index the following methodolo-gieswere used for gains estimation the classic index proposedby Smith [26] and Hazel [27] the base index of Williams[28] the free weights and parameters index of Elston [29]the index of desired gains of Pesek and Baker [30] the multi-plicative index of Subandi et al [31] the sum of ranks index ofMulamba andMock [32] and the genotype-ideotype distanceindex proposed by Cruz [25] Each index displays certainparticularities in its calculations and as such applicationis generally laborious due to the need to assign adequateeconomic weights to each trait Based on the differentanalytical procedures of selection the best populations wereidentified and the gains from selection were calculated Allstatistical analyses were carried out using Genes software [33]and a Microsoft Excelcopy spreadsheet
3 Results and Discussion
31 Genetic Variability and Heritability The analysis of vari-ance revealed significant genotype effect for almost all thetraits under study except for flag leaf area and grain yieldwhich were not significant at 005 probability level (Table 2)This provides evidence of the presence of sufficient geneticvariability among parents and hybrids that can be exploitedin wheat breeding program through selection Partitioningthe genotype effect indicated significant differences betweenall parents for HD PH SN TKW SW GN BIO and HIand significant interpopulation differences for HD PH andGN The contrast ldquoParents versus F4rdquo was highly significant
for HD PH and SN while the F4 populations exhibitedsignificant differences for nearly all the observed quantitativephenotypic traits except for CHL FLA GN and HI whichwere not significant at the 5 probability level The resultsof this study corroborate those of Abd El-Shafi [34] whoreported significant and highly significant differences amonggenotypes (families + parents) and families for all studiedtraits across three segregating generations F2 F3 and F4Thisauthor also reported that greater response to selection canbe expected from selection in cross having greater phenotypicand genotypic variances
The coefficient of variation (CV) presented values be-tween 07 and 268 for heading date and grain yield respec-tively (Table 2) The CV above 20 is considered high indi-cating high dispersion of the experimental data which mayhave been caused by the genetic and phenotypic differencesbetween the studiedmaterials HighCV estimate obtained forgrain yield can be explained by the fact that it is quantitativetrait governed by several genes and highly influenced by theenvironment
The variances values coefficient of variation and geneticparameters estimates for wheat traits studied are presentedin Table 3 Broad-sense heritability is the proportion of totalphenotypic variation due to all genetic effects The knowl-edge of the genotypic determination coefficient (ℎ2bs) allowsestablishing an estimate of the genetic gain to be obtained anddefines the best strategy to be used in the plant breeding pro-gram [35] In this study the estimated broad-sense heritabil-ity varied from 000 to 9180The highest values were foundfor heading date (9180) followed by plant height (8082)1000-kernel weight (7290) biomass (6533) and numberof grains per spike (6164) indicating that these traits arehighly heritable among the genotypes evaluatedThese resultscan be confirmed with the values obtained by the CV119892CV119890ratio that were close to or greater than 1 for these traits sug-gesting satisfactory conditions for selection [36] Moderateestimates of ℎ2bs occurred for the number of chlorophyllcontents (4031) number of spikes (5200) spikes weight(5485) and harvest index (4067) On the other handthe lowest values of ℎ2bs were found for the flag leaf area(000) and grain yield (1939) These traits exhibited alsolow CV119892CV119890 ratio values indicating the dominant effect ofthe environment on crop
Generally literature indicates widely varying narrow-sense heritability estimates Mesele et al [37] reported highheritability values for days to heading days to maturityand 1000-kernel weight moderate estimates for grain fillingperiod spike length number of spikelets per plant grainsper spike and harvest index and low values for number oftillers per plant biomass yield and grain yield Evaluatingseven F2 populations derived through cross combinations offive parental varietieslines of bread wheat Saleem et al [38]found low to high broad-sense heritability values rangingfrom 475 to 926 depending on the trait and the crossThefindings of Yaqoob [39] showed that heritability estimateswere low for number of tillers per plant (20) grainsper spike (2681) days to maturity (3013) spike length(3666) and 1000-kernel weight (3868) moderate forplant height (4579) and high for heading date (8473)
4 International Journal of Agronomy
Table 2 Analysis of variance of different bread wheat traits studied
SV Bloc Genotypes Parents F4 Parents versus F4 Error CV ()df 2 28 8 19 1 56CHL 86 190lowastlowast 95ns 238lowastlowast 36ns 86 66HD 15 90lowastlowast 183lowastlowast 25lowastlowast 587lowastlowast 07 07FLA 894 111
ns69ns 66ns 01ns 149 204
PH 91 863lowastlowast 1030lowastlowast 800lowastlowast 726lowast 166 63SN 11831 111875lowastlowast 260924lowastlowast 26578ns 539945lowastlowast 53711 159TKW 335 282lowastlowast 394lowastlowast 127ns 2321lowastlowast 77 74SW 1170346 448736lowastlowast 744904lowastlowast 182488ns 3137463lowastlowast 202598 209GN 366 447lowastlowast 613lowastlowast 370lowast 410ns 171 168BIO 811895 1310774lowastlowast 2431421lowastlowast 436017ns 8965592lowastlowast 454408 185GY 341492 157279ns 268354ns 110965ns 562059lowast 126778 268HI 1222 623lowast 1366lowastlowast 361ns 214ns 37 168CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) HI harvest index () and nslowast and lowastlowast nonsignificant and significant effect at 005 and 001 probability
Table 3 Genetic and nongenetic parameters of different bread wheat traits studied
Traits 1205902119901 1205902119890 1205902119892 ℎ2bs ℎ2ns (SK) ℎ2ns (FH) CV119892 () CV119892CV119890Chl 336 200 135 4031 2855 3013 264 047HD 301 025 276 9180 1669 3432 131 193FLA 368 498 000 000 000 000 000 000PH 2878 552 2326 8082 2727 2727 743 119SN 372917 179037 193879 5200 000 000 959 060TKW 941 255 686 7290 3164 2599 698 095SW 1495791 675325 820467 5485 866 1070 1319 064GN 1467 563 904 6164 1670 714 1228 073BIO 4369245 1514693 2854552 6533 337 593 1448 079GY 524264 422592 101671 1939 1013 1231 758 028HI 2076 1231 845 4067 1410 1497 803 048CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY Grain yield (gmminus2) HI harvest index () ℎ2bsbroad-sense heritability ℎ2ns (SK) narrow-sense heritability according to Smith and Kinman [16] and ℎ2ns (FH) narrow-sense heritability according to Freyand Horner [17]
and grain yield (9983) His results also indicated that mostof these traits exhibited low heritability under drought stressconditions suggesting the presence of high genotype times envi-ronment interactions that affected the crop behavior
Narrow-sense heritability is the proportion of the totalphenotypic variation that is due to the additive effects ofgenes This component of variation is important because it isthe only variation that natural selection can act on Henceℎ2ns determines the resemblance of offspring to their parentsand the populationrsquos evolutionary response to selectionTherecan be considerable nonadditive genetic variance but thisdoes not contribute to the resemblance between parents andoffspring or the response to selection Low to moderatenarrow-sense heritability values were recorded in this studyChlorophyll content heading date plant height 1000-kernelweight and number of grains per spike recorded the highestestimates These traits were less influenced by the environ-mental factors and would respond positively to a selection
pressure in the current breeding programThe lowheritabilityvalues can be explained by the change occurring in thesegregating lines behavior from the precedent to the currentgeneration This change may be due to nonadditive geneaction andor high environmental factors effects
Means of the variables measured showed that the bestvalues varied depending on the cross and the trait and thefew populations had the best performances for several traitsat the same time (Table 4)The best grain yielding population(5575 g) was Ain Abid times El-Wifak which had also the highestaverage for the number of grains per spike (3147 grains)spikes weight (8573 g) above ground biomass (13503 g) andharvest index (413) Ain Abid timesHidhab cross combinationhad the longest vegetative cycle with an average of 1278days and presented the highest mean for the flag leaf area(210 cm2) Acsad1069 times El-Wifak had the highest average forthe chlorophyll content (540 Spad) Acsad1069 times Mahon-Demias was the tallest (7523 cm) while Acsad1135 times Rmada
International Journal of Agronomy 5
Table4Means
ofthem
easuredtraitsfor2
0F 4
breadwheatpo
pulatio
ns
Popu
latio
nCH
LHD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Acsad 901timesM
ahon
-Dem
ias
430
1260
201
737
4483
386
6340
220
11350
3792
334
Acsad 901timesR
mada
445
1252
199
608
4424
346
6336
281
10497
4255
405
Acsad 901timesH
idhab
425
1259
198
657
4267
334
6167
230
10983
3255
296
Acsad 901timesE
l-Wifa
k451
1249
204
704
4673
332
6843
304
11490
4604
401
Acsad 899timesM
ahon
-Dem
ias
437
1257
169
708
4760
364
6260
205
11517
3668
319
Acsad 899timesR
mada
434
1250
166
595
4797
378
6257
200
10327
3688
357
Acsad 899timesH
idhab
444
1269
182
566
4060
350
5980
228
9970
3360
337
Acsad 899timesE
l-Wifa
k454
1250
181
631
4147
355
5667
242
9657
3577
370
Acsad 1135timesM
ahon
-Dem
ias
416
1261
170
681
4567
398
6040
210
10737
3834
357
Acsad 1135timesR
mada
422
1252
168
615
5037
369
6853
240
11137
4404
396
Acsad 1135timesH
idhab
415
1270
175
688
4770
395
7447
232
12687
4509
355
Acsad 1135timesE
l-Wifa
k455
1252
170
646
3970
346
4893
221
8790
3099
353
Acsad 1069timesM
ahon
-Dem
ias
438
1258
182
752
4390
392
6233
217
11937
3713
311
Acsad 1069timesR
mada
442
1250
173
617
4147
348
5810
245
9617
3608
375
Acsad 1069timesH
idhab
467
1259
185
605
4113
350
6170
266
9767
3953
405
Acsad 1069timesE
l-Wifa
k540
1250
184
665
4233
346
6110
279
9940
4033
406
Ain
AbidtimesM
ahon
-Dem
ias
420
1277
200
727
4547
388
7473
272
12867
4818
375
Ain
AbidtimesR
mada
403
1263
205
658
4357
365
6993
282
11717
4581
391
Ain
AbidtimesH
idhab
432
1278
210
602
4050
368
6933
304
11307
4628
409
Ain
AbidtimesE
l-Wifa
k436
1269
203
651
4693
377
8573
315
13503
5575
413
ParentsR
ange
391
1250
87
530
2700
300
3200
975300
1850
141
522
1320
391
820
8000
490
12600
394
23300
8090
636
F 4Ra
nge
334
1240
76420
800
190
800
931500
430
126
538
1360
364
1000
8500
510
14800
568
24200
9210
700
LSD005
48
1463
67
1197
45
2352
68
3532
1851
99CH
Lchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)andHIharvestind
ex(
)
6 International Journal of Agronomy
exhibited the highest average for the number of spikes (5037spikes) For 1000-kernel weight Acsad1135 timesMahon-Demiaswas the best population with an average of 398 g
As the contrast ldquoParents versus F4rdquo was significant forPH SN TKW SW BIO and GY and compared with theleast significant difference (LSD005) significant differencesexisted between the parents and hybrids values for thesemea-sured traits These differences were 1 to 3 times higher thanthe LSD005 and were in favor of some F4 lines indicatingthat they perform better than the parents and suggestingthe possibilities of making significant changes through effec-tive selection These findings were in harmony with thoseobtained by Abd El-Shafi [34] Loffler and Busch [40] andAlexander et al [41]
32 Genetic Gain from Direct and Indirect Selection andSelection-Based Index The gains of direct and indirect selec-tion are shown inTable 5The results showed a big variation inthe percentages of gains among the measured traitsThe totalsum of gains per selection varied from minus3611 for headingdate to 3475 for flag leaf area Generally the direct selectionbased on chlorophyll content heading date spikes weightgrain yield and harvest index resulted in negative total gainsOn the other hand flag leaf area plant height number ofspikes 1000-kernel weight number of grains per spike andbiomass recorded positive total gains
The gains obtained by direct selection were higher thanthose of indirect selection But sometimes the indirect selec-tion may be more efficient especially if the secondary traitis highly correlated with yield and is easily measurable [42]The number of grains per spike (1586) followed by aboveground biomass (1230) plant height (1210) and numberof spikes (1084) exhibited the greatest gains from directselection Inversely heading date (minus113) spikes weight(minus499) and harvest index (minus381) showed negative gainvalues Negative gain for heading date is desired in the case ofthis study as the Algerian wheat breeding program designedfor semiarid regions precocity is a crucial criterion adoptedfor selection It is related to the ability of plants to shortentheir cycle so as to decrease their exposure to the late-seasonsirocco weathering
The direct selection for chlorophyll content resulted innegative indirect gains for nearly all the other traits exceptfor the flag leaf area number of grains per spike and harvestindex that showed positive responses For heading date theindirect selection gains were only positive for chlorophyllcontent number of spikes and harvest indexThe correlativeeffects of the selection based on the flag leaf area were desir-able for grain yield although the indirect gain for the remain-ing traits was practically negative
The direct selection for plant height resulted in positiveindirect gains for heading date number of spikes 1000-kernelweight and spikes weight In addition positive responsesof the selection based on yield and yield components wereobserved for grain yield itself heading date 1000-kernelweight and spikes weight However negative indirect gainswere exhibited for other traits including chlorophyll contentflag leaf area and number of grains per spike indicatingthat indirect selection for one variable for gain in another
is unfeasible because there will be a loss in the indirectlyselected variable In cases of negative gains the model is con-sidered inappropriate for selection in this plant material
The highest correlated responses for grain yield weregenerated though indirect selection on the base of theflag leaf area (804 ie 3387 gmminus2) followed by thespike fertility (365 ie 1539 gmminus2) 1000-kernel weight(363 ie 1531 gmminus2) and above ground biomass (274 ie1154 gmminus2)These results indicated that the indirect selectionwas to be more effective in improving the primary trait thanthe indirect selection based on other traits andor on thedirect selection based on the grain yield itself
Several authors have estimated the genetic gains of traitsinvolved in yield determination The results are often incon-sistent and scarce Our results were consistent with thoseof DePauw and Shebeski [43] and Inagaki et al [44] whomentioned that direct selection on the basis of yield is ineffec-tive in early generations Benmahammed et al [45] reportedthe same findings in barley crop Their results showed thatbiomass-based direct selection appearedmore discriminatingthan yield-based selection in their plant material Howeverthe results of this study do not corroborate findings reportedby Lalic et al [11] Mitchell et al [46] Lungu et al [47] andEl-Morshidy et al [48] and who observed the effectiveness ofthe direct selection on grain yieldThedifference in the resultsmay be attributed to differences of breeding material and togenotype times environments
The gains of selection-based index are shown in Table 6The total sum of gains from the selection-based index rangedfrom minus937 to 4286 Five out of seven used indicesshowed positive total gains The base index of Williamsrecorded the highest total gain (4286) followed by theclassic index of Smith and Hazel the index desired gains ofPesek and Baker (2746) and the genotype-ideotype dis-tance index of Cruz (2207) The sum of ranks index ofMulamba and Mock had a very low gain of 374 Subandirsquosand Elstonrsquos indices exhibited negative total gains
The base index of Williams yielded positive responses forall measured traits except for chlorophyll content flag leafarea and harvest index which showed negative gains Thisindex also achieved the highest grain yield response (627ie 2641 gmminus2) compared to the other indices employed inthis study The classic index of Smith and Hazel ranked sec-ond followed by the index of desired gains of Pesek and Bakerthe genotype-ideotype distance index proposed by Cruz andthe sum of ranks index of Mulamba andMock with selectionresponses of 3693 2746 2207 and 374 respectivelyThe single-trait responses varied from one index to anotherwith a more or less balanced distribution of positive andnegative estimates among the traits This shows that themonotrait selection was inadequate because it led to a higherfinal product when considering the grain yield and generatedunfavorable responses in other traits These results indicatedthat methods that combine favorable expected gains shouldbe used in the evaluation of these progeniesThe gains expect-ed through indices for grain yield per se were larger thanthose obtained by direct and indirect monotrait selectionexcept for the flag leaf area (Tables 5 and 6) Mahdy [49]mentioned that selection-based index was predicted to be
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
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Hindawiwwwhindawicom Volume 2018
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Submit your manuscripts atwwwhindawicom
2 International Journal of Agronomy
these traits to improve grain yield in diverse environments[8ndash10]
Selection-based index is another approach certainlycomplex but can avoid the limits of the single-trait selec-tion particularly the undesirable between-trait relationsthat present an additional nuisance in breedersrsquo work [11]Selection-based index approach targets the simultaneousimprovement of several traits at the same time includingthe grain yield [12 13] The indices allow the use of a singlevalue in the selection process since the analysis is carriedout by means of linear combinations of phenotypic dataof different traits of agronomic interest with the geneticproperties of a population [14] The objective is to guaranteethe improvement of the population genotypic values and con-sequently the efficiency of the selection process maximizingthe expected genetic gain In this purpose many selectionindices have been used as an effective selection criterion inplant breeding programs on different crops [15] Howeverthe conditions determining the usefulness of an appropriateselection index may vary with individual plant breeder Theobjective of this research paper was to evaluate the efficiencyand applicability of different selection criteria based on theestimation of genetic gain in F4 segregating populations ofbread wheat evaluated under semiarid conditions
2 Materials and Methods
21 Plant Material and Experimental Design This experi-ment had 609 genotypes comprising 20 F4-derived familiesand their 9 parents The history of these families is thatafter the initial crosses in 201011 [18] 30 F2 lines wereselected in each family by using pedigree method and wereevaluated during the 201213 cropping season [19] A totalof 600 lines were planted and harvested in bulk during twoconsecutive growing seasons 201314 and 201415 The F4lines along with their parents were planted in Setif ResearchUnit (36∘151015840N 5∘871015840E 1081 masl) of the Algerian NationalInstitute of Agronomic Research (INRAA) in a modifiedrandomized complete block design with three replicationsThe experimental unit consisted of a single row plot of 2mlength with rows spaced 02m apartThe plots were fertilizedwith 100 kg haminus1 of 46 superphosphate before sowing and75 kg haminus1 of 35 urea at tillering stage Weed control wasperformed chemically using 12 g haminus1 of the Granstar [tribe-nuron methyl] herbicide According to Chennafi et al [20]the climate of the region is a semiarid type continental withcold winter and hot and dry summer Total annual rainfallis around 350mm The soil used was classified as a silty claywith high CaCO3 content [20]
22Measurements The following traits weremeasured as perplot basis Chlorophyll content (CHL Spad) was determinedat heading stage using the SPAD-502 chlorophyll meter(Minolta Camera Co Osaka Japan) Flag leaf area (FLAcm2) was obtained using the formula described by Spagno-letti Zeuli and Qualset [21] FLA (cm2) = 119871 (cm) times 119897 (cm)where 119871 is the flag leaf length and 119897 refers to the flag leafwidth Heading date (HD days) was recorded as the number
Table 1 The skeleton of the analysis of variance
Source of variation df MS 119865-testBlock 119887 minus 1 M1 M1M6Genotype 119892 minus 1 M2 M2M6
Parents 119875 minus 1 M3 M3M6F4 119899 minus 1 M4 M4M6Parents vs F4 1 M5 M5M6
Residual (119892 minus 1)(119887 minus 1) M6 - -Total 119887119892 minus 1 - - - -
of calendar days from January first to the date when 50of the spikes were half way out from flag leaf Plant height(PH cm) was measured at maturity from the soil surface tothe top of the spike awns excluded Above ground biomass(BIO gmminus2) was estimated from a harvested area of 05mlong times 020m interrow spacing which served also to obtainthe grain yield (GY gmminus2) number of spikes (SN mminus2)and spikes weight (SW gmminus2) 1000-kernel weight (TKW g)was obtained from the count and weight of 250-kernel Thenumber of grains per spike (GN) was derived from estimatedvalues of grain yield number of spikes and 1000-kernelweight Harvest index (HI ) was estimated by the ratio be-tween grain yield and above ground biomass
23 Data Analysis Data collected were subjected to analysisof variance following the procedures described by McIntosh[22] the skeleton of the analysis of variance is shown inTable 1 The statistical model used considered the completerandomized block design as 119884119894119895 = 120583 + 119892119894 + 119887119894 + 120576119894119895 where 119884119894119895is the observation of the 119894th genotype evaluated in the 119895threplicate 120583 is the overall mean of the experiment 119892119894 is theeffect of the 119894th genotype 119887119895 is the effect of the 119895th block and120576119894119895 is the effect of the 119894119895th plot
In case 119865-test was significant standard error and criticaldifferences were calculated by using the least significantdifference test at 005 probability level (LSD005) accordingto Steel and Torrie [23] LSD005 = 119905005radic21205902119890 119887 where 119905005is the tabulated value of the 119905-test at 005 probability level for(119892 minus 1)(119887 minus 1) residual degrees of freedom 1205902119890 is the residualvariance and 119887 refers to the number of replications or blocks
The genotypic variance (1205902119892) and residual variance (1205902119890)were calculated and served to determine the following geneticand nongenetic parameters for each trait The coefficient ofexperimental variation (CV) was calculated by CV () =radic1205902119890 119883 where1205902119890 is the residual variance and119883 is the generalmean of the trait The coefficient of genetic variation (CV119892)was calculated by the following equation CV119892 () = radic1205902119892119883where1205902119892 is the genotypic variance and119883 is the generalmeanof the trait The variation index was determined as the ratioof CV119892CV119890 Broad-sense heritability of average progenies(ℎ2bs) was estimated by the expression [24] ℎ2bs () = 100 times[1205902119892(1205902119892+1205902119890 )]Narrow-sense heritability of individualswithinfamilies (ℎ2ns) was determined based on the parent-offspring
International Journal of Agronomy 3
regression To do so two methods were used the first wasby the linear regression of F4 on the parental F3 individualvalues [16] while the second approach was performed withstandardized data of offspring (F4) versus standardized of thecorresponding parent (F3) according to Frey andHorner [17]
The selection gains were estimated among families basedon three selection criteria direct selection indirect selectionand selection-based index considering the selection intensityof 5 of top families The expected gains by direct selectionfor each trait evaluated were estimated by the expression [25]Δ119866119894 = ℎ2119894times119878119894 = ℎ2119894times(119883119904119894minus1198830119894) whereΔ119866119894 is the gainwith thedirect selection carried for the 119894th trait ℎ2119894 is the heritability ofthe 119894th trait 119878119894 refers to the differential selection of the 119894th trait119883119904119894 is themean of the 119894th trait for the selected individuals and1198830119894 is the mean of the 119894th trait in the base population Theexpected gain of direct selection expressed as a percentage ofthe population mean is given by Δ119866119894119894 = (Δ119866119894 times 100)1198830119894
Gains from indirect response to selection were calculatedusing the following expression [25] GS119895(119894) = ℎ2119895 times (119883119894119895 minus1198830119895) = ℎ2119895 times DS119895(119894) where GS119895(119894) is the gain on the 119895th traitwith selection based on the 119894th trait 119883119904119895 is the mean of the119895th trait for the selected individuals based on the 119894th trait1198830119895 is the mean of the 119895th trait ℎ2119895 is the heritability ofthe 119895th trait and DS119895(119894) refers to the differential selection ofthe 119895th trait in which the selected lines presented the bestperformance for the 119894th trait The expected gain of indirectselection expressed as a percentage of the population meanis given by GS119895(119894) = (GS119895(119894) times 100)1198830119894
For the selection-based index the following methodolo-gieswere used for gains estimation the classic index proposedby Smith [26] and Hazel [27] the base index of Williams[28] the free weights and parameters index of Elston [29]the index of desired gains of Pesek and Baker [30] the multi-plicative index of Subandi et al [31] the sum of ranks index ofMulamba andMock [32] and the genotype-ideotype distanceindex proposed by Cruz [25] Each index displays certainparticularities in its calculations and as such applicationis generally laborious due to the need to assign adequateeconomic weights to each trait Based on the differentanalytical procedures of selection the best populations wereidentified and the gains from selection were calculated Allstatistical analyses were carried out using Genes software [33]and a Microsoft Excelcopy spreadsheet
3 Results and Discussion
31 Genetic Variability and Heritability The analysis of vari-ance revealed significant genotype effect for almost all thetraits under study except for flag leaf area and grain yieldwhich were not significant at 005 probability level (Table 2)This provides evidence of the presence of sufficient geneticvariability among parents and hybrids that can be exploitedin wheat breeding program through selection Partitioningthe genotype effect indicated significant differences betweenall parents for HD PH SN TKW SW GN BIO and HIand significant interpopulation differences for HD PH andGN The contrast ldquoParents versus F4rdquo was highly significant
for HD PH and SN while the F4 populations exhibitedsignificant differences for nearly all the observed quantitativephenotypic traits except for CHL FLA GN and HI whichwere not significant at the 5 probability level The resultsof this study corroborate those of Abd El-Shafi [34] whoreported significant and highly significant differences amonggenotypes (families + parents) and families for all studiedtraits across three segregating generations F2 F3 and F4Thisauthor also reported that greater response to selection canbe expected from selection in cross having greater phenotypicand genotypic variances
The coefficient of variation (CV) presented values be-tween 07 and 268 for heading date and grain yield respec-tively (Table 2) The CV above 20 is considered high indi-cating high dispersion of the experimental data which mayhave been caused by the genetic and phenotypic differencesbetween the studiedmaterials HighCV estimate obtained forgrain yield can be explained by the fact that it is quantitativetrait governed by several genes and highly influenced by theenvironment
The variances values coefficient of variation and geneticparameters estimates for wheat traits studied are presentedin Table 3 Broad-sense heritability is the proportion of totalphenotypic variation due to all genetic effects The knowl-edge of the genotypic determination coefficient (ℎ2bs) allowsestablishing an estimate of the genetic gain to be obtained anddefines the best strategy to be used in the plant breeding pro-gram [35] In this study the estimated broad-sense heritabil-ity varied from 000 to 9180The highest values were foundfor heading date (9180) followed by plant height (8082)1000-kernel weight (7290) biomass (6533) and numberof grains per spike (6164) indicating that these traits arehighly heritable among the genotypes evaluatedThese resultscan be confirmed with the values obtained by the CV119892CV119890ratio that were close to or greater than 1 for these traits sug-gesting satisfactory conditions for selection [36] Moderateestimates of ℎ2bs occurred for the number of chlorophyllcontents (4031) number of spikes (5200) spikes weight(5485) and harvest index (4067) On the other handthe lowest values of ℎ2bs were found for the flag leaf area(000) and grain yield (1939) These traits exhibited alsolow CV119892CV119890 ratio values indicating the dominant effect ofthe environment on crop
Generally literature indicates widely varying narrow-sense heritability estimates Mesele et al [37] reported highheritability values for days to heading days to maturityand 1000-kernel weight moderate estimates for grain fillingperiod spike length number of spikelets per plant grainsper spike and harvest index and low values for number oftillers per plant biomass yield and grain yield Evaluatingseven F2 populations derived through cross combinations offive parental varietieslines of bread wheat Saleem et al [38]found low to high broad-sense heritability values rangingfrom 475 to 926 depending on the trait and the crossThefindings of Yaqoob [39] showed that heritability estimateswere low for number of tillers per plant (20) grainsper spike (2681) days to maturity (3013) spike length(3666) and 1000-kernel weight (3868) moderate forplant height (4579) and high for heading date (8473)
4 International Journal of Agronomy
Table 2 Analysis of variance of different bread wheat traits studied
SV Bloc Genotypes Parents F4 Parents versus F4 Error CV ()df 2 28 8 19 1 56CHL 86 190lowastlowast 95ns 238lowastlowast 36ns 86 66HD 15 90lowastlowast 183lowastlowast 25lowastlowast 587lowastlowast 07 07FLA 894 111
ns69ns 66ns 01ns 149 204
PH 91 863lowastlowast 1030lowastlowast 800lowastlowast 726lowast 166 63SN 11831 111875lowastlowast 260924lowastlowast 26578ns 539945lowastlowast 53711 159TKW 335 282lowastlowast 394lowastlowast 127ns 2321lowastlowast 77 74SW 1170346 448736lowastlowast 744904lowastlowast 182488ns 3137463lowastlowast 202598 209GN 366 447lowastlowast 613lowastlowast 370lowast 410ns 171 168BIO 811895 1310774lowastlowast 2431421lowastlowast 436017ns 8965592lowastlowast 454408 185GY 341492 157279ns 268354ns 110965ns 562059lowast 126778 268HI 1222 623lowast 1366lowastlowast 361ns 214ns 37 168CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) HI harvest index () and nslowast and lowastlowast nonsignificant and significant effect at 005 and 001 probability
Table 3 Genetic and nongenetic parameters of different bread wheat traits studied
Traits 1205902119901 1205902119890 1205902119892 ℎ2bs ℎ2ns (SK) ℎ2ns (FH) CV119892 () CV119892CV119890Chl 336 200 135 4031 2855 3013 264 047HD 301 025 276 9180 1669 3432 131 193FLA 368 498 000 000 000 000 000 000PH 2878 552 2326 8082 2727 2727 743 119SN 372917 179037 193879 5200 000 000 959 060TKW 941 255 686 7290 3164 2599 698 095SW 1495791 675325 820467 5485 866 1070 1319 064GN 1467 563 904 6164 1670 714 1228 073BIO 4369245 1514693 2854552 6533 337 593 1448 079GY 524264 422592 101671 1939 1013 1231 758 028HI 2076 1231 845 4067 1410 1497 803 048CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY Grain yield (gmminus2) HI harvest index () ℎ2bsbroad-sense heritability ℎ2ns (SK) narrow-sense heritability according to Smith and Kinman [16] and ℎ2ns (FH) narrow-sense heritability according to Freyand Horner [17]
and grain yield (9983) His results also indicated that mostof these traits exhibited low heritability under drought stressconditions suggesting the presence of high genotype times envi-ronment interactions that affected the crop behavior
Narrow-sense heritability is the proportion of the totalphenotypic variation that is due to the additive effects ofgenes This component of variation is important because it isthe only variation that natural selection can act on Henceℎ2ns determines the resemblance of offspring to their parentsand the populationrsquos evolutionary response to selectionTherecan be considerable nonadditive genetic variance but thisdoes not contribute to the resemblance between parents andoffspring or the response to selection Low to moderatenarrow-sense heritability values were recorded in this studyChlorophyll content heading date plant height 1000-kernelweight and number of grains per spike recorded the highestestimates These traits were less influenced by the environ-mental factors and would respond positively to a selection
pressure in the current breeding programThe lowheritabilityvalues can be explained by the change occurring in thesegregating lines behavior from the precedent to the currentgeneration This change may be due to nonadditive geneaction andor high environmental factors effects
Means of the variables measured showed that the bestvalues varied depending on the cross and the trait and thefew populations had the best performances for several traitsat the same time (Table 4)The best grain yielding population(5575 g) was Ain Abid times El-Wifak which had also the highestaverage for the number of grains per spike (3147 grains)spikes weight (8573 g) above ground biomass (13503 g) andharvest index (413) Ain Abid timesHidhab cross combinationhad the longest vegetative cycle with an average of 1278days and presented the highest mean for the flag leaf area(210 cm2) Acsad1069 times El-Wifak had the highest average forthe chlorophyll content (540 Spad) Acsad1069 times Mahon-Demias was the tallest (7523 cm) while Acsad1135 times Rmada
International Journal of Agronomy 5
Table4Means
ofthem
easuredtraitsfor2
0F 4
breadwheatpo
pulatio
ns
Popu
latio
nCH
LHD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Acsad 901timesM
ahon
-Dem
ias
430
1260
201
737
4483
386
6340
220
11350
3792
334
Acsad 901timesR
mada
445
1252
199
608
4424
346
6336
281
10497
4255
405
Acsad 901timesH
idhab
425
1259
198
657
4267
334
6167
230
10983
3255
296
Acsad 901timesE
l-Wifa
k451
1249
204
704
4673
332
6843
304
11490
4604
401
Acsad 899timesM
ahon
-Dem
ias
437
1257
169
708
4760
364
6260
205
11517
3668
319
Acsad 899timesR
mada
434
1250
166
595
4797
378
6257
200
10327
3688
357
Acsad 899timesH
idhab
444
1269
182
566
4060
350
5980
228
9970
3360
337
Acsad 899timesE
l-Wifa
k454
1250
181
631
4147
355
5667
242
9657
3577
370
Acsad 1135timesM
ahon
-Dem
ias
416
1261
170
681
4567
398
6040
210
10737
3834
357
Acsad 1135timesR
mada
422
1252
168
615
5037
369
6853
240
11137
4404
396
Acsad 1135timesH
idhab
415
1270
175
688
4770
395
7447
232
12687
4509
355
Acsad 1135timesE
l-Wifa
k455
1252
170
646
3970
346
4893
221
8790
3099
353
Acsad 1069timesM
ahon
-Dem
ias
438
1258
182
752
4390
392
6233
217
11937
3713
311
Acsad 1069timesR
mada
442
1250
173
617
4147
348
5810
245
9617
3608
375
Acsad 1069timesH
idhab
467
1259
185
605
4113
350
6170
266
9767
3953
405
Acsad 1069timesE
l-Wifa
k540
1250
184
665
4233
346
6110
279
9940
4033
406
Ain
AbidtimesM
ahon
-Dem
ias
420
1277
200
727
4547
388
7473
272
12867
4818
375
Ain
AbidtimesR
mada
403
1263
205
658
4357
365
6993
282
11717
4581
391
Ain
AbidtimesH
idhab
432
1278
210
602
4050
368
6933
304
11307
4628
409
Ain
AbidtimesE
l-Wifa
k436
1269
203
651
4693
377
8573
315
13503
5575
413
ParentsR
ange
391
1250
87
530
2700
300
3200
975300
1850
141
522
1320
391
820
8000
490
12600
394
23300
8090
636
F 4Ra
nge
334
1240
76420
800
190
800
931500
430
126
538
1360
364
1000
8500
510
14800
568
24200
9210
700
LSD005
48
1463
67
1197
45
2352
68
3532
1851
99CH
Lchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)andHIharvestind
ex(
)
6 International Journal of Agronomy
exhibited the highest average for the number of spikes (5037spikes) For 1000-kernel weight Acsad1135 timesMahon-Demiaswas the best population with an average of 398 g
As the contrast ldquoParents versus F4rdquo was significant forPH SN TKW SW BIO and GY and compared with theleast significant difference (LSD005) significant differencesexisted between the parents and hybrids values for thesemea-sured traits These differences were 1 to 3 times higher thanthe LSD005 and were in favor of some F4 lines indicatingthat they perform better than the parents and suggestingthe possibilities of making significant changes through effec-tive selection These findings were in harmony with thoseobtained by Abd El-Shafi [34] Loffler and Busch [40] andAlexander et al [41]
32 Genetic Gain from Direct and Indirect Selection andSelection-Based Index The gains of direct and indirect selec-tion are shown inTable 5The results showed a big variation inthe percentages of gains among the measured traitsThe totalsum of gains per selection varied from minus3611 for headingdate to 3475 for flag leaf area Generally the direct selectionbased on chlorophyll content heading date spikes weightgrain yield and harvest index resulted in negative total gainsOn the other hand flag leaf area plant height number ofspikes 1000-kernel weight number of grains per spike andbiomass recorded positive total gains
The gains obtained by direct selection were higher thanthose of indirect selection But sometimes the indirect selec-tion may be more efficient especially if the secondary traitis highly correlated with yield and is easily measurable [42]The number of grains per spike (1586) followed by aboveground biomass (1230) plant height (1210) and numberof spikes (1084) exhibited the greatest gains from directselection Inversely heading date (minus113) spikes weight(minus499) and harvest index (minus381) showed negative gainvalues Negative gain for heading date is desired in the case ofthis study as the Algerian wheat breeding program designedfor semiarid regions precocity is a crucial criterion adoptedfor selection It is related to the ability of plants to shortentheir cycle so as to decrease their exposure to the late-seasonsirocco weathering
The direct selection for chlorophyll content resulted innegative indirect gains for nearly all the other traits exceptfor the flag leaf area number of grains per spike and harvestindex that showed positive responses For heading date theindirect selection gains were only positive for chlorophyllcontent number of spikes and harvest indexThe correlativeeffects of the selection based on the flag leaf area were desir-able for grain yield although the indirect gain for the remain-ing traits was practically negative
The direct selection for plant height resulted in positiveindirect gains for heading date number of spikes 1000-kernelweight and spikes weight In addition positive responsesof the selection based on yield and yield components wereobserved for grain yield itself heading date 1000-kernelweight and spikes weight However negative indirect gainswere exhibited for other traits including chlorophyll contentflag leaf area and number of grains per spike indicatingthat indirect selection for one variable for gain in another
is unfeasible because there will be a loss in the indirectlyselected variable In cases of negative gains the model is con-sidered inappropriate for selection in this plant material
The highest correlated responses for grain yield weregenerated though indirect selection on the base of theflag leaf area (804 ie 3387 gmminus2) followed by thespike fertility (365 ie 1539 gmminus2) 1000-kernel weight(363 ie 1531 gmminus2) and above ground biomass (274 ie1154 gmminus2)These results indicated that the indirect selectionwas to be more effective in improving the primary trait thanthe indirect selection based on other traits andor on thedirect selection based on the grain yield itself
Several authors have estimated the genetic gains of traitsinvolved in yield determination The results are often incon-sistent and scarce Our results were consistent with thoseof DePauw and Shebeski [43] and Inagaki et al [44] whomentioned that direct selection on the basis of yield is ineffec-tive in early generations Benmahammed et al [45] reportedthe same findings in barley crop Their results showed thatbiomass-based direct selection appearedmore discriminatingthan yield-based selection in their plant material Howeverthe results of this study do not corroborate findings reportedby Lalic et al [11] Mitchell et al [46] Lungu et al [47] andEl-Morshidy et al [48] and who observed the effectiveness ofthe direct selection on grain yieldThedifference in the resultsmay be attributed to differences of breeding material and togenotype times environments
The gains of selection-based index are shown in Table 6The total sum of gains from the selection-based index rangedfrom minus937 to 4286 Five out of seven used indicesshowed positive total gains The base index of Williamsrecorded the highest total gain (4286) followed by theclassic index of Smith and Hazel the index desired gains ofPesek and Baker (2746) and the genotype-ideotype dis-tance index of Cruz (2207) The sum of ranks index ofMulamba and Mock had a very low gain of 374 Subandirsquosand Elstonrsquos indices exhibited negative total gains
The base index of Williams yielded positive responses forall measured traits except for chlorophyll content flag leafarea and harvest index which showed negative gains Thisindex also achieved the highest grain yield response (627ie 2641 gmminus2) compared to the other indices employed inthis study The classic index of Smith and Hazel ranked sec-ond followed by the index of desired gains of Pesek and Bakerthe genotype-ideotype distance index proposed by Cruz andthe sum of ranks index of Mulamba andMock with selectionresponses of 3693 2746 2207 and 374 respectivelyThe single-trait responses varied from one index to anotherwith a more or less balanced distribution of positive andnegative estimates among the traits This shows that themonotrait selection was inadequate because it led to a higherfinal product when considering the grain yield and generatedunfavorable responses in other traits These results indicatedthat methods that combine favorable expected gains shouldbe used in the evaluation of these progeniesThe gains expect-ed through indices for grain yield per se were larger thanthose obtained by direct and indirect monotrait selectionexcept for the flag leaf area (Tables 5 and 6) Mahdy [49]mentioned that selection-based index was predicted to be
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
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International Journal of Agronomy 3
regression To do so two methods were used the first wasby the linear regression of F4 on the parental F3 individualvalues [16] while the second approach was performed withstandardized data of offspring (F4) versus standardized of thecorresponding parent (F3) according to Frey andHorner [17]
The selection gains were estimated among families basedon three selection criteria direct selection indirect selectionand selection-based index considering the selection intensityof 5 of top families The expected gains by direct selectionfor each trait evaluated were estimated by the expression [25]Δ119866119894 = ℎ2119894times119878119894 = ℎ2119894times(119883119904119894minus1198830119894) whereΔ119866119894 is the gainwith thedirect selection carried for the 119894th trait ℎ2119894 is the heritability ofthe 119894th trait 119878119894 refers to the differential selection of the 119894th trait119883119904119894 is themean of the 119894th trait for the selected individuals and1198830119894 is the mean of the 119894th trait in the base population Theexpected gain of direct selection expressed as a percentage ofthe population mean is given by Δ119866119894119894 = (Δ119866119894 times 100)1198830119894
Gains from indirect response to selection were calculatedusing the following expression [25] GS119895(119894) = ℎ2119895 times (119883119894119895 minus1198830119895) = ℎ2119895 times DS119895(119894) where GS119895(119894) is the gain on the 119895th traitwith selection based on the 119894th trait 119883119904119895 is the mean of the119895th trait for the selected individuals based on the 119894th trait1198830119895 is the mean of the 119895th trait ℎ2119895 is the heritability ofthe 119895th trait and DS119895(119894) refers to the differential selection ofthe 119895th trait in which the selected lines presented the bestperformance for the 119894th trait The expected gain of indirectselection expressed as a percentage of the population meanis given by GS119895(119894) = (GS119895(119894) times 100)1198830119894
For the selection-based index the following methodolo-gieswere used for gains estimation the classic index proposedby Smith [26] and Hazel [27] the base index of Williams[28] the free weights and parameters index of Elston [29]the index of desired gains of Pesek and Baker [30] the multi-plicative index of Subandi et al [31] the sum of ranks index ofMulamba andMock [32] and the genotype-ideotype distanceindex proposed by Cruz [25] Each index displays certainparticularities in its calculations and as such applicationis generally laborious due to the need to assign adequateeconomic weights to each trait Based on the differentanalytical procedures of selection the best populations wereidentified and the gains from selection were calculated Allstatistical analyses were carried out using Genes software [33]and a Microsoft Excelcopy spreadsheet
3 Results and Discussion
31 Genetic Variability and Heritability The analysis of vari-ance revealed significant genotype effect for almost all thetraits under study except for flag leaf area and grain yieldwhich were not significant at 005 probability level (Table 2)This provides evidence of the presence of sufficient geneticvariability among parents and hybrids that can be exploitedin wheat breeding program through selection Partitioningthe genotype effect indicated significant differences betweenall parents for HD PH SN TKW SW GN BIO and HIand significant interpopulation differences for HD PH andGN The contrast ldquoParents versus F4rdquo was highly significant
for HD PH and SN while the F4 populations exhibitedsignificant differences for nearly all the observed quantitativephenotypic traits except for CHL FLA GN and HI whichwere not significant at the 5 probability level The resultsof this study corroborate those of Abd El-Shafi [34] whoreported significant and highly significant differences amonggenotypes (families + parents) and families for all studiedtraits across three segregating generations F2 F3 and F4Thisauthor also reported that greater response to selection canbe expected from selection in cross having greater phenotypicand genotypic variances
The coefficient of variation (CV) presented values be-tween 07 and 268 for heading date and grain yield respec-tively (Table 2) The CV above 20 is considered high indi-cating high dispersion of the experimental data which mayhave been caused by the genetic and phenotypic differencesbetween the studiedmaterials HighCV estimate obtained forgrain yield can be explained by the fact that it is quantitativetrait governed by several genes and highly influenced by theenvironment
The variances values coefficient of variation and geneticparameters estimates for wheat traits studied are presentedin Table 3 Broad-sense heritability is the proportion of totalphenotypic variation due to all genetic effects The knowl-edge of the genotypic determination coefficient (ℎ2bs) allowsestablishing an estimate of the genetic gain to be obtained anddefines the best strategy to be used in the plant breeding pro-gram [35] In this study the estimated broad-sense heritabil-ity varied from 000 to 9180The highest values were foundfor heading date (9180) followed by plant height (8082)1000-kernel weight (7290) biomass (6533) and numberof grains per spike (6164) indicating that these traits arehighly heritable among the genotypes evaluatedThese resultscan be confirmed with the values obtained by the CV119892CV119890ratio that were close to or greater than 1 for these traits sug-gesting satisfactory conditions for selection [36] Moderateestimates of ℎ2bs occurred for the number of chlorophyllcontents (4031) number of spikes (5200) spikes weight(5485) and harvest index (4067) On the other handthe lowest values of ℎ2bs were found for the flag leaf area(000) and grain yield (1939) These traits exhibited alsolow CV119892CV119890 ratio values indicating the dominant effect ofthe environment on crop
Generally literature indicates widely varying narrow-sense heritability estimates Mesele et al [37] reported highheritability values for days to heading days to maturityand 1000-kernel weight moderate estimates for grain fillingperiod spike length number of spikelets per plant grainsper spike and harvest index and low values for number oftillers per plant biomass yield and grain yield Evaluatingseven F2 populations derived through cross combinations offive parental varietieslines of bread wheat Saleem et al [38]found low to high broad-sense heritability values rangingfrom 475 to 926 depending on the trait and the crossThefindings of Yaqoob [39] showed that heritability estimateswere low for number of tillers per plant (20) grainsper spike (2681) days to maturity (3013) spike length(3666) and 1000-kernel weight (3868) moderate forplant height (4579) and high for heading date (8473)
4 International Journal of Agronomy
Table 2 Analysis of variance of different bread wheat traits studied
SV Bloc Genotypes Parents F4 Parents versus F4 Error CV ()df 2 28 8 19 1 56CHL 86 190lowastlowast 95ns 238lowastlowast 36ns 86 66HD 15 90lowastlowast 183lowastlowast 25lowastlowast 587lowastlowast 07 07FLA 894 111
ns69ns 66ns 01ns 149 204
PH 91 863lowastlowast 1030lowastlowast 800lowastlowast 726lowast 166 63SN 11831 111875lowastlowast 260924lowastlowast 26578ns 539945lowastlowast 53711 159TKW 335 282lowastlowast 394lowastlowast 127ns 2321lowastlowast 77 74SW 1170346 448736lowastlowast 744904lowastlowast 182488ns 3137463lowastlowast 202598 209GN 366 447lowastlowast 613lowastlowast 370lowast 410ns 171 168BIO 811895 1310774lowastlowast 2431421lowastlowast 436017ns 8965592lowastlowast 454408 185GY 341492 157279ns 268354ns 110965ns 562059lowast 126778 268HI 1222 623lowast 1366lowastlowast 361ns 214ns 37 168CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) HI harvest index () and nslowast and lowastlowast nonsignificant and significant effect at 005 and 001 probability
Table 3 Genetic and nongenetic parameters of different bread wheat traits studied
Traits 1205902119901 1205902119890 1205902119892 ℎ2bs ℎ2ns (SK) ℎ2ns (FH) CV119892 () CV119892CV119890Chl 336 200 135 4031 2855 3013 264 047HD 301 025 276 9180 1669 3432 131 193FLA 368 498 000 000 000 000 000 000PH 2878 552 2326 8082 2727 2727 743 119SN 372917 179037 193879 5200 000 000 959 060TKW 941 255 686 7290 3164 2599 698 095SW 1495791 675325 820467 5485 866 1070 1319 064GN 1467 563 904 6164 1670 714 1228 073BIO 4369245 1514693 2854552 6533 337 593 1448 079GY 524264 422592 101671 1939 1013 1231 758 028HI 2076 1231 845 4067 1410 1497 803 048CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY Grain yield (gmminus2) HI harvest index () ℎ2bsbroad-sense heritability ℎ2ns (SK) narrow-sense heritability according to Smith and Kinman [16] and ℎ2ns (FH) narrow-sense heritability according to Freyand Horner [17]
and grain yield (9983) His results also indicated that mostof these traits exhibited low heritability under drought stressconditions suggesting the presence of high genotype times envi-ronment interactions that affected the crop behavior
Narrow-sense heritability is the proportion of the totalphenotypic variation that is due to the additive effects ofgenes This component of variation is important because it isthe only variation that natural selection can act on Henceℎ2ns determines the resemblance of offspring to their parentsand the populationrsquos evolutionary response to selectionTherecan be considerable nonadditive genetic variance but thisdoes not contribute to the resemblance between parents andoffspring or the response to selection Low to moderatenarrow-sense heritability values were recorded in this studyChlorophyll content heading date plant height 1000-kernelweight and number of grains per spike recorded the highestestimates These traits were less influenced by the environ-mental factors and would respond positively to a selection
pressure in the current breeding programThe lowheritabilityvalues can be explained by the change occurring in thesegregating lines behavior from the precedent to the currentgeneration This change may be due to nonadditive geneaction andor high environmental factors effects
Means of the variables measured showed that the bestvalues varied depending on the cross and the trait and thefew populations had the best performances for several traitsat the same time (Table 4)The best grain yielding population(5575 g) was Ain Abid times El-Wifak which had also the highestaverage for the number of grains per spike (3147 grains)spikes weight (8573 g) above ground biomass (13503 g) andharvest index (413) Ain Abid timesHidhab cross combinationhad the longest vegetative cycle with an average of 1278days and presented the highest mean for the flag leaf area(210 cm2) Acsad1069 times El-Wifak had the highest average forthe chlorophyll content (540 Spad) Acsad1069 times Mahon-Demias was the tallest (7523 cm) while Acsad1135 times Rmada
International Journal of Agronomy 5
Table4Means
ofthem
easuredtraitsfor2
0F 4
breadwheatpo
pulatio
ns
Popu
latio
nCH
LHD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Acsad 901timesM
ahon
-Dem
ias
430
1260
201
737
4483
386
6340
220
11350
3792
334
Acsad 901timesR
mada
445
1252
199
608
4424
346
6336
281
10497
4255
405
Acsad 901timesH
idhab
425
1259
198
657
4267
334
6167
230
10983
3255
296
Acsad 901timesE
l-Wifa
k451
1249
204
704
4673
332
6843
304
11490
4604
401
Acsad 899timesM
ahon
-Dem
ias
437
1257
169
708
4760
364
6260
205
11517
3668
319
Acsad 899timesR
mada
434
1250
166
595
4797
378
6257
200
10327
3688
357
Acsad 899timesH
idhab
444
1269
182
566
4060
350
5980
228
9970
3360
337
Acsad 899timesE
l-Wifa
k454
1250
181
631
4147
355
5667
242
9657
3577
370
Acsad 1135timesM
ahon
-Dem
ias
416
1261
170
681
4567
398
6040
210
10737
3834
357
Acsad 1135timesR
mada
422
1252
168
615
5037
369
6853
240
11137
4404
396
Acsad 1135timesH
idhab
415
1270
175
688
4770
395
7447
232
12687
4509
355
Acsad 1135timesE
l-Wifa
k455
1252
170
646
3970
346
4893
221
8790
3099
353
Acsad 1069timesM
ahon
-Dem
ias
438
1258
182
752
4390
392
6233
217
11937
3713
311
Acsad 1069timesR
mada
442
1250
173
617
4147
348
5810
245
9617
3608
375
Acsad 1069timesH
idhab
467
1259
185
605
4113
350
6170
266
9767
3953
405
Acsad 1069timesE
l-Wifa
k540
1250
184
665
4233
346
6110
279
9940
4033
406
Ain
AbidtimesM
ahon
-Dem
ias
420
1277
200
727
4547
388
7473
272
12867
4818
375
Ain
AbidtimesR
mada
403
1263
205
658
4357
365
6993
282
11717
4581
391
Ain
AbidtimesH
idhab
432
1278
210
602
4050
368
6933
304
11307
4628
409
Ain
AbidtimesE
l-Wifa
k436
1269
203
651
4693
377
8573
315
13503
5575
413
ParentsR
ange
391
1250
87
530
2700
300
3200
975300
1850
141
522
1320
391
820
8000
490
12600
394
23300
8090
636
F 4Ra
nge
334
1240
76420
800
190
800
931500
430
126
538
1360
364
1000
8500
510
14800
568
24200
9210
700
LSD005
48
1463
67
1197
45
2352
68
3532
1851
99CH
Lchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)andHIharvestind
ex(
)
6 International Journal of Agronomy
exhibited the highest average for the number of spikes (5037spikes) For 1000-kernel weight Acsad1135 timesMahon-Demiaswas the best population with an average of 398 g
As the contrast ldquoParents versus F4rdquo was significant forPH SN TKW SW BIO and GY and compared with theleast significant difference (LSD005) significant differencesexisted between the parents and hybrids values for thesemea-sured traits These differences were 1 to 3 times higher thanthe LSD005 and were in favor of some F4 lines indicatingthat they perform better than the parents and suggestingthe possibilities of making significant changes through effec-tive selection These findings were in harmony with thoseobtained by Abd El-Shafi [34] Loffler and Busch [40] andAlexander et al [41]
32 Genetic Gain from Direct and Indirect Selection andSelection-Based Index The gains of direct and indirect selec-tion are shown inTable 5The results showed a big variation inthe percentages of gains among the measured traitsThe totalsum of gains per selection varied from minus3611 for headingdate to 3475 for flag leaf area Generally the direct selectionbased on chlorophyll content heading date spikes weightgrain yield and harvest index resulted in negative total gainsOn the other hand flag leaf area plant height number ofspikes 1000-kernel weight number of grains per spike andbiomass recorded positive total gains
The gains obtained by direct selection were higher thanthose of indirect selection But sometimes the indirect selec-tion may be more efficient especially if the secondary traitis highly correlated with yield and is easily measurable [42]The number of grains per spike (1586) followed by aboveground biomass (1230) plant height (1210) and numberof spikes (1084) exhibited the greatest gains from directselection Inversely heading date (minus113) spikes weight(minus499) and harvest index (minus381) showed negative gainvalues Negative gain for heading date is desired in the case ofthis study as the Algerian wheat breeding program designedfor semiarid regions precocity is a crucial criterion adoptedfor selection It is related to the ability of plants to shortentheir cycle so as to decrease their exposure to the late-seasonsirocco weathering
The direct selection for chlorophyll content resulted innegative indirect gains for nearly all the other traits exceptfor the flag leaf area number of grains per spike and harvestindex that showed positive responses For heading date theindirect selection gains were only positive for chlorophyllcontent number of spikes and harvest indexThe correlativeeffects of the selection based on the flag leaf area were desir-able for grain yield although the indirect gain for the remain-ing traits was practically negative
The direct selection for plant height resulted in positiveindirect gains for heading date number of spikes 1000-kernelweight and spikes weight In addition positive responsesof the selection based on yield and yield components wereobserved for grain yield itself heading date 1000-kernelweight and spikes weight However negative indirect gainswere exhibited for other traits including chlorophyll contentflag leaf area and number of grains per spike indicatingthat indirect selection for one variable for gain in another
is unfeasible because there will be a loss in the indirectlyselected variable In cases of negative gains the model is con-sidered inappropriate for selection in this plant material
The highest correlated responses for grain yield weregenerated though indirect selection on the base of theflag leaf area (804 ie 3387 gmminus2) followed by thespike fertility (365 ie 1539 gmminus2) 1000-kernel weight(363 ie 1531 gmminus2) and above ground biomass (274 ie1154 gmminus2)These results indicated that the indirect selectionwas to be more effective in improving the primary trait thanthe indirect selection based on other traits andor on thedirect selection based on the grain yield itself
Several authors have estimated the genetic gains of traitsinvolved in yield determination The results are often incon-sistent and scarce Our results were consistent with thoseof DePauw and Shebeski [43] and Inagaki et al [44] whomentioned that direct selection on the basis of yield is ineffec-tive in early generations Benmahammed et al [45] reportedthe same findings in barley crop Their results showed thatbiomass-based direct selection appearedmore discriminatingthan yield-based selection in their plant material Howeverthe results of this study do not corroborate findings reportedby Lalic et al [11] Mitchell et al [46] Lungu et al [47] andEl-Morshidy et al [48] and who observed the effectiveness ofthe direct selection on grain yieldThedifference in the resultsmay be attributed to differences of breeding material and togenotype times environments
The gains of selection-based index are shown in Table 6The total sum of gains from the selection-based index rangedfrom minus937 to 4286 Five out of seven used indicesshowed positive total gains The base index of Williamsrecorded the highest total gain (4286) followed by theclassic index of Smith and Hazel the index desired gains ofPesek and Baker (2746) and the genotype-ideotype dis-tance index of Cruz (2207) The sum of ranks index ofMulamba and Mock had a very low gain of 374 Subandirsquosand Elstonrsquos indices exhibited negative total gains
The base index of Williams yielded positive responses forall measured traits except for chlorophyll content flag leafarea and harvest index which showed negative gains Thisindex also achieved the highest grain yield response (627ie 2641 gmminus2) compared to the other indices employed inthis study The classic index of Smith and Hazel ranked sec-ond followed by the index of desired gains of Pesek and Bakerthe genotype-ideotype distance index proposed by Cruz andthe sum of ranks index of Mulamba andMock with selectionresponses of 3693 2746 2207 and 374 respectivelyThe single-trait responses varied from one index to anotherwith a more or less balanced distribution of positive andnegative estimates among the traits This shows that themonotrait selection was inadequate because it led to a higherfinal product when considering the grain yield and generatedunfavorable responses in other traits These results indicatedthat methods that combine favorable expected gains shouldbe used in the evaluation of these progeniesThe gains expect-ed through indices for grain yield per se were larger thanthose obtained by direct and indirect monotrait selectionexcept for the flag leaf area (Tables 5 and 6) Mahdy [49]mentioned that selection-based index was predicted to be
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
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4 International Journal of Agronomy
Table 2 Analysis of variance of different bread wheat traits studied
SV Bloc Genotypes Parents F4 Parents versus F4 Error CV ()df 2 28 8 19 1 56CHL 86 190lowastlowast 95ns 238lowastlowast 36ns 86 66HD 15 90lowastlowast 183lowastlowast 25lowastlowast 587lowastlowast 07 07FLA 894 111
ns69ns 66ns 01ns 149 204
PH 91 863lowastlowast 1030lowastlowast 800lowastlowast 726lowast 166 63SN 11831 111875lowastlowast 260924lowastlowast 26578ns 539945lowastlowast 53711 159TKW 335 282lowastlowast 394lowastlowast 127ns 2321lowastlowast 77 74SW 1170346 448736lowastlowast 744904lowastlowast 182488ns 3137463lowastlowast 202598 209GN 366 447lowastlowast 613lowastlowast 370lowast 410ns 171 168BIO 811895 1310774lowastlowast 2431421lowastlowast 436017ns 8965592lowastlowast 454408 185GY 341492 157279ns 268354ns 110965ns 562059lowast 126778 268HI 1222 623lowast 1366lowastlowast 361ns 214ns 37 168CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) HI harvest index () and nslowast and lowastlowast nonsignificant and significant effect at 005 and 001 probability
Table 3 Genetic and nongenetic parameters of different bread wheat traits studied
Traits 1205902119901 1205902119890 1205902119892 ℎ2bs ℎ2ns (SK) ℎ2ns (FH) CV119892 () CV119892CV119890Chl 336 200 135 4031 2855 3013 264 047HD 301 025 276 9180 1669 3432 131 193FLA 368 498 000 000 000 000 000 000PH 2878 552 2326 8082 2727 2727 743 119SN 372917 179037 193879 5200 000 000 959 060TKW 941 255 686 7290 3164 2599 698 095SW 1495791 675325 820467 5485 866 1070 1319 064GN 1467 563 904 6164 1670 714 1228 073BIO 4369245 1514693 2854552 6533 337 593 1448 079GY 524264 422592 101671 1939 1013 1231 758 028HI 2076 1231 845 4067 1410 1497 803 048CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY Grain yield (gmminus2) HI harvest index () ℎ2bsbroad-sense heritability ℎ2ns (SK) narrow-sense heritability according to Smith and Kinman [16] and ℎ2ns (FH) narrow-sense heritability according to Freyand Horner [17]
and grain yield (9983) His results also indicated that mostof these traits exhibited low heritability under drought stressconditions suggesting the presence of high genotype times envi-ronment interactions that affected the crop behavior
Narrow-sense heritability is the proportion of the totalphenotypic variation that is due to the additive effects ofgenes This component of variation is important because it isthe only variation that natural selection can act on Henceℎ2ns determines the resemblance of offspring to their parentsand the populationrsquos evolutionary response to selectionTherecan be considerable nonadditive genetic variance but thisdoes not contribute to the resemblance between parents andoffspring or the response to selection Low to moderatenarrow-sense heritability values were recorded in this studyChlorophyll content heading date plant height 1000-kernelweight and number of grains per spike recorded the highestestimates These traits were less influenced by the environ-mental factors and would respond positively to a selection
pressure in the current breeding programThe lowheritabilityvalues can be explained by the change occurring in thesegregating lines behavior from the precedent to the currentgeneration This change may be due to nonadditive geneaction andor high environmental factors effects
Means of the variables measured showed that the bestvalues varied depending on the cross and the trait and thefew populations had the best performances for several traitsat the same time (Table 4)The best grain yielding population(5575 g) was Ain Abid times El-Wifak which had also the highestaverage for the number of grains per spike (3147 grains)spikes weight (8573 g) above ground biomass (13503 g) andharvest index (413) Ain Abid timesHidhab cross combinationhad the longest vegetative cycle with an average of 1278days and presented the highest mean for the flag leaf area(210 cm2) Acsad1069 times El-Wifak had the highest average forthe chlorophyll content (540 Spad) Acsad1069 times Mahon-Demias was the tallest (7523 cm) while Acsad1135 times Rmada
International Journal of Agronomy 5
Table4Means
ofthem
easuredtraitsfor2
0F 4
breadwheatpo
pulatio
ns
Popu
latio
nCH
LHD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Acsad 901timesM
ahon
-Dem
ias
430
1260
201
737
4483
386
6340
220
11350
3792
334
Acsad 901timesR
mada
445
1252
199
608
4424
346
6336
281
10497
4255
405
Acsad 901timesH
idhab
425
1259
198
657
4267
334
6167
230
10983
3255
296
Acsad 901timesE
l-Wifa
k451
1249
204
704
4673
332
6843
304
11490
4604
401
Acsad 899timesM
ahon
-Dem
ias
437
1257
169
708
4760
364
6260
205
11517
3668
319
Acsad 899timesR
mada
434
1250
166
595
4797
378
6257
200
10327
3688
357
Acsad 899timesH
idhab
444
1269
182
566
4060
350
5980
228
9970
3360
337
Acsad 899timesE
l-Wifa
k454
1250
181
631
4147
355
5667
242
9657
3577
370
Acsad 1135timesM
ahon
-Dem
ias
416
1261
170
681
4567
398
6040
210
10737
3834
357
Acsad 1135timesR
mada
422
1252
168
615
5037
369
6853
240
11137
4404
396
Acsad 1135timesH
idhab
415
1270
175
688
4770
395
7447
232
12687
4509
355
Acsad 1135timesE
l-Wifa
k455
1252
170
646
3970
346
4893
221
8790
3099
353
Acsad 1069timesM
ahon
-Dem
ias
438
1258
182
752
4390
392
6233
217
11937
3713
311
Acsad 1069timesR
mada
442
1250
173
617
4147
348
5810
245
9617
3608
375
Acsad 1069timesH
idhab
467
1259
185
605
4113
350
6170
266
9767
3953
405
Acsad 1069timesE
l-Wifa
k540
1250
184
665
4233
346
6110
279
9940
4033
406
Ain
AbidtimesM
ahon
-Dem
ias
420
1277
200
727
4547
388
7473
272
12867
4818
375
Ain
AbidtimesR
mada
403
1263
205
658
4357
365
6993
282
11717
4581
391
Ain
AbidtimesH
idhab
432
1278
210
602
4050
368
6933
304
11307
4628
409
Ain
AbidtimesE
l-Wifa
k436
1269
203
651
4693
377
8573
315
13503
5575
413
ParentsR
ange
391
1250
87
530
2700
300
3200
975300
1850
141
522
1320
391
820
8000
490
12600
394
23300
8090
636
F 4Ra
nge
334
1240
76420
800
190
800
931500
430
126
538
1360
364
1000
8500
510
14800
568
24200
9210
700
LSD005
48
1463
67
1197
45
2352
68
3532
1851
99CH
Lchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)andHIharvestind
ex(
)
6 International Journal of Agronomy
exhibited the highest average for the number of spikes (5037spikes) For 1000-kernel weight Acsad1135 timesMahon-Demiaswas the best population with an average of 398 g
As the contrast ldquoParents versus F4rdquo was significant forPH SN TKW SW BIO and GY and compared with theleast significant difference (LSD005) significant differencesexisted between the parents and hybrids values for thesemea-sured traits These differences were 1 to 3 times higher thanthe LSD005 and were in favor of some F4 lines indicatingthat they perform better than the parents and suggestingthe possibilities of making significant changes through effec-tive selection These findings were in harmony with thoseobtained by Abd El-Shafi [34] Loffler and Busch [40] andAlexander et al [41]
32 Genetic Gain from Direct and Indirect Selection andSelection-Based Index The gains of direct and indirect selec-tion are shown inTable 5The results showed a big variation inthe percentages of gains among the measured traitsThe totalsum of gains per selection varied from minus3611 for headingdate to 3475 for flag leaf area Generally the direct selectionbased on chlorophyll content heading date spikes weightgrain yield and harvest index resulted in negative total gainsOn the other hand flag leaf area plant height number ofspikes 1000-kernel weight number of grains per spike andbiomass recorded positive total gains
The gains obtained by direct selection were higher thanthose of indirect selection But sometimes the indirect selec-tion may be more efficient especially if the secondary traitis highly correlated with yield and is easily measurable [42]The number of grains per spike (1586) followed by aboveground biomass (1230) plant height (1210) and numberof spikes (1084) exhibited the greatest gains from directselection Inversely heading date (minus113) spikes weight(minus499) and harvest index (minus381) showed negative gainvalues Negative gain for heading date is desired in the case ofthis study as the Algerian wheat breeding program designedfor semiarid regions precocity is a crucial criterion adoptedfor selection It is related to the ability of plants to shortentheir cycle so as to decrease their exposure to the late-seasonsirocco weathering
The direct selection for chlorophyll content resulted innegative indirect gains for nearly all the other traits exceptfor the flag leaf area number of grains per spike and harvestindex that showed positive responses For heading date theindirect selection gains were only positive for chlorophyllcontent number of spikes and harvest indexThe correlativeeffects of the selection based on the flag leaf area were desir-able for grain yield although the indirect gain for the remain-ing traits was practically negative
The direct selection for plant height resulted in positiveindirect gains for heading date number of spikes 1000-kernelweight and spikes weight In addition positive responsesof the selection based on yield and yield components wereobserved for grain yield itself heading date 1000-kernelweight and spikes weight However negative indirect gainswere exhibited for other traits including chlorophyll contentflag leaf area and number of grains per spike indicatingthat indirect selection for one variable for gain in another
is unfeasible because there will be a loss in the indirectlyselected variable In cases of negative gains the model is con-sidered inappropriate for selection in this plant material
The highest correlated responses for grain yield weregenerated though indirect selection on the base of theflag leaf area (804 ie 3387 gmminus2) followed by thespike fertility (365 ie 1539 gmminus2) 1000-kernel weight(363 ie 1531 gmminus2) and above ground biomass (274 ie1154 gmminus2)These results indicated that the indirect selectionwas to be more effective in improving the primary trait thanthe indirect selection based on other traits andor on thedirect selection based on the grain yield itself
Several authors have estimated the genetic gains of traitsinvolved in yield determination The results are often incon-sistent and scarce Our results were consistent with thoseof DePauw and Shebeski [43] and Inagaki et al [44] whomentioned that direct selection on the basis of yield is ineffec-tive in early generations Benmahammed et al [45] reportedthe same findings in barley crop Their results showed thatbiomass-based direct selection appearedmore discriminatingthan yield-based selection in their plant material Howeverthe results of this study do not corroborate findings reportedby Lalic et al [11] Mitchell et al [46] Lungu et al [47] andEl-Morshidy et al [48] and who observed the effectiveness ofthe direct selection on grain yieldThedifference in the resultsmay be attributed to differences of breeding material and togenotype times environments
The gains of selection-based index are shown in Table 6The total sum of gains from the selection-based index rangedfrom minus937 to 4286 Five out of seven used indicesshowed positive total gains The base index of Williamsrecorded the highest total gain (4286) followed by theclassic index of Smith and Hazel the index desired gains ofPesek and Baker (2746) and the genotype-ideotype dis-tance index of Cruz (2207) The sum of ranks index ofMulamba and Mock had a very low gain of 374 Subandirsquosand Elstonrsquos indices exhibited negative total gains
The base index of Williams yielded positive responses forall measured traits except for chlorophyll content flag leafarea and harvest index which showed negative gains Thisindex also achieved the highest grain yield response (627ie 2641 gmminus2) compared to the other indices employed inthis study The classic index of Smith and Hazel ranked sec-ond followed by the index of desired gains of Pesek and Bakerthe genotype-ideotype distance index proposed by Cruz andthe sum of ranks index of Mulamba andMock with selectionresponses of 3693 2746 2207 and 374 respectivelyThe single-trait responses varied from one index to anotherwith a more or less balanced distribution of positive andnegative estimates among the traits This shows that themonotrait selection was inadequate because it led to a higherfinal product when considering the grain yield and generatedunfavorable responses in other traits These results indicatedthat methods that combine favorable expected gains shouldbe used in the evaluation of these progeniesThe gains expect-ed through indices for grain yield per se were larger thanthose obtained by direct and indirect monotrait selectionexcept for the flag leaf area (Tables 5 and 6) Mahdy [49]mentioned that selection-based index was predicted to be
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
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Hindawiwwwhindawicom Volume 2018
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Submit your manuscripts atwwwhindawicom
International Journal of Agronomy 5
Table4Means
ofthem
easuredtraitsfor2
0F 4
breadwheatpo
pulatio
ns
Popu
latio
nCH
LHD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Acsad 901timesM
ahon
-Dem
ias
430
1260
201
737
4483
386
6340
220
11350
3792
334
Acsad 901timesR
mada
445
1252
199
608
4424
346
6336
281
10497
4255
405
Acsad 901timesH
idhab
425
1259
198
657
4267
334
6167
230
10983
3255
296
Acsad 901timesE
l-Wifa
k451
1249
204
704
4673
332
6843
304
11490
4604
401
Acsad 899timesM
ahon
-Dem
ias
437
1257
169
708
4760
364
6260
205
11517
3668
319
Acsad 899timesR
mada
434
1250
166
595
4797
378
6257
200
10327
3688
357
Acsad 899timesH
idhab
444
1269
182
566
4060
350
5980
228
9970
3360
337
Acsad 899timesE
l-Wifa
k454
1250
181
631
4147
355
5667
242
9657
3577
370
Acsad 1135timesM
ahon
-Dem
ias
416
1261
170
681
4567
398
6040
210
10737
3834
357
Acsad 1135timesR
mada
422
1252
168
615
5037
369
6853
240
11137
4404
396
Acsad 1135timesH
idhab
415
1270
175
688
4770
395
7447
232
12687
4509
355
Acsad 1135timesE
l-Wifa
k455
1252
170
646
3970
346
4893
221
8790
3099
353
Acsad 1069timesM
ahon
-Dem
ias
438
1258
182
752
4390
392
6233
217
11937
3713
311
Acsad 1069timesR
mada
442
1250
173
617
4147
348
5810
245
9617
3608
375
Acsad 1069timesH
idhab
467
1259
185
605
4113
350
6170
266
9767
3953
405
Acsad 1069timesE
l-Wifa
k540
1250
184
665
4233
346
6110
279
9940
4033
406
Ain
AbidtimesM
ahon
-Dem
ias
420
1277
200
727
4547
388
7473
272
12867
4818
375
Ain
AbidtimesR
mada
403
1263
205
658
4357
365
6993
282
11717
4581
391
Ain
AbidtimesH
idhab
432
1278
210
602
4050
368
6933
304
11307
4628
409
Ain
AbidtimesE
l-Wifa
k436
1269
203
651
4693
377
8573
315
13503
5575
413
ParentsR
ange
391
1250
87
530
2700
300
3200
975300
1850
141
522
1320
391
820
8000
490
12600
394
23300
8090
636
F 4Ra
nge
334
1240
76420
800
190
800
931500
430
126
538
1360
364
1000
8500
510
14800
568
24200
9210
700
LSD005
48
1463
67
1197
45
2352
68
3532
1851
99CH
Lchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)andHIharvestind
ex(
)
6 International Journal of Agronomy
exhibited the highest average for the number of spikes (5037spikes) For 1000-kernel weight Acsad1135 timesMahon-Demiaswas the best population with an average of 398 g
As the contrast ldquoParents versus F4rdquo was significant forPH SN TKW SW BIO and GY and compared with theleast significant difference (LSD005) significant differencesexisted between the parents and hybrids values for thesemea-sured traits These differences were 1 to 3 times higher thanthe LSD005 and were in favor of some F4 lines indicatingthat they perform better than the parents and suggestingthe possibilities of making significant changes through effec-tive selection These findings were in harmony with thoseobtained by Abd El-Shafi [34] Loffler and Busch [40] andAlexander et al [41]
32 Genetic Gain from Direct and Indirect Selection andSelection-Based Index The gains of direct and indirect selec-tion are shown inTable 5The results showed a big variation inthe percentages of gains among the measured traitsThe totalsum of gains per selection varied from minus3611 for headingdate to 3475 for flag leaf area Generally the direct selectionbased on chlorophyll content heading date spikes weightgrain yield and harvest index resulted in negative total gainsOn the other hand flag leaf area plant height number ofspikes 1000-kernel weight number of grains per spike andbiomass recorded positive total gains
The gains obtained by direct selection were higher thanthose of indirect selection But sometimes the indirect selec-tion may be more efficient especially if the secondary traitis highly correlated with yield and is easily measurable [42]The number of grains per spike (1586) followed by aboveground biomass (1230) plant height (1210) and numberof spikes (1084) exhibited the greatest gains from directselection Inversely heading date (minus113) spikes weight(minus499) and harvest index (minus381) showed negative gainvalues Negative gain for heading date is desired in the case ofthis study as the Algerian wheat breeding program designedfor semiarid regions precocity is a crucial criterion adoptedfor selection It is related to the ability of plants to shortentheir cycle so as to decrease their exposure to the late-seasonsirocco weathering
The direct selection for chlorophyll content resulted innegative indirect gains for nearly all the other traits exceptfor the flag leaf area number of grains per spike and harvestindex that showed positive responses For heading date theindirect selection gains were only positive for chlorophyllcontent number of spikes and harvest indexThe correlativeeffects of the selection based on the flag leaf area were desir-able for grain yield although the indirect gain for the remain-ing traits was practically negative
The direct selection for plant height resulted in positiveindirect gains for heading date number of spikes 1000-kernelweight and spikes weight In addition positive responsesof the selection based on yield and yield components wereobserved for grain yield itself heading date 1000-kernelweight and spikes weight However negative indirect gainswere exhibited for other traits including chlorophyll contentflag leaf area and number of grains per spike indicatingthat indirect selection for one variable for gain in another
is unfeasible because there will be a loss in the indirectlyselected variable In cases of negative gains the model is con-sidered inappropriate for selection in this plant material
The highest correlated responses for grain yield weregenerated though indirect selection on the base of theflag leaf area (804 ie 3387 gmminus2) followed by thespike fertility (365 ie 1539 gmminus2) 1000-kernel weight(363 ie 1531 gmminus2) and above ground biomass (274 ie1154 gmminus2)These results indicated that the indirect selectionwas to be more effective in improving the primary trait thanthe indirect selection based on other traits andor on thedirect selection based on the grain yield itself
Several authors have estimated the genetic gains of traitsinvolved in yield determination The results are often incon-sistent and scarce Our results were consistent with thoseof DePauw and Shebeski [43] and Inagaki et al [44] whomentioned that direct selection on the basis of yield is ineffec-tive in early generations Benmahammed et al [45] reportedthe same findings in barley crop Their results showed thatbiomass-based direct selection appearedmore discriminatingthan yield-based selection in their plant material Howeverthe results of this study do not corroborate findings reportedby Lalic et al [11] Mitchell et al [46] Lungu et al [47] andEl-Morshidy et al [48] and who observed the effectiveness ofthe direct selection on grain yieldThedifference in the resultsmay be attributed to differences of breeding material and togenotype times environments
The gains of selection-based index are shown in Table 6The total sum of gains from the selection-based index rangedfrom minus937 to 4286 Five out of seven used indicesshowed positive total gains The base index of Williamsrecorded the highest total gain (4286) followed by theclassic index of Smith and Hazel the index desired gains ofPesek and Baker (2746) and the genotype-ideotype dis-tance index of Cruz (2207) The sum of ranks index ofMulamba and Mock had a very low gain of 374 Subandirsquosand Elstonrsquos indices exhibited negative total gains
The base index of Williams yielded positive responses forall measured traits except for chlorophyll content flag leafarea and harvest index which showed negative gains Thisindex also achieved the highest grain yield response (627ie 2641 gmminus2) compared to the other indices employed inthis study The classic index of Smith and Hazel ranked sec-ond followed by the index of desired gains of Pesek and Bakerthe genotype-ideotype distance index proposed by Cruz andthe sum of ranks index of Mulamba andMock with selectionresponses of 3693 2746 2207 and 374 respectivelyThe single-trait responses varied from one index to anotherwith a more or less balanced distribution of positive andnegative estimates among the traits This shows that themonotrait selection was inadequate because it led to a higherfinal product when considering the grain yield and generatedunfavorable responses in other traits These results indicatedthat methods that combine favorable expected gains shouldbe used in the evaluation of these progeniesThe gains expect-ed through indices for grain yield per se were larger thanthose obtained by direct and indirect monotrait selectionexcept for the flag leaf area (Tables 5 and 6) Mahdy [49]mentioned that selection-based index was predicted to be
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
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6 International Journal of Agronomy
exhibited the highest average for the number of spikes (5037spikes) For 1000-kernel weight Acsad1135 timesMahon-Demiaswas the best population with an average of 398 g
As the contrast ldquoParents versus F4rdquo was significant forPH SN TKW SW BIO and GY and compared with theleast significant difference (LSD005) significant differencesexisted between the parents and hybrids values for thesemea-sured traits These differences were 1 to 3 times higher thanthe LSD005 and were in favor of some F4 lines indicatingthat they perform better than the parents and suggestingthe possibilities of making significant changes through effec-tive selection These findings were in harmony with thoseobtained by Abd El-Shafi [34] Loffler and Busch [40] andAlexander et al [41]
32 Genetic Gain from Direct and Indirect Selection andSelection-Based Index The gains of direct and indirect selec-tion are shown inTable 5The results showed a big variation inthe percentages of gains among the measured traitsThe totalsum of gains per selection varied from minus3611 for headingdate to 3475 for flag leaf area Generally the direct selectionbased on chlorophyll content heading date spikes weightgrain yield and harvest index resulted in negative total gainsOn the other hand flag leaf area plant height number ofspikes 1000-kernel weight number of grains per spike andbiomass recorded positive total gains
The gains obtained by direct selection were higher thanthose of indirect selection But sometimes the indirect selec-tion may be more efficient especially if the secondary traitis highly correlated with yield and is easily measurable [42]The number of grains per spike (1586) followed by aboveground biomass (1230) plant height (1210) and numberof spikes (1084) exhibited the greatest gains from directselection Inversely heading date (minus113) spikes weight(minus499) and harvest index (minus381) showed negative gainvalues Negative gain for heading date is desired in the case ofthis study as the Algerian wheat breeding program designedfor semiarid regions precocity is a crucial criterion adoptedfor selection It is related to the ability of plants to shortentheir cycle so as to decrease their exposure to the late-seasonsirocco weathering
The direct selection for chlorophyll content resulted innegative indirect gains for nearly all the other traits exceptfor the flag leaf area number of grains per spike and harvestindex that showed positive responses For heading date theindirect selection gains were only positive for chlorophyllcontent number of spikes and harvest indexThe correlativeeffects of the selection based on the flag leaf area were desir-able for grain yield although the indirect gain for the remain-ing traits was practically negative
The direct selection for plant height resulted in positiveindirect gains for heading date number of spikes 1000-kernelweight and spikes weight In addition positive responsesof the selection based on yield and yield components wereobserved for grain yield itself heading date 1000-kernelweight and spikes weight However negative indirect gainswere exhibited for other traits including chlorophyll contentflag leaf area and number of grains per spike indicatingthat indirect selection for one variable for gain in another
is unfeasible because there will be a loss in the indirectlyselected variable In cases of negative gains the model is con-sidered inappropriate for selection in this plant material
The highest correlated responses for grain yield weregenerated though indirect selection on the base of theflag leaf area (804 ie 3387 gmminus2) followed by thespike fertility (365 ie 1539 gmminus2) 1000-kernel weight(363 ie 1531 gmminus2) and above ground biomass (274 ie1154 gmminus2)These results indicated that the indirect selectionwas to be more effective in improving the primary trait thanthe indirect selection based on other traits andor on thedirect selection based on the grain yield itself
Several authors have estimated the genetic gains of traitsinvolved in yield determination The results are often incon-sistent and scarce Our results were consistent with thoseof DePauw and Shebeski [43] and Inagaki et al [44] whomentioned that direct selection on the basis of yield is ineffec-tive in early generations Benmahammed et al [45] reportedthe same findings in barley crop Their results showed thatbiomass-based direct selection appearedmore discriminatingthan yield-based selection in their plant material Howeverthe results of this study do not corroborate findings reportedby Lalic et al [11] Mitchell et al [46] Lungu et al [47] andEl-Morshidy et al [48] and who observed the effectiveness ofthe direct selection on grain yieldThedifference in the resultsmay be attributed to differences of breeding material and togenotype times environments
The gains of selection-based index are shown in Table 6The total sum of gains from the selection-based index rangedfrom minus937 to 4286 Five out of seven used indicesshowed positive total gains The base index of Williamsrecorded the highest total gain (4286) followed by theclassic index of Smith and Hazel the index desired gains ofPesek and Baker (2746) and the genotype-ideotype dis-tance index of Cruz (2207) The sum of ranks index ofMulamba and Mock had a very low gain of 374 Subandirsquosand Elstonrsquos indices exhibited negative total gains
The base index of Williams yielded positive responses forall measured traits except for chlorophyll content flag leafarea and harvest index which showed negative gains Thisindex also achieved the highest grain yield response (627ie 2641 gmminus2) compared to the other indices employed inthis study The classic index of Smith and Hazel ranked sec-ond followed by the index of desired gains of Pesek and Bakerthe genotype-ideotype distance index proposed by Cruz andthe sum of ranks index of Mulamba andMock with selectionresponses of 3693 2746 2207 and 374 respectivelyThe single-trait responses varied from one index to anotherwith a more or less balanced distribution of positive andnegative estimates among the traits This shows that themonotrait selection was inadequate because it led to a higherfinal product when considering the grain yield and generatedunfavorable responses in other traits These results indicatedthat methods that combine favorable expected gains shouldbe used in the evaluation of these progeniesThe gains expect-ed through indices for grain yield per se were larger thanthose obtained by direct and indirect monotrait selectionexcept for the flag leaf area (Tables 5 and 6) Mahdy [49]mentioned that selection-based index was predicted to be
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
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Agronomy
Hindawiwwwhindawicom Volume 2018
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Submit your manuscripts atwwwhindawicom
International Journal of Agronomy 7
Table5Selectiongain
estim
ates
(GS
)obtainedfro
mdirect(diagonal)andindirect(ofd
iagonal)selectionin
different
breadwheattraits
Traits
119883 119900119883 119904
GS(
)To
tal
Chl
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
CHL
437
464
615
(269)
minus007
(minus009)
160
(030)
minus325
(minus211)
minus406
(minus1862)
minus049
(minus019)
minus412
(minus2831
)214
(052
)minus714
(minus8330)
minus110
(minus463)
577
(209)
minus458
HD
1265
1250
143
(063)
minus113
(minus14
3)minus774
(minus14
4)minus4
69
(minus305)
176
(809)
minus235
(minus088)minus1098
(minus7543)
minus495
(minus12
1)minus1233
(minus14386)minus3
76(minus1584)
863
(313
)minus3
611
FLA
186
194
minus151
(minus066)
054
(068)
43 0
(080)
minus065
(minus042)minus4
72
(minus2169)
minus071
(minus027)
876
(6015)
1400
(343)
640
(7463)
804
(3387)
031
(011)
3475
PH64
9728
minus130
(minus057)
036
(045)
minus457
(minus085)
1210
(786)
933
(4285)
714
(268)
563
(3869)minus1579
(minus386)
1238
(14436)minus123
(minus517)minus110
8(minus401)
1298
SN4592
5089minus0
88
(minus038)
010
(013
)minus8
17(minus15
2)458
(297)
1084
(4976)
422
(158)
552
(379
0)minus1215
(minus298)
815
(9501)
114
(480)
minus437
(158)
897
TKW
375
413
minus070
(minus030)
096
(122)
minus477
(minus089)
387
(252
)1119
(513
8)993
(373)
1084
(7442)minus1446
(minus354)
1404
(16376
)363
(1531)minus741
(minus268)
2714
SW6868
6525
minus126
(minus055)
040
(050)
minus336
(minus062)minus0
17(minus011)
minus718
(minus3295)
359
(135)
minus499
(minus34
25)
minus013
(minus003)minus4
07
(minus4744)
minus505
(minus2127)minus150
(minus054)minus2
371
GN
245
284
205
(089)
053
(067)
385
(072)
minus584
(minus379)minus1050
( minus4820)
minus099
(minus037)
070
(480)
1586
(388)
minus414
(minus4830)
365
(1539)
687
(249)
1204
BIO
11664
13099
minus122
(minus053)
063
(080)
minus358
(minus067)
793
(515
)74
4(3418)
615
(231
)73
6(5055)
minus894
(minus219)
1230
(143
50)
274
(115
4)minus744
(minus269)
2339
GY
4214
4222minus2
07
(minus090)
069
(087)
minus148
(minus027)minus0
95
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
HI
3 62
348
minus091
(minus040
)045
(057)
030
(006)
minus208
(minus13
5)minus5
74(minus2635
)272
(102)
060
(409)
minus080
(minus019)
014
(163)
minus354
(minus1490)minus3
81
(minus13
8)minus1267
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
pers
pikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GY
grainyield(gmminus2)HIharvestind
ex(
)1198830traitmeanvalueof
thebase
popu
latio
nand119883119904traitm
eanvalueof
theselected
popu
lationsvaluesb
etween
parenthesesc
orrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ield
andyield
-related
traits
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
Nutrition and Metabolism
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Food ScienceInternational Journal of
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
PsycheHindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
ScienticaHindawiwwwhindawicom Volume 2018
GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Plant GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Biotechnology Research International
Hindawiwwwhindawicom Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
BotanyJournal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Veterinary Medicine International
Hindawiwwwhindawicom Volume 2018
Cell BiologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
BioMed Research International
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Submit your manuscripts atwwwhindawicom
8 International Journal of Agronomy
Table6Selectiongain
estim
ates
(GS
)obtainedfro
mselection-basedindexin
different
breadwheattraits
Indices
GS(
)To
tal
CHL
HD
FLA
PHSN
TKW
SWGN
BIO
GY
HI
Smith
ampHazel
195
(085)
097
(123)
minus093
(minus017)
874
(568)
428
(1965)
953
(357)
1020
(7002)
minus804
(minus19
7)1360
(15863)
394
(1659)
minus730
(minus264
)3693
Mulam
baampMock
minus271
(minus118)
003
(004)
minus002
(minus000)
405
(263)
minus160
(minus735)
208
(078)
minus235
(minus1611)
150
(037
)minus0
58
(minus677)
156
(657)
178
(065)
374
Williams
minus043
(minus019)
156
(198)
minus037
(minus007)
571
(371)
085
(391)
844
(317
)1243
(8535)
000
(000)
1478
(17243)
627
(2641)
minus640
(minus232)
4286
Suband
iminus137
(minus060)
078
(099)
006
(001)
327
(212
)minus9
31(minus4275)
462
(173)
minus318
(minus2185)
253
(062)
minus110
(minus1284)
minus308
(minus1299)
minus258
(minus093)
minus937
Elsto
nminus2
07
(minus090)
069
(087)
minus148
(minus027)
minus095
(minus062)minus6
49
(minus2982)
340
(127)
minus138
(minus94
5)437
(107)
minus247
(minus2884)
020
(085)
210
(076
)minus4
08
PesekampBa
ker
223
(097)
078
(099)
438
(081)
minus511
(minus332)
033
(151)
588
(221)
872
(598
9)minus0
31(minus008)
791
(9223)
542
(2283)
minus276
(minus10
0)2746
Cruz
minus146
(minus064
)095
(120)
224
(042)
015
(010
)minus799
(minus3669)
382
(143)
363
(2495)
1049
(257)
159
(1850)
576
(2427)
288
(104)
2207
CHLchloroph
yllcon
tent
(Spad)H
Dheading
date(days)FLA
flag
leafarea
(cm2)PH
plant
height
(cm)SN
num
bero
fspikesTK
W100
0-kernelweight(g)SWspikesw
eight(gmminus2)GNnum
bero
fgrains
perspikeB
IOabo
vegrou
ndbiom
ass(gmminus2)GYgrainyield(gmminus2)andHIharvestind
ex(
)andvaluesbetweenparenthesescorrespon
dto
theg
eneticgain
interm
sofp
ercent
meanfory
ieldandyield-related
traits
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
Nutrition and Metabolism
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Food ScienceInternational Journal of
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
PsycheHindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
ScienticaHindawiwwwhindawicom Volume 2018
GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Plant GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Biotechnology Research International
Hindawiwwwhindawicom Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
BotanyJournal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Veterinary Medicine International
Hindawiwwwhindawicom Volume 2018
Cell BiologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
BioMed Research International
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Submit your manuscripts atwwwhindawicom
International Journal of Agronomy 9
Table 7 Differences of the F4 selected lines relatively to the standard check Hidhab for the measured traits
Traits F4 selected lines mean Hidhab Difference Difference in of Hidhab LSD005Chl 4226 4330 minus105 minus241 480HD 12657 12967 minus310lowast minus239 140FLA 1915 1940 minus024 minus126 631PH 6867 6233 633 1016 667SN 61567 46333 15233lowast 3288 11967TKW 3997 3833 163 426 453SW 114767 82333 32433lowast 3939 23517GN 3157 2483 675lowast 2718 675BIO 183367 137667 45700lowast 3320 35315GY 75813 44133 31680lowast 7178 18510HI 4178 3223 955 2963 994CHL chlorophyll content (Spad) HD heading date (days) FLA flag leaf area (cm2) PH plant height (cm) SN number of spikes TKW 1000-kernel weight(g) SW spikes weight (gmminus2) GN number of grains per spike BIO above ground biomass (gmminus2) GY grain yield (gmminus2) and HI harvest index ()lowastSignificant effect at 005 probability
superior for yield improvement as compared to themonotraitselection method
The first three axes of the Principal Component Analysis(PCA) explainedmore than 7793of the total variation avail-able in the data subjected to analysis Heading date (0386lowast)flag leaf area (0308lowast) spikes weight (0923lowast) biomass(0853lowast) and grain yield (0866lowast) correlated significantly toPCA1 PCA2 was mainly related to the chlorophyll content(0276lowast) 1000-kernel weight (0440lowast) number of grains perspike (0655lowast) and harvest index (0547lowast) (Figure 1) Thenumber of spikes (0333lowast) was related to PCA3 (Figure 2)ThePCA biplots showed that the populations Acsad899 timesHidhab(P7) Acsad899 times El-Wifak (P8) Acsad1135 times Hidhab (P11)Acsad1135 times El-Wifak (P12) Acsad1069 times Rmada (P14) AinAbid timesMahon-Demias (P17) Ain Abid times Rmada (P18) andAinAbidtimesEl-Wifak (P20) werewell represented on the planeformed by the first axis PCA1 (Figure 1)
Ain Abid times Mahon-Demias (P17) Ain Abid times Rmada(P18) and Ain Abid times El-Wifak (P20) had positive coordi-nates with this axis They were characterized by a long vege-tative cycle and high biomass spikes weight and grain yieldvaluesThe last two populations Ain Abid timesRmada (P18) andAin Abid times El-Wifak (P20) were also distinguished by highvalues of chlorophyll content flag leaf area spikes fertilityand harvest index relatively to PCA2 but were associatedwith low number of ears relatively to PCA3 (Figures 1 and2) On the other hand Ain Abid timesMahon-Demias (P17) wasdistinguished relatively to PCA2 by high estimates of the1000-kernel weight and plant height and associatedwith highnumber of spikes relatively to PCA3 (Figures 1 and 2) Fromthese results it could be concluded that effective selectionof superior individuals within this population certainly con-tributes to the improvement of yield and yield componentsin a semi-late genetic background
33 Selection of Superior Genotypes for Grain Yield Geno-types were first ranked according to grain yield then the 5highest yielding lines were selected and theirmean yield esti-matedThe 30 lines thus selected are derived from 15 out of 20F4 populations studied Half part of these lines was equitably
P1
P2
P3
P4
P5P6
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH SN
TKW
SW
GN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4minus3minus2minus1
012345
PCA
2 (2
429
)
Figure 1 Principal Component Analysis (PCA) projections on axes1 and 2 accounting for 6356 of total variation for 20 F4 popula-tions of bread wheat
derived from Acsad1135 times Hidhab (P11) Ain Abid times Hidhab(P19) and Ain Abid times El-Wifak (P20) crosses The popula-tion Ain Abid times Mahon-Demias (P17) previously identifiedamong the promising populations contributed with fourlines Acsad1069 times Rmada (P14) and Ain Abid times Rmada (P18)participated with two lines each Seven other populationscontributed by one line each Relative to the standard checkHidhab which is the most cultivated variety in Algeriathe top 30 lines selected were characterized by significantimprovements in yield components including the numberof spikes (15233 spikes mminus2) spikes weight (32433 gmminus2)spikes fertility (675 grains spikeminus1) above ground biomass(45700 gmminus2) and grain yield (31680 gmminus2) (Table 7)Besides they were distinguished by significant reduction ofthe duration in the vegetative phase with 310 days (Table 7)
4 Conclusion
The results of this study indicated appreciable genetic varia-bility among the evaluated populations Selection based-indexwas more efficient to improve grain yield compared to direct
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
Nutrition and Metabolism
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Food ScienceInternational Journal of
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
PsycheHindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
ScienticaHindawiwwwhindawicom Volume 2018
GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Plant GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Biotechnology Research International
Hindawiwwwhindawicom Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
BotanyJournal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Veterinary Medicine International
Hindawiwwwhindawicom Volume 2018
Cell BiologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
BioMed Research International
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Submit your manuscripts atwwwhindawicom
10 International Journal of Agronomy
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11P12
P13
P14 P15
P16
P17
P18
P19
P20
CHL
HD
FLA
PH
NE
TKW
SWGN
BIO
GY
HI
minus5 minus4 minus3 minus2 minus1 0 1 2 3 4 5 6minus6PCA1 (3927)
minus4
minus3
minus2
minus1
0
1
2
3
4
5
PCA
3 (1
438
)
Figure 2 Principal Component Analysis (PCA) projections on axes1 and 3 accounting for 5365 of total variation for 20 F4 popula-tions of bread wheat
and indirect single-trait selection The analytical proceduresof the different selection methods showed possibilities ofapplications in advanced generations of breeding beingsuperior when compared with direct and indirect selectionWilliamsrsquos index was predicted to be more effective than theother selection indices for improving multiple traits at timeIt brought the highest total genetic gain and the best yieldgain per se associated with positive correlated responses formost of yield components Compared to the check cultivarHidhab the 30 F4 selected lines at 5 selection intensity werecharacterized by significant increase in grain yield and yieldrelated traits
Conflicts of Interest
The authors declare that there are no conflicts of interest re-garding the publication of this article
References
[1] ABenbelkacem ldquoAdaptation of cereal cultivars to extreme agro-ecologic environments of North Africardquo Field Crops Researchvol 45 no 1-3 pp 49ndash55 1996
[2] A Mekhlouf F Dehbi H Bouzerzour A Hannchi A Benma-hammed and A Adjabi ldquoRelationships between cold tolerancegrain yield performance and stability of durumwheat (TriticumdurumDesf) genotypes grown at high elevation area of EasternAlgeriardquo Asian Journal of Plant Sciences vol 5 no 4 pp 700ndash708 2006
[3] G A Slafer and FH Andrade ldquoChanges in physiological attrib-utes of the dry matter economy of bread wheat (Triticumaestivum) through genetic improvement of grain yield potentialat different regions of the world - A reviewrdquo Euphytica vol 58no 1 pp 37ndash49 1991
[4] S P Loss and K H M Siddique ldquoMorphological and Phys-iological Traits Associated with Wheat Yield Increases inMediterranean Environmentsrdquo Advances in Agronomy vol 52no C pp 229ndash276 1994
[5] RM TrethowanMVanGinkel K Ammar et al ldquoAssociationsamong twenty years of international bread wheat yield evalua-tion environmentsrdquo Crop Science vol 43 no 5 pp 1698ndash17112003
[6] H Bouzerzour A Djekoun A Benmahammed and K LHassous ldquoContribution de la biomasse aerienne de lrsquoindicede recolte et de la precocite a lrsquoepiaison au rendement grainde lrsquoorge (Hordeum vulgare L) en zone drsquoaltituderdquo Cahiers delrsquoAgriculture vol 8 pp 133ndash137 1998
[7] A Benmahammed H Nouar L Haddad Z Laala A Oulmiand H Bouzerzour ldquoAnalyse de la stabilite des performances derendement du ble dur (Triticum durum Desf) sous conditionssemi-aridesrdquo Biotechnologie Agronomie Societe et Environ-nement vol 14 no 1 pp 177ndash186 2010
[8] B RWhan R Knight and A J Rathjen ldquoResponse to selectionfor grain yield and harvest index in F2 F3 and F4 derived linesof two wheat crossesrdquo Euphytica vol 31 no 1 pp 139ndash150 1982
[9] M P Reynolds R P Singh A Ibrahim O A A Ageeb ALarque-Saavedra and J S Quick ldquoEvaluating physiologicaltraits to complement empirical selection for wheat in warmenvironmentsrdquo Euphytica vol 100 no 1-3 pp 85ndash94 1998
[10] M Balota A J Green C A Griffey R Pitman andWThoma-son ldquoGenetic gains for physiological traits associated with yieldin soft red winter wheat in the Eastern United States from 1919to 2009rdquo European Journal of Agronomy vol 84 pp 76ndash83 2017
[11] A Lalic D Novoselovic J Kovacevic et al ldquoGenetic gain andselection criteria effects on yield and yield components in barley(Hordeum vulgare L)rdquo Periodicum biologorum vol 112 no 3pp 311ndash316 2010
[12] MA BabarMVanGinkelM P Reynolds B Prasad andA RKlatt ldquoHeritability correlated response and indirect selectioninvolving spectral reflectance indices and grain yield in wheatrdquoAustralian Journal of Agricultural Research vol 58 no 5 pp432ndash442 2007
[13] MMCosta AODiMauro S HUneda-Trevisoli et al ldquoAnal-ysis of direct and indirect selection and indices in soybean seg-regating populationsrdquoCrop Breeding and Applied Biotechnologyvol 8 no 1 pp 47ndash55 2008
[14] J M S Viana V R Faria F F e Silva and M D V de ResendeldquoCombined selection of progeny in crop breeding using bestlinear unbiased predictionrdquo Canadian Journal of Plant Sciencevol 92 no 3 pp 553ndash562 2012
[15] C Y Lin ldquoIndex selection for genetic improvement of quanti-tative charactersrdquo Theoretical and Applied Genetics vol 52 no2 pp 49ndash56 1978
[16] J D Smith and M L Kinman ldquoThe Use of Parent-OffspringRegression as an Estimator of Heritability1rdquo Crop Science vol5 no 6 p 595 1965
[17] K J Frey and T Horner ldquoHeritability in standard unitsrdquo Agro-nomy Journal vol 49 no 2 pp 59ndash62 1957
[18] Z Fellahi A Hannachi H Bouzerzour and A BoutekrabtldquoLine times Tester Mating Design Analysis for Grain Yield andYield Related Traits in Bread Wheat (Triticum aestivumL)rdquoInternational Journal of Agronomy vol 2013 pp 1ndash9 2013
[19] Z Fellahi A Hannachi H Bouzerzour S Dreisigacker AYahyaoui and D Sehgal ldquoGenetic analysis of morpho-physio-logical traits and yield components in F2 partial diallel crossesof bread wheat (Triticum aestivum L)rdquo Revista FacultadNacional de Agronomia vol 70 no 3 pp 8237ndash8250 2017
[20] H Chennafi A Aıdaoui H Bouzerzour and A Saci ldquoYieldresponse of durumwheat (Triticum durumDesf) cultivarWahato deficit irrigation under semi arid growth conditionsrdquo AsianJournal of Plant Sciences vol 5 no 5 pp 854ndash860 2006
[21] P L Spagnoletti Zeuli and C O Qualset ldquoFlag Leaf Variationand the Analysis of Diversity in DurumWheatrdquo Plant Breedingvol 105 no 3 pp 189ndash202 1990
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
Nutrition and Metabolism
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Food ScienceInternational Journal of
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
PsycheHindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
ScienticaHindawiwwwhindawicom Volume 2018
GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Plant GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Biotechnology Research International
Hindawiwwwhindawicom Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
BotanyJournal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Veterinary Medicine International
Hindawiwwwhindawicom Volume 2018
Cell BiologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
BioMed Research International
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Submit your manuscripts atwwwhindawicom
International Journal of Agronomy 11
[22] M SMcIntosh ldquoAnalysis of combined experimentsrdquoAgronomyJournal vol 75 no 1 pp 153ndash155 1983
[23] R G D Steel and J H Torrie Principles and procedures ofstatistics McGraw-Hill Books New York NY USA 1982
[24] GAcquaahPrinciples of plant genetics and breeding JohnWileyand Sons 2009
[25] C D Cruz Programa GENES Biometria Editora UFV VicosaBrazil 1st edition 2006
[26] H F Smith ldquoA discriminates function for plant selectionrdquo An-nals of Eugenics vol 7 pp 240ndash250 1936
[27] L N Hazel ldquoThe genetic basis for constructing selection in-dexesrdquo Genetics vol 28 no 6 pp 476ndash490 1943
[28] J S Williams ldquoThe evaluation of a selection indexrdquo Biometricsvol 18 no 3 pp 375ndash393 1962
[29] R C Elston ldquoA Weight-Free Index for the Purpose of Rankingor SelectionwithRespect to Several Traits at a TimerdquoBiometricsvol 19 no 1 p 85 1963
[30] J Pesek and R J Baker ldquoComparison of predicted and observedresponses to selection for yield in wheatrdquo Canadian Journal ofPlant Science vol 51 no 3 pp 187ndash192 1971
[31] W Subandi A Compton and L T Empig ldquoComparison of theefficiencies of selection indices for three traits in two varietycrosses of cornrdquo Crop Science vol 13 no 2 pp 184ndash186 1973
[32] N N Mulamba and J J Mock ldquoImprovement of yield potentialof the ETO blanco maize (Zea mays L) population by breedingfor plant traitsrdquoThe Egyptian Journal of Genetics and Cytology vol 7 no 1 pp 40ndash51 1978
[33] C D Cruz ldquoGENES - A software package for analysis in exper-imental statistics and quantitative geneticsrdquo Acta Scientiarum -Agronomy vol 35 no 3 pp 271ndash276 2013
[34] M A Abd El-Shafi ldquoEstimates of Genetic Variability and Effi-ciency of Selection for Grain Yield and Its Components in TwoWheat Crosses (Triticum aestivum L)rdquo nternational Journal ofAgriculture and Crop Sciences vol 7 no 2 pp 83ndash90 2014
[35] R W Allard Principles of Plant Breeding John Wily and SonsInc New York NY USA 2nd edition 1960
[36] C D Cruz A J Regazi and P C S Carneiro Modelos bio-metricos aplicados ao melhoramento genetico UFV Vicosa Bra-zil 4th edition 2012
[37] A Mesele W Mohammed and T Dessalegn ldquoEstimation ofHeritability and Genetic Advance of Yield and Yield RelatedTraits in Bread Wheat (Triticum aestivum L) Genotypes atOfla District Northern Ethiopiardquo International Journal of PlantBreeding and Genetics vol 10 no 1 pp 31ndash37 2015
[38] B Saleem A KhanM Shahzad and F Ijaz ldquoEstimation of her-itability and genetic advance for various metric traits in sevenF2 populations of bread wheat (Triticum aestivum L)rdquo Journalof Agricultural Sciences Belgrade vol 61 no 1 pp 1ndash9 2016
[39] M Yaqoob ldquoEstimation of genetic variability heritability andgenetic advance for yield and yield related traits in wheat underrainfed conditionsrdquo Journal of Agricultural Research vol 54 no1 pp 1ndash14 2016
[40] C M Loffler and R H Busch ldquoSelection for Grain ProteinGrain Yield and Nitrogen Partitioning Efficiency in Hard RedSpring Wheatrdquo Crop Science vol 22 no 3 p 591 1982
[41] W L Alexander E L Smith and C Dhanasobhan ldquoA compar-ison of yield and yield component selection in winter wheatrdquoEuphytica vol 33 no 3 pp 953ndash961 1984
[42] J Kumar and P N Bahl ldquoDirect and indirect selection for yieldin chickpeardquo Euphytica vol 60 no 3 pp 197ndash199 1992
[43] R M DePauw and L H Shebeski ldquoAn evaluation of an earlygeneration yield testing procedure in Triticum aestivumrdquo Cana-dian Journal of Plant Science vol 53 no 3 pp 465ndash470 1973
[44] M N Inagaki G Varughese S RajaramM Van Ginkel and AMujeeb-Kazi ldquoComparison of bread wheat lines selected bydoubled haploid single-seed descent and pedigree selectionmethodsrdquo Theoretical and Applied Genetics vol 97 no 4 pp550ndash556 1998
[45] A Benmahammed H Bouzerzour A Djekou and K HassousldquoEfficacite de la selection precoce de la biomasse chez lrsquoorge(Hordeum vulgare L) en zone semi-ariderdquo Sciences and Tech-nologie vol C no 22 pp 80ndash85 2004
[46] J W Mitchell R Baker and D R Knott ldquoEvaluation of Honey-comb Selection for Single Plant Yield in Durum Wheatrdquo CropScience vol 22 no 4 pp 840ndash843 1982
[47] DM Lungu P J Kaltsikes and EN Larter ldquoHoneycomb selec-tion for yield in early generations of spring wheatrdquo Euphyticavol 36 no 3 pp 831ndash839 1987
[48] M A El-Morshidy K A Kheiralla M A Ali and A A S Ah-med ldquoEfficiency of pedigree selection for earliness and grainyield in two wheat populations under water stress conditionsrdquoAssiut Journal of Agricultural Sciences vol 37 pp 77ndash94 2010
[49] E EMahdy ldquoSingle andMultiple Traits Selection in a Segregat-ing Population of Wheat Triticum aestivum Lrdquo Plant Breedingvol 101 no 3 pp 245ndash249 1988
Nutrition and Metabolism
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Food ScienceInternational Journal of
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
PsycheHindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
ScienticaHindawiwwwhindawicom Volume 2018
GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Plant GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Biotechnology Research International
Hindawiwwwhindawicom Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
BotanyJournal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Veterinary Medicine International
Hindawiwwwhindawicom Volume 2018
Cell BiologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
BioMed Research International
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Submit your manuscripts atwwwhindawicom
Nutrition and Metabolism
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Food ScienceInternational Journal of
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
PsycheHindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
ScienticaHindawiwwwhindawicom Volume 2018
GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Plant GenomicsInternational Journal of
Hindawiwwwhindawicom Volume 2018
Biotechnology Research International
Hindawiwwwhindawicom Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
BotanyJournal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Veterinary Medicine International
Hindawiwwwhindawicom Volume 2018
Cell BiologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
BioMed Research International
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Submit your manuscripts atwwwhindawicom