Euphytica 76 : 63-71, 1994 .

63© 1994 Kluwer Academic Publishers. Printed in the Netherlands.

Grain-yield characteristics of oat lines surviving uniform and shuttle selectionstrategies

Carrie Young' & K.J. Frey2l KALSEC, Inc., PO Box 50511, Kalamazoo, MI 49005-0511, USA ; 2 Department of Agronomy, Iowa StateUniversity, Ames, IA 50011, USA

Received 2 June 1993 ; accepted 6 April 1994

Key words: Avena sativa, oat, grain yield, yield stability

Summary

Four selection strategies were used on four sets of oat lines to select for grain yield. Two of these used uniformenvironments whereby sequential selection of the high-yielding lines occurred in continuous high- or continuouslow-productivity environments. These are referred to as high and low uniform selection strategies, respectively. Theother two selection strategies were conducted by sequential selection of the high-yielding lines in alternating high-and low-productivity environments . They are referred to as high and low shuttle selection strategies, respectively,with high and low designating the productivity of the first environment in the sequence . After three or four cyclesof selection, the surviving lines and a random sample from each set were evaluated for mean grain yield, grainyield response to improving environments, and stability of grain yield, in a range of environments typical of oatproduction on Iowa farms .

Grain yield and regression response for all selection strategies, when calculated across all sets of lines, weresignificantly greater than corresponding values for random samples . Stability was unchanged . The uniform-highand uniform-low strategies gave the greatest and the smallest gains in mean grain yield, respectively, with theshuttle strategies giving intermediate gains . Shuttle selection in predominantly high-productivity environmentsincreased grain yield more than shuttle selection in predominantly low-productivity environments . The uniform-strategy followed by the shuttle-high strategy identified entries with superior performance in high productivityenvironments. Increased gain in mean grain yield across all environments was associated with increased number ofselection cycles conducted in high-productivity environments .

Introduction

To maximize heritability and expression of geneticdifferences, plant breeders generally test and selectamong genotypes for grain-yield performance in high-productivity environments . But selection of genotypeswith high-yield capacity in high-productivity environ-ments may overlook the need for selected genotypes tobe adapted to stress, or at least, to the suboptimal con-ditions often encountered in crop production . Thus, arecurring question for plant breeders is whether selec-tion among genotypes of a crop should be orientedtowards adaptation for high-productivity environments

* Journal Paper No . J-15252 of the Iowa Agric . and Home Econ .Exp . Stn ., Ames, Iowa 50011, USA . Project No. 2447 .

or for the full range of productivity levels encounteredin commercial production .

A procedure suggested for selecting plant geno-types generally adapted to a range of productivity lev-els is `shuttle selection' . With shuttle selection, plantgenotypes are propagated and selected in alternatingextremes or diametrical environments, in successivegenerations . Natural or artificial selection may be used .Borlaug (1968) used shuttle selection to breed widelyadapted wheat (Triticum aestivum L.) cultivars at theCentro International de Mejoramiento de Maiz y Trigo(CIMMYT) in Mexico. His procedure involved grow-ing plants in two seasons per year, a winter nursery atObregon, Sonora, Mexico (latitude = 28°N, elevation= sea level) and a summer nursery at Toluca, Mex-

64

ico (latitude = 7°N, elevation = 2800 m) . Resultingcultivars were adapted nearly anywhere in the worldbetween 40°N and 40 ° S latitude.

Lu et al . (1 967a, b) and Tsai et al . (1967) hybridizedspring, summer, and unadapted types of soybeans(Glycine max L.) and propagated successive segre-gating generations sequentially in spring and summer.Selected progenies exhibited wide adaptation and yieldstability.

St-Pierre et al . (1967), working with barley(Hordeum vulgare L.), used shuttle propagation byalternating successive generations between two loca-tions: La Pocatiere and Macdonald, Quebec, Cana-da. La Pocatiere, which had stress conditions, wasthe more effective environment for selection of widelyadapted entries . Choo et al . (1980a, b) rotated bulk pop-ulations of barley between Macdonald and La Pocatiereand found that the pressure of natural selection was tooweak to change adaptability.

Adegoke & Frey (1981) subjected bulk populationsof oat (Avena sativa L.) to shuttle and uniform selec-tion for nine generations . The former was achieved byrotating the bulk from northern, to central, to south-ern Iowa in successive generations, and the latter wasachieved by growing the bulk continuously in centralIowa. Grain yield increased gradually in the uniformline of descent, 15% over nine years, whereas yieldincrease with shuttle selection was only 5% . Means ofthe response indexes and stability values were similarfor the two selection strategies .

Shabana et al . (1980) selected oat lines in a singleyear under high-, intermediate-, and low-productivityconditions . Selection was most effective for mean yieldin high-productivity environments . Frey (1964) foundthat oat lines selected for three successive years understress exhibited strong genotype-environment interac-tion, whereas those selected in nonstress conditionsexhibited no genotype-environment interaction .

The objective of this study was to examine theeffects of four selection strategies using various com-binations of high- and low-productivity environmentsupon the grain yield characteristics of selected oatgenotypes . Selection included uniform and shuttlestrategies .

Materials and methods

General

Four sets of oat lines were studied . Uniform selectionwas conducted by continuous testing and selection ofthe high grain-yielding lines in either high- or low-productivity environments, whereas shuttle selectionwas conducted by testing and selection of the highgrain-yielding lines in alternating high- and low pro-ductivity environments. After three or four cycles ofselection with each set of oat lines, the surviving lineswere evaluated in a set of environments producing theusual range of yields encountered on Iowa farms (i .e .,one to four tons/ha) . A stability analysis was conduct-ed on the grain-yield data from each group of selectedlines to compare the effects of uniform and of shuttleselection on these characteristics of grain yield : meanyield, response to improving environments measuredby regression, and production stability measured bydeviation mean squares (Eberhart & Russell, 1966) .

Germplasm

A bulk was formed in 1957 by combining 10-g lots ofF2 seed from 250 oat matings . This bulk populationwas the germplasm base from which the four sets ofoat lines originated . The procedures used to developthe four sets were as follows :

1 . Set I: A sample of oat seed from the bulk wassubjected to thermal neutron and X-radiation ingenerations F4 to F7, after which the bulk seedlot was propagated near Ames, IA through F11 .Radiation treatment probably induced mutationsand promoted outcrossing. A second sample fromthe bulk was propagated at the same site from F2through F12, with no selection or treatment . Twentyrandom lines from each of five generations (F3, F6,F7, F8, and F12) from the nonradiated material, fourgenerations (F7, F8, F9, and F11) from the radiation-derived material, and 20 varieties and experimentallines made up Set I with 200 lines .

2. Set II: Three samples from the bulk were propa-gated from F3 through F11, one each at northern,central, and southern Iowa sites . Subsequently, 32random lines were taken from the F3, F5, F7, F9,and F11 of each line of descent to provide a total of480 lines in Set II .

3 . Set III : One sample of the bulk was propagated con-tinuously near Ames, IA . Three additional sampleswas rotated in successive generations from north-

ern to central to southern Iowa from F3 to Ft 1 .Twenty random lines from the F3, F5, F 7 , F9, andFt 1 of the four lines of descent comprised the 400lines in Set III .

4. Set IV : From a population propagated near Ames,IA from the F2 through F8, 242 F9-derived lineswere established to make up Set IV.

Testing sets of oat lines

Environments with a wide range of productivities wereused to test the four sets of oat lines . Differences inenvironmental productivity were achieved by optimaland delayed planting dates, normal and reduced seed-ing rates, and different fertility levels . These levelswere obtained via long-term crop rotation and fertiliz-er treatments at university operated research farms nearSutherland, Castana, and Kanawha, IA . Past crop rota-tions differed from continuous maize to maize-oats-meadow, and fertilization practice involved applica-tions of different amounts of nitrogen, phosphate, ormanure. Long-term averages of oat-grain yields wereused to determine the specific land areas that wouldprovide a wide range of environmental productivities .

Oat lines from Sets I, II, and III were evaluated inaugmented design experiments . An augmented designwas initiated by random division of set of lines intogroups with equal numbers . A common set of checkswas added to all groups of lines . For field evaluation,the groups of a set were assigned randomly to blocksin the experimental area, and lines within a group,including the checks, were assigned randomly to plotswithin a block. Each experimental line was tested ina single plot within an environment, whereas checkswere replicated a number of times equivalent to thenumber of blocks in the experiment . Data from checkswere analyzed to provide an estimate of error variance,and means of all checks were used to adjust grainyields of experimental lines grown in different blocksto a common basis . The 200 lines in Set I were dividedinto 2 sets of 100 lines each, and each set, along with20 checks, was tested in 14 environments . Set II with480 lines was divided into 4 sets of 120 lines each,and each set, along with 24 checks, was tested in 15environments. The 400 lines in Set III were dividedinto 4 groups of 100 lines each, and each set, alongwith 25 checks, was tested in 12 environments . Set IVof oat lines was tested using a randomized block designwith 3 replications in each of 30 environments .

Table 1 . Productivity levels (q/ha) for selection andevaluation environments

65

In all experiments, a plot was a hill sown with 30seeds (except for the seeding-rate environments), andhills were sown 30 cm apart perpendicularly . Two rowsof hill plots were sown around the perimeter of eachblock of hills to provide competition for the peripher-al plots . The environments involving variable plantingdates and seeding rates were grown in a high produc-tivity environment near Ames . All experiments werehand weeded and sprayed with a fungicide (Maneb) asnecessary to control foliar diseases .

At maturity, culms from a plot were harvested atground level, dried, and threshed, and grain yield wasrecorded for the plot .

Selection strategies

The productivity index for an environment wasobtained by computing the mean grain yield of allchecks grown in that environment . Based on produc-tivity indexes, environments used to test a set of exper-imental lines were ranked from low to high . Differentgroups of environments were used for the selectionand the evaluation phases of the study. Environmentsused for the selection phase represented high and lowextremes in productivity, whereas environments usedin the evaluation phase spanned the entire range, withmore or less equal increments of productivity (Table1) .

Three selection strategies based on grain yield wereused for each set of oat lines : uniform, shuttle, and ran-dom. Three successive cycles of selection were used for

Set Productivity levelHigh LowH1 H2 H3 H4 L1 L2 L3 L4

Set 1 29 34 26 8 21 16Set 11 24 22 23 19 15 10Set III 31 33 32 14 15 12Set IV 39 36 37 38 19 14 10 21

Evaluation environments

Set I 32 28 25 21 17 10Set II 25 24 23 21 20 16 13 10Set III 33 33 29 27 26 26 26 25 22

22 19 18 16 15 13 11 9Set IV 41 38 35 33 29 25 22 19 15 12 8

66

Table 2 . Three-phase sequential selection procedures

Sets I, II, and III, and four cycles for Set IV. For uniformselection, lines were tested and selected sequential-ly in three successive high- or low-productivity envi-ronments. For shuttle selection, lines were selectedand tested sequentially for three successive cycles inalternating high- and low-productivity environments .A fourth cycle was added to each selection strategy forSet IV by continuing the appropriate uniform alternat-ing selection patterns . Selection strategies were desig-nated as follows : uniform high (HHH), uniform low(LLL), shuttle high (HLH), and shuttle low (LHL) . Arandom sample from each set of lines was includedto provide a basis on which to measure progress fromvarious selection strategies .

The random samples of lines from Sets I, II, and IVconsisted of 12.5% of the lines within sets. For Set III,line mean grain yields across all environments wereranked from low to high ; the array was divided intodeciles, and 12.5% of lines in each decile were chosenrandomly. Thus, Set III was represented by a stratifiedrandom sample .

To illustrate how selection was achieved, we willuse Set II and the uniform-high selection strategy(Table 2). Grain yields (after adjustment for blockdifferences) of the 480 entries tested in a high-productivity environment were ranked from high tolow, and the 50% with highest grain yields (i .e ., 240)were selected. These selected entries were ranked forgrain-yield performance in a second high-productivityenvironment and again, the 50% with highest yields(i .e ., 120) were selected. Selected lines surviving thesecond cycle were ranked for grain-yield performancein the third high-productivity environment, and the50% with highest yields were chosen . Thus, after threecycles of HHH, 12 .5% (i .e ., 60) of the Set II linessurvived. Lines from Set II were selected similarly bymeans of uniform low, shuttle high, and shuttle lowstrategies . When all selection strategies had been com-

pleted on Set II, 300 entries (60 from each of fourselection strategies plus 60 in a random sample) wereavailable for evaluation. But, the 300 entries did notrepresent 300 unique oat lines because a given linemay have been selected by two or more strategies . Theselection strategies were applied similarly to the othersets of lines .

Line selection was based on individual plot datawithin each environment for Sets I and II . Individualplot data were used for the first two cycles of selectionin Set III, and the third cycle was based on mean per-formance in two environments . Line means from threereplications were used in each selection environmentfor Set IV.

The checks represented currently available varietiesfor yield and stability .

Evaluation of selected oat lines

Selected and random entries and checks were eval-uated in hill plots for grain yield . Entries from SetI were evaluated in six environments using an aug-mented design ; those from Set II were evaluated ineight environments using an augmented design ; thosefrom Set III were evaluated in 17 environments usinga randomized block design with two replications ; andentries from Set IV were evaluated in 11 environmentsusing a randomized block experiment with three repli-cations. Evaluation experiments were conducted onexperimental farms at several locations in Iowa overthe period 1972 to 1982 .

Statistical methods

Grain-yield data from the evaluation experiments wereanalyzed for yield characteristics, according to themethod of Eberhart & Russell (1966) . An indepen-dent estimate of productivity for each environment wasbased on the mean of all checks . The model for thegrain-yield analysis is :

Yjk = p + di+ Ej +i3 zi + bij + eijk

( 1 ~

where Yijk is the yield of the ith genotype in the k threplicate in the jth environment, p is the mean, di isthe genetic contribution of the ith genotype, Ej is thecontribution of the jth environment, /3 is the linearregression coefficient for the ith genotype, zi is theenvironmental index based on check means, 6 is thedeviation from regression, and eij k is the residual vari-ation of the ith genotype in the kth replicate of the jth

environment (Perkins & Jinks, 1968) .

AbbreviationUniform HHH H1- H2-+ H3-+

LLL L1-* L2-+ L3-

Shuttle HLH Hi-* L2-+ H3-.LHL Li-* H2-. L3-

Lines retained 50% 25% 12.5%Random sample 10%

Because evaluations of entries from Sets I and IIwere conducted in augmented design, analyses of datafrom the checks replicated within environments wereused to provide an estimate of error mean square (Fed-erer & Raghavarao, 1975) . The statistical model forthis analysis :

Yijk = p + d;+ Ej +d Eij +etfk ,

(2)

where the factors in the formula are as defined, withthe addition of dEij, which is the interaction of theith genotype in the j th environment. In analyses forboth replicated and augmented designs, the repli-cate/environment and the group/environment blockeffects were included in the error mean square .

Entry mean square was tested against entry-environment mean square to determine degree of vari-ation among entries selected with a given strategy. Thepooled sum of squares for environmental plus geno-type x environment was partitioned into environment(linear), entries x environment (linear), and residual .The genotype environment (linear) mean square repre-sents the magnitude of variability among regressionsof entries and was tested against the residual meansquare. The mean square for residual, which measuresdeviations from regression, was tested against the errormean square . Regression slopes of individual entriescould be tested for deviations from 1 .0, and deviationsof yields of a genotype from regression could be testedfor deviation from zero .

Results

Entry performance

Variation among entries in the evaluation experimentwas significant for all selection strategies within allsets of oat lines except for shuttle-low and shuttle-high lines of descent in Sets IV and I, respectively(Table 3). Selection reduced variation among entrieswith all strategies in all sets, except for the LLL andLHL in Set III . Generally, there was less variabilityamong entries in selection strategies involving two ormore high-productivity environments than there was instrategies involving two or more low ones .

All selection strategies increased grain yield sig-nificantly (Table 4), but means from the various selec-tion strategies within sets showed significance amongselection strategies (Table 5) . Most variation amongstrategies was due to random sample vs . selectionstrategies, but there was a significant linear trend of

Table 3 . Mean squares among grain yields of checks and entriesfrom four sets of oat lines following five selection procedures

* F test significant at 5% level .

Table 4. Means for grain yields (q/ha) of checks and entriesfrom four sets of oat lines following five selection procedures

a Means within set with the same letter are not significantlydifferent at the 5% level of probability .

increased grain yield associated with number of high-productivity environments used in a selection strategy .When averaged across sets, HHH and LLL gave thegreatest and the least gains in grain yield, respective-ly, with the shuttle strategies giving intermediate gains(Table 4). Thus, the magnitude of gain in grain yieldwas related to the number of high-productivity environ-ments in a selection strategy, regardless of whether thestrategy was uniform or shuttle. Over all sets, LLL gavean 8.1% gain in grain yield, and the successive substi-tution of a high- for a low-productivity environmentadded a gain of about 1 .4% from selection (Table 6) .On average, high-productivity environments increasedgains 1 .5 times more than did low ones .

Uniform and shuttle strategies produced nearlyidentical mean gains in grain yield when evaluatedover the entire range of environments . But entriesselected as a result of each procedure, on the basisof theory, would be expected to differ in reaction to

67

Set Selection procedureUniform Shuttle Random ChecksHHH LLL HLH LHL

Set 1 48* 55* 35 59* 86* 65*Set 11 62* 68* 68* 77* 110* 208*Set 111 93* 174* 79* 159* 149* 41*Set IV 50* 56* 47* 29 216* 171*

Mean 64 88 57 81 140 121

Set Selection procedureUniform Shuttle Random ChecksHHH LLL HLH LHL

Set I 26.8a 25 .7b 26 .6° 26 .1b 24 .3 c 22.2dSet II 27 .5° 26 .06 26.9ab 26 .5ab 22 .9° 19 .1dSet III 23 .7ab 23 .5ab 23 .9° 23.4b 22 .6 c 22.0dSet IV 27 .5a 26.3a 26 .9a 27.4° 24 .4b 22.9c

Mean 26.4° 25.4d 26.1b 25.8c 23.6e 21 .6f

68

Table 5. Analysis of variance for grain yield means fromfour sets of oat lines following five selective procedures

* F test significant at the 5% level of probability .

Table 6. Percentage increases in grain yield of oat linesrelative to the random sample, following four selectionprocedures when evaluated in low-, intermediate-, andhigh-productivity environments

low-, medium-, and high-productivity environments .To examine this possibility, mean grain yields werecalculated for selected oat lines from the four strate-gies and the random sample after evaluation in low-, intermediate-, and high-productivity environments .The LLL and LHL produced the lowest gains in allproductivity levels (Table 6) . Contrary to the overalltrend, however, oat lines selected by HLH produced2.1% more, 0 .8% more, and 1 .3% less gain than didthose selected by HHH when tested in low-, medium-,and high-productivity environments, respectively.

Another method for comparing efficiencies of thefour selection strategies involved examining the num-ber of selected lines with mean grain yield superior tothe random sample median . From 76 to 90% of linesselected by the four selection strategies were above themedian of the random sample (Table 7) . Further, thepercentages of lines significantly greater than the meangrain yield of the random sample for the various selec-

Table 7 . Percentages of selected entries and checks with grainyield means exceeding the random sample median from foursets of oat lines following five selection procedures

Table 8. Percentages of selected entries and checks from four setsof oat lines with grain yield mean significantly (0 .05 level) greaterthan (or less than) the random sample mean

tion strategies ranged from 50 to 58% (Table 8) . Only2 to 6% were significantly lower .

All four selection strategies produced gain in grainyield of oats. HHH was the most effective selectionstrategy for high-productivity environments, and theHLH was the most effective for low- and intermediateproductivity environments . But the magnitude of gainfrom selection overall was related directly to the num-ber of high productivity environments in a selectionstrategy .

Response to environmental conditions

Mean squares for heterogeneity among entry regres-sions, which measure the abilities of genotypes torespond in terms of grain yield to improved environ-ments, were significant for all selection strategies inSets II and III, for three of four strategies in Set I, andfor none of four in Set IV (Table 9) . In all sets, exceptin Set II, variability due to heterogeneity of regres-

Source Degrees of Mean F-valuefreedom squares

Sets 3 9 .5 27 .5*Selection strategies 4 5 .1 14 .7*

Random vs. strategies 1 18 .2 53 .0*Linear 1 2 .0 5 .8*Residual 2 0 .1 3 .3

Error 12 0.03

Set Selection procedureUniform Shuttle Random ChecksHHH LLL HLH LHL

Set 1 44(4) 28( 4) 36(0) 32( 8) 20(20) 5(30)Set II 80(0) 62( 3) 70(2) 67( 2) 23(28) 0(20)Set III 34(2) 44(10) 40(4) 42(12) 18(18) 2(10)Set IV 73(0) 67( 0) 73(0) 93( 0) 33(33) 13(40)

Mean 58(2) 50( 4) 55(2) 58( 6) 23(25) 5(25)

Set Selection procedureUniform Shuttle Random ChecksHHH LLL HLH LHL

Set 1 84 76 96 80 50 35Set II 95 82 93 87 50 0Set III 66 60 72 60 50 28Set IV 100 87 100 100 50 29

Mean 86 76 90 82 50 23

Environmentalproductivitylevel

Selection procedureUniform ShuttleHHH LLL HLH LHL

Low 9.2 5 .7 11 .3 7 .1Intermediate 11 .0 8 .9 11 .8 10 .2High 14.0 5 .9 12.7 8 .7

Average 12 .3 8 .1 11 .1 9 .8

sions was less for selected lines than for correspondingrandom lines (Table 9) .

The means of regressions for all selected samplesexceeded corresponding random sample means (Table10). Entries selected with HHH and HLH were moreresponsive than those selected with LLL and LHL .

Each group of selected entries, regardless of theselection strategy used, had a higher percentage ofregression coefficients significantly greater than 1 .0,than did its corresponding random sample (Table 11) .The proportions of entries with regression coefficientssignificantly greater than 1 .0 was from 0 to 32% greaterin the selected samples than in corresponding randomsamples; the greatest increase in responsiveness wasfrom strategies with two or more high-productivityenvironments .

Shuttle- and uniform-selection strategies did notselect oat lines with different response patterns of grainyield to improving environments. But strategies with

two or three high productivity environments resultedin groups of oat lines with higher regressions than didstrategies with two or three low ones .

Production stability

The mean squares for deviations from regression (Sd)were significant for all selection strategies in all sets ofoat lines (Table 12). In Sets II, III, and IV, the devia-tion mean squares tended to be larger for the selectedsamples than for the corresponding random ones, butin Set I, the converse occurred .

Discussion

Shuttle selection involves propagating and selectingplant genotypes alternately in environmental extremesto exert dual selection pressure, e .g ., selection for highgrain-yield in both nonstress and stress environments .

69

Table 9 . Mean squares for heterogeneity among regressionsfor checks and entries from four sets of oat lines following fiveselection procedures

Table 11 . Percentages of regression coefficients significantlygreater than 1 .0 for checks and entries from four sets of oatlines following five selection procedures

Set Selection procedure Set Selection procedureUniform Shuttle Random Checks Uniform Shuttle Random ChecksHHH LLL HLH LHL HHH LLL HLH LHL

Set 1Set 11Set IIISet IV

Mean

25

33*26*

38*54*

41*30

29

34

35

28*34*46*32

35

30*36*49*21

34

53*

41*29*

49*59*

2465*

22

51

34

Set 1Set 11Set 111Set IV

Mean

28

2060

3236

3640

33

41

30

28503633

37

12

12

043

28

1632

28

1253

20

7

35

22

9

* F test significant at 5% level .

Table 10. Means of regression coefficients for checks andentries from four sets of oat lines following five selection pro-cedures

Table 12 . Deviations from regression mean squares for checksand entries from four sets of oat lines following five selectionprocedures

Set Selection procedureSet Selection procedure Uniform Shuttle Random Checks

Uniform Shuttle Random Checks HHH LLL HLH LHLHHH LLL HLH LHL

Set 1 26 .1* 22 .0* 31 .6* 28 .4* 35 .9*

13.7Set 1 1 .22 1 .16 1 .35 1 .14

1 .09

1 .00 Set II 35 .5* 32 .8 32 .9* 37 .0* 23 .6*

17.5Set II 1 .56 1 .38 1 .47 1 .42

1 .27

1 .00 Set III 30 .0* 33 .4* 28 .9* 30.8* 28 .6*

19.6*Set III 1 .15 1 .17 1 .15 1 .15

1.10

1 .00 Set IV 28.0* 29 .0* 32 .6* 30.4* 26 .3

11 .9Set IV 1 .14 1 .11 1 .12 1 .17

1.02

1 .00Mean 29 .9

29.3 31 .5 31 .6 28 .6

15.7Mean 1 .27 1 .21 1 .27 1 .22

1.12

1 .00

70

Lu et al. (1967a, b) tested segregating soybean linesin both spring and summer in Taiwan and selectedthose adapted to both seasons. Borlaug (1968) used`shuttle breeding' to select high-yielding and broadlyadapted wheat cultivars . Choo et al . (1980a) applied`environmental segregation', another form of shuttleselection to barley, and found that lines from shuttlepropagation had greater genetic variability than, andtraits different from, those from uniform selection .

In our comparison of the effects of uniform andshuttle selection on the grain-yield characteristics ofselected oat lines, (a) the mean grain yields for linesselected via uniform and shuttle strategies were simi-lar, (b) HHH selected lines gave highest grain yieldsin high-productivity environments and HLH selectedlines gave the best grain yields in intermediate- andlow-productivity environments, (c) equally responsivelines were selected via HHH, HLH, or LLL and LHL,and (d) stability of production was changed little byselection strategy.

Intuitively, success of shuttle selection shoulddepend upon two factors: (a) the use of a germplasmpool encompassing genotypes with broad adaptationand (b) the use of selection environments eliciting dif-ferential responses from plant genotypes . The wheat-breeding program of Borlaug (1968) met these con-ditions . He used convergent hybridization with wheatlines from around the world to develop a broad basedpopulation, and his shuttle breeding program madeuse of two distinctly different environments . Lu et al .(1967a, b) and Tsai et al . (1967) hybridized soybeanlines adapted to spring and summer to form a gene poolfor selection, and segregates were tested in both springand summer. Although both studies produced varietiesthat were high yielding and widely adapted, no con-trol or check sample was used to determine actual gainfrom selection .

St-Pierre et al . (1967) and Choo et al . (1980a, b), incontrast, used a narrow germplasm pool, i .e ., one singlecross of barley. They did include stably propagatedsamples, but their selection environments were withinthe normal range of crop production .

Our gene pool originated from mixing F2 seedsfrom 250 crosses with many parents adapted to Iowaand many parents from other oat growing regions ofthe world. Thus, ours was a broad-based gene pool .Four sets of oat lines from this complex gene poolwere used as `biological replications' to provide inde-pendent estimates of the effects of uniform and shuttleselection . And selected entries were evaluated over thenormal range of oat-production conditions in Iowa .

Certainly, the gene pools of wheat used by Borlaug(1968), of soybean used by Lu et al . (1967a, b) andTsai et al . (1967), and of oat used by us, had the geneticpotential to produce high-yielding and broadly adaptedgenotypes . The barley gene pool used by St-Pierre etal. (1967) and Choo et al . (1980a, b), however, wasfrom one single cross, Star x M.C. 2950, and thusmight have been too narrow to produce broadly adapt-ed, high-yielding lines . Because we used a broad basedgene pool, this factor probably was not responsible forour obtaining quite similar results from shuttle- anduniform-selection strategies .

We probably did not get differential results fromshuttle and uniform selection because of the inade-quacy of our shuttle strategies . The high- and low-productivity selection environments used representedthe high and low extremes of Iowa oat yields . Butthe selection environments used for one set of oatlines all occurred in one year, and all sites were with-in Iowa. Thus, yearly variation and wide geograph-ic dispersion among test sites did not contribute toenvironmental diversity. In fact, pooled correlationsfor oat-grain yields of checks among high-productivityenvironments, among low productivity environments,and between high- and low-productivity environmentswere quite similar at 0.28, 0 .26, and 0 .23, respectively .These correlations show that all environments, whetherwith high or low productivity, elicited somewhat sim-ilar responses from the oat checks. Thus, the diversityamong environments in our study was probably notgreat enough to achieve the adaptive selection pressureassociated with the shuttle selection strategies used atCIMMYT and in Taiwan .

We found evidence for differential response fromselecting oats via HLH and HHH. Mean grain yieldof lines selected via HLH, when evaluated in low- andintermediate-productivity environments, were, respec-tively, 2.3 and 0 .8% superior to entries selected viaHHH. This result indicates that, as expected, the linesselected by the shuttle-high strategy were adapted espe-cially to low- and intermediate-productivity environ-ments .

Our study did show the relative worth of low-and high- productivity environments for making gainsfrom selection for grain yield of oat. The four selec-tion strategies differed by the sequential substitutionof three high-productivity environments for three low-productivity environments . That is, LLL, LHL, HLH,and HHH schemes gave a sequence of zero, one, two,and three high-productivity environments, respective-ly. The mean gain in grain yield from selection with the

LLL strategy was 8 .1 % of the unselected populationmeans, and each substitution of a high-productivityenvironment for a low one added 1 .4% to the gainfrom selection . This result suggests that a high produc-tivity environment was 50% more valuable than a lowone for selecting for grain yield .

References

Adegoke, A.O. & K .J . Frey, 1981 . Production traits of oat strainsfrom disruptively and non-disruptively propagated bulk popula-tions in oats. Egypt . J . Genet. Cytol . 10 : 199-212 .

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