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Genotype x environment interactions Challenges and opportunities for plant breeding and cultivar recommendations ISSN 0259-2517 Geno pe x environment interaction Challenges and opportunities for plant breeding and cultivar recommendations by Paolo Annicchiarico Senior Scientist lstituto Sperimentale per le Colture Foraggere Lodi , ltaly FOOO ANO AGRICULTURE ORGANIZATION OF THE UNITEO NATIONS Rome, 2002 FAO PLANT PRODUCTION ANO PROTECTION PAPER T h :l.s One 1111 ~ 1 ~11 111 ~~ llll ílll 111111 1~ 7CFY- FRE- FOJC Material com-.direil~u~1& The designations employed and lhe presentation oi material in this inlormalion product do not imply the expression oi any opinion whatsoever on lhe part oi the Food and Agricultura Organlzation oi lhe United Nations concerning lhe legal status oi any country, territory. city or area or oi íts authon'ttes, or concerning the delimilalíon oi its lronliers or boundaries. Thc n:fcrcnce 10 1hé differcnt co1nputcr soft"an: pro~run~' includcd 1n 'Cttions 4.5. 5.9 and 7.3 of thi<. publiea11on i~ for info1mn1ion purposcs onl)•. Tbe cboicc or i1c~ does o()( c:on,1i1u1c an cnd01scmcn1 by FAO or thc 1nanufacturer~ Of tbese co1npu1er software produ b or OÍ lhe J>'OOUCI 1hemse1' es. ISBN 92·5-104870·3 Ali righls reserved. Reproduction and dissemination oi material in this lnlormalion product lor educational or other non·commerclal purposes are authorized without any prior written permission from lhe copyright holders provided the source is fully acknowledged. Reproduction oi material in lhis inlormation producl for resaleorothercommercial purposes is prohibiled w1thout \Ynllen permission of lhe copyright holders. Applicatlons for such permísslon should be addressect to lhe Chiei, Publishing Management Service. lnlormatlon Division. FAO. Viale delle Ter medi Caracalla, 00100 Roma, llaly or by e-mail to copyright@lao.org © FAO 2002 Ma1er ai com dire tos au•orais Foreword 'fhe introducti.on of irnprovcd varieties is one of the n1ost po\vertul and cost-efficient mcans of enbancing crop productivity and farmers ' incomes. Efficicncy in varietal development itself aod in lhe proce.ss of matching varicties to production areas in1plies an understanding of plant responses to diverse environrnents and cropping syste1ns in a target production zone. M.ultiJocation testing remains the n1ain tool for underscanding varietal responses to environments, but t11e process is both tin1e-consuming and expensive. 1·he efficiency of this analytical process can be enhanced using recently developed statistical methods. 1'his publication airns to support plant brccders by cxa1nining the opponunities offered by such n1ethods. FAO hopes that this publication v.1ilJ be useful to a 'vide variety of persons intcrcsted in efticient, sustainable use of plant genetic resources, especially those focusing on the improvemeot of agriculture in food-deficit. developing countries. f ollo\ving introductory ren1arks on the impact of genotype x environ1nent interaction on agricultura! production and plant breeding (Chapter 1 ), adaptaüon and yield stability concepts are discussed in relation to breeding and the utilization of crop varieties (Chapter 2). The potential usefulness of multi-environment yield triais is also exan1il1ed (Chapters 2 and 3). ·recllniques re.lating to analysis of variance (Chaptcr 4) and n1odelling of adaptation patten1s (Chapter 5) are considered for oplimizing variety recommendation and for defining the adaptation strategy and yield stability targets in breeding progr-dn11ncs. Attention is paid to limits and opportunities for the scaling-up of results tron1 test sites to the target region. ·rhe application of selection theory to t11e analysis of 1nulti-environ1nent data as \vell as additional indications that rnay be obtained fro111 adaptive traits are also considered (Chapter 6). Concepts and n1easurcs of yit.:ld stability and yield reliabi liry, and their utilization for selection and recomn1endation of plant varieties, are highlightcd in Chaptcr 7. Lnfonnation on useful soft,vare for data analysis is provided throughout the book with special e1nphasis on IRRISTAT, a freely-available software program developcd by thc lntematio.nal Rice Research Institute. The book also presents a case study (Chapter 8), in \Vhich a large n1ulti-cnvironn1ent data set is used for exen1plifying the different analytical procedures, as \vcll as the LRRISTA'f con11nands needed for the analysis. Eric A. Kuene1nan Chief Crop and Grassland Service Plant Protcction Division iii Material com direitos autorais Acknowledgements The revision of some parts of the text by Dr Kayc E. Basford (University of Queensland), Dr Hugh O. Gauch (Comell University) and Dr Mike 'íalbot (Biomathematics & Statistics Scotland), and the suggestions made by Dr Manjit S. Kang (Louisiana State University) and Dr Bruce Walsh (University of Arizona) on specific aspects of the \VOrk, are appreciated. Dr Kamel Feliachi {lnsrin1t Technique des Grandes Cultures, Alger) and Dr Alice Pcrlini (lstituto Agronornico per l'Oltremare, Florence) are thanked for pem1itting the use of data from the AJgerian-ltalian cooperation project "Ainélioration et renforcement du systerne nationale d' adaptation variétale du bié dur en Algérie" in the case study. The lntemational Rice Researc.h lnstitute (IRRl) is thanked for perrnitting the use of tbe software IRRISTAT and its tutorial material and for allO"-'jng its distribution in thc CD attached to this publication. Dr C. Graham McLaren and Ms Violeta Bartolon1e (IRRI) provided infonnation related to the soflware. The excellent work ofMs Ruth Duffy in the editing and formatting ofthe text is greatly appreciated. Dr Helena Gómez Macpherson (Cereais Officcr, Crop and Grassland Service, f AO) made possible the prepardtion and release of this publication. IV Material com direttos autorais Table of contents List of abbreviations ., Vil l .ist of t a b les ••• V I U List of figures X 1 . l nt r oduction 1 1. 1 Backw-ound l l.2 Genorype and cnvirorunent 2 1.3 Ele1nents of a breeding strategy 3 2. Adaptation and yield stabiJity 5 2. 1 Analysis of adaptation 5 2.2 Definition of adaptation strategies 7 2.3 Wide vs. specific adaptation in breeding programmes 9 2.4 Targeting varieties 10 2.5 Assessment of yield stability and rcliability 11 2.6 Two anal}'1ical flov•-charts 12 3. Multi-environment yield triais 17 3. 1 Types of triais and rcquirements of the generated infonnation 17 3.2 Additional infonnation on environmental factors and genotypic traits 19 4. Analysis of variance (ANOVA) and estimatioo of ' 'ariance components 21 4.1 ModelsofANOVA 21 4.2 Estimation of individual effects and con1parison of mcans 25 4.3 Estimation of variànce cornponents 26 4.4 Data transfom1ation 27 4.5 Computer sofuvare 29 s. Analysis of ada~tation and idcntification of subr~ions 31 5 .1 Objcctives of the analysis 31 5.2 Joint linear regression modelling and complementary analyses 32 5.3 AMMl modelling and complementary analyses 35 5.4 Factorial regression modelling and comple1ncntary analyses 44 5.5 Panem analysis 49 5.6 Data transfonnation 51 5.7 Unba)anced data sets 55 5.8 Characterization of subregions and scaling·uE of results 56 5.9 Computer software 61 V Material com direitos autorais 6. Definition of adaptation strategy, selection environmcnts, genetic resources 67 aod adaptive traits 6.1 Adaptation srrateg)· 67 6.2 Seleccion environments 71 6.3 Gcnl!tic resources and adaptive traits 73 7. l\•leasures of yield stabilit)· and yield reliability 79 7 . 1 Yield stability 79 7 .2 Yiel<l reliability 83 7 .3 Con1puter soft'"'are 86 8. Case study:durun1 wheat in Algeria 89 8. 1 Experirncnt data 89 8.2 Adaptation stratcgy and yield stability targets for breeding 91 8.3 Cultivar rccommcndation 96 Referenccs 105 vi Material com direitos autorais A~1MI ANO\ 'A BLUP CG.IAR CINIMY1' CP DF GIS GE GL Gl.-Y GY lCARDA IRRI MS PC R.EML ss List of abbreviations additive n1ain effects and multiplicative interaction ana lysis of variance Best Linear Unbiased Prediction Consultative Group on lntemational Agricultura! Research lnternational Centre for Maizc and Whear l1nprovc1i1ent main crossover point degrees of freedorn Geographic Jnfom1ation System genotype x environ1nent genotype x location genotype x location x ye.ar genotype x year lnten1ational Centre for Agricultura! Research in the Dry Arcas lnternational Rice Research lnstitute 1nean square principal con1poncnt Restricted Maxi1nu1n Likelihood sum of squares ' vii Material com direitos autorais List of tables 2. 1 Co1nplexity of adaptation pattems as depicted by the number of signiticant PC axes in 7 lhe analysis of GE and GL data n1atriccs of six data scts 3. 1 Mcan yield of brcad \Vheat varietics ovcr two years and over four otber years in triais 18 repeated across a fixed set of locations. for hvo independent data sets 4. 1 ANOVA n1odels including the factors G = genotypc and L = location or environrnent, and 23 destimation of variance con1ponents, for triais in a randon1ized con1plete block design 4.2 ANOVA 1nodels including thc factors G = genotypc, L = location and Y = year, and 23 estin1ation of varíance components, for triais in a randornized con1plete block design repeated in sa1ne years (i.e. L and Y crossed factors) 4.3 ANOVA rnodcls including rhe factors G = genotype, L = location and Y = year, and 23 esti1nation of variance co1nponents, for triais in a randomized con1plete block design repcated in different years in cach location 4.4 Analysis of variance for 18 brcad \Vheat varieties grO\\tn for three years in 31 ltalian 24 locations, \Vith partitioning of G L, interaction by: J) joint regression analysis; 2) AMMl analysis; 3) definition of four subregions 4.5 Calculation of genotype and location 1nain effects, and GL interaction effects, frorn 25 n)ean values of genotypes at each location 4.6 Values of t · for calculation of Du11nett 's one-tailed ( or Gupta 's) n1ultiple co1nparisons of 26 the top-ranking genotype \Vith the re1nainiag eurtries 4. 7 Relationships of environrnent tnean yield \Vith experin1ental error, and of location 1nean 28 yield \Vith with~in-location phcnotypic variance or standard dcviation of gcnot)1pe valucs, '''ithin-location G Y intcraction mean squarc, and avcragc ""ithin-location phenotypic variance of annual yield values for individual genotypes, in different data sets 5. l Major differences bef\veen f\vo possible objectives tor analysis of adaptation 31 5.2 Mean yield of four genotypes in four locations and across Jocations, rnean yield of locations 52 and within-location phenotypic standard deviation of genotype rnean yields 5.3 Squared Euclidean distance betv.ieen four locations based on genotype means in each site 52 or gcnotypc x location intcraction cffccts 6. 1 Predicted yield gai11 over the r.egion of lo\vland northern ltaly fron1 selection of luceme 69 populations pecifically adapted to three or t\vo subregions relative co selection for wide adaptation viii Material com direttos autorais 6.2 Mean yield of barley lines previously selected for specific adaptation to either of l\VO 70 subregions or for wide adaptation to thc region of northern Syria, yield ratio of specific to \Vide adaptation strategies, and actual yield gajn relative to mean valuc of six control cultivars 7 .1 Criticai values ofl-Iartley's ( 1950) test for con1parison of severa! variances, applicablc to 83 cornparison of yield stability 1neasures of Type 4 8.1 Code, altitude, mean yield, sca.led score on the first GL interaction PC axis for original 90 and log10-transtorrned yield data, \ViJJter mean temperature and rainfall amount of test sites 8.2 ANOVA results and estimate of variance components, for original and transfonned yield 92 of 24 genotypes gro\vn at 14 locations for ~·o years 8.3 Comparison of analytical n1ethods for definition of t\vO subregions for durum wheat 94 breeding in Algeria. Predicted yield gain over the region rrom a specific adaptation strtltegy relative to wide adaptatíon 8.4 Mean yield. environmental variance and Kataoka's índex of yield reliability across 97 environments, Type 4 stability variance slope of regression on site mean yield, scaled score on thc first. GL interaction PC axis, and factorial regression equation for estin1ating GL effects, for eight genotypes ix Material com direitos autorais List of figures 2.1 Lirnited and large extcnt of \i/itJ1in-location variation relative to bet\A.•een-location variation 6 for a n1ajor environn1ental factor related to the occurrence of GE interaction, and its implication on the extent of GE interaction co1nponents of variance 2.2 .A.. specific adaptation strategy irnplying plant brecding at one national research centre, 9 sclect ion of novel gem1plasn1 and evaluation of genetic resources in distinct subregions, and subsequent phases of farn1er.s' selection in each subregion 2.3 Flo\v chart of stcps for definit ion of the adaptation strategy and yield stabiLity targets of 13 breeding progran1mes fron1 analysis of multilocation yield triais repeated in tin1e 2.4 Flo\v chart of steps for 1naking variery recon1n1cndatio11s from analysis of rnultilocation 15 yield triais repeated intime 5.1 Genotype yiclds 1nodelled by joint rcgression and nominal yields u1odclled by factorial 34 regression oo rainfall amount. for dun1n1 \vheat varieties across ltalian locations - 5.2 AMMI analysis of the genotype-locarion data 1natrix in Tablc 4.4 36 5.3 Scores on the first ru•o GL interaction PC axes of 18 bre-ad \Vheat genotypes and four 37 ltalian subrcgions 5.4 No1ninal vields of Jucerne varieties modelled as a function of lhe score on thc first GL 40 • interactjon PC axis of ten ltalian loc~1cions, or the first GE interaction PC axis of four artificial environments 5.5 Scores on thc first two Gf. interaction PC axcs of 20 Jtalian locations, dcfinition of four 41 subregions for brcad \vheac variety rccommcndation by grouping sites \Vith the sarne expected \\1inning genotypc, and classification of locations into five groups based on cluster analysis of site scores on PC 1 and PC 2 5.6 Cluster analysis of test locations performed on site scores on lhe first GL interaction PC 43 axis, using the lack of significant G L i11teraction \Vithin group of locations as the truncation criterion for definition of groups 5. 7 Geographic position oftest locations for lucemc in northcm ltaly, and provisional definition 43 of three subregions for a specific adaptation strategy bascd on AMMI analysis cornplcn1cnted by clusrcr analysis and additional infornlation on relevant environmental variables of sites 5.8 Yield response of two bypothetical genotypes modelled as a function of site mean yield 53 for original. log-transformed anel within-location standardized data under the assu1nption of \vithin-locat ion phenotypic standard deviation of genotype yields proportional to site rnean yield X Material com direttos autorais 5.9 Non1inal yields of genotypes 1nodelled as a function of the first GL interaction PC axis of 54 locations for original and log-transforrned data, \Vith indication of scaled PC 1 scores of sites a11d cstirnation or the main crossover point 5. 1 O Expectedpair of top-ranking cultivars for yield at each combination of J O annual rainfall 58 by 10 rnean \vinter ten1perature values of locations. according to an AMMI- 1 rnodel in \Vhich site score on PC 1 is predicted by 111ultiple regression as a ftinction of the tv.io environ1ncntal variables 6. 1 Hypothetical selection locations for thrcc adaptation strategies cornpared in ter1ns of 68 predicted yield gains 7 .1 Yield rcsponses across fíve environmcnts of hypothetical stable-yielding genotypes 80 according to m•o concepts of stability, using non-regression or regression stability measurcs 7 .2 Frequency of yield ( or relative yield) values across environn1ents of t\vo genotypes having 84 sarne mean yield and contrasting stability as mcasurcd by the variance of yield values, and estin1ation of a yield reliability index equal lo the lo\vest yield that is expected in 75% of cases 8.1 Geographic position of te-st locations and definition of t\vO subregions for a specific 89 adaptation strategy base-O on patten1 analysis and AMMJ + cluster analysis results 8.2 Non1inal yield and non1inal yield reliability as Joy,1est yield expected in 80% of cases, of 98 eight cultivars modelled as a function of the scores on the first GL interaction PC axis of 14 locations 8.3 Expected pair oftop-ranking cultivars for yield at each con1bination of 10 annual rainfa ll 99 by l O n1ean \Vinter ten1perattu·e values of locations, according to a rv.10-covariate factoria l regression 1nodel 8.4 Expected pair of top-ranking cultivars for yielcl reliability at each combination of 1 O 103 annual rainfa ll by l O mean \vinter te1nperature values of locations, according LO a tv.10- covariate tactorial regression rnodel xi Material com direitos autorais 1. lntroduction 1.1 BACKGROUNO United Nations projections estimate that the world population will continue to gro\v from thc current 6 billion to about 1 O billion by 2050 (FAO, 1996). Thc incrcasc in population and the subsequent rise in the demand for agricuJtural produce are expected to be greater in regions where production is already iosuJlicient, in particular in sub-Saharan Africa and south Asia (Pinstrup-Andersen et ai., 1999). The necessary increase in agricultura! production represents a huge challenge to local tànning systems and must come mainly from increased yield per unit area, given the limited scope for extension of cultivated land \VOrldwide (Evans, 1998). The so-callcd Green Revolution - based on the introduction of in1proved varieties with high yield potentiaJ, togetJ1er \Vith technological packages (mineral fertilizers, pesticides, inigation etc.) designe<! to significantly improve the cropping envirorunent - has greatly contributed to the increase in agricultura! production in severa! rcgions world\vide. Further expartsion of this high-input model of agriculture is not sustainable, ho,vever, due to the high costs entailed and the negative impact on natural rcsources (Conv,1ay, 1998; Singh. 2000). ln less favourable areas with poor ecological potential for crop production, \Vhere food insecurity also depends on the marked climatic fluctuations from year to year, the strategy has actually produced only Jov• and unstable economic rcturns (McCo,vn et ai., 1992). Local gennplasn1 is, therefore, often preferred to improved varieties because of its greater tolcrance 10 sevcre biotic and abiotic stresses (Byerlee and Husain, 1993; Bycrlee and Morris .. 1993; Eyzaguirre and l\vanaga, 1996; Tesen1ma and Becbere, 1998; Alinekinders et ai. , 1994). Furthennore, ongoing clin1atic changes may cause tJ1ese arcas to expand in tropical and subtropical regions (Rosenzvveig and Hillel, 1998). Plant breeding can be expected to assun1e a pivotal role in iucreasing tbe availabi lity and stabi lity of agricultura! production in the future, particularly insofar ac; increasing attention will be paid to: • tJ1e sustainability of agricultura] S)'Stems; and • d1e develop1nent of fanning systerns in less fuvourable arcas (Sleper ct ai .• 1991 ; Ceccarelli et ai. , 1992). Ho\vever, national breeding programmes, \Vhich are primarily concemcd \vith thesc objectives, rnay need to modify some elements of their Green RcvoJution strategy to produce (\vhen socio-econo1n ically convenient) improved germplasm capable of maxirnizing the agricultura! potentiaJ of speci fie areas and fanning systems, and of minin1izjng tbe occurrence of crop failures or very low yields in unfavourable years. Jntegration of fanners in the selection process, co1nmonly detined as participatory plant breeding, rnay help fulfil these objectives, in addition to facilitating Lhe adoption of novel gennplasrn (Eyzaguirre aod 1 \Vanaga, 1996; McGuire et ai. , J 999; Weltzien et ai., 1999). ln the long tenn, bioteclmology - if combined v.•ith f onns of international cooperation and intellectual property rights legislation promoting such techniques for national programrnes (f AO, 1999) - may help achieve adaptation and yield stabílity targets, especially by using plant material with increased tolerance to prevailing bioric and abiotic stresses. lmproved adaptation and yield stability may derive in the long tenn from the definition of an appropriatc breeding strategy, and in the sbort tern1 fron1 the appropríate choice of cultivars (vvhether indigenous or foreign, and either traditional or released from public or private breeding institutions). Jdeally, decisions conceming the breeding strategy and crop varieties should be based on scientific knowledge of the plant 1naterial and its relationsl1ip \Vith cropping environments \Vithin the target region. This publication highlights the cootribution that data from n1ulti-environment triais can provide in this respect. Results of studies relative to similar regions world,vide, as \VCll as common sense and practical considerations, may also contribute to the decision-making process, especially '''here there is a lack of more objective infonnation based on experimental data. It is well documented (Cooper and Byth, 1996) that the investigation in breeding progran1mes of adaptation and yield stability have been modest Th.is shortfall exists, Material com dire11os autorais Genotype x e11viró11111e11t i11tere1ctions: challe11[!.es ánd op/)Ortunities for pln11t breetli11g an(/ culrh·nr reco111111entlarions despite the irnportance of these issues, the substantial investment by public and private institutions in 111ulti- envirorunent testing, and the \Vide range of statistical methods available. Furthern1ore, varieties (or other techniques) are rarely recommcnded on thc basis of a thorough assess1nent of adaptation and yield stability characteristics. An inversion of tl1is trend probably requires that ordinary breeders and agronomists be sought as the main users of thcse 1nethods. Relatively sirnple tecluliques applicable through friendJy, inexpcnsive sot1,vare havc an obvious appeal in this context. 1.2 GENOTYPE ANO ENVIRONMENT With regard lo the comparison of plant material in a set of n1ulti-environmcnt yicld triais. tl1e tenn genotype refers to a culti var (i.e. with rnaterial genetically homogcneous, such as pure lines or clones, or heterogeneous, such as open-pollinated populations) rat11er than to an individual's genetic make-up. ·rhe tenn envirorunent relates to the set of clin1atic. soi l, biotic (pests and diseases) and 1nanagen1ent conditions in an individual triai carried out ata given location in one year (in the case of annual crops) or over several years (in the case of perennia.ls). ln particular, an environ111ent identifies a given location-year (annuals) or location-crop cycle (perennials) con1bination in tbe analysis of triais repeated over ti1ne. Purely environn1entaletfects, reflecting the difterent ccological potential of sites and 1nanagernent conditions, are nol of dircct conccrn for the breeding or recommendation of plant varieties. Genotypic 1nain effects (i.e. differences in mean yield bet\veen genotypes) provide U1c only rclcvant infonnation \vhen genotype x environn1ent (GE) interaction eflccts are abscnt or igt1orcd. However, differences between genotypcs may vary widcly among environrneuts ia the presence of GE i11teraction effects as large as those reported in extensive investigations (e.g. DeLacy et ai., 1990; An11icchiarico, l 997a). ln general, OE intcractions are considered a hindrance to crop improvement in a t.arget regíon (Kang, l 998). Morcover, such cffects rr1ay contribute , togethcr \\rith purely environmental effects, to thc temporal and spalial instability of crop yields. ·ren1poral instability, .in particular, lias a negative effect on famJers' income and, in the case of staple crops, contributes to food insecurity at natjonal and household levei. On the other hand, OE interactions may offer opportunities, especially in the selection and adoption of genotypes sho• .. ving positive interaction 'vith the location and its prevailing environmental condiLions ( exploitation of speci fie adapt.arion) or of genolypes \VÍth - - .---- 2 low frequency of poor yield or crop failure ( exploitation of yield stability) (Siinrn,onds, 199 1; Ceccarelli, 1996). Gro,ving awareness of the importance of GE interactions has led crop genotypes to be ordinarily assessed in 111ulti-environment, regional triais for cultivar recommendation or for the final stages of elite breeding rnaterial selection. GE effects should not be ignored, mther analysed using appropriate techniques, in order to explore the potential opportttnities and disadvantages. Provided the infom1ation frorn thcse triai s responds to certain prerequisitcs, it can help brceding progrrun1nes to: • better understand the type and sizc of the G E interactioas expected in a given region. and d1e reasons for tJ1eir occurrence; and • define, if necessary, a strategy to succcssfully copc \\iith the effects of interactions. ·rhe most important OE efTects for targcting culrivars or for selection of 1naterial are the crossover type affecting top-yielding gcnotypcs. Such effccts in1ply a change of ranks bet,veen environments rather than a simple variation in Lhe extent of the ditference between genotypes (Baker, 1988). J-lo\vever, ali GE interaction effects arising from lack of gcnctic corrclation a1nong environn1ents (including those relating to lov.i-yielding material and not ncccssarily of the crossover type) can be relevant if the results for a given data set are extrapolated to produce infon11ation on the GE eftects that are likely to be rnet in breeding for a target region (f\11uir et ai., 1992; Cooper et ai., 1996a). Reasons for the occurrence of GE interactions are thoroughly discussed elsewhere ( e.g. Bidinger et ai., 1996; Kang, 1998). Major interaction can bc cxpected \vhen there is: on the one hand, 'vide variation betwecn genotypes for n1orphophysiological characters conferring resistance to ( or avoidance of) one or 111ore stresses, and, on the otl1er, \Vide variation bet\vecn cnvirontnents for incidence of the san1e stress(es) (as detennined by climatic, soil, biotic and 1nanagement factors). for example, this siruation 1nay arise \vhere there is \Vide variation bet\veen material in tenns of intrinsic drought resistance or earliness of crop cycle, and bet\veen environments in tbe levei ofterminal drougbt - stress. Other pertinent examples 1nay concern the differential response of genotypes to variable leveis of stress, such as lo'v te1nperature, soil salinity, nutrient deficiency, pests, diseases, lodgíng, grazing or interspecific competition in 1nixed cropping, as a consequence of: genetic variatjon in tolerance to biotic or abioti.c stresses; ability to capture and use nutrieot resources; competi tive abi 1 ity etc. Largc GE interactions have frequently been reported Material co1n direitos autorais bet\veen pairs of environments \Vith contrasting leveis of one n1ajor stress (Ceccarelli, L 989; Brrunel-Cox, 1996), detincd as " favourable'' \vhcn charactcrizcd by low stress and high mean yield and "unfuvourable" with high stress and lo\v yield. H.o\vever, large interactions rnay also occur bct,veen pairs of unfavourablc environrncnts and cven betv.1een pairs of moderately favourable environn1ents possessing s irnilar rnean yield but \Vith differing combinations of stresses or patterns of one major stress (Annicchiarico, l 997a). For exatnple, an cnvironmcntal fact.or such as soil texturc n1ay produce sirnjlar n1can yicld of n1aterial but sizeable genotype x soil texture interaction (Koutsos e t ai., 1992). The levei of rnatching of gcnetical ly-based dcterminants of phenologícal developn1ent (e.g. photoperiod and verna li zat ion requirements) \Vith site characteristics related LO the length of the grov.1ing season (e.g. day-length and temperature patterns) is a11other deter1ninant of ren1arkable GE ínteraction, cspecially across relatively large regions (Wallace e/ ai .• J 993a, l 993b ). 1·he genetic structure of plll11t material n1ay also have a bearing on thc cxtent of GE ínteraction. Variety types characterized by lov,• levels ofheterogeneity (e.g. pure tine, clone, si11gle-cross hybrid) or heterozigosity (e.g. pure line) tcnd to intcract witJ1. thc eovironmcnt more thll11 types \Vith opposite features (e.g. open-pollinated population, 1nixture of pure tines), because lhe lo\vcr richncss in adaptive genes implied by their genetic structure makes the1n rnore susceptible to variation in enviro11n1ental conditions (Bccker and Léon, 1988; Brancourr-Huln1el et ai., 1997). Indced. harvest security is associated v,rith diversity bct\veen and within traditiona l varieties for fam1ers producing near subsistence levei (Cla\\1son, 1985). A detailed description and discussion of various aspects ofGE interaction analysis is available in nun1erous revie\v articles (Freernan, 1973; 1-lill, l 975; Denis and Vincourt, 1982; Westcott, 1986; l .in er ai., 1986; Becker and Léon~ l 988; Crossa, 1990; Romagosa and Fox, l 993; . Cooper and DeLacy, 1994; van Eeu,vijk~ 1995; Brancourt- Huhnel et ai., 1997; Kang, 1998), in papers included in the books edited by Wiltjams ( l976a), Kang ( J 990), Kang and Gauch ( 1996), Cooper and 1-Jammer ( l 996a) and Kang (2002), an<l in thc n1onographs by Gauch ( 1992), Prabhakaran and Jaín ( 1994) and 8 asford and 'f ukey (2000). This publication focuses mainly on a limited number of anaJ)itical techruques that. O\ving to the quality of inforrnation, the ease of applicarion {also in relation to the recommended software) and the lirnitcd ru11ount of input data required can bc considcred of major interest for: • brceding prograrnmes. especially narional programmes 3 lntroducrion in less developed countries. in order to increase kJ10\\rledge of GE interactions and, possibly, n1odify thc stratcgy accordingly; • breeding progran1n1es, as well as national institutions cornmitted to testing and reconunending crop varieties, ín order to asscss adaptation and yield srability pan.en1s of the available gennplas1n and exploit the infonnation for 1nore etfective selection and targeting of 1naterial. The second point 1nay be extended to the assessrnent of thc adaptability and yield stabílity of diffcrent agricultural techniques (e.g. soil tillage, 'veed control, SO\ving date) con1pared on a 1nulti-environrnent basis (Gauch and Zobel, 1996a; Picpho, 1998). 1.3 ELEMENTS OF A BREEDING STRATEGY Genetic improvement is a basic con1ponent of national policiesfor raising agricultural production in many countries. Although breeding program n1es run at intemational centres of the CGIAR (Consultative Group on lntcmational Agricultura! Research) syste1n have rnade an important contribuOon to major lood crops, national breeding prograrn1nes (public or private) rnaintain a fundamental role for crop i1nprovement in tbeir target region (usually appl icable to the whole country). particularly insofar as the exploitation of speci fic adaptation and yield stability characteristics are concen1ed, O\VÍng to their better kl10\\•ledge of. and easier access to, local gcnnplasn1 and cropping environn1ents. Thesc programrnes aim to make correct decísions on a number of issues \Vhich comprise a breeding strategy (Simmonds, 19791). Decisions may concen1, i11 particular: • adaptation strategies, yield stability and other (e.g. crop quality) targets; • gcnctic resources fonning the genetic base (indigenous or exotic, fro1n traditional or in1proved varieties); • tecl1niques for the recombination and introgression of useful genetic variation; • variety type ( e.g. síngle-cross hybrid. double-cross hybrid, u11proved population or synthetic variety, with regard to outbred species); and • brccding plan and selccríon procedures (selection environn1ents. indirectselection criteria, presence and extent of participatory breeding, experin1ental designs etc.). , lbid .. p. 186. Material com direttos autorais Genotype x e11viron111e111 i111erac1io11s: challenges and ºJ' portunities for plan/ hreediJ1g and cultivar reco111111e11da tio11s The definition of a strategy with respect to GE interactions may require decisions on 1nost of these elen1ents, nrunely: adaptation strategJ1 and stability targets, gcnetic resources, variety type, brecding plan ru1d selection procedures. lnitial decisions may change with ti1n.e as a consequence of ne\v opportun.ities otfered by scientific progress. experimental cvidence, available funding, food security policies, changcs in national sced systcrns, international cooperation etc., but they should ren1ain consistent \vith the breeding objective. For exa1nple, the inconsistency bet,veen targeting also unfavourable areas and adopting genetic resources and selection procedures producing material s.pecifically adapted to favourable environrnents has contributed to the partia! failure of a nun1ber of breeding progrrunmes carried out in the Green Revolutíon context (Simn1onds, 19792; Ceccarelli, 1994). lt is worth noting that the potentially extensive application of gcnctic engincering techniques does not elitninate the need for breeding programmes to copc with GE interactíons, because ahnost no cultivar can asse1nble ., lbid., p . 358. 4 - genes conferring superior perfonnance in ali environ1nent types within a relatively large region. This derives fro1n genetically based trade-offs bctween yield potcntial and tolerance to major stresses, e.g. drought (Ludlow and Mucho\v, 1990; Acevedo and Fereres, 1993), as \Vell as fro1n the need to choose betv.reen incompatible leveis of a key adaptive trait, such as earlíness of flo\vering (WaUace et ai. , l 993a). Also the possible selection for yield based oa molecular markerS may require a prelintinary definition of adaptation and yield stabilíty targets, sínce a remarkable portion of uscful n1arkers are cnvironrnent-specific (Paterson er ai., 1991; Hayes et ai. , J 993). Jndee<l, tbe potential benefits of exploitarion of environ1nent-specí fie n1arkers may jus ti Í)", ín the long run, grealer emphasis on specific adaptation strategies. ln a \vide adaptation prospect, marker-assisted selectioo may prove distinctly less effucti ve tban multi-environrnent, phenotypic selection for yield in the presence of relatively large GE interactions, especially when cpistatic effects contribute significantly to genetic control (Cooper et ai., 1999). Material com direitos autorais 2. Adaptation and yield stability 2.1 ANALYSIS OF ADAPTATION Objective and analysed information ln an evolutionary biology context, adaptation is a process, adaptedness is the levei of adaptation of plant material to a given environment, and adaptability is the ability to sho\v good adaptcdness in a \vide range of environn1ents (Tigerstedt, 1994). ln a plant breeding context, the first two terms relate to a condition rather than a process. indicating the ability of the material to be high-yielding \Vith respect to a given environment or given condicions (to v.1hich it is adapted) (Gallais. L992; Cooper and B)1h, l 996). ln breeding for wide adaptation (i.e. adaptability), the aim is to obtaiJ1 a variety which perforrns v.relJ iJ1 nearly ali environments; in brecdiog for spcci fic adaptation, thc ain1 is to obtain a variet)< whicb perfonns \Vell in a definite subset of environments with.in a target region. 1ne adaptive response of a variety is assessed \Vith respect to othcr genotypes and tends to undergo n1odi11cation when better- perfon11ing gennplasn1 becomes available. Breeding for \Vide adaptation and for high yield stabiLity and reliability have so1netimes been considered one and the sa1ne, insofar as the latter t-.vo tem1s indicate a consistently good yield response across environ1nents. Some authors, ho\vcver, havc applicd the yicld stabiliry concept v.1ith respect to consistency in ti1nc of genotype performance, using the adaptation concept in relation to consistency in space (Barah et ai., 1981 ; Lin and Binns, 1988; Evans, 1993). lt has also been v.•idely ackno,~rledged (Ghaderi et ai., 1980; Becker, 1984; l.~in and Butler. 1988; Bo\vman, 1989; Annicchiarico, J 992, I 997b: Romagosa and Fox, 1993; Piepho er ai., 1998) tJ1at only genot)1pe x location (GL) interacúon, rather than ali kinds of GE intttraction, is use fui for depicting adaptai ion pattems, as only this interaction can be exploited by selecting for specitic adaptation or by gro,ving specitically adaptcd genorypes. For exa1nple, the kno\vledge of specific adaptation to past years, as sho,vn by positive genotype x year (GY) interaction eflects, cannot be exploited in future years, since the climatic conditions that generate year-to-year environmental variation are not knov.in in 5 advancc. This vic\v implies that analysis of adaptation - and its i1nplications for the definirion of adaptation strategies for breeding programn1es and don1ains of cultivar recon11nendation for extcnsion services - 1nay concern only responses to locations, geographic areas, fanning practices or othcr factors that car1 be controlled or predicted prior to sowing. ln particular, the analysis of 1nulti-environment yield triais sbould f ocus pri1narily on GL interaction, \\'Íth tbe characteristics ofthe locat ions depending on climatic, soil, biorjc (pests and diseases} and crop managen1ent factors. The remaining i11teractious of genotype ""ith the tin1e factor (year for annual crop, crop cycle for perennials) sbould be dealt \Vit11 in tenns of yield stability. ln son1e cases, the conccpt and tl1c analysis of adaptation rnay concern lhe genotype r~sponses to a set of n1anagernent practices that have a crucial i1npact on GE effects, rather than the responses to locations ( e.g. Annicchiarico and Piano, 1994). ·rhe above definition of GL interacrion i1nplies that only GL effects rcpeatable in time are of practica l itnportance. These effects can be eitl1er: • exploitcd, by gro,ving specificatly adapted gennplas1n: or • n1inin1ized, by gt'O\Ving widely adapted material. Non-repeatable GL effects contribute to the second-order int.eraction (genotype x locaJio.n x year [GL Y] for annuals, and genotype x location x crop cycle for perennials). 'fhey represent the error lem1 for (repeatable)GL interaction in analysis of variance (ANO\'A) 1nodels \Vbere lhe tin1e factor (considered random) is crossed \vith the location factor. [n A NOVA models holding the time factor nested into location, non-repeatable GL effects are included in the GY interaction ,..,ilhin site, \vhich acts as che crror term for GL. interaction (see Section 4.1 ). Thc sizc of the G L e ff cct s is a lso relevant : Ln pari icu lar, i f the G l, mteraction variance con1ponent (although statistically significant) is s1nall co1npared to other con1ponents, particularly the genotypic one, it reduces U\e possible advantage of breeding for spccific adaptation. For annual crops in particular. rnultilocation triais Material com direttos autorais Genotype x environn1e111 ínteractions: cholle11ges anel opportunities for pla111 breetling an<I cultivar recon1111e11darions - FIGURE 2.1 Limited (situation 1) and large (situation 2) extent of wlthln-locat.lon varlation relative to between-location varlatlon for a major environmental factor related to the occurrence of GE interaction, and its lmplicatlon on the extent of GE lnteraction components of variance Year-to-year variation between/within two sites for a major environmental factor ( e.g. rainfall) Situation 1 Situation 2 300 Situation 1 =substantial GL interaction 350 [] 400 450 500 550 600 Situatíon 2 =limited GL interaction (large GY and GL Y effects / large GY effects within location) should be repeated in Lime 10 distinguish bet,veen repeatable and non-repeatablc GL interaction eff ects. A nun1ber of reports from various countries (Talbot, 1984; Léon and Bccker, 1988; Atl in and McRae, 1994; \Veber and \.\lestern1ann, 1994; Sneller and Oombek, 1995; Annicchiarico, l 997a), providing estin1ates of variance con1ponents or investigating the consistency of GL interaction pattcn1s across individual years, have sho\vn that a reliable assessn1ent of GL eftects is not possiblc '''ithjust one year's data, because the estimation is inflated by non-repeatable eftects. The lack of repeatabi lity is due mainJy to che year-to-ycar variation in cli rnatic factors v.ri thin locations. The emphasis on GL intcraction cíl'ccts is justificd even \Vhen analysis of adaptation relates directly to genotype responses to environmenta l factors (as \Vith the statisticaJ n1odels considered in Section 5.4). Sp~ci fica lly adapted genol)~pes can be targeted to respond \VelJ under the environmcntal condjtions prevailing in a given area, provided that these conditions are not highly variable from year to ycar. lmportant ir1fonnation rnay not be obtained if the focus is on GE ralhcr than GL effccts, becausc the inipact on the GL interaction of an environn1cntal factor that strongly affects t11e GE interaction (impact v.1hich determines thc scope for specific adaptation) may be large or lirnitcd, depending on the extent of lhe bet,veen-location 6 variation rclative lo thc \Vithin-location, year-to year variation for the factor (Fig. 2. l ). For exa111ple, gcnotype variatioo in response to rain&1H IIHl) ' appear ímporta11t when analysing OE interaction and negligible \Vhen analysing GL intcraction, ~1hen test locations have similar n1ean value and large year-to-yea r variat ion for the environ111ental variable. By concentratJng on GL interaction in analysis of adaptation, lhe process is a lso s irnpli fied, because adapta1ion pattems \Vhich are rernarkably cornplex \Vhen evaluated on a GE basis (requiring three or more dirnensions for a convenient n1ultivariate representation) beco1ne relatively s i1nple on a GL basis (requiring only one or l\\'O dirnensions), n1ainly as a consequencc of lhe largcr sizc of the appropriate error tcn1l. 'fhis is shovm in Tablc 2. 1 for difTercnt data sets, in \Vhich the nu1nbcr of din1cnsions are reprcsent.ed by the number of significant G E or OI~ inleraclion principal component axes (according to an analytical rnodel described in Section 5.3). Reducing tbe nuaiber of dimensions has practical implications, because one- or t\vo-din1ensional rnodels offer additional and simplilied procedures for thc identification of best- yielding 1naterial for variely recon1n1endatjon and to help breeding progran1mes define adaptation strategies (see Sections 5.3, 5.4 and 5.8). While carlier studies on OE interaction c,onccntrated Material co1n direitos autorais on GL interaction in the assessment of genotype adaptation and zoning of locations (Yates and Cochran, 1938; Homer and Frcy, 1957; Abou-El-Fiuouh et ai., 1969), recent developments and applications of statistical 1nethods have frequently focused on the interaction of genotypes v.iith generic environrnents (Crossa et Cl! .. 1995; DcLacy et ai. , l 996a; Gauch and Zobcl, 1997). Ho,vever, most rnethods proposed for GE interaction analysis can easily be adapted to the investigalion of GL effects. Subregions Adaptation patterns \Vith respcct to individual locations are of 1 i1nited interest per se. as the san1ple of sites is very s1nall cornpared to the large nu1nber of locations in any target region. Specific breeding, in particular, can only be directed to areas and cannot realistically be so fine-tuncd as to cxploit positive intcraction cffccts of gcnotypes with indjvidual locations. Ho,vever. sites that are siJnilar in tern1s of genotype response can be grouped by dift'erent n1ethods (discusscd in Chapter 5), and each group 1nay identify a croppingarea that is relatively unifortn because GL interaction eftects are lintited or negligible. Such areas (possibly the object of spcci fie breeding) havc becn tenned by dif:l'erent authors as subregions, subzones, subareas, n1acro-environments or mega-cnviron1nents (Homer and frey, 1957; Seif et ai. , 1979; Ceccarell i, 1989; ClMMYT. 1989). Subrcgions may also be dcf1ncd for variety reco1nrnendation: each subregion Lhen coincides v.iith a recon11nendation domain, grouping those sites \Vith the sarne best-perfon11ing genotype(s) (Gauch and Zobel, 1997). Tilc dcftnition of subregions is not justgeographical, but may also enco1npass fanning practices (e.g. irrigated or rainted croppíng). Subregions have son1etin1es becn defincd on thc basis of site sim i larity for cnvironn1ental factors that are supposcd to be itnportant but are, in fact, chosen \Vithout a definite assessmcnt of their impact on GI. inreraction (e.g. Pollak and Corbert, 1993). 1·he arbitrariness of this procedurc 1nakes its results less reliable than thosc for site sin1ilarity for GL eOects (Gauch, 19921). Ho\vever, addition.al intonnation on the clin1atic, soil, biotic and crop n1anagcn1ent variablcs closcly rclatcd to Lhe occurrencc ofGL intcraction n1ay help locate geographic boundaries for subregions (see Seccion 5.8), besides contribucing to thc understanding of causal factors for thc inreraction (Eisemann et ai., 1990; Bidinger e/ ai., 1996; van Ecuwijk 1 lbid .. p. 220. 7 il<la1Jta1io11 and yieltl stabilíty TABLE 2.1 Complexlty of adaptation patterns as depicted by the number of slgnlflcant PC axes in the analysls of GE and GL data matrices of slx data sets Data SCI No. No. No. Significant PC axesº i1e ycars genotypes OE GL Bread \vheat 31 3 18 5 2 Ourum wheat 1 6 3 9 4 2 Ourum \vheat 2 5 2 15 4 1 Oururn \vheat 3 6 2 12 4 1 "'laize 1 11 3 13 4 o Maize 2 11 3 11 3 1 • P s O.OI atcording to F011~ te.si (Cornclius, 1993). in an Al'\ll~ll analysis. So11rce: Annicchiarico. l 9<)7a. et ai., 1996). The zoning process should produce subregions tJ1at can be defined on a geographical basis or by other rneans, such as cli1natic factors or 1nanagen1ent practices, in order to be useful for brceding or cultivar reco1n1nendation. ln reality, this is not al\vaysthe case: for exan1ple. subregion defrnjtion for maize variet)' reconunendation in Jtaly revealed a leopard skin patt ern as a result of the inconsistency bet\veen geographic proximity and similarity for GL interaction effects of the sites, \vhich could not be accounted for by any of the availnble enviroru11ental variables (Annicchiarico et ai., 1995). ln tl1is case, rccom1nendations concen1ed thc \vhole region. ln other ínstances, individual locations or small groups of sites (appareat ly distinct, in tenns of GL eftects, fron1 a larger group of geographically close locarions) 1nay bc added to thc larger group to fonn a uniquc subrcgion for breeding or recom1nendation ( e.g. Annicchiarico and Perenzin, 1994). 2.2 DEFINITION OF ADAPTATION STRATEGIES Sctting adaptation strategies for breeding progra1n1nes and deJining recomrnendation domains for cultivars are distinct objectives. As such, they n1ay require partly diffcrcnc analytical approaches and provide different results \Vith regard to the definition of subregions. 111e sa1ne data set 1nay be analysed with both objectíves in r11ind. Howevcr, lhe adaptation stratcgy objcctivc focuses on the responses of a sct of gcnotypcs to obtain indícatíons and generate predictions rclative to future breeding material that may bc produccd from the gcnetic base of v.1hich the tested genotypes are assu1ned to be a representative satnple. Assessing the value of a speci fie adaptation strategy, implying a ilistinct selection progrru11111e for each subregion rather than a unique selection progra1nn1e for the whole Material com direitos autorais Ge1101ype x e11viro11me11t interactions: cha/le11ges and opportunities .for pla111 breeding an<I c11/tivar recotT1111e11da1íons target region, is of obvious interest to globally-oriented breeding programa1es of large seed companies or international research centres, where the target region may include more than one country and very di fferent environments. ln this case, each subregion may include several countries. Specific adaptation, ho\vever, may also prove a valuablc target for national breeding programmes, for \\rhich the yicld gain derived from exploitation of GL interaction effects \Vithin tbe country can also help face the iJ1creasing con1petition exerted on local seed markeLS by intemational seed companies. For public mstitutions. the brceding of cliversiiied, specifically adapted gennplasm can be a major element of a research policy enforcing sustainable agricu lture ( Brarnel-Cox et ai., 1991; Ceccarelli, 1996) by: • maximizing the potential of diffcrent areas by fitting cultivars to an environment instead of altering the environment (possibly with costly or environrnent- unfriendly inputs, such as pesticides, fertilizers and irrigation) to fit widely adapted cultivars; and • sateguarding crop biodiversity by increas ing tbe number of varieties under cuhivation, \Yith positive implicarions tor the stability of production at national levei. Furthermore, speci fie brecding may faci 1 itate thc t.ecbnological adaptation of varietics by fixi ng characteristics of specific interest to subregions (for srnall- grain cereais, shon st.ra\Y for intensive cereal fanning and long straw for extensive cereal-livestock syslcms; for cercai or food legume crops, different grain quality characteristics etc.). Jn general, breeding for spccific adaptation tcnds to imply greater genetic gains associated \Vith increased costs relative to those for a \Vide adaptation strategy. The genetic gains are derived from exploitation of GL interaction effects via useful adaptive traits (Bidinger et ai., 1996), as \vell as increased heritability of yield as a consequence of decreased GL interaction (Kang, 1998). The relatively lúgh costs may be due to increased tield testing rather tl1an to dupHcation ofbreeding stations, because crossing and hybridization operations can be centralized in a single national station providing each subregion v.•ith novel gertnplasm for local selection (and, possibly, genetic resourccs for local testing to identify parent material of specific interest). Figure 2.2 presents a hypothetical situation including a pbase of farmers' selection in a participatory plant breeding sche1ne (v.•hich may or may not be prescnt}. A comparison of 'vide vs. speciflc adaptation 8 stratcgies rnay be based on: • yieJd gains predicted for the di ff erent options according to sclcction theory, using thc sarne yi.eld data already used i.n the preliminary definition of subregions; or • actual yield gains provided by different strategies, following further research \York ( e.g. Singh et ai. , 1992; Ce.ccarellj et ai., J 998). ln fact, n1ore con1plex scenarios (including indirect selection gains in one subregion, derived from direct selection in another subregion) can also be envisaged (see Section 6.1 ). The con1parison bet\vecn adaptatjon strategies based on predicted yie ld ga ins may underestirnate the potential advantage of a specificstrdtegy, \vhen 1naterial \Vith n1arkedly specific adaptation is Wlder- represcnted among thc t.estcd varieties or breeding lines for various reasons ( e.g. previous selection for wide adaptation by local breeding or \Vide representation in a sample of foreign, 'videly adapted materiaJ). Like,vise, the potential gain of specific breeding for ttnfavourable arcas may be underestimated \vhen most tested genotypes have been selected in favourable environments. Specific adaptation could be more advantageous if it also implied the use of a distinct genetjc base for eacb subregion. Although not negligible, the positive effccts of specific breeding on tl1e biodiversity of cultivated. rnaterial are difficult to quantify. For the definition of adaptat.ion strategies, the con1parison of yield responses across environn1ents managcd for contrasting leveis of a given stress (e.g. drought or nutrient deficiency, dependent on lhe levei of irrigation or fertilization, respectively) is a valid alten1ative to lhe current approach (based on thc analysis of regional triais managed according to ordinary practices in each area), provided that the managed environments can represent actual geographic arcas or fam1ing systems within the target region. The use of n1anaged environments is currently considered 1nai11ly for genotype selection within a given adaptatjon strategy (see Section 6.2). Even if a \Vide adaptation strategy is preferred, the provisional identi fication of subregions can hclp locate crucial test sites for gennplasm selection (Abou-El-Finoub et ai., l969; Lin and Butler, 1988; Annicchiarico, 1992). ln th.is case, a fe\v sites representative of different subregions and capable of reproducing lhe n1ean responses of genotypcs across thc region can be used for selection of material eithcr conten1porarily (Brennan et ai., 198 l ) or in tum (Calhoun er ai., l994). Jdentifying crucial test sites can also be a valuable objective for the routiue evaluation of genotypes carried out by public institutions, such as Material com direttos autorais .4(/áptarion <Znd yie/(/ stability FIGURE 2.2 A speciflc adaptatlon strategy lmplying plant breedlng at one natlonal research centro, selectlon of novel germplasm and evaluatlon of genet lc resources ln dlstlnct subreglons, and subsequent phases of fanners' selectlon ln each subreglon Natlonal Research Centre (acquisition of genetic resources; recombination of geoetic variatioo and generation of novel gelTTlplasm; coordination of test work; possible selectioo for unifOITTlity and muttiplication in isolation) information on novef germpl asm • • • • Test site 1 novel germplasm • ' genellc resources • ' Test site Test siteinformalíoo on useíul nls pare 1 • • • • • in subregion A in subreglon B ln subreglon e • • • • • • • • • • • ·--· • (preliminary testing of novel gennplasm; testing of genetic resources) --.. l • • • • • • • • • novel gennplasm novel gennplasm novel gennplasm ! • • • • • ' • ' , , J F armer 1,2, ••• n Farmer 1,2, ... n Farmer 1,2, ... n • • • • • • --- . ln subreglon A ln subreglon B ln subregion e ' ··- (cycles of farmers' selection) , , ' r , , Seed systems Seed systems Seed systems in subreglon A ln subreglon B ln subreglon e (mulliplication/dístribution of novel cultivars) • , .. ~ Farmers Farmers Farmers ln subreglon A ln subreglon B ln subreglon e (cultivation of novel cultíVars) t11ose cornrnitted to the defin ition of list.5 of recom1nended varieties, or tbose responsible for the assessment of the Value fo r Cultivation and Use of ne,v ly released gem1plas111. Decisions on the adaptation strategy, \vhich can havc a considerable and lasting effect on the organii.ation of a plru1t breeding programn1e, sbould be based on the analysis of rnore data sets if available, and veritied after a rcasonable period oftin1e on thc basis of ncv.• data (DeLacy et ai., 1994). .2.3 WIDE VS. SPECIFIC ADAPTATION IN BREEDING PROGRAMMES .A..lready in the l 920s, studies of plant material (Turesson, 1922) leading to tl1e introduction of the tenn "ecot}rpe" highlighted the occurrencc of specific adaptation to certain areas and environrnental conditions. During the sarne 9 period, pioneer breeders in Great Britain (Engledow, 1925) and Strarnpelli, ltaly (Lorenzetti, 2000) advocated the importance of understanding and exploiting specific adaptation etTects io order to raise crop yields in their rcspcctive COllntr ics. Ho,vcver, breeding programmcs in the second half of lhe century mostly concentrated on the improven1ent of yield potential (Bra1nel-Cox et ai., J 991; Evans,, 1993) using - also in lcss developed countrics - favourable environments for selection (Simrnonds, 1991 ). Also the concept of plant ideotype to select for has been developed for dilTerent species to increase yields undcr favourable cropping conditions (Donald, 1968; Mock and Pearce, 1975). This trend has been favoured by numerous factors: • tJ1e perspective of rapid yield gain offered by high input leveis; • lhe greatcr profitability of target:ing sccd markcts in 1nore productive arcas; and Material com dire11os autorais Genntype x environn1en1 í11terac1iu11s: c:halle11Kes anel opporl1111ities for /Jfont breeding and cultivar r eco1111ne11datio11s - ~ - • the con1111on belicf that select ion in favourable areas produces a substantial yield increase also in lcss favourable arcas. ·rhis beliefhas been challenged by theoretical \VOrk (Jinks and Connolly, 1973; f'a lconer, 1990; Sin1monds, 199 1) and by n1ounting experin1ental evidence concerning improvemcnt of crops for environn1ents sufterjng fro1n drought stress (Nages,vara Rao et ai., 1989; Ceccarelli and Grando, 199 1; Ud-Din et ai., 1992; Byn1e et oi. , 1995; Mu11oz et ai. , 1998; Ceccarcll i et ai., 1998), nutrienl deficiency (Atlin and Frey, 1989; Banzinger et oi., 1997) or both (Cooper et ai., 1997). ·rhese works have highlighted that selecting under IO\V stress conditions i1nplies a specific adaptation target to,vards favourablc areas. ln reality, favourable, high-yielding environ1nents for selection are not necessarily so because of natural cli1natic or soil factors. Optimal management at research stations, including abundant fertilizaüon, accurate soil preparation, ti1nely so,ving, irrigation and che1nical control of \Veeds. pests and diseases, can often result in higher mean yields tJ1an in neighbouring fanners fields. Such differences in yield may also i1nply remarkable GE interaction bet,veen these envi ronn1ents (Pederson and Rathjen, 198 1; Ceccare l 1 i, 1994 ). Spaced planting or lo''' densi ty procedures adopted fo.r selection n1ay also rcsult in i1nproved cropping environments, because they i11crease the availability of resources, such as light, \Vater and nutrients (Rotili and Zannone, 1975). ln recent decades, various progran1mes run at international research centres havc n1odíficd their breeding stratcgy to produce germplasm suitable for cropping in less favourable areas. A specific adaptation strategy has been pursued for barley i111provement at the lnternational Centre for Agricultura! Research in Dry Areas (lCARDA), 'vitl1 selcction undcr conditions si1ni lar to those in the target environ1nent {favourable or drought-stressed). aod witb ímplications ext.ending also to elen1cnts of lhe breeding strat.egy, such as the choice of genetic resources and variety type (Ceccarelli, 1994). A ' 'shuttle breeding" procedure - altcmate seleclion in drought-stresscd (unfavourablc) and irrigated (favour-dble) environments - \vas established for the selection of \Videly adapted gem1plasm at CI MM YT (Edtneades et c1/., 1989; Calhoun et ai., 1994). l f o\vever, the possíbility of C;E intcraction bet\vcc11 environn1ents of si1nilar ccological potcntial (bascd on crop 1nean yield) n1ay lcad to tl1e definition of di fTercnt subregions also within unfavourable and n1oderately favourable areas. For this reason, barley breeding at ICA_RDA began producing 10 specific n1ateria l for diffcrent sets of drought-prone countries (Ceccarelli, 1996); rice breeding at IRRJ defined speci fie plant ideOl)'pcs for severa] diffcrent ecosyste1ns (Fischer. 1996 ); and 'vheat breeding at CitvfMYT is attempting a compro1nise bet\veen a \vide adaptation prospect and d1e opponunity to breed specifrcally for 12 difTcrenl 111ega-environn1ents (Braun et ai., 1996). Di fferent subregions n1ay be identified notonly within Jarge or transnational rcgions (e.g. Crossa et ai., 1991; DeLacy er ai., 1994) but also ~· ithin relatively srnall regions, as suggested by results for: • nor1hcn1 Syria (Ceccarelli. 1996); • Italy (Annicchiarico, l 997a) and northern ílaly (Annicchiarico, 1992, 2002); • Nc• .. v South Walcs (Seif et ctl., 1979; Basford and Cooper, 1998); • Queensland (DeLacy et ai., J 996c); • south~·est Canada (Saindon and Schaalje, 1993); and • Ontario (Yan et ai., 2000). 'l'herefore, lhe choice bet\veen a \Vide and a specific adaptalion stratcgy rnay be a kcy question for national breeding prog.rammes. The spec ific adapcation strategy is receiving increasing attcntion in dcveloping countries. son1etimes in co111bination ~1ith participatory plant breeding schernes. ·r hc participation of farmers can: i) support the n1ultilocational selection \vork; ii) allo\v for exploiring possible spccific adaptation effects cven \Vithin subregions; i ii) contributc to enhance thc biodivcrsity of material under cuhivation, thereby itnproving production stability; and iv) fac ilitate the seed supply to farmers via local seed systems (McGuire et ai .. 1999; \Vel.tzien et al., 1999; Ceccarelli et ai., 2000). For sn1all farrners in relatively poor countrics, such systc1ns 1nay be far rnore important than fon11al seed systcn1s (Almckinders et ai., J 994). 2.4 TARGETING VARIETIES Agricultura! techniques, to llo,ving an assessn1enl of thcir econo1nic perfonnance in regional yicld triais, can be recommended: either \Videly over the targel region or specifically for one subregion (Perrin et ai. , l 976; Shaner et ai. , 1982). For speci fic reco1n1nendation, the reco1nn1cndation don1ain of a given technique 1nay be dcíined on thc basis of gcography alone, or also on the basis of fa r1n ing practices (e.g. irrigated or rainfed cropping) or socio-economic consrraints. lna li cases, tbe int-Onnation obtained fron1 previous testing is exploited for predicting yield responses in corning years and, n1ost Material com direitos autorais frequently. in ne''' locations. The definition of reco1nntendations tor varieties is generally sirnpler than for other techniques. Usuall)' there is little difference in secd pricc among cultivars. fn this case, variable costs do not need to be taken into account in the assessn1ent. Statistical analysis concen1s yield response anel, \vhcrc thc genorypes diffcr for crop value. gross benefit. ln either case, di fferent domains (i.e. sub regions 'vhich are the object o f di stinct reco1nmendation) may be ídentificd on the basis of repeatable GL interaccion e!Tects \vhen varieties have diffcrenr adaptation patterns. 'f he dornains are charac terized by different top-yielding, .. ,vinning" genotypes (Gauch and Zobel, 1997; Ebdon and Gauch, 2002). For cach location, chc advantage of spccific vs. v.1ide recommendation can be assessed in tenns of the di fterence in predicted )iield bet .. veen: i) tlte top-ranking gcnotype(s) in the rclevant subrcgion; and ii) thc top- ra1lking genot)•pe(s) over lhe region (Gauch and Zobel, 1997). More tlian one recomrnended genotype can be a sensible choice for either scenario, particularly for ~ride reco1n1nendation, as it n1ay lin1it the risk of disasters arising due co t11e unforeseen susceptibility of the only culti var reco1nn1ended in a vast. area to a biotic or abiotic stress. This is particularly the case for the cultívation of gcnetically homogcneous variery types (pure lines, clones) (Simn1onds. 19792). Possible differences in yield stability bet\veen cuJtivars can also be taken into account tnaking reco1nn1endarions (see sections bcfO\\' and Chapter 7). Favouring the cultivation of specifica lly adapted gennplasm is generally convenient tor n1aximizing regional yields and increasing chc biodiversity of cult ivated material. However, subregions \VÍlh very lin1ited extension or negligible advantage of specitically adapted cultivars 1nay be 111erged wich larger, relatively si1nilar subregions v.1hen thc additional costs of 1nultiplying and marketing speci tically adapted gennplas1n are likely to out v.1eigb the expected benefits (Gauch, 19923). Targeting of gcnotypes is also a concern of public and private seed cornpanies \Vho 'vísh to veri fy the area of adaptation and the agronomic value of novel gem1plasn1. This information, ncccssary for planning propcr rnarkcting and advisory scherncs, is cspccially 'varrantcd when breeding has not cont.emplated a defini te adapt.ation target. ln this case, just one top-yielding genOl)'pe, across the 2 lblá .• p. 267 and p. 361. l lbid .. p. 220. 11 1t<laptatio11 (lnd .11ield s1abilir)1 region or in a distinct subregion, is usually identified for pron1otion to con1n1ercial variety status. Tech11iques envisagcd herein for defining variety recornmendations can also be usetul for this purpose. 2.5 ASSESSMENT OF YIELD STABILITY ANO RELIABILITY Yield stability High yield stabi lity usually refers to a genotype's ability to perfonn consistently, \Vhetlter at high or low yield leveis, . across a \Vide range of cnvironrncnts. As discusscd rnorc thoroughly in Section 7. 1, most stability 1neasures relate to eicher of t\vo contrasring concepts of stability: "static" (Type l) and "dyna1nic" (1ype 2) (Becker and [,,éon, 1988; Lin et ai., 1986). Static stability is analogous to the biological concept ofhorneosrasis: a stable genorype tends to maintain a constant yield across environmcnts.1'he tenn. "environrnental sensiüvity" has also been used in this respect. \Vhere greater sensitivity corresponds to lov.··er stability (Falconer, 1990; Oyke et ai. , 1995). Oynamic stability in1plies tor a stable genotype a yield response in each environ1nent that is al\l.iays parallel to the ntean rcsponse of ihe tested genotypes. i.e. zero GE interaétion. TI1e n1easure of dynarnic stability depends on the specific set of tested genotypes, unlike the n1easure of static stability (Lin e1 ai., 1986). Lin and Bu1ns (199 1) defmed a i ·ype 4 . concept of stability that is strictly related 10 thê stacic concept. Type 4 stability relates to consistency of yield exclusively in ti1ne, i.e. across years ( or crop cycles) \vithin locations, \Vhereas Type 1 stabí lity relates to consistency both in ti1nc and in space, i.e. across environrnents belonging to the sarne or different sites. As \\'ith GL effects, the GE effects contributing to yield s1ability can be either: • exploited, by breediJ1g and grO\VÜlg genotypes that are stable according to the static concept (i.e. \Vith a better response in unfavourable environments or years); or • ntinin1 ized. by using 111atcrial that is stable according to the dynan1ic concept. Static scabilit)' may be n1ore useful than dynarnic in a 'vide range of situations, cspecially in developing countries (Simmonds. l 99 1 ). From a fam1er 's point of vie,v, location is a constant - not variable - fac1or, and yield consistency over cime is thc only rclcvant componcnt of a genotype's yicld stahility. lt 'vas therefore proposed (Barah et ai., 1981; Lin and Binns, 1988) to evaluate yield stability \Vith regard to GY interaction effects \vithi n locations. Jn reality, yield Material com dire11os autorais Ge11oty1Je x enviro11n1e111 interactions: cli<1lle11ges ond opport1111ilies for fJ/ant breedíng and cultivar reco111111e11<lotions - - c-0nsistency in space also dcscrvcs consideration in tJ1e presence of sizcable GL intera.ction, since a selected or re<:ommended genotype should be stable-yielding both across years and across locations in its area of adaptation or recon1n1endation (Picpho. 1998). This is particularly so \Vhén there is a prospect of 'vide adaptarion or reco1nn1endation, because in the contcx1. of a specific adaptation or recommendation the G L effects are tninimized by the division of the target region into subregions. ln a wide prospect, the assess1nent of yield stability in relation to genotype responses to environn1ents provides a simple n1eans for considering all possibly relevant GE interaction effecrs. ln a specitic prospect, assessn1ent bascd on GY interaction e[fects \.Vithin Jocations can be recomn1ended, especially for genotypc reco1n1nendation (sec Sect ion 7 .2). Wha tever the adaptation strategy, breeding for high yield stability can be considered a useful targe1 \vhen thc rclevant Gt. interaction variation is wide. Yield reliabllity li igh )' ield stabi 1 ity 1nay be a.'!sociatcd \Vith lo'v 1nean yield (or IO\V stability v.iith high mean yicld), v.rhich con1plic,ates genotype selection or recommeodation. As an extreme exan1ple of high stabiliry associated \Vith low yicld, considera hypothetic genotype that yields jus1 above zero in ali environments (greatest stability according to its static concept), or that is consistently the least-yielding (greatest stabi lity according to the dynamic conccpt). Obviously, a less stable, higher-yielding genotype \vouJd be preferable. ·rhe practical interest of co1nbining l1igh leveis of rnean yield artd yield stability has led to the develop1nent of the yield rcliability concept (Eskridge, 1990; .Kang and Pham, 199 1; Evans, 1993). A reliable genot)rpe is charact.erized by consistently high yield across environments. The use of a yield reliability índex faci litates genotypc sclcction or reco1n1nendarion, as the n1can yield and the yield stability traits are combined into a unique measure of genotype merit. Considering yield stability in conjunction \Vith mean yield may also provide a more sensiti\re c-0n1parison of gcnotypes than \vhen using only n1ean yield, O\vingto lhe reduction in ·rype 2 error rates (Ka11g, 1993). The stability 1neasure contributing to lhe index of yield reliability may relate to the static or the dynan1ic concept of stability (see Section 7 .2). Genetic improvement Despite its potcntial interest, increased yield stability has tended to bc a n1inor objectivc in brccding progra1nroes \Vorld,vide (Rotnagosa a11d fox, 1993). A nurnber of 12 studies revie\\'ed by Becker and Léon ( 1988) and Brancourt-liuh11el e1 ai. ( 1997) confimied the early indicatioo by Allard and Bradsbaw (J 964) that variety types \vhere rhe genetic strucrure implies high leveis of heterozigosiry and/or heterogeneity are less sensitive to environ1nental variation and are, therefore, more stable- yielding. Unfortunately, such types 1nay somelimes offer fe\ver opportunities for n1axi1nizing the yield potential. Within a given varicty type, breeding success.fully for this tra it relies on thc adoption of a heritab le or repeatable stability rneasurc as a sclection criterion. ·rhere is extensivc evidence ( see Section 7. l) that heritab.ility or repeatability values tend to: • vary fron1 1noderate to low, depending on the situation; • be higher when lhe assessn1ent is done over two or more years or crop cycles; and • be somc\vhat higher for measures that relate to lhe static ( fype l or ·rype 4) concept of stability. Given tl1e high santpling crror. thc assessrnent of yield stability rcquires numcrous tesl environments (at least eight) to !:,ruarantee reliability (Kang, 1998; Piepho, 1998). Therefore, direct selecrion for yicld stability 1nay be limited by high costs and can bc rccommended. evcn whcn it has higb priority, only for elite n1aterial in lhe final testing stages. ·rhe eh o ice of parental gen11plasrn \vitl1 recognized yield stability and of, if possible, a convenientvariety type, can play a major role in breeding for more stable crop yields. Jn addition, indírect selection for higher yield stabi lity n1ay be atternpted by select ing for n1orphophysiological traits thathavc provcd to be strictly associated \vith this character (see Section 6.3). 2.6 TWO ANALYTICAL FLOW CHARTS Adaptation strategy and ylefd stability targets 111e main analyticaJ steps involved in tJ1e definition of an adaptation strategy and yield stability targets on the basis of regional triai data are stun1narized in Figure 2.3 for lhe general case of experirnents repeated also in tirne. There are six possible conclusions, i1nplying a \.vide or specifíc adaptation stratcgy and, in both cases, the inclusion or exclusion of incrcased yicld stability as a breeding target. WiU1in lhe wide adaptation strategy, indications rnay or n1ay not urge the choice of selection locations that contrast for GL interaction effects. As a preli1ninary srep, it is uscf'ul to estin1ate the variancc components for: genotype; GE interaction across environments ( location-year or locacion-crop cyc le combinations); and the t\vo determinants of GE interaction Material co1n direitos autorais Adaptation and yield stability FIGURE 2.3 Flow chart of stops for deflnition of the adaptation strategy and yield stabillty targots of broedlng programmes from analysls of multllocatlon yield triais repeated intime Start FAJRLY HIGH FAIRLY HIGH r------. lack of genetic correlation across environments variance of Gl interaction >-i.i analysis of 1-t.i provisional H~ specific adaplation advantage adaptation definition of YES subregions NO LOY/ variance of • vanance variance otherGE of olherGE of Olhe< GE interactions interactions ínteractions in subregion LOW LOW 1 1 3 LOW HIGH 5 HIGH HIGH selection for wide adaptation On few environments across reçiion) selectioo fOf wide adaptation selection for specífic adaptatioo (in few envirooments in subreçiion) (on cootrasting sites in region) 2 4 selection for ...nde adaptatioo and yield stability (in severa! envtronmeots across fe9lon) selectioo for wide adaptation and yield stability (in several environments on contrasting sites across region) selection for specific adaptation aod yield stabil~y [m several environments in su.bregioo) Note: Envirorunent as location-year or location-crop cycle cornbination (see Section 2.6 for co1nn1ent of steps). variance, i.c. thc lack of genctic correlation a1nong environ1nents for .genotype values, and thc hetcrogeneity of genotypic variance among environrnents (see Section 4.3). Adaptive responses and yield stability of genotypes as affected by heterogeneity of genotypic variance have no practical i1nportance for brccding, sincc they relate to GL and other GE effects originating fro1n a scale effect of the environrnent ar1d i1nplying no change in relative rcsponsc of gcnotypes. The larger variance of this dctcmlinant relative to the lack of genetic correlation ~ variance detinitely suggests its re.duction tlirough a suitable data transforrnation (as discussed, in particular, with respect to analysis ofadaptation - see Section 5.6). ln any case, i f the lack of genetic correlation variance con1ponent is lo\v (e.g. belo,.,. 25-30%) con1pared to tlie gcnotypic variance component, it reveals the limited extent of GE interaction etfects relevant to breeding, and supports 13 (wi thout fu rther analyses) the selection for wide adaptation \Vith no regard for yield stabi lity (Fig. 2.3). To sirnplify lhe analysis (see Section 5.6). this preliminary step n1ay be replaced by the estimation of the genotypic and GE interaction con1ponents of variance only, together with the verification of the reJationship between location n1ean yield and within-location phenotypic standard deviation of genotype yields. If no correlation is found and tJ1e OE i.nteraction variance con1ponent is larger than or about as large as lhe gcnotypic varian.ce c-0mponent (e .g. > 80-1 OOo/o ), the nex t analytical step can be considered. The next step involves the estunation of different genotypic and genotype-envi ronmental variance components through an appropriate model of cornbined A NOVA (see Scction 4. 1 ). An analysis of adaptation may already bc justified if thc GL interaction variance Material com direitos autorais Genot_vpe x environ111en1 i11ter(lctiv11s: cha/lenges an<I opportunitics for 11f(l11/ breetling (lnd cultivar reco111111enfiations con1ponent is signi ficant and only moderatcly large (e.g. > 30-35o/o rclative to the genotypic variance) (Fig. 2.3), also because the variancc of sorne of its co1nponcn1s as defined in the analysis of adaptation ( c.g. thc heterogeneity of genotypc regressions con1poncnt) may prove larger (see case study in Chapter 8). Following an appropriate n1odclli11g of the GL cff ects or the site classi fícation based on similarity for these efTects (see Chapter 5), subregions 1nay be provisionally identificd lending thcrnselvcs to a practica.J definition based on geography, environn1ental factors or farming practices. Those that cannot be charactcrized as distinct f'ron1 each other n1ay be rnerged at this stage. Likc,visc, subregions \vhich are too sn1all to bc of practical intcrcst may be merged • .. vith larger ones. \\' ide and speci fie adaptation strategies can be compared in tcrms of yicld gains pred.ictcd from original yicld data of tJle sa1nc data sct (sec Section 6.1 ). ln che case of severa! candidate subregions, speci fie adaptation 1nay contemplate othcr possibilities besides targcting cach subregion (c.g. n1crging of some subregions. neglccting subregions of n1inor ilnportance). ln n1ost cases, specific breeding "''Ould not contemplate n1ore lhan t.\VO or three subregions. A final dccision on the adaptation strategy
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