Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley

The additive main effects and multiplicative interaction (AMMI) model has emerged as a powerful analytical tool for genotype x environment studies. The objective of the present study was to assess its value in quantitative trait locus (QTL) mapping. This was done through the analysis of a large two-way table of genotype-by-environment data of barley (Hordeum vulgare L.) grain yields, where the genotypes constituted a genetic population suitable for mapping studies. Grain yield data of 150 doubled haploid lines derived from the Steptoe x Morex cross, and the two parental lines, were taken by the North American Barley Genome Mapping Project (NABGMP) at 16 environments throughout the barley production areas of the USA and Canada. Four regions of the genome were responsible for most of the differential genotypic expression across environments. They accounted for approximately 50% of the genotypic main effect and 30% of the genotype x environment interaction (GE) sums of squares. The magnitude and sign of AMMI scores for genotypes and sites facilitate inferences about specific interactions. The parallel use of classification (cluster analysis of environments) and ordination (principal component analysis of GE matrix) techniques allowed most of the variation present in the genotype x environment matrix to be summarized in just a few dimensions, specifically four QTLs showing differential adaptation to four clusters of environments. Thus, AMMI genotypic scores, when the genotypes constituted a population suitable for QTL mapping, could provide an adequate way of resolving the magnitude and nature of QTL x environment interactions.Ignacio Romagosa was on sabbatical leave from the University of Lleida and the Institut de Recerca i Tecnologia Agroalimentàries, Lleida, Spain, when this study was conducted     

甘蔗品种主要性状的基因型与环境及其互作效应分析

用AMMI模型双标图对国家第六轮甘蔗品种区域试验5个试点的12个甘蔗品种试验数据进行分析,研究甘蔗区试中不同品种的产量稳定性问题。结果表明,参试品种的6个产量性状在品种间和地点间差异显著,品种与地点的互作效应差异显著;FN30、YG16蔗茎产量和含糖量高,稳定性强,属于高产、稳产性较好的品种。AMMI模型很好地解释了甘蔗品种产量性状的基因型效应、环境效应和GE互作效应。     

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.     

Predictive and postdictive success of statistical analyses of yield trials

Summary The accuracy of a yield trial can be increased by improved experimental techniques, more replicates, or more efficient statistical analyses. The third option involves nominal fixed costs, and is therefore very attractive. The statistical analysis recommended here combines the Additive main effects and multiplicative interaction (AMMI) model with a predictive assessment of accuracy. AMMI begins with the usual analysis of variance (ANOVA) to compute genotype and environment additive effects. It then applies principal components analysis (PCA) to analyze non-additive interaction effects. Tests with a New York soybean yield trial show that the predictive accuracy of AMMI with only two replicates is equal to the predictive accuracy of means based on five replicates. The effectiveness of AMMI increases with the size of the yield trial and with the noisiness of the data. Statistical analysis of yield trials with the AMMI model has a number of promising implications for agronomy and plant breeding research programs.This research was supported by the Rhizobotany Project of the USDA-ARS     

QTLs for grain carbon isotope discrimination in field-grown barley

In several crops including cereals, carbon isotope discrimination (Delta) has been associated with drought tolerance in terms of water-use efficiency and yield stability in drought-prone environments. By using a complete genetic map generated from 167 recombinant inbred lines from a cross between Tadmor and Er/Apm, QTLs associated with grain Delta have been detected in barley grown in three Mediterranean field environments, two differing only in water availability. Ten QTLs were identified: one was specific to one environment, two presented interaction with the environment, six presented main effects across three or two environments and one presented both effects. Heading date did not contribute to the environment (E) and G x E effects acting on Delta. Seasonal rainfall and the ratio of rainfall to evapo-transpiration made large contributions to the environmental effect, but their influence on G x E was weaker. Eight QTLs for Delta co-located with QTLs for physiological traits related to plant water status and/or osmotic adjustment, and/or for agronomic traits previously measured on the same population. Some perspectives in terms of characterising drought tolerance are evoked.     

Identification of Genetic Factors Contributing to Heterosis in a Hybrid from Two Elite Maize Inbred Lines Using Molecular Markers

The use of molecular markers to identify quantitative trait loci (QTLs) affecting agriculturally important traits has become a key approach in plant genetics-both for understanding the genetic basis of these traits and to help design novel plant improvement programs. In the study reported here, we mapped QTLs (and evaluated their phenotypic effects) associated with seven major traits (including grain yield) in a cross between two widely used elite maize inbred lines, B73 and Mo17, in order to explore two important phenomena in maize genetics-heterosis (hybrid vigor) and genotype-by-environment (G x E) interaction. We also compared two analytical approaches for identifying QTLs, the traditional single-marker method and the more recently described interval-mapping method. Phenotypic evaluations were made on 3168 plots (nearly 100,000 plants) grown in three states. Using 76 markers that represented 90-95% of the maize genome, both analytical methods showed virtually the same results in detecting QTLs affecting grain yield throughout the genome, except on chromosome 6. Fewer QTLs were detected for other quantitative traits measured. Whenever a QTL for grain yield was detected, the heterozygote had a higher phenotype than the respective homozygote (with only one exception) suggesting not only overdominance (or pseudooverdominance) but also that these detected QTLs play a significant role in heterosis. This conclusion was reinforced by a high correlation between grain yield and proportion of heterozygous markers. Although plant materials were grown and measured in six diverse environments (North Carolina, Iowa and Illinois) there was little evidence for G x E interaction for most QTLs.     

QTL and QTL x environment effects on agronomic and nitrogen acquisition traits in rice

Agricultural environments deteriorate due to excess nitrogen application.Breeding for low nitrogen responsive genotypes can reduce soil nitrogen input.Rice genotypes respond variably to soil available nitrogen.The present study attempted quantification of genotype x nitrogen level interaction and mapping of quantitative trait loci (QTLs) associated with nitrogen use efficiency (NUE) and other associated agronomic traits.Twelve parameters were observed across a set of 82 double haploid (DH) lines derived from IR64/Azucena.Three nitrogen regimes namely,native (0 kg/ha; no nitrogen applied),optimum (100 kg/ha) and high (200 kg/ha) replicated thrice were the environments.The parents and DH lines were significantly varying for all traits under different nitrogen regimes.All traits except plant height recorded significant genotype x environment interaction.Individual plant yield was positively correlated with nitrogen use efficiency and nitrogen uptake.Sixteen QTLs were detected by composite interval mapping.Eleven QTLs showed significant QTL x environment interactions.On chromosome 3,seven QTLs were detected associated with nitrogen use,plant yield and associated traits.A QTL region between markers RZ678,RZ574 and RZ284 was associated with nitrogen use and yield.This chromosomal region was enriched with expressed gene sequences of known key nitrogen assimilation genes.     

Genotype by environment interactions in viability and developmental time in populations of cactophilic Drosophila

The genetic and ecological basis of viability and developmental time differences between Drosophila buzzatii and D. koepferae were analysed using the isofemale line technique. Several isofemale lines were sampled from pairs of allopatric/sympatric populations of each species. Flies were reared in media prepared with decaying tissues of two of the main natural cactus hosts of each species. This experimental design enabled us to evaluate the relative contribution of phenotypic plasticity, genetic variation and genotype by environment interaction (G x E) to total phenotypic variation for two fitness traits, viability and developmental time. Our results revealed significant G x E in both traits, suggesting that the maintenance of genetic variation can be explained, at least in part, by diversifying selection in different patches of a heterogeneous environment in both species. However, the relative importance of the factors involved in the G x E varied between traits and populations within species. For viability, the G x E can be mainly attributed to changes in the rank order of lines across cacti. However, the pattern was different for developmental time. In D. buzzatii the G x E can be mainly accounted for by changes in among line variance across cacti, whereas changes in the rank order of lines across cacti was the main component in D. koepferae. These dissimilar patterns of variation between traits and species suggest that the evolutionary forces shaping genetic variation for developmental time and viability vary between populations within species and between species.     

Seedling and adult stage resistance to net form of net blotch (NFNB) in spring barley and stability of adult stage resistance to NFNB in Morocco

This study was conducted to identify stable resistance to net form of net blotch (NFNB) in spring barley in Moroccan environments. Seedling resistance to NFNB was evaluated by inoculating 336 barley genotypes with two NFNB isolates LDNH04Ptt-19 and TD-10 in the greenhouse. These genotypes were evaluated for adult plant resistance to NFNB under seven environments in Morocco in 2015 and 2016. The disease severity was estimated at GS 77–87 on barley leaves using a double-digit scale. To investigate stability of resistance, 149 barley genotypes were subjected to AMMI analysis. At the seedling stage, differential responses of barley genotypes to different NFNB isolates were identified, whereas genotypes had variable stability to NFNB resistance at the adult stages. Five genotypes, AM-68, AM-95, AM-250, AM-267 and AM-322, were resistant to both NFNB isolates at the seedling stage. There were significant (p < .001) effects of genotype (G) and G × E interaction on NFNB severity for barley genotypes at the adult stage. The principal components, IPCA1 and IPCA2, accounted for 48.4% and 18.7% variation for NFNB severity, respectively. The AMMI stability values (ASVs) ranged from 0.01 to 15.5, and fifty-nine barley genotypes had stable responses (ASV ≤ 0.05) across all seven environments. Specifically, two stable genotypes, AM-187 and AM-244, had lower mean NFNB severities across all environments, suggesting a quantitative resistance in these genotypes. Divergent environmental responses of NFNB severity were measured in Sidi El Ayedi 2015 and Sidi Allal Tazi 2016, suggesting that these environments may be suitable to capture resistance to diverse pathotypes. These stable genotypes are valuable resources for introgression of both qualitative resistance and quantitative resistance to NFNB in future.     

Choice of selection environment for improving crop yields in saline areas

Spatial variability in salt-affected fields is normally very high. Thus, most salinity affected lands are actually comprised of many micro-environments, ranging from low to high salinity in the same field. The evidence on testing genotypes across a broad range of salinity levels shows that the genotype-by-salinity level interaction is commonly large. Thus, breeding for saline areas can be compared to what has been known as breeding for wide adaptation. The target environments both for breeding for saline soils or for wide adaptation are actually a population of many possible environments, for which there exists a significant component of genotype-by-environment(G x E) interaction. Thus it is possible to study the merit of potential strategies for breeding for salinity tolerance using the tools that have been developed for the study of breeding for wide adaptation. The evidence from selection and breeding experiments for wide adaptation seems to favour testing on a representative subset of environments, including stress and non-stress locations; but the choice of these locations is complicated by the multidimensional nature of G x E. However, in the case of salt stress, the crop-yield response functions to salinity are well known. This paper presents an attempt to systematise the choice of the optimum environment(s) to select for improved yield under saline soil conditions, based on the three-piece linear equation presented by Maas and Hoffman (1977) and the theory of direct and indirect responses to selection. It is proposed that three saline levels should be enough to make a valid estimation of the suitability of a number of selection strategies. A worked example with data from a set of grain sorghum inbred lines tested on ten saline levels shows that the same selection strategies would be chosen using the information from the ten saline levels as that obtained using the two extremes and one intermediate level.     

Interaction of gene effects with environments for malting quality of barley doubled haploids

Barley doubled haploids covering a wide range of malting quality, along with their parental cultivars and F2, F3 hybrids, were investigated in six environments (three locations, two years) to study the genotype-environment (G x E) interaction structure and the influence of environments on additive, dominance and epistatic gene effects. Grain and malt characters, such as 1000-grain weight, percentage of plump kernels, malt extract yield, protein content, Kolbach index and malt fine-coarse difference (FCD), were measured. Main effects for genetic parameters were estimated and regression analysis was used to explain the interaction of gene effects with environments. The results show that additive effects had the greatest interaction with environments for all the analysed traits, but only for malt characters this interaction was linear. Interaction of dominance effects was much lower and only in the case of 1000-grain weight, protein content and Kolbach index it proved to be significant. The results suggest that effects of heterozygous loci are more stable in contrasting environments than effects of homozygous loci.     

Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis

Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.     

Genotype x environment interaction of crossover type: detecting its presence and estimating the crossover point

Genotype-environment interaction (GEI) introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. GEI can be either qualitative (i.e., crossover type) or only quantitative (i.e., non-crossover type). Since the presence of crossover-type interaction has a strong implication for breeding for specific adaptation, it is important to assess the frequency of crossover interactions. This paper presents a test for detecting the presence of crossover-type interaction using the response-environment relationship and enumerates the frequency of crossovers and estimation of the crossover point (CP) on the environment axis, which serves as a cut-off point for the two environments groups where different/specific selections can be made. Sixty-four barley lines with various selection histories were grown in northern Syria and Lebanon giving a total of 21 environments (location-year combinations). Linear regression of the genotypic response on the environmental index represented a satisfactory model, and heterogeneity among regressions was significant. At a 5% level of significance, 38% and 19% of the pairs showed crossover interactions when the error variances were considered heterogeneous and homogeneous, respectively, implying that an appreciable number of crossovers took place in the case of barley lines responding to their environments. The CP of 1.64 t/ha, obtained as the CP of regression lines between the genotype numbers 19 and 31, provided maximum genotype x environment-group interaction. Across all environments, genotype nos. 59 and 12 stood first and second for high yield, respectively. The changes in the ranks of genotypes under the groups of environments can be used for selecting specifically adapted genotypes. Received: 25 January 1999 / Accepted: 16 March 1999     

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids

Key message

A new genomic model that incorporates genotype?×?environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids.

Abstract

The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype?×?environment interaction (G?×?E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G?×?E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G?×?E in the prediction of untested maize hybrids increases the accuracy of genomic models.

     

AMMI adjustment for statistical analysis of an international wheat yield trial

Summary Multilocation trials are important for the CIMMYT Bread Wheat Program in producing high-yielding, adapted lines for a wide range of environments. This study investigated procedures for improving predictive success of a yield trial, grouping environments and genotypes into homogeneous subsets, and determining the yield stability of 18 CIMMYT bread wheats evaluated at 25 locations. Additive Main effects and Multiplicative Interaction (AMMI) analysis gave more precise estimates of genotypic yields within locations than means across replicates. This precision facilitated formation by cluster analysis of more cohesive groups of genotypes and locations for biological interpretation of interactions than occurred with unadjusted means. Locations were clustered into two subsets for which genotypes with positive interactions manifested in high, stable yields were identified. The analyses highlighted superior selections with both broad and specific adaptation.     

QTLs for agronomic traits from a Mediterranean barley progeny grown in several environments

In order to identify quantitative trait loci (QTLs) controlling agronomic trait variation and their consistency under Mediterranean conditions in barley, a progeny of 167 recombinant inbred lines (RILs) and the parents Tadmor and Er/Apm, originating from the Mediterranean basin, were grown under Mediterranean conditions in 1995, 1996, 1997 and 1999. For the 2 first years (M95 and G96), one replicate was grown, but for the latter (M97 and M99) two rainfed (rain) and two irrigated (ir) replicates were produced. M95, G96, M97rain, M97ir, M99rain and M99ir were considered as six different environments and were compared in terms of their meteorological conditions and water supply. Grain yield and yield components were assessed, as well as heading date and plant height. Highly significant differences were noted between environments. QTLs were obtained from each environment separately and from a multiple environment analysis (simple interval mapping and simplified composite interval mapping). Despite heterogeneity between environments, numerous QTLs were common to several environments. This was particularly true for traits like plant height and thousand-grain weight. The most reliable QTLs which explained the largest part of the phenotypic variation were obtained for plant height on chromosomes 3 (3H) and 6 (6H). The multiple-environment analysis provided an opportunity to identify consistent QTLs for agronomic traits over six Mediterranean environments. A total of 24 consistent QTLs were detected. Out of these, 11 presented main effects, seven presented QTL×E interaction, and six presented both effects. In addition, 18 of the consistent QTLs were common to other published work and six seemed specific to this study. These latter QTLs could be involved in Mediterranean adaptive specificities or could be specific to the studied genetic background. Finally, when the rainfed and the irrigated environments of M97 were considered separately, a total of 16 QTLs presenting main effects over the two water conditions were identified, whereas five QTLs seemed dependent on the water conditions. Received: 31 January 2001 / Accepted: 19 February 2001     

RFLP loci associated with soybean seed protein and oil content across populations and locations

Molecular markers provide the opportunity to identify marker-quantitative trait locus (QTL) associations in different environments and populations. Two soybean [Glycine max (L.) Merr.] populations, Young x PI 416 937 and PI 97100 x Coker 237, were evaluated with restriction fragment length polymorphism (RFLP) markers to identify additional QTLs related to seed protein and oil. For the Young x PI 416937 population, 120 F₄-derived lines were secored for segregation at 155 RFLP loci. The F₄-derived lines and two parents were grown at Plains, G.a., and Windblow and Plymouth, N.C. in 1994, and evaluated for seed protein and oil. For the PI 97100 x Coker 237 population, 111 F₂-derived lines were evaluated for segregation at 153 RFLP loci. Phenotypic data for seed protein and oil were obtained in two different locations (Athens, G.a., and Blackville, S.C.) in 1994. Based on single-factor analysis of variance (ANOVA) for the Young x PI 416937 population, five of seven independent markers associated with seed protein, and all four independent markers associated with seed oil in the combined analysis over locations were detected at all three locations. For the PI 97 100 x Coker 237 population, both single-factor ANOVA and interval mapping were used to detect QTLs. Using single-factor ANOVA, three of four independent markers for seed protein and two of three independent markers for seed oil were detected at both locations. In both populations, singlefactor ANOVA, revealed the consistency of QTLs across locations, which might be due to the high heritability and the relatively few QTLs with large effects conditioning these traits. However, interval mapping of the PI 97100 x Coker 237 population indicated that QTLs identified at Athens for seed protein and oil were different from those at Blackville. This might result from the power of QTL mapping being dependent on the level of saturation of the genetic map. Increased seed protein was associated with decreased seed oil in the PI 97100 x Coker 237 population (r = –0.61). There were various common markers (P0.05) on linkage groups (LG) E, G,H,K, and UNK2 identified for both seed protein and oil. One QTL on LG E was associated with seed protein in both populations. The other QTLs for protein and oil were population specific.     

烤烟主要农艺性状的遗传与相关分析

利用包括基因型与环境互作的加性-显性遗传模型,对14个烤烟品种(系)及其配制的41个杂交组合在4个环境下的7个农艺性状表现进行遗传分析。结果表明,株高、节距、腰叶宽主要受加性效应控制,叶数、腰叶长受显性×环境互作效应影响最大,茎围以加性×环境互作效应、显性×环境互作效应为主,产量以加性效应、显性×环境互作效应为主。适应当地生态条件的品种（系）具有较高的正向加性效应。许多组合的显性主效应及在各试验点的显性×环境互作效应在方向上不尽一致,杂交组合的选配宜针对特定的生态环境进行。性状相关分析表明,大多数成对性状的各项相关系数为正值,且多以加性遗传相关为主,可利用株高对产量进行间接选择。
     

Choosing probe genotypes for the analysis of genotype-environment interaction in winter wheat trials

Genotype-environment interaction was analyzed in French multi-environment wheat (Triticum aestivum L.) trials using probe genotypes and bi-additive factorial regression. Probe genotypes are specific genotypes in which the comparisons of yield components to reference values describe the most-important environmental factors that limited grain yield. The time-period until flowering was described by the deviation of kernel number from a threshold number while the grain-filling period was described by the reduction of thousand-kernel weight from a potential value. The aim of this paper was to determine the convenient number and the characteristics of probe genotypes to include in wheat breeding trials. Two sets of genotypes were used to model genotype-environment interaction: set 1 with 12 varieties tested in 18 environments and set 2 with ten lines tested in 14 environments. Set 2 was used for validation. Seven probe genotypes described the environments by providing environmental covariates, namely differences in yield components, for further analysis of interaction in set 1 and set 2. Interaction was modelled with bi-additive factorial regressions including differences in yield components. Several rounds of models were fitted to determine the optimal number of probe genotypes (i.e. environmental covariates) to introduce. From the seven probe genotypes, all the possible combinations including one to seven genotypes were studied. Significance of the combinations was tested with critical values obtained from simulations through 1,000 random permutations. Taking into account the information available on the probe genotypes, one would think that two, three or four probe genotypes would be sufficient, otherwise the number should reach four or five genotypes. In all cases, these numbers will provide models more-parsimonious than the classical AMMI model. The important information to be known on the probe genotypes prior their first multilocation experiment is: interaction pattern, earliness, and differences in yield component. Tested for the first time, a quadruplet is better than a triplet because the probability of choosing complementary genotypes increases with their number.