Genotype Plus Genotype × Environment Research Articles

A Multi-Model Based Stability Analysis Employing Multi-Environmental Trials (METs) Data for Discerning Heat Tolerance in Chickpea (Cicer arietinum L.) Landraces.

Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype-environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia-North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype-environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which-won-where pattern of genotype plus genotype-environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

Integrating genomic selection with a genotype plus genotype x environment (GGE) model improves prediction accuracy and computational efficiency

Genotype-by-environment interaction (GEI) is one of the major factors affecting the prediction accuracy of genomic selection (GS) models. Standard models have low power to model complex GEI, and they fail to predict phenotypes in unobserved environments. Here, we developed a novel prediction model that account for GEI, named 3GS, that combines genotype plus genotype × environment (GGE) analysis with GS. The model calculates the principal components (PCs) of the environmental phenotypes using GGE analysis and predict the performance of these PCs using standard GS models before converting the GEBVs of these PCs (pcGEBVs) back to the original phenotypes. We demonstrated three advantages of the new model. First, 3GS showed significantly higher prediction accuracy primarily for deviated environments that have low to negative correlations to other environments. Second, 3GS can predict new genotypes in unobserved environments with high accuracy. Third, the computational complexity of 3GS increases linearly with increasing the number of environments and the population size, unlike the standard models that exhibit exponential increase, making it hundreds of times faster than the standard models for large data sets. 3GS can improve prediction accuracy with minimal resources in modern breeding programmes in which massive populations get multi-environment phenotypes with high-throughput techniques.

Genetic analysis of grain yield and resistance of extra‐early‐maturing maize inbreds to northern corn leaf blight

AbstractMaize (Zea mays L.) is a food security crop in sub‐Saharan Africa (SSA). Incidence of northern corn leaf blight (NCLB), caused by Exserohilum turcicum, in lowlands of SSA during the past decade has caused 30–70% reduction in maize yield. This study (a) examined the combining abilities of extra‐early maize (EEM) inbreds and classified them into heterotic groups; (b) elucidated gene action controlling resistance to NCLB; (c) assessed grain yield (GYLD) and yield stability of EEM hybrids under NCLB infection; and (d) identified testers. One hundred and fifty EEM hybrids, obtained by crossing 15 inbreds each of white‐ and yellow‐endosperm maize using the North Carolina Design II, plus six checks, were evaluated in nine environments, six of which were inoculated with an isolate of E. turcicum and three of which were non‐inoculated in 2018 and 2019. The white and yellow inbreds were placed in three heterotic groups using the heterotic grouping based on general combining ability (GCA) of multiple traits and stability of GYLD using the genotype plus genotype × environment (GGE) biplot analysis. The GCA, specific combining ability (SCA) and genotype × environment (G×E) interactions were significant for GYLD, disease severity, and other measured traits. The GCA effects were more important than the SCA effects for GYLD and NCLB severity scores across environments, implying that recurrent selection could facilitate improvement for GYLD and NCLB resistance. Three inbred testers and four single‐cross testers were identified for developing high‐yielding NCLB‐resistant hybrids. Four white and five yellow single‐cross hybrids were identified for on‐farm testing and possible commercialization.

Nonlinear Regression and Multivariate Analysis Used to Study the Phenotypic Stability of Cowpea Genotypes

This study aimed to evaluate the adaptability and phenotypic stability of cowpea genotypes using a nonlinear regression analysis and multivariate analysis. Experiments were performed at four sites in Brazil using a randomized blocks design with 20 treatments and four replications. The adaptability and stability of genotypes were evaluated by Toler nonlinear regression and genotype plus genotype × environment (GGE) biplot methodologies. Most of the genotypes revealed linear response patterns, with no differences regarding the favorable and unfavorable environments. Regarding the genotype classification for stability and adaptability, the Toler and GGE biplot methodologies are congruent. Genotypes MNC99-537F-4, MNC00-561G-6, MNC99542F-5, and Patativa have high overall adaptability and adequate yield. Therefore, they should be recommended for cultivation in the tested environments. Genotypes closer to the ideotype by the GGE biplot method are considered doubly desirable by the nonlinear method.

Biplot analysis of diallel crosses for yield and some of morphological traits in wheat

A study of combining ability for improving bread wheat yield and yield components was carried out via genotype plus genotype × environment (GGE) biplot. Six bread wheat genotypes were used to obtain F1 in a diallel cross matings at the Gachsaran Field Station, Dryland Agricultural Research Institute during 2012–2013 cropping seasons. Analysis of variance indicated significant effects of genotypes, general combining ability (GCA) and specific combining ability (SCA) for all of the studied traits. The highest values of plant height (108.15 cm), peduncle length (33.95 cm) and grain yield (265.5 g m−1) were obtained in g6 (DAMARA-6 ICW99-0427-8AP-0AP-0AP-3AP-0AP). GGE biplot analysis showed that g4 (Kouhdasht) and g6 (DAMARA-6 ICW99-0427-8AP-0AP-0AP-3AP-0AP) were as the best general combiners for days to maturity, plant height and grain yield, whereas g1 (Karim) had the highest GCA effects for spike length and thousand grain weight, and g3 (Aflak) showed the highest GCA effects in terms of peduncle length. Meanwhile, g6 showed large SCA effects for thousand grain weight and grain yield, while g4 had high SCA effects for spike length. g5 (PASTOR/2*SITTA//PBW343*2/KUKUNACMSS04Y00333S-099Y-099ZTM-099Y-099 M-3WGY-0B) had large SCA effects for peduncle length. Because of significant effects of mid-parent heterosis for grain yield, g4 × g6 was considered as the best possible cross for improvement of this trait. Based on the results of present study, GGE biplot indicated good potential for identifying suitable parents and heterotic crosses and the best hybrids according to diallel mating design.

Optimum replications and locations for cotton cultivar trials under Mediterranean conditions

SUMMARYThe number of replications and locations used in a cultivar evaluation scheme is an important factor affecting the trial heritability (H) and optimum resource allocation. The aim of the present study was to calculate the required number of replications and locations for realizing an optimumHof 0·75 and to identify the most effective test locations for cotton (Gossypium hirsutumL.) in Greece. The data for lint yield, plant height, verticillium wilt (Verticillium dahliaeKleb.) and fibre quality were derived from an 8-year experiment (2000–2007) conducted under irrigated, Mediterranean conditions at 14 locations along the Greek mega-environment. Analysis of variance was performed to calculateHas well as genotype plus genotype × environment (GGE) biplot analysis to determine the location's desirability. It was determined that the four replications currently used in the evaluation of lint yield were sufficient, whereas four locations were proposed as optimum in lieu of the current 8–14 locations used in the evaluation. Two locations excelled as the most effective for lint yield evaluation and one for selection of genotypes tolerant to verticillium wilt using as criteria: the high and consistent across yearsH(0·75), GGE biplot representativeness and discriminating ability. Moreover, one location was selected as a backup based on average trial failures. Plant height was sufficiently evaluated by four replications and two locations, while verticillium required four replications for realizing lowerH(0·60) and three locations for even lowerH(0·40). Regarding quality, an increase of replications from the two currently used to four was sufficient for evaluation of all the traits. The advantage of reducing the number of locations for evaluation of lint yield to just four casts no doubt on the evaluation precision of lint percentage, length, strength and elongation but does for micronaire, short fibre index and uniformity, which realized lowerH(0·60 or 0·50).

GGE Biplot Analysis of Yield Associations with Root Traits in a Mesoamerican Bean Diversity Panel

Common bean (Phaseolus vulgaris L.) production is subject to prolonged and crop‐failure‐threatening drought events worldwide. One of the best approaches in identifying parents and progeny with local adaptation to a target environment is the characterization of multiple environments. In this study, we used genotype plus genotype × environment (GGE) biplot analysis to examine the informativeness of the testing environments used by the bean breeding program at Michigan State University to select for drought tolerance. A group of 96 diverse Mesoamerican common bean genotypes assembled from breeding programs and germplasm collections across the United States were tested in two Michigan locations for 3 yr. Genotype plus genotype × environment biplots were used to rank genotypes according to environments and treatments (irrigated and rainfed) within environments. A genotype × trait analysis was also used to identify traits relevant to yield under rainfed and irrigated environments. Significant differentiation and nonredundancy were found among the environments, suggesting that resource use is efficient. Rainfed environments were better at discriminating high‐performing genotypes in Michigan. Genotype × trait analysis showed a significantly negative correlation between growth habit and seed yield. A common feature of the better adapted and higher yielding genotypes under Michigan conditions was the upright type II growth habit. A number of root architecture traits such as taproot diameter were independent or associated with yield in common bean under different environments. Genotypes with contrasting traits associated with yield under rainfed and irrigated conditions were identified and could be used in the development of future common bean cultivars with improved drought tolerance.

Multi-environment field testing to identify broad, stable resistance to sterility mosaic disease of pigeonpea

Sterility mosaic disease (SMD) caused by Pigeonpea sterility mosaic virus and vectored by the eriophyid mite is a serious disease of pigeonpea in almost all pigeonpea-growing areas. Managing the disease with chemicals such as acaricides is very difficult, non-eco-friendly and costly; hence, host plant resistance is the best strategy implemented to manage this disease. In this context, 28 pigeonpea genotypes identified as resistant from preliminary screening of 976 pigeonpea accessions were evaluated in field at eight different agro-ecological locations in India for the stability of their resistance against SMD during 2007/2008 and 2008/2009. Genotype plus genotype × environment (GGE) analysis partitioned main effects into genotype, environments and G × E interactions and showed significant effects (P < 0.001) for SMD percentage incidence. Environment variance had the greatest effect (76.68 %), indicating the maximum variation in the disease due to the environment. At Bangalore, Dholi and Rahuri locations, all genotypes were susceptible to SMD with mean disease incidence of 71.1, 50.4 and 32.6 % respectively. However, most of the genotypes were resistant at four locations, Akola, Badnapur, Patancheru, and Vamban, and moderately resistant at Coimbatore. The GGE biplot analysis explained about 67.26 % of total variation and identified four genotypes (ICPLs 20094, 20106, 20098, 20115) as the most stable and resistant to SMD. Three genotypes (ICPLs 20096, 20107, 20110) showed moderately stable performance against SMD. These genotypes should be included in pigeonpea breeding programs as additional sources of resistance to SMD.

Biplot Analysis of Genotype by Environment for Cooking Quality in Hybrid Rice: A Tool for Line × Tester Data

A study of combining ability for improving rice cooking quality was carried out via genotype plus genotype × environment (GGE) biplot. Four restorer lines and three male sterile lines were used to obtain F1 in a line × tester trial at the Rice Research Institute, Amol, Iran in 2009. GGE biplot analysis showed that Neda and IR56 were the best general combiners for amylose content (AC), whereas Nemat and IR28 had the highest general combining ability (GCA) effects for gelatinization temperature (GT), and IR58 and IR59 showed the highest GCA effects in terms of gel consistency (GC). Meanwhile, IR58 and IR59 showed large specific combining ability (SCA) effects for AC, while Neda and SA13 had high SCA effects for GT. Nemat and IR28 had large SCA effects for GC. Because intermediate levels of AC, GT and GC are ideal, Nemat × IR59 was considered as the best possible cross. Based on these results, the GGE biplot showed good potential for identifying suitable parents, heterotic crosses and the best hybrids in line × tester data.

Genotype Plus Genotype × Block of Environments Biplots

ABSTRACTBiplots have been widely used in recent years for the analysis of multi‐environment trials through the genotype plus genotype × environment (GGE) biplot analysis or through the additive main effects and multiplicative interaction analysis. Sometimes the environments are structured in blocks of environments, for example, regions or years. In this paper, we propose a new biplot technique for describing genotype × environment interactions that takes into account this additional information. We call it a GGB biplot for genotype plus genotype × block of environments biplot. This biplot has interesting geometrical properties that will be covered in this paper. Similarly to what was done for the GGE biplot (Laffont et al., 2007), we also establish a link between the partitioning of the total sum of squares provided by the GGB biplot and the partitioning provided by the analysis of variance. Simulated and real data are used to illustrate this new biplot.

Efficient test sites for multi-environment evaluation of sugarcane genotypes in Thailand

Multi-environment trials (METs) of crop genotypes are costly and require efficient test sites for cost effectiveness. This study aimed to identify efficient test sites for METs of sugarcane (Saccharum spp.) genotypes in Thailand, utilizing data from 10 sugarcane genotypes conducted at nine locations covering different sugarcane growing regions of the country for two crop-classes. Cluster analysis and the genotype plus genotype × environment (GGE) biplot method were used to group these sites into five subsets, based on their similarity in genotypic responses of cane and sugar yields of the planted crop and the first ratoon crop. The results showed a fair agreement between the two methods, but inconsistent results were obtained from groupings that were based on different yield traits and crop-classes. Locations appearing more consistent in certain groups were chosen as the representatives of the respective groups to constitute the set of efficient test sites. Cluster analysis and the GGE biplot, however, identified different sets of test sites that were equally effective in retaining the G×L interaction and the performance ranking of the test genotypes as the original nine test sites. The selected locations by cluster analysis which included Nakhon Ratchasima, Ratchaburi, Kamphaeng Phet, Tha Phra, Khon Kaen and Udon Thani are preferred because of their wider geographical distribution. Four sites could thus be omitted, which would substantially reduce the costs and time and greatly improve the efficiency of the METs of sugarcane genotypes in Thailand.

Using genotype plus genotype × environment (GGE) bi-plot analysis to identify promising amaranth accessions in Western Nigeria

SummaryAmaranth (Amaranthus spp.) is predominantly a self-pollinating plant and represents a promising and highly nutritious pseudo-cereal (a non-grass plant that produces edible grain) and vegetable crop. Genotype × environment (G × E) interactions exist during cultivar evaluation, indicating the need to identify and select stable, high-performing genotypes that are best suited to a given set of environmental conditions. This study was carried out to examine the effect of G × E interactions on the performance and stability of 30 amaranth accessions and to identify reference test environments in which to evaluate amaranth. All 30 amaranth accessions were evaluated for leaf dry matter (DM) contents and seed yields across four different environments using a randomised complete block design. The four growing environments were situated at two locations, Ibadan and Abeokuta, in Southwest Nigeria, between 2009 and 2010. Genotype plus G × E (GGE) biplot analysis was used. Averaged across all four environments, amaranth accessions NG174 and NG430 produced the highest leaf DM and seed yields, respectively. The test environment contributed 10.9% and 48.0% of the total variation in leaf DM and seed yield, respectively; while G × E accounted for 36.9% and 20.3% of the total variation, respectively. Significant (P ≤ 0.05) G × E interactions indicated changes in the ranking of accessions in the test environments. Ibadan was identified as the more discriminating and representative of the two locations used in the study. NG145 and NG205 produced the highest leaf DM contents and seed yields, respectively, and were the most stable accessions at Ibadan. TIBET50, NG430, and NG174 were better adapted to Abeokuta, while NH161, NG174 and AM44 were better adapted to Ibadan. These accessions could serve as potential parents for future breeding and selection programmes on amaranth.

Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S2di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha−1 for breeding lines and modern cultivar versus 2,041 kg ha−1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.

Integrating Empirical and Analytical Approaches to Investigate Genotype × Environment Interactions in Sugarcane

ABSTRACTThe causes of genotype × environment (G × E) interactions in sugarcane (Saccharum spp.) are unclear. The objectives of this study were to (i) investigate the G × E interactions and site similarity in two selection programs in South Africa, (ii) identify factors responsible for G × E interactions, and (iii) illustrate the integrated use of crop models and climatic data to better understand G × E interactions. Data from eight series of trials were analyzed using variance components, genotype plus genotype × environment (GGE) biplots, and additive main effects and multiplicative interaction (AMMI). Crop model simulations helped summarize environmental covariates before and after canopy closure. Environmental covariates and genotypic traits were correlated to AMMI scores and superimposed on biplots. The G × E interaction accounted for more variation than the main effect of genotype and the repeatable component of G × E was dominant across all series. Two sites were identified as redundant and were recommended for removal. Sites from one program were characterized by higher rainfall and lower water stress compared with sites from the other. The enriched AMMI2 biplots allowed for identification of factors that influenced genotypic traits and permitted biological interpretations and trait relations to be studied. This study has illustrated new approaches to integrate G × E studies with environmental data for comprehensive sugarcane G × E interpretations.

Variation for nodulation and plant yield of common bean genotypes and environmental effects on the genotype expression

Common bean symbiotic nitrogen fixation provides an ecological and economical alternative to increase bean production but it depends on soil fertility and climate conditions. The objectives of this work were to characterize common bean genotypes for their ability to establish symbiosis under controlled conditions and to study the effect of the environment on the expression of those genotypes. The experiment under controlled conditions was conducted in a greenhouse with 158 genotypes that represented the major dry bean market classes. The field experiment was carried out in six environments with 64 genotypes that were previously selected for their contrasting nodulation ability and/or root development under the controlled conditions experiment. Nodulation, plant, and grain yield data of the bean genotypes were measured in both experiments. There was a significant high variability in plant development responses among the studied genotypes associated with the rhizobial strain inoculated under controlled conditions. Two nodulation phenotypes were observed among the genotypes tested: the big-nodules phenotype (BNO) associated with almost 63% fewer nodules and 58% higher proportion of nodule biomass in the below-ground compartment than the small-nodules phenotype (SNO) with less developed aerial parts. Genotype plus genotype × environment (GGE) biplot model analysis enabled identification of the highest-yielding genotypes for the different environments. The soil chemical factors of these environments were associated with the nodule number or the biomass of the common bean genotypes. Genotypes with a BNO phenotype showed a good plant response, indicating that this phenotype may be more beneficial for plant growth and seed yield in environmental conditions that may limit nodule development. The amplitude of the genotypic variability found in this work confirms the potential for rhizobial symbiosis of adapted bean genotypes, which could constitute a preferential material for initial breeding of symbiotically active lines. The data also indicate the potential of bean breeding to identify environments containing effective strains of rhizobia essential for sustainable agriculture, improving productivity, and maintaining environmental quality.

Genetic variation and genotype-environment interactions in short rotation Populus plantations in southern Europe

Growth and production in the first year, as additional selection criteria, were assessed for nine poplar clones to be used as short rotation woody crops (SRWC) in the production of biomass for energy purposes. In order to identify the most promising clones in terms of growth and yield and also to assess their stability, trials were established at different locations in Spain. The majority of these clones, which form part of the European list of base materials, have frequently been used in plantations aimed at timber production but not for biomass in Mediterranean conditions. Others, such as those selected in Italy specifically for biomass production (currently provisionally admitted), are being tested for the first time under different soil and climatic conditions in Southwest Europe. The early selection of clones for rapid juvenile growth provides a valuable additional input to the clonal selection process, especially where very short rotations are desired (no more than 3 years). In any case, determining clonal stability in terms of growth is of great use not only when deciding on the clones to be used in plantations but also when developing breeding programs. ANOVA and Genotype plus Genotype × Environment (GGE) biplot analyses were used to analyse the growth and stability of the clones, which were then ranked according to mean performance and stability. Differences were detected between clones as well as between the different environments tested. The biplot analysis allowed different groups of clones to be identified according to their performance and degree of interaction displayed, thus providing useful information for the selection process. The production of aboveground biomass in the first vegetative period ranged from 1.7 to 8.0 Mg DM ha−1 at the different sites. ‘Monviso’, ‘Guardi’, ‘AF2’ and ‘2000 verde’ were the most productive clones whereas ‘Unal’, ‘Pegaso’ and ‘USA 49-177’ were the least productive. The stability analysis identified ‘AF2’, ‘Guardi’, ‘I-214’ and ‘MC’ as more stable clones while ‘Monviso’, ‘2000 verde’, ‘Unal’, ‘Pegaso’ and ‘USA 49-177’ were found to be specifically adapted to certain environments. This implies that where information on site conditions is not available, the ‘AF2’ and ‘Guardi’ clones offer greater assurance of successful establishment and higher initial growth. The growth of ‘Monviso’ ‘2000 verde’ ‘Unal’ ‘Pegaso’ and ‘USA 49-177’ clones is highly dependent on site conditions during the establishment phase. Similarly, the SH (Shore Henares river) and LT (La Tallada) sites were identified as the most highly discriminative environments for the set of clones while CS (Cubo de la Solana) and AR (Atarfe) were identified as those where performance levels were average.

Screening of superior chickpea genotypes for various environments of Iran using genotype plus genotype environment (GGE) biplot analysis

Superior crop cultivars must be identified through multi-environment trials (MET) and on the basis of multiple traits. The objective of this study was to explore the effect of genotype (G) and genotype × environment interaction (GE) on grain yield of 17 chickpea genotypes (Cicer arietinum L.) in six different research stations of Iran. GGE (G plus GE) biplot methodology was used to evaluate phenotypic stability in genotypes. A site regression (SREG) analysis to assess G × E interactions and to identify stable genotypes of chickpea was undertaken. These genotypes were developed by various breeders at different research institutes/stations of Iran and International Center for Agricultural Research in DrayAreas (ICARDA). Results indicated that the first two principal components explained 95% of the total GGE variation, with PC1 and PC2 explaining 73 and 22%, respectively. Genotypes Flip 93-93, Flip 94-123C and S 96002 had the highest mean yield and genotype Bivanij had the poorest mean yield. Thus the performance of genotype ILC 6142 was highly variable, whereas genotypes S 96003, Flip 93-48C and S 96027 were highly stable. Collective analysis of the biplots suggests four chickpea mega-environments in Iran. The first mega-environment contained locations Kermanshah, Gorgan and Ghachsaran, with genotype Flip 93-93 being the winner. Genotype Flip 85-57 × 12-071-1005 gave the highest performance in location Ilam and genotypes S 96032 and Bivanij gave the highest performance in locations Urmia. The Lorestan made up the other mega-environment with ILC 6142 as the winner. Key words: Cicer arietinum L., genotype × environment interaction, site regression analysis, GGEBiplot.

Determination of efficient test sites for evaluation of peanut breeding lines using the CSM-CROPGRO-peanut model

Efficient testing of crop breeding lines requires a set of complementary test sites that adequately sample the target environments with minimal duplication. Such test sites have been derived from actual multi-environmental trial (MET) data, which often have a limitation with respect to their environmental coverage. However, this limitation can be overcome using a crop simulation model. The goal of this study was to determine the efficient test sites for METs of peanut breeding lines in Thailand using the CSM-CROPGRO-Peanut model. The model was used to simulate pod yield for 17 peanut lines at all peanut production areas in Thailand that included 76 locations in the early-rainy season, 39 locations in the mid-rainy season and 47 locations in the dry season for 30 years. The simulated data were used to sub-divide the locations for each season into groups using cluster analysis and the genotype plus genotype × environment (GGE) biplot method. Six sets of test sites were obtained based on different scenarios for site selection that included combinations of geographical distribution and representation of location-groups as determined by the two methods. Set 1 was based on geographic distribution. Sets 2–4 were based on location grouping by cluster analysis, but with the sites distributed in all regions (Set 2), or only in the north (Set 3) or northeast (Set 4). Set 5 consisted of the sites currently used, and Set 6 was based on location grouping by the GGE biplot. Although Sets 2 and 6 appeared to capture more genotype × location interaction than the others, performance rankings of the test genotypes were almost the same for all sets. They were, therefore, considered equally effective for breeding line evaluation. The final selection was then based on the convenience, and consequently the cost, for conducting the METs. Set 4 was considered most preferable in this regard. This study demonstrated the usefulness of a crop simulation model as a tool in determining the most efficient test sites for the evaluation of peanut breeding lines.

Use of gross income as a measure of productivity in rice breeding

Rice breeders consider grain yield and milled rice percentages in developing cultivars, but usually do not consider gross income. This study’s objectives were to identify rice genotypes that produced high and stable expected gross incomes using genotype plus genotype × environment (GGE) biplot analysis. Uniform Regional Rice Nursery data on 47 long-grain genotypes grown at five locations (AR, LA, MO, MS, and TX) during 2001 to 2003 were analyzed. Gross income of each genotype was estimated based on grain yield, milled rice percentages, market prices, and direct and counter-cyclical payments. Based on GGE biplot analysis, the genotypes with the highest yield and highest gross income for the main crop were different in 12 out of 13 environments. RU0103184, Francis, and RU0003178 were the genotypes with the highest gross income in six, four, and three environments, respectively. Rice breeders should consider gross income as a selection criterion in the release of new cultivars. Key words: Rice, GGE, GE, breeding, gross income

Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran

Cultivar evaluation and mega‐environment identification are the most important objectives of multienvironment trials (MET). The objective of this study was to explore the effect of genotype and genotype × environment interaction on the grain yield of 19 barley (Hordeum vulgare L.) genotypes via GGE (genotype plus genotype × environment) biplot methodology. Experiments were conducted using a randomized complete block design with four replications for 3 yr at 10 locations. The biplot analysis identified three barley mega‐environments in Iran. The first mega‐environment contained locations Khoy, Mashhad, Miandoab, Karaj, and Nyshabour, where genotype Bahtim7‐D1/79‐w40762 was the winner; the second mega‐environment contained locations Tabriz, Hamedan, Ardabil, and Arak, where genotype Walfajre/W1‐2291 was the winner. The location of Zanjan made up the other mega‐environment, with 73‐M4‐30 as the winner. Genotypes Bahtim7‐D1/79‐w40762 and Walfajre/W1‐2291 had the highest mean yield and genotype K‐201/3‐2 had the poorest mean yield. The estimated relative yield of genotypes at Karaj station shows that genotype Bahtim7‐D1/79‐w40762 had the highest yield and genotype Owb70173‐2H‐OH had the poorest. The performances of genotypes Star/Alger and K‐201/3‐2 were highly variable, whereas genotypes Cossak/Gerbel/Harmal and Toji“S”/Robur were highly stable. The results of this study indicate the possibility of improving progress from selections under diverse location conditions by applying the GGL (genotype plus genotype × location) biplot methodology.