Genotype Location Research Articles

Estimation of Genetic Variances and Stability Components of Yield-Related Traits of Green Super Rice at Multi-Environmental Conditions in Pakistan

Identifying adopted Green Super Rice (GSR) under different agro-ecological locations in Pakistan is crucial to sustaining the high productivity of rice. For this purpose, the multi-location trials of GSR were conducted to evaluate the magnitude of genetic variability, heritability, and stability in eight different locations in Pakistan. The experimental trial was laid out in a randomized complete block (RCB) design with three replications at each location. The combined analysis of variance (ANOVA) manifested significant variations for tested genotypes (g), locations (L), years (Y), genotype × year (GY), and genotype × location (GL) interactions revealing the influence of environmental factors (L and Y) on yield traits. High broad-sense heritability estimates were observed for all the studied traits representing low environmental influence over the expression of traits. Noticeably, GSR 48 showed maximum stability than all other lines in the univariate model across the two years for grain yield and related traits data. Multivariate stability analysis characterized GSR 305 and GSR 252 as the highest yielding with optimum stability across the eight tested locations. Overall, Narowal, Muzaffargarh, and Swat were the most stable locations for GSR cultivation in Pakistan. In conclusion, this study revealed that G×E interactions were an important source of rice yield variation, and its AMMI and biplots analysis are efficient tools for visualizing the response of genotypes to different locations.

Lucerne cultivar adaptation to Italian geographic areas is affected crucially by the selection environment and encourages the breeding for specific adaptation

Plant breeding programmes frequently face the dilemma whether to breed for wide or specific adaptation. This is especially true for forage crop breeding, whose genetic resources may largely include landraces that are adapted to specific growing conditions. This study aimed to (1) investigate the extent and pattern of genotype × location (GL) interaction for biomass yield of recent lucerne cultivars across organically-managed sites of Northern and Central Italy and its relationship with cultivar selection environments, (2) provide an empirical assessment of yield gains derived from specific-adaptation and wide-adaptation breeding strategies, and (3) identify top-performing cultivars for different geographic regions. Cultivar adaptive responses, modelled by additive main effects and multiplicative interaction analysis, displayed wide GL interaction of cross-over type across test sites that reflected remarkably the geographic area and/or the growing conditions (particularly for extent of summer drought) of their selection environment. GL interaction patterns suggested three putative subregions to breed for specific adaptation, namely, Northern Italy north of the Po river, Northern Italy south of the Po river, and Central Italy. Growing specifically-adapted cultivars provided an estimated average advantage of 12.9% across subregions relative to the best-performing widely-adapted cultivar. Yield gains of recent top-performing material over an historical, widely-adapted cultivar indicated that selection for specific adaptation would provide 3.2-fold greater yield gain averaged across subregions, and 3.4-fold greater yield gain over the target region when weighting yield gains on the putative proportion of the target region of each subregion, compared with selection for wide adaptation.

A comparison of empirical BLUP with different considerations of residual error variance for genotype evaluation of multi-location trials

AbstractThe empirical best linear unbiased prediction (eBLUP) is usually based on the assumption that the residual error variance (REV) is homogenous. This may be unrealistic, and therefore limits the accuracy of genotype evaluations for multi-location trials, where the REV often varies across locations. The objective of this contribution was to investigate the direct implications of the eBLUP with different considerations about REV based on the mixed model for evaluation of genotype simple effects (i.e. genotype effects at individual locations). A series of 14 multi-location trials from a rape-breeding program in the north of China were simultaneously analyzed from 2012 to 2014 using a randomized complete block design at each location. The results showed that the model with heterogeneous REV was more appropriate than the one with homogeneous REV in all of the trials according to model fitting statistics. Whether the REV differences across locations were accounted for in the analysis procedure influenced the variance estimate of related random effects and testing of the variance of genotype-location (G-L) interactions. Ignoring REV differences by use of the eBLUP could result not only in an inflation or deflation of statistical Type I error rates for pair-wise testing but also in an inaccurate ranking of genotype simple effects for these trials. Therefore, it is suggested that in application of the eBLUP for evaluation of genotype simple effects in multi-location trials, the heterogeneity of REV should be accounted for based on mixed model approaches with appropriate variance-covariance structure.

Genotypic Association of Parameters Commonly Used to Predict Canning Quality of Dry Bean

ABSTRACTTo select dry bean (Phaseolus vulgaris L.) varieties suitable for the canning and processing industry, it is essential to understand the genetic and environmental effects on the quality parameters as well as their associations. A dataset of 6 yr of dry bean quality evaluations in the multi‐location provincial registration trials in Ontario, Canada, with data for parameters routinely used by breeding programs to predict the final canning quality of breeding materials was used. Genetic, environmental, and genotype (G) × environmental effects and multi‐variable associations were studied using the estimates of the genotypic least squared means for the balanced yearly data and the best linear unbiased predictors (BLUPs) for the overall unbalanced data. Genetic effects, accounting for 35 to 76% of the variation in navy bean and 38 to 88% of the variation in large‐seeded bean, followed by location (L) effects, accounting for 11 to 66% of the variation in navy bean and 11 to 64% of the variation in large‐seeded bean, were more important than the genotype × location (GL) interaction effects for seed composition parameters. This was not the case for physical and texture parameters, where GL interaction effects were often most important. Positive associations were observed among hydration coefficient, can yield, and protein content in the yearly analyses as well as in the multi‐year analysis. The negative association between percent washed‐drain solids and bean texture after processing was also repeatedly observed over years. The associations reported here may guide further selection efforts in bean breeding programs.

Adaptation of contrasting cocksfoot plant types to agricultural environments across the Mediterranean basin

Stress-tolerant forage resources are increasingly needed for the environmental and economic sustainability of extensive Mediterranean livestock systems. Perennial forages such as cocksfoot ( Dactylis glomerata L.) can be a valuable alternative to annuals, if they can survive across successive summer droughts. One Mediterranean cultivar of cocksfoot subsp. hispanica with complete summer dormancy (Kasbah), five non-dormant (Delta 1, Jana, Medly, Ottava) or incompletely dormant (Currie) Mediterranean cultivars of subsp. glomerata, and one Continental cultivar (Porto) of subsp. glomerata, were evaluated for dry matter yield over three years and persistence as final plant survival in six sites of Algeria, France, Italy, Morocco and Portugal, with the objectives of: (i) modeling adaptive responses and targeting cultivars as a function of environmental factors associated with genotype × location (GL) interaction; (ii) defining plant ideotypes, adaptation strategies and opportunities of international co-operation for regional breeding programmes. Adaptive responses were modeled by joint regression, additive main effects and multiplicative interaction (AMMI), and factorial regression. The most predictive models were: (i) factorial regression as a function of site spring–summer drought stress (as long-term potential evapotranspiration minus actual water available), for yield; (ii) AMMI including one GL interaction principal component related to site annual and spring–summer drought stress, for persistence. GL interaction of crossover type for yield and persistence was large and mainly associated with the summer dormancy trait. Completely dormant germplasm was specifically adapted to severe drought. Non-dormant Mediterranean cultivars tended to be specifically adapted to moderate drought stress, although they varied to some extent in adaptive response to drought-stress levels. The Continental cultivar was generally misadapted. The completely summer-dormant germplasm also tended to have greater general persistence across locations. Early flowering tended to correlate with higher yield and persistence of the cultivars across locations. Considerations on experimental conditions along with previous physiological studies from three sites suggested that water use efficiency of the cultivars tended to parallel their site-specific yield response. On the whole, the results suggest different adaptation targets, plant types, genetic resources and cultivar recommendation for northern Africa and southern Europe. Summer-dormant material of subsp. hispanica has prevalent interest for northern Africa. Breeding widely adapted, non-dormant or incompletely dormant Mediterranean cultivars of subsp. glomerata has prevalent interest for southern Europe, especially when targeted to moderate crop duration (3–4 years). However, completely summer-dormant germplasm could gain adaptive potential for Mediterranean-climate European regions in the future, to mitigate the effects of the predicted increasing drought due to climate change.

Yield Response of Winter Wheat to Agro‐Ecological Conditions Using Additive Main Effects and Multiplicative Interaction and Cluster Analysis

ABSTRACTGenetic improvement of crop performance is essential to increase yield. The aims of this paper are to present combined additive main effects and multiplicative interaction (AMMI) analysis and cluster analysis to winter wheat (Triticum aestivum L.) grain‐yield data collected in preregistration trials performed over many years in an incomplete three‐way genotype × location × year (GLY) classification and to demonstrate the usefulness of this procedure for studying the patterns of cultivar adaptation. The 21 genotypes were divided into three groups that were homogeneous in their pattern of genotype × location (GL) interactions. These groups of genotypes were further divided into subgroups with homogeneous genotype means. In this study 11 genotypes that were grouped in three subgroups had wide adaptability. Among them the cultivar TONACJA was identified as a good check cultivar for preregistration trials. One cultivar had narrow adaptation and nine were relatively poorly adapted. The combined AMMI and cluster analysis method proved effective for describing diverse patterns of yield response to agro‐ecological conditions in locations spread across most of the Polish winter wheat growing areas.

Response of common wheat varieties to organic and conventional production systems across Italian locations, and implications for selection

Specific breeding for organic systems may help reduce their yield gap relative to conventional systems by exploiting genotype × system (GS) interaction. Likewise, specific breeding for distinct subregions within a region could capitalize on genotype × location (GL) interaction. Grain yield and test weight of common wheat varieties were evaluated under organic and conventional systems in ten locations spanning from northern to southern Italy, with the objectives of: (i) comparing production systems; (ii) investigating the extent of GS and GL interactions and their relationship with genotypic and environmental characteristics; and (iii) preliminarily comparing, in terms of predicted selection gains, different strategies to cope with GS and GL effects. These effects were investigated in the 2-year Data set 1 including seven genotypes. GS effects were also assessed in the annual Data sets 2 and 3 including 13 and 11 genotypes, respectively. The yield reduction of organic systems relative to conventional ones averaged 28% in Data set 1, 29% in Data set 2 and 14% in Data set 3. Organic systems also tended to a modest test weight reduction. Genetic correlations between systems ranged from high to very high (0.88 ≤ r g ≤ 0.98) for yield and test weight, owing to nil or limited GS interaction. Broad-sense heritability tended to be higher in conventional systems than organic ones for yield in two data sets (mainly due to lower experimental error) while being similar in the two systems in the other cases. Predicted selection gains suggested nil (yield) or very modest (test weight) advantage of direct selection in organic systems relative to indirect selection in conventional systems, when targeting organic systems. The scope for selection only in conventional systems was reinforced when comparing predicted gains for selection scenarios which target both systems in relation to their foreseeable marketing importance. GL effects for yield and test weight were significant and were modeled by additive main effects and multiplicative interaction analysis. Site classification based on GL effects for yield revealed a larger subregion A including northern and central Italy and a smaller subregion B comprising southern Italy, accordingly with previous, independent studies. Yield selection only in subregion A (with indirect selection gain for subregion B) implied slightly higher predicted gain for A (+4%) and much lower gain for B (−24%) relative to independent, direct selection in each subregion. Selection for specific geoclimatic subregions may have greater importance than selection for specific production systems.

Graphic Analysis of Genotype by Environment Interaction for Lentil Yield in Iran

Selection of lentil (Lens culinaris Medik) cultivars with wide adaptability across diverse farming environments is important, before recommending them to achieve a high rate of cultivar adoption. Seed yield of 11 lentil genotypes, tested in a randomized complete‐block design with four replicates across 20 environments in Iran, was analyzed using site regression (SREG) stability model. The biplot technique facilitates a visual evaluation of superior genotypes, which is useful for cultivar recommendation and megaenvironment identification. A substantial amount of genotype × environment (GE) interaction for lentil grain yield was detected. Location (L) and genotype × location (GL) variabilities were the predominant components of total yield variation. The first two principal components (PC1 and PC2) of the SREG model accounted for 76% of the total GE interaction. There were four winning genotypes and three megaenvironments according to the SREG model. The best genotype in one location was not always so in other test locations. According to the ideal‐genotype biplot, genotype G5 was better than all other genotypes; G5 exhibited both high mean yield and high stability of performance across environments. According to G + GE sources of variations, the genotypes (G4, G7, G9, and G10) were the most suitable varieties for the lentil‐producing regions in Iran.

Cultivar Adaptation across Italian Locations in Four Turfgrass Species

Crossover genotype × location (GL) interaction may be exploited by site‐specific breeding and cultivar recommendations for distinct subregions. Turf quality and density of 110 elite cultivars belonging to four species [perennial ryegrass (Lolium perenne L.), Kentucky bluegrass (Poa pratensis L.), tall fescue (Festuca arundinacea Schreb.), and red fescue (Festuca rubra L. subsp. rubra, litoralis, and commutata)] were assessed at five locations scattered across the Italian peninsula and its main islands. The 110 cultivars were evaluated for (i) assessing the extent of GL interaction and its implications on cultivar selection and recommendation; (ii) comparing additive main effects and multiplicative interaction (AMMI) vs. joint regression modeling of GL effects; (iii) verifying the consistency between species of site similarity for GL effects; and (iv) defining test locations for the Italian national evaluation program and for local breeding programs. The trials were arranged in a Group Block Design with three replicates, grouping cultivars within species. Genotype × location interaction occurred for all traits (P < 0.05) except turf density in tall fescue and Kentucky bluegrass. The selected, one‐dimensional AMMI model always outperformed the joint regression model for predictive accuracy as estimated by the mean square value of the relevant GL interaction parameter. Wide crossover GL interaction was observed for turf quality among top‐ranking cultivars of red fescue and Kentucky bluegrass, justifying site‐specific recommendations. Variation of top‐ranking cultivars across sites also occurred for turf density in red fescue and perennial ryegrass. Site similarity for GL effects was largely inconsistent across species. However, specific adaptation either to cooler, wetter sites or warmer, drier sites emerged for cultivars of red fescue, perennial ryegrass, and tall fescue. Site‐specific breeding may be valuable especially for red fescue, exploiting the trend toward specific adaptation of its subspecies.

Defining Subregions and Estimating Benefits for a Specific‐Adaptation Strategy by Breeding Programs: A Case Study

Producing specific varieties for each subregion of a target region, instead of widely adapted varieties, may exploit positive genotype × location (GL) interactions to increase crop yields. With reference to the Algerian durum wheat (Triticum durum Desf.) region, as defined in a Geographic Information System (GIS), this study aimed at (i) comparing additive main effects and multiplicative interaction (AMMI) vs. joint regression modeling of GL effects, (ii) verifying the reliability of a GIS‐based definition of two subregions that extended the site classification on the basis of GL effects as a function of long‐term winter mean temperature in the GIS, and (iii) comparing wide vs. specific adaptation in terms of observed and predicted yield gains (the latter for scenarios of defined and undefined selection locations). Twenty‐four cultivars from international centers, Europe, and North Africa were evaluated across 3 yr in a total of 47 environments by randomized complete block designs with four replications per trial. The AMMI modeling was distinctly superior to joint regression. The AMMI + cluster analysis and pattern analysis classified test locations consistently and in good agreement with the GIS‐based subregion definition. Under the hypothesis of six selection environments assigned to subregions in proportion to their size (three sites in each of two years) for late stage selection, specific adaptation provided 2 to 7% greater gains than wide adaptation over the region at similar costs. The advantage of specific adaptation was much larger (39% determined on the basis of observed gains) for the smaller, stressful inland subregion, where specific adaptation may also enhance food security. Performing earlier specific selection, or adopting distinct genetic bases for each subregion, may further increase the advantage of specific breeding.

Repeatable genotype × location interaction and its exploitation by conventional and GIS-based cultivar recommendation for durum wheat in Algeria

Repeatable genotype × location (GL) interaction revealed by multi-locational trials may be exploited by site-specific cultivar recommendations. There is uncertainty, however, on methods for defining recommendations and extending results to non-test locations. With reference to durum wheat in Algeria, our main objective was the comparison of methods for defining the best pair of cultivars for local recommendation based on: (i) observed data; (ii) joint regression-modeled data; (iii) additive main effects and multiplicative interaction (AMMI)-modeled data; (iv) factorial regression-modeled data; (v) AMMI modeling interfaced with a geographic information system (GIS); (vi) factorial regression modeling interfaced with a GIS. The last two methods extended the recommendations to all sites in a GIS as a function of long-term climatic data. Concurrently, we aimed at assessing: (i) the repeatability over time of joint regression and AMMI parameters of adaptation; (ii) the consistency between predicted and actual yield gains derived from growing recommended cultivars in place of locally most-grown cultivars; (iii) the effect of different numbers of recommended cultivars. Modeling was based on grain yield of 24 genotypes evaluated across 2 years in a total of 31 environments. Cultivar responses at 16 sites in a third year were used for comparing methods according to average yields of recommended materials at individual sites and across sites, and for assessing repeatabilities. The selected AMMI model included one GL interaction principal component (PC 1). Winter mean temperature and rainfall over the cropping season were selected as covariates for factorial regression and as variables in a multiple regression for predicting the PC 1 score of sites in the GIS. Genotypic parameters, especially mean yield and PC 1 score, were highly repeatable ( r > 0.90). Site parameters (PC 1 score, mean yield) showed moderate to fairly low repeatability mainly due to within-site variation in annual rainfall. Recommendations based on modeled data implied less subregions (i.e. sets of locations with same top-yielding material) and provided, on the average, 4–5% higher yields and much better predictions of actual yield gains from recommendation compared with those based on observed data. GIS-based recommendations implied a slight yield decrease relative to those based on conventional modeling. However, they allowed for about 9% higher yields than those of most-grown cultivars, while enlarging the scope for site-specific recommendations and assisting national seed production and distribution systems. Factorial regression showed a slight advantage over the other models. On the average, recommending two top-yielding cultivars (reference criterion) or the top-yielding cultivar provided similar yields, somewhat higher yields than three top-yielding cultivars, and 8–10% higher yields than larger sets of statistically ( P < 0.20) not different cultivars.

Additive main effects and multiplicative interaction (AMMI) analysis of genotype-location interaction in variety trials repeated over years

Genotype-location (GL) interaction effects are of special interest for breeding programmes to identify adaptation targets, adaptive traits and test sites. These effects, generally having relatively low repeatability between years, should be studied on a multiyear basis in annual crops. Their assessment by additive main effects and multiplicative interaction (AMMI) analysis is currently defined for this situation. Two procedures based on cross validations are proposed for testing the GL-interaction principal component axes, exploiting the utilities of the computer programme MATMODEL. The use of Gollob’s F test, FGH2 test, FR test and the heuristic criterion based on the signal-to-noise ratio is also envisaged. The consistency of results provided by the testing procedures was verified on four data sets of different cereal crops. Gollob’s test tended to be the most liberal, while the FGH2 test appeared somewhat more liberal than the FR test. The signal-to-noise ratio gave results consistent with the FR test considered at a P?0.01 level of significance. These criteria disagreed in two data sets with the conclusions provided by the two cross-validation procedures which, in turn, also disagreed in one data set. Preference could be given to different testing procedures depending on the number of test years, locations and genotypes.

Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy

Joint regression and Additive Main effects and Multiplicative Interaction (AMMI) models were compared for i) capacity of describing genotype-location (GL) and genotype-environment (GE) interaction effects (environments = location-season combinations), assessed in terms of estimated variance of heterogeneity of genotype regressions and of the sum of the variances of significant interaction principal component (PC) axes, and ii) repeatability between cropping seasons of measures of genotype stability across locations. These measures were Finlay and Wilkinson's regression coefficient for joint regression, and the Euclidean distance from the origin of significant interaction PC axes (D) and the absolute value of PC 1 score (| PC 1 |) for AMMI. Shukla's stability variance (σsup2;) was considered in addition. The study included three data sets for durum wheat, two for maize and one each for bread wheat and oat. Relationships between climatic variables and GL interaction occurrence were also assessed. AMMI proved distinctly more valuable in six data sets for description of GE effects and in four for description of GL effects over seasons. Its superiority was not crop-specific and tended to occur when more, distinct environmental constraints affected genotype responses. When both methods were appropriate, they provided a similar ordination of sites and genotypes for GL effects. The models that adequately described GL interaction over seasons generally provided also stability measures that were moderately repeatable between seasons. D was better repeatable than | PC 1 | and σ& 2; in a few cases. Ordination of locations on GL interaction PC 1 tended to be consistent both between wheat and between maize data sets having either no seasons or no genotypes in common.

Genetic and Genotype ✕ Environment Effects for Rough Rice and Head Rice Yields

High rough rice and head rice yields are important to U.S. rice (Oryza sativa L.) producers for maintaining successful farming and milling operations. Unconfounded estimates of genetic and genotype ✕ environment (GE) variances for rough rice and head rice are lacking for U.S. rice breeding programs and would be useful aids to breeders in developing efficient programs. Our objectives were to estimate genetic and GE variance components for a rice population representative of a final testing stage in the Arkansas rice breeding program, to determine the relative importance of years, locations, and replicates in rice cultivar trials, and to demonstrate the usefulness of regression in genotypic stability analyses. Data were gathered on 19 genotypes at four locations in 1986, 1987, and 1988. Genotype ✕ year (GY) variance was the most important source of variation for head rice yield, whereas the genotype ✕ year ✕ location (GYL) variance was the most important source of variation for rough rice yield. The genotype ✕ location (GL) variance was the least important source of variation for both traits. An optimum experimental design in the Arkansas rice trials conducted on silt loam soils would include two locations replicated twice and repeated for a minimum of 3 yr. Regression techniques for estimating genotypic stability were most useful when regression was based on a year index, especially for head rice. The % Best statistic was significantly correlated with the mean and provided information on genotypes that consistently ranked in the top yield category [LSD (0.05)] across environments.