Adaptation Patterns and Definition of Macro-environments for Selection and Recommendation of Common-wheat Genotypes in Italy

Eighteen common-wheat varieties grown for three seasons at 31 locations in Italy were used to study the relationships between cultivar adaptation patterns, climatic factors and morphophysiological traits of genotypes, as well as defining macro-environments for the selection and recommendation of genotypes in the region. The variances of both the genotype-location and genotype–season interactions were significant, the former being almost 80% greater than the latter. Some 10.4% of genotype-location interaction was accounted for by heterogeneity of genotype regressions on site mean, 38.0% by the first two axes of a principal components analysis and 32.0% by definition of four macro-environments. Occurrence of genotype-location interaction was related to the level of late frosts, winter cold, and late drought and heat stress at each location, and to heading date and tolerance of frost, Septoria blotch and lodging in each genotype. Following estimates of broad-sense heritability, breeding for specific adaptation may be envisaged for northern and central Italy on the one hand, and southern Italy on the other. Morphophysiological traits contributing to better performance in each of these macro-environments were indicated.     

用AMMI模型分析作物区域试验中的地点鉴别力

为了比较不同试验点在区域试验中鉴别力的大小,利用AMMI模型中地点的得分向量长度衡量地点鉴别力,并与传统的联合线性回归模型中的斜率进行了比较。以1995~96和1997~2000年度黄淮南片春水组小麦区域试验4年的产量数据为例进行了地点鉴别力分析。结果表明, AMMI模型比联合线性回归模型能更好地解释基因型与环境互作效应;多     

Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis

An understanding of the characteristics of crop varieties and advanced lines could help improve their cultivation and to further enhance their potential. The objectives of this study were to estimate the genotype (G), environment (E) and genotype × environment (GE) interactions on the grain yield of Chinese spring wheat genotypes in 2000 and 2001 by the additive main effects and multiplicative interaction (AMMI) model, and to evaluate the relationships between yield and its components by correlation and path analysis. Grain yield varied from 3.9 to 5.2 t ha^?1, among which SW8188 had the highest yield performance, followed by 58769‐6 and Chuannong 16. Three interaction principal components (IPC) accounted for a total of 79.99 % and 72.96 % of the interactions with 41.05 % and 52.08 % for the corresponding degrees of freedom in 2000 and 2001, respectively. When IPC3 was significant, the stability coefficient D_i was more useful in the evaluation of the stability of each genotype. The estimates of D_i in the 2 years indicated that the D_i values varied between genotypes and years. The D_i values ranged from 1.804 to 14.665 and 2.497 to 12.481 in 2000 and 2001 respectively. The suitable locations (environments) for all genotypes were characterized. These results would be useful for improving the Chinese spring wheat cultivation and improvement.     

AMMI模型在蓖麻杂交育种中的应用

在蓖麻品种的育成与推广过程中,品种产量性状的稳定性是一个重要影响因子。由于品种的基因型和环境存在着交互作用,用一般的线性回归方程只能解释小部分交互作用。主效可加互作可乘模型（AMMI模型）,不仅最大程度地反应互作变异,而且能准确地分析品种的稳定性。本文应用AMMI模型及双标图对4个蓖麻杂交组合2008-2009年多点试验的产量性状进行稳定性分析,进而评价各参试组合的稳定性和适应性。     

基于AMMI模型的黄淮海夏大豆国家区试产量分析

为了解黄淮海夏大豆国家区试中各品种丰产性、 稳产性以及各试点鉴别力, 推动该地区大豆育种和推广工作, 利用 AMMI模型对 17个品种 （系） 在 8个试点的大豆产量表现进行分析。结果表明, 品种(g)、 试点(E), 品种×试点(g×E)互作、 交互效应主成份值(IPCA)均达极显著水平。丰产性前 3位品种依次为 ：‘SFy0803’ 、‘中黄 37’ 、‘沧豆 11’ , 稳定性前 3位品种依次为 ：‘邯豆 5号’ 、‘沧豆 11’ 、‘中作 J8032’ ,试点鉴别力前 3位依次为： 临猗、 临汾、 潍坊。试点的稳定性参数与经纬度、 生育期内平均气温、 降水、 日照的相关系数不显著。     

Inter-site transferability of crop varieties: another approach for analyzing multi-locational variety trials

Summary Improved crop varieties developed at experimental stations are intended for release for production in farmers' fields which are not necessarily the test sites. Therefore, it is important to measure the inter-site transferability of the crop variety. This study was undertaken to establish a statistical measure of inter-site transferability of a variety. Data on grain yield from two sets of multi-locational trials on barley and four sets of the wheat trials conducted by the Cereal Program, International Center for Agricultural Research in the Dry Areas, Syria, were analyzed to evaluate the inter-site transferability of the varieties. Genotype × environment interaction was significant and experimental errors were heterogeneous in each set of trials. Correlations between the slope (of linear regression of variety mean on location mean) and variety mean were significant (P < 0.01) in all trials. Correlations between the developed inter-site transferability statistic and the variety mean were significant (P < 0.05) in only two trials. Correlations between mean yield and probability of the inter-site transferability statistic were insignificant in all trials. The inter-site transferability statistic and the associated probability of the transferability may be used to select varieties which are high yielding as well as transferable to a new environment. The strong association of slope with mean yield restricted the scope of varietal selection but the introduced statistic did not suffer from this drawback.     

Stability of performance of tetraploid wheat landraces in the Ethiopian highland

Plant breeding has always been concerned with genotype-environment interaction. Normally high and stable performance are desirable attributes of cultivars. However, this is practically difficult to achieve where environmental variations are high and unpredictable and significant genotype-environment interactions occur. Stability of performance of 13 landraces evaluated at 4 different locations for 3 years in the highlands of Ethiopia was investigated. The testing locations have different climatic and edaphic conditions providing the conditions necessary for the assessment of stability. Stability parameters like b, s²d, s², r² and cv which are in common use were employed. Grain yield and 1000-kernel weight were the agronomic traits considered for the stability analysis. There were differences in the ranks of genotypes across the locations. Significant main environmental as well as interaction effects were observed showing the importance of genotype-environment interaction in both traits. Many of the landraces evaluated are rated as stable for these traits within the environmental conditions prevailing in these highland locations. Genotypes with specific adaptation to poor and favourable conditions were also identified. Certain genotypes showed similar manners of adaptation and stability for both of the traits. Grain yield showed low correlations with the stability parameters showing the possibility of attaining high yield and stability. Correlations between the stability parameters were mainly positive and significant for grain yield. Only a few of these correlations were found to be significant for kernel weight. The good adaptability of landraces should be exploited in the improvement of their yield potential. This revised version was published online in July 2006 with corrections to the Cover Date.     

Genotype × environment interactions in the expression of net blotch resistance in spring and winter barley varieties

The net blotch resistance of barley varieties widely grown in Denmark was studied using data obtained from naturally infected field plots and inoculated disease nurseries in multiple years and locations. Data were analysed by combining two statistical procedures, 1) joint regression (JR) and 2) additive main effect, multiplicative interactive effect analysis (AMMI). Of the total variation for net blotch disease severity, 61–81% could be explained by plant genotype (G) and environment (E) main effects. Of the remaining variation, 77–86% could be explained by significant G × E interactions that were due to genotype sensitivity to the mean environmental disease level and/or to specific reactions of individual genotypes in particular environments. G × E interactions led to a different ranking of varietal performance across environments. There was evidence for G × E interactions caused by the virulence characteristics of the initial inoculum sources. The spring barley varieties Alexis and Bartok and the winter barley varieties Jolante, Ludo, and Rafiki were identified as varieties with high levels of net blotch resistance in all environments. Their performance was little affected by G × E interactions, suggesting that they represent good sources for non-specific resistance. Combining JR and AMMI reduced the dimensionality of complex G × E problems greatly, identified systematic reaction patterns of varieties possibly pertaining to different resistance mechanisms, and described specific and non-specific resistance of varieties by means of few parameters while maintaining the possibility to reconstruct the original data with little loss of information. This is very useful for improving the evaluation of varietal resistance for breeding and disease management purposes. This revised version was published online in July 2006 with corrections to the Cover Date.     

Characterization of Environments in Long-Term Multi-Site Trials in Asparagus, through Yield of Standard Varieties and Use of Environmental Covariates

In genotype-environment interaction studies, in long-term multi-site trials, two main problems arise: missing values, and assessment of environments which consist of diverse locations and years. In this paper, a factorial regression model on asparagus data has been used to overcome these difficulties. These data consist in yields of seven asparagus standard varieties observed in 35 combinations of 4 locations × 9 years with missing values. Two kinds of covariates were kept in the model: (i) a biological one, e_j, which is the main environmental effect estimated with the additive model on a chosen set of genotypes (standard varieties); and (ii) three external environment covariates: the latitude of the location, the type of production (green or white asparagus), and the mean temperature during the five months preceding the beginning of the harvest. These results suggest that a significant part of genotype × environment interaction in asparagus is due to the different effects of low temperature during the dormant period on the physiology of the sub-ground crown, depending on the genotype.     

应用AMMI模型分析烤烟区试品种稳定性

AMMI模型在分析基因型与环境互作效应方面有明显的优势,可定量地描述各品种稳定性差异以及各地点对品种鉴别力的大小。为筛选适应河南省特定生态条件下的优良烤烟新品种,应用AMMI模型对2010年河南省烤烟区域试验4个试点7个参试品种的丰产性及稳产性进行分析。结果表明,所有参试新品种产量均值均高于对照‘NC89’,其中,‘Y106’和‘H8190’具有丰产稳产的特性,‘Y102’产量最高,但稳定性较差;‘8182’丰产性差,稳定性好;‘8122’丰产性较差,稳定性较差;‘优选一号’丰产性差,稳定性较好;对照‘NC89’丰产性差,稳定性一般。     

Adaptation and stability analysis of sweet potato varieties for low-input systems in Uganda

Sixteen sweet potato varieties were evaluated for fresh storage root yield in 20 trials during 2000–2001 for three seasons in four locations in Uganda. Of the 16 varieties, 11 were developed by farmers and five by a central breeding programme. The behaviour of the varieties was quantified in terms of wide adaptation (genotypic mean across trials), specific adaptation (genotypic predictions for specific locations) and stability (Shukla stability variance). With respect to all three aspects of yield behaviour, farmer varieties performed on average better than the official varieties. The results illustrate the potential that farmer varieties can have in the improvement of sweet potato in Uganda and other regions where high diversity of sweet potato landraces exists.     

Joint Selection for High Yield and Low Sensitivity in Winter Wheat (Triticum aestivum L.)

Early generation yield trials of families from a set of winter wheat crosses were grown in three contrasting environments chosen to represent differences in two important macro-environmental variables affecting the wheat, crop in Eastern England. Joint selection for high yield and low sensitivity to these macro-environmental variables over two seasons using the procedure of Brumpton et al. (1977) resulted in a group of genotypes which exhibited a much wider yield range than would have resulted from selecting for yield in each -environment separately. In spite of this, the top performing lines an yield trials grown in each of the environments during the next season exhibited good yield and stability over all three environments, demonstrating the feasibility of such a method for winter wheat breeding.     

Prediction of Yield for Specific Locations in German Winter-wheat Trials

The interaction between varieties and environment can make the prediction of crop performance difficult. The farmer is obviously interested in achieving optimum production within the particular growing environment of his farm, thus, this paper investigates if crop performance predictions can be improved by making information from other locations into account. The investigation is based on yield data from winter wheat resulting from official German variety-performance tests. The predictors are based on the single location (control), unweighted means, principal components, and weighted means, produced using regression coefficients as weights. These predictors were tested on an independent data set from another year. For the given yield data in winter wheat, the overall mean proved to be the best, which suggests that prediction for all locations should be the same, and predictors for specific locations cannot, therefore, be recommended. The main reason for this is the relatively small interaction between genotype and location, in comparison to the second-order interaction between genotypes, locations and years.     

Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression

Summary Genotype by environment interaction was investigated for yield data from the official Dutch Variety List trials for potato. The data set included 64 genotypes by 26 environments, where environments consisted of year by soil type combinations. Factorial regression models incorporating genotypic and environmental covariates in the interaction were used to analyse the data. The merits of factorial regression models were compared with those of biadditive models. Factorial regression models and biadditive models described comparable amounts of interaction, but factorial regression models provided a better basis for biological interpretation of the interaction.     

Comparison of stability statistics for yield and quality traits in bread wheat

The stability and genotypic mean of four traits, grain yield, grain protein content, alveograph W and bread volume, were evaluated in three multi-location trials, each covering two years. The stability of each genotype was evaluated by environmental variance (s² _E), interaction variance (s² _W) and variance of the ranks of the phenotypic values corrected for the genotypic effect (s² _R). The bootstrap method was used to study correlations between the genotypic mean and the three stability statistics and to calculate their accuracy. The repeatability of the stability statistics was measured by correlations between the values obtained in each of the two years. In addition, theoretical smaller trials were generated by random sampling and the stability values calculated were correlated with those of the original trial. Environmental variance appears to be usable both for yield and for quality traits, but there is a risk of counter-selecting a high genotypic mean of W. Whatever the trait and statistic envisaged, stability is poorly repeatable and its evaluation requires several years and a large number of locations per year to minimise sampling and environmental effects. This revised version was published online in August 2006 with corrections to the Cover Date.     

Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models

Summary The multi-environment trial, in which a number of genotypes is evaluated over a range of environmental conditions, is a standard experiment in plant breeding in general, and variety testing in particular. Useful statistical models for the analysis of multi-environment trials, with emphasis on the analysis of genotype by environment interaction, can be found in the classes of linear and bilinear models. Statistical properties of the most important representatives of these model classes are shortly reviewed. Structural differences between the models stem from: (1) the inclusion of random model terms in addition to fixed model terms; (2) the representation of the interaction by additive or multiplicative parameters; (3) the incorporation of concomitant variables on the levels of the environmental factor. For models with bilinear multiplicative structure for the interaction it is described how the interaction can be visualized by biplots. An illustration of the application of the models and biplots is given in a companion paper.     

Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression

Summary Genotype by environment interaction was investigated for yield data from the official Dutch Variety List trials for potato. The data set included 64 genotypes by 26 environments, where environments consisted of year by soil type combinations. Factorial regression models incorporating genotypic and environmental covariates in the interaction were used to analyse the data. The merits of factorial regression models were compared with those of biadditive models. Factorial regression models and biadditive models described comparable amounts of interaction, but factorial regression models provided a better basis for biological interpreration of the interaction.This article was previously published in Euphytica 82: 149–155.     

利用AMMI模型分析寒地水稻3个品质性状的基因型与环境互作

为了给寒地水稻品种审定和推广应用提供科学依据, 将黑龙江省垦区6个寒地水稻品种(系)种植在6个不同生态点, 利用AMMI模型对其营养食味品质性状(蛋白质含量、游离脂肪酸含量、直链淀粉含量) 进行了稳定性和适应性分析。结果表明, 蛋白质含量、游离脂肪酸含量、直链淀粉含量3项指标在基因型间、环境间及基因型×环境互作间的方差均达到极显著水平, 这3项指标的交互效应主成分值(IPCA)差异也达到显著水平;影响营养食味品质稳定性的主要环境气候因子是穗期(抽穗至成熟期)日平均气温和日均气压, 穗期温度高、光照充足和少雨的环境有利于寒地稻米良好品质的形成;6个参试品种(系) 营养食味品质稳定性为建07-1023>空育131>龙粳20>饶选06-06>北粳9005=垦稻08-924, 6个农场对水稻品质影响为查哈阳农场>梧桐河农场>853农场=军川农场>850农场>290农场。     

AMMI analysis of rainfed lowland rice (Oryza sativa) trials in Nigeria

The additive main effects and multiplicative interaction (AMMI) model is used to analyse the grain yield data of 13 rice genotypes grown in 12 rainfed lowland rice environments. The trials were organized by the International Network for Genetic Evaluation of Rice in Africa (INGER-Africa) and conducted in Nigeria. Main effects due to environments (E), genotypes (G) and G × E interaction were found to be significant (P = 0.001). Cross validation analysis suggested that an AMMI model with one interaction principal component axis (IPCA) was most useful predictively, whereas Gollobs’ test declared two components, IPCA1 and IPCA2, statistically significant (P = 0.01). The IPCAl, however, accounted for most (47.8%) of the G × E sum of squares. Correlation and regression analysis, and relative scatter of genotype and environment points on the AMMI biplot suggest that the interaction partitioned in IPCA1 resulted from differences in the days to flowering among the genotypes. The paper discusses these in relation to the occurrence of Fe toxicity at the test sites and varietal tolerance to the stress.