基于GEE的2018年我国冬油菜长江下游区域试验试点与品种评价

通过采用GGE双标图方法分析了2017—2018年度国家冬油菜品种区域试验长江下游地区参试品种的丰产性、稳产性、适应性及各试点的试验代表力、鉴别力和区分能力。结果表明:参试品种的基因型、环境、基因型与环境互作效应均达极显著水平(p<0.01),4组试验平均环境效应占处理平方和的44.23%,基因型效应占19.24%,基因型与环境互作效应占36.53%。中油600、越优1510、合油杂2705、秦油1703、16 HY 1、秦优42等6个参试品种丰产性、稳产性和适应性均比较优秀。上海市奉贤区、浙江省杭州市、安徽省芜湖市、浙江省湖州市、江苏省南京市等试点综合评价值较高,有较强的试点代表力、鉴别力和区分能力。     

福建省夏大豆区试品种稳定性与适应性分析

应用AMMI模型对2020年福建省夏大豆品种区域试验中的11个品种、7个试验地点进行综合评价,分析参试品种的丰产性、稳定性及试点的鉴别力。结果表明,华夏10号、苏闽夏2号和闽诚豆8号属高产稳产品种。福清、尤溪2个试点对品种鉴别力较强,较适合开展品种区域试验。基因型效应、环境效应和基因型×环境交互效应均达到极显著水平,AMMI模型中的主成分值,共解释总互作平方和的91.65%,有效地分析了基因与环境互作效应。     

基于GGE双标图和AMMI模型对江苏省水稻区试品种的丰产性和稳定性分析

本研究综合利用GGE双标图方法和AMMI模型对2017年江苏省杂交中粳稻区域试验的12个参试品种的丰产性和稳产性进行了分析。结果表明,基因型、环境及基因型与环境互作效应对杂交中粳稻产量有极显著的影响。在所有参试品种中,杂中区03和杂中区12是丰产稳产的广适性品种,可在粳稻适区进行推广。江苏徐淮地区淮阴农业科学研究所试点代表性最强,而江苏欢腾农业有限公司试点的鉴别力最强。AMMI和GGE双标图的综合运用,可准确直观地评价各品种的丰产性、稳定性和适应性以及各试点的鉴别力和代表性。     

不同玉米品种（系）在云南省不同生态区丰产、稳产及适应性分析

为筛选云南省不同生态区、不同栽培水平条件下的优良新品种,为该区域玉米新品种精准推广和培育提供参考依据。本研究以9个玉米品种（系）在云南省15个试点的区域试验籽粒产量数据为研究对象,通过AMMI模型和GGE双标图分析方法分析不同玉米品种（系）在云南省不同试点的丰产性、稳定性和适应性,同时综合评价参试地点的鉴别力和代表性。结果表明：基因型效应、环境效应以及基因型与环境的互作效应均对参试品种产量产生极显著影响;综合产量、AMMI模型分析及GGE双标图结果,G3（文17-115）、G6（文15-5851）和G5（文17-5313）属较理想品种;E15（普文镇试验点）和E2（石林县试验点）是综合性较好的试点,均具有较强的区分力和代表性。AMMI模型和GGE双标图分析的侧重点不同,但品种评价结果基本一致,两种方法优势互补,可以用来作为全面有效地评估品种和试点的理想工具。     

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。     

基于GGE模型对甘肃省青稞多年多点区试品种稳定性分析

GGE模型是研究基因型×环境互作的有效方法之一。本研究以2018—2020年甘肃省甘南州第十一轮青稞区域试验中7个品种(系)的产量性状数据,基于GGE模型对5个试验点的产量稳定性和适应性进行了方差分析。结果表明,区组间、品种(系)、试点、年份、品种×试点、品种×年份、试点×年份和品种×试点×年份间差异均达极显著水平(P<0.01),其中试点变异占总变异平方和比例较大,为67.43%。PC1和PC2分别解释了2018-2020年青稞不同品种(系)间环境互作效应为70.26%、88.92%、74.64%。参加区域试验的7个青稞品种(系)在丰产性与稳定性方面表现较好的依次是G07(0628)、G02(0349-1)和0429(G05),其平均单产分别比对照‘康青3号’(ck1)增产15.6%、14.7%和10.2%,比对照‘肚里黄’(ck2)增产19.2%、18.2%和13.6%。对参加区域试验5个试点的代表性与鉴别力分析发现,E3(临潭县种子工作站)和E1(甘南州农业科学研究所)是本试验中比较理想的2个试验点,不仅具有较好代表性还有强的环境鉴别力。     

浙江省早籼稻蒸煮品质的品种、地点、品种×地点互作效应研究

以浙江省1995年早籼稻品种区域试验6个试点参试品种蒸煮品质测定结果为材料,用多元分析法探讨了糊化温度、胶稠度、直链淀粉含量3个蒸煮品质的品种（基因型）效应、地点效应、品种×地点互作和蒸煮品质间的相关关系,剖析了各效应的相对重要性。分析结果表明,蒸煮品质的3个性状均以品种效应为主,而且依次以直链淀粉含量的品种间变异最大,胶稠度次之,糊化温度最小;环境对糊化温度有较大影响。相关分析显示,蒸煮品质间有相关关系,且完全来自遗传（品种）效应。     

应用 AMMI模型分析烤烟品种的产量适宜性

利用AMMI模型对2011年烤烟品种区域试验5个试点10个参试品种的产量性状进行统计分析。结果表明:(1)参试品种产量的环境效应和基因×环境互作效应均达到极显著水平。在品种试验中对产量影响作用大小顺序依次为环境、基因与环境互作、基因。(2)参试品种丰产性顺序为:AH1002>AH1001>03-8-4>03-6-1>‘K326’>6507>03-4-3>2010A7>2010A10>2010A6;稳定性顺序为:03-6-1>2010A10>AH1001>AH1002>2010A6>03-4-3>‘K326’>2010A7>6507>03-8-4。(3)参试地点环境范围内,AH1002、AH1001丰产性好,稳产性较好,适宜在各试点种植;2010A10丰产差,但适应性较好,在品种推广时需要慎重;03-8-4丰产性较好,但稳产性较差,在推广时应选择适宜的地区种植。     

江苏省棉花品种区域化试验技术研究进展

概括了近年来棉花品种区域化试验技术的研究进展：（１）进行了试点间相似性研究，调整了江苏省棉花品种区域试验的试点布局。（２）采用“环境”的方差分解方法，使联合方差分析简化、灵活，并充分利用试验信息，（３）在参试品种稳定性分析的研究中，提出区域试验精确度检验的新标准。（４）调整了区域试验的鉴定项目；研究了使用缩节安对区域试验结果的影响，引入了适度化调技术。     

贵州山区粳稻基因型与环境效应分析及其在育种上的应用研究

应用AMMI模型对2002～2009年贵州省粳稻区域试验品种（组合）产量相关性状基因型与环境效应分析结果表明：基因型、环境、基因型与环境互作对考察的7个性状影响程度各不相同,生育期、有效穗主要受环境影响,株高主要以基因型效应和环境效应为主,穗粒数、结实率和千粒重同时受基因型、环境、基因型与环境互作影响,但环境效应相对较大,对产量影响的大小顺序为基因型与环境互作＞基因型＞环境。在贵州山区粳稻育种中,需针对基因型、环境、基因型与环境互作效应进行产量相关性状选择,以提高育种效率。     

The Effectiveness of HA-GGE Biplot Application in Analyzing the Data from the National Cotton Cultivar Regional Trial in the Yangtze River Valley

A large number of regional crop trials have demonstrated the ubiquitous existence of genotype × environment interactions (G×E), which make it complicated to select superior cultivars and identify the ideal testing sites. The GGE (genotype main effect plus genotype × environment interaction) biplot is the most powerful statistical and graphical displaying tool available for regional crop trial dataset analysis. The objective of the present study was to demonstrate the effectiveness of the biplot in evaluating the high and stable yields of candidate cultivars simultaneously, and in delineating the most adaptive planting region, analyzing trial location discrimination ability and representativeness, and identifying the ideal cultivar and trial locations. The lint cotton yield dataset with nine experimental genotypes and 17 test locations (three replicates in each) was collected from the national cotton regional trial in the Yangtze River Valley (YaRV) in 2012. The results showed that: (1) the effects of genotype (G), environment (E), and genotype × environment interaction (G×E) were significant (P < 0.01) for lint cotton yield. Differences among environments accounted for 78.7% of the treatment total variation in the sum of squares, whereas the genotype main effect accounted for 8.7%, and the genotype × environment interaction accounted for 12.6%. (2) The “ideal cultivar” and “ideal location” view of the HA-GGE biplot identified Zhongcj408 (G2) and Nannon12 (G9) as the best ideal genotypes; Cixi in Zhejiang Province and Jiangling in Hubei Province were the most ideal locations.(3) The “which-won-where” view of the biplot outlined the adaptive planting region for each experimental cultivar. (4) The “similarity among locations” view clustered the trial locations into four groups, among of which the two outlier locations, Shehong (SH) and Chengdu (QBJ), located in Shichuan Basin in the upper reaches of YaRV, were clustered in one group, whereas the Nanyang (NY) of Henan Province at the northern edge of YaRV was singled out as a sole group. Such location clustering results implied an apparent association with the geographical environment.     

Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits

High prices of fish oil make linseed attractive for aquaculture and animal feed. To ensure a constant supply of linseed, the development of stable cultivars is of strategic importance. In this study, 35 linseed genotypes were evaluated in five Chilean environments (E) from 2009 to 2012. The additive main effect and multiplicative interaction analysis (AMMI), genotype (G) plus genotype by environment (GE) interaction (GGE) biplot analysis and three stability parameters were tested with the aim of identifying adapted genotypes for the development of linseed cultivars. An association mapping (AM) analysis was also conducted for four agronomic traits and the stability of the associated markers was evaluated using the QQE (QTL main effect and QTL by environment interaction) approach. Combined analysis of variance for yield, seeds per boll (SPB), plant height (PH) and days to flowering (DTF) were significant for G, E and GE (P < 0.001). The combined stability analysis identified some Canadian, Argentinean and Chilean accessions to be the best adapted and highest yielding genotypes. Coancestry analysis indicated that crossing Canadian and Chilean genotypes could maximize transgressive segregation for yield. Significant associations for DTF, PH and SPB explained up to 59 % of the phenotypic variation for these traits. The QQE and AM analyses were consistent in identifying marker LGM27B as the most stable and significant across all environments with the largest effect in reducing DTF. The combined application of the stability, AM and QQE analyses could accelerate the development of marketable linseed cultivars adapted to Southern Chile.     

GGE Biplot Analysis of Genotype by Environment Interaction in Field Pea (<Emphasis Type="Italic">Pisum sativum</Emphasis> L.) Genotypes in Northwestern Ethiopia

Ten field pea genotypes were evaluated in randomized complete block design with four replications for three consecutive years (2010-2012) main cropping seasons at four locations in each year. The objectives were to determine magnitude of genotype by environment interaction and to identify stable field pea genotype with high grain yield to be released as a cultivar to producer for Northwestern Ethiopia. The GGE [genotype main effect (G) and genotype by environment interaction (GE)] biplot graphical tool was used to analyze yield data. The combined analysis of variance revealed a significant difference (P<0.01) among genotypes, environments and genotype-by-environment interaction for grain yield. The average environment coordinate biplot revealed that EH99005-7 (G2) was the most stable and the highest yielding genotype. Polygon view of GGE-biplot showed the presence of three mega-environments. The first section includes the test environments E1 (Adet 2010), E3 (Debretabor 2010), E5 (Adet 2011), E6 (Motta 2011), E7 (Debretabor 2011), E8 (Dabat 2011), E9 (Adet 2012) and E12 (Dabat 2012) which had the variety G1 (EH99009-1) as the winner; the second section contains the environments E4 (Dabat 2010), E10 (Motta 2012) and E11 (Debretabor 2012) with G2 as the best grain yielder and the third section contains the E2 (Motta 2010) with G4 (Tegegnech X EH90026-1-3-1) as the best grain yielder. The comparison GGE- biplot of field pea genotypes with the ideal genotype showed that G2 was the closest genotype for the ideal cultivar. Among the twelve environments, E2, E6 and E10 were more discriminating and E3, E9 and E12 were less discriminating. Genotype EH99005-7 was the most stable and the highest yielding genotype. As a result it is released officially for Northwestern Ethiopia. Therefore, it is recommended to use genotype EH99005-7 for wider cultivation in Northwestern Ethiopia and similar areas.     

GGE叠图法─分析品种×环境互作模式的理想方法

本文介绍一种分析作物区域试验结果的方法－ＧＧＥ叠图法。首先，将原始产量数据减去各地 点的平均产量，由此形成的数据集只含品种主效应Ｇ和品种－环境互作效应ＧＥ，合称为ＧＧＥ。对ＧＧＥ 作单值分解，并以第一和第二主成分近似之。按照第一和第二主成分值将各品种和各地点放到一个平 面图上即形成ＧＧＥ叠图。借助于辅助线，可以直观回答以下问题：（１）什么是某一特定环境下最好的 品种；（２）什么是某一特定品种最适合的环境；（３）任意两品种在各环境下的表现如何；（４）试验中品 种×环境互作的总体模式是怎样的；（５）什么是高产、稳产品种；（６）什么是有利于筛选高产、稳产品 种的环境。     

Genotype × environment interaction and yield stability in a cowpea-based cropping system

Twenty-four cowpea genotypes were evaluated under sole cropping or additive series intercropping with sorghum from 2004 to 2005 at four sites representative of the Guinea and Sudan savannah ecologies in Ghana. The aim was to determine whether cowpea breeding programs that emphasize selection under sole-crop conditions have the potential to produce cultivars that are effective under additive series intercropping. Genotype × cropping systems interaction was significant for days to 50% flowering but not for grain yield, biomass and other studied traits. Genotypic yield reaction to cropping systems indicated that bridging the yield gap between sole cropping and intercropping systems is best addressed by agronomic interventions that reduce stress on intercrop cowpea rather than by selecting for specifically adapted genotypes for intercropping. Significant genotype × environment interactions were observed for all traits when data was pooled over cropping systems. Partitioning of the genotype × environment interaction variance indicated that days to 50% flowering was dominated by heterogeneity of genotypic variance, whereas genotype × environment interactions for grain yield and biomass was mainly due to imperfect correlations. Large differences in genotypic yield stability were observed as estimated by the among-environment variance, regression of yield on the environmental index, Kataoka’s index, and by partitioning of genotype × environment interaction sum of squares into components attributable to each genotype. The results suggest that in regions where genotype × environment interaction for yield frequently causes re-ranking across environments, genotypes with the least contribution to the interaction sum of squares are likely to be most productive. On the whole, the results support the contention that breeding under sole-crop conditions has the potential to produce cultivars effective under intercropping conditions.     

Barley adaptation and improvement in the Mediterranean basin

To study barley adaptation and improvement in the Mediterranean basin, a collection of 188 entries comprising landraces and old genotypes and current modern varieties from the Mediterranean basin and elsewhere was tested on moisture‐contrasted environments in seven Mediterranean countries, during 2004 and 2005 harvest seasons. The experimental design consisted of an unreplicated trial for all entries, augmented by four repeated checks to which a partial replicate containing a quarter of the entries was added. Best Linear Unbiased Predictions (BLUPs) representing adjusted genotypic means were generated for individual trials using a mixed model. BLUPs were used for genotype by environment interaction analysis using main effect plus genotype by environment interaction (GGE) biplots of yield ranked data and for comparisons of landraces, old and modern genotypes using analysis of variance. Mean yields ranged from near crop failure to 6 t/ha. Local landraces were better adapted to environments yielding below 2 t/ha, thus breeding has mostly benefited environments yielding above 2 t/ha where modern genotypes out yielded landraces and old cultivars by 15%. Current barley selection is leading to specifically adapted genotypes.     

Adaptation and environmental sensitivity of mungbean genotypes evaluated in the international mungbean nursery

Summary Yield data from the 5th–12th international mungbean nursery (IMN) trials conducted at 23 sites in 15 countries were analyzed by conventional stability analysis—regression of genotype mean on the environmental index, and by segmented regression analysis—fitting separate linear regressions in low yielding and high yielding environments. The gene pool base concept allows comparison of genotypes from different IMN trials grown in different years and sites. A very high positive linear relationship was observed between the regression coefficient and the average yield of cultivars, indicating that high yielding cultivars were less stable across environments. When data points of the regression of genotype mean and site mean for VC 1973A, a high yielding and widely adapted cultivar, were examined, the relationship appeared not to be linear. The segmented regression analysis improved the coefficient of determination (r²) and the genotypes were grouped based on regression coefficients in high yielding and low yielding environments. Different categories of genotypes suitable for high input environments, widely adaptable genotypes, and highly stable genotypes were identified.Texas Agricultural Experiment Station Technical Article 23208.     

Genotype × cropping system interaction in climbing beans (Phaseolus vulgaris L.) grown as sole crop and in association with maize (Zea mays L.)

The selection of cultivars for the predominant cropping systems of small farms in the tropics depends to a large extent on the information obtained by testing their performance across the different systems. The main objective of this experiment was to measure the genotype × cropping system (G × CS) interaction for yield and selected agronomic traits of climbing beans (Phaseolus vulgaris L.) grown as sole crop and intercropped with two morphologically contrasting maize (Zea mays L.) cultivars. A secondary objective was to identify the most efficient and productive bean–maize intercrop combinations. Seven climbing bean genotypes were grown as sole crop and intercropped with two maize varieties, BH 140 (Mix. 1) and Guto (Mix. 2), in a factorial arranged Randomized Complete Block Design with three replications at Bako Agricultural Research Center in western Ethiopia. Main effects due to genotype and cropping system (except days to flowering) were significant for all bean traits considered. The genotypes × cropping system interaction terms were also significant for the number of seeds per pod, 100-seed weight, harvest index and seed yield. While bean seed yield significantly correlated with the number of seeds per pod (in Mix. 1) and with harvest index (in both mixtures), positive and significant correlations occurred with the number of pods per plant and 100-seed weight under sole cropping system. The correlation between bean seed yields of Mix. 1 and Mix. 2 and between Mix. 2 and sole crop were positive and significant. No such relationship was found between Mix. 1 and sole crop. The results suggest that selection of suitable climbing bean cultivars for intercropping with maize varieties predominantly grown in the area should be made under the associated culture of the two crops. Intercropping contributed to a significant reduction in seed yield of the bean genotypes due mainly to its adverse effects on the numbers of pods per plant and seeds per pod. The index tLER₁ identified most bean–maize genotype combinations of Mix. 2 as biologically more efficient system than Mix. 1. On the other hand, tLER₂ values of more than 1.00 for all treatments of Mix. 2 demonstrated higher overall productivity of the intercrop system when the bean genotypes were grown in association with a late-maturing and high yielding maize hybrid BH 140.     

Genotype by environment interaction of tomato blossom-end scar size

Summary Large blossom-end scar is a disorder in tomato fruit which reduces its marketability. The disorder is affected by genotype and by several environmental factors and therefore the genotype by environment interaction was studied by stability analysis. Blossom-end scar size was recorded for 4 tomato cultivars grown in 6 fields. The blossom-end scar size (BSC) was affected by the genotype, the field and their interaction. Stability analysis revealed that most of the interaction resulted from different stability of the cultivars. Heterogeneity of the slopes was significant (P<0.0013). The stability slopes were 0.29, 0.74, 1.11 and 1.85 for BR-214, FA-38, Hayslip and Suncoast, respectively. The stability slopes seemed to associate with the means of the cultivars over all environments, which were 1.57, 2.92, 3.84 and 5.43, respectively. Analysis of a blossom-end scar index (BSI), which also takes fruit size into account, revealed stability similar to BSC. It was concluded, that selection for small BSC under most conditions would yield cultivars with small and stable BSC under most growing environments, however differences between genotypes in non-inducing environments are expected to be small.     

基因型、地点及其互作对内蒙古小麦主要品质性状的影响

选用来自我国春播麦区高、中、低3种筋力类型的9个品种, 于2003和2004年分别种植在内蒙古6个代表性地点, 研究了不同品种在年份和地点间籽粒硬度、蛋白质含量、和面仪参数和淀粉糊化特性等主要品质性状的变化规律。结果表明, 所测品质性状受基因型和地点效应的影响均达极显著水平, 除籽粒蛋白质含量外, 其他品质性状受基因型和地点互作效应的影响达显著或极显著水平。强筋类品种的蛋白质含量、灰分含量、沉降值、和面时间、耐揉性和峰值黏度均较高, 出粉率和稀澥值中等。中筋类品种出粉率、和面时间和耐揉性较高, 灰分含量、峰值黏度和稀澥值较低。弱筋类品种的灰分含量、峰值黏度和稀澥值较高, 籽粒硬度、蛋白质含量、出粉率、沉降值、和面时间、耐揉性低。所有品种品质性状在地点间存在较大差异, 乌海市灰分含量、和面时间和耐揉性高, 籽粒硬度、沉降值、峰值黏度和稀澥值较低。杭锦后旗出粉率高, 蛋白含量和沉降值较低, 其他性状表现中等。呼和浩特市籽粒硬度、蛋白含量、面粉灰分、沉降值、和面时间和耐揉性高, 出粉率、峰值黏度和稀澥值低。赤峰市多数性状表现中等。通辽市籽粒硬度、蛋白质含量、峰值黏度、稀澥值和耐揉性较高, 其他性状表现中等。额尔古纳市蛋白含量和沉降值较高, 和面时间和耐揉性低。初步认为强筋和中筋类品种较适于种植在呼和浩特市与乌海市, 不适于种植在额尔古纳市; 2个弱筋类品种在6个地点均不太适宜种植。