Effects of erect panicle genotype and environment interactions on rice yield and yield components

The dense and erect panicle (EP) genotype conferred by DEP1 has been widely used in the breeding of high-yield Chinese japonica rice varieties.However,the breeding value of the EP genotype has rarely been determined at the plant population level.Therefore,the effects of the interaction of EP genotype and the environment at different locations and times on rice yield and its various components were investigated in this study.Two sets of near-isogenic lines (NILs)of EP and non-EP (NEP) genotypes with Liaojing 5 (LG5) and Akitakomachi (AKI) backgrounds were grown in the field in 2016 and 2017 in Shenyang,China,and Kyoto,Japan.In 2018,these sets were grown only in Kyoto,Japan.The average yields of the EP and NEP genotypes were 6.67 and 6.13 t ha^(-1)for the AKI background,and 6.66 and 6.58 t ha^(-1)for the LG5 background,respectively.The EP genotype positively affected panicle number (PN) and grain number per square meter (GNPM),mostly resulting in a positive effect on harvest index (HI).In contrast,the EP genotype exerted a negative effect on thousand-grain weight (KGW).The ratio of the performance of the EP genotype relative to the NEP genotype in terms of yield and total biomass correlated positively with mean daily solar radiation during a 40-day period around heading.These results indicate that the effectiveness of the EP genotype depends on the availability of solar radiation,and the effect of this genotype is consistently positive for sink formation,conditional in terms of source capacity,and positive in a high-radiation environment.

Genotype-by-environment interaction for grain yield among novel cowpea(Vigna unguiculata L.) selections derived by gamma irradiation

This study determined the effects of genotype-by-environment(G × E) interaction and stability of yield among elite cowpea(Vigna unguiculata L.) selections derived by gamma irradiation. The study was conducted in Namibia at three selected sites: Bagani, Mannheim,and Omahenene, during 2014/2015 and 2015/2016. Thirty-four newly developed mutant genotypes and three local checks were evaluated using a randomized complete block design with three replications. Grain yield data were analyzed using the additive main effects and multiplicative interaction(AMMI) and the genotype main effect plus genotype-by-environment interaction(GGE) biplot methods. The AMMI and GGE biplot models explained 77.49% and 75.57% of total observed genotypic variation, respectively.Bagani and Omahenene were the environments best discriminating the test genotypes during 2014/2015 and 2015/2016, respectively. Four promising mutant genotypes: G9(Sh L3 P74), G10(Sh R3 P4), G12(Sh R9 P5), and G4(Sh L2 P4), showed wide adaptation and grain yields of 2.83, 2.06, 1.99, and 1.95 t ha^(-1), respectively. The novel mutant lines are useful genetic resources for production or future cowpea breeding programs in Namibia or similar environments.

Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran

Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.

Additive Main Effect and Multiplicative Interaction Stability Analysis of Grain Yield Performance in Cowpea Genotypes across Locations

Crops are largely influenced by climatic conditions during the growing season and therefore, minor deviation from optimal conditions can seriously threaten yield. In view of this, knowledge on the effect of environmental factors on crop growth and development could reduce the possibilities of significant yield loss. There have been statistical methods which have been developed in respect to characterizing crops but the additive main effect and multiplicative interaction (AMMI) method integrates analysis of variance and principal components analysis into a unified approach. AMMI has been used in the analysis of G × E interaction with greater precision in many crops. The objective of this study was to assess the extent of genotype x environment interaction and to select the stable cowpea genotypes in Ghanaian environments over seasons using AMMI model. Eight genotypes of cowpea released by Crops Research Institute of Ghana over two decades were selected for evaluation in two locations and two seasons using RCBD with 3 replications in forest and transitional zones of Ghana. When the mean yields of various genotypes were subjected to the AMMI model, the results showed that, a highly significant (P 0.001) genotype by location and by year interaction effects for cowpea grain yield was recorded with 63.1% of the total variation attributable to environmental effects. The AMMI Bi-plot of PC1 and GGE Bi-plot gave 80.8% and 89.3% respectively. Genotype Asontem (G3) had the highest yield and was adapted to all the environments and seasons. Genotypes Asetenapa (G1) and Soronko (G6) were however not stable with consistently low yield across all the environments. It is recommended that farmers in Forest and transitional zones of Ghana should cultivate the highly stable cowpea genotypes in order to get stable yields across environments due to climatic change.

Mixed Model, AMMI and Eberhart-Russel Comparison via Simulation on Genotype ×Environment Interaction Study in Sugarcane

Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.

Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia

To explore the effect of genotype and genotype x environment interaction on Fe concentration in rice grains, Fe concentrations of 10 genotypes were analyzed across eight paddy field environments during 2007-2008 using the AMMI-biplot method. Experiments were conducted using a randomized completely block design with three replications in eight environments. Results indicated that environment （E）, genotype （G） and genotype x environment interaction （GE） significantly affected Fe concentration in rice grains. Environment explained 74.43 % of total （G＋E＋GE） variation, whereas G and GE captured 5.60% and 19.67%, respectively. Rice genotype Barumun was desirable in terms of the highest ability and stability for Fe concentration in rice grains. Environment in genotype Cilongok was the best representative of the overall environments and the most powerful to discriminate rice genotypes.

RGxE: An R Program for Genotype x Environment Interaction Analysis

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. Univariate stability statistics calculated are regression slope (bi), deviation from regression (S2d), Shukla’s variance (σi2), S square Wricke’s ecovalence (Wi), and Kang’s yield stability (YSi). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://github.com/mahendra1/RGxE, http://cucurbitbreeding.com/todd-wehner/publications/software-sas-r-project/? and http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

AMMI analysis of interaction of genotype and environment in chalkiness of indica rice

Chalkiness is an unpleasant trait for rice con-sumer,which is known to be controlled geneti-cally and affected by environment during grainmaturing.We used the model of Additive Main

Characterization of the Main Effects, Epistatic Effects and Their Environmental Interactions of QTL on the Genetic Basis of Plant Height and Heading Date in Rice

Main-effect QTL, epistatic effects and their interactions with environment are important genetic components of quantitativetraits. In this study, we analyzed the QTL, epistatic effects and QTL by environment interactions (QE) underlying plantheight and heading date, using a doubled-haploid ( DH) population consisting of 190 lines from the cross between anindica parent Zhenshan 97 and a japonica parent Wuyujing 2, and tested in two-year replicated field trials. A geneticlinkage map with 179 SSR (simple sequence repeat) marker loci was constructed. A mixed linear model approach wasapplied to detect QTL, digenic interactions and QEs for the two traits. In total, 20 main-effect QTLs, 9 digenic interactionsinvolving 18 loci, and 5 QTL by environment interactions were found to be responsible for the two traits. No interactionswere detected between the digenic interaction and environment. The amounts of variations explained by QTLs of maineffect were 53.9% for plant height and 57.8% for heading date, larger than that explained by epistasis and QEs. However,the epistasis and QE interactions sometimes accounted for a significant part of phenotypic variation and should not bedisregarded.

Effects of genetic and environmental factors and gene-environment interaction on expression variations of genes related to stroke in rat brain

To determine if genetic and environmental (dietary) factors and gene-environment interaction impact on the expression variation of genes related to stroke, we conducted microarray experiments using two homozygous rat strains SHRSR and SHRSP fed with high and low dietary salt levels. We obtained expression data of 8,779 genes and performed the ranking analysis of microarray data. The results show that the genetic difference for stroke in rat brain has a strong effect on expression variations of genes. At false discovery rate (FDR) ≤ 5%, 534 genes were found to be differentially expressed between the genotypes resistant and prone to stroke, among which 304 genes were up-regulated in the resistant genotype and down-regulated in the prone genotype and 230 were down-regulated in the former and up-regulated in the latter. In addition, 365 were functional genes for transcription and translation, receptors (in particular, neurotransmitter receptor), channels of ions, transportation, metabolism and enzymes, and functional and structural proteins. Some of these genes are pivotal genes that cause stroke. However, dietary salt levels and GE interaction do not strongly impact on the expression variations of these genes detected on arrays.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

基于GGE双标图的玉米丰产稳产及适应性研究

本研究旨在通过GGE双标图法(Genotype Main Effects and Genotype x Environment Interaction)探讨其对玉米新品种在高海拔地区黔西北的丰产性、稳产性和适应性的综合评价能力。研究选取了5个新的玉米组合,在7个不同试点进行了产量数据收集与分析。结果显示,这些试验点可以划分为2个主要的生态区。具体来说,盘州被识别为一个独立的生态区,而六盘水、赫章、大方、纳雍、水城和威宁则构成了另一个生态区。在所测试的玉米新组合中,‘惠农单5号’在高产和稳产方面的表现尤为突出。进一步的试验地点分辨力和代表性分析表明,盘州和大方这2个试验点的鉴别力强且具有较好的代表性。因此,本研究不仅为玉米新品种的综合评价提供了科学依据,还为未来试验地点的选择提供了重要的理论支持。

Use of genotype-environment interactions to elucidate the pattern of maize root plasticity to nitrogen deficiency

Maize（Zea mays L.） root morphology exhibits a high degree of phenotypic plasticity to nitrogen（N） de ficiency,but the underlying genetic architecture remains to be investigated Using an advanced BC_4F_3 population,we investigated the root growth plasticity under two contrasted N levels and identi fied the quantitative trait loci（QTLs） with QTL-environment（Q×E）interaction effects. Principal components analysis（PCA） on changes of root traits to N de ficiency（D LN-HN） showed that root length and biomass contributed for 45.8% in the same magnitude and direction on the first PC,while root traits scattered highly on PC_2 and PC_3. Hierarchical cluster analysis on traits for D LN-HN further assigned the BC_4F_3 lines into six groups,in which the special phenotypic responses to N de ficiency was presented These results revealed the complicated root plasticity of maize in response to N de ficiency that can be caused by genotype environment（G×E） interactions. Furthermore,QTL mapping using a multi-environment analysis identi fied 35 QTLs for root traits. Nine of these QTLs exhibited signi ficant Q×E interaction effects. Taken together,our findings contribute to understanding the phenotypic and genotypic pattern of root plasticity to N de ficiency,which will be useful for developing maize tolerance cultivars to N de ficiency.

Performance and yield stability of maize hybrids in stress-prone environments in eastern Africa

Identification and deployment of high-yielding and stress-tolerant maize hybrids adapted to stress-prone agro-ecologies is important for improving the food security and livelihoods of smallholder farmers in eastern Africa.The objectives of this study were to(i)assess the performance of maize hybrids under well-watered and drought stress conditions;(ii)evaluate grain yield stability of 65 intermediate-maturing and 55 early-maturing hybrids in 24 well-watered locations and seven drought stress locations;and(iii)identify representative and/or discriminative testing locations for increasing genetic gains for the target traits.There were significant differences for grain yield among early-and intermediatematuring hybrids tested under well-watered and drought stress environments.Among the early-maturing hybrids,the top 10 hybrids produced 46.8%–73.9%and 31.2%–42.1%higher mean grain yields than the best commercial check under drought and well-watered conditions,respectively.Among the intermediate-maturing hybrids,the top 10 hybrids produced 25.2%–47.7%and 8.5%–13.5%higher grain yield than commercial checks under drought stress and well-watered conditions,respectively,suggesting improvement in the levels of drought tolerance in both early-and intermediate-maturing hybrids.GGE biplot analysis and a bi-segmented regression linear method identified specific early-maturing and intermediate-maturing hybrids that performed well under both well-watered and drought stress conditions.These hybrids could be recommended for commercial production in eastern Africa.Kakamega in Kenya was found to be the most representative and highly discriminating site among well-watered testing locations,while Kabuku in Tanzania was the least representative of test locations.For testing under drought stress conditions,Kiboko in Kenya was identified as the most representative location.This information could be useful for allocating resources and streamlining CIMMYT maize hybrid testing in eastern Africa.

Identifying key traits in high-yielding rice cultivars for adaptability to both temperate and tropical environments

Increasing rice yield potential is a continuous challenge posed by world population growth.To increase yield potential,favorable alleles of valuable genes need to be accumulated in promising germplasm.We conducted comparative yield trials for two years in Tsukuba,Japan,in a temperate region and at the International Rice Research Institute(IRRI),Philippines,in a tropical region using five high-yielding rice cultivars:Takanari and Hokuriku193,developed in Japan,and IR64,NSIC Rc158,and YTH183,developed in the Philippines.Genotype plus genotype×environment interaction(GGE)biplot analysis across four environments(two regions×two seasons)classified the five cultivars into four categories:Takanari and YTH183 showed high adaptability to both tropical and temperate regions,Hokuriku193 was suitable for temperate regions,NSIC Rc158 was suitable for the tropics,and IR64 was inferior to the other cultivars in both regions.The high yield and adaptability in Takanari and YTH183 were attributed to their large sink capacity with good grain filling.The plant type for high yield was different,however,between the two cultivars;Takanari was a panicle-weight type,whereas YTH183 was a panicle-number type.Evaluations of F2 progeny of a cross between Takanari and YTH183 showed transgressive segregation for number of panicles per plant as well as number of spikelets per panicle,leading some F2 plants to produce more spikelets per plant(corresponding to larger sink size)than the parental cultivars in both regions.These results suggest the possibility of developing rice cultivars with high yield potential in both temperate and tropical regions by crossing temperate with tropical high-yielding cultivars.

Variation and stability analysis of growth traits of poplar clones in the seedling stage in northeast China

Plant phenotypes are infl uenced by genetic and environmental factors.In this study,the growth traits of 43 one-year-old poplar clones grown at diff erent sites in northeast China(Dongling State-owned Forest Protection Center,DL;Baicheng State-owned forest farm,BC;and Cuohai Forest farm,CH)were evaluated and analyzed across clones and sites.Results show signifi cant diff erences in height and base diameter among sites and clones.Phenotypic and genetic variation coeffi cients ranged from 49.59%(BC)to 58.39%(DL)and from 49.33%(BC)to 58.06%(DL),respectively.Additive main eff ects and multiplicative interaction(AMMI)analysis showed that the eff ects of genotype,environment,and genotype×environment interaction were signifi cantly diff erent.Genetic variation was the main source of variation,accounting for 48.6%.AMMI biplot showed that clone 30 had high and stable yields at the three sites.From an evaluation of multiple traits and GGE biplot that clone 2,clone 30 and clone 25 had higher yield than the other clones at DL,CH and BC,respectively.These clones will provide material for forest regeneration in northeast China.

基于环境型鉴定技术划分生态区综合评价黄淮海青贮玉米品种

气候因子对农作物区域试验丰产性和适应性的影响较大。为准确评价青贮玉米品种在黄淮海夏播区的适应性、丰产性和稳定性,采用2002-2021年20 a的气象数据资料,依据环境型鉴定技术(ET)对2022年青贮玉米区域试验中12个试点进行生态区(ME)划分,依据品种-性状(GT)双标图和品种-产量×性状(GYT)双标图对15个参试品种的生物干重、干物质含量、倒伏率、倒折率、空秆率、小斑病、弯孢叶斑病、南方锈病、茎腐病、瘤黑粉病、生育期、株高和穗位高13个农艺性状以及全株淀粉含量、中性洗涤纤维含量、酸性洗涤纤维含量和粗蛋白质含量4个品质指标进行综合评价。结果表明,加性主效应和积性互作效应(AMMI)方差分析被测的13个农艺性状中基因型效应和环境效应均达到了极显著水平(P<0.01),除穗位高外其余性状基因型与环境互作效应也达到了极显著水平。6个省份的12个试点被划分为4个生态区,不同生态区间气象因子呈较大的变化趋势。生物干重与株高、穗位高呈极显著正相关,而与倒伏率、倒折率呈极显著负相关。GYT双标图与生态区结合,可以鉴别出不同生态区的优势品种。参试品种中渝单805在划定的4个生态区中均表现出丰产性突出、稳定性较好的特征,属于丰产稳产型品种。皖农科青贮8号、成单3601、正大511和衡玉1996等品种在ME2、ME3和ME4中丰产性和稳定性较好。安科青2号和KNX2202等品种在ME1和ME4中丰产性较差,金诚6在ME2和ME3中丰产性和稳定性均较差。基于环境型鉴定技术划分生态区和GYT双标图相结合评价青贮玉米品种的丰产性、稳定性和适应性,可以实现品种推广的精细定位。

基于GGE双标图分析新麦草种质部分性状的基因型-环境互作

评价新麦草(Psathyrostachys juncea(Fisch.)Nevski)种质资源在不同地区生长特性的差异,可为不同环境新麦草种质材料的选育提供参考依据。本研究以21份新麦草种质为材料,利用方差分析、GGE双标图模型等方法对6个性状在2个地点3年间的数据进行分析。结果表明:21份种质材料在不同环境下性状存在显著差异,变异性最大的性状为分蘖数,变异性最小的是叶长;方差分析表明基因型与年份效应对新麦草的表型影响更大;GGE双标图模型分析结果显示,叶层高最高且稳定的材料是598614,株高较高且相对稳定的材料是619565,基丛径最大的材料是502573,但基丛径稳定性最好的是502572,冠幅最大且稳定的材料是502572,分蘖数最多且稳定的材料是619565,叶长最长且稳定的材料是598610;综合区分力和适应性,较为理想的试点是包头。研究初步确定了不同种植环境下适宜的种质材料,将为新麦草种质资源利用提供参考。