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1.
Increasing awareness of heart health and disease prevention has led consumers to more proactive grocery food choices. Fibre and its associated health benefits remains an important area of research given the current interest in food, nutrition, and health. To position the potato as a good source of fibre, breeding efforts have focused on developing cultivars and germplasm with high fibre content. The current study examined eight elite potato clones and four commercial cultivars (checks) across six environments (three locations over two years) for their total dietary fibre (TDF), neutral detergent fibre (NDF), and soluble fibre (SF) content. Genotype by environment interaction (GEI) and stability analysis were conducted with SAS and GGE Biplot software. Significant genotypic (G), environmental (E) and GEI effects were found. The six environments differed in temperature and moisture levels, which were linked to levels of NDF and TDF. Some genotypes had high levels of stability for fibre content. GGE biplot analysis found no significant mega-environments for fibre components. Two elite clones (CV96044-3 and F05081) were identified as high fibre sources (13.3 and 14.4 %, respectively) compared to the other elite clones and commercial cultivars (e.g., Russet Burbank: 11.7 %). These lines may also be suitable as parents with high fibre and stability to breed into backgrounds with other desirable qualities.  相似文献   

2.
Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) × environment (E) interactions according to the Azzalini-Cox test. G × E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman’s rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype × location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs.  相似文献   

3.
Cercospora leaf spot (Cercospora canescens) is a major fungal disease which impedes mungbean production worldwide. Presence of wider host range with existence of pathogenic variability creates intricacy towards host-pathogen dynamics. Moreover, environmental factors having crucial role in augmenting severity of this disease further complicate disease management. An attempt has been made for unfolding genotype x environment interactions towards identifying and validating durable resistant genotypes against cercospora leaf spot in multi-environment testing. Preliminary screening with 246 genotypes under artificial epiphytotic condition was conducted to extract out a subset of 22 mungbean genotypes for further evaluation in field testing across six environments consecutively for two years. GGE biplot analysis detected significant environmental influence towards genotypic response and confirmed the presence of non-crossover interaction with incoherent genotypic response, thus advocating the urgency for multi-location testing. GGE biplot aptly identified “LGG 460” and “COGG 912” as “ideal” and “desirable” genotypes, respectively having durable resistance and genetic homeostasis and thus suggested for their utilization in future resistance breeding programme in mungbean against cercospora leaf spot.  相似文献   

4.
Improved winter wheat (Triticum aestivum L.) cultivars are needed for the diverse environments in Central and West Asia to improve rural livelihoods. This study was conducted to determine the performance of elite winter wheat breeding lines developed by the International Winter Wheat Improvement Program (IWWIP), to analyze their stability across diverse environments, and to identify superior genotypes that could be valuable for winter wheat improvement or varietal release. One hundred and one advanced winter wheat breeding lines and four check cultivars were tested over a 5-year period (2004–2008). Grain yield and agronomic traits were analyzed. Stability and genotypic superiority for grain yield were determined using genotype and genotype × environment (GGE) biplot analysis. The experimental genotypes showed high levels of grain yield in each year, with mean values ranging from 3.9 to 6.7 t ha−1. A set of 25 experimental genotypes was identified. These were either equal or superior to the best check based on their high mean yield and stability across environments as assessed by the GGE biplot analysis. The more stable high yielding genotypes were ID800994.W/Falke, Agri/Nac//Attila, ID800994W/Vee//F900K/3/Pony/Opata, AU//YT542/N10B/3/II8260/4/JI/Hys/5/Yunnat Esskiy/6/KS82W409/Spn and F130-L-1-12/MV12. The superior genotypes also had acceptable maturity, plant height and 1,000-kernel weight. Among the superior lines, Agri/Nac//Attila and Shark/F4105W2.1 have already been proposed for release in Kyrgyzstan and Georgia, respectively. The findings provide information on wide adaptation of the internationally important winter wheat genotypes, and demonstrate that the IWWIP program is enriching the germplasm base in the region with superior winter wheat genotypes to the benefit of national and international winter wheat improvement programs.  相似文献   

5.
Multi-environment trials (MET) play an important role in selecting the best cultivars and/or agronomic practices to be used in future years at different locations by assessing a cultivar's stability across environments before its commercial release. Objective of this study is to identify chickpea (Cicer arietinum L.) genotypes that have high yield and stable performance across different locations. The genotypes were developed by various breeders at different research institutes/stations of Iran and the International Center for Agricultural Research in Dray Areas (ICARDA), Syria. Several statistical methods were used to evaluate phenotypic stability of these test chickpea genotypes. The 17 genotypes were tested at six different research stations for two years in Iran. Three non-parametric statistical test of significance for genotype × environment (GE) interaction and ten nonparametric measures of stability analyses were used to identify stable genotypes across the 16 environments. The non-parametric measures (Kubinger, Hildebrand and Kroon/Van der) for G × E interaction were highly significant (P < 0.01), suggesting differential responses of the genotypes to the test environments. Based on high values of nonparametric superiority measure (Fox et al. 1990) and low values of Kang's (1988) rank-sum stability parameters, Flip 94-123C was identified as the most stable genotype. These non parametric parameters were observed to be associated with high mean yield. However, the other nonparametric methods were not positively correlated with mean yield and were therefore not used in classifying the genotypes. Simple correlation coefficients using Spearman’s rank correlation, calculated using ranks was used to measure the relationship between the stability parameters. To understand the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non weighted values of genotypes, was performed. The 10 stability parameters fell into three groups.  相似文献   

6.
Striga gesnerioides (Willd) Vatke, is a major destructive parasitic weed of cowpea (Vigna unguiculata (L.) Walp.) which causes substantial yield reduction in West and Central Africa. The presence of different virulent races within the parasite population contributes to significant genotype × environment interaction, and complicates breeding for durable resistance to Striga. A 3-year study was conducted at three locations in the dry savanna agro-ecology of Nigeria, where Striga gesnerioides is endemic. The primary objective of the study was to identify cowpea genotypes with high yield under Striga infestation and yield stability across test environments and to access suitability of the test environment. Data collected on grain yield and yield components were subjected to analysis of variance (ANOVA). Means from ANOVA were subjected to the genotype main effect plus genotype × environment (GGE) biplot analysis to examine the multi-environment trial data and rank genotypes according to the environments. Genotypes, environment, and genotypes × environment interaction mean squares were significant for grain yield and yield components, and number of emerged Striga plants. The environment accounted for 35.01%, whereas the genotype × environment interaction accounted for 9.10% of the variation in grain yield. The GGE biplot identified UAM09 1046-6-1 (V7), and UAM09 1046-6-2 (V8), as ideal genotypes suggesting that these genotypes performed relatively well in all study environments and could be regarded as adapted to a wide range of locations. Tilla was the most repeatable and ideal location for selecting widely adapted genotypes for resistance to S. gesnerioides.  相似文献   

7.
The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (FR) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.  相似文献   

8.
应用GGE双标图分析我国春小麦的淀粉峰值粘度   总被引：18，自引：4，他引：14  
张勇  何中虎  张爱民 《作物学报》2003,29(2):245-251
将原始数据减去各试点均值后形成的数据集中只含基因型主效G和基因型与环境互作效应GE, 合称GGE. 对GGE做单值分解, 以第一和第二主成分近似, 按第一和第二主成分值将所有品种和试点绘于同一平面图即形成GGE双标图. 以其分析我国春麦区10个试点20个品种淀粉糊化特性的峰值粘度, 结果表明铁春1号在大部分试点峰值粘度表现较好,  相似文献   

9.
Mega‐environment (ME) identification is the first step for evaluating, selecting and recommending genotypes within a target region (TR). The present study aimed to (a) identify MEs, using GGE biplot methods, in Brazilian edaphoclimatic region (ECR) 402 of soybean cultivation, located in the Mato Grosso State (the TR) and (b) compare the performance of genotypes within the TR and in each ME using fixed and mixed models. Data from three years of soybean yield trials, 19 genotypes and 22 environments were used. The biplots GGE, GGL + GGE and GGS + GGE were implemented to identify the MEs. Two MEs were identified in the TR. ME1 presents a higher altitude, farms which use a higher level of fertilizer inputs and a higher occurrence of the soybean cyst nematode (SCN) than ME2. When selection and recommendation are made based on MEs, genotypes with both broadly and specific adaptation can be selected. This action can improve grain yield in the entire target region.  相似文献   

10.
Yellow mosaic disease (YMD) caused by mungbean yellow mosaic virus (MYMV) is the most important disease of mungbean, causing great yield loss. The present investigation was carried out to study the inheritance and identify molecular markers linked with MYMV resistance gene by using F1, F2 and 167 F2 : 8 recombinant inbred lines (RILs) developed from the cross ‘TM‐99‐37’ (resistant) × Mulmarada (susceptible). The F1 was susceptible, F2 segregated in 3S:1R phenotypic ratio and RILs segregated in 1S:1R ratio in the field screening indicating that the MYMV resistance gene is governed by a single recessive gene. Of the 140 RAPD primers, 45 primers showing polymorphism in parents were screened using bulked segregant analysis. Three primers amplified specific polymorphic fragments viz. OPB‐07600, OPC‐061750 and OPB‐12820. The marker OPB‐07600 was more closely linked (6.8 cM) with a MYMV resistance gene as compared to OPC‐061750 (22.8 cM) and OPB‐12820 (25.2 cM). The resistance‐specific fragment OPB‐07600 was cloned, sequenced and converted into a sequence‐characterized amplified region (SCAR) marker and validated in twenty genotypes with different genetic backgrounds.  相似文献   

11.
Flax is an established crop in many parts of the world due to its positive health effects and numerous industrial uses. Due to increasing interest in biofuels, flax has been evaluated throughout the U.S.A. as a potential biodiesel crop. The main purpose of this research was to evaluate current and historical genotypes of flax in different regions of south‐east Texas. Twenty genotypes of flax were evaluated under dryland conditions for their agronomic and yield potential in College Station and McGregor, TX starting in 2008 thru 2011. The results suggest that all genotypes developed in Texas showed acceptable cold tolerance compared with genotypes developed in other locations. There were significant genotype–environment interactions (P < 0.001). A cross between Caldwell/Dillman (Texas genotype) was highly adapted to the environments of south‐east Texas. Nekoma and York (genotypes developed in North Dakota) yielded well in non‐cold years (>28 °F) at College Station. Utilization of cold tolerance trait identified in Texas genotypes coupled with high yield potential of modern genotypes would have a significant impact on yield improvement of flax in south‐east Texas. Overall, flax is well adapted to growth in the area surrounding College Station, TX and has potential as an oilseed crop for production in south‐east Texas.  相似文献   

12.
Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s2), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S d 2  = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability.  相似文献   

13.
A base index involving Striga damage, number of emerged Striga plants and ears per plant is used for selecting for maize (Zea mays L.) grain yield under Striga infestation. There are contradictory reports on the reliability of number of emerged Striga plants for selecting for Striga resistance. The objective of this study was to confirm reliability of the secondary traits for selecting for improved grain yield under Striga infestation. Ten Striga‐resistant extra‐early cultivars were evaluated for 3 years under artificial Striga‐infested and Striga‐free environments in Nigeria. Analysis of variance combined across years and locations showed significant mean squares for genotype, year, location and their interactions for most traits. Sequential path analysis identified ear aspect as the only trait with significant direct effect on yield under artificial Striga infestation, while GGE biplot confirmed ear aspect, ears per plant and Striga damage as the most reliable traits. Ear aspect should be included in the base index for selecting for improved grain yield of extra‐early maize under Striga infestation, while the number of emerged Striga plants should be excluded.  相似文献   

14.
Screening for drought in soybean is often a bottleneck in plant breeding programmes. Sixteen genotypes were evaluated for drought tolerance during 2012, 2013 and 2014. The experiment was conducted in a split‐plot design, and the main plots consisted of irrigated and water stress treatments, and subplots consisted of 16 genotypes. The average seed yield was highest in 2012 (1708 kg/ha), followed by 2014 (1364 kg/ha) while very low yields (958 kg/ha) were observed during 2013. The per cent reduction in average soybean yield under water stress conditions was maximum (43%) during 2014 followed by 2012 (40%) and 2013 (31%), respectively. The average yields of soybean genotypes also differed significantly, which ranged from 892 (NRC 12) to 2008 kg/ha (JS 97‐52). The maximin–minimax approach was used to classify these genotypes, and only, one genotype was identified as drought resistant and high yielding (EC 538828), three as tolerant and high yielding (JS 97‐52, EC 456548 and EC 602288) and none as low yielding and resistant, while the remaining 12 genotypes were found to be low yielding and susceptible to drought.  相似文献   

15.
Unpredictable rainfall, variations in farm inputs, crop-diseases, and the inherent potential of genotypes are among the major factors for low and variable crop yield. Fourteen elite groundnut genotypes were examined in 14 environments to analyze adaptability and stability of genotypes, and identify mega-environments if they exist. Additive main effect and multiplicative interaction (AMMI) model, cultivar-superiority measure, and genotype plus genotype-by-environment (GGE) biplot analysis were used for data analysis. The environment (69.8%) and genotype-by-environment interaction (GEI) effects (21.4%) were dominating the genotypic effect (8.8%). The GEI was significant (P < 0.01), and two distinct environments (mega-environments) were identified, suggesting separate national groundnut breeding strategies for Babile and Pawe. ICGV-94100 and ICGV-97156 were stable and had the highest-yield at Babile and Pawe, respectively. The higher heritability value was recorded in more homogeneous and favorable environments, indicating the genetic potential of groundnut genotypes were better attained in more homogeneous and favorable environments. AMMI model, cultivar-superiority measure, and GGE biplots were helpful methodologies and complemented each other to evaluate the adaptability and stability of groundnut genotypes in diverse environments.  相似文献   

16.
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.  相似文献   

17.
Yellow Mosaic disease (YMD) is one of the most destructive diseases of blackgram (Vigna mungo) causing heavy yield losses every year. Mungbean Yellow Mosaic India Virus (MYMIV) is one of the YMD causing begomoviruses prevalent in the major blackgram growing area (northern and central part) of India. Inheritance of MYMIV resistance gene was studied in blackgram using F1, F2 and F2:3 derived from cross DPU 88-31(resistant)× AKU 9904 (susceptible). The results of genetic analysis showed that a single dominant gene controls the MYMIV resistance in blackgram genotype DPU 88-31. The F2 population from the same cross was also used to tag and map the MYMIV resistance gene using SSR markers. Out of 361 markers, 31 were found polymorphic between the parents. However, marker CEDG 180 was found to be linked with resistance gene following the bulked segregant analysis. This marker was mapped in the F2 mapping population of 168 individuals at a map distance of 12.9 cm. The validation of this marker in nine resistant and seven susceptible genotypes has suggested its use in marker assisted breeding for developing MYMIV resistant genotypes in blackgram.  相似文献   

18.
Soybean mosaic virus (SMV) can cause serious yield losses in soybean. Soybean cultivar ‘RN‐9’ is resistant to 15 of 21 SMV strains. To well‐characterize this invaluable broad‐spectrum SMV‐resistance, populations (F1, F2 and F2:3) derived from resistant (R) × susceptible (S) and R × R crosses were tested for SMV‐SC18 resistance. Genetic analysis revealed that SC18 resistance in ‘RN‐9’ plus two elite SMV‐resistant genotypes (‘Qihuang No.1’ and ‘Kefeng No.1’) are controlled by independently single dominant genes. Linkage analysis showed that the resistance of ‘RN‐9’ to SMV strains SC10, SC14, SC15 and SC18 is controlled by more than one gene(s). Moreover, Rsc10‐r and Rsc18‐r were both positioned between the two simple sequence repeats markers Satt286 and Satt277, while Rsc14‐r was fine‐mapped in 136.8‐kb genomic region containing sixteen genes, flanked by BARCSOYSSR_06_0786 and BARCSOYSSR_06_0790 at genetic distances of 3.79 and 4.14 cM, respectively. Allelic sequence comparison showed that Cytochrome P450‐encoding genes (Glyma.06g176000 and Glyma.06g176100) likely confer the resistance to SC14 in ‘RN‐9’. Our results would facilitate the breeding of broad‐spectrum and durable SMV resistance in soybeans.  相似文献   

19.
Freely nodulating soybean genotypes vary in their phosphorus (P) uptake and P‐use efficiency (PUE) in low‐P soils. Understanding the genetic basis of these genotypes’ performance is essential for effective breeding. To study the inheritance of PUE, we conducted crosses using two high‐PUE genotypes, two moderate‐PUE genotypes and two inefficient‐PUE genotypes, and obtained F1, F2, BC1 and BC2 populations. The inheritance of PUE was evaluated using a randomized complete block design. A generation mean analysis of phenotypic data showed that PUE was heritable, with complex inheritance patterns and the presence of additive, dominance and epistatic gene effects. Seed P, shoot P, root P, P‐incorporation efficiency and PUE were largely quantitatively inherited traits. There were dominance, additive × additive and dominance × dominance gene effects on PUE, grain yield, shoot dry weight, 100‐seed weight, root dry weight and shoot dry matter per unit P for populations grown under low‐P conditions. Dominance effects were generally greater than additive effects on PUE‐related indices. These PUE indices can be used to select P‐efficient soybean genotypes from segregating populations.  相似文献   

20.
Soybean (Glycine max [L.] Merr.) cultivars are generally sensitive to flooding stress. The plant growth is severely affected and grain yield is largely reduced in the flooded field. It is important to develop flood‐tolerant soybean cultivars for grain production in regions of heavy rainfalls worldwide. In this study, a total of 722 soybean genotypes were evaluated for flooding tolerance at R1 stages (first flower at any node) in the 5‐year flooding screening tests. Differential soybean genotypes exhibited diverse responses to flooding stress with that plant foliar damage score (FDS) and plant survival rate (PSR) ranged from 1.9 to 8.8 and 3.4% to 81.7%, respectively (p < .0001). Based on our standard of flooding evaluation, most genotypes were sensitive to flooding with 6.0 of average FDS and 38.7% of PSR. Fifty‐two soybean genotypes showed flooding tolerance and 11 genotypes were with consistent flooding tolerance during 4‐ to 5‐year continual evaluations. In the meantime, six genotypes were identified with consistent high sensitivity to flooding. The group analysis showed that genotypes from different sources had distinguishable responses to flooding stress (p < .0001). The interacting analysis of year and flooding tolerance indicated that FDS and PSR means were significantly different among 5 years due to weather temperature and flooding treatment time influences of each year (p < .0001). Furthermore, five breeding lines with high‐yielding and flood‐tolerant traits were developed using selected consistent flood‐tolerant and high‐yielding genotypes through conventional breeding approach.  相似文献   

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