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1.
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
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Subas Malla Amir M. H. Ibrahim Rich Little Steve Kalsbeck Karl D. Glover Cuirong Ren 《Euphytica》2010,174(3):357-370
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
4.
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
5.
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
6.
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
7.
Assessment of mungbean genotypes for durable resistance to Yellow Mosaic Disease: Genotype × Environment interactions 
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下载免费PDF全文 Ashok K. Parihar Ashwani K. Basandrai Asmita Sirari Dakshinamurthy Dinakaran Deepak Singh Kamala Kannan Kailash P. S. Kushawaha Maddineni Adinarayan Mohammad Akram Tnpalayam Krshnaswamy S. Latha Vaikuntavasan Paranidharan Sanjeev Gupta 《Plant Breeding》2017,136(1):94-100
Yellow mosaic disease (YMD) is the major constraint of mungbean for realizing high productivity worldwide. Moreover, management of disease using YMD‐resistant genotypes is the simplest approach. Therefore, based on a preliminary screening of 220 genotypes during the year 2010 and 2011 at 17 locations, a set of 25 genotypes was further selected to evaluate at six locations over 2 years for identification of more stable resistant genotypes. The genotype and genotype × environment (GGE) analysis indicated that the genotypes and environment effects were significant (P < 0.001) for YMD incidence. Interestingly, the GGE biplot analysis successfully accounted for 74.71 per cent of the total variation with three genotypes (ML 818, ML 1349 and IPM 02‐14) showing high degree of resistance and stability over the locations. Notably, a strong positive association was observed between disease reaction and temperature, relative humidity and rainfall. As crop is grown in diverse growing environments, aforementioned genotypes can be used as stable/durable sources for future breeding programme to develop YMD‐resistant cultivars. 相似文献
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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. 相似文献
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Dalitso N. Yabwalo William A. Berzonsky Daniel Brabec Thomas Pearson Karl D. Glover Jonathan L. Kleinjan 《Euphytica》2018,214(7):125
Wheat (Triticum aestivum L.) market grades and prices are determined in part by test weight (TW). Millers value high TW because it is typically associated with higher flour extraction rates and better end-use quality. Test weight is expected to be influenced by other directly quantifiable grain attributes such as grain length (GL), grain width (GW), shape, single-grain-density (SGD), thousand-grain-weight (TGW), and packing efficiency (PE). The objectives of this study were to: (1) determine the primary morphological grain attributes that comprise TW measurements for winter and spring wheat classes; and (2) determine TW stability and genotype and genotype × environment interactions (GEIs) of the attributes that comprise TW. A market class representative group of 32 hard spring and 24 hard winter wheat cultivars was grown at several locations in South Dakota in 2011 and 2012. A regularized multiple regression algorithm was used to develop a TW model and determine what grain attribute reliably predicts TW. A GGE biplot was used for stability and GEI analyses whereas a linear mixed model was used for variance analyses. Data were collected on eight grain traits: TW, SGD, TGW, protein concentration, GW, GL, shape, size, and PE. Observations showed that in both spring and winter wheat, SGD accounted for over 90% of the phenotypic variation of TW. Cultivars with stable and high TW were identified in both wheat classes. Apart from TW; significant (p?<?0.0001) genotype, environment, and GEI variances were observed for GW and SGD, a more direct measure of which could help improve genetic gain for TW. 相似文献
10.
The aim of this work was to investigate the genotype × environment interaction for in vivo digestibility of organic matter and of crude fibre in silage maize evaluated with standard sheep experiments. In order to test the genotype × year interaction, the first experiment consisted of taking data subsets out of a 26-year experiment and evaluating in vivo digestibility traits at Lusignan (France) on numerous maize genotypes. In order to test the genotype × location interaction, the second experiment was a specific one whereby five hybrids were cropped in diverse locations and then evaluated from experiments with sheep, at Lusignan. The variation attributed to genotype × environment (either a year or a location) interaction for in vivo digestibility traits was distinctly lower than the variation due to the main genotypic effect. Therefore, the in vivo digestibility of organic matter and of crude fibre in maize genotypes could be accurately assessed from silages cropped in a simple experimental design, which included replicates, but only a small number of years or locations. This also confirmed the results obtained with in vitro digestibility traits from large multi-environmental designs which highlighted the low importance of genotype × environment interactions and contributed to the validation of in vitro criteria. 相似文献
11.
Reza Mohammadi Mohtasham Mohammadi Rahmatollah Karimizadeh Ahmed Amri 《Journal of Crop Science and Biotechnology》2010,13(4):267-274
This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown
in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments
(E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results
showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat
testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations
(Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association
with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ
in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments,
but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh.
The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments
in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments
or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction
data set and helped to facilitate selecting superior genotypes for target-growing sites. 相似文献
12.
Genotype × environment interaction (GEI) affects marketable fruit yield and average fruit weight of both hybrid and open-pollinated
(OP) tomato genotypes. Cultivars vary significantly for marketable fruit yield, with hybrid cultivars having, on average,
higher yield than OP cultivars. However, information is scanty on environmental factors affecting the differential response
of tomato genotypes across environments. Hence, the aim of this research was to use factorial regression (FR) and partial
least squares (PLS) regression, which incorporate external environmental and genotypic covariables directly into the model
for interpreting GEI. In this research, data from an FAO multi-environment trial comprising 15 tomato genotypes (7 hybrid
and 8 OP) evaluated in 18 locations of Latin America and the Caribbean were analyzed using FR and PLS. Environmental factors
such as days to harvest, soil pH, mean temperature (MET), potassium available in the soil, and phosphorus fertilizer accounted
for a sizeable portion of GEI for marketable fruit yield, whereas trimming, irrigation, soil organic matter, and nitrogen
and phosphorus fertilizers were important environmental covariables for explaining GEI of average fruit weight. Locations
with relatively high minimum and mean temperatures favored the marketable fruit yield of OP heat-tolerant lines CL 5915-223
and CL 5915-93. An OP cultivar (Catalina) and a hybrid (Apla) showed average marketable fruit yield across environments, while
two hybrids (Sunny and Luxor) exhibited outstanding marketable fruit yield in high yielding locations (due to lower temperatures
and higher pH) but a sharp yield loss in poor environments. Two stable hybrid genotypes in high yielding environments, Narita
and BHN-39, also showed high and stable yield in average and low yielding environments. 相似文献
13.
Genotype × environment (GE) interactions are a major problem in plant breeding programs that involve testing in diverse environments. These interactions can reduce progress from selection. Few studies have characterized the effects of weather variables on GE interactions in sorghum (Sorghum bicolor [L.] Moench). The present investigation estimated the contribution of environmental index, (?, or mean yield of all cultivars in jth environment minus ?. xor overall mean yield for all cultivars and all environments), rainfall, minimum and maximum temperature, and relative humidity, to GE interaction. Yield means of 5 full-season and 10 medium-season grain sorghum hybrids grown during 1986—1988 at four locations were used in the study. The GE interaction was significant and partitioned into σ2i, components assignable to each genotype. Weather variables (covariates) were used to remove heterogeneity from the GE interaction. The remainder of the GE interaction variance was partitioned into variance components (s2i) assignable to each genotype. In both maturity groups, the environmental index removed most, although non-significant, heterogeneity from the GE interaction sums of squares. Of all weather variables, preseason and seasonal rainfall contributed most to the GE interaction sums of squares. 相似文献
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Tazebachew Asres Yihunie Cherinet Alem Gesesse 《Journal of Crop Science and Biotechnology》2018,21(1):67-74
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
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Samia Yahiaoui Alfonso Cuesta‐Marcos Maria P. Gracia Blanca Medina José M. Lasa Ana M. Casas Francisco J. Ciudad José L. Montoya Marian Moralejo José L. Molina‐Cano Ernesto Igartua 《Plant Breeding》2014,133(2):218-226
Barley landraces from the western Mediterranean area have not been thoroughly exploited by modern breeding. This study aims at assessing the agronomic value of a core collection of lines derived from landraces of Spanish origin and to compare them with sets of successful old and modern cultivars. The agronomic performance of a set of 175 barley genotypes, comprising 159 landrace‐derived lines and 26 cultivars, was evaluated in a series of 10 field trials, carried out over 3 years and several locations. The most relevant trait of the landraces was higher grain yield at low production sites than cultivars, which may be related with better ability to fill the grain under stressful conditions. On the other hand, lateness, excessive plant height and lodging were negative traits frequently found in the landraces. Large genotype‐by‐environment interaction (GEI) for grain yield was detected, related partly with differences between germplasm groups, probably indicating local adaptation. GEI was also associated with the interaction of heading time and powdery mildew resistance with temperature. 相似文献
16.
Selecting high yielding genotypes with stable performance is the breeders’ priority but is constrained by genotype × environment (G×E) interaction. We investigated canola yield of 35 genotypes and its stability in multiple environment trials (MET) in south-western Australia and the possibility to breed broadly-adapted high yielding genotypes. The Finlay–Wilkinson (F–W) regression and factor analytic (FA) model were used to investigate the G×E interaction, yield and genotype stability and adaptability. The cross-over response in the F–W regression, substantial genetic variance heterogeneity, and the genetic correlations in the FA model demonstrated substantial G×E interaction for yield. Cluster analysis suggests low, medium and high rainfall mega-environments. F–W regression indicated that genotypes with high stability (e.g. low regression slope values) produced relatively low yield and vice versa, but also identified broadly adapted genotypes with high intercepts and steep regression slopes. The FA model provided a more detailed analysis of performance, dividing genotypes by positive, flat or negative responses to environment. In general, early flowering genotypes responded negatively to favourable environments and vice versa for late flowering genotypes. More importantly, a few early flowering hybrids with long flowering phases were consistently productive in both low and high yielding environments, showing broad adaptability. These productive hybrids were consistent with those identified earlier by high F–W intercept and slope values. Hybrids were higher yielding and more stable than open-pollinated canola, as was Roundup-Ready® canola compared to the three other herbicide tolerance groups (Clearfield®, Triazine tolerant, conventional). We conclude that yield stability and high yield are not mutually exclusive and that breeding for broadly adapted high yielding canola is possible. 相似文献
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长江流域棉花纤维比强度选择的理想试验环境筛选 总被引：1，自引：1，他引：0
采用GGE双标图方法对2000-2010年期间27组独立的长江流域棉花品种区域试验中的15个试点在纤维比强度选择上的鉴别力、代表性、理想指数和离优度指数进行了全面分析和综合评价.结果表明:南京、黄冈、常德、岳阳和南阳是以长江流域为目标环境的广适性纤维比强度选育和作为区域试验点鉴别理想品种的最理想试验环境,而江浙沿海棉区的试验环境(南通、盐城和慈溪)和四川盆地棉区试验环境(简阳和射洪)不适宜作为针对长江流域的纤维比强度选择与推荐环境.本研究充分展示了GGE双标图在区域试验环境评价方面的应用效果,也为长江流域棉花品种生态区划分和国家棉花区试方案的决策提供理论依据. 相似文献
19.
Peanut (Arachis hypogaea L.) is known to be sensitive to genotype-by-environment interaction (GEI) effects. While previous studies have reported strong GEI effects on peanut yield, most of those studies involved a relatively small number of unrelated genotypes. We examined the extent of GEI effects in elite Virginia-type peanut using a large population of recombinant inbreed lines (RILs). Two-hundred-sixty-six F7 RILs derived from different cultivars were grown in three environments. Net pod yield (NPY) was evaluated along with 11 other traits. ANOVA revealed that genotype and environment affected all of the examined traits, except for the triplet trait. The substantial influence of the environment was also demonstrated in a genetic-parameter analysis, in which the phenotypic variation coefficients were almost double those for genotypic variation. Still, relatively high heritability and genetic gain values were found for pod weight and NPY. Since NPY is the main target for selection, it was analyzed further. Path analysis showed that NPY is most directly influenced by pod weight and the shelling ratio. A significant GEI effect on NPY was identified using an AMMI model that described 42.7% of the total variation. This GEI component was subjected to a principal components analysis, which confirmed that the variability due to the different environments was greater than the variability that could be attributed to the different genotypes. Yet, several lines had stable yields across environments. These results demonstrate the importance of multi-location phenotyping for QTL analyses and crop improvement in peanut. 相似文献
20.
When evaluating genotypes, it is efficient and resourceful to identify similar testing sites and group them according to similarity.
Grouping sites ensures that breeders choose as many variable sites as possible to capture the effects of genotype-by-environment
(GE) interactions. In order to exploit these interactions and increase testing efficiency and variety selection, it is necessary
to group similar environments or mega-environments. The present mega-environments in the Southern African Development Community
(SADC) countries are confounded within each country, which limits the exchange of germplasm among them. The objective of this
study was to revise and group similar maize-testing sites across the SADC countries that are not confounded within each country.
The study was based on 3 years (1999–2001) of regional maize yield trial data and geographical information systems (GIS) parameters
from 94 sites. Sequential retrospective (Seqret) pattern analysis methodology was used to stratify testing sites and group
them according to their similarity and dissimilarity based on mean grain yield. The methodology used historical data, taking
into account imbalances of data caused by changes over locations and years, such as additions and omission of genotypes and
locations. Cluster analysis grouped regional trial sites into seven mega-environments, mainly distinguished by GIS parameters
related to rainfall, temperature, soil pH, and soil nitrogen with an overall R2 = 0.70. This analysis provides a challenge and an opportunity to develop and deploy maize germplasm in the SADC region faster
and more effectively. 相似文献