Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (9): 2137-2154.doi: 10.3724/SP.J.1006.2022.11105

• REVIEW •     Next Articles

A critical review on the principles and procedures for cultivar development and evaluation

YAN Weikai*()   

  1. Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa K1A 0C6, Ontario, Canada
  • Received:2021-11-18 Accepted:2022-03-21 Online:2022-09-12 Published:2022-03-28
  • Contact: YAN Weikai E-mail:weikai.yan@agr.gc.ca
  • Supported by:
    The work was partially supported by ASP-001

RichHTML402

50

PDF

1358

Abstract:

Plant breeding plays an indispensable role in meeting the increasing need for food and comfort of the mankind in a changing climate. Numerous concepts and procedures aiming at improving breeding efficiency have been put forward, such as various strategies for dealing with genotype by environment interaction, stability analyses, mega-environment analyses, experimental designs and analyses, biplot analyses, and selection indices. In addition, genomic selection has evolved into a stage that plant breeders must consider and cannot ignore. It is of practical importance to understand the relationships among these concepts and procedures, their usefulness, drawbacks, and pitfalls, as well as their place in the theoretical framework of plant breeding and genotype evaluation. Based primarily on personal research and experience, this article attempted to develop a systematic narrative on the principles, key concepts, and analytic procedures related to cultivar development and evaluation, with demonstrations using real-world data.

Key words: Breeder’s equation, heritability, genotype-by-environment interaction, mega-environment analysis, adequate testing, stability analysis, index selection, genomic selection

Fig. 1

A schematic presentation of the Complete Breeder’s Equation"

Table 1

The minimum probability for false selection (α) and the maximum allowable selection intensity (i) as determined by the population size (n) under the assumption of normal distribution"

n α i = z
10000 0.0001 3.71
1000 0.001 3.00
100 0.01 2.33
50 0.02 2.05

Table 2

The allowable selection intensity (ih), the culling rate (1-α), the risk of false culling (α), and the number of genotypes to be retained (N) at different levels of heritability (h2), assuming a population of n = 10,000 and a selection intensity of i = 3.7"

h2 h ih = 3.7h 1-α α N 1/N
0.0 0.00 0.00 0.5000 0.5000 5000 1/5000
0.1 0.32 1.17 0.8790 0.1210 1210 1/1210
0.2 0.45 1.65 0.9505 0.0495 495 1/495
0.3 0.55 2.03 0.9788 0.0212 212 1/212
0.4 0.63 2.34 0.9904 0.0096 96 1/96
0.5 0.71 2.62 0.9959 0.0041 41 1/41
0.6 0.77 2.87 0.9980 0.0020 21 1/21
0.7 0.84 3.10 0.9990 0.0010 10 1/10
0.8 0.89 3.31 0.9995 0.0005 5 1/5
0.9 0.95 3.51 0.9998 0.0002 2 1/2
1.0 1.00 3.70 0.9999 0.0001 1 1

Table 3

Selection/culling intensity at different stages of the breeding cycle in the Ottawa oat breeding program"

育种周期的不同阶段
Stages in a breeding cycle		 试点数
No. of
locations
(l)		 重复数
No. of
replicates
(r)		 选前群体大小
Population size
before selection
(n)		 选后群体大小
Population size
after selection
(N)		 对应遗传力
Assumed accumulative
heritability
(Hrly)
1 亲本选择和杂交
Parent selection and hybridization		 — — — — —
2 温室加代
Generation advance		 — — 10000 10000 —
3.1 第1年目测选择
Visual selection - yr1 (Observational hills)		 1 1 10000 1200 0.1
3.2 第2年目测选择
Visual selection - yr2 (Observational plots)		 1 1 1200 250 0.3
4.1 第1年预备试验
Preliminary test - yr1 (“Home test”)		 4 2 250 60 0.5
4.2 第2年预备试验
Preliminary test - yr2 (“ENCORE”)		 8-10 2-3 60 20 0.6
4.3 第1年注册试验
Registration test - yr1		 8-10 3 20 10 0.7
4.4 第2年注册试验
Registration test - yr2		 8-10 3 10 5 0.8
4.5 第3年注册试验
Registration test - yr3		 8-10 3 5 2 0.9

Fig. 2

GGE biplot to display the relative yield of 118 oat genotypes tested in 63 trials in the 2013-2019 Quebec provincial oat trials The genotypes are represented by “+” in blue and the trials by their location-year combinations in red. Each trial is displayed by the code of the relevant location and the last two digits of the year. The number immediately following the location code indicates the agro-ecological zone it belongs. For example, “CAUS3_13” refers to the trial at the location CAUS (Causapscal, a zone 3 location) in 2013."

Fig. 3

GE+GGL biplot to show the two oat mega-environments in Quebec The upper group of locations included NORM3, HEBE3, CAUS3, PRIN2, STET3, and STAU2, all from zones 2 and 3. The lower group of locations included NDHY1, STHU1, LAPO3, and OTT, from zones 1 and 3 of Quebec plus Ottawa, Ontario. The two mega-environments were not always distinct as there were overlaps between them."

Fig. 4

LG biplot to show the two oat mega-environments in Quebec Based on the yield data from the 2013-2019 Quebec provincial oat trials."

Fig. 5

GGE biplots to show the mean yield and stability of 13 oat cultivars in the two Quebec oat mega-environments a) subregion ME2a, and b) subregion ME2b. Data from the 2013-2019 Quebec provincial oat trials."

Table 4

The minimum number of years (ymin) required to achieve a heritability (H) of 0.75, estimated on the yield data of three-year spans from the Quebec provincial oat registration trials"

年份跨度
Three-year span		 共同品系数
No. of common genotypes		 σC σCY σCLY σP H ymin
2013-2015 26 0.63 0.25 0.50 0.71 0.88 1.2
2014-2016 23 0.70 0.34 0.58 0.81 0.86 1.5
2015-2017 27 0.48 0.53 0.73 0.65 0.73 3.3
2016-2018 27 0.30 0.64 0.80 0.52 0.59 6.3
2017-2019 27 0.52 0.49 0.70 0.68 0.76 2.8
2019-2020 30 0.29 0.42 0.64 0.43 0.68 4.3
平均 Mean — — — — — — 3.2

Table 5

The estimated minimum number of locations in comparison to the actual number of locations in the Quebec provincial oat trials"

年份
Year		 参试品种数
No. of entries		 实际试验点数
Actual no. of test locations		 σG σGL 最适试验点数
Estimated minimum (optimum)
no. of test locations
2013 41 8.0 91,043 339,803 11.2
2014 46 9.0 108,230 246,673 6.8
2015 44 10.0 124,695 281,369 6.8
2016 42 9.9 86,231 170,524 5.9
2017 43 10.0 129,192 354,402 8.2
2018 44 10.0 46,273 114,468 7.4
2019 46 10.0 61,079 255,019 12.5
平均Mean 9.6 8.4

Fig. 6

GGE biplots to show the highest yielding cultivar(s) in each of the trials in the two Quebec oat mega-environments a) subregion ME2a, and b) subregion ME2b. Data from 2013-2019 Quebec provincial oat trials."

Fig. 7

An example of spatial analysis using within-block multiple regression smoothing Data source: crown rust scores (0 to 9) from an oat variety trial conducted at Ottawa in 2021."

Table 6

Mean trait values of 13 oat cultivars tested in the 2013-2019 Quebec provincial oat trials and their GYT (genotype by yield×trait) index"

品种
Genotype		 性状、权重及符号 Traits and their weight and sign
产量
Yield
(kg hm-2)		 β-葡聚糖
β-glucan
(%)		 麦仁率
Groat
(%)		 油分
Oil
(%)		 蛋白质
Protein
(%)		 容重
Test weight
(kg hL-1)		 千粒重
1000-kernel
weight (g)		 倒伏
Lodging
(0-9)		 GYT指数
GYT
index
1 1 -1 1 1 1 -1
Nicolas 5948 4.3 73.9 6.1 13.2 53.4 35.8 2.9 1.3
Akina 5853 4.8 72.7 7.1 13.4 52.3 38.0 2.4 1.2
Kara 5680 4.7 71.9 8.0 14.0 54.0 38.1 2.0 0.9
Richmond 5631 3.8 71.7 5.4 12.6 55.1 39.2 3.0 0.5
Canmore 5447 4.6 71.6 7.6 14.2 54.9 39.5 3.3 0.3
Nice 5559 4.4 72.6 8.4 13.5 53.3 38.5 3.9 0.1
Orrin 5468 4.4 71.1 6.9 13.3 54.2 38.5 3.5 0.1
Adele 5354 4.6 75.3 8.4 12.8 54.0 38.7 4.5 -0.3
Dieter 5183 4.1 73.4 5.7 14.0 54.3 38.9 3.6 -0.4
Synextra 5171 4.3 72.0 7.3 14.9 56.0 37.3 3.7 -0.4
Vitality 5049 4.0 75.7 7.9 13.6 53.9 40.3 3.9 -0.9
Hidalgo 5158 4.7 74.3 8.0 13.1 53.1 34.5 4.0 -0.9
Avatar 5060 3.9 74.8 7.9 12.2 56.6 36.2 4.9 -1.3

Fig. 8

GYT biplot to show the superiority and trait profiles of 13 oat cultivars tested in the 2013-2019 Quebec provincial oat trials The arrow in red points to a higher GYT index and the arrows in blue point to strengths or weaknesses of each genotype in terms of yield-trait combinations. For example, it shows that Richmond was strong in “Y×Oil(-1)” but poor in “Y×BGL”."

[1] 赵洪璋. 小麦杂交育种工作的若干问题. 甘肃农业科技, 1982, (8): 1-3.
Zhao H Z. Several issues in wheat hybridization breeding. Gangsu Agric Sci Technol, 1982, (8): 1-3. (in Chinese)
[2] 赵洪璋. 小麦杂交育种的基本理论和三大问题. 甘肃农业科技, 1991, (2): 4-5.
Zhao H Z. Basic theories and three major issues in wheat hybridization breeding. Gangsu Agric Sci Technol, 1991, (2): 4-5 (in Chinese)
[3] 赵洪璋, 张海锋, 宋哲民. 小麦杂交育种工作中的若干问题. 陕西农业科学, 1981, (3): 1-8.
Zhao H J, Zhang H F, Song Z M. Some issues in wheat hybridization breeding. Shaanxi Agric Sci, 1981, (3): 1-8 (in Chinese)
[4] Yan W, Tinker N A, Bekele W A, Mitchell-Fetch J, Frégeau-Reid J. Theoretical unification and practical integration of conventional methods and genomic selection in plant breeding. Crop Breed Genet Genomic, 2019, 1: e190003. https://doi.org/10.20900/cbgg20190003.
[5] Yan W. A systematic narration of some key concepts and procedures in plant breeding. Front Plant Sci, 2021, 12: 1863.
[6] Duvick D N. Plant breeding, an evolutionary concept. Crop Sci, 1996, 36: 539-548.
doi: 10.2135/cropsci1996.0011183X003600030001x
[7] Rasmusson D C, Phillips R L. Plant breeding progress and genetic diversity from de novo variation and elevated epistasis. Crop Sci, 1997, 37: 303-310.
doi: 10.2135/cropsci1997.0011183X003700020001x
[8] 李振声. 小麦远缘杂交新品种——小偃6号. 山西农业科学, 1986, (5): 30.
Li Z S. New wheat cultivar from wide cross: Xiaoyan 6. Shanxi Agric Sci, 1986, (5): 30. (in Chinese)
[9] Arrones A, Vilanova S, Plazas M, Mangino G, Pascual L, Díez M J, Prohens J, Gramazio P. The dawn of the age of multi-parent MAGIC populations in plant breeding: novel powerful next- generation resources for genetic analysis and selection of recombinant elite material. Biology, 2020, 9: 229.
doi: 10.3390/biology9080229
[10] Heffner E L, Sorrells M E, Jannink J L. Genomic selection for crop improvement. Crop Sci, 2009, 49: 1-12.
doi: 10.2135/cropsci2008.08.0512
[11] Jannink J L, Lorenz A J, Iwata H. Genomic selection in plant breeding: from theory to practice. Brief Funct Genom, 2010, 9: 166-177.
doi: 10.1093/bfgp/elq001
[12] Heslot N, Jannink J L, Sorrells M E. Perspectives for genomic selection applications and research in plants. Crop Sci, 2015, 55: 1-12.
doi: 10.2135/cropsci2014.03.0249
[13] Osthushenrich T, Frisch M, Zenke-Philippi C, Jaiser H, Spiller M, Cselényi L, Krumnacker K, Boxberger S, Kopahnke D, Habekuß A, Ordon F. Prediction of means and variances of crosses with genome-wide marker effects in barley. Front Plant Sci, 2018, 9: 1899.
doi: 10.3389/fpls.2018.01899 pmid: 30627135
[14] Wang L, Zhu G, Johnson W, Kher M. Three new approaches to genomic selection. Plant Breed, 2018, 137: 673-681
doi: 10.1111/pbr.12640
[15] Jean M, Cobe E, O’Donoughue L, Rajcan I, Belzile F. Improvement of key agronomical traits in soybean through genomic prediction of superior crosses. Crop Sci, 2021, 61: 3908-3918.
doi: 10.1002/csc2.20583
[16] Eberhart S A. Factors affecting efficiencies of breeding methods. Afr Soils, 1970, 15: 655-680.
[17] Cobb J N, Juma R U, Biswas P S, Arbelaez J D, Rutkoski J, Atlin G, Hagen T, Quinn M, Ng E H. Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation. Theor Appl Genet, 2019, 132: 627-645.
doi: 10.1007/s00122-019-03317-0
[18] Zhou J, Nguyen H T. Solve the Breeder’s Equation using High- throughput Crop Phenotyping Technology. In: High-Throughput Crop Phenotyping (1-11). Wageningen, the Netherlands: Springer, 2021.
[19] Comstock R E, Moll R H. Genotype-environment interactions. Stat Gen Plant Breed, 1963, 982: 164-196.
[20] DeLacy I H, Basford K E, Cooper M, Bull J K, McLaren C G. Analysis of multienvironment trials—a historical perspective. In: Cooper M, Hammer G L, eds. Plant Adaptation and Crop Improvement. Wallingford (UK): IRRI/CABI, 1996. p 39124.
[21] Atlin G N, Baker R J, McRae K B, Lu X. Selection response in subdivided target regions. Crop Sci, 2000, 40: 7-13.
doi: 10.2135/cropsci2000.4017
[22] Yan W. Analysis and handling of G×E in a practical breeding program. Crop Sci, 2016, 2106-2118.
[23] Cooper M, Voss-Fels K P, Messina C D, Tang T, Hammer G L. Tackling G×E×M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity. Theor Appl Genet, 2021, 134: 1625-1644.
doi: 10.1007/s00122-021-03812-3 pmid: 33738512
[24] Fehr W. Principles of Cultivar Development:Theory and Technique. New York, USA: Macmillian Publishing Company, 1991.
[25] Yan W, Hunt L A, Sheng Q, Szlavnics Z. Cultivar evaluation and megaenvironment investigation based on the GGE biplot. Crop Sci, 2000, 40: 597-605.
doi: 10.2135/cropsci2000.403597x
[26] 严威凯. 双标图分析在农作物品种多点试验中的应用. 作物学报, 2010, 36: 1805-1819.
doi: 10.3724/SP.J.1006.2010.01805
Yan W. Optimal use of biplots in analysis of multi-location variety test data. Acta Agron Sin, 2010, 36: 1805-1819 (in Chinese with English Abstract).
[27] Atlin G N, Kleinknecht K, Singh K P, Piepho H P. Managing genotype × environment interaction in plant breeding programs: a selection theory approach. J Ind Soc Agric Stat, 2011, 65: 237-247.
[28] Yan W. Crop Variety Trials:Data Management and Analysis. New York: John Wiley & Sons, 2014.
[29] Yan W. Mega-environment analysis and test location evaluation based on unbalanced multiyear data. Crop Sci, 2015, 113-122.
[30] Yan W. LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data. Sci Rep, 2019, 9: 7130.
doi: 10.1038/s41598-019-43683-9
[31] Gabriel K R. The biplot graphic display of matrices with application to principal component analysis. Biometrika, 1971, 58: 453-467.
doi: 10.1093/biomet/58.3.453
[32] Yan W, Mitchell-Fetch J, Beattie A, Nilsen K T, Pageau D, DeHaan B, Hayes M, Mountain N, Cummiskey A, MacEachern D. Oat mega-environments in Canada. Crop Sci, 2021, 61: 1143-1153.
[33] Yan W, Frégeau-Reid J, Martin R, Pageau D, Mitchell-Fetch J. How many test locations and replications are needed in crop variety trials for a target region? Euphytica, 2015, 361-372.
[34] Yan W. Estimation of the optimal number of replicates in crop variety trials. Front Plant Sci, 2021, 11: 2231
[35] Becker H C. Correlations among some statistical measures of phenotypic stability. Euphytica, 1981, 30: 835-840.
doi: 10.1007/BF00038812
[36] Lin C S, Binns M R, Lefkovitch L P. Stability analysis: where do we stand? Crop Sci, 1986, 26: 894-900.
doi: 10.2135/cropsci1986.0011183X002600050012x
[37] Huehn M. Nonparametric measures of phenotypic stability. Part 2: applications. Euphytica, 1990, 47: 195-201.
doi: 10.1007/BF00024242
[38] Segherloo A E, Sabaghpour S H, Dehghani H, Kamrani M. Non-parametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica, 2008, 162: 221-229.
doi: 10.1007/s10681-007-9552-x
[39] Scapim C A, Pacheco C A P, do Amaral Jr A T, Vieira R A, Pinto R J B, Conrado T V. Correlations between the stability and adaptability statistics of popcorn cultivars. Euphytica, 2010, 174: 209-218.
doi: 10.1007/s10681-010-0118-y
[40] Eberhart S T, Russell W A. Stability parameters for comparing varieties. Crop Sci, 1966, 6: 36-40.
doi: 10.2135/cropsci1966.0011183X000600010011x
[41] Ceccarelli S. Wide adaptation: how wide? Euphytica, 1989, 40: 197-205.
doi: 10.1007/BF00024512
[42] Zobel R W, Wright M J, Gauch Jr H G. Statistical analysis of a yield trial. Agron J, 1988, 80: 388-393.
doi: 10.2134/agronj1988.00021962008000030002x
[43] Yan W, Kang M S, Ma B, Woods S, Cornelius P L. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci, 2007, 47: 643-653.
doi: 10.2135/cropsci2006.06.0374
[44] Lin C S, Binns M R. A superiority measure of cultivar performance for cultivar × location data. Can J Plant Sci, 1988, 68: 193-198.
doi: 10.4141/cjps88-018
[45] Lin C S, Binns M R. Concepts and methods for analyzing regional trial data for cultivar and location selection. Plant Breed Rev, 1994, 12: 271-297.
[46] Kang M S. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron J, 1993, 85: 754-757.
doi: 10.2134/agronj1993.00021962008500030042x
[47] Gauch Jr H G, Zobel R W. Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet, 1988, 76: 1-10.
doi: 10.1007/BF00288824 pmid: 24231975
[48] Yan W, Kang M S. GGE Biplot Analysis:a Graphical Tool for Breeders, Geneticists, and Agronomists. Boca Raton, USA: CRC Press, 2002.
[49] Gauch H G Jr. Statistical analysis of yield trials by AMMI and GGE. Crop Sci, 2006, 46: 1488-1500.
doi: 10.2135/cropsci2005.07-0193
[50] Gauch H G Jr, Piepho H P, Annicchiarico P. Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci, 2008, 48: 866-889.
doi: 10.2135/cropsci2007.09.0513
[51] Yan W. GGE biplot vs. AMMI graphs for genotype-by-environment data analysis. J Indian Soc Agri Stat, 2011, 65: 181-193.
[52] Badu-Apraku B, Oyekunle M, Obeng-Antwi K, Osuman A S, Ado S G, Coulibay N. Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. J Agric Sci, 2012, 150: 473.
doi: 10.1017/S0021859611000761
[53] Amira J O, Ojo D K, Ariyo O J, Oduwaye O A, Ayo-Vaughan M A. Relative discriminating powers of GGE and AMMI models in the selection of tropical soybean genotypes. Afr J Crop Sci, 2013, 21: 67-73.
[54] Hoyos-Villegas V, Wright E M, Kelly J D. GGE biplot analysis of yield associations with root traits in a Mesoamerican bean diversity panel. Crop Sci, 2016, 56: 1081-1094.
doi: 10.2135/cropsci2015.10.0609
[55] Oliveira T R A D, Carvalho H W L D, Oliveira G H F, Costa E F N, Gravina G D A, Santos R D D, Carvalho J L S D. Hybrid maize selection through GGE biplot analysis. Bragantia, 2019, 78: 166-174.
doi: 10.1590/1678-4499.20170438
[56] Heslot N, Yang H P, Sorrells M E, Jannink J L. Genomic selection in plant breeding: a comparison of models. Crop Sci, 2012, 52, 146-160.
doi: 10.2135/cropsci2011.06.0297
[57] Cullis B R, Gleeson A C. Spatial analysis of field experiments: an extension to two dimensions. Biometrics, 1991, 47: 1449-1460.
doi: 10.2307/2532398
[58] Cullis B R, Gogel B, Verbyla A, Thompson R. Spatial analysis of multi-environment early generation variety trials. Biometrics, 1998, 54: 1-18.
doi: 10.2307/2533991
[59] Gilmour A R, Cullis B R, Verbyla A P. Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat, 1997, 2: 269-293.
doi: 10.2307/1400446
[60] Burgueño J, Cadena A, Crossa J, Banziger M, Gilmour A R, Cullis B. User’s Guide for Spatial Analysis of Field Variety Trials using ASREML. Mexico: CIMMYT, 2000.
[61] Qiao C G, Basford K E, DeLacy I H, Cooper M. Evaluation of experimental designs and spatial analyses in wheat breeding trials. Theor Appl Genet, 2000, 100: 9-16.
doi: 10.1007/s001220050002
[62] Yang R C, Ye T Z, Blade S F, Bandara M. Efficiency of spatial analyses of field pea variety trials. Crop Sci, 2004, 44: 49-55.
doi: 10.2135/cropsci2004.4900
[63] Tanksley S D. Molecular markers in plant breeding. Plant Mol Biol Rep, 1983, 1: 3-8.
[64] Yan W, Wallace D H. Breeding for negatively associated traits. Plant Breed Rev, 1995, 13: 141-177.
[65] Asins M J. Present and future of quantitative trait locus analysis in plant breeding. Plant Breed, 2002, 121: 281-291.
doi: 10.1046/j.1439-0523.2002.730285.x
[66] Cooper M, van Eeuwijk F A, Hammer G L, Podlich D W, Messina C. Modeling QTL for complex traits: detection and context for plant breeding. Curr Opin Plant Biol, 2009, 12: 231-240.
doi: 10.1016/j.pbi.2009.01.006 pmid: 19282235
[67] Hao Y, Velu G, Peña R J, Singh S, Singh R P. Genetic loci associated with high grain zinc concentration and pleiotropic effect on kernel weight in wheat (Triticum aestivum L.). Mol Breed, 2014, 34: 1893-1902.
doi: 10.1007/s11032-014-0147-7
[68] Crespo-Herrera L A, Velu G, Singh R P. Quantitative trait loci mapping reveals pleiotropic effect for grain iron and zinc concentrations in wheat. Ann Appl Biol, 2016, 169: 27-35.
doi: 10.1111/aab.12276
[69] Simmonds N, Smartt J. Principles of Crop Improvement, 2nd edn. Oxford, UK: Blackwell Science Ltd. Press, 1999.
[70] Yan W, Frégeau-Reid J. Breeding line selection based on multiple traits. Crop Sci, 2008, 48: 417-423.
doi: 10.2135/cropsci2007.05.0254
[71] Yan W, Frégeau-Reid J. Genotype by yield×trait (GYT) biplot: a novel approach for genotype selection based on multiple traits. Sci Rep, 2018, 8: 8242.
doi: 10.1038/s41598-018-26688-8
[72] Yan W, Frégeau-Reid J, Mountain N, Kobler J. Genotype and management evaluation based on genotype by yield × trait (GYT) analysis. Crop Breed Genet Genom, 2019, 1: e190002.
[73] Boureima S, Abdoua Y A O U. Genotype by yield × trait combination biplot approach to evaluate sesame genotypes on multiple traits basis. Turk J Field Crops, 2019, 24: 237-244.
[74] de Oliveira T R A, de Amaral G G, de Moura R M, de Alcântara N F, da Cruz D P, de Oliveira G H F, da Costa J M E P Rocha R S. GYT biplot analysis: a new approach for cowpea line selection. J Exp Agric Internat, 2019, 41: 1-9.
[75] Mohammadi R. Genotype by yield × trait biplot for genotype evaluation and trait profiles in durum wheat. Cereal Res Commun, 2019, 47: 541-551.
doi: 10.1556/0806.47.2019.32
[76] Goddard M E, Hayes B J. Genomic selection. J Anim Breed Genet, 2007, 124: 323-330.
pmid: 18076469
[77] Ma F, Xu Y, Ma Z, Li L, An D. Genome-wide association and validation of key loci for yield-related traits in wheat founder parent Xiaoyan 6. Mol Breed, 2018, 38: 91.
doi: 10.1007/s11032-018-0837-7
[78] Badu-Apraku B, Bankole F A, Ajayo B S, Fakorede M A B, Akinwale R O, Talabi A O. Identification of early and extra-early maturing tropical maize inbred lines resistant to Exserohilum turcicum in sub-Saharan Africa. Crop Prot, 2021, 139: 105386.
[79] 许乃银, 赵素琴, 张芳, 付小琼, 杨晓妮, 乔银桃, 孙世贤. 基于GYT双标图对西北内陆棉区国审棉花品种的分类评价. 作物学报, 2021, 47: 660-671.
doi: 10.3724/SP.J.1006.2021.04135
Xu N Y, Zhao S Q, Zhang F, Fu X Q, Yang X N, Qiao Y T, Sun S X. Retrospective evaluation of cotton varieties nationally registered for the Northwest Inland cotton growing regions based on GYT biplot analysis. Acta Agron Sin, 2021, 47: 660-671. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.04135
[80] Tsenov N, Gubatov T, Yanchev I. Genotype selection for grain yield and quality based on multiple traits of common wheat (Triticum aestivum L.). Cereal Res Commun, 2021, 49: 119-124.
doi: 10.1007/s42976-020-00080-7
[81] Sofi P A, Saba I, Ara A, Rehman K. Comparative efficiency of GY × T approach over GT approach in genotypic selection in multiple trait evaluations: case study of common bean (Phaseolus vulgaris) grown under temperate Himalayan conditions. Agric Res (India: Online), 2021, 10.1007/s40003-021-00577-5.
doi: 10.1007/s40003-021-00577-5
[82] de Oliveira L A, da Silva C P, da Silva A Q, Mendes C T E, Nuvunga J J, Nunes J A R, Parrella R A D C, Baleste M, Filho J S D S B. Bayesian GGE model for heteroscedastic multi- environmental trials. Crop Sci, 2022, 62: in press. 10.1002/csc2.20696.
doi: 10.1002/csc2.20696
[1] ZHANG Yi,XU Nai-Yin,GUO Li-Lei,YANG Zi-Guang,ZHANG Xiao-Qing,YANG Xiao-Ni. Optimization of test location number and replicate frequency in regional winter wheat variety trials in northern winter wheat region in China [J]. Acta Agronomica Sinica, 2020, 46(8): 1166-1173.
[2] Lan-Fen WANG, Jing WU, Zhao-Li WANG, Ji-Bao CHEN, Li YU, Qiang WANG, Shu-Min WANG. Adaptability and Phenotypic Variations of Agronomic Traits in Common Bean Germplasm Resources in Different Environments [J]. Acta Agronomica Sinica, 2018, 44(03): 357-368.
[3] MA Yan-Song, LIU Zhang-Xiong, WEN Zi-Xiang, WEI Shu-Hong, YANG Chun-Ming, WANG Hui-Cai6, YANG Chun-Yan, LU Wei-Guo, XU Ran, ZHANG Wan-Hai, WU Ji-An, HU Guo-Hua, LUAN Xiao-Yan, FU . Effect of Population Structure on Prediction Accuracy of Soybean 100-Seed Weight by Genomic Selection MA Yan-Song1,2,13, LIU Zhang-Xiong1, WEN Zi-Xiang3, WEI Shu-Hong4, YANG Chun-Ming5, WANG Hui-Cai6, YANG [J]. Acta Agron Sin, 2018, 44(01): 43-52.
[4] XU Nai-Yin,JIN Shi-Qiao,LI Jian. Design of Test Location Number and Replicate Frequency in the Regional Cotton Variety Trials in China [J]. Acta Agron Sin, 2016, 42(01): 43-50.
[5] LUO Jun,XU Li-Ping,QIU Jun,ZHANG Hua,YUAN Zhao-Nian,DENG Zu-Hu,CHEN Ru-Kai,QUE You-Xiong. Evaluation of Sugarcane Test Environments and Ecological Zone Division in China Based on HA-GGE Biplo [J]. Acta Agron Sin, 2015, 41(02): 214-227.
[6] LUO Jun,ZHANG Hua,DENG Zu-Hu,XU Li-Ping,XU Liang-Nian,YUAN Zhao-Nian,QUE You-Xiong. Analysis of Yield and Quality Traits in Sugarcane Varieties (Lines) with GGE-Biplot [J]. Acta Agron Sin, 2013, 39(01): 142-152.
[7] XU Nai-Yin,ZHANG Guo-Wei,LI Jian,ZHOU Zhi-Guo. Evaluation of Cotton Regional Trial Environments Based on HA-GGE Biplot in the Yangtze River Valley [J]. Acta Agron Sin, 2012, 38(12): 2229-2236.
[8] SHU Yong-Dun, TUN Lei, WANG Dan, GUO Chang-Gong. Application of Artificial Neural Network in Genomic Selection for Crop Improvement [J]. Acta Agron Sin, 2011, 37(12): 2179-2186.
[9] CHEN Guo-Lin,WU Jian-Guo,ZHANG Li,SHI Chun-Hai. Genetic Analysis for Lysine Content of Rapeseed Cake (Brassic napus L.) at Different Periods after Anthesis [J]. Acta Agron Sin, 2010, 36(4): 680-687.
[10] YAN Wei-Kai. Optimal Use of Biplots in Analysis of Multi-Location Variety Test Data [J]. Acta Agron Sin, 2010, 36(11): 1805-1819.
[11] NI Xiao-Wen;YAN Jun;CHEN Xin-Min;XIA Xian-Chun;HE Zhong-Hu;ZHANG Yong;WANG De-Sen;Morten Lillemo. Heritability and Number of Genes Controlling Slow-Mildewing Resis-tance in Wheat Cultivar Lumai 21 [J]. Acta Agron Sin, 2008, 34(08): 1317-1322.
[12] WANG Rui;XU Xing-Fu;LI Jia-Na;TANG Zhang-Lin;CHEN Li. Analysis of Embryo, Cytoplasmic and Maternal Effects on Glucosinolate Components in Brassica napus L. [J]. Acta Agron Sin, 2007, 33(12): 2001-2006.
[13] PEI Zi-You;JIA Gao-Feng;QI Zeng-Jun; ZHUANG Li-Fang; FENG Yi-Gao;WANG Xiu-E;CHEN Pei-Du;LIU Da-Jun. Analyses of Combining Ability of DON Content in Wheat Grains [J]. Acta Agron Sin, 2007, 33(05): 731-737.
[14] NING Hai-Long;LI Wen-Xia;LI Wen-Bin. Analysis of Embryo, Cytoplasmic and Maternal Effects on Fatty Acid Components in Soybean（G. max Merill） [J]. Acta Agron Sin, 2006, 32(12): 1873-1877.
[15] ZHANG Yong;ZHANG Li-Ping;YAN Jun ;ZHANG Yan;WANG De-Sen;LIU Jian-Jun;HE Zhong-Hu. Early Generation Selection of Gluten Strength in Common Wheat [J]. Acta Agron Sin, 2006, 32(11): 1663-1670.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd