品种选育与评价的原理和方法评述
来源： 时间：2025-09-15 14:50
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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.2018.01899pmid: 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/s00122-021-03812-3pmid: 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/BF00288824pmid: 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.
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