Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses.

Runfeng Wang, Hailian Wang, Shaoming Huang, Yingxing Zhao, Erying Chen, Feifei Li, Ling Qin, Yanbing Yang, Yan'an Guan, Bin Liu, Huawen Zhang
Author Information
  1. Runfeng Wang: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  2. Hailian Wang: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  3. Shaoming Huang: Crop Development Center, University of Saskatchewan, Saskatoon, SK, Canada.
  4. Yingxing Zhao: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  5. Erying Chen: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  6. Feifei Li: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  7. Ling Qin: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  8. Yanbing Yang: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  9. Yan'an Guan: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  10. Bin Liu: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.
  11. Huawen Zhang: Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China.

Abstract

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects ( < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Keywords

References

  1. Methods Mol Biol. 2019;1931:121-140 [PMID: 30652287]
  2. Crit Rev Food Sci Nutr. 2017 Jan 22;57(2):372-390 [PMID: 25875451]
  3. Plants (Basel). 2023 May 23;12(11): [PMID: 37299058]
  4. Plants (Basel). 2023 May 30;12(11): [PMID: 37299146]
  5. Sci Rep. 2021 Nov 23;11(1):22791 [PMID: 34815427]
  6. Front Plant Sci. 2023 Jan 20;13:997429 [PMID: 36743535]
  7. Compr Rev Food Sci Food Saf. 2019 Nov;18(6):2025-2046 [PMID: 33336966]
  8. Plants (Basel). 2022 Feb 02;11(3): [PMID: 35161396]
  9. Bioinformatics. 2016 Jan 1;32(1):58-66 [PMID: 26363027]
  10. Food Sci Nutr. 2022 Jul 27;10(11):4080-4087 [PMID: 36348781]
  11. Front Plant Sci. 2022 Nov 23;13:950992 [PMID: 36507436]
  12. J Appl Genet. 2019 May;60(2):127-135 [PMID: 30877656]
  13. Front Plant Sci. 2022 Nov 10;13:1050064 [PMID: 36457517]
  14. Heliyon. 2022 Jun 10;8(6):e09690 [PMID: 35756124]
  15. ScientificWorldJournal. 2016;2016:4060857 [PMID: 27777968]
  16. Genet Mol Res. 2016 May 20;15(2): [PMID: 27323051]
  17. Theor Appl Genet. 2019 Apr;132(4):1263-1281 [PMID: 30661107]
  18. Planta. 2021 Aug 10;254(3):47 [PMID: 34374841]

Word Cloud

Created with Highcharts 10.0.0sorghumgraingenotypeyieldAMMIGGEbiplotG3×GEIperformancegenotypesvarietiesmodels0G10G11environmentsuperiortestacrosstwoeffectsinteractionstabletraitsJinzaonevarietystabilityGrainexceptionalsourcedietarynutritionoutstandingeconomicvaluesBreedingcanslowedoccurrenceinteractionscausingbiasedestimationmulti-environmentsthereforecomplicatesdirectphenotypicselectionMulti-environmenttrialsrandomizedcompleteblockdesignthreereplicationsperformed13newlydevelopedsevenlocationsChinayearsAdditivemainmultiplicative+adopteduncoverpatternseffectivelyidentifyhigh-yieldingenvironmentsYieldYLDplantheightPHdaysmaturityDTMthousandseedweightTSWpaniclelengthPLmeasuredStatisticalanalysisshowedtargetinfluencedsignificant<001broad-senseheritabilityestimatesvaried4094withinmediumhighrangecaptured663%totalvariancesuggestingsufficientapplicabilityanalyticLiaozaNo52110identified111locallyadaptedhighestyieldingpotentialsecondaveragewhereasbestadaptationlocationrecommendproductionShenyangChaoyangJinzhouJinzhongYulinPingliangYiliAssessmentperformancesanalysesGEmulti-environmenttrial

Similar Articles

Cited By