Abstract
Genotype by environment interaction (GEI) is a major limiting factor in the regional planting and breeding of cultivars. The objective of this study was to assess GEI for grain yield in foxtail millet cultivars grown in northwest China and identify promising genotypes and representative seed production environments. The study used 12 genotypes in eight environments, representative of early-maturing growing areas. Additive main effects and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) biplot analysis were used to analyze the data. AMMI analysis of variance (ANOVA) indicated that genotype (G), environment (E), and GEI effects were highly significant (p < 0.01), with contributions to total observed variation of 19.5%, 43.4%, and 28.6%. Six stability parameters based on AMMI were used to analyze yielding stability. YG35, FH9, DT29, and ZZG21 were more stable. The correlation coefficients between the six parameters were > 0.8. Subsequently, genotypes YG35, ZZG21, and DT29 were identified as stable and high yield potential based on genomic selection index (GSI). GGE biplot analysis revealed the same cultivars (YG35, ZZG21, and DT29) as the best performers for being closest to the ideal cultivar. The test sites were classified into two mega-environments using GGE biplot analysis for genotype assessment and seed production. Chifeng (CF) and Wulumuqi (XJ) were identified as the closest to the ideal test site, with relatively strong discriminatory ability and representativeness. Therefore, the findings of this study provide insights for the regional planting and breeding of foxtail millet in the future.
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Abbreviations
- AMMI:
-
Additive main effects and multiplicative interaction
- ASI:
-
AMMI Stability Index
- ASV:
-
AMMI stability value
- AVAMGE:
-
Sum across environments of absolute value of genotype-environment interaction modelled
- GEI:
-
Genotype by environment interaction
- GGE:
-
Genotype and genotype by environment
- IPCA:
-
Interaction principal component axis
- MASI:
-
Modified AMMI Stability Index
- MASV:
-
Modified AMMI stability value
- MET:
-
Multi-environment trial
- WAAS:
-
The weighted average of the absolute scores
References
Abakemal D, Shimelis H, Derera J (2016) Genotype-by-environment interaction and yield stability of quality protein maize hybrids developed from tropical-highland adapted inbred lines. Euphytica 209:757–769. https://doi.org/10.1007/s10681-016-1673-7
Ajay BC, Aravind J, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P (2018) Modified AMMI Stability Index (MASI) for stability analysis. ICAR-DGR Newsl 18:4–5
Ajay BC, Aravind J, Fiyaz RA, Kumar N, Lal C, Gangadhar K, Kona P, Dagla MC, Bera SK (2019) Rectification of modified AMMI stability value (MASV). Indian J Genet Plant Breed 79(4):726–731
Alam MA, Farhad M, Hakim MA, Barma NC, Malaker PK, Reza MM, Hossain MA, Li M (2017) AMMI and GGE biplot analysis for yield stability of promising bread wheat genotypes in Bangladesh. Pak J Bot 49(3):1049–1056
Arif A, Parveen N, Waheed MQ, Atif RM, Waqar I, Shah TM (2021) A comparative study for assessing the drought-tolerance of chickpea under varying natural growth environments. Front Plant Sci 11:607869. https://doi.org/10.3389/fpls.2020.607869
Bashir EM, Ali AM, Ali AM, Ismail MI, Parzies HK, Haussmann BI (2014) Patterns of pearl millet genotype-by-environment interaction for yield performance and grain iron (Fe) and zinc (Zn) concentrations in Sudan. Field Crop Res 166:82–91. https://doi.org/10.1016/j.fcr.2014.06.007
Bocianowski J, Liersch A, Nowosad K (2020) Genotype by environment interaction for alkenyl glucosinolates content in winter oilseed rape (Brassica napus L.) using additive main effects and multiplicative interaction model. Curr Plant Biol 21:100137. https://doi.org/10.1016/j.cpb.2020a.100137
Bocianowski J, Tratwal A, Nowosad K (2020) Genotype by environment interaction for area under the disease-progress curve (AUDPC) value in spring barley using additive main effects and multiplicative interaction model. Australas Plant Pathol 49:525–529. https://doi.org/10.1007/s13313-020-00723-7
Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci 37(4):1168–1176. https://doi.org/10.2135/cropsci1997.0011183X003700040024x
Cooper M, DeLacy IH, Basford, KE (1996) Relationships among analytical methods used to analyse genotypic adaptation in multi-environment trials. Plant adaptation and crop improvement 193–224.
Donkor EF, Nyadanu D, Akromah R, Osei K (2020) Genotype-by-environment interaction and stability of taro [Colocasia esculenta (l.) Schott.] genotypes for yield and yield components. Ecol Genet Genomics 17:100070. https://doi.org/10.1016/j.egg.2020.100070
Eberhart SA, Russel WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x
Enyew M, Feyissa T, Geleta M, Tesfaye K, Hammenhag C, Carlsson AS (2021) Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench). PLoS ONE 16(10):e0258211. https://doi.org/10.1371/journal.pone.0258211
Farshadfar E (2008) Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pak J Biol Sci 11:1791–1796
Ferfuia C, Zuliani F, Danuso F, Piani B, Cattivello C, Dorigo G, Baldini M (2021) Performance and stability of different monoecious hemp cultivars in a multi-environments trial in North-Eastern Italy. Agronomy 11(7):1424. https://doi.org/10.3390/agronomy11071424
Finlay K, Wilkinson G (1963) The analysis of adaptation in plant-breeding programme. Aust J Agric Res 14:742–754. https://doi.org/10.1071/AR9630742
Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46:1488–1500. https://doi.org/10.2135/cropsci2005.07-0193
Gauch HG, Zoble RW (1997) Identifying mega-environments and targeting genotypes. Crop Sci 37:381–385. https://doi.org/10.2135/cropsci1997.0011183X003700020002x
Gauch HG, Piepho HP, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci 48(3):866–889. https://doi.org/10.2135/cropsci2007.09.0513
Gupta P, Dhawan SS, Lal RK, Chanotiya CS, Mishra A (2021) Genotype selection over years using additive main eff; ects and multiplicative interaction (AMMI) model under the ascendancy of genetic diversity in the genus Ocimum. Ind Crops Prod 161:113198. https://doi.org/10.1016/j.indcrop.2020.113198
Gurmu F, Shimelis H, Laing M, Mashilo J (2020) Genotype-by-environment interaction analysis of nutritional composition in newly-developed sweetpotato clones. J Food Compos Anal 88:103426. https://doi.org/10.1016/j.jfca.2020.103426
Jambhulkar NN, Rath NC, Bose LK, Subudhi HN, Biswajit M, Lipi D, Meher J (2017) Stability analysis for grain yield in rice in demonstrations conducted during rabi season in India. Oryza 54(2):236–240. https://doi.org/10.5958/22495266.2017.00030.3
Joshi A, Adhikari S, Singh NK, Kumar A, Jaiswal JP, Pant U, Singh RP (2021) Responses of maize × teosinte derived backcross inbred lines (BILs) to maydis leaf blight (MLB) disease. Euphytica 217:219. https://doi.org/10.1007/s10681-021-02951-4
Kumar MV, Ramya V, Govindaraj M, Kumar CV, Maheshwaramma S, Gokenpally S, Prabhakar M, Krishna H, Sridhar M, Ramana MV, Kumar KA (2021) Harnessing sorghum landraces to breed high-yielding, grain mold-tolerant cultivars with high protein for drought-prone environments. Front Plant Sci 12:659874. https://doi.org/10.3389/fpls.2021.659874
Li C, Wang G, Li H, Wang G, Ma J, Zhao X, Huo L, Zhang L, Jiang Y, Zhang J, Liu G, Cheng R, Wei J, Yao L (2021) High-depth resequencing of 312 accessions reveals the local adaptation of foxtail millet. Theor Appl Genet 134:1303–1317. https://doi.org/10.1007/s00122-020-03760-4
Li S, Zhao W, Liu S, Li P, Zhang A, Zhang J, Wang Y, Liu Y, Liu J (2021) Characterization of nutritional properties and aroma compounds in different colored kernel varieties of foxtail millet (Setaria italica). J Cereal Sci 100:103248. https://doi.org/10.1016/j.jcs.2021.103248
Nduwumuremyi A, Melis R, Shanahan P, Theodore A (2017) Interaction of genotype and environment effects on important traits of cassava (Manihot esculenta Crantz). The Crop Journal 5(5):373–386. https://doi.org/10.1016/j.cj.2017.02.004
Ning N, Yang YJ, Hong JP, Yuan XY, Song XE, Wang HF, Guo PY (2017) Correlation between grain quality of foxtail millet (Setaria italica [L.] P. Beauv.) and environmental factors on multivariate statistical analysis. Chil J Agric Res 77(4):303–310. https://doi.org/10.4067/S0718-58392017000400303
Nowosad K, Liersch A, Popławska W, Bocianowski J (2016) Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208:187–194. https://doi.org/10.1007/s10681-015-1620-z
Nzuve F, Githiri S, Mukunya DM, Gethi J (2013) Analysis of genotype × environment interaction for grain yield in Maize hybrids. J Agric Sci 5(11):75–85
Olivoto T, Lúcio AD, da Silva JA, Marchioro VS, de Souza VQ, Jost E (2019) Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agron J 111:2949–2960. https://doi.org/10.2134/agronj2019.03.0220
Payne RW, Harding SA, Murray DA, Soutar DM, Baird DB, Glaser AI, Welham SJ, Gilmour AR, Thompson R, Webster R (2007) GenStat release 17: statistical software for windows. VSN Int., Hemel Hempstead
Perić V, Srebrić M, Nikolić A, Ristić D, Mladenović-Drinić S, Dumanović Z (2021) Stability of yield and seed composition in early maturing soybean genotypes assessed by AMMI analysis. Genetika 53(1):323–338. https://doi.org/10.2298/GENSR2101323P
Pramitha JL, Jeeva G, Ravikesavan R, Joel AJ, Vinothana NK, Meenakumari B, Raveendran M, Uma D, Hossain F, Kumar B, Rakshit S (2020) Environmental impact of phytic acid in Maize (Zea mays L.) genotypes for the identification of stable inbreds for low phytic acid. Physiol Mol Biol Plants 26:1477–1488. https://doi.org/10.1007/s12298-020-00818-x
Purchase JL, Hatting H, Van Deventer CS (2000) Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South Afr J Plant Soil 17(3):101–107. https://doi.org/10.1080/02571862.2000.10634878
Simmonds NW (1991) Selection for local adaptation in a plant breeding programme. Theoret Appl Genet 82:363–367. https://doi.org/10.1007/BF02190624
Singamsetti A, Shahi JP, Zaidi PH, Seetharam K, Vinayan MT, Kumar M, Singla S, Shikha K, Madankar K (2021) Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes. Field Crops Res 270:108224. https://doi.org/10.1016/j.fcr.2021.108224
Tadesse T, Tekalign A, Asmare B (2021) Identification of stable lentil genotypes using AMMI analysis for the highlands of bale, Southeastern Ethiopia. Chem Biomol Eng 6(4):74–79. https://doi.org/10.11648/j.cbe.20210604.12
Thungo Z, Shimelis H, Odindo A, Mashilo J (2020) Genotype-by-environment effects on grain quality among heat and drought tolerant bread wheat (Triticum aestivum L.) genotypes. J Plant Interact 15(1):83–92. https://doi.org/10.1080/17429145.2020.1748732
Trivedi AK, Arya L, Verma SK, Tyagi RK, Hemantaranjan A, Verma M, Sharma VP, Saha D (2018) Molecular profiling of foxtail millet (Setaria italica (L.) P. Beauv) from Central Himalayan Region for genetic variability and nutritional quality. J Agric Sci 156(3):333–341. https://doi.org/10.1017/S0021859618000382
Vaezi B, Pour-Aboughadareh A, Mohammadi R, Armion M, Mehraban A, Hossein-Pour T, Dorii M (2017) GGE biplot and AMMI analysis of barley yield performance in Iran. Cereal Res Commun 45:500–511. https://doi.org/10.1556/0806.45.2017.019
Wang H, Hao D, Wang X, Zhang H, Yang P, Zhang L, Ben Z (2021) Genome-wide identification and expression analysis of the SNARE genes in Foxtail millet (Setaria italica) reveals its roles in drought stress. Plant Growth Regul 95:355–369. https://doi.org/10.1007/s10725-021-00746-0
Wang T, Lu Q, Song H, Hu N, Wei Y, Li P, Liu Y, Zhao Z, Liu J, Zhang B, Peng R (2021) DNA methylation and RNA-sequencing analysis show epigenetic function during grain filling in Foxtail millet (Setaria italic L.). Front Plant Sci 12:741415. https://doi.org/10.3389/fpls.2021b.741415
Yan W (2001) GGEbiplot—a windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron J 93:1111–1118. https://doi.org/10.2134/agronj2001.9351111x
Yan W (2002) Singular-value partitioning in biplot analysis of multienvironment trial data. Agron J 94:990–996. https://doi.org/10.2134/agronj2002.9900
Yan W, Kang MS (2002) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press. https://doi.org/10.1201/9781420040371
Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86(3):623–645. https://doi.org/10.4141/P05-169
Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:641–653. https://doi.org/10.2135/cropsci2006.06.0374
Yang X, Wan Z, Perry L, Lu H, Wang Q, Zhao C, Li J, Xie F, Yu J, Cui T, Wang T (2012) Early millet use in northern China. Proc Natl Acad Sci USA 109(10):3726–3730. https://doi.org/10.1073/pnas.1115430109
Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R (2012) Evaluation of genotype × environ-ment interaction in chickpea using measures of stability from AMMI model. Ann Biol Res 3(7):3126–3136
Zewdu Z, Dessie A, Worede F, Atinaf M, Berie A, Tahir Z, Kinfe H, Bitew M (2020) Agronomic performance evaluation and yield stability analysis of upland rice (Oryza sativa L.) varieties using AMMI and GGE biplot. Plant 8(4):87–92. https://doi.org/10.11648/j.plant.20200804.11
Zhang Y, Gao J, Qie Q, Yang Y, Hou S, Wang X, Li X, Han Y (2021) Comparative analysis of flavonoid metabolites in foxtail millet (Setaria italica) with different eating quality. Life 11(6):578. https://doi.org/10.3390/life11060578
Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80(3):388–393. https://doi.org/10.2134/agronj1988.00021962008000030002x
Acknowledgements
The authors thank Qingtai Zhang for the assistant of the data analysis. This work was supported by the Basic Research program of Shanxi Province (No. 202103021223136), Doctoral Research project of Shanxi Agricultural University (No. 2021BQ40) and (No. 2021BQ60), the Doctor Foundation of Crop Science Institute of Shanxi Academy of Agricultural Sciences (No. ZB2002).
Funding
The Basic Research program of Shanxi Province, No. 202103021223136, Haiying Zhang, Doctoral Research project of Shanxi Agricultural University, No. 2021BQ40, Haiying Zhang, No. 2021BQ60, Zhiwei Feng, the Doctor Foundation of Crop Science Institute of Shanxi Academy of Agricultural Sciences, No. ZB2002, Haiying Zhang
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HZ contributed statistical analysis and drafted the manuscript; QW, JW, XY, and FQ performed the experiments and collected the data; HZ and XY prepared figures and tables; QW and XY revised the language of this manuscript; HZ, CS, and ZF conceived and designed the research.
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Zhang, H., Feng, Z., Wang, J. et al. Genotype by environment interaction for grain yield in foxtail millet (Setarai italica) using AMMI model and GGE Biplot. Plant Growth Regul 99, 101–112 (2023). https://doi.org/10.1007/s10725-022-00885-y
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DOI: https://doi.org/10.1007/s10725-022-00885-y