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Combining ability and testcross performance of low N tolerant intermediate maize inbred lines under low soil nitrogen and optimal environments

Published online by Cambridge University Press:  07 September 2020

P.F. Ribeiro ,
B. Badu Apraku Open the ORCID record for B. Badu Apraku [Opens in a new window] ,
V. Gracen ,
E.Y. Danquah ,
C. Afriyie-Debrah ,
K. Obeng-Dankwa  and
J.O. Toyinbo
P.F. Ribeiro
Affiliation:
CSIR – Crops Research Institute, P. O. Box 3785, Fumesua, Kumasi, Ghana
B. Badu Apraku*
Affiliation:
International Institute of Tropical Agriculture, P.M.B. 5320, Ibadan, Nigeria
V. Gracen
Affiliation:
West Africa Centre for Crop Improvement, University of Ghana, Legon, Ghana
E.Y. Danquah
Affiliation:
West Africa Centre for Crop Improvement, University of Ghana, Legon, Ghana
C. Afriyie-Debrah
Affiliation:
CSIR – Crops Research Institute, P. O. Box 3785, Fumesua, Kumasi, Ghana
K. Obeng-Dankwa
Affiliation:
CSIR – Crops Research Institute, P. O. Box 3785, Fumesua, Kumasi, Ghana
J.O. Toyinbo
Affiliation:
International Institute of Tropical Agriculture, P.M.B. 5320, Ibadan, Nigeria
*
Author for correspondence: B. Badu Apraku, E-mail: b.badu-apraku@cgiar.org
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Abstract

Low soil nitrogen (low N) threatens maize production in sub-Sahara Africa (SSA). We examined the mode of gene action conditioning grain yield of intermediate maturing inbreds and evaluated lines in hybrid combinations for high yield, stability and tolerance to low N. Thirty-two sets of inbreds were crossed to three elite testers (87036, 1368 and 9071) to generate 96 F1 hybrids. The testcrosses plus four hybrid checks were evaluated under low (30 kg/ha) and high (90 kg/ha) N environments at three locations for 2 years in Ghana. Significant general combining ability (GCA) and specific combining ability (SCA) effects were detected for grain yield and most measured traits across test environments, indicating that both additive and non-additive gene action governed the inheritance of the traits. GCA effects were greater than SCA effects, indicating that most traits were controlled predominantly by additive gene action and that inbreds with positive significant GCA effects for grain yield and other traits would contribute favourable alleles to progenies across environments. Hybrid CZL 0001 × 9071 possessed high GY, increased EPP, desirable EHT and PLHT and was the highest yielding under each of two research conditions. Significant genetic correlations were observed between GY and PLHT, EPP, EHT, CA and PA implying that improvement of these traits would lead to significant gains in grain yield under low-N conditions. Hybrids CLWN 247 × 9071, ZM523B-29-2-1-1-B*6 × 9071, TZD II 68 × 1368 and P43SCRq Fs100-1-1-8 × 9071 were high-yielding, stable and low-N tolerant and should be tested on-farm and commercialized.

Keywords

General and specific combining abilitygenotype-by-environment interactionGGE biplot analysis
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 158 , Issue 5 , July 2020 , pp. 351 - 370
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Abrha, SW, Habtamu, ZZ and Dagne, WG (2013) Line × tester analysis of maize inbred lines for grain yield and yield related traits. Asian Journal of Plant Science Research 3, 12–19.Google Scholar
Adebayo, MA, Menkir, A, Blay, E, Gracen, V, Danquh, E and Hearne, S (2014) Genetic analysis of drought tolerance in adapted × exotic crosses of maize inbred lines under managed stress conditions. Euphytica 196, 261–270.CrossRefGoogle Scholar
Adu, SV and Asiamah, RD (1992). Soils of the Ayensu-Densu Basin, Central, Eastern and Greater Accra Regions of Ghana. Council for Scientific and Industrial Research (CSIR) – Soil Research Institute Memoir, No. 9, 117.Google Scholar
Agbaje, S, Badu-Apraku, B and Fakorede, M (2008). Heterotic patterns of early maturing maize inbred lines in Striga-free and Striga-infested environments. Maydica, 53, 87–96, https://hdl.handle.net/10568/92263.Google Scholar
Anuarbek, SN, Abugalieva, SI, Chudinov, VA, Tuberosa, R, Pecchioni, N and Turuspekov, Y (2019) Comparative assessment of the yield components of the tetraploid wheat. World Collection Eurasian Journal of Ecology 59, 39–49.Google Scholar
Badu-Apraku, B and Akinwale, RO (2010) Cultivar evaluation and trait analysis of tropical early maturing maize under Striga-infested and Striga-free environments. Field Crop Research 121, 186–194.CrossRefGoogle Scholar
Badu-Apraku, B and Fakorede, MAB (2017) Genotype by environment interaction. In Advances in Genetic Enhancement of Early and Extra-Early Maize for Sub-Saharan Africa. Cham, Switzerland: Springer, p. 482.CrossRefGoogle Scholar
Badu-Apraku, B and Lum, AF (2010) The pattern of grain yield response of normal and quality protein maize cultivars in stress and non-stress environments. Agronomy Journal 102, 381–394.CrossRefGoogle Scholar
Badu-Apraku, B, Fakorede, MAB and Lum, AF (2007) Evaluation of experimental varieties from recurrent selection for Striga resistance in two extra-early maize populations in the savannas of West and Central Africa. Experimental Agriculture 43, 183–200.CrossRefGoogle Scholar
Badu-Apraku, B, Akinwale, RO, Ajala, SO, Menkir, A, Fakorede, MAB and Oyekunle, M (2011a) Relationships among traits of tropical early maize cultivars in contrasting environments. Agronomy Journal 103, 717–729.CrossRefGoogle Scholar
Badu-Apraku, B, Akinwale, RO, Menkir, A, Obeng-Antwi, K, Osuman, AS, Coulibaly, N, Onyibe, J, Yallou, E, Abdullai MS, GC and Didjera, A (2011b) Use of GGE biplot for targeting early maturing maize cultivars to mega-environments in West Africa. African Crop Science Journal 19, 79–96.CrossRefGoogle Scholar
Badu-Apraku, B, Akinwale, RO, Franco, J and Oyekunle, M (2012) Assessment of reliability of secondary traits in selecting for improved grain yield in drought and low-nitrogen environments. Crop Science 52, 2050–2062.Google Scholar
Badu-Apraku, B, Akinwale, RO and Oyekunle, M (2013a) Efficiency of secondary traits in selecting for improved grain yield in extra-early maize under Striga-infested and Striga-free environments. Plant Breeding Journal 133, 373–380.CrossRefGoogle Scholar
Badu-Apraku, B, Oyekunle, M, Fakorede, MAB, Vroh, I, Akinwale, RO and Aderounmu, M (2013b) Combining ability, heterotic patterns and genetic diversity of extra-early yellow inbreds under contrasting environments. Euphytica 192, 413–433.CrossRefGoogle Scholar
Badu-Apraku, B, Oyekunle, M, Menkir, A, Obeng-Antwi, K and Yallou, CG (2013c) Comparative performance of early maturing maize cultivars developed in three eras under drought stress and well-watered environments in West Africa. Crop Science 53, 1298–1311.CrossRefGoogle Scholar
Badu-Apraku, B, Annor, B, Oyekunle, M, Akinwale, RO, Fakorede, MAB, Talabi, AO, Akaogu, IC, Melaku, G and Fasanmade, Y (2015) Grouping of early maturing quality protein maize inbreds based on SNP markers and combining ability under multiple environments. Field Crops Research 183, 169–183.CrossRefGoogle Scholar
Badu-Apraku, B, Fakorede, MAB, Annor, B and Talabi, AO (2018) Improvement in grain yield and low-nitrogen tolerance in maize cultivars of three eras. Experimental Agriculture 54, 805–823.CrossRefGoogle Scholar
Baker, RJ (1978) Issues in diallel analysis. Crop Science 18, 533–536.CrossRefGoogle Scholar
Bänziger, M and Lafitte, HR (1997) Efficiency of secondary traits for improving maize for low-nitrogen target environments. Crop Science 37, 1110–1117.CrossRefGoogle Scholar
Below, FE, Brandua, PS, Lambert, RJ and Teyker, RH (1997) Combining ability for nitrogen use in maize. In Edmeades, GO, Banzinger M, Mickelson HR and Pena-Valdivia CB (eds), Developing Drought- and Low N-Tolerant Maize. Mexico, D.F: CIMMYT/UNDP, pp. 316–319.Google Scholar
Betràn, FJ, Beck, D, Bänziger, M and Edmeades, GO (2003) Genetic analysis of inbred and hybrid grain yield under stress and nonstress environments in tropical maize. Crop Science 43, 807–817.CrossRefGoogle Scholar
Buerkert, A, Bationo, A and Piepho, HP (2001) Efficient phosphorus application strategies for increased crop production in sub-Saharan West Africa. Field Crop Research 72, 1–15.CrossRefGoogle Scholar
CIMMYT (2007) Seeding Innovation. Nourishing Hope: CIMMYT Annual Report 2006–2007. CIMMYT, Mexico, D.F., Mexico.Google Scholar
Girma, CH, Sentayehu, A, Berhanu, T and Temesgen, M (2015) Test cross performance and combining ability of maize (Zea mays L.) inbred lines at Bako, Western Ethiopia. GJSFR: D Agriculture and Veterinary 15.Google Scholar
Guinto, D and Catto, W (2016) Analysis of the relationship between total nitrogen and available nitrogen in non-pastoral topsoils of New Zealand from a large soil test database. In Currie, LD and Singh, R (eds), Integrated Nutrient and Water Management for Sustainable Farming. Occasional Report No. 29. Palmerston North, New Zealand: Fertilizer and Lime Research Centre, Massey University, pp. 1–6 (http://flrc.massey.ac.nz/publications.html).Google Scholar
Ifie, B (2013) Genetic analysis of Striga resistance and low soil nitrogen tolerance in early maturing maize (Zea mays L.) inbred lines (Dissertation). University of Ghana Legon. West Africa Centre for Crop Improvement/School of Agriculture/College of Agriculture and Consumer Sciences, 110–123.Google Scholar
Jalata, Z (2011) GGE biplot analysis of multi-environment yield trials of barley (Hordeum vulgare L.) genotypes in south-eastern Ethiopia Highlands. International Journal of Plant Breeding and Genetics 5, 59–75.CrossRefGoogle Scholar
Kaya, Y, Akcura, M and Taner, S (2006) GGE biplot analysis of multi environment yield trials in bread wheat. Turkish Journal of Agriculture 30, 325–337.Google Scholar
Kempthrone, O (1957) An Introduction to Genetic Statistics. New York: John Willey and Sons. Inc.Google Scholar
Kling, JG, Oikeh, S, Akintoye, HA, Heuberge, HT and Horst, WJ (1997) Potential for developing nitrogen use efficiency maize for low input agricultural systems in the moist savanna of Africa. In Edmeades, GO, Bazinger M, Mickelson HR and Pena-Valdivia CB (eds), Developing Drought- and Low N-Tolerant Maize. Mexico, D.F: CIMMYT/UNDP, pp. 490–501.Google Scholar
Lafitte, HR and Edmeades, GO (1995) Association between traits in tropical maize inbred lines and their hybrids under high and low soil nitrogen. Maydica 40, 259–267.Google Scholar
Landon, JR (1991) Booker Tropical Soil Manual. A Handbook for Soil Survey and Agricultural Land Evaluation in the Tropic and sub Tropics. London: Addison Wesley (London) Ltd, pp. 112–114.Google Scholar
Maffouasson, AHN (2014) Genetic analysis of tolerance to low soil nitrogen in intermediate maturing maize (Zea mays L.) inbred lines (Dissertation). University of Ghana Legon, West Africa Centre for Crop Improvement/School of Agriculture/College of Agriculture and Consumer Sciences, 64–82.Google Scholar
Maffousan, HNA, Gracen, V, Yeboah, MA, Ntsomboh-Ntsefong, G, Tandzi, LN and Mutengwa, CS (2018) Genotype-by-environment interaction and yield stability of maize single cross hybrids developed from tropical inbred lines. Agronomy 8, 62.CrossRefGoogle Scholar
Makumbi, D, Betràn, FJ, Bänziger, M and Ribaut, J (2011) Combining ability, heterosis and genetic diversity in tropical maize (Zea mays L.) under stress and non-stress conditions. Euphytica 180, 143–162.CrossRefGoogle Scholar
Masuka, B, Araus, JL, Das, B, Sonder, K and Cairns, JE (2012) Phenotyping for abiotic stress tolerance in maize. Journal of Integrative Plant Biology 54, 238–249.CrossRefGoogle ScholarPubMed
Menkir, A, Badu-Apraku, B, Thé, C and Adepoju, A (2003) Evaluation of heterotic patterns of IITA's lowland white maize inbred lines. Maydica 48, 161–170.Google Scholar
Meseka, SK, Menkir, A, Ibrahim, AES and Ajala, SO (2006) Genetic analysis of performance of maize inbred lines selected for tolerance to drought under low nitrogen. Maydica 51, 487–495.Google Scholar
Meseka, SK, Menkir, A, Ibrahim, AES and Ajala, SO (2013) Genetic analysis of maize inbred lines for tolerance to drought and low nitrogen. JONARES 1, 29–36.Google Scholar
Ministry of Food and Agriculture (MoFA) (2011). Agriculture in Ghana: facts and figures. Produced by Statistics, Research, and Information Directorate: Crops Research Institute.Google Scholar
Miti, F (2007) Breeding Investigations of Maize (Zea mays L.) Genotypes for Tolerance To low Nitrogen and Drought in Zambia (Ph.D. Thesis). University of Kwa-Zulu Natal, Pietermaritzburg, South Africa.Google Scholar
Ndhlela, T (2012). Improvement strategies for yield potential, disease resistance and drought tolerance of Zimbabwean maize inbred lines (Dissertation). University of the Free State, South Africa, 295.Google Scholar
Noëlle, MAH, Richard, K, Vernon, G, Martin, YA, Laouali, MN, Liliane T, N and Godswill, N-N (2017) Combining ability and gene action of tropical maize (Zea mays L.) inbred lines under low and high nitrogen conditions. Journal of Agricultural Science 9, 222.CrossRefGoogle Scholar
Ober, ES, Bloa, ML, Clark, CJA, Royal, AK, Jaggard, W and Pidgeon, KW (2005) Evaluation of physiological traits as indirect selection criteria for drought tolerance in sugar beet. Field Crop Research 91, 231–249.CrossRefGoogle Scholar
Oikeh, SO and Horst, WJ (2001) Agro-physiological responses of tropical maize cultivars to nitrogen fertilization in the moist savanna of West Africa. In Horst, WJ (ed.), Plant-nutrition, Food Security and Sustainability of Agro-Ecosystems. Dordrecht: Kluwer Academic Publication, pp. 804–805.Google Scholar
Okweche, SI, Ogunwolu, EO and Adeyemo, MO (2013) Parameters, interrelationships with yield and use of carbofuran to control stem borers in maize (Zea mays L.) at Makurdi in the Nigerian Southern Guinea Savanna. Greener Journal of Agricultural Sciences 3, 702–706.CrossRefGoogle Scholar
Pacheco, A, Vargas, M, Alvarado, G, Rodríguez, F, Crossa, J and Burgueño, J (2016). GEA-R (genotype×environment analysis with R for Windows). Version 2.0. CIMMYT. http://hdl.handle.net/11529/10203 (accessed 20 June 2016).Google Scholar
Ribeiro, PF and Rodriguez, AVC (2020) Emerging advanced technologies to mitigate the impact of climate change in Africa. Plants 9, 381.CrossRefGoogle Scholar
Rizzi, E, Balconi, C, Nembrini, L, Stefanini, FM, Coppolino, F and Motto, M (1993) Genetic variation and relationships among N-related traits in maize. Maydica 38, 23–30.Google Scholar
Sangoi, L, Almeida, ML, Pucci, ALR, Strieder, M, Zanin, CG, Silva, L and Vieira, LJ (2008) Early nitrogen side-dress application does not increase wheat grain yield at the aluminum presence. Ciência Rural 4, 912–920.CrossRefGoogle Scholar
SAS Institute Inc (2008) Statistical Analysis Software (SAS) User's Guide, 1-18, Cary, NC: SAS Institute, Inc.Google Scholar
Sibiya, J, Tongoona, P, Derera, J and Rij, N (2012) Genetic analysis and genotype by environment (G × E) for grey leaf spot disease resistance in elite African maize (Zea mays L.) germplasm. Euphytica 185, 349–362.CrossRefGoogle Scholar
Tamilarasi, PM, Arumugachamy, S, Anantha, MS and Utharasu, S (2010) Identification of nitrogen (N) stress tolerant maize (Zea mays L.) inbred lines for low nitrogen input condition. Indian Journal of Agricultural Research 44, 53–57.Google Scholar
Yan, W (2001) GGE biplot: a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93, 1111–1118.CrossRefGoogle Scholar
Yan, W and Kang, MS (2003) GGE biplot analysis: a graphical tool for breeders. In Kang, MS (ed.), Geneticists and Agronomist. Boca Raton, FL, USA: CRC Press, p. 271.Google Scholar
Yan, W and Tinker, NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science 86, 623–645.CrossRefGoogle Scholar
Yan, W, Hunt, LA, Sheng, Q and Szlavnics, Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40, 597–605.CrossRefGoogle Scholar
Yan, J, Yang, X, Shah, T, Sánchez-Villeda, H, Li, J, Warburton, M, Zhou, Y, Crouch, J and Xu, Y (2010) High-throughput SNP genotyping with the GoldenGate assay in maize. Molecular Breeding 25, 441–451.CrossRefGoogle Scholar
Zaidi, PH, Rafique, S and Singh, NN (2003) Response of maize (Zea mays L.) genotypes to excess moisture stress: morpho – physiological effects and basis of tolerance. European Journal of Agronomy 19, 383–399.CrossRefGoogle Scholar