Selection for the improvement of maize yield under moisture-deficits

Fischer, KS; Edmeades, G.O.; Johnson, Elden

doi:10.1016/0378-4290(89)90094-4

Cited by 89 publications

(60 citation statements)

References 11 publications

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“…All of the traits showed continuous variation, typical of quantitative traits . Consistent with previous observations (Fischer et al ., 1989;Frederick et al ., 1989 ;Edmeades et al ., 1990 ;Guei & Wassom, 1992), grain yield under drought was correlated with number of ears per plant, and with tassel size ( correlated with DTS, DTP, and ASI . However, plant and ear heights did not show a significant correlation with yield.…”

Section: Drought Response and Phenotypic Correlationssupporting

confidence: 83%

Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.)

1996

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Grain yield in the maize (Zea mays L) plant is sensitive to drought in the period three weeks either side of flowering . Maize is well-adapted to the use of restriction fragment length polymorphisms (RFLPs) to identify a tight linkage between gene(s) controlling the quantitative trait and a molecular marker . We have determined the chromosomal locations of quantitative trait loci (QTLs) affecting grain yield under drought, anthesis-silking interval, and number of ears per plant . The F3 families derived from the cross SD34(tolerant) x SD35(intolerant) were evaluated for these traits in a two replicated experiment . RFLP analysis of the maize genome included non-radioactive DNA-DNA hybridization detection using chemiluminescence . To identify QTLs underlying tolerance to drought, the mean phenotypic performances of F3 families were compared based on genotypic classification at each of 70 RFLP marker loci . The genetic linkage map assembled from these markers was in good agreement with previously published maps . The phenotypic correlations between yield and other traits were highly significant . In the combined analyses, genomic regions significantly affecting tolerance to drought were found on chromosomes 1,3,5,6, and 8 . For yield, a total of 50% of the phenotypic variance could be explained by five putative QTLs . Different types of gene action were found for the putative QTLs for the three traits .

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Section: Drought Response and Phenotypic Correlationssupporting

confidence: 83%

Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.)

1996

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“…Negative association between grain yield and FMSSI has been observed under drought conditions in wheat (Fischer and Maurer, 1978), maize (Fischer et al, 1983(Fischer et al, , 1989, lentil (Hamdi and Erskine, 1986), and sunflower (Fereres et al, 1986). FMSSI has been considered to be of limited value as a selection criterion for measuring tolerance to drought in wheat (Clarke et al, 1992) and common bean (White and Singh, 1991;Schneider et al, 1997), as it does not differentiate between potential drought-resistant genotypes and those possessing overall high yield potential.…”

Section: Discussionmentioning

confidence: 99%

Comparison of selection indices to identify sorghum genotypes resistant to the spotted stemborer Chilo partellus (Lepidoptera: Noctuidae)

Singh

Rao

Sharma

2011

Int. J. Trop. Insect Sci.

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Abstract.The resistance or susceptibility of sorghum genotypes to damage by the spotted stemborer, Chilo partellus (Swinhoe) (Noctuidae: Lepidoptera) in sorghum is commonly measured in terms of leaf damage, deadheart formation and stem tunneling. Effect of the host plant on the insect survival and development (antibiosis), and the plant's response to insect damage (tolerance or recovery resistance) are also important parameters for measuring host plant resistance to stemborers. We compared 10 selection indices commonly used to select genotypes for tolerance/ resistance to various stresses, namely functional plant loss index (FPLI), antibiosis index (ABI), mean productivity index (MPI), mean relative performance (MRP), relative efficiency index (REI), tolerance index (TOL), Fischer and Maurer's stress susceptibility index (FMSSI), Fernandez stress tolerance index (FSTI), Geometric mean productivity (GMP), and Schneider's stress severity index (SSSI). The indices based on grain yield reduction can be combined with those associated with grain yield response and potential under borer-infested and -uninfested conditions, since each index assesses different biological mechanisms (such as tolerance, adaptation, and/or productivity). Amongst these, FPLI can be used to differentiate the sorghum genotypes for different components of resistance by taking into account both the foliar damage and deadheart formation. GMP, REI, and FSTI provided a better discrimination of the stemborer tolerant genotypes under borer-infested condition, and were good predictors of grain yield performance than TOL, MPI, MRP, ABI, SSSI, FMSSI, and FPLI. A strong association between * Email: h.sharma@cgiar.org 2 GMP and REI indicated that both these indices could be used to select for low grain yield loss and high productivity. However, the selection of genotypes with high grain yield potential and adaptation to borer infestation may be achieved by combining selection indices related to the mean grain yield performance under borer-infested and uninfested conditions (GMP and REI), and low levels of grain yield loss under borer infestation.

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“…Field-based, managed-environment methods have been advocated and utilised to enable genetic gain for important environmental components of the TPE that contribute to repeatable GÂE interactions (Fischer et al 1989;Cooper et al 1995Cooper et al , 1997Cooper et al , 2014Campos et al 2004Campos et al , 2006Kirigwi et al 2004;Trethowan et al 2005;Bänziger et al 2006;Weber et al 2012;Rebetzke et al 2013). Two maize breeding examples and the managed-environment solutions that have been implemented for the US corn-belt TPE to deal with important environmental components and the associated GÂE interactions are considered below: brittle snap resistance, and drought resistance.…”

Section: Improved Phenotyping Methodology: Managed Environmentsmentioning

confidence: 99%

Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

et al. 2014

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Abstract. For the foreseeable future, plant breeding methodology will continue to unfold as a practical application of the scaling of quantitative biology. These efforts to increase the effective scale of breeding programs will focus on the immediate and long-term needs of society. The foundations of the quantitative dimension will be integration of quantitative genetics, statistics, gene-to-phenotype knowledge of traits embedded within crop growth and development models. The integration will be enabled by advances in quantitative genetics methodology and computer simulation. The foundations of the biology dimension will be integrated experimental and functional gene-to-phenotype modelling approaches that advance our understanding of functional germplasm diversity, and gene-to-phenotype trait relationships for the native and transgenic variation utilised in agricultural crops. The trait genetic knowledge created will span scales of biology, extending from molecular genetics to multi-trait phenotypes embedded within evolving genotype-environment systems. The outcomes sought and successes achieved by plant breeding will be measured in terms of sustainable improvements in agricultural production of food, feed, fibre, biofuels and other desirable plant products that meet the needs of society. In this review, examples will be drawn primarily from our experience gained through commercial maize breeding. Implications for other crops, in both the private and public sectors, will be discussed.

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Selection for the improvement of maize yield under moisture-deficits

Cited by 89 publications

References 11 publications

Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.)

Mapping QTLs in breeding for drought tolerance in maize (Zea mays L.)

Comparison of selection indices to identify sorghum genotypes resistant to the spotted stemborer Chilo partellus (Lepidoptera: Noctuidae)

Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

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