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Items: 1 to 20 of 111

1.

Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture.

Shekoofa A, Emam Y, Shekoufa N, Ebrahimi M, Ebrahimie E.

PLoS One. 2014 May 15;9(5):e97288. doi: 10.1371/journal.pone.0097288. eCollection 2014.

2.

Combining ability and heterosis for some agronomic traits in crosses of maize.

Abdel-Moneam MA, Attia AN, El-Emery MI, Fayed EA.

Pak J Biol Sci. 2009 Mar 1;12(5):433-8.

PMID:
19579983
3.

A new avenue for classification and prediction of olive cultivars using supervised and unsupervised algorithms.

Beiki AH, Saboor S, Ebrahimi M.

PLoS One. 2012;7(9):e44164. doi: 10.1371/journal.pone.0044164. Epub 2012 Sep 5.

4.

Combining ability of tropical maize lines for seed quality and agronomic traits.

Moterle LM, Braccini AL, Scapim CA, Pinto RJ, Gonçalves LS, do Amaral Júnior AT, Silva TR.

Genet Mol Res. 2011 Sep 30;10(3):2268-78. doi: 10.4238/vol10-3gmr1129.

5.

Genome wide association study for drought, aflatoxin resistance, and important agronomic traits of maize hybrids in the sub-tropics.

Farfan ID, De La Fuente GN, Murray SC, Isakeit T, Huang PC, Warburton M, Williams P, Windham GL, Kolomiets M.

PLoS One. 2015 Feb 25;10(2):e0117737. doi: 10.1371/journal.pone.0117737. eCollection 2015.

6.

Wide variability in kernel composition, seed characteristics, and zein profiles among diverse maize inbreds, landraces, and teosinte.

Flint-Garcia SA, Bodnar AL, Scott MP.

Theor Appl Genet. 2009 Oct;119(6):1129-42. doi: 10.1007/s00122-009-1115-1. Epub 2009 Aug 22.

PMID:
19701625
7.

Can genetic variability for nitrogen metabolism in the developing ear of maize be exploited to improve yield?

Cañas RA, Quilleré I, Gallais A, Hirel B.

New Phytol. 2012 Apr;194(2):440-52. doi: 10.1111/j.1469-8137.2012.04067.x. Epub 2012 Feb 13.

8.

Fine mapping a major QTL for kernel number per row under different phosphorus regimes in maize (Zea mays L.).

Zhang G, Wang X, Wang B, Tian Y, Li M, Nie Y, Peng Q, Wang Z.

Theor Appl Genet. 2013 Jun;126(6):1545-53. doi: 10.1007/s00122-013-2072-2. Epub 2013 Mar 15.

PMID:
23494393
9.

Seminal quality prediction using data mining methods.

Sahoo AJ, Kumar Y.

Technol Health Care. 2014;22(4):531-45. doi: 10.3233/THC-140816.

PMID:
24898862
10.

Genomic screening for artificial selection during domestication and improvement in maize.

Yamasaki M, Wright SI, McMullen MD.

Ann Bot. 2007 Nov;100(5):967-73. Epub 2007 Aug 18. Review.

11.

Comprehensive phenotypic analysis and quantitative trait locus identification for grain mineral concentration, content, and yield in maize (Zea mays L.).

Gu R, Chen F, Liu B, Wang X, Liu J, Li P, Pan Q, Pace J, Soomro AA, Lübberstedt T, Mi G, Yuan L.

Theor Appl Genet. 2015 Sep;128(9):1777-89. doi: 10.1007/s00122-015-2546-5. Epub 2015 Jun 10.

PMID:
26058362
12.

Quantitative trait loci mapping for Gibberella ear rot resistance and associated agronomic traits using genotyping-by-sequencing in maize.

Kebede AZ, Woldemariam T, Reid LM, Harris LJ.

Theor Appl Genet. 2016 Jan;129(1):17-29. doi: 10.1007/s00122-015-2600-3. Epub 2015 Sep 24.

PMID:
26643764
13.

A large-scale screen for artificial selection in maize identifies candidate agronomic loci for domestication and crop improvement.

Yamasaki M, Tenaillon MI, Bi IV, Schroeder SG, Sanchez-Villeda H, Doebley JF, Gaut BS, McMullen MD.

Plant Cell. 2005 Nov;17(11):2859-72. Epub 2005 Oct 14.

14.

A comprehensive meta-analysis of plant morphology, yield, stay-green, and virus disease resistance QTL in maize (Zea mays L.).

Wang Y, Xu J, Deng D, Ding H, Bian Y, Yin Z, Wu Y, Zhou B, Zhao Y.

Planta. 2016 Feb;243(2):459-71. Epub 2015 Oct 16.

PMID:
26474992
15.

A non-synonymous SNP within the isopentenyl transferase 2 locus is associated with kernel weight in Chinese maize inbreds (Zea mays L.).

Weng J, Li B, Liu C, Yang X, Wang H, Hao Z, Li M, Zhang D, Ci X, Li X, Zhang S.

BMC Plant Biol. 2013 Jul 5;13:98. doi: 10.1186/1471-2229-13-98.

16.

Dissecting grain yield pathways and their interactions with grain dry matter content by a two-step correlation approach with maize seedling transcriptome.

Fu J, Thiemann A, Schrag TA, Melchinger AE, Scholten S, Frisch M.

BMC Plant Biol. 2010 Apr 12;10:63. doi: 10.1186/1471-2229-10-63.

17.

Genetic diversity, population structure, and association mapping of agronomic traits in waxy and normal maize inbred lines.

Sa KJ, Park JY, Choi SH, Kim BW, Park KJ, Lee JK.

Genet Mol Res. 2015 Jul 6;14(3):7502-18. doi: 10.4238/2015.July.3.26.

18.

Identifying genes of agronomic importance in maize by screening microsatellites for evidence of selection during domestication.

Vigouroux Y, McMullen M, Hittinger CT, Houchins K, Schulz L, Kresovich S, Matsuoka Y, Doebley J.

Proc Natl Acad Sci U S A. 2002 Jul 23;99(15):9650-5. Epub 2002 Jul 8.

19.

Genetic analysis of agronomic traits associated with plant architecture by QTL mapping in maize.

Zheng ZP, Liu XH.

Genet Mol Res. 2013 Apr 17;12(2):1243-53. doi: 10.4238/2013.April.17.3.

20.

Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize.

Hirel B, Bertin P, Quilleré I, Bourdoncle W, Attagnant C, Dellay C, Gouy A, Cadiou S, Retailliau C, Falque M, Gallais A.

Plant Physiol. 2001 Mar;125(3):1258-70.

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