Identification of quantitative trait loci for nitrogen use efficiency in maize

By H. Agrama, A. Zakaria, F. B. Said, M. Tuinstra. Molecular breeding, 2004

181 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/eef283d067a6fdce5d84481307180e97c51ec871

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Agronomy and Crop Science

1. J. Ribaut
2. Y. Fracheboud
3. Fusuo Zhang
4. Jacques Le Gouis
5. B. Hirel
6. M. Vargas
7. P. Monneveux
8. J. Lynch

In Genetics

1. J. Ribaut
2. S. Jones
3. Jiansheng Li
4. J. Dawson
5. M. C. Sanguineti
6. Roberto Tuberosa
7. K. Murphy
8. P. Monneveux

In Plant Science

1. J. Ribaut
2. Guohua Mi
3. J. Lynch
4. K. Murphy
5. Fusuo Zhang
6. S. Jones
7. S. S. Clair
8. Y. Fracheboud

Citations in the corpus

• The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. (2007, background): “Bertin and Gallais (2001) found that most of the chromosomal regions for yield, grain composition, and traits related to NUE detected at low N input corresponded to quantitative trait loci (QTLs) detected at high N input, whereas Agrama et al. (1999) detected more QTLs at low N input.”
• Mapping QTL for Grain Yield and Plant Traits in a Tropical Maize Population (2006, methodology): “…of the additive effects of the QTL were used to identify the parental origin of the favorable alleles (Austin and Lee 1996b; Khairallah et al. 1998; Agrama et al. 1999; Sibov et al. 2003; Mangolin et al. 2004); i.e., positive and negative signs indicated that L-02-03D and L-2001F contribute…”
• Decentralized selection and participatory approaches in plant breeding for low-input systems (2008, background): “For example, in CIMMYT maize lines, Agrama et al. (1999) found equal heritability estimates at high and low N for traits related to nitrogen use eYciency (NUE).”
• Mineral stress: the missing link in understanding how global climate change will affect plants in real world soils (2004, background): “Quantitative Trait Loci (QTL) have been identified for plant–nitrogen dynamics (Agrama et al., 1999; Rauh et al., 2002; Yamaya et al., 2002; Mickelson et al., 2003), aluminum tolerance (Kobayashi and Koyama, 2002; Nguyen et al., 2002; Sledge et al., 2002; Ninamango-Cardenas et al., 2003), iron…”
• Mapping QTLs for root traits under different nitrate levels at the seedling stage in maize (Zea mays L.) (2008, background): “…primary root weight and adventitious seminal roots weight in hydroponics (Tuberosa et al. 2002), vertical root pulling resistance in the field (Landi et al. 2002), grain yield (Agrama et al. 1999; Bertain and Gallais 2001), as well as whole plant N accumulation (Bertain and Gallais 2001) (Fig.”
• Quantitative trait loci for yield and correlated traits under high and low soil nitrogen conditions in tropical maize (2007, background): “The QTLs for GY under low N (Table 2) on chromosomes 1 (67 cM), 2 (18 cM), and 9 (64cM) mapped in the same bins as three QTLs identified for GY under low N by Agrama et al. (1999), near the markers bnl9.”
• Searching for quantitative trait loci controlling root traits in maize: a critical appraisal (2003, background): “Agrama et al. (1999) tested a mapping population derived from B73 × G79 at low- and high-N levels which led to the identification of six and five QTLs (LOD > 2.5), respectively, for GY.”