Associating SSR Markers with Soybean Resistance to Iron Deficiency Chlorosis-Reference-Cited by-同舟云学术

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Associating SSR Markers with Soybean Resistance to Iron Deficiency Chlorosis

Published:2003-09 Issue:10-11 Volume:26 Page:2267-2276
ISSN:0190-4167
Container-title:Journal of Plant Nutrition
language:en
Short-container-title:Journal of Plant Nutrition

Author:

Charlson Dirk V.¹,Cianzio Silvia R.²,Shoemaker Randy C.²³

Affiliation:

1. a Interdepartmental Plant Physiology Major , Iowa State University , Ames , Iowa , USA

2. b Agronomy Department , Iowa State University , Ames , Iowa , 50011‐1010 , USA

3. c USDA‐ARS, Corn Insect and Crop Genetics Research Unit , Iowa State University , Ames , Iowa , USA

Publisher

Informa UK Limited

Subject

Agronomy and Crop Science,Physiology

Link

https://www.tandfonline.com/doi/pdf/10.1081/PLN-120024280

Reference13 articles.

1. What If We Knew All the Genes for a Quantitative Trait in Hybrid Crops?

2. Agronomic Evaluation of Soybean Genotypes Resistant to Iron Deficiency Chlorosis 1

3. Variation in the Inheritance of Resistance to Iron Deficiency Chlorosis in Soybeans 1

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fine mapping of QTL conferring resistance to calcareous soil in mungbean reveals VrYSL3 as candidate gene for the resistance;Plant Science;2023-07

2. Carbonated Irrigation Assessment of Grapevine Growth, Nutrient Absorption, and Sugar Accumulation in a Tempranillo (Vitis vinifera L.) Vineyard;Agriculture;2022-05-30

3. Iron deficiency in soybean;Crop Science;2022-01

4. Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis;PLOS ONE;2021-07-09

5. Algorithmic and data modeling: Will algorithmic modeling improve predictions of traits evaluated on ordinal scales?;2020-10-07

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