Evaluating the Utility of Carbon Isotope Discrimination for Wheat Breeding in the Pacific Northwest

<div class="floats-full-page floats-full-page-figure"><h2>Figure 1</h2><div class="floats-full-page-caption"><div>Flowchart of major elements in experimental design, including field growth, phenotyping, GWAS, and data analysis.</div></div><div class="floats-full-page-content"><table class="figure-group"><tr class="fig-image"><td><object data="https://s3-eu-west-1.amazonaws.com/spj-html-parsed-preprod/articles/plantphenomics/volume-2019/4528719/figures/4528719.fig.001.svgz" name="4528719.fig.001" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 1 | Evaluating the Utility of Carbon Isotope Discrimination for Wheat Breeding in the Pacific Northwest

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Figure 1: Evaluating the Utility of Carbon Isotope Discrimination for Wheat Breeding in the Pacific Northwest

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Figure 1

About this journal

<p>The open access journal <em>Plant Phenomics</em>, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.</p>


Journal profile

Meet the editorial board

<p><em>Plant Phenomics'</em> editorial board is led by Seishi Ninomiya (University of Tokyo),&nbsp;Frédéric Baret (French National Institute of Agricultural Research), and&nbsp;Zong-Ming Cheng (Nanjing Agricultural University/University of Tennessee) and is comprised of leading experts in the field.</p>


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More special issue details

<p>Published Special Issues:</p>

<p><br />
<strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis">Image Analysis and Machine Learning for Cyber-Agricultural Systems</a></strong><br />
&nbsp;</p>

<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/statistics-bioinformatics/">Statistics, Bioinformatics, and Machine Learning Methods in Phenomics</a></strong></p>
