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        <identifier>oai:figshare.com:article/34009371</identifier>
        <datestamp>2026-09-28T05:39:07Z</datestamp>
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          <dc:title>Supplementary file 1_Spatiotemporal stability of DUS characteristics in maize and evaluation of spatial replication testing models.docx</dc:title>
          <dc:creator>Xiongfei Jiao (19331440)</dc:creator>
          <dc:creator>Jin Yu (53103)</dc:creator>
          <dc:creator>Xiaofan Shi (18188931)</dc:creator>
          <dc:creator>Yufeng Wang (274657)</dc:creator>
          <dc:creator>Jiaying Li (3250335)</dc:creator>
          <dc:creator>Leyong Feng (25119972)</dc:creator>
          <dc:creator>Zhi Zhang (55283)</dc:creator>
          <dc:creator>Ruixi Han (2213992)</dc:creator>
          <dc:creator>Han Zhang (110653)</dc:creator>
          <dc:subject>Plant Biology</dc:subject>
          <dc:subject>core characteristic</dc:subject>
          <dc:subject>DUS testing</dc:subject>
          <dc:subject>maize</dc:subject>
          <dc:subject>spatial replication mode</dc:subject>
          <dc:subject>spatio-temporal stability</dc:subject>
          <dc:description>&lt;p&gt;Distinctness, uniformity, and stability (DUS) testing is the statutory basis and core element for the protection and registration of new plant varieties. In this study, 116 genetically diverse maize accessions (inbred lines, CS hybrids, CH) were evaluated using phenotypic data from Xinzhou and Datong (2024–2025). The spatiotemporal stability of 39 DUS characteristics was systematically analyzed using coefficient of variation (CV), principal component analysis (PCA), and UPGMA clustering. The results showed the following: (1) characteristics were categorized into three stability groups based on CV quantitative analysis, with 20 characteristics identified as spatiotemporally stable to serve as a routine observation baseline, while C2, C25, and C30.2 were recognized as time-sensitive characteristic. (2) PCA indicated that the spatial environment primarily controlled phenotypic variation (cumulative variance contribution: 74.92%). An optimized evaluation system using seven core characteristics, including plant height and grain type, was established to simplify the DUS testing process. (3) Validation showed that the spatial repetition model achieved a cluster retrieval (CR) rate of 92.31%, with accuracies of 94.36% for distinctness and 96.23% for uniformity. These values significantly exceeded those of the traditional temporal model, effectively reducing the impact of environmental noise and annual climatic variation. This study established an efficient spatial repetition testing paradigm based on characteristic stability and demonstrated the scientific feasibility of reasonably shortening testing cycles, thereby providing a rigorous scientific foundation for optimizing Chinese maize DUS testing standards and enhancing the efficiency of substantive examinations.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-28T05:39:07Z</dc:date>
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          <dc:identifier>10.3389/fpls.2026.1854476.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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