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德国小麦产量、稳定性及育种进展的多环境田间试验

Multi-environment field trials for wheat yield, stability and breeding progress in Germany.

作者信息

Wang Tien-Cheng, Rose Till, Zetzsche Holger, Ballvora Agim, Friedt Wolfgang, Kage Henning, Léon Jens, Lichthardt Carolin, Ordon Frank, Snowdon Rod J, Stahl Andreas, Stützel Hartmut, Wittkop Benjamin, Chen Tsu-Wei

机构信息

Section of Intensive Plant Food Systems, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.

Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany.

出版信息

Sci Data. 2025 Jan 14;12(1):64. doi: 10.1038/s41597-024-04332-7.

DOI:10.1038/s41597-024-04332-7
PMID:39809815
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11732995/
Abstract

Multi-environmental trials (MET) with temporal and spatial variance are crucial for understanding genotype-environment-management (GxExM) interactions in crops. Here, we present a MET dataset for winter wheat in Germany. The dataset encompasses MET spanning six years (2015-2020), six locations and nine crop management scenarios (consisting of combinations for three treatments, unbalanced in each location and year) comparing 228 cultivars released between 1963 and 2016, amounting to a total of 526,751 data points covering 24 traits. Beside grain yield, ten agronomic traits, four baking quality traits, plant height, heading date, maturity date and six fungal disease infection indices are included. Additionally, we provide management records, including fertilizer use, plant protection measures, irrigation, and weather data. We demonstrate how this dataset can address four agronomic questions related to GxExM interactions. Further potential applications of the dataset include empirical analyses, genomic and enviromic analyses for breeding targets, or development of decision-supporting models for agricultural management and policy decisions.

摘要

具有时空变异的多环境试验（MET）对于理解作物中的基因型-环境-管理（GxExM）相互作用至关重要。在此，我们展示了一个德国冬小麦的MET数据集。该数据集涵盖了跨越六年（2015 - 2020年）、六个地点和九种作物管理情景（由三种处理的组合构成，在每个地点和年份中不均衡）的MET，比较了1963年至2016年间发布的228个品种，总计526,751个涵盖24个性状的数据点。除了谷物产量外，还包括十个农艺性状、四个烘焙品质性状、株高、抽穗期、成熟期以及六个真菌病害感染指数。此外，我们还提供管理记录，包括肥料使用、植物保护措施、灌溉和天气数据。我们展示了这个数据集如何能够解决与GxExM相互作用相关的四个农艺问题。该数据集的进一步潜在应用包括实证分析、针对育种目标的基因组和环境组分析，或用于农业管理和政策决策的决策支持模型的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/8852d3b3289c/41597_2024_4332_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/dce43c26e228/41597_2024_4332_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/12359a1ec064/41597_2024_4332_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/42257679ef9b/41597_2024_4332_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/d02e96f9eeb4/41597_2024_4332_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/61b7a7078dec/41597_2024_4332_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/f4ec0f2577d1/41597_2024_4332_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/7ae41813a5c5/41597_2024_4332_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/e0a49c4ca479/41597_2024_4332_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/8852d3b3289c/41597_2024_4332_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/dce43c26e228/41597_2024_4332_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/12359a1ec064/41597_2024_4332_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/42257679ef9b/41597_2024_4332_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/d02e96f9eeb4/41597_2024_4332_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/61b7a7078dec/41597_2024_4332_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/f4ec0f2577d1/41597_2024_4332_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/7ae41813a5c5/41597_2024_4332_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/e0a49c4ca479/41597_2024_4332_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4090/11732995/8852d3b3289c/41597_2024_4332_Fig9_HTML.jpg

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Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels.
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The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities.功能性状对不同作物管理强度下中欧冬小麦育种进展的贡献
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