基因型特异性p样条响应面有助于解释区域小麦对气候变化的适应

IF 2.6 Q1 AGRONOMY
Daniela Bustos-Korts, M. Boer, K. Chenu, B. Zheng, S. Chapman, F. V. van Eeuwijk
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引用次数: 5

摘要

产量是环境质量和基因型对此反应的敏感性的函数。环境质量的特征是气象数据、土壤和农艺管理,而基因型敏感性体现在生理性状的组合上,这些生理性状决定了作物对环境资源的捕获和分配。本文阐述了如何通过作物模拟和统计建模相结合来研究环境质量和基因型反应。我们通过使用农业生产系统模拟(APSIM)种植系统模型对在开花期分离的小麦群体的籽粒产量进行了基因型-环境交互作用的表征。对于东北澳大利亚小麦带的站点,我们使用APSIM整合的气象信息,根据水分、热量和霜冻胁迫对年份进行分类。结果表明,近年来,水和温度压力更严重的年份频率大幅增加。因此,未来的品种可能需要应对比过去更大的压力条件,因此选择有助于适应温度和水分压力的开花习惯很重要。根据年份类型,我们将产量响应面作为基因型、纬度和经度的函数拟合到虚拟多环境试验中。在混合模型框架中,通过二维P样条拟合响应面,以预测高空间分辨率下的产量。预测产量表明了相对基因型表现如何随地点和年份类型而变化,以及如何分析基因型与环境的相互作用。产量的预测响应面可用于性能建议、产量稳定性的量化和环境表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genotype-specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change
Yield is a function of environmental quality and the sensitivity with which genotypes react to that. Environmental quality is characterized by meteorological data, soil and agronomic management, whereas genotypic sensitivity is embodied by combinations of physiological traits that determine the crop capture and partitioning of environmental resources over time. This paper illustrates how environmental quality and genotype responses can be studied by a combination of crop simulation and statistical modelling. We characterized the genotype by environment interaction for grain yield of a wheat population segregating for flowering time by simulating it using the the Agricultural Production Systems sIMulator (APSIM) cropping systems model. For sites in the NE Australian wheat-belt, we used meteorological information as integrated by APSIM to classify years according to water, heat and frost stress. Results highlight that the frequency of years with more severe water and temperature stress has largely increased in recent years. Consequently, it is likely that future varieties will need to cope with more stressful conditions than in the past, making it important to select for flowering habits contributing to temperature and water-stress adaptation. Conditional on year types, we fitted yield response surfaces as functions of genotype, latitude and longitude to virtual multi-environment trials. Response surfaces were fitted by two-dimensional P-splines in a mixed-model framework to predict yield at high spatial resolution. Predicted yields demonstrated how relative genotype performance changed with location and year type and how genotype by environment interactions can be dissected. Predicted response surfaces for yield can be used for performance recommendations, quantification of yield stability and environmental characterization.
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来源期刊
in silico Plants
in silico Plants Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
4.70
自引率
9.70%
发文量
21
审稿时长
10 weeks
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