通过田间表型分析揭示基因型-环境-管理相互作用,用于长江流域冬季油菜籽开花的多尺度预测

IF 5.7 1区 农林科学 Q1 AGRONOMY
Chufeng Wang , Jian Zhang , Jie Kuai , Jing Xie , Wei Wu , Shuijin Hua , Mingli Yan , Hai Du , Ni Ma , Liangzhi You
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引用次数: 0

摘要

作物产量受到开花期间不利气候事件的显著影响。准确预测花期对优化作物增产策略至关重要。以前的研究使用作物模型来预测花期,由于播种日期数据有限,并且在不同品种和环境中具有普遍性,因此具有挑战性。本研究采用小区试验和高通量田间表型相结合的方法,研究了基因型-环境-管理互作(G × E × M)对长江流域冬季油菜籽花期的影响。结果表明,冬前叶面积指数能较好地反映播期对花期的影响。冬至后50 ~ 60天的积温是影响冬至的关键气候因子,冬至后50 ~ 60天的积温是影响冬至的关键气候因子。花期预测指标参照WS时间点,缓解了播期不确定的制约。综合这些指标可以预测长江流域试验区24个冬油菜籽品种的开花期,误差在4天以内。值得注意的是,在荆州市的实际农田上验证了开花预测模型的准确性,与卫星观测的开花动态吻合较好。为了将模型的应用范围扩展到区域尺度,考虑到2050年整个长江流域的升温水平为2.0°C,利用冬季后累积温度与花期的线性回归模型生成了花期分布图。结果表明,在花期,长江流域许多地区将出现较高的气温或较低的累积太阳辐射。这项研究的发现有望在未来帮助特定区域的作物种植和育种。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlock genotype-environment-management interaction via field phenotypic insights for multi-scale prediction of winter rapeseed flowering in the Yangtze River Basin
Crop yields are significantly impacted by adverse climatic events during flowering. Accurately predicting flowering periods is crucial for optimizing strategies to enhance crop yields. Previous studies used crop models to predict flowering periods, challenging due to limited sowing date data and generalizability across different cultivars and environment. In this study, plot experiments and high-throughput field phenotypes were coupled to determine the impact of genotype–environment–management interaction (G × E × M) on the flowering period of winter rapeseed in the Yangtze River Basin. The findings indicated that the pre-winter leaf area index adeptly indicated the impact of sowing dates on flowering period. The leaf color during winter distinguished the genotype effects, and the cumulative temperature between 50 and 60 days after the winter solstice (WS) was identified as the pivotal climate factor. The predictive indicators for the flowering period were referenced to the time point of the WS, alleviating the constraints of uncertain sowing dates. A combination of these indicators could be used to predict the flowering period in 24 winter rapeseed cultivars with an error of < 4 days at experimental plots across the Yangtze River Basin. Notably, the accuracy of flowering prediction model was validated on an actual farmland in Jingzhou City, aligning well with the observed flowering dynamics from satellite data. To extend the utility of the model to regional scales, distribution maps of the flowering period were generated using a linear regression model that correlated post-winter cumulative temperature with the flowering period, considering a 2.0 °C warming level by 2050 across the entire Yangtze River Basin. Results show higher temperatures or lower cumulative solar radiation during the flowering period will appear in many regions in the Yangtze River Basin. The findings of this study hold promise for aiding region-specific crop cultivation and breeding in the future.
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来源期刊
CiteScore
10.30
自引率
9.70%
发文量
415
审稿时长
69 days
期刊介绍: Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published. Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.
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