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首页> 外文期刊>Weed Science: Journal of the Weed Science Society of America >Seed burial physical environment explains departures from regional hydrothermal model of giant ragweed (Ambrosia trifida) seedling emergence in U.S. midwest.
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Seed burial physical environment explains departures from regional hydrothermal model of giant ragweed (Ambrosia trifida) seedling emergence in U.S. midwest.

机译:种子埋葬的物理环境解释了美国中西部巨型豚草(Ambrosia trifida)幼苗出苗的区域热液模型的偏离。

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摘要

Robust predictions of weed seedling emergence from the soil seedbank are needed to aid weed management. A common seed accession (Illinois) of giant ragweed was buried in replicate experimental gardens over 18 site years in Illinois, Michigan, Kansas, Nebraska, Ohio, and South Dakota to examine the importance of site and climate variability by year on seedling emergence. In a nonlinear mixed-effects modeling approach, we used a flexible sigmoidal function (Weibull) to model giant ragweed cumulative seedling emergence in relation to hydrothermal time accumulated in each site-year. An iterative search method across a range of base temperature (Tb) and base and ceiling soil matric potentials ( psi b and psi c) for accumulation of hydrothermal time identified optima (Tb=4.4 C, psi b=-2,500 kPa, psi c=0 kPa) that resulted in a parsimonious regional model. Deviations between the fits for individual site-years and the fixed effects regional model were characterized by a negative relationship between random effects for the shape parameter lrc (natural log of the rate constant, indicating the speed at which emergence progressed) and thermal time (base 10 C) during the seed burial period October through March (r=-0.51, P=0.03). One possible implication of this result is that cold winter temperatures are required to break dormancy in giant ragweed seeds. By taking advantage of advances in statistical computing approaches, development of robust regional models now is possible for explaining arable weed seedling emergence progress across wide regions.
机译:需要对土壤种子库中杂草幼苗出苗的可靠预测,以帮助杂草管理。超过18个站点年,一个常见的大型豚草种子种(Illinois)被埋在重复的实验花园中,该花园位于伊利诺伊州,密歇根州,堪萨斯州,内布拉斯加州,俄亥俄州和南达科他州,以研究站点和气候变异对幼苗出苗的重要性。在非线性混合效应建模方法中,我们使用灵活的S型函数(Weibull)来模拟豚草累积幼苗出苗与每个站点年中累积的热液时间的关系。在基础温度(T b )范围以及基础和顶棚土壤基质电势(psi b 和psi c )范围内进行迭代搜索的方法热液时间积累确定了最佳温度(T b = 4.4 C,psi b =-2,500 kPa,psi c = 0 kPa)简约的区域模型。单个站点年份的拟合与固定效应区域模型之间的偏差的特征在于,形状参数lrc(速率常数的自然对数,指示出现的速度)的随机效应与热时间(基数)之间呈负相关10 C)在十月至三月的种子埋葬期间(r = -0.51,P = 0.03)。此结果的一个可能含义是,需要寒冷的冬季温度才能打破大型豚草种子的休眠。通过利用统计计算方法的先进性,现在可以开发健壮的区域模型来解释整个地区的杂草幼苗出苗进度。

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