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An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize (Zea mays) and Common Bean (Phaseolus vulgaris)

机译:玉米(Zea Mays)和常见豆类估算根深土壤胶发策略分析(Phour Phoupolusulus)

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摘要

A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.
机译:开发了土壤核心协议,以通过深度和检测玉米和豆类的根系结构(RSA)功能重要变化的深度和检测来将根长分布(RLD)的估计。功能性结构模型OpenSimroot用于在三个不同玉米和豆类RSA表型上的六个地点进行硅土取芯片。结果将结果与两季的田间土芯和一个沟槽进行了比较。 Silico数据中的两个单侧T检验(TOST)分析表明,来自工厂基地(位置3)的行位置5cm之间,最佳估计深,中间和浅程的整体RLD / D.玉米和豆。二次判别分析表示位置3对豆具有约70%的分类精度,而工厂基地(位置6)旁边的连续位置在玉米中具有约85%的分类精度。现场数据的分析表明,越大的采样场所越大,逐年变化。在Silico和Field研究中,建议位置3是最强大的,尽管变异在季节内的复制中是显着的,但在田间季节中的复制中,以及田间土壤上皮,沟槽和模拟中。我们提出作为动态Rhizo冠层的RLD配置文件的表征有效地描述了RLD型材如何从个体植物,其邻居和踏板之间的相互作用。

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