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Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants

机译:叶绿素荧光作为鉴定油菜植物营养状况的工具

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摘要

In natural conditions, plants growth and development depends on environmental conditions, including the availability of micro- and macroelements in the soil. Nutrient status should thus be examined not by establishing the effects of single nutrient deficiencies on the physiological state of the plant but by combinations of them. Differences in the nutrient content significantly affect the photochemical process of photosynthesis therefore playing a crucial role in plants growth and development. In this work, an attempt was made to find a connection between element content in (i) different soils, (ii) plant leaves, grown on these soils and (iii) changes in selected chlorophyll a fluorescence parameters, in order to find a method for early detection of plant stress resulting from the combination of nutrient status in natural conditions. To achieve this goal, a mathematical procedure was used which combines principal component analysis (a tool for the reduction of data complexity), hierarchical k-means (a classification method) and a machine-learning method—super-organising maps. Differences in the mineral content of soil and plant leaves resulted in functional changes in the photosynthetic machinery that can be measured by chlorophyll a fluorescent signals. Five groups of patterns in the chlorophyll fluorescent parameters were established: the ‘no deficiency’, Fe-specific deficiency, slight, moderate and strong deficiency. Unfavourable development in groups with nutrient deficiency of any kind was reflected by a strong increase in F o and ΔV/Δt 0 and decline in φ Po, φ Eo δ Ro and φ Ro. The strong deficiency group showed the suboptimal development of the photosynthetic machinery, which affects both PSII and PSI. The nutrient-deficient groups also differed in antenna complex organisation. Thus, our work suggests that the chlorophyll fluorescent method combined with machine-learning methods can be highly informative and in some cases, it can replace much more expensive and time-consuming procedures such as chemometric analyses.Electronic supplementary materialThe online version of this article (10.1007/s11120-017-0467-7) contains supplementary material, which is available to authorized users.
机译:在自然条件下,植物的生长和发育取决于环境条件,包括土壤中微量元素和宏观元素的可用性。因此,不应通过确定单一营养素缺乏对植物生理状态的影响来检查营养状况,而应通过将它们的组合进行检查。营养成分的差异会显着影响光合作用的光化学过程,因此在植物生长发育中起着至关重要的作用。在这项工作中,为了找到一种方法,试图在(i)不同土壤中,(ii)在这些土壤上生长的植物叶片和(iii)选定的叶绿素a荧光参数的变化之间找到联系。用于早期检测由于自然条件下营养状况而产生的植物胁迫。为了实现此目标,使用了一种数学程序,该程序结合了主成分分析(一种用于降低数据复杂性的工具),分层k均值(一种分类方法)和一种机器学习方法(超组织图)。土壤和植物叶片中矿物质含量的差异导致光合作用机制的功能发生变化,可以通过叶绿素a荧光信号进行测量。建立了五种叶绿素荧光参数模式:“无缺乏”,铁特异性缺乏,轻度,中度和强度缺乏。 F o和ΔV/Δt0的强烈增加以及φPo,φEoδRo和φRo的下降反映了各种营养缺乏症人群的不利发育。严重缺乏症组显示光合作用机制的发展欠佳,这会影响PSII和PSI。营养缺乏的人群在天线复杂组织方面也有所不同。因此,我们的工作表明,叶绿素荧光方法与机器学习方法相结合可以提供大量信息,在某些情况下,它可以替代更昂贵且耗时的过程,例如化学计量分析。电子补充材料本文的在线版本( 10.1007 / s11120-017-0467-7)包含补充材料,授权用户可以使用。

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