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Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants
Authors:Hazem M. Kalaji  Wojciech Bąba  Krzysztof Gediga  Vasilij Goltsev  Izabela A. Samborska  Magdalena D. Cetner  Stella Dimitrova  Urszula Piszcz  Krzysztof Bielecki  Kamila Karmowska  Kolyo Dankov  Agnieszka Kompała-Bąba
Affiliation:1.Institute of Technology and Life Sciences (ITP), Falenty,Raszyn,Poland;2.White Hill Company,15-540 Bia?ystok,Poland;3.Department of Plant Ecology, Institute of Botany,Jagiellonian University,Kraków,Poland;4.Department of Plant Nutrition,Wroc?aw University of Environmental and Life Sciences,Wroc?aw,Poland;5.Department of Biophysics and Radiobiology, Faculty of Biology,St. Kliment Ohridski University of Sofia,Sofia,Bulgaria;6.Department of Plant Physiology, Faculty of Agriculture and Biology,Warsaw University of Life Sciences – SGGW,Warszawa,Poland;7.Department of Botany and Nature Protection,University of Silesia,Katowice,Poland
Abstract:
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 ΔVt 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.
Keywords:
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