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

被引:193
作者
Kalaji, Hazem M. [1 ,2 ]
Baba, Wojciech [3 ]
Gediga, Krzysztof [4 ]
Goltsev, Vasilij [5 ]
Samborska, Izabela A. [6 ]
Cetner, Magdalena D. [6 ]
Dimitrova, Stella [5 ]
Piszcz, Urszula [4 ]
Bielecki, Krzysztof [4 ]
Karmowska, Kamila [4 ]
Dankov, Kolyo [5 ]
Kompala-Baba, Agnieszka [7 ]
机构
[1] Inst Technol & Life Sci ITP, Al Hrabska 3, PL-05090 Falenty, Raszyn, Poland
[2] White Hill Co, Zurawia 71-3, PL-15540 Bialystok, Poland
[3] Jagiellonian Univ, Inst Bot, Dept Plant Ecol, Lubicz 46, PL-31512 Krakow, Poland
[4] Wroclaw Univ Environm & Life Sci, Dept Plant Nutr, Grunwaldzka 53, PL-50357 Wroclaw, Poland
[5] St Kliment Ohridski Univ Sofia, Dept Biophys & Radiobiol, Fac Biol, 8 Dr Tzankov Blvd, Sofia 1164, Bulgaria
[6] Warsaw Univ Life Sci SGGW, Fac Agr & Biol, Dept Plant Physiol, Nowoursynowska 159, PL-02776 Warsaw, Poland
[7] Univ Silesia, Dept Bot & Nat Protect, Jagiellonska 28, PL-40032 Katowice, Poland
关键词
Nutrient-deficiency detection; Nutrient status; Chlorophyll a fluorescence; OJIP test; Machine learning; Super-organising maps; FREQUENTLY ASKED QUESTIONS; PHOTOSYSTEM-II; PHOTOSYNTHETIC CHARACTERISTICS; POTASSIUM-DEFICIENCY; CO2; ASSIMILATION; NEURAL-NETWORKS; SUGAR-BEET; LEAVES; MAIZE; IMPAIRMENT;
D O I
10.1007/s11120-017-0467-7
中图分类号
Q94 [植物学];
学科分类号
071001 ;
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 Delta V/Delta t (0) and decline in phi (Po), phi (Eo) delta (Ro) and phi (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.
引用
收藏
页码:329 / 343
页数:15
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