Foliar Fertilization by the Sol-Gel Particles Containing Cu and Zn.

Silica particles with the size of 150-200 nm containing Ca, P, Cu or Zn ions were synthesized with the sol-gel method and tested as a foliar fertilizer on three plant species: maize Zea mays, wheat Triticum sativum and rape Brassica napus L. var napus growing on two types of soils: neutral and acidic. The aqueous suspensions of the studied particles were sprayed on the chosen leaves and also on the whole tested plants. At a specific stage of plant development determined according to the BBCH (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie) scale, the leaves and the whole plants were harvested and dried, and the content of Cu and Zn was determined with the AAS (atomic absorption spectroscopy) method. The engineered particles were compared with a water solution of CuSO4 and ZnSO4 (0.1%) used as a conventional fertilizer. In many cases, the copper-containing particles improved the metal supply to plants more effectively than the CuSO4. The zinc-containing particles had less effect on both the growth of plants and the metal concentration in the plants. All the tested particles were not toxic to the examined plants, although some of them caused a slight reduction in plants growth.

Systematic Investigation of the Effects of Seven Plant Extracts on the Physiological Parameters, Yield, and Nutritional Quality of Radish (Raphanus sativus var. sativus).

The modern agricultural sector faces the challenge of addressing the needs of the fast-growing global population. This process should be both high-yielding and sustainable, without creating risks for the environment and human health. Therefore, natural products are gaining attention in the production of safe and nutritious food. In a systematic effort to develop affordable and effective biostimulants, we examined the impact of botanical extracts on the growth and physiological parameters of radish plants under field conditions. Ultrasound-assisted extraction, mechanical homogenization, and water were used for the production of potential plant-based biostimulants. Foliar applications of the bio-products, developed and used in our study, have led to an increase in the examined parameters (total yield, dry weight, photosynthetic pigments, vitamin C, nitrates, and micro- and macroelements). A decrease in the total phenolic compounds content was also noted, as well as a varied impact on the steam volatile compounds, fatty acids, sterol, and glucosinolates composition. The most beneficial effects on radish, in terms of physiological and biochemical properties, were found in groups treated with extracts based on the common dandelion, valerian, and giant goldenrod. This innovative approach presented in our study could provide a valuable tool for sustainable horticultural production.

Effect of Botanical Extracts on the Growth and Nutritional Quality of Field-Grown White Head Cabbage (Brassica oleracea var. capitata).

Nutraceuticals and functional foods are gaining more attention amongst consumers interested in nutritious food. The consumption of foodstuffs with a high content of phytochemicals has been proven to provide various health benefits. The application of biostimulants is a potential strategy to fortify cultivated plants with beneficial bioactive compounds. Nevertheless, it has not yet been established whether the proposed higher plants (St. John's wort, giant goldenrod, common dandelion, red clover, nettle, and valerian) are appropriate for the production of potential bio-products enhancing the nutritional value of white cabbage. Therefore, this research examines the impact of botanical extracts on the growth and nutritional quality of cabbage grown under field conditions. Two extraction methods were used for the production of water-based bio-products, namely: ultrasound-assisted extraction and mechanical homogenisation. Bio-products were applied as foliar sprays to evaluate their impact on total yield, dry weight, photosynthetic pigments, polyphenols, antioxidant activity, vitamin C, nitrates, micro- and macroelements, volatile compounds, fatty acids, sterols, and sugars. Botanical extracts showed different effects on the examined parameters. The best results in terms of physiological and biochemical properties of cabbage were obtained for extracts from common dandelion, valerian, nettle, and giant goldenrod. When enriched with nutrients, vegetables can constitute a valuable component of functional food.

Field-Scale Evaluation of Botanical Extracts Effect on the Yield, Chemical Composition and Antioxidant Activity of Celeriac (Apium graveolens L. Var. rapaceum).

The use of higher plants for the production of plant growth biostimulants is receiving increased attention among scientists, farmers, investors, consumers and regulators. The aim of the present study was to examine the possibility of converting plants commonly occurring in Europe (St. John's wort, giant goldenrod, common dandelion, red clover, nettle, valerian) into valuable and easy to use bio-products. The biostimulating activity of botanical extracts and their effect on the chemical composition of celeriac were identified. Plant-based extracts, obtained by ultrasound-assisted extraction and mechanical homogenisation, were tested in field trials. It was found that the obtained formulations increased the total yield of leaves rosettes and roots, the dry weight of leaves rosettes and roots, the content of chlorophyll a + b and carotenoids, the greenness index of leaves, the content of vitamin C in leaves and roots. They mostly decreased the content of polyphenols and antioxidant activities in leaves but increased them in roots and conversely affected the nitrates content. Extracts showed a varied impact on the content of micro and macroelements, as well as the composition of volatile compounds and fatty acids in the celeriac biomass. Due to the modulatory properties of the tested products, they may be used successfully in sustainable horticulture.

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

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.