Effect of pyrene-induced changes in root activity on growth of Chinese cabbage (Brassica campestris L.), and the health risks caused by pyrene in Chinese cabbage at different growth stages

Background Polycyclic aromatic hydrocarbons (PAHs) pose a potential risk to ecological safety and human health. They have a range of effects on plant growth and there have been few reports on the health risks associated with ingestion of vegetable crops at different growth stages. Methodology In this study, a pot experiment in which Chinese cabbage (Brassica campestris L.) were grown in a greenhouse for 75 days was used to investigate the dose–effect relationship of pyrene with plant growth and also the exposure risk for adults of ingestion of Chinese cabbage at different growth stages. Results The results showed that low doses of pyrene (5–45 mg kg−1) promoted plant growth (20–220% and 55–97% higher than control treatment for the root biomass and shoot biomass, respectively), but significant inhibition was observed at a high dose (405 mg kg−1) (41–66% and 43–91% lower than control treatment for the root biomass and shoot biomass, respectively). High doses of pyrene reduced soil bacterial abundance and diversity during the growth of Chinese cabbage, and increased malondialdehyde (MDA) levels in the plant. The effects of pyrene on plant biomass were mainly attributed to changes in root activity induced by pyrene, as the relationship between soil pyrene concentration and biomass was similar to that between soil pyrene concentration and root activity. Furthermore, structural equation modeling analysis showed that pyrene altered growth of the vegetable by directly affecting root activity. The incremental lifetime cancer risk for adults is highest for ingestion of Chinese cabbage at the seedling stage, followed in decreasing order by the rosette stages and heading stages. Conclusions The health risk of consumers who have the possibility to ingest the Chinese cabbage planted in pyrene-contaminated soil would be decreased with the increasing growth periods. However, further studies are required to confirm the dose–effect relationship between pyrene concentration and Chinese cabbage growth on a field scale. Graphical Abstract

Temperature Effects on Tuber Production and Carbohydrates Partitioning in Different Cultivars During Consecutive Stages of Potato (Solanum tuberosum L.) Growth

The main objective of this study was to assess responses of mid-early (Spunta) and mid-late (Bellini) potato cultivars to different temperature regimes during subsequent stages of potato growth. The impact of high temperature (25/22°C day/night), low temperature (18/16°C day/night) and intermediate temperature (20/18°C day/night) was evaluated for different growth stages. Data were obtained for photosynthesis, carbohydrates in leaves, stems and tubers as well as production parameters. Enzyme activities were determined for sucrose-phosphate synthase in leaves, acid invertase in stems and acid and neutral invertases in tubers. Gene expression levels of relevant sugar metabolizing enzymes was quantified.A detailed correlation analysis revealed a strong impact of the expression level of sugar metabolizing enzymes in leaves on the final number of tubers per plant.Whereas total tuber yield increases with temperature, the number of tubers per plant was highest under low temperature conditions. Our data suggest an important role of the temperature on the length of the different growth stages.

An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development

Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest.