The exodermis is a common apoplastic barrier of the outer root cortex, with high environmentally-driven plasticity and a protective function. This study focused on the trade-off between the protective advantages provided by the exodermis and its disadvantageous reduction of cortical membrane surface area accessible by apoplastic route, thus limiting nutrient acquisition from the rhizosphere. We analysed the effect of nutrient deficiency (N, P, K, Mg, Ca, K, Fe) on exodermal and endodermal differentiation in maize. To differentiate systemic and localized effects, nutrient deficiencies were applied in three different approaches: to the root system as a whole, locally to discrete parts, or on one side of a single root. Our study showed that the establishment of the exodermis was enhanced in low-N and low-P plants, but delayed in low-K plants. The split-root cultivation proved that the effect is non-systemic, but locally coordinated for individual roots. Within a single root, localized deficiencies didn't result in an evenly differentiated exodermis, in contrast to other stress factors. The maturation of the endodermis responded in a similar way. In conclusion, N, P, and K deficiencies strongly modulated exodermal differentiation. The response was nutrient specific and integrated local signals of current nutrient availability from the rhizosphere.

• Klíčová slova
• Casparian bands, barley, exodermis, high-affinity transporters, maize, nitrogen, nutrient deficiency, split-root cultivation, suberin lamellae,
• Publikační typ
• časopisecké články MeSH

Potato (Solanum tuberosum) mutant (ST) lacking one isoform of manganese-stabilizing protein (MSPI) of photosystem II exhibited besides spontaneous tuberization also growth changes with strongly impaired root system development. Previous studies revealed marked changes in carbohydrate levels and allocation within ST plant body. To verify causal relationship between changed carbohydrate balance and root growth restriction we engaged dark grown sucrose-supplied root organ-cultures of ST plants to exclude/confirm shoot effects. Unexpectedly, in ST root cultures we observed large alterations in growth and architecture as well as saccharide status similar to those found in the intact plant roots. The gene expression analysis, however, proved PsbO1 transcript (coding MSPI protein) neither in ST nor in WT root-organ cultures. Therefore, the results point to indirect effects of PsbO1 allele absence connected possibly with some epigenetic modulations.

• Klíčová slova
• Carbohydrate allocation, In vitro cultivation, PsbO1 gene, Root branching,
• MeSH
• alely MeSH
• fotosyntéza genetika   účinky záření   MeSH
• fotosystém II (proteinový komplex) genetika   metabolismus   MeSH
• hlízy rostlin genetika   růst a vývoj   MeSH
• kořeny rostlin růst a vývoj   metabolismus   MeSH
• kultivované buňky MeSH
• mangan metabolismus   MeSH
• metabolismus sacharidů genetika   MeSH
• mutace MeSH
• mutantní proteiny chemie   genetika   metabolismus   MeSH
• protein - isoformy genetika   metabolismus   MeSH
• regulace genové exprese u rostlin genetika   fyziologie   MeSH
• rostlinné proteiny genetika   metabolismus   MeSH
• sacharosa metabolismus   MeSH
• Solanum tuberosum genetika   růst a vývoj   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• fotosystém II (proteinový komplex) MeSH
• mangan MeSH
• mutantní proteiny MeSH
• protein - isoformy MeSH
• rostlinné proteiny MeSH
• sacharosa MeSH

For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very few practical implementations have attempted to clarify how well indicator models can be built from large covariate sets combined with spatial proxies. This paper summarizes the performance of the weighted indicator-based random forest model which was used to predict exceedance probabilities for several potentially toxic elements (PTEs) in Czech farmland. The method was implemented for data mining in the Czech high-density monitoring data which had to be firstly regressed to achieve analytical harmony, and the reliability of the regression-based harmonisation was used as the input weights for the final model. The indicator-based models were trained for each PTE (As, Be, Cd, Co, Cr, Cu, Hg, Ni, Pb, V, and Zn) with two different sets of indicators, reflecting the two-tier nature of the Czech safety guidelines, which differentiate between soil textures of topsoil. The two separate predictive outputs are combined into a single probability map using a pragmatic meta-model of linear weights derived from a soil texture map generated by a compositional spatial model. Through validation with data splitting, the accuracy of the models showed relatively high predictive power for the probability distributions, but with pronounced differences between PTEs as the root mean square error in terms of exceedance probabilities ranged from 11 % (V) to 32 % (Cd and Cr) for independent validation. In addition, models based on high-resolution auxiliary variables allowed a meaningful and quantitative identification of the most important natural and anthropogenic drivers for areas with an increased rate of non-compliance with the protection thresholds for cultivated soils. Variable importance calculations showed the dominant influence of spatially explicit covariates (represented by geographical distances to quantile-based groups of points), but still significant contributions from other predictors. Among the natural factors, lithological information came to the fore, mainly due to continuous response variables such as mineral exploration density or geophysical ancillary variables (from remotely sensed gravimetry and radiometry). Among anthropogenic factors, particulate matter in the atmosphere was identified as the most important human-related pressure, followed by several land-use effects.

• Klíčová slova
• Machine learning, Potentially toxic elements, Predictive mapping, Probability of limit exceeding,
• MeSH
• farmy MeSH
• látky znečišťující půdu * analýza   MeSH
• monitorování životního prostředí * metody   MeSH
• pravděpodobnost MeSH
• půda chemie   MeSH
• strojové učení * MeSH
• zemědělství MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH
• Názvy látek
• látky znečišťující půdu * MeSH
• půda MeSH