Sugar beets (Beta vulgaris L. cv. F58-554H1) were cultured hydroponically in growth chambers. Leaf orthophosphate levels were varied nutritionally. The effect of decreased leaf phosphate (low-P) status was determined on the rate of photosynthesis (PN) and on pool sizes of leaf ribulose-l,5-bisphosphate (RuBP), 3-phosphoglycerate (PGA), triose phosphate (triose-P), fructose-1,6-bisphosphate (FBP), fructose-6-phosphate (F6P), glucose-6-phosphate (G6P), adenylates and nicotinamide nucleotides during photosynthetic induction (measured at 0, 1.5, 5 and 30 min from irradiation). p N reached 50 % of its final value in 4 min in control leaves and 10 min in low-P leaves. Hence the increase in the length of induction period in low-P leaves was most likely due to a slow build-up in RuBP: ATP, NADPH, PGA, and FBP all reached high levels in 5 min at which time RuBP was half and PN 16 % of their eventual values at 30 min. The slow-build-up of RuBP did not appear to be due to insufficient ATP and NADPH for the conversion of PGA to triose-P; rather, low-P seemed to limit photosynthetic induction somewhere in the sequence of reactions between triose-P and RuBP formation.
The paper presents a new view of the problems of relationships between complexity, credibility and uncertainty of the mathematical models that have not been investigated enough up to now. The aim of the study was to find possibilities of quantification of these relationships by means of probability theory and information theory methods. The study stems from the papers published by Conant and Klir which initiate an investigation into uncertainty of the large systems and their models. The study also analyses the bounds of application of this approach. The practical contribution of these methodical approaches lies in the intensification of analysis of the optimal model structure, in the possibility of their simplification, and in the choice of the best appropriate model according to required properties. In the application part the paper deals with the optimal structure of the linear regression stochastic model of the mean monthly flows in the Odra basin. and Studie nabízí nový pohled na zatím málo prozkoumanou problematiku vzájemných vztahů některých vlastností matematických modelů, zejména jejich složitosti, důvěryhodnosti a neurčitosti. Cílem výzkumu bylo ukázat na možnosti kvantifikace těchto vztahů metodami teorie informace a teorie pravděpodobnosti. Studie vychází především z prací Conanta a Klira, které vytvářejí základ zkoumání neurčitosti velkých systémů a jejich modelů, a analyzuje meze použití těchto přístupů. Praktický přínos nových metodických postupů je v prohloubení analýzy optimální struktury modelů, v možnosti jejich zjednodušení a ve výběru nejvhodnějšího modelu podle jeho požadovaných vlastností. V aplikační části se studie zabývá optimální strukturou lineárního regresního stochastického modelu řad průměrných měsíčních průtoků v povodí Odry.