Luxury uptake of magnesium by peas, <i>Pisum sativum</i>

Reid, JB; Trolove, Stephen; Tan, Yinling; Johnstone, Paul

doi:10.1111/aab.12044

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…The plotted points indicate values calculated for the fitting dataset. For comparison G is also plotted for Mg, using the results of Reid et al (). Feedback control increasingly limits uptake as G decreases.…”

Section: Discussionmentioning

confidence: 99%

“…Second we analyse uptake during Phase 3 at a more mechanistic level. For this we use a simple model of uptake including feedback control (Reid et al , ). The model is first parameterised separately for nitrate, using results for treatments that varied nitrate supply only, and for K using results from treatments that varied K supply only.…”

Section: Methodsmentioning

confidence: 99%

“…Simulation analysis – this was carried out on the measurements of nitrate and K uptake made during Phase 3. The model simulates uptake using a standard Michaelis–Menten analogue adjusted for feedback control exerted by the whole plant concentration of the nutrient in question (Reid et al , ). For each time step, U net is the net uptake rate of the ion by the whole root system (mol s −1 ), and

U_{normaln normale normalt} = a ((), U_{*} U_{max} - E)

where a is the surface area of the roots (m 2 , calculated from total length and average diameter), and U * is the scaled uptake rate (no units).…”

Section: Methodsmentioning

confidence: 99%

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Nitrogen or potassium preconditioning affects uptake of both nitrate and potassium in young wheat (Triticum aestivum)

et al. 2015

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Plant uptake rates of nitrate and potassium (K) are under feedback control from plant concentrations of nitrogen (N) and K, respectively. However, there is uncertainty concerning the interactions between nitrate and K uptake. We tested the hypothesis that plant concentrations of N affect K uptake and plant concentrations of K affect uptake rates of nitrate. Two experiments were carried out with wheat. Each consisted of three phases. In Phase 1, the plants were grown in complete nutrient solution for 17-18 days. In Phase 2, nitrate and K treatments were imposed -the plants were either starved of the nutrient or provided excess for 2.5 or 5 days. This generated plants of similar size but with different N and K concentrations. In Phase 3, complete nutrient solutions were restored for all plants and nitrate and K uptake was followed for 8 h. Uptake till the end of Phases 2 and 3 was examined by analysis of variance. Uptake during Phase 3 was also examined using a simple model that included Michaelis-Menten uptake kinetics and feedback control due to whole-plant N and K concentrations, but assumed no interaction between nitrate and K uptake. The model was fitted using Phase 3 measurements of nitrate uptake following nitrate starvation or excess and K uptake following K starvation or excess. There was little influence of plant N on K uptake or of plant K on nitrate uptake for periods that started with adequate plant concentrations of N and K (Phases 1 and 2). However, in Phase 3 when N starvation was broken, the plants took up more K than controls and model predictions, so feedback control of K uptake had been lessened. Similarly, when K starvation was broken, feedback control of nitrate uptake was lessened. If plants had endured both N and K starvation then on release of that they took up more K but less nitrate than predicted by the model. These results support the hypothesis under test, and suggest interactions between the mechanisms that regulate uptake of nitrate and K.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

U_{normaln normale normalt} = a ((), U_{*} U_{max} - E)

where a is the surface area of the roots (m 2 , calculated from total length and average diameter), and U * is the scaled uptake rate (no units).…”

Section: Methodsmentioning

confidence: 99%

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Nitrogen or potassium preconditioning affects uptake of both nitrate and potassium in young wheat (Triticum aestivum)

et al. 2015

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“…Mn is present in peas in very small quantities (14–15 mg/g), yet its role in growth, development and crop formation is indispensable [ 17 ]. This element actively participates in photosynthesis and various physiological processes, being a constituent of numerous ribosomes, chloroplasts, and enzymes [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of MnO2 Nanoparticles Stabilized with Cocamidopropyl Betaine on Germination and Development of Pea (Pisum sativum L.) Seedlings

Nagdalian,

Blinov,

Gvozdenko

et al. 2024

Nanomaterials

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This study aimed to synthesize, characterize, and evaluate the effect of cocamidopropyl betaine-stabilized MnO2 nanoparticles (NPs) on the germination and development of pea seedlings. The synthesized NPs manifested as aggregates ranging from 50–600 nm, comprising spherical particles sized between 19 to 50 nm. These particles exhibited partial crystallization, indicated by peaks at 2θ = 25.37, 37.62, 41.18, 49.41, 61.45, and 65.79°, characteristic of MnO2 with a tetragonal crystal lattice with a I4/m spatial group. Quantum chemical modelling showed that the stabilization process of MnO2 NPs with cocamidopropyl betaine is energetically advantageous (∆E > 1299.000 kcal/mol) and chemically stable, as confirmed by the positive chemical hardness values (0.023 ≤ η ≤ 0.053 eV). It was revealed that the interaction between the MnO2 molecule and cocamidopropyl betaine, facilitated by a secondary amino group (NH), is the most probable scenario. This ascertain is supported by the values of the difference in total energy (∆E = 1299.519 kcal/mol) and chemical hardness (η = 0.053 eV). These findings were further confirmed using FTIR spectroscopy. The effect of MnO2 NPs at various concentrations on the germination of pea seeds was found to be nonlinear and ambiguous. The investigation revealed that MnO2 NPs at a concentration of 0.1 mg/L resulted in the highest germination energy (91.25%), germinability (95.60%), and lengths of roots and seedlings among all experimental samples. However, an increase in the concentration of preparation led to a slight growth suppression (1–10 mg/L) and the pronounced inhibition of seedling and root development (100 mg/L). The analysis of antioxidant indicators and phytochemicals in pea seedlings indicated that only 100 mg/L MnO2 NPs have a negative effect on the content of soluble sugars, chlorophyll a/b, carotenoids, and phenols. Conversely, lower concentrations showed a stimulating effect on photosynthesis indicators. Nevertheless, MnO2 NPs at all concentrations generally decreased the antioxidant potential of pea seedlings, except for the ABTS parameter. Pea seedlings showed a notable capacity to absorb Mn, reaching levels of 586.5 μg/L at 10 mg/L and 892.6 μg/L at 100 mg/L MnO2 NPs, surpassing the toxic level for peas according to scientific literature. However, the most important result was the observed growth-stimulating activity at 0.1 mg/L MnO2 NPs stabilized with cocamidopropyl betaine, suggesting a promising avenue for further research.

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An algorithm to calculate the cationic composition of soil solutions. 2. Parameterisation and test

2021

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This paper presents a parameterisation and test of an algorithm to calculate distributions of the major nutrient cations between the solution and exchangeable phases of soil when cation exchange capacity (c) may vary. Two contrasting soils were considered: a volcanic subsoil where c is dominated by variable-charge surfaces, and an alluvial silt loam topsoil with stable c. Experimental treatments consisted of applying either water or solutions of CaCl2, KCl, MgCl2, or NaCl. Solution concentrations of Ca2+, K+, Mg2+, and Na+ varied by up to two orders of magnitude, and were simulated well, particularly when using log10-transformed data. The ratios of the solution concentrations of K+, Mg2+, and Na+ to Ca2+ also were generally simulated well. However, the algorithm’s description of soil acidity needs further checking. For the variable-charge soil, cation concentrations were strongly influenced by fitted parameters associated with anion exchange. For the alluvial soil, fitted parameters had little influence, and the cation calculations were dominated by information gathered from the initial (equilibrium) distributions between phases. The algorithm has strong potential for forecasting changes in solution concentrations of the major nutrient cations, using relatively small amounts of fitting data.

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Luxury uptake of magnesium by peas, Pisum sativum

Cited by 6 publications

References 47 publications

Nitrogen or potassium preconditioning affects uptake of both nitrate and potassium in young wheat (Triticum aestivum)

Nitrogen or potassium preconditioning affects uptake of both nitrate and potassium in young wheat (Triticum aestivum)

Effect of MnO2 Nanoparticles Stabilized with Cocamidopropyl Betaine on Germination and Development of Pea (Pisum sativum L.) Seedlings

An algorithm to calculate the cationic composition of soil solutions. 2. Parameterisation and test

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