Chemistry in its application to agriculture and physiology

Liebig, Justus; Playfair, Lyon; Webster, John White

doi:10.5962/bhl.title.30425

Cited by 42 publications

(11 citation statements)

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“…Liebig's law of the minimum, proposed in the nineteenth century, states that plants' growth is constrained by a single limiting nutrient. Since then, the law has been applied as a basic principle in various ecological and agronomic studies on N and P [4,5]. However, a number of studies have suggested that there are interactions between them [6][7][8].…”

Section: Historical Observation About N/p Interactionsmentioning

confidence: 99%

Nitrogen and Phosphorus interactions in plants: from agronomic to physiological and molecular insights

Krouk

Kiba

2020

Current Opinion in Plant Biology

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Section: Historical Observation About N/p Interactionsmentioning

confidence: 99%

Nitrogen and Phosphorus interactions in plants: from agronomic to physiological and molecular insights

Krouk

Kiba

2020

Current Opinion in Plant Biology

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“… and represent uptake fluxes of substrates and metabolites respectively, are metabolite secretion fluxes, and and stand for per-capita growth and death rates respectively. We used Monod kinetics for resource uptake ( and ), derived mathematical expressions for metabolite leakage ( ) and biomass production ( ) using the Liebig’s Law of the Minimum [ 28 ] (growth rate is proportional to the flux of the scarcest resource), and modelled cell death using first-order kinetics with constant specific mortality rate ( ). The functional forms of these kinetic laws and other details of model derivation are described in S1 Text .…”

Section: Resultsmentioning

confidence: 99%

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

et al. 2020

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Social interaction between microbes can be described at many levels of details: from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing an appropriate level to model microbial communities without losing generality remains a challenge. Here we show that modeling cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems is suitable to experimental data. We applied our modeling framework to three published examples of multi-strain Escherichia coli communities with increasing complexity: uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately fit empirical data and quantified metabolic exchange rates that are hard to measure experimentally, even for a complex community of 14 amino acid auxotrophies. By studying the conditions of species coexistence, the ecological outcomes of cross-feeding interactions, and each community's robustness to perturbations, we extracted new quantitative insights from these three published experimental datasets. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.

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“…Respiration is first calculated as a fixed fraction of C uptake (1 − CUE ). Two separate parameters, p and q , determine maximum allocation of C and N toward enzyme production respectively, with actual allocations constrained by enzyme stoichiometry according to Liebig's law of the minimum (Liebig, 1842). If there remain excess C or N from this initial allocation that cannot be incorporated into enzymes due to stoichiometric demand, remaining C and N are maximally incorporated into microbial biomass.…”

Section: Methodsmentioning

confidence: 99%

Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model

et al. 2021

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Advancements in microbially explicit ecosystem models incorporate increasingly accurate representations of microbial physiology and enzyme‐mediated depolymerization of soil organic matter in predicting biogeochemical responses to global change. However, a major challenge with model structural improvements is the requirement for additional parameters, which are often poorly constrained sources of uncertainty. Furthermore, it is often unclear how to best focus data collection efforts toward reducing model uncertainty. Here, we use Dual Arrhenius Michaelis‐Menten Microbial Carbon and Nitrogen Physiology, a microbially mediated, coupled soil C and N cycling model, as a tool to explore the influence of microbial physiological and enzyme kinetic parameters on model estimates. We first quantify the potential for constraining model parameters using empirical measurements of soil respiration. We then use simulated data to identify which additional sources of data collection from the field would provide the greatest impact for constraining model estimates. We find that modeled soil C and N pools and fluxes are disproportionately sensitive to only a few parameters (e.g., activation energies and microbial CUE), while others exert less influence (e.g., Michaelis‐Menten half‐saturation constants). While some parameters can be constrained by the available data on heterotrophic respiration, the collection of additional data on dissolved organic C and N pools in the soil is identified as a high‐priority data need. Improving our ability to model the interactions of soil microbial physiology, soil chemistry, enzyme activities, and environmental factors on C and N cycling will require closely considering model uncertainties and prioritizing future data collection opportunities based on their impact on model performance.

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Chemistry in its application to agriculture and physiology

Cited by 42 publications

References 0 publications

Nitrogen and Phosphorus interactions in plants: from agronomic to physiological and molecular insights

Nitrogen and Phosphorus interactions in plants: from agronomic to physiological and molecular insights

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

Identifying Data Needed to Reduce Parameter Uncertainty in a Coupled Microbial Soil C and N Decomposition Model

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