Beyond the logistic growth model for nitrous oxide emission factors from agricultural soils

2011 ◽  
Author(s):  
Kailash Prasad Thakur ◽  
Donna Giltrap ◽  
Anne-Gaelle Ausseil ◽  
Surinder Saggar ◽  
Ashish Raj
Keyword(s):  
Nitrous Oxide ◽  
Growth Model ◽  
Agricultural Soils ◽  
Emission Factors ◽  
Nitrous Oxide Emission ◽  
Logistic Growth ◽  
Logistic Growth Model

Global nitrous oxide emission factors from agricultural soils after addition of organic amendments: A meta-analysis

2017 ◽  
Vol 236 ◽  
pp. 88-98 ◽  
Author(s):  
Anaïs Charles ◽  
Philippe Rochette ◽  
Joann K. Whalen ◽  
Denis A. Angers ◽  
Martin H. Chantigny ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Agricultural Soils ◽  
Meta Analysis ◽  
Organic Amendments ◽  
Emission Factors ◽  
Nitrous Oxide Emission

Differentiation of nitrous oxide emission factors for agricultural soils

2011 ◽  
Vol 159 (11) ◽  
pp. 3215-3222 ◽  
Author(s):  
Jan Peter Lesschen ◽  
Gerard L. Velthof ◽  
Wim de Vries ◽  
Johannes Kros
Keyword(s):  
Nitrous Oxide ◽  
Agricultural Soils ◽  
Emission Factors ◽  
Nitrous Oxide Emission

Nitrous oxide emission factors in conventionally and naturally simulated cattle urine patches

2021 ◽  
Author(s):  
M. O’Neill ◽  
S. Saggar ◽  
K. G. Richards ◽  
J. Luo ◽  
B. P. Singh ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Emission Factors ◽  
Nitrous Oxide Emission

scholarly journals Extended logistic growth model for heterogeneous populations

2017 ◽  
Author(s):  
Wang Jin ◽  
Scott W McCue ◽  
Matthew J Simpson
Keyword(s):  
Cell Proliferation ◽  
Growth Model ◽  
Numerical Solutions ◽  
Cellular Level ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Living Tissues ◽  
Discrete Mathematical Model ◽  
The Continuum

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

A Neoclassical Growth Model for Population Dynamics in a Homogeneous Habitat

2001 ◽  
Author(s):  
Peter Vadasz ◽  
Alisa S. Vadasz
Keyword(s):  
Experimental Data ◽  
Growth Model ◽  
Logistic Growth ◽  
Lag Phase ◽  
Growth Stages ◽  
Initial Growth ◽  
Neoclassical Model ◽  
Wide Range ◽  
Logistic Growth Model ◽  
Special Case

Abstract A neoclassical model is proposed for the growth of cell and other populations in a homogeneous habitat. The model extends on the Logistic Growth Model (LGM) in a non-trivial way in order to address the cases where the Logistic Growth Model (LGM) fails short in recovering qualitative as well as quantitative features that appear in experimental data. These features include in some cases overshooting and oscillations, in others the existence of a “Lag Phase” at the initial growth stages, as well as an inflection point in the “In curve” of the population size. The proposed neoclassical model recovers also the Logistic Growth Curve as a special case. Comparisons of the solutions obtained from the proposed neoclassical model with experimental data confirm its quantitative validity, as well as its ability to recover a wide range of qualitative features captured in experiments.

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