Nonlinear Modeling of Crop Growth Dynamics

Nonlinear modelling of crop growth dynamics addresses the characteristic sigmoidal progression of plant development, capturing phases of establishment, exponential expansion and plateau. By fitting mathematical functions such as Logistic, Gompertz or Richards curves to biomass, height or yield over time, researchers can quantify rates of growth acceleration and deceleration, determine inflection points and estimate asymptotic maxima. Integration of mixed-effects structures allows separation of fixed factors (for example cultivar or treatment) from random sources of variation (such as year or plot), thereby improving predictive accuracy across heterogeneous environments. When coupled with accumulated thermal units or remotely sensed climatic inputs, these models support precise irrigation scheduling, nutrient management and harvest timing. Advances in computational algorithms for parameter estimation and model selection criteria (for instance information-theoretic measures and curvature diagnostics) have strengthened confidence in model-based decision support. In the context of global food security and climate variability, nonlinear growth modelling underpins adaptive management strategies and offers a robust framework for anticipating crop responses to novel genotypes, shifting agroclimatic regimes and resource constraints.

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Nonlinear Modeling of Crop Growth Dynamics publication trend

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Technical terms

Nonlinear regression model: A statistical model in which the relationship between independent and dependent variables is represented by a nonlinear function, often used to describe growth curves that cannot be captured by straight-line models.

Logistic model: A sigmoid function characterised by an initial exponential growth phase, a point of maximum growth rate (inflection) and a horizontal asymptote, frequently applied to biological growth data.

Gompertz model: A type of asymmetric sigmoid growth function in which the rate of increase decelerates exponentially after the inflection point, allowing for earlier plateau phases.

Mixed-effects model: A modelling framework that incorporates both fixed effects (systematic factors common to all observations) and random effects (sources of variability specific to clusters or subjects), improving generalisability across heterogeneous units.

Inflection point: The time or cumulative input at which a growth curve reaches its maximum instantaneous rate of increase, marking the transition from acceleration to deceleration phases.

References

1. Selecting non-linear mixed effect model for growth and development of pecan nut. Scientia Horticulturae (2023).
2. Adjusting the growth curve of sugarcane varieties using nonlinear models. Ciência Rural (2020).
3. Behavior of strawberry production with growth models: a multivariate approach. Acta Scientiarum Agronomy (2020).