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Related Concept Videos

Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...
Meristems and Plant Growth02:36

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Plants grow throughout their lives; this is called indeterminate growth, and it distinguishes plants from most animals. Although certain parts of plants stop growing (e.g., leaves and flowers), others grow continuously—like roots and stems.
Tonicity in Plants01:20

Tonicity in Plants

Plant cells maintain appropriate osmotic balance in extreme conditions. For instance, plants in dry environments store water in vacuoles, limit the opening of their stoma, and have thick, waxy cuticles to prevent unnecessary water loss. Some species of plants that live in salty environments store salt in their roots. As a result, water osmosis occurs in the root from the surrounding soil.
Tonicity
Tonicity describes the capacity of a cell to lose or gain water depending on the solute...
Tonicity in Plants00:53

Tonicity in Plants

Tonicity describes the capacity of a cell to lose or gain water. It depends on the quantity of solute that does not penetrate the membrane. Tonicity delimits the magnitude and direction of osmosis and results in three possible scenarios that alter the volume of a cell: hypertonicity, hypotonicity, and isotonicity. Due to differences in structure and physiology, tonicity of plant cells is different from that of animal cells in some scenarios.Plants and Hypotonic EnvironmentsUnlike animal cells,...
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...

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High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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Simulating coconut growth, development and yield with the InfoCrop-coconut model.

S Naresh Kumar1, K V Kasturi Bai, V Rajagopal

  • 1Central Plantation Crops Research Institute, Kasaragod-671 124, Kerala, India. nareshkumar.soora@gmail.com

Tree Physiology
|May 3, 2008
PubMed
Summary

The InfoCrop-coconut model simulates coconut crop growth and yield in various tropical and subtropical regions. This tool aids in optimizing coconut plantation management and agronomic experiments for increased efficiency.

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Area of Science:

  • Agricultural Science
  • Crop Modeling
  • Plant Physiology

Background:

  • Simulation modeling offers valuable insights for perennial crop management.
  • The InfoCrop model, successful for annual crops, was adapted for perennial species.
  • Coconut (Cocos nucifera L.) cultivation benefits from advanced modeling tools.

Purpose of the Study:

  • To develop and apply the InfoCrop-coconut model for simulating coconut growth and yield.
  • To validate the model's performance across diverse Indian agro-climatic conditions.
  • To assess the model's utility in enhancing agronomic experiments for coconut management.

Main Methods:

  • The InfoCrop-coconut model was developed based on the generic InfoCrop framework.
  • Model calibration and validation utilized published data from 1978-2005, including various water, nutrient, and variety treatments.
  • Statistical analysis assessed model efficiency and performance against observed data.

Main Results:

  • Simulated phenology, dry mass, and nut yield closely matched observed values, with a minor error of ~15%.
  • The model accurately reflected the impact of management practices and agro-climatic variations.
  • Potential yields ranged from 26-30 Mg ha⁻¹ year⁻¹, and dry mass production from 52-62 Mg ha⁻¹ year⁻¹.

Conclusions:

  • The InfoCrop-coconut model provides a reliable simulation tool for coconut cultivation.
  • The model adequately simulates coconut crop responses to environmental factors and management.
  • InfoCrop-coconut can enhance the design and efficiency of coconut agronomic research and management strategies.