Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Phosphorus Cycle

The Phosphorus Cycle

Unlike carbon, water, and nitrogen, phosphorus is not present in the atmosphere as a gas. Instead, most phosphorus in the ecosystem exists as compounds, such as phosphate ions (PO43-), found in soil, water, sediment and rocks. Phosphorus is often a limiting nutrient (i.e., in short supply). Consequently, phosphorus is added to most agricultural fertilizers, which can cause environmental problems related to runoff in aquatic ecosystems.

Phosphoinositides and PIPs

Phosphoinositides and PIPs

Phosphoinositides are a group of phospholipids containing a glycerol backbone with two fatty acid chains and a phosphate attached to a myoinositol sugar ring. The inositol head group extends into the cytoplasm, where it is modified by adding phosphate groups to form phosphatidylinositol phosphates or PIPs.
Different phosphoinositides are synthesized and recruited on the cytosolic face of the plasma membrane. The localization of specific phosphoinositides concentrated in separate membrane...

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).

Key Elements for Plant Nutrition

Key Elements for Plant Nutrition

Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the atmosphere, the...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

APLE2026: Development of a graphical user interface for the Annual Phosphorus Loss Estimator model.

Journal of environmental quality·2026

Same author

Estimating soil P pools and desorption rates using flow-through cells.

Journal of environmental quality·2025

Same author

SARLBP: Scale Adaptive Robust Local Binary Patterns for Texture Representation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

Same author

Phosphorus lability across diverse agricultural contexts with legacy sources.

Journal of environmental quality·2024

Same author

A short history of the phosphorus index and Andrew Sharpley's contributions from inception through development and implementation.

Journal of environmental quality·2023

Same author

Kinetics of phosphorous sorption to biochar-amended soils.

Chemosphere·2023

Search research articles

Related Experiment Video

Updated: May 17, 2026

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

Published on: July 22, 2019

Using a phosphorus loss model to evaluate and improve phosphorus indices.

Carl H Bolster¹, Peter A Vadas, Andrew N Sharpley

¹USDA-ARS, Bowling Green, KY, USA. carl.bolster@ars.usda.gov

Journal of Environmental Quality

|November 7, 2012

Summary

Evaluating the phosphorus (P) index (PI) is crucial for assessing P loss risk. This study demonstrates how P loss models can refine state PIs, improving accuracy and correlation with measured data.

More Related Videos

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

Published on: March 6, 2014

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

Related Experiment Videos

Last Updated: May 17, 2026

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

Published on: July 22, 2019

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading

Published on: March 6, 2014

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

Area of Science:

Environmental Science
Agronomy
Soil Science

Background:

Phosphorus (P) index (PI) is widely used to assess P loss risk.
Many state PIs lack rigorous evaluation against measured P loss data due to data scarcity.

Purpose of the Study:

To demonstrate using a P loss model for evaluating and revising PIs, using the Pennsylvania (PA) PI as a case study.
To compare multiplicative and component formulations for PI calculation.
To assess if P loss model output can improve PI weighting.

Main Methods:

Compared multiplicative and component PI formulations.
Utilized P loss model-generated data to calculate weights for modified PA PI versions.
Correlated PI values with a large, diverse measured P loss dataset.

Main Results:

The component PI formulation aligns better with P loss models and offers more accurate P loss estimates than the multiplicative formulation.
Revised PI weighting using model-generated data significantly improved the correlation between PI values and measured P loss.
Identified limitations in the original multiplicative PI formulation.

Conclusions:

A component-based PI formulation is recommended for improved accuracy in P loss risk assessment.
P loss models provide a viable method for evaluating and refining state PIs.
The demonstrated approach offers a guide for states to enhance their P loss assessment strategies.