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Pasture and Agricultural Systems
Research Guide

What is Pasture and Agricultural Systems?

Pasture and agricultural systems are managed land-use systems that integrate forage- and/or crop-based plant production with livestock and resource management to produce food and fiber while mediating ecosystem processes such as vegetation dynamics and nutrient cycling.

The research literature indexed under “Pasture and Agricultural Systems” comprises 107,565 works (5-year growth rate: N/A).

107.6K
Papers
N/A
5yr Growth
173.0K
Total Citations

Research Sub-Topics

Why It Matters

Pasture and agricultural systems matter because they underpin how vegetation condition is monitored at large scales and how farming outcomes are simulated for decision support. "Monitoring vegetation systems in the great plains with ERTS" (1974) described a method for quantitative measurement of vegetation conditions over broad regions using ERTS-1 imagery, supporting operational monitoring of rangeland phenology and climatic effects on growth conditions. "An overview of APSIM, a model designed for farming systems simulation" (2002) summarized APSIM as a modular farming-systems simulator used to represent interacting crop/soil/management processes, enabling scenario testing (e.g., management or climate variability) without conducting full-scale field trials. At a more general ecological-mechanistic level, "Principles of Terrestrial Ecosystem Ecology" (2011) and "Principles of terrestrial ecosystem ecology" (2003) synthesize processes (water/energy balance, production, decomposition, nutrient cycling) that are directly implicated in pasture productivity, persistence, and environmental impacts, and "Assessing the generality of global leaf trait relationships" (2005) provided trait relationships used to parameterize vegetation models relevant to forage species and mixed plant communities.

Reading Guide

Where to Start

Start with "An overview of APSIM, a model designed for farming systems simulation" (2002) because it provides a concrete entry point into how agricultural systems are represented as interacting modules for scenario analysis.

Key Papers Explained

"Principles of terrestrial ecosystem ecology" (2003) and "Principles of Terrestrial Ecosystem Ecology" (2011) provide the conceptual and mechanistic foundation (water/energy balance, production, decomposition, nutrient cycling) that underlies most pasture and agricultural systems reasoning. Keating et al. (2002) in "An overview of APSIM, a model designed for farming systems simulation" operationalized these kinds of mechanisms in a modular simulation framework for farming systems. Rouse et al. (1974) in "Monitoring vegetation systems in the great plains with ERTS" complements simulation by describing quantitative, broad-area measurement of vegetation condition using satellite data, which can be used for monitoring, calibration, or evaluation. Wright et al. (2005) in "Assessing the generality of global leaf trait relationships" supports model parameterization and cross-system generalization by quantifying trait relationships used in vegetation and ecosystem models.

Paper Timeline

100%
graph LR P0["Methods of Biochemical Analysis
1956 · 4.5K cites"] P1["Monitoring vegetation systems in...
1974 · 2.9K cites"] P2["Nelson Textbook of Pediatrics
1976 · 5.3K cites"] P3["An overview of APSIM, a model de...
2002 · 2.7K cites"] P4["Assessing the generality of glob...
2005 · 2.6K cites"] P5["American Journal of Mental Retar...
2008 · 3.0K cites"] P6["The Effect of Riding as an Alter...
2015 · 18.7K cites"] P0 --> P1 P1 --> P2 P2 --> P3 P3 --> P4 P4 --> P5 P5 --> P6 style P6 fill:#DC5238,stroke:#c4452e,stroke-width:2px
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Most-cited paper highlighted in red. Papers ordered chronologically.

Advanced Directions

A practical frontier is tighter coupling between observation and simulation: using regional remote sensing approaches consistent with "Monitoring vegetation systems in the great plains with ERTS" (1974) to constrain, initialize, or evaluate farming-systems simulations consistent with "An overview of APSIM, a model designed for farming systems simulation" (2002). Another frontier is improving how trait-based constraints, as synthesized in Wright et al. (2005) "Assessing the generality of global leaf trait relationships", are translated into parameters used in ecosystem-process representations emphasized in "Principles of Terrestrial Ecosystem Ecology" (2011) for managed, grazed plant communities.

Papers at a Glance

# Paper Year Venue Citations Open Access
1 The Effect of Riding as an Alternative Treatment for Children ... 2015 Integrative Medicine I... 18.7K
2 Nelson Textbook of Pediatrics 1976 The Medical Journal of... 5.3K
3 Methods of Biochemical Analysis 1956 The Medical Journal of... 4.5K
4 American Journal of Mental Retardation 2008 Encyclopedia of Specia... 3.0K
5 Monitoring vegetation systems in the great plains with ERTS 1974 NASA Technical Reports... 2.9K
6 An overview of APSIM, a model designed for farming systems sim... 2002 European Journal of Ag... 2.7K
7 Assessing the generality of global leaf trait relationships 2005 New Phytologist 2.6K
8 Principles of terrestrial ecosystem ecology 2003 Choice Reviews Online 2.2K
9 Energy and Large-Scale Patterns of Animal- and Plant-Species R... 1991 The American Naturalist 1.8K
10 Principles of Terrestrial Ecosystem Ecology 2011 1.7K

In the News

Code & Tools

GitHub - Komanawa-Solutions-Ltd/komanawa-basgra-nz-py: BASGRA or The BASic GRAssland model is a simple pasture growth model. This version, BASGRA_NZ has been specifically modified to model perennial ryegrass in New Zealand. This adaptation creates a python implementation.
github.com

BASGRA or The BASic GRAssland model is a simple pasture growth model. This version, BASGRA\_NZ has been specifically modified to model perennial ry...

GitHub - APSIMInitiative/APSIM710: APSIM
github.com

The Agricultural Production Systems sIMulator (APSIM) is internationally recognised as a highly advanced simulator of agricultural systems. It cont...

GitHub - TwinYields/farmingpy: Python library for Smart Farming data and modelling. Enables reading of ISOBUS task files, EO data from SentinelHub and interfacing APSIM simulation models.
github.com

farmingpy provides the following functionality:

GitHub - magpiemodel/magpie: Model of Agricultural Production and its Impact on the Environment (MAgPIE) - model code
github.com

The _Model of Agricultural Production and its Impact on the Environment_ (MAgPIE) is a modular open-source framework for modeling global land-syste...

GitHub - Project-AgML/AgML: AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
github.com

AgML is a comprehensive library for agricultural machine learning. Currently, AgML provides

Recent Preprints

Latest Developments

Recent developments in Pasture and Agricultural Systems research as of February 2026 include advancements in AgTech innovations such as smarter sensors, autonomous machinery, and precision farming tools supported by new financing models (icl-group.com), the integration of IoT, robotics, and AI to enhance efficiency and sustainability (dllgroup.com), and the application of computer vision systems for pasture biomass estimation to support data-driven grazing decisions (sciety-labs.elifesciences.org). Additionally, research highlights the positive impacts of temperate silvopastures on soil quality, ecosystem services, and cattle welfare without productivity loss (nature.com), and there is ongoing exploration of pasture establishment challenges and innovations in New Zealand dairy systems (frontiersin.org).

Frequently Asked Questions

What is meant by “Pasture and Agricultural Systems” in research practice?

Pasture and agricultural systems research focuses on managed vegetation (often forage or crop plants) and the management, environmental conditions, and biophysical processes that determine productivity and sustainability. "Principles of Terrestrial Ecosystem Ecology" (2011) and "Principles of terrestrial ecosystem ecology" (2003) frame these systems using core ecosystem processes such as production, decomposition, and nutrient cycling.

How do researchers monitor pasture or rangeland condition at regional scales?

Remote sensing approaches quantify vegetation condition over broad regions by using satellite observations as indicators of phenology and growth conditions. "Monitoring vegetation systems in the great plains with ERTS" (1974) reported a method developed for quantitative measurement of vegetation conditions over large areas using ERTS-1 imagery.

How do farming-systems models support pasture and mixed farming decisions?

Process-based simulation models represent interacting components of farming systems so researchers can test management and environmental scenarios computationally. "An overview of APSIM, a model designed for farming systems simulation" (2002) described APSIM as a model designed for farming systems simulation with a suite of modules to represent system components.

Which ecological mechanisms are most commonly used to interpret pasture system outcomes?

Pasture outcomes are commonly interpreted through ecosystem mechanisms including terrestrial water and energy balance, carbon input/production processes, decomposition, and nutrient cycling. These mechanisms are organized explicitly in "Principles of terrestrial ecosystem ecology" (2003) and reiterated in "Principles of Terrestrial Ecosystem Ecology" (2011).

Which plant properties help generalize pasture responses across species and environments?

Cross-species trait relationships are used to generalize plant functioning and to parameterize vegetation–climate and ecosystem models. Wright et al. (2005) in "Assessing the generality of global leaf trait relationships" compiled a global database and quantified relationships among core leaf traits relevant to modeling vegetation performance.

Which broad-scale hypotheses connect environment to biodiversity patterns relevant to grazed landscapes?

Broad-scale richness patterns have been tested against environmental predictors to evaluate competing hypotheses about why species richness varies among regions. Currie (1991) in "Energy and Large-Scale Patterns of Animal- and Plant-Species Richness" examined richness patterns across multiple vertebrate groups and compared them with regional environmental variation.

Open Research Questions

  • ? How can satellite-derived indicators of vegetation condition, as operationalized in "Monitoring vegetation systems in the great plains with ERTS" (1974), be best integrated with process-based farming systems simulators such as APSIM to improve pasture-specific decision support?
  • ? Which ecosystem-process representations emphasized in "Principles of Terrestrial Ecosystem Ecology" (2011) most constrain predictive accuracy for grazed systems when management alters decomposition and nutrient cycling pathways?
  • ? To what extent do the global leaf-trait relationships synthesized by Wright et al. (2005) in "Assessing the generality of global leaf trait relationships" transfer to forage-dominated, management-structured plant communities typical of pasture systems?
  • ? How should models reconcile regional-scale biodiversity–environment relationships discussed by Currie (1991) in "Energy and Large-Scale Patterns of Animal- and Plant-Species Richness" with local management drivers in agricultural mosaics?
  • ? What modular structure and parameterization strategy, consistent with "An overview of APSIM, a model designed for farming systems simulation" (2002), best supports credible simulation of pasture growth and persistence under variable climate and management?

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