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Citations Publications citing this paper. Goodwin , Daniel Pantzar. A discussion on simulations' visualization usage Andrew J. Manufacturing case studies: generic case studies for manufacturing simulation applications Charles R. McLean , Guodong Shao.

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  8. This article has been cited by other articles in PMC. Abstract Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions.

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    Introduction The world has become more complex in recent years due to many factors, including our growing population and its demands for more food, water, and energy, the limited arable land for expanding food production, and increasing pressures on natural resources. A brief history The history of agricultural system modeling is characterized by a number of key events and drivers that led scientists from different disciplines to develop and use models for different purposes Fig.

    Open in a separate window. Table 1 Timeline of key events that shaped the development and use of agricultural system models. Year Event Impacts s—s de Wit and van Bavel develop early computational analyses of plant and soil processes; Development of nutritional requirement tables for cattle NRC, Foundation established for the application of simulation and operations research optimization in plant-soil systems research and for modeling farm animal responses to nutrients —s Demand for policy analysis of rural development Representative farm optimization models were developed and applied by Heady and students at Iowa State University, thus establishing use of linear programming methods for agricultural production — Pioneers in soil water balance modeling WATBAL [ Slatyer, , Slatyer, , Keig and McAlpine, ; Ritchie, ; McCown, ] Water balance models proved to be useful in the evaluation of climatic constraints to agricultural development.

    Foundations for linking soil and plant models established. Duncan, R. Loomis Captured imagination of many crop and soil scientists. Prompted many to follow in their steps. Duncan, J. Hesketh, D. Baker, J. Jones, J. McKinion Creation of the Biological System Simulation Group BSSG Led to self-supported annual workshops aimed at advancing cropping system and other biological system models, continuing through s and early 80s Development of early herd dynamics simulation models Freer et al.

    Crucial for the advancement of whole livestock farm modeling and for representing disease and reproductive impacts s Gordon Conway develops concept of IPM in Malaysia. Global emphasis on reducing pesticide use, due to major increases in pesticide use globally and resistance in target pest populations.

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    Insect and disease models developed and used to help establish economic thresholds and to predict timing of threshold exceedance; some pest models were linked with crop models Mid s Discovery of chaos in ecological systems by Robert May May, and related advances in theoretical population ecology Led to new approaches to modeling predator-prey, host-disease interactions —74 Soviet Union purchase of US wheat reserves, causing major price spike see Pinter et al.

    FAO, , FAO, —81 Provided first methodology for land evaluation on a global basis, integrating soil, climate, vegetation, and socio-economic factors, leading to many applications and efforts to improve integrated assessment approaches — Early pioneers in computer simulation based decision support — SIROTAC and Australian Cotton Industry CSIRO, ; S Project on soybean modeling in the US The Australian cotton modeling was the first major initiative to put crop and pest models in the hands of farmers for decision support. Spedding Spedding, This journal helped legitimize agricultural system modeling, providing a place for scientists to publish their agricultural systems modeling and analyses as well as a collection of scholarly work in this area.

    This journal continues today with impact factor of about 2. They stimulated interest and activity in crop modeling in many parts of the world. These models simulated herbage mass and accounted for sward components, which led to a more sophisticated representation of grazing processes. The availability of the IBSNAT guidelines for data collection for crop modeling strengthened the crop model testing effort around the world. Formed the foundation for modeling low input subsistence agricultural systems and exploring development opportunities. These studies paved the way for many other national and global impact studies of climate change impacts and adaptation.

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    Its development influenced the current livestock performance models in many parts of the world. Led to acknowledgement of need for increased understanding of livestock sector for agricultural development. Over citations for models in this publication. Most of this work was done through modeling. Mid s onwards Development of global livestock models Bouwman et al. Characteristics of agricultural system models Although many factors have motivated the development of agricultural system models, there are three characteristics that stand out among them: 1 intended use of models, 2 approaches taken to develop the models, and 3 their target scales.

    Approaches for modeling agricultural systems Several dimensions are needed to describe the types of models that have been developed in the past for use in improving decisions and policies. Spatial and temporal scales of agricultural system models Users of models or information derived from them and the models themselves vary considerably across spatial and temporal scales as indicated in Fig. Field level The scope of the system is important in determining what type of model is needed and what users are being targeted.

    Farm and broader levels An agricultural system could also be defined as a farm with land area on which different crops and livestock are produced, each of which is managed by a farm family or business entity. Discussion The history of agricultural systems modeling shows that major contributions have been made by different disciplines, addressing different production systems from field to farm, landscape, and beyond. This history shows that major advances in agricultural systems modeling occurred when there were food security concerns or other crises and then decelerated afterward.

    Other studies e. Thus, it is important that we have the science and analytical tools available beforehand to act quickly to get things done while there is a window of opportunity. A strong lesson from the past is the influence of technological advances, including mainframe computers, the PC, and the Internet. New technologies and knowledge should be embraced by those who are developing next generation of agricultural systems models, data, and knowledge systems.

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    Contemporary technology examples include smart phones and telecommunications, apps and video games, molecular biology, remote sensing, open source software tools, cloud computing as a means of enabling broad access to powerful tools Foster, , Montella et al.

    Most agricultural system models have been developed using relatively narrow ranges of data, mainly because most modelers have collected their own data sets in order to develop a model. Although there are exceptions, data obtained by most biophysical agricultural scientists are lost soon after researchers collect the data and publish their results. Metadata, standards and protocols are needed to harmonize the databases that exist and to facilitate entry of data that are now mostly lost after collection and primary use.

    Major advances have occurred in the past when different disciplines joined forces, such as occurred when crop modeling and remote sensing scientists collaborated to create models for predicting wheat yield worldwide in the s, or when crop models joined up with land-surface scheme models to start to simulate crops within numerical climate models in the s. There is a need to broaden the collaboration, such as now exists to some degree among biophysical and economic modelers, in particular to include plant and animal breeders, insect and disease researchers and modelers, etc.

    It is important to have different models and approaches, but we need to develop standards and protocols to fully gain the benefits from these developments. Instead, we should aim for component models that are structured as modules that can be used alone to address specific questions such as when to apply a chemical or irrigation and, more importantly, where those modules can be integrated into holistic biophysical and economic models to address more comprehensive problems. Modular models are needed to ensure efficient scientific progress as well as model longevity and maintainability.

    The history of data and model development shows that many existing models have been developed for research purposes and then adapted to address user needs. References Acock B. Department of Agriculture; Washington, DC: Model documentation Adams R. Global climate change and U.


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    Addiscott T. Concepts of solute leaching in soils: a review of modeling approaches. Soil Sci.