Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science

By James W. Jones, J. Antle, B. Basso, Kenneth J. Boote, R. Conant, Ian T Foster, Charles J. Godfray, Mario Herrero, Richard E. Howitt, Sander Janssen and 5 more. Agricultural Systems, 2017

399 citations recorded by the source corpus.

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Subfields: Ecology, Evolution, Behavior and Systematics, General Agricultural and Biological Sciences

View Paper: https://www.semanticscholar.org/paper/7f3ec3409242473a06ed652c561d1946d69da4cf

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In Ecology, Evolution, Behavior and Systematics

In General Agricultural and Biological Sciences

Citations in the corpus

Introducing digital twins to agriculture (2021, background): “In this respect, the lack of a data culture (Jones et al., 2017) and compartmentalization of agricultural systems understanding inhibits DT development and decreases potential for adoption.”
Brief history of agricultural systems modeling (2017, neutral): “…livestock, and economic models have been combined to study farming systems as well as to analyze national and global impacts of climate change, policies, or alternative technologies, as shown in the companion paper on the state of agricultural system science (Jones et al., 2017 – in this issue).”
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change (2017, positive): “A wide range of dynamic crop growth simulation models have been developed over the past few decades, many of which are being applied to study impacts of climate change (Asseng et al., 2014; Challinor et al., 2014; Ewert et al., 2015; Jones et al., 2017a, 2017b; White et al., 2011).”
Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020, background): “Successful yield forecasting approaches using crop models in the USA (Morell et al., 2016; Togliatti et al., 2017) and Australia (Carberry et al., 2009), have shown potential to couple explanatory with predictive power to evaluate adaptive management strategies (Jones et al., 2017).”