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

TLDR

Agricultural system modeling has advanced through multidisciplinary contributions, yet open‑data initiatives require a cultural shift to supply data for diverse use cases. The authors aim to review the current state of agricultural systems science, concentrating on the capabilities and limitations of existing models. They examine model performance across five use cases—field, farm, landscape, regional, and global—spanning past, present, and future time scales. All models exhibit limitations, chiefly data scarcity and weak knowledge‑communication systems, which the authors argue are greater barriers than theoretical gaps, and they recommend multiple platforms and models to address diverse purposes.

Abstract

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.