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

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.

Crop modelling: towards locally relevant and climate-informed adaptation

“…to evaluate long-term requirements for water harvesting to address crop water deficits) on production may be a good starting point for the crop-climate modelling community to gain perspective on the magnitude of change associated with more realistic adaptation. Crop models developed for small-scale farm decision support have already developed parameter sets for some of these more refined management-based processes such as tillage, mulching or intercropping, and their impact on soil-water processes and production (Jones et al 2017). The main factor stopping these processes of management and adaptation from being scaled up with crop climate modelling studies is the perceived lack of, or lack of access to, data on management strategies at relevant scales for climate impact studies (Rivington and Koo 2010), which further supports the need for detailed descriptions of practices to enable systematic collection of management and adaptation data.…”

Cropping Systems and Climate Change in Humid Subtropical Environments

“…Such routines should be included in crop simulation models for other crops. Pest and diseases are another important component in crop production, and can have large impacts on crop yields, but usually they are not included in crop simulation models [91,92]. A major difficulty of integrating crop and pest and disease models is their different spatial and temporal scales of operation [93].…”

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

“…Information on spatial distribution of different crop rotations helps managers to perform different agriculture functions such as water/nutrient supply, crop production estimation, revenue generation in an effective way (Jones et al, 2017;Bégué et al, 2018). It optimizes the modeling of energy fluxes for food security and climate change studies in different agro-ecosystems (See et al, 2015;Leng and Huang, 2017).…”