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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.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM. However, the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral-associated organic matter (MAOM) at the subfield scale within nine farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. In contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas and that there was no significant difference in the relative contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks. [ABSTRACT FROM AUTHOR]

Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM, however the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral associated organic matter (MAOM), at the sub-field scale within 9 farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones, and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. In contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas, and that there was no significant difference in the relative contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks. [ABSTRACT FROM AUTHOR]

The feedstock supply chain of a biorefinery is critical in determining the economic and environmental performance of biofuels. This study investigated various feedstock supply chains for a centralized biorefinery based on different objective functions and how the biorefinery responds to supply chain volatility. For the analysis, a hypothetical centralized biorefinery was supplied by switchgrass grown on marginal lands in Michigan. From an economic standpoint, the minimum biofuel selling price is an important metric of the objective function, and the greenhouse gas mitigation potential is a similarly important metric for global warming impact. A trade‐off therefore exists between the economic and environmental performance of biofuels under this system configuration. However, when a carbon tax credit is applied to soil organic carbon sequestration in switchgrass production, the metric of the objective function may not be the key factor in establishing a supply chain as long as it is associated with biorefinery capacity as well as economic or environmental values. Year‐to‐year fluctuations in cellulosic biomass yield (supply chain volatility) might also prevent the biorefinery from operating at full capacity. Cellulosic biomass pellets can serve as an auxiliary feedstock for the centralized cellulosic biorefinery system to minimize the impacts of supply chain volatility. [ABSTRACT FROM AUTHOR]