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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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Page 114
Suggested Citation:"8 A Systems Approach." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
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8 A Systems Approach 1. INTRODUCTION The food system is a complex system with its variety of actors, feedback and interdependence mechanisms, heterogeneity, and spatial and dynamic complexity (IOM and NRC, 2015). The system is composed of large-scale interconnected systems of systems that include ecosystems (both natural and ag- ricultural), climate, food processing and distribution network data and information systems, and socioec- onomic systems. Models are used to better understand how various entities interact within and integrate across the system, and to understand relationships among the natural environment, society, economy, and the various biophysical components of the food system. For example, an understanding of those relation- ships enables agroecology and ecohydrology approaches to be used to simultaneously manage multiple goals (e.g., lowering water use, sequestering carbon, and minimizing environmental impacts). However, modeling complex interactions and feedback loops within and between each of these systems can be im- proved through (1) a better understanding of the drivers (e.g., physical, behavioral, market drivers) and their interactions, and (2) a greater ability to collect and analyze large amounts of data with high geospa- tial and temporal variability. The study of such a complex system will serve the ultimate goal of informing decision making in food and agriculture practices and policies. Such systems models are also needed to evaluate the impacts of a technological breakthrough on the sustainability of the food system, to rapidly respond to perturba- tions in the systems to avoid adverse outcomes, and to propose interventions that lead to overall efficien- cy gains. The socioeconomic elements of the system need to be deeply integrated with the biophysical drivers of the agroecosystem to invoke any meaningful changes. This chapter presents the many challeng- es in conducting systems-level models and analysis of the food system, and the opportunities to advance this scientific field. 2. CHALLENGES 2.1 Challenge 1: Create and Link Efficient Integrated Systems Models to Data Collection Efforts In describing subsystems of the food system, models have been successfully built that are biophys- ics-based (based on understanding of the behavior of a system’s components) and empirically based (based on observation and measurements). For example, soil and water balance models were developed to understand climate constraints on agriculture, soil models were developed to predict impacts of soil ero- sion on crop productivity, and insect and disease models were developed for integrated pest management strategies (Jones et al., 2017). In soils, great strides have been made in understanding N2 fixation and up- take and in determining types of perturbations that may impact the N2 cycle (Salvagiotti et al., 2008). Models for predicting weather at hyperregional scales are available, such as the National Center for At- mospheric Research’s Weather Research and Forecasting model and The Weather Company’s Deep Thunder (The Weather Company, 2016; NCAR, 2018). However, these various modeling efforts are gen- erally discipline-specific and the subsystem models are not yet well integrated across all the different dis- Prepublication Copy 107

Science Breakthroughs to Advance Food and Agricultural Research by 2030 ciplines within the food and agricultural system. There are many challenges to creating a reliable integrat- ed systems model for the food system. Systems Are Not Well-Defined The systems are not well defined, and in many cases the linkages between the various systems are not delineated well enough to support mechanistic-based physical models. For example, how do changes in the microbiome of soil microorganisms related to N2 fixation affect the resilience of plants against heat or salt stress, to the nutritional value of the food produced, or to feed conversion efficiency in animals? How would a mechanistic understanding of the interactions of the soil microbiome with the plant, root, and leaf microbiomes be combined with genetic tools to create more resilient plants? In addition to linkages, a significant challenge in defining any particular subsystem is determining the main drivers of change for consideration in the analysis. For example, to help identify means to ad- dress the differences in productivity and other outcomes between and within regions, it would be helpful to measure and analyze the causes (e.g., drivers) for such differences. In another example, although trade- off analysis is a useful tool for integrating biophysical models with socioeconomic models to predict the impacts of different drivers on the over sustainability of the system (Stoorvogel et al., 2004), these models require a priori assumptions about the principle drivers affecting the model outcomes, and consider only a subset of all possible drivers. Life cycle analysis (LCA) takes a more holistic approach to modeling sus- tainability indicators, and can be useful for determining, for instance, the range of the magnitude of lifecycle impacts for a given agriculture activity (e.g., milk production) or set of dietary food choices. This can be used to identify system-level opportunities for intervention to improve sustainability (Poore and Nemecek, 2018; Boehm et al., 2018). However, the outcome of the LCA analysis will depend on whether it is being optimized for water use, yield, or nutritional content (Notarnicola et al., 2017; Pour- zahedi et al., 2018). The outcome may also depend on the system boundaries that are selected. Lack of Cohesion Between the Different Modeling Communities As previously noted, the absence of standardization of data formats and reporting, data privacy con- cerns, proprietary data, and lack of data repositories are impediments to data sharing. Data standardization and open access are important since the output of one system model will likely be the input to a different system model. Lack of Methods for Integrating Models Better methods for integrating subsystem models across different spatial and temporal scales and for integrating physics-based mechanistic models with empirical/statistical models or economic models are needed (Kling et al., 2017). Mechanistic (biophysical) models of the key components of a single system may be possible, but adding only a few additional nodes to a system analysis creates significantly more connections that may each take many years of research to achieve (see Box 8-1). This precludes the abil- ity to create biophysics-based mechanistic models for the food system. The problem of scale is exacerbat- ed by the fact that many different forms of perturbations can affect the systems simultaneously (e.g., soci- ety’s values and climate). Each of the system nodes has complicated feedback loops that will need to be considered together in a systems-level analysis. Tackling this challenge will likely require the develop- ment of empirical (statistical) models, rather than purely mechanistic ones. The spatial resolution of many components of the food system may be quite coarse (e.g., soil moisture determined from satellite sensing has kilometer to tens of kilometers scale resolution), which will require averaging, simplifying assump- tions, and conducting statistics to downscale the models for more-local predictions, including analysis of differences within and between regions. However, better on ground sensing as described in Chapter 3 may improve this situation. Methods to blend these two types (and scales) of models (mechanistic and empirical) and data are needed. 108 Prepublication Copy

A Systems Approach BOX 8-1 Systems Aspects of the Food System Scale is a complicating factor in systems analysis of the food system (see figure below). Signifi- cant research has aimed at understanding the biology, chemistry, and physics to create and validate biophysics-based mechanistic models of selected subsystems. These are typically small-scale models including only the key drivers of the system. There are also models of large-scale systems, for exam- ple, global increases in temperature or precipitation due to increased CO2 in the atmosphere. These models are physics-based but often semi-empirical or data driven, rather than purely (first principles) mechanistic, and require averaging over large areas, simplifying assumptions, and statistics. The re- search community needs to effectively bridge these scales. However, coupling systems having only a few additional elements creates the need to understand significantly more causal relationships, each of which make take many years of research to achieve. The problem of linking process across scales is not unique to the food system. Overarching questions include the following:  How to bridge across scales, including spatial-temporal scales as well as with the number of pro- cesses included in the “system” being studied?  How to link mechanistic models developed for smaller, better-defined subsystems with large-scale statistical models used to predict macroscopic behaviors such as global carbon storage in soils? Updating Models Sensor development and movement toward data-driven empirical models for the food system will require massive amounts of high-quality data to calibrate the models. The current approaches for integrat- ing data into models to update model parameters are slow. Rapid (perhaps real-time) updating of the models with data is necessary, whether the subject is water- and nutrient-use in crop agriculture, feed effi- ciency in animal agriculture, or food safety and quality in food processing and distribution. 2.2 Challenge 2: Uncertainty Within and Across Multiple Elements of the System The high degree of uncertainty in the food system is a significant challenge for developing accurate models. Uncertainty is especially challenging for long-range strategic planning efforts to cope with a growing population. Uncertainty comes in many forms, for example, it is difficult to predict extremes in environmental conditions (water, heat), yields, pricing, and market conditions. While it is also difficult to predict the behavior of growers and consumers related to technology adoption and new inputs, it is im- portant for the models to take behavior into account. Incorporating information on adoption behaviors and market response is required to best quantify response to the effects of technology changes, and useful ex- amples include recent applications to water (Khanna and Zilberman, 2017; Taylor and Zilberman, 2017; Prepublication Copy 109

Science Breakthroughs to Advance Food and Agricultural Research by 2030 Zilberman et al., 2017). There is a lack of systems-level efficiency data (e.g., water-use or nitrogen-use efficiency at the watershed scale) available to calibrate the models. High degrees of uncertainty across multiple system elements can make accurate predictions of overall system behavior inherently indetermi- nate. 2.3 Challenge 3: Access to Agricultural Systems Models and Better Integration of Management and Society into the GEMS Model Agricultural systems modeling is a well-established field. Significant progress has been made in un- derstanding the biophysical processes driving agricultural productivity (Jones et al., 2017), and integrat- ing biophysical and economic models for analysis (van Wijk et al., 2014). To date, modeling efforts have focused on understanding the linkages between system components and drivers of the system responses. Challenges remain in making these models more accessible to users, in creating models that can address specific user needs (e.g., including interactions between multiple crops and between crops and livestock), and in developing user-friendly decision tools (Antle et al., 2017). Another challenge in agricultural sys- tems modeling is in data management. There are vast amounts of farm-level data available, and effective- ly storing, validating, and sharing that data is crucial to developing the next generation of agricultural sys- tems-level models. The GEMS (genotype-environment-management-society) approach to modeling agricultural sys- tems offers great opportunities to improve system efficiency by acknowledging complex interactions. Ex- panding the existing systems models to include additional societal drivers would enable better predictions of behavior in the food system, and are necessary to invoke meaningful changes to the system. For exam- ple, food choices and behaviors affecting food waste could have significant impacts on the overall sus- tainability of agriculture. The price of commodities is driven by policies (e.g., biofuel mandates), technol- ogy choice, as well as consumer demands, which in turn affects the mix of crops selected by growers. These do not always align with climate or resource availability if prices and other factors do not fully re- flect the opportunity cost of alternative uses. 3. OPPORTUNITIES This section includes 3 opportunities to advance the systems approach for food and agriculture. Breakthroughs in agricultural research will come from collaborative efforts of scientists and experts in fundamental and applied science areas, policy, and human behavior. An example of systems thinking and integrated systems modeling for watershed management is noted in Box 8-2. 3.1 Opportunity 1. Complex System Modeling to Determine Methods for Integrating Data, Models, Management Strategies, and Socioeconomic Behaviors Development of data-driven integrated food system models will inform future research efforts to- ward the most promising agricultural breakthrough opportunities to be pursued, or the best management practices for efficient resource use. The process of continuously updating and validating models with data will likely lead to more mechanistically based data-driven models and advancements in minimizing and managing uncertainty within and between systems to improve forecast reliability. Matching model com- plexity to scale and to user needs can result in better decision support tools for producers. As previously mentioned, the study of the food and agricultural system as a complex system serves the ultimate goal of informing decision making and—specifically relevant to this report—of evaluating the impacts of technological breakthroughs on the sustainability of the food system. The complexity of the food system, the integration of its various systems including the natural and built environments, and the significant need to increase its resilience and efficiency make the food system and its subsystems an ideal model for developing the capacity to model complex systems in general. Initiatives to study the food system provide the opportunity to determine appropriate boundaries for analysis within and between sys- 110 Prepublication Copy

A Systems Approach tems, to identify the key data gaps and missing linkages within systems and at the boundaries of different systems, and to identify and build the required systems analysis and decision support tools for complex agricultural systems. There is an opportunity to identify appropriate “behavior” models to include in agri- cultural systems models, and to standardize different system models through model inter-comparisons using “base-case” scenarios for model comparison. As weather and climate models increase resolution and better represent small-scale processes such as vegetative feedbacks, it will open research into soil- plant-atmosphere-water cycling from field to continental scales. Modeling the system response to climate extreme events can improve the utility of the models to growers. Scaling up models from field scale to landscape scale can also help to manage agroecosystems coupled with natural ecosystems using ecohy- drology methods. 3.2 Opportunity 2: Pioneer New Methods for Improving Data–Sharing and Privacy Issues for Public–Private Partnerships As previously noted in Chapter 7, data-driven approaches to managing agriculture offer unprece- dented opportunity for increasing the sustainability of the food system. The availability of open, harmo- nized data has already proven to be essential to creating models of the food system (Jones et al., 2017). Continued development and applications of these approaches to the food system provides an opportunity to discover new methods of data collection, provenance, and sharing. It also provides opportunities to in- tegrate data into models in real time, and to develop machine learning and artificial intelligence approach- es for managing inputs and outputs along the food value chain. There is a great opportunity to advance systems modeling by developing methods to integrate both public and private data for use in democra- tized decision support tools (Antle, et al., 2017). 3.3 Opportunity 3: Apply Systems Thinking and Convergence Methods to Agricultural Sustainability Problems The food system touches on a wide range of issues including climate change, water resource use, plant and animal genetics, food safety, nutrition, policy, and economic sustainability. The focus on indi- vidual subsystems, without consideration of the interconnectedness of the subsystems, has led to unin- tended consequences to other components of the system (Turner et al., 2016). In other words, approaching agricultural research more holistically from a system of systems perspective offers the potential to opti- mize across multiple components of the system. The basic tenet of “systems thinking” is recognizing the interconnected nature and linkages that cross multiple dimensions (subsystems). There is also tremendous opportunity to promote convergence of knowledge to solve agriculture’s most vexing problems (e.g., wa- ter use, soil degradation, and food waste). For example, combining expertise from plant biology, microbi- ology, soil science, nanotechnology, sensor design, wireless communications, and behavioral sciences may lead to innovative and socially acceptable methods of applying nanotechnology-based sensors into plants and soils to monitor plant productivity without negatively affecting the plants, environment, or the ability to sell the food produced. Combining expertise may help to convert “waste” products can to value- added products or to important sources of inputs and energy to other parts of the food system. Programs aimed at tackling all of the elements of these problems will require better integration of fundamental sci- ence and engineering with economics, medicine, behavioral sciences, and social sciences. New knowledge and research is needed on how to design technology packages and institutional structures to enhance technology adoption and new processes. Research in systems-thinking approaches to agricultural problems will ultimately trickle down into new educational programs and into practice as those graduates enter the profession. Prepublication Copy 111

Science Breakthroughs to Advance Food and Agricultural Research by 2030 BOX 8-2 Example: Management Solutions to Decrease Hypoxic Zones The formation of hypoxic zones in drainage basins provide a good example of where systems thinking and integrated systems modeling can be used to identify optimal (lowest cost) management solutions. Rabotyagov et al. (2014) developed an integrated assessment model to determine the im- pact of different cropland conservation investment decisions in more than 550 agricultural subwater- sheds that deliver nutrients into the Gulf of Mexico on the aerial extent of hypoxia. By considering the whole system simultaneously, the location, type, and extent of interventions needed to achieve a de- sired reduction in aerial extent of hypoxia in the Gulf could be estimated. The optimal locations of in- tervention and the trade-offs between cost and outcome were determined (see Figure 8-1). The ap- proach is extensible and can include other outcomes, for example, ecosystem services, impacts on water quality, and soil conservation. This enables creation of a multidimensional trade-off frontier that could be used to make more–informed policy decisions. FIGURE 8-1 The trade-offs of cost and hypoxia by various cropland conservation scenarios across subwater- sheds. Selecting the most cost-effective assignments for conservation in each subwatershed provides the lowest cost to achieve the state goal of keeping the hypoxic zone under a total area of 5,000 km2. SOURCE: Rabotyagov et al., 2014. 4. BARRIERS TO SUCCESS Barrier 1: Data provenance, security, sharing, and costs of collection and storage. Data-driven approaches to manage the food system offer great potential to improve resilience and sustainability. How- ever, issues around governance and the development of a viable business model for democratizing and sharing the benefits derived from those data are significant barriers to success (Halewood et al., 2018). Institutional and policy challenges are needed to promote the formation of public–private partnerships, determine ownership of resources, and manage sharing of benefits across countries. The lack of appropri- ate platforms, standards, and incentives for sharing of data and models is also a substantial barrier to suc- cess. Barrier 2: Systems are inherently indeterminate. There need to be realistic expectations about the applicability and accuracy of the food system models. As Box 8-1 showed, as more subsystems are in- cluded in the analysis, the number of subsystem interactions increases considerably. The causal relation- ships between many elements of the food system are not well characterized. Moreover, the high spatial and temporal variability within these systems and between the systems and the environment, as well as the unpredictability of human behaviors, may simply make the system too complex to model with reason- 112 Prepublication Copy

A Systems Approach able accuracy. As an example, the predominant methods for irrigation in water-limited western states are flood and furrow irrigation systems. Although these are highly inefficient systems, farmers fear that switching to more efficient sprinkler and drip irrigation systems will erode their stake in and ownership to their water rights (i.e., the “use it or lose it” syndrome), which is likely the reason many farmers hold back from making changes to more efficient irrigation systems (Osborn et al., 2017). Evaluating the ef- fects of policy-induced changes to incentives for switching to the more efficient irrigation systems re- quires the development of coupled models that account both for the geospatial component and water us- age as well as for human behaviors related to adaptation and induced technological change. Barrier 3: Limited incentives to consider systems-level impacts. Policies and regulations affect- ing agriculture (e.g., water policy, water quality criteria, and nutrient management) are often made with- out consideration of their impact on the system (Alfredo and Russo, 2017). The research enterprise has not been structured with the goal of applying systems approaches to problem solving across the span of the food and agricultural system, and there are limited incentives for research cooperation among disci- plines. Barrier 4: Unintended consequences are not immediately identifiable. Unidentified risks in any system or system of systems are unavoidable. The more complex and interrelated the system becomes, the greater the potential for unidentified risks. Although it is impossible to identify all of the risks of any sys- tem, a move toward systems thinking in food and agricultural research will help to reduce any unintended consequences due to interventions—for example, altering photosynthesis routes, manipulation of the soil microbiome, or environmental costs of information and communication technologies associated with widescale deployment of autonomous data-collecting sensors. 5. RECOMMENDATIONS Recommendation 1: Identify opportunities to improve the performance and adoption of integrated systems models of the food system and decision support tools. The community of stakeholders should identify current barriers to successfully applying integrated systems models to the food system. The community should also develop a roadmap to overcome key challenges specific for modeling the food system (e.g., integration of sub-systems, standardization and interoperability, data storage and sharing, data infrastructure, simplified decision support tools, crop-crop and crop-livestock interactions, validating the performance of complex systems models using data). The community should clearly delineate where the inability to model the integrated system components is limiting progress toward sustainability and resilience, rather than where other barriers exist such as poor policies or undervaluation of natural re- sources. Once the key systems and system of systems to be modeled are determined, the research community needs to improve understanding of the drivers behind the subsystem interactions. This will require im- proving the ability to collect and analyze large amounts of data with high geospatial and temporal varia- bility (e.g., sensor development). Connecting the submodels into more integrated systems models will also require consistency in applying system boundaries and assumptions and scale of analysis (Kling et al., 2016), and well as more standardized data reporting and model input/output to make the subsystems interoperable. These integrated system models can then be used to direct research needs and to avoid un- wanted consequences of technology interventions or policies. Recommendation 2: Incorporate elements of systems thinking and sustainability into all aspects of the food system (from education to research to policy). A paradigm shift is needed in the management of the food system to anticipate the effects of environmental or policy-induced changes. This will be a fun- damental change in the way the food system is viewed, and the way stakeholders are educated to operate. In research, it will require more transdisciplinary research and team science around approaching and working to solve problems in the food system. It will also require more integrative, systems-level ap- proaches that can assess policy alternatives for systems-level sustainability outcomes. It will also require adequate incentives and funding for systems-level convergent approaches to research and education. Prepublication Copy 113

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For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate.

More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face.

Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030).

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