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Animal Production Systems: Synthesis of Methods to Determine Triple Bottom Line Sustainability from Findings of Reductionist Research


Engage collaborators from the needed broad range of disciplines, institutions, and stakeholder groups to catalyze conceptual and quantitative synthesis, collaboration, and data sharing Facilitate organization, synthesis, and integration of component-based research findings and supporting data and Discover (or reveal), substantiate, and interpret the broader impacts of component-level modifications to animal-production systems.

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<p>NON-TECHNICAL SUMMARY:<br/> As a result of population growth and increases in caloric intake associated with increasing income to expend on food (FAO, 2008), food demand is expected to increase by 70% by year 2050. The demand for animal protein is expected to outpace the growth in total food consumption. However, at the same time, the quantity and quality of available land, fresh water, and energy resources are declining. Furthermore, consumers increasingly want to know how their food is produced, and as they learn, their evolving product preferences create demand for different production practices with respect to (for example) food safety, nutrition, animal welfare, and environmental protection. Balancing accelerating global demand for animal protein with finite production resources, vulnerable environments and ecosystems, economic viability of allied industries and
surrounding communities, and social acceptance of food-production practices requires an approach unlike what has been used in the past. Results from reductionist research that addressed individual or isolated components of livestock- and poultry-production systems must now be integrated in ways that reflect the complexity of the systems as a whole. A systematic and holistic approach to livestock production is the only possible means of continuously meeting global food needs while 1) protecting natural resources such that soil health, water quality and quantity, species diversity, air quality, and climate homeostasis are sustained 2) producing animal products in a manner that is socially acceptable to consumers, and 3) ensuring continued financial solvency of farm operations. The challenges faced by the livestock industry are complex and intertwined. Therefore, approaches to addressing
the Triple Bottom Line (TBL) of sustainability that is, sustainability as expressed in the ensemble of planet (environmental), people (social), and profit (economic) must be comprehensive and reveal the broader, system-wide impacts of decisions. Through the efforts in this project, we propose to evaluate more comprehensively how animal protein production practices impact all facets of the CLD by providing a more integrated view of the system. The long-term goal of the project team is to identify strategies to optimize animal protein production by balancing environmental, social, and economic drivers and effects. The overall objective of this proposal is to construct and develop an increasingly quantitative framework that conveys the system-wide impacts of decisions and the tradeoffs that result from scenarios under consideration. These tradeoffs will include environmental, social, and
economic impacts. Standard reporting measures and units (media, substances, units) to quantify impacts of animal production and effectiveness of management alternatives. Expected outcome of this project include (1) quantitative tools to evaluate pollution modifications (increases or decreases), and (2) verified systems-level model(s) to be used to evaluate the ecological impacts of changes in the animal agriculture industry. These models and the research they will inspire will lead to reduced inputs to food animal production, greater reuse and recycling of manure resources and improved environmental quality.<p>
APPROACH: <br/>OBJECTIVE 1. Facilitate organization, synthesis, and integration of component-based research findings and supporting data. The overarching goal for objective 1 is to develop trans-disciplinary research teams that are highly collaborative but also heterogeneous in that different team members may work together at different times. Task 1. Design, and find a host for, a publicly accessible database for sharing peer-reviewed, published project data that facilitates integration of system components. Sharing ideas and data is at the heart of collaborative research and development and this project team will develop a database design to support this collaboration. The project team will select parameters and appropriate units for various animal production components or unit operations such that other team participants can subsequently use or add to those results.
Identifying the parameters and units will be a primary objective of the first annual meeting. The database will be publically accessible and searchable; records will be downloadable in usable electronic form. Task 2. Design and find a host for a database accessible to project participants describing ongoing projects and data currently being collected. Another tool to enhance collaboration among participants will be a database, accessible internally to participants, that describes specific interests, special capabilities, and current projects of each participant. The types of information expected to be included here are specific research topics each participant is interested in, equipment, measurement systems, or other resources that would be useful in specific circumstances; and ongoing projects, including current data collection (parameters, functional units). With input from committee
membership and as a consequence of the annual station reports, the project chair will track the range of data types and the number of entries in the databases as a means of assessment and include the results in the annual report. OBJECTIVE 2. Discover, substantiate, and interpret the broader impacts of component-level modifications to animal-production systems. We will formalize various models, devise meaningful and measurable conceptions of sustainable livestock and poultry production, select and apply the appropriate quantitative, system-level modeling tools, and evaluate the system-level implications of information gained from our ongoing, reductionist research. Task 3. Progressively refine a modeling framework, from the conceptual to the quantitative, that describes the relationships between (a) increasing demand for animal protein produced in the United States and (b) the social,
economic, and environmental subsystems that sustain animal-protein production for the long term. We will (a) refine or replace our sustainability CLD, (b) specify the new analytic and synthetic expertise that will help us accomplish Objective 2 tasks, (c) identify structural and strategic means of improving committee operations, and (d) develop recruiting, retention, and reproduction strategies to sustain our committee's vitality for the longer term. We will learn and adapt analytic and synthetic approaches from new bodies of knowledge to integrate process-based research findings into the system framework. There may be several different forms of a generalized connectivity matrix that map important information from our CLDs. As our mechanistic understanding of the relationships between adjacent nodes is progressively developed and refined by process-level research that is, as we fill
the cells of the various forms of connectivity matrices we will document those refinements by populating a bibliographic form of the matrix with the applicable citations of peer-reviewed research. The outcome will be the systematic refinement of our whole-system CLD, transforming it from a conceptual network of nodes and linkages into an increasingly organized, quantitative, and well documented network. The CLD will be mapped to a library of closely related connectivity matrices, commonly available to all participants that contain the polarities, mechanistic descriptions, and bibliographic documentation of the relationships between adjacent nodes. Task 4. Build a dynamic, system-level, modular, simulation-modeling framework to project the concurrent flows of mass, energy, money, and various other fundamental currencies within our TBL-sustainability domain. We will build/adapt
engineering-based modules to simulate the performance of manure- and/or waste-management processes (e. g., anaerobic digesters, holding ponds, lagoons) and environmental-control systems (e. g., heating, ventilation, evaporative cooling). These, along with other modules from other disciplines from economics to sociology to soil and atmospheric sciences, will be built or adapted in accordance with an increasingly formalized, quantitatively unified framework that ensures the modules can be used together in arbitrarily large, complex simulation models. In addition, where sophisticated, modern modeling environments already exist (e. g., GAMS for economic optimization modeling, ArcGIS for spatial modeling), we will seek to design and build into our modeling framework - or adapt, as appropriate tools become known to us - a suite of tools by which our system models can call other modeling
environments as subroutines. The deliverables may be conveniently viewed as a growing, increasingly sophisticated and powerful library of sub-models of raw materials, feedstuff production, animal production, waste management, natural resources, market dynamics, and social dynamics that represent the quantitative relationships and networks in our governing, evolving CLD. Task 5. Perform dynamic simulation of subsystems to evaluate effects of newly specified causal linkages, research findings, and policy options. As project participants contribute to the enrichment of the conceptual modeling framework with refined variable sets and mathematically specified causal relationships defined in their own reductionist, process-oriented research programs, we will be able to build, test, and evaluate functioning models of dynamic systems of increasing scale and sophistication. We will collaborate
with other participants to prepare proposals to funding institutions for dynamic simulation of subsystems, including e. g. NSF's Research Collaboration Network (RCN) and Sustainability Research Network (SRN) programs and USDA's National Needs Fellowships (NNF) program. At our annual meetings and during mid-year teleconferences, we will identify research gaps within our modeling framework, available funding sources to underwrite the work, and teams of collaborators to write responsive grant proposals.</p>

Zhang, Ruihong
University of California - Davis
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