Progress 01/30/24 to 01/29/25
Outputs
Target Audience:The team invited the following stakeholders to the November 19 engagement event, titled "Interventions to Build Resilience in the Animal Protein System": Ellen Lai (ABS) Janice Rueda (ADM) Nicole Buckley Biggs (AgriWebb) Scott Higgins (American Dairy Association Mideast) Sarah Novak (American Feed Industry Association) Keith Dillon (ASN) Gwen Twillman (ASN) R Cook (Bamert Seed) Jeff Clark (C-Lock Inc.) Michelle Grogg (Cargill) Amanda Bushell (Context Network) Sarah Frank (Context Network) Matt Sutton-Vermeulen (Context Network) Kelsey King (Dairy Farmers of America) Michelle Schack (Dairy Kind) Rashel Clark (Dairy West) Fabian Bernal (DMI) Reza Afshar (DMI) Andrew Coffey (DMI) Lisa McComb (DMI) Alyssa Sundell (DMI) Karen Scanlon (DMI & Innovation Center for US Dairy) Maggie Monast (Environmental Defense Fund) April Stewart (ex-Better Cotton Initative US Ops Lead) Danielle Burke (Fairlife) LaKisha Odom (FFAR) Kathy Boomer (FFAR) Constance Awuor Gewa (FFAR) Amy TePlate Church (Food Integrity) Meredith Ellis (G Bar C Ranch) Angela Ailloni (Ginkgo Bioworks) Mitch Kanter (Global Dairy Platform) Donald Moore (Global Dairy Platform) Ashley McDonald (Grasslands Coalition) William Fox (Grazing Lands Coalition) Abby Bauer (Hoard's Dairyman/J of Nutr Mgt) Rick Naerebout (Idaho Dairyman's Association) Lara Moody (IFEEDR) Sylvia Rowe (Independent Scientist) Angela Hessinger (Innovation Center for US Dairy) Sarah Fox (JBS) Jason Weller (JBS) Sarah Klopatek (JBS) Sarah Alexander (Keystone) Jonathan Geurts (Keystone) Brad Sperber (Keystone) Cynthia French (LR Bioenergy) Jenni Tilton-Flood (Maine Dairy Promotion Board/Maine Dairy Nutrition Council) Jordan Sabine (McDonald's) Julie Mattson Ostrow (Midwest Dairy) Lauren Servick (Minnesota Pork) Mitch Breunig (Mystic Valley Dairy) Jamie Burr (National Pork Board) Marguerite Tan (National Pork Board) Jeff Piggot (National Pork Producers Council) Dan Raiten (NIH) Shelly Mayer (PDPW) Bill Salas (Regrow) Ying Wang (SAI Platform) Klaus Kraemer (Sight & Life) Chris Gambino (The Breakthrough Institute) William Burnidge (TNC) Sasha Gennet (TNC) Clare Kazanski (TNC) Naglaa El-abbadi (Tufts U) Justin Ransom (Tyson Foods) Shelby Krebs (U Minnesota) Joanne Slavin (U Minnesota) Pedro Urriola (U Minnesota) Kimberly VanderWaal (U Minnesota) Natalie Lounsberry (U New Hampshire) Frank Mitloehner (UC Davis) Jolene Griffin (United Dairy Industry of Michigan) Janice Giddens (US Dairy Export Council) Kelly Sheridan (US Dairy Export Council) Samantha Werth (US Roundtable for Sustainable Beef) Rebecca Bolton (US Roundtable for Sustainable Beef) Megan Farley (US Roundtable for Sustainable Beef) Naomi Fukagawa (USDA) Diane DeBernardo (USDA) Mike Grusak (USDA) Gregory Bohrer (Walmart Foundation) Emily Moberg (WWF) Shari Westerfield (Zoetis) As we continue to improve the FoodS3 model, we also continue to have outreach conversations with interested stakeholders. In the last year these included conversations with: Carlos Suarez (US Grains Council) Brooke Wynn (Smithfield Foods) TJ Flax (DTN) Kristen Wharton (American Egg Board) Kris Johnson (TNC) Jason Weller (JBS) Niro Johri (KFC) Angela Hessinger (Innovation Center for US Dairy) Dana Boyer (Cargill) Suzy Friedman (WWF) Simone Schenke (EDF) Erin Sweeney (PepsiCo) Jaycie Thomsen (Environmental Initiative) Ann Marie Carlton (U of CA, Irvine) Marty Heller (Blonk Sustainability) Changes/Problems:The year 2 expansion of the system dynamics modeling in support of the project has served a significant role in the stakeholder engagement process. The expansion of FoodS3 is guided by the feedback received during the workshops that have been informed through the system dynamics simulations. Because of this, and the personnel change at UMN in year 1 and 2, some funds were released back to CSU to support Dr. Sheehan's modeling efforts. As indicated in our accomplishments' sections above, we have realized that remote sensing is not feasible for lime application (Task 3.2) and training data for manure management classification is likely unavailable (Task 3.1). As such we will not be able to accomplish those tasks. What opportunities for training and professional development has the project provided?UMN has a postdoctoral researcher (Dr. Aurup Ratan Dhar) directly funded on this project, and another (Dr. Will Lockhart) working on the model underlying this work. Both postdoctoral researchers are part of the University of Minnesota's Institute on the Environment's Postdoctoral Fellowship program that creates a cohort of postdocs for peer mentorship and networking, hosts monthly professional development events, and works with each postdoc on individualized professional development plans. Stanford provided an opportunity to three Master's students to conduct a quarter-long research project on the prediction of animal farm headcount from satellite imagery. How have the results been disseminated to communities of interest?On July 10, a blog drafted by Gustafson and CSU team members (Thoma and Sheehan) was published by CSU AgNext, titled: "Research Team Led by AgNext Plans to Develop New Predictive Models Based on Data Science and Machine Learning to Maintain Resiliency and Sustainability in the Animal Protein System.' On July 26-27, team member Gustafson represented the DSAPS team by presenting a poster and attending the NIFA Project Directors meeting in Manhattan KS. On November 13, team member Schmitt was an invited speaker at the inaugural Carbon Accounting in Agricultural Supply Chains Summit in Chicago, IL where she presented work connected to this grant titled "Leveraging the FoodS3 Model to Estimate GHG Emissions & Enhance Supply Chain Connectivity" On November 19, the team held its second annual stakeholder engagement event, titled "Interventions to Build Resilience in the Animal Protein System," alongside the Sustainable Ag Summit (Nov 20-21, 2024) in Minneapolis. This project is part of the larger Food System Supply-chain Sustainability (FoodS3) platform at the Institute on the Environment at the University of Minnesota. In November 2024 we launched a FoodS3 website: foodscubed.umn.edu, to describe the model and our research (including work on this grant). This public facing website has lowered the barrier to learning about FoodS3 and made it easier to field inquiries about our research. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: As described in the project timetable, the team will host a third stakeholder engagement event, most likely again alongside the Sustainable Ag Summit, currently scheduled to take place in Anaheim CA (November 19-20). The primary purpose of this event will be to collect stakeholder feedback on the overall findings of the project, and especially on the planned-near-final draft of the paper currently titled: "Resilience in US animal protein systems can be enhanced via multiple scale-appropriate interventions." Objective 2: We are working to publish our facility optimization methodology (Task 2.1) and will finalize the data inputs required for an annual timestep for our model (Task 2.2). We will also continue gathering the data required for estimating county milk consumption and research the material flows of milk products from raw milk processing to household consumption (Task 2.3). Finally, we will draft a paper on the US dairy supply chain from feed to household based on our FoodS3 model updates (Task 2.3) The prototype system dynamics model was well received by stakeholders, who encouraged the team to continue developing it. Therefore, further development of the prototype will continue in parallel with the work on FoodS3expanding the model to include economic and inventory dynamics driven by supply, demand and price dynamics. We will evaluate whether anticipated Improvements in remote sensing of methane emissions will support direct remote measurement of manure management facilities in the near future. Such ability would bypass the need to model manure management emissions and overcome our current data limitations around estimating manure management types. Objective 3: We continue looking into Climate Trace data as an option as a training source for a machine learning model to estimate dairy and hog manure management types (Task 3.1). Objective 4: We will continue looking into relevant data sources on food affordability (Task 4.1) and jobs and income (Task 4.2). We will determine how we can reasonably include these metrics into the FoodS3 model. We have added protein quality to our FoodS3soybean data enabling us to trace protein content through the supply chain. This work is in a draft paper we plan to publish during the final year of this grant. This final year we will also be looking into milk-fat protein estimates in connection with Task 2.3. (Task 4.3) Objective 5: Team member Gustafson has circulated a draft outline for a new paper titled: "Resilience in US animal protein systems can be enhanced via multiple scale-appropriate interventions." It is intended to serve as a follow-up to a paper recently published by team member Olson and several of her colleagues during YR2: "Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures." While this new paper (Olson et al., 2024) was not a specific output of the DSAPS project, it utilized the FoodS3 model and several of its underlying data sources have now been utilized by team member Sheehan in his Systems Dynamics modeling work. Gustafson has reached out to several potential co-authors beyond the DSAPS team to gauge their interest in joining as co-authors of the proposed paper: members of the Field to Market Metrics Committee, co-PI's on another NIFA-funded project ("Diverse Corn Belt"), as well as attendees of the two DSAPS stakeholder engagements events. Several of these individuals have expressed interest. During a series of calls initiated in December 2024, Gustafson is now leading the prospective co-authors on monthly calls to review progress on the paper. A draft outline as been circulated for this paper and is presented below: 1 Background 1.1 Borrow heavily from DSAPS proposal language 1.2 Update based on recent relevant literature (an annotated table of relevant recent literature is included in the circulated draft) 2 Methods 2.1 Propose a financial-based metric for quantifying resilience benefits, per Lindbloom, see below [Resilience Metrics] 2.2 Collection of stakeholder input on relevant shocks, interventions, etc. (already done via the pair of DSPAS stakeholder engagement events) 2.3 Stella prototype - John Sheehan (CSU) 3 Results & Discussion (TBD) Resilience Metrics: 1 For the row cropping part of the overall system, score resilience based on field-scale interventions that build economic and environmental resilience (e.g., conservation tillage, cover crops, diversity of crop rotation, see next slide). 1.1 Per Lindbloom, use the "triangle method" to calculate the relative financial impact (% reduction of income loss) associated with the adoption of field-scale interventions. 1.2 Parameters calculated using statistical analysis at county, state, and national scales based on public USDA data, primarily at ERS. 2 An analogous metric could be applied at consumer end of the APS to look at reductions in price spikes or dips in per-capita protein availability.
Impacts
What was accomplished under these goals?
In 2023, we presented a simple system dynamics model of the US beef sector to industry stakeholders. The model included the cow-calf operation, stocker operations, feedlots and slaughter operations and the consumer market. Real-time testing with the stakeholders generated numerous insights about the supply chain, as well ideas for how to improve the model to allow a more in-depth understanding of the supply chain and ways that its resilience to shock could be improved. Over the past year, we have takenthese ideas into account, and began work on a more detailed and rigorous model. The updated model, which is still a work-in-progress, includes the following improvements: 1) regionalization, 2) explicit modeling of the stocker phase, 3) more operational detail in the cow calf and feedlot operations, and 4) inclusion of dairy operations as a major source of cows and calves. The original model was based on historical data at anational scale. Stakeholders felt that this ignored the important regional variability in the nature and behavior of the beef supply system. To address this, we broke the model into seven regions based on designations used by the National Cattlemen's Beef Association (NCBA). Each NCBA region was characterized by using historical data in annual calf, cow and steer populations in each phase of the supply chain. The model uses state-level annual breed herd sizes aggregated to the regional level to predict rancher, stocker and feedlot populations, as well as annual throughput in slaughter operations. The model does a good job of predicting annual calf crops, as well as the movement of animals through the supply chain, compared with historical data. An important insight from the model is that it can be used to predict the relative movement of animals across regions. For example, the model demonstrates that feedlot capacity in region I (the US northeast) is far lower than the expected number animals generated from cow calf, stocker and dairy operations. This implies that NCBA region I exportmost of its stockers to another region. Its neighboring region in the upper Midwest (NCBA region III) has excess stocker capacity and likely accepts many of these stockers. Similarly, NCBA region II (US southeast) has little stocker and feedlot capacity, and clearly must export much of its calf crop to other regions such as Region IV (US South). Using capacity and population data, we now have a way to estimate the flow of animals among all the regions. This is important in understanding the effects of drought, which is regional. Next steps include expanding the model to include economic and inventory dynamics driven by supply, demand and price dynamics. During YR2 of the project, we updated our list of stakeholders to include a total of 90 individuals. They were all invited to attend our second in-person engagement event, held on 19 Nov 2024 in Minneapolis, again alongside the Sustainable Ag Summit, which many of these stakeholders generally attend. The event was titled "Interventions to Build Resilience in the Animal Protein System" and (just as in the 2023 event) was attended by around 25 stakeholders. As in the 2023 gathering, very helpful feedback was received and is briefly summarized below: 1 Landscape trends are as important as shocks 1.1 Economic competition from other crops, urban, solar, etc. 1.2 "Natural" factors, esp. woody encroachment & noxious weeds 2 Shocks in feed and key ingredients are now top of mind 2.1 China is a major source of essential amino acids and vitamins 2.2 Biofuel policy has the potential to disrupt feed supply 3 Animal disease is also top of mind 3.1 System Dynamics models have a key role to play in optimizing responses 4 AI, robotics, and other technology will be costly and take many years to address looming workforce challenges Task 2.1: We have been working to update the beef, broiler, hog, and raw milk primary processing facility capacities to an annual timestep. Our most significant output has been updating FoodS3 with the USDA 2022 data, including facility estimates. Our previous work had facility capacities estimated every five years using a combination of datasets from industry reports detailing total production output by company and/or capacity depending on the livestock category, USDA ERS reports detailing the state and national total slaughter by livestock category, and satellite imaging to identify relative sizes of facilities in order to allocate between facilities. To standardize our methodology across animal facility types, we have improved our facility capacity estimation by aligning our data types for company data, standardizing the usage of USDA and Reference USA data, and using a consistent linear optimization to fill gaps in data. We are finalizing this update and have a draft paper outlining this new methodology that will provide annual facility estimates of production from 2017 to 2022. We also explored the idea of using imagery from Planet satellites to monitor individual locations. However, Planet satellites only see each facility twice a day. At best this data could provide a sense of monthly variation in slaughter capacities. We have decided that existing satellite data is not useful for estimating slaughter capacity for this project. Task 2.2: We are also working to "reconstruct the FoodS3 commodity flow transport optimization model's foundational structure to use monthly input and output information at livestock production, processing, and consumption nodes, to provide monthly origin-destination estimates at the commodity level." Toward this task we have assessed which data for which commodities is available at the monthly time-step in USDA NASS. We are currently assessing similar data in other USDA datasets (ERS, ARMS). Task 2.3: We have begun work on adding milk to consumers in the FoodS3 model, largely by improving our capacity to do this through additional postdoc time and an undergraduate student researcher who will work on the raw milk processing to dairy product material flows. We have also finalized, but not yet submitted, the paper with the precursor methodologies for estimating milk flows, i.e. describing our model movements of meat to consumption. Task 3:1: A team of students and a research assistant at Stanford University worked under the supervision of Dr. Lobell to develop models to estimate the number of animals in dairy, beef, and poultry farms. The model was primarily developed with data in California but subsequently tested in other states, showing reasonable performance. Our initial work focused on estimating headcount as some ground-based estimates were available for training and validation. However, despite the success of this work we have not been able to find training data for machine learning to identify manure management types and we were unsuccessful in two rounds of trying to hire undergraduate students with the knowledge and ability to create this training data.We did identify a potential existing source of such data, Climate Trace. Task 3.2: In FoodS3, over 80% of the full cradle-to-farm gate life cycle GHG emissions from crop production have been estimated using spatially explicit county-scale data for corn, soy, winter wheat, spring wheat, and durum wheat. A key remaining emission source that is likely to vary substantially across production regions is Lime application. However publicly available lime data is only available onthe national scale. Our team has reached out to two major agribusinesses about sharing their lime application data, without success. We also looked at planet imagery in Georgia to see if the brightened visual signature of lime application is visible. While we believe the signature is visible in some cases, there are other reasons that fields can be bright, and without ground data we are unable to use remote sensing and machine learning to improve spatial estimates of lime application.
Publications
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Progress 01/30/23 to 01/29/24
Outputs
Target Audience:We developed a list of 75 stakeholders, all of whom were invited to the first engagement event, held on December 5, 2023, in Charlotte, alongside the Sustainable Ag Summit (Dec 6-7), which many of these stakeholders generally attend. The in-person engagement event was entitled "Managing Shocks in the Animal Protein System" and was attended by around 25 stakeholders. The stakeholder list follows: Ellen Lai (ABS); Janice Rueda (ADM); Scott Higgins (American Dairy Association Mideast); Sarah Novak (American Feed Industry Association); Keith Dillon (ASN); Gwen Twillman (ASN); R Cook (Bamert Seed); Jeff Clark (C-Lock Inc.); Michelle Grogg (Cargill); Amanda Bushell (Context Network); Sarah Frank (Context Network); Matt Sutton-Vermeulen (Context Network); Kelsey King (Dairy Farmers of America); Michelle Schack (Dairy Kind); Rashel Clark (Dairy West); Andrew Coffey (DMI); Lisa McComb (DMI); Alyssa Sundell (DMI); Karen Scanlon (DMI & Innovation Center for US Dairy); LaKisha Odom (FFAR); Meredith Ellis (G Bar C Ranch); Donald Moore (GDF); Angela Ailloni (Ginkgo Bioworks); Ashley McDonald (Grasslands Coalition); Abby Bauer (Hoard's Dairyman/J of Nutr Mgt); Rick Naerebout (Idaho Dairyman's Association); Lara Moody (IFEEDR); Sylvia Rowe (Independent Scientist); Angela Hessinger (Innovation Center for US Dairy); Sarah Klopatek (JBS); Sarah Alexander (Keystone); Jonathan Geurts (Keystone); Brad Sperber (Keystone); Cynthia French (LR Bioenergy); Jenni Tilton-Flood (Maine Dairy Promotion Board/Maine Dairy Nutrition Council); Jordan Sabine (McDonald's); Julie Mattson Ostrow (Midwest Dairy); Lauren Servick (Minnesota Pork); Mitch Breunig (Mystic Valley Dairy); Jamie Burr (National Pork Board); Marguerite Tan (National Pork Board); Jeff Piggot (National Pork Producers Council); Dan Raiten (NIH); Shelly Mayer (PDPW); Nicki Briggs (Perfect Day, Inc.); Bill Salas (Regrow); Klaus Kraemer (Sight & Life); Chris Gambino (The Breakthrough Institute); Sasha Gennet (TNC); Clare Kazanski (TNC); Naglaa El-abbadi (Tufts U); Justin Ransom (Tyson Foods); Joanne Slavin (U Minnesota); Pedro Urriola (U Minnesota); Natalie Lounsberry (U New Hampshire); Frank Mitloehner (UC Davis); Jolene Griffin (United Dairy Industry of Michigan); Janice Giddens (US Dairy Export Council); Kelly Sheridan (US Dairy Export Council); Ying Wang (US Farmers & Ranchers in Action); Rebecca Bolton (US Roundtable for Sustainable Beef); Megan Farley (US Roundtable for Sustainable Beef); Samantha Werth (US Roundtable for Sustainable Beef); Diane DeBernardo (USDA); Naomi Fukagawa (USDA); Mike Grusak (USDA); Gregory Bohrer (Walmart Foundation); Emily Moberg (WWF); Shari Westerfield (Zoetis). Changes/Problems:UMN has had some project staffing challenges and consequential changes. UMN had originally had a life cycle analysis scientist (Dr. Rylie Pelton) included on this project. However, after the project was funded, and before it started, Dr. Pelton's employment effort at UMN went from 100% to 25%, drastically limiting her capacity to participate in this project. She is now only playing a minor advisory role. Furthermore, our original budget included funds for a software engineer who we had employed at the time of grant submission, but who had left before this grant began. Given the status of our team and the FoodS3 model at the time the grant started we pivoted from a software engineer to bringing on a postdoc. We began the process for hiring in March 2023, selected Dr. Dhar in August 2023 and he was only able to start at the end of November 2023. What opportunities for training and professional development has the project provided?UMN was able to hire a postdoctoral researcher (Dr. Aurup Ratan Dhar) in late November 2023 to join this project. While Dr. Dhar's time during the first year of the grant was minimal, we are excited to have his expertise on nitrogen and phosphorus footprinting in agri-food systems. Dr. Dhar's previous focus has been the Indian Subcontinent, and we are working to familiarize Dr. Dhar with the relevant agricultural and environmental impact data in the US. Stanford provided an opportunity to three Master's students to conduct a quarter-long research project on the prediction of animal farm headcount from satellite imagery. Dr. Lobell met with them weekly, and the work was written up and submitted to the ICML conference. How have the results been disseminated to communities of interest?The team held a December 5 stakeholder engagement event, entitled "Managing Shocks in the Animal Protein System," alongside the Sustainable Ag Summit (Dec 6-7) in Charlotte. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: As described in the project timetable, the team will host a second stakeholder engagement event, most likely again alongside the Sustainable Ag Summit, this year very conveniently located in Minneapolis (November 20-21). The primary purpose of this event will be to collect stakeholder feedback on the types of interventions that would be of greatest interest for us to include in the project. Objective 2: We will finalize our facility optimization (Task 2.1) and continue working on the monthly timestep data (Task 2.2), that will include bringing in the USDA's 2022 Ag Census data that was released in Feb 2024. We will also begin gathering the data required for estimating county milk consumption (Task 2.3). The prototype system dynamics model was well received by stakeholders, who encouraged the team to continue developing it. It was decided that, in addition to the value the simpler modeling framework brings to stakeholder engagement, the prototype approach would also be a powerful tool for guiding the implementation of dynamic elements in FoodS3. Therefore, further development of the prototype will continue in parallel with the work on FoodS3. Enhancements will include expansion of the model from its current national scale to a multi-regional scale, as well as addition of environmental impacts linked to the core supply chain. Objective 3: We will hire two summer undergraduate students to build out training data for being able to use satellite data and ML to detect hog and dairy manure management systems. With this dataset, we will then begin work on using satellite data to characterize the manure management systems for dairy and hog farms across the US (Task 3.1). We will also continue to monitor developments in this space by other groups, such as the new dataset of emissions sources released by ClimateTrace, to see if these are suitable inputs to the Foods3 model. We will also decide if we will be able to feasibly use remote sensing to estimate on field lime application amounts in a manner that can help us spatialize the greenhouse gas impacts at the county level (Task 3.2). Objective 4: We will begin looking into relevant data sources on food affordability (Task 4.1), jobs and income (Task 4.2), and nutrient availability (Task 4.3). Objective 5: This work will begin in 2025.
Impacts
What was accomplished under these goals?
The team held a kickoff workshop on March 21-23 at the offices of the University of Minnesota Institute on the Environment to develop a plan for the first year of the project. Biweekly one-hour zooms were used by the team to monitor progress. Objective 1: It was decided that the best way to engage stakeholders would be to develop a prototype dynamic model of the beef system, to help stakeholders visualize what would eventually be possible with an enhanced version of the FoodS3 model, as well as gathering feedback on what types of shocks and interventions would be of most interest. The prototype model, developed using the Stella system dynamics software package, consists of stocks and flows graphically representing the key stages of the US beef supply chain. The model dynamically tracks the movement of animals and animal products through the supply chainand includes the following activities: Breeding and pregnancy Raising calves stocker and finishing of cattle slaughterhouse processing consumer supply and demand dynamics The model was demonstrated in real time during the stakeholder meeting. Stakeholders had an opportunity to observe the dynamic response of the beef system to disease shocks at various stages of the supply chain. They could observe how decision makers at each point in the model supply chain respond to price supply and demand dynamics that flow through the supply chain from the consumer to the rancher and from the rancher back to the consumer. Running the model in real time allowed stakeholders to see the boom-and-bust cycles that can continue for years after the original disruption has ended. They immediately recognized and confirmed the kinds of patterns the model predicted. Seeing the model run in real time and walking through the graphical representation and logic of the US beef system model sparked a lively discussion with stakeholders, who were able to generate ideas for improvements as well as priorities for what kinds of disruptions they felt were most important to evaluate. We developed a list of 75 stakeholders, all of whom were invited to the first engagement event, held on December 5 in Charlotte, alongside the Sustainable Ag Summit (Dec 6-7), which many of these stakeholders generally attend. The in-person engagement event was entitled "Managing Shocks in the Animal Protein System" and was attended by around 25 stakeholders. Very helpful feedback was received at the event and is briefly summarized below: Stakeholder feedback on shocks: Seed availability shocks (corn, etc.) Stockers Demand shocks (e.g., boycotts, fear of BSE, etc.) Import/Export bans Fires at packing houses Labor/immigration Forensic analysis of past 30 years to find others Key inputs, e.g., vitamins from China Port strikes Policy changes, e.g., pricing methane, C markets, climate-smart commodities, etc. Include "Negative shocks" - e.g., disruptive tech that boosts productivity or eliminates bottlenecks Stakeholder feedback on modeling: Ensure model has decision points related to actions that can build system resilience Add stocker backgrounders Add decision factors other than just price Add feedlot operators, stockers, auction barns, packing facilities etc. Vertically-integrated supply chains Interaction between shocks? Stakeholder feedback on methodology Regionalization: Use NCBA regions, or possibly states; More localized pricing needed? Timestep: Quarterly seems fine for beef; Monthly may be needed for pork & poultry Metrics: Make sure to include metrics of interest to decision-makers, e.g., protein per capita Objective 2: Task 2.1: We have been working to update the beef, broiler, hog, and raw milk primary processing facility capacities to an annual timestep. Our previous work had facility capacities estimated every five years using a combination of datasets from industry reports detailing total production output by company and/or capacity depending on the livestock category, USDA ERS reports detailing the state and national total slaughter by livestock category, and satellite imaging to identify relative sizes of facilities in order to allocate between facilities. To standardize our methodology across animal facility types, we have improved our facility capacity estimation by aligning our data types for company data, standardizing the usage of USDA and Reference USA data, and using a consistent linear optimization to fill gaps in data. We are finalizing this update and working on a data paper outlining this new methodology. We have also begun work on building an annual time series for facility capacity using this new method. We also explored the idea of using imagery from Planet satellites to monitor individual locations. However, Planet satellites only see each facility twice a day. At best this data could provide a sense of monthly variation in slaughter capacities. We have decided to revisit this possibility later in the study after updating using non-spatial data first. Task 2.2: We are also working to "reconstruct the FoodS3 commodity flow transport optimization model's foundational structure to use monthly input and output information at livestock production, processing, and consumption nodes, to provide monthly origin-destination estimates at the commodity level." Toward this task we have assessed which data for which commodities is available at the monthly time-step in USDA NASS. We are currently assessing similar data in other USDA datasets (ERS, ARMS). Objective 3: Task 3:1: A team of students and a research assistant at Stanford University worked under the supervision of Dr. Lobell to develop models to estimate the number of animals in dairy, beef, and poultry farms. The model was primarily developed with data in California but subsequently tested in other states, showing reasonable performance. Our initial work focused on estimating headcount as some ground-based estimates were available for training and validation. Given the success of the aforementioned work we are optimistic that given appropriate training data, we can use ML to identify various manure management types at hog and dairy farms across the country, as well as to estimate the number of animals being delivered to slaughter facilities. We will be hiring students this summer to begin building the training data. Task 3.2: In FoodS3, over 80% of the full cradle-to-farm gate life cycle GHG emissions from crop production have been estimated using spatially explicit county-scale data for corn, soy, winter wheat, spring wheat, and durum wheat. A key remaining emission source that is likely to vary substantially across production regions is Lime application. However publicly available lime data is only available at the national scale. Our team has reached out to two major agribusinesses to see if they are willing to share their lime application data. Our team has looked at planet imagery in Georgia to see if the brightened visual signature of lime application is visible. We believe we have found examples of it, so it is possible an unsupervised ML model could work, but ideally have some ground truth data from an agribusiness. Objective 4: Nothing to report Objective 5: Nothing to report
Publications
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