FUTURE CHALLENGES IN ANIMAL PRODUCTION SYSTEMS: SEEKING SOLUTIONS THROUGH FOCUSED FACILITATION

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1021840
• Multistate No.: S-OLD 1074
• Project Start Date: Dec 9, 2019
• Project End Date: Sep 30, 2023
• Program Code: [(N/A)]- (N/A)

Recipient Organization
SOUTH DAKOTA STATE UNIVERSITY
PO BOX 2275A
BROOKINGS,SD 57007

Performing Department
Agricultural & Biosystems Engineering

Non Technical Summary
Odor is a top environmental issue associated with livestock production. It hampers the further expansion of the livestock industry in many places because of strong public complaints against the odor nuisance. At present, this odor issue is mitigated largely by two strategies: source control (e.g., biofilters and covered lagoons) and setback distance. Each strategy has its advantages and disadvantages. Source control can effectively reduce odor emissions but its application is often constrained by high capital or operating costs. Setback distance is inexpensive but its effectiveness varies with topographic and meteorological conditions.Herein we propose a new strategy called "smart emission" to supplement the existing approaches. Smart emission is a self-adjusted emission strategy that regulates the odor emissions from a livestock barn according to the odor concentration at the barn exhaust, the meteorological parameters that govern the odor dispersion in the atmosphere, and the odor control strategies in place. For example, when the meteorological conditions (e.g., inconvenient wind directions and surface inversion) discourage odor dilution at the neighboring communities, a smart emission system will respond by reducing the barn's ventilation rate, redirecting the exhaust air, or triggering an odor control process; and vice versa.To our knowledge, no similar concept has been proposed in the past for air quality management, including odor management. However, recent advancements in sensor, computing, and information technologies have made smart emissions technically and economically feasible. First, gas sensors are becoming increasingly capable and affordable. An industrial semi-conductor odor sensor today costs <$20. Second, miniature weather stations measuring farm-specific meteorological parameters are readily available at reasonable costs. Third, meteorological parameters can also be acquired from weather stations or weather models via the Internet. Finally, computing technology has advanced to a point that odor dispersion modeling can be done in real-time with the data pooled from sensors, weather stations, databases, and barn equipment.Since smart emission is a new concept, this project will be largely exploratory and involve three steps. First, we will further conceptualize smart emission within the context of precision livestock farming and environmental stewardship. Second, we will build computer programs to simulate the operation of smart emission systems at livestock facilities. This will include the investigation of a variety of livestock housing, emission, geographic, and meteorological scenarios. The purpose is to assess the effectiveness of the smart emission strategy and to find the best hardware setup and control algorithm for real-world applications. Finally, we will build a real system and implement it on a lab-scale simulator to characterize its mitigation performance. Once the lab testing is done, we will further validate the system on a university research farm. Although this work focuses on odor emissions from livestock facilities, a similar concept or methodology may find applications in other pollution mitigation scenarios, (e.g., agricultural runoff management).

Animal Health Component

40%

Research Effort Categories

Basic

0%

Applied

40%

Developmental

60%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
307	3510	2020	50%
723	0410	2020	50%

Knowledge Area
723 - Hazards to Human Health and Safety; 307 - Animal Management Systems;

Subject Of Investigation
3510 - Swine, live animal; 0410 - Air;

Field Of Science
2020 - Engineering;

Keywords

concentrated animal feeding operation

odor nuisance

air dispersion

mitigation

environmental management

smart emission

Goals / Objectives
Create issue-focused adaptive networks that transcend discipline and stakeholder boundaries, now and into the future Synthesizeÿ¬ data, analytical tools and communication mechanisms to evaluate and discuss animal protein supply chain sustainability metrics on various spatial and temporal scales Propose solutions, research and Extension directions to significantly contribute to sustainable animal protein systems and food security with forecasting of future trends

Project Methods
Objective 1. Conceptualize smart emission within the context of precision livestock farming and environmental stewardship Precision livestock farming (PLF) is an integrated management system for livestock production processes. It monitors livestock and relevant physical parameters, feeds the acquired information to a process controller, and sends control commands to actuators to regulate process output. The target process output can be animal growth, meat quality, mortality rate, livestock environment, etc. By this definition, smart emission falls into the scope of PLF, with the target to mitigate odor nuisance in neighboring communities. Smart emission is anticipated to be a module of future integrative PLF systems. This raises a key question: How will smart emission be integrated with other modules in a PLF system? Different modules may have different goals. The extent to which the operation of multiple modules (including smart emission) can be synergized remains an unsolved question. For example, smart emission may regulate odor emissions by reducing the ventilation rate. However, this will immediately affect the thermal environment and air quality inside an animal barn. Since these environmental parameters are closely related to animal health and growth, a holistic analysis must be performed to coordinate the smart emission module and the livestock environment controller(s). To address this question and further conceptualize smart emission (including its definition, scope, and characteristics), a comprehensive literature review will be conducted, and the analysis will be focused on the functionality and compatibility of smart emission with other PLF components that are under development or commercially available.This work will also include an analysis of the environmental stewardship of livestock production. The proposed smart emission system aims to reduce community odor nuisance. This does not necessarily result in the best environmental stewardship. For example, the accumulative odor emission could be even greater with, than without, the smart emission system because of relaxed emission control under pro-odor-dilution weather conditions. Livestock odor is caused by numerous chemicals, including ammonia, hydrogen sulfide (H2S), cresols, aldehydes, etc. Many of these chemicals have significant impacts on the environment and should be mitigated as much as techno-economically possible. Accordingly, the concept of smart emission will be further polished to accommodate this and similar demands.Objective 2. Simulate the operation of smart emission systems in various application scenarios Since smart emission is a new concept, we will run computer simulations to examine and validate its effectiveness before building a physical system. This work will be done in four steps:Selecting simulation scenarios. First, we will assume several possible smart emission systems. Assumptions will be made on individual system components and their interconnections. For example, for acquisition of meteorological data, there are three options: onsite or local weather stations, weather models (e.g., MM5 and WRF), and a combination of both. Similarly, multiple options exist for dispersion modeling and for source odor control or release. Second, we will select several typical livestock housing types. Key parameters relevant to odor emission and dispersion (e.g., odor concentrations and release height) will be determined through literature review and site survey. Third, we will locate the facilities in multiple different geographic and land-use settings. The factors affecting odor dispersion to neighboring communities will be investigated, such as terrain, farm-to-community distance, and land use (e.g., cropland and grassland). Finally, we will select the meteorological data sets representative of the upper Midwest and a few other regions. The relevant data will be downloaded from the NOAA NCDC and radiosonde databases.Building the simulation platform. The platform (computer program) will be an integration of mathematical models for individual components in a smart emission system. Accordingly, efforts will be made to first formulate the models for each system component, from odor detection to meteorological data acquisition to source odor control to air dispersion. Next, these models will be interfaced to build the simulation platform. As a closed-loop control system, the smart emission system uses the predicted odor concentrations at a neighboring community or communities as the feedback signal.Performing simulation. Once the platform is established, computer simulations will be conducted to characterize a test smart emission system and examine its performance in various application scenarios. The PI intends to work with computer scientists at SDSU during this step to optimize the computing algorithms, and the goal is to be able to run the simulated and later physical smart emission systems on a regular computer.Conducting data analysis. A number of performance indicators will be considered in this step, including odor nuisance reduction (in terms of concentration and frequency), computing power requirement, system response time, system resilience, capital costs, operating costs, etc. The PI will collaborate with electrical and mechanical engineers at SDSU to compare different systems and to find the hardware configurations and control algorithms that would work the best for commercial livestock facilities.Objective 3. Validate the effectiveness of smart emission through lab and field experiments This work will involve three steps:Constructing a prototype smart emission system. A prototype system (including hardware and software) will be constructed based upon the findings from objectives 1 and 2. Regarding hardware, potential components include miniature weather station, odor sensor, humidity and temperature sensor, ventilation fan sensor, static pressure sensor, Internet connection, data acquisition module, computer, microcontroller, and actuator (for triggering source odor control or regulating ventilation rates). For software, C++ will be used to code the programs for data acquisition, processing, and control and to interface with the odor dispersion models that are usually Fortran-based. A graphic user interface will be built to enable monitoring and human intervention of system operation.Lab validation of the prototype system. A lab-scale simulator will be built for this purpose. The key component of this simulator will be a wind tunnel with adjustable wind speeds. Inside the tunnel will be placed a model livestock house and multiple H2S sensors downstream of the house. Here, H2S will serve as a surrogate for odor and its release inside the model house will be regulated using a computerized flow meter. Sand, plastic trees, and other objects will also be placed inside the tunnel to mimic different topographic and land use conditions. Instead of odor dispersion models, a computational fluid dynamic (CFD) model will be used to predict the odor transport and dilution in the wind tunnel. Although the setup cannot duplicate real-world topographic, meteorological, and land-use conditions, it will allow us to validate the system performance and further polish the design of smart emission systems.Field assessment. The prototype system will then be installed at the SDSU Swine Education and Research Facility (SERF) for further testing. The facility currently has two biofilters in operation for odor mitigation. A tentative plan is to build bypass vents for the two biofilters to enable an on-demand odor treatment under the control of the smart emission system. Odor sensors will be installed in the vicinity of the facility to measure ground-level odor concentrations. Field assessors and observers will also be recruited to report the odor intensity in the barn vicinity before and after the implementation of the smart emission system.

Progress 12/09/19 to 09/30/20

Outputs
Target Audience:Livestock producers and industry partners. Livestock producers and industrial partners were targeted because they would be the ultimate beneficiary of the project. They were targeted through field visits, face-to-face meetings, phone calls, emails, virtual meetings, and extension activities. Last year we reached out to over 100 producers, with the majority through extension workshops. We conducted collaborative research with three companies. Academia. We collaborated with researchers from various departments at South Dakota State University (SDSU), including Agricultural and Biosystems Engineering, Animal Science, Electrical Engineering and Computer Science, Natural Resource Management, and Mechanical Engineering. We partnered with the University of Minnesota, North Dakota State University, and the University of Illinois at Urbana-Champaign in conducting research projects or exploring collaborative research and extension opportunities. We also participated in the meetings and events organized by S-1074 and ASABE which involved faculty and staff from various land-grant universities. Students. Undergraduate students were targeted through the Agricultural Waste Management course (AST453) that the PI taught in spring 2019, with totaling 38 agricultural system technology and dairy science students. Many of them plan to work in the livestock industry immediately after graduation. Graduate students were advised to develop their professional skills and research capability. General public. The general public was targeted because their perception and opinions would impact the future of livestock production systems. Scientific facts and research findings made from our research were disseminated to the general public through publications (e.g. fact sheets and articles), presentations, and extension/outreach activities. Changes/Problems:The covid-19 pandemic starting in Mar 2020 slowed down the project progress. No lab or fieldwork was done from Mar 2020 through Jun 2020 as per the SDSU university policy. Student recruitment, conference participation, S1074 team events, and extension/outreach activity were also adversely impacted. What opportunities for training and professional development has the project provided?The project provided great opportunities for students from diverse backgrounds to explore cost-effective odor control technologies/practices as a team. Last year, 4 undergraduate students and 2 graduate students participated in this project. The PI mentored the students for an average of 6 hours per week and offered advising and guidance to develop their research skills and professional preparedness. The students' research effort has resulted in two conference presentations. The following students were involved or trained in this project: Augustina Osabutey is a current Ph.D. student in ABME (Jan 2020-Present). With a background in mechanical engineering and environmental engineering, she helped the PI develop and refine the concept of smart odor emissions and worked on the air quality dispersion models for community odor simulation. Zhisheng Cen is a current Ph.D. student in ABE (Aug 2020-Present). With a background in applied chemistry and agricultural and biosystems engineering, he aided the PI in data collection and compilation, including meteorological data, terrain data, odor emission factors, community odor threshold levels, etc. Brady Cromer was an undergraduate student in Agricultural System Technology. Under the PI's mentoring, Brady and two other students conducted a four-month field project to study the distribution of airflow rates and moisture levels across two vertical biofilters (for mitigation of odor emissions). Brady graduated in Dec 2019 and currently works in the industry. Alexander Davids was an undergraduate student in Mechanical Engineering. Under the PI's mentoring, Alex and two other students worked on the biofilter project (for control of odor emissions from swine barns). Alex is currently a sophomore in Construction Management at SDSU. Logan Prouty was an undergraduate student in Civil Engineering. Under the PI's mentoring, Logan and two other students worked on the biofilter project (for control of odor emissions from swine barns). Logan is currently a sophomore in Construction Management at SDSU. Harsh Dubey was an undergraduate student in Electrical Engineering and Computer Science. He helped the PI design and fabricate a LoRaWAN-based system to monitor the operating status of swine farm equipment. Harsh started in Jan 2020 and graduated in May 2020. How have the results been disseminated to communities of interest?Two conference presentations were made in the first year of the project, along with one article published in an agricultural magazine. Presentations and training were given through the SDSU and multistate extension programs to promote the awareness of livestock odor issues and the importance of proper management to the public, in particular livestock producers. Face-to-face and virtual meetings were also held to disseminate the results of our research to livestock producers, policymakers, and other stakeholders. What do you plan to do during the next reporting period to accomplish the goals?We will continue to collaborate with researchers with various backgrounds across the U.S. to promote the sustainability of animal production systems. We will also maintain a close working relationship with field extension specialists, animal producers, and facility builders to facilitate technology transfer and knowledge dissemination. Among the three goals of the Multistate Research Project S1074, we will focus on Goal 3 -- Propose solutions through the development of innovative technologies and management practices for livestock odor management. Goal One: Create issue-focused adaptive networks that transcend discipline and stakeholder boundaries, now and into the future We will continue to participate in the meetings and events organized by S1074 and proactively explore opportunities to serve the S1074 community, including serving on subcommittees and coordinating certain events. We will reach out to animal scientists, agricultural engineers, field extension specialists, and animal producers in South Dakota to inform them of and engage them in the team effort of S1074. An additional professor at SDSU will likely join S1074 in 2020-21. Goal Two: Synthesize data, analytical tools and communication mechanisms to evaluate and discuss animal protein supply chain sustainability metrics on various spatial and temporal scales We will continue to review the technical documents drafted by other S1074 team members (through subcommittees) and be actively involved in the life cycle assessment of animal production systems. Goal Three: Propose solutions, research and extension directions to significantly contribute to sustainable animal protein systems and food security with forecasting of future trends Our effort next year will be focused on Aims 1 and 2 of the smart odor emission sub-project. We will continue the literature review on PLF and the environmental stewardship of livestock production systems. We will formulate the mathematical models for odor emissions, source odor control, investigate community odor annoyance, etc. and integrate them with the selected air dispersion model to build a simulation platform (a computer program) for proposed smart odor emission systems. We will start to develop scenarios for the simulation study.

Impacts
What was accomplished under these goals? Goal One: Create issue-focused adaptive networks that transcend discipline and stakeholder boundaries, now and into the future (15% Accomplished) There are three tasks under this goal: (1) Task 1A - Conduct web-based work sessions for issue teams; (2) Task 1B - Facilitate face-to-face workshops that translate techniques and methods among issue teams and S1074 members; and (3) Task 1C - Strengthen the next generation of thinkers to engage in animal protein sustainability discussions. For Task 1A, since the PI joined the Multistate Research Project (S1074) a year after it started, he did not coordinate or organize any web-based work sessions. He attended all the web-based work sessions last year organized by S1074. In addition, the PI participated in two web-based work sessions for livestock agriculture and agronomy at SDSU. For Task 1B, no face-to-face multistate project workshops were held last year due to the COVID-19 pandemic. For Task 1C, the PI hired and advised two graduate students and mentored four undergraduate students last year. They all worked on sustainable animal agriculture-related research projects. Goal Two: Synthesize data, analytical tools and communication mechanisms to evaluate and discuss animal protein supply chain sustainability metrics on various spatial and temporal scales (0% Accomplished) There are three tasks under this goal: (1) Task 2A - Align sustainability definitions and metrics; (2) Task 2B - Develop data dictionaries appropriate for sharing data in the open-access arena; and (3) Task 2C - Move from causality to mechanistic. The PI was not involved in Task 2A or 2B. The PI made a minor contribution to Task 2C by reviewing the technical documents drafted by other S-1074 project members. Goal Three: Propose solutions, research and extension directions to significantly contribute to sustainable animal protein systems and food security with forecasting of future trends (25% Accomplished) This goal contains only one task: Task 3A - Propose solutions to "respond to potential external perturbations in future animal production systems." The PI's effort was focused on developing smart odor emission, an innovative solution to livestock odor management. As aforementioned, this included three specific aims. For Aim 1 - Conceptualization of smart emission, we did a comprehensive literature review (of ~80 publications) about recent advancements in precision livestock farming (PLF) and focused on the PLF systems that included environmental control elements. We reviewed ~50 articles discussing various aspects of environmental stewardship of livestock production systems. In addition, we did a thorough analysis of thermal environment requirements for different livestock animals, as well as existing odor mitigation strategies for livestock facilities. Through analyzing the existing literature and brainstorming, the concept and engineering framework of smart emission were established and refined. We found that the proposed smart odor emission system would be most useful for mechanically ventilated swine and poultry barns and it would be most compatible with in-barn odor control technologies (e.g. water or oil sprinkling) and low-pressure-drop end-of-pipe abatement technologies (e.g. electric air filters). We also found that smart odor emission can be readily incorporated in existing or future PLF systems, with minimal additional costs required. For Aim 2 - Simulation of the operation of smart emission systems, our effort of last year was focused on preparing the tools and input data for simulation, including meteorological datasets, terrain datasets, odor emission factors, odor mitigation efficiencies, and community odor threshold levels. Two air dispersion models were selected for simulating odor concentrations in the neighboring communities of a livestock facility: AERMOD and WindTrak. A graduate student was trained to understand and use these modeling tools. For Aim 3 - Lab and field validation of smart emission systems, we designed and fabricated an Internet-of-Thing (IoT)-based system to measure the real-time air exchange rate (i.e. the flow rate of exhaust air from mechanically ventilated livestock barns). We also developed a low-cost air quality monitoring system to monitor hydrogen sulfide and odor concentrations at the barn exhaust. With the two systems, we were able to determine odor emissions from livestock barns in a time-responsive and cost-effective manner. The same systems will be used for lab and field validation of smart emission systems. Meanwhile, we investigated two end-of-pipe mitigation technologies: vertical biofilters and electric air filter walls through field experiments. Through our study, we identified several limitations of the vertical biofilter technology and proposed some major changes in biofilter design and operation. Relevant results were presented at the 2021 ABABE Annual Conference. The study of electric air filter walls was a joint effort between SDSU and the University of Minnesota. All the tests were done and a report was submitted. Both mitigation technologies, as well as the monitoring systems we developed, may potentially be incorporated in future smart odor emission systems.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Cromer, B., Davids, A., Prouty, L., Osabutey, A., Thaler, R., Nicolai, R., Yang, X. 2020. Distribution of air velocity and media moisture content across vertical biofilters. ASABE 2020 Annual Conference. July, 14. (virtual).
• Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Yang, X.(202). IoT and its applications to CAFOs. Minnkota Annual Meeting. Mar 12-13. Sioux Falls, SD.
• Type: Other Status: Published Year Published: 2020 Citation: Yang, X. 2020. Dry handling systems are being proactively discussed for livestock manure management. The Advocator (fall 2020). Sioux Nation Ag Center, Sioux Falls, SD.