Performing Department
(N/A)
Non Technical Summary
The future of agriculture and sustainable food systems is at a critical juncture for a rapidly growing worldwide population. This project endeavors to overcome challenges threatening their progress, such as resource inefficiencies and limited access to cutting-edge technologies. It addresses the pressing need to equip the future agricultural workforce with the skills and knowledge to tackle modern food, agriculture, natural resources, and human sciences (FANH) challenges. With a focus on the Langston University, Sherman Lewis School of Agriculture and Applied Sciences (SL/SAAS) graduates, the project aims to integrate hands-on experiences with cutting-edge AI-integrated dairy farming and food processing technologies into their study plans. By doing so, these HBCU graduates will be a highly skilled leading workforce that will confidently drive innovation, promote sustainability, and proactively tackle the multifaceted challenges of the FANH sector. This goal will be supported by setting up a cutting-edge data collection hub to aid in AI integration in SL/SAAS farms. This AI integration will enhance productivity, sustainability, and efficiency, which can act as a blueprint for small goat farmers to follow and learn from.Moreover, by conducting comprehensive market research and needs assessments, the project aims to identify the strengths, weaknesses, opportunities, and threats within the small-scale goat production sector, focusing on herd management practices, farm management strategies, marketing techniques, technology utilization, and training needs. Through the results of this assessment, the project team aims to develop an engaging educational resource package and an Agribusiness Incubator space tailored to the specific needs of small-scale goat producers, providing them with the knowledge and tools necessary to incorporate cutting-edge technologies, such as AI technology, to enhance their farming practices and promote agribusiness development. The project's success promises significant societal benefits, including increased agricultural productivity and sustainability, enhanced environmental stewardship, and improved food security for communities nationwide.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
The project aims to equip SL/SAAS students with practical, hands-on experiences in modern agribusiness practices, AI integration, and goat farming. The primary aim is to prepare students for a successful career in the agricultural industry by implementing classroom knowledge with real-world applications. The project intends to promote innovation in the goat farming industry and enhance sustainable agricultural practices through research, education, and outreach activities. By integrating AI technologies and optimizing dairy and meat production processes through research, the project seeks to upgrade the quality and efficiency of milk and cheese production and the quality of goat meat. The project aims to transform the SL/SAAS Main Farm into a state-of-the-art dairy goat farm and turn the South Farm into a model for meat goat farming. Moreover, the project intends to develop an educational package that will cultivate the growth of local agribusinesses, including small goat farmers, by providing training, educational resources, and support. By encouraging "made-in-Oklahoma products" and engaging with the local community, the project seeks to contribute to regional economic development and sustainability.The key supporting objectives are:1. Develop a skilled workforce for sustainable agriculture, incorporating hands-on experiences with AI-integrated dairy farming and food processing technologies into their study plan. Collaborations with academic and industry experts will enhance the learning experience, ensuring graduates are well-prepared for modern FANH challenges.2. Establish an advanced data collection mainframe computer for AI integration: The action plan comprehensively assesses the Goat Dairy Farm's current data collection capabilities. Parameters pertinent to herd management will be identified and mapped. Automated data entry systems will lead to the creation of a centralized database for future integration with AI.3. Develop a user-friendly educational resource package: The team will create an educational resource package on sustainable farming practices and value-added goat products to foster the capacitation of small goat farmers.
Project Methods
Procedures for Accomplishing Objectives:Objective 1: Develop a skilled workforce for sustainable agricultureOver three years, these steps will be taken:1. Curriculum Enhancement and Integration: - Task: Enhance the Animal Science and Management (ASM) Plan of Study incorporating modules on sustainable agriculture, AI integration, and food technology. - Effort: Provide hands-on training experiences for SL/SAAS students as three-hour guided practical sessions on the LU dairy and meat farms where they actively participate in goat farming operations. Similar sessions will be implemented for milk processing operations at the Main Farm Creamery. These sessions will involve manufacturing procedures following food safety GMP guidelines. - Procedure: Collaborate with faculty members to identify critical areas for curriculum enhancement. Working with the course professor, we will develop and integrate new practical sessions into existing ASM courses, like "ASM 4123 Small Ruminant Management" and "ASM4313 Range & Pasture Mgmt." They will include feeding, breeding, milking, and managing the herd. The food processing practical sessions will be part of the new course, "ASM 3233 Dairy Technology." Students will elaborate on different dairy products in the pilot plant creamery. - Metric: Document the integration of new modules into the ASM curriculum and evaluate the student's performance in those practical sessions.2. Internship Program: - Task: Establish a structured internship and practical training program for students. - Effort: Coordinate with the collaborating partners, AIFARMS and Homestead Meat & Processing, to provide hands-on training opportunities within an internship framework. - Procedure: The first is an eight-week food processing internship at Homestead Meat & Processing plant, where students will receive training in meat processing following HACCP protocols. The second is a ten-week project with AIFarms researchers at the University of Illinois, where students will learn how to integrate AI technologies into farming operations. - Metric: Track student participation in internships and assess their acquired skills through performance evaluations.3. Research Activities: - Task: Engage students in research and production activities related to sustainable agriculture. - Effort: Integrate students into ongoing SL/SAAS goat farms research projects. - Procedure: In the Animal Science course "ASM 4621 Topical Seminar", students will work on small tasks related to processing data from two farms. This will help them learn about basic AI operations such as data collection, analysis, and interpretation. - Metric: Monitor student involvement in research activities and assess their contributions to project outcomes.Objective 2: Establish an advanced data collection mainframe for AI integration.The three-year plan involved in this process is as follows:Year 1:1. Data Collection Baseline Assessment:- Task: Identify the currently collected data, collection methods, and utilization.- Procedure: Conduct a comprehensive assessment of existing data collection methods, including types of sensors, data points collected, and frequency of data collection.- Metric: Document the current state of data collection, including strengths and limitations.2. Plan Development for Expanding Data Collection:- Task: Identify additional data needed for comprehensive analysis.- Procedure: Collaborate with SL/SAAS Main Farm and Evans Allen experiments to determine gaps in data collection.- Metric: Document the specific parameters identified for expansion and the rationale for their importance.3. Data Collection Plan Expansion and Implementation:- Task: Install sensors and equipment for expanded data collection.- Procedure: Select appropriate sensors and equipment based on the identified parameters. Train staff on proper usage and maintenance.- Metric: Document successful installation and staff training completion.Year 2:4. Refinement of Data Collection Plan:- Task: Evaluate and improve data collection methods.- Procedure: Review data collected in Year 1 to identify any issues, limitations, or opportunities for improvement. Consider changes to sensors, equipment, or collection frequency.- Metric: Document changes made to the data collection plan and the rationale behind them.5. Data Analysis for Parameter Effects:- Task: Analyze data to understand parameter effects.- Procedure: Apply relevant statistical analyses to correlate collected data with milk and meat quality and yield. Identify trends and relationships.- Metric: Document findings and correlations between selected parameters and quality/yield outcomes.Year 3:6. Full Implementation of Data Collection Plan:- Task: Ensure complete functionality of the data collection infrastructure.- Procedure: Verify that all sensors and equipment are fully operational and provide accurate data. Conduct a final review of staff training and proficiency.- Metric: Document successful implementation and operational status of all components.7. Evaluation of Data Collection Plan Effectiveness:- Task: Assess the success of the data collection plan.- Procedure: Compare the program's goals with the actual data collected. Determine if the collected data aligns with the project's objectives and whether any modifications are necessary.- Metric: Document the evaluation results, including any modifications recommended for the data collection plan.Objective 3: Develop a comprehensive and user-friendly educational resource packageThe steps to be implemented over three years:1. Market Research and Needs Assessment: - Task: Conduct a thorough market research and needs assessment to identify critical areas for educational resource development. - Effort: Design and implement surveys targeting small-scale goat producers to gather insights on current practices, challenges, and training needs. - Procedure: We will survey 50 participants from Oklahoma to analyze small-scale goat production practices, management strategies, and training needs. We will use qualitative and quantitative methods, including thematic analysis for open-ended responses and descriptive statistics for Likert scale responses. - Metric: Analyze the collected data to identify trends and opportunities.2. Content Development and Design: - Task: Develop engaging and informative educational resources based on the market research findings. - Effort: Collaborate with subject matter experts to create multimedia content that addresses the identified needs and preferences of the target audience - Procedure: We'll develop an educational resource package based on adult learning theory. Our approach will focus on active engagement, experiential learning, and problem-based instruction. It will be accessible through secure user authentication. - Metric: Evaluate the quality and relevance of educational resources through expert review and user feedback.3. Usability Testing and Iterative Improvement: - Task: Test the usability and effectiveness of educational resources through pilot studies and feedback sessions. - Procedure: Conduct usability testing with small-scale goat producers participating in the Agribusiness Incubator space to assess the resources' clarity, effectiveness, and user-friendliness. - Metric: Use feedback surveys to measure user satisfaction and engagement with the educational resources.4. Dissemination and Outreach: - Task: Promote the educational resources to the target audience and facilitate knowledge dissemination. - Procedure: Attend SL/SAAS workshops, training sessions, and programs like OKLAS and "Field Day and Small Farms Conference." There, we will offer hands-on learning experiences, practical demonstrations, and expert-led discussions based on market research. - Metric: Track the reach and impact of dissemination efforts through event attendance and participation surveys.