Source: VIRGINIA POLYTECHNIC INSTITUTE submitted to NRP
FOSTERING RESPONSIBLE INNOVATION AND GOVERNANCE OF BIG DATA AND ARTIFICIAL INTELLIGENCE IN PRECISION AGRICULTURE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1030813
Grant No.
2023-67023-40216
Cumulative Award Amt.
$649,396.00
Proposal No.
2022-11541
Multistate No.
(N/A)
Project Start Date
Aug 1, 2023
Project End Date
Jul 31, 2026
Grant Year
2023
Program Code
[A1642]- AFRI Foundational - Social Implications of Emerging Technologies
Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
(N/A)
Non Technical Summary
This project aims to encourage innovators, researchers, practitioners, and policy makers to respond to the social and ethical challenges of big data and Artificial Intelligence (AI) in precision agriculture (PA) and to generate insights and strategies that are suitablefor stakeholders across the PA value chain.The project has three primary objectives: (1) map stakeholders' perceptions and expectations about the societal implications of big data and AI in PA; (2) deploy digital serious games to understand farmers' and farm advisors' risk and information preferences in response to different levels of AI reliability and uncertainty, and preferences to forms of farm data ownership, and (3) create opportunities for responsible innovation in PA through interdisciplinary education, policy recommendations, and outreach activities.Key project outcomes include: (1) synthesized information about the challenges and opportunities offered by big data and AI in PA; (2) identified strategies for responsible innovation; (3) Integrating societal implications of PA technologies into existing university courses, (4) Data Science for Public Good summer training program, and (5) an interactive science museum exhibit at The Science Museum of Western Virginia in Roanoke, Virginia. Project outcomes will be disseminated through peer-reviewed manuscripts, policy briefs, and conference presentations.The project uses a transparent, inclusive, and iterative approach to provide legitimacy to innovation and policy processes and outcomes, and has the potential to stimulate learning and reflection among stakeholders in the U.S. food and agricultural systems.The proposed work advances the "Economic and Social Implications of Food and Agricultural Technologies" program area (Priority Code A1642).
Animal Health Component
50%
Research Effort Categories
Basic
30%
Applied
50%
Developmental
20%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
8037310308080%
8037410208020%
Knowledge Area
803 - Sociological and Technological Change Affecting Individuals, Families, and Communities;

Subject Of Investigation
7310 - Experimental design and statistical methods; 7410 - General technology;

Field Of Science
2080 - Mathematics and computer sciences; 3080 - Sociology;
Goals / Objectives
This project aims to encourage innovators, researchers, practitioners, and policy makers to respond to the social and ethical challenges of big data and Artificial Intelligence (AI) in precision agriculture (PA) and to generate insights and strategies that are more broadly applicable to stakeholders across the PA value chain. The project has three primary objectives: (1) map stakeholders' perceptions and expectations about the societal implications of big data and AI in PA; (2) deploy digital serious games to understand farmers' and farm advisors' risk and information preferences in response to different levels of AI reliability and uncertainty, and preferences to forms of farm data ownership, and (3) create opportunities for responsible innovation in PA through interdisciplinary education, policy recommendations, and outreach activities.
Project Methods
Efforts:Conducting a 'horizon scanning' of trends in published articles, conference proceedings, and gray literature (e.g. policy papers) about some of the potential social, ethical, economic, and environmental challenges and impacts of big data and AI in PA.Summary of the horizon scanning trends will be presented at scenario-based foresight workshops, held online, where a wide range of stakeholders across the food system value chain will be invited to participate in homogenous (by sector / expertise) groups. The workshop participants will co-produce scenarios to examine plausible futures, which 'could happen', and explore how the society would have to change if certain trends were to strengthen or weaken. The workshop participants will then discuss the implications of AI and big data in each scenario by responding to questions such as: What role will big data and AI play in this plausible future? What are the social and ethical implications that might arise as a result of emerging technologies.The team will use the benefits and risks of big data and AI in PA obtained from the foresight workshops to conduct a Multicriteria Decision Analysis (MCDA) process. We will use a web-based platform (EngagementHQ) for conducting the MCDA. Participants will complete a survey-using a centralized online hub (EngagementHQ)-which will include questions about the benefits and risks associated with big data and AI in PA as well as the most desirable strategies identified by foresight workshop participants. Participants will rank the benefits and risks of big data and AI technologies and strategies for responsible innovation and governance using a Likert scale ranging from 1 to 5. Their rankings will be converted into variable weights using the rank-order centroid (ROC) method. ROC performs better than other methods of ranking because it minimizes the maximum error of each weight by identifying the centroid of all possible weights. We will then create an overall index for each group of participants so that shared objectives and strategies to support the responsible innovation of big data and AI in PA are clearly identified.Content from foresight workshop will be analyzed using: (1) topic modelling to identify stakeholders' interests and concerns related to innovation and governance of big data and AI in precision agriculture; and (2) network analysis to map collaborations and relationships between stakeholders across the food system value chain who may want to strategize to develop effective governance approaches. We will use a method called Latent Dirichlet Allocation (LDA), which is a probabilistic model designed to identify latent or hidden topics in a collection of documents (corpus).The LDA model assumes a document is a mixture of topics and a topic is a collection words with probabilities attached to them. Therefore, the topic proportions will be specific to a document but topics are shared by a whole collection of documents. Following this model, a transcript from a single foresight workshop participant is imagined as a mixture of topics, with each topic being a mixture of words. Moreover, network analysis will be used to identify clusters of participants who share similar characteristics, such as: perceived risks/benefits of big data and AI, desired strategies, and experience with developing or using PA.We will design a digital serious game using the Unity Development Platformand host the game online using WebGL. Serious games will be designed and employed to determine how farmers and farm advisors' respond to different technology and policy incentives. The game will simulate a 4x3 experimental design. The first dimension of our 4x3 experimental design will simulate AI information provision to producers from an AI platform with (a) zero, (b) low, (c) medium, and (d) high level of uncertainty. The second dimension will simulate three data ownership conditions: (a) "farmer owns data", (b) "Agritech owns data" and (c) "farmer can purchase their data from agritech for dollars." Therefore, the game design will have four conditions for AI uncertainty and three conditions for data ownership, for a total of twelve conditions that all players will engage with in the game. The serious game will be used to compare and contrast the behavioral preferences of farmers and farm advisors across the 2 factors and 3-4 levels.To pre-test our serious game, we will distribute it--with a 4x3 full factorial design--to 1000 participants on Amazon Mechanical Turk (MTurk). Participants will complete a survey before and after playing the game. The survey will include questions about respondents' risk preferences, trust in AI (in general), and suggestions for improving the game play. Once the game has been pre-tested by about 1,000 MTurk workers, we will use the data to refine the game design and finalize it for wider dissemination.Both difference-in-difference time series regression models and unsupervised machine learning models will be applied on the experimental data generated from the serious games to identify conditions under which data ownership and levels of AI uncertainty are more or less trusted.We will create a science museum exhibit and aneducational block-based coding game where the players (children) will be asked to complete two levels. For the novice coders (level 1), the players will be asked to complete simple coding (e.g. arrange blocks in some order) to fly a drone over a farm. The player will be to take off, maneuver, and land the drone safely to complete the level. The player will get the opportunity to view results from the different sensors on the drone. The next level will gamify a water conservation problem under climate change scenarios. In wetter conditions, the player will fly the drone and precisely apply irrigation to areas of the field that require it urgently. The cost to the environment (in terms of gallons of water saved) and farm economy (e.g. yield) will be shared with the participants and they will be asked to make trade-offs where needed. Once the game is over, the player will be asked to evaluate the game, answer questions about technology and sustainabilityEvaluation:A program evaluator from the Virginia Tech Center for Educational Networks and Impact will assess the impact of research and education activities. The project evaluator will apply a developmental evaluation approach to project evaluation. Developmental evaluation engages the project team in an ongoing learning process using data as it is collected and analyzed as well as the observations of project team members. This approach requires a preliminary evaluation workshop to identify team-generated questions that can guide the reflective processes embedded in team meetings, quarterly team reflections, and yearly evaluation summits. Two evaluation tools will supplement these processes. First, we will use a journey mapping process to detail potential solutions and their solutions as the team identifies specific work tasks to understand the social and ethical implications of big data and AI in PA and strategize responsible innovation. Second, the project evaluator will work with the team to create ongoing ripple effect maps related to how the project evolved and which outcomes and impacts can be attributed to the project. Data to support the ongoing learning process will include both formative - did we do what we said we would do - and summative data, that is, did our efforts lead to the change we predicted.

Progress 08/01/24 to 07/31/25

Outputs
Target Audience:During this reporting period, our project engaged a diverse set of target audiences across scientific, educational, and public-facing domains. Each group was strategically included to advance our broader goals around responsible and trustworthy AI in precision agriculture. Certified Crop Advisors (CCAs) were a key stakeholder group. Their deep involvement in guiding farm-level decisions makes them central to the adoption of AI-enabled precision agriculture (PA) tools. We targeted this group through a partnership with the American Society of Agronomy (ASA), culminating in the publication of our research findings in CSA News, a widely read professional magazine. This outreach aimed to elicit advisor perspectives on issues of trust, precision, and accountability in digital tools, especially around the explainability and reliability of AI systems. Graduate and undergraduate students were engaged through the upcoming second edition of the Precision Agriculture Basics textbook. Our team contributed a chapter on the adoption of PA, which will reach classrooms across the U.S. and internationally. The new edition, which is an expansion of the original 2018 best-seller published by the Tri-Societies (ASA, SSSA, and CSA), now includes 30 chapters, 15 of which are brand-new. Students were also invited to participate in the peer review process, offering a unique opportunity for early academic engagement with cutting-edge PA research. We also engaged with science communication audiences, particularly through collaboration with the Science Museum of Western Virginia. Here, our work contributes to the development of an interactive museum exhibit focused on soil and space science. The exhibit is designed to spark curiosity among children and families by illustrating the importance of soil health and plant diversity through multisensory, immersive experiences. As part of the exhibit's development, we are organizing a design workshop in July 2025 that brings together agricultural scientists, space scientists, designers, and museum educators. Finally, the project continues to support and mentor graduate and undergraduate students involved in research and development. These students are contributing to interdisciplinary efforts through game design and behavioral testing in Unity, hypothesis development to explore trust in AI systems, and data integration work focused on farmer priorities. Their contributions not only support the project's objectives but also build valuable technical and ethical expertise in the next generation of agricultural and data scientists. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project team currently includes two graduate students, one undergraduate student, and threefaculty members, including the Principal Investigator. The students have taken strong ownership of their respective project activities, each of which is directly contributing to a chapter of their dissertation. The team meets biweekly and has developed an effective, collaborative working dynamic. Students are actively learning not only from the PI but also from one another, fostering a peer-supported environment for intellectual growth. In addition, I am facilitating opportunities for them to engage with stakeholders beyond the lab, including individuals from the private sector, academia, and the farming community. These interactions are designed to enhance the project's relevance while supporting student training and professional development.The students are also beginning to prepare their work-in-progress for writing workshops and upcoming presentations at national and international conferences. These activities will allow them to integrate early-stage feedback into their research, strengthening both the rigor and impact of their contributions. How have the results been disseminated to communities of interest?To broaden the impact of our work and make it accessible to a wide range of audiences, including those not typically engaged in research activities, our team has initiated a major revision of the Precision Agriculture Basics book, originally published in 2018 and widely recognized as a foundational resource for agronomists, farmers, students, and educators. The upcoming second edition reflects the rapid advancements in the field over the past six years, particularly in areas such as artificial intelligence, machine learning, and sustainability. This outreach includes voices that often bridge science with practice, such as farmers, extension specialists, and education experts, ensuring that the book remains accessible and relevant beyond traditional research audiences. To support outreach and learning, the book will be accompanied by classroom-ready materials, including curated datasets and presentation slides, which will be freely available to educators and practitioners. These resources are designed not only to support higher education but also to enhance public understanding of how data-driven and sustainable approaches can shape the future of agriculture. In doing so, the project contributes to a broader effort to spark interest in science, technology, and the humanities among diverse and often underrepresented communities. What do you plan to do during the next reporting period to accomplish the goals?During the next reporting period, we will move into the implementation and dissemination phases of our three primary objectives. For objective 1,we will prioritize the analysis and dissemination of results from our two-wave Certified Crop Advisor (CCA) survey, conducted in 2024 and 2025. This survey offers valuable insights into how agricultural professionals evaluate emerging AI-based decision support systems, particularly in terms of precision, accuracy, and key features that contribute to trustworthiness, such as explainability and accountability. These findings will form the basis of forthcoming publications focused on the role of perceived trust and technical reliability in the adoption of AI tools in agriculture. The survey data will also help us identify design and governance principles that support responsible innovation in precision agriculture. In parallel, we will begin a second line of work focused on a more specific digital agriculture application: digital traceability systems. To examine these technologies, we will conduct case studies that draw on semi-structured interviews, policy document analysis, and stakeholder discussions. Our goal is to better understand how digital traceability systems are being implemented, the governance challenges they pose, and how various actors across the agricultural value chain, such as farmers, agribusinesses, regulators, and consumers, perceive their risks and benefits. This two-pronged strategy will allow us to compare general perspectives on AI-enabled decision tools (via the CCA survey) with more detailed insights into specific digital innovations (via traceability case studies), thus enriching our broader understanding of data governance, trust, and AI futures in agriculture. For objective 2,we will release the finalized version of our serious game to begin structured data collection for research. The game is designed to test how different governance arrangements influence stakeholder preferences around data sharing and AI-based decision support. We will deploy the game through workshops, online platforms, and partner organizations to reach a diverse group of agricultural stakeholders. Data collected through gameplay will be analyzed to assess patterns in trust, decision-making, and willingness to engage with various actors in the agricultural data ecosystem. For objective 3, we intend to finalize the design and functionality of the prototype for the science museum exhibit, with a focus on accessibility, interactivity, and age-appropriateness. The exhibitwill be tested with children and caregivers to ensure usability and engagement. We will begin fabrication of the exhibit infrastructure and continue co-design sessions with both agricultural experts and educators to refine content. We will continue to disseminate research findings through peer-reviewed publications, academic and policy conferences, and cooperative extension events where feasible. These dissemination efforts aim to broaden the impact of our work, spark dialogue around AI governance in agriculture, and encourage interest in science, technology, and responsible innovation among public audiences.

Impacts
What was accomplished under these goals? For Objective 1, we have adopted a two-pronged strategy to examine the implications of emerging digital technologies in precision agriculture (PA), particularly in the context of foresight and governance. First, we are focusing on generic AI-based decision support systems (AI-DSS), some of which are currently available in the market, while others are anticipated to emerge within the next few years. We have already tested stakeholder preferences for these AI-DSS tools through a discrete choice experiment with certified crop advisors across North America. Second, we are investigating specific, currently deployed technologies, namely'digital traceability systems' that leverage big data and AI to enhance transparency and accountability across agricultural value chains. This two-track approach allows us to capture both broad butforward-looking insights, as well as detailed andcontext-specific analysis. For Objective 2, we have made significant progress in developing a serious game focused on data governance in agriculture. This interactive game explores whether stakeholders are more inclined to trust local farmer networks or agribusinesses when it comes to sharing data and receiving input recommendations, such as those related to fertilizer use. We have solicited and integrated feedback from farmers and agricultural stakeholders through our ongoing projects, which has led to improved graphics, more engaging gameplay, and the refinement of our research hypotheses. The latest version of the game has been finalized, and we are on track to begin data collection for research purposes in the next reporting period. For Objective 3, we have continued our collaboration with the Western Virginia Science Museum to develop an interactive public education exhibit. The prototype takes the form of an instrument and interative device, designed to engage children in envisioning alternative futures of agriculture while reflecting on key questions about the governance of new technologies. In parallel, we are conducting co-design workshops with agricultural stakeholders to ensure that the exhibit is not only imaginative and educational but also grounded in real-world concerns and scientific discoveries in soil and space sciences.We aim to finalize the exhibit design in Year 2 and launch the prototype of themuseum installation beginning in Year 3.

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2025 Citation: Carcamo, P., Gardezi, M., Ryan, B., & Stock, R. The Future Governance of Artificial Intelligence in Agriculture. in Philipp Hacker (ed.), Oxford Intersections: AI in Society (Oxford, online edn, Oxford Academic, 20 Mar. 2025)


Progress 08/01/23 to 07/31/24

Outputs
Target Audience:This is the first year of the project. We have spent considerable time fine-tuning our methods and approach to conducting foresight, serious games, and science museum exhibition.The next step is to actually conduct workshops with a range of stakeholders. This will be achieved in year 2 of the grant. Changes/Problems:During the first few months of the project, we had to drop our co-PI Brianna Posadas from the project. Brianna had some medical concerns that made it difficult for her to continue the role. She also had to leave Virginia Tech due to health concerns. The project remains on track and no changes or problems are anticipated. What opportunities for training and professional development has the project provided?There are currently three graduate students, two undergraduate students, and two faculty (including the PI) working on this team. The students have taken complete ownership of the project activities, and each activity is leading directly to the student's dissertation chapter. The team meets once every two weeks and have been working well together. The students have been learning from each other as well as the PI. I am also creating opportunities for them to interact with people from outside the lab (private sector, academia, farming community).This way, the project is directly geared toward providing opportunities for training and professional development. The students are also beginning to prepare their work-in-progress in writing workshops and to be presented at national and internationalconferences. This will help incorporate constructive feedback into their research during the early stages of the project. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?For objective 1, we plan to conduct several foresight workshops with stakeholders across the agricultural value chain. For objective 2, we plan to release the serious game for research and data collection. For objective 3, we intend to finalize and test the prototype for the science museum exhibit and fabricate designs that are suitable for children. We plan to continue to publish and present our research at conferences and cooperative extensionevents (where possible)across the country.

Impacts
What was accomplished under these goals? For objective 1, we have spent considerable time together as a team to fine-tune our approach to foresight. In line with the project goals of helping stakeholders envision alternative futures, we are relying onmore creative and emancipatory methods of foresight. We have also designed the foresight workshops so that they can generate the most effective conversations and visions of the future, while ensuring that we canlearn more about possible and potential data and AI governance approaches and their implications.We aim to conduct foresight workshops during year 2. For objective 2, we have been working on designing a seroius game, along with bringing the prototype to farmers and other stakeholders in our other projects (to gather feedback). This feedback has helped us redefine the goal for the serious game, improve graphics and game play, and carefully think of testable hypothesis for research.The game is currently being fine-tuned and we aim to release it for research at the beginning of year 2.For objective 3, we have partnered with the Western Virginia Science Museum and prototyped an exhibit that will be in the form of "Choose your own adventure game". The adventure game will allow children visiting the museum to envision alternative futures of agriculture and answer questions pertaining to governance of technology. We plan to finalize this exhibit design in year 2 and then showcase it in year 3 onward.

Publications

  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Gardezi, M., Abuayyash, H., Adler, P. R., Alvez, J. P., Anjum, R., Badireddy, A. R., ... & Zia, A. (2024). The role of living labs in cultivating inclusive and responsible innovation in precision agriculture. Agricultural Systems, 216, 103908. https://doi.org/10.1016/j.agsy.2024.103908
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Ishtiaque, A., Krupnik, T., Krishna, V. Uddin, M., Aryal, J., Srivastava, A., Kumar, S., Shahzad, M., Bhatt, R., Gardezi, M., Bahinipati., R., Nazu, S., Ghimire, R., Anik, R., Sapkota, T., Ghosh, M., Subedi, R., Sardar, Asif., Uddin, K.M., Khatri-Chhetri, A., Rahman, M., Singh, B., Jain, M. (2024). Overcoming barriers to climate smart agriculture in South Asia. Nature Climate Change. https://www.nature.com/articles/s41558-023-01905-z
  • Type: Journal Articles Status: Under Review Year Published: 2024 Citation: A socio-technical framework for analyzing crop advisors' preferences for AI-based decision support systems. Journal: Technological Forecasting & Social Change