Abstract: College teaching can be enhanced by a deeper understanding of student motivation. A highly motivated student can often outperform students with less enthusiasm and ambition. Reflection and consideration of student motivation allows teachers to develop and implement learning environments to maximize student learning outcomes. The objective of this research is to identify the major determinants of student motivation for learning in an academic environment, using an economic model of household production theory. The determinants of student motivation are identified by the construction of a mathematical model of human capital acquisition. The model provides useful implications concerning how college-level instructors could implement strategies that use student motivation to enhance student effort level and learning outcomes. Timely and useful strategies for teachers are derived from the economic model."

Abstract: Despite the economic importance of the food retailing industry, the literature suggests that food retailers face a major talent recruitment challenge mostly stemming from the negative perceptions of the industry. Therefore, it is crucial that higher education food and agribusiness programs help students better understand and appreciate potential career opportunities and professional career paths. This research develops a teaching innovation that enhances student engagement in asynchronous online learning and explores how such an innovative pedagogy relates to students’ attitudes toward careers in food retailing, with relevance extending across educational formats and disciplines. Through an exploratory quasi-experiment, it is illustrated that innovative pedagogy in online learning may make a difference with respect to attracting students to food retailing careers. The outcomes included more positive attitudes toward a career in food retailing and higher interest levels among the students who were exposed to the teaching innovation compared to students in the control group."

Abstract: This article highlights the critical role of data visualization in applied economics education and outreach. We first outline some general principles for teaching graph literacy and data visualization principles in and out of the classroom. We then discuss the mechanics of visualizing data—collection, preparation, and visualization—with an emphasis on how instructors can teach each step using the R and/or Python statistical environments. We ultimately contend that the requisite skills for successful data visualization are indispensable for students trained in today’s agricultural and applied economics programs to communicate their research effectively."

Abstract: This manuscript provides an overview of an online decision support system (DSS) developed to help growers implement integrated pest management (IPM) practices by delivering short-term risk forecasts for crop management, pest control, and disease prevention. Launched in 1995 by Cornell University’s New York State Integrated Pest Management (NYSIPM) Program, the Network for Environment and Weather Applications (NEWA) leverages local weather data from over a thousand ground-based sensors across the United States to deliver pest risk assessments for 32 models covering fruit, vegetable, ornamental, and agronomic crops. Through real-time weather data summaries, insect and plant disease models, and tailored crop tools, NEWA offers essential resources for agricultural professionals. The platform includes automated alerts for data interruptions and quality-controlled data processing to ensure reliable and timely weather inputs crucial for accurate crop and pest models. NEWA’s open-source framework allows users to customize and expand the system, making it adaptable to diverse agricultural settings. Moreover, historical climate data aids in trend analysis and long-term planning, supporting precision agriculture. The platform empowers Extension educators by providing a foundation for demonstrating sustainable and effective IPM strategies, making NEWA a vital tool for enhancing agricultural resilience and data-driven decision-making."

Abstract: Recognized by Western Agricultural Economics Association (WAEA) and Agricultural and Applied Economics Association (AAEA) as an innovative adult education (Extension/outreach) program. Testing Agriculture Performance Solutions (TAPS) was developed at the University of Nebraska-Lincoln’s (UNL) West Central Research, Extension, Education Center (WCREEC) in North Platte, NE. This program was created to enhance Extension education by increasing stakeholder engagement and commitment. This engagement comes in the form of a series of season-long contests, the application of andragogical principles, and the support of Extension programming and materials. Four key groups make this program viable: facilitators, competitors, integrators, and followers. This program is hosted and maintained by the university facilitators, with help from integrators and agribusinesses. Competitors make production and management choices recorded and acted upon by the facilitators, with reports and publications made available to all, including followers. This paper describes the reasoning and application of the program with accompanying feedback by competitors. The current program focuses on farm profitability in conjunction with nitrogen and irrigation efficacy and efficiency. While the program is effective, it is costly and requires special resources that are limited. To address these issues, a virtual version is being developed. This new virtual TAPS will increase flexibility and reduce costs, making it more accessible and useful."

Abstract: Teaching faculty across institutions find themselves entrenched in the same challenges as the pre-COVID-19 world. However, research and anecdotal evidence point to enhanced traditional challenges along with new ones altogether. Finding ways to encourage academic curiosity and the true value of genuine student learning have never been more difficult. To address this challenge, five mid-career faculty evaluated Blum’s (2020) book, Ungrading: Why Rating Students Undermines Learning (and What to Do Instead), over a series of four seminars. The book revealed that applications of ungraded teaching methodologies must fit within a formally graded framework. The book also offered positive results along with the challenges of implementation and other outcomes. This teaching commentary is a report of the seminars and provides different suggestions to incorporate the positive aspects of the ungraded classroom into a traditional graded environment."

Abstract: This study explores the integration of an AI-powered tutor into agricultural policy education at ETH Zürich to enhance the learning experience and provide insights into AI tools in higher education. Based on a large language model (ChatGPT-4.0), the AI tutor was designed to facilitate interactive learning about agricultural policy, specifically tailored to a textbook on Swiss agricultural policy. It provided functionalities such as concept clarification, summaries, knowledge tests, and open-ended discussions for undergraduate students. Over the course of the semester, 15 students used the tutor independently and for exam preparation. Analysis of student interactions revealed that 79 percent of the tutor’s use was for explaining and clarifying concepts, while 9 percent was for summaries and 4 percent for assessments. Students rated their overall satisfaction with the tutor as 3.8 out of 5 and perceived it as a supplementary learning tool. The results provide insights into the benefits, challenges, and ethical considerations of AI in education and highlight the potential for broader applications in other courses. The study contributes to the discourse on AI in higher education and guides the development and integration of AI-enhanced learning tools to improve student engagement and learning outcomes."

Abstract: Since the release of artificial intelligence tools like ChatGPT, instructors are wrestling with how to maintain academic honesty when students have access to artificial intelligence (AI) tools that can assist them with completing course work. This paper explores the key ethical considerations that an instructor should evaluate when managing students’ use of AI tools. It reviews the syllabus guidance provided by 95 universities that house agribusiness and applied economics programs regarding ethical considerations for managing AI usage. A checklist of key considerations is created to guide instructors who are considering developing course policies for managing the use of AI in their classes. The checklist is used on a data analytics course to demonstrate how to create syllabus statements for the use of AI."