AE 330 - Economics of Precision Agriculture

3 Credits

1. recognize statistical principles, gaining proficiency in using statistical software for data analysis and identify appropriate data for economic analysis.
2. create meaningful data visualizations, enabling to interpret and draw meaningful conclusions from graphical information when conducting analytical comparisons.
3. appraise the economic performance of both livestock and crop enterprises and explain how management decisions around technology influence profitability.
4. assess and evaluate appropriate assumptions and recognize that analyses is specific to the individual technology and operation.
5. develop ROI case studies for livestock and crop enterprises and various technologies for each.
6. demonstrate the ability to provide logical input into analyses and ability to collaborate to fully assess the positive and negative benefits of technology implementation.

Hoshmand, R., 2018. Design of experiments for agriculture and the natural sciences.

Chapman and Hall/CRC.

Kay, R. D., Edwards, W. M., & Duffy, P. A. (2016). Farm management. McGraw-Hill

Education.

Pearson, R.K. (2018). Exploratory Data Analysis Using R (1st ed.). Chapman and Hall/

CRC.

Pedersen, S.M. & Lind, K.M. (Eds.). (2017). Precision agriculture: Technology and

Economic Perspectives. Springer International Publishing.

Roulstone, D.B. & Phillips, J.J. (2008). ROI for technology projects: measuring and

delivering value (1^st ed.). Butterworth-Heinemann.

Uzayr, S. (2023). Mastering R: A Beginner’s Guide (1st ed.). CRC Press.

Additional resources are provided on Desire to Learn (D2L).

Conduct of Course:
This course consists of lectures. The scheduled class time may include lectures, guest speakers, group activities, field trip(s) and/or time to for discussions.

Students are expected to complete assigned readings, watch, or listen to any assigned videos and/or podcasts prior to each lecture. Students are also expected to complete course assignments outside of class time.

Attendance is taken for each lecture and contributes to the students’ participation mark.

Students are evaluated through participation, a term-paper on tech-economics applied to farming systems and, a final examination.

The instructors hold open office hours. Students can make appointments to speak with the instructors outside of office hours, if needed. If emailing an instructor to request support related to the course, responses can be expected within 48 hours. Please plan accordingly.

Classroom and laboratory attendance is considered vital to the learning process and as significant to the students’ evaluation as examinations and reports.

1. Students having a combination of excused and/or unexcused absence of 20 percent or higher for the scheduled course hours are required to withdraw and automatically receive a “RW” (required withdrawal) for the course, regardless of any other evaluation results. (RW is a failing grade.)
2. An excused absence is one that is verified with your instructor. Verification should be prior to the absence or the next class day following the absence. Verification of the absence may take the form of a note from your doctor/College nurse regarding illness, or a note from another instructor regarding a field trip or other activity, or authorization by your instructor. An unexcused absence is anything NOT verified by the instructor prior to the absence or the next class day following the absence.

It is the students’ responsibility to know their own absentee record.

Normal hours are 8:30 a.m. to 6:30 p.m., with potential for evening courses, exams or extended field trips. Students are expected to be available for classes during these times.

Content of Course:
 

1. Introduce the basics of research trial design
2. Basic statistical knowledge for data analysis
3. Use of software such as R programming and excel for data analysis
4. The influence of micro and macro-economic factors on ag tech adoption
5. Return on investment of agriculture technology
6. Assumptions - differentiating between correct and incorrect comparisons for analysis
7. Qualitative versus quantitative information in analyses
8. Economics of precision agriculture for crop production
9. Economics of precision agriculture for livestock production