Course Syllabus

Welcome to Agronomy 513 at Iowa State University!

Instructor: Dr. Marin Harbur

Office Hours: ​Key activities in Office Hours include answering unclear content from modules, review of key module content, assignment issues/questions, and preparation for exams.


Course Description: Quantitative methods for analyzing and interpreting agronomic information. Principles of experimental design, hypothesis testing, analysis of variance, regression, correlation, and graphical representation of data. Use of SAS and Excel for organizing, analyzing, and presenting data. Required course for the Master of Science in Agronomy degree program.

Course Prerequisites: AGRON 181 or equivalent, MATH 140, STAT 104. Restricted to graduate students enrolled in MS Agronomy online degree program at ISU. Students from other departments must get permission.

Look for my welcome message in Canvas to learn more about my background. You will introduce yourself as part of Discussion Topic 1.1.

Course Overview

Welcome to Agronomy 513!  I've been involved with Agronomy 513 since 1998, when I was a graduate student at Iowa State University.  I've taught this course since 2012. Until last fall, the course stayed pretty much the same -- a typical statistics course, only with agricultural datasets.  

But after 10 years in industry, first in retail agriculture at a regional cooperative in Ohio, and then as a data scientist for Winfield United (Land O Lakes), I gained a better sense of what statistics are regularly used outside academia.  In many cases, you need a broad overview of statistics to better understand reports from your own full-time statisticians.  In other cases, you need how-to recipes to run basic statistics on your own.     

Thus, we will start with how to summarize yield results from a yield map, then work our way through sampling from multiple fields, "side-by-side" trials, and more extensive hybrid or product comparisons.  We will also look at how quantitative treatments (like different product rates) can be modelled and yield responses predicted.  We will learn how to present data, and how spatial data is used to create fertilizer application maps.  And we will spend a unit on machine learning, to better understand how "Ag Tech" tools you use make predictions and find patterns in data.

Besides changing course content, I also moved course exercises from SAS to R.  R is open-source software (meaning you can access it long after you graduate); more so, it has amazing capabilities to model and visualize (plot, graph, and map) data.  It is command-line software -- you need to write code to run it.

Statistics, and math in general, can be a source of anxiety for many.  I get it: I am not a math prodigy.  Furthermore, a lot of mathematical and statistical texts are awful -- filled with poorly-explained mathematical notation and stingy about reminding us what variables represent.  I am constantly turning back pages to remember the latter.

That is a shame, for statistics, at its heart, is the beautiful process of describing shapes in our data.  How spread out is our data?  Where is its center value?  How much do observations from different treatments overlap?  Do the data form a line that represents a relationship, or a meaningless cloud?  

My intention with this course is to challenge you to learn a lot of practical statistics -- and make it as painless as possible for you to do so.  It will require attention and time from you.  But this should not wasted time.  I have agonized over what to include in this course and how to explain it, all because I don't want you to get stuck.  But when you do, I want you to reach out right away.  There are no bonus points in this course for frustration.  Let's keep you on track and learning as efficiently as possible.  

Course Philosophy

This purpose of this course is to learn how to apply various statistical tools to design experiments and interpret data. We want to focus on understanding and using the tools, rather than only memorizing formulas and definitions. Therefore, the best strategy for success may vary somewhat for this course from those used for other courses.

A useful analogy is learning to play a musical instrument. You would not expect to be able to learn how to play the piano by reading about it in a book. Of course, you could learn the theory behind the instrument, but translating that into the ability to play would be a stretch. To learn to play it, you have to … practice. To be an accomplished pianist you have to practice a lot.

Course Objectives

      • Understand fundamentals of experimental design and how statistics describe the distributions of data.
        1. Recognize whether data have normal or non-normal distribution
        2. Appreciate the importance of treatment replication and randomization to experimental design and statistical testing
      • Use common statistical tests to determine whether observed differences between or among treatments are likely the result of chance sampling, or likely to occur in the population itself.
        1. Use the t-distribution to test the difference between two sample means
        2. Use analysis of variance (ANOVA) to test differences among multiple treatment means
        3. Understand how sources of variation included in ANOVA tests differ among completely randomized design (CRD), factorial design, and randomized complete block design (RCBD) trials
      • Apply linear and non-linear regressions to determine whether relationships among variables are likely the result of chance sampling, or likely to occur in the population itself.
        1. Use simple linear regression to analyze the relationship between two variables.
        2. Use multiple linear regression to analyzed the relationship among three or more variables
        3. Use non-linear regression to fit curves to non-linear relationships between variables
        4. Understand how regression models are properly used to predict data

Course Structure

Module Format

The online course materials in Agronomy 513 consist of 14 modules modules and a midterm and final exam.

Each module is designed to take one week to complete. Your weekly activities will typically include the following:

      • Reading the online modules and utilizing the included learning tools.
      • Reading any required textbook pages.
      • Completing and submitting each Homework Assignment. Homework assignments allow you to practice the concepts you have learned in each module.  They are also a de facto study guide for the exams.  I am lenient in grading, if it appears that you have made a solid effort, but I don’t generally allow re-dos, especially after I have distributed the answer key.  If you get stuck, reach out to me before you submit your homework!
      • Responding thoughtfully to each Discussion Topic. There are ten discussion topics that are assigned throughout the semester.  You will be randomly divided into subgroups of 4 – 6 students for each topic.  These questions are designed to explore important principles.  Not every module has a discussion topic; and Module 8 has two discussions. To get full credit for each discussion topic, post your own answer to the discussion question and write one response to another group member’s post.
      • Completing and submitting each Module Reflection. At the end of each module you will be asked to respond to three questions:
        1. In your own words, write a short nontechnical summary (about 150 words) for this module.  Please be specific about the module content.  The summary need not be perfect, it is an exercise to help you synthesize the material. 
        2. List the most valuable concept you’ve learned from the module.  This helps us to identify what areas to expand or reduce in future versions of the course.
        3. Describe module concepts that are hardest to understand.  This helps us understand what sections may need more work.
      • Participation in Office Hours.
      • Completing the four Quizzes (see below).  

Required Textbook

You may also find additional course material in Canvas modules. These materials include links to exercises in RStudio Cloud and videos explaining the lecture and exercises.  Please review the module folders frequently in Canvas.

Required Technology

We will use R for this course and access it using a cloud-based service called RStudio Cloud.  R is the language that runs the analysis.  RStudio is an environment (user interface) that allows us to write code, submit it, and view results.  It also displays and organizes the datasets we create.

Elsewhere in the course introduction, I will provide instructions how to link to RStudio Cloud for this course.  You will need to create a free account.  From there, you will be able to download modules for the course, which will include the lecture, exercises, and assignments.

Instructor Interactions

Office Hours: ​Key activities in Office Hours include answering unclear content from modules, review of key module content, assignment issues/questions, and preparation for exams.

Email: Feel free to message the instructor via the Canvas Inbox if you have any questions or concerns.

Discussions: Instructors actively read all discussion posts, and will engage the discussion early in the week. Feel free to draw the instructor into the discussion with a question. 

Graded Feedback: I am committed to providing you timely feedback on your work. Assignments are typically graded and returned within a few days of being turned in. Please monitor your progress carefully and contact me during the semester if you are concerned about your progress. I will not adjust grades after the end of the semester – you are responsible for earning your grade during the semester.

Grading Procedures

Letter grades for the course will be assigned as follows:
A: 93-100
A-: 90-92
B+: 87-89
B: 83-86
B-: 80-82
C+: 77-79
C: 74-76
C-: 70-73
D+: 68-69
D: 65-67
F: <65

Please don’t count on a curve. Last semester, grades were only adjusted 1.5 points by the curve.



The homework assignments in this course are designed to let you practice the skills and techniques that have been presented to you. The best way to master the course material and learn the methods is to do the homework. Any shortcuts you take there will show up later when you are asked to apply the methods as part of a course exam. Please do not short change yourself by turning in the work of someone else as your homework. This hampers your learning, is unethical and represents academic dishonesty. Homework is part of the learning process. I am relatively lenient in grading – especially if it appears that you have made a solid effort. As a rule, I don’t allow re-dos, especially once I have sent out the answer key.

I will also be available to help you online as a group with homework and other questions. I host Office Hours on Thursday evenings from 7:30 to 8:30 pm CST. For each session, I will be available via Adobe Connect promptly at the beginning of the hour. I will remain available unless no students have logged on by 7:45pm, or the last student has logged off.

Hopefully, the two sessions will allow you flexibility in planning your work week, shuttling kids around on the weekends, etc. I enjoy the online study group, and students who participate in them typically perform well on the homework and exams.

Module Reflections

At the end of each module you will be asked to respond to four questions:

  1. In your own words, write a short summary (150 words) of each week’s module. Be specific about the module content, not your general feelings about the module (save that for the third question), but represent the entire module. I want to be sure that you have kept up with the material.
  2. What is the most valuable concept you learned from the module? Why is this concept valuable to you?
  3. What concepts in the module are still unclear/the least clear to you? These help me develop the Questions and Answers section at the end of each module!

Module reflections are an essential way for us to communicate. I read your summaries to assess whether or not you understand the material and will let you know if you make a mistake.

A note on Question 3: every semester, I get a few students who answer week after week that “the module was well taught” and “everything was clear.” Nonsense! This is confusing material and my instruction is far from perfect. If you are not confused, you have not thoroughly absorbed the material. Re-read the text and I am sure you will find something that bothers you!

Please answer Question 3 each week. I know your answers to Question 3 may be very similar from week to week, but please do not answer “the same as last time” – my memory just isn’t that good.


There are ten discussion topics that are assigned throughout the semester. I realize that in many cases there is less to discuss after the first few students have made a post. However, the questions are designed to make you think about important principles and I would like for you to be as thoughtful and creative as possible in answering them. You may be surprised what you can learn from each other by taking the time to view posts and respond to them. To get full credit for each discussion topic, you must post to the topic, respond to at least one post from another student, and read at least 10 responses made by other students.


Each quiz will cover the previous three units.  There are four quizzes in this course, covering Units 1 - 3, 4 - 6, 7 - 9, and 10 - 12.  They are open-book and untimed, and you will have over a week to complete them.  While memorization is great, it is more important you learn how to find your way back to answers.  I hope this approach will reduce your stress.  I will send out reminders as the quizzes become available.


Communication Policy

All communication within the course should adhere to university standards of Netiquette at ISU. Specifically, communication should be scholarly, respectful, professional, and polite. You are encouraged to disagree with other students, but such disagreements need to be based upon facts and documentation. It is my goal to promote an atmosphere of mutual respect in our interactions. I expect you to approach this course with the same degree of professionalism you take to your work. Timeliness in completing assignments, respect for me and co-participants, and personal responsibility are essential. This course is a learning contract between you and me: I commit explaining the material and helping you through it in a timely manner, and by registering for this course you commit to completing the material and corresponding with me whenever you have questions about the material.

I know that you, like me, have a day job, children who seem to be mainlining espresso, and other distractions of daily life.  In addition, we are still navigating COVID, with unexpected illnesses, school closures, and general anxiety and mental stress.  I ask you to be timely and professional with your work, whenever possible, but communicate with me if issues arrive so we keep you caught up in the course.

Please email me at for any questions about the content of Agronomy 513 or if you have suggestions for improving the interactions in this course. I will do my best to respond within 24 hours unless you have been notified in advance of travel times. You can also use the Course Questions forum in Canvas (or email) to ask questions, share an interesting article or observation, or comment on current and relevant events. 

If you are struggling with the course, contact me immediately and make the time to attend Office Hours.. Note that there are firm and specific deadlines for course drops, tuition refunds, incompletes, etc., that we cannot override. It is your responsibility to monitor your progress in the course and be aware of these deadlines.

For computer-related problems or problems with connections via the internet, contact the Agronomy Development Lab staff at (515)-294-6602 or by email:

General announcements will be posted to the Announcements section of Canvas.

Be sure to properly configure your Notification settings or commit yourself to checking Canvas daily for new communication.

Feedback Policy

I am committed to providing you timely feedback on your work. Assignments are typically graded and returned within a few days of being turned in. Please monitor your progress carefully and contact me during the semester if you are concerned about your progress. I will not adjust grades after the end of the semester – you are responsible for earning your grade during the semester.

Personalized feedback will be provided for each homework and reflection. In addition, a homework key and summary of common issues students encountered will be provided for each module. 


All deadlines are posted on the course calendar in Canvas. Deadlines for Homework, Reflections, and Discussions are firm to be fair to each student and keep us on track with the learning. I want to provide rapid feedback on the homework, and I cannot do that if assignments are outstanding.

Module materials will be made available several weeks in advance, so that you can work ahead in the event you have a business trip. Assignments turned in late will not be graded. No exceptions. For me to do otherwise is to be unfair to other students who may have compromised their own obligations to get their work in on time.

Study Tips

Questions and Answers

At the end of each Module in the online course materials, you will find a Questions and Answers section. These are actual questions posed by previous victims of this course. If you are confused, you may very well find that someone else has asked a similar question. (This indicates we are consistently confusing!) Seriously, though, it may help you to see your question answered in slightly different language than that used in the text, or it may help answer a question that the main module overlooked.

Homework Tips

Each week, I will message the class with tips on the upcoming Homework assignment. Be sure to read these carefully as they will better prepare you to complete the homework.

Sample Study Plan

A sample study plan is provided here to help you make efficient use of your time.









Week 1

Start Module 1

Read the Module 1 online materials:
make notes to aid with Module Reflection and compile list of questions to ask during Office Hours

Reply to DT 1.1

Work on Module 1 homework


Participate in Office Hours

Post initial
DT response

Participate in Office Hours

Week 2

Work on Module 1 homework

Read the Module 2 online materials:
make notes to aid with Module Reflection and compile list of questions to ask during Office Hours

Reply to DT 2.1

Work on Module 2 homework

Start Module 2

Module 1 Assignments, Discussions, Reflection due

Participate in Office Hours

Post initial
DT response

Participate in Office Hours



Category Description Action

Course Content Support

Questions related to course content or grading should be directed to the course instructor. Instructor via Canvas Inbox

Student Support

The Center for Excellence in Learning and Teaching is an organization dedicated to supporting, promoting, and enhancing teaching effectiveness and student learning at ISU.

Self-guided orientation which you may find useful.

CELT: Online Learner Support

Self-Guided Orientation

Canvas Technical Support

If you experience any technical issues while using Canvas, contact the Solution Center. The Solution Center's hours are posted on their website.

Solution Center

Technology support

If you have any technical issues while using the University Library's Course Reserves system, please refer to the Library's FAQ page.

For all other technical issues, contact Agron DevLab Support. The Agronomy Development Lab staff is guaranteed to respond to requests within 24 hours during regular business hours. All requests made during the weekend will be addressed first thing Monday morning.

Course Reserves FAQ


Agron DevLab Support

Writing Support

The MS Agronomy program has built a Writing Guide to help answer some of the questions you may have while working on your courses.

Ms. Amy Pollpeter is available for one-on-one consultations and can assist you with any part of the writing process. Schedule an appointment with Amy through the CELT's website or via email.

Writing Guide

CELT's website or via email.

Library and research support

Anita Kay is the liaison librarian to the Department of Agronomy. She can help find any article, book or any other piece of information that you want assistance finding.  Anita has also built a really useful Agronomy Research Guide (Links to an external site.).

Anita Kay
Agronomy Research Guide (Links to an external site.)

Department Contact

Contact Dr. Mary Wiedenhoeft, Associate Chair for Academics in Agronomy, if issues persist after working with the support systems listed above. Dr. Mary Wiedenhoeft

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Course Summary:

Date Details Due