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CROP 590: Experimental Design in Agriculture
Additional links will be activated as the term progresses.
Announcements will be posted on the Blackboard as needed.
Time and Place:
Lectures: MWF 8:00 CRPS 122
Section 1 H 8:00 – 9:20 am CRPS 150
Section 2 H 3:00 – 4:20 pm CRPS 150
Help Sessions (optional): T 8:00 – 9:00 in CRPS 150
Office: CRPS 221
Office Hours: W 9:00 – 10:00 am
Office: CRPS 221
Office Hours: F 10:00 – 11:00 am
There is no required text for this class. Recommended references are on reserve in the library.
Kuehl, Robert O. (2000) Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd edition. Duxbury Press. (an excellent general reference, but uses slightly different notation than we do in class) Q182.3.K84 2000
Clewer, A. G. and D. H. Scarisbrick (2001) Practical Statistics and Experimental Design for Plant and Crop Science. John Wiley & Sons. (A good reference for those who are not too familiar with statistics; the emphasis on agricultural research is also very relevant for the course) QK51.C58 2001
Cody, Ronald P. and Jeffrey K. Smith (2006) Applied Statistics and the SAS Programming Language, 5th edition. Prentice Hall, New Jersey. QA276.4 .C53 2006
Petersen, Roger G. (1994) Agricultural Field Experiments: Design and Analysis. Marcel Dekker, New York. (Much of the lecture material was adapted from this text, but it contains some errors. Two copies of the text are on reserve in the library, should you need any clarification on lecture material.) S540.F5 P47 1994
Other Suggested References:
Cochran, W. G., and G. M. Cox (1957) Experimental Designs, 2nd ed., Wiley, New York.
Cox, D. R. (1958) Planning Experiments, Wiley, New York.
Gomez, K. A. and A. A. Gomez (1984) Statistical Procedures for Agricultural Research, 2nd ed. Wiley, New York.
Little, T. M., and F. J. Hills (1978) Agricultural Experimentation, Wiley, New York.
Mead, R., R. N. Curnow, and A. M. Hasted (2003) Statistical Methods in Agriculture and Experimental Biology, 3rd ed., CRC Press, Boca Raton, Fl.
Montgomery, Douglas C. (1991) Design and Analysis of Experiments, 3rd ed., Wiley, New York.
Petersen, R. G. (1985) Design and Analysis of Experiments, Marcel Dekker, New York.
Quinn, G. P., and M. J. Keough (2002) Experimental Design and Data Analysis for Biologists, Cambridge University Press, Cambridge, UK.
Ramsey, F. L. and D. W. Schafer (2002) The Statistical Sleuth: A Course in Methods of Data Analysis, 2nd ed., Brooks/Cole, CA.
Snedecor, G. W., and W. G. Cochran (1980) Statistical Methods, 7th ed., Iowa State University Press, Ames, IA.
Sokal, R. R. and F. J. Rohlf (2011) Biometry : the Principles and Practice of Statistics in Biological Research, 4th ed., W.H. Freeman, New York, NY.
Steel, R. G. D., J. H. Torrie and D. A. Dickey (1997) Principles and Procedures of Statistics, 3rd ed., McGraw-Hill, New York.
Other SAS References:
Der, G. and B. S. Everitt (2002) A Handbook of Statistical Analyses using SAS, 2nd ed., Chapman & Hall/CRC.
Littell, R. C., W. W. Stroup, and R. J. Freund (2002) SAS for Linear Models, 4th ed. SAS Series in Statistical Applications.
Announcements will be posted on the Blackboard as needed.
For information on how to access and use the Blackboard, go to the OSU Extended Campus website login page at http://my.oregonstate.edu/
Grades for homework and exams will be posted on the Blackboard under CROP_590_001_W2014.
You can access the course website through the Blackboard (click on the course information button) or directly at this url:
General Course Description
This course addresses the needs of the student preparing for a career in agricultural research or consultation and is intended to assist the scientist in the design, plot layout, analysis and interpretation of field and greenhouse experiments. Emphasis is placed on experimental designs used in agronomy and plant breeding research with more emphasis toward applied statistics rather than statistical theory. Many numerical examples and problems will be presented and the recitation will allow students to explore analysis using SAS and Excel.
Students should have an introductory understanding of statistical methods including the ideas of interval estimation, significance testing, simple linear regression and correlation. Familiarity with such common statistical tables as Student’s t, F, and chi-square is expected. The necessary mathematical background is minimal. At most, a knowledge of college algebra is required.
Assessment/Evaluation of Student Performance
|Proposal for experiment||15%|
|Final exam (part take-home, part in-class)||20%|
Grades will be assigned according to the following point system:
|97-100 = A+||87-89 = B+||77-79 = C+||67-69 = D+||<=59 = F|
|93-96 = A||83-86 = B||73-76 = C||63-66 = D|
|90-92 = A-||80-82 = B-||70-72 = C-||60-62 = D-|
There will be seven graded assignments in this class, which will be due one week after they are assigned. It is highly recommended that you use Excel to complete the assignments, and that you submit them electronically via the Assignment function in Blackboard.
You will have the opportunity in Recitation to gain some hands-on experience with SAS analyses. You will not be expected to turn in work from your recitation sections, but attendance is required. Self-assessments will be available on Blackboard to encourage you to review the lab material and ensure that you have understood the major concepts presented each week. You will also need to be able to write simple SAS programs in order to do some of your assignments later in the term, and you may be asked to interpret SAS output on exams.
You may bring one 8.5” x 11” piece of paper and a calculator with you to each exam. You may write any formulas or notes that you think you will need on the front and back of the paper. Exams are comprehensive, but emphasis will always be on the material since the last exam. For the second midterm and final, you may bring the 8.5” x 11” papers that you prepared for earlier exams, along with a new sheet for the exam that you are taking. The take-home portion of the final will be given to you during the 9th week of class and will be due at the time of your final exam (Tuesday, March 19). There will most likely be two components to your take-home:
1) You will be presented with an experimental scenario and asked to describe an appropriate experimental design based on concepts presented in class.
2) You will be given some experimental data and asked to complete the analysis on your own using SAS.
The format for the in-class portion of the final will be similar to your midterm exams and should take no more than one hour to complete.
Instructional Objectives and Student Learning Outcomes:
Upon completion of the course, students should be able to:
- Define experimental error
- Enumerate and explain at least five ways that experimental error can be reduced
- Discuss the relationship of plot size, shape, and placement to experimental error
- Identify objectives of a field experiment
- Summarize and give examples of the assumptions of the ANOVA. Use statistical tools to detect violations of ANOVA assumptions and apply appropriate transformations
- Discuss the difference between sampling, replication and blocking
- Explain and compute the components of an analysis of variance for the following experimental designs and list the advantages and disadvantages of each:
- Complete randomized
- Randomized complete block
- Latin square
- Split plot
- Strip plot
- Explain the difference between fixed and random effects and be able to interpret computer output from mixed model analyses
- Select and justify a particular experimental design to meet the experimental objectives
- Use Excel to generate random numbers, organize and summarize data, display data graphically and perform simple statistical analysis
- Explain the difference between main effects and interactions and how these affect the interpretation of results
- Form appropriate orthogonal contrasts to answer specific questions raised by an experiment
- Demonstrate the appropriate use of mean separation techniques
- Set up an ANOVA table for multilocational or multiyear trials and explain how the F tests would be computed
- Use SAS to generate an analysis of variance for all of the experimental designs discussed
Outline of Topics Covered
- Week 1 Basic Principles
- Kinds of field experiments
- Site selection
- Experimental error
- Field uniformity
- Steps in experimentation
- Types of data to collect
- Hypothesis testing
- Review of t tests
- Week 2 Field Plot Fundamentals
- The importance of randomization
- Control of experimental error
- Plot shape and orientation
- Border effects
- Completely randomized design
- Week 3 Experimental Design Strategies
- Monday, Jan. 20 - Martin Luther King holiday (no class)
- Concepts of replication and blocking
- Randomized block design
- Optimum and convenient plot size
- Number of replications and power calculations
- Week 4 Refining the Model
- ANOVA assumptions
- Checking ANOVA assumptions and transformations
- Fixed, Random, and Mixed Models
- Introduction to Generalized Linear Mixed Models
- First Test (on material from Weeks 1, 2, and 3)
- Week 5 More Designs + Mean Comparisons
- Latin square design
- Structured versus unstructured experiments
- Mean separation techniques
- Nested (hierarchical) designs
- Week 6 Factorial Experiments and Contrasts
- Factorial experiments
- Main effects and interactions
- Orthogonal contrasts
- Week 7 Different Plot Sizes within an Experiment
- Split-plot designs
- Strip-plot designs
- Repeated measures
- Second Test (on material from Weeks, 4, 5, and 6)
- Week 8 Regression in the ANOVA
- Regression in the ANOVA
- Orthogonal polynomial contrasts
- Analysis of covariance
- Week 9 Multiple Sites and Unreplicated Experiments
- Combined experiments (multilocational trials)
- Unreplicated experiments, augmented designs
- Week 10 Experiments with Large Numbers of Treatments
- Incomplete block designs, Lattice designs
- Student proposals - poster session, Wed., March 12, 2014
- Exam Week
- Final - Wednesday, March 19, 2014 at 12:00 in CRPS 122
Tentative Recitation Schedule
Week 1 Introduction to SAS; review of basic statistics
Week 2 One-Way ANOVA (CRD) (Excel and SAS)
Week 3 Two-way ANOVA (RBD), Power calculations
Week 4 ANOVA Assumptions, Transformations, Mixed Models
Week 5 Latin Square ANOVA, Multiple Comparison Tests
Week 6 Nested Designs, Factorials and Contrasts
Week 7 Split Plot, Strip Plot, Repeated Measures
Week 8 Regression and Analysis of Covariance
Week 9 Across Site Analyses (Combined Experiments)
Week 10 Augmented Designs, Lattice Designs