Back to Jayfred's Experiment

Now we are back to Jayfred's experiment. Your assignments for this module are based upon this experiment, so let's understand the details of this experiment.

Jayfred also followed an augmented design for his experimental design.  Overall, he is testing 190 new wheat genotypes (varieties). In his data files these are each referred as an "entry" and are indexed from 1 through 190.  Rather than replicating the genotypes in the field, they are randomized across 5 blocks, with no single new genotype planted twice in the experiment. Jayfred used three spring wheat varieties as checks in his experiment:  Kelse and Scarlet, both are Washington varieties.  Kelse is a hard red spring wheat that was bred for high rainfall regions. Scarlet is also a hard red spring wheat, but was bred for semi-arid region. Crosses were made from these two parental lines to form the population of segregating genotypes which are being tested. The third variety is Jefferson.

The Kelse and Scarlet checks are replicated 3 times in each block and Jefferson is replicated 4 times (see image below for field details). Now doing the math, we have 190 testing (new) genotypes which are distributed across the 5 blocks. Since a new entry may be only represented once with a total count of 190  + 2 * 3 * 5 checks (2 checks x 3 replications of each *  5 blocks ) +  1 * 4 * 5 checks (1 check x 4 replications * 1 block) gives us a total of 240 total plots in the experiment.

Their hypothesis is that Scarlet would perform better when exposed to high temperatures. In addition, they are thinking there will be segregation seen in the genes associated to heat tolerance in the new population.   In other words, they are hypothesizing that the new population, which has 190 different genotypes, will contain many different genes which lead to heat tolerance. The population was made by crossing .

This is where statistical analysis comes into play. Using the proper statistical design and analysis will allow Jayfred to adjust for experimental errors, spatial variations, etc. giving them good estimates of phenotypic performance (tolerance to heat) which will then be useful for future work in using QTL mapping or actually locating the genetic regions that give spring wheat plants good tolerance to heat. They can use R to correlate canopy spectral reflectance data with wheat physiological trait data especially under heat stress and test if this is a good index that can be used as a selection parameter.  By selection parameter, they mean that if there is a good correlation between a CSR type of data and the actual wheat physiology, that they could then use the CSR as an easier tool to help quickly identify those individual plants which have high tolerance to heat stress.   

Simplified Field Layout of Jayfred's Experment

Shown below is a diagram with a simplified view of the field layout and experimental design. On the top first row of the diagram we represent the 5 blocks that comprise the experiment. Under each of those blocks we see the 48 different plots along with an indicators regarding the wheat variety planted in that plot. Finally, in the last three rows of the diagram we see the summary of the number of check entries in each of the blocks.

Experiment Layout 

Below is a quick summary of the experiment: 

1.  The 190 new genotypes are:

• Not replicated, and 

• Are randomized across 5 blocks

2. Two check varieties replicated 3 times per block IKelse and Scarlet)

3. One check variety is replicated 4 times per block (Jefferson)

As explained earlier, among the other experimental observations, the domain scientists, Jayfred and Mike, are recording reflectance (CSR) data in their spring wheat plots at difference dates throughout the growing season. Additionally they are recording trait data on the plots (e.g. yield, biomass, etc.). Specifically, the goals of this study are: (1) compare reflectance indices across different dates, (2) compare traits among genotypes and checks, and (3) correlating reflectance indices with traits. In this lesson we will work with the second objective i.e. comparing the traits (specifically, yield) among genotypes and checks. 

Please follow the above experimental description to solve the assignment for this module.



Question: The method that you learned in this lesson - Analysis of Augmented Block Design Using R Part 3: Analyzing Treatments as Random Effects, with respect to Jayfred's data has the goal to:

Looks Good! Correct: Explanation: Our goal is to distinguish if our new traits are any different than existing ones.