Repeated measures and subsampling with perennial crops

Published at December 4, 2023 ·  5 min read

In a recent post, I have talked about repeated measures, for a case where measurements were taken repeatedly in the same plots across years see here. Previously, in another post, I had talked about subsampling, for a case where several random samples were taken from the same plot see here.

Repeated measures and subsampling are vastly different: in the first case I am specifically interested in the ‘evolution’ of the response over time (or space, sometimes). In the second case (subsampling), I only want to improve the precision/accuracy of my measurements, by taking multiple random samples in each plot.

Subsampling in field experiments

Published at March 29, 2023 ·  11 min read

Subsampling is very common in field experiments in agriculture. It happens when we collect several random samples from each plot and we submit them to some sort of measurement process. Some examples? Let’s imagine that we have randomised field experiments with three replicates and, either,:

1. we collect the whole grain yield in each plot, select four subsamples and measure, in each subsample, the oil content or some other relevant chemical property, or
2. we collect, from each plot, four plants and measure their heights, or
3. we collect a representative soil sample from each plot and perform chemical analyses in triplicate.

For all the above examples, we end up with 3 by 4 equal 12 data for each treatment level. The question is: do we have 12 replicates? This is exactly the point: subsamples should never be mistaken for true-replicates, as the experimental treatments were not independently allocated to each one of them. In literature, subsamples are usually known as sub-replicates or pseudo-replicates: for the above examples, we have three true-replicates and four pseudo-replicates per true-replicate. Let’s see how to handle pseudo-replicates in data analysis. But, first of all, do not forget that: experiments with pseudo-replicates are valid only when we also have true-replicates! If we only have pseudo-replicates… well, there is nothing we can do in data analysis that transforms our experiment into a valid one…