Thank you for bringing this matter to our attention!. It is still a great help and a real pleasure to read your posts
Generally speaking: as many replications as you can afford! The more replications, the better the precision of the comparison of the treatment means. Practically this means in field tests more replications than in laboratory tests, because of the controlled factors in a lab and uncontrolled factors in the field (e.g. weather conditions). In field trials, as a ballpark figure the number of blocks should be enough to give a minimum of 12 residual degrees of freedom: Rdf = (t-1) x (r-1) where t is treatments and r is blocks or replications (Clewer & Scarisbrick, 2001, Practical Statistics and Experimental Design for Plant and Crop Science). E.g. if you have 5 treatments you could start with 4 replications as a minimum under field conditions, and more would be better. However, the bigger the experiments, the higher the variation in measurements tend to get, so there is a balance between precision and resources.
Sample size is the minimum number of individuals taken from a population to obtain a statistically significant result. Repeats or repeated measurements are repetitions of the sample per treatment with the aim of reducing experimental error. Replication is the repetition of all the treatment combinations to be compared in an experiment to be able to estimate variability.
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