Abstract
Using adequate sample size in experimental units improves the efficiency of the research. In the agricultural year of 2004/2005, an experiment was conducted in Santa Maria, Rio Grande do Sul State, Brazil, with the objective of estimating sample size for the following traits: ear length, ear and cob diameter, ear weight, weight of grains per ear, cob weight and the weight of 100 grains, number of grain rows per ear, number of grains per ear and length of grains for two single hybrids (P30F33 and P Flex), two three-way hybrids (AG8021 and DG501) and two double hybrids (AG2060 and DKB701) of maize. For a 5% (D5) precision, the weight traits (dehusked ear weight, weight of grains per ear, cob weight and weight of 100 grains) can be sampled with 21 ears; the size traits (ear length, ear diameter, cob diameter and grain length) with eight ears; and the number traits (number of grain and rows) with 13 ears. Sample size varies as a function of ear trait and the type of hybrid i.e. single, three-way or double. Genetic variability among ears does not correspond to the increasing genetic variability i.e. single, three-way and double for the sample size of traits per ear.
Highlights
The assessment of genotypes in maize improvement programs is made difficult due to the large number of measurements in each experimental unit
The difference between the amount estimated in the sample and the amount of the same parameter in the population is known as sampling error, which obviously decreases as the sample size increases
No differences were observed for yield of grains between the six hybrids assessed, whose average was equal to 8.75 t ha-1
Summary
The assessment of genotypes in maize improvement programs is made difficult due to the large number of measurements in each experimental unit. Sampling number is an alternative, it is necessary to know beforehand the population variance and the desired accuracy degree for each trait to be assessed, as well as possible genetic interferences (Martin et al, 2005a; 2005b) when creating a single random sampling. In finite populations there are three basic alternatives for sampling: random sampling, systematic sampling and arbitrary sampling (Steel et al, 1997). The difference between the amount estimated in the sample and the amount of the same parameter in the population is known as sampling error, which obviously decreases as the sample size increases
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
