Evaluation of Oat Kernel Size Uniformity

Abstract

Oat (Avena sativa L.) kernel size uniformity is important to the oat milling industry because oat‐processing mills separate oats according to size to optimize dehulling efficiency. In this study, we compared two different approaches for analyzing oat kernel size uniformity, namely the sequential sieving of oat samples with a gradient of slotted sieve sizes and digital image analysis. Image analysis of size fractions provided evidence that sieving separated oat kernels according to their depth, whereas, digital image analysis measured kernel length and width, and derived a measure of the area of the oat kernel image. Samples identified by sieving with superior uniformity were those with greater proportions of large kernels. Histograms of oat kernel sizes derived from digital image analysis suggested oat kernel sizes were (within a genotype and location) composed of bimodal populations. A new statistical analysis allowed for the derivation of means and variances of each of these subpopulations, the numerical balance between the two subpopulations, and the extent of bimodality. Oat samples with lower levels of bimodality tended to be of higher test weight and groat percentage and thus, of better milling quality. Both methods appear satisfactory for evaluating oat kernel size uniformity, although the sequential sieving method is likely to be more useful to breeding programs because of its relative technical ease and simplicity.