Variation in the hectolitre weight of wheat grain for equipment and sample size

• Autor(a) principal: Martin,Thomas Newton
• Data de Publicação: 2022
• Outros Autores: Cargnelutti Filho,Alberto, Deak,Evandro Ademir, Cechin,Joanei, Burg,Giovane Matias, Grün,Eduarda
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Ciência Rural
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782022000600201
• topic: Triticum aestivum L.; bulk density; grain measurement; sample variance; classification system.

ABSTRACT: Hectolitre weight (HW) is the principal quantitative parameter used by receiving units as an indicator of the quality of wheat grain (Triticum aestivum L.). The moisture content and the equipment used can be considered important sources of variation to assess a batch, requiring correct sizing of the sample used to measure the HW. This research identified the variability between the equipment used to evaluate HW with or without correcting the grain moisture content, and to estimate the ideal number of samples for the commercial classification of wheat batches. The experiment was carried out in a randomised block design with four replications. Seven batches of wheat grain from different cultivars and/or production sites were used to measure the HW using three types of equipment (Equipment ‘A’, Equipment ‘B’ and Equipment ‘C’), with and without correcting the grain moisture content to 13%. Higher values for HW were determined with Equipment ‘A’ (77.15) compared to Equipment ‘B’ (75.08) and Equipment ‘C’ (74.69), classifying the wheat according to type. The moisture content affected the HW, but did not change the final classification of the wheat in terms of type. The ideal number of samples for HW ranged from 1 to 18 at the lowest level of precision (HW=mean±1 kg hl-1) and from 67 to 1820 samples for the highest precision (HW=mean±0.1 kg hl-1). Equipment ‘B’ requires a smaller number of samples (possibly as few as one) for the same level of precision (HW=mean±1 kg hl-1), whereas Equipment ‘A’ requires a greater number of samples for the majority of batches. Correctly classifying wheat by HW therefore depends on a larger number of samples.