Studying standard and rheological quality parameters of winter wheat by Python visualisation
Zoltán Magyar | Péter Pepó
This study was carried out to present an innovative solution for interpreting large data sets in agri-statistics with the invocation of programmed visualisation. Moreover, the following polyfactorial long-term experiment embodies a comprehensive study of 18 wheat quality parameters. The effect of increasing dosages of fertiliser (control, N90PK, N150PK) was examined on 3 winter wheat cultivars (KG Kunhalom, GK Csillag, Hybiza) in two consecutive growing seasons (2018-2019). The ecological conditions of 2018 gave a significantly higher yield, meanwhile 2019 significantly augmented gluten spread, alveographic tenacity, alveographic deformation work, valorigraphic stability and quality group and loaf volume. N90PK dosage was enough to realise yield and quality potential as well. Fertilising significantly improved 13 indices, namely yield, crude protein, Zeleny index, wet gluten content, alveographic extensibility, alveographic deformation work, valorigraphic water absorption, quality number and group, dough development time, stability, softening and loaf volume. Considering yield, cv. Hybiza performed better, while cvs. KG Kunhalom and GK Csillag possessed significantly better protein-linked postharvest attributes. One of the most important findings is that waffle chart, joint plot, correlation matrix and complexradar of Python provide a very powerful tool in agri-statistics. Also, the results can potentially improve the knowledge about cultivar-specific agronomy practice, wheat quality and the connections between these parameters.
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