Variation of Protein and Protein Fraction Content in Wheat in Relation to NPK Mineral Fertilization

Wheat is a crucial crop for human nutrition, and the demand for high-quality indicators within the &ldquo:from farm to fork&rdquo: concept is increasing. Based on this premise, this study examined how, at the farm level, the fertilization system can influence key quality indicators relevant to wheat production and final products. This research was conducted under specific conditions of the Western Plain of Romania at the Agricultural Research and Development Station (ARDS), Lovrin, during 2015&ndash:2017. Fertilization involved the autumn application of phosphorus (concentrated superphosphate: 0, 40, 80, 120, 160 kg ha&minus:1 active substance, a.s.) and potassium (potassium chloride: 0, 40, 80, 120 kg ha&minus:1 a.s.). Nitrogen (ammonium nitrate: 0, 30, 60, 90, 120 kg ha&minus:1 active substance) was applied in spring in two stages. The combination of these three fertilizers resulted in 18 fertilized variants (T2 to T19), tested alongside an unfertilized control (T1). The experimental variants were arranged in four randomized replications. Grain quality was assessed based on protein content (PRO, %), gluten (GLT, g 100 g&minus:1), gliadins (Gliad, %), glutenins (Glut, g 100 g&minus:1), high-molecular-weight glutenins (HMW, g 100 g&minus:1), low-molecular-weight glutenins (LMW, g 100 g&minus:1), and the gliadin/glutenin ratio (Gliad/Glut). Compared to the average values for each indicator across the experiment, certain variants produced values above the mean, with statistical significance. Variant T16 stood out by producing values above the mean for all indicators, with statistical confidence. Multivariate analysis showed that five indicators with very strong (PRO, GLT) and strong (HMW, Glut, LMW) influence grouped in PC1, while two indicators (Gliad, Gliad/Glut) with very strong and strong influence grouped in PC2. The analysis revealed varying levels of correlation between the applied fertilizers, with nitrogen (N) showing very strong and strong correlations with most indicators, while phosphorus and potassium showed moderate-to-weak correlations. Regression analysis generated mathematical models that statistically described how each indicator varied in relation to the fertilizers applied.