Electronic Journal of Polish Agricultural Universities (EJPAU) founded by all Polish Agriculture Universities presents original papers and review articles relevant to all aspects of agricultural sciences. It is target for persons working both in science and industry,regulatory agencies or teaching in agricultural sector. Covered by IFIS Publishing (Food Science and Technology Abstracts), ELSEVIER Science - Food Science and Technology Program, CAS USA (Chemical Abstracts), CABI Publishing UK and ALPSP (Association of Learned and Professional Society Publisher - full membership). Presented in the Master List of Thomson ISI.
Volume 22
Issue 1
DOI:10.30825/5.ejpau.168.2019.22.1 , EJPAU 22(1), #01.
Available Online: http://www.ejpau.media.pl/volume22/issue1/art-01.html


Jan Rozbicki1, Dariusz Gozdowski2, Marcin Studnicki2, Wiesław Mądry2, Jan Golba1, Grzegorz Sobczyński1, Magdalena Wijata1
1 Department of Agronomy, Warsaw University of Life Sciences (SGGW), Poland
2 Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland



Management, genetic and environmental factors affect bread wheat quality, mainly in terms of their effects on grain protein content and its composition. The objectives of this study were to determine the impacts of management intensity factors on bread-making wheat quality and contributions of cultivar, environment, and their interaction on the variation in quality traits, and to analyze the relationships between grain parameters. The data used for the analyses were obtained from 8 locations in Poland from post-registration multi-environment trials using 25 winter wheat cultivars at two crop management levels. Our results indicate that most sources of variation in grain quality traits were significant and that the variability of these traits, to a great extent, was dependent on the environments rather than cultivar. The most sensitive variables to environment were the grain yield and grain protein content. Moreover, yearly weather conditions affected the set of variables that describe the grain quality of winter wheat. On the basis of the mean values and standard deviations of the examined cultivars, it was possible to select cultivars that are characterized by stable grain quality, which is expected by farmers and the baking industry.

Key words: Genotype-by-environment interaction, Crop management, Grain yield, Wheat (winter), Quality traits.


CV – coefficient of variability;
E – environment;
FB – fiber content;
FN – Hagberg falling number;
G – cultivar;
GI – gluten index;
GY – grain yield;
HI – high input management;
L – location;
LI – low input management;
M – (crop) management;
NIR – near-infrared spectroscopy;
PC – grain protein content;
PCA – Principal Component Analysis;
SD – standard deviation;
SC – starch content;
SV – Zeleny sedimentation value;
TGW – thousand grain weight;
TW – test weight;
WG – wet gluten content;
Y – year.


Wheat is the most widely cultivated food crop and one of the most traded commodities in world markets [10, 15]. Currently, Poland is the fourth largest wheat producer in the European Union, with about 11 million tons per year, of which 4 million tons is allocated for food; this grain is a basic staple food that is mainly consumed as a bread product [25]. It is expected that global wheat production to 2050 should increase by 38% [2].

Grain quality is an essential component of wheat production because it determines the commercial value of wheat. The wheat grain quality is a complex trait that is described by many parameters, including physical grain properties, protein content and composition, and starch content. A full evaluation of bread-making quality involves multiple traits and includes parameters of the grain and flour quality, dough and baking quality traits. Because it is costly and time-consuming, trade grain quality is evaluated in a simplified manner, based on grain end-use quality traits such as the following: test weight (TW), Hagberg falling number (FN), grain protein content (PC), Zeleny sedimentation value (SV) or wet gluten content (WG), and gluten index (GI) [33]. The highly significant correlation found between the PC and SV and the rheological properties of dough make it possible to use simple and fast measurements, such as PC and SV, that are valuable for predicting the rheological parameters of dough [35]. The glutomatic system determines the gluten characteristics for both wheat and flour. However, using the GI parameter as the sole or main indicator of wheat bread-making quality attributes is problematic because of its variability and responsiveness to sowing date [5].

While methods based on the near-infrared spectroscopy (NIR) technique are accepted worldwide for evaluating cereal quality control in trade, especially because it is capable of generating rapid results for several quality parameters, the NIR is of great interest because it is an easy, rapid, and non-destructive method. Although different countries have established their own systems for classifying wheat on the basis of different quality parameters, wheat grading systems are commonly based on the wheat protein content and test weight [30, 33]. The NIR technique is also of great interest to breeders for assessing grain hardness. As reported by Salmanowicz et al. [36], the NIR parameter determining grain hardness is significantly positively correlated with the wet gluten and sedimentation values and with most of the rheological parameters and bread yield. The grain hardness evaluated by the NIR technique may be used as a criterion in the breeding for selection of wheat with improved quality.

The cultivar (G), environment (E), and their interactions G x E play an important role in grain yields and grain quality attributes [4, 12, 45, 47, 48, 50].

Certain quality traits, i.e., hardness, flour yield, and SV, are highly influenced by G [8, 50], whereas other parameters, i.e., PC, TGW, TW, and FN, are mostly influenced by the environment [8, 23, 50].

In general, the interaction effect of G x E has been found to be smaller than that of G or E alone. Furthermore, variations in the relative contributions of G, E and G x E to various quality parameters mainly due to different cultivars and environments studied have been observed [24, 32]. Genetic and environmental factors affect bread wheat quality mainly in terms of their effects on grain protein content and its composition [14, 17], but TGW and TW are also responsive to these factors and are important as they determine the yield of flour [24, 41, 50].

A larger contribution from cultivar in explaining the variability of the quality traits were shown in experiments evaluating the breeding progress of agronomic and bread-making quality traits of bread or durum wheat cultivars, usually released over a longer period of time [4, 6, 13, 19, 31, 37] The group of experiments that found a strong influence of cultivar on quality traits should also include those performed in one or a few environments with a large number of old and modern cultivars originating from different countries, regions, and breeding programs, as in the papers published by Denčić et al. [12], Zhang et al. [51] and Sanchez-Garcia et al. [37]. However, when currently grown cultivars were evaluated for variability of quality traits influenced by G, the influence was much lower [48].

An important portion of the variability observed in quantitative parameters, i.e., GY, TGW, PC, and gluten content, has been determined predominantly by environment Y and L [24, 41, 48].

The grain quality may also be substantially improved by crop/farming management practices, exploiting the synergism between cultivar and the environment [23, 35, 41]. Cormier et al. [9] and Bhatta et al. [3] reported that cultivar × nitrogeninteractions, as a management variable, were significant for yield and grain protein content. The nitrogen supply was the main factor affecting variation in protein content and composition. The total protein content increased with the higher supply of nitrogen, and as the grain protein increased, the gliadin and glutenin contents and their ratio increased [45].

However, the impacts of management factors and their interactions with cultivar across different environments (G x E x M) on other grain quality traits have rarely been investigated and are less well understood [20]. This particularly refers to the level of management intensity in different farming systems and regions.

Understanding these effects is essential to identifying zones in which a cultivar is able to express its full yield and quality potential and is of primary importance for breeders and farm advisors. Variation among cultivars in their response to environment and management practices would further improve the prediction and identification of cultivars with superior bread-making quality and should allow farmers to produce wheat grain with a high level of uniformity to meet the demands of the modern baking industry and the automated processing facilities that they use [16].

The objectives of this study were as follows: i) to determine the impacts two levels of management intensity factors on bread-making wheat quality; ii) to determine the contributions of cultivar, environment, and their interaction effects on the variation in bread-making traits of 25 cultivars tested across 8 locations and 2 years; and iii) to analyze the relationships between grain parameters.


Twenty-five winter wheat cultivars originating from 4 European countries, mainly Poland, were grown in a strip-plot (split-block) experimental design with 2 replicates; the experiments were conducted across 8 locations at which post-registration multi-environment trials were carried out by COBORU (The Research Centre for Cultivar Testing in Poland) over two consecutive growing seasons – 2008/2009 and 2009/2010. The sample of studied winter wheat cultivars was chosen across a range of grain and quality attributes. The cultivars studied in the trials and their origins have been presented by Golba et al. [18]. The second experimental factor was the crop management level (2 levels, LI – low-input and HI – high-input). LI management is defined as moderately intensive technology involving nitrogen fertilization, approximately 100 kg N ha-1 (40 kg N at ZGS 29 + the rest at ZGS 49 ) as ammonium nitrate, with no fungicide protection against leaf diseases, and HI management involves intensive wheat production with additional mineral fertilization of 40 kg N ha-1, two fungicide treatments against diseases, foliar nutrition with microelements and one treatment against lodging. A detailed description of the trials, including the characteristics of crop management, was described by Golba et al. [18].

The trials were located in the main Polish wheat-producing regions [27], and these locations cover environmental variability where winter wheat is grown. The soil and weather conditions in each location are presented in Table 1. According to Kong et al. [23], when the cumulative post-anthesis precipitation is higher than 50 mm, it can have negative influences on grain protein content and quality. The total seasonal precipitation in the trial seasons was 578 mm in 2008/2009 and 769 mm in 2009/2010, while post-anthesis precipitation in the present study was highly variable in the seasons and the location and was between 117–313 mm.

Table 1. Characteristics of soil and weather conditions at the experimental sites
Location Latitude, Longitude Season Soil texture‡ Soil pH
in KCl
June-July [mm]
Radostowo 53.9857°N, 18.7557°E 2008/2009 CL 6.1 159
2009/2010 SiL 6.5 119
Głubczyce 50.1934°N, 17.8260°E 2008/2009 SiL 6.4 291
2009/2010 SiL 6.1 313
Seroczyn 52.0108°N, 21.9194°E 2008/2009 LS 6.4 203
2009/2010 LS 7.1 117
Węgrzce 50.1189°N, 19.9788°E 2008/2009 SiL 6.2 217
2009/2010 SiL 6.5 308
Głębokie 52.6448°N, 18.4384°E 2008/2009 SL 6.0 205
2009/2010 SL 7.3 199
Nowa Ujska Wieś 53.0349°N, 16.7495°E 2008/2009 LS 5.4 185
2009/2010 LS 6.0 148
Marianowo 53.2105°N, 22.1036°E 2008/2009 SL 5.9 223
2009/2010 SL 5.9 181
Kościelna Wieś/
51.7845°N, 18.0137°E 51.2543°N, 18.6359°E 2008/2009 SL 6.4 181
2009/2010 LS 6.7 203
† in 2008/2009, research was conducted at Kościelna Wieś and in 2009/2010 at Masłowice
‡ CL – clay loam, SiL – silt loam, LS – loamy sand, SL – sandy loam

The agronomic traits, GY and TGW were measured during harvest on the basis of a 1-m2 sample from the plot. End-use grain quality traits, including TW, PC, WG, SV, SC, FB, and FN, were recorded to evaluate the effects of cultivar, environment, management, and their interaction on the bread-making quality attributes of grains.

Most of the quality traits i.e. TW, PC, WG, SC, and FB were evaluated using NIR spectrometry with the Foss NIR System (Infratec 1241 Grain Analyzer), which measures the reflectance in the 400–2498 nm wavelength, after calibration against quality traits determined by AACC methods [1]. The Hagberg falling number was determined using the Falling Number Test Apparatus, type SWD by AACC (Method 56–81B).

To evaluate the variability of the examined traits, the following statistical parameters were calculated: the means, standard deviations, coefficients of variability, and the minimal and maximal values. A four way analysis of variance based on a random effects-based linear model was used for significance testing of the examined main and adequate interaction effects of the studied factors (year, location, cultivar and crop management) and to estimate their variance components. Multivariate relationships between the examined traits were evaluated on the basis of a principal component analysis (PCA). For all of the statistical tests, the level of significance was set at 0.05. The analyses were conducted using SAS 9.3 (using PROC MIXED) and Statistica 10 statistical packages.


The examined traits were characterized by the variabilities caused by cultivars and environments (year x location) (Tab. 2). The range was broader among environments than among cultivars for traits known to be highly influenced by environment, such as grain yield (560 – 1022 g m-1 and 677 – 777 g m-1 for environments and cultivars, respectively), protein content (87.2 – 112 g kg-1 and 94.9 – 110 g kg-1), wet gluten (20.1 – 31.0% and 22.7 – 27.3%), falling number (338 – 544 s and 337 – 526 s), and thousand grains weight (32.3 – 49.8 g and 37.1 – 49.9 g), as reported by Kong et al. [23], Taghouti et al. [44], Yong et al. [50], Kaya and Akcura [22] and Vázquez et al. [48]. The variabilities caused by management were similar to the variability caused by cultivars.  Ranges in all of the analyzed parameters were broader across environments than across cultivars [28], which was opposite of the high ranges for PC, WG, and FN that have been reported as genetic variability by Denčić et al. [12] and Zhang et al. [51]. This was a consequence of the high diversity of compared cultivars, which originated from 28 different counties [12] or were from different areas of the country (China) and different breeding programs [51].

Table 2. Basic statistical parameters of variability of the examined traits for both environmental and genotypic levels
Type of effect and traits Abbreviation Mean Min. Max. SD CV
a. Environment [year x location]
Grain yield [g/m2] GY 728 560 1022 122 16.7
Thousand grains weight [g] TGW 43.4 32.3 49.8 4.86 11.2
Test weight [kg/hl] TW 79 74.3 83 2.45 3.14
Grain protein content [g kg-1] PC 103 87.2 112 7.63 7.41
Wet gluten content [%] WG 24.7 20.1 31 3.51 14.2
Zeleny sedimentation value [cm3] SV 41.8 32.7 50 5.69 13.6
Hagberg falling number [s] FN 431 338 544 52.9 12.3
Starch content [%] SC 69.3 68.1 71.2 0.81 1.21
Fiber [%] FB 2.83 2.62 2.93 0.08 2.70
b. Cultivar
Grain yield [g/m2] GY 728 677 777 22.4 3.01
Thousand grains weight [g] TGW 43.4 37.1 49.9 2.81 6.53
Test weight [kg/hl] TW 79.7 73.3 81.9 2.24 2.82
Grain protein content [g kg-1] PC 103 94.9 110 3.8 3.78
Wet gluten content [%] WG 24.7 22.7 27.3 1.25 5.17
Zeleny sedimentation value [cm3] SV 41.8 36.6 47.7 3.19 7.64
Hagberg falling number [s] FN 431 337 526 54.1 12.6
Starch content [%] SC 69.3 68.4 70.3 0.50 0.70
Fiber [%] FB 2.83 2.71 2.97 0.06 2.20
c. Crop management
Grain yield [g/m2] GY 728 680 776 68 6.3
Thousand grains weight [g] TGW 43.4 42.6 44.3 1.21 2.80
Test weight [kg/hl] TW 79.0 78.4 79.6 0.80 1.05
Grain protein content [g kg-1] PC 103 98.4 107.6 6.45 6.31
Wet gluten content [%] WG 24.7 23.2 26.2 2.13 8.56
Zeleny sedimentation value [cm3] SV 41.8 38.9 44.7 4.11 9.94
Hagberg falling number [s] FN 431 430 432 0.51 0.11
Starch content [%] SC 69.3 68.9 69.6 0.41 0.63
Fiber [%] FB 2.83 2.71 2.84 0.14 0.20

Our data suggest that the GY and PC were the most sensitive variables to environment; however, even protein quality parameters, i.e., SV, which has been reported to be more dependent on cultivars [25, 50], were more influenced by environment and management. Ranges in the TW and FB were similar for cultivars and environments. Very low variability for cultivars and environments (CV below 4%) was observed for TW, FB and SC.

For all of the traits, a significant effect of cultivar and the interaction of Y x L by ANOVA was observed (Tab. 3). Other factors and their interactions had a lower number of significant effects. As expected, variability of grain yield was to a very high degree (38.2% in total variability) caused by year, i.e., mainly by variations in weather conditions and by the interaction of year x location (29.0%) for total variability, which was in agreement with study Solomon et al. [39] and Bhatta et al. [3]. The smaller year effect found Bilgin et al. [4], probably due to not having very different weather conditions between the two cropping seasons. A weaker but significant effect of cultivar and crop management on GY was observed. TGW and TW were mainly determined by cultivar and the interaction of year and location (i.e., environment) as well interaction of management and environment, and in the case of TGW, a very high effect of location was observed, which is consistent with Bilgin et al. [4] compared highly diverse set of cultivars.

Table 3. P-values for the effects of the factors and their two-way interactions for the examined traits obtained using the random effect model-based analysis of variance and the share of the effects of variance components in the total variability of examined traits (share is presented as percentages in brackets)
Y – year 0.005*
L – location 0.749
G – cultivar 0.115
M – management 0.005*
YxL 0.009*
YxG 0.666
YxM 0.562
LxG 0.925
LxM 0.041*
GxM 0.574
GxYxL 0.184
MxLxY 0.299
GxMxY 0.363
GxLxM 0.479
GxLxMxY 0.370
GY – Grain yield, TGW – Thousand grains weight, TW – Test weight, PC – Grain protein content , WG – Wet gluten content, SV – Zeleny sedimentation value, FN – Hagberg falling number, SC – Starch content, FB – Fiber
* – significant effect at the 0.05 probability level

The strongest effect on PC and WG variability in the total variability had the year, location, and year x location interaction. However, crop management had a significant impact on these traits (variability – 28.8% for both traits) as a result of HI management, with higher doses of nitrogen and a full program of plant protection, which was in agreement with previous findings [9, 11, 23, 35, 41].

The effect of cultivar on both traits was very weak. Other traits of grain quality, i.e., SV, FN, SC, and FB, were mainly influenced by the effects of cultivar and year x location interactions, with the exception of SV, which was also highly sensitive to crop management. These two effects explained about 50% of the total variability measured by the variance components (Tab. 3). Significant interaction of cultivar and environment (location x year) was observed for FN and FB as well interaction of management and environment for SC.

A number of positive correlations among the analyzed traits were found (Tab. 4). Very strong positive correlations (r=0.91) were found between the SV and WG, which means that these two traits are almost completely proportional. Proteins were reported as the most important component of wheat grains, governing the technological and rheological properties of flour, and are closely associated with end-use quality [52]. The grain protein content (PC) exhibited high positive correlations with WG, similar to the previously reported values [12, 42, 43, 48]; this was understood as having to do with gluten being a part of the total protein. PC, particularly glutenin content, is positively correlated with the SV [40, 43] and is highly negatively correlated with SC, TGW, and TW in accordance with Mladenov et al. [29] and Rharrabti et al. [34]. Positive correlations between TW and TGW were also observed [46]. The SC and PC were highly negatively correlated, but the SC and TW indicated positive correlations [7, 21]. The GY was significantly positively correlated with TGW, as observed by Bhatta et al. [3], which means that high GY was obtained if TGW was high. Moreover, GY was positively correlated with TW and weakly correlated with PC and SV but negatively correlated with FN. Generally, high-grain-yielding cultivars have been associated with lower-quality parameters and mainly have a negative correlation between GY and PC as a side effect of breeding progress [6, 13, 26]. However, in the group of cultivars currently grown, GY and PC were positively correlated, giving farmers a chance to obtain GY and PC at a high level.

Table 4. Correlations between examined traits
GY   0.38* 0.20* 0.16* 0.24* 0.21* -0.13* 0.02 -0.10*
TGW 0.38*   0.53* -0.26* -0.31* -0.16* -0.09 0.45* -0.27*
TW 0.20* 0.53*   -0.12* -0.22* 0.04 0.22* 0.38* 0.06
PC 0.16* -0.26* -0.12*   0.90* 0.90* 0.09 -0.85* 0.02
WG 0.24* -0.31* -0.22* 0.90*   0.91* -0.01 -0.75* -0.08
SV 0.21* -0.16* 0.04 0.90* 0.91*   0.13* -0.66* -0.13*
FN -0.13* -0.09 0.22* 0.09 -0.01 0.13*   0.01 -0.05
SC 0.02 0.45* 0.38* -0.85* -0.75* -0.66* 0.01   -0.24*
FB -0.10* *-0.27 0.06 0.02 -0.08 -0.13* -0.05 -0.24*  
* – significant correlation at the 0.05 probability level

Principal component analysis revealed that WG, SV and PC were strongly negatively correlated with PC1, whereas GY, TW and TGW were strongly correlated with PC2 (Fig. 1a). PC1 explains 36.9% of the total variability of the set of examined traits. This means that WG, SV, PC (negatively correlated with PC1), and SC (positively correlated with PC1) are the traits that have the highest share of total variability of grain quality. TW and TGW are less variable traits because PC2 explains only 21.4% of the total variability. The other two traits, i.e., FC and FB, were not correlated with PC1 and PC2 because both traits are of rather small importance in the total variability and to a high degree are determined without correlation with other traits.

GY – Grain yield, TGW – Thousand grains weight, TW – Test weight, PC – Grain protein content, WG – Wet gluten content, SV – Zeleny sedimentation value, FN – Hagberg falling number, SC – Starch content, FB – Fiber
LI – low input crop management, HI – high input crop management
Fig. 1. Loadings for the examined traits (a) and plot of PC1 and PC2 for the different management levels and years (b)

To compare multivariate variability between the examined cultivars, standard deviations for combinations of each cultivar were calculated for the first and second principal components (PC1 and PC2) (Tab. 5). Very low standard deviations for PC1 and PC2 were observed for the following cultivars: Smuga, Muszelka and Tonacja. This means that these cultivars were stable in various environments and agronomic conditions according to their grain quality traits. The most variable cultivars according to the examined traits were Akteur and Mulan.

Table 5. Means and standard deviations (SD) calculated for the first two principal components (PC1 and PC2) for the examined combinations separately for each cultivar
Cultivar Mean PC1 Mean PC2 SD PC1 SD PC2
Akteur -0.63 -0.16 2.33 1.44
Alcazar -0.34 -1.27 1.78 1.04
Anthus 1.21 -0.05 1.84 1.19
Bogatka -0.48 1.19 1.92 1.19
Boomer 0.19 -0.19 1.69 1.43
Figura 0.48 0.55 1.87 1.29
Finezja -0.78 0.50 1.79 1.22
Garantus 0.42 -0.85 1.68 1.19
Jenga 0.82 -0.07 1.82 1.37
Kohelia 0.27 0.53 1.61 1.38
Legenda -0.46 0.36 1.93 1.21
Ludwig -0.73 0.96 2.05 1.05
Markiza -0.89 -0.14 1.64 1.26
Meteor -0.58 -0.40 1.46 1.57
Mulan 0.42 -0.24 2.01 1.47
Muszelka 0.57 -0.33 1.51 1.07
Nadobna 0.64 -0.46 1.64 1.29
Naridana -0.21 0.20 1.67 1.21
Ostroga* 0.55 0.58 1.60 1.08
Rapsodia 0.17 -1.33 1.75 1.46
Satyna -0.56 -0.29 1.82 1.31
Smuga -0.32 0.05 1.47 1.00
Tonacja 0.42 0.87 1.60 1.11
Türkis 0.24 -0.16 1.68 1.44
Wydma -0.38 0.22 1.58 1.33
* Awned cultivar

Multivariate differences between the examined combinations (cultivar x crop management x year x location) are presented in Figure 1b on the basis of the PCA results (as values of PC1 and PC2). Because the number of examined combinations was very high, Figure 1b presents only the results of selected cultivars (i.e. Akteur, Ludwig, Legenda, Wydma, Rapsodia, Tonacja and Ostroga) whose share of the sown area in Poland is large.

The mean values of PC1 and PC2 for these cultivars are presented in Table 5. On the basis of these values, it is possible to estimate the mean values of examined grain quality traits. For example, Akteur had a low PC1 and rather low PC2, which indicated that WG, SV, PC, FC and FN are high for that cultivar, whereas GY, TW, TGW, and SC were low for that cultivar.

These results indicate the effect of crop management (two management intensities) and year (different weather conditions, especially precipitation) on the examined traits. High input (HI) crop management caused higher quality grain yield in comparison with low input (LI) crop management. That effect was especially visible for the following traits: SV, WG, GY, TW and TGW, which had higher values in HI crop management than LI crop management (Fig. 1). This means that intensive crop management can significantly improve grain quality, especially SV and TW.

Similar differences were found between years, i.e., for year 2009, most of the grain quality traits exhibited higher values in comparison with the year 2010. These differences between years were probably caused by the distribution of precipitation at the end of the vegetation stage in wheat. In the year 2009, a larger amount of precipitation in June and a lower amount of precipitation in July were observed compared with the year 2010.


The two-year tests of 25 currently grown cultivars across 8 locations in central European areas indicated that most sources of variation in bread-making traits were significant and that the variability of quality traits was to a greater extent dependent on the environment (Y – year and L – location) than cultivar. The most sensitive variables to the environment were the GY and PC, but even the protein quality parameters, i.e., SV, were less dependent on cultivar and considerably influenced by environment. The range in test weight (TW) and fiber content (FB) was similar among cultivars and environments and was very low (below 4%).

Studies have also shown that crop management practices have a significant impact not only on the variability of such traits as GY, PC, WG, what has been confirmed earlier, but also on quality traits as SV and TW. The grain yield and grain quality, based on protein content and quality, may be substantially improved by high-input crop management, especially N fertilization [3]. Especially late N dose is more effective in improving wheat grain quality than increase in total N [49] because of that it is important to split the nitrogen fertilization during vegetation. Split of the nitrogen fertilization is more important on sandy soils while on heavy textured deeply developed soils the effect of splitting of nitrogen doses is much weaker [38]. The results of principal component analysis reviled a strong effect of crop management intensity level on the multivariate variability of examined cultivars in different environments.

Strong relationships were observed between many of the examined traits, which allows application of a principal component analysis for simplified evaluation of multivariate variability of the examined combinations: cultivar x environment x management. In particular, strong positive correlations were found between PC, SV and WG, and simultaneously, these three traits were strongly negatively correlated with starch content (SC). Proven in earlier papers as having a highly significant correlation between PC, WG and SV and the rheological properties of dough [35], simple and fast measurements such as PC and SV were confirmed to be important grain quality trait. Significant positive correlations were also observed between GY, TW and TGW, indicating a beneficial effect of TW and TGW on the grain yield.


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Received: 5.07.2018
Reviewed: 21.01.2019
Accepted: 4.02.2019

Jan Rozbicki
Department of Agronomy, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska Street 159
02-776 Warsaw

Dariusz Gozdowski
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska 159
02-776 Warsaw
Phone: +48 (22) 59 32 730
email: dariusz_gozdowski@sggw.pl

Marcin Studnicki
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska 159
02-776 Warsaw
email: marcin_studnicki@sggw.pl

Wiesław Mądry
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska 159
02-776 Warsaw

Jan Golba
Department of Agronomy, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska Street 159
02-776 Warsaw

Grzegorz Sobczyński
Department of Agronomy, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska Street 159
02-776 Warsaw

Magdalena Wijata
Department of Agronomy, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska Street 159
02-776 Warsaw

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