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.
2016
Volume 19
Issue 3
Topic:
Agronomy
ELECTRONIC
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES
Weber R. , Bujak H. , Zalewski D. 2016. SPATIAL VARIATION OF WINTER WHEAT CULTIVARS YIELDS IN THE LOWER SILESIAN AREA, SOUTH-WEST POLAND, EJPAU 19(3), #03.
Available Online: http://www.ejpau.media.pl/volume19/issue3/art-03.html

SPATIAL VARIATION OF WINTER WHEAT CULTIVARS YIELDS IN THE LOWER SILESIAN AREA, SOUTH-WEST POLAND

Ryszard Weber1, Henryk Bujak2, Dariusz Zalewski2
1 Institute of Soil Science and Plant Cultivation, Department of Ecology and Soil Tillage, National Research Institute, Puławy, Poland
2 Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Science, Poland

 

ABSTRACT

The objective of the study was classification of the experimental stations of the Post-registration Variety Testing (PDO) Programme in Lower Silesia into groups with notable similarity in the variation of the winter wheat cultivars performance. The analysis covered yields of the winter wheat obtained in seven PDO experiments in the years 2009–2011 carried out at high level of cultivation, comprising full chemical protection against pests and fungal diseases, application of an anti-lodging agent, and supplementary foliar nutrition. The research was based on the results of grain yields of 11 winter wheat cultivars; the experiments set up in the two-factor strip-plot design. The results obtained permitted identification within the experimental stations analyzed of three groups of localities differing significantly in the yield variability of the wheat cultivars studied. The first group includes Tomaszów and Naroczycze. The second group, composed of a single element, is made up by the experimental station of Pawłowice, which is characterized by lack of correlation with the other localities. The third group includes Zybiszów, Kobierzyce, Kondratowice and Tarnów. The significant changes taking place in the soil-climate environment indicate that micro-regionalization of the winter wheat cultivars under the diverse conditions of certain areas in the provinces of South Poland may contribute to an increase in yields of this cereal crop.

Key words: variability, factor analysis, matrix, correlation coefficients.

INTRODUCTION

The area of the winter wheat cultivation in Poland occupies 1.8–2.0 million hectares. The species is grown both on soils classified among the black earths or czernozems, and also at localities with less favourable water-air relations. The winter wheat cultivation on light soils involves a high risk of reduced yields, especially in years of temporary deficits of precipitation during the growing season. The significant variation in the agro-climatic conditions in the particular provinces of Poland lies behind the ongoing search for cultivars characterized by broad adaptation capabilities. The general adaptation ability or broad adaptation is a term used to define the ability of a cultivar to produce relatively high yields under variable environmental conditions of a region, across years, or at different tillage modes [1, 15, 20, 21, 24], whereas the narrow adaptation of a cultivar refers to its ability of high-level performance under specific environmental conditions – sub-regions, or pertaining to specific systems of cultivation [8, 18]. Cultivars with broad adaptation abilities perform at a high and stable level in relation to other cultivars [16, 19, 22]. Those traits of cultivars are often the result of considerable tolerance to biotic and abiotic stresses [3]. Yielding stability of cultivars is largely dependent on their response to water stress [2]. Specific breeding programmes are conducted in the search for genotypes characterized by high resistance to the environment salinity and water deficits during vegetation [7]. If the genotypic variance outweighs the variance of interaction effects (genotype x experimental station), the breeder can achieve effective selection of breeding lines with a high stability in a given region [12, 13]. In the reverse situation, the strategy of selecting genotypes with narrow adaptation may prove to be the better choice [9]. Studies on Polish cultivars indicate their notable interaction with years and localities, irrespective of the level of cultivation intensity [4, 23]. In the selection of the winter wheat cultivars in Poland there is a lack of forms characterized by high tolerance to drought [5]. Therefore, varied levels of precipitation in the area of several communes may lead to considerable differences in the yields of particular cultivars. The substantial edaphic variability and varied precipitation in the Lower Silesian area indicate a possibility to define microregions distinguishable by different soil and climate conditions as well as diversified yields of cultivars.

The study presented herein was aimed at division of the experimental stations engaged in the Post-registration Variety Testing in Lower Silesia into groups with significant similarity in performance by the winter wheat cultivars in the period under analysis. The outcome will enable determination of the “sub-region environmental value”. Thus, a breeder will be given an opportunity to choose cultivars that are best adapted to the climatic and edaphic conditions of a particular farm, based on sound statistical data which take into account the genotype-environment interaction, not relying exclusively on the offer of seed producers. Moreover, it is commonly agreed that a substantial number of experiments improves the efficacy of yielding evaluation, and, on the other hand, the division into sub-regions may contribute to the optimization of the costs of research within the frames of the PDO programme through a potential reduction of the number of experiments.

Fig. 1. Location experiences in the Lower Silesia region

MATERIAL AND METHODS

The study was conducted with the use of the results of the winter wheat cultivars yielding in a series of PDO experiments at seven localities situated in the Province of Lower Silesia that are characterized by varied soil-climate conditions. The analysis covered the winter wheat yields obtained in the PDO experiments (unpublished data made available by COBORU = Research Centre for Cultivar Testing) at the intensive level of cultivation (including full chemical protection against pests and fungal diseases, the use of an anti-lodging agent, and supplementary foliar nutrition), in the span of 2009–2011 (Tab. 1). The experiments were set up in two replications. The analysis included the results of grain yields of 11 winter wheat cultivars tested at all of the seven localities. The PDO experiments were established in the two-factor system of split-block design; the area of the experimental plots was 15 m2.

Table 1. Characterisation of PDO experimental stations in Lower Silesia
Locality
Elevation
above
sea level
[m]
SOILS
Sum of precipitation X–III [mm]
Sum of precipitation IV–IX [mm]
2008–2009
2009–2010
2010–2011
2008–2009
2009–2010
2010–2011
Naroczyce
110
5 Bw pgl:gl
269
203
207
342
489
429
Kobierzyce
140
1Dz płi
222
239
453
515
Zybiszów
130
1Dz płi
188
222
181
436
515
404
Kondratowice
150
2Dz gsp:ps
239
211
170
451
493
380
Pawłowice
115
4A pgl:gl
221
256
227
441
541
452
Tarnów
300
4Bw pgl:gl
210
232
206
413
626
360
Tomaszów
200
5A glp.pl
284
263
247
420
649
418
5 Bw pgl:gl – dystric cambisols developed on loamy sand on medium-deep sandy loam;
4Bw pgl:gl – dystric cambisols developed on loamy sand on loam;
5A glp.pl – luvisols developed on silty loam on medium-deep sand;
4 A płz:pl – luvisols developed on silt on sand;
1Dz płi   – faeozems developed on silty clay;
2D glp – czernozems developed on loam;

The results were processed statistically by means of factor analysis [Stevens 1986]. The objective of factor analysis is reduction of the size of the correlation matrix of the objects studied through limitation of the number of correlation coefficients. This statistical method permits the search for hidden structures among the variables studied, ones that are difficult to identify on the basis of the original data. The factors obtained with the method define the relations among groups of data initially analyzed. If two of the variables studied display a considerable correlation with the same factor, a significant part of the correlation between the variables studied finds an explanation in the identified common factor. The starting point was the primary matrix of the correlation coefficients for the winter wheat yields from the seven experimental stations situated within the area of the Lower Silesian province. After finding, by means of the Bartlett test, that the correlation matrix under analysis is not a single-element matrix, the particular stages of factor analysis were carried out. The decision on the number of factors was made on the basis of the eigenvalues of the variance of a given factor with the assumption that the range of initial variance (after standardization) is 1. On the basis of the Kaiser criterion [10] and the Cattell scree test, such factors were selected whose eigenvalues were higher than unity. The variance of each factor defines what percentage of the total variability of all primary variables (localities) is accounted for by a given factor. Next, the resources of common variability defined by a given number of factors were analyzed. Reduction of the correlation matrix allowed determination of the factor matrix, and subsequent calculation of factor loadings without and with the Varimax rotation, between the primary variables and the factors.

RESULTS

Table 2 presents the mean grain yields of the cultivars tested at the seven localities. A comparison of the mean grain yields among the experimental stations shows that the soil conditions and total precipitation during the vegetation season had a decisive impact on the level of yielding by the winter wheat cultivars under study (Tab. 1). This is evidenced by higher yields of the winter wheat on the better soil complexes in Kondratowice, Kobierzyce and Zybiszów as related to the lowest performance on luvisols in Tomaszów.

Table 2. Mean yields of the tested cultivars [T · ha-1] at experimental stations in (2009–2011)
Cultivar
Locality
Mean
Naroczyce
Kobierzyce
Zybiszów
Kondratowice
Pawłowice
Tarnów
Tomaszów
Bogatka
8.25
9.78
10.70
9.55
7.48
7.79
6.93
8.64
Figura
7.89
9.86
9.24
9.50
7.63
7.62
6.97
8.39
Muszelka
8.71
10.29
10.23
9.49
8.71
7.76
7.62
8.97
Nadobna
8.60
10.63
9.86
10.24
8.03
7.43
6.78
8.80
Rapsodia
8.77
9.58
9.74
9.78
8.93
7.86
6.88
8.79
Boomer
8.33
10.10
10.07
9.64
8.33
8.35
6.98
8.83
Kohelia
8.60
10.09
10.49
9.21
7.51
8.23
6.62
8.68
Mulan
8.94
10.21
10.42
9.70
8.67
8.04
6.97
8,99
Cubus
8.42
9.90
10.11
9.94
7.93
8.02
6.92
8.75
Brillant
8.60
10.04
9.88
9.78
8.21
7.87
7.04
8.77
Ostroga
8.54
9.87
10.01
10.02
8.14
7.88
6.50
8.71
Mean
8.51
10.03
10.07
9.71
8.14
7.89
6.93
8.75
LSD Localities = 1.091;
LSD Cultivars = 0.373;
LSD Localities x Cultivars = 1.543

Table 3 presents the matrix of spatial correlation, containing seven variables (localities) that characterize the yielding by the 11 winter wheat cultivars at the intensive level of cultivation. Analysing the particular coefficients of correlation, one can note that the highest value (r=0.86) was found for the pair of variables representing the localities of Kondratowice and Tarnów.

Table 3. Matrix of simple correlation coefficients for yields of wheat varieties at experimental stations
Sites
Naroczyce
Kobierzyce
Zybiszów
Kondratowice
Pawłowice
Tarnów
Tomaszów
Naroczyce
1.00
-0.34
-0.56*
-0.78*
0.32
-0.82*
0.82*
Kobierzyce
-0.34
1.00
0.36
0.58*
-0.20
0.51*
-0.27
Zybiszów
-0.56*
0.36
1,00
0.60*
0.06
0.71*
-0.74*
Kondratowice
-0.78*
0.58*
0.60*
1.00
-0.38
0.86*
-0.68*
Pawłowice
0.32
-0.20
0.06
-0.38
1.00
-0.16
0.11
Tarnów
-0.82*
0.51*
0.71*
0.86*
-0.16
1.00
-0.78*
Tomaszów
0.82*
-0.27
-0.74*
-0.68*
0.11
-0.78*
1.00

* significant at the 0.05 probability level

The successive correlation coefficients formed the core of the second group, composed of Tomaszów and Naroczyce (r=0.84), and the third, including Tarnów and Zybiszów (r=0.71). An analysis of negative correlation coefficients has revealed negative correlation between the following pairs of localities: Naroczyce and Tarnów (r=-0.82), Naroczyce and Kondratowice (r=-0.78), and Naroczyce and Zybiszów (r=-0.56). Negative coefficients of correlation were also determined for the localities of Tomaszów and Tarnów (r=-0.78), Tomaszów and Zybiszów (r=-0.74), and Tomaszów and Kondratowice (r=-0.68). The negative correlations between the experimental stations under analysis indicate a different ranking of yielding by the cultivars. One can conclude, therefore, that the localities of Tomaszów and Naroczyce constitute separate micro-regions in the Province of Lower Silesia. In the primary matrix of correlations – in spite of the inclusion of the significant correlations between the stations studied – it is hard to identify the maximum similarity or the greatest differences between the localities since it is impossible to identify the hidden relations that are the basis for the division into groups. Application of factor analysis can provide a more complete answer to those doubts.

The results from the experimental stations were treated as the primary variables, and an attempt was made at their reduction with the method of factor analysis. The first step in factor analysis is transformation of the seven correlated variables (localities) into “n” non-correlated factors. The decision concerning the number of factors is taken with the help of analysis of the eigenvalues (Tab. 4). With the application of the Kaiser criterion and the Cattell scree test, the first two factors were chosen, i.e. U1 and U2, whose values are greater than unity [Kaiser 1960]. These factors account for 77.5% of the total variation of the set under analysis. The resources of common variability of winter wheat yields at the particular localities are presented in Table 5. The last column of the table presents the coefficient of determination. Based on Table 5, a conclusion can be made that 86, 83, and 81%, respectively, of the total variation of yields in the localities of Tarnów, Kondratowice and Naroczyce is explained by the remaining experimental stations, whereas the yield variability in Kobierzyce and Pawłowice is similar to a lesser extent to the yielding at the other localities.

Table 4. Eigen values-extraction: principal components
Value
Eigen value
Percentage of total variance
Cumulated Eigen value
Cumulated percent
U1
4.2830
61.1858
4.2830
61.18
U2
1.1423
16.3191
5.4253
77.5

Table 5. Communalities – (unrotated)
Variable
1st factor (U1)
2nd factor (U2)
R2
Naroczyce
0.7895
0.7919
0.8129
Kobierzyce
0.3312
0.4061
0.4014
Zybiszów
0.6012
0.7815
0.6570
Kondratowice
0.8374
0.8745
0.8274
Pawłowice
0.0850
0.8588
0.3415
Tarnów
0.8857
0.8931
0.8583
Tomaszów
0.7526
0.8191
0.7978

The resources of common variability from the factor analysis were used for estimation of the suitability of the particular experimental stations. Values close to zero mean that the variable does not fit the adopted factor model. The breakdown of the correlation matrix into factors resulted in acquisition of a factor matrix of each variable with each factor (Tab. 6). Factor loadings are an equivalent of coefficients of correlation between given variables and the particular factors. Table 6 presents the values of non-rotated factor loadings. It demonstrates that the localities of Tarnów, Zybiszów and Kondratowice are highly and positively correlated with the first factor whereas the variables Naroczyce and Tomaszów display a strong negative correlation with the same factor. Therefore, factor U1 is a bipolar factor, and in its identification one should take into account the correlations between two groups of localities. The first group should include Tarnów, Zybiszów and Kondratowice. The second group is formed by Naroczyce and Zybiszów, with a strong positive correlation of yields but with negative coefficients of correlation with the first group of localities.

Table 6. Factor loadings (unrotated); extraction: principal components
Variable
1st factor (U1)
2nd factor(U2)
Naroczyce
-0.8885*
0.0488
Kobierzyce
0.5755
-0.2737
Zybiszów
0.7754*
0.4245
Kondratowice
0.9151*
-0.1925
Pawłowice
-0.2915
0.8796*
Tarnów
0.9411*
0.0857
Tomaszów
-0.8675*
-0.2578
explained variance
4.2830
1.1423
participation
0.6118
0.1631

The second factor indicates a considerable correlation with the variable Pawłowice, which is characterized by a small communality. The performance by the wheat cultivars at that experimental station displays a lack of correlation with the yields at the remaining localities. The last line of Table 6 for the intensive variant illustrates the total resources of common variability. The first and the second factor accounts for 61 and 16% of total yield variability respectively. To validate the interpretation of the above results for the intensive cultivation variant, rotation of the factors with the Varimax method was performed (Tab. 7). The rotation was made for the purpose of maximization of the variance of crude loadings for each of the factors. For each factor such an arrangement of loadings was obtained that represented the greatest possible differentiation, which permits easy interpretation of results. The data in Table 7 confirm the interpretation of factor loadings described above.

Table 7. Factor loadings (varimax normalized);  extraction: principal components
Variable
1st factor (U1)
2nd factor (U2)
Naroczyce
0.8418*
-0.2886
Kobierzyce
-0.4793
0.4199
Zybiszów
-0.8616*
-0.1976
Kondratowice
-0.8282*
0.4341
Pawłowice
0.0413
-0.9258*
Tarnów
-0.9290*
0.1734
Tomaszów
0.9050*
0.0122
explained variance
4.0507
1.3746
  participation
0.5786
0.1963

In the literature there are different approaches for grouping genotypes or environments. Abou-El-Fittouch et al. [1969] used cluster and distance coefficient analysis for classifying locations. Lin [1982] slightly modified their distance coefficient analysis in order to obtain a direct link between the cluster method and the genotype x environment (GxE) interaction mean square. The new index was constructed in each cluster for any groups and was shown to be equivalent to the GxE mean square within a group. Lin and Butler [1988] compared Lin’s [1982] cluster method with the method based on the largest GxE interaction mean square for any number of locations. Based on their results, these authors recommended using both methods in the choice of test locations for regional trials.

DISCUSSION

The results obtained enabled to identify among the experimental stations analyzed three groups of localities differing significantly in the variation of yielding by the wheat cultivars under study. The first group included Tomaszów and Naroczycze, situated on light soils. The second group, a single-element one, is represented by the experimental station Pawłowice, characterized by a lack of correlation with the remaining localities. The third group comprises Zybiszów, Kobierzyce, Kondratowice and Tarnów. The localities of Tomaszów and Naroczyce, forming the first micro-region, are characterized by relatively long distances from the remaining experimental stations. Probably, a different distribution of the sum of precipitation at those stations, as well as poorer soils compared with the remaining experimental stations, contributed to the different ranking of performance by the cultivars studied in comparison with the other localities. Light soils are characterized by low water capacity. Short-lasting deficits of water on those soils can inhibit to a greater extent the yielding of certain cultivars than in the case of better soils, classified as faeozems or czernozems. The lack of significant correlation of the winter wheat cultivars performance between Pawłowice (luvisols) and the remaining experimental stations supports the above hypothesis. The recent reports from all over the world indicate global climate warming due to increasing concentration of greenhouse gases [14, 17]. Climate changes affect the agricultural production through waves of hot weather, drought, and intensive rainfalls. In recent years we have been observing periods of drought interspaced with intensive rainfalls [6, 11]. Even during a few hours, a heavy rainfall can have an especially destructive effect on the structure of light soils, disturbing their water-air relations. Violent changes in the soil environment can be the main cause of the different ranking of yields by the winter wheat cultivars on light soils as compared with the results obtained on soils of the faeozem or czernozem type. The demonstrated high coefficients of correlation between the localities characterized by light soils and the experimental stations situated on soils classified among the black earths support the above suppositions. The significant changes in the soil-climate environment indicate that micro-regionalization of the winter wheat cultivars under the conditions of certain areas within the provinces of south Poland may contribute to an increase in the winter wheat yield values. The Post-registration Variety Testing experiments should be utilized to a greater extent for the selection of cultivars characterized by narrow adaptation, performing at a high level in the area of a specific commune.

CONCLUSION

REFERENCES

  1. Abou-El-Fittouh, Rawllings J.O., Miller P.A., 1969. Classification of environments to control genotype by environment interactions with an application to cotton. Crop Sci., 9, 2, 135–140.
  2. Ahmad R., Nadir S., Ahmad N., Shah K.H., 2003. Yield potential and stability of nine wheat varieties under water stress conditions. Int. J. Agr. Biol., 5, (1), 7–9.
  3. Albrizio R., Todorovic M., Macic T., Stellacc A.M., 2010. Comparing the interactive effects of water and nitrogen on durum wheat and barley grown in a Mediterranean environment. Field Crops Res., 115, 179–190.
  4. Bujak H., Tratwal G., 2011. Ocena stabilności plonowania odmian pszenicy ozimej na podstawie doświadczeń porejestrowych w Polsce [Estimation of winter wheat yielding stability according to postregistration variety trials in Poland]. Biuletyn IHAR, 260/261, 69–79 [In Polish].
  5. Drzazga T., Krajewski P., Śmiałowski T., Śmiałek E., 2011. Ocena odmian pszenicy ozimej pod względem tolerancji na suszę  [Drought resistance assessment of winter wheat cultivars]. Biuletyn IHAR, 260/261, 135–142 [In Polish].
  6. Erekul O., Gotz K.P., Gurbuz T., 2012. Effect of supplemental irrigation on yield and bread – making quality of wheat (Triticum aestivum L.) varieties under the Mediterranean climatical conditions. Turk. J. Field Crops, 17, 1, 78–86.
  7. Farooq S., Azam F., 2007. A new allopolyploid wheat for stressed lands and poverty alleviation. Field Crops Res., 100, 2–3, 369–373.
  8. Gauch H.G., Piepho H.P., Annicchiarico P., 2008. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci., 48, 866–889.
  9. Grünberg W.J., Manrique K., Hang D., Hermann M., 2005. Genotype x environment interactions for a diverse set of sweet potato clones evaluated across varying ecogeographic conditions in Peru. Crop Sci., 45, 2160–2171.
  10. Kaiser H.F., 1960. The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151.
  11. Kundzewicz Z.W., Kędziora A., 2010. Zmiany klimatu i ich wpływ na środowisko i gospodarkę (Obserwacje i Projekcje) [Climate change and its impact on the environment and economy (Observations and Projections)]. Studia i Raporty IUNG-PIB, 19, 115–132 [In Polish].
  12. Lin C.S., 1982. Grouping genotypes by a cluster method directly related to genotype-environment mean square. Theor. Appl. Genet., 62, 277–280.
  13. Lin C.S., Butler G., 1988. A data-based approach for selecting locations for regional trials. Can. J. Plant Sci., 68, 651–659.
  14. Long S.P., Ainsworth E.A., Leakey A.D., Nosberger J., Ort D.R., 2006. Food for Thought: Lower-than-expected crop yield stimulation with rising CO2 concentrations. Science, 119, 1918–1921.
  15. Mądry W., Iwańska M., 2011. Przydatność metod oraz miar statystycznych do oceny stabilności i adaptacji odmian [Usefulness of statistical methods and measures for evaluating cultivar stability and adaptation: an overview of research]. Biuletyn IHAR, 260/261, 193–218 [In Polish].
  16. Mohammadi M., Karimizadeh B., Sabaghina N., Shefazadeh M.K., 2012. Genotype x environment interaction and yield stability analysis of new improved bread wheat genotypes. Turk. J. Field Crops, 17(1), 67–73.
  17. Olsen J.E., Trnka M., Kersebaum K.C., Skjelvåg A.O., Seguin B., Peltonen-Sainio P., Rossi F., Kozyra J., Micale F., 2011. Impacts and adaptation of European crop production systems to climate change. Europ. J. Agron., 34, 96–112.
  18. Przystalski M., Osman A., Thiemt E.M., Rolland B., Ericsson L., Østergård H., Levy L., Wolfe M., Büchse A., Piepho H.P., Krajewski P., 2008. Comparing the performance of cereal varieties in organic and nonorganic cropping systems in different European countries. Euphytica, 163, 417–433.
  19. Roozeboom K.L., Schapaugh W.T., Tuinstra M.R., Vanderlip R.L., Milliken G.A., 2008. Testing wheat in variable environments: genotype, environment, interaction effects, and grouping test locations. Crop Sci., 48, 317–330.
  20. Salomon K.F., Smit H.A., Malan E., Du Toit W.J., 2008. Parametric model based assessment of genotype x environment interactions for grain yield in durum wheat under irrigation. IJPP, 2, 1, 23–36.
  21. Singh R.P., Huerta-Espino J., Sharma R., Joshi A.K., Trethowan R., 2007. High yielding spring bread wheat germplasm for global irrigated and rainfed production systems. Euphytica, 157, 351–363.
  22. Sharma R.C., Morgounov A.I., Braun H.J., Akin B., Keser M., Bedoshvili D., Bagci A., Martusi C., van Ginkel M., 2010. Identifying high yielding stable winter wheat genotypes for irrigated environments in Central and West Asia. Euphytica, 171, 53–64.
  23. Weber, R., Bujak H., Kaczmarek J., Gacek E., 2011. Plonowanie odmian pszenicy ozimej w Polsce południowo-zachodniej. [Analysis of yield variability in winter wheat cultivars in South-Western Poland]. Biuletyn IHAR, 260/261, 121–134 [In Polish].
  24. Williams R.M., O’Brien L., Eagles H.A., Solah V.A., Jayasena V., 2008. The influence of genotype environment and genotype x environment interaction on wheat quality. Aust. J. Agric. Res., 95, 95–111.

Accepted for print: 1.08.2016


Ryszard Weber
Institute of Soil Science and Plant Cultivation, Department of Ecology and Soil Tillage, National Research Institute, Puławy, Poland
Orzechowa 61
50-540 Wrocław, Poland
Phone: +48 71 363 8707
email: rweber@iung.pulawy.pl

Henryk Bujak
Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Science, Poland
pl. Grunwaldzki 24a
50-363 Wrocław
Poland

Dariusz Zalewski
Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Science, Poland
pl. Grunwaldzki 24a
50-363 Wrocław
Poland

Responses to this article, comments are invited and should be submitted within three months of the publication of the article. If accepted for publication, they will be published in the chapter headed 'Discussions' and hyperlinked to the article.