Corresponding author: Gorjanović Biljana, Dušana Jerkovića 22, 22320 Inđija, 065
8432167, e-mail: email@example.com
Original scientific paper
EFFECT OF NITROGEN RATE ON GRAIN YIELD OF BREAD
Biljana GORJANOVIĆ 1*, Miroslav ZORIĆ 2, Marija KRALJEVIĆ-BALALIĆ1
1Faculty of agricultural, Novi Sad, Serbia
2Faculty of technology, Novi Sad, Serbia
Gorjanović B., M. Zorić, and M. Kraljević Balalić (2010): Effect of
nitrogen rate on grain yield of bread wheat genotypes .- Genetika, Vol 42,
No. 2, 279 -286.
The improvement in grain yield is the main objective of bread wheat
breeding programs. Numerous studies indicate that nitrogen is the key
factor of yield and quality in the wheat. The goal of this paper is to
investigate variability of grain yield, of twelve bread wheat genotypes, on
three nitrogen level. ANOVA showed that this trait was mostly under
influence of the genotype (36, 3%), year × genotype interaction (26, 3%),
year of investigation (14, 1%), and in the smallest amount of the nitrogen
rate (8, 8%). On all three nitrogen level, the highest grain yield was found in
the variety Malyska. The lowest grain yield in control was found in the
variety Nevesinjka, while in the N75 and N100 rates it was found in the
variety Tamaro. The mean performance of individual cultivars, in nine
280 GENETIKA, Vol. 42, No. 2, 279-286, 2010
environments (three years × three nitrogen rates), was depicted using
which-won-were view of SREG2 biplot. The nine environments fall into
two sectors, which is an indication of a strong crossover GE interaction.
Genotype Malyska was the winner (the highest yielding variety) in first
sector containing seven environments, while genotypes Pertrana and Axis
were the winners in second sector containing two environments.
Key words: grain yield, nitrogen, sites regression (SREG) model,
The constant increase of use of nitrogen fertilizers leads to numerous
ecological and health problems. One of the ways to solve these problems is creating
genotypes that will give the appropriate yield with limited application of mineral
fertilizers, i.e. genotypes with increased nitrogen uptake and nitrogen use efficiency.
MOMČILOVIĆ et al. (1990) stated that differences among genotypes in terms of
nitrogen concentration in plant tissue were not so pronounced, while KRALJEVIĆ-
BALALIĆ et al. (1991), MAY et al. (1997), LE GOUIS et al. (2000) and KRALJEVIĆ-
BALALIĆ (2001) found significant variation among genotypes in plant nitrogen
concentration. Parameters such as nitrogen harvest index, nitrogen remobilization
efficiency and straw nitrogen content could be used in the development of cultivars
with desired nitrogen use efficiency (VAN SANFORD and MC KOWN, 1987;
KRALJEVIĆ-BALALIĆ et al., 1995; BARBOTTIN et al., 2005; GORJANOVIĆ et al., 2010).
Nitrogen (N) is the key factor of yield and quality in the cereals. Numerous
studies indicate that N fertilization can increase both wheat grain yield and grain
protein content (FOWLER et al., 1990; EHDAIE and WAINES, 2001; SUBEDI et al., 2007;
GORJANOVIĆ and KRALJEVIĆ-BALALIĆ, 2008). The goal of this paper is to investigate
variability of grain yield, of twelve bread wheat genotypes, on three nitrogen level,
in the three-year period.
MATERIALS AND METHODS
The 12 bread wheat cultivars were studied in the three-year (2004-05, 2005-
06 and 2006-07 growing seasons) field trial with three nitrogen rates (0, 75, 100
kg/ha N). Five cultivars originated from Serbia (Evropa 90, Nevesinjka, Pobeda,
Zlatka and Sonata), five from Slovakia (Ilona, Malyska, Vanda, Petrana and Axis),
one from France (Renan) and one from Switzerland (Tamaro). The experiment was
conducted at the experimental field of the Institute of Field and Vegetable Crops,
Novi Sad. The sowing rate was 600 grains/m2. Plot size was 5 m2. In all three years
45 kg/ha of each N, P and K before plowing were applied. In spring three N levels
were applied (0, 75, 100 kg/ha N). Standard agronomic practices were used to keep
the plots free of diseases. The grain yield was determinate at maturity.
The mean performance of individual cultivars, in nine environments (three
years × three nitrogen rates: E1N1, 2004/05-N0; E1N2, 2004/05-N75; E1N3,
2004/05-N100; E2N1, 2005/06-N0; E2N2, 2005/06-N75; E2N3, 2005/06-N100; E3N1,
2006/07-N0; E3N2, 2006/07-N75; E3N3, 2006/07-N100), was depicted using which-
B. GORJANOVIĆ et al.: NITROGEN EFFECT ON WHEAT YIELD 281
won-were view of sites regression-SREG2 biplot (Yan et al., 2000). The SREG
grain yield of ith genotype at jth environment; µ is grand mean;
effect of jth environment;
bilinear terms (PC1 and PC2, respectively);
genotype for PC1 and PC2, respectively;
environment for PC1 and PC2, respectively;
variation associated with ith genotype at jth environment. All statistical analyses were
done using R software (R Development Core Team, 2009).
RESULTS AND DISCUSSION
ANOVA showed that grain yield was mostly under influence of the
genotype (36, 3%), year × genotype interaction (26, 3%), year of investigation (14,
1%), and in the smallest amount of the nitrogen rate (8, 8%). These results are in
contradiction with the results of RHARRABTI et al. (2001) and FARER et al. (2006)
who found that grain yield was mostly under influence of environmental factors.
Significant differences were found between N0 and N75 rate, N0 and N100 rate, while
there were no significant differences between N75 and N100 rates (Table 1). MA ET
AL. (2004) found that the general trend was that higher N applications produces
higher yield, but in some environments, higher N rates produced lower yields,
associated with increased incidence of Fusarium head blight and other foliar
Table 1. ANOVA for grain yield
ij y is the mean
β is the main
1 λ and
2 λ are the sngular values for the first and second
1 j γ
ε is the residual of unexplained
are eigenvectors of ith
are eigenvectors of jth
Source of variation DF Mean squares F %
Year × Genotype
Year × N rate
Genotype × N rate
Significance of differences between N rates
* p<0.05; ** p<0.01
On all three nitrogen levels the grain yield was highest in 2005/06 growing
season ( x =6,34 t/ha, x =6,92 t/ha, x =7,08 t/ha), and a smallest in 2006/07 growing
season ( x =5,50 t/ha, x =6,12 t/ha, x =6,16 t/ha) (Table 2).
282 GENETIKA, Vol. 42, No. 2, 279-286, 2010
On N0 rate, the highest grain yield was found in the variety Malyska ( x =7,13
t/ha), while the lowest grain yield in control was found in the variety Nevesinjka
( x =5,08 t/ha). On N75 rate, the most yielding varieties were Malyska ( x =7,71 t/ha)
and Vanda ( x =7,13 t/ha), while the lowest grain yield was found in the variety
Tamaro ( x =5,31 t/ha). On the highest N rate, the highest grain yield was found in
the varieties Malyska ( x =8,00 t/ha), Petrana ( x =7,78 t/ha) and Vanda ( x =7,17
t/ha), while the lowest grain yield was found in the variety Tamaro ( x =5,19 t/ha),
Table 2. Grain yield on thre nitrogen level
N0 N75 N100
The mean performance of individual cultivars, in nine environments (three
years × three nitrogen rates), was depicted using which-won-were view of SREG2
biplot (Figure 1). This type of biplot are effective tool for study of mega-
environments, defined as groups of test locations that share the best cultivar(s)
consistently across yeas (YAN et al., 2000; BALALIĆ et al., 2008).
A polygon is first drawn on genotypes that are furthest from the origine so
that all other genotypes are contained within the polygon. Then perpendicular lines
to each side of the poligon are drawn, starting from the biplot origine. Genotypes
located on the vertices of the polygon performed either the best or the poorest in one
or the more environments (YAN et al., 2000).
B. GORJANOVIĆ et al.: NITROGEN EFFECT ON WHEAT YIELD 283
Figure 1. The “which-won-where” view of the SREG2 biplot for the genotype × environment
two-way data, based on grain yield of 12 wheat genotypes. Abbreviations are: 1, Evropa 90; 2,
Nevesinjka; 3, Pobeda; 4, Zlatka; 5, Sonata; 6, Renan; 7, Tamaro; 8, Ilona; 9, Malyska; 10,
Vanda; 11, Petrana; 12, Axis; E1N1, 2004/05-N0; E1N2, 2004/05-N75 ; E1N3, 2004/05-N100;
E2N1, 2005/06-N0; E2N2, 2005/06-N75; E2N3, 2005/06-N100; E3N1, 2006/07-N0; E3N2,
2006/07-N75; E3N3, 2006/07-N100.
The distance between two genotypes approximates the Euclidean distance
between them, which is the measure of overall dissimilarity between them. The
dissimilarity can be due to difference in mean yield (G) and/or in interaction with the
environments (GE) . Genotypes 5 (Sonata) and 3 (Pobeda), 6 (Renan) and 4 (Zlatka)
and 7 (Tamaro) and 8 (Ilona) were quite similar (Figure 1).
The length of the genotype vector, which is the distance between a genotype
and a biplot origin, measures the difference of the genotype from the „average“
genotype, i.e. its contribution to either G and GE or both. Therefore, genotype
located near the biplot origine have little contribution to both G and GE and
genotypes with longer vectors have large contributions to either G or GE or both.
Therefore, genotypes with the longest vectors are either the best (Malyska) or the
284 GENETIKA, Vol. 42, No. 2, 279-286, 2010
The equality lines divide the biplot into sectors and the winning genotype for
each sector is the one located on the respective vertex (YAN ET AL., 2000). The
nine environments fall into two sectors, which is an indication of a strong crossover
GE interaction. Genotype 9 (Malyska) was the winner (the highest yielding variety)
in first sector containing seven environments (E3N2, E3N1, E3N3, E2N1, E2N2,
E2N3, E1N2), while genotype 11 (Pertrana) and closely positioned genotype 12
(Axis) were the winners in second sector containing two environments (E1N1,
E1N3) Figure 1.
Genotype Malyska performed best in E2 environments (2005/2006 growing
season), which had the favourable weather conditions during the winter and
vegetative growth, and E3 environments (2006/2007 growing season), which was
caracterised by high temperatures in the winter and drought in the spring. Genotypes
Petrana and Axis perfomed best in E1 environments (2004/2005 growing season)
which was caracterised by water surplus at the beginning and at the end of the
season, and low temperatures in the winter.
The work was conducted in the frame of research project number 20090, financed by
the Serbian Ministry of Science. Authors are thankful to Dr. Chen Chanyou for help
with statistical analysis and all Gene banks and Institutes for being kind donors of
the Trifolium pratense collections.
Received February 01st, 2010
Accepted June 18th, 2010
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286 GENETIKA, Vol. 42, No. 2, 279-286, 2010 Download full-text
UTICAJ DOZE AZOTA NA PRINOS GENOTIPOVA HLEBNE PŠENICE
Biljana GORJANOVIĆ1, Miroslav ZORIĆ2 i Marija KRALJEVIĆ-BALALIĆ1
1Poljoprivredni fakultet, Novi Sad
2Tehnološki fakultet, Novi Sad
I z v o d
Povećanje prinosa je glavni cilj u programima oplemenjivanja pšenice. Veliki broj
autora navodi da je azot jedan od ključnih faktora u formiranju prinosa i kvaliteta
kod pšenice. Cilj ovog rada je da se u trogodišnjem periodu ispita fenotipska
varijabilnost prinosa dvanaest genotipova hlebne pšenice, na tri nivoa ishrane
azotom. ANOVA je pokazala da je na ispoljavanje ovog svojstva najveći uticaj imao
genotip (36,3%), zatim interakcija godina × genotip (26,3%) i godina (14,1%), a
najmanji uticaj imala je doza azota (8,8%). Sorta Malyska je na sva tri nivoa ishrane
imala najveći prinos. Najmanji prinos u kontroli je zabeležen kod sorte Nevesinjka, a
na dozama N75 i N100 kod sorte Tamaro. Srednja vrednost genotipova, u devet
sredina (kombinacija doza azota × godina), prikazana je pomoću „which-won-were“
- SREG2 biplota. Devet sredina svrstano je u dva sektora, što je indikacija jake
ukrštene (crossover) interakcije genotipa i spoljne sredine. Sorta Malyska je bila
najprinosnija u prvom sektoru koji sadrži sedam sredina, dok su sorte Petrana i Axis
bile najprinosnija u drugom sektoru koji sadrži dve sredine.
Primljeno 01. II. 2010.
Odobreno 18. VI. 2010.