ARTICLES
RIA / Vol. 42 N.º 3
Relationship between efficiency of
nitrogen use and industrial quality in
Argentine varieties of Bread Wheat
(Triticum Aestivum L.) with different
gluten composition
LERNER, S.E.; ARATA, A.F.1*; ARRIGONI, A.C.1
ABSTRACT
The efficient use of nitrogen in crops can minimize environmental risks and maximize returns to farmers. This
can be achieved by adjusting the fertilization management and/or using the genetic variability. In addition to
its effect on yield, N has a significant effect on the quality of wheat grains. The latter is also influenced by the
gluten protein composition and the environment. The aim of this work was to study the relationship between N
recovery efficiency and its partitioning between grain and stover with industrial quality attributes in Argentine
wheat bread varieties of different cycle and with different allelic composition of gliadins and glutenins. In the
current work, field trials of 27 Argentine bread wheat varieties of different cycle were carried out during 2011/12
season, in Azul, Province of Buenos Aires, Argentina. The fertilizer treatments were: control treatment (T) and
fertilized treatment (F) with split application of urea at variable rates to reach 210 kg n/ha. The following characteristics were determined: grain yield and its components, parameters of N use efficiency, alveographic parameters, sedimentation volume, protein content, wet gluten content and N stover content. The protein pattern
of each variety was identified by polyacrylamide gels. The genotype was the main source of variation for total
recovery efficiency, grain recovery efficiency and stover recovery efficiency. The N harvest index decreased
from 0.81 in control to 0.73 in fertilized treatments on average, and variety*treatment interaction was signi
cant. The protein content in grains and the dough extensibility were highly dependent on the level of n while
the baking strength it was of the genotype. The ability to partition n to grain explained the relationship between
the extensibility and the n recovery efficiency. The assessment of high molecular weight glutenin alleles was
not strictly coincided with quality group of the variety showing the in uence of other protein fractions and their
interaction with the environment. The expression of the genetic potential attributed to the allelic composition of
gluten may be regulated in part by n use efficiency attributes.
Keywords: nitrogen recovery, end use quality, glutenins, gliadins.
Departamento de Producción Vegetal, Cereales y Oleaginosas, Laboratorio de Valoración de Calidad Industrial de Trigo, Facultad de
Agronomía, UNCPBA. Rep. de Italia 780, 7300, Azul, Buenos Aires, Argentina. Correo electrónico: [email protected]
1
Received October 28, 2014 // Accepted July 6, 2015 // Published online December 9, 2015
Relationship between efficiency of nitrogen use and industrial quality in Argentine varieties of Bread Wheat (Triticum Aestivum L.) (...)
April 2016, Argentina
INTRODUCTION
Nitrogen (N) is one of the main inputs in cereal cultivation and has become an important agricultural pollution
factor through leaching and fertilizer runoff. Therefore, a
more efficient use of N in production represents a current
challenge for agronomic research, and especially for plant
breeding programs, with the aim of minimizing environmental risks and maximizing producers’ incomes (Muurinen et al. Al., 2006).
The response to fertilization in different production situations is markedly affected by genetic differences in regards
to potential yield and N absorption and retranslocation to
harvestable organs (Van Sanford and MacKown, 1987).
The duration of the cycle of the variety is another important
variation factor (Rostami and O Brien, 1996). The genotype and N level affect the nutrient use efficiency in terms of
yield and captured N (Barraclough et al., 2010), as well as
the grain recovery efficiency of the applied fertilizer (Guarda et al., 2004).
There are two ways of increasing the N use efficiency
(EUN) in wheat crops, adjusting the management of fertilization or achieving better varieties. The efficiency of
fertilization can be increased with managements and crop
rotations that impact on the N levels in the soil, as well as
fertilizer applications adjusted to the crop’s demand, taking
into account the achievable yield and the climate. The other
way to improve EUN is to develop varieties able of recovering more N from soils and fertilizers and to use it to generate more grain (Barraclough et al., 2010). It should be noted
that the captured N not translocated to grains returns to the
system by means of mineralization, and becomes available
(to a greater or lesser extent) for the next crop. Although the
latter does not generate a direct productive result, it contributes to improve sustainability.
In addition to its effect on yield, the N captured and translocated to grains exerts an important effect on the amount of protein in grains and other quality parameters. The
protein content in grain, strongly associated with the gluten
content, is partially determinant of industrial quality (Labuschagne et al., 2006; Pinilla Quezada et al., 2008). The type
of proteins of gluten (gliadins and glutenins) are characteristic of each variety and provide specific properties related
to their end use.
The effect of N on the rheological parameters of the
dough, such as those obtained using the Chopin Alveograph: baking capacity (W), tenacity (P) and dough extensibility (L) (Alzueta et al., 2008; Fuertes-Mendizábal et al., 2010,
2012). The dough extensibility (L) is the alveographic parameter most influenced by the availability of N (Park et al.,
2006), and thus stable genotypes in N recovery efficiency
in grain and agronomic efficiency show less variation in the
values of this parameter even between years with different
rainfall distribution (Lerner et al., 2013). However, little is
known about the relationship between the industrial quality
of the flours and the determinants of grain N recovery: capture and partition.
Genes in the chromosomes in groups 1 through 6 control
the coding of gluten proteins. The glu-A1 loci, glu-B1 and
glu-D1 loci encode high molecular weight glutenin (HMWG)
subunits. Payne et al. (1987) observed that HMWG subunits were directly associated with gluten strength and, by
analyzing numerous varieties of bread wheat, established
an index called Scoreglu-1, where a quality value was assigned to each variety (from 3 to 10) depending on the subunit composition of these proteins. For the construction of
this classification, the sedimentation test (SDSS) (Axford,
1979) was used to estimate gluten strength, which is very
important to determine the final use of flours (Peña, 2002;
Cornish et al., 2006).
Low molecular weight glutenins (LMWG) are controlled
by genes from the loci glu-A3, glu-B3 and glu-D3 located on
the short arms of chromosomes 1AS, 1BS and 1DS, near
the genes encoding certain gliadins (γ- and ω-gliadins) and
therefore they are partially jointly inherited (Metakovsky et
al., 1990). In a first glance, there are two broad groups of
gliadins, those resembling the Chinese Spring (CSS) variety and those similar to the Cheyenne variety (CNN). The
latter provides better rheological properties to the flours.
On the other hand, the substitution of the short arm of
the chromosome 1B of wheat by the short arm of the chromosome 1R of rye (1B/1R translocation) has a negative
impact on wheat bread quality due to the loss of a group
of LMWG and gliadins (ω-gliadins), which is replaced by
secalines (Martín and Carrillo, 1999). This last factor has
been corrected in the aforementioned Score. Another factor
that may affect the relationship between different protein
groups is the overexpression of the Bx7 subunit (HMWG),
which only associates to subunit 8 (Marchylo et al., 1992)
and improves quality.
The objective of the present work was to study the relationship between the efficiency of N capture and its partition
between grain and stover with attributes of industrial quality
in Argentine varieties of wheat bread of different cycles and
with different allelic composition of gliadins and glutenins.
MATERIALS AND METHODS
Field tests
The tests were conducted during the 2011/12 campaign
in the experimental field of the Faculty of Agronomy, National University of the Center of the province of Buenos Aires, Azul, Buenos Aires (36º 49 ‘53’’ South latitude, 59º 53’
23’’ West longitude). We included twenty-seven Argentine
long-cycle (LC) bread wheat (Triticum aestivum L.): ACA
201 and 304, BIOINTA 2005, Buck Baqueano, Malevo,
55 CL2, SY 100 and SY 200, Klein Carpincho, Gladiador
and Yarará, Relmó INIA Torcaza; and short-cycle (SC): ACA
801, BIOINTA 1003, Buck AGP Fast, Meteoro and SY 300,
Don Mario Atlax and Arex, Klein León, Nutria, Proteo and
Rayo, Santa Rosa BAT 102, 111 and 112 (experimentals),
Sursem SRM 2331. Sowing dates were July 5 and August
8, 2011, and the target densities were 400 and 450 plants/
m2 for the first and second group, respectively.
LERNER, S.E.; ARATA, A.F.1*; ARRIGONI, A.C.1
ARTICLES
RIA / Vol. 42 N.º 3
We worked with two levels of nitrogen (N): control (T),
which corresponded to emergency base fertilization with
a dose of urea equivalent to 50 kg/ha of diammonium
phosphate (9 kg N/ha), and fertilized (F), with split application of urea, 40% at emergence and 60% at the end of
tillering, at variable doses according to soil analysis at planting in order to achieve a total of 210 kg/ha of N (210 kg N/
ha - N Soil) that correspond to the requirements of N for an
objective yield of 7 t/ha. Triple superphosphate was applied
at pre-sowing to ensure phosphorus non-deficiency. Pests,
diseases and weed were controlled when appropriate.
Rainfall and temperature
Figure 1 shows the the average monthly rainfall and
temperatures for the campaign (Regional Center for Agrometeorology, Centro Regional de Agrometeorología, FAUNCPBA, 2011). During the crop cycle the cumulative precipitations were 229.3 mm for long cycles and 174.6 mm for
short cycles. It should be noted that in November a total of
79.9 mm was concentrated.
Measurements and calculations in the crop
At harvest and on a 1m2 plot we determined total dry matter (TDM), grain yield (GY), dry matter of stubble (DMS),
grain weight (P1000). We calculated number of grains
(NG), harvest index (HI) and number of spikes (Nsp). We
determined percentage of proteins in grain (%Pro) (NIR)
and percentage of N in stover (% NS) (Micro-Kjeldahl) and
we calculated total absorbed N (NabsT), N absorbed in grain (NabsG), N absorbed in stover (NabsS) and N harvest
index (ICN= NabsG/NabsT). We calculated the Total N Recovery Efficiency (TRE), in grain (REG) and in stover (RES)
from the calculated percentage of N in grain (N= Protein /
5.75) (% NG) and the percentage of N in stover, according
to the following formulas derived from Guarda et al. (2004):
- Total N recovery efficiency (%): {[Ngrain+stover treat. N
(kg/ha) - N grain+stover treat. T (kg/ha)]/ N applied (kg/ha)}
x 100
- Recovery efficiency of N in grain (%) ={[N grain treat. N
(kg/ha) - N grain treat. T (kg/ha)] / N applied (kg/ha)} x
100ç
- Recovery efficiency of N in stover (%) = {[N stover treat. N
(kg/ha) - N stover treat. T (kg/ha)]/ N applied (kg/ha)} x 100
Quality assessment
Industrial quality rheological parameters were obtained
from the flour obtained from grinding the harvest grains: baking capacity (W), tenacity (P), extensibility (L) and P/L ratio using a Chopin Alveograph (AACC International , 1999).
We performed the sodium dodecyl sulphate sedimentation
(SDSS) sedimentation test (Dick and Quick, 1983) on wholegrain flour. Varieties were classified according to their
quality group (INASE, 2014).
90
25
80
70
60
15
50
40
10
30
Precipitations (mm)
Mean temperature (ºC)
20
20
5
10
0
Jun
Jul
Aug
Precipitations (mm)
Sep
Oct
Nov
Dec
0
Mean temperature (ºC)
Figura 1. Mean temperatures (ºC) and rainfall (mm) during the crop cycle (Regional Center for Agrometeorology FA-UN-CPBA, 2011).
Prepared for this edition.
Relationship between efficiency of nitrogen use and industrial quality in Argentine varieties of Bread Wheat (Triticum Aestivum L.) (...)
April 2016, Argentina
Identification of the protein pattern
Electrophoresis was performed on polyacrylamide gels
(SDS-PAGE, one-dimensional, T= 13.5%), with sequential
extraction of gliadins and glutenins according to Gupta and
Mc Ritchie (1991). The quality of the different variables was
measured according to Score glu-1 (Payne, 1987), which
takes into account the HMWG composition and the 1B/1R
transposition correction. In turn, the patterns of LMWG
were numbered by electrophoretic mobility similarity, the
type of gliadins was determined as well as the presence of
overexpression of the Bx7 subunit and 1B/1R translocation.
Experimental design and statistical analysis
The experimental design was in three complete random
blocks with split plot. Macro-plots of 13.3 m2 were used,
randomizing the varieties in the main plots and the fertilization treatments in the secondary plots. The results were
analyzed by means of Analysis of Variance (ANOVA) and
comparison of means by Duncan’s test (α≤0.05) and Principal Component Analysis (PCA) using the Infostat statistical
package (Di Rienzo et al., 2014). For each attribute studied
by ANOVA, the percentage of sum of squares of each source of variation with respect to the sum of squares of the
model (% SC) was calculated, in order to estimate the contribution of each factor on the variability of these attributes.
RESULTS AND DISCUSSION
Yield and components
The variables TDM, GY, DMS, HI and Nsp were significantly differed between varieties and treatments (Table 1).
Lerner et al. (2013) reported similar effects of the variety
and nitrogen fertilization treatment on GY, although their
interaction was significant. On the other hand, the interaction variety*N was significant for the variables %NG,%NS,
NabsT, NabsG and NabsS, although the value %SC value
was low in all cases (Table 2).
This indicates that the genotypic differences in N capture and its partition to grain depended on the availability of
the nutrient for the crop. In contrast, Le Gouis et al. (2000)
found that the interaction variety*N was not significant for
%NG in experiments conducted in France, although it was
significant for yield and its components. The main effect of
the fertilization treatment on NabsG was due to responses
in both GY and %NG. Similarly, the average response in
NabsS corresponded to variations in DMS and %NS, determining higher NabsT values (Table 3).
Significant differences were observed between cycles for
TDM, GY, DMS, HI, Nsp, %NG, %NS, NabsT, NabsG and
NabsS (Tables 1 and 2). The effect of this factor in favor of
long-cycle varieties on NabsG was due to the responses of
GY and %NG (Table 3). The interaction cycle*N was significant for TDM, DMS,% NG, NabsT and NabsS (Tables 1
and 2). Also, we noted a higher response to fertilization in
%NG for short-cycle varieties, whereas for NabsS a higher
response to the addition of N was found in the long-cycle
varieties, probably due to the longer stage of biomass accumulation (Table 3).
Nitrogen use efficiency
Significant differences were found between varieties in
terms of total N recovery efficiency (TRE), in grain (REG)
and in stover (RES)(Table 4 and Figure 2). Genotype proved to be a significant source of variation for the recovery
efficiencies, as reported by Guarda et al. (2004). On the
other hand, Figure 2 shows that the behavior of the different varieties in REG did not strictly coincide with what was
observed in RES and therefore in TRE. This can be partially
explained by the genotypic variability found in the N harvest
index (NHI) (Table 2 and Figure 3), which determines different patterns of N partitioning within the plant, similar to the
report of Barraclough et al. (2010).
Long-cycle varieties presented significantly higher TRE
(LC= 43.30 a; SC= 33.55 b) and RES (LC= 19.33 a; SC=
13.04 b) values when compared with short cycle varieties,
although there were no differences in REG (LC= 23.97 a,
SC= 20.51 a). Therefore, the differences found between
cycles in N capture were reflected in changes in the ability
to allocate N in stubble rather than in grains. This may be
due in part to the different doses applied to the different
cycles, which are involved in the calculation of RES, due
to the variation in the availability of N in the soil at the time
of sowing.
We observed significant differences between varieties
and between treatments in NHI, but not between cycles
(Table 2 and Figure 3). Fertilized treatments presented, on
average, lower values of this variable than the controls (T=
0.81 a, N= 0.73 b). This shows a limitation in the capacity
of retranslocation of N to harvestable destinations with high
doses of fertilizer, which is in agreement with Le Gouis et
al. (2000). The interactions cycle*N and variety*N were significant, although with reduced %SC. However, it is worth
mentioning some varieties that did not respond to generality, such as BIO1003, AGPFAST, Meteoro, León, Proteo,
Torcaza, BAT111 and BAT112, which were stable in ICN
between fertilization treatments.
Industrial quality
The percentage of protein in grains (% Pro), which is a
parameter of commercial quality partially associated to industrial use, presented significant effects of the cycle, the
variety and the level of N. There were significant interactions cycle*N and variety*N. The most important sources
of variation were fertilization and genotype, accounting for
55.8% and 26.7% of variability, respectively, while %SC
and cycle interactions were low (Table 5).
The baking capacity (W), the main attribute of industrial
quality, presented significant differences between cycles,
varieties and N levels, and the interaction variety*N was significant. But in this case, the genotype, clearly associated
with the protein composition of gluten, was the source of preponderant variation followed by fertilization. As for the deter-
LERNER, S.E.; ARATA, A.F.1*; ARRIGONI, A.C.1
ARTICLES
RIA / Vol. 42 N.º 3
TDM
SV
GY
DMS
HI
Nsp
%SC
p-value
%SC
p-value
%SC
p-value
%SC
p-value
%SC
p-value
Cycle
15
***
21,5
***
8,53
***
3,85
**
6,57
**
Variety
37,9
***
40,4
***
41
***
50
**
38,4
**
Nitrogen (N)
19,1
***
11,9
***
18,2
***
1,88
*
8,32
**
Cycle*N
0,85
*
0,51
ns
0,83
*
0,28
ns
0
ns
Variety*N
5,37
ns
4,77
ns
6,96
ns
11,5
ns
14,5
ns
Table 1. Percentage of sum (TDM), grain yield of squares (%SC) and p-value of the sources of variation (SV) considered for total dry
matter (GY), dry matter of stover (DMS), harvest index (HI) Harvest (IC) and number of spikes per m2 (Nsp).
*p-value<0,05; **p-value<0,01; ***p-value<0,0001.
Prepared for this edition.
%NG
SV
%SC
%NS
p-valor %SC
NabsT
NabsG
p-valor %SC p-valor
NabsS
NHI
%SC
p-valor
Cycle
0,6
*
0,9
**
12,2
***
15,3
***
%SC p-valor %SC p-valor
4,9
***
0,0
ns
Variety
26,4
***
15,9
**
12,7
**
20,4
***
13,2
*
32,4
***
Nitrogen (N)
55,7
***
63,4
***
56,5
***
44,1
***
57,9
***
39,7
***
Cycle*N
1,1
**
0,1
ns
0,4
*
0,1
ns
1,4
**
0,6
*
Variety*N
6,3
**
6,2
**
5,6
**
6,6
*
6,1
*
7,4
*
Table 2. Percentage of sum of squares (%SC) and p-value of the sources of variation (SV) considered for percentage of nitrogen in grain
(%NG), percentage of nitrogen in stubble (%NS), total absorbed nitrogen (NabsT), nitrogen absorbed in grain (NabsG), nitrogen absorbed
in stover (NabsS) and nitrogen harvest index (NHI).
*p-value<0,05; **p-value<0,01; ***p-value<0,0001. The variables expressed as percentage were transformed (√) for analysis.
Prepared for this edition.
Treat
MST
RGR
MSR
kg/ha
IC
Nesp
esp/m
2
%NG
%NR
%
%
NabsT
NabsG
NabsS
kg/ha
LC F
15362 a
5614 a
9748 a
0,37 b
604 b
2,29 a
0,51 a
178 a
128 a
49 a
SC F
13086 b
4629 b
8457 b
0,35 b
658 a
2,32 a
0,46 b
146 b
107 b
38 b
LC T
12807 b
4836 b
7971 b
0,38 a
543 c
1,93 b
0,26 c
115 c
93
c
21 c
SC T
11407 c
4114 c
7293 c
0,36 b
598 b
1,82 c
0,24 c
93 d
75
d
18 d
Table 3. Means of total dry matter (TDM, kg/ha), grain yield (GY, kg/ha), dry matter of stover (DMS, kg/ha), harvest index (HI), number
of spikes per m2 (Nsp, sp/m2), percentage of nitrogen in grain (%NG), percentage of nitrogen in stover (%NS), total absorbed nitrogen
(NabsT, kg/ha), nitrogen absorbed in grain (NabsG, kg/ha) and nitrogen absorbed in stover (NabsS, kg/ha) for the combinations of long
cycles (LC) and short cycles (SC) and control (T) and fertilized (N) treatments. Means with a common letter are not significatively different
(Duncan, α= 0.05). The variables expressed as percentage were transformed (√) for analysis.
Prepared for this edition.
minants of W, the variability in tenacity (P) was explained to
a greater extent by the variability followed by the interaction
variety*N, while the extensibility (L) showed a greater influence of nitrogen fertilization in both cycles, which coincides with
the reports of Miceli et al. (1992) and Park et al. (2006).
variety*N for P, 74% of the genotypes (20 varieties) were
stable between levels of N, whereas 22% (LS: Malevo, 55
CL, Yarará, Torcaza, SC: Protoo, Ray) showed a negative
response to fertilization, and 4% a positive response (SC:
SRM2331) (data not shown).
Although significant interactions between factors were
detected for the% Pro, W and L variables, their SC% of
these were reduced, so the discussion of the results focused on the main effects (Table 5). As for the interaction
The sedimentation volume (SDSS) was poorly affected by
the environment (cycle and N) and was determined to a greater extent by the variety, and is therefore an excellent method
for assessing intrinsic quality (Table 5), according to Axford et
Relationship between efficiency of nitrogen use and industrial quality in Argentine varieties of Bread Wheat (Triticum Aestivum L.) (...)
April 2016, Argentina
TRE
SV
REG
al. (1979), Dick and Quick (1983), Carrillo et al. (1990) and
Lerner and Ponzio (2004).
RES
%SC
p-value
%SC
p-value
%SC
p-value
Cycle
14,9
**
4,59
ns
21,8
***
Variety
79,1
**
81,9
*
70,8
**
The PCA that included parameters of industrial quality and
efficiency determinants of the use of N (Figure 4) indicated
that the main component 1 (CP1) was conformed by the variables P (+), W (+), SDSS (+ ), TRE (+) and NabsT (+) and
explained 31.4% of the variability observed, and the main
component 2 (CP2) by L (+), NHI (+) and %Pro (+) accounted
for 28.9% of the variability. The formation of groups according
to the variety cycle was not observed, so that that factor did
not present discriminative capacity for the studied variables.
Table 4. Percentage of sum of squares (%SC) and p-value of sources of variation (SV) considered for the total recovery efficiency
(TRE), in grain (REG) and stover (RES).
*p-value<0,05; **p-value<0,01; ***p-value<0,0001.
Prepared for this edition.
Nitrogen recovery efficiency (%)
80
70
60
50
40
30
20
10
0
ERT
ERG
ERR
Figure 2. Total recovery efficiency (TRE, %), in grain (REG, %) and stover (RES, %) of the analyzed varieties.
1
0,9
Harvest index of N
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
T
F
Figure 3. Nitrogen harvest index (NHI) of the analyzed varieties for control (T) and fertilized (F) treatments.
Prepared for this edition.
LERNER, S.E.; ARATA, A.F.1*; ARRIGONI, A.C.1
ARTICLES
RIA / Vol. 42 N.º 3
Considering the variables associated to CP1, the alveographic parameter W and the sedimentation volume presented a high correlation with each other, being mainly determined by the ability to capture N by the crop (NabsT).
According to CP2, extensibility (L) was strongly associated
with %Pro, in agreement to the observations of Godfrey et
al. (2010) and was determined by the NHI. This last fact evidences that the relationship between the extensibility and
the efficiency in N recovery (REG) reported by Lerner et al.
(2013) would be related to a greater capacity to partition N
to harvestable destinations (NHI) and not to the capture of
N from the soil (NabsT).
Protein composition
Of the loci coding for subunits of high molecular
weight glutenins (HMWG), glu-B1 showed the highest variability followed by glu-A1, and the allelic variants 7 + 9 and 2* were the most frequent for the first
and the second, respectively. In contrast, the glu-D1
locus was more homogeneous and the pair of subunits 5+10 were the most preponderant, except for the
variety León, which presented the allele 2 + 12, of
lorer quality (Table 6). These results coincide with that
reported by Lerner et al. (2009) for the Argentine collection of bread wheat.
For the subunits of low molecular weight glutenin (LMWG),
we identified and grouped band patterns, and found a great
variability in the electrophoretic mobility (Figure 5). Regarding the composition of gliadins, the CSS type was the most
frequent and with lower quality when than CNN, according
to Masci et al. (1991). Only four varieties presented introgression with rye (1B/1R) and were located in the low quality
quadrants of the PCA, due to the above mentioned factors
concerning the replacement of a group of LMWG and gliadins (ω-gliadins) by secalines (Martín and Carrillo, 1999).
The ACA304 variety was the only one with overexpression
of band 7 (7oe), and presented a good performance for the
variables efficiency and quality (Table 6 and Figure 4).
The Score glu-1, which ranks according to the HMWG composition, did not coincide strictly with the Quality Group (QG)
SV
%PRO
classification; we found heterogeneity in the Score within each
QG or vice versa (Table 6). This may be due to the influence of
other protein fractions such as LMWG and gliadins (Branlard
and Dardevet, 1985). The first accounts for about one-third
of total protein in grains and 60% of total gluten (Shuaib et
al., 2007) and have a significant influence on the final use of
wheat flour (Wang et al. 2009), and are important in this case
due to the great variability found in the band patterns.
In the PCA of Figure 6, shaped in the same way as in Figure
4, the varieties are grouped according to phenotypic attributes
of use of N and industrial quality, which may also contribute
to explain the lack of association between the Score and the
QG. The varieties ACA201, Malevo, SY100 and SRM2331,
belonging to QG 2 but possess Score 10, except for SY100
that has Score 8; presented high values in the CP1 and intermediate values in the CP2. This indicates que they had high
baking capacity (SDSS, W) and tenacity (P), associated with
intermediate NHI and high values of TRE and NabsT.
Yarará and ACA304 belong to QG 1 and have a Score 9
and 10 respectively. They presented positive values in CP 1
and CP 2. This indicates that they had high baking capacity
and a balanced P/L ratio, in association with higher NHI than
the previous. The variety 55CL, from QG 2 and Score 9, presented negative values in CP1 and intermediate values in
CP2; as well as the varieties León and AGPFAST varieties,
from QG3, which indicated a reduced baking capacity.
Although these genotypes presented an intermediate
NHI, their TRE and NabsT values are very low which may
partially explain this response. On the other hand, BAT112
(Score 9) was in the same group as the Carpincho and Baqueano varieties, from QG3 and with Score 6. These genotypes displayed intermediate values in CP1 and negatives
values in CP2, thus indicating an intermediate baking capacity, but with reduced extensibility (L) due to low values
of %Pro and NHI. An interesting case is that of variety Arex
which, despite having Score 9, is in QG3.
This could be due to its low capacity to partition N to grain
(NHI), which reduced the %Pro and the extensibility of the
dough (negative values ​​in CP2). On the other hand, varieties Meteoro, Torcaza and Rayo, despite having a high
W
P
L
SDSS
%SC
p-valor
%SC
p-valor
%SC
p-valor
%SC
p-valor
%SC
p-valor
Cycle
0,5
*
6,5
***
3,96
***
0,1
ns
2,45
**
Variety
26,7
***
50,7
***
66
***
38,5
***
69,5
***
Nitrogen (N)
55,8
***
24,1
***
1,74
**
36
***
6,09
***
Cycle*N
1,4
**
0,19
ns
0,14
ns
0,46
ns
0,03
ns
Variety*N
6,4
**
6,42
*
18,5
***
7,02
*
7,55
ns
Table 5. Percentage of sum of squares (% SC) and p-value of the sources of variation (SV) considered for protein percentage in grain
(%Pro), baking capacity (W), tenacity (P), extensibility (L) and volume of sedimentation (SDSS).
*p-value<0,05; **p-value<0,01; ***p-value≤0,0001. The variables expressed as percentage were transformed (√) for analysis.
Prepared for this edition.
Relationship between efficiency of nitrogen use and industrial quality in Argentine varieties of Bread Wheat (Triticum Aestivum L.) (...)
April 2016, Argentina
4,00
Proteo
Rayo
L
Meteoro
%Pro
2,00
ICN
ACA304
Yarara
W
CP 2 (28,9%)
Torcaza
BIO2005SY300
Leon
NabsT
0,00
ACA801
55CL
AGPFAST
Gladiador
Nutria
SY100
BAT111
Arex
Atlax
BAT102
P
SRM2331
BIO1003
SY200
Baqueano
-2,00
-4,00
ACA201
SDSS
ERT
Carpincho
BAT112
-4,00
-2,00
0,00
2,00
4,00
CP 1 (31,4%)
Figure 4. Biplot of the first and second principal component for total nitrogen recovery efficiency (TRE), total absorbed nitrogen (NabsT),
nitrogen harvest index (NHI), protein percentage (%Pro), sedimentation volume (SDSS), baking capacity (W), tenacity (P) and extensibility (L) of 27 wheat varieties. The genotypes are represented by the points and the vectors represent the variables of use of nitrogen and
industrial quality.
Prepared for this edition.
Score and being in QG1, were not located within the group
of higher W (negative values ​​in CP1). This could be explained by its high NHI and consequent increase of %Pro and
extensibility (high values ​​in CP2). Thus, imbalances can be
produced in the proportions of each type of protein, leading
to very extensible doughs and therefore affecting the final
quality (Godfrey et al., 2010). In turn, the variety Proteo presented a very high %Pro but associated with a somewhat
higher baking capacity, when compared to the previous
ones (Table 6 and Figure 6). For all of the above, the ability
of the crop to capture the N of the soil and to partition it to
grain modified its content and protein balance, which would
affect the expression of the genetic potential attributed to
the allelic pattern of gliadins and glutenins.
CONCLUSIONS
Genotype was the main source of variation for the recovery efficiency of total N, grain and stover.
The ability to retranslocate N to harvestable destinations
was affected by variety, N level and their interaction. It decreased with high doses of fertilizer in most of the genotypes.
The duration of the cycle affected the recovery efficiency
of N due to changes in the recovery efficiency in stover,
although differences between genotypes were also detected within each cycle.
The protein content in the grains and the extensibility of
doughs were highly dependent on the level of N, while the
baking capacity - main attribute of industrial quality - was
determined to a greater extent by the genotype.
The relationship between extensibility and recovery efficiency of N in grain was related to the crop’s greater ability
to partition N to harvestable destinations and not to the capture of N from the soil.
The score glu-1, which ranks according to HMWG alleles,
did not strictly coincide with the Quality Group of the variety, evidencing the influence of other gluten protein fractions
and their interaction with the environment.
The expression of the genetic potential attributed to the
allelic composition of gliadins and glutenins could be regulated in part by attributes of efficiency in the use of N.
ACKNOWLEDGEMENTS
To Agronomist Daniel Pérez for his support in the laboratory work and to the National University of the Center
[Universidad Nacional del Centro] for the partial funding
of this work.
LERNER, S.E.; ARATA, A.F.1*; ARRIGONI, A.C.1
ARTICLES
RIA / Vol. 42 N.º 3
Variety
Glu-A1
Glu-B1
Glu- D1
ACA201
2*
17+18
5+10
ACA304
2*
7+8
5+10
Baqueano
1
7+9
5+10
Malevo
2*
7+8
SY100
2*
6+8
Gli
LMWG
1B/1R
7oe
QG
Score
CSS
3
CNN
15
2
10
X
1
CSS
10
8
X
2
6
5+10
5+10
CNN
17
2
10
CSS
11
2
8
SY200
2*
6+8
5+10
CSS
11
2
8
BIO2005
2*
7+9
5+10
CSS
1
2
9
Carpincho
1
7+9
5+10
CSS
7
X
3
6
Gladiador
1
7+9
5+10
CNN
17
X
3
6
Yarará
2*
7+9
5+10
CSS
14
1
9
Torcaza
2*
17+18
5+10
CSS
16
1
10
ACA801
2*
7+9
5+10
CSS
4
2
9
AGPFAST
ni
13+16
5+10
CSS
12
3
sd
Meteoro
2*
7+9
5+10
CNN
10
1
9
55CL
2*
7+9
5+10
CNN
16
2
9
Rayo
2*
7+8
5+10
CSS
17
1
10
SRM2331
1/2*
17+18
5+10
CSS
15
2
10
SY300
2*
6+8
5+10
CSS
11
2
8
Nutria
2*
7+9
5+10
CSS
19
X
3
6
León
N
7+9
2+12
CSS
12
3
5
BIO1003
2*
7+9
5+10
CSS
2
2
9
Atlax
1
7+9
5+10
CSS
9
2
9
Proteo
1
7+9
5+10
CSS
18
1
9
BAT102
2*
14+15
5+10
CSS
5
sd
sd
BAT111
2*
14+15
5+10
CSS
6
sd
sd
BAT112
2*
7+9
5+10
CSS
13
sd
9
Arex
2*
7+9
5+10
CSS
9
3
9
Table 6. Allelic composition of high molecular weight glutenin (Glu-A1, Glu-B1, Glu-D1), type of gliadins (Gli), low molecular weight glutenins (LMWG), presence of introgression with rye (1B/1R), presence of overexpression of the Bx7 subunit (7oe), Quality Group (QG) and
Score Glu1 (Score) for the analyzed varieties.
Prepared for this edition.
Figure 5. Electrophoretic mobility patterns of low molecular weight glutenins (LMWG). CS: Chinese Spring (marker); P1: BIO2005; P2:
BIO1003; P3: ACA201; P4: ACA801; P5: BAT102; P6: BAT111; P7: Carpincho; P8: Baqueano; P9: Atlax, Arex; P10: Meteo- ro; P11:
SY100, SY200, SY300; P12: AGPFAST, León; P13: BAT112; P14: Yarará; P15: ACA304, SRM2331; P16: Torcaza, 55CL; P17: Malevo,
Gladiador, Rayo; P18: Proteo; P19: Nutria.
Prepared for this edition.
Relationship between efficiency of nitrogen use and industrial quality in Argentine varieties of Bread Wheat (Triticum Aestivum L.) (...)
April 2016, Argentina
4,00
Proteo-GC1-S9
Rayo-GC1-S9
CP 2 (28,9%)
2,00
Meteoro-GC1-S9
L
%Pro
ICN
ACA304-GC1-S10
Yarara-GC1-S9
Torcaza-GC1-S10
W
ERT
NabsT
Leon-GC3-S5
0,00
ACA201-GC2-S10
Malevo-GC2-S10
SY100-GC2-S8
55CL-GC2-S9
AGPFAST-GC3-sd
Arex-GC3-S9
P
SRM2331-GC2-S10
Baqueano-GC2-S6
-2,00
BAT112-sd-S9
-4,00
SDSS
-4,00
-2,00
Carpincho-GC3-S6
0,00
2,00
4,00
CP 1 (31,4%)
Figura 6. Biplot of the first and second principal component for total nitrogen recovery efficiency (TRE), total absorbed nitrogen (NabsT),
nitrogen harvest index (NHI), protein percentage (% Pro), sedimentation volume (SDSS), baking capacity (W), tenacity (P) and extensibility (L) of 27 wheat varieties. The genotypes are represented by the points and the vectors represent the variables of use of nitrogen and
industrial quality. The denomination of the variety, Quality Group (QG) and Score glu-1 (S) were included for the genotypes mentioned in
the previous discussion.
Prepared for this edition.
LERNER, S.E.; ARATA, A.F.1*; ARRIGONI, A.C.1