GT BIPLOT ANALYSIS FOR YIELD AND RELATED TRAITS IN SOME SUGAR BEET VARIETIES AS AFFECTED BY COMPOST UNDER SALINE SOIL

GT BIPLOT ANALYSIS FOR YIELD AND RELATED
TRAITS IN SOME SUGAR BEET VARIETIES AS
AFFECTED BY COMPOST UNDER SALINE SOIL
Abu-Ellail, F.F.B.1* and A. H. Sasy2
1Breeding and Genetics Dept., 2Sugar Technology, Res. Dept.,
Sugar Crops Research Institute, Agriculture Research Center, Giza, Egypt.
1*Email – farrag_abuellail@yahoo.com
Key Words: Compost fertilizer, Correlation, GT biplot, Mono and
Multigerm Sugar Beet Varieties.
ABSTRACT
Two field trials were carried out during two successive seasons of
2018/2019 and 2019/2020 at farmer’s field, Tamiya, (latitude of 29.58o
N, longitude of 30.96o E and altitude of 34 m above sea level), Al
Fayoum Governorate, Egypt, to evaluate the performance of eight sugar
beet varieties under four levels of compost (without, 2, 4, and 6 ton /fed)
in saline soil. A split-plot design with three replications was used in both
seasons. The results revealed that treating the soil with 6-ton compost/fed
significantly increased root diameter, root fresh weight/plant, sucrose and
extractable sugar percentages, sodium content, root and sugar yields/fed
as well as alpha-amino N content decreased in both seasons. Meantime,
the proline, potassium, sodium contents and sugar lost to molasses%
were insignificantly affected by compost levels in 1st season and the
second one. Indira-KWS mono-germ variety exhibited superiority over
the all other tested varieties, which recorded the highest values of root
diameter, root fresh weight/plant, sucrose%, root and sugar yields/fed as
well a significant decrease in proline content in both seasons. While, both
mono-germ Carma variety and multi-germ Shrb 21802 variety had the
lowest value of sodium content without significant difference between
them in both seasons, compared with the other varieties. There was a
highly significant and positive correlation between root yield and each
root diameter and root weight. The genotype by trait (GT) biplot graph
was used to compare varieties based on multiple traits. It proved to be a
reliable and easy-to-interpret analysis and visualization of the results.
interpret analysis and visualization of the results. Under conditions of this
work, planting mono-germ variety (Indira-KWS) and fertilized it with 6-
ton compost/fed can be recommended to get the higher root and sugar
yields/fed under saline soil condition.
INTRODUCTION
Most soils in Al-Fayum governorate are affected by soil salinity
very around Lake Qaroun in large areas connected to most of the city,
such as Tamiya, also the salt rates range from 2 dsm-1 to 17 dsm-1 and
have a significant impact on the growth of crops and reduce agricultural
Egypt. J. of Appl. Sci., 36 (3) 2021 66-83
production in general. Evaluate imported sugar beet varieties under
saline soil is essential to select recommended best variety under these
conditions. Also, to cultivate this soil, need good fertilizing and good
agricultural practices to be available for planting.
Sugar beet (Beta vulgaris L.) is one of the most important crops for
sugar production in Egypt, and it has the ability to grown on newly
reclaimed soils that suffer from salinity. Sugar beet became an important
crop in the newly reclaimed sandy and saline soils, increasing sugar
crops cultivated area and sugar production per unit area is considered the
important national target to minimize the gap between sugar consumption
and production. The total sugar beet cultivated area reached about 5985
thousand fed., with an average of 18 tons fed-1(Annual Report of Sugar
Crops Council, December 2020). Recently, the sugar beet crop has been
of favorable importance in local crop rotation as a winter crop not only
infertile soils but also in poor, saline, alkaline and calcareous soils.
Moreover, it could be economically grown in newly reclaimed soils.
Nowadays, compost is being extensively used as a robust tool to
maximize crop productivity. Deficiency of soil nutrients such as nitrogen,
phosphorus, potassium, zinc and boron has been identified as the major
constraints in sugar beet crop production and, based on plant needs,
should be added to the soil (Ali, 2015).
Soil salinity is a major abiotic stress that has adverse effects on the
physiological and metabolic processes of plants leading to diminished
growth and yield of plants (Azizpour et al., 2010). Plant growth is
suppressed severely at high salinity stress due to factors such as osmotic
stress, mineral nutrition absorption imbalance, and specific ion toxicity,
all combining to reduce nutrient uptake consequentially causing
physiological drought to plants (David, 2007). Fertilization plays an
important role in promoting plants to tolerate salt stress and specialize
from this fertilization compost which, has a positive effect on soil
fertility as well as the productivity of the field crops. Hence, adding
significant quantities of agricultural residues as compost in saline sandy
soils improves their physical, chemical and biological properties. In this
connection, Wallace and Carter (2007) showed that the use of compost
increases soil fertility which led to increasing sugar beet root yield by
7%. Siddiqui et al., (2009) explained that compost is an organic soil
amendment and is an important source of fertilization, which is found as
a result of organic material decomposition. Also, compost is improving
the soil's physical and chemical properties and increasing water holding
capacity. El-Nagdi and Abd El Fattah (2011) showed that all plant
residues, bio fertilizer, and organic compost alone or in combination with
biocides significantly increased the fresh weight of roots and shoots of
sugar beet plants. Compost is a low cost as organic fertilizers and soil
67 Egypt. J. of Appl. Sci., 36 (3) 2021
amendment. When applied to soils, it positively affects the structure,
porosity, water holding capacity, nutrient contents and organic matter all
of which improve plant growth and crop yield (Rajaa and Saadi, 2011).
Masri et al., (2015) found that adding compost (2 ton/fed) gave the
maximum values of root yield, as well as improved juice quality traits of
sugar-beet. Also, the application of 12 tons/ha of compost improved the
root yield of sugar beet. As for the differences between varieties, Enan,
et al., (2016) and Makhlouf et al., (2021) indicated that the tested beet
varieties differed significantly in the studied traits. The aim of this work
is to evaluate eight sugar beet varieties that fertilized with different
compost levels for growth, yield, and technological traits. Also, estimate
GT biplot analysis and estimate the correlation coefficient between yield
and related traits were investigated.
MATERIALS AND METHODS
Two field trials were carried out during two successive seasons of
2018/2019 and 2019/2020 at farmer’s field, Tamiya, (latitude of 29.58o
N, longitude of 30.96o E and altitude of 34 m above sea level) Al
Fayoum Governorate, Egypt, to evaluate the performance of eight sugar
beet varieties namely (Indira, Dipendra, Carma and Vangelis) as a
multigerm variety and (Shantala, Melodia, MK 4199 and Shrb21802) as
a monogram variety under four levels of compost (without, 2, 4, and 6
ton /fed) in saline soil. A split-plot design with three replications was
used in both seasons. The four levels applied of compost fertilization
allocated in the main plots and the eight tested varieties were randomly
distributed in the sub-plots. The plot area was 18 m2, which included 5
ridges of 6.0 m in length and 0.6 m in width. Compost was applied before
sowing during seedbed preparation. Phosphorous was added in the form
of superphosphate (15 %) at the rate of 30 kg P2O5/fed during seedbed
preparation. Nitrogen fertilizer was added in the form of ammonium
nitrate (33.5% N) at the rate of 80 kg N/fed in three equal doses; after
thinning (4 true leaf stage) and after 3-week intervals later. Potassium
was added in the form of potassium sulfate (48%) at the rate of 48 kg
K2O/fed with the first and third dose of nitrogen fertilizer. Multi-germ
and mono-germ sugar beet varieties were sown in the 2nd week of
October in the 1st and 2nd seasons, while harvesting took place at age of
210 days after sowing in both seasons. All other cultural practices were
maintained to assure optimum growth and production throughout the
whole season. The country of origin of the tested sugar beet varieties is
manifested in Table (1). The chemical properties and contents of the
plant compost are presented in Table (2). As shown by Jackson (1958),
soil samples were taken for mechanical and chemical analyses before
Egypt. J. of Appl. Sci., 36 (3) 2021 68
sowing from each location at 0-30 cm depth from the soil surface (Table
3).
Table 1: Country of origin and source* of the evaluated sugar beet
(Beta vulgaris var. saccharifera, L.) varieties
Sugar beet varieties Types Company Country of origin
Indira –KWS Monogerm KWS Germany
Dipendra-KWS Monogerm KWS Germany
Carma Monogerm MARIBO Denmark
Vangelis Monogerm SCHREIBOERS USA
Shantala-KWS Maltigerm KWS Germany
Melodia Maltigerm KHBC Poland
MK 4199(Emperator) Maltigerm KUHN USA
Shrb21802(Echnaton) Maltigerm STRUBE Netherlands
*Source: Sugar Crops Research Institute, Agricultural Research Centre, Giza, Egypt
Table 2: Chemical properties of compost plant
Moisture
content
EC
dSm-1
1:10
PH C/N
ratio
Organic
Matter
Organic
carbon
Ashe Ammonium
nitrogen
Nitrate
nitrogen
Total
nitrogen
Total
phosphoric
Total
potassium
21% 2.65
6.5 1:20 41.85% 37.9% 35.9% 320ppm 22ppm 2.77% 1.09% 0.64%
Table 3: Soil properties of the experimental site in 2018-2019 and 2019-
2020 seasons
Particle size distributions 2018/2019 season 2019/2020 season
Sand% 33.15 28.52
Silt% 45.32 47.23
Clay% 21.53 24.25
Texture Sandy loam Sandy loam
pH at (1:2.5) soil: water
suspension
8.2 7.4
EC (dS/m) 6.18 6.00
O.M (g/kg) 6.69 8.23
CaCO3 (g/kg) 84.69 80.45
Cations (meq/l)
Na+ 41.28 39..92
K+ 1.96 1.58
Ca++ 8.77 6.83
Mg++ 9.89 8.57
Anions (meq/l)
Cl- 36.85 32.22
HCO3- 12.61 11.15
So4- 12.44 13.53
Available NPK (mg/kg soil)
Available Nitrogen 11.19 46.31
Available P2O5 4.35 6.78
Available K2O 155. 21 165.26
69 Egypt. J. of Appl. Sci., 36 (3) 2021
The recorded data:
After 105 days from sowing, random samples of sugar beet plants
were taken from each sub plot to determine the following traits:
1. Proline concentration (u moles/g leaf fresh weight) was estimated
using the method of Bates et al., (1973).
At harvest, a sample of ten plants was randomly collected from the
middle rows of each plot to determine the following traits:
1. Root diameter (cm).
2. Root fresh weight/plant (g)
3. Sucrose (Pol %) was estimated in the fresh samples of sugar
beet roots using Saccharometer according to the method
described by A.O.A.C. (2005)
4. Impurities (K, Na and α-amino N) in roots were determined in
El-Fayoum Sugar Company Laboratories, by an Automated
Analyzer as described by Cooke and Scott (1993).
5. Sugars lost to molasses percentage (SLM %) was calculated
according to the following formula as shown by Devillers
(1988): SLM% = 0.14 (Na + K) + 0.25 (α-amino N) + 0.5
6. Extracted sugar percentage was calculated according the
formula of Dexter et al., (1967) as follows:
Extracted sugar % = sucrose% - SLM% - 0.6
7. Quality index (QZ%) = (extracted sugar % / sucrose%) × 100.
8. Root yield/fed (ton), which were determined on sub plot
weight (kg) and converted to tons/fed.
9. Sugar yield/fed (ton) was calculated according to the following
method of Devillers (1988): Sugar yield/fed (ton) = root
yield/fed (ton) x extracted sugar% /100
Statistical analysis
Results were statistically analyzed using COSTATC software.
The ANOVA test was used to determine significantly (p≤0.01 or p≤0.05)
treatment effect and LSD was used to compare among treatment means.
Yan and Rajcan (2002) used the genotype by trait (GT) biplot, which is
an application of the GGE biplot to study the genotype by trait data.
Because the traits were measured in different units, the biplot procedure
was generated using the standardized values of the trait means. SPSS
version 10 was used for assessing the magnitudes of correlation among
variables. Phenotypic correlation coefficients were calculated among all
the traits according to (Falconer, 1989).
RESULTS AND DISCUSSIONS
Effect of the tested sugar beet varieties:
Except for potassium and alpha-amino nitrogen contents, sugar
lost to molasses%, and quality index data in Table 4 indicated that the
tested sugar beet varieties differed significantly in proline content, root
Egypt. J. of Appl. Sci., 36 (3) 2021 70
diameter, fresh weight/plant, sucrose, and extractable sugar percentages,
as well as sodium content, root and sugar yields/fed in both seasons.
Indira-KWS mono-germ variety recorded a significant decreased in
proline content. These results may be due to the positive correlation
between proline accumulation and plant stress where it plays a beneficial
role in plants exposed to various stress conditions. At the same time, it
surpassed the other varieties, whether it is mono or multigerm seeds, with
respect to root diameter, root fresh weight/plant, sucrose%, root, and
sugar yields/fed. While, both mono-germ Carma and multi-germ Shrb
21802 varieties had the lowest value of sodium content without
significant difference between them in both seasons, compared with the
other varieties. This result may be attributed to that plant growth,
development, and finally, the yield of root and sugar which is the result
of genetic composition and environmental effects. Sugar beet varieties
markedly differed significantly root fresh weight per plant, root and sugar
yields, as well as sucrose% and extractable sugar% (Enan et al., 2016,
Abu-Ellail et al., 2020 and El-Kady et al., 2021).
Table 4: Some traits of the tested sugar beet varieties as affected by
compost levels in 2018/2019 and 2019/2020 seasons.
Sugar beet
varieties
2018/2019 season
Proline
(μ moles/g)
RD
(cm)
RW
(kg)
S% Alpha Na K SLM% ES% QZ RY
(ton/fed)
SY
contents (ton/fed)
(meq/100 g)
(beet)
Indira 2.40 11.04 0.876 16.25 1.43 2.61 2.65 1.59 14.06 86.52 20.72 2.91
Dipendra 3.10 10.16 0.631 15.87 1.49 2.50 2.65 1.59 13.68 86.20 18.85 2.58
Carma 3.20 10.26 0.666 15.56 1.46 2.37 2.57 1.56 13.40 86.12 20.17 2.70
Vangelis 3.50 9.81 0.621 15.98 1.48 2.31 2.41 1.53 13.85 86.67 18.76 2.60
Shantala 3.20 10.56 0.705 16.33 1.22 2.21 2.55 1.47 14.26 87.32 18.64 2.64
Melodia 2.90 10.57 0.864 15.68 1.51 2.15 2.85 1.58 13.50 86.10 19.57 2.66
MK 4199 3.10 10.36 0.695 15.59 1.53 2.24 2.74 1.58 13.41 86.02 19.47 2.61
Shrb21802 2.90 10.19 0.690 15.59 1.76 2.36 2.37 1.60 13.39 85.89 19.22 2.57
LSD at 5% 0.32 0.15 0.04 0.08 NS 0.10 NS NS 0.14 NS 0.26 0.03
2019/2020 season
Indira 1.50 10.96 0.699 17.14 1.43 2.50 2.62 1.57 14.97 87.34 20.45 3.06
Dipendra 2.50 10.18 0.618 16.04 1.49 2.41 2.63 1.58 13.86 86.41 19.18 2.63
Carma 2.30 10.21 0.653 16.07 1.46 2.23 2.54 1.55 13.92 86.62 19.74 2.75
Vangelis 2.20 9.78 0.589 16.68 1.41 2.33 2.38 1.51 14.57 87.35 19.00 2.79
Shantala 2.10 10.56 0.677 16.54 1.22 2.38 2.53 1.47 14.47 87.48 18.44 2.72
Melodia 2.10 10.67 0.691 16.14 1.51 2.39 2.82 1.58 13.96 86.49 19.51 2.79
MK 4199 1.90 10.15 0.645 16.03 1.53 2.27 2.71 1.58 13.85 86.40 19.25 2.67
Shrb21802 2.10 10.19 0.617 16.27 1.51 2.26 2.34 1.54 14.13 86.85 19.78 2.65
LSD at 5% 0.23 0.13 0.07 0.05 NS 0.13 NS NS 0.16 NS 0.12 0.12
RD= Root diameter (cm), RW= Root weight (kg), S= Sucrose%, Alpha = α-amino nitrogen content,
Na= Sodium and K=Potassium contents. SLM=Sugar lost in molasses %, ES= Extractable sugar%,
QZ= Quality index, RY=root yield ((ton/fed), SY=Sugar yield (ton/fed).
71 Egypt. J. of Appl. Sci., 36 (3) 2021
Effect of compost fertilizer on the above-mentioned traits
Results illustrated in Table 5 revealed that except for proline, sodium,
potassium contents, sugar lost to molasses% and quality index, other traits
were significantly affected by increasing compost level from zero up to 6
ton/fed. An application of 6-ton compost/fed gave the thickest, heaviest
roots and the highest values of sucrose and extractable sugar percentages,
root and sugar yields/fed. Meantime, alpha-amino N content decreased in 1st
season and the second one. Applying 6-ton compost/fed significantly
increased root weight amounted to 0.04 and 0.11 g/plant and was
accompanied by an increase in root and sugar yields/fed amounted to 3.87
%-ton roots and 8.66 % tons sugar in 1st season, while 4.51%-ton roots and
9.97%-ton sugar in 2nd season over that those gained 4-ton compost/fed.
These results coincide with that obtained by Makhlouf et al., (2021) and
Wallace and Carter (2007) who explained the effect of compost on the
yield of sugar beet on various soil types (sandy loam, clay loam, sandy clay
loam and sandy silt loam). They showed that the application of compost
improves soil fertility. Key benefits were quantified relating to the physical
condition of the soil (organic matter, soil structure and water relations); soil
chemistry (soil pH and nutrients) and soil biology (increased microbial
populations and activity).
Table 5: Some traits of the tested sugar beet varieties as affected by
compost levels in 2018/2019 and 2019/2020 seasons.
Compost
levels
(ton/fed)
2018/2019 season
Proline
(μ moles/g)
Root
diameter
(cm)
Root
weight
(kg)
Sucrose
%
Alpha Na K
SLM% ES% QZ
Root
yield/fed
(ton)
Sugar
yield/fed
contents (ton)
0 4.20 8.43 0.59 15.72 1.64 3.97 2.72 1.71 13.41 85.31 14.81 1.99
2 3.30 8.65 0.62 16.21 1.49 2.38 2.69 1.67 14.06 86.74 16.98 2.37
4 2.60 9.25 0.64 16.46 1.44 2.23 2.56 1.58 14.30 86.88 17.81 2.54
6 2.40 9.90 0.68 17.05 1.37 2.20 2.23 1.54 14.82 86.92 18.50 2.76
LSD at
5%
NS 0.66 0.04 0.06 0.06 NS NS NS 0.18 NS 0.95 0.06
2019/2020 season
0 3.20 9.74 0.66 15.32 1.76 2.21 2.85 1.45 13.25 86.49 16.70 2.22
2 2.30 10.00 0.72 16.01 1.53 2.15 2.74 0.80 13.89 86.76 19.29 2.82
4 1.80 10.25 0.76 16.66 1.51 2.24 2.55 0.81 14.48 86.91 20.39 3.11
6 1.60 11.30 0.87 17.46 1.22 2.36 2.37 0.83 15.22 87.17 21.31 3.42
LSD at
5%
NS 1.00 0.10 0.09 0.13 NS NS NS 0.05 NS 0.90 0.10
Alpha = α-amino nitrogen content, Na= Sodium and K=Potassium contents. SLM=Sugar lost in
molasses %, ES= Extractable sugar%, QZ= Quality index.
Egypt. J. of Appl. Sci., 36 (3) 2021 72
The interaction between compost fertilization levels and sugar beet
varieties:
Among the studied traits only, root diameter, fresh weight/plant,
and yield/fed (ton) were significantly affected by the interaction between
compost levels and the evaluated varieties in both seasons (Table 6). The
highest value of root diameter, fresh weight/plant, and yield/fed (12.30
cm, 0.955 g, and 21.72 ton/fed) were produced from fertilized monogerm
(Indira-KWS) variety with 6 ton/fed compost in 1st season. In the
second one, the mono-germ variety (Indira-KWS) had the same trend, it
was surpassed the all rest of the varieties and achieved the highest values
of the previously mentioned traits. These increases were clearly
magnitude when mono-germ varieties fertilized with this level (6 ton/fed)
compared to all multi or mono-germ varieties. These results may be
attributed to the positive effect of compost in increased total microbial
count in soil amended with organic matter which indicated the act of
simple organic carbon compounds found in compost that were readily
assimilated by microorganisms as well as, the difference in their genetic
structure and the chemical properties of soil of the experimental site.
These results are in line with those obtained with El-Nagdi and Abd El
Fattah (2011), Enan, et al., (2016), and Makhlouf et al., (2021).
Table 6: Interaction effect between sugar beet varieties and compost
levels on root traits during two seasons.
Sugar beet
varieties
2018/ 2019 season
Root diameter Root weight Root Yield
Compost levels (ton /fed)
0 2 4 6 0 2 4 6 0 2 4 6
Indira 9.67 9.67 10.87 12.30 0.827 0.835 0.903 0.955 18.11 21.41 21.63 21.72
Dipendra 9.13 9.40 10.76 11.17 0.620 0.621 0.635
0.648 16.40 19.31 19.54 20.16
Carma 9.47 10.03 10.20 11.33 0.603 0.637 0.665 0.757 18.08 20.43 20.55 21.62
Vangelis 9.27 9.47 9.89 10.40 0.570 0.627 0.637 0.650 16.05 18.02 19.63 21.34
Shantala 9.63 9.83 10.47 11.57 0.638 0.682 0.729 0.770 15.93 18.97 19.32 20.35
Melodia 10.37 10.81 11.07 11.73 0.782 0.858 0.893 0.911 16.48 18.90 21.03 21.87
MK 4199 9.90 10.00 10.70 10.73 0.641 0.688 0.696 0.756 17.39 18.78 20.81 20.92
Shrb21802 9.33 9.57 9.67 11.2 0.562 0.633 0.767 0.797 17.59 19.01 19.31 20.98
LSD at 5% 0.42 0.14 1.09
2019/ 2020 season
Indira 10.07 10.25 11.09 11.82 0.563 0.644 0.649 1.016 19.00 19.45 21.65 21.94
Dipendra 9.20 10.13 10.93 10.93 0.535 0.597 0.598 0.684 16.10 19.31 19.89 20.69
Carma 9.52 9.70 10.51 11.07 0.524 0.576 0.610 0.633 16.70 20.3 20.51 21.44
Vangelis 9.17 9.87 10.23 10.27 0.853 0.884 0.908 0.956 16.90 18.58 19.62 21.62
Shantala 9.73 10.21 10.53 11.70 0.718 0.744 0.827 0.897 15.50 18.15 19.40 20.72
Melodia 10.55 10.73 10.93 11.43 0.805 0.830 0.913 0.984 16.45 18.61 21.04 21.70
MK 4199 10.08 10.57 10.87 11.23 0.699 0.725 0.811 0.879 15.36 20.18 20.61 20.85
Shrb21802 9.63 9.70 10.47 11.17 0.607 0.784 0.788
0.901 17.56 19.71 20.38 21.48
LSD at 5% 0.11 0.20 0.84
73 Egypt. J. of Appl. Sci., 36 (3) 2021
Sucrose%, extractable sugar %, and sugar yield were affected
significantly by the interaction between compost levels and sugar beet
varieties (Table 7). Results found a significant difference between monogerm
variety (Indira-KWS) and all tested varieties whether these varieties
are mono or multi-germ in sucrose% trait when it was fertilized with 6-
ton compost/fed in 1st season only. However, in the second season,
Shrb21802 and Melodia multi-germ varieties without a significant
difference between them showed better performance and gave the highest
values of sucrose% in both seasons, extractable sugar % and sugar
yield/fed in 2nd season only compared to other mono or multi varieties of
embryos when they received the higher dose of compost (6 ton/fed).
These results may be due to variable genetic structure, positively
interacted with the mentioned compost levels. These results coincide
with those obtained by (Masri et al., 2015), Abu-Ellail et al., (2020),
and EL-Kady et al., (2021) who found sugar beet varieties differed
significantly in sucrose%, extractable sugar% and sugar yields.
Table 7: Interaction effect between sugar beet varieties and compost
levels on quality traits during two seasons.
Sugar beet
varieties
2018/ 2019 season
Sucrose% Extractable sugar% Sugar Yield (ton/fed)
Compost levels (ton /fed)
0 2 4 6 0 2 4 6 0 2 4 6
Indira 16.24 16.27 16.70 17.80 14.17 14.18 14.47 15.55 2.57 3.04 3.13 3.38
Dipendra 15.55 16.08 16.19 17.22 13.35 13.81 14.11 15.11 2.19 2.67 2.76 3.05
Carma 15.33 15.86 16.07 16.99 13.26 13.62 13.92 14.82 2.40 2.78 2.86 3.20
Vangelis 15.68 16.21 16.62 17.39 13.71 14.07 14.39 15.19 2.20 2.54 2.82 3.24
Shantala 16.39 16.93 16.97 17.01 14.28 14.89 14.91 14.91 2.27 2.82 2.88 3.03
Melodia 15.35 16.01 16.63 16.72 13.24 13.83 14.37 14.54 2.18 2.61 3.02 3.18
MK 4199 15.60 16.22 16.26 16.31 13.54 13.92 14.01 14.18 2.35 2.61 2.92 2.97
Shrb21802 15.60 16.06 16.27 16.93 13.39 13.91 14.10 14.61 2.36 2.64 2.72 3.07
LSD at 5% 0.24 0.06 0.03
2019/ 2020 season
Indira 14.23 15.97 17.29 17.48 12.01 13.58 14.69 15.06 2.28 2.64 3.18 3.27
Dipendra 15.74 15.79 16.16 16.46 13.43 13.50 13.80 14.10 2.16 2.61 2.74 2.92
Carma 14.99 15.63 16.30 17.34 12.63 13.48 14.02 15.03 2.11 2.74 2.88 3.22
Vangelis 16.11 16.06 17.18 17.36 13.75 13.80 14.89 15.02 2.32 2.56 2.92 3.25
Shantala 15.51 16.08 17.05 17.10 13.17 13.44 14.87 15.31 2.04 2.44 2.88 3.17
Melodia 16.26 16.83 16.95 18.51 13.94 14.20 14.63 16.17 2.29 2.64 3.08 3.55
MK 4199 15.44 15.85 16.42 16.42 13.13 13.56 13.83 14.18 2.02 2.74 2.85 2.96
Shrb21802 14.27 15.89 15.95 18.97 11.97 13.48 13.66 16.58 2.10 2.66 2.78 3.56
LSD at 5% 0.89 0.92 0.14
Egypt. J. of Appl. Sci., 36 (3) 2021 74
Genotype by Trait (GT) biplot graph
The polygon view of a genotype by trait (GT) biplot graph is an
effective tool to study the interaction patterns between genotypes and
traits provided the biplot should explain a high percentage of the total
variation. The biplot graph (Fig. 1) presents the relationship among the
aimed sugar beet genotypes using the root and sugar yields and their
related attributes. The GT biplot of the mean performance of the sugar
beet data in 1st season explained 86.33 % of the total variation of the
standardized data. The first and two principal components (PC1 and PC2)
explained 57.73 % and 28.6 %, respectively. While in the 2nd season,
total variation equaled 82.77 % and the first and two principal
components (PC1 and PC2) explained 56.69 % and 26.08 %,
respectively. This relatively moderate proportion reflects the complexity
of the relationships among the genotypes and the measured traits. Yan
and Kang (2003) mentioned that the first two PC's should reflect more
than 60 % of the total variation in order to achieve the goodness of fit for
GT biplot model. The perpendicular lines to the polygon sides facilitate
comparison between neighboring vertex varieties. It is obvious that
variety Indira-KWS recorded high values of root yield (RY), sugar yield
(SY) and related traits. Also, varieties Carma and Melodia located in the
same sector and reflected similar behavior toward the same traits. It is
noted that the points of these varieties and traits placed into one sector
and the angles among them were acutely reflecting the positive
associations among them. On the other hand, the four varieties
(Dipendra-KWS, Vangelis, Shantala-KWS and MK 4199) recorded the
lowest values of RY and SY because obtuse angles were found between
these genotypes and the two characters. It is worth mentioning that the
current varieties groups are consistent with those obtained by the mean
performance. Accordingly, the GT biplot graph is considered a successful
and effective technique to select the best variety for muti-traits.
Undoubtedly, GT biplot graph is preferred because it easy to interpret
and more informative. These results are in line with Korshid, (2016),
Ober et al., (2005), and Abbasi et al., (2014) who found that GT biplot
showed that yield-related traits (i.e., root and sugar yields/fed) had the
same discriminating values for the genotypes as did the extraction
coefficient of sugar content, and sugar extractable percentage. Traits with
short vectors were less variable among varieties.
75 Egypt. J. of Appl. Sci., 36 (3) 2021
1st Season
2nd Season
Fig. (1): Polygon view Genotype by Trait (GT) biplot showing which
varieties had the highest values for which traits for eight sugar beet
varieties at 1st and 2nd season respectively.
Egypt. J. of Appl. Sci., 36 (3) 2021 76
Genotypes by treatments (GT) biplot graph
Data in Fig. 2 and 3 showed the polygon view of a genotype by
treatments (GT) biplot graph. Figure 2 cleared the GT biplot for the sugar
beet dataset of root yield explained 88.17 and 88.40 % of the total
variation in the first and the second years, respectively. The first two PC's
(PC1 and PC2) explained 71.60 and 16.58 %, respectively while the first
two PC's described 64.97 and 23.43 %, respectively. With respect to
sugar yield dataset, Figure 3 showed that GT biplot graph explained
84.84 % and 84.89 % of the total variation in the first and the second
years, respectively. The first two PC's (PC1 and PC2) accounted for
about (68.55% and 16.29%) in 1st season and (63.47% and 21.03%) in 2nd
season of the total variation, respectively. This relatively high percentage
reflects the efficiency of GT biplot graph in interpreting the
responsibility of sugar beet varieties to the treatments for root and sugar
yields at both experimental years. The polygon view of the GT biplot
helps identify varieties (genotypes) with good responsibility for one or
more treatments. Results showed that variety (Indira-KWS) gave the best
root and sugar yields under most or all treatments in 1st and 2nd seasons
followed by variety Carma in the 1st season and variety Sharb21802 and
Melodia in the 2nd season. These results are in agreement with Ober et
al., (2005) and Korshid, (2016) who found that genotype × trait biplots
(GT) showed superior genotypes with relatively greater expression of
combinations of favorable traits. The results suggest that root weight and
patterns of water use could help identify elite sugar beet varieties. These
data should enable tools to be developed for the indirect determination of
varieties suited to stress environments.
Root yield in 1st Root yield in 2nd
Fig. (2): Polygon view of genotype × treatments biplot of eight sugar
beet varieties for root yield at the 1st and 2nd season.
77 Egypt. J. of Appl. Sci., 36 (3) 2021
Sugar yield in 1st Sugar yield in 2nd
Fig. (3): Polygon view of genotype × treatments biplot of eight sugar
beet varieties for sugar at the 1st and 2nd season.
Relationships among yield-trait combinations
Data are given in Table 8 presents the simple correlation coefficients
among root and sugar yields and their related attributes estimated across the
two seasons. The results showed that there was a significant positive
correlation between RY and each of RD (0.284*), RW (0.411*), and SY
(0.800**). It is suggested that the RY of these sugar beet varieties may be
raised through selection for the biggest root and those that had the highest
fresh root weight. However, insignificant and positive associations were
obtained between RY and the other traits indicating that these traits may be
independent in their genetic behavior under the tested varieties. The yield
components exhibited various trends of associations among themselves.
Highly significant and positive associations were observed among S%,
ES%, and SY (correlation coefficients > 0.25) reporting that the highest root
yield varieties were highly sucrose%, extractable sugar%, and sugar yield.
The highest sucrose% and extractable sugar % varieties produced the lowest
α-amino-N, Na, K, and MLS% according to the highly significant negative
associations between extractable sugar % and each of MLS% (-0.656 **)
and α-amino-N (-0.554 *). It is worthy to understand the negative
associations between proline accumulation and each of RD (-0.003), S%(-
0.234), and ES%(-0.320). This trend of interrelationships among yield
attributes sometimes called offset, buffer, or compensation effects. This
relation means that increasing sucrose% or extractable sugar% did not
necessarily result in a high Proline. On the other hand, the magnitude of the
Egypt. J. of Appl. Sci., 36 (3) 2021 78
correlation coefficients among other traits was trivial and insignificant.
These results concur with those reported by Sklenar et al., (1997), Abu-
Ellail et al., (2020) and Danojević et al., (2011) they found a significant
and positive correlations were obtained for root weight and root yield. Also
reported that extractable sugar % and root yield in both seasons were
significantly (P ≤ 0.01) contributed to variations in sugar yield (ton/fed).
Table (8): Correlation coefficients among root and sugar yields/fed
and its related attributes computed from eight sugar beet
varieties evaluated in both seasons.
RD RW RY S% ES% SY N Na K MLS% Proline
RD 1
RW 0.365* 1
RY 0.284* 0.411* 1
S% 0.063 -0.183 0.103 1
ES% 0.023 -0.253 0.048 0.992 ** 1
SY 0.366 0.424 0.800 ** 0.279* 0.215* 1
N -0.175 0.012 0.238 -0.554 * -0.556 * -0.080 1
Na -0.293 0.120 0.531 0.243 0.185 0.441 0.126 1
K 0.704 0.410 0.191 -0.213 -0.249 0.115 -0.012 -0.288 1
MLS% 0.319 0.532 * 0.171 -0.656 ** -0.730 ** 0.267 0.334 0.030 0.477 1
Proline -0.003 0.276 0.152 -0.234 -0.320 0.428 -0.033 0.384 -0.108 0.595 * 1
*, ** Correlation is significant at the 0.05 and 0.01 level respectively
Abbreviations: RD =Root diameter, RW= Root weight, RY=Root yield, SLM=Sugar lost in
molasses, S=Sucrose, ES= Extractable sugar, SY=Sugar yield, N= α-amino nitrogen %, Na=
Sodium and K=Potassium.
CONCLUSION
The obtained results by GT biplot graphs have coincided with those
obtained by correlation matrix, indicating that the GT biplot graph is
considered a successful and effective technique besides. Undoubtedly, the
GT biplot graph is preferred because it is easy to interpret and gives more
information. The varieties with the best performance for each group were
(mono-germ varieties Indira-KWS and Carma as well multi-germ variety
Melodia). The combination between root and sugar yields with proline and
MLS should not be used to select varieties with good performance for the
other groups of related yield traits. Correlation exhibits a high effect of root
diameter, and root weight at harvest on root yield in crops.
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لممحصول والصفات ذات الصمة لبعض أصناف )GT( تحميل المحاور الثنائية
بنجر السکر المتأثرة بالتسميد بالکمبوست تحت التربة الممحية
ف ا رج فرغل برعى ابوالميل 1 ، أنور حامد ساسي 2
1قسم التربية والو ا رثة ، 2 قسم تکنولوجيا بحوث السکر، معيد بحوث المحاصيل السکرية
، مرکز البحوث الز ا رعية ، الجيزة ، مصر.
2020 في / 2012 و 2012 / تم إج ا رء تجربتين ميدانيتين خلال موسمين متتاليين 2012
حقل م ا زرع خاصة بمرکزطامية )خط عرض 22.52 درجة شمالا وخط طول 30.26 درجة
شرقا وارتفاع 34 م عن سطح البحر( بمحافظة الفيوم ، مصر ، لتقييم أداء ثمانية أصناف من
4 و 6 طن / فدان( في ، بنجر السکر تحت أربعة مستويات من سماد الکمبوست )بدون ، 2
التربة المالحة. تم استخدام تصميم القطعة المنشقة بثلاثة مکر ا رت في الموسمين. أظيرت النتائج
أن تسميد نباتات البنجر بمعدل 6 طن کمبوست/ فدان أدى إلى زيادة معنوية في قطر الجذر ،
ووزن الجذر الطازج لمنبات ، ونسب السکروز والسکر القابل للاستخلاص ، ومحتوى
الصوديوم ، وانتاجية محصول الجذور والسکر / فدان ، وکذلک محتوى ألفا أمينونيتروجين الذى
انخفض في کلا الموسمين. تأثر محتوى البرولين والبوتاسيوم والصوديوم والسکر المفقود فى
المولاس بشکل ضئيل بمستويات التسميد في الموسم الأول والثاني. أظير صنف )أندريا( أحادي
الجنين تفوقًا عمى جميع الأصناف المختبرة الأخرى ، حيث سجل أعمى قيم لقطر الجذر ، ووزن
Egypt. J. of Appl. Sci., 36 (3) 2021 82
الجذر الطازج لمنبات ، ونسبة السکروز ، وانتاجية محصول الجذور ومحصول السکر لمفدان،
بالإضافة إلى انخفاض معنوي في محتوى البرولين في کلا الموسمين. بينما کان لکل من
الصنف )کومي ا ر( أحادي الجنين والصنف متعدد الأجنة ) شيرب 21202 ( أقل قيمة لمحتوى
الصوديوم دون اختلاف معنوي بينيما في کلا الموسمين مقارنة بالأصناف الأخرى. کانت
ىناک علاقة ارتباط موجبة وعالية المعنوية بين محصول الجذ ور وقطر الجذر ووزن الجذر. تم
لمقارنة الأصناف بناءً ا )GT( استخدام الرسم البياني لمنمط الجيني حسب صفات التحميل الثنائى
عمى سمات متعددة. لقد ثبت أنو تحميل موثوق بو وسيل التفسير وتصور النتائج. في ظل
ظروف ىذا العمل ، يمکن التوصية بز ا رعة الصنف أحادي الجنين )أندريا( وتسميده بمعدل 6
طن کمبوست لمفدان لمحصول عمى محصول عالى من الجذور والسکر لمفدان تحت ظروف
التربة الممحية.
83 Egypt. J. of Appl. Sci., 36 (3) 2021