Introduction
Sustainability of rice (Oryza sativa L.) production is highly
dependent on the available water resources. Increasingly limited resources of
fresh water are likely to become a major factor limiting the optimal growth of
plants (Tietenberg and Lewis 2009). Many
plant-breeding programs focus on producing new crop varieties with traits that
will help enhance food security. Most modern rice cultivars are susceptible to
abiotic stresses (Swamy et al. 2017). Rice uses a lot of water for growth; on
average, 2500 liter of water is needed to produce 1 kg of rice (Bouman 2009). However, fresh water is becoming
limited in various regions of the world due to global climate change (Malaysian Meteorological Department 2017).
Thus, it is important to develop new rice lines that can tolerate limited water
availability while still producing high yields.
The year 2016 was the hottest year on
record in Malaysia, with an average temperature of 27.66°C (Malaysian Meteorological Department 2017).
This year was even hotter than 1998, when the most severe drought on record struck Malaysia. In 2016, drought caused by a super
El-Niño event occurred throughout Malaysia between February
and May (Malaysian Meteorological Department,
2017). The 2016 El-Niño event created considerable anxiety among rice
farmers, who needed to adjust their planting schedules to avoid the worst
effects of the drought. Rice farmers in granary areas are less affected by
drought because they can acquire an adequate water supply, but most farmers
outside the main granary areas where access to water is limited and reliant
solely on rain for irrigation.
The development of drought-tolerant
rice cultivars is one approach for helping farmers reduce the impact of drought
and obtain better yields. However, until 2018, no drought-tolerant cultivars
have been released in Malaysia despite numerous cultivars being released in
neighboring countries. In 2013, the rice cultivar MR1A was released for
cultivation. This cultivar can be grown under aerobic conditions and requires
less water than typical varieties, but its yield is low; 2.0 to 3.0 tons per
hectare (Othman et al. 2014). Collaborative research by Universiti
Kebangsaan Malaysia (UKM) and the International Rice Research Institute (IRRI)
developed drought-tolerant lines by pyramiding drought yield QTLs (qDTY) into local cultivars MR219 and
MRQ74. These two drought-tolerant rice lines showed better yields than local
cultivars under drought stress trials (Shamsudin
et al. 2016a, b). It is hoped
that the pyramided lines (PLs) can be used by farmers to mitigate the disastrous
effects of droughts.
However, yield performance of a
genotype can vary from one environment to another due to GEI (Fasahat et al.
2014). Genes controlling yield might be expressed differently in a
different environment, making one genotype superior to others in any given
environment. A genotype is stable when its yield is consistent among
environments. Therefore, evaluating genotypic stability is important prior to
releasing a cultivar to farmers so they can be assured of obtaining similar
yields in different locations.
According to Huehn (1990), parametric procedure is a good
attribute when it relies on certain statistical assumption such as interaction
effects and normal distribution errors. A drawback with this approach is that
the statistical assumption is sensitive to the significance of variances and
variance-related measures. Here is where non-parametric measures provide an
alternative approach since the procedure is conducted without the underlying
specific assumptions. The non-parametric methods are based on the ranks of
genotypes in different environments. The genotypes are considered stable if
their rank is similar despite the environmental differences. Regardless of
merits and demerits from both approaches, each of them can complement and
supplement each other, hence creating a better picture of interaction for GEI
interpretation (Dehghani et al. 2016).
Researchers frequently use genotype and
genotype-by-environment interaction (GGE) models and additive main effects and
multiplicative interaction (AMMI) models to obtain yield trial data on genetic
crosses (Gauch 2006). The concept of
using GGE biplots, as proposed by Yan et al. (2000) is that genotype
(G) and genotype-by-environment interaction (GE) must be considered
simultaneously when making selection decisions. The biplot data can be used to
determine crossover interactions or rank changes in yields of cultivars under
various environmental conditions.
Choices for GGE biplot scaling include
genotype-focused scaling, environment-focused scaling, symmetric scaling, and
equal-space scaling, all of which display a “which-won-where” pattern. Although
none of these scaling approaches is perfect, each complements the others. It is
believed that the use of GGE biplots can identify highly adapted and
phenotypically stable lines across a wide range of environments (Oladosu et al.
2017). In contrast, the AMMI model proposed (Gauch 1992) combines a univariate (ANOVA) to assess the main
effect of the genotypes and environment together with a multivariate technique;
usually principal component analysis (PCA) to assess genotype-by-environment
interactions. The principal components of a PCA usually represent the response
of genotypes that are proportional and not proportional to the environment.
AMMI models are usually called AMMI(n), with n defined
as the number of components used to study the interactions. The use of these
two models has been critically reviewed and compared (Gauch 2006; Yan et al. 2007; Gauch et al. 2008). Numerous studies have used the GGE biplot
method to study the genotypic stability of varieties (Akter et al. 2015; Balakrishnan
et al. 2016; Shahriari et al. 2018). We believe that the
use of the GGE biplot approach is more informative than the AMMI approach, despite the
criticisms conveyed by Oladosu et al. (2017). The present study
uses the GGE model, univariate and non-parametric approaches to analyze the
performance and genotypic stability of drought-tolerant PLs under drought and
water-limited environments across Malaysia.
Materials and
Methods
Plant materials, location and management practices
Designation |
Type |
|
Source |
IR
98010-134-4-1-2-1-1 |
MRQ74 PL |
|
UKM |
IR
99784-156-137-1-3-1-1 |
MR219 PL |
|
UKM |
IR
99784-226-335-1-2-1-1 |
MR219 PL |
|
UKM |
IR
99784-226-335-1-5-1-1 |
MR219 PL |
|
UKM |
IR
99784-255-68-1-7-1-1 |
MR219 PL |
|
UKM |
IR
99784-255-7-2-5-1-1 |
MR219 PL |
|
UKM |
IR
99784-255-91-1-1-1-1 |
MR219 PL |
|
UKM |
MR219 |
Stable check
(modern cultivar) |
|
MARDI |
MRQ74 |
Check (modern
cultivar) |
|
MARDI |
IR
77298-14-1-2-10 |
Check (drought
tolerant line) |
|
IRRI |
Location |
Year |
Irrigation regime |
Range of soil moisture content (%) |
Mean soil water potential (kPa) |
Maximum temperature (°C) |
Minimum temperature (°C) |
Mean temperature (°C) |
Mean monthly rainfall (mm) |
Soil series |
|
TC1 |
Teluk Chengai |
2015 |
Normal |
65-68 |
-4 |
30.2 |
25.0 |
28.1 |
260.60 |
Chengai |
TC1RS |
Teluk Chengai |
2015 |
Drought |
24-27 |
-60 |
30.2 |
25.0 |
28.1 |
260.60 |
Chengai |
TC2 |
Teluk Chengai |
2016 |
Normal |
62-68 |
-8 |
28.7 |
24.5 |
27.3 |
142.45 |
Chengai |
TC2RS |
Teluk Chengai |
2016 |
Water-limited |
45-51 |
-30 |
28.7 |
24.5 |
27.3 |
142.45 |
Chengai |
TC3 |
Teluk Chengai |
2017 |
Normal |
69-71 |
-3 |
29.8 |
24.5 |
27.2 |
248.15 |
Chengai |
TC3RS |
Teluk Chengai |
2017 |
Water-limited |
48-52 |
-35 |
29.8 |
24.5 |
27.2 |
248.15 |
Chengai |
SR |
Sawah Ring |
2017 |
Normal |
67-72 |
-10 |
31.0 |
23.0 |
27.0 |
150.00 |
Sedu |
SRRS |
Sawah Ring |
2017 |
Drought |
26-28 |
-65 |
31.0 |
23.0 |
27.0 |
150.00 |
Sedu |
PB |
Parit Buntar |
2017 |
Normal |
71-73 |
-4 |
28.2 |
24 |
26.7 |
293.73 |
Selangor |
PBRS |
Parit Buntar |
2017 |
Drought |
21-23 |
-75 |
28.2 |
24 |
26.7 |
293.73 |
Selangor |
BM |
Kampung Bukit Merah |
2016 |
Normal |
65-70 |
-7 |
29.2 |
24.9 |
27.2 |
181.30 |
Kranji |
BMRS |
Kampung Bukit Merah |
2016 |
Drought |
26-30 |
-79 |
29.2 |
24.9 |
27.2 |
181.30 |
Kranji |
TS |
Parit Buntar |
2017 |
Normal |
68-71 |
-8 |
25.0 |
29.9 |
27.9 |
117.20 |
Selangor |
Six
drought-tolerant pyramided lines (PLs) of MR219 and one of MRQ74 selected from
a previous advanced yield trial (AYT) (Shamsudin
et al. 2016a, b; Ikmal et al. 2018, 2019) together with
three checks namely IR 77298-14-2-10 (drought tolerant line) and the two
recipient parents; MR219 and MRQ74 (Table 1). These drought-tolerant PLs
generated from crosses between donors of drought yield QTL (qDTY) from the International Rice
Research Institute (IRRI), Philippines and the recipient parents, MR219 and
MRQ74 from Malaysia. These PLs have different combinations of qDTYs viz., qDTY2.2,
qDTY3.1, and qDTY12.1. Table 2 shows the
13 environments used to conduct this study which was carried out in 2015, 2016
and 2017. Environments were defined as the combination of location, treatment
and years. Randomized complete block design (RCBD) with three replications was
used in this study. Plot size, 2 m × 5 m with planting spacing of 25 cm between
rows and hills was used. Rice cultivation guidelines given by the Department of
Agriculture Malaysia (DOA) were used for field cultural practices. Fertilizer
(17 N: 20 P: 10 K) was given at two separate occasions; firstly, during the
early growth (seven days after germination) at the rate of 140 kg per ha and
secondly after 50 days of germination at the rate of 100 kg per ha. Urea
fertilizer was applied during the active tillering period at the rate of 80 kg
per ha. Chemical fungicides and insecticides were sprayed to the field to
control diseases and pests. Standing water in the plot was only allowed for 30
days after transplanting and drained on the 31st day to create drought stress
or water-limited condition. If rain happened to occur, the drains at the plot
were opened to allow water to flow out from the plot to ensure the low soil
moisture. In TS (Parit Buntar), the drought evaluation was carried out in a
concrete containing soil and equipped with out-flow channels made from
polyvinyl chloride (PVC) pipes to drain out excessive water to create drought
condition. For water-limited condition, water was supplied at the field
capacity instead of 5 cm standing water above the soil surface for normal
condition.
Phenotyping
Grain
yield was obtained after grains were harvested, dried, weighed and adjusted to
14% moisture content from ten inner rows, leaving 50 cm at both ends per plot
(or two hills) as border area of the experimental plot. The final weights were
converted to kilogram per hectare (kg ha-1). The Standard Evaluation
System (SES) for Rice (IRRI
2013) used as a guide for all other yield related traits such as the
number of panicle (NP), spikelet per panicle (SPP), filled spikelets (FS) and
thousand-grain weight (TGW). NP, SPP, FS and TGW were recorded from trials in
Parit Buntar, Teluk Chengai and Bukit Merah.
Data analysis
Combined
analysis of variance (ANOVA) was computed for genotype (G), environment (E) and
GE interaction for grain yield using Statistical Tool for Agricultural Research
(STAR) version 2.0.1. Rstudio used to produce GGE biplot using the GGEBiplotGUI
package (Frutos et al. 2014). Stability measures used in this study viz., linear regression coefficient (bi),
Shukla stability variances (σi2)
(Shukla 1972), deviation from regression
(S2d) (Eberhart and Russell 1966) and Wricke’s ecovalence (Wi2) (Wricke 1962). Kang’s stability statistics (YSi) (Magari and Kang 1993) were computed in R
studio using Agricolae package.
Non-parametric measures of stability (Nassar and
Huhn 1987; Kang 1988; Thennarasu 1995) also computed in R studio using Phenability
package. The relationship between all stability measures calculated in R Studio
using Spearman rank correlation and visualized using Corrplot package.
Results
All locations selected as the test
environments received medium amount of monthly rainfall that was between 100 –
300 mm. The period of June to July is classified as the driest month in most
states of Malaysia while November, December and January are months with the
maximum amount of rainfall by Malaysian Meteorological Department. Parit Buntar
recorded the lowest and the highest amount of mean monthly rainfall during the
planting seasons (Table 2). Drought condition is achieved when the soil moisture
content dropped below 30% and at the permanent wilting point while
water-limited condition is when the soil moisture content between 50–60%.
Combined analysis of variance
DF |
Sum of Square |
Mean Square |
F-Value |
Pr (> F) |
%SS |
|
Environment (E) |
12 |
750020796.13 |
62501733.01 |
23.30 |
*** |
72.00 |
Genotype (G) |
9 |
75040580.40 |
8337842.27 |
7.74 |
*** |
7.20 |
Genotype × Environment (GE) |
108 |
216615870.36 |
2005702.50 |
1.86 |
*** |
20.79 |
Replicate (Environment) |
26 |
69746412.48 |
2682554.33 |
2.49 |
*** |
|
Pooled Error |
234 |
252207608.78 |
1077810.29 |
|||
Total |
389 |
1363631268.15 |
Genotype |
Grain
yield (kg ha-1) |
Number
of panicle |
Length
of panicle (cm) |
Spikelet
per panicle |
Filled
spikelets |
Thousand-grain
weight (g) |
IR
98010-134-4-1-2-1-1 |
5393.99bcd |
17.97
a |
25.72a |
184.25a |
164.50a |
23.94a |
IR
99784-156-137-1-3-1-1 |
6328.79a |
14.26
c |
25.44a |
179.67a |
161.33a |
27.30a |
IR
99784-226-335-1-2-1-1 |
6024.26abc |
15.13
bc |
25.44a |
169.25a |
158.00a |
27.20a |
IR
99784-226-335-1-5-1-1 |
6388.31a |
16.38
ab |
24.39a |
153.67a |
142.33a |
28.23a |
IR
99784-255-68-1-7-1-1 |
6086.41ab |
15.90
bc |
24.14a |
180.67a |
162.50a |
26.63a |
IR
99784-255-7-2-5-1-1 |
6256.98a |
15.15
bc |
25.23a |
165.92a |
154.17a |
27.24a |
IR
99784-255-91-1-1-1-1 |
6200.37a |
16.49
ab |
25.23a |
168.83a |
159.58a |
26.12a |
MR219 |
5705.24abcd |
15.41
bc |
25.33a |
173.08a |
160.33a |
26.87a |
MRQ74 |
5287.13cd |
16.95
ab |
25.32a |
162.67a |
148.83a |
25.70a |
IR
77298-14-1-2-10 |
5146.55d |
16.23
ab |
25.48a |
162.83a |
148.17a |
27.05a |
Mean |
5881.80 |
15.99 |
25.17 |
170.13 |
155.97 |
26.63 |
SE |
94.81 |
0.21 |
0.19 |
3.24 |
3.00 |
0.30 |
CV
(%) |
17.65 |
26.34 |
8.41 |
20.87 |
21.07 |
12.33 |
Min |
1293.22 |
7.00 |
20.40 |
73.00 |
67.00 |
18.80 |
Max |
11720.00 |
34.00 |
30.60 |
289.00 |
287.00 |
35.00 |
Coefficient of variation (CV), standard error (SE),
minimum (min), maximum (max). Mean values with different letter are
significantly different (Tukey’s HSD, P <
0.05)
Combined analysis of variance revealed
that genotype (G), environment (E) and genotype-by-environment (GE) interaction for
grain yield were highly significant (P
< 0.001) (Table 3). We found that G explained 7.20% of the total variance, E
explained 72.00% of the total variance, and GE explained 20.79% of the total
variance.
The mean values for genotype comparison
Table 4 shows the grand mean values of grain
yield and the yield related traits for each genotype across environments. IR
99784-226-335-1-5-1-1 has the highest grain yield (6388.31 kg ha-1)
followed by IR 99784-156-137-1-3-1-1 (6328.79 kg ha-1) while IR
77298-14-1-2-10 has the lowest grain yield (5146.55 kg ha-1). The best genotype showed 21 and 24%
higher yield than MR219 and MRQ74 respectively. The drought-tolerant MR219 PLs
produced 319.02–683.07 kg ha−1 higher yield than MR219 (G8),
whereas MRQ74 PL produced 106.86 kg ha−1 higher yield than
MRQ74 (G9). Mean number of panicle (NP) for all genotypes were significantly
different from each other across environment with IR 98010-134-4-1-2-1-1
recorded the highest while IR 99784-156-137-1-3-1-1 recorded the lowest NP.
Length of panicle (LP) for all genotypes was more than 25.00 cm except IR
99784-226-335-1-5-1-1 and IR 99784-255-68-1-7-1-1. Meanwhile, number of
spikelet per panicle (SPP) for every genotypes were more than 150 with IR
98010-134-4-1-2-1-1 had the highest SPP. The same result also can be seen for
number of filled spikelet per panicle (FS), where genotypes with high number of
SPP will have high number of FS. IR 99784-226-335-1-5-1-1 recorded the highest
thousand-grain weight (TGW) while IR 98010-134-4-1-2-1-1 had the lowest TWG.
The mean values of grain yield and the other yield contributing traits for each
environment is provided in Supplementary Tables S1, S2 and S3.
GGE biplot analysis
The GGE biplot explained 66.09%
(PC1=47.13%, PC2=18.96%) of the total variation that was related to G and GE
interaction. The what-won-where pattern for grain yield was shown (Fig. 1A).
The what-won-where polygon pattern of GGE biplot was constructed in such a way
that all the tested genotypes were contained within the polygon. The straight
line originating from the center of the biplot divides the polygon into
different sections. The genotypes at the vertex or edge of the polygon in a
sector represent the winning genotype for entire environments in that sector.
Also, the lines that originate from the biplot center perpendicular to the
polygon represent a hypothetical environment in which the two genotypes that
represent the two sides of the polygon are said to perform equally. IR
99784-156-137-1-3-1-1 is the best genotype in the environments TC1, TC1RS, PB,
PBRS, SR, SRRS, TC2, and TC3RS. Meanwhile, IR 99784-226-335-1-5-1-1 is the
winning genotype in TC3, TS, and TC2RS. IR 98010-134-4-1-2-1-1 is the best in
BM and BMRS. IR 99784-156-137-1-3-1-1 and IR 99784-226-335-1-5-1-1 performed
the same in environments TC2 and TC3RS while IR 99784-226-335-1-5-1-1 and IR
98010-134-4-1-2-1-1 performed similarly in environment TC2RS and TS.
Fig. 2: (A) Ranking
of environments compared to the “ideal environment” located in the innermost concentric
circle as pointed by the arrowhead. (B)
Relationship among environments biplot showing the angles between vectors of
environments to each other
Genotype |
Mean |
bi |
S2d |
σi2 |
Wi2 |
YSi |
S1 |
S2 |
S3 |
S6 |
N1 |
N2 |
N3 |
N4 |
KRS |
IR
98010-134-4-1-2-1-1 |
5393.99 |
0.64 |
3651191.3** |
4306655.5** |
43750736 |
-7 |
0.04 |
10.69 |
25.44 |
7.41 |
2.77 |
0.35 |
0.46 |
0.01 |
10 |
IR
99784-156-137-1-3-1-1 |
6328.79 |
1.26 |
2059493.4* |
2386139.5* |
25313782 |
7+ |
0.05 |
11.03 |
16.40 |
4.96 |
2.77 |
0.69 |
0.72 |
0.01 |
12 |
IR
99784-226-335-1-2-1-1 |
6024.26 |
1.05 |
988267.0ns |
882327.2ns |
10877184 |
6+ |
0.08 |
6.91 |
11.58 |
4.47 |
2.15 |
0.43 |
0.49 |
0.01 |
11 |
IR
99784-226-335-1-5-1-1 |
6388.31 |
1.23 |
995947.1ns |
1313325.1ns |
15014764 |
12+ |
0.10 |
9.27 |
10.44 |
3.33 |
2.46 |
0.82 |
0.81 |
0.03 |
10 |
IR
99784-255-68-1-7-1-1 |
6086.41 |
1.10 |
1975828.8* |
1844699.3ns |
20115956 |
5+ |
0.09 |
11.58 |
17.70 |
5.26 |
2.62 |
0.65 |
0.69 |
0.02 |
12 |
IR
99784-255-7-2-5-1-1 |
6256.98 |
1.20 |
1793834.1ns |
1903810.7ns |
20683426 |
7+ |
0.06 |
6.56 |
7.93 |
3.36 |
2.15 |
0.72 |
0.60 |
0.02 |
11 |
IR
99784-255-91-1-1-1-1 |
6200.37 |
0.91 |
975675.4ns |
915937.3ns |
11199841 |
8+ |
0.04 |
4.50 |
10.64 |
3.80 |
1.69 |
0.34 |
0.43 |
0.01 |
7 |
MR219 |
5705.24 |
1.13 |
490829.8ns |
547590.1ns |
7663708 |
3+ |
0.03 |
4.76 |
14.83 |
5.23 |
1.46 |
0.21 |
0.31 |
0.00 |
13 |
MRQ74 |
5287.13 |
0.65 |
1725149.8ns |
2517108.1** |
26571080 |
-8 |
0.01 |
9.44 |
23.69 |
8.35 |
2.62 |
0.29 |
0.42 |
0.00 |
10 |
IR
77298-14-1-2-10 |
5146.55 |
0.82 |
3532576.0** |
3439432.3** |
35425393 |
-9 |
0.01 |
13.73 |
27.71 |
8.86 |
3.31 |
0.41 |
0.47 |
0.00 |
14 |
Non-significant (ns). *significant at P < 0.05, **significant at P < 0.01
Fig. 3: Graphical correlation matrix showing the relationship
among tested environments and the significant levels. *** P < 0.001, ** P <
0.01, * P < 0.05
The mean versus stability of the tested genotypes shows
that the green line with a single arrowhead is the average environment
coordinate (AEC) abscissa (Fig. 1B). The green lines perpendicular to the AEC
are the AEC ordinate in which a particular genotype is considered less stable
when its projection line is longer. The vertical line passing through the
origin and is perpendicular to the AEC abscissa divides the genotypes into
higher than the overall mean performance and lower than the overall mean
performance across environments. As shown that, IR 99784-156-137-1-3-1-1, IR
99784-226-335-1-2-1-1, IR 99784-226-335-1-5-1-1, IR 99784-255-68-1-7-1-1, IR
99784-255-7-2-5-1-1, and IR 99784-255-91-1-1-1-1 are the genotypes with higher
mean grain yields than the overall mean grain yield while IR
98010-134-4-1-2-1-1, MR219, MRQ74, IR 77298-14-1-2-10 are the genotypes with
lower mean grain yields than the overall mean grain yield (Fig. 1B). The
arrowhead is pointing towards the genotypes with higher mean grain yields and
consequently ranked the genotypes with respect to the grain yield. IR
99784-226-335-1-5-1-1 is the most stable and high yielding followed by IR
99784-226-335-1-5-1-1 and IR 99784-255-7-2-5-1-1. Although IR
99784-156-137-1-3-1-1 won in more environments than IR 99784-226-335-1-5-1-1,
its stability is lower as indicated by the longer projection of IR
99784-156-137-1-3-1-1 than IR 99784-226-335-1-5-1-1. MR219 had almost similar
stability to IR 99784-255-7-2-5-1-1 but grain yield of MR219 is much lower than
IR 99784-255-7-2-5-1-1. IR 98010-134-4-1-2-1-1 is the least stable genotype
evidenced by the longest projection from the AEC abscissa.
The
concentric circles are used to illustrate the distance between the genotypes
and the “ideal genotype” that is located at the center or the innermost circle
(Fig. 1C). An “ideal genotype” should both be high yielding and have great
stability. IR 99784-226-335-1-5-1-1
had the shortest distance from the innermost circle followed by IR
99784-255-7-2-5-1-1 and IR 99784-156-137-1-3-1-1. Three genotypes namely IR
98010-134-4-1-2-1-1, MRQ74, and IR 77298-14-1-2-10 had the furthest distance from the
innermost circle. The ranking of the genotypes for grain yield is IR 99784-226-335-1-5-1-1> IR 99784-255-7-2-5-1-1> IR
99784-156-137-1-3-1-1> IR 99784-255-91-1-1-1-1> IR 99784-226-335-1-2-1-1>
IR 99784-255-68-1-7-1-1> MR219> MRQ74> IR 98010-134-4-1-2-1-1> IR
77298-14-1-2-10.
Discriminativeness and the
representativeness of the environments are illustrated in Fig. 1D. The former
ability is measured by the length of the environmental markers' vector while
the latter is measured by the size of the angle of the environmental marker's
vector to the AEC abscissa. TC1 has the longest vector from the origin of the
biplot followed by TC1RS and TS, while SRRS has the shortest vector. The angles
between the vectors of TC3RS and TC2 are the top two smallest to the AEC
abscissa compared to other environments. BMRS has the largest angle to the AEC
abscissa. Fig. 2A shows the ranking of environments compared to the ‘ideal
environment’ where TC1RS and TC3 are the closest environments to the ‘ideal
environment’.
The relationship between test
environments have been shown that fifty-two environment combinations were
positively correlated, and the remaining 26 environment combinations were
negatively correlated (Fig. 2B). The significantly positively correlated
environment combinations are TS/TC2RS, PB/TC1, PB/TC3RS, PB/TC1RS, TC1/TC1RS,
TC1/TC2, TC3/TC1RS, TC3/TC2, TC3RS/TC1RS, TC3RS/TC2, and TC1RS/TC2. The
graphical correlation matrix between the test environments showed that
positively correlated environments are closely related and any genotypes tested
in that environments will produce almost similar grain yield but the opposite
will happen if the environments are negatively correlated to each other (Fig. 3).
The linear regression coefficient (bi) ranged from 0.64 to 1.26. IR 99784-226-335-1-2-1-1 recorded
the best value of bi, 1.05 that is the closest to 1.00 at P < 0.05, followed by IR
99784-255-91-1-1-1-1 with the bi of 0.91 at P < 0.05. MR219, IR 99784-255-91-1-1-1-1,
and IR 99784-226-335-1-2-1-1 are the top three genotypes with the lowest values
of S2d compared
to the other genotypes (Table 5). Their values of deviation form regression (S2d) were not significantly different
from zero. IR 98010-134-4-1-2-1-1 showed the highest S2d value was significantly different from
zero. According to the values of Shukla stability variance (σi2) and Wricke’s ecovalence
(Wi2), MR219 has the lowest values,
followed by IR 99784-226-335-1-2-1-1 and IR 99784-255-91-1-1-1-1, while IR
98010-134-4-1-2-1-1 has the highest values. For the yield stability statistic (YSi), seven genotypes were
marked with the “+” signs, are IR 99784-156-137-1-3-1-1, IR
99784-226-335-1-2-1-1, IR 99784-226-335-1-5-1-1, IR 99784-255-68-1-7-1-1, IR
99784-255-7-2-5-1-1, IR 99784-255-91-1-1-1-1, and MR219. IR
99784-226-335-1-5-1-1 has the highest value of YSi. As shown in Table 5, MRQ74, and IR 77298-14-1-2-10
have the lowest values of the non-parametric stability measure of the mean of
the absolute rank differences of a genotype over the n environments (S1) compared to the other
genotypes. Meanwhile, IR 99784-255-91-1-1-1-1, MR219 and IR 99784-255-7-2-5-1-1
had the lowest values of the variance among the ranks over the k environments (S2). For the other two non-parametric statistics, the
sum of the absolute deviations (S3)
and the relative sum of squares of rank for each genotype (S6) showed that IR 99784-226-335-1-5-1-1, IR
99784-255-7-2-5-1-1, and IR 99784-255-91-1-1-1-1 were the top three genotypes
with the lowest values, whereas IR 77298-14-1-2-10 recorded the highest values.
The values of N1 showed
that IR 99784-226-335-1-2-1-1 and IR 99784-255-7-2-5-1-1 are the lowest ranked
genotypes. MR219 has the lowest value of N2
while IR 99784-255-68-1-7-1-1 has the highest value. For N3, MR219 has the lowest value while the highest value
recorded by IR 99784-226-335-1-5-1-1. Three genotypes recorded 0.00 values for N4 despite the unclear
differences of the N4
values for all genotypes. It was also found that IR 99784-255-91-1-1-1-1 is the
genotype with the lowest value of the Kang’s rank sum (KRS) stability statistics, while IR 77298-14-1-2-10 has the highest
value of 14.00.
Discussion
The ten genotypes, including G8
(MR219), which is a stable mega-variety (Zainuddin
et al. 2012) and G9 (MRQ74),
which is a high-quality, specialty rice cultivar (Asfaliza et al. 2008; Suhaimee et al. 2009) were evaluated in present
study. In partitioning of total variation, the large percentage of
variation (due to GE interaction than to G) suggests that there were large
differences in the genotype’s performance across environments. The existence of
a different mega-environment is inferred where there is a significant amount of
variation in GE interactions; that is, for each mega-environment, a different
genotype has the best yield (Yan and Kang 2002).
A previous study also reported a
significant effect of the environment on mutant rice genotypes (Oladosu et al.
2017). Changes in the relative rankings of genotypes across environments
suggest that even though yield depends on genetics and environmental factors
may also play a significant role in modifying yields (Oladosu et al. 2017).
Therefore, it is important to test genotypes from targeted environments to
select the best-performing genotypes for any given environment or across all
environments examined. A multilocation trial involves several rice genotypes
for testing, but GE interactions increase the difficulty of selecting the best
genotypes for release to farmers. Therefore, to benefit the most farmers,
breeders must make an extra effort when selecting the best genotype for all
environments.
The which-won-where pattern is based on
relative genotypic stabilities and mean performances of genotypes in tested
environments. This pattern has also the ability to explain the presence or
absence of crossover GE interactions in explaining the potential existence of
various mega-environments (Yan and Rajcan 2002). Yan et al.
(2000) stated that crossover and non-crossover types of GE interactions
constitute MET data, whereby the former term shows a shift in yield ranking,
whereas the latter term indicates constant yield performance. If a single
variety wins in all environments tested, then no crossover GE interaction would
be detected, and a single mega-environment exists. The genotypes located within
polygons are judged as being less responsive to environmental conditions than
genotypes located at the corners or vertices (Yan
et al. 2007). Genotypes in
vertices perform poorly across all environments if no environmental marker
falls within its sector. The test environments with different winning genotypes
(vertex genotypes) are defined as the mega-environments (Sserumaga et al. 2015).
The qDTYs
showed a positive effect on the enhancement yields under drought or
water-limited conditions, as evidenced by the higher mean values of grain yield
than MR219 and MRQ74 (for all PLs), even though the yield advantage of IR
98010-134-4-1-2-1-1 over MRQ74 was only 106.86 kg ha−1.
Previous studies also reported that lines with qDTYs performed better than parents without qDTYs (Shamsudin et al. 2016a, b; Ikmal et al. 2018, 2019).
Interestingly, all genotypes (excluding IR 99784-226-335-1-5-1-1) recorded at
least one better grain yielding genotype under water-limited conditions than under normal-irrigation
conditions (Supplementary Table S1). Better grain yields under water-limited
conditions have been previously reported (Zhang et al. 2009; Poli et al. 2018). One possible reason for this disparity might
be the presence of qDTY3.1
in genotypes that reportedly cause lower yields under irrigated conditions (Venuprasad et al. 2009; Dixit et al. 2014, 2017; Ikmal et al. 2018). Yield depends on
the severity of water stress and the inherent genotypic differences where
limited irrigation improves root growth, photosynthetic rate, and root
oxidation activities (Poli et al. 2018).
IR 99784-226-335-1-5-1-1, IR
99784-255-7-2-5-1-1, and IR 99784-255-91-1-1-1-1 showed better genotypic
stability, evidenced by their shorter projections than MR219, which is known to
be stable and has been grown successfully in 90% of Malaysia’s rice-growing
regions for more than 20 seasons (Zainuddin et al. 2012). Grain yield is
associated with many yield-related traits, such as TGW, NP, LP, SPP and FS. The
genotype with the highest grain yield (IR 99784-226-335-1-5-1-1) had a high NP
and thousand-grain weight; even though its LP, SPP, and FS were lower than the
other genotypes we tested (Supplementary Table S3). Furthermore, the seed width
of genotype IR 99784-226-335-1-5-1-1 is broader than the seeds of the other
genotypes (Supplementary Fig. S1 and S2), which contribute to
a higher TGW. Previous study also reported that mutant rice with high
tiller numbers produces higher yields in stressed environments (Poli et al.
2018). Because the number of panicles (or tillers) is one determinant of
yield, we suggest that this trait be used in selecting stable and high-yielding
rice genotypes in Malaysia (Table S2).
The length of an environmental vector
from its biplot origin is used to determine the discriminating ability of a
given environment. Longer vectors represent environments that have a higher
ability to discriminate among genotypes. This means that genotypes associated with
short vectors will perform similarly in those environments. The angle between
vectors and the AEC abscissa for environments is used to determine the
representativeness of those environments to the mega-environment. An
environment is more like the mega-environment than to other environments when
its angle to the AEC abscissa is smaller than the angles of other environments
to the abscissa. Based on vector lengths and angles, test environments can be
grouped into three categories: (1) environments that give no or little
information on differences among genotypes, (2) environments that are useful
for selecting superior genotypes, and (3) environments that are useful for
discarding low-performing or the least-stable genotypes. If a tested
environment is highly representative of the mega-environment, the trial for
that environment is sufficient for testing traits; thus, the cost of cultivars
evaluation is reduced.
TC1 had the longest vector for grain
yield, which means that it is the best environment to discriminate genotypes
for selecting the best genotype (Fig. 1D). TC1 also had the smallest angle,
which makes it the most similar environment to the mega-environment. Most
drought environments had a shorter projection than typical irrigated
environments. This situation shows that most of the drought-tolerant PLs we
tested perform similarly, despite carrying different qDTYs. However, mild drought stress might be another reason that
the yields of these genotypes were so similar. Severe stress conditions are
needed to clearly identify the most drought-tolerant genotypes.
An environment can be identified as an
“ideal environment” if it has a high ability to discriminate genotypes and
representativeness. The ideal environment in present study was denoted by the
arrowhead located at the innermost circle of biplot (Fig. 2A). If an ideal
environment does not exist, the ideal environment in the biplot serves to
indicate the similarity of a test environment to the ideal environment (Yan and Kang 2002; Oladosu et al. 2017). The environments located closest to the ideal
environment in our biplot were the granary areas of Malaysia, which are
considered the highest yielding and most productive rice cultivated areas in
Malaysia.
The genotypes with bi values that approached 1 (unity value) and S2d of zero are
considered the most stable. If a genotype has bi < 1, it can adapt better to the unfavorable
environmental conditions, while bi
> 1 shows that the genotype can be adapted to favorable or high yielding
environment. IR 99784-226-335-1-2-1-1 and IR 99784-255-91-1-1-1-1 have the best
bi values that approached
1.00 and accompanied by high GY are considered the best genotype with greater
stability based on this measure. On the other hand, the genotype with a low
value of S2d
which is not significantly different from zero is considered more stable.
Therefore, IR 99784-226-335-1-2-1-1, IR 99784-255-91-1-1-1-1, and MR219 are the
most stable genotypes that met the required criteria. According to Shukla (1972) and Wricke (1962), the genotypes that have the lowest stability
variance are considered as the most stable. Eberhart
and Russell (1966) also stated that the desired variety is the one with
high yield mean, bi =
1.00 and as low as possible S2d.
According to the yield stability statistic (YSi),
the genotypes that were marked with the “+” sign have the YSi values which are above the YSi mean besides having greater stability across the
environments. The other stability statistics which was developed by Thennarasu (1995), and Nassar and Huhn (1987) ranked the genotypes for stability from the
lowest value to the highest value. The stability parameters developed by Thennarasu (1995) were based on the adjusted
ranks of genotypes within each test environment. Genotypes with low S1 values have low mean of
the differences in rank over the 13 environments while genotypes with low S2 have low variance among
the rank over all tested environments. Meanwhile, genotypes with low S3 have low sum of all the
absolute deviations of yield in all the environments. A low S6 value indicated that the
genotype have low relative sum of squares of rank which means that the GY in
every environment are almost the same. The KRS
proposed by Kang (1988) assigned the
lowest rank (1) to the most stable genotype, which has the highest yield and
the lower stability variance (Vaezi et al. 2018).
Conclusion
In this study, environmental variation
was the largest contributor to the variation in grain yield. Therefore,
breeders need information on cultivar yield (specific to rice cultivating
areas) to provide appropriate guidance to farmers on what cultivar to plant,
particularly where yield problems occur and persist for long periods. It was
found that the genotypes evaluated in this study grew best in specific
environments, and so each environment had a different “winning” genotype. These
winning genotypes should be recommended to farmers for cultivation in an
environment-specific manner. Based on our results of genotypes with acceptable
yields and stability, it is recommended that the following genotypes be
included in further trials for cultivar release: IR 99784-156-137-1-3-1-1, IR
99784-226-335-1-5-1-1, IR 99784-255-7-2-5-1-1, and IR 99784-255-91-1-1-1-1.
Acknowledgements
We would like to thank the Department of
Agriculture Malaysia (Paddy, Industrial Crops and Floriculture Division) for
their field assistance and support in the research station. We are grateful to
Universiti Kebangsaan Malaysia for research grants GUP-2017-033 and
DCP-2017-004/2.
We would also like to extend our gratitude to Dr. Arvind Kumar and Dr. B.P.
Mallikarjuna Swamy from the International Rice Research Institute for their
suggestions and recommendations. Thanks to Dr. Yusuff Oladosu from Institute of
Tropical Agriculture, Universiti Putra Malaysia for helping in results
interpretation and Prof. Manjit S. Kang for his critical comments and
suggestions for improvement of the manuscript.
Author Contributions
AMI, AASN conducted the field experimental design and
phenotyping, data analysis and manuscript preparation. TATNAR, ZPDE conducted
phenotyping and analysis in two of the trial sites. ZSA helped in preparing the
manuscript and RW revised the manuscript. All authors read and finalized the
manuscript.
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