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Screening of Submergence Tolerant Rice under Artificial Condition Based on Multiple Selection Indices
Plant Breed. Biotech. 2019;7:360-374
Published online December 1, 2019
© 2019 Korean Society of Breeding Science.

Rina Hapsari Wening1,2, Indrastuti Apri Rumanti2, Bambang Sapta Purwoko1*, Willy Bayuardi Suwarno1, Nurul Khumaida1

1Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Kampus IPB Darmaga, Bogor 16680, Indonesia
2Indonesian Center for Rice Research, Jl. Raya 9, Sukamandi, Subang, West Java 41256, Indonesia
Corresponding author: *Bambang Sapta Purwoko,, Tel: +62-251-862-9353, Fax: +62-251-862-9353
Received September 16, 2019; Revised October 28, 2019; Accepted October 28, 2019.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Flooding often occurs during the vegetative stage in freshwater swamps and rainfed lowlands, and therefore submergence tolerant rice varieties are needed. This study was aimed at evaluating rice lines to submergence stress. The experiments were conducted at Indonesian Center for Rice Research (ICRR) experimental station in Sukamandi, Subang, West Java, each in different environmental conditions: (1) submergence condition, which was conducted in a submergence pool and (2) optimal condition, which was conducted in the irrigation field. Ninety-five lines and four check varieties, namely Inpari 30 Ciherang Sub1, IR42, Limboto, and IR20 were used. An augmented design with five blocks was followed in each environment. Three selection methods were used: (1) selection based on survival rate and vigor, (2) selection based on productivity under submergence and sensitivity index on productivity character, (3) selection based on the weighted selection index using sensitivity index variables of morphological and agronomic characters. The clustergram analysis with heatmaps method was used to compile of three previous selection methods to facilitate scientist in clarifying tolerant and sensitive lines. There were eighteen lines selected as tolerant to submergence stress, i.e., line no. 9, 15, 34, 46, 47, 48, 49, 50, 53, 55, 56, 57, 59, 60, 61, 62, 89, and 90. These lines are considered promising for breeding improved rice varieties tolerant to submergence.
Keywords : Rice, Submergence, Selection, Tolerant

Rice is a staple food for more than half of the world’s population. Asia is the largest producer and consumer in the world (FAO 2013). In 2010, rice production in the world reached 701,128 thousand tons, with average productivity of 4.3 t/ha, whereas Asia became the largest producer of 633,746 thousand tons (FAO 2013). The population in Asia increases rapidly, up to 1.8% increase per year (Abdullah et al. 2019), and the world’s population is expected to reach 9 billion by 2050. Therefore, it demands efforts to increase food production (Kush 2005; Collard et al. 2013a).

The current climate change is a serious threat to agricultural production. Submergence stress caused by flooding is critical abiotic stress that cannot be predicted in rice cultivation. This stress often occurs in Southeast Asia, South Asia, and Africa (Collard et al. 2013b). Submergence is likely to occur frequently in the coming years as a result of climate change (Mirza 2011; Schiermeier 2011). Submergence results in rice production decrease. Dey and Upadhyaya (1996) stated that economic losses caused by submergence reached USD 1 billion per year in the world, while in Indonesia it reached 353.7 million US dollars per year (Manikmas 2008).

Submergence can be divided into two types, namely stagnant flooding and flash flooding. Stagnant flooding means that water stagnates for a longer duration, commonly more than a month, and occur in deepwater and floating rice areas (Kato et al. 2014; Kuanar et al. 2017). Submergence stress can occur in all phases of growth from seedling to flowering plants with 1-2 weeks of flood duration (flash flood) (Mackill et al. 1993; Nugraha et al. 2013). Submergence stress can occur in flood-prone areas both in irrigation and rainfed areas. In Asia, rainfed lowland is widely distributed in South Asia such as India, Bangladesh, Nepal, and Southeast Asia (Singh et al. 2013), whereas in Indonesia, the type of lowland that most often suffered by submergence is shallow and middle swampland. The area of shallow and middle swampland in Indonesia covers about 7,512,080 ha, and it has great potential to be used for food crop production (Subagyo 2006). An alternative strategy to reduce the impact of submergence stress is by planting tolerant rice varieties (Mackill et al. 1993; Zeigler and Puckridge 1995; Wassman et al. 2009). The use of tolerant varieties will not increase production costs for farmers and production stability can be maintained (Septiningsih et al. 2013a; Rumanti et al. 2016a).

The effect of submergence on the morphology and physiology of rice plants has been widely studied (Hairmansis et al. 2011; Ahmed et al. 2013; Nugraha et al. 2013). Likewise, quantitative trait loci (QTL) and genes associated with submergence tolerant have been successfully identified and utilized in molecular breeding programs (Septiningsih et al. 2012; Septiningsih et al. 2013b). In Indonesia, several tolerant and high yielding lines have been developed and need to be selected to obtain the best variety. Selection to obtain lines that are tolerant to abiotic stress can be done through various testing methods and statistical analysis methods. This study was aimed at selecting lines tolerant to submergence with multiple selection indices.


The experiment was conducted in Sukamandi experiment station, Subang, West Java from April to November 2017. The material used was 99 genotypes consisting of 95 promising lines (63 advanced generation lines of freshwater swampland lines, 30 rainfed lowland lines, and two introduced lines from IRRI), and 4 check varieties. The 63 lines were selected from previous study done by Indrastuti Apri Rumanti (Rumanti et al. 2016b) The 30 rainfed lowland were obtained from Dr. Untung Susanto (Susanto et al. 2016). The two lines from IRRI were introduced via expedition by Dr. R.K. Singh. The experiment was carried out in two environmental conditions, i.e., submergence stress and optimal conditions. The submergence stress treatment was carried out in a submergence pool with an area of 30 m × 28 m and a depth of submergence of 85 cm. The experimental design used was an augmented design with five blocks. Each block consisted of 19 lines and four check varieties, namely Inpari 30 Ciherang Sub1 as a tolerant check, IR42 as a sensitive check, plus Limboto and IR20. Seedlings aged 19 days after sowing (DAS) were transplanted with plant spacing of 20 cm × 20 cm, in a 100 cm × 260 cm plot area or adjusted to the availability of seedlings. The submergence treatment was given ten days after planting (DAP) with a water level of 85 cm above the ground. The submergence was stopped when IR42 showed sensitive symptoms with characteristic of wilting or dying plants, after 11 days of submergence. The plants were then maintained until harvest with standard agronomic practices. The documentation of experiments method could be seen at Fig. 1.

In the experiment with submergence treatment, observations on the number of individuals and plant vigor was carried out before submergence and seven days after the treatment was stopped. The survival rate is the percentage of the number of survived plants compared to the number of plants before submergence. Plant vigor was observed according to Standard Evaluation System for Rice (SES): 1 = extra vigorous (very fast growing); 3 = vigorous (fast-growing); 5 = normal; 7 = weak (no-tillers); 9 = very weak (wilting of leaves) (IRRI 2014). Other variables observed included days to flowering, days to maturity, plant height, number of tillers, panicle length, number of filled grains per panicle, number of empty grains per panicle, panicle fertility, 1,000-grain weight and yield per plot both in stress and control plots, phenotypic acceptability or PAcp during generative phase. The days of maturity means the number of days from seeding to grain ripening (85% of grains on panicle are mature). The PAcp score was based on SES: 1 = excellent; 3 = good; 5 = fair; 7 = poor; 9 = unacceptable (IRRI 2014). Calculation of productivity (t/ha) in the experiment without stress, was carried out by this formula:

Y=250000(the number of plant in a plot)×yield per plot(kg)×(100MCgrain)86÷1000


Y=yield(ton/ha)250000=the number of plants in 1 ha with a spacing of 20×20cm86=100-14,whereas 14 is 14% moisture content of (paddy) riceMCgrain=observed grain moisture content

In the stress experiment, the same formula was used except the number of plants in a plot was changed to the initial population size.

Data were subjected to analysis of variance following an augmented design using SAS. Genotype by environment interactions (G × E) were tested using combined analysis across two environments. The number of plant per plot was used as a covariate for anova on yield for reducing experimental error as well as adjusting the genotype means. The combined data were used to determine the estimation of genetic variance, environmental variance, G × E variance, and broad-sense heritability (repeatability). In this paper, a sensitivity index analysis of submergence stress was also conducted to compare the performance of plants between stress and control treatment. The sensitivity index is an index used to see a decrease in an observation variable caused by stress environment compared to the optimal environment (Fischer and Maurer 1978). The formula used to calculate the sensitivity to stress index follows Fischer and Maurer (1978).

The method used to select submergence tolerant lines consisted of three methods. First, the selection was based on survival rate and vigor. Second, the selection was based on productivity under submergence and sensitivity index of productivity. Third, the selection was based on the weighted selection index (WINDEX) using the sensitivity indices of morphological and agronomic characters. The values of morphological and agronomic characters were standardized to negate the differences caused by variable scale and type. Standardization was done using mean square (MS) errors on each character in the combined analysis. The weighted selection index was carried out using the modified equation of Falconer and Mackay (1967).

I=a1Z1+a2Z2+a3Z3+.....+ZnI=weighted selection indexan=weight of each variablesZn=standardized variables Zn=(Xaverage of n)/sqrt(MS error/r)X=value of each genotypesr=number of blocks

The values from the three selection approaches were then used for the clustergram analysis with the heatmaps method to facilitate researchers in classifying tolerant and sensitive lines. According to Bowers (2010), clustergram analysis with a combination of heatmaps and dendrograms will simplify the visualization of the analysis.


Phenology effect

Analysis of variance of days to flowering and maturity in submergence treatment, control, and combined analysis are shown in Table 1. There were significant differences among lines both in the single analyses and in the combined analysis. The results of the combined analysis showed differences between the environment, and there were interactions between lines and environments. Submergence delayed flowering and maturity of the lines. The lines having delay days to flowering and maturity significantly shorter than Inpari 30 Ciherang Sub1 are shown in Table 2.

The effect of submergence on average delay in the days to flowering is 13.6 days, while the delay in days to maturity is 23.6 days (Table 2). In the submergence treatment, the days to flowering of Inpari 30 Ciherang Sub1was delayed by 15.2 days and days to maturity was delayed by 24.2 days compared to the control treatment. IR20 as well as line no. 10 and line no. 90 has relatively fixed days to flowering on both control and submergence. Those lines show no stagnation due to submergence. IR20 responded the shortest in the delay, while line no. 2 had the shortest delay in maturity age of 10 days, followed by line no. 63 and line no. 12 which had a delay of 11 days. Besides, there were six other lines which had a shorter maturity delay than Inpari 30 Ciherang Sub1, i.e., line no 25, 6, 27, 47, 4 and 90.

Genetic and environment variability estimation

A combined analysis across two locations showed differences among lines on the characters of plant height, panicle length, 1000 grain weight, grain weight per plant, and productivity. The environment affected differences on character of plant height, 1000-grains weight, grain weight per plant, and productivity. On the character of grain weight per plant and productivity there were interactions between lines and environment (Table 3).

The heritability of 1000-grains weight was the highest (58.93%) and was in the high heritability category (Table 3). The heritability of plant height, number of tillers, panicle length, and yield ranged from 20 to 50% and was in a moderate heritability category. The heritability of filled grain number, empty grain number, panicle fertility, and grain weight per plant had less than 20% and were in the low heritability category.

Selection based on survival rate and vigor

The survival rate showed the survived plants after critical period of submergence stress. Based on the SES (IRRI 2014), there were ten lines classified as very tolerant with a percentage of survival rate of 100%, and four lines classified as tolerant with survival rate between 95-99%. Eight lines of the 14 lines had good vigor after submergence, with a score of 1 which means extra vigorous (Table 4). Based on these two variables, the lines classified as tolerant to submergence stress in the vegetative phase were line no. 9, 24, 47, 49, 52, 56, 60, and 62.

Selection based on productivity

Based on productivity in submergence treatment and submergence sensitivity index value, the lines classified as tolerant to submergence lines were lines having higher productivity and lower submergence sensitive index of productivity than those of Inpari 30 Ciherang Sub1. The lines classified as tolerant to submergence stress based on productivity and submergence sensitive index were 20 lines, i.e., line no. 15, 32, 33, 34, 42, 46, 47, 48, 50, 53, 55, 56, 57, 59, 61, 81, 84, 87, 89, and 90 (Table 4). The twenty lines have a productivity range of 2.65 -5.97 tons/ha under submergence.

Selection based on weighted selection index

In this selection, only 91 lines were used based on the complete data of all characters and treatments. Standardization was done using MS errors from each character in the combined analysis. The weight could be given based on economic or agronomic value (Ramos et al. 2014; Setyono 2016; Hidayatullah et al. 2018). In this study, the weight was given in the submergence sensitivity index of plant height, number of tillers, panicle length, 1000-grains weight, and PAcp were -1, -2, -1, -1, and -3. The weight of PAcp was 3 in a negative direction because the PAcp is a visual appearance or performance of plants with the better were small in value. The result of weighted selection index analysis indicated that tolerant lines had a higher index value than the Inpari 30 Ciherang Sub1 (2.56). There were 30 tolerant lines as shown in Table 5. Inpari 30 Ciherang Sub1 was used as a parameter because these varieties are comparable tolerant variety and the best check variety.

Clustering the lines based on tolerance

The number of genotypes used in the cluster analysis was the same as the weighted selection index analysis (91 lines and four check varieties). The characters included in the cluster program were a combination of the three previous selection methods as a consensus to facilitate grouping lines that were tolerant and sensitive. The clustergram shows that the darker the color, the higher value of a variable, and vice versa. Therefore, in understanding the color distribution of the cluster programs it was necessary to look at the direction of each variable. The vigor variable and submergence sensitivity index of productivity (SSI Yield) had a negative direction, so in this case the brighter color was the better. Variable productivity on submergence treatment (YieldS) and survival rate (% live) and WINDEX had a positive direction, so the darker color was the better. Lee et al. (2016) stated that the color intensity in clustergram analysis showed the level of strength or degree of genotype in each variable.

Based on clustergram analysis, there were three groups. Group 1 consisted of 19 genotypes (18 lines and one check variety Inpari 30 Ciherang Sub1). Group 2 subgroup 1 consisted of 29 lines, subgroup 2 consisted of 11 lines, and subgroup 3 consisted of 16 genotypes (15 lines and one check variety IR20). Group 3 consisted of 20 genotypes, 18 lines and two check varieties of Limboto and IR42.

Group 1 had good vigor and SSI Yield, high productivity in submergence treatment so that it can be categorized as submergence tolerant. Most of the genotypes in this group were selected lines on the three methods used. Inpari 30 Ciherang Sub1, a tolerant check variety, included in the group. Lines belong to this group were line no. 9, 15, 34, 46, 47, 48, 49, 50, 53, 55, 56, 57, 59, 60, 61, 62, 89, and 90. The morphology and agronomy characters of those lines are shown in Table 6.

Group 2 had the medium SSI Yield and WINDEX values. The group was divided into 3 subgroups based on WINDEX value and productivity under submergence. Subgroup 2 and 3 had equal WINDEX value and productivity. Both subgroups were significantly distinct with subgroup 1. Subgroup 1 had lower WINDEX and higher pro ductivity. Based on these variables, the subgroup could be categorized into two tolerance categories. Group 2 subgroup 1 could be categorized as moderate tolerant to submergence stress. Subgroups 2 and 3 can be categorized as moderate sensitive to submergence stress. Even though both subgroups were distinct for the survival rate, whereas subgroup 2 has a lower survival rate compared to subgroup 3.

Group 3 had low productivity and WINDEX, low vigor, and SSI Yield. A high SSI Yield indicated that there was high gap productivity between stress and control treatment. IR42 variety as sensitive check variety included in this group. Group 3 can be categorized as the most sensitive group to submergence. Genotypes included in group 3 are not recommended for planting in areas with risk of flooding or in swampland. The lines included in group 3 were line no. 26, 75, 68, 69, 71, 25, 72, and 93.

The results of the clustergram analysis with the concept of heatmaps also produce character groupings which are shown as columns. There were three groups where the survival rate and productivity under submergence (YieldS) as the first group, WINDEX as the second group, and vigor and submergence sensitivity index of the productivity character as the third group.


In general, all types of stress will cause changes in plant phenology. Yullianida et al. (2015) reported that stagnant flooding caused a delay in days to flowering and maturity in rice. Nugraha et al. (2013) also reported that the delay in flowering occurred both after submergence and during stagnant flooding. In this study, the average delay on days to flowering and maturity age was 13.6 days and 23.6 days, respectively. Plants needs longer time to recover and have normal vegetative growth. In this study, seven lines had a shorter maturity age delay than Inpari 30 Ciherang Sub1. This type of lines is needed by farmers in the swamp area or flood-prone irrigation areas so that harvesting is delayed shorter.

Heritability can be used to predict the phenotypic expression of the next generation which is controlled by breeding values (Falconer and Mackay 1996). Breeding value is the value used to estimate potential genetic of parents (Putra et al. 2014). The diversity of populations used allows for genetic diversity testing and assessing heritability as it is random. The phenotypic value of each genotype can be assessed by measuring it directly, but the breeding value that will affect the next generation cannot be measured. Variance analysis was carried out to determine the genotype and environment variations, in terms of studying the heritability value of each character. According to Sujiprihati et al. (2003), the value of broad-sense heritability was low if it is less than 20%, moderate if it was between 20-50%, and high if was higher than 50%. However, these values were very dependent on the method and population used (Sujiprihati et al. 2003).

Variance analysis in a combined analysis was carried out by using the effect of genotype by environment interaction on all observed characters. Low heritability was shown the number of filled grains per panicle, number of empty grains per panicle, panicle fertility, and grain weight per plant. It was indicated that these characters were influenced by environment and genotypes by environment interaction. Barmawi et al. (2013) stated that heritability determined the response to selection. The higher value of heritability, the greater the response to selection. Therefore, in this study, the selection characters were determined by high heritability, i.e., plant height, number of productive tillers, panicle length, 1000-grains weight, and productivity.

According to SES (IRRI 2014), tolerant genotypes can be determined by the survival rate. The survival rate or recovery was significantly correlated with yield (Yullianida et al. 2014). Survival rate only shows how many the number of plants recovered from the submergence stress. Plant vigor after submergence also needs to be observed and considered in determining tolerant lines. In this study, the determination of tolerant genotypes considered the two variables at once.

Selection can also be done by productivity. High productivity under stress conditions shows that the line is adaptive. According to Anshori (2019), varieties adapted to stress depend only on productivity. Besides, the tolerance of lines to stress can be seen based on sensitivity index value which compares stress to optimal condition. A low sensitivity index indicates that there is no significant yield decrease under stress condition and the lines can be categorized as tolerant (Akbar et al. 2018). This method could be applied for characters having a positive direction, or the higher, the better. Three lines were highly tolerant to submergence because they showed higher productivity in submergence than optimal treatment. They are line no. 15, 53, and 55 (Table 4).

Weighted selection index was widely used by scientists to select the desired genotype (Undang 2012; Hidayatullah et al. 2018; Wening et al. 2018). According to Wirnas et al. (2006), characters that could be used to develop a selection index were based on the significant correlation with yield, high heritability, and economic value. In this study, the weighted selection index analysis used submergence sensitivity index (SSI) data on morphological and agronomic characters, which showed relatively high heritability and PAcp score. PAcp is the value or score of the overall appearance of line observed in the generative phase. The score is a description of the morphology and agronomy of a line.

All variables used in the weighted selection index analysis have a negative direction, which means the smaller the value, the better, and so, the weighting will have a negative value. Higher weight was given to higher biological relationship or agronomic value between character and productivity. Setyono (2016) used the production index, stem lodging, root lodging, and ear height with weights 6, -3, -1, and 1 in the corn selection. Hidayatullah et al. (2018) and Ramos et al. (2014) used the economic and agronomic value as weightage given to each character on rice and papaya selection. In this study, the weighted selection index analysis provided a selection pressure of approximately 30%, and therefore there were 30 lines selected among 91 tested lines.

Cluster analysis is widely used by researchers to classify genetic material used (Tomita et al. 2017; Anshori et al. 2018). Grouping of tolerance levels in lines by clustergram analysis with the concept of heatmaps visualization was apparent (Fig. 2). According to Yuan et al. (2016) character grouping in clustergram analysis with the concept of heatmaps is based on the similarity of reciprocal relationships among characters and the lines tested. This is expected to improve the efficiency of selection so that the right selected lines are obtained (Anshori et al. 2018). From the clustergam analysis, there were 19 genotypes (18 lines and one variety) categorized as tolerant to submergence stress. Performance of several selected lines could be seen at Fig. 3.

The clustergram analysis provided a clear visualization of the three selection approaches, namely selection based on survival rate and vigor, productivity, and weighted selection index. Clustergram analysis has classified 19 genotypes as tolerant (18 lines and one variety), 29 genotypes as moderate tolerant, 27 genotypes as moderate sensitive (26 lines and one variety) and 20 genotypes as sensitive (18 lines and two variety). The tolerant genotypes did not have significant productivity decrease under the stress condition when compared to the optimal condition.


This study was supported by competitive grant Kerjasama Penelitian, Pengkajian, dan Pengembangan Pertanian Strategis (KP4S) No: 76.31/PL.040/H.1/04/2017.K to Indrastuti Apri Rumanti, Ministry of Agriculture, Indonesian Agency for Agricultural Research and Development (IAARD) and team. The authors would like to thank Dr. Untung Susanto and Dr. R.K. Singh for providing the seeds used in the research.

Fig. 1.

Experiment process. (A) Seedling (+/‒7 days after transpanting, before submergence treatment), (B) observation of sensitive check’s survival, (C) plant condition after water recede, (D) generative stage performance.

Fig. 2.

Clustergram analysis based on heatmaps concept. SR: survival rate, YieldS: productivity on submergence, SSI Yield: submergence stress sensitivity index of yield.

Fig. 3.

Phenotypic appearance of selected lines. (A) B13925E-KA-1, (B) BP20452e-PWK-0-SKI-1-1, (C) BP20452e-PWK-0-SKI-2-3, (D) IR11T210, (E) BP20452e-PWK-0-SKI-2-4, (F) IR11T230.


Single anova in submergence and control treatment, and combined anova of days to flowering and maturity character.

Source of variance Control Submergence Combine

Days to flowering Days to maturity Days to flowering Days to maturity Days to flowering Days to maturity
Environment (E) . . . . ** **
Block ns ns ns ns ns ns
Genotype (G) ** ** ** ** ** **
G*E . . . . ** **
R2 0.9693 0.9978 0.9916 0.9431 0.9920 0.9977
CV 3.58 0.74 1.01 2.03 2.52 1.62

CV: coefficient of variation.

Adjusted genotype means of the days to flowering and maturity due to 11 days submergence.

No of lines Lines Days to flower (days) Days to maturity (days)

Sub Control Delay Sub Control Delay
2 B13983E-KA-12-3 99.8 87.7 12.6 127 117.2 9.9
63 BP29790d-PWK-1-SKI-1-1 95 88.2 5.2 127 116.7 10.8
12 B13983E-KA-12-2 99.3 88.7 10.6 128.8 117.2 10.9
25 B13507E-MR-19 100 80.4 20.1 129 117.5 11.6
6 B13925E-KA-46 98 84.7 13.6 131 118.2 11.9
27 B14366E-KY-2 100.3 86.2 12.2 128.8 116.7 12.8
47 IR11T230 94.8 87.7 7.6 129.5 114.2 13.9
4 B13983E-KA-7-3 103.8 84.2 20.6 130 117 13.9
10 B14039E-KA-15 97.3 102.2 ‒6.8 138.8 116.7 22.8
90 BP30704b-2Dalam-0-0 93.8 93.2 ‒0.8 128 113.7 15.8
89 BP30704b-1Genjah-0-0 97.8 93.2 3.2 128 108.7 20.8
46 IR11T210 92.8 87.2 4.2 122.5 101.7 20.8
24 B14301E-KA-37 91.8 86.2 4.2 127.5 103.7 23.8
A Inpari 30 Ciherang Sub1 98.8 83.6 15.2 128 103.8 24.2
B IR42 109.2 96.6 12.6 138.2 116.6 21.6
C Limboto 96.8 79.8 18.7 129.5 101.4 28.1
D IR20 98.4 96.6 1.8 133.6 116 17.6
Average 97.5 83.9 13.7 129.8 106.1 23.6
LSD 3.3 10 9.5 8.9 2.6 8.1

Sub: submergence, LSD: least significance difference.

Bold numbers indicate significantly shorter than Inpari 30 Ciherang Sub1; an upright number indicates the equivalent of Inpari 30 Ciherang Sub1. The displayed value is the adjusted value of the augmented design.

Combined anova of morphological and agronomy character of submergence and control treatment.

Source of variation PH NT PL 1000G FGt UGt PFt GW/Pt Yield
Environment (E) ** ns ns ** ns ns ns ** **
Block ns ns ns ns ns ns ns ns ns
Genotype (G) ** ns ** ** ns ns ns ns **
Line vs Check ** ns ns ** ns ns ns ** **
Check (C) ns ns ** ** ns * ns ** **
Lines (L) ** ns ** ** ns ns ns ns **
G × E ns ns ns ns ns ns ns ns **
(L vs C) × E ns ns ns ** ns ** ns ns ns
C × E * ns ns ** ns ** ns ns **
L × E ns ns ns ns ns ns ns ns **
R2 0.97 0.96 0.96 0.98 0.95 0.98 0.91 0.95 0.99
CV% 4.69 22.28 5.31 5.60 12.28 5.6 11.6 11.16 15.59
LSD 11.72 6.70 3.25 3.41 3.13 3.41 2.21 1.43 1.39
VG 19.17 3.90 1.20 3.74 0.05 3.74 0.03 0.01 5.12
VP 64.16 10.62 2.65 6.34 2.72 6.34 0.79 0.62 17.25
h2bs 29.88 36.75 45.47 58.93 1.71 58.93 3.75 1.3 29.66

E: environment, Check: check varieties, Line vs Check: lines means compare with varieties means, PH: plant height, NT: number of tillering, PL: panicle length, FG: filled grain, UG: unfilled grain, PF: panicle fertility, 1000W: 1000 grain weight, GW/P: grain weight per plant, VG: genotypic variance, VP: phenotypic variance, t: transformation + 0.5, CV: coefficient of variation, h2bs: broad sense heritability.

*Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

Survival rate, vigor, and productivity of lines at submergence treatments.

No of lines Lines Survival rate Vigor Productivity (t/ha) SSI Y

Submergence Control
9 B13926E-KA-1 97.83 1 3.29 6.21 0.75
15 B13925E-KA-1 90.00 1 5.04 4.11 ‒0.36
24 B14301E-KA-37 100.00 1 1.44 7.76 1.3
32 B14366E-KY-34 100.00 7 3.19 4.10 0.36
33 B13926E-KA-49 100.00 7 3.99 6.43 0.61
34 B14599E-KA-20 91.67 1 4.64 5.01 0.12
42 B14366E-KY-3 90.48 5 4.11 5.69 0.45
46 IR11T210 81.58 1 4.63 6.28 0.42
47 IR11T230 95.45 1 4.69 6.65 0.47
48 BP20452e-PWK-0-SKI-1-1 88.89 1 4.94 5.00 0.02
49 BP20452e-PWK-0-SKI-1-2 100.00 1 2.43 5.02 0.83
50 BP20452e-PWK-0-SKI-1-3 93.02 1 3.33 4.69 0.47
52 BP20452e-PWK-0-SKI-1-5 96.15 1 1.26 5.37 1.22
53 BP20452e-PWK-0-SKI-2-1 92.11 1 5.68 5.24 ‒0.13
55 BP20452e-PWK-0-SKI-2-3 92.11 1 5.97 5.30 ‒0.20
56 BP20452e-PWK-0-SKI-2-4 100.00 1 4.75 6.09 0.35
57 BP20452e-PWK-0-SKI-2-5 92.31 1 4.36 4.73 0.13
58 BP20452e-PWK-0-SKI-3-1 94.12 1 1.92 5.67 1.06
59 BP20452e-PWK-0-SKI-3-2 73.53 3 3.21 4.24 0.39
60 BP20452e-PWK-0-SKI-3-3 98.04 1 3.11 5.74 0.73
61 BP20452e-PWK-0-SKI-3-4 85.71 1 3.67 5.25 0.48
62 BP20452e-PWK-0-SKI-3-5 100.00 1 3.46 6.37 0.73
81 BP30703b-8-0-0 28.57 9 2.70 3.87 0.48
84 BP30586e 73.53 7 3.55 4.54 0.35
87 BP30679e 56.67 7 2.65 4.37 0.63
89 BP30704b-1Genjah-0-0 77.27 1 2.79 4.59 0.63
90 BP30704b-2Dalam-0-0 82.50 3 4.16 6.69 0.60
A Inpari 30 Ciherang Sub1 94.08 1 2.54 4.50 0.70
B IR42 27.03 9 0.52 5.76 1.46
C Limboto 27.28 7 0.18 3.09 1.51
D IR20 52.44 5 0.99 5.55 1.32
LSD 2.66 2.39

SSI Y: Submergence stress sensitivity index of yield character. Bold numbers indicate significantly better than Inpari 30 Ciherang Sub1.

Weighted selection index of submergence stress sensitivity index of plant height, number of tillers, panicle length, 1000 grain weight, and PAcp at submergence.


Weight ‒1 ‒2 ‒1 ‒1 ‒3
2 B13983E-KA-12-3 ‒0.09 ‒0.97 ‒0.76 ‒0.22 ‒1.04 6.14
3 B13983E-KA-13-1 0.05 ‒0.27 ‒0.19 ‒0.97 ‒1.04 4.78
6 B13925E-KA-46 1.95 ‒2.00 ‒0.92 ‒0.63 0.31 2.65
9 B13926E-KA-1 ‒0.25 0.21 ‒1.98 ‒0.92 ‒1.04 5.85
13 B13522E-KA-5-B ‒0.76 ‒0.34 ‒1.17 ‒1.09 ‒1.04 6.83
16 B13926E-KA-13 0.26 ‒1.73 1.15 ‒1.16 ‒1.04 6.35
17 B13926E-KA-44 ‒1.27 0.10 ‒1.23 ‒0.91 ‒1.04 6.35
18 B13582E-KA-6-B ‒0.05 ‒1.75 ‒0.68 0.60 0.31 2.69
19 B13982E-KA-30 ‒0.43 ‒0.42 ‒1.57 ‒0.01 ‒1.04 5.97
24 B14301E-KA-37 0.22 ‒0.39 0.08 ‒0.08 ‒1.04 3.7
34 B14599E-KA-20 ‒0.84 0.35 0.76 ‒0.33 ‒1.04 2.84
35 B14299E-KY-46 0.14 ‒2.30 ‒0.74 0.74 0.31 3.52
41 BP29829E-SKI-18 0.14 ‒0.57 1.47 ‒0.47 ‒1.04 3.12
42 B14366E-KY-3 ‒0.77 0.69 0.01 ‒0.33 ‒1.04 2.86
46 IR11T210 0.19 ‒0.78 0.06 ‒1.39 ‒2.40 9.91
47 IR11T230 ‒1.12 0.32 ‒3.17 ‒0.79 ‒1.04 7.58
48 BP20452e-PWK-0-SKI-1-1 ‒2.62 1.44 ‒1.05 0.13 ‒1.04 3.79
49 BP20452e-PWK-0-SKI-1-2 ‒1.14 0.49 0.91 ‒1.09 ‒1.04 3.47
52 BP20452e-PWK-0-SKI-1-5 ‒1.42 ‒0.28 0.35 ‒0.39 ‒1.04 5.15
55 BP20452e-PWK-0-SKI-2-3 ‒2.14 0.32 ‒1.09 0.94 ‒1.04 4.78
56 BP20452e-PWK-0-SKI-2-4 ‒0.70 0.98 ‒0.49 ‒0.80 ‒2.40 7.24
59 BP20452e-PWK-0-SKI-3-2 ‒0.85 0.21 ‒0.90 ‒0.09 ‒1.04 4.56
62 BP20452e-PWK-0-SKI-3-5 ‒0.68 0.14 ‒0.83 ‒0.37 ‒1.04 4.74
65 BP29790d-PWK-1-SKI-1-3 0.22 ‒1.55 ‒0.36 ‒0.86 0.31 3.15
85 BP30604e ‒0.76 ‒1.45 ‒0.62 0.25 0.31 3.09
86 BP30663e ‒0.12 ‒1.53 ‒0.23 0.26 ‒1.04 6.29
89 BP30704b-1Genjah-0-0 ‒0.81 ‒0.13 0.14 ‒0.12 ‒1.04 4.17
90 BP30704b-2Dalam-0-0 ‒1.00 ‒0.93 ‒1.22 ‒0.52 ‒1.04 7.73
95 IR94391-131-152-3-B-3-1-1 ‒0.98 ‒1.10 ‒1.87 ‒1.57 ‒1.04 9.77
98 IR96321-1447-651-B-1-1-2 ‒0.61 0.04 0.56 ‒0.98 ‒1.04 4.07
A Inpari 30 Ciherang Sub1 ‒0.38 0.35 ‒0.06 0.32 ‒1.04 2.56
B IR42 0.19 0.71 ‒0.04 0.87 0.31 ‒3.38
C Limboto 1.18 0.73 0.57 0.51 1.67 ‒8.74
D IR20 0.71 ‒0.48 ‒0.19 ‒0.53 0.31 0.03

Z: standardized value, SSIPH: submergence stress sensitivity index of plant height, SSINT: submergence stress sensitivity index of number of tillering, SSIPL: submergence stress sensitivity index of panicle length, SSI1000: submergence stress sensitivity index of 1000 grain weight, PAcp: phenotypic acceptability, WINDEX: weighted selection index.

Morphology and agronomy characters of selected lines.

No Lines DM PH NT PL FG UG 1000W Yield per plant Yield
9 B13926E-KA-1 118.6 bc 108.7 ab 12.2 ab 24.7 ab 170.2 bc 17 ab 24.6 b 39.61 bc 4.95 cd
15 B13925E-KA-1 117.1 bc 110.9 ab 14.7 bc 27.8 bc 150.4 bc 42 ab 25.7 bc 34.87 bc 4.43 bc
34 B14599E-KA-20 116.0 ab 105.4 ab 16.4 bc 27.1 bc 96.7 ab 20.2 ab 29.2 cd 34.98 bc 4.57 cd
46 IR11T210 112.1 ab 111.9 b 16.0 bc 31.1 c 117.5 bc 48.5 ab 26.8 bc 29.59 ab 5.52 cd
47 IR11T230 121.9 c 106.5 ab 13.8 bc 27.6 bc 189 c 18.4 ab 28.9 cd 35.8 bc 5.31 cd
48 BP20452e-PWK-0-SKI-1-1 118.0 bc 121.8 bc 11.3 ab 28.9 c 106.9 ab 88.1 b 28.2 c 26.72 ab 4.9 cd
49 BP20452e-PWK-0-SKI-1-2 115.5 ab 117.4 bc 11.6 ab 28.3 bc 84.7 ab 76.4 b 28.7 cd 32.38 bc 3.8 bc
50 BP20452e-PWK-0-SKI-1-3 116.2 b 122.3 bc 11.5 ab 29.2 c 112.7 b 38 ab 30.1 cd 35.61 bc 4.5 c
53 BP20452e-PWK-0-SKI-2-1 114.2 ab 111.5 ab 12.2 b 27.9 bc 171.8 bc 55.8 ab 31.7 d 32.72 bc 4.47 bc
55 BP20452e-PWK-0-SKI-2-3 111.7 a 116.1 b 10.1 ab 29.5 c 115.4 bc 64.3 b 30.9 cd 29.7 ab 5.59 cd
56 BP20452e-PWK-0-SKI-2-4 112.0 ab 121.6 bc 11.0 ab 29.5 c 154 bc 35 ab 30.1 cd 35.5 bc 5.4 cd
57 BP20452e-PWK-0-SKI-2-5 116.3 b 113.6 b 11.1 ab 29.1 c 105.7 ab 82.2 b 30.7 cd 33.79 bc 4.04 bc
59 BP20452e-PWK-0-SKI-3-2 117.2 bc 104.7 ab 12.0 ab 29 c 122.7 bc 97.5 b 29.4 cd 40.57 bc 2.85 ab
60 BP20452e-PWK-0-SKI-3-3 118.9 bc 120.4 bc 10.4 ab 26.8 bc 100 ab 52.1 ab 27.1 bc 35.23 bc 4.35 bc
61 BP20452e-PWK-0-SKI-3-4 116.9 bc 112.9 b 10.6 ab 28.9 c 100.4 ab 76.6 b 27.9 bc 23.65 ab 3.8 bc
62 BP20452e-PWK-0-SKI-3-5 117.4 bc 121.2 bc 11.1 ab 27.6 bc 123.5 bc 73.2 b 28.3 cd 34.34 bc 4.44 bc
89 BP30704b-1Genjah-0-0 118.4 bc 111.0 ab 14.4 bc 25.6 b 152.3 bc 41.6 ab 26.9 bc 32.37 b 3.51 bc
90 BP30704b-2Dalam-0-0 120.9 c 101.3 ab 12.8 bc 23.9 ab 133 bc 30.6 ab 26.3 bc 32.87 bc 5.42 cd
Inpari 30 Ciherang Sub1 115.9 ab 107.0 ab 12.6 b 26.2 bc 113.9 bc 32.9 ab 27.9 bc 33.15 bc 3.73 bc
IR42 127.4 d 104.5 ab 14.8 bc 26.6 bc 130.0 bc 49.1 ab 22.3 ab 27.71 ab 3.16 bc
Limboto 115.4 ab 104.6 ab 9.2 ab 29.7 c 138.7 bc 68.8 b 27.8 bc 21.28 ab 1.7 a
IR20 124.8 cd 105.4 ab 16.0 bc 26.1 bc 97.6 ab 46.2 ab 21.6 ab 21.38 ab 3.36 bc
BNT 4.3 11.7 0.3 3.2 9.3 14.6 3.4 1.54 1.39

DM: days to maturity (DAP), PH: plant height (cm), NT: number of tillers, PL: panicle length (cm), FG: filled grain per panicle, UG: unfilled grain per panicle, 1000W: 1000-grain weight, Yield per plant (g), Yield: productivity (t/ha).

  1. Abdullah AH, Kobayashi H, Matsumura I, Ito S. 2019. World rice demand towards 2050: Impact of decreasing demand of per capita rice consumption for China and India.
  2. Ahmed F, Rafii MY, Ismail MR, Juraimi AS, Rahim HA, Asfaliza R, et al. 2013. Waterlogging tolerance of crops: breeding, mechanism of tolerance, molecular approach, and future prospects. Biomed. Res. Int. 2013: 1-10.
    Pubmed KoreaMed CrossRef
  3. Akbar MR, Purwoko BS, Dewi IS, Suwarno WB. 2018. Penentuan indeks seleksi toleransi kekeringan galur haploid padi sawah tadah hujan pada fase perkecambahan (abstract in English). J. Agron. Indonesia 46: 133-139.
  4. Anshori MF. 2019. Perakitan galur-galur dihaploid padi (Oryza sativa L.) toleran dan adaptif terhadap cekaman salinitas (in Indonesian). Disertation, IPB University. pp 187.
  5. Anshori MF, Purwoko BS, Dewi IS, Ardie SW, Suwarno WB, Safitri H. 2018. Heritabilitas, karakterisasi, dan analisis clustergram galur-galur padi dihaploid hasil kultur anthera (abstract in English). J. Agron. Indonesia 46: 119-125.
  6. Barmawi M, Sa’diyah N, Yantama E. 2013. Kemajuan genetik dan heritabilitas karakter agronomi kedelai (Glycine max [L.] Merrill) Generasi F2 Persilangan Wilis dan Mlg2521 (abstract in English). Prosiding Semirata FMIPA Universitas Lampung.
  7. Bowers AJ. 2010. Analyzing the longitudinal K-12 grading histories of entire cohorts of students: grades, data driven decision making, dropping out and hierarchical cluster analysis. Pract. Assess. Res. Eval. 15: 1-18.
  8. Collard BCY, Jogloy S, Lertrat S, Redona E, Srinives P. 2013a. The 12th SABRAO congress on plant breeding towards 2025: challenges in a rapidly changing world. SABRAO J. Breed. Genet. 45: 1-5.
  9. Collard BCY, Septiningsih EM, Das SR, Caradang JJ, Pamplona AM, Sanchez DL, et al. 2013b. Developing new flood-tolerant varieties at the International Rice Research Institute (IRRI). SABRAO J. Breed. Genet. 45: 42-56.
  10. Dey MM, Upadhyaya HK. 1996. Yield loss due to drought, cold and submergence in Asia, p. 291-303. In: RE. Evenson, RW. Herdt, M. Hossain (eds.). Rice Research in Asia, Progress and Priorities, Oxford University Press, North Carolina, U.S.A.
  11. Falconer DS, Mackay TFC. 1996. Introduction to quantitative genetics. Longman Group, Ltd., England. pp. 464.
  12. Fischer RA, Maurer R. 1978. Drought resistance in spring wheat cultivar: I. Grain yield responses. Aust. J. Agric. Res. 29: 897-912.
  13. Food and Agriculture Organization of the United Nations (FAO). 2013. FAO Statistical Yearbook 2013: World Food and Agriculture. pp. 289.
  14. Hairmansis A, Supartopo S, Kustianto B, Pane H. 2011. Karakter agronomi dan hasil galur padi toleran rendaman (abstract in English). Penelitian Pertanian Tanaman Pangan. 30: 1-8.
  15. Hidayatullah A, Purwoko BS, Dewi IS, Suwarno WB. 2018. Agronomic performance and yield of doubled haploid rice lines in advanced yield trial. SABRAO J. Breed. Genet. 50: 242-253.
  16. International Rice Research Institute (IRRI). 2014. Standard Evaluation System for Rice. Los Banos.
  17. Kato Y, Collard BCY, Septiningsih EM, Ismail AM. 2014. Physiological analysis of traits associated with tolerance of long-term partial submergence in rice. AoB Plants 6: plu058.
    Pubmed KoreaMed CrossRef
  18. Khush GS. 2005. What it will take to feed 5.0 billion rice consumers in 2030. Plant Mol. Biol. 59: 1-6.
    Pubmed CrossRef
  19. Kuanar SR, Yar A, Sethi SK, Chattopadhyay K, Sarkar RK. 2017. Physiological basis of stagnant flooding tolerance in rice. Rice Sci. 24: 73-84.
  20. Lee JE, Recker M, Bowers AJ, Yuan M. 2016. Hierarchical cluster analysis heatmaps and pattern analysis: an approach for visualizing learning management system interaction data. Proceeding of The 9th International Conference on Educational Data Mining, North Carolina, U.S.A.
  21. Mackill DJ, Amante MM, Vergara BS, Sarkarung S. 1993. Improved semidwarf rice lines with tolerance to submergence of seedlings. Crop Sci. 33: 749-753.
  22. Manikmas MO. 2008. Developing submergence-tolerant rice varieties in Indonesia. Sub1 News 2: 4-5
  23. Mirza MMQ. 2011. Climate change, flooding in South Asia and implications. Reg. Environ. Change 11: 95-107.
  24. Nugraha Y, Vergara GV, Mackill DJ, Ismail AM. 2013. Response of Sub1 in introgression lines of rice to various flooding conditions. Indones. J. Agric. Sci. 14: 15-26.
  25. Putra WPB, Sumadi, Hartatik T. 2014. The estimation of breeding value and most probable producing ability of production traits Aceh cattle at Indrapuri district Aceh province. Bullet. Anim. Sci. 38: 1-7.
  26. Ramos HCC, Pereira MG, Viana AP, da Luz LN, Cardoso DL, Ferreguetti GA. 2014. Combined selection in backcross population of papaya (Carica papaya L.) by the mixed model methodology. Am. J. Plant Sci. 5: 2973-2983.
  27. Rumanti IA, Nugraha Y, Wening RH, Gonzaga ZJC, Suwarno, Nasution A, et al. 2016a. Development of high-yielding rice varieties suitable for swampy lands in Indonesia. Plant Breed. Biotech. 4: 413-425.
  28. Rumanti IA, Suwarno, Nafisah, Wening RH, Nasution A, Supartopo. 2016b. Annual report: development of swampy rice variety (summary in English). Indonesian Center for Rice Research.
  29. Schiermeier Q. 2011. Increased flood risk linked to global warming. Nature 470: 316.
    Pubmed CrossRef
  30. Septiningsih EM, Collard BCY, Heuer S, Bailey-Serres J, Ismail AM, Mackill DJ. 2013a. Applying genomics tools for breeding submergence tolerance in rice, p. 9-30. In: RK. Varshney, R. Tuberosa (eds.). Translational Genomics for Crop Breeding 2: Abiotic Stress, Yield and Quality. John Wiley and Sons Inc., Hoboken, New Jersey, U.S.A.
  31. Septiningsih EM, Ignacio JC, Sendon PM, Sanchez DL, Ismail AM, Mackill DJ. 2013b. QTL mapping and confirmation for tolerance of anaerobic conditions during germination derived from the rice landrace Ma-Zhan Red. Theor. Appl. Genet. 126: 1357-1366.
    Pubmed CrossRef
  32. Septiningsih EM, Sanchez DL, Singh N, Sendon PM, Pamplona AM, Heuer S, et al. 2012. Identifying novel QTLs for submergence tolerance in rice cultivars IR72 and Madabaru. Theor. Appl. Genet. 124: 867-874.
    Pubmed CrossRef
  33. Setyono. 2016. Contoh penggunaan indeks sederhana pada seleksi jagung (abstract in English). Jurnal Agronnida. 2: 87-98.
  34. Singh US, Dar MH, Singh S, Zaidi NW, Bari MA, Mackill DJ, et al. 2013. Field performance, dissemination, impact and tracking of submergence tolerant (Sub1) rice varieties in South Asia. SABRAO J. Breed. Genet. 45: 112-131.
  35. Subagyo. 2006. Klasifikasi dan penyebaran lahan rawa, p. 1-22. Dalam: I. Las, D. Ardi, U. Kurnia, H.S Mamat, W Hartatik, D. Setyorini. Pengelolaan Lahan Rawa. Balai Besar Penelitian Dan Pengembangan Sumberdaya Lahan Pertanian. Badan Penelitian dan Pengembangan Pertanian. Departemen Pertanian (in Indonesian language).
  36. Sujiprihati S, Saleh GB, Ali ES. 2003. Heritability, performance and correlation studies on single cross hybrids of tropical maize. Asian J. Plant Sci. 2: 51-57.
  37. Susanto U, Wening RH, Rohaeni WR, Nugraha Y, Satoto. 2016. Annual report: development of rainfed lowland rice variety (summary in English). Indonesian Center for Rice Research.
  38. Tomita A, Sato T, Uga Y, Ohara M, Fukuta Y. 2017. Genetic variation of root angle distribution in rice (Oryza sativa L.) seedlings. Breed. Sci. 67: 181-190.
    Pubmed KoreaMed CrossRef
  39. Undang. 2012. Seleksi berbagai genotipe jagung manis, cabai, dan kacang panjang hasil pemuliaan IPB sebagai penyedia benih unggul (abstract in English). Jurnal Sains Terapan Edisi II. 2: 1-15.
  40. Wassman R, Jagadish SVK, Heuer S, Ismail A, Redona E, Serraj R, et al. 2009. Climate change affecting rice production: the physiological and agronomic basis for possible adaptation strategies. Adv. Agron. 101: 59-122.
  41. Wening RH, Supartopo, Rumanti IA, Samaullah MY. 2018. Seleksi generasi menengah galur-galur padi rawa tahan hawar daun bakteri berdasarkan indeks seleksi terboboti (abstract in English). Jurnal Penelitian Pertanian. 2: 67-75.
  42. Wirnas D, Widodo I, Sobir, Trikoesoemaningtyas, Sopandie D. 2006. Pemilihan karakter agronomi untuk menyusun indeks seleksi pada 11 populasi kedelai generasi F6 (abstract in English). Bul. Agron. 34: 9-24.
  43. Yuan J, Murphy A, Koeyer DD, Lague M, Bizimungu B. 2016. Effectiveness of the field selection parameters on potato yield in Atlantic Canada. Can. J. Plant Sci. 96: 701-710.
  44. Yullianida, Suwarno, Ardhie SW, Aswidinnoor H. 2014. Uji cepat toleransi tanaman padi terhadap cekaman rendaman pada fase vegetatif (abstract in English). J. Agron. Indonesia 42: 89-95.
  45. Yullianida, Ardhie SW, Suwarno, Aswidinnoor H. 2015. Respon dan produktivitas padi rawa terhadap cekaman rendaman stagnan untuk pengembangan di lahan rawa lebak (abstract in English). J. Agron. Indonesia 43: 15-22.
  46. Zeigler RS, Puckridge DW. 1995. Improving sustainable productivity in rice-based rainfed lowland systems of South and Southeast Asia. GeoJournal 35: 307-324.

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