Integration of Gamma Irradiation Breeding from Doubled haploid and Systematic Screening to Develop Adapted Rice Mutants under Salinity Stress

Integration of Gamma Irradiation Breeding from Doubled haploid and Systematic Screening to Develop Adapted Rice Mutants under Salinity Stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integration of Gamma Irradiation Breeding from Doubled haploid and Systematic Screening to Develop Adapted Rice Mutants under Salinity Stress Muhammad Fuad Anshori, Bambang Sapta Purwoko, Nono Carsono, Iswari Saraswati Dewi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7431509/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Plant Biology → Version 1 posted 16 You are reading this latest preprint version Abstract Gamma radiation breeding represents a significant approach for developing salt-adapted rice varieties that are crucial for food security, particularly in archipelagic nations such as Indonesia. The generated mutation diversity is inherently random and requires a large population size. Optimizing mutations requires a systematic methodology that encompasses induction potential and selection processes. This can be accomplished by optimizing mutations in double-haploid lines and screening for salt tolerance within extensive mutant populations. This study aimed to optimize the mutagenic radiation dose in double haploid rice lines and develop a method for screening salt tolerance from the germination to seedling stage. The M1 generation underwent gamma ray irradiation at doses of 0, 200, 400, 600, 800, and 1000 Gy on the double haploid line HS4-15-1-63. The optimized M1 results advanced to M2 generation. This process was executed through salinity screening in three stages: germination under salt stress, seedling phase screening in saline soil, and evaluation of the growth and production of adaptive mutants. Research findings show that the optimal radiation dose for inducing diversity in Generation M1 ranges from 200 to 400 Gy, with 200 Gy yielding the highest diversity in Generations M1 and M2. A dose of 200 Gy enhanced the adaptability of HS4-15-1-63 to salt stress. The screening method integrating germination and seedling phases with saline soil in trays proved efficient. Therefore, the 200 Gy dose and this systematic selection concept are recommended for optimizing mutant adaptability from double haploid lines to salinity stress. adaptation double haploid gamma radiation mutation breeding Oryza sativa salinity tolerance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Rice is an essential element of economic sustainability and food security for a substantial portion of the global population. The leading rice-producing countries include China, India, Indonesia, Bangladesh, and Vietnam [ 1 ], rendering fluctuations in rice production a significant threat to food security in these regions. Although rice production has demonstrated a relative annual increase, growth has been gradual. Conversely, the population has surged, correlating with an increased demand for rice [ 2 ]. This dynamic is crucial for maintaining food security in numerous countries. Furthermore, this instability is intensified by the impacts of global warming [ 3 ]. Global warming exacerbates suboptimal conditions for rice cultivation, such as drought, salinity, and flooding. Salinity stress, in particular, adversely affects island nations like Indonesia. Global warming can result in rising sea levels, which are associated with seawater intrusion into terrestrial areas [ 4 ]. This intrusion can elevate soil salinity levels, leading to significant production declines or even crop failures in rice-growing areas near coastal regions. This issue is critical for Indonesia, where 15% of total rice production is situated near coastal areas [ 5 ]. Therefore, addressing rice-related challenges in coastal regions is imperative to maintain the resilience and stability of Indonesia's rice production. The issue of salinity in rice cultivation can be effectively addressed through plant breeding strategies. This approach constitutes a fundamental component in resolving production challenges in agriculture, including salinity stress. Generally, plant breeding focuses on the genetic modification of plants to develop new varieties with optimal potential based on specific objectives [ 6 ]. The Indonesian government has released several rice varieties that exhibit tolerance to salinity stress. However, the dynamics of environmental changes continue to evolve, necessitating the ongoing development and enhancement of adaptive varieties in alignment with technological advancements. Several studies have reported progress in the development of salt-tolerant and adaptive rice varieties, including [ 7 , 8 ], which conducted the development of a haploid rice variety HS4-15-1-63 line (F37) that is adaptive to salt stress. Nonetheless, [ 9 ]demonstrated a reduction in the salinity stress tolerance of the F37 line when screened in saline soil in trays. This finding indicates that this line still requires enhanced adaptability to effectively address the challenges posed by ongoing climate change. Consequently, genetic modification of this line is necessary, which can be achieved through the concept of mutation breeding. Mutagenesis is a straightforward and efficacious technique for generating a diverse base population. This method is employed to develop additional varieties with specific novel traits derived from the genetic composition of existing varieties[ 10 ]. Numerous studies have substantiated the efficacy of variety development through the application of mutations. Generally, this concept can be realized through the use of chemical and physical mutagens. However, the majority of studies employ physical mutation or irradiation to create populations exhibiting a wide to extreme level of diversity. According to a bibliometric analysis related to rice irradiation mutation, gamma-ray mutagens are the most prevalent method (Fig. 1). This mutagen is considered relatively cost-effective, with commendable efficacy in establishing a mutation base population. Nevertheless, the mutation concept is highly contingent upon the wild-type genetic construct or M0, as the mutations that occur are concentrated solely on the genetic base of the wild type, without any contribution from other genetic constructs. The haploid F37 rice line, which possesses a genetic construct sufficiently adaptive to salinity stress, is deemed suitable for enhancement using the mutation concept. However, the process of inducing mutation diversity, which is highly stochastic, renders the direction of selection challenging to predict as desired. This suggests that the success and efficacy of the process are heavily reliant on the size of the selected population. The larger the population, the greater the probability of identifying desired traits. However, a large population necessitates substantial costs and systematic methods in the selection process. Therefore, the selection of F37 mutant lines must be conducted systematically and efficiently to augment their adaptability to salinity stress. The development of a salinity screening concept for mutants can be accomplished through the optimization of mutation diversity in M1 and systematic salinity screening selection in M2. The optimization of mutation diversity in M1 represents the initial stage in the mutation process. Determination of radiosensitivity through LD50 is frequently employed in mutation breeding, including in rice. LD50 serves as an indicator of effectiveness in mutation breeding [ 11 – 14 ]. A higher LD50 corresponds to greater diversity produced, although the number of surviving individuals decreases. Conversely, a lower LD50 results in fewer surviving individuals, making LD50 the optimal diversity point with a sufficient percentage of surviving individuals for the selection process [ 15 – 18 ]. Based on data from several reports (Table 1 ), the LD50 of rice in gamma radiation mutation ranges from 200 to 600 Gy. This indicates that each genotype exhibits a relatively diverse pattern in responding to the administered mutagen dose. Furthermore, the lines used originate from double haploid technology, suggesting that the mutation response pattern for the F37 line may differ from existing patterns. Therefore, optimizing the gamma radiation mutagen dose for the F37 line is necessary to achieve high diversity for selecting salt stress adaptability. Table 1 LD50 results from several rice mutation studies No Source Wild Type LD 50 dose (Gy) 1 [ 19 ] local black upland rice 347.00 2 [ 11 ] White Ponni 354.80 3 [ 11 ] BPT 5204 288.40 4 [ 20 ] MR269 351.00 5 [ 20 ] MRQ74 365.00 6 [ 21 ] ADT (R) 47 RICE 235.00 7 [ 12 ] CV. Sidikalang 586.44 8 [ 13 ] Mira-1 677.27 9 [ 13 ] Bestari 683.68 10 [ 22 ] CR1009 152.52 11 [ 22 ] CR1009 sub1 284.77 12 [ 23 ] Basmati 217 555.68 13 [ 23 ] Basmati 370 354.35 14 [ 23 ] ITA310 517.90 15 [ 23 ] Komboka 385.12 16 [ 24 ] CR5272 674.00 17 [ 25 ] Madang Pulau 333.58 18 [ 25 ] Putiah Papanai 377.62 19 [ 25 ] Banang Kuning 291.14 20 [ 26 ] Singgam Putih 300.00 21 [ 16 ] ElonElon 340.00 22 [ 16 ] Milagrosa 329.00 23 [ 16 ] Kandaman 322.00 24 [ 16 ] C4-63 336.00 The development of a systematic selection framework for salinity tolerance screening can be executed in three distinct phases: germination, seedling, and reproductive. The seedling phase is extensively utilized in salinity screening, as it is considered a critical juncture for rice in response to salinity stress and is conducted during the vegetative phase [ 27 ]. This phase allows for the rapid and straightforward determination of potential tolerance [ 7 , 28 , 29 ]. However, selection during this phase frequently employs hydroponic methods, which are criticized for inadequately reflecting the interactions between soil and salinity. Moreover, the tolerance traits identified through this method do not strongly correlate with adaptability during the reproductive phase [ 9 ]. A salinity selection method utilizing small containers has been developed, which aligns with general screening on saline soil in pots and can be adapted for the seedling phase. Such adaptations can be applied to seedling trays, facilitating large-scale selection capacity at M2. Furthermore, integrating this method with the germination phase can enhance the stability of tolerance traits during the vegetative phase. This approach has not been previously implemented, particularly in mutant rice, thus its application could represent a novel advancement in the selection of mutant rice with a large population. Consequently, optimizing irradiation on the double haploid line F37 and conducting systematic selection based on the germination-seedling phase in seedling trays for M2 mutants is highly promising. Therefore, the objectives of this study are (1) to determine the optimal mutagenic irradiation dose for double haploid lines; and (2) to assess the effectiveness of developing a new method for screening salt tolerance in mutant rice during the germination-seedling phase. 2. Materials and methods 2.1. Experimental Design The research was conducted at the Breeding Laboratory and Greenhouse Center of Excellence (CoE) Experimental Garden, Faculty of Agriculture, Hasanuddin University, located in the Tamalanrea District, Makassar City, South Sulawesi. The site was situated at an altitude of 0–25 meters above sea level, with greenhouse temperatures averaging between 21.4°C and 41.4°C. The study spanned from March to June 2024 for the M1 generation and from July to December 2024 for the M2 generation. The investigation of the M1 generation concentrated on the application of gamma ray mutagenic irradiation to the haploid line HS4-15-1-63 (F37) from Anshori et al. [ 7 ]. The mutation process is carried out at the National Nuclear Energy Agency (BATAN) of Indonesia using a gamma cell device. The device operates at a radiation dose rate of 2.13 kGy/hour. The mutagenic doses were administered at six levels: 0 Gy (wild type), 200 Gy, 400 Gy, 600 Gy, 800 Gy, and 1000 Gy. All irradiated mutants were cultivated until the reproductive phase. The seeds produced in the M1 generation will be utilized in the subsequent M2 generation study. The M2 mutant rice generation study comprised three stages: (1) screening for salinity stress during the germination phase, (2) screening for salinity stress during the seedling phase, and (3) evaluation of growth and production of adaptive mutants. Each stage was conducted in a sequential and systematic manner as shown in Fig. 2 2.2. Research Procedure 2.2.1. Optimization of gamma ray irradiation mutagen dose in M1 generation mutants The mutated seeds (M1) were planted in a 108-hole seedling tray. The tray contained a growing medium produced by combining soil and compost in a ratio of 3:1 (v/v). The seeds were first soaked and then planted evenly in each hole of the tray according to the mutagenic radiation dose. After two weeks, the seedlings were transferred to pots containing the same growing medium as in the trays. The pots used had a volume of 10 L, and the growing medium filled the pots to a volume of 8 L. Rice seedlings were planted in the pots at a rate of 1 seedling per pot. Each pot was fertilized with NPK 15:15:15 at a dose of 5 g/pot 7 days after sowing (DAS). Subsequently, additional fertilizer was applied at 30 DAS with 3 g of urea per pot. Maintenance at this stage included irrigation, weeding, and pest and disease control. Irrigation was done regularly when the water in the pots starts to decrease. Weeding was done by pulling out weeds growing in each pot. Disease control was done chemically by applying the fungicide Antracol at a concentration of 3 grams per liter of water, and pest control is done mechanically by manually removing pests and then killing them. Rice plants were harvested when the grains have entered the physiological ripening phase, with criteria of 80% of the panicles appearing yellow and the rice grains at the base of the panicles having hardened. Rice harvesting was done by cutting the lower part of the panicle base using scissors. The harvested rice was then placed into sample envelopes. 2.2.2. Selection of the germination phase in M2 mutant rice lines The first stage of research on the M2 generation was conducted during the germination stage under salinity stress. The genotypes included were the F7 mutant at a dose of 200 Gy (F37_200) and F37 at a dose of 400 Gy (F37_400). Each mutation dose group included 500 seeds in the salinity germination screening. The screening was conducted using the paper test method. The screening was performed in plastic trays measuring 17 cm × 10 cm × 6 cm with a concentration of 100 mmol/L (5.84 g/L) NaCl or equivalent to 10 dS/m at the EC level of the solution. Each plastic tray was filled with three layers of tissue paper, and each tray contained 100 seeds depending on the treatment, with 50 seeds per treatment. Specifically, the wild-type genotypes F37 and IR29 were each sown with 100 seeds of good seed quality and divided into two groups: 50 seeds were subjected to salt stress treatment, and 50 seeds were not subjected to salt stress treatment. All small container seeds were covered to maintain seed moisture, and the germination test was conducted over 7 days. Seed maintenance in plastic trays was carried out by placing the trays in a dark location for 3 days and in a lighted location for 4 days. 2.2.3. Selection of the seedling phase of selected M2 mutant rice varieties through saline soil in seedling trays The second stage of the M2 generation focused on all seeds that could grow in the seedling salinity screening, including wild type and IR29. This method used trays with specifications identical to those used in the M generation study. The test method involved using trays with a NaCl concentration of 60 mM/L (3.50 g/L) NaCl or equivalent to 6 dS/m on the EC scale. As in the first stage, wild type and IR29 under normal germination conditions were also planted in trays without salinity treatment or normal conditions. Wild type and IR29 under salinity conditions were placed in each tray as a control for comparing tolerance between trays. Before the normally germinated seeds were transplanted, the transplanting medium was prepared using soil and compost in a 3:1 (v/v) ratio. The seeds that had been transferred to the germination trays were allowed to grow normally for 7 DAS. After this period, salinity stress was initiated by pouring 3 liters of NaCl solution, equivalent to 6.34 dS/m, onto the tray base. Each tray was placed on the tray base to stimulate salinity stress on the seedlings. Maintenance of the trays after NaCl application focused on maintaining water availability, so water was added periodically according to the solution limit at intervals of 3 to 15 days after treatment. After 15 days, the seedlings were observed, and the saline solution was replaced with normal water. Salinity adjustment and recovery were conducted over 5 days. Seedlings that survive and recover at this stage were proceeded to the third stage to evaluate the potential of mutant lines against the effects of salinity stress. 2.2.3. Evaluation of the response and impact of M2 mutant rice growth and production on salinity stress during the seedling-nursery phase. The third stage in the M2 generation study was the evaluation of the adaptability potential of the mutant rice lines that have been selected in the second stage. The method used in this stage involves seeds that were adaptively recovered from salinity stress (score 1–5) in pots and is carried out under non-saline conditions. In this stage, several comparison varieties were added, namely Inpari13, Padjajaran, and M70D, which were germinated and sown alongside the lines selected in stages 1 and 2 of the M2 generation. All these varieties were not subjected to salinity stress during the germination and sowing process or were independent. All maintenance and harvesting processes followed the cultivation concepts of the M1 generation. 2.3. Data Observation and Analysis The observation parameters of this study consisted of three parts, namely germination, seedling cultivation, and growth and production evaluation. (1) Parameters in the germination phase focus on two characteristics, namely the percentage of germinated seeds (%) and plumule height (cm). Both parameters were measured at 7 DAS. (2) Parameters in the seedling phase also focus on two characteristics, namely the percentage of live seedlings (%), seedling height (cm), and tolerance score based on IRRI SES. Both observations were conducted after 15 days of salinity stress treatment. The growth and production phases were used for optimization and evaluation of the growth adaptability of mutant rice M1 and M2. For the M1 generation, the focus was on several characteristics, namely: Germination rate (%): percentage of seeds that germinate per number of seeds planted Vigor percentage after transplanting (%): the number of plants that survive after eight days after planting (DAP). Plant height (cm), measured from the base of the stem to the tip of the highest leaf (measured before harvest). Number of productive tillers or number of panicles (stems or panicles), counting all tillers that produce panicles (observed before harvest). Number of grains per panicle Yield per plant (g), determined by weighing all grains on each plant. Growth evaluation observations at the M2 stage also include characters observed at M1 and additional growth characters. Additional observation characters at this stage include: Number of total tillers (stems), counting all tillers formed (observed during the primordial phase). Number of productive tillers or number of panicles (stems or panicles), counting all tillers that produce panicles (observed before harvest). Flag leaf length (cm), measured from the base of the flag leaf to the tip of the leaf, (observed before harvest). Days to flowering (DAP), calculated as the number of days from germination until flowering of each pot. Days to harvest (DAP), calculated as the number of days from germination until harvest, marked by 80% of the plants turning yellow. Panicle length (cm), measured from the base of the panicle to the tip of the panicle, (observed after harvest). Number of total grains per panicle (grains), counting all filled and empty grains, (observed after harvest). Percentage of filled grains (%), the ratio between the number of filled grains and the total number of spikelets. Percentage of empty grains (%), the ratio between the number of empty grains and the total number of spikelets. Number of panicle branches, counted as the number of branches on each panicle within a clump, (observed after harvest). Weight of 100 grains (g), weighed 100 grains contained in each sample of the line and comparative variety at a moisture content of 14%. The data analysis in this study was conducted in distinct stages for both the M1 and M2 generations. In the M1 generation, the analysis concentrated on radiosensitivity through regression analysis and the determination of the LD50. The LD50 was calculated using Curve Expert software [ 23 ]. Additionally, a biplot diversity analysis based on Principal Component Analysis was performed for this generation to identify patterns of diversity distribution among each mutant dose group, including the wild type [ 30 ]. This analysis was executed using the Rstudio program and the factoextra package [ 31 ]. In the analysis of salinity screening during the M2 generation's sprout and seedling phases, the focus was on fitness analysis and the relative decline compared to the wild type. Additionally, broad-sense heritability analysis was conducted at both stages to estimate the extent of environmental influence on this study [ 32 ]. Furthermore, the adaptability of M2 mutants to salinity stress was assessed by determining evaluation criteria through factor analysis and path analysis. Generally, the combination of factor analysis and path analysis was integral to structural equation modeling (SEM) [ 33 ], which emphasized the interrelationship between variables within a system. Factor analysis reduced variables by grouping correlated characteristics into principal factors [ 34 ], thereby forming a robust structure of relationships among relevant characteristics. Conversely, characteristics lacking significant correlations tend not to contribute to factor construction[ 31 ]. This concept was further reinforced by path analysis, which determines a variable's potential to influence the total variance of a principal factor [ 35 ], in this case, biological yield. This combination enhanced the evaluation of the growth potential of adaptive lines in salinity screening of seedlings. The analyses were conducted using Minitab v 17 [ 36 ]and Rstudio with the Agricolae package [ 37 , 38 ]. The evaluation criteria obtained were further analyzed through adaptability potential analysis by comparing the Least Significant Difference to the reference. The evaluation involved comparing the combination of mutant dose groups and their tolerance through SES scoring. This combination was further refined through PCA biplot analysis, employing the same concept as in generation M1. 3. Results 3.1. Analysis of the diversity of F37 rice line mutations to various doses of gamma irradiation in Generation M1 The results of the lethal dose (LD) 50 mutation analysis of the F37 line are shown in Fig. 3. Based on this figure, the mutation dose at 2 weeks after sowing shows a linear pattern with the formula − 0.1071 gamma ray dose (GRD) + 102.57. This formula has a determination coefficient of 0.86 and an LD₅₀ value at a dose of 502 Gy. Conversely, the mutation mortality response at 8 weeks after sowing showed a quadratic pattern with the formula 9x10-5GRD2- 0.182 GRD + 91.143. This formula also has a high coefficient of determination (0.67) with an LD 50 at a dose of 273 Gy. The results of the analysis of growth response to the M1 mutant line based on gamma irradiation dose were shown in Fig. 4. The figure focused on four main characteristics, namely plant height, number of panicles per culm (NPC), percentage of filled grains, and biological yield. Based on plant height, the gamma ray dose response showed a negative quadratic pattern with a high coefficient of determination (0.987) and a peak at a dose of 254.67 Gy (125.31 cm). Based on the number of panicles, the gamma ray dose response showed a negative quadratic pattern with a fairly good coefficient of determination (0.73) and a peak at a dose of 400 Gy (15 panicles). Doses of 200 and 400 Gy exhibited relatively high variability compared to the wild type and the 600 Gy mutant. Based on the percentage of filled grains (PFG), the gamma ray dose response also showed a positive quadratic pattern with a minimum at 600 Gy (around 5%). The 200 Gy dose was the best mutant dose compared to other doses, with a potential percentage of 10%. Based on biological yield, the mutation dose response showed the same pattern as the PFG characteristics, namely a positive polynomial with the highest weight at the 200 Gy dose (5 g). The results of the principal component analysis of the overall growth characteristics in the M1 generation were shown in Fig. 5. The analysis maps the potential at each gamma irradiation dose and its wild type. Based on the figure, the 200 Gray dose exhibits the highest variability. The 400 Gy and 600 Gy doses showed variability that is partially overlapping with the variability of the 200 Gy dose mutant. However, the 400 Gy dose exhibited higher variability compared to the 600 Gy dose. In contrast, the wild type exhibited narrower diversity compared to 200 and 400 Gy. However, the range and position of the wild type's variance differed from those of its mutant diversity. 3.2. Analysis of diversity and tolerance potential of F37 mutant rice lines of the M2 generation to systematic screening of salinity stress in the seedling phase. The tolerance and growth responses of M2 rice mutants to salinity stress screening in the seedling phase were shown in Table 2 . Based on the table, normal IR29 had the highest percentage of germinating seeds, average plumule height, and fitness value. The F37_400Gy line had better germination percentage and fitness values than F37_200_Gy, F37_salin, and IR29_salin, but not better than F37_normal and IR29_normal. In terms of plumule height, the F37_400Gy line had higher average plumule height and fitness values than F37 200 and F37 saline. However, its potential was not higher than that of F37_normal, IR29_normal, and IR29_salin. The relative decrease for the comparison showed that IR29 experienced the highest relative decrease of 24% in UDK, and F37 experienced the highest relative decrease of 55.11% in plumule height when comparing normal and salinity treatments. Table 2 Percentage results of germination rates and plumule height Genotype (Percentage of Live Seedlings) Plumula Height Value (%) Fitness Relative Decline (%) Value (%) Fitness Relative Decline (%) F37 Normal 90 0.9 4.5 0.87 F37 Saline 80 0.8 11.11 2.02 0.39 55.11 F37 200 83.8 0.84 6.88 2.47 0.48 45.11 F37 400 88 0.88 2.22 2.68 0.52 40.44 IR29 Normal 100 1 5.17 1 IR29 Saline 76 0.76 24 2.89 0.56 44.1 Note: F37 Normal = F37 not irradiated and without NaCl treatment, F37 Salin = F37 not irradiated with 100 mM/L NaCl salinity treatment, F37 200 = F37 irradiated with 200 Gy and 100 mM/L NaCl salinity treatment,F37 400 = F37 irradiated at 400 Gy with 100 mM/L NaCl salinity treatment, IR29 Normal = IR29 not irradiated and without NaCl treatment, and IR29 Salin = IR29 not irradiated with 100 mM/L NaCl salinity treatment. The results of the potential tolerance of the Mutant M2 rice line in screening for salinity stress in the seedling phase were shown in Table 3 . Based on this table, F37 under normal conditions had the highest percentage of live seedlings and fitness value. IR29 under saline conditions showed the death of all seedlings that continued from the previous phase. The F37_200Gy line exhibited better seedling survival rates and fitness values than F37_400Gy, F37_salin, and IR29_salin. However, this potential was not better than that of F37_normal. In terms of seedling height, F37_400Gy had a higher average seedling height and fitness value than F37_200Gy, F37_normal, and F37_salin. The relative decrease for the comparison showed that F37 experienced a 91.31% decrease in the number of live seeds and a 7.89% decrease in seedling height. The heritability of both the cotyledon and seedling screening phases was shown in Table 4 . Based on this table, the heritability of plumules in both mutant groups ranged from 28 to 36%. When classified, plumule height was categorized as moderate, both when compared to the potential of F37 under normal conditions and under saline conditions. Conversely, the heritability of seedling length showed values > 50% or ranging from 67–68%. This indicated that seedling length was categorized as high, both when compared to F37 under normal conditions and F37 under saline conditions. Table 3 Percentage of live seedlings and seedling height Genotype (Percentage of Live Seedlings) Plumula Height Value (%) Fitness Relative Decline (%) Value (cm) Fitness Relative Decline (%) F37 Normal 82.14 1 11.4 0.72 F37 Saline 7.14 0.09 91.31 10.5 0.66 7.89 F37 200 17.18 0.21 79.04 15.34 0.96 -34.56 F37 400 12.72 0.15 84.51 15.93 1 -39.73684211 IR29 Saline 0 0 0 0 Note: F37 Normal = F37 not irradiated and without NaCl treatment, F37 Salin = F37 not irradiated with 60 mM/L NaCl salinity treatment, F37 200 = F37 irradiated with 200 Gy with 60 mM/L NaCl salinity treatment, F37 400 = F37 irradiated at 400 Gy with a salinity treatment of 60 mM/L NaCl, and IR29 Salin = IR29 not irradiated with a salinity treatment of 60 mM/L NaCl. Table 4 Heritability of rice mutant line M2 for plumule height Mutant Dosage Heritability Comparison Plumula Height (cm) Criteria Seedling Length (%) Criteria F37 200 Normal 33 Moderate 68 High Saline 36 Moderate 81 High F37 400 Normal 28 Moderate 67 High Saline 32 Moderate 80 High Note: F37 200 (N) = F37 irradiated with 200 Gy compared to F37 not irradiated under normal conditions, F37 200 (S) = F37 irradiated with 200 Gy compared to F37 not irradiated under conditions of 100 mM/L NaCl salinity, F37400 (N) = F37 irradiated with 400 Gy compared to non-irradiated F37 under normal conditions, and F37400 (S) = F37 irradiated with 400 Gy compared to non-irradiated F37 under conditions of 100 mM/L NaCl salinity. Based on the potential for salinity tolerance (Table 5 ), F37_200Gy has the highest adaptive mutant potential with a percentage of 10.2% or 51 lines. These 51 lines are divided into three classes: highly tolerant (score 1 = 33 lines), tolerant (score 3 = 7 lines), and moderate (score 5 = 11 lines). Conversely, F37_400Gy had 30 adaptive mutants and was divided into three classes. The first class was highly tolerant (score 1) with 20 lines. The second class was tolerant (score 3) with 8 lines. Finally, the moderate class (score 5) consisted of two lines. 3.3. Analysis of the growth potential of components in pots from the Mutan M2 rice line, which is considered adaptive in the salinity screening phase of seedlings. The factor analysis focused on four factor dimensions based on the optimal percentage of variance, which reached 86% (Table 6 ). Based on this analysis, biological yield variability was found in factor 1 with a loading value of 0.26. This potential was also in line with the total number of tillers, the number of productive tillers, flowering age, harvest age, panicle length, and grain number per panicle. However, flowering age (0.08) and panicle length (0.05) have very low loading values below 0.1, so these two traits were not recommended for further analysis. Therefore, total tiller number, productive tiller number, flowering age, and grain number per panicle become the ideal criteria to proceed with in the path analysis of biological yield. Table 5 Tolerance score of mutant rice M2 after salinity stress in the seedling phase Tolerance Score Number of genotypes F37 200 F37 400 F37 IR29 1 33 20 0 0 3 7 8 1 0 5 11 2 1 0 7 32 17 3 0 9 417 453 20 50 Percentage of survival (%) 10.2 6 0.4 0 Fitness 1.00 0.59 0.04 0.00 The results of the path analysis were shown in Table 7 , which summarizes the potential diversity of 53%. Based on this table, the total number of tillers (0.83), the number of productive tillers (0.85), and the number of grains per panicle (0.71) have a high positive correlation with biological yield. On the other hand, harvest age has a negative correlation of -0.42 with biological yield. Based on its direct effect, the number of productive tillers has a dominant positive direct effect (1.30) on biological yield. This was followed by the number of grains per panicle, which has a positive direct effect of 0.29. Conversely, the total number of tillers has a large negative direct effect (-0.71) on biological yield, followed by flowering age (-0.14). Based on this, the number of productive tillers and the number of grains per panicle can be recommended as evaluation criteria alongside biological yield. Table 6 Factor analysis of growth characteristics of M2 mutant rice lines tolerant to salinity stress during the seedling stage Variable Factor1 Factor2 Factor3 Factor4 Communality PH (cm) 0.00 0.02 -0.39 0.06 0.78 NTT (stems) 0.34 -0.01 0.14 0.11 0.94 NPT (stems) 0.34 -0.01 0.15 0.10 0.94 FLL (cm) -0.26 0.16 -0.64 0.05 0.82 DF (DAP) 0.08 -0.61 0.09 -0.19 0.98 DH (DAP) 0.11 -0.59 0.13 -0.14 0.97 PL (cm) 0.05 0.08 -0.32 0.14 0.67 NTG (grains) 0.22 -0.17 -0.02 -0.09 0.77 PFG (%) -0.08 -0.12 0.07 -0.61 0.87 W100G (g) -0.03 -0.21 0.06 -0.63 0.83 Yield 0.26 -0.07 0.12 -0.11 0.87 Variance 3.63 2.02 1.99 1.80 9.43 % Var 0.33 0.18 0.18 0.16 0.86 Note: PH = plant height, NTT = number of total tillers, NPT = number of productive tillers, FLL = flag leaf length, DF = days to flowering, DH = days to harvest, PL = panicle length, NTG = number of total grain, PFG = percentage of filled grains, W100G = weight of 100 grains Table 7 Path analysis of selected traits against biological yield of M2 mutant rice lines that are tolerant to salinity stress Character Direct Effect Indirect Effect Correlation JAT JAP UP JGPM NTT -0.71 1.29 0.05 0.20 0.83 NTG 1.30 -0.70 0.05 0.20 0.85 DH -0.14 0.25 -0.48 -0.05 -0.42 NTG 0.29 -0.49 0.89 0.02 0.71 The potential tolerance of mutant rice line M2 to the three main evaluation criteria was shown in Table 8 . In general, the classification in the salinity stress tolerance scoring showed that highly tolerant plants have better potential than tolerant and moderate classifications. Additionally, the potential of F37_200Gy performs better than F37_400Gy across all levels of salt stress tolerance. The F37_200Gy mutant line group with a score of 1 (highly tolerant) has the highest number of panicles (21.10) and a significant difference compared to the wild-type line under both conditions (saline = 6, normal = 9.64) and the M70D variety. This was also accompanied by a relative increase of 1.19 compared to the potential of the wild-type line under normal conditions. Additionally, this mutant group also exhibited a significantly better biological yield compared to the wild type under saline conditions, with a relative increase of 0.33. However, for the “number of total grains” trait, all mutant groups showed potential that was not better than the wild type under both conditions and compared to other reference varieties. Table 8 performance analysis of each group of putative salt-tolerant mutants based on evaluation criteria. Gamma Ray Doses Tolerance score Number of Panicles Number of total grains Biological Yield mean sig/LSD RI mean sig/LSD RI mean sig/LSD RI F37_200Gy 1 21.10 a,b,e 1.19 150.52 - -0.13 34.28 a 0.33 F37_400Gy 1 11.17 - 0.16 122.63 - -0.29 17.00 - -0.34 F37_200Gy 3 12.25 - 0.27 154.28 - -0.11 21.94 - -0.15 F37_400Gy 3 8.07 - -0.16 107.47 - -0.38 9.34 - -0.64 F37_200Gy 5 10.91 - 0.13 128.04 - -0.26 15.76 - -0.39 F37_400Gy 5 3.47 - -0.64 104.90 - -0.40 3.18 - -0.88 WT_sal (a) 5 6.00 8.2 149.40 66.75 10.00 14.03 WT_norm (b) - 9.64 173.41 25.73 Inpari 13 (c) - 18.75 182.40 29.85 Padjajaran (d) - 20.50 139.25 48.58 M70D (e) - 12.00 93.10 25.60 Biobestari (f) - 13.25 149.20 34.50 Note: sig/LSD: significant by least squares difference The results of the analysis of the diversity potential of each mutant tolerance group according to the growth evaluation criteria were shown in Fig. 6. In general, all mutant groups overlap with each other. However, the diversity of the F37_200Gy mutant, both in the highly tolerant, tolerant, and moderate groups, showed a wide range of diversity. Nevertheless, the diversity of the highly tolerant F37_200Gy mutant showed the greatest diversity compared to other groups. In addition, most of the diversity range was not compressed or overlaps with other diversity groups. On the other hand, the F37_400Gy mutant group across all tolerance groups exhibits centralized diversity and overlaps with the diversity of other groups, except for line 38. Furthermore, the potential of the F37 wildtype showed a significantly different position compared to most mutant groups, particularly in relation to the highly tolerant F37_200Gy. 4. Discussion 4.1. Potential diversity and assessment of the effectiveness of F37 rice line mutation to various doses of gamma irradiation in Generation M1 According to this study, the pattern of lethal dose reduction in relation to germination percentage is linear, with the LD50 identified at a dose of 502 Gy. These findings align with the LD50 range presented in Table 1 and the review report by [ 39 ]. However, during the adaptation phase following transplantation, the reduction in lethal dose assumes a quadratic form, with the LD50 occurring at a dose of 273 Gy. This suggests that the LD50 observed in this study is relatively high compared to the general LD50 range. Nonetheless, when evaluating the percentage of surviving plants post-transplantation, the LD50 in this study also falls within the general LD50 range. The discrepancy in LD50 between germination percentage in trays and the percentage of surviving plants in pots indicates an accumulation of stress between the mutation treatment and transplantation, as also reported by [ 40 ]. Generally, mutation treatment results in a significant increase in reactive oxygen species (ROS) [ 41 – 45 ]. These ROS can inflict damage on membranes, nucleic acids, and various cellular components, leading to plant tissue death [ 46 – 50 ]. This effect is further intensified by the transplantation process, which also contributes to increased ROS production. As noted by [ 49 ], environmental changes in plants can trigger ROS accumulation, resulting in an extremely high accumulation that shifts the stress status from elastic or linear to plastic or quadratic. Consequently, a dose of 273 Gy at the 8-week post-sowing or 6-week post-planting stage is deemed optimal for estimating the LD50 in the F37 double haploid mutant line. Analysis of the performance of mutant rice lines across various mutation doses reveals that mutation induction generally results in a reduction in the mean values of five critical growth traits, with the exception of plant height. This suggests that mutation induction significantly affects generative traits, a phenomenon corroborated by previous studies [ 51 – 54 ]. Typically, mutations induce physiological and genetic imbalances within plant tissues [ 55 , 56 ], which in turn alter the performance of rice plants, particularly in tiller formation. This results in a reduced number of tillers compared to the M0 or wild type [ 40 , 43 , 55 – 57 ]. The decrease in tiller number is directly correlated with the number of panicles produced, thus the observed reduction in panicles in this study is attributable to the mutation [ 58 , 59 ]. Notably, the M1 mutant line exhibits a pronounced reduction in the percentage of filled grains, characterized by an exponential decline. This observation is consistent with previous reports [ 43 , 54 , 60 ]. The significant decrease in filled grains suggests that the mutation process induces genetic imbalances in the reproductive tissues of rice plants, thereby impairing optimal meiosis [ 43 , 61 , 62 ]. Similar sterility due to mutations has been documented in other plant species, including black gram [ 63 ], wheat [ 64 ], Pachyrhizus erosus [ 65 ], and Adlay [ 66 ]. Such disruptions in cell division can lead to genetic imbalances, culminating in seed sterility [ 43 ]. Consequently, the biological yield in mutant plants follows a similarly extreme decline in grain percentage. However, this pronounced decline also necessitates consideration of the potential genetic diversity generated. The results of the PCA biplot analysis indicate that most mutant lines exhibit distinct diversity compared to their wild-type counterparts. This suggests that the mutation process exerts differential effects on each seed, leading to a broad diversity that diverges from the potential of the wild type. This phenomenon has been similarly reported by [ 65 , 67 – 69 ]. Furthermore, the 200 Gray dose demonstrated the greatest diversity, despite the number of surviving plants not exceeding that of the 400 Gy dose. This suggests that the damage and induction of diversity at the 400 Gy dose were relatively more targeted compared to the 200 Gy dose, resulting in the diversity produced by the 200 Gy dose being more random and varied. The overall findings reinforce the assessment of performance across mutation dose groups. Consequently, the mutation process in this study is deemed effective and holds the potential to generate extensive genetic diversity in the M2 generation. This serves as the foundational basis for the development of mutant lines tolerant to salinity stress. 4.2. Potential tolerance of M2 mutant rice to systematic salinity stress during the seedling phase The findings from the seedling phase tolerance screening indicate that the majority of lines exhibit resilience to this stage of stress. This is demonstrated by the variations in percentage and fitness between mutant lines and wild types under both normal and saline conditions. Generally, during the seedling phase, rice demonstrates a relatively high tolerance to salinity stress [ 28 , 70 – 72 ]. This resilience enables rice seeds to endure relatively high salinity levels during the seedling phase. Nonetheless, the impact of salinity stress is apparent in the reduction of plumule height, with potential decreases of up to 50% compared to the wild type under normal conditions, as also reported by [ 73 , 74 ]. Salinity stress continues to impede cell division, allowing for a provisional estimation of tolerance potential based on plumule height potential [ 74 , 75 ]. However, this estimation remains imprecise and complex, further complicated by the moderate heritability potential of the plumule. Consequently, the assessment of tolerance potential must be extended into the seedling phase. Analysis of the seedling phase screening results reveals a significant increase in mortality due to salinity stress, exceeding 50% across all mutant dose groups. Notably, IR29 and the wild-type F37 exhibited the highest mortality rates, surpassing 90%. Conversely, the 200 Gy mutant dose group demonstrated superior fitness relative to other groups. This observation suggests that stress during the seedling phase results in substantial stress accumulation, thereby diminishing the potential for salinity tolerance at this stage. As noted in [ 71 , 76 – 78 ], the seedling phase is a critical period for rice under salinity stress. Typically, hydroponic screening is employed during this phase [ 71 , 76 ]. However, this study utilized soil screening in confined seedling trays, aligning with findings in [ 9 ], where screening in saline soil within small containers produced stress levels comparable to those in pot containers. This approach does not incorporate systematic stress during the seedling phase. Furthermore, the high potential of the seedlings indicates that mutant lines outperform their wild-type counterparts under both normal and saline conditions, supported by the high heritability of this trait. Collectively, these findings suggest that the selection methodology employed in this study is effective for selecting salt tolerance, particularly during the vegetative phase. Consequently, the selection approach presented here offers a novel method for extensively estimating salt tolerance, especially in large mutant populations. According to salinity tolerance scores, the wild type F37 exhibits a markedly low level of tolerance in comparison to the mutant dose groups. Notably, the F37_200 Gy mutant group contains the highest number of lines that demonstrate adaptability to salinity stress, as indicated by Scores 1, 3, and 5. These findings align with the previously observed potential of M1, wherein mutants subjected to a 200 Gy mutation dose display greater diversity relative to other mutant dose groups, including the wild type. This suggests a synergistic interaction between the diversity potential of M1 and the diversity potential of M2 tolerance in systematic screening for salinity stress. Consequently, the 200 Gy irradiation dose is deemed optimal for enhancing rice tolerance to salinity stress. 4.3. Growth and production performance in pots of M2 mutant lines assessed as adaptive in seedling screening Systematic evaluation of crop performance is essential, with a focus on production potential [ 79 ]. This approach aims to enhance the robustness of the evaluation process by first establishing evaluation criteria [ 80 , 81 ]. The determination of these criteria can be achieved through the application of multivariate analysis, incorporating both factor analysis and path analysis. This methodological combination has been documented in previous studies [ 82 – 84 ]. The integration of these analyses indicates that the number of productive tillers and the number of grains per panicle are significant determinants of productivity. These findings align with observations in M1, where both traits are correlated with productivity. Consequently, these two traits, along with productivity, can serve as evaluation criteria for assessing the growth and production of adaptive mutant rice lines during salinity screening at the seedling stage. Upon evaluating the criteria obtained, it is evident that the number of productive tillers significantly impacts the differentiation of potential among tolerance groups. Generally, in rice cultivation, the concept of clumping positions productive tillers as a key indicator of productivity. An increase in the number of productive tillers correlates with enhanced productivity [ 58 , 85 , 86 ]. Furthermore, several studies have identified the number of productive tillers as a selection criterion for assessing rice tolerance and adaptability to salinity stress [ 7 , 55 , 82 , 87 – 91 ]. According to [ 92 ], rice subjected to salinity stress accumulates reactive oxygen species (ROS) in its leaves and main stem. Genotypes that are sensitive or less adaptive exhibit suboptimal ROS scavenging capacity, leading to increased ROS accumulation [ 93 , 94 ]. This accumulation disrupts cell division and enzymatic processes in sensitive genotypes, compelling them to prioritize the stability of enzymatic processes in the main stem to avert mortality [ 95 ]. Consequently, this phenomenon inhibits tiller formation, which is associated with panicle development, resulting in fewer productive tillers in less adaptive sensitive genotypes compared to tolerant or adaptive genotypes. Therefore, the potential differences among salinity tolerance classes can be discerned through the number of productive tillers or panicles. Evaluating the potential of each tolerance group, the 200 Gy mutant group demonstrated commendable adaptability following exposure to salinity stress during the seedling phase. This is evident from the overall potential of the 200 Gy mutant tolerance group (score 1–3), which consistently surpassed that of the 400 Gy dose. This enhancement is significantly attributed to the increase in the number of productive tillers in the 200 Gy mutant. This observation underscores the pattern that rice tolerance to salinity stress is highly contingent upon a genotype's capacity to produce productive tillers. The potential of the 200 Gy mutant group is further corroborated by the diversity pattern observed in the PCA biplot analysis, where this group exhibits greater diversity compared to other groups, rendering selection within this mutant dose group effective. The comprehensive results presented in Table 8 and Fig. 5. also suggest that salinity tolerance diversity in mutation breeding is influenced by the diversity pattern observed in the M1 generation. This phenomenon aligns with findings in soybeans [ 18 ]and cotton [ 96 ]. When compared to the potential of the wild type, mutations with a dose of 200 Gy are considered to enhance tolerance and adaptability potential relative to its parent F37. The F37 line, as noted in [ 7 , 82 ], exhibits sufficient adaptive potential under salinity stress. However, this new screening concept with elevated stress levels results in moderate potential for the haploid F37 line. This indicates that the 200 Gy mutation dose can augment the adaptability potential of a rice genotype that is already sufficiently adaptive. Conversely, a sufficiently high dose (400 Gy) is suspected to significantly alter the potential of its wild type. This is consistent with the opinion of [ 6 ], where the higher the mutation dose, the greater the resulting diversity. However, the resulting diversity may not be related to adaptability to salinity stress. Therefore, the salinity tolerance potential of the 400 Gy dose is not as favorable as that of the 200 Gy group. Based on the aforementioned analysis, a mutation dose of 200 Gy for F37 rice seeds is recommended for forming a population with enhanced adaptability potential compared to the wild type, particularly in the development of adaptive and salt-tolerant rice varieties. 5. Conclusion The research series determined that irradiation doses between 200 Gy and 400 Gy are optimal for inducing genetic diversity in rice populations. A high level of diversity observed in the M1 generation was also reflected in the M2 generation, with the 200 Gy dose yielding the greatest diversity across the M1-M2 generations. This dose is also deemed optimal for enhancing adaptability in the haploid F37 rice line, which exhibits sufficient resilience to salinity stress. In contrast, the 400 Gy dose is evaluated to have similar or reduced adaptability compared to the wild type. The study introduces a novel and effective method for selecting salt tolerance in mutant rice lines within large populations by combining vegetative screening with the seedling-seedling phase. Consistently productive seedlings are identified as the optimal selection criterion for determining rice salt tolerance. Thus, optimizing selection based on productive seedlings is crucial for detecting rice tolerance to salinity stress, particularly in the 200 Gy mutant rice population. The findings of this study should be extended to the M3 and M4 generations using a systematic shuttle breeding-based approach to enhance mutant tolerance to salinity stress. Abbreviations DAP Days after planting EC Electrical conductivity FLL Flag leaf length Gy Gray LD50 Lethal Dose 50% M Mutant generation NPC Number of panicles per culm NPT Number of productive tillers NTG Number of total grains PCA Principal component analysis PFG Percentage of filled grains PL Panicle length ROS Reactive oxygen species SEM Structural equation model SES Standard evaluation system W100G Weight of 100 grains Declarations Consent for publication Not Applicable Competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study. Ethical Approval and Consent to Participate not applicable Funding The author(s) declare that financial support was received for the Ongoing Research Funding Program (ORF-2025-751), King Saud University, Riyadh, Saudi Arabia, is acknowledged. The study was also funded by the Indonesian Collaboration Research (Riset Kolaborasi Indonesia (RKI)) scheme A with number 01319/UN4.22/PT.01.03/2025 Author Contribution Conceptualization, MFA, BSP, NC, ISD, WBS, SWA; Formal analysis, MFA; Funding acquisition. MFA, BSP, NC, MFS, MA; Methodology, MFA, BSP, ISD, WBS, SWA; Software, MFA, WBS; Data curation, MFA, BSP, NC, ISD, WBS, WMI; Investigation, MFA, AKB, RR: Resources, BSP, ISD; Supervision, BSP, NC, ISD, WBS, SWA, AHB: Validation, AHB, WMI, MA, NA, MFS; Visualization, MFA, AKB; Writing—original draft preparation, MFA; All authors reviewed the manuscript Acknowledgement The support of the Ongoing Research Funding Program (ORF-2025-334), King Saud University, Riyadh, Saudi Arabia, is acknowledged. Besides that, we are grateful to Hasanuddin University for providing funding for this research through the Indonesian collaboration research (Riset Kolaborasi Indonesia (RKI) scheme A with number 01319/UN4.22/PT.01.03/2025 Data Availability All the data is available within the manuscript. References Mohapatra PK, Sarkar RK, Panda D, Kariali E. Origin and Evolution of Rice as Domesticated Staple Food Crop. Tillering Behavior of Rice Plant. Singapore: Springer Nature Singapore; 2025. pp. 1–17. Chen W, Zhao X. Understanding Global Rice Trade Flows: Network Evolution and Implications. Foods. 2023;12:3298. Muluneh MG. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agric Food Secur. 2021;10:36. Isiaka IO, Ndukwe KO, Chibuike US. Mean Sea Level: The Effect of the Rise in the Environment. J Appl Sci Technol Trends. 2023;4:94–100. Oelviani R, Adiyoga W, Suhendrata T, Bakti IGMY, Sutanto HA, Fahmi DA, et al. Effects of soil salinity on rice production and technical efficiency: Evidence from the northern coastal region of Central Java, Indonesia. Case Stud Chem Environ Eng. 2024;10:101010. Salgotra RK, Chauhan BS. Genetic Diversity, Conservation, and Utilization of Plant Genetic Resources. Genes (Basel). 2023;14. Anshori MF, Purwoko BS, Dewi IS, Ardie SW, Suwarno WB. A New Approach to Select Doubled Haploid Rice Lines under Salinity Stress Using Indirect Selection Index. Rice Sci. 2021;28:368–78. Anshori MF, Farid M, Nasaruddin, Musa Y, Iswoyo H, Sakinah AI, et al. Development of image-based phenotyping for selection characters of rice adaptability on the seedling salinity screening. IOP Conf Ser Earth Environ Sci. 2021;807:032022. Anshori MF, Purwoko BS, Dewi IS, Suwarno WB, Ardie SW. Systematic selection to adaptive doubled haploid rice lines under different environments of submergence screening methods. J Agric Food Res. 2023;14:100775. Yali W, Mitiku T. Mutation Breeding and Its Importance in Modern Plant Breeding. J Plant Sci. 2022;10:64. Ramchander S, Ushakumari R, Arumugam Pillai M. Lethal dose fixation and sensitivity of rice varieties to gamma radiation. Indian J Agric Res. 2015;49:24–31. Naibaho D, Purba E, Hanafiah DS, Hasibuan S. Radiosensitivity and effect of gamma ray irradiation on upland rice CV. Sidikalang. IOP Conf Ser Earth Environ Sci. 2021;782. Solim MH, Rahayu S. Radiosensitivity of rice varieties of Mira-1 and Bestari mutants using gamma rays irradiation. IOP Conf Ser Earth Environ Sci. 2021;911. Siregar DS, Rosmayati, Hanafiah DS, Lubis K. Initial vegetative growth and determination of the lethal dose of local rice arias putih at various doses of gamma irradiation. IOP Conf Ser Earth Environ Sci. 2024;1352. Yunita R, Dewi IS, Lestari EG, Purnamanengsih R, Rahayu S. Mastur. Formation of upland rice drought-tolerant mutants by mutation induction and in vitro selection. Biodiversitas. 2020;21:1476–82. Faustino GP, Habana EJH, Malabanan-bauan KB, Abonitalla DB, Borromeo TH, Calayugan MIC. Identification of Median Lethal Dose (LD 50) and Shoot and Root Growth Reduction (GR 50) Dose in Four Rice Varieties Irradiated with Cobalt-60. Philipp J Sci. 2024;153:1899–910. Rawat AS, Bhuj BD, Srivastava R, Chand S, Singh NK, Bisht YS, et al. Determination of lethal and mutation induction doses of gamma rays for gladiolus (Gladiolus grandifloras Hort.) genotypes. Heliyon. 2024;10:e37387. Djarot P, Lestari EG, Anshori MF, Budiarto R. In vitro Culture and Field Evaluation of Two Generations of Sorghum Mutants Induced by Gamma Ray Irradiation. Asian J Plant Sci. 2021;20:648–58. Warman B, Sobrizal S, Suliansyah I, Swasti E, Syarif A. Perbaikan Genetik Kultivar Padi Beras Hitam Lokal Sumatera Barat Melalui Mutasi Induksi. J Ilm Apl Isot dan Radiasi. 2016;11:125. Kadhimi AA, Alhasnawi AN, Isahak A, Ashraf MF, Mohamade A, Yusoffa WMW, Zaina CC. Gamma radiosensitivity study on MRQ74 and MR269, two elite varieties of rice. Life Sci J. 2016;13:85–91. Nagaraj RA, Geethalakshmi V, Manonmani S, Ravikumar R, Murugananthi D, Bhuvaneswari K, et al. Comprehensive insights into the risks of climatic factors on rice production and its value chain- A Review. Plant Sci Today. 2024;11:1–12. Khannetah K, Pushpam R, Ganesan K, KK Kumar, Pillai C, Chandrashekar, et al. Appraising LD50 dosage for physical mutagen (Gamma rays) in CR1009 and CR1009 sub1 rice varieties.J … 2715–9. Njoroge W, Kinyua M, Gichuhi E, Ankamah-Yeboah T, Offei KS, Ofori K. Radio-Sensitivity of four selected rice (Oryza sativa L.) varieties in Kenya, as a pre-requisite for mutation breeding. Afr J plant Sci. 2022;16:210–23. Madriz-Martínez M, Fernández-Acuña A, Hernandez-Villalobos S, Orozco-Rodríguez R, Argüello-Delgado J. Radiosensitivity of rice (Oryza sativa L. var CR5272) to gamma irradiation in Costa Rica. Cultiv Trop. 2022;43:8. Puspita Sari H, Suliansyah I, Dwipa I, Hervani D. Orientasi Dosis Iradiasi Efektif Pada Perbaikan Genetik Padi (Oryza sativa L.) Lokal Padang Pariaman Melalui Mutasi Induksi. Produksi Tanam. 2023;011:408–21. Marlina G, Syarif A, Gustian G, Yusniwati Y. Orientation Of Effective Irradiation Dosage In Local Genetic Rice Improvement (Oryza sativa L.) Kuantan Singingi District Using Induced Mutation. J Agron Tanam Trop. 2024;6:119–29. Kakar N, Jumaa SH, Redoña ED, Warburton ML, Reddy KR. Evaluating rice for salinity using pot-culture provides a systematic tolerance assessment at the seedling stage. Rice. 2019;12:57. Farid M, Nasaruddin, Anshori MF, Musa Y, Iswoyo H, Sakinah AI. Interaction of rice salinity screening in germination and seedling phase through selection index based on principal components. Chil J Agric Res. 2021;81:368–77. Sakinah AI, Farid M, Musa Y, Hairmansis A, Anshori MF. Seedling Stage Image-Based Phenotyping Selection Criteria through Tolerance Indices on Drought and Salinity Stress in Rice. Plant Breed Biotechnol. 2024;12:43–58. Fatima S, Rashid M, Hameed A, Fiaz S, Rebouh NY, Zaman QU. Development of rice mutants with enhanced resilience to drought and brown spot (Bipolaris oryzae) and their physiological and multivariate analysis. BMC Plant Biol. 2025;25:1040. Abdelsalam KMH, Shaalan AM, AbouEl-Soud GM, El-Dalil MAE, Marei AM, El-Moneim DA, et al. Comprehensive quality profiling and multivariate analysis of rice (Oryza sativa L.) cultivars: integrating physical, cooking, nutritional, and micronutrient characteristics for enhanced varietal selection. BMC Plant Biol. 2025;25:492. Sadimantara GR, Yusuf DN, Febrianti E, Leomo S. The performance of agronomic traits, genetic variability, and correlation studies for yield and its components in some red rice (Oryza sativa) promising lines. Biodiversitas J Biol Divers. 2021;22:3994–4001. Agah LJ, Akpa EA, Ekpoto DF, Njoku EA, Obok EE, Ogbonna PE, et al. Multivariate Correlation and Fit for Path Analysis of 25 African Yam Bean (Sphenostylis stenocarpa) Accessions Using Structural Equation Models. Trop J Nat Prod Res. 2025;9:2886–94. Shrestha N. Factor Analysis as a Tool for Survey Analysis. Am J Appl Math Stat. 2021;9:4–11. Ashraf ATMH, Rahman MM, Hossain MM, Sarker U. Study of Correlation and Path Analysis in the Selected Okra Genotypes. Asian Res J Agric. 2020;:1–11. Ghannam HA, I RA, Ibrahim MA, Mousa MI, Soliman AA. Genetic Analysis for Some Faba Bean Agronomic Traits. Sci J Agric Sci. 2024;6:72–91. Rathod S, Kumar AT. Statistical Procedures for Analyzing Agricultural Data using R. Hyderabad: ICAR- Indian Institute of Rice Research; 2023. Jaldhani V, Suman K, Rao DS, Travis T, Rao IS, Rathod S, et al. Superior haplotypes to enhance grain filling in rice (Oryza sativa L.) identified through GWAS. Discov Plants. 2024;1:70. Baadu R, Chong KP, Gansau JA, Zin MRM, Dayou J. A systematic review on physical mutagens in rice breeding in Southeast Asia. PeerJ. 2023;11:1–28. Harding S. Effect of Gamma Rays on Seed Germination, Seedling Height, Survival Percentage and Tiller Production in Some Rice Varieties Cultivated in Sierra Leone. Am J Exp Agric. 2012;2:247–55. Sakai A, Nakanishi M, Yoshiyama K, Maki H. Impact of reactive oxygen species on spontaneous mutagenesis in Escherichia coli. Genes Cells. 2006;11:767–78. Ragu S, Faye G, Iraqui I, Masurel-Heneman A, Kolodner RD, Huang ME. Oxygen metabolism and reactive oxygen species cause chromosomal rearrangements and cell death. Proc Natl Acad Sci U S A. 2007;104:9747–52. Choi H, Il, Han SM, Jo YD, Hong MJ, Kim SH, Kim JB. Effects of acute and chronic gamma irradiation on the cell biology and physiology of rice plants. Plants. 2021;10:1–14. Li S, Liu S, Zhang Q, Cui M, Zhao M, Li N, et al. The interaction of ABA and ROS in plant growth and stress resistances. Front Plant Sci. 2022;13:1–16. Yanting L, Bingkui W, Mengchao Z, Jing Y, Shenghai Y. Sensitivity of genotypically diverse rice varieties to radiation and the related changes to antioxidant enzyme activities. Int J Radiat Biol. 2024;100:453–65. Farnese FS, Menezes-Silva PE, Gusman GS, Oliveira JA. When bad guys become good ones: The key role of reactive Oxygen species and Nitric Oxide in the plant responses to abiotic stress. Front Plant Sci. 2016;7:1–15. Huang H, Ullah F, Zhou DX, Yi M, Zhao Y. Mechanisms of ROS regulation of plant development and stress responses. Front Plant Sci. 2019;10:1–10. Juan CA, de la Lastra JMP, Plou FJ, Pérez-Lebeña E. The chemistry of reactive oxygen species (Ros) revisited: Outlining their role in biological macromolecules (dna, lipids and proteins) and induced pathologies. Int J Mol Sci. 2021;22. Panda SK, Gupta D, Patel M, Vyver C, Van Der, Koyama H. Functionality of Reactive Oxygen Species (ROS) in Plants: Toxicity and Control in Poaceae Crops Exposed to Abiotic Stress. Plants. 2024;13. Sood M. Reactive oxygen species (ROS): plant perspectives on oxidative signalling and biotic stress response. Discov Plants. 2025;2. Shrivastava P, Kumar R. Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J Biol Sci. 2015;22:123–31. Herwibawa B, Sakhidin, Dwi Haryanto TA. Agronomic performances of aromatic and non-aromatic M1 rice under drought stress. Open Agric. 2019;4:575–84. Riyanto A, Oktaviani E, Wulansari NK, Haryanto TAD. Genetic parameters of yield component and yield in M1 rice (Oryza sativa L.) generation irradiated with gamma-ray. Kultivasi. 2024;23:187–200. Suliartini NWS, Adekayanti S, Sudharmawan AAK, Aryana IGPM. Effect of Mutations Induction on Vegetative and Generative Characters of G16 Rice (Oryza sativa L). J Penelit Pendidik IPA. 2023;9:3790–9. Hase Y, Satoh K, Kitamura S, Oono Y. Physiological status of plant tissue affects the frequency and types of mutations induced by carbon-ion irradiation in Arabidopsis. Sci Rep. 2018;8:1–10. Ma L, Kong F, Sun K, Wang T, Guo T. From Classical Radiation to Modern Radiation: Past, Present, and Future of Radiation Mutation Breeding. Front Public Heal. 2021;9:1–11. Widian J, Nurhidayah S, Rahayu S, Firmansyah E, Yunita R. Morphology Performance On Six Black Rice Accessions (Oryza Sativa L.) In M1 Generation Irradiated By Gamma Rays. JERAMI Indones J Crop Sci. 2021;3:62–7. Yuan R, Mao Y, Zhang D, Wang S, Zhang H, Wu M et al. The Formation of Rice Tillers and Factors Influencing It. Agronomy. 2024;14. Zhang N, Liu Y, Gui S, Wang Y. Regulation of tillering and panicle branching in rice and wheat. J Genet Genomics. 2025;52:869–86. Zhang R, Wang Y, Hussain S, Yang S, Li R, Liu S, et al. Study on the Effect of Salt Stress on Yield and Grain Quality Among Different Rice Varieties. Front Plant Sci. 2022;13:1–14. De Micco V, Arena C, Pignalosa D, Durante M. Effects of sparsely and densely ionizing radiation on plants. Radiat Environ Biophys. 2011;50:1–19. Caplin N, Willey N. Ionizing radiation, higher plants, and radioprotection: From acute high doses to chronic low doses. Front Plant Sci. 2018;9:1–20. Ramaiyan S, Vijayakumar P, Balasubramanian R alias, Nallathambi B. Influence of nano-zinc oxide and fortified rice residue compost on rice productivity, zinc biofortification, zinc use efficiency, soil quality, zinc fractions and profitability in different rice production systems. J Plant Nutr. 2023;46:4063–84. Nazarenko M. Parameters of winter wheat growing and development after mutagen action. Bull Transilv Univ Brasov Ser II Wood Ind Agric Food Eng. 2016;9:109–16. Dewi Hayati PK, Witari S, Rozen N, Sutoyo, Widiarsih S. Effect of gamma irradiation on the germination, pollen viability, and morpho-agronomic of Pachyrhizus erosus cv. Kota Padang Biodiversitas. 2022;23:1231–8. Adhi RK, Qosim WA, Rachmadi M, Damayanti F, Amien S, Anas, et al. Mutagenic Effectiveness and Efficiency of Gamma-rays Treatment on Adlay (Coix lacryma-jobi L). Int J Agric Biosci. 2024;13:683–8. Guo H, Yan Z, Li X, Xie Y, Xiong H, Liu Y, et al. Development of a high-efficient mutation resource with phenotypic variation in hexaploid winter wheat and identification of novel alleles in the TaAGP.L-B1 gene. Front Plant Sci. 2017;8:1–9. Kitamura S, Satoh K, Oono Y. Detection and characterization of genome-wide mutations in M1 vegetative cells of gamma-irradiated Arabidopsis. PLoS Genet. 2022;18:1–23. Abdelhameed AA, Ali M, Darwish DBE, AlShaqhaa MA, Selim DAFH, Nagah A, et al. Induced genetic diversity through mutagenesis in wheat gene pool and significant use of SCoT markers to underpin key agronomic traits. BMC Plant Biol. 2024;24:1–14. Hoang TML, Tran TN, Nguyen TKT, Williams B, Wurm P, Bellairs S, et al. Improvement of salinity stress tolerance in rice: Challenges and opportunities. Agronomy. 2016;6:1–23. Singh RK, Kota S, Flowers TJ. Salt tolerance in rice: seedling and reproductive stage QTL mapping come of age. Theor Appl Genet. 2021;134:3495–533. Hartatik S, Rozzita N, Wibowo S, Choirunnisa E, Sakanti SAS, Puspito AN, et al. Characterization of rice varieties under salinity level and the response of defense-related genes during the germination stage. Biodiversitas. 2024;25:1536–43. Shakri T, Che-Othman MH, Isa NM, Sukiran NL, Zainal Z. Morpho-Physiological and Stress-Related Gene Expression of Rice Varieties in Response to Salinity Stress at Early Vegetative Stage. Agric. 2022;12. Alkahtani J, Dwiningsih Y. Analysis of Morphological, Physiological, and Biochemical Traits of Salt Stress Tolerance in Asian Rice Cultivars at Seedling and Early Vegetative Stages. Stresses. 2023;3:717–35. Baranova EN, Gulevich AA. Asymmetry of plant cell divisions under salt stress. Symmetry (Basel). 2021;13. Farid M, Nasaruddin, Anshori MF, Musa Y, Iswoyo H, Sakinah AI. Interaction of rice salinity screening in germination and seedling phase through selection index based on principal components. Chil J Agric Res. 2021;81:368–77. Zheng C, Liu C, Liu L, Tan Y, Sheng X, Yu D, et al. Effect of salinity stress on rice yield and grain quality: A meta-analysis. Eur J Agron. 2023;144:126765. Osei-Wusu MO, Adjei RR, Appiah KS, Ankamah-Yeboah T, Adusei-Fosu K. Advanced breeding strategies for combating rice salinity stress in Ghana: A critical review and future perspective. Sci Afr. 2025;29:e02784. Anshori MF, Musa Y, Farid M, Jayadi M, Bahrun AH, Yassi A, et al. A new concept in assessing adaptability index for superior potential cropping intensity in early-maturing rice. Front Sustain Food Syst. 2024;8:1–12. Musa Y, Farid M, Nasaruddin N, Anshori MF, Adzima AF, Maricar MF et al. Sustainability approach in cropping intensity (CI) 400 through optimizing the dosage of compost and chemical fertilizers to early-maturing rice varieties based on multivariate analysis. J Agric Food Res. 2023;14 December:100907. Padjung R, Farid M, Musa Y, Nasaruddin N, Nurfaida N, Anshori MF et al. Yield and vegetation index of different maize varieties and nitrogen doses under normal irrigation. Open Agric. 2025;10. Anshori MF, Purwoko BS, Dewi IS, Suwarno WB, Ardie SW. Salinity tolerance selection of doubled-haploid rice lines based on selection index and factor analysis. AIMS Agric Food. 2022;7:520–35. Anshori MF, Musa Y, Farid M, Jayadi M, Padjung R, Kaimuddin K, et al. A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties. Front Plant Sci. 2024;15:1–12. Ridwan I, Farid M, Haring F, Widiayani N, Yani A, Amier N, et al. Optimized framework for evaluating F3 transgressive segregants in cayenne pepper. BMC Plant Biol. 2025;25:156. Yan Y, Ding C, Zhang G, Hu J, Zhu L, Zeng D, et al. Genetic and environmental control of rice tillering. Crop J. 2023;11:1287–302. Kalaitzidis A, Kadoglidou K, Mylonas I, Ghoghoberidze S, Ninou E, Katsantonis D. Investigating the Impact of Tillering on Yield and Yield-Related Traits in European Rice Cultivars. Agric. 2025;15. Anshori MF, Purwoko BS, Dewi IS, Ardie SW, Suwarno WB. Selection index based on multivariate analysis for selecting doubled-haploid rice lines in lowland saline prone area. Sabrao J Breed Genet. 2019;51:161–74. Arifuddin M, Musa Y, Farid M, Anshori MF, Nasaruddin N, Nur A, et al. Rice screening with hydroponic deep-flow technique under salinity stress. Sabrao J Breed Genet. 2021;53:435–46. Khatab IA, Farid MA, Abu Amo AG, El-Refaee YZ. Screening of some rice genotypes for salinity tolerance using agro-morphological and SSR markers. Chil J Agric Res. 2022;82:211–24. Rasheed A, Li H, Nawaz M, Mahmood A, Hassan MU, Shah AN, et al. Molecular tools, potential frontiers for enhancing salinity tolerance in rice: A critical review and future prospective. Front Plant Sci. 2022;13:1–18. Saleem MA, Khan A, Tu J, Huang W, Liu Y, Feng N, et al. Salinity Stress in Rice: Multilayered Approaches for Sustainable Tolerance. Int J Mol Sci. 2025;26:1–23. Zuo G, Zhang R, Feng N, Zheng D. Photosynthetic Responses to Salt Stress in Two Rice (Oryza sativa L.) Varieties. Agronomy. 2024;14:2134. Li Q, Yang A, Zhang WH. Comparative studies on tolerance of rice genotypes differing in their tolerance to moderate salt stress. BMC Plant Biol. 2017;17:1–13. Tavu LEJ, Redillas MCFR. Oxidative Stress in Rice (Oryza sativa): Mechanisms, Impact, and Adaptive Strategies. Plants. 2025;14. Zhang R, Zheng D, Feng N, Linfeng L, Ma J, Yuan X et al. Effect of salt stress on different tiller positions in rice and the regulatory effect of prohexadione calcium. PeerJ. 2024;12. Hassan A, Naseer A, Shahani AAA, Aziz S, Khalid MN, Mushtaq N, et al. Assessment of Fiber and Yield Related Traits in Mutant Population of Cotton. Int J Agric Biosci. 2022;11:95–102. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7431509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511916333,"identity":"87120016-d0d8-4c8a-9538-06b5fda0c952","order_by":0,"name":"Muhammad Fuad Anshori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBAC9gYGBgkQwwCID3xgYEiASkgwNuDQwnMAScvBGSRrYeZBaGHArUUi+eENhpo79ubsvQ8P27bdyePvP8D44QeDhSxuLWnGFgzHniXu7DlucDi37VmxxI0EZskeBgljXFrsJRLMJBjYDicY3EhjAGo5nNhwg4FBGujaRNy2pH+TYPh32B6sxRKoZf75A8y/8WvJMZNgbDvMuAGkBchI3HAggQ2/LTxvii0S+4AqzxxjONhz7nDixhuJbZY9Brj9wsOevvHGh29Ahx1vY/7wo+xw4rzzhw/f+FFRhzPEwCABlQuKEQN86kfBKBgFo2AUEAIAmMJdTHjB9NcAAAAASUVORK5CYII=","orcid":"","institution":"Hasanuddin University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Fuad","lastName":"Anshori","suffix":""},{"id":511916334,"identity":"097060d0-e1f8-4c99-b7c1-657d30a90856","order_by":1,"name":"Bambang Sapta Purwoko","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"Bambang","middleName":"Sapta","lastName":"Purwoko","suffix":""},{"id":511916336,"identity":"e1979768-5eec-462b-a94a-4a04ab466eed","order_by":2,"name":"Nono Carsono","email":"","orcid":"","institution":"Universitas Padjadjaran","correspondingAuthor":false,"prefix":"","firstName":"Nono","middleName":"","lastName":"Carsono","suffix":""},{"id":511916337,"identity":"a08cff29-2480-4aff-945a-2a12b40092a9","order_by":3,"name":"Iswari Saraswati Dewi","email":"","orcid":"","institution":"National Research and Innovation Agency","correspondingAuthor":false,"prefix":"","firstName":"Iswari","middleName":"Saraswati","lastName":"Dewi","suffix":""},{"id":511916338,"identity":"0b3a322d-fb82-4231-95bd-d60ab30d8fb7","order_by":4,"name":"Abd Haris Bahrun","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Abd","middleName":"Haris","lastName":"Bahrun","suffix":""},{"id":511916339,"identity":"fe668053-ee01-4dcd-b8e9-ff97de459f9c","order_by":5,"name":"Achmad Kautsar Baharuddin","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Achmad","middleName":"Kautsar","lastName":"Baharuddin","suffix":""},{"id":511916340,"identity":"24956d4b-7ed3-4cba-b973-58707be55816","order_by":6,"name":"Reskiana Rahman","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Reskiana","middleName":"","lastName":"Rahman","suffix":""},{"id":511916341,"identity":"026314c9-14d4-4b89-aba7-87cfaa6c054c","order_by":7,"name":"Wijaya Murti Indriatama","email":"","orcid":"","institution":"National Research and Innovation Agency","correspondingAuthor":false,"prefix":"","firstName":"Wijaya","middleName":"Murti","lastName":"Indriatama","suffix":""},{"id":511916342,"identity":"6ff08ca0-b79b-4f30-aaa4-522fffd58cd8","order_by":8,"name":"Majed Alotaibi","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"","lastName":"Alotaibi","suffix":""},{"id":511916343,"identity":"452646aa-909d-4bc2-ab9f-952470cdcd22","order_by":9,"name":"Nawab Ali","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Nawab","middleName":"","lastName":"Ali","suffix":""},{"id":511916344,"identity":"f21df824-5e2c-420e-82c6-ed881850db93","order_by":10,"name":"Mahmoud F. Seleiman","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"F.","lastName":"Seleiman","suffix":""}],"badges":[],"createdAt":"2025-08-22 06:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7431509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7431509/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07542-2","type":"published","date":"2025-11-14T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90941766,"identity":"64d20fba-7715-4628-84fc-9e6c837dbf56","added_by":"auto","created_at":"2025-09-09 18:31:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":624971,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of bibliometric keyword interactions related to rice irradiation mutations\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/ec720daa65ce1e4c302cab90.png"},{"id":90941621,"identity":"5bb078e9-f6e5-41d6-bb30-72d4cf4ba8bf","added_by":"auto","created_at":"2025-09-09 18:23:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236663,"visible":true,"origin":"","legend":"\u003cp\u003eFramework for developing putative mutants adapted to saline stress.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/058397891dd8e353ab2476ed.jpeg"},{"id":90941765,"identity":"a1d4ba41-8374-4e84-aaeb-ecfd2cc566f6","added_by":"auto","created_at":"2025-09-09 18:31:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29443,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the lethal dose of gamma irradiation mutase on the wild type line F.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/a48625cafc59159a36611a93.png"},{"id":90941620,"identity":"0bdc0685-01d6-469d-94f6-07b0b61f51a7","added_by":"auto","created_at":"2025-09-09 18:23:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97524,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the response of gamma irradiation mutase to growth characteristics.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/7af7330bf2535fdf1e08d424.png"},{"id":90941624,"identity":"a654a19a-41fd-4234-8808-90d442703b37","added_by":"auto","created_at":"2025-09-09 18:23:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":62631,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot analysis of the main components of growth characteristics of mutant rice M1 based on gamma irradiation dose\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/f7d2b9aaa6f8960b60315360.png"},{"id":90941643,"identity":"87631299-37a5-4636-910c-866efc3fbd37","added_by":"auto","created_at":"2025-09-09 18:23:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":49680,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot analysis of main components against evaluation criteria for the growth of M2 mutant rice tolerant to salinity stress during the seedling stage.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/e4c411fd05f229174f128224.png"},{"id":96105087,"identity":"9effff4d-fd8b-499d-aecc-006ef4293bcb","added_by":"auto","created_at":"2025-11-17 16:08:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2860104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7431509/v1/4a71eec4-f6e3-4d65-b853-7d38d3f98d37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integration of Gamma Irradiation Breeding from Doubled haploid and Systematic Screening to Develop Adapted Rice Mutants under Salinity Stress","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice is an essential element of economic sustainability and food security for a substantial portion of the global population. The leading rice-producing countries include China, India, Indonesia, Bangladesh, and Vietnam [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], rendering fluctuations in rice production a significant threat to food security in these regions. Although rice production has demonstrated a relative annual increase, growth has been gradual. Conversely, the population has surged, correlating with an increased demand for rice [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This dynamic is crucial for maintaining food security in numerous countries. Furthermore, this instability is intensified by the impacts of global warming [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Global warming exacerbates suboptimal conditions for rice cultivation, such as drought, salinity, and flooding. Salinity stress, in particular, adversely affects island nations like Indonesia. Global warming can result in rising sea levels, which are associated with seawater intrusion into terrestrial areas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This intrusion can elevate soil salinity levels, leading to significant production declines or even crop failures in rice-growing areas near coastal regions. This issue is critical for Indonesia, where 15% of total rice production is situated near coastal areas [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, addressing rice-related challenges in coastal regions is imperative to maintain the resilience and stability of Indonesia's rice production.\u003c/p\u003e\u003cp\u003eThe issue of salinity in rice cultivation can be effectively addressed through plant breeding strategies. This approach constitutes a fundamental component in resolving production challenges in agriculture, including salinity stress. Generally, plant breeding focuses on the genetic modification of plants to develop new varieties with optimal potential based on specific objectives [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Indonesian government has released several rice varieties that exhibit tolerance to salinity stress. However, the dynamics of environmental changes continue to evolve, necessitating the ongoing development and enhancement of adaptive varieties in alignment with technological advancements. Several studies have reported progress in the development of salt-tolerant and adaptive rice varieties, including [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which conducted the development of a haploid rice variety HS4-15-1-63 line (F37) that is adaptive to salt stress. Nonetheless, [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]demonstrated a reduction in the salinity stress tolerance of the F37 line when screened in saline soil in trays. This finding indicates that this line still requires enhanced adaptability to effectively address the challenges posed by ongoing climate change. Consequently, genetic modification of this line is necessary, which can be achieved through the concept of mutation breeding.\u003c/p\u003e\u003cp\u003eMutagenesis is a straightforward and efficacious technique for generating a diverse base population. This method is employed to develop additional varieties with specific novel traits derived from the genetic composition of existing varieties[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Numerous studies have substantiated the efficacy of variety development through the application of mutations. Generally, this concept can be realized through the use of chemical and physical mutagens. However, the majority of studies employ physical mutation or irradiation to create populations exhibiting a wide to extreme level of diversity. According to a bibliometric analysis related to rice irradiation mutation, gamma-ray mutagens are the most prevalent method (Fig.\u0026nbsp;1). This mutagen is considered relatively cost-effective, with commendable efficacy in establishing a mutation base population. Nevertheless, the mutation concept is highly contingent upon the wild-type genetic construct or M0, as the mutations that occur are concentrated solely on the genetic base of the wild type, without any contribution from other genetic constructs. The haploid F37 rice line, which possesses a genetic construct sufficiently adaptive to salinity stress, is deemed suitable for enhancement using the mutation concept. However, the process of inducing mutation diversity, which is highly stochastic, renders the direction of selection challenging to predict as desired. This suggests that the success and efficacy of the process are heavily reliant on the size of the selected population. The larger the population, the greater the probability of identifying desired traits. However, a large population necessitates substantial costs and systematic methods in the selection process. Therefore, the selection of F37 mutant lines must be conducted systematically and efficiently to augment their adaptability to salinity stress.\u003c/p\u003e\u003cp\u003eThe development of a salinity screening concept for mutants can be accomplished through the optimization of mutation diversity in M1 and systematic salinity screening selection in M2. The optimization of mutation diversity in M1 represents the initial stage in the mutation process. Determination of radiosensitivity through LD50 is frequently employed in mutation breeding, including in rice. LD50 serves as an indicator of effectiveness in mutation breeding [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A higher LD50 corresponds to greater diversity produced, although the number of surviving individuals decreases. Conversely, a lower LD50 results in fewer surviving individuals, making LD50 the optimal diversity point with a sufficient percentage of surviving individuals for the selection process [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Based on data from several reports (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the LD50 of rice in gamma radiation mutation ranges from 200 to 600 Gy. This indicates that each genotype exhibits a relatively diverse pattern in responding to the administered mutagen dose. Furthermore, the lines used originate from double haploid technology, suggesting that the mutation response pattern for the F37 line may differ from existing patterns. Therefore, optimizing the gamma radiation mutagen dose for the F37 line is necessary to achieve high diversity for selecting salt stress adaptability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLD50 results from several rice mutation studies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLD 50 dose (Gy)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003elocal black upland rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e347.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhite Ponni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e354.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBPT 5204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e288.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMR269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e351.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMRQ74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e365.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eADT (R) 47 RICE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e235.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCV. 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align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e683.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCR1009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e152.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCR1009 sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e284.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBasmati 217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e555.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBasmati 370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e354.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eITA310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e517.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKomboka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e385.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCR5272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e674.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMadang Pulau\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e333.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePutiah Papanai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e377.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBanang Kuning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e291.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSinggam Putih\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e300.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElonElon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e340.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMilagrosa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e329.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKandaman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e322.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC4-63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e336.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe development of a systematic selection framework for salinity tolerance screening can be executed in three distinct phases: germination, seedling, and reproductive. The seedling phase is extensively utilized in salinity screening, as it is considered a critical juncture for rice in response to salinity stress and is conducted during the vegetative phase [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This phase allows for the rapid and straightforward determination of potential tolerance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, selection during this phase frequently employs hydroponic methods, which are criticized for inadequately reflecting the interactions between soil and salinity. Moreover, the tolerance traits identified through this method do not strongly correlate with adaptability during the reproductive phase [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A salinity selection method utilizing small containers has been developed, which aligns with general screening on saline soil in pots and can be adapted for the seedling phase. Such adaptations can be applied to seedling trays, facilitating large-scale selection capacity at M2. Furthermore, integrating this method with the germination phase can enhance the stability of tolerance traits during the vegetative phase. This approach has not been previously implemented, particularly in mutant rice, thus its application could represent a novel advancement in the selection of mutant rice with a large population. Consequently, optimizing irradiation on the double haploid line F37 and conducting systematic selection based on the germination-seedling phase in seedling trays for M2 mutants is highly promising. Therefore, the objectives of this study are (1) to determine the optimal mutagenic irradiation dose for double haploid lines; and (2) to assess the effectiveness of developing a new method for screening salt tolerance in mutant rice during the germination-seedling phase.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Experimental Design\u003c/h2\u003e\n\u003cp\u003eThe research was conducted at the Breeding Laboratory and Greenhouse Center of Excellence (CoE) Experimental Garden, Faculty of Agriculture, Hasanuddin University, located in the Tamalanrea District, Makassar City, South Sulawesi. The site was situated at an altitude of 0\u0026ndash;25 meters above sea level, with greenhouse temperatures averaging between 21.4\u0026deg;C and 41.4\u0026deg;C. The study spanned from March to June 2024 for the M1 generation and from July to December 2024 for the M2 generation. The investigation of the M1 generation concentrated on the application of gamma ray mutagenic irradiation to the haploid line HS4-15-1-63 (F37) from Anshori et al. [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. The mutation process is carried out at the National Nuclear Energy Agency (BATAN) of Indonesia using a gamma cell device. The device operates at a radiation dose rate of 2.13 kGy/hour. The mutagenic doses were administered at six levels: 0 Gy (wild type), 200 Gy, 400 Gy, 600 Gy, 800 Gy, and 1000 Gy. All irradiated mutants were cultivated until the reproductive phase. The seeds produced in the M1 generation will be utilized in the subsequent M2 generation study. The M2 mutant rice generation study comprised three stages: (1) screening for salinity stress during the germination phase, (2) screening for salinity stress during the seedling phase, and (3) evaluation of growth and production of adaptive mutants. Each stage was conducted in a sequential and systematic manner as shown in Fig.\u0026nbsp;2\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Research Procedure\u003c/h2\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1. Optimization of gamma ray irradiation mutagen dose in M1 generation mutants\u003c/h2\u003e\n\u003cp\u003eThe mutated seeds (M1) were planted in a 108-hole seedling tray. The tray contained a growing medium produced by combining soil and compost in a ratio of 3:1 (v/v). The seeds were first soaked and then planted evenly in each hole of the tray according to the mutagenic radiation dose. After two weeks, the seedlings were transferred to pots containing the same growing medium as in the trays. The pots used had a volume of 10 L, and the growing medium filled the pots to a volume of 8 L. Rice seedlings were planted in the pots at a rate of 1 seedling per pot. Each pot was fertilized with NPK 15:15:15 at a dose of 5 g/pot 7 days after sowing (DAS). Subsequently, additional fertilizer was applied at 30 DAS with 3 g of urea per pot. Maintenance at this stage included irrigation, weeding, and pest and disease control. Irrigation was done regularly when the water in the pots starts to decrease. Weeding was done by pulling out weeds growing in each pot. Disease control was done chemically by applying the fungicide Antracol at a concentration of 3 grams per liter of water, and pest control is done mechanically by manually removing pests and then killing them. Rice plants were harvested when the grains have entered the physiological ripening phase, with criteria of 80% of the panicles appearing yellow and the rice grains at the base of the panicles having hardened. Rice harvesting was done by cutting the lower part of the panicle base using scissors. The harvested rice was then placed into sample envelopes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2. Selection of the germination phase in M2 mutant rice lines\u003c/h2\u003e\n\u003cp\u003eThe first stage of research on the M2 generation was conducted during the germination stage under salinity stress. The genotypes included were the F7 mutant at a dose of 200 Gy (F37_200) and F37 at a dose of 400 Gy (F37_400). Each mutation dose group included 500 seeds in the salinity germination screening. The screening was conducted using the paper test method. The screening was performed in plastic trays measuring 17 cm \u0026times; 10 cm \u0026times; 6 cm with a concentration of 100 mmol/L (5.84 g/L) NaCl or equivalent to 10 dS/m at the EC level of the solution. Each plastic tray was filled with three layers of tissue paper, and each tray contained 100 seeds depending on the treatment, with 50 seeds per treatment. Specifically, the wild-type genotypes F37 and IR29 were each sown with 100 seeds of good seed quality and divided into two groups: 50 seeds were subjected to salt stress treatment, and 50 seeds were not subjected to salt stress treatment. All small container seeds were covered to maintain seed moisture, and the germination test was conducted over 7 days. Seed maintenance in plastic trays was carried out by placing the trays in a dark location for 3 days and in a lighted location for 4 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3. Selection of the seedling phase of selected M2 mutant rice varieties through saline soil in seedling trays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe second stage of the M2 generation focused on all seeds that could grow in the seedling salinity screening, including wild type and IR29. This method used trays with specifications identical to those used in the M generation study. The test method involved using trays with a NaCl concentration of 60 mM/L (3.50 g/L) NaCl or equivalent to 6 dS/m on the EC scale. As in the first stage, wild type and IR29 under normal germination conditions were also planted in trays without salinity treatment or normal conditions. Wild type and IR29 under salinity conditions were placed in each tray as a control for comparing tolerance between trays. Before the normally germinated seeds were transplanted, the transplanting medium was prepared using soil and compost in a 3:1 (v/v) ratio. The seeds that had been transferred to the germination trays were allowed to grow normally for 7 DAS. After this period, salinity stress was initiated by pouring 3 liters of NaCl solution, equivalent to 6.34 dS/m, onto the tray base. Each tray was placed on the tray base to stimulate salinity stress on the seedlings. Maintenance of the trays after NaCl application focused on maintaining water availability, so water was added periodically according to the solution limit at intervals of 3 to 15 days after treatment. After 15 days, the seedlings were observed, and the saline solution was replaced with normal water. Salinity adjustment and recovery were conducted over 5 days. Seedlings that survive and recover at this stage were proceeded to the third stage to evaluate the potential of mutant lines against the effects of salinity stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3. Evaluation of the response and impact of M2 mutant rice growth and production on salinity stress during the seedling-nursery phase.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe third stage in the M2 generation study was the evaluation of the adaptability potential of the mutant rice lines that have been selected in the second stage. The method used in this stage involves seeds that were adaptively recovered from salinity stress (score 1\u0026ndash;5) in pots and is carried out under non-saline conditions. In this stage, several comparison varieties were added, namely Inpari13, Padjajaran, and M70D, which were germinated and sown alongside the lines selected in stages 1 and 2 of the M2 generation. All these varieties were not subjected to salinity stress during the germination and sowing process or were independent. All maintenance and harvesting processes followed the cultivation concepts of the M1 generation.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Data Observation and Analysis\u003c/h2\u003e\n\u003cp\u003eThe observation parameters of this study consisted of three parts, namely germination, seedling cultivation, and growth and production evaluation. (1) Parameters in the germination phase focus on two characteristics, namely the percentage of germinated seeds (%) and plumule height (cm). Both parameters were measured at 7 DAS. (2) Parameters in the seedling phase also focus on two characteristics, namely the percentage of live seedlings (%), seedling height (cm), and tolerance score based on IRRI SES. Both observations were conducted after 15 days of salinity stress treatment. The growth and production phases were used for optimization and evaluation of the growth adaptability of mutant rice M1 and M2. For the M1 generation, the focus was on several characteristics, namely:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eGermination rate (%): percentage of seeds that germinate per number of seeds planted\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVigor percentage after transplanting (%): the number of plants that survive after eight days after planting (DAP).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePlant height (cm), measured from the base of the stem to the tip of the highest leaf (measured before harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNumber of productive tillers or number of panicles (stems or panicles), counting all tillers that produce panicles (observed before harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNumber of grains per panicle\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYield per plant (g), determined by weighing all grains on each plant.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGrowth evaluation observations at the M2 stage also include characters observed at M1 and additional growth characters. Additional observation characters at this stage include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eNumber of total tillers (stems), counting all tillers formed (observed during the primordial phase).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNumber of productive tillers or number of panicles (stems or panicles), counting all tillers that produce panicles (observed before harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFlag leaf length (cm), measured from the base of the flag leaf to the tip of the leaf, (observed before harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDays to flowering (DAP), calculated as the number of days from germination until flowering of each pot.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDays to harvest (DAP), calculated as the number of days from germination until harvest, marked by 80% of the plants turning yellow.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePanicle length (cm), measured from the base of the panicle to the tip of the panicle, (observed after harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNumber of total grains per panicle (grains), counting all filled and empty grains, (observed after harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePercentage of filled grains (%), the ratio between the number of filled grains and the total number of spikelets.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePercentage of empty grains (%), the ratio between the number of empty grains and the total number of spikelets.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNumber of panicle branches, counted as the number of branches on each panicle within a clump, (observed after harvest).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWeight of 100 grains (g), weighed 100 grains contained in each sample of the line and comparative variety at a moisture content of 14%.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe data analysis in this study was conducted in distinct stages for both the M1 and M2 generations. In the M1 generation, the analysis concentrated on radiosensitivity through regression analysis and the determination of the LD50. The LD50 was calculated using Curve Expert software [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, a biplot diversity analysis based on Principal Component Analysis was performed for this generation to identify patterns of diversity distribution among each mutant dose group, including the wild type [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. This analysis was executed using the Rstudio program and the factoextra package [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn the analysis of salinity screening during the M2 generation's sprout and seedling phases, the focus was on fitness analysis and the relative decline compared to the wild type. Additionally, broad-sense heritability analysis was conducted at both stages to estimate the extent of environmental influence on this study [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, the adaptability of M2 mutants to salinity stress was assessed by determining evaluation criteria through factor analysis and path analysis. Generally, the combination of factor analysis and path analysis was integral to structural equation modeling (SEM) [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], which emphasized the interrelationship between variables within a system. Factor analysis reduced variables by grouping correlated characteristics into principal factors [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], thereby forming a robust structure of relationships among relevant characteristics. Conversely, characteristics lacking significant correlations tend not to contribute to factor construction[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. This concept was further reinforced by path analysis, which determines a variable's potential to influence the total variance of a principal factor [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], in this case, biological yield. This combination enhanced the evaluation of the growth potential of adaptive lines in salinity screening of seedlings. The analyses were conducted using Minitab v 17 [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]and Rstudio with the Agricolae package [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. The evaluation criteria obtained were further analyzed through adaptability potential analysis by comparing the Least Significant Difference to the reference. The evaluation involved comparing the combination of mutant dose groups and their tolerance through SES scoring. This combination was further refined through PCA biplot analysis, employing the same concept as in generation M1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Analysis of the diversity of F37 rice line mutations to various doses of gamma irradiation in Generation M1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the lethal dose (LD) 50 mutation analysis of the F37 line are shown in Fig.\u0026nbsp;3. Based on this figure, the mutation dose at 2 weeks after sowing shows a linear pattern with the formula \u0026minus;\u0026thinsp;0.1071 gamma ray dose (GRD)\u0026thinsp;+\u0026thinsp;102.57. This formula has a determination coefficient of 0.86 and an LD₅₀ value at a dose of 502 Gy. Conversely, the mutation mortality response at 8 weeks after sowing showed a quadratic pattern with the formula 9x10-5GRD2- 0.182 GRD\u0026thinsp;+\u0026thinsp;91.143. This formula also has a high coefficient of determination (0.67) with an LD 50 at a dose of 273 Gy.\u003c/p\u003e\n\u003cp\u003eThe results of the analysis of growth response to the M1 mutant line based on gamma irradiation dose were shown in Fig.\u0026nbsp;4. The figure focused on four main characteristics, namely plant height, number of panicles per culm (NPC), percentage of filled grains, and biological yield. Based on plant height, the gamma ray dose response showed a negative quadratic pattern with a high coefficient of determination (0.987) and a peak at a dose of 254.67 Gy (125.31 cm). Based on the number of panicles, the gamma ray dose response showed a negative quadratic pattern with a fairly good coefficient of determination (0.73) and a peak at a dose of 400 Gy (15 panicles). Doses of 200 and 400 Gy exhibited relatively high variability compared to the wild type and the 600 Gy mutant. Based on the percentage of filled grains (PFG), the gamma ray dose response also showed a positive quadratic pattern with a minimum at 600 Gy (around 5%). The 200 Gy dose was the best mutant dose compared to other doses, with a potential percentage of 10%. Based on biological yield, the mutation dose response showed the same pattern as the PFG characteristics, namely a positive polynomial with the highest weight at the 200 Gy dose (5 g).\u003c/p\u003e\n\u003cp\u003eThe results of the principal component analysis of the overall growth characteristics in the M1 generation were shown in Fig.\u0026nbsp;5. The analysis maps the potential at each gamma irradiation dose and its wild type. Based on the figure, the 200 Gray dose exhibits the highest variability. The 400 Gy and 600 Gy doses showed variability that is partially overlapping with the variability of the 200 Gy dose mutant. However, the 400 Gy dose exhibited higher variability compared to the 600 Gy dose. In contrast, the wild type exhibited narrower diversity compared to 200 and 400 Gy. However, the range and position of the wild type's variance differed from those of its mutant diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Analysis of diversity and tolerance potential of F37 mutant rice lines of the M2 generation to systematic screening of salinity stress in the seedling phase.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tolerance and growth responses of M2 rice mutants to salinity stress screening in the seedling phase were shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on the table, normal IR29 had the highest percentage of germinating seeds, average plumule height, and fitness value. The F37_400Gy line had better germination percentage and fitness values than F37_200_Gy, F37_salin, and IR29_salin, but not better than F37_normal and IR29_normal. In terms of plumule height, the F37_400Gy line had higher average plumule height and fitness values than F37 200 and F37 saline. However, its potential was not higher than that of F37_normal, IR29_normal, and IR29_salin. The relative decrease for the comparison showed that IR29 experienced the highest relative decrease of 24% in UDK, and F37 experienced the highest relative decrease of 55.11% in plumule height when comparing normal and salinity treatments.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage results of germination rates and plumule height\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGenotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(Percentage of Live Seedlings)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePlumula Height\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValue (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFitness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRelative Decline (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValue (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFitness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRelative Decline (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 Normal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 Saline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR29 Normal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR29 Saline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eNote: F37 Normal\u0026thinsp;=\u0026thinsp;F37 not irradiated and without NaCl treatment, F37 Salin\u0026thinsp;=\u0026thinsp;F37 not irradiated with 100 mM/L NaCl salinity treatment, F37 200\u0026thinsp;=\u0026thinsp;F37 irradiated with 200 Gy and 100 mM/L NaCl salinity treatment,F37 400\u0026thinsp;=\u0026thinsp;F37 irradiated at 400 Gy with 100 mM/L NaCl salinity treatment, IR29 Normal\u0026thinsp;=\u0026thinsp;IR29 not irradiated and without NaCl treatment, and IR29 Salin\u0026thinsp;=\u0026thinsp;IR29 not irradiated with 100 mM/L NaCl salinity treatment.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the potential tolerance of the Mutant M2 rice line in screening for salinity stress in the seedling phase were shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on this table, F37 under normal conditions had the highest percentage of live seedlings and fitness value. IR29 under saline conditions showed the death of all seedlings that continued from the previous phase. The F37_200Gy line exhibited better seedling survival rates and fitness values than F37_400Gy, F37_salin, and IR29_salin. However, this potential was not better than that of F37_normal. In terms of seedling height, F37_400Gy had a higher average seedling height and fitness value than F37_200Gy, F37_normal, and F37_salin. The relative decrease for the comparison showed that F37 experienced a 91.31% decrease in the number of live seeds and a 7.89% decrease in seedling height.\u003c/p\u003e\n\u003cp\u003eThe heritability of both the cotyledon and seedling screening phases was shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on this table, the heritability of plumules in both mutant groups ranged from 28 to 36%. When classified, plumule height was categorized as moderate, both when compared to the potential of F37 under normal conditions and under saline conditions. Conversely, the heritability of seedling length showed values\u0026thinsp;\u0026gt;\u0026thinsp;50% or ranging from 67\u0026ndash;68%. This indicated that seedling length was categorized as high, both when compared to F37 under normal conditions and F37 under saline conditions.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage of live seedlings and seedling height\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGenotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(Percentage of Live Seedlings)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePlumula Height\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValue (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFitness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRelative Decline (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValue (cm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFitness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRelative Decline (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 Normal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 Saline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e91.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-34.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-39.73684211\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIR29 Saline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eNote: F37 Normal\u0026thinsp;=\u0026thinsp;F37 not irradiated and without NaCl treatment, F37 Salin\u0026thinsp;=\u0026thinsp;F37 not irradiated with 60 mM/L NaCl salinity treatment, F37 200\u0026thinsp;=\u0026thinsp;F37 irradiated with 200 Gy with 60 mM/L NaCl salinity treatment, F37 400\u0026thinsp;=\u0026thinsp;F37 irradiated at 400 Gy with a salinity treatment of 60 mM/L NaCl, and IR29 Salin\u0026thinsp;=\u0026thinsp;IR29 not irradiated with a salinity treatment of 60 mM/L NaCl.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHeritability of rice mutant line M2 for plumule height\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMutant Dosage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHeritability Comparison\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePlumula Height (cm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCriteria\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSeedling Length (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCriteria\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eF37 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSaline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eF37 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSaline\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: F37 200 (N)\u0026thinsp;=\u0026thinsp;F37 irradiated with 200 Gy compared to F37 not irradiated under normal conditions, F37 200 (S)\u0026thinsp;=\u0026thinsp;F37 irradiated with 200 Gy compared to F37 not irradiated under conditions of 100 mM/L NaCl salinity, F37400 (N)\u0026thinsp;=\u0026thinsp;F37 irradiated with 400 Gy compared to non-irradiated F37 under normal conditions, and F37400 (S)\u0026thinsp;=\u0026thinsp;F37 irradiated with 400 Gy compared to non-irradiated F37 under conditions of 100 mM/L NaCl salinity.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on the potential for salinity tolerance (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), F37_200Gy has the highest adaptive mutant potential with a percentage of 10.2% or 51 lines. These 51 lines are divided into three classes: highly tolerant (score 1\u0026thinsp;=\u0026thinsp;33 lines), tolerant (score 3\u0026thinsp;=\u0026thinsp;7 lines), and moderate (score 5\u0026thinsp;=\u0026thinsp;11 lines). Conversely, F37_400Gy had 30 adaptive mutants and was divided into three classes. The first class was highly tolerant (score 1) with 20 lines. The second class was tolerant (score 3) with 8 lines. Finally, the moderate class (score 5) consisted of two lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Analysis of the growth potential of components in pots from the Mutan M2 rice line, which is considered adaptive in the salinity screening phase of seedlings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe factor analysis focused on four factor dimensions based on the optimal percentage of variance, which reached 86% (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Based on this analysis, biological yield variability was found in factor 1 with a loading value of 0.26. This potential was also in line with the total number of tillers, the number of productive tillers, flowering age, harvest age, panicle length, and grain number per panicle. However, flowering age (0.08) and panicle length (0.05) have very low loading values below 0.1, so these two traits were not recommended for further analysis. Therefore, total tiller number, productive tiller number, flowering age, and grain number per panicle become the ideal criteria to proceed with in the path analysis of biological yield.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTolerance score of mutant rice M2 after salinity stress in the seedling phase\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTolerance Score\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eNumber of genotypes\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF37 200\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF37 400\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF37\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIR29\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e417\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of survival (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFitness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the path analysis were shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, which summarizes the potential diversity of 53%. Based on this table, the total number of tillers (0.83), the number of productive tillers (0.85), and the number of grains per panicle (0.71) have a high positive correlation with biological yield. On the other hand, harvest age has a negative correlation of -0.42 with biological yield. Based on its direct effect, the number of productive tillers has a dominant positive direct effect (1.30) on biological yield. This was followed by the number of grains per panicle, which has a positive direct effect of 0.29. Conversely, the total number of tillers has a large negative direct effect (-0.71) on biological yield, followed by flowering age (-0.14). Based on this, the number of productive tillers and the number of grains per panicle can be recommended as evaluation criteria alongside biological yield.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFactor analysis of growth characteristics of M2 mutant rice lines tolerant to salinity stress during the seedling stage\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCommunality\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePH (cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNTT (stems)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.34\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNPT (stems)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.34\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFLL (cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF (DAP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDH (DAP)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePL (cm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNTG (grains)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.22\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePFG (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eW100G (g)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYield\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVariance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% Var\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: PH\u0026thinsp;=\u0026thinsp;plant height, NTT\u0026thinsp;=\u0026thinsp;number of total tillers, NPT\u0026thinsp;=\u0026thinsp;number of productive tillers, FLL\u0026thinsp;=\u0026thinsp;flag leaf length, DF\u0026thinsp;=\u0026thinsp;days to flowering, DH\u0026thinsp;=\u0026thinsp;days to harvest, PL\u0026thinsp;=\u0026thinsp;panicle length, NTG\u0026thinsp;=\u0026thinsp;number of total grain, PFG\u0026thinsp;=\u0026thinsp;percentage of filled grains, W100G\u0026thinsp;=\u0026thinsp;weight of 100 grains\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePath analysis of selected traits against biological yield of M2 mutant rice lines that are tolerant to salinity stress\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDirect Effect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eIndirect Effect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCorrelation\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eJAT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eJAP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eJGPM\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe potential tolerance of mutant rice line M2 to the three main evaluation criteria was shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. In general, the classification in the salinity stress tolerance scoring showed that highly tolerant plants have better potential than tolerant and moderate classifications. Additionally, the potential of F37_200Gy performs better than F37_400Gy across all levels of salt stress tolerance. The F37_200Gy mutant line group with a score of 1 (highly tolerant) has the highest number of panicles (21.10) and a significant difference compared to the wild-type line under both conditions (saline\u0026thinsp;=\u0026thinsp;6, normal\u0026thinsp;=\u0026thinsp;9.64) and the M70D variety. This was also accompanied by a relative increase of 1.19 compared to the potential of the wild-type line under normal conditions. Additionally, this mutant group also exhibited a significantly better biological yield compared to the wild type under saline conditions, with a relative increase of 0.33. However, for the \u0026ldquo;number of total grains\u0026rdquo; trait, all mutant groups showed potential that was not better than the wild type under both conditions and compared to other reference varieties.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eperformance analysis of each group of putative salt-tolerant mutants based on evaluation criteria.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGamma Ray Doses\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTolerance score\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNumber of Panicles\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNumber of total grains\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBiological Yield\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esig/LSD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esig/LSD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003esig/LSD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37_200Gy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea,b,e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37_400Gy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e122.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37_200Gy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e154.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37_400Gy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37_200Gy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e128.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF37_400Gy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWT_sal (a)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e8.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e149.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e66.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e14.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWT_norm (b)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e173.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInpari 13 (c)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e182.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePadjajaran (d)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e139.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM70D (e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiobestari (f)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e149.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003eNote: sig/LSD: significant by least squares difference\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the analysis of the diversity potential of each mutant tolerance group according to the growth evaluation criteria were shown in Fig.\u0026nbsp;6. In general, all mutant groups overlap with each other. However, the diversity of the F37_200Gy mutant, both in the highly tolerant, tolerant, and moderate groups, showed a wide range of diversity. Nevertheless, the diversity of the highly tolerant F37_200Gy mutant showed the greatest diversity compared to other groups. In addition, most of the diversity range was not compressed or overlaps with other diversity groups. On the other hand, the F37_400Gy mutant group across all tolerance groups exhibits centralized diversity and overlaps with the diversity of other groups, except for line 38. Furthermore, the potential of the F37 wildtype showed a significantly different position compared to most mutant groups, particularly in relation to the highly tolerant F37_200Gy.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cb\u003e4.1. Potential diversity and assessment of the effectiveness of F37 rice line mutation to various doses of gamma irradiation in Generation M1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccording to this study, the pattern of lethal dose reduction in relation to germination percentage is linear, with the LD50 identified at a dose of 502 Gy. These findings align with the LD50 range presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the review report by [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, during the adaptation phase following transplantation, the reduction in lethal dose assumes a quadratic form, with the LD50 occurring at a dose of 273 Gy. This suggests that the LD50 observed in this study is relatively high compared to the general LD50 range. Nonetheless, when evaluating the percentage of surviving plants post-transplantation, the LD50 in this study also falls within the general LD50 range. The discrepancy in LD50 between germination percentage in trays and the percentage of surviving plants in pots indicates an accumulation of stress between the mutation treatment and transplantation, as also reported by [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Generally, mutation treatment results in a significant increase in reactive oxygen species (ROS) [\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These ROS can inflict damage on membranes, nucleic acids, and various cellular components, leading to plant tissue death [\u003cspan additionalcitationids=\"CR47 CR48 CR49\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This effect is further intensified by the transplantation process, which also contributes to increased ROS production. As noted by [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], environmental changes in plants can trigger ROS accumulation, resulting in an extremely high accumulation that shifts the stress status from elastic or linear to plastic or quadratic. Consequently, a dose of 273 Gy at the 8-week post-sowing or 6-week post-planting stage is deemed optimal for estimating the LD50 in the F37 double haploid mutant line.\u003c/p\u003e\u003cp\u003eAnalysis of the performance of mutant rice lines across various mutation doses reveals that mutation induction generally results in a reduction in the mean values of five critical growth traits, with the exception of plant height. This suggests that mutation induction significantly affects generative traits, a phenomenon corroborated by previous studies [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Typically, mutations induce physiological and genetic imbalances within plant tissues [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], which in turn alter the performance of rice plants, particularly in tiller formation. This results in a reduced number of tillers compared to the M0 or wild type [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The decrease in tiller number is directly correlated with the number of panicles produced, thus the observed reduction in panicles in this study is attributable to the mutation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Notably, the M1 mutant line exhibits a pronounced reduction in the percentage of filled grains, characterized by an exponential decline. This observation is consistent with previous reports [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The significant decrease in filled grains suggests that the mutation process induces genetic imbalances in the reproductive tissues of rice plants, thereby impairing optimal meiosis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Similar sterility due to mutations has been documented in other plant species, including black gram [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], wheat [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], \u003cem\u003ePachyrhizus erosus\u003c/em\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and Adlay [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Such disruptions in cell division can lead to genetic imbalances, culminating in seed sterility [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Consequently, the biological yield in mutant plants follows a similarly extreme decline in grain percentage. However, this pronounced decline also necessitates consideration of the potential genetic diversity generated.\u003c/p\u003e\u003cp\u003eThe results of the PCA biplot analysis indicate that most mutant lines exhibit distinct diversity compared to their wild-type counterparts. This suggests that the mutation process exerts differential effects on each seed, leading to a broad diversity that diverges from the potential of the wild type. This phenomenon has been similarly reported by [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Furthermore, the 200 Gray dose demonstrated the greatest diversity, despite the number of surviving plants not exceeding that of the 400 Gy dose. This suggests that the damage and induction of diversity at the 400 Gy dose were relatively more targeted compared to the 200 Gy dose, resulting in the diversity produced by the 200 Gy dose being more random and varied. The overall findings reinforce the assessment of performance across mutation dose groups. Consequently, the mutation process in this study is deemed effective and holds the potential to generate extensive genetic diversity in the M2 generation. This serves as the foundational basis for the development of mutant lines tolerant to salinity stress.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Potential tolerance of M2 mutant rice to systematic salinity stress during the seedling phase\u003c/h2\u003e\u003cp\u003eThe findings from the seedling phase tolerance screening indicate that the majority of lines exhibit resilience to this stage of stress. This is demonstrated by the variations in percentage and fitness between mutant lines and wild types under both normal and saline conditions. Generally, during the seedling phase, rice demonstrates a relatively high tolerance to salinity stress [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. This resilience enables rice seeds to endure relatively high salinity levels during the seedling phase. Nonetheless, the impact of salinity stress is apparent in the reduction of plumule height, with potential decreases of up to 50% compared to the wild type under normal conditions, as also reported by [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Salinity stress continues to impede cell division, allowing for a provisional estimation of tolerance potential based on plumule height potential [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. However, this estimation remains imprecise and complex, further complicated by the moderate heritability potential of the plumule. Consequently, the assessment of tolerance potential must be extended into the seedling phase.\u003c/p\u003e\u003cp\u003eAnalysis of the seedling phase screening results reveals a significant increase in mortality due to salinity stress, exceeding 50% across all mutant dose groups. Notably, IR29 and the wild-type F37 exhibited the highest mortality rates, surpassing 90%. Conversely, the 200 Gy mutant dose group demonstrated superior fitness relative to other groups. This observation suggests that stress during the seedling phase results in substantial stress accumulation, thereby diminishing the potential for salinity tolerance at this stage. As noted in [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], the seedling phase is a critical period for rice under salinity stress. Typically, hydroponic screening is employed during this phase [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. However, this study utilized soil screening in confined seedling trays, aligning with findings in [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], where screening in saline soil within small containers produced stress levels comparable to those in pot containers. This approach does not incorporate systematic stress during the seedling phase. Furthermore, the high potential of the seedlings indicates that mutant lines outperform their wild-type counterparts under both normal and saline conditions, supported by the high heritability of this trait. Collectively, these findings suggest that the selection methodology employed in this study is effective for selecting salt tolerance, particularly during the vegetative phase. Consequently, the selection approach presented here offers a novel method for extensively estimating salt tolerance, especially in large mutant populations.\u003c/p\u003e\u003cp\u003eAccording to salinity tolerance scores, the wild type F37 exhibits a markedly low level of tolerance in comparison to the mutant dose groups. Notably, the F37_200 Gy mutant group contains the highest number of lines that demonstrate adaptability to salinity stress, as indicated by Scores 1, 3, and 5. These findings align with the previously observed potential of M1, wherein mutants subjected to a 200 Gy mutation dose display greater diversity relative to other mutant dose groups, including the wild type. This suggests a synergistic interaction between the diversity potential of M1 and the diversity potential of M2 tolerance in systematic screening for salinity stress. Consequently, the 200 Gy irradiation dose is deemed optimal for enhancing rice tolerance to salinity stress.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.3. Growth and production performance in pots of M2 mutant lines assessed as adaptive in seedling screening\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSystematic evaluation of crop performance is essential, with a focus on production potential [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This approach aims to enhance the robustness of the evaluation process by first establishing evaluation criteria [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The determination of these criteria can be achieved through the application of multivariate analysis, incorporating both factor analysis and path analysis. This methodological combination has been documented in previous studies [\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. The integration of these analyses indicates that the number of productive tillers and the number of grains per panicle are significant determinants of productivity. These findings align with observations in M1, where both traits are correlated with productivity. Consequently, these two traits, along with productivity, can serve as evaluation criteria for assessing the growth and production of adaptive mutant rice lines during salinity screening at the seedling stage.\u003c/p\u003e\u003cp\u003eUpon evaluating the criteria obtained, it is evident that the number of productive tillers significantly impacts the differentiation of potential among tolerance groups. Generally, in rice cultivation, the concept of clumping positions productive tillers as a key indicator of productivity. An increase in the number of productive tillers correlates with enhanced productivity [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Furthermore, several studies have identified the number of productive tillers as a selection criterion for assessing rice tolerance and adaptability to salinity stress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan additionalcitationids=\"CR88 CR89 CR90\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. According to [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], rice subjected to salinity stress accumulates reactive oxygen species (ROS) in its leaves and main stem. Genotypes that are sensitive or less adaptive exhibit suboptimal ROS scavenging capacity, leading to increased ROS accumulation [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. This accumulation disrupts cell division and enzymatic processes in sensitive genotypes, compelling them to prioritize the stability of enzymatic processes in the main stem to avert mortality [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Consequently, this phenomenon inhibits tiller formation, which is associated with panicle development, resulting in fewer productive tillers in less adaptive sensitive genotypes compared to tolerant or adaptive genotypes. Therefore, the potential differences among salinity tolerance classes can be discerned through the number of productive tillers or panicles.\u003c/p\u003e\u003cp\u003eEvaluating the potential of each tolerance group, the 200 Gy mutant group demonstrated commendable adaptability following exposure to salinity stress during the seedling phase. This is evident from the overall potential of the 200 Gy mutant tolerance group (score 1\u0026ndash;3), which consistently surpassed that of the 400 Gy dose. This enhancement is significantly attributed to the increase in the number of productive tillers in the 200 Gy mutant. This observation underscores the pattern that rice tolerance to salinity stress is highly contingent upon a genotype's capacity to produce productive tillers. The potential of the 200 Gy mutant group is further corroborated by the diversity pattern observed in the PCA biplot analysis, where this group exhibits greater diversity compared to other groups, rendering selection within this mutant dose group effective. The comprehensive results presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;5. also suggest that salinity tolerance diversity in mutation breeding is influenced by the diversity pattern observed in the M1 generation. This phenomenon aligns with findings in soybeans [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]and cotton [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. When compared to the potential of the wild type, mutations with a dose of 200 Gy are considered to enhance tolerance and adaptability potential relative to its parent F37. The F37 line, as noted in [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], exhibits sufficient adaptive potential under salinity stress. However, this new screening concept with elevated stress levels results in moderate potential for the haploid F37 line. This indicates that the 200 Gy mutation dose can augment the adaptability potential of a rice genotype that is already sufficiently adaptive. Conversely, a sufficiently high dose (400 Gy) is suspected to significantly alter the potential of its wild type. This is consistent with the opinion of [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], where the higher the mutation dose, the greater the resulting diversity. However, the resulting diversity may not be related to adaptability to salinity stress. Therefore, the salinity tolerance potential of the 400 Gy dose is not as favorable as that of the 200 Gy group. Based on the aforementioned analysis, a mutation dose of 200 Gy for F37 rice seeds is recommended for forming a population with enhanced adaptability potential compared to the wild type, particularly in the development of adaptive and salt-tolerant rice varieties.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe research series determined that irradiation doses between 200 Gy and 400 Gy are optimal for inducing genetic diversity in rice populations. A high level of diversity observed in the M1 generation was also reflected in the M2 generation, with the 200 Gy dose yielding the greatest diversity across the M1-M2 generations. This dose is also deemed optimal for enhancing adaptability in the haploid F37 rice line, which exhibits sufficient resilience to salinity stress. In contrast, the 400 Gy dose is evaluated to have similar or reduced adaptability compared to the wild type. The study introduces a novel and effective method for selecting salt tolerance in mutant rice lines within large populations by combining vegetative screening with the seedling-seedling phase. Consistently productive seedlings are identified as the optimal selection criterion for determining rice salt tolerance. Thus, optimizing selection based on productive seedlings is crucial for detecting rice tolerance to salinity stress, particularly in the 200 Gy mutant rice population. The findings of this study should be extended to the M3 and M4 generations using a systematic shuttle breeding-based approach to enhance mutant tolerance to salinity stress.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDAP Days after planting\u003c/p\u003e\u003cp\u003eEC Electrical conductivity\u003c/p\u003e\u003cp\u003eFLL Flag leaf length\u003c/p\u003e\u003cp\u003eGy Gray\u003c/p\u003e\u003cp\u003eLD50 Lethal Dose 50%\u003c/p\u003e\u003cp\u003eM Mutant generation\u003c/p\u003e\u003cp\u003eNPC Number of panicles per culm\u003c/p\u003e\u003cp\u003eNPT Number of productive tillers\u003c/p\u003e\u003cp\u003eNTG Number of total grains\u003c/p\u003e\u003cp\u003ePCA Principal component analysis\u003c/p\u003e\u003cp\u003ePFG Percentage of filled grains\u003c/p\u003e\u003cp\u003ePL Panicle length\u003c/p\u003e\u003cp\u003eROS Reactive oxygen species\u003c/p\u003e\u003cp\u003eSEM Structural equation model\u003c/p\u003e\u003cp\u003eSES Standard evaluation system\u003c/p\u003e\u003cp\u003eW100G Weight of 100 grains\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eCompeting interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\u003cp\u003enot applicable\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe author(s) declare that financial support was received for the Ongoing Research Funding Program (ORF-2025-751), King Saud University, Riyadh, Saudi Arabia, is acknowledged. The study was also funded by the Indonesian Collaboration Research (Riset Kolaborasi Indonesia (RKI)) scheme A with number 01319/UN4.22/PT.01.03/2025\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, MFA, BSP, NC, ISD, WBS, SWA; Formal analysis, MFA; Funding acquisition. MFA, BSP, NC, MFS, MA; Methodology, MFA, BSP, ISD, WBS, SWA; Software, MFA, WBS; Data curation, MFA, BSP, NC, ISD, WBS, WMI; Investigation, MFA, AKB, RR: Resources, BSP, ISD; Supervision, BSP, NC, ISD, WBS, SWA, AHB: Validation, AHB, WMI, MA, NA, MFS; Visualization, MFA, AKB; Writing—original draft preparation, MFA; All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe support of the Ongoing Research Funding Program (ORF-2025-334), King Saud University, Riyadh, Saudi Arabia, is acknowledged. Besides that, we are grateful to Hasanuddin University for providing funding for this research through the Indonesian collaboration research (Riset Kolaborasi Indonesia (RKI) scheme A with number 01319/UN4.22/PT.01.03/2025\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data is available within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMohapatra PK, Sarkar RK, Panda D, Kariali E. Origin and Evolution of Rice as Domesticated Staple Food Crop. Tillering Behavior of Rice Plant. Singapore: Springer Nature Singapore; 2025. pp. 1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W, Zhao X. Understanding Global Rice Trade Flows: Network Evolution and Implications. Foods. 2023;12:3298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuluneh MG. Impact of climate change on biodiversity and food security: a global perspective\u0026mdash;a review article. Agric Food Secur. 2021;10:36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsiaka IO, Ndukwe KO, Chibuike US. Mean Sea Level: The Effect of the Rise in the Environment. J Appl Sci Technol Trends. 2023;4:94\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOelviani R, Adiyoga W, Suhendrata T, Bakti IGMY, Sutanto HA, Fahmi DA, et al. Effects of soil salinity on rice production and technical efficiency: Evidence from the northern coastal region of Central Java, Indonesia. Case Stud Chem Environ Eng. 2024;10:101010.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalgotra RK, Chauhan BS. Genetic Diversity, Conservation, and Utilization of Plant Genetic Resources. Genes (Basel). 2023;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Purwoko BS, Dewi IS, Ardie SW, Suwarno WB. A New Approach to Select Doubled Haploid Rice Lines under Salinity Stress Using Indirect Selection Index. Rice Sci. 2021;28:368\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Farid M, Nasaruddin, Musa Y, Iswoyo H, Sakinah AI, et al. Development of image-based phenotyping for selection characters of rice adaptability on the seedling salinity screening. IOP Conf Ser Earth Environ Sci. 2021;807:032022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Purwoko BS, Dewi IS, Suwarno WB, Ardie SW. Systematic selection to adaptive doubled haploid rice lines under different environments of submergence screening methods. J Agric Food Res. 2023;14:100775.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYali W, Mitiku T. Mutation Breeding and Its Importance in Modern Plant Breeding. J Plant Sci. 2022;10:64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamchander S, Ushakumari R, Arumugam Pillai M. Lethal dose fixation and sensitivity of rice varieties to gamma radiation. Indian J Agric Res. 2015;49:24\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNaibaho D, Purba E, Hanafiah DS, Hasibuan S. Radiosensitivity and effect of gamma ray irradiation on upland rice CV. Sidikalang. IOP Conf Ser Earth Environ Sci. 2021;782.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSolim MH, Rahayu S. Radiosensitivity of rice varieties of Mira-1 and Bestari mutants using gamma rays irradiation. IOP Conf Ser Earth Environ Sci. 2021;911.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiregar DS, Rosmayati, Hanafiah DS, Lubis K. Initial vegetative growth and determination of the lethal dose of local rice arias putih at various doses of gamma irradiation. IOP Conf Ser Earth Environ Sci. 2024;1352.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYunita R, Dewi IS, Lestari EG, Purnamanengsih R, Rahayu S. Mastur. Formation of upland rice drought-tolerant mutants by mutation induction and in vitro selection. Biodiversitas. 2020;21:1476\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaustino GP, Habana EJH, Malabanan-bauan KB, Abonitalla DB, Borromeo TH, Calayugan MIC. Identification of Median Lethal Dose (LD 50) and Shoot and Root Growth Reduction (GR 50) Dose in Four Rice Varieties Irradiated with Cobalt-60. Philipp J Sci. 2024;153:1899\u0026ndash;910.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRawat AS, Bhuj BD, Srivastava R, Chand S, Singh NK, Bisht YS, et al. Determination of lethal and mutation induction doses of gamma rays for gladiolus (Gladiolus grandifloras Hort.) genotypes. Heliyon. 2024;10:e37387.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDjarot P, Lestari EG, Anshori MF, Budiarto R. In vitro Culture and Field Evaluation of Two Generations of Sorghum Mutants Induced by Gamma Ray Irradiation. Asian J Plant Sci. 2021;20:648\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWarman B, Sobrizal S, Suliansyah I, Swasti E, Syarif A. Perbaikan Genetik Kultivar Padi Beras Hitam Lokal Sumatera Barat Melalui Mutasi Induksi. J Ilm Apl Isot dan Radiasi. 2016;11:125.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKadhimi AA, Alhasnawi AN, Isahak A, Ashraf MF, Mohamade A, Yusoffa WMW, Zaina CC. Gamma radiosensitivity study on MRQ74 and MR269, two elite varieties of rice. Life Sci J. 2016;13:85\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagaraj RA, Geethalakshmi V, Manonmani S, Ravikumar R, Murugananthi D, Bhuvaneswari K, et al. Comprehensive insights into the risks of climatic factors on rice production and its value chain- A Review. Plant Sci Today. 2024;11:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhannetah K, Pushpam R, Ganesan K, KK Kumar, Pillai C, Chandrashekar, et al. Appraising LD50 dosage for physical mutagen (Gamma rays) in CR1009 and CR1009 sub1 rice varieties.J \u0026hellip; 2715\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNjoroge W, Kinyua M, Gichuhi E, Ankamah-Yeboah T, Offei KS, Ofori K. Radio-Sensitivity of four selected rice (Oryza sativa L.) varieties in Kenya, as a pre-requisite for mutation breeding. Afr J plant Sci. 2022;16:210\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadriz-Mart\u0026iacute;nez M, Fern\u0026aacute;ndez-Acu\u0026ntilde;a A, Hernandez-Villalobos S, Orozco-Rodr\u0026iacute;guez R, Arg\u0026uuml;ello-Delgado J. Radiosensitivity of rice (Oryza sativa L. var CR5272) to gamma irradiation in Costa Rica. Cultiv Trop. 2022;43:8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuspita Sari H, Suliansyah I, Dwipa I, Hervani D. Orientasi Dosis Iradiasi Efektif Pada Perbaikan Genetik Padi (Oryza sativa L.) Lokal Padang Pariaman Melalui Mutasi Induksi. Produksi Tanam. 2023;011:408\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarlina G, Syarif A, Gustian G, Yusniwati Y. Orientation Of Effective Irradiation Dosage In Local Genetic Rice Improvement (Oryza sativa L.) Kuantan Singingi District Using Induced Mutation. J Agron Tanam Trop. 2024;6:119\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKakar N, Jumaa SH, Redo\u0026ntilde;a ED, Warburton ML, Reddy KR. Evaluating rice for salinity using pot-culture provides a systematic tolerance assessment at the seedling stage. Rice. 2019;12:57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarid M, Nasaruddin, Anshori MF, Musa Y, Iswoyo H, Sakinah AI. Interaction of rice salinity screening in germination and seedling phase through selection index based on principal components. Chil J Agric Res. 2021;81:368\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSakinah AI, Farid M, Musa Y, Hairmansis A, Anshori MF. Seedling Stage Image-Based Phenotyping Selection Criteria through Tolerance Indices on Drought and Salinity Stress in Rice. Plant Breed Biotechnol. 2024;12:43\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFatima S, Rashid M, Hameed A, Fiaz S, Rebouh NY, Zaman QU. Development of rice mutants with enhanced resilience to drought and brown spot (Bipolaris oryzae) and their physiological and multivariate analysis. BMC Plant Biol. 2025;25:1040.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdelsalam KMH, Shaalan AM, AbouEl-Soud GM, El-Dalil MAE, Marei AM, El-Moneim DA, et al. Comprehensive quality profiling and multivariate analysis of rice (Oryza sativa L.) cultivars: integrating physical, cooking, nutritional, and micronutrient characteristics for enhanced varietal selection. BMC Plant Biol. 2025;25:492.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSadimantara GR, Yusuf DN, Febrianti E, Leomo S. The performance of agronomic traits, genetic variability, and correlation studies for yield and its components in some red rice (Oryza sativa) promising lines. Biodiversitas J Biol Divers. 2021;22:3994\u0026ndash;4001.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgah LJ, Akpa EA, Ekpoto DF, Njoku EA, Obok EE, Ogbonna PE, et al. Multivariate Correlation and Fit for Path Analysis of 25 African Yam Bean (Sphenostylis stenocarpa) Accessions Using Structural Equation Models. Trop J Nat Prod Res. 2025;9:2886\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrestha N. Factor Analysis as a Tool for Survey Analysis. Am J Appl Math Stat. 2021;9:4\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshraf ATMH, Rahman MM, Hossain MM, Sarker U. Study of Correlation and Path Analysis in the Selected Okra Genotypes. Asian Res J Agric. 2020;:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhannam HA, I RA, Ibrahim MA, Mousa MI, Soliman AA. Genetic Analysis for Some Faba Bean Agronomic Traits. Sci J Agric Sci. 2024;6:72\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRathod S, Kumar AT. Statistical Procedures for Analyzing Agricultural Data using R. Hyderabad: ICAR- Indian Institute of Rice Research; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaldhani V, Suman K, Rao DS, Travis T, Rao IS, Rathod S, et al. Superior haplotypes to enhance grain filling in rice (Oryza sativa L.) identified through GWAS. Discov Plants. 2024;1:70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaadu R, Chong KP, Gansau JA, Zin MRM, Dayou J. A systematic review on physical mutagens in rice breeding in Southeast Asia. PeerJ. 2023;11:1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarding S. Effect of Gamma Rays on Seed Germination, Seedling Height, Survival Percentage and Tiller Production in Some Rice Varieties Cultivated in Sierra Leone. Am J Exp Agric. 2012;2:247\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSakai A, Nakanishi M, Yoshiyama K, Maki H. Impact of reactive oxygen species on spontaneous mutagenesis in Escherichia coli. Genes Cells. 2006;11:767\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRagu S, Faye G, Iraqui I, Masurel-Heneman A, Kolodner RD, Huang ME. Oxygen metabolism and reactive oxygen species cause chromosomal rearrangements and cell death. Proc Natl Acad Sci U S A. 2007;104:9747\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi H, Il, Han SM, Jo YD, Hong MJ, Kim SH, Kim JB. Effects of acute and chronic gamma irradiation on the cell biology and physiology of rice plants. Plants. 2021;10:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Liu S, Zhang Q, Cui M, Zhao M, Li N, et al. The interaction of ABA and ROS in plant growth and stress resistances. Front Plant Sci. 2022;13:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYanting L, Bingkui W, Mengchao Z, Jing Y, Shenghai Y. Sensitivity of genotypically diverse rice varieties to radiation and the related changes to antioxidant enzyme activities. Int J Radiat Biol. 2024;100:453\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarnese FS, Menezes-Silva PE, Gusman GS, Oliveira JA. When bad guys become good ones: The key role of reactive Oxygen species and Nitric Oxide in the plant responses to abiotic stress. Front Plant Sci. 2016;7:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang H, Ullah F, Zhou DX, Yi M, Zhao Y. Mechanisms of ROS regulation of plant development and stress responses. Front Plant Sci. 2019;10:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJuan CA, de la Lastra JMP, Plou FJ, P\u0026eacute;rez-Lebe\u0026ntilde;a E. The chemistry of reactive oxygen species (Ros) revisited: Outlining their role in biological macromolecules (dna, lipids and proteins) and induced pathologies. Int J Mol Sci. 2021;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanda SK, Gupta D, Patel M, Vyver C, Van Der, Koyama H. Functionality of Reactive Oxygen Species (ROS) in Plants: Toxicity and Control in Poaceae Crops Exposed to Abiotic Stress. Plants. 2024;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSood M. Reactive oxygen species (ROS): plant perspectives on oxidative signalling and biotic stress response. Discov Plants. 2025;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrivastava P, Kumar R. Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J Biol Sci. 2015;22:123\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerwibawa B, Sakhidin, Dwi Haryanto TA. Agronomic performances of aromatic and non-aromatic M1 rice under drought stress. Open Agric. 2019;4:575\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiyanto A, Oktaviani E, Wulansari NK, Haryanto TAD. Genetic parameters of yield component and yield in M1 rice (Oryza sativa L.) generation irradiated with gamma-ray. Kultivasi. 2024;23:187\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuliartini NWS, Adekayanti S, Sudharmawan AAK, Aryana IGPM. Effect of Mutations Induction on Vegetative and Generative Characters of G16 Rice (Oryza sativa L). J Penelit Pendidik IPA. 2023;9:3790\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHase Y, Satoh K, Kitamura S, Oono Y. Physiological status of plant tissue affects the frequency and types of mutations induced by carbon-ion irradiation in Arabidopsis. Sci Rep. 2018;8:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa L, Kong F, Sun K, Wang T, Guo T. From Classical Radiation to Modern Radiation: Past, Present, and Future of Radiation Mutation Breeding. Front Public Heal. 2021;9:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWidian J, Nurhidayah S, Rahayu S, Firmansyah E, Yunita R. Morphology Performance On Six Black Rice Accessions (Oryza Sativa L.) In M1 Generation Irradiated By Gamma Rays. JERAMI Indones J Crop Sci. 2021;3:62\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan R, Mao Y, Zhang D, Wang S, Zhang H, Wu M et al. The Formation of Rice Tillers and Factors Influencing It. Agronomy. 2024;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang N, Liu Y, Gui S, Wang Y. Regulation of tillering and panicle branching in rice and wheat. J Genet Genomics. 2025;52:869\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang R, Wang Y, Hussain S, Yang S, Li R, Liu S, et al. Study on the Effect of Salt Stress on Yield and Grain Quality Among Different Rice Varieties. Front Plant Sci. 2022;13:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Micco V, Arena C, Pignalosa D, Durante M. Effects of sparsely and densely ionizing radiation on plants. Radiat Environ Biophys. 2011;50:1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaplin N, Willey N. Ionizing radiation, higher plants, and radioprotection: From acute high doses to chronic low doses. Front Plant Sci. 2018;9:1\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamaiyan S, Vijayakumar P, Balasubramanian R alias, Nallathambi B. Influence of nano-zinc oxide and fortified rice residue compost on rice productivity, zinc biofortification, zinc use efficiency, soil quality, zinc fractions and profitability in different rice production systems. J Plant Nutr. 2023;46:4063\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNazarenko M. Parameters of winter wheat growing and development after mutagen action. Bull Transilv Univ Brasov Ser II Wood Ind Agric Food Eng. 2016;9:109\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDewi Hayati PK, Witari S, Rozen N, Sutoyo, Widiarsih S. Effect of gamma irradiation on the germination, pollen viability, and morpho-agronomic of Pachyrhizus erosus cv. Kota Padang Biodiversitas. 2022;23:1231\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdhi RK, Qosim WA, Rachmadi M, Damayanti F, Amien S, Anas, et al. Mutagenic Effectiveness and Efficiency of Gamma-rays Treatment on Adlay (Coix lacryma-jobi L). Int J Agric Biosci. 2024;13:683\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo H, Yan Z, Li X, Xie Y, Xiong H, Liu Y, et al. Development of a high-efficient mutation resource with phenotypic variation in hexaploid winter wheat and identification of novel alleles in the TaAGP.L-B1 gene. Front Plant Sci. 2017;8:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKitamura S, Satoh K, Oono Y. Detection and characterization of genome-wide mutations in M1 vegetative cells of gamma-irradiated Arabidopsis. PLoS Genet. 2022;18:1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdelhameed AA, Ali M, Darwish DBE, AlShaqhaa MA, Selim DAFH, Nagah A, et al. Induced genetic diversity through mutagenesis in wheat gene pool and significant use of SCoT markers to underpin key agronomic traits. BMC Plant Biol. 2024;24:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoang TML, Tran TN, Nguyen TKT, Williams B, Wurm P, Bellairs S, et al. Improvement of salinity stress tolerance in rice: Challenges and opportunities. Agronomy. 2016;6:1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh RK, Kota S, Flowers TJ. Salt tolerance in rice: seedling and reproductive stage QTL mapping come of age. Theor Appl Genet. 2021;134:3495\u0026ndash;533.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartatik S, Rozzita N, Wibowo S, Choirunnisa E, Sakanti SAS, Puspito AN, et al. Characterization of rice varieties under salinity level and the response of defense-related genes during the germination stage. Biodiversitas. 2024;25:1536\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShakri T, Che-Othman MH, Isa NM, Sukiran NL, Zainal Z. Morpho-Physiological and Stress-Related Gene Expression of Rice Varieties in Response to Salinity Stress at Early Vegetative Stage. Agric. 2022;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkahtani J, Dwiningsih Y. Analysis of Morphological, Physiological, and Biochemical Traits of Salt Stress Tolerance in Asian Rice Cultivars at Seedling and Early Vegetative Stages. Stresses. 2023;3:717\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaranova EN, Gulevich AA. Asymmetry of plant cell divisions under salt stress. Symmetry (Basel). 2021;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarid M, Nasaruddin, Anshori MF, Musa Y, Iswoyo H, Sakinah AI. Interaction of rice salinity screening in germination and seedling phase through selection index based on principal components. Chil J Agric Res. 2021;81:368\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng C, Liu C, Liu L, Tan Y, Sheng X, Yu D, et al. Effect of salinity stress on rice yield and grain quality: A meta-analysis. Eur J Agron. 2023;144:126765.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsei-Wusu MO, Adjei RR, Appiah KS, Ankamah-Yeboah T, Adusei-Fosu K. Advanced breeding strategies for combating rice salinity stress in Ghana: A critical review and future perspective. Sci Afr. 2025;29:e02784.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Musa Y, Farid M, Jayadi M, Bahrun AH, Yassi A, et al. A new concept in assessing adaptability index for superior potential cropping intensity in early-maturing rice. Front Sustain Food Syst. 2024;8:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMusa Y, Farid M, Nasaruddin N, Anshori MF, Adzima AF, Maricar MF et al. Sustainability approach in cropping intensity (CI) 400 through optimizing the dosage of compost and chemical fertilizers to early-maturing rice varieties based on multivariate analysis. J Agric Food Res. 2023;14 December:100907.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadjung R, Farid M, Musa Y, Nasaruddin N, Nurfaida N, Anshori MF et al. Yield and vegetation index of different maize varieties and nitrogen doses under normal irrigation. Open Agric. 2025;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Purwoko BS, Dewi IS, Suwarno WB, Ardie SW. Salinity tolerance selection of doubled-haploid rice lines based on selection index and factor analysis. AIMS Agric Food. 2022;7:520\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Musa Y, Farid M, Jayadi M, Padjung R, Kaimuddin K, et al. A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties. Front Plant Sci. 2024;15:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRidwan I, Farid M, Haring F, Widiayani N, Yani A, Amier N, et al. Optimized framework for evaluating F3 transgressive segregants in cayenne pepper. BMC Plant Biol. 2025;25:156.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan Y, Ding C, Zhang G, Hu J, Zhu L, Zeng D, et al. Genetic and environmental control of rice tillering. Crop J. 2023;11:1287\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalaitzidis A, Kadoglidou K, Mylonas I, Ghoghoberidze S, Ninou E, Katsantonis D. Investigating the Impact of Tillering on Yield and Yield-Related Traits in European Rice Cultivars. Agric. 2025;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnshori MF, Purwoko BS, Dewi IS, Ardie SW, Suwarno WB. Selection index based on multivariate analysis for selecting doubled-haploid rice lines in lowland saline prone area. Sabrao J Breed Genet. 2019;51:161\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArifuddin M, Musa Y, Farid M, Anshori MF, Nasaruddin N, Nur A, et al. Rice screening with hydroponic deep-flow technique under salinity stress. Sabrao J Breed Genet. 2021;53:435\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhatab IA, Farid MA, Abu Amo AG, El-Refaee YZ. Screening of some rice genotypes for salinity tolerance using agro-morphological and SSR markers. Chil J Agric Res. 2022;82:211\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRasheed A, Li H, Nawaz M, Mahmood A, Hassan MU, Shah AN, et al. Molecular tools, potential frontiers for enhancing salinity tolerance in rice: A critical review and future prospective. Front Plant Sci. 2022;13:1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaleem MA, Khan A, Tu J, Huang W, Liu Y, Feng N, et al. Salinity Stress in Rice: Multilayered Approaches for Sustainable Tolerance. Int J Mol Sci. 2025;26:1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZuo G, Zhang R, Feng N, Zheng D. Photosynthetic Responses to Salt Stress in Two Rice (Oryza sativa L.) Varieties. Agronomy. 2024;14:2134.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Q, Yang A, Zhang WH. Comparative studies on tolerance of rice genotypes differing in their tolerance to moderate salt stress. BMC Plant Biol. 2017;17:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTavu LEJ, Redillas MCFR. Oxidative Stress in Rice (Oryza sativa): Mechanisms, Impact, and Adaptive Strategies. Plants. 2025;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang R, Zheng D, Feng N, Linfeng L, Ma J, Yuan X et al. Effect of salt stress on different tiller positions in rice and the regulatory effect of prohexadione calcium. PeerJ. 2024;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHassan A, Naseer A, Shahani AAA, Aziz S, Khalid MN, Mushtaq N, et al. Assessment of Fiber and Yield Related Traits in Mutant Population of Cotton. Int J Agric Biosci. 2022;11:95\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"adaptation, double haploid, gamma radiation, mutation breeding, Oryza sativa, salinity tolerance","lastPublishedDoi":"10.21203/rs.3.rs-7431509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7431509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGamma radiation breeding represents a significant approach for developing salt-adapted rice varieties that are crucial for food security, particularly in archipelagic nations such as Indonesia. The generated mutation diversity is inherently random and requires a large population size. Optimizing mutations requires a systematic methodology that encompasses induction potential and selection processes. This can be accomplished by optimizing mutations in double-haploid lines and screening for salt tolerance within extensive mutant populations. This study aimed to optimize the mutagenic radiation dose in double haploid rice lines and develop a method for screening salt tolerance from the germination to seedling stage. The M1 generation underwent gamma ray irradiation at doses of 0, 200, 400, 600, 800, and 1000 Gy on the double haploid line HS4-15-1-63. The optimized M1 results advanced to M2 generation. This process was executed through salinity screening in three stages: germination under salt stress, seedling phase screening in saline soil, and evaluation of the growth and production of adaptive mutants. Research findings show that the optimal radiation dose for inducing diversity in Generation M1 ranges from 200 to 400 Gy, with 200 Gy yielding the highest diversity in Generations M1 and M2. A dose of 200 Gy enhanced the adaptability of HS4-15-1-63 to salt stress. The screening method integrating germination and seedling phases with saline soil in trays proved efficient. Therefore, the 200 Gy dose and this systematic selection concept are recommended for optimizing mutant adaptability from double haploid lines to salinity stress.\u003c/p\u003e","manuscriptTitle":"Integration of Gamma Irradiation Breeding from Doubled haploid and Systematic Screening to Develop Adapted Rice Mutants under Salinity Stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 18:23:41","doi":"10.21203/rs.3.rs-7431509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T07:25:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T18:03:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T14:50:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T16:31:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302671398326771837699598441584653494563","date":"2025-09-08T10:10:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25021915101256451200587744602021287846","date":"2025-09-08T09:04:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152782204604157705296931075598037310135","date":"2025-09-06T07:14:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319688902322315081432380043548700985191","date":"2025-09-06T03:03:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232309683416672815778831460832628598990","date":"2025-09-04T02:45:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127025471441010787246102017363422598365","date":"2025-09-03T11:53:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319975517687580838603867259566534689546","date":"2025-09-03T10:06:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T07:57:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T07:49:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-01T07:38:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-30T01:36:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-08-30T01:32:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b6dacb3-80ed-4668-8c50-b23dd06f1b14","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:02:54+00:00","versionOfRecord":{"articleIdentity":"rs-7431509","link":"https://doi.org/10.1186/s12870-025-07542-2","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-11-14 15:58:22","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-09-09 18:23:41","video":"","vorDoi":"10.1186/s12870-025-07542-2","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07542-2","workflowStages":[]},"version":"v1","identity":"rs-7431509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7431509","identity":"rs-7431509","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}