Diversity Analysis and Comprehensive Evaluation of Agronomic Traits in Perennial Chinese Rice Germplasm for breeding

Diversity Analysis and Comprehensive Evaluation of Agronomic Traits in Perennial Chinese Rice Germplasm for breeding | 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 Diversity Analysis and Comprehensive Evaluation of Agronomic Traits in Perennial Chinese Rice Germplasm for breeding Yongshu Liang, Junyi Gong, Yuxin Yan, Tingshen Peng, Jiuyan Lu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5794229/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Perennial Chinese rice is a novel type of rice germplasm native to China. This study comprehensively evaluated the variation in the agronomic traits of 20 perennial Chinese rice germplasm across four different planting seasons to explore the genetic diversity of perennial Chinese rice and effectively utilize them. A total of 16 agronomic traits, including heading date, plant height, and thousand-grain weight, were investigated based on the field phenotypic values. The findings revealed significant variations among these traits with a broad range of Shannon–Wiener indices, which ranged from 1.46 to 3.33 in 2021MC, 1.49 to 1.96 in 2021RC, 1.50 to 2.10 in 2022MC, and 1.31 to 2.10 in 2022RC. The coefficients of variation among 16 traits ranged from 4.40–64.34% in 2021 MC, 5.53–74.24% in 2021RC, 3.91–56.90% in 2022MC, and 3.55–92.57% in 2022RC. The 20 germplasm were divided into five distinctive clusters in 2021MC, 2021RC, and 2022MC and six distinctive clusters in 2022RC based on the analysis of hierarchical clustering, but divided into six categories by 13 pairs of SSR primers with good polymorphism. The M-TOPSIS exhaustive evaluation method based on correlation and the principal component analysis (PCA) of 16 traits was applied for the 20 germplasm, and the top one germplasm LN1 that displayed stable field performance on agronomic traits was screened repeatedly across four different planting seasons and adapted to the ecological areas of Chongqing in Southwest China. This study will provide a reference for the screening of potential perennial rice germplasm and the further research on perennial rice genetics and breeding. perennial Chinese rice agronomic traits genetic diversity comprehensive evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Rice ( Oryza sativa L) is one of the most important food crops throughout the world. It transformed from a perennial wild species into an annual cultivated species after nearly 10 000 years of artificial selection by humans [1]. It feeds more than half of the world’s population [2] and has been playing an important role in ensuring that China has a sustainable national food supply [3]. The production mode of annual crop, which necessitates ploughing every year, has generated a series of social, economic, and ecological environmental problems, such as large seed input, overused pesticides and fertilizers, increased agricultural machinery and tool input, water loss and soil erosion, nutrient imbalance, and soil structure and ecosystem damage. In particular, Chinese rice production is facing numerous challenges, including rapid urbanization, a declining number of farmers, widespread land abandonment, and the growing shortage of labor forces, leading to reductions in rice planting area [4]. In particular, rising labor costs increase rice production costs and further weaken the core competitiveness of the Chinese rice industry in the international agricultural market, potentially threatening Chinese food security [5]. Consequently, in China, how to develop a set of sustainable grain production technologies to ensure the balance between food and ecological security has become the current main issue involved in the implementation of rural revitalization strategies. Jackson and Glover argued that a shift from annual to perennial food crop production is one of the most feasible strategies for addressing the problems mentioned above [6–8]. Subsequently, a series of exploratory studies have been implemented on perennial Chinese rice variety breeding [9–14]. A successful hybrid between the wild perennial rice Oryza longistaminata and cultivated annual Asian rice RD23 has been reported [15]. Several perennial Chinese rice varieties have been successfully developed and commercially released to farmers [16–18]. The perennial Chinese rice variety is strongly preferred by farmers and exhibits numerous advantages in rice production over the preferred annual rice variety [19]. First, the perennial Chinese rice variety presents the advantage of stable field performance on grain yield and quality across multiple years and sites in Southern China and Laos. It shows an average yield of 6.8 Mg ha − 1 harvest − 1 versus the 6.7 Mg shown by replanted annual rice. Second, the perennial Chinese rice variety displays numerous advantages, such as cost savings and increased efficiency, in the international rice market. Moreover, it saves 58.1% of labor and 49.2% of input costs in each regrowth cycle. Importantly, the perennial Chinese rice variety has the advantage of maintaining important ecosystem functions. Perennial crops can increase nitrogen retention and soil carbon accumulation as a result of their permanent living cover and deep root systems [20–24]. In summary, the successful breeding and promotion of perennial Chinese rice varieties are attributed to the exploitation and innovative utilization of the novel rice germplasm O. longistaminata . Therefore, exploring a series of novel perennial rice germplasm is important prerequisite of the future perennial Chinese rice genetic and breeding projects. China is rich in rice germplasm resources. Tens of thousands of rice germplasms and thousands of commercially developed rice varieties exist in China. However, the perennial habit of the current commercially released Chinese rice varieties has been largely ignored and remains unknown. Consequently, comprehensively evaluating the field performance on agronomic traits and genetic diversity of perennial rice germplasm across different planting seasons is necessary for theirs further utilization. In our previous studies, a series of perennial Chinese rice germplasm were screened from existing Chinese cultivated rice varieties under the cold-winter environment [25, 49]. In this study, we investigated and comprehensively evaluated the phenotypic variation of sixteen agronomic traits and genetic diversity of the 20 perennial Chinese rice germplasm across two major crop (MC) seasons and two ratooning crop (RC) seasons in two consecutive years (2021 and 2022) and rank the investigated agronomic traits by both statistical analysis and molecular strategy. This study will be greatly beneficial for screening of potential perennial rice germplasm and commercial release of perennial rice varieties with stable field performance on agronomic traits across different planting seasons. It also provide a reference for the further utilization of perennial Chinese rice germplasm and even the genetic improvement of important agronomic traits, as well as a theoretical basis for the breeding of new perennial rice varieties in the future. 2. Materials and methods 2.1 Plant materials and experimental sites Over the past 5 years, we surveyed the perennial habit of 1034 Chinese rice cultivars collected throughout China [25]. We identified 20 perennial Chinese rice germplasm that could withstand cold tolerance to − 1°C of the daily minimum temperature for one day, 0 ℃ of the daily minimum temperature for four days, and 1 ℃ of the daily minimum temperature for four days in January 2021 throughout the cold–winter season of Chongqing province, China. These germplasm included Anhui (AH, 3), Hubei (HB, 3), Jiangsu (JS, 4), Jilin (JL, 1), Liaoning (LN, 2), Tianjin (TJ, 2), and Zhejiang (ZJ, 5). The germplasm, which sprouted from rice tillering nodes in March 2021, exhibited perennial characteristics (Figs. 1 A–D, 2 A–D, and 3 A–G) and were distributed in seven provinces of China at longitude 108°21′42ʹʹ–131°19′ and latitude 27°02'–46°18′ (Fig. 4 ). We performed a phenotypic evaluation of these perennial Chinese rice germplasm across the four different planting seasons in two consecutive years. The field experiment was conducted with a randomized complete block design and three replicates at the Biotechnology Testing Station of Chongqing Normal University at the altitude of 285.8 meters, University Town, Shapingba District, Chongqing, China (29°.52ʹ66.25ʹʹN and 106°.49ʹ25.08ʹʹE). In 2021MC, the seeds of the 20 germplasm were sown on 18 March 2021, and 35-day-old seedlings of all germplasm were transplanted into four-row plots with six plants per row with spacing of 20 cm × 30 cm between the plants and rows. In the fall, all germplasm were sampled for 16 agronomic traits evaluations. In 2021RC, 30 cm rice stubs of all tested germplasm were retained after autumn harvest in 2020MC. They survived through the natural cold winter season, germinated, flowered, and were harvested for the evaluation of these traits. In 2022MC, seeds collected from the 20 germplasms were sown on 9 March 2022, and 35-day-old seedlings of all germplasm were transplanted into four-row plots with six plants per row with spacing of 20 cm × 30 cm between plants and rows. In 2022RC, after autumn harvest in 2021MC, 30 cm stubs of all tested rice germplasm were retained. They survived through the natural cold winter season, germinated, flowered, and were harvested for grain yield and quality evaluation (Figs. 1 A–D and 2 A–D). All germplasm were sampled for the evaluation of these traits. A special rice compound fertilizer, including pure N 150–180 kg hm − 2 , 100 kg hm − 2 P 2 O 5 , and 150 kg hm − 2 K 2 O, was selected as the base fertilizer and applied before rice seedling transplantation. Pure N (75–150 kg hm − 2 ) was applied at 2 weeks after seedling transplantation. The water management strategy was involved into applying shallow water at the tillering stage and flooding at midseason with drainage–reflooding–moist intermittent irrigation, timely sunning of the field, and control of ineffective tillering. Weed control, pest management, and disease treatment were conducted in accordance with local conventional high-yield cultivation. 2.2 Measurement of agronomic traits A random sample of five plants per plot within each germplasm for each replication across 2021MC, 2021RC, 2022MC, and 2022RC was collected to measure phenotypic agronomic traits in accordance with the method described by Shen (1995) with some modifications [26]. In this study, 16 agronomic traits, including 13 quantitative traits and three derived traits, were measured. The field phenotypic data of each agronomic trait of five plants within each individual genotype with three replicates were calculated for statistical analysis. 2.2.1 Measurement of agronomic traits Data on 11 agronomic traits, including nine quantitative traits and two derived traits, were evaluated. Nine quantitative traits traits included heading date (HD, days), plant height (PH, cm), panicle number plant − 1 (PP), panicle length (PL, cm), filled grain number panicle − 1 (FGP), empty grain number panicle − 1 (EGP), spikelet number panicle − 1 (SP), grain yield major panicle − 1 (GYMP, g), and grain yield plant − 1 (GYP). Two derived traits were calculated as follows: grain setting rate (GSR) = (FGP/SP) × 100% and grain setting density (GSD) = SP/PL. 2.2.3 Measurement of grain shape traits Grain shape traits, including grain length (GL, mm), grain width (GW, mm), and grain thickness (GT, mm), were measured by using a Mitutoyo absolute digimatic caliper with a precision of 0.01 mm (model 500 − 173). Thousand-grain weight (TGW) was measured by using an electronic balance with a precision of 0.01 g. The derived trait length-to-width ratio (LWR) was calculated as GL/GW. 2.3 DNA extraction and PCR amplification The 20 perennial Chinese rice germplasm and two sequenced rice varieties Nipponbare and 9311 were sampled at the rice tillering stage. Nipponbare and 9311 were selected as the control DNA. Rice genomic DNA was extracted in reference to the sodium dodecyl sulfate method described by Cuthbert (2008) with some modifications [27]. Thirty pairs of simple sequence repeat (SSR) primers with good polymorphisms were employed to evaluate the genetic diversity of the 20 perennial Chinese rice germplasm. PCR amplifications were performed in reference to the protocol described by Greer (2008) with some modifications [28]. DNA products were separated by using 8% polyacrylamide gel electrophoresis. 2.4 Statistical analysis All experiments were performed in triplicate. All phenotypic agronomic trait data collected from all tested rice germplasm across 2021MC, 2021RC, 2022MC, and 2022RC were juxtaposed in Microsoft Excel 2010 for statistical analysis. All phenotypic agronomic traits were classified into 10 grades: grade 1 X + 2σ. Each interval between grades 1 and 10 was 0.5σ. X and σ are the mean and standard deviation, respectively. Morphological diversity was evaluated on the basis of the frequency of these trait dispersion and Shannon–Wiener diversity index ( H ʹ). The statistics of agronomic trait parameters were measured. They included mean, standard deviation (SD), coefficient of variation (CV %), phenotypic correlation coefficients (PCCs), and H′ . The H′ for each trait was calculated by using the formula H′ = −∑ Pi ln Pi , where Pi is the proportion of the individual number of this trait in the total number of individuals [29]. The CV for all traits was calculated as CV = S /X, where S is the standard deviation, and‾X is the mean [30]. IBM SPSS Statistics version 20.0 software (SPSS Inc., Chicago, IL, USA) was employed to perform multiple comparison analysis and principal component analysis (PCA). Multiple comparison analysis was conducted on the basis of Duncan’s new multiple range method at the 0.05 probability. PCCs among the 16 traits were obtained on the basis of Pearson correlation coefficients by using IBM SPSS Statistics 20.0 version software. A correlation heat map was drawn by applying OrigniLab OriginPro2021 version. 0 and 1 represent nonamplified and amplified bands, respectively, in PCR amplification with 13 pairs of SSR primers with good polymorphism and were used to arrange molecular data in Microsoft Excel 2010. All data on the phenotypic agronomic trait were normalized to 0 or 1 for cluster analysis with NTSYSpc software version 2.10. Cluster analysis was performed by using the unweighted pair-group method with arithmetic mean in NTSYSpc software version 2.10 (Applied Biostatistics, Port Jefferson, New York, USA). A clustering heat map was drawn by using iTOL software ( https://itol.embl.de/ ). 3. Results 3.1 Variation characteristics of the agronomic traits in perennial Chinese rice germplasm The 16 agronomic traits of the 20 perennial Chinese rice germplasm exhibited an irregular variance in terms of field phenotypic values and high phenotypic diversity across four different planting seasons and were sensitive to external environmental factors (Fig.5; Tables 1 and S1–16). The 16 traits of the 20 germplasm presented wide phenotypic variation with CV% values that ranged from 4.34% for GL to 64.34% for EGP in 2021MC, from 4.61% for GL to 74.24% for EGP in 2021RC, from 3.91% for GL to 56.90% for EGP in 2022MC, and from 3.55% for GL to 82.57% for EGP in 2022RC. Moreover, they demonstrated high phenotypic diversity and high H' values that ranged from 1.46 for HD to 3.33 for SP in 2021MC, from 1.49 for HD and GSR to 1.96 for GL in 2021RC, from 1.5 for HD to 2.20 for PP in 2022MC, and from 1.31 for TGW to 1.96 for GYP in 2022RC. Among the individual agronomic traits in the 20 germplasm across four different planting seasons, four agronomic traits (PH, GL, GW, and LWR) displayed relatively stable field performance values with CV values of less than 10% that ranged from 7.53% in 2022RC to 9.25% in 2021RC; from 3.55% in 2022RC to 4.61% in 2021RC; from 5.42% in 2022MC to 7.46% in 2021RC; and from 5.98% in 2022MC to 7.42% in 2022RC, respectively. The remaining 12 traits (HD, PP, PL, FGP, EGP, SP, GSR, GSD, GYMP, GYP, GT, and TGW) displayed wide phenotypic variation with CV values beyond 10% that ranged from 12.64% in 2021RC to 16.62% in 2022RC; from 20.12% in 2022MC to 25.75% in 2022RC; from 14.86% in 2021MC to 18.90% in 2022RC; from 19.09% in 2021MC to 52.14% in 2022RC; from 56.90% in 2021MC to 92.57% in 2022RC; from 14.88% in 2021MC to 26.90% in 2022RC; from 8.28% in 2022MC to 39.83% in 2022RC; from 16.22% in 2021MC to 27.81% in 2022RC; from 24.08% in 2021MC to 61.81% in 2022RC; from 25.05% in 2022MC to 75.67% in 2021RC; from 4.90% in 2022MC to 21.52% in 2022RC; and from 9.15% in 2021RC to 45.73% in 2022RC, respectively. Among the individual germplasm evaluated 16 agronomic traits across four different planting seasons, three germplasm (LN1, LN2, and ZJ5) had more than 10 stable field performance on agronomic traits with CV values of less than 10%. Among these germplasm, LN1 displayed relatively stable field performance values for 13 traits (HD, PH, PL, FGP, SP, GSR, GSD, GYMP, GL, GW, GT, LWR, and TGW) with CV values that ranged from 1.67% for GSR to 6.23% for HD. However, it had unstable field performance values for three traits (PP, EGP, and GYP) with CV values beyond 10% that ranged from 28.82% for EGP to 40.78% for GYP. LN2 displayed a relatively stable phenotypic value for 12 traits (FGP, HD, PH, PL, GSD, GSR, GYMP, GL, GT, GW, LWR, and TGW) with CV values that ranged from 1.73% for GSR to 9.93% for FGP. However, it had unstable phenotypic values for four traits (PP, EGP, SP, and GYP) with CV values beyond 10% that ranged from 10.22% for SP to 65.83% for GYP. ZJ5 displayed stable phenotypic values for 11 traits (HD, PH, PL, SP, FGP, GL, GW, LWR, GT, GSR, and GSD) with CV values that ranged from 1.37% for GL to 9.14% for FGP. Nevertheless, it exhibited unstable phenotypic values for five traits (PP, EGP, GYMP, GYP, and TGW) with CV values beyond 10% that ranged from 19.45% for TGW to 57.23% for GYP. Four germplasm (HB1, JS2, JS3, and JS4) had more than 10 unstable traits with CV values beyond 10%. Among them, JS3 displayed unstable phenotypic values for 11 traits (EGP, FGP, GSD, GYMP, GYP, PL, PP, SP, GSR, TGW, and GT) with CV values ranging from 10.30% for PL to 59.94% for GYP. However, it presented stable phenotypic values for five traits (HD, PH, GL, GW, and LWR) with CV values of less than 10% that ranged from 2.04% for GL to 6.83% for HD. HB1 displayed unstable phenotypic values for 10 traits (PP, EGP, FGP, SP, GSD, GSR, GYMP, GYP, GT, and TGW) with CV values that ranged from 10.89% for GT to 60.45% for GYP. Nevertheless, it had stable phenotypic values for six traits (HD, PH, PL, GW, GL, and LWR) with CV values of less than 10% that ranged from 3.16% for GL to 8.23% for HD. JS2 displayed unstable phenotypic values for 10 traits (PP, EGP, FGP, SP, GSD, GSR, GYMP, GYP, GT, and TGW) with CV values that ranged from 14.42% for SP to 92.09% for GYP. However, it exhibited stable phenotypic values for six traits (HD, PH, PL, GL, GW, and LWR) with CV values of less than 10% that ranged from 1.64% for GL to 8.75% for PH. JS4 displayed unstable phenotypic values for 10 traits (PP, EGP, FGP, SP, GSR, GYMP, GYP, GT, LWR, and TGW) with CV values that ranged from 10.16% for LWR to 66.70% for FGP. Nevertheless, it had stable phenotypic values for six traits (HD, PH, PL, GSD, GL, and GW) with CV values of less than 10% that ranged from 1.47% for GL to 9.69% for GSD. 3.2 Correlation analysis Altogether, 346 pairs of significant relationship values among the 16 agronomic traits in the 20 perennial Chinese rice germplasm across four different planting seasons were estimated on the basis of correlation analysis and Pearson correlation coefficients (Fig.6 A–D; Table 2). In 2021MC, 83 pairs of significant PCC values, including 53 pairs of positive correlations and 30 pairs of negative correlations, were identified in the 20 germplasm (Fig.6 A; Table 2). Among them, PH had significantly positive correlations with PH, PP, EGP, GYP, GL, GW, and GT with coefficients that ranged from 0.27 to 0.65 but had significantly negative correlations with FGP, GSR, and GYMP with coefficients that ranged from −0.28 to −0.67. PP had significantly negative correlations with PL, FGP, SP, GSR, GSD, GYMP, GW, GT, and TGW with coefficients that ranged from −0.22 to −0.58. SP presented significantly positive correlations with PL, FGP, EGP, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.16 to 0.86. GW showed strong positive correlations with HD, PH, PL, FGP, EGP, SP, GYMP, GT, and TGW with coefficients that ranged from 0.19 to 0.78. TGW had significantly positive correlations with PL, FGP, EGP, SP, GYMP, GYP, GL, GW, and GT with coefficients that ranged from 0.17 to 0.55. GYMP exhibited significantly positive correlations with PL, FGP, SP, GSR, GSD, GYP, GW, GT, and TGW with coefficients that ranged from 0.15 to 0.91. GYP had significantly positive correlations with HD, PP, FGP, SP, GSD, GYMP, and TGW with coefficients that ranged from 0.15 to 0.51. However, in 2021RC, 85 pairs of significant PCC values among the 16 traits in the 20 germplasm, including 41 pairs of positive correlations and 44 pairs of negative correlations, were calculated (Fig.6 B; Table 2). HD had significantly negative correlations with the nine traits PH, PP, PL, FGP, SP, GSR, GYMP, GYP, and TGW with coefficients that ranged from −0.17 to −0.53. FGP had significantly positive correlations with PH, PP, PL, SP, GSR, GSR, GSD, GYMP, and GYP with coefficients that ranged from 0.32 to 0.86. TGW exhibited significantly positive correlations with GYMP, GL, GW, LWR, and GT with coefficients that ranged from 0.27 to 0.57. GYMP demonstrated significantly positive correlations with PP, PL, FGP, SP, GSD, GSR, GYP, and TGW with coefficients that ranged from 0.25 to 0.70. GYP had significantly positive correlations with PP, PL, FGP, SP, GSR, and GYMP with coefficients that ranged from 0.27 to 0.71 but had significantly negative correlations with HD, EGP, GL, GW, GT, and TGW with coefficients that ranged from −0.20 to −0.45. In 2022MC, 88 pairs of significant PCC values among the 16 traits in the 20 germplasm, including 49 pairs of positive correlations and 39 pairs of negative correlations, were calculated (Fig.6 C; Table 2). HD had significantly negative correlations with PL, FGP, SP, GSR, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from −0.15 to −0.69. PL presented significantly positive correlations with PH, FGP, SP, GSR, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.16 to 0.52. FGP had significantly positive correlations with PH, PL, SP, GSR, GSD, GYMP, GYP, GT and TGW with coefficients that ranged from 0.26 to 0.92. GYMP exhibited significantly positive correlations with PH, PL, FGP, SP, GSR, GSD, GYP, GT, and TGW with coefficients that ranged from 0.21 to 0.91. GYP had significantly positive correlations with PL, FGP, SP, GSR, GSD, GYMP, GL, GW, GT, and TGW with coefficients that ranged from 0.19 to 0.70 but had significantly negative correlations with HD and EGP, with coefficients of −0.56 and −0.34. However, in 2022RC, 90 pairs of significant PCC values among the 16 traits in the 20 germplasm, including 55 pairs of positive correlations and 35 pairs of negative correlations, were calculated for the 20 germplasm (Fig.6 D; Table 2). HD exhibited significantly negative correlations with PH, PL, FGP, SP, GSR, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from −0.16 to −0.72. PH had significantly positive correlations with PL, FGP, SP, GSR, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.23 to 0.68. FGP presented significantly positive correlations with PH, PL, SP, GSR, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.31 to 0.95. EGP had significantly negative correlations with PH, PP, PL, FGP, GSR, GYMP, GYP, GL, GW, GT, and TGW with coefficients that ranged from −0.33 to −0.96. GYMP showed significantly positive correlations with PH, PL, FGP, SP, GSR, GSD, GYP, GW, GT, and TGW with coefficients that ranged from 0.33 to 0.95. GYP had significantly positive correlations with PH, PP, PL, FGP, SP, GSR, GSD, GYMP, GW, GT, and TGW with coefficients that ranged from 0.16 to 0.83 but had significantly negative correlations with HD, EGP, GL, and LWR with coefficients of −0.26 and −0.50. In summary, the number, direction, and size of significant PCC values among the sixteen traits of the 20 germplasm across four different planting seasons displayed a series of irregular variations and were easily affected by external environmental factors. 3.3 Cluster analysis In this study, hierarchical clustering was performed to analyze the relationships among the 20 perennial Chinese rice germplasm based on the field performance of the 16 agronomic traits across four different planting seasons (Fig.7 A–D). In 2021MC, all germplasm was divided into five distinctive clusters on the basis of phenotypic traits (Fig.7 A). Cluster I contained two germplasm (LN1 and AH2) with a large phenotypic value for PL. Cluster II consisted of three germplasm (JS3, ZJ2, and ZJ5) with a large phenotypic value for HD. Cluster III contained six germplasm (LN2, AH1, TJ1, AH3, ZJ3, and JL1) with a large phenotypic value for GSR and a small phenotypic value for EGP, and was split into two sub-clusters. The first sub-cluster in cluster III consisted of three germplasm (LN2, AH1, and TJ1) with large phenotypic values for FGP and SP and a small phenotypic value for HD. The second sub-cluster in cluster III also consisted of three germplasm (AH3, ZJ3, and JL1) with small phenotypic values for FGP and SP. Cluster IV also contained three germplasm (TJ2, ZJ4, and JS2) with a large phenotypic value for FGP. Cluster V contained six germplasm (JS4, ZJ1, HB1, HB2, HB3, and JS1) with a large phenotypic value for EGP. However, in 2021RC, all germplasm can be assigned to five distinctive clusters (Fig.7 B). Cluster I consisted of three germplasm (ZJ1, ZJ5, and JS3) with large phenotypic values for HD and FGP. Cluster II consisted of four germplasm (ZJ4, ZJ2, ZJ3, and HB1) with large phenotypic values for HD and EGP. Cluster III contained four germplasm (JS2, HB3, JS1, and AH3) with large phenotypic values for PH and PP. Cluster IV consisted of three germplasm (LN1, LN2, and AH1) with a large phenotypic value for GSR. Cluster V contained six germplasm (TJ2, TJ1, JS4, JL1, HB2, and AH2) with large phenotypic values for EGP and GYP. In 2022MC, all germplasm can be assigned to five distinctive clusters based on phenotypic value on agronomic traits (Fig.7 C). Cluster I contained only one germplasm (TJ1) and was distinguished by a large phenotypic value for TGW and a small phenotypic value for GSD. Cluster II consisted of six germplasm (LN2, LN1, AH1, JS1, JS3 and AH3) with large phenotypic values for GSD and GYP. Cluster III contained two germplasm (TJ2 and JS2) with a large phenotypic value for PP and a small phenotypic value for SP. Cluster IV also consisted of two germplasm (JL1 and AH2) with a large phenotypic value for GYP and PL. Cluster V only contained nine germplasm (HB3, ZJ5, ZJ3, HB2, HB1, ZJ4, ZJ1, JS4, and ZJ2) with large phenotypic values for HD and EGP and small phenotypic values for GSR and TGW. However, in 2022RC, all germplasm can be assigned to six distinctive clusters (Fig.7 D). Cluster I contained only one germplasm (LN1) with large phenotypic values for PH, PL, SP, GSR, and GYP. Cluster II also consisted of only one germplasm (JS2) with a large phenotypic value for EGP and small phenotypic values for GSR, GYMP, GYP, GT, and TGW. Cluster III contained two germplasm (AH3 and HB1) with large phenotypic values for PH and PP. Cluster IV consisted of two germplasm (AH1 and ZJ3) with a large phenotypic value for PP and a small phenotypic value for PH. Cluster V consisted of five germplasm (ZJ5, TJ2, TJ1, ZJ4, and ZJ1) with large phenotypic values for GYP and GSR and a small phenotypic value for EGP. It was split into two sub-clusters. The first sub-cluster of V cluster consisted of two germplasm (ZJ5 and TJ2) with a large phenotypic value for PL and a small phenotypic value for PP. The second sub-cluster of cluster V also consisted of three germplasm (TJ1, ZJ4, and ZJ1) with a large phenotypic value for PP and small phenotypic values for FGP and SP. Cluster VI consisted of nine germplasm (JS1, HB2, ZJ2, JS4, AH2, LN2, JS3, JL1, and HB3) with a large phenotypic value for EGP and a small phenotypic value for TGW and was split into two sub-clusters. The first sub-cluster of cluster VI consisted of three germplasms (JS1, HB2, and ZJ2) with a large phenotypic value for PL and a small phenotypic value for TGW. The second sub-cluster of cluster VI also consisted of six germplasm (JS4, AH2, LN2, JS3, JL1, and HB3) with large phenotypic values for PP, GSD, and GYP and small phenotypic values for GYMP and TGW. In summary, the field phenotypic values on sixteen phenotypic traits in the 20 germplasm that directly determined the number of cluster and cluster contained germplasm were easily affected by external environmental factors across four different planting seasons. 3.4 Genetic diversity analysis The 20 perennial Chinese rice germplasm, Nipponbare, and 9311 were roughly divided into five clusters by 13 pairs of SSR primers with good polymorphism (Fig.8; Table 3), and exhibited abundant genetic diversity between indica and japonica rice. Cluster I contained only one indica rice germplasm (9311) and exhibited significant genetic differences from the 20 germplasm and Nipponbare. Cluster II consisted of six germplasm (JL1, HB3, HB2, LN2, TJ2, and TJ1) and was divided into three sub-clusters. Only one germplasm (JL1) clustered into the first sub-cluster II, two germplasm (HB3 and HB2) clustered into the second sub-cluster II, and three germplasm (LN2, TJ2, and TJ1) clustered into the third sub-cluster II. Cluster III consisted of only one germplasm (ZJ1). Cluster IV contained five germplasm (JS1, AH2, LN1, JS3, and ZJ5) was divided into two sub-clusters. The two germplasm (JS1 and AH2) clustered into the first sub-cluster IV. The three germplasm LN1, JS3, and ZJ5 clustered into the second sub-cluster IV. Cluster V contained nine germplasm (ZJ2, AH1, Nip, JS4, JS2, AH3, ZJ3, HB1, and ZJ4) , which clustered into three sub-clusters. The three germplasm (ZJ2, AH1, and Nipponbare) clustered into the first sub-cluster V. The six germplasm (JS4, JS2, AH3, ZJ3, HB1, and ZJ4) clustered into the second sub-cluster V. In summary, 13 pairs of SSR primers can accurately distinguish between indica rice 9311, japonica rice Nipponbare, and 20 perennial Chinese rice germplasm (Fig. 9). 3.5 Principal component analyses The PCA graph of the 20 perennial Chinese rice germplasm was obtained to demonstrate the distribution of germplasm in accordance with the differences on the field phenotypes of traits. PCA revealed distinctive separations among the 20 germplasm in accordance with phenotypic traits. Therefore, these agronomic phenotypic parameters can be used as essential criteria for defining perennial rice germplasm. PCA was performed to identify the main distinguishing characters of the 16 agronomic traits. The dimensions implied by the 16 quantitative agronomic traits was reduced to five significant components in 2021MC, accounted for 85.38% of the total variance comparised of EGP, SP, GYP, PH, and GL (Fig.10 A;Table 4), so it was referred to as grain yield factor and plant height factor. The first six significant components in 2021RC, accounted for 86.34% of the total variance comparised of FGP, GSD, GSR, PL, LWR, and GYP (Fig.10 B;Table 4) , so it was referred to as grain yield factor. The first six significant components in 2022MC, accounted for 93.35% of the total variance comparised of GYMP, GSR, GW, GL, PP, and GT (Fig.10 C; Table 4) , so it was referred to as grain yield factor. The first four significant components in 2022RC accounted for 84.47% of the total variance comparised of FGP, SP, GL, and GW, so it was referred to as grain yield factor (Fig.10 D; Table 4). The results of PCA indicated that the number of siginificant principal components and the principal factors displayed a series of irregulal variations across four different planting seasons.` 3.6 M-TOPSIS comprehensive evaluation The TOPSIS method is widely used in multi-objective decision analysis and comprehensively employed to evaluate germplasm [31–32]. It is a ranking method that is based on the similarity between a limited number of objects and an idealized target. It is used to evaluate the relative merits and demerits of existing objects. In this study, 16 agronomic traits, including HD, PH, GYP, and TGW, were employed to evaluate the 20 perennial Chinese rice germplasm on the basis of correlation and principal component analysis (Table 5). Subsequently, a comprehensive evaluation model of perennial Chinese rice germplasm was constructed by M-TOPSIS. The top one germplasm LN1 that displayed stable field phenotypic traits was screened repeatedly across four different planting seasons and adapted to the ecological areas of Chongqing in Southwest China. 4. Discussion 4.1 Phenotypic variation of perennial Chinese rice germplasm The field phenotypes of the agronomic traits of crop germplasm resources is an important basis for rice geneticists and breeders to elucidate the genetic mechanisms of complex traits and crop varieties undergoing improvement [33–35]. In particular, the evaluation of the genetic diversity of crop germplasm resources provides important information for mining new genes and breeding new varieties [36–37]. Phenotypic diversity is helpful for understanding the diversity degree of agronomic traits as a whole and provides a theoretical and practical basis for mining and utilizing excellent resources. In the present study, we analyzed the diversity of 16 agronomic traits in 20 perennial Chinese rice germplasm across four different planting seasons. The present results indicated that the CV values of 16 agronomic traits displayed wide variation and a great degree of variability. Only GT displayed a high CV value of 21.52% in 2022RC. TGW displayed high CVs of 16.89% in 2022MC and 45.73% in 2022RC. The top three CV values in 2021MC were exhibited by PP, EGP, and GYP. The top three CV values in 2021 were shown by EGP, GYP, and FGP. The top three CV values in 2022MC were presented by EGP, GYMP, and FGP. The top three CV values in 2022RC were presented by EGP, GYP, and GYMP. This finding suggested that the seven agronomic traits of PP, EGP, FGP, GYMP, GYP, GT, and TGW displayed a higher degree of phenotypic variation than other 9 agronomic traits and were easily affected by external environmental factors such as high temperature during the rice heading and filling stages in Chongqing Southwest China. Therofore, special attention should be paid toward these varibly agronomic traits owing to external environment factors in the future perennial rice breeding project and before commericaly released to farmers. 4.2 Cluster analysis of the agronomic traits in perennial Chinese rice germplasm Cluster analysis can gather perennial rice germplasm with similar genetic information into one cluster. It is conducive for elucidating the genetic relationships among germplasm from different ecological areas throughout the world [38–39]. In this study, seven germplasm (ZJ1-5 and HB1-2) from Zhejiang and Hubei Provinces in 2021MC, 2022MC, and 2021RC clustered into clusters I, III, and I, respectively, indicating the existence of a certain correlation between the agronomic traits and geographical locations of different germplasm. However, in 2021RC, three germplasm (HB1, HB2, and ZJ3) from Hubei and Zhejiang Provinces clustered into cluster I, three germplasm (ZJ2, ZJ4, and ZJ5) from Zhejiang Province clustered into cluster II, and only ZJ1 clustered into cluster V (Fig. 7 ). The same germplasm clustered into different clusters across four different planting seasons, and cluster analysis based on the field phenotypic data of agronomic traits across different planting seasons was not stable because the field performances of agronomic traits were easily affected by external environmental factors, such as light, water, and fertilizer, especially for high temperature during the rice heading and filling stages in Chongqing Southwest China [40]. However, the field phenotypes of important agronomic traits are the basis of the breeding of new rice varieties by breeders in rice breeding projects [42–43] (Yuan 1966; Cheng et al., 1998; Birchler 2015). Nowadays, SSR markers are widely employed to elucidate the genetic diversity of rice germplasm resources [44–46]. In the present study, three germplasm (ZJ4, HB1, and ZJ3) clustered into the first sub-cluster I, ZJ2 clustered into the third sub-cluster I, ZJ5 clustered into the first sub-cluster II, HB2 clustered into the second sub-cluster II, and ZJ1 clustered into cluster VI. The cluster analysis based on SSR markers was different from those of agronomic traits in 20 germplasm. Geographical distribution may not play a decisive role in the genetic diversity of germplasm. In this study, some germplasm from different geographical locations was divided into the same cluster. The sixteen agronomic traits in 20 germplasm frequently exhibit phenotypic variation even if these germplasm were planted in the same experimental field of Chongqing in Southwest China across different planting seasons as result of the varible field phenotypes of agronomic traits owing to both internal genetic factors and external environmental factors. Consequently, the cluster analysis based on SSR genetic loci is more accurate and reliable than that based on the field phenotypic data of agronomic traits. 4.3 Correlation and principal component analyses between the quantitative agronomic traits In this study, we observed significant correlations among the 16 agronomic traits of the 20 perennial Chinese rice germplasm (Fig. 6 A–D). However, a series of irregular correlations were observed in the 20 germplasm across four different planting seasons. The number, strength, and direction of significant correlations among the 16 agronomic traits of the 20 germplasm across four different planting seasons exhibited significant differences and were affected by genotype and genotype × environment interactions. For example, GYP showed no significant correlations with PH and PP in 2022MC. However, in 2022RC, susceptible GYP exhibited significant positive correlations with PH and PP. This result indicated that GYP was codetermined by a series of variably agronomical traits owing to both internal genetic factors and external environmtental factors. It also agreed with previous findings that revealed complex correlations among crop agronomic traits [47–49]. PCA is an effective method for reducing the dimensionality of large datasets. It can maximize interpretability, minimize information loss and determine the most suitable agronomic traits that mostly contribute to the variation in selected materials by analyzing the internal correlations of field phenotypic data [50–51]. In this study, PCA confirmed that the first five components explained the vast majority of the variation, concentrating on five agronomic traits, such as the EGP, SP, GYP, PH, and GL in 2021MC; the first six significant components explained the vast majority of the variation, concentrating on six agronomic traits,such as the FGP, GSD, GSR, PL, LWR, and GYP in 2021RC; the first six significant components explained the vast majority of the variation, concentrating on six agronomic traits, such as the GYMP, GSR, GW, GL, PP, and GT in 2022MC; the first four significant components comprised of FGP, SP, GL, and GW in 2022RC explained the vast majority of the variation. The results suggested that these concentrated agronomic traits are suitable for the assessment of genetic diversity and field performance characterization of perennial Chinese rice germplasm and affected by high temperature during the rice heading and filling stages in Chongqing Southwest China. Therefore, special attention should be paid toward the ecological adaptability of perennial rice before commericaly released to farmers. 4.4 Perennial Chinese rice germplasm for further breeding utilization Rice germplasm resources are the important basis of rice genetics and breeding [41, 52–54]. A series of new rice varieties with good quality, high yield, multi-resistance, and wide adaptability have been successfully bred and commercially released to farmers due to the innovation and utilization of excellent rice germplasm resources. In particular, the innovative utilization of O. longistaminata germplasm has filled the gap in the breeding of new perennial Chinese rice varieties [16, 54]. In this study, we evaluated the field phenotypes of agronomic traits and genetic diversity of perennial Chinese rice germplasm across four different planting seasons. One germplasm LN1 displayed stable field phenotypic agronomic traits across four different planting seasons was adapted to the ecological areas of Chongqing in Southwest China. They might be directly applied in rice production. The stability of the agronomic traits of the remaining 19 germplasm should be considered for evaluation outside Chongqing. The presenting perennial rice germplasm are suitable for elucidating the molecular and genetic mechanisms of perennial characteristics and even sustainable agricultural development in China. 5 Conclusions In this study, 16 agronomic traits, including HD, PH, and TGW, were observed across four different planting seasons to assess the phenotypic diversity of 20 perennial Chinese rice germplasm. Meanwhile, 13 pairs of SSR primers with good polymorphism were employed to reveal the genetic diversity of the 20 germplasm. Ample phenotypic variations and abundant DNA genetic diversity were observed in the 20 germplasm. A comprehensive evaluation model of perennial Chinese rice germplasm was constructed by M-TOPSIS. The top one germplasm LN1 that displayed stable phenotypic agronomic traits was screened repeatedly across four different planting seasons and adapted to the ecological areas of Chongqing in Southwest China was screened repeatedly across four different planting seasons. This study will provide a reference for the further utilization of perennial Chinese rice germplasm and genetic improvement of agronomic traits, and even sustainable agricultural development in China Declarations Author contributions YSL: Conceptualization, Funding acquisition, Supervision, Writing-original draft, and Writing-review & editing; JYG : Resources and Methodology; YXY : Investigation, Data curation, and Formal analysis; TSP and LT : Investigation and Data curation; JYL : Investigation and Methology; XJQ : Software and Formal analysis; ML and WBN : Data curation and Validation. Funding This work was supported by the State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China (SKL-KF202226), the Chongqing Natural Science Foundation of China (cstc2021jcyj-msxm X0007), and the Open Project Program of State Key Laboratory of Rice Biology (20190202). Data availability The published article and its supplementary materials include all the data generated in this study. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no known competing financial interests. References Jackson W. New roots for agriculture[M]. New Edition. Lincoln: University of Nebraska Press 1980. Zhou K, Robards S, Helliwell C, Blanchard. Composition and functional properties of rice. International Journal of Food Science Technology. 2002; 37: 849–868. Cheng SH. Chinese super rice breeding. Science Press, Beijing, China 2010. Fischer G, Winiwarter W, Cao GY, Ermolieva T, Hizsnyik E, Klimont Z, Wiberg D, Zheng XY. Implications of population growth and urbanization on agricultural risks in China. Population and Environment. 2012; 33: 243–258. Han J. China: Food security and agricultural going global. American Academic Press. 2014. JacksonW. New roots for agriculture [M]. New Edition. Lincoln: University of Nebraska Press, 1980. Glover JD, Reganold JP, Bell LW, Borevitz J, Brummer EC, Buckler ES, Cox CM, Cox TS, Crews TE, Culman SW, DeHaan LR, Eriksson D, Gill BS, Holland J, Hu F, Hulke BS, Ibrahim AMH, Jackson W, Jones SS, Murray SC. Increased food and ecosystem security via perennial grains. Science. 2010; 328: 1638–1639. Crews TE, Carton W, Olsson L. Is the future of agriculture perennial? Imperatives and opportunities to reinvent agriculture by shifting from annual monocultures to perennial polycultures. Global Sustainability. 2018; 1: 1–18. Lin YL. Research progress on resistance breeding of perennial rice. Fujian Science and Technology of Rice and Wheat. 1991; 1: 33–34. Li QX. Perennial rice cultivars I Interspecific crosses for breeding perennial lines in Oryza. Southwest China Journal of Agricultural Sciences. 1997; 10: 9–14. Sacks EJ, Roxas JP, Cruz MT. Developing perennial upland rice I: Field performance of Oryza sativa / O.rufipogon F 1 , F 4 , and BC 1 F 4 progeny. Crop Science. 2003a; 43: 120–128. Sacks EJ, Roxas JP, Cruz MT. Developing perennial upland rice II: field performance of S1 families from an intermediated Oryza sativa / O.longistaminata population. Crop Science. 2003b; 43:129–134. Crews TE, Cattani DJ. Strategies, advances, and challenges in breeding perennial grain crops. Sustainability. 2018; 10: 2192. Sacks EJ, Dhanapala MP, Tao DY, Cruz MT, Sallan R. Breeding for perennial growth and fertility in an Oryza sativa / O.longistaminata population. Field Crops Research. 2006; 95: 39–48. Tao DY, Sripichitt P. Preliminary report on transfer traits of vegetative propagation from wild rice species to Oryza sativa via distant hybridization and embryo rescue. Kasetsart Journal. 2000; 34: 1–11. Zhang SL, Hu J, Yang CD, Liu HT, Yang F, Zhou JH, Samson BK, Boualaphanh C, Huang LY, Huang GF, Zhang J, Huang WQ, Tao DY, Harnpichitvitaya D, Wade LJ, Hu FY. Genotype by environment interactions for grain yield of perennial rice derivatives ( Oryza sativa L/ Oryza longisaminata ) in southern China and Laos. Field Crops Research. 2017; 207: 62–70. Samson BK, Voradeth S, Zhang SL, Tao DY, Xayavong S, Khammone T, Douangboupha K, Sihathep V, Sengxua P, Phimphachanhvongsod V, Bouahom B, Jackson T, Harnpichitvitaya D, Hu FY, Wade LJ. Performance and survival of perennial rice derivatives ( Oryza sativa L/ Oryza longistaminata ) in Lao PDR. Experimental Agriculture. 2018; 54: 592–603. Huang GF, Qin SW, Zhang SL, Cai XL, Wu SK, Dao JR, Zhang J, Huang LY, Harnpichitvitaya D, Wade LJ, Hu FY. Performance, economics and potential impact of perennial rice PR23 relative to annual rice cultivars at multiple locations in Yunnan province of China. Sustainability. 2018; 10: 1–18. Zhang SL, Huang GF, Zhang YJ, Lv XT, Wan KJ, Liang J, Feng YP, Dao JR, Wu SK, Zhang L, Yang X, Lian XP, Huang LY, Shao L, Zhang J, Qin SW, Tao DY, Crews TE, Sacks EJ, Lyu J, Wade LJ, Hu FY. Sustained productivity and agronomic potential of perennial rice. Nature Sustainability. 2022; 6: 28–38. Culman SW, Snapp SS, Ollenburger M, Basso B, Dehaan LR. Soil and water quality rapidly responds to the perennial grain kernza wheatgrass. Agronomical Journal. 2013; 105: 735–744. Jungers JM, DeHaan LH, Mulla DJ, Sheaffer CC, Wyse DL. Reduced nitrate leaching in a perennial grain crop compared to maize in the Upper Midwest, USA. Agriculture, Ecosystems & Evironment. 2019; 272: 63–73. Oliveira GD, Brunsell NA, Sutherlin CE, Crews TE, DeHaan LR. Energy, water and carbon exchange over a perennial kernza wheatgrass crop. Agricultural and Forest Meteorology. 2018; 249: 120–137. ZhangYJ, Huang GF, Zhang SL, Zhang J, Gan SX, Cheng M, Hu J, Huang LY, Hu FY. An innovated crop management scheme for perennial rice cropping system and its impacts on sustainable rice production. European Journal of Agronomy. 2021; 122: 126186. Vander Pol L, Nester B, Schlautman B, Crews TE, Contrufo MF. Perennial grain kernza fields have higher particulate organic carbon at depth than annual grain fields. Canadian Journal of Soil Science. 2022;102: 1005–1009. Liang YS, Gong JY, Yan YX, Wang BB, Gong WA, Wen H, Wu Q, Nan WB, Qin XJ, Zhang HM. Survey of overwintering trait in Chinese rice cultivars ( Oryza sativa L). Euphytica. 2022; 218: 94. Shen ZT. Crop breeding experiment. China Agriculture Press, Beijing. 1995. Wang DY, Zhang H, Wang L, Jia CH, Zhao B, Zuo N, Tong XP, Zhao F, Pei ZY. Molecular detection and analysis of blast resistance genes and restored genes in 107 japonica rice. Molecular Plant Breeding. 2021; 19: 2644–2659. Cuthbert J, Somers D, Brule-Babel A, Brown P, Crow G. Molecular mapping of quantitative trait loci for yield and yield components in spring wheat ( Triticum Aestivum L). Theoretical Applied Genetics. 2008;117: 595–608. Lei QY, Zhou JG, Zhang WH, Luo J, Wu K, Long CN. Morphological diversity of panicle traits in kam fragrant glutinous rice ( Oryza sativa L). Genetic Resources and Crop Evolution. 2018; 65:775–786. Das R, Divakara BN. Variability and divergence in Pongamia pinnata for further use in tree improvement. Journal of Forestry Research. 2011; 22:193–200. Hwang CL, Yoon K. Methods for multiple attribute decision making[M]//Multiple Attribute Decision Making. Berlin: Springer-Verlag, 1981: 58–191. Chen T, Liu L, Zhou YL, Zheng Q, Luo SY, Xiang TT, Zhou LJ, Feng SL, Yang HY, Ding CB. Characterization and comprehensive evaluation of phenotypic characters in wild Camellia oleifera germplasm for conservation and breeding. Frontiers in Plant Science. 2023,10.3389/fpls.2023.1052890 Soleri D, Cleveland DA. Farmer selection and conservation of crop varieties[C]//GOODMAN RMed. Encyclopedia of Plant and Crop Science. New York: Marcel Dekker Incorporated. 2004: 433–438. Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nature Genetics. 2008; 40: 761–767. Campbll DR. Using phenotypic manipulations to study multivariate selection of floral trait associations. Annals of Botany. 2009;103:1557–1566. Zhao KY, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications. 2011; 2: 467. Glaszmann JC, Kilian B, Upadhyaya HD, Varshney RK. Accessing genetic diversity for crop improvement. Current Opinion in Plant Biology. 2010; 13: 167–173. Dalmaijer ES, Nord CL, Astle DE. Statistical power for cluster analysis. BMC Bioinformation. 2022:23;1–28. Devi NS, Zaman S, Sarma M, Bhuyan J, Saharia DD, Nath MP, Talukdar JC, Ahmed ASM. Genetic diversity analysis for yield and quality traits in rice cultivars of manipur and assam. International Journal of Plant Soil & Science. 2022; 34: 1293–1304. NasimR, Mohammadreza JT. Agronomic characteristics and grain yield of 30 Iranian rice genotypes as affected by genotype x environment interaction. Ecological Genetics and Genomics. 2023; 26: 100153. Yuan LP. A preliminary report on male sterility in rice, Oryza sativa L. Chinese Science Bulltin. 1966; 4: 322. Cheng SH, Liao XY, Min SK. China’s super rice research: background, goals and issuers. China Rice. 1998; 1: 3–5. Birchler JA. Heterosis: the genetic basis of hybrid vigour. Nature Plants. 2015; 1: 15020. Zeng Y, Zhang H, Wang L, Pu X, Du J, Yang S, Liu J. of rice landraces ( Oryza sativa L) in Yunnan, China. Breeding Science. 2007; 57: 91–99. Thomson MJ, Septiningsih EM, Suwardio F, Santoso TJ, Silitonga TS, McCouch SR. Genetic diversity analysis of traditional and improved Indonesian rice ( Oryza sativa L) germplasm using microsatellite markers. Theoretical Applied Genetics. 2007; 114: 559–568. Wei XH, Yuan XP, Yu HY, Wang YP, Xu Q, Tang SX. Temporal changes in SSR allelic diversity of major rice cultivars in China. Journal of Genet Genomics. 2009; 36: 363–370. Xu L, Zhang YD, Zhu Z, Zhao L, Zhao QY, Zhang QF, Wang CL. Dissection of QTLs in two years for component tratis in rice ( Oryza sativa ). Chinese Journal Rice Science. 2008; 22: 370–376. Lu K, Shen GZ, Liang Y, Fu MZ, He B, Tie LM, Zhang Y, Peng L, Li JN. Analysis of yield components with high harvest index in Brassica napus under environments fitting different yield levels. Acta Agronomica Sinica. 2017; 43: 82–96. Liang YS, Nan WB, Qin XJ, Zhang HM. Field performance on grain yield and quality and genetic diversity of overwintering cultivated rice ( Oryza sativa L.) in southwest China. Scientific Reports. 2021;11:1846. NardoM, Saisana M, Saltelli A, Tarantola S. Tools for composite indicators building. European Comission Ispra. 2005; 15: 19–20. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philosophical Transactions A. 2016; 374: 2065. Shi MS. The discovery and study of the photosensitive recessive male-sterile rice. Scientia Agriculture Sinica. 1985; 2: 44–48. Zeng QC, Zhou KD, Zhu Z, Luo Q. Current status in the use of hybrid rice heterosis in China. Chinese Journal Rice Science. 2000; 14: 243–246. Zeng B, Gong JY, Zhang F. Analysis and prospects of extension of main varieties of hybrid rice with high quality in China. Chinese Journal Rice Science. 2022; 36: 439–446. Tables Table 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx Table5.docx SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable4.docx SupplementaryTable5.docx SupplementaryTable6.docx SupplementaryTable7.docx SupplementaryTable8.docx SupplementaryTable9.docx SupplementaryTable10.docx SupplementaryTable11.docx SupplementaryTable12.docx SupplementaryTable13.docx SupplementaryTable14.docx SupplementaryTable15.docx SupplementaryTable16.docx thefulluncroppedGelsandBlotsimages.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:14:42","extension":"rar","order_by":22,"title":"","display":"","copyAsset":false,"role":"supplement","size":27168544,"visible":true,"origin":"","legend":"","description":"","filename":"thefulluncroppedGelsandBlotsimages.rar","url":"https://assets-eu.researchsquare.com/files/rs-5794229/v1/1f563104ec607b745105c315.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diversity Analysis and Comprehensive Evaluation of Agronomic Traits in Perennial Chinese Rice Germplasm for breeding","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L) is one of the most important food crops throughout the world. It transformed from a perennial wild species into an annual cultivated species after nearly 10 000 years of artificial selection by humans [1]. It feeds more than half of the world\u0026rsquo;s population [2] and has been playing an important role in ensuring that China has a sustainable national food supply [3]. The production mode of annual crop, which necessitates ploughing every year, has generated a series of social, economic, and ecological environmental problems, such as large seed input, overused pesticides and fertilizers, increased agricultural machinery and tool input, water loss and soil erosion, nutrient imbalance, and soil structure and ecosystem damage. In particular, Chinese rice production is facing numerous challenges, including rapid urbanization, a declining number of farmers, widespread land abandonment, and the growing shortage of labor forces, leading to reductions in rice planting area [4]. In particular, rising labor costs increase rice production costs and further weaken the core competitiveness of the Chinese rice industry in the international agricultural market, potentially threatening Chinese food security [5]. Consequently, in China, how to develop a set of sustainable grain production technologies to ensure the balance between food and ecological security has become the current main issue involved in the implementation of rural revitalization strategies. Jackson and Glover argued that a shift from annual to perennial food crop production is one of the most feasible strategies for addressing the problems mentioned above [6\u0026ndash;8]. Subsequently, a series of exploratory studies have been implemented on perennial Chinese rice variety breeding [9\u0026ndash;14]. A successful hybrid between the wild perennial rice \u003cem\u003eOryza longistaminata\u003c/em\u003e and cultivated annual Asian rice RD23 has been reported [15]. Several perennial Chinese rice varieties have been successfully developed and commercially released to farmers [16\u0026ndash;18]. The perennial Chinese rice variety is strongly preferred by farmers and exhibits numerous advantages in rice production over the preferred annual rice variety [19]. First, the perennial Chinese rice variety presents the advantage of stable field performance on grain yield and quality across multiple years and sites in Southern China and Laos. It shows an average yield of 6.8 Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e harvest\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e versus the 6.7 Mg shown by replanted annual rice. Second, the perennial Chinese rice variety displays numerous advantages, such as cost savings and increased efficiency, in the international rice market. Moreover, it saves 58.1% of labor and 49.2% of input costs in each regrowth cycle. Importantly, the perennial Chinese rice variety has the advantage of maintaining important ecosystem functions. Perennial crops can increase nitrogen retention and soil carbon accumulation as a result of their permanent living cover and deep root systems [20\u0026ndash;24]. In summary, the successful breeding and promotion of perennial Chinese rice varieties are attributed to the exploitation and innovative utilization of the novel rice germplasm \u003cem\u003eO. longistaminata\u003c/em\u003e. Therefore, exploring a series of novel perennial rice germplasm is important prerequisite of the future perennial Chinese rice genetic and breeding projects.\u003c/p\u003e \u003cp\u003eChina is rich in rice germplasm resources. Tens of thousands of rice germplasms and thousands of commercially developed rice varieties exist in China. However, the perennial habit of the current commercially released Chinese rice varieties has been largely ignored and remains unknown. Consequently, comprehensively evaluating the field performance on agronomic traits and genetic diversity of perennial rice germplasm across different planting seasons is necessary for theirs further utilization. In our previous studies, a series of perennial Chinese rice germplasm were screened from existing Chinese cultivated rice varieties under the cold-winter environment [25, 49]. In this study, we investigated and comprehensively evaluated the phenotypic variation of sixteen agronomic traits and genetic diversity of the 20 perennial Chinese rice germplasm across two major crop (MC) seasons and two ratooning crop (RC) seasons in two consecutive years (2021 and 2022) and rank the investigated agronomic traits by both statistical analysis and molecular strategy. This study will be greatly beneficial for screening of potential perennial rice germplasm and commercial release of perennial rice varieties with stable field performance on agronomic traits across different planting seasons. It also provide a reference for the further utilization of perennial Chinese rice germplasm and even the genetic improvement of important agronomic traits, as well as a theoretical basis for the breeding of new perennial rice varieties in the future.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials and experimental sites\u003c/h2\u003e \u003cp\u003eOver the past 5 years, we surveyed the perennial habit of 1034 Chinese rice cultivars collected throughout China [25]. We identified 20 perennial Chinese rice germplasm that could withstand cold tolerance to \u0026minus;\u0026thinsp;1\u0026deg;C of the daily minimum temperature for one day, 0 ℃ of the daily minimum temperature for four days, and 1 ℃ of the daily minimum temperature for four days in January 2021 throughout the cold\u0026ndash;winter season of Chongqing province, China. These germplasm included Anhui (AH, 3), Hubei (HB, 3), Jiangsu (JS, 4), Jilin (JL, 1), Liaoning (LN, 2), Tianjin (TJ, 2), and Zhejiang (ZJ, 5). The germplasm, which sprouted from rice tillering nodes in March 2021, exhibited perennial characteristics (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;D, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e A\u0026ndash;D, and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e A\u0026ndash;G) and were distributed in seven provinces of China at longitude 108\u0026deg;21\u0026prime;42ʹʹ\u0026ndash;131\u0026deg;19\u0026prime; and latitude 27\u0026deg;02'\u0026ndash;46\u0026deg;18\u0026prime; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We performed a phenotypic evaluation of these perennial Chinese rice germplasm across the four different planting seasons in two consecutive years.\u003c/p\u003e \u003cp\u003eThe field experiment was conducted with a randomized complete block design and three replicates at the Biotechnology Testing Station of Chongqing Normal University at the altitude of 285.8 meters, University Town, Shapingba District, Chongqing, China (29\u0026deg;.52ʹ66.25ʹʹN and 106\u0026deg;.49ʹ25.08ʹʹE). In 2021MC, the seeds of the 20 germplasm were sown on 18 March 2021, and 35-day-old seedlings of all germplasm were transplanted into four-row plots with six plants per row with spacing of 20 cm \u003cb\u003e\u0026times;\u003c/b\u003e 30 cm between the plants and rows. In the fall, all germplasm were sampled for 16 agronomic traits evaluations. In 2021RC, 30 cm rice stubs of all tested germplasm were retained after autumn harvest in 2020MC. They survived through the natural cold winter season, germinated, flowered, and were harvested for the evaluation of these traits. In 2022MC, seeds collected from the 20 germplasms were sown on 9 March 2022, and 35-day-old seedlings of all germplasm were transplanted into four-row plots with six plants per row with spacing of 20 cm \u003cb\u003e\u0026times;\u003c/b\u003e 30 cm between plants and rows. In 2022RC, after autumn harvest in 2021MC, 30 cm stubs of all tested rice germplasm were retained. They survived through the natural cold winter season, germinated, flowered, and were harvested for grain yield and quality evaluation (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;D and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e A\u0026ndash;D). All germplasm were sampled for the evaluation of these traits.\u003c/p\u003e \u003cp\u003eA special rice compound fertilizer, including pure N 150\u0026ndash;180 kg hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 100 kg hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e, and 150 kg hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e K\u003csub\u003e2\u003c/sub\u003eO, was selected as the base fertilizer and applied before rice seedling transplantation. Pure N (75\u0026ndash;150 kg hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) was applied at 2 weeks after seedling transplantation. The water management strategy was involved into applying shallow water at the tillering stage and flooding at midseason with drainage\u0026ndash;reflooding\u0026ndash;moist intermittent irrigation, timely sunning of the field, and control of ineffective tillering. Weed control, pest management, and disease treatment were conducted in accordance with local conventional high-yield cultivation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement of agronomic traits\u003c/h2\u003e \u003cp\u003e A random sample of five plants per plot within each germplasm for each replication across 2021MC, 2021RC, 2022MC, and 2022RC was collected to measure phenotypic agronomic traits in accordance with the method described by Shen (1995) with some modifications [26]. In this study, 16 agronomic traits, including 13 quantitative traits and three derived traits, were measured. The field phenotypic data of each agronomic trait of five plants within each individual genotype with three replicates were calculated for statistical analysis.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Measurement of agronomic traits\u003c/h2\u003e \u003cp\u003eData on 11 agronomic traits, including nine quantitative traits and two derived traits, were evaluated. Nine quantitative traits traits included heading date (HD, days), plant height (PH, cm), panicle number plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (PP), panicle length (PL, cm), filled grain number panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (FGP), empty grain number panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (EGP), spikelet number panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (SP), grain yield major panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GYMP, g), and grain yield plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (GYP). Two derived traits were calculated as follows: grain setting rate (GSR) = (FGP/SP) \u0026times; 100% and grain setting density (GSD)\u0026thinsp;=\u0026thinsp;SP/PL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Measurement of grain shape traits\u003c/h2\u003e \u003cp\u003eGrain shape traits, including grain length (GL, mm), grain width (GW, mm), and grain thickness (GT, mm), were measured by using a Mitutoyo absolute digimatic caliper with a precision of 0.01 mm (model 500\u0026thinsp;\u0026minus;\u0026thinsp;173). Thousand-grain weight (TGW) was measured by using an electronic balance with a precision of 0.01 g. The derived trait length-to-width ratio (LWR) was calculated as GL/GW.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 DNA extraction and PCR amplification\u003c/h2\u003e \u003cp\u003eThe 20 perennial Chinese rice germplasm and two sequenced rice varieties Nipponbare and 9311 were sampled at the rice tillering stage. Nipponbare and 9311 were selected as the control DNA. Rice genomic DNA was extracted in reference to the sodium dodecyl sulfate method described by Cuthbert (2008) with some modifications [27]. Thirty pairs of simple sequence repeat (SSR) primers with good polymorphisms were employed to evaluate the genetic diversity of the 20 perennial Chinese rice germplasm. PCR amplifications were performed in reference to the protocol described by Greer (2008) with some modifications [28]. DNA products were separated by using 8% polyacrylamide gel electrophoresis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll experiments were performed in triplicate. All phenotypic agronomic trait data collected from all tested rice germplasm across 2021MC, 2021RC, 2022MC, and 2022RC were juxtaposed in Microsoft Excel 2010 for statistical analysis. All phenotypic agronomic traits were classified into 10 grades: grade 1\u0026thinsp;\u0026lt;\u0026thinsp;X\u0026thinsp;\u0026minus;\u0026thinsp;2σ to grade 10\u0026thinsp;\u0026gt;\u0026thinsp;X\u0026thinsp;+\u0026thinsp;2σ. Each interval between grades 1 and 10 was 0.5σ. X and σ are the mean and standard deviation, respectively. Morphological diversity was evaluated on the basis of the frequency of these trait dispersion and Shannon\u0026ndash;Wiener diversity index (\u003cem\u003eH\u003c/em\u003eʹ). The statistics of agronomic trait parameters were measured. They included mean, standard deviation (SD), coefficient of variation (CV %), phenotypic correlation coefficients (PCCs), and \u003cem\u003eH\u0026prime;\u003c/em\u003e. The \u003cem\u003eH\u0026prime;\u003c/em\u003e for each trait was calculated by using the formula \u003cem\u003eH\u0026prime; =\u003c/em\u003e \u0026minus;\u0026sum;\u003cem\u003ePi\u003c/em\u003eln\u003cem\u003ePi\u003c/em\u003e, where \u003cem\u003ePi\u003c/em\u003e is the proportion of the individual number of this trait in the total number of individuals [29]. The CV for all traits was calculated as CV\u0026thinsp;=\u0026thinsp;\u003cem\u003eS\u003c/em\u003e/X, where \u003cem\u003eS\u003c/em\u003e is the standard deviation, and\u0026oline;X is the mean [30].\u003c/p\u003e \u003cp\u003eIBM SPSS Statistics version 20.0 software (SPSS Inc., Chicago, IL, USA) was employed to perform multiple comparison analysis and principal component analysis (PCA). Multiple comparison analysis was conducted on the basis of Duncan\u0026rsquo;s new multiple range method at the 0.05 probability. PCCs among the 16 traits were obtained on the basis of Pearson correlation coefficients by using IBM SPSS Statistics 20.0 version software. A correlation heat map was drawn by applying OrigniLab OriginPro2021 version. 0 and 1 represent nonamplified and amplified bands, respectively, in PCR amplification with 13 pairs of SSR primers with good polymorphism and were used to arrange molecular data in Microsoft Excel 2010. All data on the phenotypic agronomic trait were normalized to 0 or 1 for cluster analysis with NTSYSpc software version 2.10. Cluster analysis was performed by using the unweighted pair-group method with arithmetic mean in NTSYSpc software version 2.10 (Applied Biostatistics, Port Jefferson, New York, USA). A clustering heat map was drawn by using iTOL software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Variation characteristics of the agronomic traits in perennial Chinese rice germplasm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 16 agronomic traits of the 20 perennial Chinese rice germplasm exhibited an irregular variance in terms of field phenotypic values and high phenotypic diversity across four\u0026nbsp;different planting seasons and were sensitive to external environmental factors (Fig.5; Tables 1 and S1\u0026ndash;16). The 16 traits of the 20 germplasm presented wide phenotypic variation with CV% values that ranged from 4.34% for GL to 64.34% for EGP in 2021MC, from 4.61% for GL to 74.24% for EGP in 2021RC, from 3.91% for GL to 56.90% for EGP in 2022MC, and from 3.55% for GL to 82.57% for EGP in 2022RC. Moreover, they demonstrated high phenotypic diversity and high \u003cem\u003eH\u0026apos;\u003c/em\u003e values\u0026nbsp;that ranged from 1.46 for HD to 3.33 for SP in 2021MC, from 1.49 for HD and GSR to 1.96 for GL in 2021RC, from 1.5 for HD to 2.20 for PP in 2022MC, and from 1.31 for TGW to 1.96 for GYP in 2022RC.\u003c/p\u003e\n\u003cp\u003eAmong the individual agronomic traits in the 20 germplasm across four different planting seasons, four agronomic traits (PH, GL, GW, and LWR) displayed relatively stable field performance values with CV values of less than 10% that ranged from 7.53% in 2022RC to 9.25% in 2021RC; from 3.55% in 2022RC to 4.61% in 2021RC; from 5.42% in 2022MC to 7.46% in 2021RC; and from 5.98% in 2022MC to 7.42% in 2022RC, respectively. The remaining 12 traits (HD, PP, PL, FGP, EGP, SP, GSR, GSD, GYMP, GYP, GT, and TGW) displayed wide phenotypic variation with CV values beyond 10% that ranged\u0026nbsp;from 12.64% in 2021RC to 16.62% in 2022RC; from 20.12% in 2022MC to 25.75% in 2022RC; from 14.86% in 2021MC to 18.90% in 2022RC; from 19.09% in 2021MC to 52.14% in 2022RC; from 56.90% in 2021MC to 92.57% in 2022RC; from 14.88% in 2021MC to 26.90% in 2022RC; from 8.28% in 2022MC to 39.83% in 2022RC; from 16.22% in 2021MC to 27.81% in 2022RC;\u0026nbsp;from 24.08% in 2021MC to 61.81% in 2022RC;\u0026nbsp;from 25.05% in 2022MC to 75.67% in 2021RC;\u0026nbsp;from 4.90% in 2022MC to 21.52% in 2022RC; and from 9.15% in 2021RC to 45.73% in 2022RC, respectively.\u003c/p\u003e\n\u003cp\u003eAmong the individual germplasm evaluated 16 agronomic traits across four\u0026nbsp;different planting seasons, three germplasm (LN1, LN2, and ZJ5) had more than 10 stable field performance on agronomic traits with CV values of less than 10%. Among these germplasm, LN1 displayed relatively stable field performance values for 13 traits (HD, PH, PL, FGP, SP, GSR, GSD, GYMP, GL, GW, GT, LWR, and TGW) with CV values that ranged from 1.67% for GSR to 6.23% for HD. However, it had unstable field performance values for three traits (PP, EGP, and GYP) with CV values beyond 10% that ranged from 28.82% for EGP to 40.78% for GYP. LN2 displayed a relatively stable phenotypic value for 12 traits (FGP, HD, PH, PL, GSD, GSR, GYMP, GL, GT, GW, LWR, and TGW) with CV values that ranged from 1.73% for GSR to 9.93% for FGP. However, it had unstable phenotypic values for four traits (PP, EGP, SP, and GYP) with CV values beyond 10% that ranged from 10.22% for SP to 65.83% for GYP. ZJ5 displayed stable phenotypic values for 11 traits (HD, PH, PL, SP, FGP, GL, GW, LWR, GT, GSR, and GSD) with CV values that ranged from 1.37% for GL to 9.14% for FGP. Nevertheless, it exhibited unstable phenotypic values for five traits (PP, EGP, GYMP, GYP, and TGW) with CV values beyond 10% that ranged from 19.45% for TGW to 57.23% for GYP. Four germplasm (HB1, JS2, JS3, and JS4) had more than 10 unstable traits with CV values beyond 10%. Among them, JS3 displayed unstable phenotypic values for 11 traits (EGP, FGP, GSD, GYMP, GYP, PL, PP, SP, GSR, TGW, and GT) with CV values ranging from 10.30% for PL to 59.94% for GYP. However, it presented stable phenotypic values for five traits (HD, PH, GL, GW, and LWR) with CV values of less than 10% that ranged from 2.04% for GL to 6.83% for HD. HB1 displayed unstable phenotypic values for 10 traits (PP, EGP, FGP, SP, GSD, GSR, GYMP, GYP, GT, and TGW) with CV values that ranged from 10.89% for GT to 60.45% for GYP. Nevertheless, it had stable phenotypic values for six traits (HD, PH, PL, GW, GL, and LWR) with CV values of less than 10% that ranged from 3.16% for GL to 8.23% for HD. JS2 displayed unstable phenotypic values for 10 traits (PP, EGP, FGP, SP, GSD, GSR, GYMP, GYP, GT, and TGW) with CV values that ranged from 14.42% for SP to 92.09% for GYP. However, it exhibited stable phenotypic values for six traits (HD, PH, PL, GL, GW, and LWR) with CV values of less than 10% that ranged from 1.64% for GL to 8.75% for PH. JS4 displayed unstable phenotypic values for 10 traits (PP, EGP, FGP, SP, GSR, GYMP, GYP, GT, LWR, and TGW) with CV values that ranged from 10.16% for LWR to 66.70% for FGP. Nevertheless, it had stable phenotypic values for six traits (HD, PH, PL, GSD, GL, and GW) with CV values of less than 10% that ranged from 1.47% for GL to 9.69% for GSD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAltogether, 346 pairs of significant relationship values among the 16 agronomic traits in the 20 perennial Chinese rice germplasm across four different planting seasons were estimated on the basis of correlation analysis and Pearson correlation coefficients (Fig.6 A\u0026ndash;D; Table 2).\u0026nbsp;In 2021MC,\u0026nbsp;83 pairs of significant PCC values, including 53 pairs of positive\u003cem\u003e\u0026nbsp;\u003c/em\u003ecorrelations and 30 pairs of negative correlations, were identified in the 20 germplasm (Fig.6 A; Table 2). Among them, PH had significantly positive correlations with PH, PP, EGP, GYP, GL, GW, and GT with coefficients that ranged from 0.27 to 0.65 but had significantly negative correlations with FGP, GSR, and GYMP with coefficients that ranged from \u0026minus;0.28 to \u0026minus;0.67. PP had significantly negative correlations with PL, FGP, SP, GSR, GSD, GYMP, GW, GT, and TGW with coefficients that ranged from \u0026minus;0.22 to \u0026minus;0.58. SP presented significantly positive correlations with PL, FGP, EGP, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.16 to 0.86. GW showed strong positive correlations with HD, PH, PL, FGP, EGP, SP, GYMP, GT, and TGW with coefficients that ranged from 0.19 to 0.78. TGW had significantly positive correlations with PL, FGP, EGP, SP, GYMP, GYP, GL, GW, and GT with coefficients that ranged from 0.17 to 0.55. GYMP exhibited significantly positive correlations with PL, FGP, SP, GSR, GSD, GYP, GW, GT, and TGW with coefficients that ranged from 0.15 to 0.91. GYP had significantly positive correlations with HD, PP, FGP, SP, GSD, GYMP, and TGW with coefficients that ranged from 0.15 to 0.51. However, in 2021RC, 85 pairs of significant PCC values among the 16 traits in the 20 germplasm, including 41 pairs of positive correlations and 44 pairs of negative correlations, were calculated (Fig.6 B; Table 2). HD had significantly negative correlations with the nine traits PH, PP, PL, FGP, SP, GSR, GYMP, GYP, and TGW with coefficients that ranged from \u0026minus;0.17 to \u0026minus;0.53. FGP had significantly positive correlations with PH, PP, PL, SP, GSR, GSR, GSD, GYMP, and GYP with coefficients that ranged from 0.32 to 0.86. TGW exhibited significantly positive correlations with GYMP, GL, GW, LWR, and GT with coefficients that ranged from 0.27 to 0.57. GYMP demonstrated significantly positive correlations with PP, PL, FGP, SP, GSD, GSR, GYP, and TGW with coefficients that ranged from 0.25 to 0.70. GYP had significantly positive correlations with PP, PL, FGP, SP, GSR, and GYMP with coefficients that ranged from 0.27 to 0.71 but had significantly negative correlations with HD, EGP, GL, GW, GT, and TGW with coefficients that ranged from \u0026minus;0.20 to \u0026minus;0.45.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn 2022MC, 88 pairs of significant PCC values\u0026nbsp;among the 16 traits in the 20 germplasm, including 49 pairs of positive correlations and 39 pairs of negative correlations, were calculated (Fig.6 C; Table 2). HD had significantly negative correlations with PL, FGP, SP, GSR, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from \u0026minus;0.15 to \u0026minus;0.69. PL presented significantly positive correlations with PH, FGP, SP, GSR, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.16 to 0.52. FGP had significantly positive correlations with PH, PL, SP, GSR, GSD, GYMP, GYP, GT and TGW with coefficients that ranged from 0.26 to 0.92. GYMP exhibited significantly positive correlations with PH, PL, FGP, SP, GSR, GSD, GYP, GT, and TGW with coefficients that ranged from 0.21 to 0.91. GYP had significantly positive correlations with PL, FGP, SP, GSR, GSD, GYMP, GL, GW, GT, and TGW with coefficients that ranged from 0.19 to 0.70 but had significantly negative correlations with HD and EGP, with coefficients of \u0026minus;0.56 and \u0026minus;0.34. However, in 2022RC, 90 pairs of significant PCC values among the 16 traits in the 20 germplasm, including 55 pairs of positive correlations and 35 pairs of negative correlations, were calculated for the 20 germplasm (Fig.6 D; Table 2). HD exhibited significantly negative correlations with PH, PL, FGP, SP, GSR, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from \u0026minus;0.16 to \u0026minus;0.72. PH had significantly positive correlations with PL, FGP, SP, GSR, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.23 to 0.68. FGP presented significantly positive correlations with PH, PL, SP, GSR, GSD, GYMP, GYP, GW, GT, and TGW with coefficients that ranged from 0.31 to 0.95. EGP had significantly negative correlations with PH, PP, PL, FGP, GSR, GYMP, GYP, GL, GW, GT, and TGW with coefficients that ranged from \u0026minus;0.33 to \u0026minus;0.96. GYMP showed significantly positive correlations with PH, PL, FGP, SP, GSR, GSD, GYP, GW, GT, and TGW with coefficients that ranged from 0.33 to 0.95. GYP had significantly positive correlations with PH, PP, PL, FGP, SP, GSR, GSD, GYMP, GW, GT, and TGW with coefficients that ranged from 0.16 to 0.83 but had significantly negative correlations with HD, EGP, GL, and LWR with coefficients of \u0026minus;0.26 and \u0026minus;0.50.\u003c/p\u003e\n\u003cp\u003eIn summary, the number, direction, and size of significant PCC values among the sixteen traits of the 20 germplasm across four\u0026nbsp;different planting seasons displayed a series of irregular variations and were easily affected by external environmental factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Cluster\u0026nbsp;analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, hierarchical clustering was performed to analyze the relationships among the 20 perennial Chinese rice germplasm based on the field performance of the 16 agronomic traits across four different planting seasons (Fig.7 A\u0026ndash;D). In 2021MC, all germplasm was divided into five distinctive clusters on the basis of phenotypic traits (Fig.7 A). Cluster I contained two germplasm (LN1 and AH2) with a large phenotypic value for PL. Cluster II consisted of three germplasm (JS3, ZJ2, and ZJ5) with a large phenotypic value for HD. Cluster III contained six germplasm (LN2, AH1, TJ1, AH3, ZJ3, and JL1) with a large phenotypic value for GSR and a small phenotypic value for EGP, and was split into two sub-clusters. The first sub-cluster in cluster III\u0026nbsp;consisted of three germplasm (LN2, AH1, and TJ1) with large phenotypic values for FGP and SP and a small phenotypic value for HD. The second sub-cluster in cluster III\u0026nbsp;also consisted of three germplasm (AH3, ZJ3, and JL1) with small phenotypic values for FGP and SP.\u0026nbsp;Cluster IV\u0026nbsp;also contained three germplasm (TJ2, ZJ4, and JS2) with a large phenotypic value for FGP.\u0026nbsp;Cluster V\u0026nbsp;contained six germplasm (JS4, ZJ1, HB1, HB2, HB3, and JS1) with\u0026nbsp;a large phenotypic value for EGP. However, in 2021RC, all germplasm can be assigned to five distinctive clusters (Fig.7 B). Cluster\u0026nbsp;I\u0026nbsp;consisted of three germplasm (ZJ1, ZJ5, and JS3) with large phenotypic values for HD and FGP. Cluster II\u0026nbsp;consisted of four germplasm (ZJ4, ZJ2, ZJ3, and HB1) with large phenotypic values for HD and EGP. Cluster\u0026nbsp;III\u0026nbsp;contained four germplasm (JS2, HB3, JS1, and AH3) with large phenotypic values for PH and PP. Cluster\u0026nbsp;IV\u0026nbsp;consisted of three germplasm (LN1, LN2, and AH1) with a\u0026nbsp;large phenotypic value for GSR. Cluster\u0026nbsp;V\u0026nbsp;contained six germplasm (TJ2, TJ1, JS4, JL1, HB2, and AH2) with large phenotypic values for EGP and GYP.\u003c/p\u003e\n\u003cp\u003eIn 2022MC, all germplasm can be assigned to five distinctive clusters based on phenotypic value on agronomic traits (Fig.7 C). Cluster I contained only one germplasm (TJ1) and was distinguished by a large phenotypic value for TGW and a small phenotypic value for GSD. Cluster II\u0026nbsp;consisted of six germplasm (LN2, LN1, AH1, JS1, JS3 and AH3) with large phenotypic values for GSD and GYP. Cluster\u0026nbsp;III\u0026nbsp;contained two germplasm (TJ2 and JS2) with a large phenotypic value for PP and a small phenotypic value for SP. Cluster\u0026nbsp;IV\u0026nbsp;also consisted of two germplasm (JL1 and AH2) with a large phenotypic value for GYP and PL. Cluster\u0026nbsp;V\u0026nbsp;only contained nine germplasm (HB3, ZJ5, ZJ3, HB2, HB1, ZJ4, ZJ1, JS4, and ZJ2) with large phenotypic values for HD and EGP and small phenotypic values for GSR and TGW.\u0026nbsp;However, in 2022RC, all germplasm can be assigned to six distinctive clusters (Fig.7 D). Cluster\u0026nbsp;I\u0026nbsp;contained only one germplasm (LN1) with large phenotypic values for PH, PL, SP, GSR, and GYP. Cluster II\u0026nbsp;also consisted of only one germplasm (JS2) with a large phenotypic value for EGP and small phenotypic values for GSR, GYMP, GYP, GT, and TGW. Cluster\u0026nbsp;III\u0026nbsp;contained two germplasm (AH3 and HB1) with large phenotypic values for PH and PP. Cluster\u0026nbsp;IV\u0026nbsp;consisted of two germplasm (AH1 and ZJ3) with a large phenotypic value for PP and a small phenotypic value for PH. Cluster\u0026nbsp;V\u0026nbsp;consisted of five germplasm (ZJ5, TJ2, TJ1, ZJ4, and ZJ1) with large phenotypic values for GYP and GSR and a small phenotypic value for EGP. It was split into two sub-clusters. The first sub-cluster of\u0026nbsp;V\u0026nbsp;cluster\u0026nbsp;consisted of two germplasm (ZJ5 and TJ2) with a large phenotypic value for PL and a small phenotypic value for PP. The second sub-cluster of cluster\u0026nbsp;V\u0026nbsp;also consisted of three germplasm (TJ1, ZJ4, and ZJ1) with a large phenotypic value for PP and small phenotypic values for FGP and SP. Cluster\u0026nbsp;VI\u0026nbsp;consisted of nine germplasm (JS1, HB2, ZJ2, JS4, AH2, LN2, JS3, JL1, and HB3) with a large phenotypic value for EGP and a small phenotypic value for TGW and was split into two sub-clusters. The first sub-cluster of cluster\u0026nbsp;VI\u0026nbsp;consisted of three germplasms (JS1, HB2, and ZJ2) with a large phenotypic value for PL and a small phenotypic value for TGW. The second sub-cluster of cluster\u0026nbsp;VI\u0026nbsp;also consisted of six germplasm (JS4, AH2, LN2, JS3, JL1, and HB3) with large phenotypic values for PP, GSD, and GYP and small phenotypic values for GYMP and TGW.\u003c/p\u003e\n\u003cp\u003eIn summary, the field phenotypic values on sixteen phenotypic traits in the 20 germplasm that directly determined the number of cluster and cluster contained germplasm were easily affected by external environmental factors across four different planting seasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Genetic diversity analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 20 perennial Chinese rice germplasm, Nipponbare, and 9311 were roughly divided into five clusters by 13 pairs of SSR primers with good polymorphism (Fig.8; Table 3), and exhibited abundant genetic diversity between \u003cem\u003eindica\u0026nbsp;\u003c/em\u003eand \u003cem\u003ejaponica\u003c/em\u003e rice. Cluster I contained only one \u003cem\u003eindica\u003c/em\u003e rice germplasm (9311) and exhibited significant genetic differences from the 20 germplasm and Nipponbare. Cluster II consisted of six germplasm (JL1, HB3, HB2, LN2, TJ2, and TJ1) and was divided into three sub-clusters. Only one germplasm (JL1) clustered into the first sub-cluster II, two germplasm (HB3 and HB2) clustered into the second sub-cluster II, and three germplasm (LN2, TJ2, and TJ1) clustered into the third sub-cluster II. Cluster III consisted of only one germplasm (ZJ1). Cluster IV contained five germplasm (JS1, AH2, LN1, JS3, and ZJ5) was divided into two sub-clusters. The two germplasm (JS1 and AH2) clustered into the first sub-cluster IV. The three germplasm LN1, JS3, and ZJ5 clustered into the second sub-cluster IV. Cluster V contained nine germplasm (ZJ2, AH1, Nip, JS4, JS2, AH3, ZJ3, HB1, and ZJ4) , which clustered into three sub-clusters. The three germplasm (ZJ2, AH1, and Nipponbare) clustered into the first sub-cluster V. The six germplasm (JS4, JS2, AH3, ZJ3, HB1, and ZJ4) clustered into the second\u003csup\u003e\u0026nbsp;\u003c/sup\u003esub-cluster V. In summary, 13 pairs of SSR primers can accurately distinguish between indica rice 9311, japonica rice Nipponbare, and 20 perennial Chinese rice germplasm (Fig. 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Principal component analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCA graph of the 20\u0026nbsp;perennial Chinese rice\u0026nbsp;germplasm was obtained to demonstrate the distribution of germplasm in accordance with the differences on the field phenotypes of traits. PCA revealed distinctive separations among the 20 germplasm in accordance with phenotypic traits. Therefore, these agronomic phenotypic parameters can be used as essential criteria for defining perennial rice germplasm. PCA was performed to identify the main distinguishing characters of the 16 agronomic traits. The dimensions implied by the 16 quantitative agronomic traits was reduced to five significant components in 2021MC, accounted for 85.38% of the total variance comparised of\u0026nbsp;EGP, SP,\u0026nbsp;GYP,\u0026nbsp;PH, and GL\u0026nbsp;(Fig.10 A;Table 4), so it was referred to as grain yield factor and plant height factor. The first six significant components in 2021RC, accounted for 86.34% of the total variance comparised of\u0026nbsp;FGP,\u0026nbsp;GSD,\u0026nbsp;GSR, PL,\u0026nbsp;LWR, and\u0026nbsp;GYP\u0026nbsp;(Fig.10 B;Table 4) , so it was referred to as grain yield factor. The first six significant components in 2022MC, accounted for 93.35% of the total variance comparised of\u0026nbsp;GYMP, GSR, GW, GL, PP, and GT\u0026nbsp;(Fig.10 C; Table 4) , so it was referred to as grain yield factor. The first four significant components in 2022RC accounted for 84.47% of the total variance comparised of FGP, SP, GL, and GW, so it was referred to as grain yield factor (Fig.10 D; Table 4). The results of PCA indicated that the number of siginificant principal components and the principal factors displayed a series of irregulal variations\u0026nbsp;across four different planting seasons.`\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 M-TOPSIS comprehensive evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TOPSIS method is widely used in multi-objective decision analysis and comprehensively employed to evaluate germplasm [31\u0026ndash;32]. It is a ranking method that is based on the similarity between a limited number of objects and an idealized target. It is used to evaluate the relative merits and demerits of existing objects. In this study, 16 agronomic traits, including HD, PH, GYP, and TGW, were employed to evaluate the 20 perennial Chinese rice germplasm on the basis of correlation and principal component analysis (Table 5). Subsequently, a comprehensive evaluation model of perennial Chinese rice germplasm was constructed by M-TOPSIS. The top one germplasm LN1 that displayed stable field phenotypic traits was screened repeatedly across four different planting seasons and adapted to the ecological areas of Chongqing in Southwest China.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Phenotypic variation of perennial Chinese rice germplasm\u003c/h2\u003e \u003cp\u003eThe field phenotypes of the agronomic traits of crop germplasm resources is an important basis for rice geneticists and breeders to elucidate the genetic mechanisms of complex traits and crop varieties undergoing improvement [33\u0026ndash;35]. In particular, the evaluation of the genetic diversity of crop germplasm resources provides important information for mining new genes and breeding new varieties [36\u0026ndash;37]. Phenotypic diversity is helpful for understanding the diversity degree of agronomic traits as a whole and provides a theoretical and practical basis for mining and utilizing excellent resources. In the present study, we analyzed the diversity of 16 agronomic traits in 20 perennial Chinese rice germplasm across four different planting seasons. The present results indicated that the CV values of 16 agronomic traits displayed wide variation and a great degree of variability. Only GT displayed a high CV value of 21.52% in 2022RC. TGW displayed high CVs of 16.89% in 2022MC and 45.73% in 2022RC. The top three CV values in 2021MC were exhibited by PP, EGP, and GYP. The top three CV values in 2021 were shown by EGP, GYP, and FGP. The top three CV values in 2022MC were presented by EGP, GYMP, and FGP. The top three CV values in 2022RC were presented by EGP, GYP, and GYMP. This finding suggested that the seven agronomic traits of PP, EGP, FGP, GYMP, GYP, GT, and TGW displayed a higher degree of phenotypic variation than other 9 agronomic traits and were easily affected by external environmental factors such as high temperature during the rice heading and filling stages in Chongqing Southwest China. Therofore, special attention should be paid toward these varibly agronomic traits owing to external environment factors in the future perennial rice breeding project and before commericaly released to farmers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Cluster analysis of the agronomic traits in perennial Chinese rice germplasm\u003c/h2\u003e \u003cp\u003eCluster analysis can gather perennial rice germplasm with similar genetic information into one cluster. It is conducive for elucidating the genetic relationships among germplasm from different ecological areas throughout the world [38\u0026ndash;39]. In this study, seven germplasm (ZJ1-5 and HB1-2) from Zhejiang and Hubei Provinces in 2021MC, 2022MC, and 2021RC clustered into clusters I, III, and I, respectively, indicating the existence of a certain correlation between the agronomic traits and geographical locations of different germplasm. However, in 2021RC, three germplasm (HB1, HB2, and ZJ3) from Hubei and Zhejiang Provinces clustered into cluster I, three germplasm (ZJ2, ZJ4, and ZJ5) from Zhejiang Province clustered into cluster II, and only ZJ1 clustered into cluster V (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The same germplasm clustered into different clusters across four different planting seasons, and cluster analysis based on the field phenotypic data of agronomic traits across different planting seasons was not stable because the field performances of agronomic traits were easily affected by external environmental factors, such as light, water, and fertilizer, especially for high temperature during the rice heading and filling stages in Chongqing Southwest China [40]. However, the field phenotypes of important agronomic traits are the basis of the breeding of new rice varieties by breeders in rice breeding projects [42\u0026ndash;43] (Yuan 1966; Cheng et al., 1998; Birchler 2015). Nowadays, SSR markers are widely employed to elucidate the genetic diversity of rice germplasm resources [44\u0026ndash;46]. In the present study, three germplasm (ZJ4, HB1, and ZJ3) clustered into the first sub-cluster I, ZJ2 clustered into the third sub-cluster I, ZJ5 clustered into the first sub-cluster II, HB2 clustered into the second sub-cluster II, and ZJ1 clustered into cluster VI. The cluster analysis based on SSR markers was different from those of agronomic traits in 20 germplasm. Geographical distribution may not play a decisive role in the genetic diversity of germplasm. In this study, some germplasm from different geographical locations was divided into the same cluster. The sixteen agronomic traits in 20 germplasm frequently exhibit phenotypic variation even if these germplasm were planted in the same experimental field of Chongqing in Southwest China across different planting seasons as result of the varible field phenotypes of agronomic traits owing to both internal genetic factors and external environmental factors. Consequently, the cluster analysis based on SSR genetic loci is more accurate and reliable than that based on the field phenotypic data of agronomic traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Correlation and principal component analyses between the quantitative agronomic traits\u003c/h2\u003e \u003cp\u003eIn this study, we observed significant correlations among the 16 agronomic traits of the 20 perennial Chinese rice germplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;D). However, a series of irregular correlations were observed in the 20 germplasm across four different planting seasons. The number, strength, and direction of significant correlations among the 16 agronomic traits of the 20 germplasm across four different planting seasons exhibited significant differences and were affected by genotype and genotype \u0026times; environment interactions. For example, GYP showed no significant correlations with PH and PP in 2022MC. However, in 2022RC, susceptible GYP exhibited significant positive correlations with PH and PP. This result indicated that GYP was codetermined by a series of variably agronomical traits owing to both internal genetic factors and external environmtental factors. It also agreed with previous findings that revealed complex correlations among crop agronomic traits [47\u0026ndash;49].\u003c/p\u003e \u003cp\u003ePCA is an effective method for reducing the dimensionality of large datasets. It can maximize interpretability, minimize information loss and determine the most suitable agronomic traits that mostly contribute to the variation in selected materials by analyzing the internal correlations of field phenotypic data [50\u0026ndash;51]. In this study, PCA confirmed that the first five components explained the vast majority of the variation, concentrating on five agronomic traits, such as the EGP, SP, GYP, PH, and GL in 2021MC; the first six significant components explained the vast majority of the variation, concentrating on six agronomic traits,such as the FGP, GSD, GSR, PL, LWR, and GYP in 2021RC; the first six significant components explained the vast majority of the variation, concentrating on six agronomic traits, such as the GYMP, GSR, GW, GL, PP, and GT in 2022MC; the first four significant components comprised of FGP, SP, GL, and GW in 2022RC explained the vast majority of the variation. The results suggested that these concentrated agronomic traits are suitable for the assessment of genetic diversity and field performance characterization of perennial Chinese rice germplasm and affected by high temperature during the rice heading and filling stages in Chongqing Southwest China. Therefore, special attention should be paid toward the ecological adaptability of perennial rice before commericaly released to farmers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Perennial Chinese rice germplasm for further breeding utilization\u003c/h2\u003e \u003cp\u003eRice germplasm resources are the important basis of rice genetics and breeding [41, 52\u0026ndash;54]. A series of new rice varieties with good quality, high yield, multi-resistance, and wide adaptability have been successfully bred and commercially released to farmers due to the innovation and utilization of excellent rice germplasm resources. In particular, the innovative utilization of \u003cem\u003eO. longistaminata\u003c/em\u003e germplasm has filled the gap in the breeding of new perennial Chinese rice varieties [16, 54]. In this study, we evaluated the field phenotypes of agronomic traits and genetic diversity of perennial Chinese rice germplasm across four different planting seasons. One germplasm LN1 displayed stable field phenotypic agronomic traits across four different planting seasons was adapted to the ecological areas of Chongqing in Southwest China. They might be directly applied in rice production. The stability of the agronomic traits of the remaining 19 germplasm should be considered for evaluation outside Chongqing. The presenting perennial rice germplasm are suitable for elucidating the molecular and genetic mechanisms of perennial characteristics and even sustainable agricultural development in China.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn this study, 16 agronomic traits, including HD, PH, and TGW, were observed across four different planting seasons to assess the phenotypic diversity of 20 perennial Chinese rice germplasm. Meanwhile, 13 pairs of SSR primers with good polymorphism were employed to reveal the genetic diversity of the 20 germplasm. Ample phenotypic variations and abundant DNA genetic diversity were observed in the 20 germplasm. A comprehensive evaluation model of perennial Chinese rice germplasm was constructed by M-TOPSIS. The top one germplasm LN1 that displayed stable phenotypic agronomic traits was screened repeatedly across four different planting seasons and adapted to the ecological areas of Chongqing in Southwest China was screened repeatedly across four different planting seasons. This study will provide a reference for the further utilization of perennial Chinese rice germplasm and genetic improvement of agronomic traits, and even sustainable agricultural development in China\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYSL:\u003c/strong\u003eConceptualization, Funding acquisition, Supervision, Writing-original draft, and Writing-review \u0026amp; editing; \u003cstrong\u003eJYG\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Resources and Methodology; \u003cstrong\u003eYXY\u003c/strong\u003e: Investigation, Data curation, and Formal analysis; \u003cstrong\u003eTSP and LT\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Investigation and Data curation; \u003cstrong\u003eJYL\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation and Methology; \u003cstrong\u003eXJQ\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Software and Formal analysis; \u003cstrong\u003eML and WBN\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eData curation and Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China (SKL-KF202226), the Chongqing Natural Science Foundation of China (cstc2021jcyj-msxm X0007), and the Open Project Program of State Key Laboratory of Rice Biology (20190202).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe published article and its supplementary materials include all the data generated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJackson W. New roots for agriculture[M]. New Edition. Lincoln: University of Nebraska Press 1980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou K, Robards S, Helliwell C, Blanchard. Composition and functional properties of rice. International Journal of Food Science Technology. 2002; 37: 849\u0026ndash;868.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng SH. Chinese super rice breeding. Science Press, Beijing, China 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer G, Winiwarter W, Cao GY, Ermolieva T, Hizsnyik E, Klimont Z, Wiberg D, Zheng XY. Implications of population growth and urbanization on agricultural risks in China. Population and Environment. 2012; 33: 243\u0026ndash;258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan J. China: Food security and agricultural going global. American Academic Press. 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacksonW. New roots for agriculture [M]. New Edition. Lincoln: University of Nebraska Press, 1980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlover JD, Reganold JP, Bell LW, Borevitz J, Brummer EC, Buckler ES, Cox CM, Cox TS, Crews TE, Culman SW, DeHaan LR, Eriksson D, Gill BS, Holland J, Hu F, Hulke BS, Ibrahim AMH, Jackson W, Jones SS, Murray SC. Increased food and ecosystem security via perennial grains. Science. 2010; 328: 1638\u0026ndash;1639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrews TE, Carton W, Olsson L. Is the future of agriculture perennial? Imperatives and opportunities to reinvent agriculture by shifting from annual monocultures to perennial polycultures. Global Sustainability. 2018; 1: 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin YL. Research progress on resistance breeding of perennial rice. Fujian Science and Technology of Rice and Wheat. 1991; 1: 33\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi QX. Perennial rice cultivars I Interspecific crosses for breeding perennial lines in Oryza. Southwest China Journal of Agricultural Sciences. 1997; 10: 9\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacks EJ, Roxas JP, Cruz MT. Developing perennial upland rice I: Field performance of \u003cem\u003eOryza sativa\u003c/em\u003e /\u003cem\u003eO.rufipogon\u003c/em\u003e F\u003csub\u003e1\u003c/sub\u003e, F\u003csub\u003e4\u003c/sub\u003e, and BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e4\u003c/sub\u003e progeny. Crop Science. 2003a; 43: 120\u0026ndash;128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacks EJ, Roxas JP, Cruz MT. Developing perennial upland rice II: field performance of S1 families from an intermediated \u003cem\u003eOryza sativa\u003c/em\u003e/\u003cem\u003eO.longistaminata\u003c/em\u003e population. Crop Science. 2003b; 43:129\u0026ndash;134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrews TE, Cattani DJ. Strategies, advances, and challenges in breeding perennial grain crops. Sustainability. 2018; 10: 2192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacks EJ, Dhanapala MP, Tao DY, Cruz MT, Sallan R. Breeding for perennial growth and fertility in an \u003cem\u003eOryza sativa\u003c/em\u003e/\u003cem\u003eO.longistaminata\u003c/em\u003e population. Field Crops Research. 2006; 95: 39\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao DY, Sripichitt P. Preliminary report on transfer traits of vegetative propagation from wild rice species to \u003cem\u003eOryza sativa\u003c/em\u003e via distant hybridization and embryo rescue. Kasetsart Journal. 2000; 34: 1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang SL, Hu J, Yang CD, Liu HT, Yang F, Zhou JH, Samson BK, Boualaphanh C, Huang LY, Huang GF, Zhang J, Huang WQ, Tao DY, Harnpichitvitaya D, Wade LJ, Hu FY. Genotype by environment interactions for grain yield of perennial rice derivatives (\u003cem\u003eOryza sativa\u003c/em\u003e L/\u003cem\u003eOryza longisaminata\u003c/em\u003e) in southern China and Laos. Field Crops Research. 2017; 207: 62\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamson BK, Voradeth S, Zhang SL, Tao DY, Xayavong S, Khammone T, Douangboupha K, Sihathep V, Sengxua P, Phimphachanhvongsod V, Bouahom B, Jackson T, Harnpichitvitaya D, Hu FY, Wade LJ. Performance and survival of perennial rice derivatives (\u003cem\u003eOryza sativa\u003c/em\u003e L/\u003cem\u003eOryza longistaminata\u003c/em\u003e) in Lao PDR. Experimental Agriculture. 2018; 54: 592\u0026ndash;603.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang GF, Qin SW, Zhang SL, Cai XL, Wu SK, Dao JR, Zhang J, Huang LY, Harnpichitvitaya D, Wade LJ, Hu FY. Performance, economics and potential impact of perennial rice PR23 relative to annual rice cultivars at multiple locations in Yunnan province of China. Sustainability. 2018; 10: 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang SL, Huang GF, Zhang YJ, Lv XT, Wan KJ, Liang J, Feng YP, Dao JR, Wu SK, Zhang L, Yang X, Lian XP, Huang LY, Shao L, Zhang J, Qin SW, Tao DY, Crews TE, Sacks EJ, Lyu J, Wade LJ, Hu FY. Sustained productivity and agronomic potential of perennial rice. Nature Sustainability. 2022; 6: 28\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCulman SW, Snapp SS, Ollenburger M, Basso B, Dehaan LR. Soil and water quality rapidly responds to the perennial grain kernza wheatgrass. Agronomical Journal. 2013; 105: 735\u0026ndash;744.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJungers JM, DeHaan LH, Mulla DJ, Sheaffer CC, Wyse DL. Reduced nitrate leaching in a perennial grain crop compared to maize in the Upper Midwest, USA. Agriculture, Ecosystems \u0026amp; Evironment. 2019; 272: 63\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira GD, Brunsell NA, Sutherlin CE, Crews TE, DeHaan LR. Energy, water and carbon exchange over a perennial kernza wheatgrass crop. Agricultural and Forest Meteorology. 2018; 249: 120\u0026ndash;137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhangYJ, Huang GF, Zhang SL, Zhang J, Gan SX, Cheng M, Hu J, Huang LY, Hu FY. An innovated crop management scheme for perennial rice cropping system and its impacts on sustainable rice production. European Journal of Agronomy. 2021; 122: 126186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVander Pol L, Nester B, Schlautman B, Crews TE, Contrufo MF. Perennial grain kernza fields have higher particulate organic carbon at depth than annual grain fields. Canadian Journal of Soil Science. 2022;102: 1005\u0026ndash;1009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang YS, Gong JY, Yan YX, Wang BB, Gong WA, Wen H, Wu Q, Nan WB, Qin XJ, Zhang HM. Survey of overwintering trait in Chinese rice cultivars (\u003cem\u003eOryza sativa\u003c/em\u003e L). Euphytica. 2022; 218: 94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen ZT. Crop breeding experiment. China Agriculture Press, Beijing. 1995.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang DY, Zhang H, Wang L, Jia CH, Zhao B, Zuo N, Tong XP, Zhao F, Pei ZY. Molecular detection and analysis of blast resistance genes and restored genes in 107 japonica rice. Molecular Plant Breeding. 2021; 19: 2644\u0026ndash;2659.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuthbert J, Somers D, Brule-Babel A, Brown P, Crow G. Molecular mapping of quantitative trait loci for yield and yield components in spring wheat (\u003cem\u003eTriticum Aestivum\u003c/em\u003e L). Theoretical Applied Genetics. 2008;117: 595\u0026ndash;608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei QY, Zhou JG, Zhang WH, Luo J, Wu K, Long CN. Morphological diversity of panicle traits in kam fragrant glutinous rice (\u003cem\u003eOryza sativa\u003c/em\u003e L). Genetic Resources and Crop Evolution. 2018; 65:775\u0026ndash;786.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas R, Divakara BN. Variability and divergence in Pongamia pinnata for further use in tree improvement. Journal of Forestry Research. 2011; 22:193\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang CL, Yoon K. Methods for multiple attribute decision making[M]//Multiple Attribute Decision Making. Berlin: Springer-Verlag, 1981: 58\u0026ndash;191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Liu L, Zhou YL, Zheng Q, Luo SY, Xiang TT, Zhou LJ, Feng SL, Yang HY, Ding CB. Characterization and comprehensive evaluation of phenotypic characters in wild Camellia oleifera germplasm for conservation and breeding. Frontiers in Plant Science. 2023,10.3389/fpls.2023.1052890\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoleri D, Cleveland DA. Farmer selection and conservation of crop varieties[C]//GOODMAN RMed. Encyclopedia of Plant and Crop Science. New York: Marcel Dekker Incorporated. 2004: 433\u0026ndash;438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q. Natural variation in \u003cem\u003eGhd7\u003c/em\u003e is an important regulator of heading date and yield potential in rice. Nature Genetics. 2008; 40: 761\u0026ndash;767.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbll DR. Using phenotypic manipulations to study multivariate selection of floral trait associations. Annals of Botany. 2009;103:1557\u0026ndash;1566.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao KY, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications. 2011; 2: 467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlaszmann JC, Kilian B, Upadhyaya HD, Varshney RK. Accessing genetic diversity for crop improvement. Current Opinion in Plant Biology. 2010; 13: 167\u0026ndash;173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalmaijer ES, Nord CL, Astle DE. Statistical power for cluster analysis. BMC Bioinformation. 2022:23;1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevi NS, Zaman S, Sarma M, Bhuyan J, Saharia DD, Nath MP, Talukdar JC, Ahmed ASM. Genetic diversity analysis for yield and quality traits in rice cultivars of manipur and assam. International Journal of Plant Soil \u0026amp; Science. 2022; 34: 1293\u0026ndash;1304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasimR, Mohammadreza JT. Agronomic characteristics and grain yield of 30 Iranian rice genotypes as affected by genotype x environment interaction. Ecological Genetics and Genomics. 2023; 26: 100153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan LP. A preliminary report on male sterility in rice, \u003cem\u003eOryza sativa\u003c/em\u003e L. Chinese Science Bulltin. 1966; 4: 322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng SH, Liao XY, Min SK. China\u0026rsquo;s super rice research: background, goals and issuers. China Rice. 1998; 1: 3\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirchler JA. Heterosis: the genetic basis of hybrid vigour. Nature Plants. 2015; 1: 15020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng Y, Zhang H, Wang L, Pu X, Du J, Yang S, Liu J. of rice landraces (\u003cem\u003eOryza sativa\u003c/em\u003e L) in Yunnan, China. Breeding Science. 2007; 57: 91\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomson MJ, Septiningsih EM, Suwardio F, Santoso TJ, Silitonga TS, McCouch SR. Genetic diversity analysis of traditional and improved Indonesian rice (\u003cem\u003eOryza sativa\u003c/em\u003e L) germplasm using microsatellite markers. Theoretical Applied Genetics. 2007; 114: 559\u0026ndash;568.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei XH, Yuan XP, Yu HY, Wang YP, Xu Q, Tang SX. Temporal changes in SSR allelic diversity of major rice cultivars in China. Journal of Genet Genomics. 2009; 36: 363\u0026ndash;370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, Zhang YD, Zhu Z, Zhao L, Zhao QY, Zhang QF, Wang CL. Dissection of QTLs in two years for component tratis in rice (\u003cem\u003eOryza sativa\u003c/em\u003e). Chinese Journal Rice Science. 2008; 22: 370\u0026ndash;376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu K, Shen GZ, Liang Y, Fu MZ, He B, Tie LM, Zhang Y, Peng L, Li JN. Analysis of yield components with high harvest index in Brassica napus under environments fitting different yield levels. Acta Agronomica Sinica. 2017; 43: 82\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang YS, Nan WB, Qin XJ, Zhang HM. Field performance on grain yield and quality and genetic diversity of overwintering cultivated rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) in southwest China. Scientific Reports. 2021;11:1846.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNardoM, Saisana M, Saltelli A, Tarantola S. Tools for composite indicators building. European Comission Ispra. 2005; 15: 19\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philosophical Transactions A. 2016; 374: 2065.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi MS. The discovery and study of the photosensitive recessive male-sterile rice. Scientia Agriculture Sinica. 1985; 2: 44\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng QC, Zhou KD, Zhu Z, Luo Q. Current status in the use of hybrid rice heterosis in China. Chinese Journal Rice Science. 2000; 14: 243\u0026ndash;246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng B, Gong JY, Zhang F. Analysis and prospects of extension of main varieties of hybrid rice with high quality in China. Chinese Journal Rice Science. 2022; 36: 439\u0026ndash;446.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"perennial Chinese rice, agronomic traits, genetic diversity, comprehensive evaluation","lastPublishedDoi":"10.21203/rs.3.rs-5794229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5794229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePerennial Chinese rice is a novel type of rice germplasm native to China. This study comprehensively evaluated the variation in the agronomic traits of 20 perennial Chinese rice germplasm across four different planting seasons to explore the genetic diversity of perennial Chinese rice and effectively utilize them. A total of 16 agronomic traits, including heading date, plant height, and thousand-grain weight, were investigated based on the field phenotypic values. The findings revealed significant variations among these traits with a broad range of Shannon\u0026ndash;Wiener indices, which ranged from 1.46 to 3.33 in 2021MC, 1.49 to 1.96 in 2021RC, 1.50 to 2.10 in 2022MC, and 1.31 to 2.10 in 2022RC. The coefficients of variation among 16 traits ranged from 4.40\u0026ndash;64.34% in 2021 MC, 5.53\u0026ndash;74.24% in 2021RC, 3.91\u0026ndash;56.90% in 2022MC, and 3.55\u0026ndash;92.57% in 2022RC. The 20 germplasm were divided into five distinctive clusters in 2021MC, 2021RC, and 2022MC and six distinctive clusters in 2022RC based on the analysis of hierarchical clustering, but divided into six categories by 13 pairs of SSR primers with good polymorphism. The M-TOPSIS exhaustive evaluation method based on correlation and the principal component analysis (PCA) of 16 traits was applied for the 20 germplasm, and the top one germplasm LN1 that displayed stable field performance on agronomic traits was screened repeatedly across four different planting seasons and adapted to the ecological areas of Chongqing in Southwest China. This study will provide a reference for the screening of potential perennial rice germplasm and the further research on perennial rice genetics and breeding.\u003c/p\u003e","manuscriptTitle":"Diversity Analysis and Comprehensive Evaluation of Agronomic Traits in Perennial Chinese Rice Germplasm for breeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 14:14:35","doi":"10.21203/rs.3.rs-5794229/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5c13794-7b3c-42c9-988c-b247ff8876b6","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T10:08:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 14:14:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5794229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5794229","identity":"rs-5794229","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}