Rapid generation advancement of RIL population and unlocking the potential of Rhizobium nodulation for improving crop yields in chickpea

Rapid generation advancement of RIL population and unlocking the potential of Rhizobium nodulation for improving crop yields in chickpea | 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 Article Rapid generation advancement of RIL population and unlocking the potential of Rhizobium nodulation for improving crop yields in chickpea Nandigam SwathiRekha, Mahesh Damodhar Mahendrakar, Srungarapu Rajasekhar, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598881/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Chickpeas, a widely cultivated legume, actively fix atmospheric nitrogen in root nodules through a symbiotic relationship with rhizobia bacteria. A recombinant inbred line (RIL) population, progressing from F2 to F7 generations, was developed in a short-period of 18 months using the Rapid Generation Advancement (RGA) protocol. The F 7 RILs were evaluated during the 2020-21 and 2021-22 crop seasons under typical field conditions to quantify the effects of nodulation on seed yield (SY) and its associated traits. The analysis of variance revealed a highly significant difference (P < 0.01) among genotypes for seed yield and other agronomic traits, with no significant seasonal effect. In the pooled analysis, nodulating genotypes (NG) exhibited a substantial increase (P < 0.01) in SY (62.55%), 100-seed weight (SW100; 12.21%), harvest index (HI; 6.40%), number of pods per plant (NPPP; 39.55%), and number of seeds per plant (NSPP; 44.37%) compared to non-nodulating genotypes (NNG). Both NG and NNG exhibited a significant (P < 0.01) positive correlation between SY and NPPP (r=0.64 and 0.63), NSPP (r=0.66 and 0.61), HI (r=0.27), and number of primary branches per plant (PBr) (r=0.31), respectively. The top-performing genotypes for yield and related traits were predominantly nodulating. Genotype-trait bi-plot analysis identified nine nodulating genotypes as the most adaptable across the two seasons—six for SY, plant height, SW100, and three for days to first flowering and maturity. These findings underscore the critical role of nodulation in maximizing chickpea yields and the significant yield penalties associated with non-nodulation. To boost chickpea production, future breeding efforts should focus on developing genotypes with high compatibility with rhizobium strains. Biological sciences/Plant sciences Biological sciences/Plant sciences/Plant breeding Chickpea rhizobium Nodulation Rapid Generation Advancement (RGA) Recombinant Inbred Line (RIL) population Nodulating and Non-nodulating genotypes Genetic variability. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Legumes have a specific mutualistic relationship with soil bacteria that fix the atmospheric nitrogen into plant-usable ammonical form in root nodules 49 , in contrast to other plants. Development of nodules and nitrogen fixation depends majorly on the legume cultivar and its specific rhizobial strain 39 , 52 , 73 which will in turn improve the overall crop growth, productivity 34 , 57 and soil health. Around 20 to 22 million tonnes of nitrogen per annum is fixed in the agricultural systems by the Leguminosae family members 33 which contributes significantly towards reducing the global carbon footprint. Chickpea ( Cicer arietinum L.) is a highly nutritious, diploid (2n = 2x = 16) legume crop grown in an area of 14.2 million hectares across 56 countries globally 21 , of which, India is the largest producer and consumer. It is considered a storehouse of proteins, complex carbohydrates, vitamins, and micronutrients that are required for human nutrition 14 , 15 , 23 , 37 , 65 . Being a leguminous crop, chickpea fixes the atmospheric nitrogen by association with Rhizobium species 30 which differentiates it from other cereal crops 79 . It stores the fixed nitrogen in the nodules present in its root system and converts it into ammonia that can be used by the plant 22 , 84 . It was estimated that about 70 kg of nitrogen per hectare is fixed annually by chickpea 78 , which helps in providing nitrogen not only to the host but also to the subsequent crops grown 45 thereby, helping the farmers in reducing the cost of production. Root nodulation is a complex process requiring the recognition of symbiotic bacteria, Nod-factor induced infection, and root growth 43 , 58 , 70 . Polyphenols named flavonoids released from the roots, stimulate Nod-factor production in rhizobia thereby initiating curling and colonization of root hair and the formation of root nodules 48 . Phytohormone signaling plays a major role during this process between the bacteria and the host 10 . The symbiotic nitrogen fixation (SNF) efficiency is dependent on the host, rhizobial strains, soil conditions 85 , availability of phosphorous 80 , and environmental conditions 44 . Under water stress, the biochemical activity in the nodules will get disrupted resulting in the senescence of nodules 3 , 6 , 36 along with downgraded leg-hemoglobin content and nitrogenase activity 20 . The ability of the nodules to supply energy, transport, regulate oxygen molecules, and assimilate ammonia to the plants will help in increasing plant growth and yield 19 . However, the consequences of the chickpea roots being unable to make a symbiotic relationship with rhizobium are not well understood and documented. Several studies reported in chickpea 16 , 17 , 30 , 59 as well as in other leguminous crops viz ., soybean 82 , cowpea 36 , lupin 9 , groundnut 5 , etc., were focused majorly on the external application of biochar, artificial fertilizers, growth hormones and the findings of strain-specific effect on nodulation and yield-related traits. However, the information on the impact of nodulation over non-nodulation on yield and yield-contributing traits was minimal. In this context, the current study aimed to 1) broaden the knowledge on the association between nodulation, yield, and its associated traits; and 2) quantify the value gain of the traits in a RIL population segregating for nodulation trait. In addition, we successfully showcased the utility of rapid generation advancement methods for developing mapping populations in a short period. Results Analysis of variance The results of the combined analysis of variance (ANOVA) revealed significant differences (Prob < 0.01) among the genotypes for the traits under study while for the factors, block and replication, no significant difference was observed (Table 1 ). For nodulating genotypes (NG), mean performance of agro-morphological and yield-related traits over the seasons was in the range of 42.74–47.37 (Days to first flowering- DF), 47.57–51.76 (Days to 50% flowering- DFF), 94.24-100.92 (Days to maturity- DM), 38.74–44.36 (Plant height- PH), 1.94–2.17 (Number of primary branches per plant- PBr), 6.03–8.48 (Number of secondary branches per plant- SBr), 293.76-343.36 (Number of pods per plant- NPPP), 349.08-395.66 (Number of seeds per pod- NSPP), 17.12–17.26 (100 seed weight- SW100), 34.50-36.85 (Harvest index- HI), 2472.78-2689.62 (Seed yield- SY) (Table 2 ). Similarly, non-nodulating genotypes (NNG) exhibited comparable trends over the seasons ranging from 44.58–50.60 (DF), 49.27–54.87 (DFF), 96.20-103.46 (DM), 34.86–39.28 (PH), 1.98–2.10 (PBr), 6.97–8.42 (SBr), 194.53-265.36 (NPPP), 214.48–30.23 (NSPP), 14.29–16.27 (SW100), 33.19–33.87 (HI) and 1638.78-1665.05 (SY) (Table 2 ). In addition to this, checks exhibited a range of 42.61–43.38 (DF), 47.50-48.53 (DFF), 93.26–98.52 (DM), 54.02–59.63 (PH), 1.99–2.07 (PBr), 5.06–7.81 (SBr), 296.37-321.63 (NPPP), 320.70–338.00 (NSPP), 21.55–21.67 (SW100), 31.68–34.34 (HI), 2644.09-2667.64 (SY) (Table 2 ) over the seasons. The median values (black solid line) for agro-morphological and yield component traits across seasons were provided using Violin plot analysis (Fig. 1 ). All the traits exhibited almost consistent median values except for PH, PBr, and SW100 for both NG and NNG over the seasons, highlighting the importance of seasonal variations in the breeding selection process. Notably, the observed ranges of agro-morphological traits and yield components of NG, NNG, and checks, emphasize the inherent variability within the genotypes (nodulating and non-nodulating) across the seasons, which underscores the superiority of nodulating genotypes in terms of productivity. Table 1 Analysis of Variance (ANOVA) for agro-morphological, yield, and yield component traits in chickpea genotypes under pooled conditions Fixed Effects Df DF DFF DM PH PBr SBr NPPP NSPP SY SW100 HI Season 1 5304.05** 3420.17** 5658.18** 1710.99** 144.85** 341.85** 1.8 15.35 178.75** 809.92** 87.48** Rep (Season) 4 2.19 4.6 3.51 4.95 4.77 14.08** 0.3 1.45 3.81 0.4 5.49 Genotype 229 27.82** 23.31** 14.83** 36.13** 3.77** 1.53** 7.69** 10.26** 20.95** 46.96** 8.49** Season×Genotype 229 12.58** 10.47** 9.14** 6.15** 3.63** 1.93** 6.04** 7.49** 8.17** 11.07** 7.81** Random Effects Block (Season Rep) 0.03 0.07 0.05 0.20 0 0.09 0.27 0.32 0 0 0.17 Residual1 1.19 1.23 1.83 2.13 0.03 3.63 2.40 2.49 138066 0.27 2.57 Residual2 0.95 1.11 1.54 3.16 0.03 2.87 2.54 1.94 34966 0.20 1.68 Comparison of Nodulation Vs Non-nodulation Nodulation Yes 45.06 49.67 97.58 41.55 2.05 7.26 314.97 368.36 2685.23 17.26 35.67 No 47.59 52.07 99.83 37.07 2.04 7.70 225.7 255.15 1651.91 15.38 33.53 Mean difference -2.55** -2.40** -2.25** 4.48** 0.02 -0.44** 2.76** 3.24** 1033.32** 1.88** 2.15** SE 0.07 0.07 0.09 0.10 0.01 0.10 0.11 0.10 19.79 0.03 0.10 %Change in nodulation 5.33(↓) 4.62(↓) 2.26(↓) 12.08(↑) 0.74(↑) 5.74(↓) 39.55 (↑) 44.37 (↑) 62.55(↑) 12.21 (↑) 6.40 (↑) **-Significance at 1% level, (↓)-% decrease, (↑)-% increase, DF-Days to first flowering, DFF-Days to 50 percent flowering, DM-Days to maturity, PH-Plant Height, PBr-Number of primary branches per plant, SBr-Number of secondary branches per plant, NPPP- Number of pods per plant, NSPP-Number of seeds per pod, SY-Seed yield, SW100-100 seed weight, HI-Harvest Index. Table 2 Mean performance (± SE) of nodulating and non-nodulating genotypes of RIL population agro-morphological, yield and yield components traits in chickpea Character Season Nodulating genotypes Non-nodulating genotypes Checks Agro-morphological traits Days to first flowering Rabi 2020-21 Rabi 2021-22 Pooled 47.37 ± 0.75 42.74 ± 0.68 45.12 ± 0.50 50.60 ± 0.75 44.58 ± 0.68 47.60 ± 0.51 43.38 ± 0.78 42.61 ± 0.70 42.99 ± 0.52 Days to 50 percent flowering Rabi 2020-21 Rabi 2021-22 Pooled 51.76 ± 0.77 47.57 ± 0.72 49.72 ± 0.53 54.87 ± 0.77 49.27 ± 0.73 52.08 ± 0.53 48.53 ± 0.80 47.50 ± 0.73 48.01 ± 0.54 Days to maturity Rabi 2020-21 Rabi 2021-22 Pooled 94.24 ± 0.93 100.92 ± 0.86 97.63 ± 0.63 96.20 ± 0.93 103.46 ± 0.87 99.85 ± 0.64 93.26 ± 0.97 98.52 ± 0.81 95.89 ± 0.63 Plant Height Rabi 2020-21 Rabi 2021-22 Pooled 38.74 ± 0.95 44.36 ± 1.19 41.07 ± 0.76 34.86 ± 0.93 39.28 ± 1.19 37.05 ± 0.76 54.02 ± 1.07 59.63 ± 1.29 56.82 ± 0.84 Number of primary branches per plant Rabi 2020-21 Rabi 2021-22 Pooled 2.17 ± 0.13 1.94 ± 0.12 2.05 ± 0.09 2.10 ± 0.12 1.98 ± 0.12 2.04 ± 0.09 1.99 ± 0.14 2.07 ± 0.12 2.03 ± 0.09 Number of secondary branches per plant Rabi 2020-21 Rabi 2021-22 Pooled 8.48 ± 1.12 6.03 ± 0.99 7.29 ± 0.75 8.42 ± 1.11 6.97 ± 0.99 7.70 ± 0.75 7.81 ± 1.11 5.06 ± 0.99 6.44 ± 0.75 Yield component traits Number of pods per plant Rabi 2020-21 Rabi 2021-22 Pooled 343.36 ± 1.13 293.76 ± 1.12 316.20 ± 0.80 194.53 ± 1.12 265.36 ± 1.13 225.45 ± 0.80 321.63 ± 1.15 296.37 ± 1.01 304.34 ± 0.77 Number of seeds per pod Rabi 2020-21 Rabi 2021-22 Pooled 395.66 ± 1.16 349.08 ± 1.04 370.74 ± 0.78 214.48 ± 1.15 307.23 ± 1.04 254.91 ± 0.78 338.00 ± 1.17 320.70 ± 1.00 324.48 ± 0.77 100 seed weight Rabi 2020-21 Rabi 2021-22 Pooled 17.25 ± 0.36 17.26 ± 0.31 17.12 ± 0.24 14.29 ± 0.36 16.47 ± 0.32 15.38 ± 0.24 21.55 ± 0.37 21.67 ± 0.32 21.61 ± 0.24 Harvest index Rabi 2020-21 Rabi 2021-22 Pooled 36.85 ± 1.15 34.50 ± 0.92 35.75 ± 0.74 33.19 ± 1.16 33.87 ± 0.93 33.53 ± 0.75 31.68 ± 1.11 34.34 ± 0.91 33.01 ± 0.72 Seed yield Rabi 2020-21 Rabi 2021-22 Pooled 2472.8 ± 132.1 2689.6 ± 147.0 2685.2 ± 263.8 1665.1 ± 263.8 1638.8 ± 132.7 1652.3 ± 147.7 2667.6 ± 263.1 2644.1 ± 132.4 2655.9 ± 147.3 Genetic parameters for the yield and agro-morphological traits The values of the genetic parameters for yield, yield components, and agro-morphological traits in NG and NNG of chickpea are presented in Supplementary Tables 2 and 3 respectively. For NG (Supplementary Table 2), low estimates of the Genotypic coefficient of variation (GCV) and Phenotypic coefficient of variation (PCV) were recorded for DF, DFF, and DM; moderate to low for PH; low to moderate for PBr recorded and high to low for SBr. Estimates of the genetic advance of mean (GAM) were low for DM; low to moderate for DF and DFF; low to high for SBr; and moderate to high for PH and PBr in pooled. For yield and yield component traits, PCV and GCV were recorded as low for HI, moderate to low for SW100, and high to moderate for NPPP, NSPP, and SY. Estimates of the GAM were high for NPPP, NSPP, and SY whereas for SW100 and HI the estimates were moderate and low to moderate in pooled. In NNG, the estimates of PCV and GCV were recorded as low for DF, DFF, and DM; moderate to low for PH, and PBr, and high to low for SBr (Supplementary Table 3). For GAM, low to moderate estimates were recorded for DF and DFF; low for DM; moderate to high for PH; and low to high for PBr and SBr. The estimates of PCV, GCV, heritability, and GAM for yield and yield components were high for NPPP, NSPP, and SY. PCV and GCV estimates are low to moderate for SW100 and HI; and high for SY. Promising NG and NNG were identified for yield and yield components (Table 3 ). Of the NG, NG-172 recorded the highest yield (3966.7 kg/ha) followed by NG-88 (3730.7 kg/ha), NG-188 (3584.7 kg/ha), NG-159 (3563.8 kg/ha), NG-13 (3561.6 kg/ha). Even though the increase in SY was non-significant, it was higher than the best checks RVG 204 and Phule Vikram (Table 3 ), Among the NNG, NNG-206 recorded the highest yield (3153.7 kg/ha) followed by NNG-182, NNG-163, NNG-80, and NNG-152 with yields of 2881.3 kg/ha, 2676.4 kg/ha, 2661.9 kg/ha, 2648.5 kg/ha respectively. These NNGs showed on-par seed yield with the best checks. Correlation of agro-morphological, yield, and yield component traits Karl Pearson’s correlation coefficients were calculated for the agro-morphological, yield, and yield component traits of NG and NNG (Supplementary Tables 4 and 5). In NG under both seasons, SY showed a significant positive correlation with PH, NPPP (except 2020-21), NSPP, and SW100. Similarly, in pooled, SY was positively associated with PH, PBr, NPPP, NSPP, and HI. DF and DFF traits showed a significant negative correlation with DM and SW100 in both the crop seasons and pooled data. Whereas PH showed a significant positive correlation with SW100 in both the seasons and pooled. For NPPP, a positive significant correlation was recorded with NSPP and HI whereas NSPP recorded a significant positive correlation with HI alone (Fig. 2 ). In NNG, the SY showed a significant negative correlation with NPPP in Rabi 2020-21; PH, NPPP, and NSPP in Rabi 2021-22 whereas it was significantly positively correlated with NPPP and NSPP in pooled. In both the cropping seasons and pooled season a significant positive correlation was recorded between DF and DFF, PH and SW100, and NPPP and NSPP (Fig. 2 ). Table 3 Details of promising nodulating and non-nodulating genotypes identified for yield and yield related traits S.No Genotype Days to first flowering Days to 50% flowering Days to maturity Plant Height (cm) Number of primary branches per plant Number of secondary branches per plant Number of pods per plant Number of seeds per plant 100 seed weight (g) Harvest Index (%) Seed yield (kg/ha) Nodulating genotypes 1 NG-172 51.28 55.85 104.09 44.32 2.11 8.11 425.19 562.11 15.71 35.14 3966.7 2 NG-88 46.74 51.39 99.15 41.09 2.35 5.86 294.62 335.90 17.49 34.98 3730.7 3 NG-188 47.29 51.52 100.34 40.14 2.43 7.99 408.33 502.47 15.91 33.64 3584.7 4 NG-159 42.52 47.13 96.21 44.65 1.87 7.99 350.80 446.68 16.80 36.86 3563.8 5 NG-13 46.20 50.52 97.71 43.22 2.34 7.00 430.07 514.43 18.17 36.60 3561.6 Non-nodulating genotypes 6 NNG-206 48.74 53.41 99.14 42.12 2.06* 8.26 466.59 531.88 15.48 35.05 3153.7 7 NNG-182 49.40 54.25 97.31 38.43 2.04* 6.50 232.36 243.31 16.40 35.24 2881.3 8 NNG-163 53.32 58.08 101.52 69.55 2.29 6.87 242.60 248.73 24.83 30.26 2676.4 9 NNG-80 49.44 52.70 99.93 38.71 2.04 8.33 375.88 401.02 15.57 34.67 2661.9 10 NNG-152 46.80 51.35 97.54 40.07 1.82 8.21 383.81 407.67 14.76 34.09 2648.5 Checks 12 Phule Vikram 43.47 47.93 96.63 57.14 2.05 5.68 363.23 375.45 17.57 35.13 3201.6 13 RVG 204 43.54 48.96 96.40 59.97 2.26 6.98 381.76 409.69 21.01 32.76 3478.4 14 NBeG 47 45.02 49.72 95.71 60.69 1.73 6.47 167.34 180.91 25.57 28.92 1501.8 15 JG 14 39.95 45.44 94.82 49.50 2.09 6.61 305.02 331.87 22.31 35.23 2441.8 16 ICC 4918NN 51.26 46.77 97.53 39.13 1.88 7.05 248.72 278.34 14.87 33.35 1614.4 17 ICC 4918 49.75 45.47 97.20 42.99 2.12 6.50 198.44 234.43 17.69 35.88 2231.4 CD (0.05) 3.71 3.57 3.3 4.88 0.1 0.1 82.72 107.91 1.94 1.54 912 Scatter Plot for yield and yield component traits in chickpea RILs The relationships between two numeric variables in the data set for the target traits and the performance of the genotypes in two environments were analyzed using a scatter plot (Fig. 3 ). The RILs that were in the 2nd and 4th coordinates are adaptable when considering the environment as the main factor while, those in the 1st and 3rd coordinates are stable across the environments. Specifically, genotypes situated in the 1st and 3rd coordinates were identified as NG and NNG, respectively. The scatter plot analysis revealed distinct performance patterns for yield and yield component traits across different seasons in the NG and NNG (Fig. 3 ). In pooled (2nd and 3rd coordinate), RILs 172, 88, 188, 159, and 13 for seed yield; RILs 206, 156, 13, 172 and 17 for NPPP; the RILs 172, 40, 206, 13, 56 for NSPP; RILs 92, 199, 194, 17 and 63 for HI; RILs 163, 157, 194, 22, 68 for SW100 exhibited better performance, emphasizing the trait-specific adaptability of genotypes (Fig. 3 ). For Rabi 2020-21 (4th coordinate), the RILs 172, 88, 188, 178, 159 for SY; the RILs 172, 188, 178, 40, 159 NPPP; the RILs 172, 98, 188, 221, 13 for NSPP; the RILs 92, 199, 194, 17 and 63 for HI; RILs 163, 22, 207, 68, 157 for SW100 whereas for Rabi 2021-22 (2nd coordinate), the RILs 209, 101, 110, 151, 136 for SY; the RILs 156, 94, 4, 199, 196 for NPPP; RILs 156, 4, 94, 102, 175 for NSPP; the RILs 114, 22, 67, 199, 194 for HI; the RILs 163, 157, 169, 185, 208 for SW100 (Fig. 3 ) were scattered in their respective coordinates from other RILs depicting their better performance in their respective seasons, highlighting their potential for adaptability and yield enhancement. Using scatter plots to distinguish the genotypes based on their mean performance aligns with previous studies conducted in chickpea 1 , 35 and mungbean 67 , 77 . The strategic use of this analytical tool enhances the precision of genotype selection, providing a valuable resource for chickpea breeding programs. Genotype × Trait (GT) bi-plot The GT bi-plot analysis revealed that 53.92% of the trait variation could be explained, with PC1 and PC2 accounting for 40.72% and 13.20% of the total variance, respectively (Fig. 4 ). Apart from the checks, genotypes 68, 159, 13, 17, 187, and 123 demonstrated superior adaptability for the traits SY, PH, and SW100 across the seasons. Conversely, genotypes 213, 128, 106, 109, and 46 exhibited the highest adaptability for traits DFF and DM across the seasons (Fig. 4 ). Of the checks, PhuleVikram and RVG 204 were best adaptable for the traits SY, PH, and SW100 in both the seasons (Fig. 4 ). Notably, there were differences in genotype rankings for HI between seasons, as indicated by angles greater than 90° between vectors. Based on the vectors, the ranking of the genotypes for SY, PH, and SW100 can be observed as, PhuleVikram = RVG 204 > RILs 68 > 159 > 13 > 17 > 187 > 123. The polygon view depicted in Fig. 4 illustrated the distribution of genotypes in the trait bi-plot, highlighting important yield, yield components, and agro-morphological traits in chickpea. Discussion The formation of rhizobium nodulation is a key symbiotic mechanism in legume crops for their adaptation to marginal environments. The quantitative assessment of their impact on plant growth and economic yields is crucial for cultivar improvement and optimizing agricultural productivity. In this study, the selected parents are landraces, distinguishing one as a non-nodulating mutant (ICC 4918NN) derived from the other germplasm (ICC 4918). By employing the RGA approach, we successfully generated a RIL population within a mere 18 months, a testament to the efficiency and robustness of the methodology utilized 66 . While the potential of RGA has been acknowledged in other crops such as pea 61 , pigeon pea 69 , barley and wheat 81 , and canola 81 , its application in practical breeding programs remains largely unexplored. The variance analysis revealed a significant difference (P < 0.01) among the RILs for all the traits under the study. This indicates the presence of ample variability for the traits in the population (Table 1 ). Significant genetic variability among genotypes was observed in earlier studies evaluated for nodulation-related traits in chickpea 31 , 32 . A notable finding in our study is the substantial increase in yield (62.55%) in NG compared to NNG. The improvement was majorly contributed by the increase in the NPPP (39.5%) and NSPP (44.4%). On a moderate level, nodulation has reduced the flowering time and maturity and enhanced the PH, SBr, SW100 and HI (Table 1 ). These results indicate that NGs were more efficient in synthesizing photosynthates, which led to produce more number of flowers for generating a large number of pods and seeds 46 , 71 than NNG. In addition, the yield advantage reflects not only the inherent genetic potential of the legume plant but also the synergistic effects of the established symbiosis between roots and rhizobial bacteria. The result agrees with an earlier study in chickpea, which reported a 31% higher yield in NG compared to NNG counterparts 63 , 64 . The current study, with a more pronounced yield advantage, emphasizes the significance of rhizobial nodulation in optimizing chickpea productivity. The poorer performance of NNG may be attributed to the deficiency of nitrogen fixation through nodulation, emphasizing its crucial role in chickpea productivity. The reliance on alternative nitrogen sources becomes crucial for NNG genotypes, and supplementing nitrogen in the form of fertilizers may be necessary to enhance yield 41 . This aligns with previous findings in chickpea and groundnut, where NNGs in nitrogen-rich soils could attain yields on par with NG’s 62 , 27 . Though the degree of reliance on nodulation for nitrogen fixation varies throughout legumes, it is perpetually present. For instance, in soybean, nodulation can contribute to a significant proportion of the plant's nitrogen needs, with non-nodulating variants often displaying stunted growth and reduced yields 85 . Similarly, nodulation is critical for optimal growth in common beans, especially under nitrogen-deficient conditions 18 . Further investigations into the performance of NNG and NG under varying nitrogen availability can offer insights into their adaptability and yield potential across different soil conditions. The present investigation aimed to bridge a crucial gap in the existing knowledge base by assessing the impact of nodulation on agro-morphological and yield traits in chickpea. Our results align with existing literature on the positive effects of rhizobial nodulation on various growth characteristics, emphasizing the importance of this symbiotic association. The observed higher values in PH, PBr, biomass, and yield traits in NG compared to NNG underscore the pivotal role of rhizobial nodulation in enhancing plant growth and productivity in chickpea (Table 2 ) and mobilization of insoluble nutrients in the soil, leading to improved nutrient uptake in other legumes 4 , 12 . In addition, the absence of rhizobium nodulation resulted in a significant reduction in various growth parameters which might be due to a deficit in (a) host-dependent strain fitness 11 , (b) up-regulated expression of nif genes related to flavonoid synthesis 83 , and (c) maintenance of plant Pi (Inorganic phosphate) levels 50 . The estimation of genetic variability and inheritance through GCV, PCV and heritability allows the breeders to identify the traits with substantial genetic control and potential for selection in crop improvement programs. For phenological traits, the small difference between GCV and PCV values suggests a predominant influence of genetic factors on their variance. HI exhibited low estimates of both PCV and GCV, indicating a more substantial influence of environment (Supplementary table 2 and 3). This aligns with previous studies highlighting the major role of genetic components in the inheritance of flowering, maturity, and the environmental factors in determining HI 8 , 51 , 56 , 76 . For the traits NPPP, NSPP, HI, PBr, and SBr, a magnitude of low to high heritability was observed, suggesting several genetic factors controlling the inheritance of these traits. In particular, SY demonstrated a high magnitude of GCV, PCV, and heritability emphasizing its potential for genetic improvement through simple selection even in early generations efficiently 35 , 40 , 42 , 51 , 76 . This diverse heritability pattern underscores the importance of trait-specific breeding strategies to achieve improvements in chickpea agronomics 25 , 47 , 56 , 76 . High heritability coupled with high GAM was recorded for SY and SW100 across seasons (Supplementary table 2 and 3), indicating the predominance of additive gene action for these traits. Similar findings were reported under diverse genetic backgrounds in chickpea 42 , 54 , 60 , blackgram 13 as well as in cowpea 53 , 26 . Interestingly, the association among SY, NPPP, and NSPP was significantly positive in both NG and NNG (Fig. 2 ). This indicates the possibility of simultaneous improvement of multiple traits in chickpea genotypes and cowpea advanced breeding lines 8 , 26 , 53 , 59 . The ability to enhance multiple traits concurrently is crucial for developing improved chickpea varieties with enhanced agronomic performance. Further analysis of the genotypes for their yield and stability using GT bi-plot identified the checks, Phule Vikram and RVG 204 on its vertex with high values for all the traits under the study which can be considered the best adaptable genotypes (Fig. 4 ). For SY, PH, and SW100, the NG #68, 159, 13, 17, 187, and 123 while 106, 109, and 46 for DFF and DM were best adaptable over the seasons. Comparable research of this kind in various crops 7 , 55 , 75 , provide a valuable insight for the selection of superior genotypes with enhanced adaptability and nodulating nature in the chickpea breeding programs. The impact of rhizobium nodulation on grain yield and its association with yield-related traits was prominent in different legume crops. Earlier studies in chickpea emphasized the role of nodulation in enhancing seed yield and pod development 8 , 42 . Similarly, the seed yield was significantly improved by 40% in cowpea 2 , 33% in common bean 18 , and 45.6–50% in faba bean 24 , 74 compared to control or non-inoculated treatments, indicating the critical role of symbiotic nitrogen fixation in achieving higher seed yields in legumes. Moreover, the crop-specific strains, soils, and weather factors are crucial in achieving yields. These shared trends highlight the conserved nature of the nodulation mechanism inherited historically for thriving legume crops under diverse agro-ecologies. The comprehensive assessment of genetic variability, heritability, and genetic advance in this study provides a robust foundation for future chickpea breeding programs. The identification of traits with high heritability and substantial genetic advance, coupled with positive correlations among yield-related parameters, highlights avenues for effective genetic improvement. However, it's crucial to recognize the crop-specific nature of these findings and to tailor breeding strategies accordingly. Future research should delve deeper into the molecular mechanisms underpinning nodulation in chickpea and explore comparative studies across leguminous crops to enhance our understanding of these complex interactions. Materials and methods Description of the environment The present investigation was carried out at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India located at 17 ◦ 30′ N latitude and 78 ◦ 16′ E longitude with an altitude of 549 m above sea level during the normal crop season ie., Rabi 2020-21 and 2021-22. The average rainfall, humidity, minimum, and maximum temperatures were recorded as 3.1mm, 73.8%, 14.7°C and 29.7°C respectively during Rabi 2020-21 whereas in Rabi 2021-22 they were recorded as 8.0 mm, 63.3%, 15.7°C and 30.0°C respectively ( http://icrisatintranet/nrmp/agroclimatology/weather.asp ). Development of the RIL population through Rapid generation advancement (RGA) The RIL population was developed using the Rapid Generation Advancement (RGA) method 66 by crossing between two parents ICC 4918NN and ICC 4918 (Annegiri). The hybridity confirmed F 1 seeds were advanced to F 2 in a glasshouse. From F 2 the generations were advanced by selfing and single seed decent method. In 2019-20, 307 F 2 seeds were harvested in the glasshouse and advanced to F 3 through RGA where the immature pods were harvested 50 days after sowing (DAS) and sown on the same day for generation advancement. Four generations starting from F 2 to F 6 advanced in one year (Table 4 ). A maximum temperature of 25 ± 1°C, 70–80% humidity, and artificial light with the help of incandescent bulbs was provided for a period of 12 hrs (from 18:00–6:00) during the RGA experiment. The F 6 − 7 and F 7 − 8 seeds were sown in the field in Rabi 2020-21 and 2021-22 for agronomic characterization (Fig. 5 ). A total of 230 RILs (130 nodulating genotypes, 94 non-nodulating genotypes), four checks (Phule Vikram, RVG 204, NBeG 47, and JG 14), and two parents (ICC 4918NN and ICC 4918) were used in our experiment. The parental genotypes were obtained from the genebank of ICRISAT, India. The genotypes (RIL population) were categorized based on the presence or absence of nodules in Supplementary Table 1. Table 4 Development of RIL mapping population of nodulating and non-nodulating genotypes S.No Filial generations Year Growth condition No. of plants advanced Plant Type Nodulating genotypes Non-nodulating genotypes 1 Crossing 2018-19 Field - * * 2 F 1 − 2 2018-19 Field 1 * * 3 F 2 − 3 2019-20 Glass House 307 * * 4 F 3 − 4 2019-20 Glass House 302 * * 5 F 4 − 5 2019-20 Glass House 281 * * 6 F 5 − 6 2019-20 Glass House 270 * * 7 F 6 − 7 2020-21 Field evaluation 224 130 94 8 F 7 − 8 2021-22 Field evaluation 224 130 94 Field experiment The experimental material was sown on Vertisols in Alpha Lattice Design with three replications. Each genotype was sown with a spacing of 60 × 15 cm (inter and intra) covering an area of 1.2 m 2 . Planting of the population was completed during the first week of November in Rabi , 2020, and in the second week of November in Rabi , 2021. All the standard agronomic practices were followed for better crop establishment. Two irrigations were provided, one at the initial stage of planting and the other at the podding stage to maintain sufficient moisture regime for better crop establishment. Data related to the agronomic traits such as DF, DFF, DM, PH, PBr, SBr were recorded in each plot. The genotypes were harvested when all the plants matured completely. The post-harvest data related to the NPPP, NSPP, SW100, HI and SY were recorded from their respective plots. Screening for nodulation To distinguish the nature of the genotypes in terms of nodulation, the RIL population was grown in semi-controlled glasshouse condition as per the protocol outlined in the previous studies of chickpea 30 . The experimental material was surface sterilized with sodium hypochlorite and treated individually with the rhizobial strains IC59 and IC76A, the predominant nodulating bacteria in south-central India 62 , 28 . The seeds of each genotype were sown in 9” pots filled with potting mixture composed of sterile soil, sand, and vermicompost (3:2:2). After germination, the seedlings were thinned to three and uprooted at 35 days after sowing 29 , to determine the presence or absence of nodules in the planting material. Each genotype was designated as NG and NNG based on the presence or absence of root nodules (Fig. 5 ). Statistical analysis A combined analysis of variance was carried out using the SAS MIXED procedure 68 , to test the significance of the main and interaction effects of seasons and genotypes, considering the season, genotype, replication as fixed, and block as random. Individual season variance was modeled into combined analysis with repeated statements using the REML (Restricted Maximum Likelihood) method. To analyze the fixed effects, BLUEs (Best Linear Unbiased Estimates) are estimated for both main and interaction effects from the pooled analysis. The significance of NG and NNG means were compared using t-statistics from the combined analysis. Karl Pearson’s correlation was performed among the agronomic traits by using the SAS CORR procedure 68 . The magnitude of variation and its utility can be explained by genetic parameters like GCV, PCV, broad sense heritability, and GAM. These parameters were also estimated for the target traits using the SAS CORR procedure 68 . The estimates of PCV and GCV were classified as high (> 20%), moderate (10–20%) and low ( 60%), moderate (30–60%), low ( 20%), moderate (10–20%) and low (< 10%) 38 . To visualize the associations among the traits and the trait profiles of the genotypes a Genotype × Trait (GT) bi-plot was generated. It is based on Single Value Decomposition (SVD) of trait-wise standardized data (Z ~ N (0,1)). The correlation between the traits was interpreted by using cosine angles of the vectors in between the traits. The angle 90°, and equal to 90° states the positive, negative, and no correlation between the traits, respectively. As the vector length of a particular trait explains the variation in genotypes, the longest vector explains more variation among the genotypes and the shortest vector depicts a very low level of variation among genotypes for a given trait. Conclusion The RIL mapping population was developed in less than two years, the first report in chickpea to show the practical application of the RGA protocol in developing breeding material. The influence of nodulation on yield was distinctly favorable in nodulating genotypes, characterized by increased SY, biomass, and PH compared to NNG. The stable performing genotypes with high yield and early flowering nature were mostly nodulating in nature depicting the beneficial effect of nodulation on the crop growth under moisture stress conditions. The promising genotypes identified can serve as donors for use in chickpea breeding programs. Furthermore, the results emphasize the critical role of compatible Rhizobium strain/s in achieving optimal yields. The study highlights that the absence of nodulation can lead to substantially lower yields in chickpea. Therefore, the distribution and presence of Rhizobium strains in cultivated fields emerge as influential factors affecting final yield levels. To enhance the understanding of nodulation in chickpea, further research on identifying the genomic regions/QTLs/markers associated with nodulation is in progress to enhance our knowledge and provide valuable insights into the molecular mechanisms controlling nodulation in chickpea, ultimately contributing to improved crop productivity and promoting sustainable agricultural practices. Declarations Acknowledgments We, the authors, are thankful to the chickpea team members of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) for providing research facilities and funds for the field trials, Acharya N. G. Ranga Agricultural University (ANGRAU) for providing fellowship during the research and International Center for Biosaline Agriculture (ICBA) for technical backstopping in development and submission of the manuscript. Supplementary information The online version contains supplementary material. Funding The current research was supported by the Indian Council of Agricultural Research-International Crops Research Institute for the Semi-Arid Tropics (ICAR-ICRISAT) and The CGIAR Research Program on Grain Legumes and Dryland Cereals (CRP-GLDC) projects. Conflict of interest The authors declare that they had no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. International guidelines and legislation This experimental research on chickpea ( Cicer arietinum L.) crop adheres to international guidelines and legislation to ensure ethical, sustainable, and scientifically robust practices. The study complies with institutional, national, and international standards, including the Convention on Biological Diversity (CBD) and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), ensuring the sustainable use and conservation of biodiversity. Author contributions Nandigam SwathiRekha: Data collection and curation, Trial establishment, Software, Visualization, Writing-original draft preparation. Mahesh Damodhar Mahendrakar: Data curation, formal analysis, validation, writing the manuscript. Uttam Chand: Field layout, Intercultural operations, Data curation. S Subramaniam Gopalakrishnan: Conceptualization of rhizobium inoculation, Methodology, Review, and editing the manuscript. Srinivasa Rao Vatluri: Methodology, Review, and editing the manuscript. Vadlamudi Srinivas: Rhizobium culture preparation and application, glasshouse evaluations. Srinivas Thati: Conceptualization, Review and editing the manuscript. Srungarapu Rajasekhar: Data curation, Formal analysis, Editing of the manuscript. Anilkumar Vemula: Statistical Analysis, Validation of results, Visualization. Himabindu Kudapa: Methodology and editing the manuscript. Samineni Srinivasan: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing the manuscript. All authors read and approved the final manuscript. Data Availability The data of this article supporting the conclusions will be made available by the corresponding author upon request. References Admas, S., Tesfaye, K., Haileselassie, T., Shiferaw, E & Flynn, K. C. Genetic variability and population structure of Ethiopian chickpea ( Cicer arietinum L.) germplasm. Plos one . 16 (11), e0260651 (2021). Ahmet, E. K & Halime Özdamar Ünlü. Effect of rhizobium inoculation on yield and some quality properties of fresh cowpea. Cogent food agric . 9 , 2 (2023). Ahmad, M. et al. Appraising endophyte–plant symbiosis for improved growth, nodulation, nitrogen fixation and abiotic stress tolerance: An experimental investigation with chickpea ( Cicer arietinum L.). Agronomy . 9 , 621 (2019). Ali, M. A., Naveed, M., Mustafa, A. & Abbas, A. The good, the bad, and the ugly of rhizosphere microbiome in Probiotics and Plant Health . 253–290 (Springer, 2017). Asante, M., Ahiabor, B. D. K & Atakora, W. K. Growth, Nodulation, and Yield Responses of groundnut ( Arachis hypogaea L.) as influenced by combined application of rhizobium inoculant and phosphorus in the Guinea Savanna zone of Ghana. Int. J. Agron . 1–7, p.8691757 (2020). Ashraf, M & Iram, A. Drought stress induced changes in some organic substances in nodules and other plant parts of two potential legumes differing in salt tolerance. Flora . 200 , 535–546 (2005). Bakhshi, B. et al. Trait profiling and genotype selection in oilseed rape using genotype by trait and genotype by yield* trait approaches. Food Sci. Nutr . 11 (6), 3083-3095 (2023). Barmukh, R. et al. Construction of a high-density genetic map and QTL analysis for yield, yield components and agronomic traits in chickpea ( Cicer arietinum L.). Plos one . 16 (5), p.e0251669 (2021). Bourebaba, Y. et al . Diversity of Bradyrhizobium strains nodulating Lupinus micranthus on both sides of the Western Mediterranean: Algeria and Spain. Syst. Appl. Microbiol . 39 , 266-274 (2016). Buhian, W. P & Bensmihen, S. Mini-review: nod factor regulation of phytohormone signaling and homeostasis during rhizobia-legume symbiosis. Front. Plant Sci . 9, 1247 (2018). Burghardt, L. T., Epstein, B., Hoge, M., Trujillo, D. I & Tiffin, P. Host-Associated Rhizobial Fitness: Dependence on Nitrogen, Density, Community Complexity, and Legume Genotype. Appl. Environ. Microbiol . 88 , e00526-22 (2022). Cakmakci, R., Donmez, M. F & Erdogan, U. The effect of plant growth promoting rhizo bacteria on barley seedling growth, nutrient uptake, some soil properties, and bacterial counts. Turk. J. Agr. For . 31 , 189–199 (2007). Chauhan, S., Mittal, R. K., Sood, V. K & Patial, R. Evaluation of genetic variability, heritability and genetic advance in blackgram [ Vigna mungo (L.) Hepper]. Legum. Res. 43 (4), 488-494 (2020). Diapari, M. et al. Genetic diversity and association mapping of iron and zinc concentrations in chickpea ( Cicer arietinum L.). Genome . 57 , 459–468 (2014). Diapari, M., Sindhu, A., Warkentin, T. D., Bett, K & Tar’an, B. Population structure and marker-trait association studies of iron, zinc and selenium concentrations in seed of field pea ( Pisum sativum L.). Mol. Breed . 35 , 1–14 (2015). Ditta, A. et al. Rock phosphate enriched organic fertilizer with phosphate solubilizing microorganisms improves nodulation, growth and yield of legumes. Commun. Soil Sci. Plant Anal . 49 , 2715–2725 (2018a). Ditta, A. et al. Application of rock phosphate enriched composts increases nodulation, growth and yield of chickpea. Int. J. Recycl. Org. Waste Agric . 7 , 33–40 (2018b). Dos Santos Sousa, W. et al. Effects of Rhizobium inoculum compared with mineral nitrogen fertilizer on nodulation and seed yield of common bean. A meta-analysis. Agron. Sustain. Dev. 42 , 52 (2022). Dutta, S & Podile, A. R. Plant growth promoting rhizobacteria (PGPR): the bugs to debug the root zone. Crit. Rev. Microbiol . 36 , 232-244 (2010). Figueiredo, M. V. B., H, L, A, Burity., C, R, Martïnez & Chanway, C. P. Alleviation of drought stress in the common bean ( Phaseolus vulgaris L.) by co-inoculation with Paenibacillus polymyxa and Rhizobium tropici . Appl. Soil. Ecol . 40 , 182–88 (2008). Food and Agriculture Organization of the United Nations. FAOSTAT statistical database. Rome: FAO (2022). Fox, A. R. et al . Major cereal crops benefit from biological nitrogen fixation when inoculated with the nitrogen fixing bacterium Pseudomonas protegens Pf5 X940. Environ. Microbiol . 18 , 3522-3534 (2016). Gaur, P. M. et al. Inheritance of protein content and its relationships with seed size, grain yield and other traits in chickpea. Euphytica. 209 , 253–260 (2016). Gedamu, S.A., Tsegaye, E.A. & Beyene, T.F. Effect of rhizobial inoculants on yield and yield components of faba bean ( Vicia fabae L.) on vertisol of Wereillu District, South Wollo, Ethiopia. CABI Agric Biosci . 2 , 8 (2021). Gediya, L. N. et al. Phenotypic variability, path analysis and molecular diversity analysis in chickpea ( Cicer arietinum L.). Vegetos . 32 , 167–180 (2019). Gerrano, A. S., Jansen van Rensburg, W. S & Kutu, F. R. Agronomic evaluation and identification of potential cowpea ( Vigna unguiculata L. Walp) genotypes in South Africa. Acta Agric. Scand. Sec- B Soil Plant Sci . 69 , 295-303 (2019). Giller, K. E., Nambiar, P. T. C., Srinivasa Rao, B., Dart, P. J & Day, J. M. A comparison of nitrogen fixation in genotypes of groundnut ( Arachis hypogaea L.) using 15 N-isotope dilution. Biol. Fertil. Soils . 5 , 23-25 (1987). Gopalakrishnan, S. et al. Assessment of nodulation potential in mini-core genotypes and land races of chickpea. J. Food Legum . 30 , 65-72 (2017a). Gopalakrishnan, S., Srinivas, V & Samineni, S. Nitrogen fixation, plant growth and yield enhancements by diazotrophic growth-promoting bacteria in two cultivars of chickpea ( Cicer arietinum L.). Biocatal. Agric. Biotechnol . 11 , 116-123 (2017b). Gopalakrishnan, S., Srinivas, V., Vemula, A., Samineni, S & Rathore, A. Influence of diazotrophic bacteria on nodulation, nitrogen fixation, growth promotion and yield traits in five cultivars of chickpea. Biocatal. Agric. Biotechnol . 15 , 35-42 (2018). Gul R., Khan, H., Khan, N. U & Khan, F. Y. Characterization of chickpea germplasm for nodulation and effect of Rhizobium inoculation on nodules number and seed yield. J. Anim. Plant Sci. 24 (5), 1421-1429 (2014). Gul, R., Khan, H., Khan, N.U., Latif, A & Harada, K. Characterization for nodulation and detection of duplicate gene action of dominant epistasis controlling root nodulation in chickpea ( Cicer arietinum ). Int. J. Agric. Biol . 20 : 683‒688 (2018). Herridge, D. F., Peoples, M. B & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant soil . 311 , 1-18 (2008) Huang, X. F. et al. Rhizosphere interactions: root exudates, microbes, and microbial communities. Botany . 92, 267–275 (2014). Hussain, S. A. et al. Estimating genetic variability among diverse lentil collections through novel multivariate techniques. Plos one . 17 (6), e0269177 (2022). Jemo, M. et al. Comparative analysis of the combined effects of different water and phosphate levels on growth and biological nitrogen fixation of nine cowpea varieties. Front. Plant Sci . 8 , 2111 (2017). Jha, U. C. & Shil, S. A. N. D. I. P. Association analysis of yield contributing traits of chickpea genotypes under high temperature condition. Trends Biosci . 8 , 2335-2341 (2015). Johnson, H. W., Robinson, H. F & Comstock, R. E. Estimates of genetic and environmental variability in soybeans. Agron. J . 47 , 314–318 (1955). Keneni, G. et al. Phenotypic diversity for symbio-agronomic characters in Ethiopian chickpea ( Cicer arietinum L.) germplasm accessions. Afr. J. Biotechnol . 11 , 12634-12651 (2012). Khan, M. M. H., Rafii, M. Y., Ramlee, S. I., Jusoh, M. A. & Mamun, M. Genetic analysis and selection of Bambara groundnut ( Vigna subterranea [L.] Verdc.) landraces for high yield revealed by qualitative and quantitative traits. Sci. Rep . 11 (1), 1-21 (2021). Koevoets, I. T., Venema, J. H., Elzegna, J. T. M. & Testerink, C. Roots withstanding their environment: Exploiting root system architecture responses to abiotic stress to improve crop tolerance. Front. Plant Sci . 7, 1335 (2016). Kushwah, A. et al . Phenotypic evaluation of genetic variability and selection of yield contributing traits in chickpea recombinant inbred line population under high temperature stress. Physiol. Mol. Biol. Plants . 27 , 747-767 (2021). Limpens, E. et al . LysM domain receptor kinases regulating rhizobial Nod factor-induced infection. Sci. 302, 630–633 (2003). Liu, Y. Y., Wu, L. H., Baddeley, J. A. & Watson, C. A. Models of biological nitrogen fixation of legumes. A review. Agron. Sustain. Dev . 31, 155–172 (2011). Lupwayi, N. Z., Clayton, G. W., Hanson, K. G., Rice, W. A. & Biederbeck, V. O. Endophytic rhizobia in barley, wheat and canola roots. Can. J. Plant Sci . 84 : 37-45 (2004). Mallikarjuna, B. P., Viswanatha, K. P., Samineni, S. & Gaur, P. M. Association of flowering time with phenological and productivity traits in chickpea. Euphytica . 215 , 1-8 (2019). Mallu, T. S. et al. Assessment of genetic variation and heritability of agronomic traits in chickpea ( Cicer arietinum L). Int. J. Agron. Agric. Res . 5 (4), 76–88 (2014). Mathesius, U. Comparative proteomic studies of root–microbe interactions. J. Proteome . 72 , 353-366 (2009). McCauley, A. M. Nitrogen fixation by annual legume green manures in a semi-arid cropping system. Dissertation, Montana State University-Bozeman (2011). Míguez-Montero, M. A., Valentine, A. & Pérez-Fernández, M. A. Regulatory effect of phosphorus and nitrogen on nodulation and plant performance of leguminous shrubs. AoB Plants . 12 , 1-11 (2019). Misra, G. et al. Baseline status and effect of genotype, environment and genotype× environment interactions on iron and zinc content in Indian chickpeas ( Cicer arietinum L.). Euphytica. 216 (9), 1-16 (2020). Ouma, E. W., Asango, A. M., Maingi, J. & Njeru, M. Elucidating the potential of native rhizobial isolates to improve biological nitrogen fixation and growth of common bean and soybean in smallholder farming systems of Kenya. Int. J. Agron . 1–7 (2016). Owusu, E. Y. et al. Genetic variability, heritability and correlation analysis among maturity and yield traits in Cowpea ( Vigna unguiculata (L) Walp) in Northern Ghana. Heliyon. 7 (9), e07890 (2021). Parashi, V. S., Lad, D. B., Mahse, L. B., Kute, N. S. & Sonawane, C. J. Genetic diversity studies in chickpea ( Cicer arietinum L.). BIOINFOLET . 10 (1b), 337-341 (2013). Parissi, Z. et al. Analysis of Genotypic and Environmental Effects on Biomass Yield, Nutritional and Anti nutritional Factors in Common Vetch. Agron . 12 , 1678 (2022). Paul, P. J. et al. Capturing genetic variability and selection of traits for heat tolerance in a chickpea recombinant inbred line (RIL) population under field conditions. Euphytica . 214, 1-14 (2018). Perrig, D. et al. Plant-growth-promoting compounds produced by two agronomically important strains of Azospirillum brasilense , and implications for inoculant formulation. Appl. Microbiol. Biotechnol . 75, 1143-1150 (2007). Radutoiu, S. et al. Plant recognition of symbiotic bacteria requires two LysM receptor-like kinases. Nature. 425 , 585–592 (2003). Rafique, M. et al. The combined effects of gibberellic acid and Rhizobium on growth, yield and nutritional status in chickpea ( Cicer arietinum L.). Agron. 11 , 105 (2021). Rajasekhar, S. et al. Genetic Variation for Grain Protein, Fe and Zn Content traits in Chickpea Reference Set. J. Food Compos. Anal . p.104774 (2022). Ribalta, F.M. et al. Precocious floral initiation and identification of exact timing of embryo physiological maturity facilitate germination of immature seeds to truncate the lifecycle of pea. Plant Growth Regul . 81 , 345–353 (2017). Rupela, O. P. Natural occurrence and salient characters of non nodulating chickpea plants. Crop Sci . 32 , 349-352 (1992). Rupela, O. P. & Johansen, C. Identification of non-nodulating, and low and high nodulating plants in pigeonpea. Soil Bio. Biochem . 27 , 539-544 (1995). Rupela, O. P., Sharma, L. C. & Danso, S. K. A. Evaluation of N 2 fixation by nodulation-variants of chickpea in India. In Improving Yield and Nitrogen Fixation of Grain Legumes in the Tropics and Sub-tropics of Asia. Int. Atomic Energy Agency . 1027 , 99-119 (1997). Saha. S., Chakraborty, D., Sehgal, V. K. & Pal, M. Potential impact of rising atmospheric CO 2 on quality of grains in chickpea ( Cicer arietinum L.). Food Chem . 187, 431–436 (2015). Samineni, S., Sen, M., Sajja, S. B. & Gaur, P. M. Rapid generation advance (RGA) in chickpea to produce up to seven generations per year and enable speed breeding. Crop J . 8, 164-169 (2020). Samyuktha, S. M. et al. Delineation of genotype× environment interaction for identification of stable genotypes to grain yield in mungbean. Front. Agron . 2 , 577911 (2020). SAS Institute Inc. SAS/STAT® 15.1 User’s Guide. Cary, NC: SAS Institute Inc 2018. Saxena, K., Saxena, R.K. & Varshney, R.K. Use of immature seed germination and single seed descent for rapid genetic gains in pigeonpea. Plant Breed . 136 , 954–957 (2017). Sharma, V. et al. Molecular Basis of Root Nodule Symbiosis between Bradyrhizobium and ‘Crack-Entry’ Legume Groundnut ( Arachis hypogaea L.). Plants. 9 , 276 (2020). Siddique, K. H. M. & Khan, T. N. Early-flowering and high-yielding chickpea lines from ICRISAT ready for release in Western Australia. Int. Chickpea and Pigeonpea Newslett . 3 , 22-24 (1996). Sivasubramanian, S. & Madhavamenon, P. Genotypic and phenotypic variability in rice. Madras Agric. J . 60 , 1093–1096 (1973). Stajkovic, O. et al . Improvement of common bean growth by co-inoculation with Rhizobium and plant growth-promoting bacteria. Rom. Biotechnol. Lett . 16 , 5919-5926 (2011). Talaat, N. B. & Abdallah, A. M. Response of fababean ( Vicia faba L.) to dual inoculation with Rhizobium and VA mycorrhiza under different levels of N and P fertilization. J. Appl. Sci. Re s. 4 , 1092–1102 (2008). Gayacharan et al. Understanding genetic variability in the mungbean ( Vigna radiata L.) genepool. Annals App. Bio . 177 (3), 346-357 (2020). Tsehaye, A., Fikre, A. & Bantayhu, M. Genetic variability and association analysis of Desi-type chickpea ( Cicer arietinum L.) advanced lines under potential environment in North Gondar, Ethiopia. Cogent Food Agri . 6 (1), 1806668 (2020). Ullah, H. et al. Selecting high yielding and stable mungbean [ Vigna radiata (L.) Wilczek] genotypes using GGE biplot techniques. Can. J. Plant Sci. 92 , 951-960 (2012). Unkovich, M., Baldock, J. & Peoples, M. Prospects and problems of simple linear models for estimating symbiotic N 2 fixation by crop and pasture legumes. Plant soil . 329 , 75-89 (2010). Wang, E. T., Chen, W. F., Tian, C. F., Young, J. P. W. & Chen, W. X. Ecology and Evolution of Rhizobia, Principles and Applications in Springer . 154-157 (2019). Ward, A. Phosphorus Limitation of Soybean and Alfalfa Biological Nitrogen Fixation on Organic Dairy Farms. Dissertation, Nova Scotia Agricultural College (2011). Watson, A. et al. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat. Plants . 4 , 23–29 (2018). Xia, X., Ma, C., Dong, S., Xu, Y. & Gong, Z. Effects of nitrogen concentrations on nodulation and nitrogenase activity in dual root systems of soybean plants. J. Soil Sci. Plant Nutr . 63 , 1–13 (2017). Xu, T. et al. Revealing the underlying mechanisms mediated by endophytic actinobacteria to enhance the rhizobia-chickpea ( Cicer arietinum L.) symbiosis. Plant soil . 1-20 (2022). Yang, Y. et al. Characterization of genetic basis on synergistic interactions between root architecture and biological nitrogen fixation in soybean. Front. Plant Sci . 8 , 1466 (2017). Yang, Z. W., Shen, Y. Y., Xie, T, L. & Tan, G. Y. Biological nitrogen fixation efficiency in soybean under different levels of nitrogen supply. Acta Bot. Boreali Occident Sin . 29 , 574–579 (2009). Additional Declarations No competing interests reported. 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Institute for Semi-arid Tropics","correspondingAuthor":false,"prefix":"","firstName":"Mahesh","middleName":"Damodhar","lastName":"Mahendrakar","suffix":""},{"id":322858794,"identity":"92100b72-bba7-48c5-987e-5387cb3be592","order_by":2,"name":"Srungarapu Rajasekhar","email":"","orcid":"","institution":"International Crops Research Institute for Semi-arid Tropics","correspondingAuthor":false,"prefix":"","firstName":"Srungarapu","middleName":"","lastName":"Rajasekhar","suffix":""},{"id":322858798,"identity":"b371e19b-9944-49b2-a981-fb59c73e75f7","order_by":3,"name":"Uttam Chand","email":"","orcid":"","institution":"International Crops Research Institute for Semi-arid Tropics","correspondingAuthor":false,"prefix":"","firstName":"Uttam","middleName":"","lastName":"Chand","suffix":""},{"id":322858801,"identity":"d1265bed-a70f-4593-9229-1dc7e69ca4f6","order_by":4,"name":"Subramaniam Gopalakrishnan","email":"","orcid":"","institution":"International Institute of Tropical Agriculture (IITA)","correspondingAuthor":false,"prefix":"","firstName":"Subramaniam","middleName":"","lastName":"Gopalakrishnan","suffix":""},{"id":322858805,"identity":"06e8d9f8-50f0-4769-a5ec-9d789ec6f11e","order_by":5,"name":"Srinivas Thati","email":"","orcid":"","institution":"Acharya N G Ranga Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Srinivas","middleName":"","lastName":"Thati","suffix":""},{"id":322858809,"identity":"c3428969-f11f-40bc-8c0b-d8f7494506e1","order_by":6,"name":"Srinivasa Rao Vatluri","email":"","orcid":"","institution":"Acharya N G Ranga Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Srinivasa","middleName":"Rao","lastName":"Vatluri","suffix":""},{"id":322858813,"identity":"1cfe0277-8814-41df-852f-9b8208a9a538","order_by":7,"name":"Vadlamudi Srinivas","email":"","orcid":"","institution":"International Crops Research Institute for Semi-arid Tropics","correspondingAuthor":false,"prefix":"","firstName":"Vadlamudi","middleName":"","lastName":"Srinivas","suffix":""},{"id":322858818,"identity":"98d453fa-bb4c-4170-8340-81edc59da44d","order_by":8,"name":"Anilkumar Vemula","email":"","orcid":"","institution":"International Crops Research Institute for Semi-arid Tropics","correspondingAuthor":false,"prefix":"","firstName":"Anilkumar","middleName":"","lastName":"Vemula","suffix":""},{"id":322858821,"identity":"df477193-968a-4e65-a5cd-1b828e81aea2","order_by":9,"name":"Himabindu Kudapa","email":"","orcid":"","institution":"International Crops Research Institute for Semi-arid Tropics","correspondingAuthor":false,"prefix":"","firstName":"Himabindu","middleName":"","lastName":"Kudapa","suffix":""},{"id":322858823,"identity":"7e4b1797-b5e4-4942-940f-1b9f41fc15f5","order_by":10,"name":"Samineni Srinivasan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie2RsUrEQBCG51jYNJtLuyCYV9gQSBDx8iobAlelEGxS5QLCXnmvkkrbyIJVwFZQJMdBWoOFHGjhJPWqZyfHfrBMMfMx87MAFsu/ReAj5B4k1jk5XKHLSaGHKRMsmgr9bc6vnO3b4+Xzaeyw964rSp867l0HxcuicnRjPKlh4Uku+vDs2r0VstWBIvNMQHuVVWwpzSkYoKLTWrs3PFXNTBEW8ZmSGUAuvjls94HKqtasR6VMUIn3k+K9DsYwDUTjFik0o6iQdNwCqCyA50YDJ6NzVIJa03jMkqESctlKSXlvPmy93j3ln9oXD7rf7ovyYuO1wTAUMvG8rDOuMX+cBEiVsfMTyZ8Ni8ViOVa+AI5xV2xgwaB/AAAAAElFTkSuQmCC","orcid":"","institution":"International Center for Biosaline Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Samineni","middleName":"","lastName":"Srinivasan","suffix":""}],"badges":[],"createdAt":"2024-06-18 09:22:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4598881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4598881/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98965-2","type":"published","date":"2025-04-22T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59699069,"identity":"78a91e1a-07bf-432a-9723-dd5a9e75e0d2","added_by":"auto","created_at":"2024-07-05 03:59:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation in agro-morphological and yield component traits under nodulating and non-nodulating genotypes (pooled analysis)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/9e1b752491f5d9507e1d534b.png"},{"id":59698740,"identity":"68c60029-a312-4b2f-9da8-757d2f1ddd99","added_by":"auto","created_at":"2024-07-05 03:51:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of yield and yield components in nodulating and non-nodulating \u0026nbsp;chickpea genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(DF-Days to first flowering, DFF-Days to 50 percent flowering, DM- Days to maturity, PH-Plant height, PBr- Number of primary branches per plant, SBR- Number of secondary branches per plant, NPPP- Number of pods per plant, NSPP- Number of seeds per plant, SW100-100 seed weight, HI-Harvest index, SY-Seed yield)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/12c3ba3556f3c2d7810c3527.png"},{"id":59698741,"identity":"b248bda6-504d-44c8-b0bd-2cc85e531cc7","added_by":"auto","created_at":"2024-07-05 03:51:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter Plot for yield and yield component traits in the RILs of ICC 4918NN x ICC 4918 of chickpea\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/8d1032b89b046481939df5d6.png"},{"id":59698745,"identity":"31f746fd-0c19-4e44-8de3-56bed98917fd","added_by":"auto","created_at":"2024-07-05 03:51:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePolygon view of genotypes in trait bi-plot for important yield, yield components, and agro-morphological traits in chickpea\u003c/strong\u003e (DF-Days to first flowering, DFF-Days to 50 percent flowering, PH-Plant height, SY-Seed yield, SW100-100 seed weight, HI-Harvest index)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/64d75ecf21daa4c90b92db3a.png"},{"id":59699400,"identity":"680b51b2-1d67-487f-a719-cb7bfcf37f5b","added_by":"auto","created_at":"2024-07-05 04:07:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":738735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNodulating and non-nodulating genotypes uprooted at 35 days after sowing (DAS) with intact root system\u003c/strong\u003e [Nodulating genotypes #108,109, 117, and 118) (Top), Non-nodulating genotypes #121, 122, 132, and 135) (Bottom)]\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/acd0c236b88dc01fdf3fdf32.png"},{"id":81570199,"identity":"5ed5c90d-5acc-49ed-b1f6-d2f8d2f341b9","added_by":"auto","created_at":"2025-04-28 16:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3251488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/7eede914-02fe-422f-b56f-33a7f5fb7838.pdf"},{"id":59699070,"identity":"741de8a0-d2ea-431a-934a-bb6bfef5720e","added_by":"auto","created_at":"2024-07-05 03:59:15","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":307200,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.doc","url":"https://assets-eu.researchsquare.com/files/rs-4598881/v1/4ce57deb8b332bef5e98d1fe.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid generation advancement of RIL population and unlocking the potential of Rhizobium nodulation for improving crop yields in chickpea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLegumes have a specific mutualistic relationship with soil bacteria that fix the atmospheric nitrogen into plant-usable ammonical form in root nodules\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, in contrast to other plants. Development of nodules and nitrogen fixation depends majorly on the legume cultivar and its specific rhizobial strain\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e which will in turn improve the overall crop growth, productivity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and soil health. Around 20 to 22\u0026nbsp;million tonnes of nitrogen per annum is fixed in the agricultural systems by the Leguminosae family members\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e which contributes significantly towards reducing the global carbon footprint.\u003c/p\u003e \u003cp\u003eChickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) is a highly nutritious, diploid (2n\u0026thinsp;=\u0026thinsp;2x\u0026thinsp;=\u0026thinsp;16) legume crop grown in an area of 14.2\u0026nbsp;million hectares across 56 countries globally\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, of which, India is the largest producer and consumer. It is considered a storehouse of proteins, complex carbohydrates, vitamins, and micronutrients that are required for human nutrition\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Being a leguminous crop, chickpea fixes the atmospheric nitrogen by association with \u003cem\u003eRhizobium\u003c/em\u003e species\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e which differentiates it from other cereal crops\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. It stores the fixed nitrogen in the nodules present in its root system and converts it into ammonia that can be used by the plant\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. It was estimated that about 70 kg of nitrogen per hectare is fixed annually by chickpea\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, which helps in providing nitrogen not only to the host but also to the subsequent crops grown\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e thereby, helping the farmers in reducing the cost of production.\u003c/p\u003e \u003cp\u003eRoot nodulation is a complex process requiring the recognition of symbiotic bacteria, \u003cem\u003eNod-factor\u003c/em\u003e induced infection, and root growth\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Polyphenols named flavonoids released from the roots, stimulate \u003cem\u003eNod-factor\u003c/em\u003e production in rhizobia thereby initiating curling and colonization of root hair and the formation of root nodules\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Phytohormone signaling plays a major role during this process between the bacteria and the host\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The symbiotic nitrogen fixation (SNF) efficiency is dependent on the host, rhizobial strains, soil conditions\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, availability of phosphorous\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e, and environmental conditions\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Under water stress, the biochemical activity in the nodules will get disrupted resulting in the senescence of nodules\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e along with downgraded leg-hemoglobin content and nitrogenase activity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The ability of the nodules to supply energy, transport, regulate oxygen molecules, and assimilate ammonia to the plants will help in increasing plant growth and yield\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, the consequences of the chickpea roots being unable to make a symbiotic relationship with rhizobium are not well understood and documented.\u003c/p\u003e \u003cp\u003eSeveral studies reported in chickpea\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e as well as in other leguminous crops \u003cem\u003eviz\u003c/em\u003e., soybean\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, cowpea\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, lupin\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, groundnut\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, etc., were focused majorly on the external application of biochar, artificial fertilizers, growth hormones and the findings of strain-specific effect on nodulation and yield-related traits. However, the information on the impact of nodulation over non-nodulation on yield and yield-contributing traits was minimal. In this context, the current study aimed to 1) broaden the knowledge on the association between nodulation, yield, and its associated traits; and 2) quantify the value gain of the traits in a RIL population segregating for nodulation trait. In addition, we successfully showcased the utility of rapid generation advancement methods for developing mapping populations in a short period.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of variance\u003c/h2\u003e\n \u003cp\u003eThe results of the combined analysis of variance (ANOVA) revealed significant differences (Prob\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among the genotypes for the traits under study while for the factors, block and replication, no significant difference was observed (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For nodulating genotypes (NG), mean performance of agro-morphological and yield-related traits over the seasons was in the range of 42.74\u0026ndash;47.37 (Days to first flowering- DF), 47.57\u0026ndash;51.76 (Days to 50% flowering- DFF), 94.24-100.92 (Days to maturity- DM), 38.74\u0026ndash;44.36 (Plant height- PH), 1.94\u0026ndash;2.17 (Number of primary branches per plant- PBr), 6.03\u0026ndash;8.48 (Number of secondary branches per plant- SBr), 293.76-343.36 (Number of pods per plant- NPPP), 349.08-395.66 (Number of seeds per pod- NSPP), 17.12\u0026ndash;17.26 (100 seed weight- SW100), 34.50-36.85 (Harvest index- HI), 2472.78-2689.62 (Seed yield- SY) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, non-nodulating genotypes (NNG) exhibited comparable trends over the seasons ranging from 44.58\u0026ndash;50.60 (DF), 49.27\u0026ndash;54.87 (DFF), 96.20-103.46 (DM), 34.86\u0026ndash;39.28 (PH), 1.98\u0026ndash;2.10 (PBr), 6.97\u0026ndash;8.42 (SBr), 194.53-265.36 (NPPP), 214.48\u0026ndash;30.23 (NSPP), 14.29\u0026ndash;16.27 (SW100), 33.19\u0026ndash;33.87 (HI) and 1638.78-1665.05 (SY) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition to this, checks exhibited a range of 42.61\u0026ndash;43.38 (DF), 47.50-48.53 (DFF), 93.26\u0026ndash;98.52 (DM), 54.02\u0026ndash;59.63 (PH), 1.99\u0026ndash;2.07 (PBr), 5.06\u0026ndash;7.81 (SBr), 296.37-321.63 (NPPP), 320.70\u0026ndash;338.00 (NSPP), 21.55\u0026ndash;21.67 (SW100), 31.68\u0026ndash;34.34 (HI), 2644.09-2667.64 (SY) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) over the seasons.\u003c/p\u003e\n \u003cp\u003eThe median values (black solid line) for agro-morphological and yield component traits across seasons were provided using Violin plot analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). All the traits exhibited almost consistent median values except for PH, PBr, and SW100 for both NG and NNG over the seasons, highlighting the importance of seasonal variations in the breeding selection process. Notably, the observed ranges of agro-morphological traits and yield components of NG, NNG, and checks, emphasize the inherent variability within the genotypes (nodulating and non-nodulating) across the seasons, which underscores the superiority of nodulating genotypes in terms of productivity.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Variance (ANOVA) for agro-morphological, yield, and yield component traits in chickpea genotypes under pooled conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDFF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePBr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSBr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPPP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNSPP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSW100\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5304.05**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3420.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5658.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1710.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e341.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e809.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRep (Season)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.82**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.13**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.26**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.95**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.96**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeason\u0026times;Genotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.58**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.04**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.49**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.07**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"13\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eRandom Effects\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Season Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"13\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eComparison of Nodulation Vs Non-nodulation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNodulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2685.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1651.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1033.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e%Change in nodulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33(\u0026darr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62(\u0026darr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26(\u0026darr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.08(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.74(\u0026darr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.55 (\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.37 (\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.55(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.21 (\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.40 (\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e**-Significance at 1% level, (\u0026darr;)-% decrease, (\u0026uarr;)-% increase, DF-Days to first flowering, DFF-Days to 50 percent flowering, DM-Days to maturity, PH-Plant Height, PBr-Number of primary branches per plant, SBr-Number of secondary branches per plant, NPPP- Number of pods per plant, NSPP-Number of seeds per pod, SY-Seed yield, SW100-100 seed weight, HI-Harvest Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean performance (\u0026plusmn;\u0026thinsp;SE) of nodulating and non-nodulating genotypes of RIL population agro-morphological, yield and yield components traits in chickpea\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNodulating\u003c/p\u003e\n \u003cp\u003egenotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-nodulating genotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChecks\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAgro-morphological traits\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to first flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003cp\u003e42.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003cp\u003e45.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003cp\u003e44.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003cp\u003e47.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003cp\u003e42.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\n \u003cp\u003e42.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to 50 percent flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003cp\u003e47.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003cp\u003e49.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003cp\u003e49.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003cp\u003e52.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n \u003cp\u003e47.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003cp\u003e48.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003cp\u003e100.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003cp\u003e97.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003cp\u003e103.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003cp\u003e99.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003cp\u003e98.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003cp\u003e95.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003cp\u003e44.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003cp\u003e41.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003cp\u003e39.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003cp\u003e37.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n \u003cp\u003e59.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\n \u003cp\u003e56.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of primary branches per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of secondary branches per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n \u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003cp\u003e7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003cp\u003e6.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield component traits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of pods per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003cp\u003e293.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n \u003cp\u003e316.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n \u003cp\u003e265.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003cp\u003e225.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003cp\u003e296.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\n \u003cp\u003e304.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of seeds per pod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\n \u003cp\u003e349.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\n \u003cp\u003e370.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003cp\u003e307.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\n \u003cp\u003e254.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\n \u003cp\u003e320.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\n \u003cp\u003e324.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 seed weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003cp\u003e17.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003cp\u003e17.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003cp\u003e16.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003cp\u003e15.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n \u003cp\u003e21.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003cp\u003e21.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarvest index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003cp\u003e34.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003cp\u003e35.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\n \u003cp\u003e33.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003cp\u003e33.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003cp\u003e34.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003cp\u003e33.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2020-21\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRabi\u003c/em\u003e 2021-22\u003c/p\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2472.8\u0026thinsp;\u0026plusmn;\u0026thinsp;132.1\u003c/p\u003e\n \u003cp\u003e2689.6\u0026thinsp;\u0026plusmn;\u0026thinsp;147.0\u003c/p\u003e\n \u003cp\u003e2685.2\u0026thinsp;\u0026plusmn;\u0026thinsp;263.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1665.1\u0026thinsp;\u0026plusmn;\u0026thinsp;263.8\u003c/p\u003e\n \u003cp\u003e1638.8\u0026thinsp;\u0026plusmn;\u0026thinsp;132.7\u003c/p\u003e\n \u003cp\u003e1652.3\u0026thinsp;\u0026plusmn;\u0026thinsp;147.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2667.6\u0026thinsp;\u0026plusmn;\u0026thinsp;263.1\u003c/p\u003e\n \u003cp\u003e2644.1\u0026thinsp;\u0026plusmn;\u0026thinsp;132.4\u003c/p\u003e\n \u003cp\u003e2655.9\u0026thinsp;\u0026plusmn;\u0026thinsp;147.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic parameters for the yield and agro-morphological traits\u003c/h2\u003e\n \u003cp\u003eThe values of the genetic parameters for yield, yield components, and agro-morphological traits in NG and NNG of chickpea are presented in Supplementary Tables\u0026nbsp;2 and 3 respectively. For NG (Supplementary Table\u0026nbsp;2), low estimates of the Genotypic coefficient of variation (GCV) and Phenotypic coefficient of variation (PCV) were recorded for DF, DFF, and DM; moderate to low for PH; low to moderate for PBr recorded and high to low for SBr. Estimates of the genetic advance of mean (GAM) were low for DM; low to moderate for DF and DFF; low to high for SBr; and moderate to high for PH and PBr in pooled. For yield and yield component traits, PCV and GCV were recorded as low for HI, moderate to low for SW100, and high to moderate for NPPP, NSPP, and SY. Estimates of the GAM were high for NPPP, NSPP, and SY whereas for SW100 and HI the estimates were moderate and low to moderate in pooled. In NNG, the estimates of PCV and GCV were recorded as low for DF, DFF, and DM; moderate to low for PH, and PBr, and high to low for SBr (Supplementary Table\u0026nbsp;3). For GAM, low to moderate estimates were recorded for DF and DFF; low for DM; moderate to high for PH; and low to high for PBr and SBr.\u003c/p\u003e\n \u003cp\u003eThe estimates of PCV, GCV, heritability, and GAM for yield and yield components were high for NPPP, NSPP, and SY. PCV and GCV estimates are low to moderate for SW100 and HI; and high for SY. Promising NG and NNG were identified for yield and yield components (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Of the NG, NG-172 recorded the highest yield (3966.7 kg/ha) followed by NG-88 (3730.7 kg/ha), NG-188 (3584.7 kg/ha), NG-159 (3563.8 kg/ha), NG-13 (3561.6 kg/ha). Even though the increase in SY was non-significant, it was higher than the best checks RVG 204 and Phule Vikram (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), Among the NNG, NNG-206 recorded the highest yield (3153.7 kg/ha) followed by NNG-182, NNG-163, NNG-80, and NNG-152 with yields of 2881.3 kg/ha, 2676.4 kg/ha, 2661.9 kg/ha, 2648.5 kg/ha respectively. These NNGs showed on-par seed yield with the best checks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation of agro-morphological, yield, and yield component traits\u003c/h2\u003e\n \u003cp\u003eKarl Pearson\u0026rsquo;s correlation coefficients were calculated for the agro-morphological, yield, and yield component traits of NG and NNG (Supplementary Tables 4 and 5). In NG under both seasons, SY showed a significant positive correlation with PH, NPPP (except 2020-21), NSPP, and SW100. Similarly, in pooled, SY was positively associated with PH, PBr, NPPP, NSPP, and HI. DF and DFF traits showed a significant negative correlation with DM and SW100 in both the crop seasons and pooled data. Whereas PH showed a significant positive correlation with SW100 in both the seasons and pooled. For NPPP, a positive significant correlation was recorded with NSPP and HI whereas NSPP recorded a significant positive correlation with HI alone (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn NNG, the SY showed a significant negative correlation with NPPP in \u003cem\u003eRabi\u003c/em\u003e 2020-21; PH, NPPP, and NSPP in \u003cem\u003eRabi\u003c/em\u003e 2021-22 whereas it was significantly positively correlated with NPPP and NSPP in pooled. In both the cropping seasons and pooled season a significant positive correlation was recorded between DF and DFF, PH and SW100, and NPPP and NSPP (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetails of promising nodulating and non-nodulating genotypes identified for yield and yield related traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDays to first flowering\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDays to 50% flowering\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlant Height (cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of primary branches per plant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of secondary branches per plant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of pods per plant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of seeds per plant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e100 seed weight (g)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHarvest Index (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeed yield (kg/ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"14\"\u003e\n \u003cp\u003e\u003cstrong\u003eNodulating genotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG-172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3966.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG-88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3730.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG-188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e408.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e502.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3584.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG-159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e446.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3563.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e514.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3561.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"14\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-nodulating genotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNNG-206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e466.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e531.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3153.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNNG-182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2881.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNNG-163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e242.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2676.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNNG-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2661.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNNG-152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e383.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e407.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2648.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"14\"\u003e\n \u003cp\u003e\u003cstrong\u003eChecks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhule Vikram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e363.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3201.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRVG 204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3478.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNBeG 47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1501.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJG 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2441.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC 4918NN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1614.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICC 4918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2231.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eScatter Plot for yield and yield component traits in chickpea RILs\u003c/h2\u003e\n \u003cp\u003eThe relationships between two numeric variables in the data set for the target traits and the performance of the genotypes in two environments were analyzed using a scatter plot (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The RILs that were in the 2nd and 4th coordinates are adaptable when considering the environment as the main factor while, those in the 1st and 3rd coordinates are stable across the environments. Specifically, genotypes situated in the 1st and 3rd coordinates were identified as NG and NNG, respectively.\u003c/p\u003e\n \u003cp\u003eThe scatter plot analysis revealed distinct performance patterns for yield and yield component traits across different seasons in the NG and NNG (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In pooled (2nd and 3rd coordinate), RILs 172, 88, 188, 159, and 13 for seed yield; RILs 206, 156, 13, 172 and 17 for NPPP; the RILs 172, 40, 206, 13, 56 for NSPP; RILs 92, 199, 194, 17 and 63 for HI; RILs 163, 157, 194, 22, 68 for SW100 exhibited better performance, emphasizing the trait-specific adaptability of genotypes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For \u003cem\u003eRabi\u003c/em\u003e 2020-21 (4th coordinate), the RILs 172, 88, 188, 178, 159 for SY; the RILs 172, 188, 178, 40, 159 NPPP; the RILs 172, 98, 188, 221, 13 for NSPP; the RILs 92, 199, 194, 17 and 63 for HI; RILs 163, 22, 207, 68, 157 for SW100 whereas for \u003cem\u003eRabi\u003c/em\u003e 2021-22 (2nd coordinate), the RILs 209, 101, 110, 151, 136 for SY; the RILs 156, 94, 4, 199, 196 for NPPP; RILs 156, 4, 94, 102, 175 for NSPP; the RILs 114, 22, 67, 199, 194 for HI; the RILs 163, 157, 169, 185, 208 for SW100 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) were scattered in their respective coordinates from other RILs depicting their better performance in their respective seasons, highlighting their potential for adaptability and yield enhancement. Using scatter plots to distinguish the genotypes based on their mean performance aligns with previous studies conducted in chickpea\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and mungbean\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The strategic use of this analytical tool enhances the precision of genotype selection, providing a valuable resource for chickpea breeding programs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eGenotype \u0026times; Trait (GT) bi-plot\u003c/h2\u003e\n \u003cp\u003eThe GT bi-plot analysis revealed that 53.92% of the trait variation could be explained, with PC1 and PC2 accounting for 40.72% and 13.20% of the total variance, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Apart from the checks, genotypes 68, 159, 13, 17, 187, and 123 demonstrated superior adaptability for the traits SY, PH, and SW100 across the seasons. Conversely, genotypes 213, 128, 106, 109, and 46 exhibited the highest adaptability for traits DFF and DM across the seasons (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Of the checks, PhuleVikram and RVG 204 were best adaptable for the traits SY, PH, and SW100 in both the seasons (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, there were differences in genotype rankings for HI between seasons, as indicated by angles greater than 90\u0026deg; between vectors. Based on the vectors, the ranking of the genotypes for SY, PH, and SW100 can be observed as, PhuleVikram\u0026thinsp;=\u0026thinsp;RVG 204\u0026thinsp;\u0026gt;\u0026thinsp;RILs 68\u0026thinsp;\u0026gt;\u0026thinsp;159\u0026thinsp;\u0026gt;\u0026thinsp;13\u0026thinsp;\u0026gt;\u0026thinsp;17\u0026thinsp;\u0026gt;\u0026thinsp;187\u0026thinsp;\u0026gt;\u0026thinsp;123. The polygon view depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrated the distribution of genotypes in the trait bi-plot, highlighting important yield, yield components, and agro-morphological traits in chickpea.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe formation of rhizobium nodulation is a key symbiotic mechanism in legume crops for their adaptation to marginal environments. The quantitative assessment of their impact on plant growth and economic yields is crucial for cultivar improvement and optimizing agricultural productivity. In this study, the selected parents are landraces, distinguishing one as a non-nodulating mutant (ICC 4918NN) derived from the other germplasm (ICC 4918). By employing the RGA approach, we successfully generated a RIL population within a mere 18 months, a testament to the efficiency and robustness of the methodology utilized\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. While the potential of RGA has been acknowledged in other crops such as pea\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, pigeon pea\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, barley and wheat\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, and canola\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, its application in practical breeding programs remains largely unexplored. The variance analysis revealed a significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among the RILs for all the traits under the study. This indicates the presence of ample variability for the traits in the population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Significant genetic variability among genotypes was observed in earlier studies evaluated for nodulation-related traits in chickpea\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA notable finding in our study is the substantial increase in yield (62.55%) in NG compared to NNG. The improvement was majorly contributed by the increase in the NPPP (39.5%) and NSPP (44.4%). On a moderate level, nodulation has reduced the flowering time and maturity and enhanced the PH, SBr, SW100 and HI (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results indicate that NGs were more efficient in synthesizing photosynthates, which led to produce more number of flowers for generating a large number of pods and seeds\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e than NNG. In addition, the yield advantage reflects not only the inherent genetic potential of the legume plant but also the synergistic effects of the established symbiosis between roots and rhizobial bacteria. The result agrees with an earlier study in chickpea, which reported a 31% higher yield in NG compared to NNG counterparts\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. The current study, with a more pronounced yield advantage, emphasizes the significance of rhizobial nodulation in optimizing chickpea productivity.\u003c/p\u003e \u003cp\u003eThe poorer performance of NNG may be attributed to the deficiency of nitrogen fixation through nodulation, emphasizing its crucial role in chickpea productivity. The reliance on alternative nitrogen sources becomes crucial for NNG genotypes, and supplementing nitrogen in the form of fertilizers may be necessary to enhance yield\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This aligns with previous findings in chickpea and groundnut, where NNGs in nitrogen-rich soils could attain yields on par with NG\u0026rsquo;s\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Though the degree of reliance on nodulation for nitrogen fixation varies throughout legumes, it is perpetually present. For instance, in soybean, nodulation can contribute to a significant proportion of the plant's nitrogen needs, with non-nodulating variants often displaying stunted growth and reduced yields\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Similarly, nodulation is critical for optimal growth in common beans, especially under nitrogen-deficient conditions\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Further investigations into the performance of NNG and NG under varying nitrogen availability can offer insights into their adaptability and yield potential across different soil conditions.\u003c/p\u003e \u003cp\u003eThe present investigation aimed to bridge a crucial gap in the existing knowledge base by assessing the impact of nodulation on agro-morphological and yield traits in chickpea. Our results align with existing literature on the positive effects of rhizobial nodulation on various growth characteristics, emphasizing the importance of this symbiotic association. The observed higher values in PH, PBr, biomass, and yield traits in NG compared to NNG underscore the pivotal role of rhizobial nodulation in enhancing plant growth and productivity in chickpea (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and mobilization of insoluble nutrients in the soil, leading to improved nutrient uptake in other legumes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In addition, the absence of rhizobium nodulation resulted in a significant reduction in various growth parameters which might be due to a deficit in (a) host-dependent strain fitness\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, (b) up-regulated expression of \u003cem\u003enif\u003c/em\u003e genes related to flavonoid synthesis\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, and (c) maintenance of plant Pi (Inorganic phosphate) levels\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe estimation of genetic variability and inheritance through GCV, PCV and heritability allows the breeders to identify the traits with substantial genetic control and potential for selection in crop improvement programs. For phenological traits, the small difference between GCV and PCV values suggests a predominant influence of genetic factors on their variance. HI exhibited low estimates of both PCV and GCV, indicating a more substantial influence of environment (Supplementary table 2 and 3). This aligns with previous studies highlighting the major role of genetic components in the inheritance of flowering, maturity, and the environmental factors in determining HI\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. For the traits NPPP, NSPP, HI, PBr, and SBr, a magnitude of low to high heritability was observed, suggesting several genetic factors controlling the inheritance of these traits. In particular, SY demonstrated a high magnitude of GCV, PCV, and heritability emphasizing its potential for genetic improvement through simple selection even in early generations efficiently\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. This diverse heritability pattern underscores the importance of trait-specific breeding strategies to achieve improvements in chickpea agronomics\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. High heritability coupled with high GAM was recorded for SY and SW100 across seasons (Supplementary table 2 and 3), indicating the predominance of additive gene action for these traits. Similar findings were reported under diverse genetic backgrounds in chickpea\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, blackgram\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e as well as in cowpea\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, the association among SY, NPPP, and NSPP was significantly positive in both NG and NNG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This indicates the possibility of simultaneous improvement of multiple traits in chickpea genotypes and cowpea advanced breeding lines\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The ability to enhance multiple traits concurrently is crucial for developing improved chickpea varieties with enhanced agronomic performance. Further analysis of the genotypes for their yield and stability using GT bi-plot identified the checks, Phule Vikram and RVG 204 on its vertex with high values for all the traits under the study which can be considered the best adaptable genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For SY, PH, and SW100, the NG #68, 159, 13, 17, 187, and 123 while 106, 109, and 46 for DFF and DM were best adaptable over the seasons. Comparable research of this kind in various crops\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, provide a valuable insight for the selection of superior genotypes with enhanced adaptability and nodulating nature in the chickpea breeding programs.\u003c/p\u003e \u003cp\u003eThe impact of rhizobium nodulation on grain yield and its association with yield-related traits was prominent in different legume crops. Earlier studies in chickpea emphasized the role of nodulation in enhancing seed yield and pod development\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Similarly, the seed yield was significantly improved by 40% in cowpea\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, 33% in common bean\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and 45.6\u0026ndash;50% in faba bean\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e compared to control or non-inoculated treatments, indicating the critical role of symbiotic nitrogen fixation in achieving higher seed yields in legumes. Moreover, the crop-specific strains, soils, and weather factors are crucial in achieving yields. These shared trends highlight the conserved nature of the nodulation mechanism inherited historically for thriving legume crops under diverse agro-ecologies.\u003c/p\u003e \u003cp\u003eThe comprehensive assessment of genetic variability, heritability, and genetic advance in this study provides a robust foundation for future chickpea breeding programs. The identification of traits with high heritability and substantial genetic advance, coupled with positive correlations among yield-related parameters, highlights avenues for effective genetic improvement. However, it's crucial to recognize the crop-specific nature of these findings and to tailor breeding strategies accordingly. Future research should delve deeper into the molecular mechanisms underpinning nodulation in chickpea and explore comparative studies across leguminous crops to enhance our understanding of these complex interactions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the environment\u003c/h2\u003e \u003cp\u003eThe present investigation was carried out at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India located at 17\u003csup\u003e◦\u003c/sup\u003e30\u0026prime; N latitude and 78\u003csup\u003e◦\u003c/sup\u003e16\u0026prime; E longitude with an altitude of 549 m above sea level during the normal crop season ie., \u003cem\u003eRabi\u003c/em\u003e 2020-21 and 2021-22. The average rainfall, humidity, minimum, and maximum temperatures were recorded as 3.1mm, 73.8%, 14.7\u0026deg;C and 29.7\u0026deg;C respectively during \u003cem\u003eRabi\u003c/em\u003e 2020-21 whereas in \u003cem\u003eRabi\u003c/em\u003e 2021-22 they were recorded as 8.0 mm, 63.3%, 15.7\u0026deg;C and 30.0\u0026deg;C respectively (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://icrisatintranet/nrmp/agroclimatology/weather.asp\u003c/span\u003e\u003cspan address=\"http://icrisatintranet/nrmp/agroclimatology/weather.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of the RIL population through Rapid generation advancement (RGA)\u003c/h2\u003e \u003cp\u003eThe RIL population was developed using the Rapid Generation Advancement (RGA) method\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e by crossing between two parents ICC 4918NN and ICC 4918 (Annegiri). The hybridity confirmed F\u003csub\u003e1\u003c/sub\u003e seeds were advanced to F\u003csub\u003e2\u003c/sub\u003e in a glasshouse. From F\u003csub\u003e2\u003c/sub\u003e the generations were advanced by selfing and single seed decent method. In 2019-20, 307 F\u003csub\u003e2\u003c/sub\u003e seeds were harvested in the glasshouse and advanced to F\u003csub\u003e3\u003c/sub\u003e through RGA where the immature pods were harvested 50 days after sowing (DAS) and sown on the same day for generation advancement. Four generations starting from F\u003csub\u003e2\u003c/sub\u003e to F\u003csub\u003e6\u003c/sub\u003e advanced in one year (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A maximum temperature of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, 70\u0026ndash;80% humidity, and artificial light with the help of incandescent bulbs was provided for a period of 12 hrs (from 18:00\u0026ndash;6:00) during the RGA experiment. The F\u003csub\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;7\u003c/sub\u003e and F\u003csub\u003e7\u0026thinsp;\u0026minus;\u0026thinsp;8\u003c/sub\u003e seeds were sown in the field in \u003cem\u003eRabi\u003c/em\u003e 2020-21 and 2021-22 for agronomic characterization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A total of 230 RILs (130 nodulating genotypes, 94 non-nodulating genotypes), four checks (Phule Vikram, RVG 204, NBeG 47, and JG 14), and two parents (ICC 4918NN and ICC 4918) were used in our experiment. The parental genotypes were obtained from the genebank of ICRISAT, India. The genotypes (RIL population) were categorized based on the presence or absence of nodules in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDevelopment of RIL mapping population of nodulating and non-nodulating genotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFilial generations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrowth condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of plants advanced\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePlant Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNodulating genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-nodulating genotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlass House\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlass House\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e4\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlass House\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e5\u0026thinsp;\u0026minus;\u0026thinsp;6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlass House\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eField evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e7\u0026thinsp;\u0026minus;\u0026thinsp;8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eField evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eField experiment\u003c/h2\u003e \u003cp\u003eThe experimental material was sown on Vertisols in Alpha Lattice Design with three replications. Each genotype was sown with a spacing of 60 \u0026times; 15 cm (inter and intra) covering an area of 1.2 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Planting of the population was completed during the first week of November in \u003cem\u003eRabi\u003c/em\u003e, 2020, and in the second week of November in \u003cem\u003eRabi\u003c/em\u003e, 2021. All the standard agronomic practices were followed for better crop establishment. Two irrigations were provided, one at the initial stage of planting and the other at the podding stage to maintain sufficient moisture regime for better crop establishment. Data related to the agronomic traits such as DF, DFF, DM, PH, PBr, SBr were recorded in each plot. The genotypes were harvested when all the plants matured completely. The post-harvest data related to the NPPP, NSPP, SW100, HI and SY were recorded from their respective plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening for nodulation\u003c/h2\u003e \u003cp\u003eTo distinguish the nature of the genotypes in terms of nodulation, the RIL population was grown in semi-controlled glasshouse condition as per the protocol outlined in the previous studies of chickpea\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The experimental material was surface sterilized with sodium hypochlorite and treated individually with the rhizobial strains IC59 and IC76A, the predominant nodulating bacteria in south-central India\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The seeds of each genotype were sown in 9\u0026rdquo; pots filled with potting mixture composed of sterile soil, sand, and vermicompost (3:2:2). After germination, the seedlings were thinned to three and uprooted at 35 days after sowing\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, to determine the presence or absence of nodules in the planting material. Each genotype was designated as NG and NNG based on the presence or absence of root nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA combined analysis of variance was carried out using the SAS MIXED procedure\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, to test the significance of the main and interaction effects of seasons and genotypes, considering the season, genotype, replication as fixed, and block as random. Individual season variance was modeled into combined analysis with repeated statements using the REML (Restricted Maximum Likelihood) method. To analyze the fixed effects, BLUEs (Best Linear Unbiased Estimates) are estimated for both main and interaction effects from the pooled analysis. The significance of NG and NNG means were compared using t-statistics from the combined analysis. Karl Pearson\u0026rsquo;s correlation was performed among the agronomic traits by using the SAS CORR procedure\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe magnitude of variation and its utility can be explained by genetic parameters like GCV, PCV, broad sense heritability, and GAM. These parameters were also estimated for the target traits using the SAS CORR procedure\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The estimates of PCV and GCV were classified as high (\u0026gt;\u0026thinsp;20%), moderate (10\u0026ndash;20%) and low (\u0026lt;\u0026thinsp;10%)\u003csup\u003e72\u003c/sup\u003e; heritability as high (\u0026gt;\u0026thinsp;60%), moderate (30\u0026ndash;60%), low (\u0026lt;\u0026thinsp;30%)\u003csup\u003e38\u003c/sup\u003e; GAM as high (\u0026gt;\u0026thinsp;20%), moderate (10\u0026ndash;20%) and low (\u0026lt;\u0026thinsp;10%)\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo visualize the associations among the traits and the trait profiles of the genotypes a Genotype \u0026times; Trait (GT) bi-plot was generated. It is based on Single Value Decomposition (SVD) of trait-wise standardized data (Z\u0026thinsp;~\u0026thinsp;N (0,1)). The correlation between the traits was interpreted by using cosine angles of the vectors in between the traits. The angle\u0026thinsp;\u0026lt;\u0026thinsp;90\u0026deg;, \u0026gt;\u0026thinsp;90\u0026deg;, and equal to 90\u0026deg; states the positive, negative, and no correlation between the traits, respectively. As the vector length of a particular trait explains the variation in genotypes, the longest vector explains more variation among the genotypes and the shortest vector depicts a very low level of variation among genotypes for a given trait.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe RIL mapping population was developed in less than two years, the first report in chickpea to show the practical application of the RGA protocol in developing breeding material. The influence of nodulation on yield was distinctly favorable in nodulating genotypes, characterized by increased SY, biomass, and PH compared to NNG. The stable performing genotypes with high yield and early flowering nature were mostly nodulating in nature depicting the beneficial effect of nodulation on the crop growth under moisture stress conditions. The promising genotypes identified can serve as donors for use in chickpea breeding programs. Furthermore, the results emphasize the critical role of compatible \u003cem\u003eRhizobium\u003c/em\u003e strain/s in achieving optimal yields. The study highlights that the absence of nodulation can lead to substantially lower yields in chickpea. Therefore, the distribution and presence of \u003cem\u003eRhizobium\u003c/em\u003e strains in cultivated fields emerge as influential factors affecting final yield levels. To enhance the understanding of nodulation in chickpea, further research on identifying the genomic regions/QTLs/markers associated with nodulation is in progress to enhance our knowledge and provide valuable insights into the molecular mechanisms controlling nodulation in chickpea, ultimately contributing to improved crop productivity and promoting sustainable agricultural practices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe, the authors, are thankful to the chickpea team members of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) for providing research facilities and funds for the field trials, Acharya N. G. Ranga Agricultural University (ANGRAU) for providing fellowship during the research and International Center for Biosaline Agriculture (ICBA) for technical backstopping in development and submission of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u0026nbsp;\u003c/strong\u003eThe online version contains supplementary material. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe current research was supported by the Indian Council of Agricultural Research-International Crops Research Institute for the Semi-Arid Tropics (ICAR-ICRISAT) and The CGIAR Research Program on Grain Legumes and Dryland Cereals (CRP-GLDC) projects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare that they had no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternational guidelines and legislation\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis experimental research on chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) crop adheres to international guidelines and legislation to ensure ethical, sustainable, and scientifically robust practices. The study complies with institutional, national, and international standards, including the Convention on Biological Diversity (CBD) and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), ensuring the sustainable use and conservation of biodiversity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eNandigam SwathiRekha:\u0026nbsp;Data collection and curation, Trial establishment, Software, Visualization, Writing-original draft preparation. Mahesh Damodhar Mahendrakar:\u0026nbsp;Data curation, formal analysis, validation, writing the manuscript. Uttam Chand: Field layout, Intercultural operations,\u0026nbsp;Data curation. S Subramaniam Gopalakrishnan:\u0026nbsp;Conceptualization of rhizobium inoculation, Methodology, Review, and editing the manuscript. Srinivasa Rao Vatluri: Methodology, Review, and editing the manuscript. Vadlamudi Srinivas:\u0026nbsp;Rhizobium culture preparation and application, glasshouse evaluations.\u0026nbsp;Srinivas Thati:\u0026nbsp;Conceptualization, Review and editing the manuscript. Srungarapu Rajasekhar:\u0026nbsp;Data curation, Formal analysis, Editing of the manuscript. Anilkumar Vemula:\u0026nbsp;Statistical Analysis, Validation of results, Visualization. Himabindu Kudapa: Methodology and editing the manuscript. Samineni Srinivasan: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003eThe data of this article supporting the conclusions will be made available by the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdmas, S., Tesfaye, K., Haileselassie, T., Shiferaw, E \u0026amp; Flynn, K. C. Genetic variability and population structure of Ethiopian chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) germplasm. \u003cem\u003ePlos one\u003c/em\u003e. \u003cstrong\u003e16\u003c/strong\u003e(11), e0260651 (2021).\u003c/li\u003e\n \u003cli\u003eAhmet, E. K \u0026amp; Halime \u0026Ouml;zdamar \u0026Uuml;nl\u0026uuml;. Effect of rhizobium inoculation on yield and some quality properties of fresh cowpea. \u003cem\u003eCogent food agric\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 2 (2023).\u003c/li\u003e\n \u003cli\u003eAhmad, M. \u003cem\u003eet al.\u003c/em\u003e Appraising endophyte\u0026ndash;plant symbiosis for improved growth, nodulation, nitrogen fixation and abiotic stress tolerance: An experimental investigation with chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eAgronomy\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 621 (2019).\u003c/li\u003e\n \u003cli\u003eAli, M. A., Naveed, M., Mustafa, A. \u0026amp; Abbas, A. The good, the bad, and the ugly of rhizosphere microbiome in \u003cem\u003eProbiotics and Plant Health\u003c/em\u003e. 253\u0026ndash;290 (Springer, 2017).\u003c/li\u003e\n \u003cli\u003eAsante, M., Ahiabor, B. D. K \u0026amp; Atakora, W. K. Growth, Nodulation, and Yield Responses of groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) as influenced by combined application of rhizobium inoculant and phosphorus in the Guinea Savanna zone of Ghana. \u003cem\u003eInt. J. Agron\u003c/em\u003e. 1\u0026ndash;7, p.8691757 (2020).\u003c/li\u003e\n \u003cli\u003eAshraf, M \u0026amp; Iram, A. Drought stress induced changes in some organic substances in nodules and other plant parts of two potential legumes differing in salt tolerance. \u003cem\u003eFlora\u003c/em\u003e. \u003cstrong\u003e200\u003c/strong\u003e, 535\u0026ndash;546 (2005).\u003c/li\u003e\n \u003cli\u003eBakhshi, B. \u003cem\u003eet al.\u003c/em\u003e Trait profiling and genotype selection in oilseed rape using genotype by trait and genotype by yield* trait approaches. \u003cem\u003eFood Sci. Nutr\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e(6), 3083-3095 (2023).\u003c/li\u003e\n \u003cli\u003eBarmukh, R. \u003cem\u003eet al.\u003c/em\u003e Construction of a high-density genetic map and QTL analysis for yield, yield components and agronomic traits in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003ePlos one\u003c/em\u003e. \u003cstrong\u003e16\u003c/strong\u003e(5), p.e0251669 (2021).\u003c/li\u003e\n \u003cli\u003eBourebaba, Y. \u003cem\u003eet al\u003c/em\u003e. Diversity of \u003cem\u003eBradyrhizobium\u0026nbsp;\u003c/em\u003estrains nodulating \u003cem\u003eLupinus micranthus\u003c/em\u003e on both sides of the Western Mediterranean: Algeria and Spain.\u0026nbsp;\u003cem\u003eSyst.\u0026nbsp;\u003c/em\u003e\u003cem\u003eAppl. Microbiol\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003e39\u003c/strong\u003e, 266-274 (2016).\u003c/li\u003e\n \u003cli\u003eBuhian, W. P \u0026amp; Bensmihen, S. Mini-review: nod factor regulation of phytohormone signaling and homeostasis during rhizobia-legume symbiosis. \u003cem\u003eFront. Plant Sci\u003c/em\u003e. \u003cstrong\u003e9,\u003c/strong\u003e 1247 (2018).\u003c/li\u003e\n \u003cli\u003eBurghardt, L. T., Epstein, B., Hoge, M., Trujillo, D. I \u0026amp; Tiffin, P. Host-Associated Rhizobial Fitness: Dependence on Nitrogen, Density, Community Complexity, and Legume Genotype.\u0026nbsp;\u003cem\u003eAppl. Environ. Microbiol\u003c/em\u003e. \u003cstrong\u003e88\u003c/strong\u003e, e00526-22 (2022).\u003c/li\u003e\n \u003cli\u003eCakmakci, R., Donmez, M. F \u0026amp; Erdogan, U. The effect of plant growth promoting rhizo bacteria on barley seedling growth, nutrient uptake, some soil properties, and bacterial counts. \u003cem\u003eTurk. J. Agr. For\u003c/em\u003e. \u003cstrong\u003e31\u003c/strong\u003e, 189\u0026ndash;199 (2007).\u003c/li\u003e\n \u003cli\u003eChauhan, S., Mittal, R. K., Sood, V. K \u0026amp; Patial, R. Evaluation of genetic variability, heritability and genetic advance in blackgram [\u003cem\u003eVigna mungo\u003c/em\u003e (L.) Hepper]. \u003cem\u003eLegum. Res.\u003c/em\u003e\u003cstrong\u003e43\u003c/strong\u003e(4), 488-494 (2020).\u003c/li\u003e\n \u003cli\u003eDiapari, M. \u003cem\u003eet al.\u003c/em\u003e Genetic diversity and association mapping of iron and zinc concentrations in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eGenome\u003c/em\u003e. \u003cstrong\u003e57\u003c/strong\u003e, 459\u0026ndash;468 (2014).\u003c/li\u003e\n \u003cli\u003eDiapari, M., Sindhu, A., Warkentin, T. D., Bett, K \u0026amp; Tar\u0026rsquo;an, B. Population structure and marker-trait association studies of iron, zinc and selenium concentrations in seed of field pea (\u003cem\u003ePisum sativum\u003c/em\u003e L.). \u003cem\u003eMol. Breed\u003c/em\u003e. \u003cstrong\u003e35\u003c/strong\u003e, 1\u0026ndash;14 (2015).\u003c/li\u003e\n \u003cli\u003eDitta, A. \u003cem\u003eet al.\u003c/em\u003e Rock phosphate enriched organic fertilizer with phosphate solubilizing microorganisms improves nodulation, growth and yield of legumes. \u003cem\u003eCommun. Soil Sci. Plant Anal\u003c/em\u003e. \u003cstrong\u003e49\u003c/strong\u003e, 2715\u0026ndash;2725 (2018a).\u003c/li\u003e\n \u003cli\u003eDitta, A. \u003cem\u003eet al.\u003c/em\u003e Application of rock phosphate enriched composts increases nodulation, growth and yield of chickpea. \u003cem\u003eInt. J. Recycl. Org. Waste Agric\u003c/em\u003e. \u003cstrong\u003e7\u003c/strong\u003e, 33\u0026ndash;40 (2018b).\u003c/li\u003e\n \u003cli\u003eDos Santos Sousa, W. \u003cem\u003eet al.\u003c/em\u003e Effects of \u003cem\u003eRhizobium\u003c/em\u003e inoculum compared with mineral nitrogen fertilizer on nodulation and seed yield of common bean. A meta-analysis. \u003cem\u003eAgron. Sustain. Dev.\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 52 (2022).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDutta, S \u0026amp; Podile, A. R. Plant growth promoting rhizobacteria (PGPR): the bugs to debug the root zone. \u003cem\u003eCrit. Rev. Microbiol\u003c/em\u003e. \u003cstrong\u003e36\u003c/strong\u003e, 232-244 (2010).\u003c/li\u003e\n \u003cli\u003eFigueiredo, M. V. B., H, L, A, Burity., C, R, Mart\u0026iuml;nez \u0026amp; Chanway, C. P. Alleviation of drought stress in the common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) by co-inoculation with \u003cem\u003ePaenibacillus polymyxa\u003c/em\u003e and \u003cem\u003eRhizobium tropici\u003c/em\u003e. \u003cem\u003eAppl. Soil. Ecol\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, 182\u0026ndash;88 (2008).\u003c/li\u003e\n \u003cli\u003eFood and Agriculture Organization of the United Nations. FAOSTAT statistical database. Rome: FAO (2022).\u003c/li\u003e\n \u003cli\u003eFox, A. R. \u003cem\u003eet al\u003c/em\u003e. Major cereal crops benefit from biological nitrogen fixation when inoculated with the nitrogen fixing bacterium \u003cem\u003ePseudomonas protegens\u003c/em\u003e Pf5 X940. \u003cem\u003eEnviron. Microbiol\u003c/em\u003e. \u003cstrong\u003e18\u003c/strong\u003e, 3522-3534 (2016).\u003c/li\u003e\n \u003cli\u003eGaur, P. M. \u003cem\u003eet al.\u003c/em\u003e Inheritance of protein content and its relationships with seed size, grain yield and other traits in chickpea. \u003cem\u003eEuphytica.\u003c/em\u003e\u003cstrong\u003e209\u003c/strong\u003e, 253\u0026ndash;260 (2016).\u003c/li\u003e\n \u003cli\u003eGedamu, S.A., Tsegaye, E.A. \u0026amp; Beyene, T.F. Effect of rhizobial inoculants on yield and yield components of faba bean (\u003cem\u003eVicia fabae\u003c/em\u003e L.) on vertisol of Wereillu District, South Wollo, Ethiopia. \u003cem\u003eCABI Agric Biosci\u003c/em\u003e. \u003cstrong\u003e2\u003c/strong\u003e, 8 (2021).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGediya, L. N. \u003cem\u003eet al.\u003c/em\u003e Phenotypic variability, path analysis and molecular diversity analysis in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eVegetos\u003c/em\u003e. \u003cstrong\u003e32\u003c/strong\u003e, 167\u0026ndash;180 (2019).\u003c/li\u003e\n \u003cli\u003eGerrano, A. S., Jansen van Rensburg, W. S \u0026amp; Kutu, F. R. Agronomic evaluation and identification of potential cowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e L. Walp) genotypes in South Africa. \u003cem\u003eActa Agric. Scand. Sec- B Soil Plant Sci\u003c/em\u003e. \u003cstrong\u003e69\u003c/strong\u003e, 295-303 (2019).\u003c/li\u003e\n \u003cli\u003eGiller, K. E., Nambiar, P. T. C., Srinivasa Rao, B., Dart, P. J \u0026amp; Day, J. M. A comparison of nitrogen fixation in genotypes of groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) using \u003csup\u003e15\u003c/sup\u003eN-isotope dilution. \u003cem\u003eBiol. Fertil. Soils\u003c/em\u003e. \u003cstrong\u003e5\u003c/strong\u003e, 23-25 (1987).\u003c/li\u003e\n \u003cli\u003eGopalakrishnan, S. \u003cem\u003eet al.\u003c/em\u003e Assessment of nodulation potential in mini-core genotypes and land races of chickpea. \u003cem\u003eJ. Food Legum\u003c/em\u003e. \u003cstrong\u003e30\u003c/strong\u003e, 65-72 (2017a).\u003c/li\u003e\n \u003cli\u003eGopalakrishnan, S., Srinivas, V \u0026amp; Samineni, S. Nitrogen fixation, plant growth and yield enhancements by diazotrophic growth-promoting bacteria in two cultivars of chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eBiocatal. Agric. Biotechnol\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 116-123 (2017b).\u003c/li\u003e\n \u003cli\u003eGopalakrishnan, S., Srinivas, V., Vemula, A., Samineni, S \u0026amp; Rathore, A. Influence of diazotrophic bacteria on nodulation, nitrogen fixation, growth promotion and yield traits in five cultivars of chickpea. \u003cem\u003eBiocatal. Agric. Biotechnol\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e,\u0026nbsp;35-42 (2018).\u003c/li\u003e\n \u003cli\u003eGul R., Khan, H., Khan, N. U \u0026amp; Khan, F. Y. Characterization of chickpea germplasm for nodulation and effect of \u003cem\u003eRhizobium\u003c/em\u003e inoculation on nodules number and seed yield. \u003cem\u003eJ. Anim. Plant Sci.\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e(5), 1421-1429 (2014).\u003c/li\u003e\n \u003cli\u003eGul, R., Khan, H., Khan, N.U., Latif, A \u0026amp; Harada, K. Characterization for nodulation and detection of duplicate gene action of dominant epistasis controlling root nodulation in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e). \u003cem\u003eInt. J. Agric. Biol\u003c/em\u003e. \u003cstrong\u003e20\u003c/strong\u003e: 683‒688 (2018).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHerridge, D. F., Peoples, M. B \u0026amp; Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. \u003cem\u003ePlant soil\u003c/em\u003e. \u003cstrong\u003e311\u003c/strong\u003e, 1-18 (2008)\u003c/li\u003e\n \u003cli\u003eHuang, X. F. \u003cem\u003eet al.\u003c/em\u003e Rhizosphere interactions: root exudates, microbes, and microbial communities. \u003cem\u003eBotany\u003c/em\u003e. \u003cstrong\u003e92,\u003c/strong\u003e 267\u0026ndash;275 (2014).\u003c/li\u003e\n \u003cli\u003eHussain, S. A. \u003cem\u003eet al.\u003c/em\u003e Estimating genetic variability among diverse lentil collections through novel multivariate techniques. \u003cem\u003ePlos one\u003c/em\u003e. \u003cstrong\u003e17\u003c/strong\u003e(6), e0269177 (2022).\u003c/li\u003e\n \u003cli\u003eJemo, M. \u003cem\u003eet al.\u003c/em\u003e Comparative analysis of the combined effects of different water and phosphate levels on growth and biological nitrogen fixation of nine cowpea varieties. \u003cem\u003eFront. Plant Sci\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e,\u0026nbsp;2111 (2017).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJha, U. C. \u0026amp; Shil, S. A. N. D. I. P. Association analysis of yield contributing traits of chickpea genotypes under high temperature condition. \u003cem\u003eTrends Biosci\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 2335-2341 (2015).\u003c/li\u003e\n \u003cli\u003eJohnson, H. W., Robinson, H. F \u0026amp; Comstock, R. E. Estimates of genetic and environmental variability in soybeans. \u003cem\u003eAgron. J\u003c/em\u003e. \u003cstrong\u003e47\u003c/strong\u003e, 314\u0026ndash;318 (1955).\u003c/li\u003e\n \u003cli\u003eKeneni, G. \u003cem\u003eet al.\u003c/em\u003e Phenotypic diversity for symbio-agronomic characters in Ethiopian chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) germplasm accessions. \u003cem\u003eAfr. J. Biotechnol\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 12634-12651 (2012).\u003c/li\u003e\n \u003cli\u003eKhan, M. M. H., Rafii, M. Y., Ramlee, S. I., Jusoh, M. A. \u0026amp; Mamun, M. Genetic analysis and selection of Bambara groundnut (\u003cem\u003eVigna subterranea\u003c/em\u003e [L.] Verdc.) landraces for high yield revealed by qualitative and quantitative traits. \u003cem\u003eSci. Rep\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e(1), 1-21 (2021).\u003c/li\u003e\n \u003cli\u003eKoevoets, I. T., Venema, J. H., Elzegna, J. T. M. \u0026amp; Testerink, C. Roots withstanding their environment: Exploiting root system architecture responses to abiotic stress to improve crop tolerance. \u003cem\u003eFront. Plant Sci\u003c/em\u003e. \u003cstrong\u003e7,\u003c/strong\u003e 1335 (2016).\u003c/li\u003e\n \u003cli\u003eKushwah, A. \u003cem\u003eet al\u003c/em\u003e. Phenotypic evaluation of genetic variability and selection of yield contributing traits in chickpea recombinant inbred line population under high temperature stress. \u003cem\u003ePhysiol. Mol. Biol. Plants\u003c/em\u003e. \u003cstrong\u003e27\u003c/strong\u003e, 747-767 (2021).\u003c/li\u003e\n \u003cli\u003eLimpens, E. \u003cem\u003eet al\u003c/em\u003e. LysM domain receptor kinases regulating rhizobial Nod factor-induced infection. \u003cem\u003eSci.\u003c/em\u003e\u003cstrong\u003e302,\u003c/strong\u003e 630\u0026ndash;633 (2003).\u003c/li\u003e\n \u003cli\u003eLiu, Y. Y., Wu, L. H., Baddeley, J. A. \u0026amp; Watson, C. A. Models of biological nitrogen fixation of legumes. A review. \u003cem\u003eAgron. Sustain. Dev\u003c/em\u003e. \u003cstrong\u003e31,\u003c/strong\u003e 155\u0026ndash;172 (2011).\u003c/li\u003e\n \u003cli\u003eLupwayi, N. Z., Clayton, G. W., Hanson, K. G., Rice, W. A. \u0026amp; Biederbeck, V. O. Endophytic rhizobia in barley, wheat and canola roots. \u003cem\u003eCan. J. Plant Sci\u003c/em\u003e. \u003cstrong\u003e84\u003c/strong\u003e: 37-45 (2004).\u003c/li\u003e\n \u003cli\u003eMallikarjuna, B. P., Viswanatha, K. P., Samineni, S. \u0026amp; Gaur, P. M. Association of flowering time with phenological and productivity traits in chickpea. \u003cem\u003eEuphytica\u003c/em\u003e. \u003cstrong\u003e215\u003c/strong\u003e, 1-8 (2019).\u003c/li\u003e\n \u003cli\u003eMallu, T. S. \u003cem\u003eet al.\u003c/em\u003e Assessment of genetic variation and heritability of agronomic traits in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L). \u003cem\u003eInt. J. Agron. Agric. Res\u003c/em\u003e. \u003cstrong\u003e5\u003c/strong\u003e(4), 76\u0026ndash;88 (2014).\u003c/li\u003e\n \u003cli\u003eMathesius, U. Comparative proteomic studies of root\u0026ndash;microbe interactions. \u003cem\u003eJ. Proteome\u003c/em\u003e. \u003cstrong\u003e72\u003c/strong\u003e, 353-366 (2009).\u003c/li\u003e\n \u003cli\u003eMcCauley, A. M. Nitrogen fixation by annual legume green manures in a semi-arid cropping system. Dissertation, Montana State University-Bozeman (2011).\u003c/li\u003e\n \u003cli\u003eM\u0026iacute;guez-Montero, M. A., Valentine, A. \u0026amp; P\u0026eacute;rez-Fern\u0026aacute;ndez, M. A. Regulatory effect of phosphorus and nitrogen on nodulation and plant performance of leguminous shrubs. \u003cem\u003eAoB Plants\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e, 1-11 (2019).\u003c/li\u003e\n \u003cli\u003eMisra, G. \u003cem\u003eet al.\u003c/em\u003e Baseline status and effect of genotype, environment and genotype\u0026times; environment interactions on iron and zinc content in Indian chickpeas (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eEuphytica.\u003c/em\u003e\u003cstrong\u003e216\u003c/strong\u003e(9), 1-16 (2020).\u003c/li\u003e\n \u003cli\u003eOuma, E. W., Asango, A. M., Maingi, J. \u0026amp; Njeru, M. Elucidating the potential of native rhizobial isolates to improve biological nitrogen fixation and growth of common bean and soybean in smallholder farming systems of Kenya. \u003cem\u003eInt. J. Agron\u003c/em\u003e.\u0026nbsp;1\u0026ndash;7 (2016).\u003c/li\u003e\n \u003cli\u003eOwusu, E. Y. \u003cem\u003eet al.\u003c/em\u003e Genetic variability, heritability and correlation analysis among maturity and yield traits in Cowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e (L) Walp) in Northern Ghana. \u003cem\u003eHeliyon.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e(9), e07890 (2021).\u003c/li\u003e\n \u003cli\u003eParashi, V. S., Lad, D. B., Mahse, L. B., Kute, N. S. \u0026amp; Sonawane, C. J. Genetic diversity studies in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eBIOINFOLET\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003e10\u003c/strong\u003e(1b), 337-341\u0026nbsp;(2013).\u003c/li\u003e\n \u003cli\u003eParissi, Z. \u003cem\u003eet al.\u003c/em\u003e Analysis of Genotypic and Environmental Effects on Biomass Yield, Nutritional and Anti nutritional Factors in Common Vetch. \u003cem\u003eAgron\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e,\u0026nbsp;1678\u0026nbsp;(2022).\u003c/li\u003e\n \u003cli\u003ePaul, P. J. \u003cem\u003eet al.\u003c/em\u003e Capturing genetic variability and selection of traits for heat tolerance in a chickpea recombinant inbred line (RIL) population under field conditions.\u0026nbsp;\u003cem\u003eEuphytica\u003c/em\u003e. \u003cstrong\u003e214,\u003c/strong\u003e 1-14\u0026nbsp;(2018).\u003c/li\u003e\n \u003cli\u003ePerrig, D. \u003cem\u003eet al.\u003c/em\u003e Plant-growth-promoting compounds produced by two agronomically important strains of \u003cem\u003eAzospirillum brasilense\u003c/em\u003e, and implications for inoculant formulation. Appl.\u0026nbsp;\u003cem\u003eMicrobiol. Biotechnol\u003c/em\u003e. \u003cstrong\u003e75,\u003c/strong\u003e 1143-1150\u0026nbsp;(2007).\u003c/li\u003e\n \u003cli\u003eRadutoiu, S. \u003cem\u003eet al.\u003c/em\u003e Plant recognition of symbiotic bacteria requires two LysM receptor-like kinases. \u003cem\u003eNature.\u003c/em\u003e\u003cstrong\u003e425\u003c/strong\u003e, 585\u0026ndash;592 (2003).\u003c/li\u003e\n \u003cli\u003eRafique, M. \u003cem\u003eet al.\u003c/em\u003e The combined effects of gibberellic acid and \u003cem\u003eRhizobium\u003c/em\u003e on growth, yield and nutritional status in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eAgron.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e,\u0026nbsp;105 (2021).\u003c/li\u003e\n \u003cli\u003eRajasekhar, S. \u003cem\u003eet al.\u003c/em\u003e Genetic Variation for Grain Protein, Fe and Zn Content traits in Chickpea Reference Set. \u003cem\u003eJ. Food Compos. Anal\u003c/em\u003e. p.104774 (2022).\u003c/li\u003e\n \u003cli\u003eRibalta, F.M. \u003cem\u003eet al.\u003c/em\u003e Precocious floral initiation and identification of exact timing of embryo physiological maturity facilitate germination of immature seeds to truncate the lifecycle of pea. \u003cem\u003ePlant Growth Regul\u003c/em\u003e. \u003cstrong\u003e81\u003c/strong\u003e, 345\u0026ndash;353 (2017).\u003c/li\u003e\n \u003cli\u003eRupela, O. P. Natural occurrence and salient characters of non nodulating chickpea plants. \u003cem\u003eCrop Sci\u003c/em\u003e. \u003cstrong\u003e32\u003c/strong\u003e, 349-352 (1992).\u003c/li\u003e\n \u003cli\u003eRupela, O. P. \u0026amp; Johansen, C. Identification of non-nodulating, and low and high nodulating plants in pigeonpea. \u003cem\u003eSoil Bio. Biochem\u003c/em\u003e. \u003cstrong\u003e27\u003c/strong\u003e, 539-544 (1995).\u003c/li\u003e\n \u003cli\u003eRupela, O. P., Sharma, L. C. \u0026amp; Danso, S. K. A. Evaluation of N\u003csub\u003e2\u003c/sub\u003e fixation by nodulation-variants of chickpea in India. In Improving Yield and Nitrogen Fixation of Grain Legumes in the Tropics and Sub-tropics of Asia. \u003cem\u003eInt. Atomic Energy Agency\u003c/em\u003e. \u003cstrong\u003e1027\u003c/strong\u003e, 99-119 (1997).\u003c/li\u003e\n \u003cli\u003eSaha. S., Chakraborty, D., Sehgal, V. K. \u0026amp; Pal, M. Potential impact of rising atmospheric CO\u003csub\u003e2\u003c/sub\u003e on quality of grains in chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). \u003cem\u003eFood Chem\u003c/em\u003e. \u003cstrong\u003e187,\u003c/strong\u003e 431\u0026ndash;436 (2015).\u003c/li\u003e\n \u003cli\u003eSamineni, S., Sen, M., Sajja, S. B. \u0026amp; Gaur, P. M. Rapid generation advance (RGA) in chickpea to produce up to seven generations per year and enable speed breeding. \u003cem\u003eCrop J\u003c/em\u003e. \u003cstrong\u003e8,\u003c/strong\u003e 164-169 (2020).\u003c/li\u003e\n \u003cli\u003eSamyuktha, S. M. \u003cem\u003eet al.\u003c/em\u003e Delineation of genotype\u0026times; environment interaction for identification of stable genotypes to grain yield in mungbean. \u003cem\u003eFront. Agron\u003c/em\u003e. \u003cstrong\u003e2\u003c/strong\u003e,\u0026nbsp;577911 (2020).\u003c/li\u003e\n \u003cli\u003eSAS Institute Inc. SAS/STAT\u0026reg; 15.1 User\u0026rsquo;s Guide. Cary, NC: SAS Institute Inc 2018.\u003c/li\u003e\n \u003cli\u003eSaxena, K., Saxena, R.K. \u0026amp; Varshney, R.K. Use of immature seed germination and single seed descent for rapid genetic gains in pigeonpea. \u003cem\u003ePlant Breed\u003c/em\u003e. \u003cstrong\u003e136\u003c/strong\u003e, 954\u0026ndash;957 (2017).\u003c/li\u003e\n \u003cli\u003eSharma, V. \u003cem\u003eet al.\u003c/em\u003e Molecular Basis of Root Nodule Symbiosis between \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u0026lsquo;Crack-Entry\u0026rsquo; Legume Groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.). \u003cem\u003ePlants.\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 276 (2020).\u003c/li\u003e\n \u003cli\u003eSiddique, K. H. M. \u0026amp; Khan, T. N. Early-flowering and high-yielding chickpea lines from ICRISAT ready for release in Western Australia. \u003cem\u003eInt. Chickpea and Pigeonpea Newslett\u003c/em\u003e. \u003cstrong\u003e3\u003c/strong\u003e, 22-24 (1996).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSivasubramanian, S. \u0026amp; Madhavamenon, P. Genotypic and phenotypic variability in rice. \u003cem\u003eMadras Agric. J\u003c/em\u003e. \u003cstrong\u003e60\u003c/strong\u003e, 1093\u0026ndash;1096 (1973).\u003c/li\u003e\n \u003cli\u003eStajkovic, O. \u003cem\u003eet al\u003c/em\u003e. Improvement of common bean growth by co-inoculation with \u003cem\u003eRhizobium\u003c/em\u003e and plant growth-promoting bacteria. \u003cem\u003eRom. Biotechnol. Lett\u003c/em\u003e. \u003cstrong\u003e16\u003c/strong\u003e, 5919-5926 (2011).\u003c/li\u003e\n \u003cli\u003eTalaat, N. B. \u0026amp; Abdallah, A. M. Response of fababean (\u003cem\u003eVicia faba\u003c/em\u003e L.) to dual inoculation with \u003cem\u003eRhizobium\u003c/em\u003e and VA mycorrhiza under different levels of N and P fertilization. \u003cem\u003eJ. Appl. Sci. Re\u003c/em\u003es. \u003cstrong\u003e4\u003c/strong\u003e, 1092\u0026ndash;1102 (2008).\u003c/li\u003e\n \u003cli\u003eGayacharan \u003cem\u003eet al.\u003c/em\u003e Understanding genetic variability in the mungbean (\u003cem\u003eVigna radiata\u003c/em\u003e L.) genepool. \u003cem\u003eAnnals App. Bio\u003c/em\u003e. \u003cstrong\u003e177\u003c/strong\u003e(3), 346-357 (2020).\u003c/li\u003e\n \u003cli\u003eTsehaye, A., Fikre, A. \u0026amp; Bantayhu, M. Genetic variability and association analysis of Desi-type chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) advanced lines under potential environment in North Gondar, Ethiopia. \u003cem\u003eCogent Food Agri\u003c/em\u003e. \u003cstrong\u003e6\u003c/strong\u003e(1), 1806668 (2020).\u003c/li\u003e\n \u003cli\u003eUllah, H. \u003cem\u003eet al.\u003c/em\u003e Selecting high yielding and stable mungbean [\u003cem\u003eVigna radiata\u003c/em\u003e (L.) Wilczek] genotypes using GGE biplot techniques. \u003cem\u003eCan. J. Plant Sci.\u003c/em\u003e\u003cstrong\u003e92\u003c/strong\u003e, 951-960 (2012).\u003c/li\u003e\n \u003cli\u003eUnkovich, M., Baldock, J. \u0026amp; Peoples, M. Prospects and problems of simple linear models for estimating symbiotic N\u003csub\u003e2\u003c/sub\u003e fixation by crop and pasture legumes. \u003cem\u003ePlant soil\u003c/em\u003e. \u003cstrong\u003e329\u003c/strong\u003e, 75-89 (2010).\u003c/li\u003e\n \u003cli\u003eWang, E. T., Chen, W. F., Tian, C. F., Young, J. P. W. \u0026amp; Chen, W. X. Ecology and Evolution of Rhizobia, Principles and Applications in \u003cem\u003eSpringer\u003c/em\u003e. 154-157 (2019).\u003c/li\u003e\n \u003cli\u003eWard, A. Phosphorus Limitation of Soybean and Alfalfa Biological Nitrogen Fixation on Organic Dairy Farms. Dissertation, Nova Scotia Agricultural College (2011).\u003c/li\u003e\n \u003cli\u003eWatson, A. \u003cem\u003eet al.\u003c/em\u003e Speed breeding is a powerful tool to accelerate crop research and breeding. \u003cem\u003eNat. Plants\u003c/em\u003e. \u003cstrong\u003e4\u003c/strong\u003e, 23\u0026ndash;29 (2018).\u003c/li\u003e\n \u003cli\u003eXia, X., Ma, C., Dong, S., Xu, Y. \u0026amp; Gong, Z. Effects of nitrogen concentrations on nodulation and nitrogenase activity in dual root systems of soybean plants. \u003cem\u003eJ. Soil Sci. Plant Nutr\u003c/em\u003e. \u003cstrong\u003e63\u003c/strong\u003e, 1\u0026ndash;13\u0026nbsp;(2017).\u003c/li\u003e\n \u003cli\u003eXu, T. \u003cem\u003eet al.\u003c/em\u003e Revealing the underlying mechanisms mediated by endophytic actinobacteria to enhance the rhizobia-chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) symbiosis. \u003cem\u003ePlant soil\u003c/em\u003e. 1-20 (2022).\u003c/li\u003e\n \u003cli\u003eYang, Y. \u003cem\u003eet al.\u003c/em\u003e Characterization of genetic basis on synergistic interactions between root architecture and biological nitrogen fixation in soybean. \u003cem\u003eFront. Plant Sci\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 1466 (2017).\u003c/li\u003e\n \u003cli\u003eYang, Z. W., Shen, Y. Y., Xie, T, L. \u0026amp; Tan, G. Y. Biological nitrogen fixation efficiency in soybean under different levels of nitrogen supply. \u003cem\u003eActa Bot. Boreali Occident Sin\u003c/em\u003e. \u003cstrong\u003e29\u003c/strong\u003e, 574\u0026ndash;579 (2009).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chickpea, rhizobium, Nodulation, Rapid Generation Advancement (RGA), Recombinant Inbred Line (RIL) population, Nodulating and Non-nodulating genotypes, Genetic variability. ","lastPublishedDoi":"10.21203/rs.3.rs-4598881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChickpeas, a widely cultivated legume, actively fix atmospheric nitrogen in root nodules through a symbiotic relationship with rhizobia bacteria. A recombinant inbred line (RIL) population, progressing from F2 to F7 generations, was developed in a short-period of 18 months using the Rapid Generation Advancement (RGA) protocol. The F\u003csub\u003e7\u003c/sub\u003e RILs were evaluated during the 2020-21 and 2021-22 crop seasons under typical field conditions to quantify the effects of nodulation on seed yield (SY) and its associated traits. The analysis of variance revealed a highly significant difference (P \u0026lt; 0.01) among genotypes for seed yield and other agronomic traits, with no significant seasonal effect. In the pooled analysis, nodulating genotypes (NG) exhibited a substantial increase (P \u0026lt; 0.01) in SY (62.55%), 100-seed weight (SW100; 12.21%), harvest index (HI; 6.40%), number of pods per plant (NPPP; 39.55%), and number of seeds per plant (NSPP; 44.37%) compared to non-nodulating genotypes (NNG). Both NG and NNG exhibited a significant (P \u0026lt; 0.01) positive correlation between SY and NPPP (r=0.64 and 0.63), NSPP (r=0.66 and 0.61), HI (r=0.27), and number of primary branches per plant (PBr) (r=0.31), respectively. The top-performing genotypes for yield and related traits were predominantly nodulating. Genotype-trait bi-plot analysis identified nine nodulating genotypes as the most adaptable across the two seasons—six for SY, plant height, SW100, and three for days to first flowering and maturity. These findings underscore the critical role of nodulation in maximizing chickpea yields and the significant yield penalties associated with non-nodulation. To boost chickpea production, future breeding efforts should focus on developing genotypes with high compatibility with rhizobium strains.\u003c/p\u003e","manuscriptTitle":"Rapid generation advancement of RIL population and unlocking the potential of Rhizobium nodulation for improving crop yields in chickpea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-05 03:51:10","doi":"10.21203/rs.3.rs-4598881/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-14T04:50:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"165618285576886896194494130032317472526","date":"2025-01-27T18:35:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-20T14:33:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-16T09:27:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288499289798832947596430260140129604915","date":"2025-01-08T14:23:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120838148853641004628557550868094932600","date":"2025-01-08T10:34:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278689521168643050948649416567784057955","date":"2024-11-11T09:40:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-23T10:48:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108165577873198376850614360404425516228","date":"2024-09-12T19:56:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245772613451237909573740885947685480152","date":"2024-09-12T19:31:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-12T18:36:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-10T05:39:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-21T18:49:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-19T11:35:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-18T09:21:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24715175-1128-464d-95a1-f8210eacf183","owner":[],"postedDate":"July 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34145447,"name":"Biological sciences/Plant sciences"},{"id":34145448,"name":"Biological sciences/Plant sciences/Plant breeding"}],"tags":[],"updatedAt":"2025-04-28T16:08:10+00:00","versionOfRecord":{"articleIdentity":"rs-4598881","link":"https://doi.org/10.1038/s41598-025-98965-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-22 15:57:30","publishedOnDateReadable":"April 22nd, 2025"},"versionCreatedAt":"2024-07-05 03:51:10","video":"","vorDoi":"10.1038/s41598-025-98965-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98965-2","workflowStages":[]},"version":"v1","identity":"rs-4598881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598881","identity":"rs-4598881","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}