Performance Evaluation of Quinoa Genotypes for Agronomic Traits and Stress Resilience Under Arid Conditions in Rajasthan

Performance Evaluation of Quinoa Genotypes for Agronomic Traits and Stress Resilience Under Arid Conditions in Rajasthan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance Evaluation of Quinoa Genotypes for Agronomic Traits and Stress Resilience Under Arid Conditions in Rajasthan Shourabh Joshi, Neeshu Joshi, Samarpal Singh, Dinesh Jinger, Jitendar Kumar Sharma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8838444/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Quinoa ( Chenopodium quinoa Willd.), a highly nutritious and climate-resilient pseudocereal native to the Andean region, has emerged as a promising crop for cultivation in marginal environments globally, including the hot arid zones of India. This study aimed to evaluate the performance of 24 quinoa genotypes under agroclimatic conditions in Nagaur, Rajasthan, during the Rabi season of 2022–2023. The agro-morphological traits, yield components, and biochemical characteristics, including total phenolic, moisture, and ash contents, were assessed. Significant variability in plant height, the number of primary branches, inflorescence length, and grain yield was detected among the genotypes. Cluster analysis divided the genotypes into two distinct groups, with Cluster 1 genotypes (e.g., EC896062 and EC896218) exhibiting superior agronomic and nutritional traits. Early and stable flowering behavior was noted in EC896237 and HIMSHAKTI, whereas high phenolic retention after processing was recorded in SHQ5 and EC896276. The study concluded that quinoa can be effectively cultivated under the saline and drought-prone conditions of Rajasthan when sown between November and April. Genotypes EC896062 and EC896218 were identified as the most promising lines, combining high yield potential and superior grain quality traits, and may serve as parental lines for future breeding programs aimed at enhancing quinoa adaptation and nutritional value in arid environments. Ash content Chenopodium quinoa genotype evaluation salinity tolerance cluster analysis phenolic content hot-arid regions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Chenopodium quinoa Willd., commonly known as quinoa, belongs to the Amaranthaceae family native to the Andean region (Alandia et al., 2020 ). Its natural distribution extends from northern Colombia to southern Chile, and it can be cultivated at a wide range of altitudes, from sea level to 4,000 m above sea level (Zurita-Silva et al., 2014 ). In recent decades, the cultivation of this crop has expanded worldwide, although the main producers in the world are still Bolivia and Peru (Bazile et al., 2016 ). One of the reasons for the increased interest in cultivating quinoa is the capacity for adaptation and its resilience to extreme conditions (Jacobsen et al., 2003 ). Quinoa can tolerate drought, high soil salinity, frost, and low temperatures (Jacobsen et al., 2005 , 2012 ; Pulvento et al., 2010 ; Adolf et al., 2012 ), which makes it an ideal crop to exploit and introduce into marginal environments (Choukr- Allah et al., 2016). The National Academy of Sciences of the United States considers quinoa among the "golden grains" because of its high nutritional value. NASA has incorporated quinoa into the diet of astronauts (Carrasco and Soto, 2010 ). The FAO, in its thirty-seventh session of the General Conference, declared the year 2013 the International Year of Quinoa, considering its potential to fight hunger and malnutrition (UN, 2011). In India, the Himalayan region and North Indian plains have cultivated crops with good yields. CSIRNBRI and Lucknow initiated systematic trials of quinoa cultivation in Northeast India (Bhargava et al., 2006 ). Quinoa was successfully grown under the project “Ananta” in the Hyderabad and Anantapur regions of Andhra Pradesh (Padmashree et al., 2018 ). Although quinoa is considered a superfood, its consumption is very limited in India, and the majority of the Indian population is still unaware of its potential health benefits (Padmashree et al., 2018 ). Although statistics on its exact area and production are not available, one report mentioned that quinoa in India is cultivated in an area of 440 hectares with a production of 1053 tonnes (Srinivasa Rao, 2015 ). In 2013, Uttarakhand state reportedly signed a research agreement with Peru to grow quinoa in the state and research institutes in Andhra Pradesh. Rajasthan State Seeds Corporation encouraged some farmers to grow this crop on an experimental basis and managed to produce more than 20,000 quintals of seeds. Few farmers in the Fazilka district of Panjab, adjoining the Rajasthan border, cultivated this crop for the first time from 2017–18. In Karnataka, as a part of the research programme in all India Co-ordinated Research Network on Potential Crops, Bengaluru initiated compatibility studies and evaluations of some quinoa germplasms. Quinoa seeds have a high protein content, ranging from 12–23% depending on genotype, which is higher than that of common grains but lower than that of oilseeds and legumes. Furthermore, quinoa is one of the few plant foods that contains nine essential amino acids (Dakhili et al., 2019 ). In addition, quinoa contains fiber, magnesium, vitamin B, iron, potassium, calcium, phosphorus, and vitamin E. (Awadalla and Morsy, 2017 ). Another interesting aspect of quinoa seed composition is its lack of gluten, which makes this food suitable for people with coeliac disease (Peñas et al., 2014 ). On the other hand, many antinutritional substances, such as saponins, phytic acid, tannins, and trypsin inhibitors, are found in quinoa, which may have detrimental effects on the growth and performance of monogastric animals because quinoa is the main source of food energy [Improta & Kellems ( 2001 ); Satheesh & Fanta ( 2018 )]. On the other hand, despite its potential for production and adaptation to extreme conditions, the cultivation of quinoa remains limited by many factors that reduce its widespread cultivation at a large scale, such as sensitivity to temperature, frost, photoperiod, length of cycle duration and bitter taste due to the presence of saponins, which can affect the absorption and digestibility of nutrients (Akram et al., 2024 ; Kabir et al., 2023 ). Temperature plays an important role in quinoa growth, whereas hot temperatures result in problems with seed viability (García-Parra et al., 2020 ). It is a “facultative” halophyte and is well suited to overcome abiotic stressors such as drought and salinity (Hinojosa et al., 2018 ; Jacobsenet al., 2003 ). It can tolerate salinity at levels closer to sea water. Many studies have elucidated several mechanisms that contribute to quinoa’s high salinity tolerance, and most of them attribute this tolerance to its efficient sodium (Na+) sequestration in leaf vacuoles, oxidative stress protection, and potassium (K+) retention (Adolf et al., 2013 ; Iqbalet al., 2020 ; Shabala et al., 2012 ). However, salinity tolerance has been shown to vary widely among quinoa cultivars/genotypes (Adolf et al., 2012 ; Peterson & Murphy, 2015 ) and between different growth stages (Derbali et al., 2020 ; Maleki et al., 2018 ). Seed germination and seedling establishment are critical stages for the establishment of plant populations under saline conditions (Maleki et al., 2018 ). Manjarres-Hernández et al. ( 2021 ) evaluated the phenological and yield relationships among 30 quinoa genotypes and reported that panicle length, seed weight, and seed diameter were the traits with the greatest influence on yield. Therefore, the main objective of this study was to evaluate the differences in the germination potential and seedling growth of quinoa genotypes and to identify suitable accessions on the basis of their salinity tolerance for yield and agronomic yield components. Additionally, superior accessions for desired traits, such as high grain yield, low grain weight, saponin content, and optimal growth duration, were identified in the hot arid areas of Rajasthan. 2. Materials and methods 2.1. Plant Materials and Experimental Site The experiment was conducted during the Rabi season from 2022–2023 at the Agricultural Research Farm, College of Agriculture, Nagaur (Rajasthan), Agriculture University, Jodhpur (27.19 0 N, 73.75 0 E), which has a dry and hot climate with an altitude of 302 m. For this study, 24 quinoa genotypes ( Chenopodium quinoa Willd.) were chosen. Seeds of these 24 accessions were procured from the National Bureau of Plant Genetic Resources, ICAR, New Delhi. The experimental soil was sandy to sandy loam in texture with an alkaline pH (8.2), an electrical conductivity of saturation extract of 1.25 dS/m, low organic carbon (0.18), available N (168.95 kg/ha), available P2O5 (18.1 kg/ha) and medium in available K2O (158.50 kg/ha). 2.2. Growth conditions and experimental design The precipitation and average temperature from November–April are given in Table 1 . Seeds were sown on November 7, 2022, at a rate of 15 kg/ha. After germination, thinning was performed. The organic matter and fertility of the experimental soil were improved by incorporating 8–10 tonnes/ha of well-decomposed farmyard manure before sowing. The crop was supplied with the recommended dose of fertilizer, i.e., 60 kg N, 40 kg P2O5 or 40 kg K2O/ha, in the form of urea, diammonium phosphate (DAP) or muriate of potash (MOP), respectively. The entire doe of P, K and a half dose of N was applied as a base through placement in the furrows, with hand hoes 5 cm away from the seed rows and at a depth of 2 cm below the seed zone. The remaining 50% of N was top dresses during intercultivation at 30 DAS. Irrigation was given as needed, especially during the germination stage, to ensure good germination. Quinoa varieties were sown manually by dibbling 3–4 seeds into the soil at a depth of 1–2 cm in the furrows. The experimental design for each treatment was a randomized complete block with three replicates per genotype. The plot size was 4 m × 3 m, with a plant-to-plant distance of 15 cm and a row-to-row distance of 45 cm. The rows were oriented in the north‒south direction. Throughout the crop season, hand weeding was performed as needed without the application of any herbicides. Table 1 Precipitation and temperature at experimental site November December January February March April Precipitation (mm) 91.42 96.77 0.00 3.00 33.30 16.90 Average Temperature ( 0 C) 18.74 15.26 11.78 20.16 22.59 26.49 2.3. Collection and management of plant agro-morphological data The number of days to flowering (DF) and maturity (DM) was estimated from seedling emergence to flowering and physiological maturity. For each plot, plant height was measured for five plants as detailed elsewhere (Hussain et al., 2018 ). Seed yield (SY) and biomass were estimated from a one-square-meter (m²) area, and thousand seed weight (TSW) was subsequently assessed. The dry weight of the biomass (DW) was determined by drying the samples in the sun for two days initially and then in a forced-air oven at 80°C for 48 hours, after which the samples were stored at 4°C with a relative humidity of 30% for further analyses. 2.4. Estimation of moisture content, ash content and phenol content The moisture content was determined via the oven-drying method at 105 ± 1°C (AOAC 945.15). The ash content was determined by incineration at 550°C in a Milestone modified microwave muffle furnace. MLS-1200 Pyro (Monroe, CT, USA) was used for steps (250°C/30 min, 550°C/15 min, 550°C/20 h, and 100°C/30 min) to optimize the process and minimize the volatilization of minerals (Mateos-Aparicio et al., 2010 ). The Folin‒Ciocalteu reagent method (Adom et al., 2003 ) was used to determine the total phenolic compounds. To achieve this, 0.5 L of distilled water was mixed with 125 µL of the phenolic compound mixture and 125 µL of Folin-phenol. The mixture was then allowed to react for 6 min before 1.25 mL of Na2CO3 solution (7%) was added, followed by the addition of 1 mL of distilled water. The resulting mixture was kept at room temperature in the dark for 90 min. A control was prepared by replacing the sample with methanol. The absorbance at 765 nm was measured using gallic acid as the standard. The total polyphenol content was expressed as mg gallic acid equivalent GAE/100 g dry weight (DW). 2.5. Statistical analysis The data for the agromorphological and biochemical traits evaluated in this study were subjected to analysis of variance (ANOVA), and the significant variance among the treatments was tested via the least significant difference (LSD) method. The critical difference was calculated wherever the F test was found to be significant at the 5 percent probability level, and the values were furnished. For visualization of the data clustering, principal component analysis was performed via the ggfortify package in RStudio (version 2023.03.0-daily + 82.pro2), and a cluster ggplot was generated. 3. Results The cluster plot and the cluster centers help evaluate the distinctness of the clusters. Therefore, we suggest that cluster analysis should be executed properly. The results of the agronomic trait cluster analysis are shown in Fig. 6 . The 24 quinoa resources could be divided into two categories on the basis of a squared Euclidean distance of 7.25. Nine materials were in the first category, representing 37.5% of the test material, namely, EC896062, EC896097, EC896115, EC896218, EC896219, IGKVC12, EC896213, EC896276 and EC896208. The average plant height was 116.36, the number of primary branches was 20.97, the inflorescence length was 26.14, the number of inflorescences per plant was 25.53, and the grain yield was 1.15. It is a quinoa source of yield attributes and yield, as its agronomic traits are greater than those of the other categories. A total of 15 materials were tested in the second category, representing 62.5% of all the materials tested. In terms of plant height, primary branches, inflorescence length, number of inflorescences per plant and grain yield, the average values were 92.16, 18.18, 22.92, 21.41 and 1.07, respectively, which are excellent quinoa resources for transformation; however, all the agronomic indices indicate that these values are slightly lower than those of the first category. The above data revealed that, compared with Cluster 2, Cluster 1 presented relatively greater plant height, a greater number of primary branches, greater inflorescence length, greater inflorescence number per plant and greater grain yield. Therefore, we can say that the two clusters are relatively distinct. The centroids of each cluster indicated distinct patterns in trait combinations, highlighting potential differences in genetic or agronomic performance. Table 2 Cluster means Cluster number Plant height Primary branches Inflorescence length No. of Inflorescence/plant Grain yield 1 116.36667 20.96778 26.14444 25.53000 1.153333 2 92.15867 18.17867 22.92200 21.41533 1.068667 The clustering results provide insight into the variability among genotypes. The Cluster 1 genotype may be prioritized for yield-focused breeding, whereas the Cluster 2 genotype may represent balanced trait performance, although lower yields might be associated with other desirable traits or stress tolerance for specific environments. Cluster 1 indicates a smaller group of more similar observations. Cluster 2 indicates a greater spread out and larger size and greater internal variability or a more diverse group of observations. There is good separation between the clusters along PC1, indicating that the clustering has captured the real structure in the data. The minimal overlap suggests that the K-means algorithm effectively groups similar observations. 3.1. Growth attributes of quinoa 3.1.1. Flower bud initiation in quinoa The data on flower bud initiation in different quinoa genotypes are presented in Fig. 1 . Among all the genotypes, early and high levels of flower bud initiation as early as 3rd January were observed in genotypes EC896237 (10.8), followed by EC896246 (13.2) and EC896219 (9.6). These genotypes consistently maintained greater flower initiation across all dates, indicating early and sustained reproductive development. However, minimal to no flower initiation was recorded in genotype EC896069 (0.1), followed by EC896276 (0.3). Some lines, such as HIMSHAKTI and EC896246, gradually increased and sustained bud initiation, peaking at approximately 14–28 Jan. These findings may indicate stable flowering performance. However, genotypes such as SHQ1 to SHQ5 presented very low and uniform bud initiation throughout, indicating possible late maturity or unresponsiveness under current conditions. Early - flowering genotypes (e.g., EC896237 and HIMSHAKTI) are likely better suited for short growing seasons or early harvests (Rathore et al., 2019 ). 3.1.2. Flower opening The data presented in Fig. 2 revealed that a high flower opening response was observed in genotypes such as EC896079 (104), followed by EC896069 (77.2) and EC896201 (64.4), which tended to increase, with both early and prolonged flowering. The genotypes with medium to low responders were EC896219, EC896208, SHQ2 and SHQ5. They have stable and upward trends and are less suited to the prevailing growing conditions. EC896237 and EC896201 could be potential candidates for high- yield varieties. 3.1.3. Phenolic content Phenolics are a large and diverse class of compounds comprising hydroxyl group(s) attached to at least one aromatic hydrocarbon ring. Phenolics have high structural stability, which determines the strong antioxidant potential of these compounds ( Woldemichael & Wink, 2001) . Quinoa grains contain free phenolic compounds in the range of 167.2–308.3 mg gallic acid equivalents per 100 g dry weight [Harborne & Williams ( 2000 ), Han et al. ( 2019 )]. The free fraction of the total phenolic content in the seven varieties of quinoa ranged from 25.5% to 51.0%. Quinoa grains contain a lower amount of bound phenolics than free phenolics [Tang et al. ( 2015 ), Harborne & Williams ( 2000 ) , Renard et al. ( 1999 )]. Bound phenolic compounds are mostly present in the leaves of quinoa but not in the seeds (Da-Silva et al., 2007 ). The total amount of phenolic acids varied from 16.8 to 59.7 mg/100 g, and the proportion of soluble phenolic acids varied from 7% to 61% (Repo-Carrasco-Valencia et al., 2010 ). The phenolic contents of different quinoa genotypes are shown in Fig. 3 . The phenolic content of 24 quinoa varieties ranging from unprocessed seeds ranged from 0.32 to 0.51 mg/g GAE, with an average of 0.42 mg/g GAE. Those for the processed quinoa seed samples ranged between 0.25 and 0.41 mg/g GAE, with an average of 0.35 mg/g GAE. EC896062, EC896097, EC896109, EC896218, and EC896246 had phenol contents above 0.48 mg/g GAE, with EC896218 showing one of the highest levels before processing, whereas SHQ5 and EC896276 maintained relatively high phenol levels even after processing (~ 0.4 mg/g GAE), indicating better retention of phenolics. However, some genotypes, such as EC896069, EC896275, and SHQ5, presented relatively minor differences between the unprocessed and processed states, indicating better stability or phenol retention. Therefore, overall, there is removal or degradation of phenol compounds during processing, except for SHQ5 and EC896276, which showed promising phenol retention. 3.1.4. Moisture content All the samples had moisture levels below 12% (from 5.28 to 10.01%), making them suitable for storage according to Spehar ( 2006 ). In the majority of the samples, the moisture content of the processed quinoa grains was lower than that of the unprocessed ones, indicating effective moisture reduction through processing. Among the unprocessed samples, EC896079 had the highest moisture content (~ 10.5%), whereas the lowest values were observed for SHQ3 and SHQ4 (6%). However, EC896098 and SHQ5 presented relatively high moisture contents (~ 9.5%) among the processed quinoa genotypes, whereas SHQ3 and SHQ4 presented relatively low moisture contents (5.5%). The EC896109 and EC896079 genotypes presented noticeable decreases in moisture content upon processing. The results are in accordance with those of Pellegrini et al. ( 2018 ), who reported moisture content ranging between 5.2 and 8.6%. In addition, other studies have shown higher moisture contents (9.3–14%) (Carrasco-Valencia & Serna et al. 2011; Hemalatha et al., 2016 , Perreira et al., 2019). Processing generally results in a decrease in moisture content in quinoa grains, which is beneficial for improving shelf-life and reducing postharvest losses. 3.1.5. Ash content The ash content of quinoa (2.6 ± 0.2 g/100 g) is similar to that obtained by Carrasco-Valencia et al., 2010 , Vilcacundo and Hernandez-Ledesma, 2017 , Jimenez et al., 2019 , Perreira et al., 2019). EC896079 (5.3%), EC896276 (5.1%), and SHQ4 (5.3%) had the highest ash values, exceeding 5.0%, with EC896079 peaking above 5.2%, whereas SHQ5 and SHQ4 retained relatively high ash contents even after processing (~ 4.2–4.5%), suggesting better mineral retention. The minimum changes in ash content before and after processing were observed in SHQ5, SHQ4, and EC896213. 4. Discussion This study revealed that quinoa has good adaptability and can be successfully cultivated in marginal areas of the hot arid lands of Rajasthan. The crop was successful during the November–April season. Indeed, quinoa cultivation depends on the adaptation of genotypes to climate and soil conditions. The plant height of quinoa varied significantly between genotypes (Table 3 ). These differences may have resulted from the variability in genetic structure. According to Spehar & da-Silva-Rocha (2009), Pulvento et al. ( 2010 ), Tan & Temel (2018), and Shams ( 2018 ), plant heights differ among quinoa varieties and populations. Our results were lower than those reported by Shams ( 2018 ), who reported that plant height varied between 135 and 146 cm under similar agronomic conditions (sowing in November and in sandy soil), which can probably be explained by differences in other crop practices, particularly nitrogen fertilizer (214.2 kg N/ha), compared with our case (150 kg N/ha). Omar et al. ( 2014 ) also evaluated five quinoa genotypes under saline conditions and reported highly significant differences among all the genotypes for branches per plant. Yilmaz et al. ( 2021 ) reported that the highest plant height in quinoa was obtained from the dough stage and that the plant height increased by approximately 11% in the dough stage compared with the flowering stage. According to Temel and Yolcu ( 2020 ), the annual temperature, precipitation amount, and distribution are the primary factors influencing the change in plant height in quinoa. Inflorescence length is one of the crucial yield characteristics contributing to higher yields in quinoa. Kishore et al. ( 2007 ) reported similar observations for inflorescence length, and a positive association between seed yield and inflorescence length was shown by Kunj Chandra and Kute (2017) in different grain amaranthus varieties. Agronomic traits of germplasm resources that can be used for estimating grain yield and quality [Gupta et al. (2006); Moles et al. (2005)]. One of the most effective ways to improve the nutritional value of grain is by selecting and breeding varieties that have excellent agronomic attributes. By analyzing plant height, primary branches, inflorescence length, and the number of inflorescences per plant, excellent agronomic traits were selected. The results of the cluster analysis of quinoa yield and subsequent analysis of agronomic traits across different yield categories provide valuable insights into the relationships between yield and various plant characteristics. The cluster analysis identified two distinct yield categories on the basis of grain yield: low-yield (LY) and high-yield (HY) genotypes. These categories provide a basis for understanding the performance of different quinoa cultivars in terms of yield potential. Yield components such as primary branch number and inflorescence number per plant varied significantly among yield categories. High-yielding varieties presented fewer effective branches but more inflorescence numbers per plant, suggesting efficient resource allocation for grain production. The environmental conditions strongly influence the duration of growth of the studied quinoa genotypes, which are predominantly self-pollinated. Cluster analysis was used to classify 24 quinoa resources on the basis of agronomic traits and quality parameters at a squared Euclidean distance of 7.25. The genotypes with high yield and genetic proximity were clustered into one category; for cultivation, genotypes with excellent agronomic qualities were selected to increase yield and quality. This study revealed that high grain yield and good grain quality in quinoa cultivation are possible under the hot-arid extreme environmental conditions (high salinity and drought) of Rajasthan, provided that the cropping season occurs from November–April. Our findings could be used to identify quinoa genotypes particularly suitable for cultivation under such conditions in this region. However, notably, the conditions of the field trial allowed each genotype to express its potential separately. While some genotypes are interesting in terms of yield, others have shown high performance in terms of quality (phenol and ash content). Consequently, the decision to grow a specific quinoa genotype must rely on and respond to the production objective. During the cropping season, the twenty-four genotypes tested did not significantly differ in terms of most agronomic performance, yield parameters (plant height, primary branches, inflorescence length) or grain quality traits (phenol and ash contents), which indicates the strong potential for the stabilization of these characteristics. Therefore, it should be possible to select better adapted genotypes with high yields and nutritional quality. Six quinoa materials, EC896062, EC896097, EC896115, EC896218, EC896213, and EC896208, were selected, and all had better agronomic and quality traits. SHQ5 was selected as the most sensitive material. Two materials, EC896062 and EC896218, provided the best results in terms of phenol, ash and moisture contents; plant height; primary branch length; inflorescence number per plant; and grain yield. They can be the most promising parental genotypes for the development of high-yield and high-quality genotypes and for optimal adaptation to the pedoclimatic conditions of hot drylands worldwide Table 3 Data on various yield and yield attributing traits of quinoa S.No. Entries Plant height(cm) Primary branches Inflorescence length (cm) No. of Inflorescence/plant Seed yield (kg/ha) 1. EC896062 102.04 23.84 26.13 27.04 187 2. EC896064 94.98 21.41 22.44 21.49 125 3. EC896097 117.44 23.71 22.80 25.84 111 4. EC896098 87.27 17.23 19.10 22.83 154 5. EC896109 90.60 18.47 21.93 29.27 111 6. EC896115 110.87 19.43 23.33 23.77 146 7. EC896218 105.40 20.50 27.77 26.20 196 8. EC896219 112.00 21.67 26.33 26.22 94 9. IGKVC12 119.00 22.73 21.47 28.07 74 10. HIMSHAKTI 99.33 18.12 22.33 26.62 96 11. EC896069 97.73 16.60 23.93 26.87 64 12. EC896079 103.93 14.27 24.20 18.67 84 13. EC896237 88.33 18.93 20.47 20.53 82 14. EC896246 90.07 22.47 23.73 22.40 117 15. EC896213 130.53 21.20 27.80 27.47 142 16. EC896275 116.33 17.20 25.60 17.93 78 17. EC896276 108.33 19.47 25.07 27.67 77 18. EC896201 85.87 13.80 26.40 18.87 89 19. EC896208 127.40 22.80 35.13 26.60 120 20. SHQ1 99.13 15.77 25.07 18.45 121 21. SHQ2 102.13 18.17 23.00 18.67 159 22. SHQ3 61.83 14.80 22.00 15.40 91 23. SHQ4 75.67 20.60 19.63 19.23 67 24. SHQ5 103.47 18.20 23.47 14.89 56 SE(m) 1.73 0.27 0.49 0.38 1.93 C.D. 4.95 0.77 1.40 1.09 5.52 SEd 2.45 0.38 0.70 0.54 2.73 Declarations Data Availability Statement : All data generated or analysed during this study are included in this published article. Disclosure statement No potential conflicts of interest were reported by the author(s). Funding Declaration No funding was received for the research to be carried out. Clinical Trial Number : Not applicable. Ethics and Consent to Participate, and Consent to Publish declarations : Seeds of these 24 accessions were procured from the National Bureau of Plant Genetic Resources, ICAR, New Delhi (India) and complied with National guidelines. Permission to collect the plants : Seeds have been procured from the National Bureau of Plant Genetic Resources, ICAR, New Delhi (India). Source of the plant used in study : Already mentioned in Materials and methods. References Adolf, V.I., Jacobsen, S.E., Shabala, S., 2013. Salt tolerance mechanisms in quinoa ( Chenopodium quinoa Willd.). Environ. Exp. Bot. 92, 43–54. Adolf, V.I., Shabala, S., Andersen, M.N., Razzaghi, F., Jacobsen, S.E., 2012. Varietal differences of quinoa’s tolerance to saline conditions. Plant Soil 357, 117–129. https://doi.org/10.1007/s11104-012-1133-7 Adom, K.K., Sorrells, M.E., Liu, R.H., 2003. Phytochemical profiles and antioxidant activity of wheat varieties. J. Agric. Food Chem. 51, 7825–7834. 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Padmashree, A., Negi, N., Handu, S., Khan, M., Semwal, A., et al., 2018. Effects of germination on the nutritional, antinutritional and rheological characteristics of Chenopodium quinoa . Def. Life Sci. J. 4(1), 55–60. https://doi.org/10.14429/dlsj.4.12202 Peñas, E., Ballabio, C., Brandolini, A., Restani, P., di Lorenzo, C., & Uberti, F. 2014. Biochemical and immunochemical evidence supporting the inclusion of quinoa ( Chenopodium quinoa Willd.) as a gluten‑free ingredient. Plant Foods for Human Nutrition 69: 297–303. https://doi.org/10.1007/s11130-014-0449-2 Pellegrini, M., Lucas‑Gonzales, R., Ricci, A., Fontecha, J., Fernández‑López, J.A. Pérez‑Álvarez, & Viuda‑Martos, M. 2018. Chemical, fatty acid, polyphenolic profile, techno‑functional and antioxidant properties of flours obtained from quinoa ( Chenopodium quinoa Willd) seeds. 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Field trial evaluation of two Chenopodium quinoa genotypes grown under rain‑fed conditions in a typical Mediterranean environment in South Italy. Journal of Agronomy and Crop Science 196(6): 407–411. https://doi.org/10.1111/j.1439-037X.2010.00431.x Rathore, S., Bala, M., Gupta, M., & Kumar, R. 2019. Introduction of multipurpose agro‑industrial crop quinoa ( Chenopodium quinoa ) in western Himalayas. Indian Journal of Agronomy 64(2): 287–292. Renard, C.M., Wende, G., & Booth, E.J. 1999. Cell wall phenolics and polysaccharides in different tissues of quinoa ( Chenopodium quinoa Willd). Journal of the Science of Food and Agriculture 79: 2029–2034. Repo‑Carrasco‑Valencia, R., Hellström, J.K., Pihlava, J., & Mattila, P. 2010. Flavonoids and other phenolic compounds in Andean indigenous grains: quinoa ( Chenopodium quinoa ), kañiwa (Chenopodium pallidicaule) and kiwicha (Amaranthus caudatus). Food Chemistry 120(1): 128–133. https://doi.org/10.1016/j.foodchem.2009.09.087 Satheesh, N., & Fanta, S. 2018. Review on structural, nutritional and anti‑nutritional composition of Teff (Eragrostis tef) in comparison with quinoa ( Chenopodium quinoa Willd.). Cogent Food & Agriculture 4: 1–27. Shabala, L., Mackay, A., Tian, Y., Jacobsen, S.-E., Zhou, D., & Shabala, S. 2012. Oxidative stress protection and stomatal patterning as components of salinity tolerance mechanism in quinoa ( Chenopodium quinoa ). Physiologia Plantarum 146(1): 26–38. https://doi.org/10.1111/j.1399-3054.2012.01599.x Shams, A. 2018. Preliminary evaluation of new quinoa genotypes under sandy soil conditions in Egypt. Agriculture Sciences 9: 1444–1456. https://doi.org/10.4236/as.2018.911100 Spehar, C.R. 2006. Adaptação da quinoa ( Chenopodium quinoa Willd.) para incrementar a diversidade agrícola e alimentar no Brasil. Cadernos de Ciência & Tecnologia 23: 41–62. Spehar, C.R., & da Silva‑Rocha, J.E. 2009. Effect of sowing density on plant growth and development of quinoa, genotype 4.5, in the Brazilian savannah highlands. Bioscience Journal 25(4): 53–58. Srinivasa Rao, K. 2015. Sarikotha panta quinoa. Sakhi News , pp. 10. (No DOI) Tang, Y., Li, X., Chen, P.X., Zhang, B., Hernandez, M., Zhang, H., Marcone, M.F., Liu, R., & Tsao, R. 2015. Characterization of fatty acid, carotenoid, tocopherol/tocotrienol compositions and antioxidant activities in seeds of three Chenopodium quinoa Willd. genotypes. Food Chemistry 174: 502–508. Temel, S. & S. Yolcu. 2020. The effect of different sowing time and harvesting stages on the herbage yield and quality of quinoa ( Chenopodium quinoa Willd.). Turkish Journal of Field Crops 25(1): 41-49. United Nations. 2013. Launching ceremony of the International Year of Quinoa, United Nations General Assembly, in New York, 2011. (Published 2013) Vilcacundo, R., & Hernandez‑Ledesma, B. 2017. Nutritional and biological value of quinoa ( Chenopodium quinoa Willd.). Current Opinion in Food Science 14: 16. https://doi.org/10.1016/j.cofs.2016.11.007 Woldemichael, G.M., & Wink, M. 2001a. Identification and biological activities of triterpenoid saponins from Chenopodium quinoa . Journal of Agriculture & Food Chemistry 49: 2327–2332. https://doi.org/10.1021/jf0013499 Yilmaz, S., I. Ertekin & I. Atis, 2021. Forage yield and quality of quinoa ( Chenopodium quinoa Willd.) genotypes harvested at different cutting stages under mediterranean conditions. Turkish Journal of Field Crops 26(2): 202-209. Zurita‑Silva, A., Fuentes, F., Zamora, P., Jacobsen, S.E., & Schwember, A.R. 2014. Breeding quinoa ( Chenopodium quinoa Willd.): potential and perspectives. Molecular Breeding 34: 13–30. https://doi.org/10.1007/s11032-014-0023-5 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8838444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598973224,"identity":"18404808-8c7f-436a-8284-875fe3152452","order_by":0,"name":"Shourabh Joshi","email":"","orcid":"","institution":"Agriculture University, Jodhpur","correspondingAuthor":false,"prefix":"","firstName":"Shourabh","middleName":"","lastName":"Joshi","suffix":""},{"id":598973225,"identity":"610ec4a9-7b79-412a-aeb8-6bcffc22fe88","order_by":1,"name":"Neeshu 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12:19:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106420,"visible":true,"origin":"","legend":"\u003cp\u003eDays to 50% Flower open in Quinoa\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8838444/v1/a72f8fc168d60ff9edae282d.png"},{"id":104403453,"identity":"c903cb31-f322-4fe3-b1b1-caf3e9e42208","added_by":"auto","created_at":"2026-03-11 12:18:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83014,"visible":true,"origin":"","legend":"\u003cp\u003ePhenolic content of quinoa genotypes (at harvest)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8838444/v1/124a59156a0092e34e219d87.png"},{"id":104116061,"identity":"b6cca144-e8b9-4427-bef6-c585cf729409","added_by":"auto","created_at":"2026-03-07 05:04:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68935,"visible":true,"origin":"","legend":"\u003cp\u003eAsh content of Quinoa genotypes (at harvest)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8838444/v1/32d55edfb3a6dc0dfcf6c4af.png"},{"id":104116066,"identity":"de116bec-de7a-4688-b923-b3a958fcf3d2","added_by":"auto","created_at":"2026-03-07 05:04:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53840,"visible":true,"origin":"","legend":"\u003cp\u003eMoisture content of Quinoa genotypes\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8838444/v1/587548b9a87138c8e1f67e24.png"},{"id":104404024,"identity":"9db556c1-3e0b-4045-9de3-b73865aaad35","added_by":"auto","created_at":"2026-03-11 12:19:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":160749,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of 24 quinoa genotypes. (a) Within cluster sum of squares -Elbow method; (b) k-means Cluster analysis of Agronomic traits.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8838444/v1/1dae073f83a1836df144918b.png"},{"id":104408910,"identity":"a7eeb4f5-9555-4ca7-a9c6-7ee171102e02","added_by":"auto","created_at":"2026-03-11 12:43:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1987863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8838444/v1/52eeb1a0-a340-4002-8106-270dbe52fcd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Evaluation of Quinoa Genotypes for Agronomic Traits and Stress Resilience Under Arid Conditions in Rajasthan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd., commonly known as quinoa, belongs to the Amaranthaceae family native to the Andean region (Alandia et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Its natural distribution extends from northern Colombia to southern Chile, and it can be cultivated at a wide range of altitudes, from sea level to 4,000 m above sea level (Zurita-Silva et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In recent decades, the cultivation of this crop has expanded worldwide, although the main producers in the world are still Bolivia and Peru (Bazile et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). One of the reasons for the increased interest in cultivating quinoa is the capacity for adaptation and its resilience to extreme conditions (Jacobsen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Quinoa can tolerate drought, high soil salinity, frost, and low temperatures (Jacobsen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pulvento et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Adolf et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which makes it an ideal crop to exploit and introduce into marginal environments (Choukr- Allah et al., 2016).\u003c/p\u003e \u003cp\u003eThe National Academy of Sciences of the United States considers quinoa among the \"golden grains\" because of its high nutritional value. NASA has incorporated quinoa into the diet of astronauts (Carrasco and Soto, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The FAO, in its thirty-seventh session of the General Conference, declared the year 2013 the International Year of Quinoa, considering its potential to fight hunger and malnutrition (UN, 2011). In India, the Himalayan region and North Indian plains have cultivated crops with good yields. CSIRNBRI and Lucknow initiated systematic trials of quinoa cultivation in Northeast India (Bhargava et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Quinoa was successfully grown under the project \u0026ldquo;Ananta\u0026rdquo; in the Hyderabad and Anantapur regions of Andhra Pradesh (Padmashree et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although quinoa is considered a superfood, its consumption is very limited in India, and the majority of the Indian population is still unaware of its potential health benefits (Padmashree et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although statistics on its exact area and production are not available, one report mentioned that quinoa in India is cultivated in an area of 440 hectares with a production of 1053 tonnes (Srinivasa Rao, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In 2013, Uttarakhand state reportedly signed a research agreement with Peru to grow quinoa in the state and research institutes in Andhra Pradesh. Rajasthan State Seeds Corporation encouraged some farmers to grow this crop on an experimental basis and managed to produce more than 20,000 quintals of seeds. Few farmers in the Fazilka district of Panjab, adjoining the Rajasthan border, cultivated this crop for the first time from 2017\u0026ndash;18. In Karnataka, as a part of the research programme in all India Co-ordinated Research Network on Potential Crops, Bengaluru initiated compatibility studies and evaluations of some quinoa germplasms. Quinoa seeds have a high protein content, ranging from 12\u0026ndash;23% depending on genotype, which is higher than that of common grains but lower than that of oilseeds and legumes. Furthermore, quinoa is one of the few plant foods that contains nine essential amino acids (Dakhili et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, quinoa contains fiber, magnesium, vitamin B, iron, potassium, calcium, phosphorus, and vitamin E. (Awadalla and Morsy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother interesting aspect of quinoa seed composition is its lack of gluten, which makes this food suitable for people with coeliac disease (Pe\u0026ntilde;as et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On the other hand, many antinutritional substances, such as saponins, phytic acid, tannins, and trypsin inhibitors, are found in quinoa, which may have detrimental effects on the growth and performance of monogastric animals because quinoa is the main source of food energy [Improta \u0026amp; Kellems (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); Satheesh \u0026amp; Fanta (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)]. On the other hand, despite its potential for production and adaptation to extreme conditions, the cultivation of quinoa remains limited by many factors that reduce its widespread cultivation at a large scale, such as sensitivity to temperature, frost, photoperiod, length of cycle duration and bitter taste due to the presence of saponins, which can affect the absorption and digestibility of nutrients (Akram et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kabir et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Temperature plays an important role in quinoa growth, whereas hot temperatures result in problems with seed viability (Garc\u0026iacute;a-Parra et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is a \u0026ldquo;facultative\u0026rdquo; halophyte and is well suited to overcome abiotic stressors such as drought and salinity (Hinojosa et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jacobsenet al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It can tolerate salinity at levels closer to sea water. Many studies have elucidated several mechanisms that contribute to quinoa\u0026rsquo;s high salinity tolerance, and most of them attribute this tolerance to its efficient sodium (Na+) sequestration in leaf vacuoles, oxidative stress protection, and potassium (K+) retention (Adolf et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Iqbalet al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shabala et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, salinity tolerance has been shown to vary widely among quinoa cultivars/genotypes (Adolf et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Peterson \u0026amp; Murphy, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and between different growth stages (Derbali et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Maleki et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Seed germination and seedling establishment are critical stages for the establishment of plant populations under saline conditions (Maleki et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Manjarres-Hern\u0026aacute;ndez et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) evaluated the phenological and yield relationships among 30 quinoa genotypes and reported that panicle length, seed weight, and seed diameter were the traits with the greatest influence on yield. Therefore, the main objective of this study was to evaluate the differences in the germination potential and seedling growth of quinoa genotypes and to identify suitable accessions on the basis of their salinity tolerance for yield and agronomic yield components. Additionally, superior accessions for desired traits, such as high grain yield, low grain weight, saponin content, and optimal growth duration, were identified in the hot arid areas of Rajasthan.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Plant Materials and Experimental Site\u003c/h2\u003e \u003cp\u003eThe experiment was conducted during the \u003cem\u003eRabi\u003c/em\u003e season from 2022\u0026ndash;2023 at the Agricultural Research Farm, College of Agriculture, Nagaur (Rajasthan), Agriculture University, Jodhpur (27.19\u003csup\u003e0\u003c/sup\u003e N, 73.75\u003csup\u003e0\u003c/sup\u003e E), which has a dry and hot climate with an altitude of 302 m. For this study, 24 quinoa genotypes (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd.) were chosen. Seeds of these 24 accessions were procured from the National Bureau of Plant Genetic Resources, ICAR, New Delhi. The experimental soil was sandy to sandy loam in texture with an alkaline pH (8.2), an electrical conductivity of saturation extract of 1.25 dS/m, low organic carbon (0.18), available N (168.95 kg/ha), available P2O5 (18.1 kg/ha) and medium in available K2O (158.50 kg/ha).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Growth conditions and experimental design\u003c/h2\u003e \u003cp\u003eThe precipitation and average temperature from November\u0026ndash;April are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Seeds were sown on November 7, 2022, at a rate of 15 kg/ha. After germination, thinning was performed. The organic matter and fertility of the experimental soil were improved by incorporating 8\u0026ndash;10 tonnes/ha of well-decomposed farmyard manure before sowing. The crop was supplied with the recommended dose of fertilizer, i.e., 60 kg N, 40 kg P2O5 or 40 kg K2O/ha, in the form of urea, diammonium phosphate (DAP) or muriate of potash (MOP), respectively. The entire doe of P, K and a half dose of N was applied as a base through placement in the furrows, with hand hoes 5 cm away from the seed rows and at a depth of 2 cm below the seed zone. The remaining 50% of N was top dresses during intercultivation at 30 DAS. Irrigation was given as needed, especially during the germination stage, to ensure good germination. Quinoa varieties were sown manually by dibbling 3\u0026ndash;4 seeds into the soil at a depth of 1\u0026ndash;2 cm in the furrows. The experimental design for each treatment was a randomized complete block with three replicates per genotype. The plot size was 4 m \u0026times; 3 m, with a plant-to-plant distance of 15 cm and a row-to-row distance of 45 cm. The rows were oriented in the north‒south direction. Throughout the crop season, hand weeding was performed as needed without the application of any herbicides.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrecipitation and temperature at experimental site\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecipitation (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage Temperature (\u003c/b\u003e\u003csup\u003e\u003cb\u003e0\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.49\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Collection and management of plant agro-morphological data\u003c/h2\u003e \u003cp\u003eThe number of days to flowering (DF) and maturity (DM) was estimated from seedling emergence to flowering and physiological maturity. For each plot, plant height was measured for five plants as detailed elsewhere (Hussain et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Seed yield (SY) and biomass were estimated from a one-square-meter (m\u0026sup2;) area, and thousand seed weight (TSW) was subsequently assessed. The dry weight of the biomass (DW) was determined by drying the samples in the sun for two days initially and then in a forced-air oven at 80\u0026deg;C for 48 hours, after which the samples were stored at 4\u0026deg;C with a relative humidity of 30% for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Estimation of moisture content, ash content and phenol content\u003c/h2\u003e \u003cp\u003eThe moisture content was determined via the oven-drying method at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C (AOAC 945.15). The ash content was determined by incineration at 550\u0026deg;C in a Milestone modified microwave muffle furnace. MLS-1200 Pyro (Monroe, CT, USA) was used for steps (250\u0026deg;C/30 min, 550\u0026deg;C/15 min, 550\u0026deg;C/20 h, and 100\u0026deg;C/30 min) to optimize the process and minimize the volatilization of minerals (Mateos-Aparicio et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Folin‒Ciocalteu reagent method (Adom et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) was used to determine the total phenolic compounds. To achieve this, 0.5 L of distilled water was mixed with 125 \u0026micro;L of the phenolic compound mixture and 125 \u0026micro;L of Folin-phenol. The mixture was then allowed to react for 6 min before 1.25 mL of Na2CO3 solution (7%) was added, followed by the addition of 1 mL of distilled water. The resulting mixture was kept at room temperature in the dark for 90 min. A control was prepared by replacing the sample with methanol. The absorbance at 765 nm was measured using gallic acid as the standard. The total polyphenol content was expressed as mg gallic acid equivalent GAE/100 g dry weight (DW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data for the agromorphological and biochemical traits evaluated in this study were subjected to analysis of variance (ANOVA), and the significant variance among the treatments was tested via the least significant difference (LSD) method. The critical difference was calculated wherever the F test was found to be significant at the 5 percent probability level, and the values were furnished. For visualization of the data clustering, principal component analysis was performed via the ggfortify package in RStudio (version 2023.03.0-daily\u0026thinsp;+\u0026thinsp;82.pro2), and a cluster ggplot was generated.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe cluster plot and the cluster centers help evaluate the distinctness of the clusters. Therefore, we suggest that cluster analysis should be executed properly. The results of the agronomic trait cluster analysis are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The 24 quinoa resources could be divided into two categories on the basis of a squared Euclidean distance of 7.25. Nine materials were in the first category, representing 37.5% of the test material, namely, EC896062, EC896097, EC896115, EC896218, EC896219, IGKVC12, EC896213, EC896276 and EC896208. The average plant height was 116.36, the number of primary branches was 20.97, the inflorescence length was 26.14, the number of inflorescences per plant was 25.53, and the grain yield was 1.15. It is a quinoa source of yield attributes and yield, as its agronomic traits are greater than those of the other categories. A total of 15 materials were tested in the second category, representing 62.5% of all the materials tested. In terms of plant height, primary branches, inflorescence length, number of inflorescences per plant and grain yield, the average values were 92.16, 18.18, 22.92, 21.41 and 1.07, respectively, which are excellent quinoa resources for transformation; however, all the agronomic indices indicate that these values are slightly lower than those of the first category. The above data revealed that, compared with Cluster 2, Cluster 1 presented relatively greater plant height, a greater number of primary branches, greater inflorescence length, greater inflorescence number per plant and greater grain yield. Therefore, we can say that the two clusters are relatively distinct. The centroids of each cluster indicated distinct patterns in trait combinations, highlighting potential differences in genetic or agronomic performance.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\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\u003eCluster means\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlant height\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimary branches\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInflorescence length\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Inflorescence/plant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrain yield\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\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.36667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.96778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.14444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.53000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.153333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.15867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.17867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.92200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.41533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.068667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe clustering results provide insight into the variability among genotypes. The \u003cstrong\u003eCluster 1 genotype\u003c/strong\u003e may be prioritized for yield-focused breeding, whereas the \u003cstrong\u003eCluster 2\u003c/strong\u003e genotype may represent balanced trait performance, although lower yields might be associated with other desirable traits or stress tolerance for specific environments. Cluster 1 indicates a smaller group of more similar observations. Cluster 2 indicates a greater spread out and larger size and greater internal variability or a more diverse group of observations. There is \u003cstrong\u003egood separation\u003c/strong\u003e between the clusters along PC1, indicating that the clustering has captured the real structure in the data. The minimal overlap suggests that the K-means algorithm effectively groups similar observations.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. \u003cem\u003eGrowth attributes of quinoa\u003c/em\u003e\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1. Flower bud initiation in quinoa\u003c/h2\u003e\n \u003cp\u003eThe data on flower bud initiation in different quinoa genotypes are presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Among all the genotypes, early and high levels of flower bud initiation as early as 3rd January were observed in genotypes EC896237 (10.8), followed by EC896246 (13.2) and EC896219 (9.6). These genotypes consistently maintained greater flower initiation across all dates, indicating early and sustained reproductive development. However, minimal to no flower initiation was recorded in genotype EC896069 (0.1), followed by EC896276 (0.3). Some lines, such as HIMSHAKTI and EC896246, gradually increased and sustained bud initiation, peaking at approximately 14\u0026ndash;28 Jan. These findings may indicate stable flowering performance. However, genotypes such as SHQ1 to SHQ5 presented very low and uniform bud initiation throughout, indicating possible late maturity or unresponsiveness under current conditions. Early\u003cstrong\u003e-\u003c/strong\u003eflowering genotypes (e.g., EC896237 and HIMSHAKTI) are likely better suited for short growing seasons or early harvests (Rathore et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2. Flower opening\u003c/h2\u003e\n \u003cp\u003eThe data presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e revealed that a high flower opening response was observed in genotypes such as EC896079 (104), followed by EC896069 (77.2) and EC896201 (64.4), \u003cstrong\u003ewhich tended to increase, with\u003c/strong\u003e both early and prolonged flowering. The genotypes with medium to low responders \u003cstrong\u003ewere\u003c/strong\u003e EC896219, EC896208, SHQ2 and SHQ5. They have stable and upward \u003cstrong\u003etrends\u003c/strong\u003e and are less suited\u003c/p\u003e\n \u003cp\u003eto the prevailing growing conditions. EC896237 and EC896201 could be potential candidates for high-\u003cstrong\u003eyield\u003c/strong\u003e varieties.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3. Phenolic content\u003c/h2\u003e\n \u003cp\u003ePhenolics are a large and diverse class of compounds comprising hydroxyl group(s) attached to at least one aromatic hydrocarbon ring. Phenolics have high structural stability, which determines the strong antioxidant potential of these compounds (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWoldemichael \u0026amp; Wink, 2001)\u003c/span\u003e. Quinoa grains contain free phenolic compounds in the range of 167.2\u0026ndash;308.3 mg gallic acid equivalents per 100 g dry weight [Harborne \u0026amp; Williams (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), Han et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)]. The free fraction of the total phenolic content in the seven varieties of quinoa ranged from 25.5% to 51.0%. Quinoa grains contain a lower amount of bound phenolics than free phenolics [Tang et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), Harborne \u0026amp; Williams (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, Renard et al. (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e)]. Bound phenolic compounds are mostly present in the leaves of quinoa but not in the seeds (Da-Silva et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The total amount of phenolic acids varied from 16.8 to 59.7 mg/100 g, and the proportion of soluble phenolic acids varied from 7% to 61% (Repo-Carrasco-Valencia et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe phenolic contents of different quinoa genotypes are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The phenolic content of 24 quinoa varieties ranging from unprocessed seeds ranged from 0.32 to 0.51 mg/g GAE, with an average of 0.42 mg/g GAE. Those for the processed quinoa seed samples ranged between 0.25 and 0.41 mg/g GAE, with an average of 0.35 mg/g GAE. EC896062, EC896097, EC896109, EC896218, and EC896246 had phenol contents above 0.48 mg/g GAE, with EC896218 showing one of the highest levels before processing, whereas SHQ5 and EC896276 maintained relatively high phenol levels even after processing (~\u0026thinsp;0.4 mg/g GAE), indicating better retention of phenolics. However, some genotypes, such as EC896069, EC896275, and SHQ5, presented relatively minor differences between \u003cstrong\u003ethe\u003c/strong\u003e unprocessed and processed states, indicating better stability or phenol retention. Therefore, overall, there is removal or degradation of phenol compounds during processing, except for SHQ5 and EC896276, which showed promising phenol retention.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4. Moisture content\u003c/h2\u003e\n \u003cp\u003eAll the samples had moisture levels below 12% (from 5.28 to 10.01%), making them suitable for storage according to Spehar (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the majority of the samples, the moisture content of the processed quinoa grains \u003cstrong\u003ewas\u003c/strong\u003e lower than that of the unprocessed ones, indicating effective moisture reduction through processing. Among the unprocessed samples, EC896079 had the highest moisture content (~\u0026thinsp;10.5%), whereas the lowest values were observed for SHQ3 and SHQ4 (6%). However, EC896098 and SHQ5 presented relatively high moisture contents (~\u0026thinsp;9.5%) among the processed quinoa genotypes, whereas SHQ3 and SHQ4 presented relatively low moisture contents (5.5%). The EC896109 and EC896079 genotypes presented noticeable decreases in moisture content upon processing. The results are in accordance with those of Pellegrini et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), who reported moisture content ranging between 5.2 and 8.6%. In addition, other studies have shown higher moisture contents (9.3\u0026ndash;14%) (Carrasco-Valencia \u0026amp; Serna et al. 2011; Hemalatha et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e, Perreira et al., 2019). Processing generally results in a decrease in moisture content in quinoa grains, which is beneficial for improving shelf-life and reducing postharvest losses.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.5. Ash content\u003c/h2\u003e\n \u003cp\u003eThe ash content of quinoa (2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g/100 g) is similar to that obtained by Carrasco-Valencia et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e, Vilcacundo and Hernandez-Ledesma, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e, Jimenez et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, Perreira et al., 2019). EC896079 (5.3%), EC896276 (5.1%), and SHQ4 (5.3%) had the highest ash values, exceeding 5.0%, with EC896079 peaking above 5.2%, \u003cstrong\u003ewhereas\u003c/strong\u003e SHQ5 and SHQ4 retained relatively high ash contents even after processing (~\u0026thinsp;4.2\u0026ndash;4.5%), suggesting better mineral retention. The minimum changes in ash content before and after processing were observed in SHQ5, SHQ4, and EC896213.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study revealed that quinoa has good adaptability and can be successfully cultivated in marginal areas of the hot arid lands of Rajasthan. The crop was successful during the November\u0026ndash;April season. Indeed, quinoa cultivation depends on the adaptation of genotypes to climate and soil conditions. The plant height of quinoa varied significantly between genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These differences may have resulted from the variability in genetic structure. According to Spehar \u0026amp; da-Silva-Rocha (2009), Pulvento et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Tan \u0026amp; Temel (2018), and Shams (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), plant heights differ among quinoa varieties and populations. Our results were lower than those reported by Shams (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who reported that plant height varied between 135 and 146 cm under similar agronomic conditions (sowing in November and in sandy soil), which can probably be explained by differences in other crop practices, particularly nitrogen fertilizer (214.2 kg N/ha), compared with our case (150 kg N/ha). Omar et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also evaluated five quinoa genotypes under saline conditions and reported highly significant differences among all the genotypes for branches per plant. Yilmaz et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that the highest plant height in quinoa was obtained from the dough stage and that the plant height increased by approximately 11% in the dough stage compared with the flowering stage. According to Temel and Yolcu (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the annual temperature, precipitation amount, and distribution are the primary factors influencing the change in plant height in quinoa.\u003c/p\u003e \u003cp\u003eInflorescence length is one of the crucial yield characteristics contributing to higher yields in quinoa. Kishore et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) reported similar observations for inflorescence length, and a positive association between seed yield and inflorescence length was shown by Kunj Chandra and Kute (2017) in different grain amaranthus varieties. Agronomic traits of germplasm resources that can be used for estimating grain yield and quality [Gupta et al. (2006); Moles et al. (2005)]. One of the most effective ways to improve the nutritional value of grain is by selecting and breeding varieties that have excellent agronomic attributes. By analyzing plant height, primary branches, inflorescence length, and the number of inflorescences per plant, excellent agronomic traits were selected.\u003c/p\u003e \u003cp\u003eThe results of the cluster analysis of quinoa yield and subsequent analysis of agronomic traits across different yield categories provide valuable insights into the relationships between yield and various plant characteristics. The cluster analysis identified two distinct yield categories on the basis of grain yield: low-yield (LY) and high-yield (HY) genotypes. These categories provide a basis for understanding the performance of different quinoa cultivars in terms of yield potential. Yield components such as primary branch number and inflorescence number per plant varied significantly among yield categories. High-yielding varieties presented fewer effective branches but more inflorescence numbers per plant, suggesting efficient resource allocation for grain production. The environmental conditions strongly influence the duration of growth of the studied quinoa genotypes, which are predominantly self-pollinated. Cluster analysis was used to classify 24 quinoa resources on the basis of agronomic traits and quality parameters at a squared Euclidean distance of 7.25. The genotypes with high yield and genetic proximity were clustered into one category; for cultivation, genotypes with excellent agronomic qualities were selected to increase yield and quality.\u003c/p\u003e \u003cp\u003eThis study revealed that high grain yield and good grain quality in quinoa cultivation are possible under the hot-arid extreme environmental conditions (high salinity and drought) of Rajasthan, provided that the cropping season occurs from November\u0026ndash;April. Our findings could be used to identify quinoa genotypes particularly suitable for cultivation under such conditions in this region. However, notably, the conditions of the field trial allowed each genotype to express its potential separately. While some genotypes are interesting in terms of yield, others have shown high performance in terms of quality (phenol and ash content). Consequently, the decision to grow a specific quinoa genotype must rely on and respond to the production objective. During the cropping season, the twenty-four genotypes tested did not significantly differ in terms of most agronomic performance, yield parameters (plant height, primary branches, inflorescence length) or grain quality traits (phenol and ash contents), which indicates the strong potential for the stabilization of these characteristics. Therefore, it should be possible to select better adapted genotypes with high yields and nutritional quality. Six quinoa materials, EC896062, EC896097, EC896115, EC896218, EC896213, and EC896208, were selected, and all had better agronomic and quality traits. SHQ5 was selected as the most sensitive material. Two materials, EC896062 and EC896218, provided the best results in terms of phenol, ash and moisture contents; plant height; primary branch length; inflorescence number per plant; and grain yield. They can be the most promising parental genotypes for the development of high-yield and high-quality genotypes and for optimal adaptation to the pedoclimatic conditions of hot drylands worldwide\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData on various yield and yield attributing traits of quinoa\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\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant\u0026nbsp;height(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimary\u0026nbsp;branches\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInflorescence\u0026nbsp;length\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo.\u0026nbsp;of\u0026nbsp;Inflorescence/plant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeed\u0026nbsp;yield\u003c/p\u003e \u003cp\u003e(kg/ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896062\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896097\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896098\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896109\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEC896218\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e196\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\u003e\u003cb\u003eEC896219\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e 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colname=\"c3\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e : All data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest were reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for the research to be carried out.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e : Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e: Seeds of these 24 accessions were procured from the National Bureau of Plant Genetic Resources, ICAR, New Delhi \u0026nbsp; (India) and complied with National guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to collect the plants\u003c/strong\u003e : \u0026nbsp; Seeds have been procured \u0026nbsp;from the National Bureau of Plant Genetic Resources, ICAR, New Delhi \u0026nbsp;(India).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of the plant used in study\u003c/strong\u003e : \u0026nbsp;Already mentioned in Materials and methods.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n 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Preliminary evaluation of new quinoa genotypes under sandy soil conditions in Egypt. \u003cem\u003eAgriculture Sciences\u003c/em\u003e 9: 1444\u0026ndash;1456. https://doi.org/10.4236/as.2018.911100\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSpehar, C.R.\u003c/strong\u003e 2006. Adapta\u0026ccedil;\u0026atilde;o da quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd.) para incrementar a diversidade agr\u0026iacute;cola e alimentar no Brasil. \u003cem\u003eCadernos de Ci\u0026ecirc;ncia \u0026amp; Tecnologia\u003c/em\u003e 23: 41\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSpehar, C.R., \u0026amp; da Silva‑Rocha, J.E.\u003c/strong\u003e 2009. Effect of sowing density on plant growth and development of quinoa, genotype 4.5, in the Brazilian savannah highlands. \u003cem\u003eBioscience Journal\u003c/em\u003e 25(4): 53\u0026ndash;58.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSrinivasa Rao, K.\u003c/strong\u003e 2015. 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Forage yield and quality of quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd.) genotypes harvested at different cutting stages under mediterranean conditions. \u003cem\u003eTurkish Journal of Field Crops\u003c/em\u003e 26(2): 202-209.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZurita‑Silva, A., Fuentes, F., Zamora, P., Jacobsen, S.E., \u0026amp; Schwember, A.R.\u003c/strong\u003e 2014. Breeding quinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd.): potential and perspectives. \u003cem\u003eMolecular Breeding\u003c/em\u003e 34: 13\u0026ndash;30. https://doi.org/10.1007/s11032-014-0023-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ash content, Chenopodium quinoa, genotype evaluation, salinity tolerance, cluster analysis, phenolic content, hot-arid regions","lastPublishedDoi":"10.21203/rs.3.rs-8838444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8838444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuinoa (\u003cem\u003eChenopodium quinoa\u003c/em\u003e Willd.), a highly nutritious and climate-resilient pseudocereal native to the Andean region, has emerged as a promising crop for cultivation in marginal environments globally, including the hot arid zones of India. This study aimed to evaluate the performance of 24 quinoa genotypes under agroclimatic conditions in Nagaur, Rajasthan, during the Rabi season of 2022\u0026ndash;2023. The agro-morphological traits, yield components, and biochemical characteristics, including total phenolic, moisture, and ash contents, were assessed. Significant variability in plant height, the number of primary branches, inflorescence length, and grain yield was detected among the genotypes. Cluster analysis divided the genotypes into two distinct groups, with Cluster 1 genotypes (e.g., EC896062 and EC896218) exhibiting superior agronomic and nutritional traits. Early and stable flowering behavior was noted in EC896237 and HIMSHAKTI, whereas high phenolic retention after processing was recorded in SHQ5 and EC896276. The study concluded that quinoa can be effectively cultivated under the saline and drought-prone conditions of Rajasthan when sown between November and April. Genotypes EC896062 and EC896218 were identified as the most promising lines, combining high yield potential and superior grain quality traits, and may serve as parental lines for future breeding programs aimed at enhancing quinoa adaptation and nutritional value in arid environments.\u003c/p\u003e","manuscriptTitle":"Performance Evaluation of Quinoa Genotypes for Agronomic Traits and Stress Resilience Under Arid Conditions in Rajasthan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-07 05:04:33","doi":"10.21203/rs.3.rs-8838444/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T06:22:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T17:27:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276975940195059504684145209383650968467","date":"2026-04-13T16:52:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286313741071098357806548214158120424284","date":"2026-04-08T13:03:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T08:32:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215614794407169833193642795401756635410","date":"2026-03-07T02:23:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T01:43:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-01T12:55:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T11:41:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T11:05:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Agriculture","date":"2026-02-23T10:59:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb1f6ec7-e685-4d9e-aca5-292f3fde1def","owner":[],"postedDate":"March 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T07:55:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-07 05:04:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8838444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8838444","identity":"rs-8838444","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}