Trait discovery for yield, related attributes and quality parameters and identification of potential stable donors for genetic improvement of groundnuts through evaluation of germplasm originated from different countries in two contrasting environments

Trait discovery for yield, related attributes and quality parameters and identification of potential stable donors for genetic improvement of groundnuts through evaluation of germplasm originated from different countries in two contrasting environments | 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 Trait discovery for yield, related attributes and quality parameters and identification of potential stable donors for genetic improvement of groundnuts through evaluation of germplasm originated from different countries in two contrasting environments Kirti Rani, Ajay BC, Sandip Kumar Bera, Mahesh Kumar Mahatma, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3309986/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Oct, 2023 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 7 You are reading this latest preprint version Abstract The study aimed to analyze a varied collection of 371 germplasm obtained from various countries, with regards to yield-related characteristics, oil, protein, sugar, free amino acids, and total phenolics, in two distinct environments. The purpose was to assess the extent of inherent genotypic diversity and the potential for improving productivity and quality through breeding. The study identified germplasm that exhibited superior and stable performance with two or more desirable traits. Specifically, NRCGs-10366, 10480, 10485, and 10844 were found to be superior in terms of yield (PY), hundred kernel weight (HKW), and seed shape (SHP). NRCG-11390 was identified as superior for yield (PY), protein content, and sugar content. Additionally, NRCGs-12469, 14089, and 16492 were found to be superior for important biochemical traits such as oil, protein, and sugar content. NRCG-11101 was identified as superior for HKW, protein content, and oil content, while NRCGs-11154 and 11164 were found to be superior for HKW, protein content, and sugar content. The study has identified germplasm with a grain protein content exceeding 34% and an oil content ranging from 47% to 48%. Germplasm exhibiting specific traits were identified for their potential utilisation as donors in the groundnut improvement program. The Spanish collection presents a potential source of valuable breeding traits for enhancing groundnut productivity and improving the content of oil, protein, sugar, phenol, and FAA, in various combinations. Groundnut germplasm yield biochemical traits stability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Protein energy malnutrition is a major public health issue, especially in underdeveloped countries (Ritchie and Roser 2017). This effect affects everyone, especially cardiovascular patients. Unhealthy diets and global food system changes affect human nutrition and food security (Anand et al. 2015). Protein and energy deficiency can harm the immune system, development, and reproduction (Shao et al. 2012). Groundnuts were oilseed crops in India. In recent years, groundnut consumption has changed due to urbanisation and cheaper oil alternatives. Groundnuts are becoming a major food crop as well as oilseeds. Due to their functional components like tocopherol, niacin, and folic acid, mineral components like Cu, Fe, Zn, Mn, K, Ca, and P, dietary fibres, phytosterols like resveratrol and beta-sitosterol, and flavonoids and phenolic acids, groundnuts can provide long-term health benefits. Groundnuts have 25% protein and 45–50% oil (Francisco and Resurreccion 2008). Approximately 80% of the groundnut's fatty acid content is monounsaturated oleic acid and polyunsaturated linoleic acid (Bera et al. 2019). According to Ojiewo et al. (2020) groundnut has helped resource-poor farmers achieve nutritional security. In dryland farming system, groundnut is important as both animal food and seed. Groundnut is mostly grown in tropical and subtropical climates, where water scarcity reduces production (Happy et al. 2018). Singh et al. (2021) reported that 85% of groundnut farming relies on rainfall for water. This area has no irrigation infrastructure and is 80% dryland. The yield of agricultural produce decreases due to water deficit stress during its growing cycle. Vincent and Pasala (2016) found that dryness during pod and seed formation stages can reduce groundnut pod output by 56–85%. Drought stress reduces productivity and quality. Drought stress reduces seed quality, producing smaller and medium-sized seeds. Stress also reduces seed vigour and germination. Under water stress, Alqudah et al. (2010) and Dwivedi et al. (1996) found that protein, oleic acid, and stearic acid increased while oil and linoleic acid decreased. Reddy et al. (2003) examined how irrigation levels affected groundnut seed protein. Plants with adequate watering produced more kernels, total proteins, and oil. Water stress affects leafy vegetable biochemistry. It increases sugars and free amino acids and decreases leaf turgidity, chlorophyll, ascorbic acid, and soluble protein. Thus, drought-tolerant genotypes with consistent nutritional components across conditions are crucial. Successful breeding programs require a large genetic pool and excellent genotypes for hybridization. According to Gupta et al. (2015), groundnut genetic variation has decreased due to insufficient genetic diversity and underutilization of elite breeding lines and variations. Groundnut breeding programs depend on germplasms to increase genetic variety. Despite germplasm sets and beneficial alleles that have yet to be fully utilised, plant breeding requires germplasm assessment. Pramanik et al. (2019), Rathore et al. (2022) and Tomar et al. (2022) reported that assessing morphological, agronomical, and biochemical diversity helps characterise groundnut germplasm collections and identify breeding parents. Groundnut yield and nutritional properties vary due to environmental circumstances. Thus, germplasms must be tested in various environments to determine their stability and identify genotypes with reliable and consistent results. Unfortunately, germplasm accessions in groundnut for the identification of stable donors in terms of oil content, protein content, nutritionally balanced ingredients like total sugar and total free amino acids, and functional compounds like phenolics with high kernel yield have rarely been evaluated under diverse environmental conditions. This data may help identify high-quality groundnut germplasm for breeding programs. This study screened groundnut germplasm in Junagadh (Gujarat) and Anantapur (Andhra Pradesh) to discover stable donors with excellent yield and nutritional attributes. Junagadh has few sunny hours and no water stress, but Anantapur has many. Materials and methods Experimental materials Characterising germplasm helps identify potential germplasm for breeding programmes and improves groundnut. 371 groundnut germplasm from diverse origins were randomly selected from early, medium, and late maturity groups (Table 1S). These germplasm collections came from Australia ( 1 ), East Africa ( 19 ), Middle Africa ( 5 ), North Africa ( 3 ), West Africa ( 33 ), South Africa ( 34 ), Europe ( 8 ), North America (48), South America ( 29 ), South Asia (127), Southeast Asia ( 12 ), East Asia ( 14 ), Middle East Asia ( 3 ), West Asia ( 3 ), and unknown origin. The Gene bank, ICAR-Directorate of Groundnut Research, in Junagadh, Gujarat, India, provided groundnut genetic resources for this research. Experimental sites The ICAR-Directorate of Groundnut Research in Junagadh, Gujarat, and the Regional Research Station (RRS) in Anantapur, Andhra Pradesh, assessed 371 genotypes from various countries in 2021. The former is in medium black calcareous soil in latitude 21 o 31' N and longitude 70 o 36' E, whereas the latter is in red sandy loam soil at 14 o 69' N and 77 o 69' E. Rainy season evaluations were done. Figure 1 a and 1 b show the precipitation amounts, mean weekly maximum and lowest temperatures, and evaporation rates for Anantapur and Junagadh, respectively, over the study. In 2021, Anantapur had 603 mm of precipitation while Junagadh 1305 mm during crop cultivation. May and June are the warmest months in Anantapur and Junagadh, with average weekly maximum temperatures of 30.1–38.2°C and 31.9–35.0°C. These months average 16.2–27.3°C and 17.8–23.7°C low temperatures. Compared to Junagadh, Anantapur had a greater average weekly evaporation rate of 5.2–8.7 mm. Field Evaluation and Data Collection In mid-June at ICAR-DGR, Junagadh and in the first week of July at RRS, ICAR-DGR, Anantapur, 371 genotypes were sown. An 8-block augmented incomplete block design (AIBD) was used for each setting. Each genotype was represented once throughout the experiment by randomly assigning the 371 genotypes to 3-metre blocks. Both regions used uniform agronomic methods and plant protection. After drying pods, hundred kernel weight (HKW, g), shelling outturn (SHP, %), and pod yield (PY, g/m 2 ) were measured. A random sample of 200 grammes of fully developed pods calculated the shelling outturn (%). Near Infrared (NIR) Spectroscopy (Dickey John, Instalab 700) measured oil, protein, total soluble sugar, phenols, and free amino acids (FAA) in the crushed seed combination. Statistical analysis Data analysis used R version 4.2.1 (R core team 2021). Univariate analysis determined mean, standard deviation, range, coefficient of variation, and correlation for all parameters. Percentage distribution analysed quantitative attributes. The Shapiro-Wilk test determined distribution normality. One-factor analysis of variance (ANOVA) was used to compare normal distribution data mean values. For non-normal data, Kruskal-Walli's test was used. The stability variance (σi 2 ) provided by Shukla (1972) and Kang's rank-sum approach (Kang 1991) were computed using online statistical program 'STABILITYSOFT' (Pour-Aboughadareh et al. 2019) to determine parametric and non-parametric stability statistics. Eliminating environmental factors determines a genotype's stability variance. According to Shukla (1972), genotypes with lower levels are more stable. The genotype with the highest mean yield (Y) was ranked 1. The projected value with the lowest magnitude was ranked 1 for σi 2 . The genotypes with the lowest rank-sum (KR) were the most preferred, based on yield (Y) and stability variance (σi 2 ). Kang's 1988 rank-sum approach uses mean performance and σi 2 . This parameter weights mean performance and stability statistics equally to identify genotypes with good yield and stability. The genotype with the highest yield and lowest σi 2 ranks first in this study. PAST 3.25 was used to analyse variance and simple correlation coefficients (Hammer et al. 2001). Results Genotypic Variability Agronomic traits The entire variability range for hundred kernel weight (g) in Anantapur was 12–88 (CV = 17.43%) and 48–87 (CV = 7.45%) at Junagadh. Pod yield (g/m 2 ) at Anantapur was 25.50 to 904.20 (CV = 48.97) and 15.60 to 260 (CV = 30.96%) at Junagadh. At both locations, pod yield data was more dispersed from mean than hundred kernel weights. Both locations had normal Shapiro–Wilk test statistics for all agronomic variables. At Junagadh most accessions had pod yields of 100–150 g/m 2 , shelling outturns of 70.1–75.0% (134), and hundred kernel weights of 30-35g (133). In Anantapur, most accessions had pod yields > 250 g/m 2 , shelling outturns of 70.1–75.0% (136), and HKWs of 35-40g (200) (Fig. 2 ). Thus, two areas had similar agronomic trait distributions. Thus, genotypes performed better in Anantapur than Junagadh. The shelling percentage fluctuated from 28.04 to 91.79 in Anantapur (CV = 8.91) and 22 to 56 at Junagadh (CV = 15.34%) (Fig. 2 ). The features exhibited significant diversity across accessions, and the coefficient of variation of various parameters ranged from 7.45 to 48.97%, showing that the trial and data recording were precise. Biochemical traits The range, mean, and variation of the germplasm were measured using the univariate analysis (Fig. 2 ). The germplasm analysed showed significant phenotypic variation across two environments. The Shapiro-Wilk test showed normal distribution of biochemical features at both locations, except for oil and protein concentration at Anantapur. The two locations had different biochemical characteristic data dispersion. Oil and protein content dispersed more than FAA and phenol content. This study examines the overall variable range of oil, protein, phenol, sugar, and frees amino acids at both locations. At Anantapur oil, protein, phenol, sugar, and free amino acids ranged from 41.36–47.75 (CV = 2.67%), 0.20–0.30 (CV = 8), 4.84–8.39 (CV = 10.11), and 0.50–0.77 (CV = 7.57%). At Junagadh's oil content ranged from 40.49–52.40% (CV = 5.23%), protein from 23.54–35.24% (CV = 6.05%), phenol from 0.00-0.32% (CV = 7.14%), sugar from 3.73–9.04% (CV = 16.08%), and free amino acids from 0.1–0.78% (CV = 63.41%). Plant biochemical properties depend on species, varietal, environmental, and regional factors. Groundnut oil and protein content indicate its economic and nutritional value. In Junagadh, overall variability for free amino acids (%) ranged from 0.11–0.78 (CV = 63.41%) (Fig. 2 ). This biochemical characteristic fluctuates due to crop genetics and environment. Water stress reduces oil output and oleic acid content, (Hashim et al. 1993 and Chakraborty et al. 2013) and increases protein and sugar (Dwivedi et al. 1996). Under water stress, Chakraborty et al. (2013) found higher linoleic acid levels. Figure 2 shows frequencies distribution among groundnut germplasm for different nutritional traits. Most traits had a near-normal distribution, however accessions varied. Except for free amino acids, oil, and phenol content in Junagadh, most accessions had mean or near-mean values for most characteristics in both habitats. Free amino acid distribution differed biochemically between two places. 25 germplasm from Junagadh had oil contents over 50.1%, whereas none from Anantapur did. Anantapur's germplasm had 44–46% oil and 32–34% protein. Figure 2 shows that 38 Anantapur germplasms and 45 Junagadh germplasms have protein contents over 34%. The impact of water deficit stress on oilseed crops is noteworthy as it leads to a significant reduction in oil yield and consequent alteration of kernel composition. Mean performance (Y), stability variance (σi 2 ), and rankings ( 𝘒R) Agronomic traits Figure 3 shows the average performance and stability of the genotypes in both scenarios. NRCG-3693 had the highest average pod yield of 285.27 g/m 2 across two conditions. NRCG-10570, NRCG-10580, and NRCG-10682 were the best genotypes for hundred kernel weight (HKW) in two different environments. Shelling percentage (SHP) is commercially significant and were found to be high in NRCG-5360, NRCG-11379, and NRCG-10331 accessions. Breeding programs aim to create cultivars with high yield potential and adaptability to various agro-ecological zones. Thus, yield-stability selection indices are necessary. Genotype-Environment-Interaction (GEI) decreases association between genotypic and phenotypic values, following selection (Ajay et al. 2020a, 2020b, 2021 and 2022). Assuming a normal trait distribution in the population, Shukla's stability variance (σi 2 ) was used to analyze genotypic stability of reported genotypic responses under two environmental situations. The study conducted by Kang and Pham (1991) involved an assessment of various techniques for the joint selection of yield and stability. The researchers introduced the rank-sum approach, which integrates the Shukla stability variance and yield into a unified metric. This metric assigns uniform significance to both the Shukla variance and yield rank within a singular measure. The rank of 1 was assigned to the highest genotype's yield, while the genotype with the lowest variance of Shukla was given a rank of 1. According to Kang and Pham's (1991) findings, genotypes with lower Kang's rank-sum (KR) values exhibit high yield and lowest variance and are deemed the most favorable. Mean performance, Shukla stability (σi2), and rankings (𝘒R) of germplasm for pod yield, hundred kernel weight and shelling percent is provided in Fig. 3 . For pod yield 𝘒R varied from 34 to 720, NRCG-14027 ( 34 ) was most desirable followed by NRCG-10366 (53), NRCG-103960 (68) and NRCG-10585 (69). In the context of HKW, germplasm listed in the Fig. 3 exhibiting 𝘒R = 0 within the 0-727 range were deemed analogous.. SHP (KR = 0-756) was the most variable agronomic parameter in the germplasm studied and NRCG-10272, 11546, and 12229 had the largest variability, with KR = 0 performing best. Biochemical traits The mean performance of top performing genotypes from two different environments for biochemical traits is presented in Fig. 4 . The mean performance of the tested genotypes across two environments showed NRCG-10312 as superior for oil content (48.22%); NRCG-14039 (34.71%), NRCG-10501 (34.62%) and NRCG-10413 (34.12%) for protein content; a maximum of 0.29% phenol content (NRCG-1613, 4236, 4508, 5573, 6450 and 9235); 0.74% free amino acids (NRCG-1688, and 6450) and 8.30% (NRCG-10501), 7.84% (NRCG-14039), 7.53% (NRCG-11340) sugar content. So, the data indicate that the genotypes exhibited differential behavior as a response to the two climatic conditions. The most desirable genotypes are those with low values of Kang's rank-sum (𝘒R). 𝘒R ranged from 0-750 for oil content, 0-735 for protein content, 0-672 for sugar content, 0-601 for phenol and 0-599 for free amino acids. So, the great variability was observed for oil and protein content in the tested germplasm. This index revealed that all the germplasm mentioned in Fig. 4 had the lowest values of 𝘒R for FAA and phenol content and therefore were the most stable with high mean performance. For oil content, as per 𝘒R, NRCGs-11270, 11515, 11732, 12380, 12465, 12470 and 13945 were most desirable. While for protein content, germplasm with lowest values, NRCGs-11562, 11732, 13959, 14067, 14072, 14073 and 14545 were identified as best performing. For sugar content listed germplasm with lowest values were most stable and best performing, followed by NRCGs-10501 ( 1 ), 14039 ( 4 ), 11340 ( 7 ), 14074 ( 11 ). Inter-Relationship among various traits and Identification of superior accessions for multiple traits Figure 5 shows trait correlation coefficients. Oil content was significantly adversely linked with protein (r = − 0.22(ATP); r = − 0.33(JND)) and sugar (r = − 0.21(ATP); r = − 0.24(JND)). Oil content was favorably linked with phenol content (r = + 0.18(ATP); +0.19(JND)). Oil was positively connected (r = + 0.19) at Anantapur and negatively correlated (r = − 0.39) at Junagadh for FAA. Protein and sugar were favorably connected (r = + 0.93(ATP); +0.54(JND)), as were phenol and FAA (r = + 0.89ATP; +0.21 JND). HKW and SHP significantly correlated with pod yield. As expected, HKW and SHP were positively correlated (r = + 0.30 (ATP); r = + 0.18 (JND). Table 3S shows superior germplasm for two-or more character combinations identified on the basis of mean performance, Kang's rankings, and characters associations, NRCGs-10366, 10480, 10485, and 10844 were superior for PY, HKW, and SHP; NRCG-4559 for PY, FAA, and phenol content; NRCG-11390 for PY, protein, and sugar; NRCGs-10382, 10366 for PY, FAA, and SHP; NRCG-4781 for phenol, sugar, and FAA; and NRCGs-12469, 14089, and 16492 for important biochemical traits (oil, protein, and sugar content); NRCG-9344 for FAA, oil and phenol;NRCG-11101 for HKW, protein & oil;NRCGs-11154, 11164 for HKW, protein and sugar; and NRCG-7081 for FAA, SHP and phenol content. Discussion A better understanding of the extent of natural genotypic variation existing in the germplasm of cultivated groundnut is helpful in identification of genotypes likely with greater yield potential and nutritional values that can be utilized as parental material for breeding of improved cultivars. Characterization of 371 germplasm originating from more than fourteen countries, resulted in the identification of trait specific and multi-traits germplasm from the global composite collection. Genotypic Variability The prerequisite for successful breeding program is to find sufficient amount of variability, in which desired lines are to be selected for further manipulation to achieve the target. Genetic variability is of paramount importance in selecting the best genotypes for making rapid improvement in yield and desirable characters as well as for selection of the most promising parents for further breeding program. Study of genetic variability reveals variation in different quantitative traits. The success of breeder in selecting suitable quality parameters lies largely on existence and exploitation of genetic variability to the fullest extent (Allard, 1960). The coefficient of variability for hundred kernel weight (g) was 17.43%; 7.45% at two locations, 48.97; 30.96% for pod yield (g/m 2 ) and 8.91; 15.34% for shelling percentage. So, the traits showed significant variation among accessions, with the coefficient of variation of various parameters had a range value (7.45–48.97%), indicating that the trial and data recording was carried out with sufficient precision. In other studies, low values of coefficient of variability were recorded, suggesting better improvement scope for these traits. The coefficients of variations measures magnitude of variability present in the population. In other studies, high values of coefficient of variability were recorded, suggesting better improvement scope for these traits by selection of parents with desirable attributes for breeding program. The frequency distribution of germplasm for yield and related attributes in different groups indicated the presence of high level of genotypic diversity among the germplasm evaluated at the two locations. Germplasm identification involves identifying accessions with consistent and extraordinary agronomic traits, and biochemical traits such as high oil, sugar, and protein levels. This study found that most biochemical parameters were within normal ranges when compared to past research. Both places tested showed variability in germplasm for biochemical traits. Junagadh has more diversified germplasm for the traits under research than Anantapur. The total variable range for oil, protein, and sugar concentrations in Junagadh was 40.49 to 52.40%, 23.54 to 35.924%, and 3.73 to 9.04%, respectively. At Anantapur, oil, protein, and sugar ranged from 41.36 to 47.75%, 28.33 to 35.96%, and 4.84 to 8.39%, respectively. Oil and protein content values in the population showed continuity, indicating a quantitative inheritance pattern. Genetic and environmental factors cause this metabolic characteristic to fluctuate significantly. The study found that 58.49% of Anantapur germplasm and 36.65% of Junagadh germplasm had oil content between 44.1–46% and 46.1–48%. 53.9% and 37.46% of germplasm from Anantapur and Junagadh had seed protein values between 32.1–34%. Researchers worldwide have found similar seed oil and protein concentrations. Jambunathan et al. 1985 studied 6840 germplasm accessions at ICRISAT in tiny batches over several years. The results showed that accessions had 32–55% oil and 16–34% protein. Upadhyaya et al. 2003 found 45–55% oil content heterogeneity in the groundnut minicore collection, wild Arachis accessions in the ICRISAT gene bank (Upadhyaya et al. 2011), and USDA groundnut germplasm repository (Wang et al. 2012). According to unpublished data from S.N. Nigam of ICRISAT in 2006, genotypes with high oil or protein content in one environment were unsustainable when systematically assessed in multiple environments. Heba et al. (2021) and Meena et al. (2022) found that increased moisture stress can reduce nutrient uptake, affecting growth and development of groundnut. Breeding program needs reliable sources with high oil concentration, and superior for agronomic traits. The coefficient of variation compares quantitative trait genetic variability. At two locations, oil and protein levels had low coefficient of variability (CV) values of 2.67% and 3.76% and 5.23% and 6.05%, respectively. Asibuo et al. (2008) analysed 20 Ghanaian landraces and found protein (1.50%) and oil (0.34%) with very low CVs. According to Lynch and Walsh (2001), the oil and protein contents exhibited low values, which is indicative of a significant degree of genotypic variability in these traits. Oil, was lower in Anantapur than Junagadh. The mean values of protein, sugar, and FAA at Anantapur were higher than those at Junagadh, indicating more diversity across germplasm accessions. Mid-season drought decreased groundnut oil content (Conkerton et al. 1989), and according to Vaidya et al. (2015) and Yadav et al. (2013) drought stress increased genetic variability, FAA, and total soluble protein as observed at Anantapur condition than that was found at Junagadh. A higher kernel protein content and stress-induced protein breakdown may explain the observed increase in free amino acids. Stress heterogeneously affected total phenol content. Genetic variability makes it easy to identify genotypes with drought-stress-related features for breeding. Small-molecular-mass proteins accumulate quicker than fatty acids in drought-stressed plants. De novo synthesis or amino acid degradation enhances total soluble protein content. Protein increases may help plants tolerate drought by balancing osmotic pressure (Yadav et al. 2013). The current study confirmed previous findings that Anantapur, a location that endures repeated droughts during the rainy season, accumulated less oil but more proteins, carbohydrates, and free amino acids. Chakraborty et al. (2016) found that oil content decreased during drought stress as observed under Anantapur as well. Mean performance (Y), stability variance (σi 2 ), and rankings ( 𝘒R) Anantapur and Junagadh's groundnut germplasm's pod yield, hundred kernel weight (HKW), and shelling percentage (SHP) mean values were 223.66 and 134.47 g/m 2 , 34.75 and 36.38 g, and 72.11 and 71.72%, respectively. Junagadh has lower pod yield and SHP than Anantapur. Table 2S shows that precipitation, sunshine exposure, and temperature may explain the differences between the two experimental sites. During the rainy season, Anantapur received 630 mm and Junagadh 1305 mm. The rainy season lowest and maximum temperatures at Anantapur and Junagadh were 17.8°C and 35.0°C, and 16.2°C and 38.2°C, respectively. Anantapur had 39.87% greater pod yield and 49.55% higher SHP than Junagadh. In Anantapur and Junagadh, seasonal solar exposure was explored on photosynthesis, photosynthate accumulation and translocation, yield, and related traits. Anantapur's continuous daily sunlight hours above 5 hours may have optimised photosynthetic processes and yields. Figure 1 b shows that Junagadh's erratic rainfall and lack of sunshine during pod setting may have reduced yields. Solar radiation interception increases Radiation Use Efficiency (RUE), dry matter production, and yield in several Arachis sp. cultivars (Oluwasemire and Odugbenra, 2014). Junagadh's typical daylight hours of 1–3 hours/day have affected yield traits throughout critical crop growth stages including flowering and pod formation. During kharif season, high moisture stress, disease pressure, and insufficient sunshine in Junagadh impair crop performance. HKW in Junagadh was 51.54% greater than Anantapur's. The observed variation may be attributed to distinct climatic and edaphic factors associated with the cultivation of groundnut. A breeding program aims to generate cultivars with high yield potential and adaptability to various agro-ecological zones. The mean performance and stability statistic of NRCG-14027, NRCG-10272, NRCG-11546, and NRCG-12229 indicated the best cultivars for specific outturn (SHP) and NRCG-14027, NRCG-10366 and NRCG-13960 were best cultivars for pod yield. The mean values for oil content, protein content, sugar content, phenol and FAA of germplasm over two environments, ANT and JND was 44.62; 46.59%, 32.40; 31.37%, 6.43; 5.97%, 0.25; 0.27%, and 0.66; 0.41% respectively. The biochemical data indicate that the genotypes exhibited different behavior for different traits in response to the two different environments. However, mean values of biochemical traits were comparable from two locations that could help in identifying stable performing germplasm. For oil content, NRCGs-11270, 11515, 11732, 12380, 12465, 12470 and 13945 were most desirable. While for protein content, germplasm viz. , NRCGs-11562, 11732, 13959, 14067, 14072, 14073 and 14545 were identified as best performing. Available reports indicate that water stress reduces oil yield (Hashim et al. 1993 and Chakraborty et al. 2016) whereas protein (Dwivedi et al. 1996) and sugar content (Chakraborty et al. 2016) increases. Findings of present study are in agreement with previous reports wherein Anantapur location which faces frequent dry spells during rainy season had reduced accumulation of oil but with increased accumulation of proteins and sugars. This indicates that drought stress at optimum or higher air temperature (Fig. 1 a) can significantly affect groundnut oil content. The lack of adequate C-supply from the source tissue (both due to reduced photosynthesis and conversion of assimilate for biosynthesis of organic osmo-protectants) resulted in reduction in kernel oil content, but a relative increase in protein content (Chakraborty et al. 2016). Inter-Relationship among various traits and Identification of superior accessions for multiple traits Understanding inter-character correlation is essential to select best genotypes from the population. However, selection for a trait may reduce other attributes. Figure 5 shows groundnut physicochemical and agronomic connections. Trait relationships were examined in two contexts i.e Junagadh and Anantapur conditions seperately. Junagadh data suggests no negative association between pod yield and protein content. Thus, both factors can be improved simultaneously. High oil yield per unit area is difficult due to the negative association between pod yield and oil content. Chiow and Wynne (1983) found a negative association between these variables. In both the environments, oil content and 100-kernel weight had no significant negative connection (0.05 & 0.17**). Two factors were negatively correlated as reported by Janila et al. (2016). When they examined 33 genotypes, Dwivedi et al. (1990) found a positive correlation between kernel oil content and seed mass. Ajay et al. (2012) found that kernel protein and oil content affect confectionery groundnut cultivar preferences. Groundnut seed contains oil, crude protein and carbohydrates (Francisco and Resurreccion 2008). An increase in any of these ingredients should decrease one or more of the others. Assimilates are distributed across several seed components, therefore protein concentration and other important constituents are intrinsically linked. Thus, changing one component affects the others. The correlation coefficients of + 0.93 (ATP) and + 0.54 (JND) show that protein and sugar are positively correlated. NRCGs-10366, 10480, 10485, and 10844 were superior for PY, HKW, and SHP; NRCG-11390 for PY, protein, and sugar; and NRCGs-12469, 14089, and 16492 for important biochemical traits (oil, protein, and sugar content); NRCG-11101 for HKW, protein, and oil; and NRCGs-11154, 11164 for HKW, protein, and sugar. Table 4S describes the morphology of exceptional germplasm with numerous desirable features. After adaptive trials, stable genotypes with better mean performance can be grown and employed as breeding parents. Conclusion The findings of this study showed that the examined accessions have significant stable genetic variation that might be used in groundnut breeding. The ability of a species to adapt to its environment is based on genetic variation. Ideal genotypes should combine desired stable agronomic traits high yielding capacity and quality traits to qualify as being adapted or elite. Optimal moisture during crucial growth phases and increased sunshine hours have had a greater impact on improving groundnut production and quality attributes. In the current study, superior and stable performing germplasm with two or multiple desirable traits were identified based on evaluation from two contrasting environments. The sources of valuable breeding traits for high productivity, as well as high content of oil, protein, sugar, phenol and FAA in different combinations in Spanish collection could be used in groundnut improvement. Declarations Acknowledgements Authors acknowledge the support and facilities received from the Director, ICAR-DGR, Junagadh, Gujarat. Competing interest The authors have no relevant financial or non-financial interests to disclose Funding This work was supported by the facilities received from the ICAR-Directorate of Groundnut Research, Junagadh, Gujarat India. Handling plant materials The collection and handling of plant were in accordance with all the relevant guidelines Data availability All relevant data are within the manuscript and its Supporting Information files. References Ajay BC, Gowda MVC, Rathnakumar AL, Kusuma VP, Abdul fiyaz R, Holajjer P, Ramya KT, Govindaraj G, Prashanth babu H (2012) Improving Genetic Attributes of Confectionary Traits in Peanut ( Arachis hypogaea L.) Using Multivariate Analytical Tools. J Agr Sci 4: 247-258. Ajay BC, Bera SK, Singh AL, Kumar N, Gangadhar K, Kona P (2020a) Evaluation of Genotype × Environment Interaction and Yield Stability Analysis in Peanut Under Phosphorus Stress Condition Using Stability Parameters of AMMI Model. Agric Res 9: 477–486. Ajay BC, Ramya KT, Abdul Fiyaz R, Govindaraj G, Bera SK, Kumar N, Gangadhar K, Kona P, Singh GP, Radhakrishnan T (2020b) R-AMMI-LM: Linear-fit Robust-AMMI model to analyze genotype-by environment interactions. Indian J Genet 81: 87-92 Ajay BC, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P, Rani K, Radhakrishnan T (2022) Higher Order AMMI (HO-AMMI) analysis: A novel stability model to study genotype-location interactions. Indian J Genet 82: 25-30. Ajay BC, Bera SK, Singh AL, Kumar N, Dagla MC, Gangadhar K, Meena HN, Makwana AD, et al. (2021) Identification of stable sources for low phosphorus conditions from groundnut ( Arachis hypogaea L.) germplasm accessions using GGE biplot analysis. Indian J Genet 81: 300-306. Allard RW (1960) Principles of plant breeding. John Wiley and Sons, New York: 585 pp Alqudah AM, Samarah NH, Mullen RE (2010) Drought stress effect on crop pollination, seed set, yield and quality. In: Lichtfouse, E. (ed.) Alternative Farming Systems, Biotechnology, Drought Stress and Ecological Fertilization. Berlin: Springer, 193-213 Anand SS, Hawkes C, de Souza RJ, Mente A, Dehghan M, et. al. (2015) Food Consumption and its Impact on Cardiovascular Disease: Importance of Solutions Focused on the Globalized Food System. J. Am. Col. Cardio. 66: 1590-1614. Asibuo JY, Akromah R, Safo-Kantanka O, Adu-Dapaah HK, Ohemeng-Dapaah S, Agyeman A (2008) Chemical composition of groundnut, Arachis hypogaea (L.) landraces. Afr J Biotechnol 7: 2203-2208. Bera SK, Kamdar JH, Kasundra SV, Patel SV, Jasani MD, et al. (2019) Steady expression of high oleic acid in peanut bred by marker-assisted backcrossing for fatty acid desaturase mutant alleles and its effect on seed germination along with other seedling traits. PloSone 14: e0226252. Chakraborty K, Mahatma MK, Thawait LK, Bishi SK, Kalariya KA, Singh AL (2016) Water deficit stress affects photosynthesis and sugar profile in source and sink tissues of groundnut ( Arachis hypogaea L.) and impacts kernel quality. J Appl Bot Food Quality 89: 98-104. Chakraborty K, Bishi SK, Singh AL, Kalariya KA, Kumar L (2013) Moisture deficit stress affects yield and quality in groundnut seeds. Indian J. Plant Physiol 18: 136–141. Chiow HW, Wynne JC (1983) Heritabilities and genetic correlations for yield and quality traits of advanced generations in a cross of peanut. Peanut Sci 10: 13–17 Conkerton EJ, Ross LF, Daigle DJ, Kvien CS, McCombs C (1989) The effect of drought stress on peanut seed composition. II. Oil, protein and minerals. Oleagineux 44: 593-599. Dwivedi SL, Jambunathan R, Nigam SN, Raghunath, Shanka RK, Nagabhushanam GVS (1990) Relationship of seed mass to oil and protein contents in peanut ( Arachis hypogaea L.). Peanut Sci. 17: 48–52 Dwivedi SL, Nigam SN, Rao NRC, Singh U, Rao KVS (1996) Effect of drought on oil, fatty acids and protein contents of groundnut ( Arachis hypogaea L.) seeds. Field Crop Res 48: 125-133. Francisco ML, Resurreccion AV (2008) Functional components in peanuts. Crit Rev Food Sci Nutr 48 715–746. Gupta SK, Baek J, Carrasquilla-Garcia N, Penmetsa RV (2015) Genome-wide polymorphism detection in peanut using next-generation restriction-site-associated DNA (RAD) sequencing. Mol Breed 35: 145. Hammer O, Harper D, Ryan P (2001) PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica 4: 1-9. Happy D, Hussein S, Mark L, Patrick O, Omari M (2018) Groundnut production constraints, farming systems, and farmer-preferred traits in Tanzania. J Crop Imp 32: 812−828. Hashim IB, Koehler PE, Eitenmiller RR, Kvien K (1993) Fatty acid composition and tocopherol content of drought stressed Florunner peanuts. Peanut Sci 20: 21-24. Heba MN, Rana DS, Choudhary AK, Dass A, Rajanna GA, Pande P (2021) Improving productivity, quality and biofortification in groundnut ( Arachis hypogaea L.) through sulfur and zinc nutrition in alluvial soils of the semi-arid region of India. J Plant Nutrition 44: 1151-1174. Jambunathan R, Raju SM, Barde SP (1985) Analysis of oil content of peanuts by nuclear magnetic resonance spectrometry. J Sci Food Agric 36:162–166 Janila P, Manohar SS, Patne N, Variath MT, Nigam SN (2016) Genotype× environment interactions for oil content in peanut and stable high‐oil‐yielding sources. Crop Sci 56: 2506-2515. Kang MS, Pham HN (1991) Simultaneous selection for high yielding and stable crop genotypes. Agron J 83:161-165. Lynch M, Walsh B (2001) Genetics and analysis of quantitative traits. Am J Hum Genet 68: 548–549. Meena HN, Ajay BC, Rajanna GA, Yadav RS, Jain NK, Meena MS (2022) Polythene mulch and potassium application enhances peanut productivity and biochemical traits under sustained salinity stress condition. Agr Water Manage. 273: 107903. Ojiewo CO, Janila P, Bhatnagar-Mathur P, Pandey MK, Desmae H, et al. (2020) Advances in crop improvement and delivery research for nutritional quality and health benefits of groundnut ( Arachis hypogaea L.). Front Plant Sci 11: 29. Oluwasemire KO, Odugbenra GO (2014) Solar Radiation Interception, Dry Matter Production and Yield among Different Plant Densities of Arachis spp. in Ibadan, Nigeria. Agr Sci 5: 48962. Pour-Aboughadareh AM, Yousefian H, Moradkhani P, Poczai P, Siddique KHM (2019). STABILITYSOFT: A new online program to calculate parametric and non-parametric stability statistics for crop traits. Appl Plant Sci 7: e1211. Pramanik A, Tiwari S, Tomar RS, Tripathi MK, Singh AK (2019) Molecular characterization of groundnut ( Arachis hypogaea L.) germplasm lines for yield attributed traits. Indian J Genet 79: 56-658. R core team. (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Rathore MS, Tiwari S, Tripathi MK, Gupta N, Yadav S, Singh S, Tomar RS (2022) Genetic diversity analysis of groundnut germplasm lines in respect to early and late leaf spot diseases and biochemical traits. Legume Res 1: 6. Reddy TY, Reddy VR, Anbumozhi V (2003) Physiological responses of groundnut ( Arachis hypogea L.) to drought stress and its amelioration: a critical review. Plant Growth Regul 41, 75-88. Ritchie H, Roser M (2017) Micronutrient deficiency. Our World in data . https://ourworldindata.org/micronutrient-deficiency, accessed on 3 rd July 2023 Shao T, Verma HK, Pande B, Costanzo V, Ye W, Cai Y, Bhaskar LVKS (2012) Physical Activity and Nutritional Influence on Immune Function: An Important Strategy to Improve Immunity and Health Status. Front Physiol 8: 751374 Shukla GK (1972) Some statistical aspects of partitioning genotype-environment components of variability. Heredity 29: 237-245 Singh AL, Chaudhari V. Rani K, Kona P, Mahatma MK, Verma A, Reddy KK, Sushmita, Thawait LK, Patel CB (2021) In book: Arachis hypogaea: Cultivation, Production and Nutritional Value (Richard J. Whitworth ed) Agriculture Issues and Policies Publisher: Nova Science Publishers, Inc. USA. Nova Science Publishers, Inc. USA. Tomar YS, Tiwari S, Tripathi MK, Singh S, Gupta N (2022) Genetic diversity, population structure and biochemical parameters estimations driving variations in groundnut germplasm. Legume Res 10.18805/LR-4965. Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2003) Development of a peanut core collection using taxonomical, geographical and morphological descriptors. Genet Resou Crop Evo 50: 139–148. Upadhyaya HD, Dwivedi SL, Nadaf HL, Singh S (2011) Phenotypic diversity and identification of wild Arachis accessions with useful agronomic and nutritional traits. Euphytica 182: 103– 115. Vaidya S, Vanaja M, Lakshmi NJ, Sowmya P, Anitha Y, Sathish P (2015) Variability in drought stress induced responses of groundnut ( Arachis hypogaea L.) genotypes. Biochem Physiol 4: 149. Vincent V, Pasal R (2016) High transpiration efficiency increases pod yield under intermittent drought in dry and hot atmospheric conditions but less so under wetter and cooler conditions in groundnut ( Arachis hypogaea (L.)). Field Crop Res 193: 16–23. Wang ML, Raymer P, Chinnan M, Pittman RN (2012) Screening of the USDA peanut germplasm for oil content and fatty acid composition. Biomass Bioenergy 39: 336–343 Yadav SK, Jyothi Lakshmi N, Singh V, Patil A, Tiwari YK, Nagendram E, Sathish P, Vanaja M, Maheswari M, Venkateswarlu B (2013) In vitro screening of Vigna mungo genotypes for PEG induced moisture deficit stress. Indian J Plant Physi 18: 55-60. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.docx Cite Share Download PDF Status: Published Journal Publication published 20 Oct, 2023 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Major revision 25 Sep, 2023 Reviews received at journal 11 Sep, 2023 Reviewers agreed at journal 30 Aug, 2023 Reviewers invited by journal 30 Aug, 2023 Submission checks completed at journal 30 Aug, 2023 Editor assigned by journal 30 Aug, 2023 First submitted to journal 30 Aug, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3309986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229869867,"identity":"2d4692a0-3ccc-4d45-a6a5-3e03ce9720b0","order_by":0,"name":"Kirti Rani","email":"","orcid":"","institution":"ICAR-Directorate of Groundnut Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kirti","middleName":"","lastName":"Rani","suffix":""},{"id":229869868,"identity":"e8d7b941-c876-4dfa-977a-48d69d22b93b","order_by":1,"name":"Ajay 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1","display":"","copyAsset":false,"role":"figure","size":210634,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly weather data of Anantapur (a) and Junagadh (b) during 2021 crop growth period\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/bc70f7f66a64867d56e7ab62.jpg"},{"id":42517808,"identity":"de8a795e-acba-4445-874c-478394e0e2b8","added_by":"auto","created_at":"2023-09-01 20:55:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":207373,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of 371 germplasm accessions evaluated at Anantapur and Junagadh locations for Oil, protein, sugar and Phenol contents, FAA, Pod yield and Shelling out turn\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/f1e345e26614a349986f9a14.jpg"},{"id":42517516,"identity":"8814bf75-e797-4d90-8a2f-78c8efb6370d","added_by":"auto","created_at":"2023-09-01 20:47:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean performance (Y), stability variance (σi\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e), and rankings (𝘒R) of germplasm for agronomic traits\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/ade39fb0925901b0b3b7231b.jpg"},{"id":42517513,"identity":"b3aca0fe-a3a7-4629-852d-d6483e618010","added_by":"auto","created_at":"2023-09-01 20:47:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":128370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean performance (Y), stability variance (σi\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e), and rankings (𝘒R) of germplasm for different biochemical traits\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/d9e5131a058255799af554a4.jpg"},{"id":42517514,"identity":"b188ed0b-7be3-4d08-9bab-87f28d607ba4","added_by":"auto","created_at":"2023-09-01 20:47:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePearson’s correlation coefficients among different studied parameters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/b7fc60f040ef919ef60c075a.jpg"},{"id":45091318,"identity":"fdcbaf1f-b3aa-44dd-95fa-827333ae0677","added_by":"auto","created_at":"2023-10-23 15:09:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":803239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/9806decc-1b77-46bd-9897-5c78f9b553be.pdf"},{"id":42517518,"identity":"4f808a4b-42c3-4141-bc0b-71072e9a3d51","added_by":"auto","created_at":"2023-09-01 20:47:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":92788,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-3309986/v1/dee64e97959d7697ef179180.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trait discovery for yield, related attributes and quality parameters and identification of potential stable donors for genetic improvement of groundnuts through evaluation of germplasm originated from different countries in two contrasting environments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProtein energy malnutrition is a major public health issue, especially in underdeveloped countries (Ritchie and Roser 2017). This effect affects everyone, especially cardiovascular patients. Unhealthy diets and global food system changes affect human nutrition and food security (Anand et al. 2015). Protein and energy deficiency can harm the immune system, development, and reproduction (Shao et al. 2012). Groundnuts were oilseed crops in India. In recent years, groundnut consumption has changed due to urbanisation and cheaper oil alternatives. Groundnuts are becoming a major food crop as well as oilseeds. Due to their functional components like tocopherol, niacin, and folic acid, mineral components like Cu, Fe, Zn, Mn, K, Ca, and P, dietary fibres, phytosterols like resveratrol and beta-sitosterol, and flavonoids and phenolic acids, groundnuts can provide long-term health benefits. Groundnuts have 25% protein and 45\u0026ndash;50% oil (Francisco and Resurreccion 2008). Approximately 80% of the groundnut's fatty acid content is monounsaturated oleic acid and polyunsaturated linoleic acid (Bera et al. 2019). According to Ojiewo et al. (2020) groundnut has helped resource-poor farmers achieve nutritional security.\u003c/p\u003e \u003cp\u003eIn dryland farming system, groundnut is important as both animal food and seed. Groundnut is mostly grown in tropical and subtropical climates, where water scarcity reduces production (Happy et al. 2018). Singh et al. (2021) reported that 85% of groundnut farming relies on rainfall for water. This area has no irrigation infrastructure and is 80% dryland. The yield of agricultural produce decreases due to water deficit stress during its growing cycle. Vincent and Pasala (2016) found that dryness during pod and seed formation stages can reduce groundnut pod output by 56\u0026ndash;85%. Drought stress reduces productivity and quality. Drought stress reduces seed quality, producing smaller and medium-sized seeds. Stress also reduces seed vigour and germination. Under water stress, Alqudah et al. (2010) and Dwivedi et al. (1996) found that protein, oleic acid, and stearic acid increased while oil and linoleic acid decreased. Reddy et al. (2003) examined how irrigation levels affected groundnut seed protein. Plants with adequate watering produced more kernels, total proteins, and oil. Water stress affects leafy vegetable biochemistry. It increases sugars and free amino acids and decreases leaf turgidity, chlorophyll, ascorbic acid, and soluble protein. Thus, drought-tolerant genotypes with consistent nutritional components across conditions are crucial. Successful breeding programs require a large genetic pool and excellent genotypes for hybridization. According to Gupta et al. (2015), groundnut genetic variation has decreased due to insufficient genetic diversity and underutilization of elite breeding lines and variations. Groundnut breeding programs depend on germplasms to increase genetic variety. Despite germplasm sets and beneficial alleles that have yet to be fully utilised, plant breeding requires germplasm assessment. Pramanik et al. (2019), Rathore et al. (2022) and Tomar et al. (2022) reported that assessing morphological, agronomical, and biochemical diversity helps characterise groundnut germplasm collections and identify breeding parents.\u003c/p\u003e \u003cp\u003eGroundnut yield and nutritional properties vary due to environmental circumstances. Thus, germplasms must be tested in various environments to determine their stability and identify genotypes with reliable and consistent results. Unfortunately, germplasm accessions in groundnut for the identification of stable donors in terms of oil content, protein content, nutritionally balanced ingredients like total sugar and total free amino acids, and functional compounds like phenolics with high kernel yield have rarely been evaluated under diverse environmental conditions. This data may help identify high-quality groundnut germplasm for breeding programs. This study screened groundnut germplasm in Junagadh (Gujarat) and Anantapur (Andhra Pradesh) to discover stable donors with excellent yield and nutritional attributes. Junagadh has few sunny hours and no water stress, but Anantapur has many.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental materials\u003c/h2\u003e \u003cp\u003eCharacterising germplasm helps identify potential germplasm for breeding programmes and improves groundnut. 371 groundnut germplasm from diverse origins were randomly selected from early, medium, and late maturity groups (Table\u0026nbsp;1S). These germplasm collections came from Australia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), East Africa (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Middle Africa (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), North Africa (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), West Africa (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), South Africa (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), Europe (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), North America (48), South America (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), South Asia (127), Southeast Asia (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), East Asia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), Middle East Asia (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), West Asia (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and unknown origin. The Gene bank, ICAR-Directorate of Groundnut Research, in Junagadh, Gujarat, India, provided groundnut genetic resources for this research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExperimental sites\u003c/h2\u003e \u003cp\u003eThe ICAR-Directorate of Groundnut Research in Junagadh, Gujarat, and the Regional Research Station (RRS) in Anantapur, Andhra Pradesh, assessed 371 genotypes from various countries in 2021. The former is in medium black calcareous soil in latitude 21\u003csup\u003eo\u003c/sup\u003e31' N and longitude 70\u003csup\u003eo\u003c/sup\u003e36' E, whereas the latter is in red sandy loam soil at 14\u003csup\u003eo\u003c/sup\u003e69' N and 77\u003csup\u003eo\u003c/sup\u003e69' E. Rainy season evaluations were done. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb show the precipitation amounts, mean weekly maximum and lowest temperatures, and evaporation rates for Anantapur and Junagadh, respectively, over the study. In 2021, Anantapur had 603 mm of precipitation while Junagadh 1305 mm during crop cultivation. May and June are the warmest months in Anantapur and Junagadh, with average weekly maximum temperatures of 30.1\u0026ndash;38.2\u0026deg;C and 31.9\u0026ndash;35.0\u0026deg;C. These months average 16.2\u0026ndash;27.3\u0026deg;C and 17.8\u0026ndash;23.7\u0026deg;C low temperatures. Compared to Junagadh, Anantapur had a greater average weekly evaporation rate of 5.2\u0026ndash;8.7 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eField Evaluation and Data Collection\u003c/h2\u003e \u003cp\u003eIn mid-June at ICAR-DGR, Junagadh and in the first week of July at RRS, ICAR-DGR, Anantapur, 371 genotypes were sown. An 8-block augmented incomplete block design (AIBD) was used for each setting. Each genotype was represented once throughout the experiment by randomly assigning the 371 genotypes to 3-metre blocks. Both regions used uniform agronomic methods and plant protection. After drying pods, hundred kernel weight (HKW, g), shelling outturn (SHP, %), and pod yield (PY, g/m\u003csup\u003e2\u003c/sup\u003e) were measured. A random sample of 200 grammes of fully developed pods calculated the shelling outturn (%). Near Infrared (NIR) Spectroscopy (Dickey John, Instalab 700) measured oil, protein, total soluble sugar, phenols, and free amino acids (FAA) in the crushed seed combination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis used R version 4.2.1 (R core team 2021). Univariate analysis determined mean, standard deviation, range, coefficient of variation, and correlation for all parameters. Percentage distribution analysed quantitative attributes. The Shapiro-Wilk test determined distribution normality. One-factor analysis of variance (ANOVA) was used to compare normal distribution data mean values. For non-normal data, Kruskal-Walli's test was used. The stability variance (σi\u003csup\u003e2\u003c/sup\u003e) provided by Shukla (1972) and Kang's rank-sum approach (Kang 1991) were computed using online statistical program 'STABILITYSOFT' (Pour-Aboughadareh et al. 2019) to determine parametric and non-parametric stability statistics. Eliminating environmental factors determines a genotype's stability variance. According to Shukla (1972), genotypes with lower levels are more stable. The genotype with the highest mean yield (Y) was ranked 1. The projected value with the lowest magnitude was ranked 1 for σi\u003csup\u003e2\u003c/sup\u003e. The genotypes with the lowest rank-sum (KR) were the most preferred, based on yield (Y) and stability variance (σi\u003csup\u003e2\u003c/sup\u003e). Kang's 1988 rank-sum approach uses mean performance and σi\u003csup\u003e2\u003c/sup\u003e. This parameter weights mean performance and stability statistics equally to identify genotypes with good yield and stability. The genotype with the highest yield and lowest σi\u003csup\u003e2\u003c/sup\u003e ranks first in this study. PAST 3.25 was used to analyse variance and simple correlation coefficients (Hammer et al. 2001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic Variability\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eAgronomic traits\u003c/h2\u003e \u003cp\u003eThe entire variability range for hundred kernel weight (g) in Anantapur was 12\u0026ndash;88 (CV\u0026thinsp;=\u0026thinsp;17.43%) and 48\u0026ndash;87 (CV\u0026thinsp;=\u0026thinsp;7.45%) at Junagadh. Pod yield (g/m\u003csup\u003e2\u003c/sup\u003e) at Anantapur was 25.50 to 904.20 (CV\u0026thinsp;=\u0026thinsp;48.97) and 15.60 to 260 (CV\u0026thinsp;=\u0026thinsp;30.96%) at Junagadh. At both locations, pod yield data was more dispersed from mean than hundred kernel weights. Both locations had normal Shapiro\u0026ndash;Wilk test statistics for all agronomic variables. At Junagadh most accessions had pod yields of 100\u0026ndash;150 g/m\u003csup\u003e2\u003c/sup\u003e, shelling outturns of 70.1\u0026ndash;75.0% (134), and hundred kernel weights of 30-35g (133). In Anantapur, most accessions had pod yields\u0026thinsp;\u0026gt;\u0026thinsp;250 g/m\u003csup\u003e2\u003c/sup\u003e, shelling outturns of 70.1\u0026ndash;75.0% (136), and HKWs of 35-40g (200) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, two areas had similar agronomic trait distributions. Thus, genotypes performed better in Anantapur than Junagadh. The shelling percentage fluctuated from 28.04 to 91.79 in Anantapur (CV\u0026thinsp;=\u0026thinsp;8.91) and 22 to 56 at Junagadh (CV\u0026thinsp;=\u0026thinsp;15.34%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The features exhibited significant diversity across accessions, and the coefficient of variation of various parameters ranged from 7.45 to 48.97%, showing that the trial and data recording were precise.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical traits\u003c/h2\u003e \u003cp\u003eThe range, mean, and variation of the germplasm were measured using the univariate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The germplasm analysed showed significant phenotypic variation across two environments. The Shapiro-Wilk test showed normal distribution of biochemical features at both locations, except for oil and protein concentration at Anantapur. The two locations had different biochemical characteristic data dispersion. Oil and protein content dispersed more than FAA and phenol content. This study examines the overall variable range of oil, protein, phenol, sugar, and frees amino acids at both locations. At Anantapur oil, protein, phenol, sugar, and free amino acids ranged from 41.36\u0026ndash;47.75 (CV\u0026thinsp;=\u0026thinsp;2.67%), 0.20\u0026ndash;0.30 (CV\u0026thinsp;=\u0026thinsp;8), 4.84\u0026ndash;8.39 (CV\u0026thinsp;=\u0026thinsp;10.11), and 0.50\u0026ndash;0.77 (CV\u0026thinsp;=\u0026thinsp;7.57%). At Junagadh's oil content ranged from 40.49\u0026ndash;52.40% (CV\u0026thinsp;=\u0026thinsp;5.23%), protein from 23.54\u0026ndash;35.24% (CV\u0026thinsp;=\u0026thinsp;6.05%), phenol from 0.00-0.32% (CV\u0026thinsp;=\u0026thinsp;7.14%), sugar from 3.73\u0026ndash;9.04% (CV\u0026thinsp;=\u0026thinsp;16.08%), and free amino acids from 0.1\u0026ndash;0.78% (CV\u0026thinsp;=\u0026thinsp;63.41%). Plant biochemical properties depend on species, varietal, environmental, and regional factors. Groundnut oil and protein content indicate its economic and nutritional value. In Junagadh, overall variability for free amino acids (%) ranged from 0.11\u0026ndash;0.78 (CV\u0026thinsp;=\u0026thinsp;63.41%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This biochemical characteristic fluctuates due to crop genetics and environment. Water stress reduces oil output and oleic acid content, (Hashim et al. 1993 and Chakraborty et al. 2013) and increases protein and sugar (Dwivedi et al. 1996). Under water stress, Chakraborty et al. (2013) found higher linoleic acid levels. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows frequencies distribution among groundnut germplasm for different nutritional traits. Most traits had a near-normal distribution, however accessions varied. Except for free amino acids, oil, and phenol content in Junagadh, most accessions had mean or near-mean values for most characteristics in both habitats. Free amino acid distribution differed biochemically between two places. 25 germplasm from Junagadh had oil contents over 50.1%, whereas none from Anantapur did. Anantapur's germplasm had 44\u0026ndash;46% oil and 32\u0026ndash;34% protein. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that 38 Anantapur germplasms and 45 Junagadh germplasms have protein contents over 34%. The impact of water deficit stress on oilseed crops is noteworthy as it leads to a significant reduction in oil yield and consequent alteration of kernel composition.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMean performance (Y), stability variance (σi\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003e), and rankings (\u003c/b\u003e\u0026#120338;R)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAgronomic traits\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the average performance and stability of the genotypes in both scenarios. NRCG-3693 had the highest average pod yield of 285.27 g/m\u003csup\u003e2\u003c/sup\u003e across two conditions. NRCG-10570, NRCG-10580, and NRCG-10682 were the best genotypes for hundred kernel weight (HKW) in two different environments. Shelling percentage (SHP) is commercially significant and were found to be high in NRCG-5360, NRCG-11379, and NRCG-10331 accessions.\u003c/p\u003e \u003cp\u003eBreeding programs aim to create cultivars with high yield potential and adaptability to various agro-ecological zones. Thus, yield-stability selection indices are necessary. Genotype-Environment-Interaction (GEI) decreases association between genotypic and phenotypic values, following selection (Ajay et al. 2020a, 2020b, 2021 and 2022). Assuming a normal trait distribution in the population, Shukla's stability variance (σi\u003csup\u003e2\u003c/sup\u003e) was used to analyze genotypic stability of reported genotypic responses under two environmental situations. The study conducted by Kang and Pham (1991) involved an assessment of various techniques for the joint selection of yield and stability. The researchers introduced the rank-sum approach, which integrates the Shukla stability variance and yield into a unified metric. This metric assigns uniform significance to both the Shukla variance and yield rank within a singular measure. The rank of 1 was assigned to the highest genotype's yield, while the genotype with the lowest variance of Shukla was given a rank of 1. According to Kang and Pham's (1991) findings, genotypes with lower Kang's rank-sum (KR) values exhibit high yield and lowest variance and are deemed the most favorable. Mean performance, Shukla stability (σi2), and rankings (\u0026#120338;R) of germplasm for pod yield, hundred kernel weight and shelling percent is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For pod yield \u0026#120338;R varied from 34 to 720, NRCG-14027 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) was most desirable followed by NRCG-10366 (53), NRCG-103960 (68) and NRCG-10585 (69). In the context of HKW, germplasm listed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e exhibiting \u0026#120338;R\u0026thinsp;=\u0026thinsp;0 within the 0-727 range were deemed analogous.. SHP (KR\u0026thinsp;=\u0026thinsp;0-756) was the most variable agronomic parameter in the germplasm studied and NRCG-10272, 11546, and 12229 had the largest variability, with KR\u0026thinsp;=\u0026thinsp;0 performing best.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical traits\u003c/h2\u003e \u003cp\u003eThe mean performance of top performing genotypes from two different environments for biochemical traits is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The mean performance of the tested genotypes across two environments showed NRCG-10312 as superior for oil content (48.22%); NRCG-14039 (34.71%), NRCG-10501 (34.62%) and NRCG-10413 (34.12%) for protein content; a maximum of 0.29% phenol content (NRCG-1613, 4236, 4508, 5573, 6450 and 9235); 0.74% free amino acids (NRCG-1688, and 6450) and 8.30% (NRCG-10501), 7.84% (NRCG-14039), 7.53% (NRCG-11340) sugar content. So, the data indicate that the genotypes exhibited differential behavior as a response to the two climatic conditions. The most desirable genotypes are those with low values of Kang's rank-sum (\u0026#120338;R). \u0026#120338;R ranged from 0-750 for oil content, 0-735 for protein content, 0-672 for sugar content, 0-601 for phenol and 0-599 for free amino acids. So, the great variability was observed for oil and protein content in the tested germplasm. This index revealed that all the germplasm mentioned in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e had the lowest values of \u0026#120338;R for FAA and phenol content and therefore were the most stable with high mean performance. For oil content, as per \u0026#120338;R, NRCGs-11270, 11515, 11732, 12380, 12465, 12470 and 13945 were most desirable. While for protein content, germplasm with lowest values, NRCGs-11562, 11732, 13959, 14067, 14072, 14073 and 14545 were identified as best performing. For sugar content listed germplasm with lowest values were most stable and best performing, followed by NRCGs-10501 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), 14039 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), 11340 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), 14074 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInter-Relationship among various traits and Identification of superior accessions for multiple traits\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows trait correlation coefficients. Oil content was significantly adversely linked with protein (r = \u0026minus;\u0026thinsp;0.22(ATP); r = \u0026minus;\u0026thinsp;0.33(JND)) and sugar (r = \u0026minus;\u0026thinsp;0.21(ATP); r = \u0026minus;\u0026thinsp;0.24(JND)). Oil content was favorably linked with phenol content (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.18(ATP); +0.19(JND)). Oil was positively connected (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.19) at Anantapur and negatively correlated (r = \u0026minus;\u0026thinsp;0.39) at Junagadh for FAA. Protein and sugar were favorably connected (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.93(ATP); +0.54(JND)), as were phenol and FAA (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.89ATP; +0.21 JND). HKW and SHP significantly correlated with pod yield. As expected, HKW and SHP were positively correlated (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.30 (ATP); r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.18 (JND).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3S shows superior germplasm for two-or more character combinations identified on the basis of mean performance, Kang's rankings, and characters associations, NRCGs-10366, 10480, 10485, and 10844 were superior for PY, HKW, and SHP; NRCG-4559 for PY, FAA, and phenol content; NRCG-11390 for PY, protein, and sugar; NRCGs-10382, 10366 for PY, FAA, and SHP; NRCG-4781 for phenol, sugar, and FAA; and NRCGs-12469, 14089, and 16492 for important biochemical traits (oil, protein, and sugar content); NRCG-9344 for FAA, oil and phenol;NRCG-11101 for HKW, protein \u0026amp; oil;NRCGs-11154, 11164 for HKW, protein and sugar; and NRCG-7081 for FAA, SHP and phenol content.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA better understanding of the extent of natural genotypic variation existing in the germplasm of cultivated groundnut is helpful in identification of genotypes likely with greater yield potential and nutritional values that can be utilized as parental material for breeding of improved cultivars. Characterization of 371 germplasm originating from more than fourteen countries, resulted in the identification of trait specific and multi-traits germplasm from the global composite collection.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic Variability\u003c/h2\u003e \u003cp\u003eThe prerequisite for successful breeding program is to find sufficient amount of variability, in which desired lines are to be selected for further manipulation to achieve the target. Genetic variability is of paramount importance in selecting the best genotypes for making rapid improvement in yield and desirable characters as well as for selection of the most promising parents for further breeding program. Study of genetic variability reveals variation in different quantitative traits. The success of breeder in selecting suitable quality parameters lies largely on existence and exploitation of genetic variability to the fullest extent (Allard, 1960). The coefficient of variability for hundred kernel weight (g) was 17.43%; 7.45% at two locations, 48.97; 30.96% for pod yield (g/m\u003csup\u003e2\u003c/sup\u003e) and 8.91; 15.34% for shelling percentage. So, the traits showed significant variation among accessions, with the coefficient of variation of various parameters had a range value (7.45\u0026ndash;48.97%), indicating that the trial and data recording was carried out with sufficient precision. In other studies, low values of coefficient of variability were recorded, suggesting better improvement scope for these traits. The coefficients of variations measures magnitude of variability present in the population. In other studies, high values of coefficient of variability were recorded, suggesting better improvement scope for these traits by selection of parents with desirable attributes for breeding program. The frequency distribution of germplasm for yield and related attributes in different groups indicated the presence of high level of genotypic diversity among the germplasm evaluated at the two locations.\u003c/p\u003e \u003cp\u003eGermplasm identification involves identifying accessions with consistent and extraordinary agronomic traits, and biochemical traits such as high oil, sugar, and protein levels. This study found that most biochemical parameters were within normal ranges when compared to past research. Both places tested showed variability in germplasm for biochemical traits. Junagadh has more diversified germplasm for the traits under research than Anantapur. The total variable range for oil, protein, and sugar concentrations in Junagadh was 40.49 to 52.40%, 23.54 to 35.924%, and 3.73 to 9.04%, respectively. At Anantapur, oil, protein, and sugar ranged from 41.36 to 47.75%, 28.33 to 35.96%, and 4.84 to 8.39%, respectively. Oil and protein content values in the population showed continuity, indicating a quantitative inheritance pattern. Genetic and environmental factors cause this metabolic characteristic to fluctuate significantly. The study found that 58.49% of Anantapur germplasm and 36.65% of Junagadh germplasm had oil content between 44.1\u0026ndash;46% and 46.1\u0026ndash;48%. 53.9% and 37.46% of germplasm from Anantapur and Junagadh had seed protein values between 32.1\u0026ndash;34%. Researchers worldwide have found similar seed oil and protein concentrations. Jambunathan et al. 1985 studied 6840 germplasm accessions at ICRISAT in tiny batches over several years. The results showed that accessions had 32\u0026ndash;55% oil and 16\u0026ndash;34% protein. Upadhyaya et al. 2003 found 45\u0026ndash;55% oil content heterogeneity in the groundnut minicore collection, wild \u003cem\u003eArachis\u003c/em\u003e accessions in the ICRISAT gene bank (Upadhyaya et al. 2011), and USDA groundnut germplasm repository (Wang et al. 2012). According to unpublished data from S.N. Nigam of ICRISAT in 2006, genotypes with high oil or protein content in one environment were unsustainable when systematically assessed in multiple environments.\u003c/p\u003e \u003cp\u003eHeba et al. (2021) and Meena et al. (2022) found that increased moisture stress can reduce nutrient uptake, affecting growth and development of groundnut. Breeding program needs reliable sources with high oil concentration, and superior for agronomic traits. The coefficient of variation compares quantitative trait genetic variability. At two locations, oil and protein levels had low coefficient of variability (CV) values of 2.67% and 3.76% and 5.23% and 6.05%, respectively. Asibuo et al. (2008) analysed 20 Ghanaian landraces and found protein (1.50%) and oil (0.34%) with very low CVs. According to Lynch and Walsh (2001), the oil and protein contents exhibited low values, which is indicative of a significant degree of genotypic variability in these traits. Oil, was lower in Anantapur than Junagadh. The mean values of protein, sugar, and FAA at Anantapur were higher than those at Junagadh, indicating more diversity across germplasm accessions. Mid-season drought decreased groundnut oil content (Conkerton et al. 1989), and according to Vaidya et al. (2015) and Yadav et al. (2013) drought stress increased genetic variability, FAA, and total soluble protein as observed at Anantapur condition than that was found at Junagadh. A higher kernel protein content and stress-induced protein breakdown may explain the observed increase in free amino acids. Stress heterogeneously affected total phenol content. Genetic variability makes it easy to identify genotypes with drought-stress-related features for breeding. Small-molecular-mass proteins accumulate quicker than fatty acids in drought-stressed plants. De novo synthesis or amino acid degradation enhances total soluble protein content. Protein increases may help plants tolerate drought by balancing osmotic pressure (Yadav et al. 2013). The current study confirmed previous findings that Anantapur, a location that endures repeated droughts during the rainy season, accumulated less oil but more proteins, carbohydrates, and free amino acids. Chakraborty et al. (2016) found that oil content decreased during drought stress as observed under Anantapur as well.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMean performance (Y), stability variance (σi\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003e), and rankings (\u003c/b\u003e\u0026#120338;R)\u003c/p\u003e \u003cp\u003eAnantapur and Junagadh's groundnut germplasm's pod yield, hundred kernel weight (HKW), and shelling percentage (SHP) mean values were 223.66 and 134.47 g/m\u003csup\u003e2\u003c/sup\u003e, 34.75 and 36.38 g, and 72.11 and 71.72%, respectively. Junagadh has lower pod yield and SHP than Anantapur. Table\u0026nbsp;2S shows that precipitation, sunshine exposure, and temperature may explain the differences between the two experimental sites. During the rainy season, Anantapur received 630 mm and Junagadh 1305 mm. The rainy season lowest and maximum temperatures at Anantapur and Junagadh were 17.8\u0026deg;C and 35.0\u0026deg;C, and 16.2\u0026deg;C and 38.2\u0026deg;C, respectively. Anantapur had 39.87% greater pod yield and 49.55% higher SHP than Junagadh. In Anantapur and Junagadh, seasonal solar exposure was explored on photosynthesis, photosynthate accumulation and translocation, yield, and related traits. Anantapur's continuous daily sunlight hours above 5 hours may have optimised photosynthetic processes and yields. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb shows that Junagadh's erratic rainfall and lack of sunshine during pod setting may have reduced yields. Solar radiation interception increases Radiation Use Efficiency (RUE), dry matter production, and yield in several \u003cem\u003eArachis\u003c/em\u003e sp. cultivars (Oluwasemire and Odugbenra, 2014). Junagadh's typical daylight hours of 1\u0026ndash;3 hours/day have affected yield traits throughout critical crop growth stages including flowering and pod formation. During \u003cem\u003ekharif\u003c/em\u003e season, high moisture stress, disease pressure, and insufficient sunshine in Junagadh impair crop performance. HKW in Junagadh was 51.54% greater than Anantapur's. The observed variation may be attributed to distinct climatic and edaphic factors associated with the cultivation of groundnut. A breeding program aims to generate cultivars with high yield potential and adaptability to various agro-ecological zones. The mean performance and stability statistic of NRCG-14027, NRCG-10272, NRCG-11546, and NRCG-12229 indicated the best cultivars for specific outturn (SHP) and NRCG-14027, NRCG-10366 and NRCG-13960 were best cultivars for pod yield.\u003c/p\u003e \u003cp\u003eThe mean values for oil content, protein content, sugar content, phenol and FAA of germplasm over two environments, ANT and JND was 44.62; 46.59%, 32.40; 31.37%, 6.43; 5.97%, 0.25; 0.27%, and 0.66; 0.41% respectively. The biochemical data indicate that the genotypes exhibited different behavior for different traits in response to the two different environments. However, mean values of biochemical traits were comparable from two locations that could help in identifying stable performing germplasm. For oil content, NRCGs-11270, 11515, 11732, 12380, 12465, 12470 and 13945 were most desirable. While for protein content, germplasm \u003cem\u003eviz.\u003c/em\u003e, NRCGs-11562, 11732, 13959, 14067, 14072, 14073 and 14545 were identified as best performing. Available reports indicate that water stress reduces oil yield (Hashim et al. 1993 and Chakraborty et al. 2016) whereas protein (Dwivedi et al. 1996) and sugar content (Chakraborty et al. 2016) increases. Findings of present study are in agreement with previous reports wherein Anantapur location which faces frequent dry spells during rainy season had reduced accumulation of oil but with increased accumulation of proteins and sugars. This indicates that drought stress at optimum or higher air temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) can significantly affect groundnut oil content. The lack of adequate C-supply from the source tissue (both due to reduced photosynthesis and conversion of assimilate for biosynthesis of organic osmo-protectants) resulted in reduction in kernel oil content, but a relative increase in protein content (Chakraborty et al. 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInter-Relationship among various traits and Identification of superior accessions for multiple traits\u003c/h2\u003e \u003cp\u003eUnderstanding inter-character correlation is essential to select best genotypes from the population. However, selection for a trait may reduce other attributes. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows groundnut physicochemical and agronomic connections. Trait relationships were examined in two contexts i.e Junagadh and Anantapur conditions seperately. Junagadh data suggests no negative association between pod yield and protein content. Thus, both factors can be improved simultaneously. High oil yield per unit area is difficult due to the negative association between pod yield and oil content. Chiow and Wynne (1983) found a negative association between these variables. In both the environments, oil content and 100-kernel weight had no significant negative connection (0.05 \u0026amp; 0.17**). Two factors were negatively correlated as reported by Janila et al. (2016). When they examined 33 genotypes, Dwivedi et al. (1990) found a positive correlation between kernel oil content and seed mass. Ajay et al. (2012) found that kernel protein and oil content affect confectionery groundnut cultivar preferences. Groundnut seed contains oil, crude protein and carbohydrates (Francisco and Resurreccion 2008). An increase in any of these ingredients should decrease one or more of the others. Assimilates are distributed across several seed components, therefore protein concentration and other important constituents are intrinsically linked. Thus, changing one component affects the others. The correlation coefficients of +\u0026thinsp;0.93 (ATP) and +\u0026thinsp;0.54 (JND) show that protein and sugar are positively correlated. NRCGs-10366, 10480, 10485, and 10844 were superior for PY, HKW, and SHP; NRCG-11390 for PY, protein, and sugar; and NRCGs-12469, 14089, and 16492 for important biochemical traits (oil, protein, and sugar content); NRCG-11101 for HKW, protein, and oil; and NRCGs-11154, 11164 for HKW, protein, and sugar. Table\u0026nbsp;4S describes the morphology of exceptional germplasm with numerous desirable features. After adaptive trials, stable genotypes with better mean performance can be grown and employed as breeding parents.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study showed that the examined accessions have significant stable genetic variation that might be used in groundnut breeding. The ability of a species to adapt to its environment is based on genetic variation. Ideal genotypes should combine desired stable agronomic traits high yielding capacity and quality traits to qualify as being adapted or elite. Optimal moisture during crucial growth phases and increased sunshine hours have had a greater impact on improving groundnut production and quality attributes. In the current study, superior and stable performing germplasm with two or multiple desirable traits were identified based on evaluation from two contrasting environments. The sources of valuable breeding traits for high productivity, as well as high content of oil, protein, sugar, phenol and FAA in different combinations in Spanish collection could be used in groundnut improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledge the support and facilities received from the Director, ICAR-DGR, Junagadh, Gujarat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the facilities received from the ICAR-Directorate of Groundnut Research, Junagadh, Gujarat India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHandling plant materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collection and handling of plant were in accordance with all the relevant guidelines\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the manuscript and its Supporting Information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjay BC, Gowda MVC, Rathnakumar AL, Kusuma VP, Abdul fiyaz R, Holajjer P, Ramya KT, Govindaraj G, Prashanth babu H (2012) Improving Genetic Attributes of Confectionary Traits in Peanut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) Using Multivariate Analytical Tools. J Agr Sci 4: 247-258. \u003c/li\u003e\n\u003cli\u003eAjay BC, Bera SK, Singh AL, Kumar N, Gangadhar K, Kona P (2020a) Evaluation of Genotype \u0026times; Environment Interaction and Yield Stability Analysis in Peanut Under Phosphorus Stress Condition Using Stability Parameters of AMMI Model. Agric Res 9: 477\u0026ndash;486.\u003c/li\u003e\n\u003cli\u003eAjay BC, Ramya KT, Abdul Fiyaz R, Govindaraj G, Bera SK, Kumar N, Gangadhar K, Kona P, Singh GP, Radhakrishnan T (2020b) R-AMMI-LM: Linear-fit Robust-AMMI model to analyze genotype-by environment interactions. Indian J Genet\u003cem\u003e \u003c/em\u003e81: 87-92 \u003c/li\u003e\n\u003cli\u003eAjay BC, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P, Rani K, Radhakrishnan T (2022) Higher Order AMMI (HO-AMMI) analysis: A novel stability model to study genotype-location interactions. 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J Sci Food Agric\u003cem\u003e \u003c/em\u003e36:162\u0026ndash;166 \u003c/li\u003e\n\u003cli\u003eJanila P, Manohar SS, Patne N, Variath MT, Nigam SN (2016) Genotype\u0026times; environment interactions for oil content in peanut and stable high‐oil‐yielding sources. Crop Sci 56: 2506-2515. \u003c/li\u003e\n\u003cli\u003eKang MS, Pham HN (1991) Simultaneous selection for high yielding and stable crop genotypes. Agron J 83:161-165. \u003c/li\u003e\n\u003cli\u003eLynch M, Walsh B (2001) Genetics and analysis of quantitative traits. Am J Hum Genet 68: 548\u0026ndash;549. \u003c/li\u003e\n\u003cli\u003eMeena HN, Ajay BC, Rajanna GA, Yadav RS, Jain NK, Meena MS (2022) Polythene mulch and potassium application enhances peanut productivity and biochemical traits under sustained salinity stress condition. Agr Water Manage. 273: 107903. \u003c/li\u003e\n\u003cli\u003eOjiewo CO, Janila P, Bhatnagar-Mathur P, Pandey MK, Desmae H, et al. 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Whitworth ed) Agriculture Issues and Policies Publisher: Nova Science Publishers, Inc. USA. Nova Science Publishers, Inc. USA.\u003c/li\u003e\n\u003cli\u003eTomar YS, Tiwari S, Tripathi MK, Singh S, Gupta N (2022) Genetic diversity, population structure and biochemical parameters estimations driving variations in groundnut germplasm. Legume Res 10.18805/LR-4965. \u003c/li\u003e\n\u003cli\u003eUpadhyaya HD, Ortiz R, Bramel PJ, Singh S (2003) Development of a peanut core collection using taxonomical, geographical and morphological descriptors. Genet Resou Crop Evo 50: 139\u0026ndash;148. \u003c/li\u003e\n\u003cli\u003eUpadhyaya HD, Dwivedi SL, Nadaf HL, Singh S (2011) Phenotypic diversity and identification of wild \u003cem\u003eArachis\u003c/em\u003e accessions with useful agronomic and nutritional traits. Euphytica 182: 103\u0026ndash; 115. \u003c/li\u003e\n\u003cli\u003eVaidya S, Vanaja M, Lakshmi NJ, Sowmya P, Anitha Y, Sathish P (2015) Variability in drought stress induced responses of groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) genotypes. Biochem Physiol 4: 149. \u003c/li\u003e\n\u003cli\u003eVincent V, Pasal R (2016) High transpiration efficiency increases pod yield under intermittent drought in dry and hot atmospheric conditions but less so under wetter and cooler conditions in groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e (L.)). Field Crop Res 193: 16\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eWang ML, Raymer P, Chinnan M, Pittman RN (2012) Screening of the USDA peanut germplasm for oil content and fatty acid composition. Biomass Bioenergy 39: 336\u0026ndash;343 \u003c/li\u003e\n\u003cli\u003eYadav SK, Jyothi Lakshmi N, Singh V, Patil A, Tiwari YK, Nagendram E, Sathish P, Vanaja M, Maheswari M, Venkateswarlu B (2013) In vitro screening of Vigna mungo genotypes for PEG induced moisture deficit stress. Indian J Plant Physi 18: 55-60. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Groundnut, germplasm, yield, biochemical traits, stability","lastPublishedDoi":"10.21203/rs.3.rs-3309986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3309986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aimed to analyze a varied collection of 371 germplasm obtained from various countries, with regards to yield-related characteristics, oil, protein, sugar, free amino acids, and total phenolics, in two distinct environments. The purpose was to assess the extent of inherent genotypic diversity and the potential for improving productivity and quality through breeding. The study identified germplasm that exhibited superior and stable performance with two or more desirable traits. Specifically, NRCGs-10366, 10480, 10485, and 10844 were found to be superior in terms of yield (PY), hundred kernel weight (HKW), and seed shape (SHP). NRCG-11390 was identified as superior for yield (PY), protein content, and sugar content. Additionally, NRCGs-12469, 14089, and 16492 were found to be superior for important biochemical traits such as oil, protein, and sugar content. NRCG-11101 was identified as superior for HKW, protein content, and oil content, while NRCGs-11154 and 11164 were found to be superior for HKW, protein content, and sugar content. The study has identified germplasm with a grain protein content exceeding 34% and an oil content ranging from 47% to 48%. \u0026nbsp;Germplasm exhibiting specific traits were identified for their potential utilisation as donors in the groundnut improvement program. The Spanish collection presents a potential source of valuable breeding traits for enhancing groundnut productivity and improving the content of oil, protein, sugar, phenol, and FAA, in various combinations.\u003c/p\u003e","manuscriptTitle":"Trait discovery for yield, related attributes and quality parameters and identification of potential stable donors for genetic improvement of groundnuts through evaluation of germplasm originated from different countries in two contrasting environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-01 20:47:51","doi":"10.21203/rs.3.rs-3309986/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-25T11:39:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-11T10:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3ddf65ee-629b-4d4b-927b-f2569b881902","date":"2023-08-30T13:05:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-30T11:56:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-08-30T11:47:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-30T11:47:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2023-08-30T11:27:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"93eebe63-ba34-4f91-9064-157a11a8a2d8","owner":[],"postedDate":"September 1st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-23T15:08:05+00:00","versionOfRecord":{"articleIdentity":"rs-3309986","link":"https://doi.org/10.1007/s10722-023-01761-y","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2023-10-20 15:01:32","publishedOnDateReadable":"October 20th, 2023"},"versionCreatedAt":"2023-09-01 20:47:51","video":"","vorDoi":"10.1007/s10722-023-01761-y","vorDoiUrl":"https://doi.org/10.1007/s10722-023-01761-y","workflowStages":[]},"version":"v1","identity":"rs-3309986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3309986","identity":"rs-3309986","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}