Phenotyping and Crossability parameters studies in Foxtail millet (Setaria italica L.) genotypes

Introduction

Indian agriculture is well known for its various functions from providing environmental and food securities, employment and livelihood to nutritional (Sharma et al., 2025) [20] . The global population is projected to reach nearly 10 billion by 2050, intensifying the demand for food (Kumari et al., 2024) [12] . Employing high doses of fertilizers to achieve more yields is indispensable (Sharma et al., 2020) [21] under intensive cropping, but blanket application of chemical fertilizers is considered as one of the major constraints to sustain crop production (Sharma et al., 2021; Sharma et al., 2025 and Kumari et al., 2025) [22,20,11] . Recently nextgeneration precision farming technologies to enhance sustainable crop production and environmental sustainability (Yadav et al., 2026b and Kumari et al., 2026) [28,10] . With the global population steadily increasing, there is a growing need to boost agricultural productivity in order to ensure sufficient food supply for future generations (Gt et al., 2025) [7] . Millets are traditional, small-grained, annual cereals cultivated in the Indian subcontinent for over 5000 years. These hardy, rainfed crops require less water and fertilizers than major cereals like rice and wheat, making them ideal for cultivation in arid and semi-arid regions. They have historically served as staple foods in Asia and Africa due to their adaptability, affordability and nutritional benefits. Among them, foxtail millet (Setaria italica L.), also known as Italian millet, holds significant importance. It is widely grown in Karnataka, India, and is native to Southern Asia. China's Yellow River basin accounts for 80% of global production, with India ranking second at around 10%. Globally, millets rank sixth among cereal crops, with 2020 production at 89.17 million metric tons over 74 million hectares. Sorghum and pearl millet dominate production, followed by foxtail, finger, proso, barnyard, little, and kodo millets. In India, during 2021-22, millets were cultivated on 13.83 lakh hectares, producing 15.53 million tons at a productivity of 1248 kg/ha. Foxtail millet had the highest productivity at 2166 kg/ha. Despite having 5.88 million hectares of cultivable land, Chhattisgarh recorded low millet productivity in 2006 at just 210 kg/ha. Nutritionally, millets are rich in vitamins, minerals, fiber, sulphur-containing amino acids (Yadav et al., 2026a) [27] and phytochemicals, earning them the term "Nutri-cereals." Foxtail millet supports a steady glucose release, making it suitable for diabetic and heart-healthy diets. It also finds use in cosmetics and poultry feed. Crop improvement programs for foxtail millet rely on genetic variability, morphological characterization, and understanding of genotypeenvironment interactions. Heritability and genetic advance estimates guide effective selection, while genetic divergence helps identify diverse parent lines for hybridization (Kumari et al., 2026) [10] . Thus, genetic evaluation and trait-based clustering are essential for developing improved foxtail millet cultivars.

Methods and materials

The present study was conducted during Kharif 2024 at the Upland Research cum Instructional Farm, Lamker, under Shaheed Gundadhoor College of Agriculture and Research Station, Jagdalpur, Chhattisgarh (19°4'0" N, 82°2'0" E; 552 m AMSL). The region experienced temperatures between 19.4°C and 32.9°C, with total rainfall of 1080 mm, highest in July (432 mm). The experiment involved 23 foxtail millet (Setaria italica L.) germplasm lines under the ICAR-AICRP on Small Millets to evaluate crossability, performance, and phenotypic traits. The trial followed a randomized block design with three replications. Each plot (7.5 m²) had 10 rows (3 m length), with 2.5 m inter-row and 10 cm intra-row spacing. Sowing was done on July 5, 2024 and standard agronomic practices were followed. Observations were recorded from five randomly selected plants per plot for all traits except days to 50% flowering and days to maturity, which were recorded on a plot basis, following ICAR-IIMR guidelines. A total of 17 traits were studied, including 14 quantitative (e.g., days to panicle initiation, anthesis, maturity, plant height, flag leaf dimensions, panicle length, test weight, fodder and grain yield, and productive tillers) and 3 qualitative traits (plant pigmentation, panicle color and seed color). The collected data were used for statistical analysis to study genetic variability and adaptability of the genotypes under local agro-climatic conditions.

Result and Discussion

Qualitative traits description

In genetics, qualitative characters are typically controlled by either a single gene (monogenic) or multiple genes (polygenic), with their expression remaining unaffected by environmental conditions. These traits were traditionally treated as separate variables, using counts and ratios to determine their frequencies. Qualitative traits generally do not exhibit transgressive segregation, tend to have high heritability, and show significant variation across different family lines. Three qualitative traits were specifically selected for analysis: plant pigmentation at the junction of the leaves, panicle color and seed color presented in figure 1. There were total of 23 genotypes assessed. Among these, 8 genotypes (35%) showed purple pigmentation at the leaf juncture and remaining 8 genotypes (65%) showed green pigmentation at the leaf juncture, which was regarded as the absence of pigmentation shown in table 1.

Similar result was also found by Tiwari (2024) [25] . Out of 23 genotypes there were 20 genotypes (87%) with brown seeds, 2 genotypes (9%) with pale yellow color seeds, 1 genotype (4%) with pale orange seeds shown in table 2. Similarly, Charitha (2023) [3] studied 64 genotypes and showed similar result. Among 23 genotypes, 8 genotypes (35%) showed pigmented panicle (purple color) and remaining 15 genotypes (65%) showed non-pigmented panicle or green color shown in table 3. Similar result also found by Nanda and Agrawal (2008) [15] . [18] performed an experiment and found similar result. Among 23 genotypes under investigation, 8 genotypes (35%) showed medium duration flowering (45-55 DAS) and 15 genotypes (65%) displayed late duration flowering (>55 DAS) with mean value of 57 and a coefficient of variation of 2.44 were recorded for this character. Similar result was found by Elangovan et al. (2022) [5] who performed an [18] performed an experiment and found similar result. Among the 23 [18] also performed an experiment and found that the mean panicle length was [16] also found significant variation was found for this trait. Number of leaves ranged from 4 (RPCAU-XM-2) to 8 (IIMR FXM-18). Among 23 out of 23 genotypes consisted of minimum number of leave with mean value of 7 and a coefficient of variation of 6.64 were recorded for this character.

Similar result was also showed by Upadhyaya et al. (2016) [26] . Fodder yield ranged from 1. [18] who also performed an experiment and found similar result.

Genetic variability, heritability and genetic advance

The study showed considerable genetic variability across traits using GCV and PCV. Grain yield recorded the highest GCV (28.98%) and PCV (29.19%), while days to maturity had the lowest (GCV: 2.97%, PCV: 3.59%). Number of productive tillers also showed high variability (GCV: 25.94%, PCV: 28.32%). High heritability was observed in fodder yield (98.57%), grain yield (89.21%), and plant height (85.64%). Genetic advance was highest in plant height (14.09) and lowest in productive tillers (0.08). As a percentage of mean, fodder yield (59.28%) and grain yield (37.80%) had the highest values, while test weight (3.24%) and days to maturity (5.08%) were lowest consistent with Anuradha et al. (2020) [1] , Jyothsna et al. (2016) [9] and Tiwari (2024) [18] .

Character association studies

Character association studies refer to the statistical analysis of relationships between different plant traits (characters) to understand how they co-vary. These studies commonly use correlation coefficients and path coefficient analysis. [6] and Shinde et al. (2014) [23] .

Correlation analysis

Phenotypic Path Coefficient analysis

At phenotypic path among all the component, fodder yield kg/plot exhibited the highest direct effect (0.7167) on the grain yield followed by days to 50% flowering (0.2882), days to panicle initiation (0.1823), flag leaf length (0.1632), test weight (0.1453), plant height (0.0902), number of leaves (0.0198), flag leaf width (0.0093) and days to anthesis completion (-0.3707), panicle length (-0.1645), days to maturity (-0.01645) and number of productive tillers (-0.0575) recorded negative direct effect on grain yield at phenotypic level which is depicted in table 7. Above findings revealed that among all traits evaluated, fodder yield exhibited the highest positive direct effect on grain yield, indicating that biomass accumulation plays a major and direct role in enhancing grain production. These findings are in agreement with earlier results of Kumari and Nagarajan (2008) [13] , Sirisha et al. (2009) [24] , Sathya et al.

Genetic Divergence

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) was used to reduce dimensionality and explore the underlying structure of agronomic and physiological traits. These included phenological traits (e.g., days to flowering, maturity), morphological traits (e.g., plant height, panicle length, leaf dimensions) and yield components such as fodder yield, test weight, and grain yield. By analyzing eigenvectors, the contribution of each trait to principal components was assessed, aiding in understanding trait relationships and their impact on grain yield. A total of 14 principal components (PCs) were extracted. The first five principal components had eigenvalues greater than or close to 1 and together accounted for approximately 85.1% of the total variation in the dataset. The principal component analysis revealed that the first component (PC1) contributed the most to genotype divergence, accounting for 46% of total variation with an eigenvalue of 6.43. The second, third, and fourth canonical vectors added 17.0%, 9.8%, and 6.9% respectively, while the fifth and sixth components contributed 5.4% which is depicted in table 9. [17] also studied and showed similar result. Medium-duration flowering and maturing genotypes along with pigmented and non-pigmented types identified based on key morphological markers such as panicle and node colour were found suitable for potential hybridization programs. Highest inter cluster distance was found between cluster I and cluster III (5.86) followed by cluster I and cluster II (5.73), cluster II and cluster III (2.77). So, the cross between the genotypes of Cluster III and cluster I followed by cluster I and cluster Iet I will generate heterotic individual. These findings are in accordance with the results of Dodake et al. (1998) [4] and Gupta et al. (2012) [8] in finger millet.

Conclusion

The study revealed significant genetic variability among foxtail millet genotypes across both qualitative and quantitative traits, with grain yield showing the highest variation and strong positive associations with fodder yield, plant height, test weight, and flag leaf dimensions. High heritability estimates for key traits like fodder yield, grain yield, and plant height indicate the potential effectiveness of selection in breeding programs. Path and regression analyses consistently identified fodder yield as the most influential trait on grain yield, while PCA and genetic divergence analyses highlighted substantial genetic diversity and grouped genotypes into distinct clusters, offering opportunities for heterotic hybrid combinations. Genotypes such as SIA 4247, RPCAU-FXM-2 and KOPFx2107 emerged as promising parents for future hybridization efforts, emphasizing the study's utility in guiding targeted selection and breeding strategies for foxtail millet improvement.

Acknowledgement

AcknowledgementThe research was a part of M.Sc. thesis conducted at Shaheed Gundadhur College of Agriculture and Research Station, Jagdalpur, Bastar-494001, Chhattisgarh. The author duly acknowledge AICRP small millets and DRS, IGKV, Raipur the institute for technical and financial support for research.

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