Elucidating the genetic diversity for morpho-physiological parameters in foxtail millet cultivars

Introduction

Foxtail millet (Setaria italica (L.) P. Beauv.) is one of the world's earliest cultivated crops which was domesticated over 8000 years ago in East Asia (Lu et al., 2009) [8] . This is found to be an arid crop which is rich in high-quality protein, dietary fibre, and vital minerals like iron, calcium, magnesium, phosphorus, and potassium (Kumar et al., 2019) [10] . Thus, foxtail millet is also known to be a hardy crop with a diploid chromosome number of 2n = 2x = 18. This is a dual-purpose crop and its mode of pollination is self-pollination. Although it is selfpollinated, foxtail millet has been reported to have immense variability in its genetic resources which is crucial for effective selection and crop improvement (Suthamathi et al., 2026) [15] . India's total foxtail millet production is estimated around 0.66 lakh tonnes (66,000 tonnes) annually, with 0.87 lakh hectares nationwide. Foxtail millet is a hardy crop known for its climate resilience from the earlier periods of history until now (Sun et al., 2024). This is predominantly cultivated by the farmers in the arid zones. In India, Karnataka is the largest producer of foxtail millet followed by Andhra Pradesh and Telangana. Recently, foxtail millet has gained momentum owing to its nutritional values in the market. This is rich in protein, dietary fibre and essential minerals like iron and calcium (Beulah & Lavanya, 2024). Thus, day by day, the demand for foxtail millet grains and processed products is increasing and the efforts for breeding new varieties for higher grain and fodder yield needs to be focused to sustain food and nutritional security (Wang et al., 2024) [14] Genetic Variability for effective selection is an initial prerequisite that allows breeders to choose diverse parents to exploit heterosis. Foxtail millet is reported to have immense variability in its germplasm which needs to be further explored for developing elite cultivars in future (Xue et al., 2025) [16] . Recently, 30 genotypes evaluated by Rao and Chaturvedi (2024) [12] across five environments in Nagaland for eleven morpho-physiological traits and single plant yield revealed, intra-cluster distances. In inter-cluster distances, clusters III and V were the highest in and the test weight was the major contributor for genetic divergence across environments.

Successively, another study conducted with 50 germplasm lines characterized for 16 morpho-physiological traits, grouped the genotypes into six clusters. Cluster Ⅰ had the largest intra-cluster distance. Cluster Ⅳ and Ⅴ were found to have the maximum inter-cluster distances. The largest contributor for genetic divergence was photosynthetic rate. The results found seven accessions viz., SiA 3290, SiA 4345, SiA 4391, SiA 4281, SiA 4389, SiA 4396, and SiA 3159 as desired genotypes for future breeding based on performance and diversity (Battula et al., 2025) [1] . Recently, Ram et al. (2025) [11] evaluated 40 foxtail millet genotypes for 18 morpho-physiological traits. The genotypes were classified into six clusters. The maximum inter-cluster distance was observed between Clusters Ⅱ and Ⅴ and the highest intra-cluster distance was within Cluster Ⅰ. The results exhibited the protein content as the highest contributor towards genetic divergence. Although, variability is being explored in foxtail millet, the genetic diversity of physiological and morphological traits in the germplasm facilitates the overall improvement in crop production and varietal development. Thus, in a view of this the current study was conducted to analyse the genetic variability and diversity among 31 genotypes by examining both morphological and physiological parameters.

Materials and Methods

The experiment was carried out with thirty-one foxtail millet cultivars collected from research stations in India at the Karunya Institute of Technology and Sciences in Coimbatore, during the season Kharif 2025 (Table 1). The genotypes were raised in a Randomized Block Design (RBD) with three replications under the spacing of 20×10 cm. From each genotype, under a replication five plants were chosen for recording of morphological and physiological traits. Twelve traits namely, flag leaf blade length (cm), flag leaf blade width (cm), 1000 seed weight (g), single plant yield (g), intercellular CO₂ Concentration (μmol mol⁻¹ (ppm), photosynthetic rate (μmol CO₂ m⁻² s⁻¹), cuvette temperature (°C), stomatal conductance (mol H₂O m⁻² s⁻¹), transpiration rate (mmol H₂O m⁻² s⁻¹), leaf temperature (°C), vapor pressure deficit (kPa), and water use efficiency (μmol CO₂/mmol H₂O (A/E) were observed. The recommended package of farming practices for raising the genotypes were followed (Indian Council of Agricultural Research, 2025) [4] and the collected data was subjected to a D 2 statistics developed by Mahalanobis (1936). For grouping of genotypes, hierarchical clustering by principal components was followed. The data analysis was carried out in Metan' in R software version 4.5.3.

Results and Discussion

The results of ANOVA for 12 quantitative traits revealed significant differences among 31 genotypes indicating the presence of genotypic variation for all the traits (Table 2). It was observed that, all traits exhibited highly significant differences among genotypes.

Mean Performance for yield and physiological traits

The overall mean performance for traits is presented in the Table 3. Among all for yield-related traits, the genotypes viz., Narasimharaya, Garuda and ATL 1 were identified as highest performers for single plant yield and thousand grain weight. Similar results for single plant yield and its attributing traits were reported by Zhang et al. (2017) [18] . For physiological traits; among 31 genotypes; the genotypes viz., Narasimharaya, Andhra Local, DHFt 109-3 and K2 recorded a highest per se performance. Thus, genotypes with higher yield and photosynthetic efficiency could be forwarded in other breeding programs for developing high yielding cultivars in future.

Genetic Diversity for morphological and physiological traits Among all 12 traits, the major contribution towards the genetic divergence was conferred by water use efficiency followed by that the transpiration rate and stomatal conductance were the major contributors for clustering of genotypes (Table 4). Similar results for genetic divergence were reported by (Zhao et al., 2026) [19] . The 31 genotypes were subjected to D 2 analysis and were grouped into five clusters (Table 5). Among all, cluster Ⅰ and cluster Ⅲ were found to be the largest with nine genotypes respectively. Followed by that, cluster Ⅱ and cluster Ⅴ had eight genotypes each. Finally, the cluster Ⅳ had only 2 genotypes namely, DHF t 109-3 and ALT-2. Thus, all the genotypes were grouped based on the phenotypic variability present among them. None of the genotypes were found to be present in a solitary cluster (Figure 1).

Cluster Mean Among all, cluster Ⅲ recorded the highest mean for single plant yield and thousand grain weight (Table 6). This projects that the cluster comprised of the genotypes with higher performance for yield and thousand grain weight. Similar findings for yield related traits were reported by (Kandel et al., 2020) [5] . For flag leaf blade length and flag leaf blade width, cluster IV and for photosynthetic traits cluster II and cluster V had genotypes conferring desirable photosynthetic efficiency. Cluster I recorded the highest mean for water use efficiency and cluster III had the highest mean for stomatal gas conductance. Thus, the genotypes in the respective above-mentioned clusters could utilized in breeding genotypes with higher photosynthetic and water use efficiency. Similar findings for photosynthetic traits were reported by Battula et al. (2025) [1] .

Genetic Distance

Among five clusters, the highest inter cluster distance was found between cluster Ⅰ and cluster Ⅱ. Followed by that, it was in cluster Ⅲ and cluster I (Table 7). Therefore, the genotypes from these clusters namely ATL 1, SR 16, GPUF-3, SiA 3159, Red Foxtail, Andhra Local, Kottatara thenai, GPUF-16, Kolli local, and from cluster I and SiA 3085, RAU 2, SiA 326, Renadu, GPUF-16, Kolli local, HMT 100-1 and SiA 3156 from cluster II, could be used as parents in hybridization programs to develop high yielding segregants. Further, among all; the cluster Ⅰ had the highest intra cluster distance. This presented that genotypes grouped under this cluster were variable for the traits, other than the contributing characters Similar findings for inter and intra cluster distance were reported by (Ram et al., 2025; Rao and Chaturvedi. 2024) [11,12] .

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