Studies on genetic variability of sweet potato under Bastar plateau of Chhattisgarh

Introduction

Sweet potato [Ipomoea batatas (L.)] commonly known as "Shakarkand" is one of the most popular and important tuber crops in India and abroad because of its yield potential and high calorific value. It is a globally important tuber crop, ranked second most important tropical and subtropical staple tuber crop in area and production after cassava (FAOSTAT, 2014) [11] . In Chhattisgarh it is locally known as "Kalmal Kanda", 'Maati Kanda' and 'Kevat Kanda' is one of the most popular and important tubers crops in India and abroad because of its yield potential and high calorific value. In India, it is grown in an area of 116 thousand ha and produces 1186 thousand MT with a productivity of 10.22 t/ha (Anon., 2020) [1] . In Chhattisgarh state, it is cultivated in an area of 4.65 thousand hectare area with the production of 54.19 thousand MT and productivity of 11.65 t/ha. In Chhattisgarh Balrampur is leading district in area and production of sweet potato, where productivity is 9.94 t/ha in Balrampur district (Anon., 2023) [2] . Sweet potato rich in vitamin A and potassium. The orange fleshed sweet potato rich in beta carotene whereas the purple-fleshed are richer in anthocyanin. Sweet potato tuber are rich in starch (20-28%), carbohydrates (28.2%) and vitamins (Dhaliwal, 2017) [9] . For fruitful breeding programme information about extends of genetic variability, correlation is basic requirement. In any crop improvement programme, evaluation of germplasm to assess the existing variability is a preliminary step.

Materials and Methods

The experiment was carried out during the year 2024-2025 at Instructional cum Research Farm of K.D. College of Horticulture and Research Station, Jagdalpur, Bastar. The experimental material comprised of fifteen genotypes along with two checks Indira Naveen as local check and Sree Bhadhara as national check. The experiment was laid out in a randomized block design with three replications at the spacing of 60 cm between rows and 20 cm between plants to plant. A net plot size of 3.6 m 2 was kept for each genotype. All the recommended cultural practices were taken to grow a healthy crop. Data were recorded on five randomly selected plants for thirteen characters viz., vine length (cm), vine length of internode (cm), diameter of vine (mm), neck length of tuber (cm), tuber length (cm), tuber diameter (cm), number of tubers per plant, dry matter% of vine, dry matter% of tuber. Three important characters viz., tuber yield per plant tuber yield (t/ha), marketable tuber yield per plant and tuber yield per plant (g) were calculated on the basis of observed data.

Results and Discussion

The analysis of variance for tubers yield and its component characters indicated that mean sum of squares due to genotypes were highly significant for all the characters under study. Significant mean sum of squares due to tubers yield and attributing characters revealed existence of considerable variability in material studied for improvement of various traits. These findings were closely associated with the reports of Anshebo et al (2004) [3] , Teshome et al (2004) [18] , Chaurasiya (2012) [5] , Mohanty (2013) [15] , Dash et al (2014) [8] , Bhadauriya et al (2018) [4] . In general, the genotypic correlations were observed to be higher than the corresponding phenotypic correlations for all the character combinations in present investigation, thus indicating the suppression of phenotypic expression under the influence of environmental factors (Table 2). Nedunzhiyan and Reddy (2000) [16] , Choudhary et al (2000) [6] also found similar results in their studies on sweet potato.

Tuber yield

Tuber yield (t/ha) showed high significant positive correlation with tuber yield per plant at both phenotypic and genotypic level (0.969) and (0.988) followed by marketable tuber yield per plant (0.556) and (0.627), number of tubers per plant (0.433) and (0.590), diameter of vine (0.362) and (0.525), tuber diameter (0.430) at genotypic level only and significant negative correlation observed with dry matter% of vine (-0.374) and (-0.485) at both phenotypic and genotypic level followed by neck length of tuber (-0.358) and (-0.462), tuber length (-0.349) and (-0.515). Similar result had been also reported by Choudhary (2000) [6] for tuber yield, Hussain (2000) [12] for tuber weight, number of tuber per plant, Sahu (2003) [17] for tuber diameter and harvest index, Sahu (2005) [17] for diameter of tuber.

Path coefficient analysis

The path coefficient analysis which splits total correlation coefficient of different characters into direct and indirect effect of yield attributing characters on tubers yield (t/ha) was presented (Table 3). The data revealed that genotypic path, tuber yield per plant showed maximum positive direct effect on tuber yield tone per hectare (0.594) followed by number of tubers per plant (0.390), tuber diameter (0.296), vine length of internode (0.152), marketable tuber yield per plant (0.051), dry matter% of vine (0.030) and neck length of tuber (0.017) which indicated that these are the main contributors to the tuber yield which is in consonance with the findings of Hossain et al. (2000) [12] , Tirkey et al. (2011) [19] , Bhadauriya et al. (2018) [4] . Whereas, tuber length showed maximum negative direct effect (-0.167) followed by diameter of vine (-0.073), vine length (-0.044), and dry matter% of tuber (-0.021).

Conclusion

In this present study the correlation between tuber yield and a character is due to direct effect of tuber yield per plant, number of tubers per plant, tuber diameter and marketable tuber yield per plant revealed true relationship between them and direct selection for this trait would be rewarding for yield improvement. Overall, in this study selection of genotypes having higher tuber yield or selection of tuber yield characters for further breeding program will improve the tuber yield per hectare. In genotypic path, tuber yield per plant showed maximum positive direct effect on tuber yield tone per hectare followed by number of tubers per plant, tuber diameter, vine length of internode, marketable tuber yield per plant, dry matter% of vine and neck length of tuber which indicated that these are the main contributor to the tuber yield.

References

1. Anonymous. Directorate of Horticulture and Farm Forestry, Chhattisgarh, Department of Agriculture, Government of Chhattisgarh, Raipur. Horticulture statistics: area and production district-wise. 2020:5.
2. Anonymous. Directorate of Horticulture and Farm Forestry, Chhattisgarh, Department of Agriculture, Government of Chhattisgarh. Horticulture statistics 2023-24. 2023.
3. Anshebo T, Veeraragavathatham D, Kannan M. Genetic variability and correlation studies in sweet potato (Ipomoea batatas L.). Madras Agric J. 2004;91(7):420424.
4. Bhadauriya PS, Deo C, Ram CN, Verma SK, Singh S. Studies on genetic variability, heritability, genetic advance, correlation coefficient and divergence analysis in sweet potato (Ipomoea batatas L.). Indian J Hill Farm. 2018;31(1).
5. International Journal of Advanced Biochemistry Research https://www.biochemjournal.com [PhD thesis]. Raipur: Indira Gandhi Krishi Vishwavidyalaya; 2012.
6. Choudhary SC, Kumar H, Verma VS, Naskar SKT, Kumar M. Genetic divergence in sweet potato (Ipomoea batatas L.). J Res. 2000;10(1):186-188.
7. International Potato Center (CIP). Towards food system transformation. Annual report. 2018:26.
8. Dash SP, Singh J, Panigrahi T, Thakur P. Evaluation of sweet potato (Ipomoea batatas L.) genotypes for yield and processing traits. Plant Arch. 2015;15(2):595-699.
9. Dhaliwal MS. Handbook of vegetable crops. 3rd ed. New Delhi: Kalyani Publishers; 2017. p. 327.
10. Engida T, Sastry D, Dechassa N. Correlation and path analysis in sweet potato and their implications for clonal selection. J Agron. 2006;5:391-395.
11. FAOSTAT. FAO statistical database. 2014. Available from: http://www.fao.org/statistics/en/
12. Hossain MD, Rabbani MG, Mollah MLR. Genetic variability, correlation and path analysis of yield contributing characters in sweet potato (Ipomoea batatas L.). Pak J Sci Res. 2000;43:314-318.
13. Jha G. Increasing productivity of sweet potato (Ipomoea batatas L.) through clonal selection of ideal genotypes from open pollinated seedling population. Int J Farm Sci. 2012;2(2):17-27.
14. Johnson HW, Robinson HF, Comstock RE. Genotypic and phenotypic correlations in soybean and their implication in selection. Agron J. 1955;47:477-483.
15. Mohanty P. Studies on genetic variability and correlation analysis in sweet potato (Ipomoea batatas L.) genotypes [MSc thesis]. Venkataramannagudem: Dr YSR Horticultural University; 2013. p. 16.
16. Nedunzhiyan M, Reddy SD. Correlation and regression studies in sweet potato (Ipomoea batatas L.). J Root Crops. 2000;26(1):34-37.
17. Sahu GD, Singh J, Mehta N. Correlation and path analysis in sweet potato. Environ Ecol. 2005;23(2):207211.
18. Teshome A, Veeraragavathatham D, Kannan M. Genetic variability and correlation studies in sweet potato (Ipomoea batatas L.). Madras Agric J. 2004;91(7-12):420-424.
19. Tirkey PL, Singh J, Chaurasia PC, Sarnaik DA. Character association and path coefficient studies in sweet potato (Ipomoea batatas L.) genotypes. J Plant Dev Sci. 2011;3(1-2):137-143.