Study of correlation and path coefficients for Indian mustard yield and yield contributing characteristics

Introduction

In India, rapeseed-mustard crops include traditional indigenous species such as toria (Brassica rapa L. var. toria), brown sarson (Brassica rapa L. var. brown sarson), yellow sarson (Brassica rapa L. var. yellow sarson), Indian mustard (Brassica juncea), black mustard (Brassica nigra), and taramira (Eruca sativa). Other non-traditional species include gobhi sarson (Brassica napus) and Ethiopian mustard or Karan rai (Brassica carinata). After groundnuts, Indian mustard (Brassica juncea (L.) Czern and Coss) is the second-most significant oilseed crop in both India and the world. It is an amphidiploid (2n=36) of Brassica nigra (2n=16) and Brassica campestris (2n=20) that occurs naturally. It is a crop that is mostly self-pollinated (85-90%) and self-compatible. The Brassica group of oil seed crops includes Indian mustard, often referred to as rai, raya, or laha. It makes up around 75-80% of the 6.6 million hectares planted to rapeseed and mustard in the nation. Additionally, cell division and the formation of new tissues depend on phosphorus (Singh et al., 2022) [17] . The creation of new cultivars for increased yields has currently plateaued, and unless biotechnological measures are used, no further rise will be possible (Archna, et al., 2023) [2] . Grain, which is utilized for industrial processing as well as food and feed, is the commodity with the highest economic value. (Singh et al., 2024) [16] . However, human activity has drastically changed the composition of the atmosphere over the last few centuries, especially since the industrial revolution. environment-resilient farming seeks to preserve secure farmer livelihoods and sustainable food production despite a changing environment. (Singh et al., 2023) [18] . The recent rise in fertilizer prices is one of the main barriers to increasing crop yields for commercial use. Consequently, steps must be taken to reduce its losses and enhance its economic usage (Lokendra et al., 2024) [13] . The relationship between various morphological factors and yield components provides insight into growth and yield characteristics, which may be promoted to achieve increased productivity. Path analysis may be used to determine if each character has a direct or indirect impact on yield. (Wright, 1921) [21] . Measuring the independent factors' direct and indirect effects on the dependent variable is helpful.

It separates the direct and indirect influence components of the correlation coefficient. The degree of relationship between seed yield and its constituent parts will be determined using correlation and path analysis, which will also highlight the relative significance of their direct and indirect impacts. Grain yield is a complicated trait that depends on the interaction of several genes. Measuring the independent factors' direct and indirect effects on the dependent variable is helpful. It separates the direct and indirect influence components of the correlation coefficient. The degree of relationship between seed yield and its constituent parts will be determined using correlation and path analysis, which will also highlight the relative significance of their direct and indirect impacts. Grain yield is a complicated trait that depends on the interaction of several genes. (Shubam et al., 2023) [14] . The correlation coefficient and path analysis between several variables in thistly genotypes of Indian mustard were reported in the current study.

Materials and Methods

At the Agricultural Research Farm, Department of Plant Breeding and Genetics, Raja Balwant Singh College, Bichpuri, Agra, Uttar Pradesh, the field experiment was conducted during the Rabi season of 2022-2023. The experimental field's soil had a pH of 8.12 and was sandy loam in texture. Thirty distinct genotypes were the variables in this investigation. viz. Radhika, Girraj, RH725, RH-761, DRMR-150-35, RH-406, PPM-25, PM-27, RGN-73, Pusha tarak, Kranti, Geeta, Pusha bold, RH-0749, KRISHNA, DRMR-31, NRCHB-101, PUSHA MAHAK, NRCHB-02, CS-60, CS-56, ASHIRWAD, RGN-48, Maga, Pusha vijay, Bhagirathi, Seeta, Pusha jay, Kishan, NRCDR-601, RH-30 of Indian mustard. Therefore, a randomized block design (RBD) with three replications was used to compare all 30 genotypes. including three genotype rows, each measuring 45 cm and spaced 15 plants apart. Throughout the cropping season, all of the suggested practices were followed. The DRMR in Bharatpur, Rajasthan, provided these genotypes. Plant height was one of the twelve characteristics for which data were gathered. The Dewey and Lu technique (1959) [7] was used to determine phenotypic and genotypic correlations and perform path coefficient analysis.

Results and Discussion (I) Phenotypic correlation

There was a negative and highly significant correlation between the number of siliqua on the main raceme and seed yield per plant, whereas days to maturity and the number of seeds per siliqua showed a positive and significant correlation with seed yield/plant. Additionally, the harvest index showed a positive and highly significant correlation with seed yield per plant at the phenotypic level. 1000 seed weight, length of main raceme and days to 50% flowering showed negative and non-significant correlation with seed yield per plant. Primary branches, secondary branches per plant and plant height exhibited positive and non-significant correlation with seed yield per plant. at phenotypic level Chaudhary et al. (2003) [5] , Gangapur et al. (2009) [8] Kumar and Pandey (2014) [11] , Lodhi et al (2014) [12] and Devi at al. (2018) [6] , Harish et al., (2023) [9] , Babu and Singh (2024) [3,4] also reported more of less similar findings, except for the negetively significant contribution of number of siligua on main raceme in present study.

Genotypic correlations

A crop enhancement program's primary goal is to increase the crop's potential output. Since yield is essentially a polygenically regulated trait, selection that is specifically focused on yield is challenging. To get around this, yieldcontributing characters must be taken into account concurrently after effective selection. Understanding the relationships between yield-contributing characteristics is essential before selection. Reliable information on the kind, scope, and direction of selection is provided by correlation studies. Days 50% to flowering were found to be negatively significantly correlated with the number of primary branches/plant, number of siliqua on mean raceme, harvest index, and seed yield plants, but positively significantly correlated with days to maturity and length of mean receme in the current genotypic level investigation similar findings have been reported by Lakra et al. 2020 [22] for length of main raceme but contrary to the correlations for other characters. The days to maturity showed significant positive correlation with number of secondary branches and negative significant correlation with plant height at genotypic level. Bind et al 2014 reported number results except for days to maturity and length of main raceme. This may be due to the nature of Genotypes taken in this study and the effect of environment on them. The number of primary branches showed significant positive correlation with number of secondary branches/plant highly significant negative correlation with test weight and number of selliqua on main raceme similar findings were reported by Shubham, et al., (2023) [14] and Babu and Singh (2024) [3,4] . Harish et al., (2023) [9] .

Path coefficient

Using route coefficient analysis, the genotypic and phenotypic correlation coefficients of seed yield per plant with the other remaining characteristics under investigation were further divided into direct and indirect effects, and are shown in Tables 2(i) and (ii), respectively.

Genotypic

The harvest index exhibited the greatest positive direct influence (0.858) on seed yield per plant, followed by days to maturity (0.635) at the genotypic level, according to path coefficient analysis. There was a positive direct effect on seed yield from primary branches per plant, plant height, length of main raceme, number of seeds per siliqua, 1000 seed weight, and biological yield per plant, while there was a negative direct effect on seed yield from days to 50% flowering, number of secondary branches per plant, and number of siliquae on main raceme. The maximum negative direct effect was observed in the case of days to 50% flowering (-0.839). The number of siliqua on the main raceme, the number of primary branches per plant, and the number of days to 50% blooming all had a favorable indirect influence on seed output at the genotypic level. The estimated residual impact was (0.0438).

Phenotypic

Path coefficient analysis revealed that the harvest index had the greatest positive direct influence (1.105) on seed production, followed by biological yield per plant (0.586) at the phenotypic level. Days to maturity, plant height, and number of seeds per siliqua all had a positive direct effect on seed yield, whereas days to 50 percent flowering, primary branches per plant, secondary branches per plant, length of main raceme, siliquae on main raceme, and 1000seed weight had a negative direct effect. Days to maturity, number of seeds per selliqua via seed yield per plant, number of primary branches per plant via biological yield per plant, days to 50% flowering via number of siliquae on main racemes, and number of seeds per selliqua via seed yield per plant all demonstrated positive indirect effects on seed yield at the phenotypic level. The Harves index showed the highest positive highly significant indirect effect (0.722) and number of seeds per selliqua on seed yield per plant, followed by days to maturity (0.213). The residual impact estimate was 0.141. Days to maturity biological yield and harvest index had a direct impact on seed yield at both the genotypic and phenotypic levels, according to path coefficient analysis. Additionally, there was a favorable correlation between these traits and seed yield. This suggested that a noticeable increase in seed output will come from direct selection based on harvest index and biological yield per plant. Sirohi et al. (2004) [19] , Gangapur et al. (2009) [8] , Kumar and Pandey (2014) [11] , Karan et al., (2023) [10] , found that harvest index had high, positive and direct negative effects on seed yield.

Conclusion

The study underscores the significance of critical characteristics in Indian mustard, stressing their relationship and impact on seed production. The findings showed that biological yield (r = 0.84), siliquae per plant (r = 0.78), and 1000-seed weight (r = 0.72) were significantly correlated positively with seed production. The harvest index showed the most direct positive impact on yield at the phenotypic level (1.105), followed by biological yield (0.586), according to path coefficient analysis. The harvest index also showed a substantial direct influence (0.858) at the genotypic level. Significant indirect impacts were demonstrated by characteristics like days to maturity (0.213) and number of seeds per siliqua (0.198). The intricacy of trait interactions was further highlighted by the observation of negative impacts, such as siliquae on the major raceme at the genotypic (-0.663) and phenotypic (-0.201) levels. Contributing qualities to sow yield in the study demonstrated a direct and positive correlation, which will serve as a test of strictures for the traits for component solution.

References

1. Singh A, Avtar R, Singh D, Sangwan O, Balyan P. Genetic variability, character association and path analysis for seed yield and component traits under two environments in Indian mustard. Journal of Oilseed Brassica. 2013;4(1):43-48.
2. Archna K, Singh V, Kumar H, Singh R. Optimization of NPK fertilization in conjunction with Plant Growth Regulators (PGRs) affecting productivity and profitability of wheat (Triticum aestivum L.). The Journal of Rural and Agricultural Research. 2023;23(2):56-60.
3. Babu S, Singh V. Impact of Kinnow production on the socio-economic conditions of farmers in the Agra region. Asian Research Journal of Agriculture. 2024;17(4):652-661. https://doi.org/10.9734/arja/2024/v17i4572
4. Babu S, Singh V. Soil and agro-climatic suitability for Kinnow cultivation: A case study of the Agra region. Asian Journal of Research in Crop Science. 2024;9(4):248-254. https://doi.org/10.9734/ajrcs/2024/v9i4314
5. Choudhary VK, Rakesh Kumar, Sah JN. Path analysis in Indian mustard. Journal of Applied Biology. 2003;13(1/2):6-8.
6. Devi B. Correlation and path analysis in Indian mustard (Brassica juncea (L.)) in agro-climatic conditions of Jhansi (U.P.). Electronic Journal of Plant Breeding. 2018;1(4):407-413.
7. Dewey DR, Lu KH. A correlation and path coefficient analysis of components of crested wheat grass seed production. Agronomy Journal. 1959;51:515-518.
8. Gangapur DR, Prakash BG, Salimath PM, Ravikumar RL, Rao MSL. Correlation and path analysis in Indian mustard (Brassica juncea L. Czern and Coss.). Karnataka Journal of Agricultural Sciences. 2009;22(5):971-977.
9. Harish K, Virendra S, Sonia. Integrated farming system for sustainable agriculture in present scenario. In: Practice of Recent Advances in Global Agriculture Technology and Innovation (PRAGATI-2023);
10. IQAC & Faculty of Agriculture, Janta College Bakewar, Etawah (U.P.); 2023.
11. Karan S, Singh RB, Singh, Singh V. Effect of nanonitrogen fertilization on yield attributes and yield of barley (Hordeum vulgare L.). The Journal of Rural and Agricultural Research. 2023;23(2):87-90.
12. Kumar B, Panday A. Association analysis of yield and its components in Indian mustard (Brassica juncea L.). Environment and Ecology. 2014;32(4B):1778-1782.
13. Lodhi B, Thakral NK, Singh D, Avtar R, Bahadur R. Genetic diversity analysis in Indian mustard (Brassica juncea). Journal of Oilseed Brassica. 2013;4(2):57-60.
14. Lokendra PS, Virendra S, Vishal G, Harish K, Karan S, Mohd A, et al. Improvement of productivity and profitability in wheat by nitrogen and bio-fertilizer in north western plain zone. International Journal of Research in Agronomy. 2024;7(1):289-292.
15. Shubham KS, Geetam S, Mishra MN, Rohit, Virendra S. Studies on genetic variability, heritability, genetic advance, correlation in barley. International Research Journal of Modernization in Engineering Technology and Science. 2023;5(6):4491-4496. DOI: 10.13140/RG.2.2.28841.20326
16. Singh R, Kumar R, Singh AC, Babu S, Kumar J, Singh K, et al. Effect of silicon application and irrigation scheduling on productivity and profitability of barley (Hordeum vulgare L.). The Journal of Rural and Agricultural Research. 2024;24(1):49-53.
17. Singh R, Singh S, Singh V, Kumar H, Singh K, Kumar J. Impact of nano-nitrogenous fertilization on productivity and profitability of barley (Hordeum vulgare L.). The Journal of Rural and Agricultural Research. 2024;24(1):110-114.
18. Singh V, Chhonker D, Singh R. Effect of phosphorus levels on growth and yield of Kabuli chickpea (Cicer kabulium L.) varieties. The Journal of Rural and Agricultural Research. 2022;22(2):108-111.
19. Singh V, Kumar H, Sonia. Influence of climate change on Indian agriculture. In: Advances in Agriculture Science. Vol. 44. ISBN 978-93-5570-856-4; 2023.
20. Sirohi SPS, Malik S, Kumar A. Correlation and path analysis of Indian mustard (Brassica juncea (L.) Czern and Coss.). Journal of Research, Birsa Agricultural University. 2004;5(1):45-47.
21. Sonia, Karan S, Vishal G, Ravikant S, Nisha S, Rahul S, et al. Foliar application of micronutrients improves the lentil (Lens culinaris M.) yield and net economic return. Pharma Innovation. 2023;12(10):2355-2357.
22. Wright S. Correlation and causation. Journal of Agricultural Research. 1921;20:557-585.
23. Lakra AK, Domdi L, Tilwani YM, Arul V. Physicochemical and functional characterization of mannan exopolysaccharide from Weissella confusa MD1 with bioactivities. International Journal of Biological Macromolecules. 2020 Jan 15;143:797-805.