Correlation and path analysis for yield contributing characters in spine gourd (Momordica dioica Roxb.) genotypes

Introduction

Spine gourd (Momordica dioica Roxb.) is nutritionally important dioceious, perennial and underutilized vegetable crop. It belongs to the Cucurbitaceae family having chromosome number (2n = 28), in the genus Momordica (Raj et al., 1993) [9] . The major constraints to commercial cultivation of spine gourd has a various problems which includes lack of improved varieties, low marketable yield, reduced marketability of fruits due to hard spines and yellow colour, low rate of seed germination or inability to germinate due to presence of hard seed coat, low rate of tuber development, fruits become non edible at maturity due to aging of seeds and become hard, effect of dormancy on propagation by seeds and tubers and uncertain sex ratio in seedling progeny (Ali et al., 1991) [14] in spine gourd. The correlation studies revealed the nature and extent of relationship between various characters and help in improving the yield contributing characters in spine gourd. Yield of crop is a dependent character which is linked with a number of component characters. Hence, direct selection for yield is not effective. The genetic enhancement in dependent traits was obtained by applying strong selection procedure to characters which are generally associated with the dependent character. Partitioning of total correlation into direct and indirect effects by path coefficient analysis aids in making the selection procedure more effective. The path analysis points out the association of component traits with yield is due to their direct effect on yield, or it is a consequence of their indirect effect through some other characters. The steps of improving the crop by involving indigenous germplasm has showed new way in breaking the yield barriers. Therefore, the present investigation was aimed to determine the association of different yield contributing parameters with yield, direct and indirect effects of traits towards yield to identify high yielding spine gourd genotypes (Triveni et al., 2021 in bitter gourd) [13] .

Materials and Methods

Experimental site

The field experiment was set up at Research Farm, Department of Horticulture, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. The experimental site was geographically located at latitude: 19°16′0″N and longitude: 76°47′0″E with at an altitude of about 408m from mean sea level. Parbhani district comes under semi-arid climate, with a dry and hot summer, humid monsoon and cold winter.

Experimental material

Eighteen promising spine gourd genotypes were obtained from various locations of Maharashtra and evaluated in kharif season of 2022-23. The source of all the germplasms which were utilized in the study are given in Table1.

Experimental details and observations recorded

The experiment was set up in randomized block design replicated thrice with plant spacing of 1 m x 0.9 m. To produce a healthy crop, all cultural operations and plant protection techniques were uniformly applied to each treatment. The data were recorded on five randomly selected uniform plants from each treatment on seventeen traits viz, plant height (cm), no. of primary branches plant -1 , internodal length, days to first flowering, days to 50% flowering, days to first harvest, days to last harvest, fruit length (cm), fruit diameter (cm), fruit pedicel length (cm), no. of fruits plant -1 , ascorbic acid content (mg/100 gm), total soluble solids (TSS), average fruit weight (g), fruit yield plant -1 (g), fruit yield plot -1 (kg), fruit yield ha -1 (q).

Statistical analysis

The correlation coefficients were compared with table value of correlation coefficient at 1 percent and 5 percent level of significance (Snedecor and Cochran, 1967) [12] . The coefficients genotypic and phenotypic correlations were used to determine their direct and indirect path as suggested by Dewey and Lu (1959) [3] .

Results

Correlation coefficients analysis:

The data represented in Table 2, clearly showed that fruit yield ha -1 of spine gourd had showed that positive significant relation at both genotypic and phenotypic level with no. of primary branches plant -1 (0.036** G and 0.039** P), fruit diameter (0.781** G and 0.574** P), pedicel length (0.746** G and 0.669** P), average fruit weight (0.837** G and 0.651** P), no. of fruits plant -1 (0.99** G and 0.853**), fruit yield plant - 1 (0.976** G and 0.889** P) and fruit yield plot -1 (0.987** G and 0.904** P). Hence, these traits indicating to improve yield (Sandliya et al., 2020) [10] in spine gourd. It had also recorded that positive but non-significant relation with fruit length (0.138 G and 0.07 P), ascorbic acid (0.116 G and 0.11) and total soluble solids (0.129 G and 0.116 P) at both the levels. Fruit yield ha -1 had showed negative significant relation at both genotypic and phenotypic level with days to first flowering (-0.722** G and-0.538** P), days to first harvest (-0.774** G and-0.488** P) and days to last harvest (-0.545* G and-0.296* P). It had also recorded that negative but non-significant relation with plant height (-0.088 G and-0.06 P), internodel length (-0.234 G and-0.206 P), days to 50% flowering (-0.064 G and-0.079 P) at both the levels. Hence, those traits show non-significant and negatively correlated with yield revealed that improvement of this traits shall reduce the yield. The results obtained from this study were in agreement with those obtained by Sandliya et al., (2020) [10] , Aliya et al., (2014) [2] , Patil et al., (2022) [8] in spine gourd and Sinha et al., (2024) [11] in pointed gourd.

Path coefficient analysis: Results of Path analysis are presented in Table 3 and4. Path analysis showed that, the traits viz. fruit yield ha. -1 was exhibited with plant height (0.0640 G and 0.0764 P), days to first harvest (0.0207 G and 0.0143 P), fruit length (0.2061 G and 0.0297 P), average fruit weight (0.2874 G and 0.1780 P), no. of fruit plant -1 (0.08154 G and 0.2632 P), fruit yield plant -1 (0.1523 G and 0.1613 P), fruit yield plot -1 (0.5359 G and 0.3780 P) and total soluble solids (0.1997 G and 0.0568 P) had showed positive direct effect on fruit yield ha. -1 . Similar results were found by Patil et al. (2022) [8] in spine gourd, Sinha et al., (2024) [11] in pointed gourd and Aishwarya et al., (2024) in bitter gourd. So, the genotypes which exhibited better performance for these characters can be used in further improvement of spine gourd (Triveni et al., 2021) [13] in bitter gourd. Whereas no. of primary primary branches plant -1 (-0.2174 G and-0.1249 P), internodel length (-0.1284 G and-0.1063 P), days to last harvest (-0.2817 G and-0.0892 P) and ascorbic acid (-0.0312 G and-0.1093 P) had showed negative direct effect on fruit yield ha. -1 at both genotypic and phenotypic level. Thus, these findings showed that trait was neglected duration selection, as it did not contribute in production.

Discussion

Correlation coefficients analysis: The correlation coefficient indicated the relation between two traits and forms basis for selecting a desirable plant type. It helps to identify traits or combination of traits which might be useful as an indicator of high yield. Study on correlation of components traits with yield help in planning of an effective selection programme (Triveni et al., 2021) [13] in bitter gourd and Mane et al., 2024. In the present investigation, genotypic and phenotypic correlation coefficient between yield and its sixteen component characters are presented in table 2. In general, genotypic correlation were greater than phenotypic correlation, which revealed that the association was largely due to genetic basis and masked efficiently by environment effects which had to the expression of the characters thereby reduced the phenotypic expression at a bit of lesser extent (Islam et al., 2014) [5] in snake gourd.

In general, the relation of the characters could be shown in a way that total change in yield is controlled mainly by changing in one or more components; though all changes in the components as in each of cases were not expressed in yield due to decisive ratings of desirable and undesirable relation of the components with yield/plant (Graffius, 1964) [4] . Moreover, the knowledge of nature and extent of association between yield and its components is quite necessary for effective selection procedure in advance generation. Correlations between pairs of characters were brought up due to either linkage of genes or pleiotropic gene action (Narolia et al., 2012) [7] in tomato. So, focus for selection of the components in positive direction for higher fruit yield ha -1 might be suggested for effective yield enhancement in spine gourd.

Path coefficient analysis: Path coefficient analysis divide the correlation coefficients into direct and indirect effects to access the relative impact on each character. Yield being a complex polygenic character, direct selection may not be an appropriate approach on account of being highly affected by environmental factors. So, it becomes imperative to identify the component characters through which yield improvement could be achieved. Even Tough correlations reflect about the components of a complex character like yield, but it laid to generate correct picture of relative importance of the direct and indirect of the component characters to yield. Hence, correlation in addition with path analysis would project a better insight into cause-and-effect relationship between different pairs of characters (Triveni et al., 2021) [13] in bitter gourd.

Table 3: Genotypic path coefficient analysis of the yield and its components in spine gourd.

Traits PH NPBPP IL DFF D50%F DFH DLH FL FD PL AFW NFPP FYPP FYPP AA TSS PH 0.0640 -0.0296 -0.0797 0.0017 -0.0309 -0.0019 -0.0635 -0.0132 0.0030 0.0418 0.0792 -0.00997 -0.0226 -0.0642 0.0174 0.0205 NPBPP 0.0087 -0.2174 0.0189 -0.0010 0.0220 -0.0103 0.0756 0.1476 0.0108 -0.0588 -0.0506 0.00643 0.0232 0.1249 -0.0445 -0.0195 IL 0.0398 0.0321 -0.1284 0.0017 -0.0336 0.0030 -0.0226 -0.1089 -0.0163 0.0555 -0.0618 -0.00806 -0.0352 -0.1355 0.0523 0.1326 DFF 0.0251 0.0474 -0.0483 0.0044 -0.0620 0.0139 -0.2206 -0.0283 -0.0191 0.1761 -0.1161 -0.05722 -0.1180 -0.3701 0.0556 -0.0046 D50%F 0.0279 0.0673 -0.0609 0.0038 -0.0710 0.0025 -0.0452 -0.1733 -0.0144 0.0815 0.0492 0.00377 -0.0395 -0.0277 0.0624 0.0696 DFH -0.0058 0.1081 -0.0183 0.0029 -0.0086 0.0207 -0.2008 -0.0296 -0.0211 0.1310 -0.1536 -0.06106 -0.1210 -0.4330 0.0326 -0.0160 DLH 0.0144 0.0583 -0.0103 0.0034 -0.0114 0.0147 -0.2817 0.0592 -0.0030 0.1404 -0.1185 -0.04545 -0.0757 -0.3084 0.0337 -0.0144 FL -0.0041 -0.1557 0.0678 -0.0006 0.0596 -0.0030 -0.0810 0.2061 0.0210 -0.0401 -0.0011 0.00227 0.0310 0.1321 -0.0684 -0.0289 FD 0.0060 -0.0739 0.0661 -0.0027 0.0322 -0.0138 0.0263 0.1368 0.0317 -0.1637 0.2504 0.05239 0.1384 0.4398 -0.0593 -0.0863 PL -0.0120 -0.0570 0.0316 -0.0034 0.0258 -0.0121 0.1765 0.0368 0.0231 -0.2240 0.1825 0.05601 0.1260 0.4127 -0.0505 0.0331 AFW 0.0176 0.0382 0.0276 -0.0018 -0.0122 -0.0111 0.1161 -0.0008 0.0276 -0.1423 0.2874 0.05212 0.1145 0.3815 -0.0192 -0.0384 NFPP -0.0078 -0.0172 0.0127 -0.0031 -0.0033 -0.0155 0.1571 0.0057 0.0204 -0.1539 0.1837 0.08154 0.1458 0.5158 0.0001 0.0676 FYPP -0.0095 -0.0331 0.0296 -0.0034 0.0184 -0.0164 0.1401 0.0419 0.0288 -0.1854 0.2161 0.07807 0.1523 0.5224 -0.0212 0.0168 FYPP -0.0078 -0.0516 0.0331 -0.0031 0.0037 -0.0170 0.1652 0.0518 0.0265 -0.1758 0.2085 0.07998 0.1512 0.5259 -0.0208 0.0168 AA -0.0102 -0.0886 0.0613 -0.0022 0.0405 -0.0061 0.0868 0.1289 0.0172 -0.1035 0.0505 -0.00007 0.0296 0.1001 -0.1093 -0.0788 TSS 0.0066 0.0213 -0.0852 -0.0001 -0.0247 -0.0017 0.0203 -0.0298 -0.0137 -0.0371 -0.0553 0.02758 0.0128 0.0442 0.0431 0.1997 Bold numbers indicate direct effects and remaining all indirect effects of particular character; Residual effect:-0.0428 PH-Plant height (cm), NPBPP-No. of primary branches plant -1 , IL-Internodal length (cm), DFF-Days to first flowering, D50% F-Days to 50% flowering, DFH-Days to first harvest, DLH-Days to last harvest, FL-Fruit length (cm), FD-Fruit diameter (cm), PL-Pedicel length (cm), AFW-Average fruit weight (g), NFPP-No. of fruit plant -1 , FYPP-Fruit yield plant -1 (g), FYPP-Fruit yield plot -1 (kg), AA-Ascorbic acid (Mg/100 g), TSS-Total soluble soiled (%) and FYPH-Fruit yield ha. -1 (q) Table 4: Phenotypic path coefficient analysis of the yield and its components in spine gourd.

Traits PH

NPB IL DFFO D50.F DFH DLH FL FD PL AFW NFP FYP FYPL AA TSS PH 0.0764 -0.0150 -0.0532 -0.0142 0.0043 -0.0004 -0.0144 -0.0003 0.0052 -0.0023 0.0303 -0.031 -0.0221 -0.0338 0.0051 0.0058 NPB 0.0092 -0.1249 0.0136 0.0078 -0.0045 -0.0038 0.0197 0.0110 0.0137 0.0039 -0.0194 0.0152 0.0228 0.0906 -0.0112 -0.0051 IL 0.0382 0.0160 -0.1063 -0.0145 0.0069 0.0009 0.0038 -0.0075 -0.0145 -0.0032 -0.0198 -0.0265 -0.0342 -0.0898 0.0123 0.0326 DFFO 0.0176 0.0157 -0.0250 -0.0618 0.0059 0.0053 -0.0356 -0.0051 -0.0172 -0.0086 -0.0406 -0.1261 -0.0887 -0.1858 0.0126 0.0003 D50.F 0.0165 0.0285 -0.0369 -0.0184 0.0198 0.0012 -0.0035 -0.0085 -0.0110 -0.0036 -0.0122 -0.0269 -0.0236 -0.0188 0.0102 0.0082 DFH -0.0019 0.0335 -0.0065 -0.0227 0.0017 0.0143 -0.0346 -0.0053 -0.0172 -0.0061 -0.0411 -0.1496 -0.0897 -0.1660 0.0073 -0.0039 DLH 0.0123 0.0275 0.0045 -0.0246 0.0008 0.0056 -0.0892 0.0053 -0.0049 -0.0049 -0.0210 -0.0496 -0.0430 -0.1199 0.0059 -0.0004 FL -0.0007 -0.0463 0.0270 0.0106 -0.0057 -0.0025 -0.0158 0.0297 0.0096 0.0032 -0.0076 0.0330 0.0176 0.0349 -0.0101 -0.0077 FD 0.0087 -0.0377 0.0341 0.0235 -0.0048 -0.0055 0.0096 0.0063 0.0453 0.0080 0.0873 0.1123 0.0990 0.2198 -0.0134 -0.0187 PL -0.0114 -0.0312 0.0217 0.0339 -0.0046 -0.0056 0.0279 0.0061 0.0231 0.0156 0.0682 0.1572 0.1162 0.2552 -0.0128 0.0085 AFW 0.0130 0.0136 0.0118 0.0141 -0.0014 -0.0033 0.0105 -0.0013 0.0222 0.0060 0.1780 0.1069 0.0882 0.2046 -0.0031 -0.0097 NFP -0.009 -0.0072 0.0107 0.0296 -0.0020 -0.0082 0.0168 0.0037 0.0194 0.0093 0.0723 0.2632 0.1352 0.3012 -0.0009 0.0186 FYP -0.0105 -0.0177 0.0226 0.034 -0.0029 -0.0080 0.0236 0.0032 0.0278 0.0113 0.0974 0.2206 0.1613 0.3269 -0.0063 0.0052 FYPL -0.0068 -0.0299 0.0253 0.0304 -0.0010 -0.0063 0.0283 0.0026 0.0264 0.0106 0.0964 0.2097 0.1395 0.3780 -0.0042 0.0050 AA -0.0126 -0.0446 0.0418 0.0250 -0.0064 -0.0034 0.0169 0.0096 0.0194 0.0064 0.0179 0.0078 0.0326 0.0508 -0.0312 -0.0204 TSS 0.0078 0.0111 -0.0609 -0.0003 0.0029 -0.0010 0.0006 -0.0040 -0.0149 0.0023 -0.0303 0.0858 0.0147 0.0333 0.0111 0.0568 Bold numbers indicate direct effects and remaining all indirect effects of particular character; Residual effect: 0.0696 PH-Plant height (cm), NPBPP-No. of primary branches plant -1 , IL-Internodal length (cm), DFF-Days to first flowering, D50% F-Days to 50% flowering, DFH-Days to first harvest, DLH-Days to last harvest, FL-Fruit length (cm), FD-Fruit diameter (cm), PL-Pedicel length (cm), AFW-Average fruit weight(g), NFPP-No. of fruit plant -1 , FYPP-Fruit yield plant -1 (g), FYPP-Fruit yield plot -1 (kg), AA-Ascorbic acid (Mg/100 g), TSS-Total soluble soiled (%) and FYPH-Fruit yield ha. -1 (q).

Conclusion

Based on findings of present studies on correlation and path analysis, it may be concluded that the fruit yield plant -1 , no. of fruit plant -1 , fruit yield plot -1 , fruit diameter, average fruit weight, fruit length these traits being easily observable should be given primary focus during the selecting of superior genotypes for improving the genetic yield potential of spine gourd.

Acknowledgement

AcknowledgmentThe authors of the research article are very thankful to the Vasantrao Naik Marathwada Agriculture University, Parbhani, Maharashtra, India.

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