Correlation and path coefficient analysis of seed yield and yield components of French bean (Phaseolus vulgaris L.) genotypes in sub-tropical region.


This research aims to determine the relationship between yield and yield components of French bean using multivariate statistical techniques. The study was conducted at Horticulture Research Station, Dailekh, Nepal for consecutive three year of 2016-2018. In this study, six French bean genotypes were used. The following measurements and observations were made: germination (%), pod length (cm), pod diameter (mm), individual pod weight and number of seeds pod-1, number of dry pods plant-1, dry pod yield plant-1 and dry pod yield (t/ha), 100 seed weight (g) and seed yield (t/ha). Highly significant and positive correlation was recorded between seed yield and individual pod weight (r=0.659***), number of seed pod-1 (r=0.548**) and pod length (0.459*). Conversely, pod diameter showed a negative and non-significant (-0.025) correlation on seed yield. Based on simple regression analysis, linear regression of individual pod weight, number of seed pod-1 and pod length leads to an increase in the seed yield (t/ha) by 0.188, 0.285 and 0.103 units respectively. From the path coefficient analysis maximum positive direct effect on seed yield was recorded by number of seeds pod-1 (0.767) followed by 100 seed weight (0.530) and individual pod weight (0.429). Also, stepwise multiple linear regression analysis revealed that three traits including individual pod weight, 100 seed weight and number of dry pod plant-1 with R2 = 68.22% showed their overall contribution towards seed yield. The rest of the variance (R2 = 31.78%) was contributed by the variables that were not included in the study. Results concerning four statistical methods showed that individual pod weight appeared to be most effective contributor of seed yield followed by the number of seed pod-1, 100 seed weight, and pod length. Therefore, attention should be paid to these characters in any breeding program during the selection criteria for improving the seed yield.

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Preprints for Agriculture and Allied Sciences
Advisory Board
  • Leisa Armstrong, Edith Cowan University, Australia
  • Arianna Becerril García, Autonomous University of the State of Mexico, Redalyc/AmeliCA, Mexico
  • Susmita Das, Bangladesh Agricultural Research Council
  • Abeer Elhalwagi, National Gene Bank, Egypt
  • Gopinath KA, Central Research Institute for Dryland Agriculture
  • Niklaus Grünwald, USDA Agricultural Research Service
  • Sridhar Gutam, ICAR IIHR/Open Access India
  • Vinodh Ilangovan, Max Planck Institute for Biophysical Chemistry
  • Jayalakshmi M, ANGRAU, India
  • Khelif Karima, Institut National de la Recherche Agronomique d'Algérie
  • Dinesh Kumar, Indian Agricultural Statistics Research Institute
  • Satendra Kumar Singh, Indian Council of Agricultural Research
  • Devika P. Madalli, DRTC/Indian Statistical Institute, India
  • Prateek Mahalwar, Cellulosic Technologies UG, Germany
  • Bernard Pochet, University of Liège - Gembloux Agro-Bio Tech
  • Vassilis Protonotarios, NEUROPUBLIC
  • Andy Robinson, CABI
  • Paraj Shukla, King Saud University
  • Chandni Singh, Indian Institute for Human Settlements
  • Kuldeep Singh Jadon, ICAR-Central Arid Zone Research Institute, India
  • Rajeev K Varshney, CGIAR/ICRISAT, India
  • Sumant Vyas, ICAR- National Research Centre on Camel, India
  • Oya Yildirim Rieger, Ithaka S+R/ITHAKA, USA
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