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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | A Principal Component Regression Approach to Determine the Relationship Between Yam Yield and Some Climatic Determinants |
| Authors Name: | Amokaha, O.A , Apeagee, B. B , Agada, I. O , Akosu, A. M |
| Unique Id: | IJSDR2305114 |
| Published In: | Volume 8 Issue 5, May-2023 |
| Abstract: | This work applied the principal component regression approach in modelling the relationship between yam yield and some climatic variables in Makurdi, Benue State, Nigeria. Secondary data were sourced from Benue Agricultural and Rural Development Authority (BNARDA) and Nigerian Meteorological Agency Headquarters, Tactical Air Command, Makurdi – Airport, Benue State. It was established that there was multi-collinearity among the climatic variables. In this study, an attempt has been made to apply the concept of Principal Component Regression as a remedial solution to this problem. After establishing the existence of high collinearity between the independent variables, the concept of Principal Component Regression was applied to mitigate the effect of collinearity and find the best possible linear combinations of variables that can produce large variance with the best entropy. This study results include the fact that four principal components each were obtained for the first, second, and third farming seasons, yielding 81.11 %, 84.15 %, and 89.97 % of the total variability for the first, second, and third phases respectively. The first phase results of Principal Component Regression analysis obtained for eigenvalues were between 2.483233 to 0.063056, Incremental percentage of 35.47 to 0.9, and Condition number of 1.00 to 39.38. The second phase had eigenvalues between 1.976513 to 0.456305, an Incremental percentage of 32.94 to 7.61, and a Condition number of 1.00 to 4.33. And lastly, the third phase had eigenvalues between 1.609677 to 0.501677, an Incremental percentage of 32.19 to 10.03, and a Condition number of 1.00 to 3.21. The study has shown that the application of Principal Component Regression for mitigating the presence of multi-collinearity between the independent variable of climatic data can significantly be reduced and the best possible obtainable linear combination for independent components fit is achieved for estimation and predications of yam yield. |
| Keywords: | principal, component, regression, climate, determinants, yields. |
| Cite Article: | "A Principal Component Regression Approach to Determine the Relationship Between Yam Yield and Some Climatic Determinants", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.754 - 765, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305114.pdf |
| Downloads: | 000337070 |
| Publication Details: | Published Paper ID: IJSDR2305114 Registration ID:205886 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): https://doi.org/10.5281/zenodo.8373693 Page No: 754 - 765 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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