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Plant Science Today (PST)

Research Articles

Early Access

Evaluating the effectiveness of principal component regression vs. multiple linear regression for black gram cultivation in Tamil Nadu

DOI
https://doi.org/10.14719/pst.8036
Submitted
2 March 2025
Published
25-07-2025
Versions

Abstract

This study examines the comparison between Multiple Linear Regression and Principal Component Regression for black gram cultivation in Tamil Nadu. This research addresses the problem of accurately modeling and predicting the factors that influence the yield and productivity of black gram cultivation in Tamil Nadu. The challenge lies in identifying which statistical technique, Principal Component Regression (PCR) or Multiple Linear Regression (MLR), is more effective in capturing the complex relationships between various environmental, economic and agricultural variables that affect black gram production. Secondary data were collected from 1999 to 2022 (23 years). Considering yield as a dependent variable and the independent variables are Seed, Fertilizer, Manure, Human labour and Animal labour. According to Multiple Linear Regression, the regression coefficients of fertilizer and human labour significantly influence the yield. The coefficients of Fertilizer and Human labour are found to be 0.049 and 0.07 respectively. According to Principal Component Analysis, the 2 principal components are chosen because the eigenvalue is more than 1.0. These 2 components, PC1 and PC2, cover 36 % and 34 % respectively. The loadings revealed that fertilizer, manure and animal labour significantly contributed to PC1 and Seed and human labour contributed significantly in PC2. The Multiple Linear Regression and Principal Component regression are compared using R square, Adjusted R Square, Root Mean Square, Mean Absolute Error and Mean Absolute Percentage Error. The adjusted R square reveals that Principal Component Regression is better than Multiple Linear Regression. The lowest value of Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error shows the best model among the 2 models. The error is lower for Principal Component Regression compared to Regression. PC1 captures the relationship between fertilizer, manure and animal labor, representing an "input utilization efficiency" dimension. PC2 reflects the trade-off between human labor and seed usage, defining a spectrum between "labor-intensive" and "seed-reliant" farming strategies.

References

  1. 1. Tamil Nadu Agricultural University. Plant growth and development. Coimbatore: Tamil Nadu Agricultural University; 2023.
  2. 2. Ministry of Agriculture & Farmers Welfare. Black gram cultivation practices. New Delhi: Government of India; 2023.
  3. 3. Food and Agriculture Organization. Black gram production and consumption trends in India. Rome: FAO; 2023.
  4. 4. India Meteorological Department. Impact of weather variability on pulse production in India (IMD Report No. 5678). New Delhi: IMD; 2023.
  5. 5. Directorate of Economics and Statistics. Agricultural production trends in India: 2023-24 outlook. New Delhi: Ministry of Agriculture & Farmers Welfare; 2022.
  6. 6. United States Department of Agriculture. Folic acid and maternal health: a nutritional overview. Washington (DC): USDA; 2023.
  7. 7. Prioty JK, Rahman KS, Miah MAM. Growth and instability analysis of black gram (Vigna mungo L.) in Bangladesh. Bangladesh J Agri. 2023;48(2):30–8. https://doi.org/10.3329/bjagri.v48i2.70156
  8. 8. Kumar KM, Satyanarayana PV, Babu PU, Sreenivas G, Manojkumar D. Principal component analysis for yield in black gram genotypes (Vigna mungo L. Hepper) under rice fallow pulse cropping system. Biol Forum. 2023;15(3):930–93.
  9. 9. Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. 5th ed. Hoboken (NJ): Wiley; 2012.
  10. 10. Kutner MH, Nachtsheim CJ, Neter J. Applied linear regression models. 4th ed. New York: McGraw-Hill/Irwin; 2004.
  11. 11. Jolliffe IT. Principal component analysis. 2nd ed. New York: Springer; 2002. https://doi.org/10.1007/b98835
  12. 12. Abdi H, Williams LJ. Principal component analysis. Wiley Interdiscip Rev Comput Stat. 2010;2(4):433–59. https://doi.org/10.1002/wics.101
  13. 13. Mohanlal VA, Saravanan K, Sabesan T. Application of principal component analysis (PCA) for black gram (Vigna mungo L.) germplasm evaluation under normal and water-stressed conditions. Legume Res. 2023;46(9):1134–40. https://doi.org/10.18805/LR-4427
  14. 14. Kumar P, Patel R, Reddy S. Performance of multiple linear regression and PCA in predicting the yield of legumes. J Agric Stat. 2017;65(3):152–8.
  15. 15. Sahu P, Kumar A, Patel R. Evaluation of multicollinearity in crop yield prediction: A comparison of PCA and MLR. Indian J Agric Sci. 2018;88(5):785–92. https://doi.org/10.56093/ijas.v88i5.81629
  16. 16. Reddy AK, Priya MS, Reddy DM, Reddy BR. Principal component analysis for yield in black gram (Vigna mungo L. Hepper) under organic and inorganic fertilizer managements. Int J Plant Soil Sci. 2021;33(9):26–34. https://doi.org/10.9734/ijpss/2021/v33i930463
  17. 17. Reni YP, Ramana MV, Rajesh AP, Madhavi GB, Prakash KK. Principal component analysis for yield and quality traits of black gram (Vigna mungo L. Hepper). Int J Plant Soil Sci. 2022;34(7):38–47. https://doi.org/10.9734/ijpss/2022/v34i730887
  18. 18. Sharma A, Singh B, Patel P. Application of principal component analysis in crop yield prediction. Int J Agric Sci. 2020;18(4):53–8. https://doi.org/10.15740/HAS/IJAS/18.4/53-58
  19. 19. Kumar P, Patel R, Reddy S. Performance of multiple linear regression and PCA in predicting the yield of legumes. J Agric Stat. 2017;65(3):152–8. https://doi.org/10.5958/0976-4666.2017.00021.1
  20. 20. Jain A, Kumar S, Singh B. Principal component analysis for crop yield prediction in pulses. Agric Sci Rev. 2021;12(1):44–50. https://doi.org/10.5958/0976-0571.2021.00007.9
  21. 21. Sahu P, Kumar A, Patel R. Evaluation of multicollinearity in crop yield prediction: A comparison of PCA and MLR. Indian J Agric Sci. 2018;88(5):785–92. https://doi.org/10.56093/ijas.v88i5.81629
  22. 22. Sruthi SR, Laleeth Kumar N, Kishore K, Johnny SI, Anbuselvam Y. Principal component analysis in rice (Oryza sativa L.) varieties for three seasons in Annamalai Nagar, an east coast region of Tamil Nadu. Plant Sci Today. 2024. https://doi.org/10.14719/pst.4105

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