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Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Growth dynamics, yield behaviour and economic returns of Phyllanthus emblica L. under agroforestry conditions across Tamil Nadu

DOI
https://doi.org/10.14719/pst.14632
Submitted
21 March 2026
Published
15-09-2026

Abstract

Indian gooseberry (Phyllanthus emblica L.) has become increasingly important in India due to rising demand for Ayurvedic and natural health products. This study examines the growth and yield patterns of Indian gooseberry over its lifespan. Trees aged 6 to 35 years were studied in different conditions. The research was carried out in two regions of Tamil Nadu—the North Western Zone (NWZ) and North Eastern Zone (NEZ). These regions differ in climate and soil, providing a comprehensive assessment of how the species performs in varied environments. Biometric parameters including total tree height and diameter at 2 m vertical intervals were recorded using a tree telescope. Multiple linear regression (MLR) analysis was employed to develop zone-specific yield prediction equations and yield tables, bypassing
conventional form-factor assumptions. The yield prediction model common to both zones takes the form Y = -0.059 + 0.001X1 + 1.67X₂ + 0.004X₃ - 0.038X₄, while monetary yield is expressed as Y = 0.093 + 0.001X₁ + 0.302X₂ + 3.545X₃ - 0.039X₄. Pronounced inter-zonal differences in growth and productivity emerged from the results, underscoring the significant influence of site-specific soil conditions and local climate in shaping tree performance. The yield tables derived from this analysis offer a statistically grounded and field-applicable framework for estimating productivity, guiding management decisions and supporting the sustainable use of Indian gooseberry across the varied regional environments of Tamil Nadu.

References

  1. 1. Avinash PG, Hamid, Shams R, Dash KK, Shaikh AM, Ungai D, et al. Recent insights into the morphological, nutritional and phytochemical properties of Indian gooseberry (Phyllanthus emblica) for the development of functional foods. Plants (Basel). 2024;13(5):574. https://doi.org/10.3390/plants13050574
  2. 2. Kumar V, Kumar R, Singh J, editors. Contaminants in agriculture and environment: Health risks and remediation. Agro Environ Media, Publication Cell of AESA, Agriculture and Environmental Science Academy; 2019. https://doi.org/10.26832/AESA-2019-CAE
  3. 3. Suryani M, Yulyana A, Sumaiyah S, Fitri K, Lubis LD, Daulay W, et al. Microwave-assisted extraction enhances the antioxidant and anti-diabetic activities of polyphenol-rich Phyllanthus emblica fruit extract. Discov Food. 2025;5(1):244. https://doi.org/10.1007/s44187-025-00532-1
  4. 4. Clutter JL, Jones EP, Jones EP. Prediction of growth after thinning in old-field slash pine plantations. Asheville (NC): Department of Agriculture, Forest Service, Southeastern Forest Experiment Station; 1980.
  5. 5. Gregersen H, Contreras A. Economic assessment of forestry project impacts. Etude FAO: Forets No. 106. Rome: Food and Agriculture Organization of the United Nations; 1994.
  6. 6. Stier JC. Review of forest resource economics and finance, by Klemperer WD. Land Econ. 1997;73(3):440–2. https://doi.org/10.2307/3147178
  7. 7. Cubbage F, Koesbandana S, Mac Donagh P, Rubilar R, Balmelli G, Olmos VM, et al. Global timber investments, wood costs, regulation, and risk. Biomass Bioenergy. 2010;34(12):1667–78. https://doi.org/10.1016/j.biombioe.2010.05.008
  8. 8. Nair PR. Agroforestry systems and environmental quality: Introduction. J Environ Qual. 2011;40(3):784–90. https://doi.org/10.2134/jeq2011.0076
  9. 9. Avery TE, Burkhart HE. Forest measurements. 3rd ed. New York: McGraw-Hill; 1983. p. 331.
  10. 10. Husch B, Beers TW, Kershaw JA Jr. Forest mensuration. New York: John Wiley & Sons; 2002.
  11. 11. Khanna LS, Chaturvedi AN. Forest mensuration. Dehradun: International Book Distributors; 1994.
  12. 12. Pretzsch H. Forest dynamics, growth and yield. Berlin: Springer; 2009. https://doi.org/10.1007/978-3-540-88307-4
  13. 13. Philip MS. Measuring trees and forests. 2nd ed. Wallingford: CAB International; 1994. https://doi.org/10.1079/9780851988832.0000
  14. 14. Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. Hoboken (NJ): John Wiley & Sons; 2021.
  15. 15. Alder D. Growth modelling for mixed tropical forests. Oxford: Oxford Forestry Institute, University of Oxford; 1995.
  16. 16. Alder D, Synnott T. Permanent sample plot techniques for mixed tropical forest. Oxford: Oxford Forestry Institute, University of Oxford; 1992.
  17. 17. Vanclay JK. Modelling forest growth and yield: Applications to mixed tropical forests. Wallingford: CAB International; 1994.
  18. 18. Gujarati DN, Porter DC. Basic econometrics. New York: McGraw-Hill Irwin; 2009.
  19. 19. Raymond HM, Montgomery DC, Vining GG, Robinson JT. Generalized linear models with applications in engineering and the sciences. Wiley; 2012. https://doi.org/10.1080/07408170304405
  20. 20. Kutner MH, Nachtsheim CJ, Neter J. Applied linear statistical models. 5th ed. New York: McGraw-Hill Irwin; 2005.
  21. 21. Kittur BH, Sudhakara K, Mohan Kumar B, Kunhamu TK, Sureshkumar P. Bamboo-based agroforestry systems in Kerala, India: Performance of turmeric (Curcuma longa L.) in the subcanopy of differentially spaced seven-year-old bamboo stand. Agrofor Syst. 2016;90(2):237–50. https://doi.org/10.1007/s10457-015-9849-z
  22. 22. Draper NR, Smith H. Applied regression analysis. 3rd ed. New York: John Wiley & Sons; 1998. https://doi.org/10.1002/9781118625590
  23. 23. Belsley DA, Kuh E, Welsch RE. Regression diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley & Sons; 2004.
  24. 24. Fox J. Applied regression analysis and generalized linear models. 3rd ed. Thousand Oaks (CA): SAGE Publications; 2016.
  25. 25. Seber GA, Lee AJ. Linear regression analysis. Hoboken (NJ): John Wiley & Sons; 2003. https://doi.org/10.1002/9780471722199
  26. 26. Rawlings JO, Pantula SG, Dickey DA, editors. Applied regression analysis: A research tool. New York: Springer; 1998. https://doi.org/10.1007/b98890
  27. 27. Chatterjee S, Hadi AS. Regression analysis by example. Hoboken (NJ): John Wiley & Sons; 2015.
  28. 28. Tomé M, Burkhart HE. Modeling forest trees and stands. Dordrecht: Springer Netherlands; 2012. https://doi.org/10.1007/978-90-481-3170-9
  29. 29. Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol. 2014;20(10):3177–90. https://doi.org/10.1111/gcb.12629
  30. 30. Kirilenko AP, Sedjo RA. Climate change impacts on forestry. Proc Natl Acad Sci U S A. 2007;104(50):19697–702. https://doi.org/10.1073/pnas.0701424104
  31. 31. Pearson K. On lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci. 1901;2(11):559–72. https://doi.org/10.1080/14786440109462720
  32. 32. Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans R Soc A Math Phys Eng Sci. 2016;374(2065):20150202. https://doi.org/10.1098/rsta.2015.0202
  33. 33. Kaufman L, Rousseeuw PJ. Finding groups in data: An introduction to cluster analysis. Hoboken (NJ): John Wiley & Sons; 2009.
  34. 34. Niklas KJ. Plant allometry: The scaling of form and process. Chicago: University of Chicago Press; 1994.
  35. 35. Parresol BR. Assessing tree and stand biomass: A review with examples and critical comparisons. For Sci. 1999;45(4):573–93. https://doi.org/10.1093/forestscience/45.4.573
  36. 36. Stage AR, Salas C. Interactions of elevation, aspect, and slope in models of forest species composition and productivity. For Sci. 2007;53(4):486–92. https://doi.org/10.1093/forestscience/53.4.486
  37. 37. Enquist BJ, Niklas KJ. Global allocation rules for patterns of biomass partitioning in seed plants. Science. 2002;295(5559):1517–20. https://doi.org/10.1126/science.1066360
  38. 38. Borders BE, Bailey RL. A compatible system of growth and yield equations for slash pine fitted with restricted three-stage least squares. For Sci. 1986;32(1):185–201. https://doi.org/10.1093/forestscience/32.1.185
  39. 39. Fang Z, Bailey RL. Height-diameter models for tropical forests on Hainan Island in southern China. For Ecol Manage. 1998;110(1–3):315–27. https://doi.org/10.1016/S0378-1127(98)00297-7
  40. 40. Kozak A. My last words on taper equations. For Chron. 2004;80(4):507–15. https://doi.org/10.5558/tfc80507-4
  41. 41. Burnham KP, Anderson DR. Model selection and multimodel inference: A practical information-theoretic approach. 2nd ed. New York: Springer; 2004. p. 488. https://doi.org/10.1007/b97636
  42. 42. Zhang L, Bi H, Cheng P, Davis CJ. Modeling spatial variation in tree diameter-height relationships. For Ecol Manage. 2004;189(1–3):317–29. https://doi.org/10.1016/j.foreco.2003.09.004
  43. 43. Temesgen H, Monleon VJ, Hann DW. Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests. Can J For Res. 2008;38(3):553–65. https://doi.org/10.1139/X07-104

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