Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy


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Authors

  • BHABANI PRASAD MONDAL ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
  • RABI NARAYAN SAHOO ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
  • NAYAN AHMED ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
  • RAJIV KUMAR SINGH ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
  • BAPPA DAS ICAR-Central Coastal Agricultural Research Institute, Goa
  • NILIMESH MRIDHA ICAR-National Institute of Natural Fibre Engineering and Technology, Kolkata
  • SHALINI GAKHAR ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India

https://doi.org/10.56093/ijas.v91i9.116080

Keywords:

Available sulphur, Multivariate models, PLSR, Reflectance spectroscopy, RF

Abstract

Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.

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2021-09-27

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2021-09-27

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How to Cite

MONDAL, B. P., SAHOO, R. N., AHMED, N., SINGH, R. K., DAS, B., MRIDHA, N., & GAKHAR, S. (2021). Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy. The Indian Journal of Agricultural Sciences, 91(9), 1328–1332. https://doi.org/10.56093/ijas.v91i9.116080
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