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VIS-NIR Reflectance Spectroscopy as an Alternative Method for Rapid Estimation of Soil Available Potassium

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Title VIS-NIR Reflectance Spectroscopy as an Alternative Method for Rapid Estimation of Soil Available Potassium
Bhabani Prasad Mondal
Bharpoor S. Sekhon
Priya Paul
Arijit Barman
Arghya Chattopadhyay
Nilimesh Mridha
 
Creator Not Available
 
Subject Available potassium
reflectance spectroscopy
root mean square error
residual prediction deviation
 
Description Not Available
Potassium (K) is an important macronutrient for crop plant and plays a crucial role in crop production.
Therefore, accurate and rapid estimation of soil available K is necessary for judicious application of
available K in an intensively cropped region. However, traditional soil chemical analysis for assessing soil
available K is very much laborious, expensive and time consuming. The visible near-infrared (VIS-NIR)
reflectance spectroscopy is considered as a promising alternative technique for rapid, non-destructive and
ecofriendly estimation of available K and other soil properties. An experiment was carried out in an
intensively cultivated region of Ludhiana district of Punjab to investigate the potential of VIS-NIR technique
for accurate prediction of available K using multivariate model. A total of 170 georeferenced surface soil
samples (0-15 cm) were collected from the study site for both chemical and spectral analysis of available K.
A popular statistical technique namely, partial least square regression (PLSR) was employed to develop
spectral model for K prediction. Important statistical diagnostics like coefficient of determination (R2
), root
mean square error (RMSE) and residual prediction deviation (RPD) were used to evaluate the efficacy of
prediction model. The results showed that the R2
and RMSE and RPD values were 0.41, 0.09 and 1.44,
respectively for independent validation dataset of PLSR model. The RPD value indicated acceptable
prediction accuracy for soil available K with PLSR model. Comparatively lower performance of the studied
prediction model could be ascribed to the less variation in the collected spectra of soil samples and the use
of linear multivariate model. Therefore, the study suggested to explore advanced non-linear data mining
techniques for achieving better prediction accuracy for soil available K
Not Available
 
Date 2021-07-23T09:28:07Z
2021-07-23T09:28:07Z
2020-09-30
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/49576
 
Language English
 
Relation Not Available;
 
Publisher Not Available