NON-DESTRUCTIVE ASSESSMENT OF SOIL ORGANIC CARBON USING NEAR INFRARED TECHNOLOGY

Siti Mechram*
Graduate Studies Program, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia Department of Agricultural Engineering Universitas Syiah Kuala, Jl. T Hasan Krueng Kalee No.3, Banda Aceh 23111, Indonesia

Bambang Rahadi
Department of Agricultural Engineering, Brawijaya University, Malang – Indonesia

Zaenal Kusuma
Department of Soil Science, Brawijaya University, Malang – Indonesia

Soemarno
Department of Soil Science, Brawijaya University, Malang – Indonesia *Email: mechram@unsyiah.ac.id

Vol 17, No 06 ( 2022 )   |  Doi: 10.5281/zenodo.6628767   |   Licensing: CC 4.0   |   Pg no: 30-40   |   Published on: 09-06-2022

Abstract
Soil organic carbon (C-organic) is a major component of soil quality that influences the composition of organic materials and the properties of soil mixtures. This C-organic has a practical value and importance in agriculture as well. Normally, conventional and time-consuming procedures were used to determine C-organic. However, this method is costly, time consuming, involves chemical materials, and may result in pollution. As a result, an alternative fast and environmentally friendly method for determining C-organic in soil is required. The near infrared reflectance spectroscopy (NIRS) technique can be considered for use because it is quick, non-destructive, requires little preparation, and produces no pollution. As a result, the primary goal of this research is to use the NIRS technique to predict C-organics and classify soils based on geographical characteristics. Soil samples were collected from four different site locations, and spectra data were collected in the range of 4000-10 000 cm-1. NIR spectra data and partial least square regression (PLSR) were used to create a C-organic prediction model, while principal component analysis was used to create a classification model (PCA). The results demonstrated that the NIRS technique could predict C-organic with a maximum correlation coefficient (r) of 0.96 and a residual predictive deviation (RPD) index of 4.05, indicating excellent prediction model performance. It is possible to conclude that the NIRS technique can be used to predict C-organic and classify soil characteristics in a quick and non-destructive manner.

Keywords:
NIRS, Technology, Agriculture, Soil, Carbon.

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