Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties
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2020
Authors
Takele, C.
Iticha, B.
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Journal article
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Heliyon, 2020; 6(10):e05269-1-e05269-12
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Chalsissa Takele, Birhanu Iticha
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Abstract
The main aim of this research was to assess the use of mid-infrared (MIR) spectroscopy and geostatistical model for the evaluation and mapping of the spatial variability of some selected soil properties across a field. It is with the view of aiding site-specific soil management decisions. The performance of the model for the prediction of the components (soil parameters) was reported using the coefficient of determination (R²) and root mean square error (RMSE) values of the validation data set. Results revealed that least square regression model performed better in predicting cation exchange capacity-CEC (R² = 0.88 and RMSE = 8.98), soil organic carbon-OC (R² = 0.88, RMSE = 0.55), and total nitrogen-TN (R² = 0.91 and RMSE = 0.04). The first five principal components (PC) accounted for 78.17% of the total variance (PC1 = 25.75%, PC2 = 18.06%, PC3 = 13.85%, PC4 = 11.12%, and PC5 = 9.39%) and represented most of the variation within the data set. The coefficient of variation ranged from 6.73% for soil pH to 57.02% for available phosphorus (av. P). The soil pH values ranged from 4.21 to 6.57. The mean soil OC density was 2.14 kg m‾² within 50 cm soil depth. Nearly 96–97% of the soils contained av. P and sulfur (SO₄ ²‾-S) below the critical levels. The overall results revealed that soil properties varied spatially. Hence, we suggest that mapping the spatial variability of soils across a field is a cost-effective solution for soil management.
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© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync- nd/4.0/).