Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties
| dc.contributor.author | Takele, C. | |
| dc.contributor.author | Iticha, B. | |
| dc.date.issued | 2020 | |
| dc.description.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. | |
| dc.description.statementofresponsibility | Chalsissa Takele, Birhanu Iticha | |
| dc.identifier.citation | Heliyon, 2020; 6(10):e05269-1-e05269-12 | |
| dc.identifier.doi | 10.1016/j.heliyon.2020.e05269 | |
| dc.identifier.issn | 2405-8440 | |
| dc.identifier.issn | 2405-8440 | |
| dc.identifier.orcid | Iticha, B. [0000-0003-1737-0764] [0000-0003-4436-3068] | |
| dc.identifier.uri | https://hdl.handle.net/2440/145756 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Sci Ltd | |
| dc.rights | © 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/). | |
| dc.source.uri | https://doi.org/10.1016/j.heliyon.2020.e05269 | |
| dc.subject | Infrared spectroscopy; Geostatistical model; Soil survey; Soil variability; Digital soil mapping; Soil management; Variable rate technology; Precision agriculture; Chemistry; Agricultural science; Environmental science; Soil science; Environmental geochemistry; Earth sciences; Biogeochemistry; Biological sciences | |
| dc.title | Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties | |
| dc.type | Journal article | |
| pubs.publication-status | Published |
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