CHEMICAL CHARACTERIZATION OF VOLCANIC SOILS USING NEAR INFRARED SPECTROSCOPY (NIRS)
ABSTRACT The quantification of nutrient concentration in soils is important for the implementation of adequate fertilization strategies, and also to improve agricultural productivity. In recent decades, new instrumental methods have been developed, including the use of near infrared spectroscopy (NI...
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| Autores principales: | , , , |
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| Lenguaje: | English |
| Publicado: |
Universidad de Concepción. Facultad de Agronomía, Facultad de Ingeniería Agricola y Facultad de Ciencias Veterinarias
2021
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-38902021000100032 |
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| Sumario: | ABSTRACT The quantification of nutrient concentration in soils is important for the implementation of adequate fertilization strategies, and also to improve agricultural productivity. In recent decades, new instrumental methods have been developed, including the use of near infrared spectroscopy (NIRS). Although its potential has been recognized by soil scientists for a few decades, the use of NIRS for routine soil analysis has been, to the best of our knowledge, poorly developed in Chile. Calibration models were developed by partial least squares regression (PLSR). The objective of this study was to assess the use of NIRS for the determination of concentrations of Calcium, Magnesium, Phosphorus, Potassium, pH (water), organic matter, Sodium, exchangeable aluminum, ammonium and nitrate in volcanic soils of southern Chile. The coefficient of determination in the calibration obtained ranged from 0.79 to 0.95 and 0.79 to 0.89 in Andisols and Ultisols, respectively. The residual prediction deviation (RPD) values varied between 2.1 and 4.4 in Andisols, and between 2.2 and 3.1 in Ultisols. It was not possible to generate robust calibration models for Al Exch, Na and NH4 + in Andisols and Al Exch, Mg, NO3 - and Na in Ultisols. Although some of the models developed presented high R2, this methodology requires further validation, including a greater number of samples from a wider spatial distribution, covering a variety of climatic and agricultural conditions. |
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