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Influence of moisture and grain sizes on the analysis of nutrients in agricultural soils using LIBS
(2018)
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression(PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
In den letzten Jahrzehnten ist die Nachfrage nach kostengünstigen und flächendeckenden Kartierungsmöglichkeiten im Hinblick auf eine ertragssteigernde und umweltfreundlichere Bewirtschaftung von landwirtschaftlichen Nutzflächen stark gestiegen. Hierfür eignen sich spektroskopische Methoden wie die Röntgenfluoreszenzanalyse (RFA), Raman- und Gammaspektroskopie sowie die laserinduzierte Plasmaspektroskopie (LIBS). In Abhängigkeit von der Funktionsweise der jeweiligen Methoden werden Informationen zu verschiedensten Bodeneigenschaften wie Nährelementgehalt, Textur und pH-Wert erhalten.
Ziel dieser Arbeit ist die Entwicklung eines Online-LIBS-Verfahrens zur Nährelementbestimmmung und Kartierung von Ackerflächen. Die LIBS ist eine schnelle und simultane Multielementanalyse bei der durch das Fokussieren eines hochenergetischen Laserpulses Probenmaterial von der Probenoberfläche ablatiert wird und in ein Plasma überführt wird. Beim Abkühlen des Plasmas wird Strahlung emittiert, welche Rückschlüsse über die elementare Zusammensetzung der Probe gibt. Diese Arbeit ist im Teilprojekt I4S (Intelligenz für Böden) im Forschungsprogramm BonaRes (Boden als nachhaltige Ressource für die Bioökonomie) des Bundesministerium für Bildung und Forschung (BMBF) entstanden. Es wurden insgesamt 651 Bodenproben von verschiedenen Test-Agrarflächen unterschiedlichster Standorte Deutschlands gemessen, ausgewertet und zu Validierungszwecken mit entsprechender Referenzanalytik wie die Optische Emissionsspektroskopie mittels induktiv gekoppeltem Plasma (ICP-OES) und die wellenlängendispersive Röntgenfluoreszenzanalyse (WDRFA) charakterisiert.
Für die Quantifizierung wurden zunächst die Messparameter des LIBS-Systems auf die Bodenmatrix optimiert und für die Elemente geeignete Linien ausgewählt sowie deren Nachweisgrenzen bestimmt. Es hat sich gezeigt, dass eine absolute Quantifizierung basierend auf einem univariaten Ansatz aufgrund der starken Matrixeffekte und der schlechten Reproduzierbarkeit des Plasmas nur eingeschränkt möglich ist. Bei Verwendung eines multivariaten Ansatz wie der Partial Least Squares Regression (PLSR) für die Kalibrierung konnten für die Nährelemente im Vergleich zur univariaten Variante Analyseergebnisse mit höherer Güte und geringeren Messunsicherheiten ermittelt werden. Die Untersuchungen haben gezeigt, dass das multivariate Modell weiter verbessert werden kann, indem mit einer Vielzahl von gut analysierten Böden verschiedener Standorte, Bodenarten und einem breiten Gehaltsbereich kalibriert wird. Mithilfe der Hauptkomponentenanalyse (PCA) wurde eine Klassifizierung der Böden nach der Textur realisiert. Weiterhin wurde auch eine Kalibrierung mit losem Bodenmaterial erstellt. Trotz der Signalabnahme konnten für die verschiedenen Nährelemente Kalibriergeraden mit ausreichender, analytischer Güte erstellt werden.
Für den Einsatz auf dem Acker wurde außerdem der Einfluss von Korngröße und Feuchtigkeit auf das LIBS-Signal untersucht. Die unterschiedlichen Korngrößen haben nur einen geringen Einfluss auf das LIBS-Signal und das Kalibriermodell lässt sich durch entsprechende Proben leicht anpassen. Dagegen ist der Einfluss der Feuchtigkeit deutlich stärker und hängt stark von der Bodenart ab, sodass für jede Bodenart ein separates Kalibriermodell für verschiedene Feuchtigkeitsgehalte erstellt werden muss. Mithilfe der PCA kann der Feuchtigkeitsgehalt im Boden grob abgeschätzt werden und die entsprechende Kalibrierung ausgewählt werden.
Diese Arbeit liefert essentielle Informationen für eine Echtzeit-Analyse von Nährelementen auf dem Acker mittels LIBS und leistet einen wichtigen Beitrag zu einer fortschrittlichen und zukunftsfähigen Nutzung von Ackerflächen.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set.
Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate Determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.
