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In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer.
The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements
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.
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.
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.
Bei der vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Förderinitiative „BonaRes“ steht die nachhaltige Nutzung der knappen Ressource Boden im Fokus. Das durch BonaRes geförderte Verbundprojekt „Intelligence for Soil“ (I4S) hat sich die Entwicklung eines integrierten Systems zur ortsspezifischen Steuerung der Bodenfruchtbarkeit zur Aufgabe gemacht, welches Empfehlungen zur Anpassung der Düngung und anderer Maßnahmen gibt, um die Bodenfunktionen zu verbessern und Umweltbelastungen zu vermindern. Das System soll aus drei Hauptkomponenten bestehen:
- Mobile Sensorplattform zur hochauflösenden Kartierung der Bodeneigenschaften (BonaRes Mapper). Die Sensorplattform enthält Bodensensoren zur komplexen Erfassung bewirtschaftungsrelevanter Bodeneigenschaften in situ.
- Dynamische Bodenmodelle (Nährstoff-, Wasser-, C-Haushalt) zur Verarbeitung der Sensordaten und Erzeugung entscheidungsrelevanter Informationen,
- Entscheidungsunterstützungssystem (EUS), welches in die Bodenmodelle agronomische und sozioökonomische Randbedingungen integriert, Düngungsempfehlungen ausspricht und Bodenfunktionen bewertet. Der Output des EUS sind insbesondere Düngungskarten für die ortsspezifische Düngung im Rahmen von Precision Agriculture.
Der Erhalt und die Verbesserung der Fruchtbarkeit von Böden durch landwirtschaftliche Bewirtschaftungsmaßnahmen erfordert sorgfältig geplante Entscheidungen, die auf einer detaillierten Erfassung der Bodeneigenschaften und einem vertieften Verständnis der Bodenprozesse beruhen. Durch konventionelle, flächeneinheitliche Bewirtschaftungen (z.B. Düngung) können zum einen Ertragsverluste durch zu geringe Bewirtschaftungsintensität auf dem einen Teil der Fläche verursacht werden, während andere Teile des Feldes zu hohe Dosen erhalten und es dadurch zu Umweltbelastungen oder zu Verschwendung von Ressourcen kommt. Trotz der Verfügbarkeit von Technologien für ortsspezifische Düngung ist die Akzeptanz ortsspezifischer Bewirtschaftung (Precision Agriculture) in der Praxis noch gering. Ein wesentlicher Grund dafür ist das Fehlen von kostengünstigen Methoden zur Erfassung der bewirtschaftungsrelevanten Bodenmerkmale. Für die elementanalytische Untersuchung von Böden vor-Ort ist eine zuverlässige Analytik und Prozessüberwachung wie z.B. durch RFA oder LIBS erforderlich.
Bestimmung von Makro- und Mikronährstoffen in Böden mittels laserinduzierter Plasmaspektroskopie
(2018)
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. The research projects I4S (intelligence for soil) has the goal to develop a system for this purpose. I4S is one of ten interdisciplinary research project associations of the innovation programme called BonaRes, which is funded by the German Federal Ministry of Education and Research (BMBF).
The system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of major and minor nutrients in soils, like calcium, magnesium, potassium. LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm) was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated. When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. For this purpose, the research project I4S (intelligence for soil) has the goal to develop an integrated system. This system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS.
LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm) was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated.[1] When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. The research projects I4S (intelligence for soil) has the goal to develop a system for this purpose. I4S is one of ten interdisciplinary research project associations of the innovation programme called BonaRes, which is funded by the German Federal Ministry of Education and Research (BMBF).
The system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of major and minor nutrients in soils, like calcium, magnesium, potassium. LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm)was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated. When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.