Analysis of Agronomic Categories in Different Soil Texture Classification Systems

Different soil texture classification systems are used in Poland. The system most widely used in agriculture is named after Polish Soil Science Society (PSSS) and is described in the soil classification norm BN-78/9180-11 (BN1978 standard). The last edition of soil classification system and soil texture classes published by PSSS in 2008 (PSSS 2008 classification) is different from BN1978 standard. The aim of this paper is a quantitative and qualitative comparison of the compatibility of agronomic categories created according to the old textural classes (BN1978 standard) and the new textural classes (PSSS 2008 classification). The representative set of soil samples (n=316) for arable mineral soils in Poland were divided into agronomic categories according to these two soil classification systems. The agronomic categories, which comply with soil classification systems PSSS 1978 are widely used in agricultural advisory. The results of the study showed differences in the amount of soil samples classified for the corresponding agronomic category. The study also showed discrepancies in the fine particle (<0.02 mm) and colloidal fraction (<0.002 mm) content in the corresponding categories. The differences may affect the assessment of soil fertility in nutrients(abundance) such as potassium, magnesium and of soil liming needs, as well as appropriate determination of fertiliser doses

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

This paper presents preliminary results of the use of visible and near-infrared (VIS -NIR) spectroscopy for soil classification and soil profile examination. Three experiments involving (1) three different soil types (Albic Luvisol, Gleyic Phaeozem, Brunic Arenosol), (2) three artificial micro-plots with similar texture (loamy sand, Gleyic Phaeozem) but different soil organic carbon (SOC) content and (3) a soil profile (Fluvisol) have been investigated using VIS -NIR spectroscopy. Results indicated that VIS -NIR is a promising technique for preliminary soil description and can classify soils according to soil properties (especially SOC ) and horizons. Instead of complex chemical and physical analyses involved in routine soil profile classification, VIS-NIR spectroscopy is suggested as a useful, rapid, and inexpensive tool for soil profile investigation

Visible and near-infrared spectroscopy in Poland: from the beginning to the Polish Soil Spectral Library

Worldwide, there is a growing interest in the use of vi-sible and near-infrared spectroscopy (VIS-NIRS) to characterise soils. The method is largely used in the agricultural (foods and cereals) sector but is only in the research phase for soil analysis despite the fact that it is a suitable tool for precision agriculture. A quick search at the Web of Science (WoS) Core Collection con-firmed that the method, although very popular in different fields of research, is still new within soils studies in Poland. Furthermo-re, the method only occasionally involved arable soils. This paper briefly describes how VIS-NIRS is used in Poland and demon-strates with a few examples the main advantages of the method over classical analytical method for mineral soil analysis. As an illustration of the method potential, soil organic carbon (SOC) and clay content were predicted using partial least-square (PLS) regression at field and national scale. The models were robust at field scale and revealed a high agreement between measured and predicted values with e.g. r2 = 0.65 and RMSEv = 0.11% for SOC. Prediction results at national scale are promising but less robust. VIS-NIRS is a suitable technique to estimate several soil proper-ties at different scales and at a relatively low cost

Screening Risk Assessment of Agricultural Areas under a High Level of Anthropopressure Based on Chemical Indexes and VIS-NIR Spectroscopy

Intensive anthropogenic activity may result in uncontrolled release of various pollutants that ultimately accumulate in soils and may adversely affect ecosystems and human health. Hazard screening, prioritisation and subsequent risk assessment are usually performed on a chemical-by-chemical basis and need expensive and time-consuming methods. Therefore, there is a need to look for fast and reliable methods of risk assessment and contamination prediction in soils. One promising technique in this regard is visible and near infrared (VIS-NIR) spectroscopy. The aim of the study was to evaluate potential environmental risk in soils subjected to high level of anthropopressure using VIS-NIR spectroscopy and to calculate several risk indexes for both individual polycyclic aromatic hydrocarbons (PAHs) and their mixture. Results showed that regarding 16PAH concentration, 78% of soil samples were contaminated. Risk assessment using the most conservative approach based on hazard quotients (HQ) for 10 individual PAHs allowed to conclude that 62% of the study area needs further action. Application of concentration addition or response addition models for 16PAHs mixture gave a more realistic assessment and indicates unacceptable risk in 23% and 55% of soils according to toxic units (TUm) and toxic pressure (TPm) approach. Toxic equivalency quotients (TEQ) were below the safe limit for human health protection in 88% of samples from study region. We present here the first attempt at predicting risk indexes using VIS-NIR spectroscopy. The best results were obtained with binary models. The accuracy of binary model can be ordered as follows: TPm (71.6%) < HI (85.1%) < TUm (87.9%) and TEQ (94.6%). Both chemical indexes and VIS-NIR can be successfully applied for first-tier risk assessment

Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties

The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P2O5, K2O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes. The best results were obtained for SOC and N content prediction using the full dataset with cross-validation (r² = 0.76, RMSECV = 0.04, RPD = 2.02 and r² = 0.81, RMSECV = 0.01, RPD = 2.20, respectively) and with an independent validation dataset (r² = 0.70, RMSEP = 0.04, RPD = 1.80 and r² = 0.73, RMSEP = 0.03, RPD = 1.22, for SOC and N content, respectively). The use of fertilizers and the type of crop rotation appear to have a significant impact on soil spectral properties; the SVM methodology with a linear kernel function was able to classify soil samples as functions of the applied doses of organic and inorganic fertilizers with 75% accuracy with cross-validation and the type of crop rotation with more than 90% accuracy with full validation of separate datasets.El objetivo del presente estudio fue analizar los resultados de un experimento a largo plazo, concerniente a las propiedades espectrales del suelo y desarrollar un modelo de pronóstico de estas propiedades (el contenido del carbón orgánico del suelo, N, pH, pH, Hh, P2O5, K2O, Ca, Mg, K, y Na) a base de muestras homogéneas en términos de textura (arena arcillosa). Para este propósito se han aplicado métodos quimiométricos, tales como la regresión de mínimos cuadrados parciales y la clasificación de las máquinas de soporte vectorial. Los resultados demuestran que la espectroscopia visible y del infrarrojo cercano e espectroscopia (VIS-NIRS) constituye un método adecuado para el pronóstico de propiedades de las muestras homogéneas en términos de textura. Los efectos de la aplicación del fertilizante eran suficientes para la modificación de la composición química del suelo hasta alcanzar el rango que permitió el uso de VIS-NIRS para la calibración y modelización. Los mejores resultados para el pronóstico del contenido del carbón orgánico del suelo y N se obtuvieron utilizando el conjunto completo de datos con la validación cruzada (r² = 0,76, error cuadrático medio con validación cruzada (RMSECV) = 0,04, diferencia porcentual relativa (RPD) = 2,02 y r² = 0,81, RMSECV = 0,01, RPD = 2,20, respectivamente) y el conjunto de datos con validación independiente (r² = 0,70, error cuadrático medio de predicción (RMSEP) = 0,04, RPD = 1,80 y r² = 0,73, RMSEP = 0,03, RPD = 1,22, respectivamente). El uso de fertilizantes y el tipo de rotación de cultivos parecen tener impacto significativo sobre las propiedades espectrales den suelo, ya que con el uso de la metodología de máquinas de soporte vectorial con función lineal nuclea se logró la identificación de muestras del suelo como funciones de dosis administradas de fertilizantes orgánicos e inorgánicos con el 75% de exactitud con validación cruzada y el tipo de rotación de cultivos con la exactitud superior al 90% con validación completa de conjuntos de datos separados

In Situ VIS-NIR Spectroscopy for a Basic and Rapid Soil Investigation

Visible and near-infrared (VIS-NIR) spectroscopy is extensively used in the field of soil science to predict several soil properties, mostly in laboratory conditions. When measured in situ, contact probes are used, and, very often, time-consuming methods are applied to generate better spectra. Unfortunately, spectra obtained by these methods differ greatly from spectra remotely acquired. This study tried to address this issue by measuring reflectance spectra directly with a fibre optic or a 4° lens on bare untouched soils. C, N content and soil texture (sand, silt, and clay) prediction models were established using partial least-square (PLS) and support vector machine (SVM) regression. With spectral pre-processing, some satisfactory models were obtained, i.e., for C content (R2 = 0.57; RMSE = 0.09%) and for N content (R2 = 0.53; RMSE = 0.02%). Some models were improved when using moisture and temperature as auxiliary data for the modelling. Maps of C, N and clay content generated with laboratory and predicted values were presented. Based on this study, VIS-NIR spectra acquired with bare fibre optic and/or a 4° lens could be used to build prediction models in order to obtain basic preliminary information on soil composition at the field scale. The predicting maps seem suitable for a fast but rough field screening

Diffuse Reflectance Spectroscopy for Black Carbon Screening of Agricultural Soils under Industrial Anthropopressure

Visible and near-infrared spectroscopy (VIS-NIRS) is a fast and simple method increasingly used in soil science. This study aimed to investigate VIS-NIRS applicability to predict soil black carbon (BC) content and the method’s suitability for rapid BC-level screening. Forty-three soil samples were collected in an agricultural area remaining under strong industrial impact. Soil texture, pH, total nitrogen (Ntot) and total carbon (Ctot), soil organic carbon (SOC), soil organic matter (SOM), and BC were analyzed. Samples were divided into three classes according to BC content (low, medium, and high BC content) and scanned in the 350–2500 nm range. A support vector machine (SVM) was used to develop prediction models of soil properties. Partial least-square with SVM (PLS-SVM) was used to classify samples for screening purposes. Prediction models of soil properties were at best satisfactory (Ntot: R2 = 0.76, RMSECV = 0.59 g kg−1, RPIQ = 0.65), due to large kurtosis and data skewness. The RMSECV were large (16.86 g kg−1 for SOC), presumably due to the limited number of samples available and the wide data spread. Given our results, the VIS-NIRS method seems efficient for classifying soil samples from an industrialized area according to BC content level (training accuracy of 77% and validation accuracy of 81%)

Characterization of Soil Organic Matter Individual Fractions (Fulvic Acids, Humic Acids, and Humins) by Spectroscopic and Electrochemical Techniques in Agricultural Soils

The objective of this paper was to investigate the molecular characterization of soil organic matter fractions (humic substances (HS): fulvic acids-FAs, humic acids-HAs, and humins-HNs), which are the most reactive soil components. A wide spectrum of spectroscopic (UV–VIS and VIS–nearIR), as well as electrochemical (zeta potential, particle size diameter, and polydispersity index), methods were applied to find the relevant differences in the behavior, formation, composition, and sorption properties of HS fractions derived from various soils. Soil material (n = 30) used for the study were sampled from the surface layer (0–30 cm) of agricultural soils. FAs and HAs were isolated by sequential extraction in alkaline and acidic solutions, according to the International Humic Substances Society method, while HNs was determined in the soil residue (after FAs and HAs extraction) by mineral fraction digestion using a 0.1M HCL/0.3M HF mixture and DMSO. Our study showed that significant differences in the molecular structures of FAs, Has, and HNs occurred. Optical analysis confirmed the lower molecular weight of FAs with high amount of lignin-like compounds and the higher weighted aliphatic–aromatic structure of HAs. The HNs were characterized by a very pronounced and strong condensed structure associated with the highest molecular weight. HAs and HNs molecules exhibited an abundance of acidic, phenolic, and amine functional groups at the aromatic ring and aliphatic chains, while FAs mainly showed the presence of methyl, methylene, ethenyl, and carboxyl reactive groups. HS was characterized by high polydispersity related with their structure. FAs were characterized by ellipsoidal shape as being associated to the long aliphatic chains, while HAs and HNs revealed a smaller particle diameter and a more spherical shape caused by the higher intermolecular forcing between the particles. The observed trends directly indicate that individual HS fractions differ in behavior, formation, composition, and sorption properties, which reflects their binding potential to other molecules depending on soil properties resulting from their type. The determined properties of individual HS fractions are presented as averaged characteristics over the examined soils with different physico-chemical properties

Cutting-edge mass spectrometry methods for the multi-level structural characterization of antibody-drug conjugates

International audienceAntibody drug conjugates (ADCs) are highly cytotoxic drugs covalently attached via conditionally stable linkers to monoclonal antibodies (mAbs) and are among the most promising next-generation empowered biologics for cancer treatment. ADCs are more complex than naked mAbs, as the heterogeneity of the conjugates adds to the inherent microvariability of the biomolecules. The development and optimization of ADCs rely on improving their analytical and bioanalytical characterization by assessing several critical quality attributes, namely the distribution and position of the drug, the amount of naked antibody, the average drug to antibody ratio, and the residual druglinker and related product proportions. Here brentuximab vedotin (Adcetris®) and trastuzumab emtansine (Kadcyla®), the first and gold-standard hinge-cysteine and lysine drug conjugates, respectively, were chosen to develop new mass spectrometry (MS) methods and to improve multiplelevel structural assessment protocols