Rapid determination of lime requirement by mid‐infrared spectroscopy: A promising approach for precision agriculture

Mid‐infrared spectroscopy (MIRS) has proven to be a cost‐effective, high throughput measurement technique for soil analysis. After multivariate calibration mid‐infrared spectra can be used to predict various soil properties, some of which are related to lime requirement (LR). The objective of this study was to test the performance of MIRS for recommending variable rate liming on typical Central European soils in view of precision agriculture applications. In Germany, LR of arable topsoils is commonly derived from the parameters organic matter content (SOM), clay content, and soil pH (CaCl₂) as recommended by the Association of German Agricultural Analytical and Research Institutes (VDLUFA). We analysed a total of 458 samples from six locations across Germany, which all revealed large within‐field soil heterogeneity. Calcareous topsoils were observed at some positions of three locations (79 samples). To exclude such samples from LR determination, peak height at 2513 cm^?1 of the MIR spectrum was used for identification. Spectra‐based identification was accurate for carbonate contents > 0.5%. Subsequent LR derivation (LR_SPP) from MIRS‐PLSR predictions of SOM, clay, and pH (CaCl₂) for non‐calcareous soil samples using the VDLUFA look‐up tables was successful for all locations (R² = 0.54–0.82; RMSE = 857–1414 kg CaO ha^?1). Alternatively, we tested direct LR prediction (LR_DP) by MIRS‐PLSR and also achieved satisfactory performance (R² = 0.52–0.77; RMSE = 811–1420 kg CaO ha^?1; RPD = 1.44–2.08). Further improvement was achieved by refining the VDLUFA tables towards a stepless algorithm. It can be concluded that MIRS provides a promising approach for precise LR estimation on heterogeneous arable fields. Large sample numbers can be processed with low effort which is an essential prerequisite for variable rate liming in precision agriculture.     

基于局部加权回归的土壤全氮含量可见-近红外光谱反演

全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。     

Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

The objective of this study was to develop a near‐infrared (NIR) imaging system to determine rice moisture content. The NIR imaging system fitted with 15 band‐pass filters (wavelengths of 870–1,014 nm) was used to capture the spectral image. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both near‐infrared spectrometry (NIRS) and the NIR imaging system to determine the moisture content of rice. Comprehensive performance comparison among MLR, PLSR, and ANN approaches has been conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six significant wavelengths selected by the MLR model, which had high correlation with the moisture content of rice, were used as the input data of the ANN. The performance of the developed system was evaluated through experimental tests for rice moisture content. This study adopted the coefficient of determination (r_val²), the standard error of prediction (SEP), and the relative performance determinant (RPD) as the performance indices of the NIR imaging system with respect to the tests of rice moisture content. Utilizing these three models, the analysis results of r_val², SEP, and RPD for the validation set were within 0.942–0.952, 0.435–0.479%, and 4.2–4.6, respectively. From experimental results, the performance of NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided a high prediction capacity for the determination of moisture in rice samples. These results indicated that the NIR imaging system developed in this study can be used as a device for the measurement of rice moisture content.     

Is soil water repellency a function of soil order and proneness to drought? A survey of soils under pasture in the North Island of New Zealand

We conducted a survey of the occurrence of soil water repellency (SWR) in the top 40 mm of soils across 50 sites under pastoral land use in the North Island of New Zealand. The sites represented ten soil orders and covered five classes of proneness to drought. We found at least a moderate persistence of SWR in 35 out of 50 sites (70%) in summer 2009/2010 and a moderate potential persistence of SWR in 49 out of 50 sites (98%) after drying the soils. The soil orders had an influence on the degree and persistence of SWR. Both the degree and persistence of SWR were greatest for the soil orders Podzol, Organic and Recent, and least for the soil order Allophanic. On average, all soil orders had contact angles larger than 94°, with the exception of the soil order Allophanic. We found no relationship between SWR and drought‐proneness. The degree of SWR and its persistence for air‐dried samples were positively correlated with soil carbon and nitrogen contents and negatively with soil bulk density. The persistence of SWR for field‐fresh samples was additionally negatively correlated with the soil water content. We identified a close relationship (R² = 0.84) between the degree and persistence of SWR. The survey results indicate that SWR is at least moderately persistent in a soil with a contact angle larger than 93.8°. Using a water‐drop penetration time of 60 s as the threshold for SWR being moderately persistent, we found that moderately persistent SWR occurred only for volumetric water contents below 45% or a relative saturation of 60%. The latter can be considered to be a generic value of the critical water content for the onset of SWR at the scale of the North Island of New Zealand.     

Near infrared reflectance spectroscopy: A tool to characterize the composition of different types of exogenous organic matter and their behaviour in soil

In addition to total organic carbon and nitrogen, potential organic carbon mineralization under controlled laboratory conditions and indicators such as the indicator of remaining organic carbon in soil (I_ROC), based on Van Soest biochemical fractionation and short-term carbon mineralization in soil, are used to predict the evolution of exogenous organic matter (EOM) after its application to soils. The purpose of this study was to develop near infrared reflectance spectroscopy (NIRS) calibration models that could predict these characteristics in a large dataset including 300 EOMs representative of the broad range of such materials applied to cultivated soils (plant materials, animal manures, composts, sludges, etc.). The NIRS predictions of total organic matter and total organic carbon were satisfactory (R²_P = 0.80 and 0.85, ratio of performance to deviation, RPD_P = 2.2 and 2.6, respectively), and prediction of the Van Soest soluble, cellulose and holocellulose fractions were acceptable (R²_P = 0.82, 0.73 and 0.70, RPD_P = 2.3, 1.9 and 1.8, respectively) with coefficients of variation close to those of the reference methods. The NIRS prediction of carbon mineralization during incubation was satisfactory and indeed better regarding the short-term results of mineralization (R²_P = 0.78 and 0.78, and RPD_P = 2.1 and 2.0 for 3 and 7 days of incubation, respectively). The I_ROC indicator was predicted with fairly good accuracy (R²_P = 0.79, RPD_P = 2.2). Variables related to the long-term C mineralization of EOM in soil were not predicted accurately, except for I_ROC which was based on analytical and well-identified characteristics, probably because of the increasing interactions and complexity of the factors governing EOM mineralization in soil as a function of incubation time. This study demonstrated the possibility of developing NIRS predictive models for EOM characteristics in heterogeneous datasets of EOMs. However, specific NIRS predictive models still remain necessary for sludges, organo-mineral fertilizers and liquid manures.     

Determination of total soil organic C and hot water‐extractable C from VIS‐NIR soil reflectance with partial least squares regression and spectral feature selection techniques

The calibration of soil organic C (SOC) and hot water‐extractable C (HWE‐C) from visible and near‐infrared soil reflectance spectra is hindered by the complex spectral interaction of soil chromophores that usually varies from one soil or soil type to another. The exploitation of spectral variables from spectroradiometer data is further affected by multicollinearity and noise. In this study, a set of soil samples (Fluvisols, Podzols, Cambisols and Chernozems; n = 48) representing a wide range of properties was analysed. Spectral readings with a fibre‐optics visible to near‐infrared instrument were used to estimate SOC and HWE‐C contents by partial least squares regression (PLS). In addition to full‐spectrum PLS, spectral feature selection techniques were applied with PLS (uninformative variable elimination, UVE‐PLS, and a genetic algorithm, GA‐PLS). On the basis of normalized spectra (mean centring + vector normalization), the order of prediction accuracy was GA‐PLS ? UVE‐PLS > PLS for SOC; for HWE‐C, it was GA‐PLS > UVE‐PLS, PLS. With GA‐PLS, acceptable cross‐validated (cv) prediction accuracies were obtained for the complete dataset (SOC, , RPD_cv = 2.42; HWE‐C_cv, , RPD_cv = 2.13). Splitting the soil data into two groups with different basic properties (Podzols compared with Fluvisols/Cambisols; n = 21 and n = 23, respectively) improved SOC predictions with GA‐PLS distinctly (Podzols, , RPD_cv = 3.14; Fluvisols/Cambisols, , RPD_cv = 3.64). This demonstrates the importance of using stratified models for successful quantitative approaches after an initial rough screening. GA selection frequencies suggest that the spectral region over 1900 nm, and in particular the hydroxyl band at 2200 nm are of great importance for the spectral prediction of both SOC and HWE‐C.     

Application of VNIR and machine learning technologies to predict heavy metals in soil and pollution indices in mining areas

Purpose

Soil pollution indices are an effective tool in the computation of metal contamination in soil. They monitor soil quality and ensure future sustainability in agricultural systems. However, calculating a soil pollution index requires laboratory measurements of multiple soil heavy metals, which increases the cost and complexity of evaluating soil heavy metal pollution. Visible and near-infrared spectroscopy (VNIR, 350–2500 nm) has been widely used in predicting soil properties due to its advantages of a rapid analysis, non-destructiveness, and a low cost.

Methods

In this study, we evaluated the ability of the VNIR to predict soil heavy metals (As, Cu, Pb, Zn, and Cr) and two commonly used soil pollution indices (Nemerow integrated pollution index, NIPI; potential ecological risk index, RI). Three nonlinear machine learning techniques, including cubist regression tree (Cubist), Gaussian process regression (GPR), and support vector machine (SVM), were compared with partial least squares regression (PLSR) to determine the most suitable model for predicting the soil heavy metals and pollution indices.

Results

The results showed that the nonlinear machine learning models performed significantly better than the PLSR model in most cases. Overall, the SVM model showed a higher prediction accuracy and a stronger generalization for Zn (R²_V?=?0.95, RMSE_V?=?6.75 mg kg^?1), Cu (R²_V?=?0.95, RMSE_V?=?8.04 mg kg^?1), Cr (R²_V?=?0.90, RMSE_V?=?6.57 mg kg^?1), Pb (R²_V?=?0.86, RMSE_V?=?4.14 mg kg^?1), NIPI (R²_V?=?0.93, RMSE_V?=?0.31), and RI (R²_V?=?0.90, RMSE_V 3.88). In addition, the research results proved that the high prediction accuracy of the three heavy metal elements Cu, Pb, and Zn and their significant positive correlations with the soil pollution indices were the reason for the accurate prediction of NIPI and RI.

Conclusion

Using VNIR to obtain soil pollution indices quickly and accurately is of great significance for the comprehensive evaluation, prevention, and control of soil heavy metal pollution.

     

两种常用方法测定土壤斥水性结果的相关性研究

土壤斥水性不利于农业生产和环境的可持续性发展,引起了很多土地利用问题。为了合理地选择土壤斥水特性的测定方法,该文采用常用的滴水穿透时间法与酒精溶液入渗法两种方法测定了内蒙古锡林浩特羊草草原土壤的斥水性,并分析了两种方法的相关性。结果表明：两种方法测定的土壤斥水性具有较强的相关性,在一定范围内可以通过文中提出的多项式进行转换,并获得了中度斥水性等级以下的酒精溶液入渗法的土壤斥水性等级指标。     

Use of different chemometric approaches for an estimation of soil properties at field scale with near infrared spectroscopy

Visible and near infrared spectroscopy (vis‐NIRS) may be useful for an estimation of soil properties in arable fields, but the quality of results are often variable depending on the applied chemometric approach. Partial least squares regression (PLSR) may be replaced by approaches which employ supervised learning methods or variable selection procedures in order to increase the proportion of informative wavelengths used in the estimation procedure, to reduce the noise of the spectra and to find the best fitting solution. Objectives were (1) to compare the usefulness of PLSR with either PLSR combined with a genetic algorithm (GA‐PLSR) or support vector machine regression (SVMR) for an estimation of soil organic carbon (SOC), total nitrogen (N), pH, cation exchange capacity (CEC) and soil texture for surface soils (0–5 cm, n = 144) of an arable field in Bangalore (India) and (2) to test and optimize different calibration strategies for GA‐PLSR for an improved estimation of soil properties. PLSR was useful for an estimation of SOC, N, sand and clay. In the cross‐validation (n = 96), accuracies of estimated soil properties generally decreased in the order GA‐PLSR > SVMR > PLSR. However, the order of estimation accuracies for the random validation sample (n = 48) changed to SVMR > GA‐PLSR > PLSR for SOC, N, pH, and CEC, whereas for clay the order changed to SVMR > PLSR > GA‐PLSR. A sequential procedure, which used the most frequently selected wavelengths of the GA‐PLSR runs, proved to be useful for an improved estimation of SOC and N. Overall, SVMR especially improved estimations of SOC and clay, whereas GA‐PLSR was particularly useful for SOC and N and it was the only approach which successfully estimated CEC in cross‐validation and validation.     

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

基于EM38和WorldView-2影像的土壤盐渍化建模研究

在干旱半干旱地区,土壤盐渍化是常见的土地退化问题之一。本研究选取于田县克里雅河上游边缘典型盐渍化区域作为研究靶区,通过EM38大地电导率仪实测土壤表观电导率,提取不同系数下的土壤调节植被指数(SAVI),分析了SAVI指数与土壤电导率间的相关性,并利用同时期WorldView-2影像的敏感波段建立了基于高分辨率影像数据的土壤盐渍化偏最小二乘回归(PLSR)模型并进行了精度验证。结果表明:①从遥感影像提取SAVI指数时,在系数(L)调节范围内选取固定系数值,系数值(间隔为0.1)从0.1变化到1.0的过程中,相应提取的SAVI指数与土壤电导率的相关性明显提升,相关性系数(r)从0.30提高到0.50,并通过显著性检验(P0.01)。②选取的SAVI1.0、B6、B7、B8四种变量中,以SAVI1.0+B6+B8为变量组合所建立的PLSR模型为最优,该模型较其他变量组合建模的决定系数(R2p)提高了0.11,因此,在研究区该模型具有更好的预测能力,模型精度为RMSEC=0.77dS/m、RC2=0.68、RMSEP=0.79 dS/m、RP2=0.66、RPD=2.2。     

Non-destructive estimation of winter wheat leaf moisture content using near-ground hyperspectral imaging technology

ABSTRACT

Accurate monitoring of crop moisture content is very important for irrigation scheduling and yield increase. This study aims to construct an optimal estimation model of winter wheat leaf moisture content (LMC) through spectral data processing and feature band selection. LMC and spectral reflectance were measured in 2017-2018 to construct models using simple linear regression (SLR), principal components regression (PCR), and partial least square regression (PLSR); feature bands for modelling were selected through correlation analysis and the effects of feature band number on estimation accuracy were compared. The results showed that data transformation significantly enhanced the correlation between spectral features and LMC. However, the band position corresponding to the maximum correlation coefficient for each transformation was not fixed. The accuracy of PLSR models were significantly higher than that of PCR and SLR models. The comparison of relative percent deviation (RPD) values indicated that the RPD values increased rapidly and then tended to be stable with the increase of feature band number. The R′′ -PLSR model constructed with 28 feature bands (R²_c = 0.8517; RPD > 2.0) estimated the LMC more accurately than other models. This study provides a good method for non-destructive monitoring of crop moisture content.     

Prediction of volumetric soil organic carbon from field‐moist intact soil cores

This study investigated the potential for visible–near‐infrared (vis–NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field‐moist soil cores. One hundred cores were collected from a 71‐ha arable field. The vis–NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3 m. Cores were then divided into 0.1‐m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least‐squares regression (PLSR) models for bulk density (ρ_b), gravimetric SOC (SOC_g) and volumetric SOC (SOC_v). Accurate predictions were obtained from averages of spectra from those 0.1‐m increments for SOC_g (ratio of performance to inter‐quartile (RPIQ) = 5.15; root mean square error (RMSE) = 0.38%) and SOC_v (RPIQ = 5.25; RMSE = 4.33 kg m^?3). The PLSR model for ρ_b performed least well, but still produced accurate results (RPIQ = 3.76; RMSE = 0.11 Mg m^?3). Predictions for ρ_b and SOC_g were combined to compare indirect and direct predictions of SOC_v. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOC_v is possible. The PLSR models calibrated on the 10‐cm depth intervals were also applied to the spectra originally recorded on a 1‐cm depth increment. While a bigger bias was observed for 1‐cm than for 10‐cm predictions (1.13 and 0.19 kg m^?3, respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis–NIR spectroscopy on field‐moist soil cores to predict SOC at high depth resolutions (1 cm) with locally derived calibrations.     

Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection

Purpose

Fast and real-time prediction of leaf nutrient concentrations can facilitate decision-making for fertilisation regimes on farms and address issues raised with over-fertilisation. Cacao (Theobroma cacao L.) is an important cash crop and requires nutrient supply to maintain yield. This project aimed to use chemometric analysis and wavelength selection to improve the accuracy of foliar nutrient prediction.

Materials and methods

We used a visible-near infrared (400–1000 nm) hyperspectral imaging (HSI) system to predict foliar calcium (Ca), potassium (K), phosphorus (P) and nitrogen (N) of cacao trees. Images were captured from 95 leaf samples. Partial least square regression (PLSR) models were developed to predict leaf nutrient concentrations and wavelength selection was undertaken.

Results and discussion

Using all wavelengths, Ca (R²_CV?=?0.76, RMSE_CV?=?0.28), K (R²_CV?=?0.35, RMSE_CV?=?0.46), P (R²_CV?=?0.75, RMSE_CV?=?0.019) and N (R²_CV?=?0.73, RMSE_CV?=?0.17) were predicted. Wavelength selection increased the prediction accuracy of Ca (R²_CV?=?0.79, RMSE_CV?=?0.27) and N (R²_CV?=?0.74, RMSE_CV?=?0.16), while did not affect prediction accuracy of foliar K (R²_CV?=?0.35, RMSE_CV?=?0.46) and P (R²_CV?=?0.75, RMSE_CV?=?0.019).

Conclusions

Visible-near infrared HSI has a good potential to predict Ca, P and N concentrations in cacao leaf samples, but K concentrations could not be predicted reliably. Wavelength selection increased the prediction accuracy of foliar Ca and N leading to a reduced number of wavelengths involved in developed models.

     

Cadmium Release in Soil Solutions at Low Moisture Content

Abstract

Speciation of cadmium (Cd) was studied in four spiked agricultural soils at moisture content corresponding to 1.2 times field moisture capacity (FMC) and in the range from 1.2 FMC to soil–water 1∶10. Cadmium desorption isotherms were nonlinear in all soils, resulting in the decrease in Cd partition coefficient with loading. The Windermere Humic Aqueous Model (WHAM VI) was applied to predict Cd concentration in the solutions, and predicted values were compared with the measured ones. Based on total Cd content in soils, with concentrations of dissolved organic carbon (DOC), calcium (Ca), magnesium (Mg), and sodium (Na) and soil solution pH as the input variables, WHAM VI predicted Cd concentration in soil solutions with the root‐mean‐square error (RMSE) of log[Cd] RMSE_log[Cd]=0.54 (n=37). Using total Cd content in soils, average concentrations of Ca and DOC in soil solutions, and soil pH instead of soil solution pH enabled prediction of Cd concentration in soil solution with RMSE_log[Cd]=0.56. Calculation of Cd concentration as a function of moisture content resulted in RMSE_log[Cd]=0.25 (n=20).     

Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms

The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R²) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R² and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.     

The use of Vis-NIR spectral reflectance for determining root density: evaluation of ryegrass roots in a glasshouse trial

This paper reports the use of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm^?3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm^?3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm^?3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r² cross‐validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross‐validation (RMSECV) = 0.60 mg cm^?3; with removing outliers, r² cross‐validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm^?3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.     

Use of FTIR‐PAS combined with chemometrics to quantify nutritional information in rapeseeds (Brassica napus)

Fast acquisition of nutritional information of rapeseeds is important for rapeseed breeding programs, evaluation of soil nutrient conditions and even fertilization recommendations. Fourier transform mid‐infrared photoacoustic spectroscopy (FTIR‐PAS) was employed to determine nitrogen, phosphorus and potassium content in rapeseeds. Calibration models were developed using both partial least squares (PLS) and partial least squares combined with direct orthogonal signal correction (DOSC‐PLS). According to the values of RPD (ratio of prediction to deviation), the PLS models for nitrogen and phosphorus were acceptable, while the PLS model for potassium needed to be improved. By contrast, DOSC‐PLS models obtained the better predictive accuracy with RPD values of 2.54, 2.10 and 1.94 for nitrogen, phosphorus and potassium, respectively. This work demonstrates the good performance of FTIR‐PAS for rapid and non‐destructive quantification of nutritional information in rapeseeds.     

Ash and Mineral Contents in Leaves of Woody Species: Analysis by Near‐Infrared Reflectance Spectroscopy

Abstract

Near‐infrared reflectance spectroscopy (NIRS) was evaluated for its effectiveness to determine ash and mineral concentrations [potassium (K), magnesium (Mg), copper (Cu), iron (Fe), and zinc (Zn)], in a total of 182 leaf samples of 17 woody species located in the central‐western region of the Iberian Peninsula. Chemical analysis revealed great variability in all leaf mineral elements. This variability was mainly related to differences in leaf habit (deciduous versus evergreen) and to differences in mean leaf longevity and among leaf age classes within evergreen species. A set of samples including all 17 species and leaf age classes was used to develop the calibration equations using multiple linear regression (MLR) and partial‐least squares regression (PLSR). The set of samples that did not enter in the calibration was used for external validation. In general, the most satisfactory results were obtained using PLSR and derivative transformations. Despite the strong heterogeneity of the samples included in the study, the results showed that NIRS can be employed as an effective tool, alternative to the more time‐consuming standard methods. The best predictive model was obtained for ash content. Models with acceptable accuracy were obtained in the prediction of K and Mg contents. However, their applicability for the determination of trace elements was more limited.     

Application of Near Infrared Reflectance Spectroscopy for the Assessment of Soil Quality in a Long‐Term Pasture

Abstract

The objective of the present study was to assess the ability of near infrared reflectance spectroscopy (NIRS) to analyze chemical soil properties and to evaluate the effects of different phosphorus (P) and potassium (K) fertilization rates on soil quality in different layers of a long‐term pasture. The NIRS calibrations were developed for humus, total Kjeldahl nitrogen (N_Kjeldahl), and several humic substances (HA1, “mobile” humic acids fraction; ΣHA, sum of humic acids; FA1, “mobile” fulvic acids; ΣFA, sum of fulvic acids, etc.) using soil samples of rather heterogeneous origin, collected during 1999–2003. Different spectral preprocessing and the modified partial least squares (MPLS) regression method were explored to enhance the relation between the spectra and measured soil properties. The equations were employed for the quality prediction of a sod gleyic light loam (Cambisol) in five PK fertilization treatments. The soil was sampled in 2000 and 2003 in three field replicates at depths of 0–10, 10–20, 20–30, and 30–50 cm, n=60 samples yr^?1. The best coefficients of correlation, R², between the reference and NIRS‐predicted data were as follows: for N_Kjeldahl, 0.965; humus, 0.938; HA1, 0.903; HA2, 0.905; HA3, 0.924; ΣHA, 0.904; and FA1, 0.911; and ΣFA, 0.885. Our findings suggest that it is feasible to use NIRS for the assessment of the effects of the inorganic PK fertilizer on the soil quality in different depths of a long‐term pasture.