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.     

Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid‐infrared diffuse reflectance spectroscopy

This study investigated the suitability of mid‐infrared diffuse reflectance Fourier transform (MIR‐DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off‐shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross‐validation of soil property predictions was assessed by the coefficient of determination (R²), the root mean square error of cross‐validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross‐validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross‐validation appeared to be the most accurate (R² = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross‐validation for OC and IC (R² = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R² = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.     

Accounting for the effects of water and the environment on proximally sensed vis–NIR soil spectra and their calibrations

Visible–near infrared (vis–NIR) spectroscopy can be used to estimate soil properties effectively using spectroscopic calibrations derived from data contained in spectroscopic databases. However, these calibrations cannot be used with proximally sensed (field) spectra because the spectra in these databases are recorded in the laboratory and are different to field spectra. Environmental factors, such as the amount of water in the soil, ambient light, temperature and the condition of the soil surface, cause the differences. Here, we investigated the use of direct standardization (DS) to remove those environmental factors from field spectra. We selected 104 sensing (sampling) sites from nine paddy fields in Zhejiang province, China. At each site, vis–NIR spectra were recorded with a portable spectrometer. The soils were also sampled to record their spectra under laboratory conditions and to measure their soil organic matter (SOM) content. The resulting data were divided into training and validation sets. A subset of the corresponding field and laboratory spectra in the training set (the transfer set) was used to derive the DS transfer matrix, which characterizes the differences between the field and laboratory spectra. Using DS, we transferred the field spectra of the validation samples so that they acquired the characteristics of spectra that were measured in the laboratory. A partial least squares regression (PLSR) of SOM on the laboratory spectra of the training set was then used to predict both the original field spectra and the DS‐transferred field spectra. The assessment statistics of the predictions were improved from R² = 0.25 and RPD = 0.35 to R² = 0.69 and RPD = 1.61. We also performed independent predictions of SOM on the DS‐transferred field spectra with a PLSR derived using the Chinese soil spectroscopic database (CSSD), which was developed in the laboratory. The R² and RPD values of these predictions were 0.70 and 1.79, respectively. Predictions of SOM with the DS‐transferred field spectra were more accurate than those treated with external parameter orthogonalisation (EPO), and more accurate than predictions made by spiking. Our results show that DS can effectively account for the effects of water and environmental factors on field spectra and improve predictions of SOM. DS is conceptually straightforward and allows the use of calibrations made with laboratory‐measured spectra to predict soil properties from proximally sensed (field) spectra, without needing to recalibrate the models.     

Influence of Soil Particle Size on the Measurement of Sodium by Near-Infrared Reflectance Spectroscopy

This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0–10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100–2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R²) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg^–1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg^–1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R² and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg^–1) and 0.80 (RMSECV: 0.03 mg kg^–1) for SPS-0.053 and SPS-0.212 samples, respectively.     

基于可见/近红外光谱技术的土壤总磷分析研究

Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350--2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R²) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R² in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.     

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.     

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.     

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 visible and near‐infrared spectroscopy for the analysis of soil water repellency

This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R²_val = 0.61, RPD_val = 1.60, RMSE_val = 0.59) and good prediction for %C (R²_val = 0.82, RPD_val = 2.30, RMSE_val = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.     

Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Seffected South African Soils

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.     

Measurement of Phosphorus in Soils by Near Infrared Reflectance Spectroscopy: Effect of Reference Method on Calibration

Abstract

The use of ultraviolet (UV), visible (VIS), near infrared reflectance (NIR), and midinfrared (MIR) spectroscopy techniques have been found to be successful in determining the concentration of several chemical properties in soils. The aim of this study was to evaluate the effect of two reference methods, namely Bray and Resins, on the VIS and NIR calibrations to predict phosphorus in soil samples. Two hundred (n=200) soil samples were taken in different years from different locations across Uruguay with different physical and chemical characteristics due to different soil types and management. Soil samples were analyzed by two reference methods (Bray and Resins) and scanned using an NIR spectrophotometer (NIRSystems 6500). Partial least square (PLS) calibration models between reference data and NIR data were developed using cross‐validation. The coefficient of determination in calibration (R²) and the root mean square of the cross validation (RMSECV) were 0.58 (RMSECV: 3.78 mg kg^?1) and 0.61 (RMSECV: 2.01 mg kg^?1) for phosphorus (P) analyzed by Bray and Resins methods, respectively, using the VIS and NIR regions. The R² and RMSECV for P using the NIR region were 0.50 (RMSECV: 3.78 mg kg^?1) and 0.58 (RMSECV: 2.01 mg kg^?1). This study suggested that differences in accuracy and prediction depend on the method of reference used to develop an NIR calibration for the measurement of P in soil.     

Microbial CO2 production, CH4 dynamics and nitrogen in a wetland soil (New York State, USA) associated with three plant species (Typha, Lythrum, Phalaris)

Plants furnish soil with organic carbon (OC) compounds that fuel soil microorganisms, but whether individual plant species – or plants with unique traits – do so uniquely is uncertain. We evaluated soil microbial processes within a wetland in which areas dominated by a distinct plant species (cattail –Typha sp.; purple loosestrife –Lythrum salicaria L.; reed canarygrass –Phalaris arundinacea L.) co‐mingled. We also established an experimental plot with plant shoot removal. The Phalaris area had more acidic soil pH (7.08 vs. 7.27–7.57), greater amount of soil organic matter (19.0% vs. 9.0–11.5%), and the slowest production rates of CO₂ (0.10 vs. 0.21–0.46 μmol kg⁻¹ s⁻¹) and CH₄ (0.040 vs. 0.054–0.079 nmol kg⁻¹ s⁻¹). Nitrogen cycling was dominated by net nitrification, with similar rates (17.2–18.9 mg kg⁻¹ 14 days⁻¹) among the four sampling areas. In the second part of the study, we emplaced soil cores that either allowed root in‐growth or excluded roots to evaluate how roots directly affect soil CO₂ and CH₄. The three plant species had similar amounts of root growth (ca 290 g m⁻² year⁻¹). Fungal biomass was similar in soils with root in‐growth versus root exclusion, regardless of dominant plant species. Rates of soil CO₂ production did not differ with root in‐growth versus root exclusion, and added glucose increased CO₂ production rates by only 35%. Root in‐growth did lead to greater rates of CH₄ production; albeit, addition of glucose had much greater effect on CH₄ production (1.24 nmol kg⁻¹ s⁻¹) compared with controls without added glucose (0.058 nmol kg⁻¹ s⁻¹). Our data revealed relatively few subtle differences in soil characteristics and processes associated with different plant species; albeit, roots had little effect, even inhibiting some microbial processes. This research highlights the need for both field and experimental studies in long‐established monocultures of plant species to understand the role of plant biodiversity in soil function.     

Effect of land‐use on soil nutrients and microbial biomass of an alpine region on the northeastern Tibetan plateau,China

Land‐use patterns affect the quantity and quality of soil nutrients as well as microbial biomass and respiration in soil. However, few studies have been done to assess the influence of land‐use on soil and microbial characteristics of the alpine region on the northeastern Tibetan plateau. In order to understand the effect of land‐use management, we examined the chemical properties and microbial biomass of soils under three land‐use types including natural grassland, crop‐field (50 + y of biennial cropping and fallow) and abandoned old‐field (10 y) in the area. The results showed that the losses of soil organic carbon (SOC) and total nitrogen (TN) were about 45 and 43 per cent, respectively, due to cultivation for more than 50 y comparing with natural grassland. Because of the abandonment of cultivation for about a decade, SOC and TN were increased by 27 and 23 per cent, respectively, in comparison with the crop field. Microbial carbon (ranging from 357·5 to 761·6 mg kg⁻¹ soil) in the old‐field was intermediate between the crop field and grassland. Microbial nitrogen (ranging from 29·9 to 106·7 mg kg⁻¹ soil) and respiration (ranging from 60·4 to 96·4 mg CO₂‐C g⁻¹ C_mic d⁻¹) were not significantly lower in the old‐field than those in the grassland. Thus it could be concluded that cultivation decreased the organic matter and microbial biomass in soils, while the adoption of abandonment has achieved some targets of grassland restoration in the alpine region of Gansu Province on the northeastern Tibetan plateau. Copyright © 2010 John Wiley & Sons, Ltd.     

The effect of compaction on soil electrical resistivity: a laboratory investigation

Among the geophysical tools used in soil science, electrical methods are considered as potentially useful to characterize soil compaction intensity. A laboratory investigation was undertaken on agricultural and forest soils in order to study the impact of compaction on bulk soil electrical resistivity. Samples taken from four different types of loamy soils were compacted at three bulk densities (1.1, 1.3 and 1.6 g cm⁻³). Bulk soil resistivity was measured at each compacted state for gravimetric water contents ranging from 0.10 to 0.50 g g⁻¹. A specific experimental procedure allowed the control of the water‐filling of the intra‐aggregate pores and the inter‐aggregate pores. Soil resistivity decreased significantly with increase in density and typically for gravimetric water contents less than 0.25 g g⁻¹. The decrease was more pronounced for the drier soils, indicating the strong impact of the surface conductance, especially in agricultural soils. The experimental data were in good agreement with simulated values given by the petro‐physical model of Waxman‐Smits (1968) , at least for water saturation greater than 0.3. The analysis of the petro‐physical parameters derived from the experimental data suggested that: (i) the electrical tortuosity of the loamy agricultural soil was significantly affected by compaction and (ii) the forest soil had a singular microstructure from an electrical point of view and had isolated conductive zones associated with clay embedded in a poorly conductive medium comprised mainly of soil solution and quartz grains. Our results provide the phenomenological basis for assessing, in the field, the relationship between soil electrical resistivity and compaction intensity.     

The phosphorus source determines the arbuscular mycorrhizal potential and the native mycorrhizal colonization of tall fescue and wheatgrass

The study assesses the effect of two phosphate (P) sources (soluble superphosphate (SP) and rock phosphate (RP)) on the arbuscular mycorrhizal potential (AMP), the root arbuscular mycorrhizal colonization (AMC) and the growth of tall fescue and wheatgrass of a grassland soil from Argentina. Mycorrhizal potential was assessed with soil samples collected from 2 years for tall fescue and wheatgrass swards before and after field plots were fertilized with 0 and 60 kg P ha⁻¹ as SP or RP. Mycorrhizal potential both at unfertilized and at RP fertilized plots was high (12–14 AM propagules g⁻¹), however fertilization with SP caused a decrease in AMP (0.70–0.95 AM propagules g⁻¹). A range of soil P between 4 and 46 mg P kg⁻¹ and a range of root AMC between 6% and 50% were obtained after fertilization with four rates of SP and RP (0, 15, 30, and 60 kg P ha⁻¹) in plots where tall fescue and wheatgrass were grown during 2 years. Soil P and root mass were higher in the top 10-cm depth than in the 20-cm of the soil profile, but AMC did not change with depth. Shoot dry matter (SDM) production of both grasses did not differ after fertilization with SP or RP, particularly at second year. The AMP positively correlated with the indigenous AMC, and they were not different between tall fescue or wheatgrass. Lineal-plateau relationships between soil P, relative SDM and AMC were established. Highest relative SDM was attained at 6.5 mg P kg⁻¹ in plots fertilized with RP, and at 15.2 mg P kg⁻¹ with SP. Variability in colonization was well accounted by the soil P (at 0–10 cm depth) fertilized with SP (r² = 0.48, P 0.01), but any relationship was found with RP. The AMC decreased with increasing available soil P from plots with SP until 18.3 mg kg⁻¹ (a decrease of 2.2% per mg P kg), after that AMC was stabilized at about 6.9%. Our study clearly showed that fertilization with SP or RP produced similar available soil P content and grasses SDM production. Mycorrhiza root colonization and propagules decreased after fertilization with SP, but fertilization with RP did not decrease mycorrhizal propagules nor colonization. It can be concluded that RP fertilization instead SP could allow obtaining acceptable tall fescue and wheatgrass yield enhancing mycorrhizal potential of soils and indigenous colonization of plants and thus maximizing the use of fertilizer.     

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.     

Towards development of on-line soil moisture content sensor using a fibre-type NIR spectrophotometer

As the soil moisture content (w) is a deterministic factor for site-specific irrigation, seeding, transplanting and compaction detection, an on-line measurement system will bring these applications into practice. A fibre-type visible (VIS) and near-infrared (NIR) spectrophotometer, with a light reflectance measurement range of 306.5–1710.9 nm was used to measure w during field operation. The spectrophotometer optical unit was attached to the subsoiler chisel backside to perform the light reflectance measurement from the soil surface on the bottom of the trench opened by the proceeded chisel. The spectrophotometer–optical unit system was calibrated for w under stationary laboratory conditions on samples collected from an Arenic Cambisol field with different soil textures. A partial least square analysis was carried out in order to establish a statistical model relating soil light spectra with the gravimetric w of the 0.005–0.26 kg kg⁻¹ range. This model was validated with the full cross validation method resulting in a small root mean square error of cross validation (RMSECV) of 0.0175 kg kg⁻¹ and a high validation correlation of 0.978. Further validation of the model developed in the laboratory under stationary state showed also a small root mean square error of prediction (RMSEP) of 0.0165 kg kg⁻¹ and a prediction correlation of 0.982. When the NIR sensor-model system was used to determine w, based on on-line field measurement, a relatively larger RMSEP of 0.025 kg kg⁻¹ and lower prediction correlation of 0.75 were found. However, a reasonably similar spatial distribution of w was found between the on-line NIR measurement and oven drying methods. Therefore, the on-line NIR w sensor developed is recommended to provide valuable information towards the site-specific applications in soils.     

Phosphorus acquisition and wheat growth are influenced by shoot phosphorus status and soil phosphorus distribution in a split‐root system

Root proliferation and greater uptake per unit of root in the nutrient‐rich zones are often considered to be compensatory responses. This study aimed to examine the influence of plant phosphorus (P) status and P distribution in the root zone on root P acquisition and root and shoot growth of wheat (Triticum aestivum L.) in a split‐root soil culture. One compartment (A) was supplied with either 4 or 14 mg P (kg soil)^–1, whereas the adjoining compartment (B) had 4 mg P kg^–1 with a vertical high‐P strip (44 mg kg^–1) at 90–110 mm from the plant. Three weeks after growing in the split‐root system, plants with 4 mg P kg^–1 (low‐P plants) started to show stimulatory root growth in the high‐P strip. Two weeks later, root dry weight and length density in the high‐P strip were significantly greater for the low‐P plants than for the plants with 14 mg P (kg soil)^–1. However, after 8 weeks of growth in the split‐root system, the two P treatments of compartment A had similar root growth in the high‐P strip of compartment B. The study also showed that shoot P concentrations in the low‐P plants were 0.6–0.8 mg g^–1 compared with 1.7–1.9 mg g^–1 in the 14 mg P kg^–1 plants after 3 and 5 weeks of growth, but were similar (1.1–1.4 mg g^–1) between the two plants by week 8. The low‐P plants had lower root P concentration in both compartments than those with 14 mg P kg^–1 throughout the three harvests. The findings may indicate that root proliferation and P acquisition under heterogeneous conditions are influenced by shoot P status (internal) and soil P distribution (external). There were no differences in the total root and shoot dry weight between the two P treatments at weeks 3 and 5 because enhanced root growth and P uptake in the high‐P strip by the low‐P plants were compensated by reduced root growth elsewhere. In contrast, total plant growth and total root and shoot P contents were greater in the 14 mg P kg^–1 soil than in the low‐P soil at week 8. The two P treatments did not affect the ratio of root to shoot dry weight with time. The results suggest that root proliferation and greater P uptake in the P‐enriched zone may meet the demand for P by P‐deficient plants only for a limited period of time.     

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BP神经网络的不同人为干扰强度下盐渍土SO42-定量分析

SO_4~(2-)是盐渍土阴离子中的主要离子,但目前针对不同人为干扰区域土壤中SO_4~(2-)反演研究却鲜有报道。土壤高光谱与土壤某元素间的关系表现为非线性,传统线性偏最小二乘模型(PLSR)对土壤元素的反演精度有限。本文以新疆昌吉回族自治州境内不同人为干扰区域的盐渍化土壤为研究对象,以土壤的野外高光谱和SO_4~(2-)含量为数据源,对原始(R)和对数(LogR)变换后的高光谱分别进行0阶、一阶和二阶微分预处理,选择通过0.05显著性水平的波段为敏感波段,将敏感波段对应的高光谱反射率作为非线性BP神经网络模型的输入变量,并设定BP的隐藏节点为300,学习速率为0.01,最大迭代次数为1 000,训练函数为trainscg。从SO_4~(2-)的真实值与预测值的散点图、拟合效果图和BP训练过程3个方面,定量分析无人为干扰(A区)和有人为干扰(B区)土壤SO_4~(2-)含量,并与PLSR对比预测精度。仿真显示, A区二阶微分后的BP预测精度优于一阶微分,而B区一阶微分后的BP预测精度优于二阶微分。且不论在A区还是B区, LogR光谱变换的反演精度均优于R。最佳BP模型的相对预测性能(RPD)、决定系数(R2)、均方根误差(RMSE)和迭代次数,在A区分别为3.309、0.906、0.253和8次,在B区分别为2.234、0.844、0.786和45次,表明BP对A区SO_4~(2-)的预测能力非常强(RPD2.5),对B区SO_4~(2-)的预测能力较强(RPD为2.0～2.5)。而在A区和B区两种光谱变换的一阶和二阶微分中, PLSR的RPD值均在1.4与1.8之间,其预测性能一般;在B区的0阶微分中, PLSR的RPD值均小于1.0,其不能对SO_4~(2-)进行预测。因此, BP模型能对不同人为干扰区域的SO_4~(2-)进行有效的定量分析。