Prediction of model pools for a long‐term experiment using near‐infrared spectroscopy

Fourty‐one soil samples from the “Eternal Rye” long‐term experiment in Halle, Germany, were used to test the usefulness of near‐infrared spectroscopy (NIRS) to differentiate between C derived from C₃ and C₄ plants by using the isotopic signature (δ¹³C) and to predict the pools considered in the Rothamsted Carbon (RothC) model, i.e., decomposable plant material, resistant plant material, microbial biomass, humified organic matter, and inert organic matter. All samples were scanned in the visible‐light and near‐infrared region (400–2500 nm). Cross‐validation equations were developed using the whole spectrum (first to third derivative) and a modified partial least‐square regression method. δ¹³C values and all pools of the RothC model were successfully predicted by NIRS as reflected by RSC values (ratio between standard deviation of the laboratory results and standard error of cross‐validation) ranging from 3.2 to 3.4. Correlations analysis indicated that organic C can be excluded as basis for the successful predictions by NIRS in most cases, i.e., 11 out of 16.     

Use of near‐ and mid‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils

The presence of relatively inert organic materials such as char has to be considered in calibrations of soil C models or when calculating C‐turnover times in soils. Rapid and cheap spectroscopic techniques such as near‐infrared (NIRS) or mid‐infrared spectroscopy (MIRS) may be useful for the determination of the contents of char‐derived C in soils. To test the suitability of both spectroscopic techniques for this purpose, artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals) and forest‐floor Oa material were produced. The total C content of these mixtures (432 samples) ranged from 0.5% to 6% with a proportion of char‐derived C amounting to 0%, 20%, 40%, 50%, 60%, or 80%. All samples were scanned in the visible and near‐IR region (400–2500 nm). Cross‐validation equations for total C and N, C and N derived from char (C_char, N_char) and Oa material were developed using the whole spectrum (first and second derivative) and a modified partial least‐square regression method. Thirty‐six samples were additionally scanned in the middle‐IR and parts of the near‐IR region (7000–400 cm^–1 which is 1430–25,000 nm) in the diffuse‐reflectance mode. All properties investigated were successfully predicted by NIRS as reflected by RSC values (ratio of standard deviation of the laboratory results to standard error of cross‐validation) > 4.3 and modeling efficiencies (EF) ≥ 0.98. Near‐infrared spectroscopy was also able to differentiate between the different coals. This was probably due to structural differences as suggested by wavelength assignment. Mid‐IR spectroscopy in the diffuse‐reflectance mode was also capable to successfully predict the parameters investigated. The EF values were > 0.9 for all constituents. Our results indicated that both spectroscopic techniques applied, NIRS and MIRS, are able to predict C and N derived from different sources in soil, if closed populations are considered.     

Use of mid‐infrared spectroscopy in the diffuse‐reflectance mode for the prediction of the composition of organic matter in soil and litter

Mid‐infrared spectroscopy (MIRS) is assumed to be superior to near‐infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid‐infrared region including part of the near‐infrared region (7000–400 cm^–1) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C CPMAS‐NMR spectroscopy. A partial least‐square method and cross‐validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid‐infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF ≥ 0.70, 0.8 ≤ a ≤ 1.2, r ≥ 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O‐alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl–to–aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse‐reflectance mode were generally not superior to those of NIRS.     

Near‐infrared spectroscopy can predict the composition of organic matter in soil and litter

The usefulness and limitations of near‐infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near‐infrared reflectance (VIS‐NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS‐NIR region (400–2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C‐CPMAS‐NMR spectroscopy. A modified partial least‐square method and cross‐validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near‐infrared spectroscopy predicted well the C : N ratios, the percentages of O‐alkyl C and alkyl C, the ratio of alkyl C to O‐alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross‐validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 ≤ a ≤ 1.2, r ≥ 0.8, and 1.4 ≤ RSC ≤ 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N‐mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter.     

Rainforest litter quality and chemical controls on leaf decomposition with near‐infrared spectrometry

Plant‐litter chemical quality is an important driver of many ecosystem processes, however, what actually constitutes high‐ or low‐quality litter (chemical potential for fast and slow decomposition, respectively) is often interpreted by the indices available. Here, near‐infrared spectroscopy (NIRS) was used to explore leaf‐litter chemical quality and the controls on decomposition in the tropical rainforest region of north Queensland Australia. Leaf‐litter samples from litterfall collections and litterbag studies were used. NIRS was used to calibrate the chemical compositions of the material (N, P, C, Mg, Ca, acid detergent fiber, acid detergent lignin, α‐cellulose, and total phenolics) from a smaller sample set covering the spectral range in the full set of samples. Calibrations were compared for both separate (local) and combined models, for litterbags, and litterfall. Coefficients of determination (r²) in the local models ranged from 0.88 (litterbag Mg) to 0.99 (litterfall N), with residual prediction deviation ratios > 3 for all constituents except Mg (≈ 2.5). Mass loss in the litterbags was strongly related to the NIR spectra, with model r²'s of 0.75 (in situ leaves) and 0.76 (common control leaf). In situ decomposability was determined from modeling the initial NIR spectra prior to decomposition with litterbag exponential‐decay rates (model r² of 0.81, n = 85 initial samples). A best subset model including litter‐quality, climate, and soil variables predicted decay better than the NIR decomposability model (r² = 0.87). For litter quality alone the NIR model predicted decay rate better than all of the best predictive litter–chemical quality indices. The decomposability model was used to predict in situ decomposability in the litterfall samples. The chemical variables explaining NIR decomposability for litterfall were initial P, C, and phenolics (linear model r² = 0.80, n = 2471). NIRS is a holistic technique that is just as, if not more accurate, than litter–chemical quality indices, when predicting decomposition and decomposability, shown here in a regional field study.     

Use of near infrared spectroscopy to determine biological and chemical characteristics of organic layers under spruce and beech stands

The chemical composition of organic layers of forest soils shows a high spatial variability and fast methods may be required for its study at a landscape level. The objective was to assess the applicability of near infrared spectroscopy (NIRS) to measure several chemical and biological properties of organic layers in spruce, beech, and mixed spruce‐beech stands. Spectra in the VIS‐NIR region (400—2500 nm) were recorded for 406 samples representing Oi, Oe, and Oa layers of forest soils from Solling (Germany), 195 of them were used for calibration and 211 for validation. The calibration equations for each constituent were developed using the whole spectrum (0^th to 3^rd derivative). Humus samples were analyzed for contents of C and N and contents of P, S, Na, K, Ca, Mg, Mn, Fe, and Al after pressure digestion in HNO₃. Additionally, basal respiration and microbial C (C_mic) were measured. NIRS predicted well the contents of C, N, P, S, Ca, Na, K, Fe, and Al and C/N and C/P ratios: the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater or equal 0.9. C_mic (a = 0.87, r = 0.83) was predicted satisfactorily, whereas the prediction of the basal respiration (a = 0.74, r = 0.87) was less satisfactory. Due to liming of some of the plots NIRS failed to predict contents of Mg (a = 1.27, r = 0.68). For all chemical and biological characteristics the best prediction performances were achieved using the whole sample population. Splitting the samples into smaller groups according to a dominant tree species or an organic layer did not improve the predictions.<?show $6#>     

Sorption of iron‐cyanide complexes on goethite investigated in long‐term experiments

The iron‐cyanide complexes ferrocyanide, [Fe^II(CN)₆]^4–, and ferricyanide, [Fe^III(CN)₆]^3–, are anthropogenic contaminants in soil. We investigated their sorption on goethite, α‐FeOOH, in batch experiments in a time range from 1 d to 1 yr, their desorption by phosphate and chloride as well as their surface complexes on goethite by Fourier‐transform infrared spectroscopy (FTIR). The sorption of both complexes continued over the whole time range. Percent desorption of ferricyanide by phosphate decreased, whereas that of ferrocyanide increased until it amounted to approximately 87% for both complexes. By FTIR spectroscopy inner‐sphere complexation of both complexes on the goethite surface was indicated. With both complexes, a Berlin‐Blue‐like layer (Fe₄[Fe(CN)₆]₃) was formed initially on the goethite surface which disappeared with increasing reaction time. After at least 30 d reaction time, ferricyanide was the only sorbed iron‐cyanide complex detected even when ferrocyanide was initially added. This resulted from slow oxidation of ferrocyanide, most probably by dissolved oxygen. Based on all results, we propose that ferricyanide forms monodentate inner‐sphere complexes on the goethite surface.     

不同类型近红外模型在苹果汁检测中的应用

为了探讨近红外光谱分析中模型的稳定性和适应范围,该研究建立了4种模型（局部模型、转移的局部模型、全局模型、优化全局模型）,分别对5种鲜榨苹果汁的3种品质参数（可溶性固形物SSC、pH值、电导率）进行分析。在对苹果汁品质的分析中,SSC的预测准确度较高（r=0.93,相对预测标准差3.7%）;电导率与近红外光谱之间间接相关,其预测准确度较低（r=0.84,相对预测标准差12.7%）;pH全局模型的相关系数较高（r=0.94）,但其分辨能力较差（其局部模型的参考值标准偏差与预测标准差的比值为1.1）。采用相对预测标准差、参考值标准偏差与预测标准差的比值等参数来评价各种模型的稳定性和适应范围,通过对4种近红外模型的稳定性、适配性及准确度的比较,发现模型的适应范围对预测结果的影响很大,对不同的分析要求应该建立不同的模型,具体为：采用单一品种建立的局部模型准确度高,但稳定性较差,一般只适用于本品种样品的预测。通过在现有局部模型中加入少数几个待测品种的样本重新建立模型,可以实现模型的转移,使之适用于其它品种样品的预测。采用多个品种建立的全局模型稳定性高,其准确度较之局部模型稍有下降。通过挑选有代表性的样品来建立优化全局模型,可以在保持模型性能的同时降低建模工作量,是值得推荐的建模方法。     

Biological soil properties in a long‐term tillage trial in Germany

After 37 years of different soil‐tillage treatments in a long‐term field experiment in Germany, a number of biological soil characteristics was measured. The field trial comprised six major treatments with different implements and various depths. In this paper, results from a comparison of long‐term use of a plow (to 25 cm depth), a chisel plow (to 15 cm depth), and no‐tillage are presented. The biological soil characteristics measured include the soil‐organic‐carbon (SOC) content, microbial biomass, enzyme activities, and the abundance and biomass of earthworms. Long‐term use of a chisel plow and no‐tillage increased the organic‐C content in the uppermost soil layer (0–10 cm) compared with the plow treatment. The microbial biomass and the enzyme activities arginine‐ammonification, β‐glucosidase, and catalase decreased with depth in all treatments. Arginine‐ammonification and catalase were higher in the plow treatment in soil layers 10 to 30 cm. Additionally, the chisel plow caused an increase in number and biomass of earthworms compared to both other tillage treatments. Differences in earthworm numbers and biomass between plowing and no‐tillage were not statistically significant.     

北京典型耕作土壤养分的近红外光谱分析

为研究土壤养分含量分布信息,以从北京郊区一块试验田采集的72个土壤样品为试验材料,应用傅里叶变换近红外光谱技术分析了土样的全氮、全钾、有机质养分含量和pH值。采用偏最小二乘法(PLS)对光谱数据与土壤养分实测值进行回归分析,建立预测模型,以模型决定系数(R2)、校正标准差(RMSECV)、预测标准差(RMSEP)和相对分析误差(RPD)作为模型精度的评价指标。结果表明,利用该模型与光谱数据对土壤全氮、全钾、有机质养分含量和pH值进行预测,结果与实测数据具有较好的一致性,最高决定系数R2达到0.9544。偏最小二乘回归方法建立的养分预测模型能准确地对北京地区褐土土质全氮、有机质、全钾和pH值4种养分进行预测。     

应用近红外光谱法测定土壤的有机质和pH值

为了满足精细农业对土壤快速实时测试的需要,对未经过粉碎、过筛等处理的土壤,采集了4000～12500 cm-1范围的近红外光谱。研究了土壤的光谱特性,并采用偏最小二乘回归分析方法建立了一阶微分光谱的光谱吸光度与有机质含量和pH值之间的定量分析模型。试验分析表明：有机质的预测相关系数为0.818,预测标准偏差SEP为0.069,预测均方根误差为RMSEP为0.085;pH值的预测相关系数为0.834,SEP为0.095,RMSEP为0.114。表明采用近红外光谱仪经一阶微分处理可以很好地预测经过简单处理的土样中的有机质含量和pH值, 该结论为今后田间快速土壤特性光谱测量奠定了基础。     

Temporal changes in chemical properties of air‐dried stored soils and their interpretation for long‐term experiments

The usefulness of stored soils from long‐term experiments is often questioned because of changes that might occur during storage. We examined changes during long‐term storage (8–69 years) in the chemical properties of soils with a range of pH values (3.4–8.1 in water) from woodland and grassland experiments at Rothamsted Experimental Station in the UK. No significant changes during storage were measured for total C and N. Large but erratic changes in exchangeable Na⁺ content between 1959 and 1991 were probably caused by contamination of the 1959 samples by perspiration and from sodium‐based glassware. Exchangeable K⁺ increased during storage but only by a small amount. Small changes in exchangeable Ca²⁺ and Mg²⁺ were measured in some samples but not in others. Generally the amount of exchangeable cations increased slightly during storage. This is probably linked to the decreases of 0.4 units in the pH of acid soils, which we attribute to the hydrolysis of approximately 0.25% of the exchangeable Al³⁺. A doubling of the amount of exchangeable Mn²⁺ during storage for 32 years was probably caused by re‐equilibration of Mn species. The most practicable way to prepare soil samples for long‐term storage is to dry them in air. However, those who study changes in soil by re‐analysing samples of the soil stored for a long time must (i) use the same methods of analysis, or (ii) demonstrate that different methods lead to the same results, and (iii) know what changes can arise during storage.     

The application of biochar on soil acidity and other physico‐chemical properties of soils in southern Ethiopia

The aim of this research was to investigate the effect of biochar amendment on soil acidity and other physico‐chemical properties of soil in Southern Ethiopia using a field experiment of three treatments: (1) biochar made of corn cobs, (2) biochar made of chopped Lantana camara stem, and (3) biochar made of Eucalyptus globulus feedstock and a control, in which neither of the biochar was used. Each treatment had three levels of 6, 12 and 18 t ha⁻¹. The experiment was setup with RCBD in a factorial arrangement with three replications. In this regard, a total of 36 plots (each 2 × 2 m size) were applied with three replications to the depth of 0–15cm. From these 36 plots, composite soil samples were collected to the depth of 0–30 cm and analyzed for bulk density, total porosity, pH, soil organic carbon, total nitrogen, available phosphorus, potassium, and exchangeable acidity using standard procedures before and after biochar application. Two‐way ANOVA was also used to analyze the impact of the biochars on soil acidity and other properties. For the treatments that had significant effects, a mean separation was made using Least Significance Difference (LSD) test. The results showed the application of biochar significantly reduced, soil bulk density and exchangeable acidity when compared with a control (p < 0.05). Moreover, the total soil porosity, soil pH, total nitrogen, soil organic carbon, available phosphorus, and potassium were significantly increased in the soil. From among applied biochar treatments, Lantana camara applied at the level of 18 t ha⁻¹ had a higher impact in changing soil physico‐chemical properties. In general, the study suggests that the soil acidity can be reduced by applying biochar as it can amend other soil physico‐chemical properties.     

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.     

Investigation and prediction spatial variability in chemical properties of agricultural soil using geostatistics

Determination of the chemical characteristics of soil for balanced fertilization on large scales is an important factor in achieving a precision agriculture. Laboratory analyses of soil properties are usually expensive and time consuming. Surmounting these problems is possible using geostatistics. Therefore, this research aims at selecting a proper interpolation method using 213 soil samples for alfalfa farmland in Hamadan Province, Iran. Various factors such as pH, EC, , , K, P, Fe, Zn, B and Co were measured. Ordinary kriging and co-kriging were assessed to derive maps of soil physico-chemical properties, using mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE) and average kriging standard error (AKSE) as statistical criteria. Variography analysis indicated that the ranges of influence for pH, EC, , , K, P, Zn, Fe, B and Co were 65, 55, 78, 79, 75, 60, 50, 65, 70 and 30 km, respectively, and the measuring error varied between 0.366 and 0.843. The results revealed that, based on precision criteria, co-kriging was the best method for interpolating the chemical properties of soil. Finally, using to the co-kriging for each determined variable, a related zoning map for fertility management of the study area was prepared.     

长期施用有机肥与化肥对潮土土壤化学及生物学性质的影响

研究长期施用有机肥与化肥对潮土土壤化学和生物学性质的影响结果表明,有机肥和化肥均使土壤有机质、全N、全P、速效磷、速度钾、阳离子交换性提高,增加土壤微生物数量和活性,但有机肥在培肥地力、创造有利于微生物生长繁育的土壤环境方面明显优于化学肥料。     

基于近红外光谱土壤水分检测模型的适应性

由于土壤水分的近红外光谱定量分析模型精度依赖于样品状态,故土壤水分定量分析模型的适应性极其重要。以湖北地区的3种土壤为研究对象,利用偏最小二乘法交叉验证建立了处理后样品下的土壤水分分析模型,模型预测值与标准值的决定系数R2为0.9946,交叉验证预测均方差为0.801%,模型预测决定系数R2为0.9919,预测均方差为0.912%;利用主成分分析了未处理土壤样品与处理土壤样品得分图的差异,结果表明定量分析模型对未处理样品的预测精度降低;采用斜率/截距的方法修正了12个未处理样品的模型预测值,预测平均绝对值误差从0.78%降低到0.38%,结果表明斜率/截距校正法能较好的提高近红外光谱土壤水分定量分析模型的适应性。     

Isolation and characterization of labile organic phosphorus pools in soils from the Askov long‐term field experiments

Isolierung und Kennzeichnung des labilen organischen Phosphor‐Pools in Böden des Langzeitdüngungsexperimentes Askov Labiler organischer Phosphor (P_o) im Boden spielt eine wichtige Rolle in der P‐Ernährung der Pflanzen und ist bedeutend hinsichtlich der Gewässereutrophierung. Im Rahmen dieser Arbeit werden neuere Ergebnisse zu den Eigenschaften des labilen P_o und seiner Reaktion auf unterschiedliche Düngungssysteme diskutiert. Die Untersuchungen fanden an Böden des Langzeitexperimentes zur organischen und anorganischen Düngung in Askov statt. Unser analytischer Ansatz basierte auf einer Kombination der Extraktion von labilem P_o mittels makroporösem Anionenaustauscher‐Harz und der Kennzeichnung von Struktur und Herkunft des NaOH‐extrahierbaren P_o mittels ³¹P‐NMR‐Spektroskopie. Die Analysen wurden an der Feinerde und an Korngrößenfraktionen durchgeführt. Die Ergebnisse zeigen, dass Harz‐Extraktion einen aktiven Pool an P_o isoliert, welcher v.a. aus mikrobiell synthetisierten Strukturen besteht. Die Größe dieses Pools variiert im Jahresgang und hängt von der P‐Düngung ab. Die Art des Düngers (NPK gegenüber Stallmist und Gülle) scheint demgegenüber den labilen P_o kaum zu beeinflussen. Der größte Teil des leicht verfügbaren P_o ist in der Tonfraktion lokalisiert. Es ist daher zu schließen, dass diese Fraktion wichtig im kurzfristigen Umsatz von P_o ist.     

赣南脐橙园土壤全磷和全钾近红外光谱检测

为建立一种能够同时快速检测土壤全磷和全钾的定量估计模型,该文采用近红外漫反射技术对赣南脐橙果园的土壤进行研究,对56个土样风干、过筛,然后进行光谱采集和化学分析。光谱经过Savitzky-Golay平滑后再用一阶微分变换的方法进行预处理,分别应用偏最小二乘回归（partial least square regress PLS）、主成分回归（principal component regression PCR）和最小二乘支持向量机（least squares support vector machine LS-SVM）3种方法,在4 000～7 500 cm-1波数范围内,建立赣南脐橙果园土壤全磷和全钾快速定量检测模型。结果发现在建立土壤全磷模型时,PLS和PCR的预测模型效果均不理想,但LS-SVM建立的模型较为理想, 其预测相关系数（correlation coefficient of prediction RP）为0.884,预测集均方根误差（the root mean square error of prediction RMSEP）为0.341,预测相对分析误差（residual predictive deviation RPD）为2.59。在建立土壤全钾模型时,PLS、PCR和LS-SVM 建立3种模型效果均理想,其中以LS-SVM模型最理想,其预测相关系数（RP）为0.971,预测集均方根误差（RMSEP）为0.714,预测相对分析误差（RPD）为5.12。研究表明,采用LS-SVM建立的土壤全磷和全钾模型对实现土壤全磷和全钾含量快速检测具有可行性。