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1.
高阳县农田土壤速效养分空间变异特征研究   总被引:10,自引:0,他引:10       下载免费PDF全文
土壤养分空间变异的研究对指导测土配方施肥具有重要意义。为了便于土壤养分的管理, 以河北省保定市高阳县为例, 应用地统计学和GIS相结合的方法, 研究了农田土壤速效氮、磷、钾含量的空间变异特征。结果表明: 土壤速效氮、磷、钾的含量范围分别为10.50~210.00 mg·kg-1、1.02~197.75 mg·kg-1和14.51~376.18 mg·kg-1, 平均值分别为76.32 mg·kg-1、22.28 mg·kg-1和128.34 mg·kg-1, 变异系数范围为36.11%~79.71%, 属于中等强度变异。速效氮、磷、钾的C0/(C0+C)值均介于25%~75%, 表现出中等强度的空间自相关, 空间变异是结构因素和随机因素共同作用的结果, 空间相关距离分别为43.96 km、1.05 km和51.94 km。通过插值误差的比较得出最优拟合模型, 速效氮、磷、钾最好的理论模型分别为球状模型、指数模型和球状模型, 趋势效应参数宜选取0阶。然后用普通克里格方法绘制了土壤速效氮、磷、钾的空间分布图, 速效氮含量绝大部分属低等水平, 无明显分布特征, 速效磷空间分布呈条带状, 速效钾空间分布呈条带状和岛状分布相结合的特点。  相似文献   

2.
近红外光谱结合偏最小二乘法快速评估土壤质量   总被引:9,自引:0,他引:9  
以长江中下游粮食主产区水稻土为研究对象,采集17种不同施肥处理下共136个土壤样品在350 ~2 500 nm范围的近红外光谱,利用偏最小二乘回归分析结合交叉验证法建立了近红外漫反射光谱与传统化学分析方法测得的全碳、全氮、碳氮比、速效钾、速效磷、电导率、土壤pH等土壤指标之间的定量分析模型。模型的决定系数(R2)以及化学分析值标准差(SD)与模型的内部交叉验证均方差(RMSECV)的比值RSC用于判定建立的模型的好坏。结果表明:全碳、全氮、碳氮比和pH模型的R2和RSC分别为:R2=0.94,RSC=4.31;R2=0.95,RSC=4.35;R2=0.97,RSC=5.60;R2=0.92,RSC=3.37,说明上述土壤指标的预测结果很好。速效钾模型的R2和RSC分别为:R2=0.87,RSC=2.23,表明预测结果尚好。而速效磷和电导率模型的R2和RSC分别为:R2=0.18,RSC=1.16;R2=0.37,RSC=1.31,说明两者的预测结果均很不理想。综上所述,水稻土的土壤质量相关指标(全碳、全氮、碳氮比、速效钾和土壤pH)可以通过近红外光谱结合偏最小二乘法(NIR-PLS)快速评估。  相似文献   

3.
哈尔滨市辖区黑土速效养分空间异质性分析   总被引:1,自引:0,他引:1  
研究在哈尔滨市所辖黑土区的6个市县进行,共采集表层(0~20cm)黑土土样411个,测定了全部样点的有机质、全氮的养分含量,同时测定了部分样点的碱解氮、速效磷、速效钾的养分含量,利用地统计学中协同克立格分析方法并结合经典统计学和GIS技术分析碱解氮、有效磷、速效钾养分空间异质性。分析表明有效磷空间分布符合指数模型,碱解氮、速效钾符合高斯模型,其块金值与基台值之比分别为50.0%、39.7%、6.3%,有效磷和碱解氮为中等程度空间相关水平,速效钾为强度空间相关水平;与克里格对比,应用协克里格插值绘制的三种速效养分空间分布图精度显著提高,为协克里格方法在土壤养分空间异质性分析上的应用提供了实践依据。  相似文献   

4.
黑土养分含量的航空高光谱遥感预测   总被引:3,自引:3,他引:0       下载免费PDF全文
为监测黑龙江省黑土典型区土壤的养分元素含量,综合利用统计理论与光谱分析方法,研究建三江农场黑土土壤的3类养分含量与土壤光谱之间的关系,建立土壤全氮、有效磷、速效钾含量高光谱反演模型,实现土壤养分元素含量定量预测。对黑土土壤航空高光谱数据进行处理,应用偏最小二乘回归(PLSR)和BP神经网络方法分别建立土壤养分元素含量的高光谱定量反演模型,结果表明:全氮PLSR和BP神经网络预测模型的RPIQ值(样本观测值第三和第一四分位数之差与均方根误差的比值)分别为2.42和2.80;有效磷PLSR和BP神经网络模预测型的RPIQ值分别为0.83和1.67;速效钾PLSR和BP神经网络模型的RPIQ值分别为2.00和2.33。试验证明土壤全氮和速效钾的光谱定量预测模型具备较好的精度和预测能力。但有效磷的预测效果不是特别理想,仅可达到近似定量预测的要求;全氮、有效磷和速效钾的预测精度,BP神经网络建模相比偏最小二乘建模有更好的精度和预测能力,预测精度分别提高6.5%、10.1%和6.6%。  相似文献   

5.
云南甘蔗主产区土壤有机质和速效养分分布研究   总被引:1,自引:0,他引:1  
对2005年10月至2008年12月在云南甘蔗主产区的1450份土壤养分的分析研究,结果表明:云南甘蔗主产区土壤有机质、碱解氮、速效磷处在中等偏低水平,速效钾处在中等偏高水平;90%以上的有机质含量在4~40 g kg-1之间,碱解氮含量在15~160 mg kg-1,速效磷含量在0.5~29 mg kg-1,速效钾含量在30~210 mg kg-1。在集中分布区,土壤有机质、碱解氮呈近似正态分布,速效钾、速效磷呈近似偏正态分布。土壤有效养分的变异程度PKN。  相似文献   

6.
为了得到白酒工业中酒精度的快速检测技术,将偏最小二乘法与傅立叶变换近红外光谱相结合,通过解析白酒样品的近红外光谱图和对光谱进行不同的预处理,结果表明:用最大最小归一化法预处理光谱,光谱范围选择9747.1~7498.3 cm-1和6102~5446.3 cm-1,采用内部交叉验证建立模型,决定系数(R2)为99.99%,交互验证均方根差(RMSECV)为0.165%,主成分数为4,此条件下建模效果较好;模型进行验证结果表明预测集相关系数(R2)为99.80%,预测标准偏差(RMSEP)为0.264%,模型的预测效果很好,具有较高的精密度和良好的稳定性,能满足生产中白酒酒精度的快速检测要求。  相似文献   

7.
灌溉水中悬浮固体对土壤水分入渗性能的影响   总被引:1,自引:1,他引:0       下载免费PDF全文
为监测黑龙江省黑土典型区土壤的养分元素含量,综合利用统计理论与光谱分析方法,研究建三江农场黑土土壤的3类养分含量与土壤光谱之间的关系,建立土壤全氮、有效磷、速效钾含量高光谱反演模型,实现土壤养分元素含量定量预测。对黑土土壤航空高光谱数据进行处理,应用偏最小二乘回归(PLSR)和BP神经网络方法分别建立土壤养分元素含量的高光谱定量反演模型,结果表明:全氮PLSR和BP神经网络预测模型的RPIQ值(样本观测值第三和第一四分位数之差与均方根误差的比值)分别为2.42和2.80;有效磷PLSR和BP神经网络模预测型的RPIQ值分别为0.83和1.67;速效钾PLSR和BP神经网络模型的RPIQ值分别为2.00和2.33。试验证明土壤全氮和速效钾的光谱定量预测模型具备较好的精度和预测能力。但有效磷的预测效果不是特别理想,仅可达到近似定量预测的要求;BP神经网络建模相比偏最小二乘建模有更好的精度和预测能力,预测精度分别提高6.5%、10.1%和6.6%。  相似文献   

8.
精料补充料中肉骨粉含量的近红外光谱检测   总被引:4,自引:1,他引:3  
为了保证饲料安全,精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法,123个样品作为校正集,采用偏最小二乘法(PLS),分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型,以最大的决定系数(R2)和最小的标准差(RMSEC)为选择依据,通过比较,以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好,其预测值与测量值的决定系数(R2)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证,决定系数(r2)和标准差(RMSEP)分别为0.9749和0.420,平均绝对误差和相对误差分别为0.326和13.89%。结果表明,利用近红外分析技术可以检测精料补充料中肉骨粉的含量。  相似文献   

9.
基于LS-SVM的草莓固酸比和可滴定酸近红外光谱定量模型   总被引:1,自引:1,他引:0  
为提高草莓固酸比和可滴定酸近红外光谱定量模型的性能,该文采用偏最小二乘法提取的潜在变量作为最小二乘-支持向量机模型的输入变量,建立了两指标的近红外定量模型,并与偏最小二乘模型结果进行了比较,建模所使用的光谱范围为6000~12500 cm-1。结果表明,草莓可滴定酸和固酸比偏最小二乘模型校正相关系数、校正和预测均方根误差分别为0.430、0.096%、0.096%及0.688、0.926和1.190,而两指标的前10个潜在变量得分作为输入变量的最小二乘—支持向量机模型各项性能均远优于偏最小二乘模型,其校正和预测相关系数、校正和预测均方根误差以及剩余预测偏差分别为:可滴定酸0.965、0.967、0.028%、0.027%、3.881;固酸比0.980、0.973、0.258、0.373、3.111。研究表明,潜在变量作为最小二乘支持向量机模型的输入变量可在较大程度上改善草莓可滴定酸和固酸比指标近红外定量模型的预测性能和稳定性。  相似文献   

10.
四川省盐源县植烟土壤氮磷钾空间变异特征及影响因素   总被引:3,自引:1,他引:2  
利用地统计学和回归分析方法分析四川省盐源县植烟土壤碱解氮、有效磷、速效钾含量空间变异特征以及不同因素对其空间变异的影响,以期为盐源烤烟种植区划、科学施肥和产质量提升提供科学依据。结果表明:1碱解氮、有效磷和速效钾的均值分别为102.84、10.18、148.10 mg/kg,整体处于中等水平,变异系数分别为38.66%、90.08%、60.65%,达到中等变异程度,空间分布不均;2碱解氮、有效磷空间分布符合指数模型,具有中等空间自相关,速效钾符合高斯模型,具有明显空间自相关,空间变异受结构性因素和随机性因素共同影响;3盐源植烟土壤碱解氮、有效磷和速效钾空间变异的主控因素有所不同,碱解氮为坡度和前茬作物,有效磷为土壤类型和前茬作物,速效钾为海拔和坡度,因此植烟区规划和施肥需要综合考虑不同的影响因素。  相似文献   

11.
This article describes a proof-of-concept exercise to examine the ability of near infrared spectroscopy (NIRS)–based methods to predict the major nutrient properties of sugar mill by-products, particularly mill mud, ash, and mixtures of mud and ash. Sixty mill mud, mixed mud/ash, and ash samples were subsampled three times and analyzed using traditional analytical techniques for carbon (C), nitrogen (N), silicon (Si), phosphorus (P), and potassium (K), and the NIR spectra were recorded. Two different partial least squares (PLS) regression models were constructed, one using all samples and the other without the ash samples included in the model development. Three mud, one mixed mud/ash, and two ash samples were retained for predictive purposes and were not included in the model development process. R2 values in the range of 0.77 to 0.98 were obtained for all constituents across both sets of PLS models. The standard errors of prediction (SEP) were similar for both models for N (0.10 and 0.08), P (0.17 and 0.16), and K (0.05 and 0.05). However, the SEP obtained for Si (3.53 and 1.04) and C (1.92 and 1.00) varied between the two models. These preliminary results are very encouraging. Future research will extend to robust NIRS calibrations for these nutrients and develop applications for their use within laboratory or field situations to permit nutrient monitoring in various sugar mill by-products.  相似文献   

12.
Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (NIRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R(2)) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R(2) = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R(2) = 0.847 and standard error of calibration (SEC) = 0.050% and a R(2) = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C═O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.  相似文献   

13.
Near-infrared spectroscopy (NIRS) was used for the simultaneous prediction of exopolysaccharide (EPS; 0-3 g/L) and lactic acid (0-59 g/L) productions as well as lactose (0-68 g/L) concentration in supernatant samples from pH-controlled batch cultures of Lactobacillus rhamnosus RW-9595M in supplemented whey permeate medium. To develop calibration equations, the correlation between the second derivative of 164 NIRS transmittance spectra and concentration data obtained with reference methods was calculated at the wavelength between 1653-1770 and 2041-2353 nm, using a partial least-squares method (PLS). The lactic acid and lactose concentrations were measured by HPLC, and the EPS concentration was estimated by a new ultrafiltration method. The PLS correlation coefficient (R(2)) and the standard error of cross-validation for the calibrations were 91% and 0.26 g/L for EPS, 99% and 2.54 g/L for lactic acid, and 98% and 3.32 g/L for lactose, respectively. The calibration equations were validated with 45 randomly selected culture samples from 6 cultures that were not used for calibration. A high agreement between data of the reference methods and those of NIRS was observed, with correlation coefficients and standard errors of prediction of 99% and 1.64 g/L for lactic acid, 99% and 4.5 g/L for lactose, and 91% and 0.32 g/L for EPS. The results suggest that NIRS could be a useful method for rapid monitoring and control of EPS lactic fermentations.  相似文献   

14.
ABSTRACT

Standards of optimum nutrition are not readily available for mature trees of the Canadian boreal forest. The objective of this study was to determine foliar nutritional standards for white spruce for all major nutrients [nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and manganese (Mn)] using critical values (CVA) and compositional nutrient diagnosis (CND). Trees were sampled at two locations in Ontario and Quebec to cover a gradient of soil fertility levels. A boundary-line approach was used in combination with quadratic regression models to estimate the relationship between growth and foliar-nutrient concentrations or CND scores when free of the effects of interacting environmental factors. White spruce optimum nutrition ranges were computed from significant relationships (P ≤ 0.10) for N, P, K, Ca, and Mn concentrations and for N, P, and K CND scores. Optimum concentrations for first-year needles were 12.3, 1.9, 7.3, 6.5, and 0.39 mg g?1 for N, P, K Ca, and Mn, respectively, whereas optimum CND scores were 0.17, ?1.65, ?0.40, and ?0.30 for N, P, K, and Ca, respectively. Samples from a broader range of environmental conditions will be required in order to establish standards for all major nutrients and to ascertain toxicity levels of most nutrients.  相似文献   

15.
Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination ( r (2)) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r (2) of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods ( r (2) < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.  相似文献   

16.
Abstract

Near‐infrared reflectance spectroscopy (NIRS) has potential to provide rapid estimates of phosphorus (P) and nitrogen (N) concentrations in broiler litter to assist managers in establishing application rates of litter to grazing lands that fall within productive and environmentally safe levels. An experiment was conducted to determine accuracy of NIRS estimates of moisture, P, N, and acid detergent fiber (ADF) concentrations in broiler litter. Broiler litter samples were collected from various farms to develop sample sets that were either with or without bedding material, and each sample set was subdivided into processed (i.e., dried and ground) and unprocessed samples to develop local equations for each constituent. Equations were developed by using all samples from each set and using samples following random removal of 20% of total for equation validation. Moisture was determined to be accurately measured by using NIRS based on a high R2 (≥0.96), low SEC (<10 g kg?1), and high sx/SECV (>5.0). ADF also had a high R2 (0.96), but the Sx/SEC (3.00) value was too low for the equation to be considered truly accurate. Estimations of P and N by calibrations that included all samples had a moderate to high R2 values, but estimations for the validation set were relatively low in R2 (≤0.78) and Sx/SEC (≤2.00). Concentrations of P and N were not estimated by NIRS with a high degree of accuracy, but other methodologies could enhance the usefulness of this technology to rapidly provide these nutrient measures.  相似文献   

17.
Advances in laboratory instrumentation and chemometrics provide alternatives to traditional methods of conducting soil chemical analysis. One of these is infrared diffuse reflectance spectroscopy in the near-infrared spectral range (NIRS). Herein we report the results of a multinational study to develop useful calibrations associating NIRS spectra with laboratory-measured results for total soil carbon (C), total soil nitrogen (N), δ13C, and δ15N from a single soil site in Mexico subjected to zero- and conventional-tillage regimens with and without crop residues and crop rotations of maize and wheat across 16 years. Modified partial least squares regression (MPLS) was used to obtain useful NIR predictions for total soil C and N, with ratio performance deviation (RPD) values of 6.8 and 2.6, respectively. Corresponding multiple correlation coefficients (RSQs) for C and N were 0.98 and 0.85, with standard errors of prediction (SEPs) of ±0.45 g C kg–1 and ±0.09g Nkg–1, respectively. The generation of δ15N and δ13C models produced different NIR recordings in soils with and without crop residues. Application of discriminant partial least squares (DPLS) statistics to the NIR spectral data allowed us to discriminate soils with and without residues. The prediction confidence for stable isotopes was 90% (internal validation) and 94% (external validation). Modified partial least squares regression was used to estimate δ15N and δ13C. Ratio performance deviation, RSQ, and SEP values obtained for δ13C and δ15N were 2.44 and 3.57, 0.83 and 0.81, ±0.5‰ (parts per thousand) and ±0.45‰ in soils with residues and 2.5 and 3.8, 0.93 and 0.92, and ±0.2‰ and ±0.23‰ in soils without residues, respectively. Overall, results obtained with NIRS were comparable to those obtained using conventional analytical methods, a finding that has wide relevance to agricultural soils and environmental studies in tropical locations. However, further testing is necessary to confirm that the calibration models are neither site nor instrument specific.  相似文献   

18.
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.  相似文献   

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