Evaluation of a Near‐Infrared Reflectance Spectrometer as a Granulation Sensor for First‐Break Ground Wheat: Studies with Six Wheat Classes

In flour milling, a granulation sensor for ground wheat is needed for automatic control of a roller mill's roll gap. A near‐infrared (NIR) reflectance spectrometer was evaluated as a potential granulation sensor of first‐break ground wheat using offline methods. Sixty wheat samples, ground independently, representing six classes and five roller mill gaps, were each used for calibration and validation sets. Partial least squares regression was used to develop the models with cumulative mass of size fraction as the reference value. Combinations of four data pretreatments (log (1/R), baseline correction, unit area normalization, and derivatives) and three wavelength regions (700–1,500, 800–1,600, and 600–1,700 nm) were evaluated. Unit area normalization combined with baseline correction or second derivative yielded models that predicted well each size fraction of first‐break ground wheat. Standard errors of performance of 4.07, 1.75, 1.03, and 1.40 and r² of 0.93, 0.90, 0.88, and 0.38 for the >1,041‐, >375‐, >240‐, and >136‐μm size ranges, respectively, were obtained for the best model. Results indicate that the granulation sensing technique based on NIR reflectance is ready for online evaluation.     

基于NGO-CNN-SVM的高标准农田灌溉工程施工成本预测

为提高高标准农田项目施工成本的预测精度,控制施工成本在合理范围,减少投资风险,该研究从单体灌溉工程施工成本预测角度出发,通过随机森林（random forest,RF）筛选出高标准农田灌溉工程施工成本的关键影响因素,结合卷积神经网络（convolutional neural networks,CNN）和支持向量机（support vector machine,SVM）两种模型的优点,通过北方苍鹰优化算法（northern goshawk optimization,NGO）对模型里的惩罚因子和核参数进行寻优,构建基于NGO-CNN-SVM的施工成本预测模型。通过辽宁省2018—2023年高标准农田工程中灌溉工程的施工成本数据,选取样本决定系数R²、平均绝对误差MAE、平均绝对百分比误差MAPE和均方根误差RMSE作为精度指标进行分析,结果表明：基于NGO-CNN-SVM的施工成本预测模型在渠道工程中MAE低于0.615万元,RMSE低于0.512万元,R²达到0.968以上,相对误差小于4.210%;在进水闸工程中MAE低于0.610万元,RMSE低于0.536万元,R²达到0.966以上,相对误差小于4.410%;在桥涵工程中MAE低于0.494万元,RMSE低于0.477万元,R²达到0.970以上,相对误差小于3.548%,并相比较于反向传播神经网络,CNN和CNN-SVM模型,NGO-CNN-SVM模型的预测结果均最优。通过特征选择、模型融合、算法优化以及不同模型对比表明NGO-CNN-SVM模型具有更高的预测准确率和泛化性,可为高标准农田灌溉工程施工成本预测提供理论依据。     

Prediction of Triticale Grain Quality Properties,Based on Both Chemical and Indirectly Measured Reference Methods,Using Near‐Infrared Spectroscopy

The increasing demand for triticale as food, feed, and fuel has resulted in the availability of cultivars with different grain quality characteristics. Analyses of triticale composition can ensure that the most appropriate cultivars are obtained and used for the most suitable applications. Near‐infrared (NIR) spectroscopy is often used for rapid measurements during quality control and has consequently been investigated as a method for the measurement of protein, moisture, and ash contents, as well as kernel hardness (particle size index [PSI]) and sodium dodecyl sulfate (SDS) sedimentation from both whole grain and ground triticale samples. NIR spectroscopy prediction models calculated using ground samples were generally superior to whole grain models. Protein content was the most effectively modeled quality property; the best ground grain calibration had a ratio of the standard error of test set validation to the standard deviation of the reference data of the test set (RPD_test) of 4.81, standard error of prediction (SEP) of 0.52% (w/w), and r² of 0.95. Whole grain protein calibrations were less accurate, with optimum RPD_test of 3.54, SEP of 0.67% (w/w), and r² of 0.92. NIR spectroscopy calibrations based on direct chemical reference measurements (protein and moisture contents) were better than those based on indirect measurements (PSI, ash content, and SDS sedimentation). Calibrations based on indirect measurements would, however, still be useful to identify extreme samples.     

基于1D-CNN的土壤全氮近红外光谱预测模型

摘要：【目的】传统的基于近红外光谱数据预测土壤全氮的方法需要对原始光谱数据做复杂的预处理，筛选出与土壤全氮含量相关性高的敏感波长之后进行模型的回归拟合。本文提出一种一维卷积神经网络（1D-CNN）模型，可以在对数据进行简单预处理甚至无处理的情况下达到非常理想的结果，实现用近红外光谱技术对土壤全氮含量的预测。【方法】于江苏无锡采集410个土壤样品，利用半微量开氏法(NY/T 53-1987)测定土壤的全氮含量，并利用NIR Quest 512光谱仪，在室内环境下对每份土壤样品做光谱检测，并用均值中心化(CT)、标准正态变换(SNV)、趋势校正(DT)对光谱进行预处理，运用偏最小二乘回归（PLS）、BP神经网络、1D-CNN方法建立土壤全氮含量的回归预测模型。每种模型在采用不同预处理方法的数据集上做十折交叉验证，记录预测模型的决定系数(R2)和均方根误差(RMSE)的平均值，并对比三种预处理方法对模型精度的影响。【结果】证明了本文提出的1D-CNN模型基于土壤近红外光谱数据预测土壤全氮含量的可靠性。使用原始数据与经均值中心化、标准正态变换、趋势校正预处理的数据训练得到的1D-CNN模型的决定系数分别为0.907、0.931、0.922、0.964，构建的PLS回归模型决定系数为0.856、0.863、0.861、0.880，训练的BP神经网络的决定系数为0.874、0.907、0.901、0.911。【结论】本文提出的1D-CNN模型在原始数据和经预处理的光谱数据上的表现都优于PLS和BP神经网络，且可以证明，对光谱数据进行预处理能够有效提高1D-CNN模型的性能，尤其是趋势校正对模型的提升效果最明显。研究表明，1D-CNN能更好地提取光谱特征并建立其与含氮量的映射关系，有效地避免过拟合，在未经过预处理的光谱数据上依然能够达到一定的精度。     

利用土壤传递函数估算土壤水力学特性研究进展

Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. This paper reviews the development of pedotransfer functions (PTFs) used as an alternative tool to estimate soil hydraulic properties during the last two decades. Modern soil survey techniques like satellite imagery/remote sensing has been used in developing PTFs. Compared to mechanistic approaches, empirical relationships between physical properties and hydraulic properties have received wide preference for predicting soil hydraulic properties. Many PTFs based on different parametric functions can be found in the literature. A number of researchers have pursued a universal function that can describe water retention characteristics of all types of soils, but no single function can be termed generic though van Genuchten (VG) function has been the most widely adopted. Most of the reported parametric PTFs focus on estimation of VG parameters to obtain water retention curve (WRC). A number of physical, morphological and chemical properties have been used as predictor variables in PTFs. Conventionally, regression algorithms/techniques (statistical/neural regression) have been used for calibrating PTFs. However, there are reports of utilizing data mining techniques, e.g., pattern recognition and genetic algorithm. It is inferred that it is critical to refine the data used for calibration to improve the accuracy and reliability of the PTFs. Many statistical indices, including root mean square error (RMSE), index of agreement (d), maximum absolute error (ME), mean absolute error (MAE), coefficient of determination (r²) and correlation coefficient (r), have been used by different researchers to evaluate and validate PTFs. It is argued that being location specific, research interest in PTFs will continue till generic PTFs are developed and validated. In future studies, improved methods will be required to extract information from the existing database.     

Screening of Wheat for Flour Swelling Volume by Near-Infrared Spectroscopy

Selection for starch quality is an important consideration in the breeding of wheat for Asian noodles, particularly Japanese udon, and the flour swelling volume (FSV) test was developed for this purpose. Near-infrared reflectance spectroscopy (NIRS) analysis has also been a key tool in recent years in wheat quality selection. The development and validation of NIRS calibrations for the prediction of FSV on whole grain involved 22 cultivars and breeding lines grown at four locations in two seasons. Eight calibrations were developed, each based on samples from seven trials, with the eighth trial used for validation. Over the eight calibrations, r² between predicted and actual values was 0.56–0.86 (mean 0.74) and the standard error of prediction (SEP) was 0.77–1.65 (mean 1.14) mL/g of dry meal. Separate calibrations were also developed for hard (n = 461), soft (n = 150), and soft+hard grain (n = 616), with standard errors of cross-validation (SECV) of 1.03, 1.39, and 1.21 mL/g of dry meal, respectively. Corresponding r² between predicted and actual values were 0.76, 0.78, and 0.76, respectively. Thus, NIRS offers good potential for the screening of early-generation lines to identify those with high or low FSV.     

SDS-Protein Gel Test for Prediction of Bread Loaf Volume

Flour dispersed in aqueous solutions of sodium dodecyl sulfate (SDS) forms a proteinaceous gel when centrifuged at high speed. The conventional methodology for SDS gel testing was modified to develop a small-scale (<1 g of flour or wheat meal) screening test for evaluation of the protein quality of wheat for breadmaking. The principal modification involved centrifugation with a swinging-bucket rotor to facilitate direct measurement of gel height, which is the primary test parameter. The effects of suspension temperature and time, centrifugation speed, sample size, and sieving of ground wheat or flour on the efficacy of the test were examined. Gel height, wet weight, and protein content were assessed as test parameters. In the standard test procedure that was developed, 0.67 g of flour or ground whole wheat was dispersed in 13.5 mL of 1.5% SDS solution for 15 min at 20°C, followed by centrifugation at 80,000 × g for 30 min. The test was evaluated using seven Canadian commercial wheat flours with diverse breadmaking quality. For the samples, gel height was strongly related to loaf volume (R² = 0.89 and 0.95 for flour and ground wheat, respectively). Sieving flour through a 75-μm sieve slightly increased the predictive power of the test (R² = 0.94). SDS gel height gave better discrimination of samples for prediction of loaf volume than did the traditional SDS sedimentation test. The performance of the sedimentation test improved when sieved ground wheat was used. The relationship between gel height or protein content and flour protein content was comparatively poor (R² = 0.25). The SDS gel test appears to primarily measure the effects of flour protein quality.     

Evaluation of a New Viscometer Performance in Predicting the Technological Quality of Soft Wheat Flour

Gluten aggregation properties were investigated by means of the GlutoPeak device, a viscometer recently proposed as a rapid and sensitive test for measurement of wheat flour technological performance. In this study, 62 soft wheat flour samples of different quality and end use were utilized to evaluate if the GlutoPeak parameters could adequately predict chemical and rheological characteristics of soft wheat flour dough, that is, protein content measured by the Kjeldahl method, dough strength measured by a Chopin alveograph, and dough stability and water absorption measured by a Brabender farinograph. Linear correlation analysis showed that most GlutoPeak curve parameters were strongly correlated with protein content, dough strength, and water absorption. The statistical models, obtained by a stepwise multiple regression method, showed the GlutoPeak device to be a promising tool to characterize wheat flour (R_adj² = 0.84 for protein content, R_adj² = 0.71 for dough strength, and R_adj² = 0.67 for water absorption). The rather high accuracy of the prediction models for the three mentioned parameters confirmed that GlutoPeak parameters are well correlated with other frequently used flour quality parameters and are able to describe flour technological performance.     

Using a nitrogen mineralization index will improve soil productivity rating by artificial neural networks

ABSTRACT

In the Pampas, nitrogen fertilization rates are low and soil organic matter impacts crop yield. Wheat (Triticum aestivum L.) yield was related to total soil nitrogen (total N) and to nitrogen mineralization potential (mineralized N) to determine whether the effects of organic matter may be attributed to its capacity to act as a nitrogen source or to the improvement of the soil physical condition. Data of 386 sites from throughout the region comprised in a recent soil survey were used, in which climate and soil properties to 1 m depth were determined. Artificial neural networks were applied for total N and mineralized N estimation using climate and soil variables as inputs (R² = 0.59–0.70). The models allowed estimating total N and mineralizable N at county scale and related them to statistical yield information. Neural networks were also used for yield prediction. The best productivity model fitted (R² = 0.85) showed that wheat yield could be predicted by rainfall, the photothermal quotient, and mineralized N. The soil organic matter effect on crop yield seems to be mainly related to its nitrogen mineralization capacity. Using mineralized N as predictor would be a valuable tool for rating soil productivity.     

Impact of tillage and crop establishment methods on crop yields,profitability and soil physical properties in rice–wheat system of Indo‐Gangetic Plains of India

Conservation agriculture (CA) based on best‐bet crop management practices may increase crop and water productivity, as well as conserve and sustain soil health and natural resources. In a 2‐year study, we assessed the effects of tillage and crop establishment (TCE) methods on productivity, profitability and soil physical properties in a rice–wheat (RW) system. The six TCE treatments were used to study the impact, which are puddled transplanted rice followed by conventionally tilled wheat (CTPR–CTW), direct‐seeded rice on the flat followed by zero‐till wheat (CTDSR–ZTW), zero‐till direct‐seeded rice with residue followed by zero‐till wheat with residue (ZTDSR+R–ZTW+R), transplanted rice after rotavator puddling followed by zero‐till wheat (RTTPR–ZTW), transplanted rice after rotavator puddling followed by rotary till wheat (RTTPR–RTW) and farmer practice rice–wheat (FP–RW). Result of the study revealed that mean rice yield was not significantly affected by different TCE methods. Wheat planted with ZTDSR+R–ZTW+R gave 30% larger grain yield than FP‐RW. Overall, among all the TCE treatments, the RW system yields and net returns were maximum under ZTDSR+R–ZTW+R. The fastest mean infiltration rate (0.10 cm hr^–1) was registered in ZTDSR+R–ZTW+R plots, whereas the slowest was in FP‐RW plots (0.05 cm hr^–1). Bulk density at 15–20 cm soil depth was least in ZTDSR+R–ZTW+R (1.70 Mg m^–3) and greatest in FP‐RW (1.73 Mg m^–3). Results from this study revealed that conventionally tilled (CT) and transplanting of rice could be successfully replaced by adoption of the profitable double ZT–RW system.     

Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province,Northwest Iran

The aim of this research is to study the efficiency of pedotransfer functions (PTFs) and artificial neural networks (ANNs) for cationic exchange capacity (CEC) prediction using readily available soil properties. Here, 417 soil samples were collected from the calcareous soils located in East-Azerbaijan province, northwest Iran and readily available soil properties, such as particle size distribution (PSD), organic matter (OM) and calcium carbonate equivalent (CCE), were measured. The entire 417 soil samples were divided into two groups, a training data set (83 soil samples) and test data set (334 soil samples). The performances of several published and derived PTFs and developed neural network algorithms using multilayer perceptron were compared, using a test data set. Results showed that, based on statistics of RMSE and R², PTFs and ANNs had a similar performance, and there was no significant difference in the accuracy of the model results. The result of the sensitivity analysis showed that the ANN models were very sensitive to the clay variable (due to the high variability of the clay). Finally, the models tested in this study could account for 85% of the variations in cationic exchange capacity (CEC) of soils in the studied area.

Abbreviations: ANN: arti?cial neural networks; MLP: multilayer perceptron; MLR: multiple linear regression; PTFs: Pedotransfer Functions; RBF: Radial Basis Function; MAE: mean absolute error; MSE: mean square error; CEC: cationic exchange capacity     

基于Logistic回归和RBF神经网络的土壤侵蚀模数预测

[目的]寻求估算土壤侵蚀模数的新方法,并通过GIS实现对土壤侵蚀空间分布情况的预测。[方法]采用土壤侵蚀模数作为判别条件,分别验证基于Logistic回归和RBF神经网络而建立的土壤侵蚀预报模型的适用性,进而构建并验证改进模型——LOG-RBF神经网络土壤侵蚀预测模型。[结果](1)Logistic回归模型判别目标土地是否发生土壤侵蚀的优势明显,未发生和发生土壤侵蚀的预测正确率分别为77.4%和97.9%,总预测正确率为94.9%。(2)RBF神经网络模型估计土壤侵蚀模数的能力较强,模拟结果的相对误差和平方和误差分别为0.612%和13.292,R2为0.57。(3)LOG-RBF神经网络土壤侵蚀预测模型预测结果的相对误差和平方和误差比RBF神经网络模型模拟结果分别降低了0.157%和2.601。R2为0.82,拟合程度上优于RBF神经网络模型。随着土壤侵蚀模数的增大,错估现象呈逐渐减少趋势。通过受试者工作特征曲线的判别,LOG-RBF神经网络模型的曲线下面积值比RBF神经网络模型大0.063,模型判断的准确性更高。[结论]利用LOG-RBF神经网络土壤侵蚀预测模型可更准确地估计土壤侵蚀模数,基于GIS能够预测土壤侵蚀的空间分布情况。     

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.     

Solvent Retention Capacity Values in Relation to Hard Winter Wheat and Flour Properties and Straight‐Dough Breadmaking Quality

Solvent retention capacity (SRC) was investigated in assessing the end use quality of hard winter wheat (HWW). The four SRC values of 116 HWW flours were determined using 5% lactic acid, 50% sucrose, 5% sodium carbonate, and distilled water. The SRC values were greatly affected by wheat and flour protein contents, and showed significant linear correlations with 1,000‐kernel weight and single kernel weight, size, and hardness. The 5% lactic acid SRC value showed the highest correlation (r = 0.83, P < 0.0001) with straight‐dough bread volume, followed by 50% sucrose, and least by distilled water. We found that the 5% lactic acid SRC value differentiated the quality of protein relating to loaf volume. When we selected a set of flours that had a narrow range of protein content of 12–13% (n = 37) from the 116 flours, flour protein content was not significantly correlated with loaf volume. The 5% lactic acid SRC value, however, showed a significant correlation (r = 0.84, P < 0.0001) with loaf volume. The 5% lactic acid SRC value was significantly correlated with SDS‐sedimentation volume (r = 0.83, P < 0.0001). The SDS‐sedimentation test showed a similar capability to 5% lactic acid SRC, correlating significantly with loaf volume for flours with similar protein content (r = 0.72, P < 0.0001). Prediction models for loaf volume were derived from a series of wheat and flour quality parameters. The inclusion of 5% lactic acid SRC values in the prediction model improved R² = 0.778 and root mean square error (RMSE) of 57.2 from R² = 0.609 and RMSE = 75.6, respectively, from the prediction model developed with the single kernel characterization system (SKCS) and near‐infrared reflectance (NIR) spectroscopy data. The prediction models were tested with three validation sets with different protein ranges and confirmed that the 5% lactic acid SRC test is valuable in predicting the loaf volume of bread from a HWW flour, especially for flours with similar protein contents.     

Estimation of bulk density of waterlogged soils from basic properties

Pedotransfer functions (PTFs) to predict bulk density (BD) from basic soil data are presented. Available data pertaining to seasonally impounded shrink–swell soils of Jabalpur district in the Madhya Pradesh state of India were used for the study. The data included horizon-wise information of 41 soil profiles in the study area covering nearly 5 million ha. Six independent variables, namely textural data (sand, silt and clay), field capacity (FC), permanent wilting point (PWP) and organic carbon content (OC) were used as input in hierarchical steps to establish dependencies, with bulk density as the dependent variable, using statistical regression and artificial neural networks. The PTFs derived using neural networks [average root mean square error (RMSE) 0.05] were relatively better than statistical regression PTFs (average RMSE > 0.1). The best-performing PTFs required input data on sand, silt content, FC and PWP, with lowest prediction errors (RMSE 0.01, maximum absolute error (MAE) 0.01) and highest values of index of agreement (d, 0.95) and R ² (0.65). Use of measures of structure, as well as information on pore structure, was found to be essential to derive acceptable PTFs. Inclusion of OC as an input variable showed relatively better fitting to the training data set, implying an underlying relationship between OC and BD, but the neural networks could not mimic the relationship when tested against subset.     

Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (R_v), 1.119 g kg^?1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with R_v = 0.952, RMSEP = 0.633 g kg^?1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.     

Hardness Measurement of Bulk Wheat by Single‐Kernel Visible and Near‐Infrared Reflectance Spectroscopy

Reflectance spectra (400 to 1700 nm) of single wheat kernels collected using the Single Kernel Characterization System (SKCS) 4170 were analyzed for wheat grain hardness using partial least squares (PLS) regression. The wavelengths (650 to 700, 1100, 1200, 1380, 1450, and 1670 nm) that contributed most to the ability of the model to predict hardness were related to protein, starch, and color differences. Slightly better prediction results were observed when the 550–1690 nm region was used compared with 950–1690 nm region across all sample sizes. For the 30‐kernel mass‐averaged model, the hardness prediction for 550–1690 nm spectra resulted in a coefficient of determination (R²) = 0.91, standard error of cross validation (SECV) = 7.70, and relative predictive determinant (RPD) = 3.3, while the 950–1690 nm had R² = 0.88, SECV = 8.67, and RPD = 2.9. Average hardness of hard and soft wheat validation samples based on mass‐averaged spectra of 30 kernels was predicted and compared with the SKCS 4100 reference method (R² = 0.88). Compared with the reference SKCS hardness classification, the 30‐kernel (550–1690 nm) prediction model correctly differentiated (97%) between hard and soft wheat. Monte Carlo simulation technique coupled with the SKCS 4100 hardness classification logic was used for classifying mixed wheat samples. Compared with the reference, the prediction model correctly classified mixed samples with 72–100% accuracy. Results confirmed the potential of using visible and near‐infrared reflectance spectroscopy of whole single kernels of wheat as a rapid and nondestructive measurement of bulk wheat grain hardness.     

基于地类分层的土壤有机质光谱反演校正样本集的构建

以江汉平原滨湖地区不同土地利用类型的土壤样本为例,比较了基于目标土壤理化性质的浓度梯度法、扩展的基于多种理化性质的综合法(P-KS)、基于光谱信息的KS法、最邻近样本去除法(reduce nearest neighbor samples,RNNS)法和基于浓度分层并结合光谱信息的C-KS、C-RNNS法,基于地类分层再结合上述方法,构建具有不同层次土壤信息代表性的校正集,采用偏最小二乘回归法,建立土壤有机质可见光/近红外光谱反演模型。结果表明,具有单一代表性的浓度梯度法、KS法、RNNS法难以建立适用模型;具有光谱与理化性质二元代表性的C-KS方法模型预测精度得到了明显的提升,相对分析误差(ratio of performance to standard deviation,RPD)为1.66;考虑土地利用类型后,浓度梯度法、RNNS法与C-KS法模型预测精度有明显的提升,RPD分别达到了1.84、1.51、1.75,模型具有良好的适用性。说明具有多层次土壤信息代表性的校正集构建方法对提高土壤有机质可见光/近红外光谱反演模型的适用性具有较好作用。     

Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data

Prediction of daily reference evapotranspiration (ET ₀) is the basis of real-time irrigation scheduling. A multiple regression method for ET ₀ prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T _max), minimum temperature (T _min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET ₀ prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522 mm d^?1, respectively. ET ₀ prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3 K is likely to result in acceptable ET ₀ predictions, with AAE and average absolute relative error (AARE) <0.142 mm d^?1 and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET ₀ prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET ₀ based on public weather data.     

Determining Weight and Moisture Properties of Sound and Fusarium‐Damaged Single Wheat Kernels by Near‐Infrared Spectroscopy

Single kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near‐infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R²) of 0.77 and standard error of cross validation (SECV) of 1.03%. This model predicted the MC of the wheat cultivar 2137 with R² of 0.81 and a standard error of prediction (SEP) of 1.02% and RPD of 2.2. Calibration models developed using all kernels from both cultivars predicted MC, fresh weight, dry weight, or water mass in kernels better than models that used only undamaged kernels from both cultivars. Single kernel water mass was more accurately estimated using the actual fresh weight of kernels and MC predicted by calibrations that used all kernels or undamaged kernels. The necessity for evaluating and expressing constituent levels in single kernels on a mass/kernel basis rather than a percentage basis was elaborated. The need to overcome the effects of kernel size and water mass on single kernel spectra before using in calibration model development was also highlighted.