Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.     

Evaluation of soil aggregate stability in Algerian northwestern soils using pedotransfer functions and artificial neural networks

Since, aggregate stability is the main physical property regulating erodibility; its observations can act as a useful indicator for monitoring and managing soil degradation. In this context, this study carried out in the alluvial plain of Cheliff, a semi-arid area aimed to predict aggregate stability through Mean Weight Diameter (MWD), using pedotransfer functions (PTFs) with different stratifications (textural, salinity and organic-textural) and artificial neural networks (ANNs). Results showed that the best MWD predictions were those related to organic-textural PTFs, in this stratification the silty-clay moderately rich OM class showed the highest significant determination coefficient R² (0.65) and the lowest mean square error (0.03), whereas, the textural and salinity PTFs were a very weak predictors with a very low R². It was also found that the performances of ANNs in predicting MWD were better than those of PTFs, regarding ANNs input variables the best predictions were those obtained with a large number of input variables, furthermore, by using a large number of hidden neurons, the performances of Radial Basis Function (RBF) were better than those of Multilayer Perceptron (MLP). It was also noted that the best RBF results were always related to the Gaussian hidden activation, whereas, MLP was not related to a specific hidden activation.     

A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression,Classification and Regression Tree,and Random Forest Models

Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0–20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R² value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R² values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale.     

黄土丘陵区土壤质量评价指标研究

针对黄土丘陵区侵蚀土壤最主要的功能--生产力和抗侵蚀能力,运用敏感性分析、主成分分析和判别分析法,对10种土地利用类型、208个样点的32项土壤属性指标进行了筛选.结果表明,在黄土丘陵区,土壤速效磷含量、抗冲性、渗透系数、活性有机碳、有机质、脲酶作为土壤质量评价的高度敏感指标,是土壤质量恢复与调控的主要目标.土壤生物指标属于高度敏感和中度敏感指标.黄土丘陵区侵蚀土壤的29项理化及生物属性指标可以被归纳为5个土壤质量因子:有机质因子、质地因子、磷因子、孔隙因子和微结构因子.5个因子中,孔隙因子在不同土地利用方式之间差异不显著,其余4个质量因子在不同土地利用方式之间差异极显著.黄土丘陵区侵蚀土壤质量评价指标为有机质、渗透系数、抗冲性、CEC、蔗糖酶、团聚体平均重量直径、速效磷、微团聚体平均重量直径.其中,有机质、渗透系数、抗冲性是表征黄土丘陵区侵蚀土壤质量的关键指标.     

Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms

Machine learning (ML) along with high volume of satellite images offers an alternative to agronomists in crop yield predictions for decision support systems. This research exploited the possibility of using monthly image composites from Sentinel-2 imageries for rice crop yield predictions one month before the harvesting period at the field level using ML techniques in Taiwan. Three ML models, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), were designed to address the research question of yield predictions in four consecutive growing seasons from 2019 to 2020 using field survey data. The research findings of yield modeling and predictions showed that SVM slightly outperformed RF and ANN. The results of model validation, obtained from SVM models using the data from transplanting to ripening, showed that the root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) values were 5.5% and 4.5% for the 2019 second crop, and 4.7% and 3.5% for the 2020 first crop, respectively. The results of yield predictions (obtained from SVM) for the 2019 second crop and the 2020 first crop evaluated against the government statistics indicated a close agreement between these two datasets, with the RMSPE and MAPE values generally smaller than 11.2% and 9.2%. The SVM model configuration parameters used for rice crop yield predictions indicated satisfactory results. The comparison results between the predicted yields and the official statistics showed slight underestimations, with RMSPE and MAPE values of 9.4% and 7.1% for the 2019 first crop (hindcast), and 11.0% and 9.4% for the 2020 second crop (forecast), respectively. This study has successfully proven the validity of our methods for yield modeling and prediction from monthly composites from Sentinel-2 imageries using ML algorithms. The research findings from this research work could useful for agronomists to timely formulate action plans to address national food security issues.     

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.     

Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

Background

Few studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.

Methodology/Principal Findings

A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.

Conclusions/Significance

Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.     

轮牧方式对荒漠草原土壤团聚体及有机碳特征的影响

合理的草地轮牧方式对草原的科学管理具有重要意义.以宁夏荒漠草原为研究对象,对围封禁牧、连续放牧和二区、四区及六区轮牧下0~30 cm土层土壤团聚体分布特征、稳定性、有机碳含量及其贡献率进行了研究.结果表明: 除围封禁牧草地以机械稳定性大团聚体为主外,其他处理的土壤水稳性团聚体均以微团聚体为主;增加轮牧分区有利于表层土壤水稳性团聚体含量的保持及大团聚体含量增加.机械稳定性团聚体分形维数在连续放牧下最大,增加轮牧分区则呈现减小趋势,但水稳性团聚体分形维数无明显变化规律;团聚体平均重量直径(MWD)及几何平均直径(GMD)在禁牧草地最大,且随着轮牧分区的增加而增大;MWD和GMD与微团聚体含量呈显著负相关.水稳定性大团聚体有机碳含量以六区轮牧和围封禁牧较高,二区轮牧和连续放牧较低;试验区微团聚体有机碳对土壤有机碳含量贡献率较高,但0~20 cm土层中,轮牧分区越多则大团聚体有机碳贡献率越高.从土壤团聚体及其有机碳特征考虑,六区轮牧为研究区荒漠草原最适宜的轮牧方式.     

Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm-Gaussian processes

In this article, we discuss the application of the Gaussian process (GP) and other statistical methods (PLS, ANN, and SVM) for the modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands. Divided physicochemical property scores of amino acids, involving significant hydrogen bond, electronic, hydrophobic, and steric properties, was used to characterize the peptide structures, and quantitative structure-affinity relationship models were then constructed by PLS, ANN, SVM, and GP coupled with genetic algorithm-variable selection. The results show that: (i) since the significant flexibility and high complexity possessed in polypeptide structures, linear PLS method was incapable of fulfilling a satisfying behavior on SH3 domain binding peptide dataset; (ii) the overfitting involved in training process has decreased the predictive power of ANN model to some extent; (iii) both SVM and GP have a good performance for SH3 domain binding peptide dataset. Moreover, by combining linear and nonlinear terms in the covariance function, the GP is capable of handling linear and nonlinear-hybrid relationship, and which thus obtained a more stable and predictable model than SVM. Analyses of GP models showed that diversified properties contribute remarkable effect to the interactions between the SH3 domain and the peptides. Particularly, steric property and hydrophobicity of P(2), electronic property of P(0), and electronic property and hydrogen bond property of P(-3) in decapeptide (P(4)P(3)P(2)P(1)P(0)P(-1)P(-2)P(-3)P(-4)P(-5)) significantly contribute to the binding affinities of SH3 domain-peptide interactions.     

轮耕与施肥对渭北旱作玉米田土壤团聚体和有机碳含量的影响

2007—2012年在陕西合阳连作玉米田进行保护性轮耕与施肥长期定位试验,设置免耕/深松(NT-ST)、深松/翻耕(ST-CT)和翻耕/免耕(CT-NT)3种隔年交替轮耕处理和连续免耕(NT-NT)、连续深松(ST-ST)、连续翻耕(CT-CT)3种连耕处理及平衡施肥、低肥和常规施肥3种施肥处理,分析了0～40 cm土壤团聚体分布、平均质量直径(MWD)、几何平均直径(GMD)、分形维数(D)及0～60 cm土壤有机碳(SOC)含量.结果表明: 随着耕作强度的增加,土壤团聚体总含量减小,稳定性降低,有机碳损失增大;连续免耕和轮耕增大了土壤团聚体MWD和GMD,减小分形维数,增加了粒径大于0.25 mm团聚体(R_0.25)和SOC含量.在相同施肥处理下,团聚体R_0.25表现为NT-NT>NT-ST>NT-CT>ST-ST>CT-ST>CT-CT;在相同耕作方式下,平衡施肥和低肥处理下土壤团聚体比常规施肥更稳定.通过对土壤团聚体分形维数进行数学拟合,干筛法和湿筛法所测土壤团聚体的分形维数分别为2.247～2.681和2.897～2.976. 0～30 cm土层土壤团聚体分形维数均表现为连续免耕和轮耕显著低于连续翻耕(CT-CT),随土层加深分形维数增大,在40 cm处趋于稳定.施肥对不同土层有机碳含量的影响差异显著(P<0.05),随土层加深有机碳含量呈递减趋势,平衡施肥处理下有机碳积累量较常规施肥增加了6.9%.土壤有机碳含量随团聚体粒径的增大而增加,0.25～2 mm粒径土壤团聚体含量对有机碳积累的影响达到显著水平(P<0.01),确定系数R²为0.848.     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

Investigation of ecological factors controlling quality of flue-cured tobacco (Nicotiana tabacum L.) using classification methods

The growth and quality of tobacco are associated with ecological conditions, such as soil, climate or weather, and geographical attributes. Tobacco, especially flue-cured tobacco, is an important cash crop and widely cultivated in southwestern China. However, knowledge about critical ecological indicators affecting quality of flue-cured tobacco is limited in this region. In the current study, two well-known clustering algorithms, i.e., k-means and classification and regression trees (CART), were applied to investigate the critical ecological indicators controlling quality of flue-cured tobacco. On the basis of six quality indices and Davis–Bouldin index, a total of 142 flue-cured tobacco leaf samples were classified into three groups with low, medium, and high quality using k-means algorithm. The results obtained by CART model showed that geographical attributes (altitude, latitude, and longitude) and weather indicators had high effects on the quality of flue-cured tobacco followed by soil properties and varieties. Flue-cured tobacco plants with high quality preferred to be grown in areas with low values of altitude, rainfall and relative humidity, high values of latitude, longitude, sunshine hours, and temperature-related indices (mean, maximum and minimum temperatures and their difference), and low concentrations of soil nutrients in this study area. Nevertheless, further study should be conducted to understand the interaction among the ecological variables.     

Restoration of wetlands: successes and failures on scalds comprising an iron oxide clogged layer with areas of acid sulfate soils

Background and aims

We evaluated the influence of plant species and life forms on soil aggregate distribution among size-classes, total macroaggregate mass and aggregate mean weight diameter (MWD), and examined how specific root traits were related to these aggregation variables.

Methods

We analyzed the soil attached to the roots (i.e., rhizospheric soil) under 13 Mediterranean species grown in monocultures in a common garden experiment for four years, and compared it to a bare soil. The mass distribution of aggregates in six size-classes and aggregate MWD were calculated, both on a rhizospheric soil and root biomass basis.

Results

Compared to bare soil, macroaggregate mass increased by an average of 13% in the presence of plants, with a strong effect of species and life forms (both P < 0.0001); some species such as Sanguisorba minor showing increases of up to ~40%. Although the soil under graminoids had a greater macroaggregate mass, their MWD was lower than under non-woody dicots. Large (2000–1000 μm) and intermediate (1000–500 μm) macroaggregate mass increased with root mass and length density and decreased with root lignin concentration, while very large macroaggregate (6000–2000 μm) mass and the MWD increased with root soluble compound concentration.

Conclusions

Species and life forms differently influenced the distribution of macroaggregates among size-classes and aggregate MWD. Easily-decomposable roots with traits related to resource acquisition (i.e., high fine root length, high water-soluble compound concentration) are more favorable for the development of water-stable macroaggregates than roots traits related to resource conservation (high lignin concentration, thick roots).

     

不同施氮量对麦田土壤水稳性团聚体和N2O排放的影响

过量施氮可破坏农田土壤结构,增加温室气体排放量。为揭示不同施氮量对土壤团聚体和N₂O排放的影响,于2018—2020年基于氮肥定位试验,设置秸秆原位还田条件下施氮 0 (N₀)、120 (N₁₂₀)、180 (N₁₈₀)、240 (N₂₄₀)、300 (N₃₀₀)、360 kg·hm^-2 (N₃₆₀) 6个处理,研究不同施氮量对麦田土壤N₂O排放、土壤充水孔隙度(WFPS)、土壤温度、硝态氮、铵态氮含量、水稳性团聚体的组成及稳定性的影响。结果表明: 土壤N₂O排放量与氮肥用量之间呈显著正相关关系,WFPS与施氮量之间无显著相关关系,0~10 cm土壤温度随氮肥施用量的增加而显著降低,土壤硝态氮、铵态氮含量与氮肥施用量间存在显著正相关关系。随氮肥施用量的增加,直径>2 mm的水稳性团聚体含量降低,直径<0.5 mm的水稳性团聚体含量增加,土壤水稳性团聚体的粒径也逐渐减小。氮肥施用量与团聚体平均重量直径(MWD)、几何平均直径之间呈显著负相关关系,但与分形维数之间并无显著相关性。MWD (x)与N₂O排放通量(y)之间的拟合方程为:y=3928.3e^-2.171x (R²=0.55,P<0.001),表明当MWD减小时,N₂O排放量将会剧烈升高。可见,麦田施氮量的增加会降低0~10 cm土壤温度,增加土壤硝态氮和铵态氮含量,减小耕层土壤水稳性团聚体的平均粒径,降低团聚体的稳定性,增加N₂O的排放量。     

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.     

套种对南方红壤坡耕地经济果园土壤团聚体分布及稳定性的影响

为研究红壤丘岗区经济果园套种模式对新改坡耕地土壤团聚体分布及稳定性的影响,以坡度约12°、坡长18 m、宽度1.5 m的12个猕猴桃种植试验小区为对象,设计连续3年套种紫薯(PSP)、毛叶苕子(HV)、荒草(W)3种模式,以无植被覆盖裸地(CK)为对照,测定不同坡位0～15 cm表层土壤团聚体指标。结果表明:套种处理的土壤水稳定性团聚体(WR_>0.25)数量和大小均有不同程度增大趋势,其增幅顺序为PSP>HV>W>CK。土壤团聚体结构破坏率(PAD)和分形维数(D)的顺序为CK>W>HV>PSP,表明套种紫薯坡地土壤团聚体的稳定性最好,毛叶苕子次之。沿顺坡方向,所有处理WR_>0.25含量、团聚体平均重量直径(MWD)和几何平均直径(GMD)均呈减小趋势,而PAD和D呈逐渐增大趋势,表明新改坡耕地土壤的结构性沿坡长方向呈现变差的趋势。D与MWD、GMD、>0.25 mm团聚体含量均呈显著负相关。套种能够增加丘岗坡地经济果园土壤团聚体数量和大小,改善土壤团粒结构,提高土壤质量。     

Which spatial distribution model best predicts the occurrence of dominant species in semi-arid rangeland of northern Iran?

Rangelands with more than 8000 plant species occupy nearly 54.6% of the land area of Iran and thus are accounted for a rich plant genetic storage. Mazandaran province has 378,000 ha of rangelands with high plant species richness and diversity due to its climate conditions but plants distribution is at risk because of non-principle management, land use change and as a result changing environmental factors. Vegetation management strategies can be guided by models that predict plant species distribution based on governing environmental variables. This is especially useful for the dominant species that determine ecosystem processes. In fact, modelling algorithm in each SDM determines its suitability for different ecosystems. Our aim was to compare the predictive power of a number of SDMs and to evaluate the importance of a range of environmental variables as predictors in the context of semi-arid rangeland vegetation. The selected study area, the Sarkhas rangelands (northern Iran, 36°10′ 42˝ N - 51°19′ 11˝ E), covers approximately 4358.9 ha of Mazandaran province. The efficacy of four different modelling techniques as well as Ensemble model was evaluated to predict the distribution of five dominant forage plant species (Vicia villosa, Stachys lavandulifolia, Coronilla balansae, Sanguisorba minor and Alopecurus textilis). The used models included artificial neural network (ANN), boosted regression trees (BRT), classification and regression trees (CART), and random forest (RF). Ensemble, RF and CART had the highest area under curve. The AUC obtained for Vicia villosa, Stachys lavandulifolia, Coronilla balansae, Sanguisorba minor and Alopecurus textilis, were 0.90, 0.72, 0.76, 0.69 and 0.75 respectively. Ensemble model was the model that most consistently demonstrated high predictive power across species in the rangeland context investigated here. BRT exhibited the least predictive power. An importance analysis of variables showed that soil organic C according to the CART model (0.396) and K according to the RF model (0.396) were the most important environmental variables.     

Detection of dairy fouling: Combining ultrasonic measurements and classification methods

Fouling and cleaning in heat exchangers are severe and costly (up to 0.3% of gross national product) issues in dairy and food processing. Therefore, reducing cleaning time and cost is urgently needed. In this study, two classification methods [artificial neural network (ANN) and support vector machine (SVM)] for detecting protein and mineral fouling presence and absence based on ultrasonic measurements were presented and compared. ANN is based on a multilayer perceptron feed forward neural network, whereas SVM is based on clustering between fouling and no fouling using a hyperplane. When both fouling types (1239 datasets) were combined, ANN showed an accuracy of 71.9% while SVM displayed an accuracy of 97.6%. Separate fouling detection of mineral/protein fouling by ANN/SVM was comparable: dependent on fouling type detection accuracies of 100% (protein fouling, ANN and SVM), and 98.2% (SVM), and 93.5% (ANN) for mineral fouling was reached. It was shown that it was possible to detect fouling presence and absence offline in a static setup using ultrasonic measurements in combination with a classification method. This study proved the applicability of combining classification methods and fouling measurements to take a step toward reducing cleaning costs and time.     

生草栽培对果园土壤团聚体及其有机碳分布的影响

以福建尤溪玉池生草果园定位观测点为平台,研究了生草栽培对果园土壤团聚体有机碳分布的影响。结果表明,生草栽培处理后,0~20 cm土壤团聚体中>0.25 mm水稳性团聚体的比例(R0.25)、平均重量直径(MWD)和几何平均直径(GWD)分别比顺坡清耕和梯台清耕处理的高3.78%~5.90%、16.82%~20.94%、5.86%~50.31%和3.81%~13.82%、13.33%~19.95%、7.50%~60.63%,分形维数比顺坡清耕和梯台清耕处理的低1.54%~2.35%和1.09%~9.64%。同时,生草栽培可提高>2 mm土壤团聚体内有机碳贮量和大团聚体有机碳贮量占总有机碳的比例,但其影响主要集中于0~20 cm土层。这说明生草栽培处理更有利于提高土壤团聚体稳定性,增强土壤有机碳的保护和碳汇作用。