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Abstract Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing soil quality and achieving precision agriculture. The cropping system is an important factor that affects the soil carbon cycle in farmlands, and different agricultural managements under different cropping systems lead to spatial heterogeneity of SOC. However, current research often ignores differences in the main controlling factors of SOC under different cropping systems, especially when the cropping pattern is complex, which is not conducive to farmland zoning management. This study aims to (i) obtain the spatial distribution map of six cropping systems by using multi-phase HJ-CCD satellite images; (ii) explore the stratified heterogeneous relationship between SOC and environmental variables under different cropping systems by using the Cubist model; and (iii) predict the spatial map of SOC. The Xiantao, Tianmen, and Qianjiang cities, which are the core agricultural areas of the Jianghan Plain, were selected as the study area. Results showed that the SOC content in rice–wheat rotation was the highest among the six cropping systems. The Cubist model outperformed random forest, ordinary kriging, and multiple linear regression in SOC mapping. The results of the Cubist model showed that cropping system, climate, soil attributes, and vegetation index were important influencing factors of SOC in farmlands. The main controlling factors of SOC under different cropping systems were different. Specifically, summer crop types had a greater influence on spatial variations in SOC than winter crops. Paddy–upland rotation was more affected by river distance and NDVI, while upland–upland rotation was more affected by irrigation-related factors. This work highlights the differentiated main controlling factors of SOC under different cropping systems and provides data support for farmland zoning management. The Cubist model can improve the prediction accuracy of SOC under complex cropping systems.

The United States (U.S.) is a global leader in the production and exportation of soybeans and corn. Accurate monitoring and estimation of soybean and corn yields in the U.S. is essential for improving global food security. However, there is currently a lack of publicly available spatial distribution datasets with high temporal and spatial resolution for U.S. corn and soybean yields, which hampers related research and policy-making. Therefore, in this study, we proposed a statistical downscaling framework to produce spatially explicit crop yield estimates by utilizing multisource environmental covariates and ensemble machine learning methods. We produced distribution maps of soybean and corn yields in the U.S. from 2006 to 2021 at a 1-km resolution through the optimal Cubist model, resulting in the USASoy&CornYield1km dataset. The results demonstrated stable accuracy, with R2 values for corn ranging from 0.70 to 0.89 (average of 0.80) and for soybeans ranging from 0.74 to 0.90 (average of 0.81) during the period 2006–2021. Comparison with the spatial production allocation model (SPAM) dataset further confirmed the reliability of this dataset, with correlations of 0.84 for soybeans and 0.78 for corn when compared to SPAM2010. Spatial uncertainty analysis showed that the yield estimation uncertainty was 14.04% for soybeans and 20.49% for corn, indicating a generally low level of uncertainty. Overall, the USASoy&CornYield1km dataset offers higher spatial and temporal resolution, captures yield variations within counties, and covers a long time span. This study provides significant insights for analyzing U.S. soybean and corn yields and improving agricultural production.

In materials design, it is very difficult to accurately design forward problems owing to the variety of scales and phenomena to be considered and the increasing number of input and output variables. In addition, it is necessary to reduce the number of computationally expensive calculations to perform backward problem to obtain the optimal set of parameters for the input to a forward problem. In this paper, we describe the development of the MIOpt (MI Optimizer), a framework for backward problem. The process – structure – property – performance relationship (PSPP relation) of materials is modeled as forward problems in MInt, and constructed in terms of materials engineering concepts. This can be linked to backward analysis via MIOpt, which greatly improves the efficiency of trial – and – error materials design on a computer. In addition, two proprietary optimization algorithms are implemented, allowing the use of an extension of Bayesian optimization and an update algorithm for machine learning models, which reduces the actual time required for optimization. The benchmark problems were the minimum search problem for the analytical function and the heat input condition optimization problem for creep rupture time in welds. Efficient Bayesian optimization method was confirmed to be faster and more stable than the conventional sequential Bayesian optimization method.

Accurate calculation of reference evapotranspiration is one of the basic tasks to achieve optimal management of water resources. In this research, the evapotranspiration of Bushehr reference was calculated by the Fau Penman Mantis method. Then, temperature methods (Hargreaves Samani and Blaney Cridle) and radiation methods (modified Mc King, Tork and Prestley Taylor) were also used to calculate evaporation transpiration. The results obtained from these methods were compared with the combined Fau Penman Mantis method. The results showed that among the two temperature methods, the Hargreaves Samani method and among the radiation methods, the Prestley Taylor method had closer results to the combined Fau Penman Mantis method. Artificial intelligence, support vector machine, random forest and cubist models were also used to estimate reference evaporation transpiration. The data used included minimum, maximum and average temperature, relative humidity, sunshine hour and wind speed during a thirty year statistical period from 1370 to 1400. In order to check the results of the mentioned models, the standard evaluation criteria of error mean square, absolute mean error and R2 explanation coefficient were used. The results showed that all three models were highly accurate in simulating evaporation transpiration. Cubist model with higher R2 (0.95), the lowest mean squared error (0.87) and the lowest absolute mean error (0.38) was chosen as the best method for evaporation transpiration. [ABSTRACT FROM AUTHOR]

Background and Objectives: The physical properties of the soil, which cannot be easily measured, play an important role in the design of irrigation and drainage systems. Since the direct measurement of these characteristics is time-consuming and expensive, therefore, to estimate these parameters, most researchers use indirect methods such as transfer functions. This research aims to investigate and determine the best model for estimating soil moisture content in Field capacity (FC) and Permanent Wilting Point (PWP) using easily measured soil characteristics and pedotransfer functions in the R software environment and choosing the most suitable model for the soils of the Ravansar region in Kermanshah province. Materials and Methods: In this research, the easily measurable properties of soil were used as input variables for five transfer functions of the multivariable linear, artificial neural network, Cubist, random forest, and support vector machine. At first, in the study area, the location of 120 profiles was determined using the Latin hypercube method. In these observation points, soil profile was dug and studied and samples were taken from its horizons. Then, laboratory analysis including measurement of electrical conductivity, pH, calcium carbonate equivalent, organic carbon, and percentage of sand, silt, and clay was performed on soil samples. Based on the range of changes of these characteristics, especially the soil texture, 75 surface soil samples and 33 soil samples from ten different soil profiles were selected. PWP measurement was performed on 33 samples and FC measurement was performed on surface and depth samples, i.e., 108, and then modeling operations were performed on them. Root mean square error (RMSE), mean absolute error (MAE), and R2 indices were used to evaluate the models. Results: The results showed that the accuracy of pedotransfer functions in estimating PWP is higher than FC (R2 and RMSE values of the Cubist model for PWP are 0.81 and 0.054 and for FC are 0.53 and 0.085, respectively). Also, the results for FC showed that between the models, the Cubist and the artificial neural network have low MAE (0.066 and 0.068) and RMSE (0.085) and high R2 (0.53 and 0.54) respectively, compared to other models. Conclusion: The overall results showed that Cubist, Artificial Neural Network, and Random Forest models with lower error and higher R2 have higher efficiency for soil moisture estimation in FC than other models. The results showed that the Cubist and random forest models were the best models for estimating moisture at the PWP in terms of comparing the coefficient of determination. This research showed the importance of using new machine learning methods in studies related to soil transfer functions to estimate difficult-to-measure soil properties. Also, the results of this research are acceptable for a wide range of plains in Kermanshah province, which has similar soil formation conditions to the Ravansar region. [ABSTRACT FROM AUTHOR]

In this study, machine learning (ML) techniques were used to model surveillance test data of nuclear power plants from an international database of the ASTM E10.02 committee. Regression modeling was conducted using various techniques, including Cubist, XGBoost, and a support vector machine. The root mean square deviation of each ML model for the baseline dataset was less than that of the ASTM E900-15 nonlinear regression model. With respect to the interpolation, the ML methods provided excellent predictions with relatively few computations when applied to the given data range. The effect of the explanatory variables on the transition temperature shift (TTS) for the ML methods was analyzed, and the trends were slightly different from those for the ASTM E900-15 model. ML methods showed some weakness in the extrapolation of the fluence in comparison to the ASTM E900-15, while the Cubist method achieved an extrapolation to a certain extent. To achieve a more reliable prediction of the TTS, it was confirmed that advanced techniques should be considered for extrapolation when applying ML modeling.

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands. [ABSTRACT FROM AUTHOR]

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

We tested one front‐end data fusion method to combine visible near‐infrared (vis–NIR) and portable X‐ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south‐central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air‐dried soil samples were scanned with vis–NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis–NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front‐end data fusion approach, including: (a) 10 PCs from vis–NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis–NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF‐estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models. PXRF spectra and elemental data can predict soil texture with validation R2 > 0.85, better than using solely vis–NIR spectra, and combining vis–NIR and PXRF spectra improved the prediction. Combining PXRF and vis–NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis–NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN. Highlights: The use of front‐end data fusion method to combine vis–NIR and PXRF spectra for predicting soil properties.Different preprocessing algorithms were compared systematically for PXRF spectra.Spectral smoothing should be applied to the PXRF spectra prior to developing predictive models.PXRF spectra can be used solely to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN. [ABSTRACT FROM AUTHOR]

Probability of safety (reliability) analysis is a major concern of any structure, especially of rock mechanics. This paper used different machine learning (ML) techniques (cubist model, extreme learning machine (ELM) and multivariate adaptive regression splines (MARS)) for reliability analysis of rock slopes. The performance of these ML models was assessed using different statistical parameters, such as Nash-Sutcliff coefficient (NS), coefficient of determination (R2), root mean square error (RMSE), variance account factor (VAF), expanded uncertainty (U95), mean absolute error (MAE), ... etc. A comparative study was performed to test the adaptability of cubist, ELM and MARS models. It is evident from the results that MARS model shows excellent results in terms of fitness parameters. This study reflects that cubist, ELM and MARS models are well capable of predicting the reliability of slope in terms of the factor of safety (FOS) of rock slope considering statistical predictands. [ABSTRACT FROM AUTHOR]

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpecTM (ASD) and NeoSpectraTM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R2 = 0.89 (RMSE = 0.12%) and R2 = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R2 = 0.75 (RMSE = 0.45%) and R2 = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices. [ABSTRACT FROM AUTHOR]

Large-scale soil organic carbon (SOC) stock assessment is expensive as a large number of samples must be collected and then their time-consuming measurements must be made in the laboratory. Previous studies have shown that visible-near-infrared reflectance (vis-NIR) spectroscopy can quickly predict SOC content at a low cost. However, the application of this method at the large scale remains challenging due to the high spatial heterogeneity of SOC and the spatially dependent relationships of soil spectra and SOC content. Here, we conducted large-scale soil sampling across China's forests and established the Chinese forest soil spectral library (CFSSL) by measuring SOC content and scanning the vis-NIR reflectance of 11, 213 soil samples. Compared with the traditional global partial least squares regression (PLSR) modeling method (R 2 = 0.75, RPIQ = 1.95), the clustering by fast research and find of density peak in combination with the Cubist model significantly improved the prediction ability of SOC content (R 2 = 0.96, RPIQ = 5.83). This study provided a cost-efficient spectroscopic methodology, including measurement and prediction modeling, for large-scale SOC estimation. • Chinese forest soil vis-NIR spectral library was developed with 11, 213 soil samples. • PLSR model is not able to precisely predict SOC content at large-scale. • DP + Cubist model significantly improves the spectroscopic prediction of SOC. [ABSTRACT FROM AUTHOR]

Abstract Soil legacy data is ubiquitous and usually contains routine soil analysis information. In Iran, like most places, legacy soil data constitutes genetic horizon soil information recorded from excavated soil profiles. Describing and sampling from each genetic horizon is assumed to be heterogeneous from site to site. Digital soil mapping (DSM) using observed data is valuable because it provides a means to exploit the available information together with leveraging commonly available information by way of environmental covariates. It creates a much more detailed view of soil at the landscape scale. The purpose of this paper is to model and map the spatial distribution of nitrogen, phosphorous and boron at four standardized depths: 0–15, 15–30, 30–60, 60–100 cm, in an area of 7300 ha in the north west of Iran, and compare different model types. To circumvent the issue of heterogeneous soil depth observations from site to site, mass-preserving soil depth function splines were used to harmonise the soil profile observed data to the aforementioned standard depths. This facilitated the spatial modelling of each of the target variables for each standard depth with the aim of creating digital soil maps. Twenty-three covariates were extracted from a publically available digital elevation model (DEM) as well as freely available Landsat 8 ETM+ imagery. The DEM-derivative covariates used in this study were divided into three main categories: i) Morphometry; ii) hydrology; and iii) lighting visibility. Both Random Forest and Cubist were assessed as candidate models for predicting each target variable. The results showed that Cubist was the most accurate method. Terrain attributes play an important role in estimating N, P, and B, while optical images do not have significant role. The most important findings of this paper in terms of environmental hazards are that the inundated regions in the west part of the study area are susceptible to boron contamination, providing future guidance for remediation. Highlights • Digital Soil Mapping was used to assess vertical distribution of soil nutrients. • DEM derivatives and Landsat imagery indices relate to soil nutrients. • Cubist models outperformed Random Forest for Nitrogen, Phosphorous and Boron. • Soil model uncertainty is greatest at lower soil depths. [ABSTRACT FROM AUTHOR]

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• Soil profile had a considerable variation of soil properties and SQI with depth and within horizons. • Cubist model can be used to predict SQI. • The grid soil sampling can be used to characterize soil properties and SQI in soil profile wall. • Combined Vis-NIR and pXRF spectra had better prediction performance than individual Vis-NIR and pXRF spectra. A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The objectives of this study were to (i) characterize vertical and horizontal variations of some soil properties and SQI in a Fluventic Haploxerepts profile in Eskişehir, Turkey, (ii) compare interpolation methods for mapping the distribution of soil properties and SQI in the profile wall, and (iii) assess cubist model for predicting soil properties and SQI using individual and combined Vis-NIR and pXRF spectra in the profile. The soil profile wall (1 m deep × 1 m wide) was divided into a raster of 10 × 10 cm intervals for collecting a total of 100 soil samples. The soil samples were analyzed for soil physical, chemical properties, and elemental concentrations and scanned using Vis-NIR (350–2500 nm) and pXRF (0–8 and 0–45 kV) spectrometers. Standard scoring function was used for standardization of indicators. Weighted linear combination method was used to calculate SQI and F-AHP for assigning weights of indicators. The 100 samples were split into calibration (70%) and validation (30%) datasets. Individual or combined Vis-NIR and pXRF spectra were used in cubist model to evaluate the feasibility for predicting soil properties and SQI in the Inceptisol. Results suggested that the soil profile had a considerable variation of soil properties and SQI with depth and within horizons. The A horizon had the highest SQI. In addition, Vis-NIR had better prediction performances for pH, SOM, Fe, and Mn (R2 = 0.42–0.88), whereas pXRF had better prediction performances for EC, CaCO 3 , P, and Zn (R2 = 0.18–0.90). In addition, combining Vis-NIR, pXRF8, and pXRF45 had better prediction performance for sand, silt, clay, K, Cu, and SQI (R2 = 0.81–0.97). We concluded that Vis-NIR and pXRF spectra can be successfully used to predict SQI in Fluventic Haploxerepts. Combined Vis-NIR and pXRF spectra had better prediction performance for SQI. [ABSTRACT FROM AUTHOR]

• Soil properties were used to predict various color coordinates using Cubist models. • The 45−125, 125−250, and 1000−2000 µm fractions give sandy soils their color. • Cubist models from one dataset cannot be used to predict color of another dataset. Color is an important soil property and often used to infer soil properties and delineate soil horizons. We investigated the effect of soil texture, carbon, total elements, and particle-size fractions on the color of sandy soils using five color models. In total, 915 soil samples were collected to a maximum depth of 220 cm at 400 locations in the Wisconsin Central Sand Plains, and the soils were sandy throughout. The samples were scanned using a visible-near infrared spectrometer, with soil color models (HSV, RGB, CIE L*a*b* , CIE L*u*v* , and redness index (RI)) extracted from the reflectance spectra. Cubist models were used to predict each of the soil color coordinates from the soil properties. The models showed high prediction accuracy for V, R, S RGB , L* , a* , b* , u* , and v* color coordinates for both calibration and validation. The CIE L*a*b* color model was generally better than other color models. Silt and Fe were used in all of the Cubist models, while sand, clay, carbon, Al, Si, Mn, and Zn were used in most models. The 45–125 µm, 125–250 µm, and 1000–2000 µm fractions affected the soil color as opposed to the 250–500 µm and 500–1000 µm fractions. A clustering analysis on the CIE L*a*b* color model showed that soil lightness was higher with higher sand content, but lower with an increase in silt and clay content, carbon, Fe, Al, Zn, and Mn. The LUCAS dataset has approximately 20,000 soil samples and was used to explore the relationships between soil color and soil properties in sandy soils and to test the robustness of the model established with the Central Sands dataset. The Cubist models developed from either dataset (Central Sands, LUCAS) were not useful to predict CIE L*a*b* color using the other dataset. [ABSTRACT FROM AUTHOR]

Biomass conversion and expansion factors (BCEFs) are important parameters for estimating carbon storage in forest biomass. Clarifying the source of differences in estimating BCEFs could reduce uncertainties in forest biomass carbon estimation. The decision tree models of ensemble learning can be used to properly figure out the source of differences in estimating BCEFs. However, the comparison of different decision tree models for analyzing differences in estimating BCEFs has never been reported. In this study, three models [the boosted regression trees (BRT), random forest(RF), and Cubist] and data of 331 masson pine plots from the 8 th Chinese National Forest Inventory for Fujian Province were used to analyze the differences in estimating BCEFs (including above- and below-ground). The results showed that BCEFs were following right-skewed distribution, with the mean, minimum and maximum value being 0.69 t·m -3 , 0.67 t·m -3 and 0.71 t·m -3 , respectively. All three models performed well in BCEFs prediction and fitting, and could explain more than 92.8% variations of BCEFs. All three models showed that average DBH and volume were the top two highest relative importance predictors. BCEFs decreased with the increases of average DBH and volume. Stand characteristics factors, such as average DBH, volume, average age and average height, had great influence on BCEFs. Both soil factors and topographic factors had little influence on BCEFs. Using a few variables (such as average DBH, volume, average age and avera-ge height) which contained more BCEFs prediction information could have preferable forecasting precision when building BCEFs models. Moreover, widely representative samples with different average tree ages, average DBH and volume should be chosen to calculate BCEFs when applying constant BCEFs.