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As precious cultural heritage, earthen sites are susceptible to various natural factors, leading to diverse forms of degradation. To protect earthen sites, the effects of super absorbent polymers (SAPs) on soil water retention, physical properties, and color compatibility at different concentrations were studied. After applying SAP treatment to an earthen site with different degrees of weathering, we drew the following conclusions. SAP-2 improved soil water retention capacity, increased soil water content, and slowed down the precipitation of soluble salts. At the same time, SAP-2 had the least effect on soil color difference and reduced the development of cracks by filling soil pores and enhancing the cohesion between soil particles, thus giving the earthen sites better weathering resistance. Therefore, the results provide a useful reference for the surface cracking of earthen sites in semi-arid areas and the degradation caused by flaking and block spalling. [ABSTRACT FROM AUTHOR]

Appropriate environment management requires an understanding of how mining activity alters environmental characteristics and how those changes affect an area. Therefore, to reduce the adverse effects of mining activity on the land, it becomes crucial to have relevant information about responses to environmental degradation. This study aims to assess the impact of semi-mechanised and artisanal mining activities on the land cover using remote sensing data and photogrammetric analysis, in the Mbale locality, Northern Cameroon. For this purpose, the maximum likelihood classification algorithm of the supervised classification method combined with field surveys was used to map environmental changes, based on Sentinel-2 images of 2019, 2021, and 2023. Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Brithness index (BI), and Soil crust index (SCI), were calculated to assess changes in vegetation, bare soil, water body, and exploited area. The orthophoto obtained from photogrammetric processing was performed to outline river network change through visual interpretation techniques and to calculate the volume of pits created by mining. The result of classified images indicated that vegetation cover decreased by 11.74% over the studied years. However, bare soil and exploited areas increased by 9.2% and 5.4% respectively. The calculated spectral indices show that between 2019 and 2023 the locality of Mbale considerably lost its vegetation cover, in favor of bare soil. The color of the soil and the granulometric size of the topsoil have also changed. The photogrammetry analysis highlighted the deviation of the main river and estimated the volume of pits created by mining activity to 22188.7 m3. The mining activities caused a loss of the vegetation cover, generated big pits, and multiple deviations of the Lom River from its natural course, which have a substantial negative influence on the ecosystem. Such data can be used for long-term environmental management, reclamation and rehabilitation monitoring, and mining area restoration. [ABSTRACT FROM AUTHOR]

1-Introduction Soil erosion is one of the most important environmental hazards. Investigating the impact of soil erosion on soil quality is possible through the evaluation of soil quality indices in eroded and non-eroded points. Studying the severity of soil erosion in order to protect the soil and investigate and evaluate its risks is of particular importance. There is a significant relationship between increasing soil erosion and decreasing soil quality, therefore soil quality and erosion are closely related phenomena. The main prerequisite for dealing with all types of erosion, including gully erosion, is to predict the risk of gully formation and its effects in different places. The purpose of this study in the south of Ardabil city is to calculate the soil quality indicators and investigate the effect of soil erosion on the quality indicators, compare the correlation between the most important characteristics affecting the soil quality (MDS) and the total characteristics affecting the soil quality (TDS) and provide an index distribution map. The quality of the soil is determined with the help of geostatistical methods. 2-Methodology The studied area is a part of Mullah Ahmed watershed near Amin Lo village, 10 km south of Ardabil city. A part of this area was selected with an area of about 50 hectares, which had active Gullies. After identifying and investigating the study area, 48 samples were taken from the depth of 0 to 30 cm of the soil surface, including 24 non-eroded and 24 eroded points. Sampling from each point was done based on the change of topographical conditions and variety of soil characteristics, especially soil color, and in the form of paired points. After laboratory analysis, the total of the measured features was considered as the total data set (TDS) and the principal component analysis (PCA) method was used to select the minimum data set (MDS) (Doran and Parkin, 1994). The contribution of each feature (COM) was calculated by factor analysis (FA) in TDS and MDS sets (Shukla et al; 2004). Then, soil quality indices were calculated using IQI and NQI relationships (Doran and Parkin, 1994). After calculating the soil quality indices at the sampling points in the region, continuous maps of the distribution of soil quality indices were obtained using two methods, Kriging and IDW, with the help of GS Plus 5.1 and Arc GIS 10.8 software. 3-Results According to the results of PCA test, Sand, Clay, pH and Ec variables in eroded points and BD, Sand, Clay and n variables in non-eroded points were selected as MDS set. Pearson's correlation for soil quality indices in two sets of TDS and MDS in eroded and non-eroded points was significant at 5% level. According to the significant results, the use of the MDS set can optimally calculate the soil quality indices in non-eroded points instead of the TDS set. According to the distribution maps of soil quality and the findings of Qi (2009), the studied area is in grade III to II in terms of soil quality. In eroded points, in IQI and NQI indices, Kriging method was the best in TDS set and IDW method was the best in MDS set. While in the non-eroded points, except for the NQI index in the TDS set, in the rest of the soil quality indices, the Kriging method showed a better and more accurate performance. 4-Discussion & Conclusions Due to the significant correlation in non-eroded points, the use of MDS can acceptably calculate the soil quality indices (IQI and NQI) in non-eroded points, and in these points the IQI and NQI indices calculated using TDS are much superior. It is not compared to MDS. In general, in the studied area, based on the coefficient of explanation (R2) and the mean square error, the kriging method was more effective in showing the distribution of soil quality indices compared to the IDW method. The results of all these researches show that the optimal and appropriate method for estimation and estimating data can be different depending on the variable. The limited size of the studied area, sample intervals, the number of samples, the heterogeneity of the area in terms of variables and the existence of a trend can also influence the selection of estimation methods. According to the results obtained from the studied area, soil erosion has a significant effect on some soil quality characteristics. According to this research, conducting similar researches in other parts of Iran under different soil and climate conditions, along with a wide range of erosion effects on soil quality, general modeling of soil quality is important. The result of this can help to develop and create a national index of soil quality, and the existence of this index can provide the possibility of permanent monitoring of the soil condition at the level of agricultural and natural areas of the country and predict the possibility of soil destruction and erosion. [ABSTRACT FROM AUTHOR]

The aim of this research was to map and classify some soils of coastal plain sands origin in Edo State, Nigeria. The study covered two areas, including Site A (4 hectares) and Site B (12 hectares). The methodology used was a systematic soil survey using a rigid grid at detailed scales (1:5000 for Site A and 1:10,000 for Site B), with two mapping units delineated at each site. A representative pedon was excavated in each mapping unit, described, and sampled for analysis. Soil samples were analyzed using standard laboratory methods, and the data were processed with descriptive statistics. Soil classification followed USDA soil taxonomy, the World Reference Base for Soil Resources, and local systems. The morphological properties showed reddish soil colors when moist, with structures varying from single grain crumb to sub-angular blocky, and textures from sand to sandy clay loam. The content of sand, silt, and clay ranged from =742.0 to =886.00 g/kg, from =15.00 to =26.00 g/kg, and from =88.00 to =190.00 g/kg, respectively. Meanwhile, pH, organic carbon, cation exchange capacity, and base saturation ranged from =5.24 to =5.58, from =4.80 to =13.70, from =6.71 to =11.90 cmol/kg, and from =9.80 to =21.60 cmol/kg, consecutively. Pedons 2A and 2B were classified as Rhodic kandiudults by USDA, Rhodic Nudiargic Acrisols (Arenic, Vetic) by WRB, and locally as the Orlu series. Pedon 1A was classified as Typic Udipsamments by USDA, Eutric Rhodic Arenosols (Transportic) by WRB, and locally as the Ahiara series. Pedon 1B was classified as Typic Eutrudepts by USDA, Eutric Rhodic Cambisols (Arenic, Ochric) by WRB, and locally as the Kulfo series. [ABSTRACT FROM AUTHOR]

Soil color recognition through AI approaches is crucial for efficient and rapid soil analysis in geotechnical engineering. It enables improved site characterization and supports informed decision-making in engineering projects. The integration of soil color data with other geospatial information enhances geological mapping and modeling capabilities. The expansion of deep learning can assist different stockholders in soil classification as a significant study in recent years. Therefore, this paper presented an efficient IoT-based hybrid CNN-SVM model to classify five soil classes through soil images. The recommended framework utilizes a hybrid model based on CNN as feature selection. After that, a multiclass SVM as a classifier is utilized for the soil classification task in an effective manner as well as new real-time IoT-based portable soil detection devices for geotechnical engineers in Geo-sites. The proposed framework is estimated using a dataset that comprehends a total of 252 soil images for investigation purposes with different evaluation metrics. The proposed hybrid framework using CNN models such as Squeezenet, Alexnet, and Resnet50 with the multiclass SVM classifier gives good accuracy of 86%, 96%, and 95%, respectively. In contrast, the CNN models only give accuracy of 80%, 89%, and 87% for the Squeezenet, Alexnet, and Resnet50, respectively. From the obtained results, we observed that the offered hybrid Alexnet-SVM gives a higher performance while the Squeezenet-SVM gives the lowest concert. Overall, the attained results showed that the recommended hybrid framework gives the best concert for soil classification, which can help in making an efficient support decision-making system for real-time geotechnical engineering applications. [ABSTRACT FROM AUTHOR]

The present study examined how the use of soil color can help build and evaluate clay content prediction models from laboratory visible and near infrared spectroscopic data. This study was based on a regional database containing 449 soil samples collected over Karnataka state in India, which has been divided into red soils (240 samples) and black soils (209 samples) based on their Munsell soil color. Partial least squares regression models were calibrated and validated from both the regional datasets and subsets stratified as red and black soils. In addition, a random forest model was used to classify the validation soil samples into black and red classes to evaluate models' performance. First, while the clay content predicted by the regression model built from regional data was evaluated as correct at regional scale (R2val of 0.75), this model was evaluated as more accurate over black (R2val of 0.8) than red (R2val of 0.63) soil samples. Second, the regression models built from subsets stratified per soil color provided different performances than the regression model built from the regional data, both at the regional scale and soil color scale. In conclusion, this study demonstrated that (1) predictions are highly dependent on calibration data, (2) the interpretation of prediction performances relies heavily on validation data, and (3) pedological knowledge, such as soil color, can be effectively employed as an encouraging covariate in both the construction and evaluation of regression models. Core Ideas: Prediction of clay from visible and near infrared spectroscopy was analyzed based on soil color.Models were calibrated from regional database and subsets stratified by soil color.Global model yielded over‐ and underestimation of accuracy over red and black soils, respectively.Predictions are highly dependent on the calibration dataset.Model performance evaluation is highly dependent on validation data stratification. [ABSTRACT FROM AUTHOR]

Context: Understanding genetic structure at multiple spatial scales and identifying drivers of genetic isolation are important for developing comprehensive conservation plans including for grassland conservation efforts. However, few studies account for multiple genetic isolation processes nor partition genetic variance among these processes. Objectives: We assess key processes that can create spatial genetic patterns including isolation by barrier (IBB), isolation by distance (IBD), and isolation by environment (IBE) for a widespread pocket gopher species (Geomys bursarius) and a spatially restricted subspecies (Geomys bursarius illinoensis). We further partition genetic variation to each isolating effect and identify genetic variation that was shared between processes. Methods: We used seven microsatellites to determine spatial genetic clustering and identify environmental factors impacting genetic similarities. Then, we used redundancy analysis to partition variance explained by IBB, IBD, and IBE. Results: Major rivers including the Mississippi River acted as barriers and explained the most genetic variation across the species. In contrast, IBD explained the most genetic variation for G. b. illinoensis. Gophers had genetic associations to soil sand percent and soil color, but IBE uniquely explained a small amount of genetic structure for G. bursarius, with additional variation shared with other isolating processes. Conclusions: Gopher genetic structure resulted from barriers, distance, and environmental factors at the species range as well as for a subspecies' region, but the relative amount of genetic variance assigned to unique isolating processes differed between scales. Delineation of conservation units should consider major rivers as natural boundaries, and finer-scale management should identify and protect areas close to source populations with similar soil friability. Our study exemplifies how analyzing gene flow at rangewide and regional scales can aid managers in developing localized strategies that fit within broader conservation units. [ABSTRACT FROM AUTHOR]

This work aimed to propose multivariate selection strategies reconciling the agronomic ideotype and vegetation indices to obtain genotypes with high tillering, early cycle, tolerant to lodging, and foliar diseases. The sowing of white oat lines was carried out on April 27, 2023, allocated in an experimental design of blocks augmented with interspersed controls. The regular treatments correspond to 593 lines of white oat and the common treatments were represented by the cultivars URS Olada, URS Altaneira, and IPR Artêmis arranged in four replications. The measurements of variables of agronomic interest took place in each experimental unit as follows: percentage of tillering, days to flowering, percentage of lodging, percentage of leaf rust, percentage of stem rust, and percentage of leaf spots. The phenomic analyses took place through flights carried out with a specialized drone, equipped with a 20 MP resolution camera and standard height (80 m). The best proposed indices were index for reducing the percentage of lodging with normalized green (NG), green–red ratio (IGR), soil color index (SCI), green leaf index (GLI), visible atmospheric resistant index (VARI), green leaf index 2 (GLI2), red–blue (RB), blue green pigment (BGI), normalized red–blue difference index (NRBDI), green–blue ratio, BIM, normalized green–blue difference index (NGBDI), and ExGR; index for reducing days to flowering with brightness index (BI), NRBDI, days to flowering (DF), gray (GRAY), IGR, average intensity (L), normalized red (NR), RGRI, SCI, BGI, BIM, GLI, NGBDI, and NG; and index for reducing the percentage of stem rust with NG, BI, Gray2, GLI2, RGRI, GLI, VARI, BGI, L, NR, RB, and coloration index. Only two lines were selected concomitantly with multivariate indices, line L167 (M5, SA) and L426 (M6, ETMS), that combine lodging tolerance and leaf rust genes. Core Ideas: New selection strategies for plant breeding with precision and speed.Genotype selection using remote sensing.Multivariate strategy using predicted data.Innovative protocol using phenomics in white oat trials.Agronomic Ideotype combined with indirect selection strategies by unmanned vehicles. [ABSTRACT FROM AUTHOR]

As interest in sustainable agricultural land management continues to grow, there is a need for advanced techniques that enable easy and rapid quantification of soil characteristics. Soil organic matter (SOM) is a critical factor in determining soil health. Unfortunately, contemporary techniques for SOM content analysis are laborious, time consuming, and resource intensive. In response to this challenge, our study has developed a statistical model for forecasting the SOM content using soil color indices and machine learning algorithms. Color indices, including brightness, hue, and saturation, were derived from the soil images captured by a smartphone. The correlation between color indices and SOM reveals the negative correlations between brightness (−0.790) and hue (−0.420) with the SOM content at significance level (p) <0.01. Conversely, the saturation of soil color, which performed best with the predictive model, exhibited a positive relationship (+0.120, p<0.05). The predictive performance of our model outperformed random forest (RF) algorithm compared to both multiple linear regression (MLR) and support vector machine (SVM). The RF algorithm achieved the highest coefficient of determination (R2) value of 0.984. Furthermore, it demonstrated the lowest error metrics. Notably, the root mean squared error value for the RF algorithm was only 0.025% with the training dataset, whereas the MLR and SVM algorithms yielded relatively higher values at 0.029% and 0.110%, respectively. These findings highlight the presence of a nonlinear relationship in predicting the SOM content, which the RF algorithm effectively captures. This approach offers accurate predictions of the SOM content, supporting sustainable agricultural land management through rapid and easy quantification. Core Ideas: Machine learning algorithm‐based technique can aid in achieving efficient agricultural land management.Prediction‐based analytical techniques are a potential alternative for efficient management of agricultural soils.Soil color and organic matter exhibit a nonlinear relationship, which was explained by random forest algorithm.Soil color‐based predictive models in sunlight‐limited conditions minimize environmental interference. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to accurately estimate soil organic carbon (SOC) for the protection of soil quality. Materials and methods: Soil color analysis using three colors (tricolor) has been suggested as a potential tool to estimate SOC, but its accuracy can be affected by other factors such as iron (Fe) content. This study used an iron-removal learning machine (ILM) method, developed using machine learning concepts, to analyze and remove the influence of total soil Fe on SOC estimates. Results and discussion: Compared with the random forest model, the SOC model developed using ILM exhibited better validation performance, with a coefficient of determination of 0.878, a mean relative error of 13.8%, a root mean square error of 2.316 g/kg, a ratio of performance to deviation 2.465, a ratio of performance to interquartile distance of 4.903, and a concordance correlation coefficient of 0.934. Conclusions: Results indicate that the ILM method employed here has great potential for estimating SOC. To improve real-time estimates of SOC, future work should include Fe indices within ILM. [ABSTRACT FROM AUTHOR]

The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis and three commercial entries. The entries were evaluated agronomically and two flights with Remotely Piloted Aircraft (RPA) were conducted. Clustering was performed to validate the existence of genetic variability among the entries using an artificial neural network to produce a Kohonen's self-organizing map. The genotype–ideotype distance index was used to verify the best entries. Genetic variability among the tropical carrot entries was evidenced by the formation of six groups. The Brightness Index (BI), Primary Colors Hue Index (HI), Overall Hue Index (HUE), Normalized Green Red Difference Index (NGRDI), Soil Color Index (SCI), and Visible Atmospherically Resistant Index (VARI), as well as the calculated areas of marketable, unmarketable, and total roots, were correlated with agronomic characters, including leaf blight severity and root yield. This indicates that tropical carrot materials can be indirectly evaluated via remote sensing. Ten entries were selected using the genotype–ideotype distance (2, 15, 16, 22, 34, 37, 39, 51, 52, and 53), confirming the superiority of the entries. [ABSTRACT FROM AUTHOR]

A consensus has not yet been reached on effects of climate change and driving mechanisms between the Tibetan Plateau (TP) and adjacent monsoonal areas during the Marine Isotope Stage 3 (MIS 3). Loess–paleosol sequences from the TP provide valuable information about the MIS 3 environmental history. Detailed color index and a diffuse reflectance spectral (DRS) analysis of Zhouqu (ZQ) loess from the Western Qinling Mountains were conducted to investigate climate change on the eastern margin of the TP during the MIS 3. Our results show that the variations in color index and iron oxide content in ZQ loess are mainly caused by the pedogenesis and climate conditions. The lightness (L*) value and hematite (Hm) content were used to reconstruct the precipitation history and temperature changes, respectively. The reconstructed records revealed that climate change during the MIS 3 was characterized by high frequency and large amplitude, with millennial-scale changes superimposed on orbital-scale changes. Warm–humid climate occurred in the late MIS 3, while the early climate of MIS 3 was relatively cold–dry. The Indian summer monsoon (ISM) and temperature variations during the MIS 3 mainly occurred due to obliquity and precession. The North Atlantic cooling led to the southward movement of the Intertropical Convergence Zone, and the downstream cooling of the atmosphere by the westerly jet could result in events on a millennial-scale in the eastern margin of the TP. The interhemispheric forcing may play an important role in driving the strong summer monsoon in the late MIS 3. [ABSTRACT FROM AUTHOR]

A uniform application of phosphorus (P) fertilizers to spatially variable soils often results in under-fertilization in low P-localities and over-fertilization in high P-localities. This study aimed to evaluate the variable rate applicability of P fertilizers on a 300-ha sloping landscape under rainfed winter wheat cultivation for over 70 years. The soils were sampled (155 samples) using a random spatial sampling technique based on visual differences in soil color and topographic factors. Plant available soil P content (Pav) and other variables of soil samples were analyzed. The spatial variability of Pav was evaluated and the area was divided into three uniform zones (low, medium, high) for fertilizer P application based on the spatial variation of Pav. The values of Pav showed moderate variablity (CV= 21.3%). The fertilizer recommended by the Ministry of Agriculture and Forestry (MAF) was calculated for five identically-sized subregions. The results showed that P fertilizer rates calculated for all five sub-regions based on MAF were identical, suggesting that the MAF was insensitive to spatial variability of Pav in the study soils. Both semivariograms and surface maps of soil properties indicated a strong spatial association between Pav and each of plant available water content (PAWC) and aggregate stability index (ASI), suggesting that yield limitation casued by PAWC should be considered in a variable P-application program in the study area. A more comprehensive study is needed to evaluate the efficiency and cost-benefit economics of variable P application in the study soils. [ABSTRACT FROM AUTHOR]

Using digital images, we have created a three-dimensional (3D) model of spatial distribution of soil horizons with a mosaic structure. The technique was tested on the developed soddy–podzolic soil of the Chashnikovo Training and Experimental Soil Ecological Center. The 11 sections of the soil profile used to create the model show an area of 30 × 45 cm with a distance of 2.5 cm between the section. The correctness of color rendering of photographic images was checked using a portable spectrophotometer. The color rendition of the best images was corrected using the external standard method. The resulting images were used to create a batch processing file in SAGA GIS to obtain a three-dimensional data array on soil color in the CIE L*a*b* system. Using the Voxler 4 software, we have constructed a 3D model with dimensions of 45 cm (X) × 30 cm (Y) × 25 cm (Z) and a resolution of 0.5 cm (X) × 0.5 cm (Y) × 2.5 cm (Z). Analysis of the spatial distribution of color indicators and the use of the threshold value algorithm have identified horizons A1A2, A2, and A2B. Carbon stocks calculated for the same volume of soil using the 3D model are significantly (by 25%) lower than those calculated using the 2D model. The authors believe that the optical mapping method based on color indicators in the CIE L*a*b* system quite accurately reproduces the natural structure of the boundaries of soil horizons and can be used in further works on modeling and studying soil profiles with a mosaic structure. [ABSTRACT FROM AUTHOR]

The seven islands in the Ionian Sea form one of the smallest states in the world - Malta. The Phoenicians called it Malat, the Greeks called it Melita or Melite, and the Maltese called it Ghasel, meaning honey. The Maltese gave it this name because of the specific color of the soil, the rocks, but also because of the tradition of honey cultivation on the territory of Malta. Malta's cultural heritage is inversely proportional to the size of the state. Seven megalithic temples are found on the islands of Malta and Gozo, each the result of a distinct stage of cultural development. The two temples on the island of Gozo, dating from the Bronze Age, are impressive for their gigantic structures. Some other examples in this regard, given by UNESCO experts, are the monuments: Auberge de Baviere, Church of the Shipwreck of Saint Paul the Apostle, Mantel Theatre, the Library located in the heart of the capital Valletta and many others. From the strong cultural wealth of Malta we cannot omit the The Sovereign Military Order of Malta (SMOM), officially the Sovereign Military Hospitaller Order of Saint John of Jerusalem, of Rhodes and of Malta. The birth of the Knights Hospitaller dates back to around 1048. The original hospitaller mission became the main activity of the order, growing ever stronger during the 20th century, most especially because of the contribution of the activities carried out by the Grand Priories and National Associations in many countries around the world. [ABSTRACT FROM AUTHOR]

The representation of the texture and color of the soil on the Mansell scale, measured to a depth of 135 cm with an interval of 5 cm on the transect (length 6 km) with a step of 20 m, in hyperspectral data of the Hyperion instrument for nine measurement periods from January to September is studied. It was found that the satellite image for May 24 is most closely related to all the characteristics under consideration. For the soil texture, the largest relationships are observed for depths of 7–15, 45, 75, and 120 cm; for color Hue, 3–10 and 95–135 cm; for color Value, 7–15, 25–30, and 65–70 cm; for color Сhroma, 7–10, 40–50, 75, and 100 cm. The texture of soil is most related to the wavelengths 579–702 and 529 nm; the color Hue to 641–691 nm; the color Value to 569–702 and 518–539 nm; and the color Chroma to 569–702, 508–529, and 732–763 nm. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) is one of the critical indicators in various fields such as geotechnical engineering and agriculture. To avoid the time-consuming, destructive, and laborious drawbacks of conventional SWC measurements, the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis. In this study, a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images, aiming to alleviate the interference of complex surface conditions with color information extraction. In addition, an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images. Meanwhile, a subset of 10 parameters is identified through three variable analytical methods. Then, four machine learning models for SWC prediction based on partial least squares regression (PLSR), random forest (RF), support vector machines regression (SVMR), and Gaussian process regression (GPR), are established using 32 and 10 characteristic parameters, and their performance is compared. The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions. The RGB, CIEXYZ, and CIELCH color spaces and lightness parameters, as the inputs, are more suitable for the SWC prediction models. Furthermore, it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy, and the GPR model has the best prediction performance (R2 ≥ 0.95, RMSE ≤ 2.01%, RPD ≥ 4.95, and RPIQ ≥ 6.37). The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction, providing valuable insights for mapping soil moisture fields.

BACKGROUND: Rice cultivation under film mulching with no flooding is widely used as an effective water‐saving technology. Different colors of film mulch have different effects on the soil hydrothermal environment and crop growth because of their different optical properties. However, the effects of different colors of film mulch on soil temperature and rice physiological growth are not clearly understood. RESULTS: Field experiments were conducted in 2019 and 2020 to investigate the effects of different color mulches on soil temperature and rice growth in a non‐flooded condition. Transparent film (TM), black film (BM), two‐color film (BWM, silver on the front and black on the back), and no film (NM) in a non‐flooded condition were designed. Soil temperature variation at different soil depths of 0–0.25 m and rice plant height, stem thickness, dry matter, yield and quality were monitored. The results showed that compared to no mulching, the mulching treatment effectively increased the average soil temperature during the whole rice growth stage with the soil temperature ranked TM > BM > BWM. Compared with NM, the BM and BWM treatments increased rice yield by 12.1–17.7% and 6.4–14.4% in 2019 and 2020, respectively. The BWM had 18.2% and 6.8% greater gel consistency than NM in 2019 and 2020, respectively. CONCLUSION: Transparent film should be applied with care because of the high soil temperature stress. Black film and two‐color film (silver on the front and black on the back) could be better option for rice yield, increasing and quality improving in a non‐flooded condition. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Soil salinity/sodicity is becoming a challenge for crop production in Ethiopia's semi-arid and arid regions. However, more information on soil salinity/sodicity needs to be available around Abaya and Chamo Lakes, South Ethiopia Rift Valley. This study aimed to assess and characterize soil salinity/sodicity and determine salt-affected soils' morphological, physical, and chemical properties. The representative soil pits that were 60 ∗ 60 ∗ 60 cm in size were examined, and samples were taken from 0–20, 20–40, and 40–60 cm depths based on the criteria set for agricultural salt-affected soil studies. The soil properties determined include soil color, structure, consistency, bulk density, particle density, porosity, texture, pH, EC, SAR, ESP, CEC, BS, OC, TN, available P, CaCO3−, exchangeable bases, and soluble ions (Na+, Ca2+, Mg2+, K+, Cl−, SO4−2, NO3−, CO3−2, and HCO3−. The soil-analyzed results were rated and interpreted following a guide to standardized analysis methods for soil data. The results of this study reveal that the soils had considerable heterogeneity in soil morphological, physical, and chemical properties. The soils of the study site were highly alkaline and had very high sodium content, very high CEC value, and low levels of organic carbon and exchangeable calcium. The dominant soluble cation was sodium, followed by magnesium, calcium, and potassium in all soil depths of the pits. Similarly, Cl− was dominant among the anions throughout the soil depth, followed by HCO3−, SO42−, and NO3−. The findings of this study imply that removing sodium and salts from the soil depth may improve the salt-affected soils' productivity in the study area. Application of organic amendments, including manures and crop residues, may also be beneficial in increasing fertility and organic matter content. [ABSTRACT FROM AUTHOR]

'Mruda' is a Marathi term that signifies soil, and 'MrudaTest' shows soil testing. Crop yield is influenced by soil pH and macronutrients such as nitrogen (N), phosphorus (P), and potassium (K). Precision agriculture, which has gained appeal on a global scale, can be achieved in part by managing fertilizers properly in accordance with soil health. The standard laboratory test is time-consuming and resource-intensive. Sample testing takes longer duration due to overworked soil testing labs, which discourages farmers from performing soil fertility tests. To address the aforementioned issue, portable, farmer-friendly, rapid-testing technology is required, and our effort is geared in that direction. Soil samples were treated with reagents that turn the clear solution into color as per soil nutrients. An android phone was used to detect and quantify color information using a built-in camera, computing hardware, graphical user interface, image processing, and machine learning classification techniques. The machine learning model was trained using 410 samples collected under various lighting situations, followed by labeling and validation. On 100 samples, it was examined, and the estimated variance was determined to be between 2–11%. Additionally, we used unidentified soil samples to confirm our method, and we estimated an average variance of 8.9–11.7% for the stated nutrients, which is similar to the PUSA STFR kit data. [ABSTRACT FROM AUTHOR]

• Color of sandy soils. • Spectroscopy. • Particle composition. • Soil moisture content. • Spectral signature. It is a well-known phenomenon that the color of sand changes due to moisture. As the moisture content increases, the sand will typically become darker. In our research, we are looking for the answer to the exact function according to which this darkening process changes and what relationship there may be between the different, initial (dry) soil colors and how this relationship can influence the evolution of the darkening process. Using the suitably chosen range of the color spectrum, we created a color parameter to characterize the color of the measured sandy soil, and then, in addition to determining the moisture content, we also looked for other uses of the color spectrum for sandy soil. Thus, based on the color spectrum and other input parameters, not only the moisture content can be calculated, but in the case of two components, the grain composition can also be determined. [ABSTRACT FROM AUTHOR]

In the cool highlands agricultural area in the Kanto region in Japan, large-scale vegetable cultivation is taking place in sloping fields where Andosols are distributed. In some steeply sloping fields in the area, soil erosion has resulted in the loss of surface soil and its redeposition, causing heterogeneity of soil productivity. In this study, a high-resolution soil map (1 m resolution) was delineated using drone images and machine learning to understand the status of soil productivity in sloping vegetable fields. A digital elevation model (DEM) and orthoimages were created from the analysis of images taken by a drone. Then, 13 topographic index maps, such as slopes, were created from the DEM. The orthoimages were then converted to black and white images to quantify surface soil color. Based on the black and white images and topographic indices of the field, the distribution map of 1) organic carbon content of surface soil and 2) layer thickness of A horizon in the study area were delineated by the Regression-Kriging method. The Empirical Bayesian Kriging method was used to delineate maps of 3) gravel content in the soil profile (0–60 cm) and 4) depth to the gravel layer. Using the 13 topographic index maps and the maps from 1) to 4) as features, a predicted soil map was delineated using the random forest method with eight soil series groups as the map unit. Nine features were selected by the best-predicted model. High-Humic Cumulic Allophanic Andosols and Skeletal Cumulic Allophanic Andosols were generally covered on gentle slopes with low LS-Factor, which was calculated by slope and specific catchment area. Skeletal Low-humic Allophanic Andosols were mainly distributed on steep slopes with high LS-Factor and susceptible to soil erosion. A high-resolution soil map reflecting soil erosion was able to delineate using drone images and machine learning in Andosol's sloping upland field. [ABSTRACT FROM AUTHOR]

Purple soil is a type of global soil that is referred to by various names in different countries, which makes it difficult to understand, utilize, and ameliorate purple soil internationally. Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB) are the most widely used soil classification systems in the world. The aim of this study was to clarify the classification of purple soil in ST and the WRB and to establish a reference between different classification systems of purple soil. Therefore, based on the current principles and methods of the ST and WRB systems, 18 typical purple soil profiles in the eastern Sichuan Basin were identified, retrieved, and classified. Then, the soil units of the WRB were compared with those of ST and the Chinese Soil Taxonomy (CST). The results revealed that the 18 typical purple soil profiles could be classified into three soil orders, four soil group orders, and seven soil subgroups in ST and four reference soil groups (RSGs) in the WRB; each profile had its own unique principal and supplementary qualifier combinations within the soil units. It was found that when compared with the ST system, the WRB and CST systems had stronger abilities to distinguish purple soil. In addition, the WRB system was able to more comprehensively consider soil characteristics such as soil layer thickness, ferric horizon, soil color, texture mutations, and carbonate through qualifiers. However, the CST system added diagnostic characteristics, such as the lithologic characteristics of purplish sandstones and shales and the ferric properties and alic properties at the soil group and subgroup levels, which enhanced the differentiation ability of the purple soil at the subgroup level. [ABSTRACT FROM AUTHOR]

The soil organic carbon (C) content played an important factor in soil fertility for crop yield, but in wider regions, the estimation in the fields require costly to analyze. The study objectives were to use soil color as an indicator for assessment and correlation with the soil organic C content as the soil degradation indicator from 82 soil samples of the study area in Hau Giang province, Vietnam. The results showed that when the soil has the same humidity, the soil has a different Value and Chroma but the same Munsell Hue color. The organic C content is ranging from 1.32% to 5.6%. There was the negative significant correlation between Munsell soil color properties and organic C content, as Value (r = -0.75** air-dry, r = -0.74** moist); Chroma (r= -0.55** air-dry, r = -0.66** moist). Especially for topsoil layers, soil color is a useful predictor of organic C content. This study indicates that the Munsell soil colors can predict the fruit garden soil organic C in moist conditions. However, more analysis on the other soils with different soil properties for a further recommendation. [ABSTRACT FROM AUTHOR]

Soil color has historically been estimated visually, but inaccuracies with this method have been documented, including disagreement in evaluators, color blindness, and differences in physical color books due to weathering. As new technologies become available to soil scientists, a reliable digital method to determine soil color in the field could offer a solution to these inaccuracies. This research aimed to assess if visual estimates of soil matrix color from 111 soil horizons differed from digital measurements of Munsell soil color across three South Carolina regions. Visual estimates of soil color were conducted using Munsell color books. The XRite Capsure gave digital measurements in Munsell notation specific for soils. The absolute difference between hues was similar among assessment methods. Visual estimates of color value and chroma were statistically greater than digital measurements. However, color values and chromas were less than one chip different, suggesting no practical difference between the two assessment methods. Utilizing a digital soil color assessment tool can assist with training and validate human estimates. Core Ideas: Digital measurements and visual estimates of hue were similar.Visual estimates of value and chroma were significantly higher than digital measurements.The difference in assessment methods was less than one chip and not of practical significance.Utilizing a digital soil color assessment tool can help train and validate human estimates. [ABSTRACT FROM AUTHOR]

By the example of arable horizon samples taken from three soil types (sod-podzolic, gray forest, and leached chernozem) the peculiarities of electromagnetic waves reflection from their different particle size fractions were studied. The extraction of fractions by dry sieving was carried out using Retsch AS 200 BASIC equipment. As a result, 14 fractions ranging in size from less than 20 microns to more than 5,000 microns were isolated. Spectral reflectance was determined for each fraction and for the soil sample before sieving in the electromagnetic wave range from 350 to 2,500 nm using a SR-6500 field spectroradiometer (Spectral Evolution, USA). Analysis of similarities and differences in the obtained spectral reflectance curves of individual fractions was carried out using their visual analysis, the method of similarity dendrogram construction, as well as regression analysis between light reflectance and fraction particle size. It was confirmed that at a more detailed level of analysis compared to the one carried out by other researchers earlier, the general patterns of light reflectance of the samples do not change. A higher reflection of waves by thinner fractions and a lower reflection by more coarse fractions are observed. At the same time, spectral reflection curves for individual fractions are out of the general pattern, the level of intensity of local extremes of the curves’ changes. This confirms the difference of the material composition, which forms the color of soils, of these fractions from others. The color of the mixed sample is a spectral mixture of colors of its separate fractions. Presumably, this is the main reason for such a phenomenon as change of spectral reflectivity of open surface of soils under the influence of atmospheric precipitation.

Global warming is a wide-scale problem and soil carbon sequestration is its local scale, natural solution. Role of soil as carbon sink has been researched extensively but the knowledge regarding the role of soil variables in predicting soil carbon uptake and its retention is scarce. The current study predicts SOC stocks in the topsoil of Islamabad-Rawalpindi region keeping the soil properties as explanatory variables and applying the partial least square regression model on two different seasons' datasets. Samples collected from the twin cities of Islamabad and Rawalpindi were tested for soil color, texture, moisture-content, SOM, bulk density, soil pH, EC, SOC, sulphates, nitrates, phosphates, fluorides, calcium, magnesium, sodium, potassium, and heavy metals (nickel, chromium, cadmium, copper and manganese) by applying standard protocols. Afterwards, PLSR was applied to predict the SOC-stocks. Although, current SOC stocks, ranged from 2.4 to 42.5 Mg/hectare, but the outcomes of PLSR projected that if soil variables remain unaltered, the SOC stocks would be likely to get concentrated around 10 Mg/hectare in the region. The study also identified variable importance for both seasons' datasets so that noisy variables in the datasets could be ruled out in future researches and precise and accurate estimations could be made. [ABSTRACT FROM AUTHOR]

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In this study we present an in-depth description of the colorimetric values for the lowest section of the Dukatar Loess Palaeosol Sequence (LPS) pedocomplex S5. Formed during the Marine Isotope Stage (MIS) 13-15, it represents the oldest pedocomplex exposed at the base of the Titel loess plateau (TLP), near the confluence of the Tisa and Danube rivers in Vojvodina (northern Serbia). The results of low-field magnetic susceptibility measurements (X/r)were compared to colour properties (obtained by conventional methods as well as instrumental measuring) and quantified Soil Development Indices (SDI). Of these measurements we found that the Redness Index (RI1) yielded the most useful results, as this index appears most sensitive to lithological changes and soil development intensity. It was also observed that a high level of correlation existed between X/r, and a* chromaticity. The initial results of this study highlight the utility of colorimetric methods as an interdisciplinary tool when evaluating the presence of ferromagnetics, and the application of rock magnetism to the Middle and Upper Pleistocene LPS of the Middle Danube Basin. The presented approach can be used to observe the evolution of climatic and ecological conditions in the given study area, and for establishing correlations between sites extending over the Eurasian LPS provinces. [ABSTRACT FROM AUTHOR]

Soil color is a critical property, providing important information on soil properties. Soil color highly spatially varies on cultivated semi-arid sloping landscapes, indicating differences in soil properties that affect soil fertility. This study evaluated the relationships between color variables (L*: soil brightness, a*: redness, and b*: yellowness) and some basic soil properties on air dry and wet (around field capacity) soil samples, in a semi-arid sloping landscape having been under wheat cultivation for a long time. The values of color variables and soil properties were graphed and relationships between them were modeled using most proper regression models. The soil properties were poorly related to values of a* and b*, while CaCO3, sand, clay, and K contents and EC were highly significantly correlated with values of L*-wet (L*-values obtained on moist soil samples). Soil EC and CaCO3 content can be safely predicted by L*-wet in the study area. Also, the L*-wet should be preferred over L*-dry in predicting soil properties from soil color components in the study area and similar soils. [ABSTRACT FROM AUTHOR]

Soil's load-bearing capacity is a crucial property from which the ability of soil to resist the vertical deformation resulting from a normal load can be determined, and this property is essential for analyzing a vehicle's performance over soil terrain in terramechanics studies. Soil's moisture content has a significant impact on its load-bearing capacity and spectral behavior. This study aims to show the relation between the load-bearing capacity and the spectral behavior of sandy loam soil. The study presents the load-bearing capacity and color results of sandy loam soil at different moisture contents. The load-bearing capacity was measured using the Bevameter technique, and the color was measured using spectrophotometer technology that sends waves in the visible range (400–700 nm). The pressure–sinkage results of the tested soil show that with an increase in the moisture content, the bearing capacity of the soil decreases, and the color results show a decrease in the color reflectance with the increase in the moisture content. The measurements were performed in the laboratory of the vehicle technology department at Hungarian University of Agriculture and Life Sciences (MATE). [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in the use of soil color as a climatic proxy. We investigated the variation of soil chromaticity parameters in subtropical and tropical coastal areas in China and their relationships with climatic factors (mean annual precipitation and temperature). Latitudinal zonal soil profiles in the coastal areas of Fujian and Guangdong were selected for study, and chromaticity analysis of soil samples was performed using the CIELAB color system. The principal results are as follows: (1) Soil color in the coastal areas of Fujian and Guangdong is mainly controlled by hematite and goethite. Redness (a∗) varies significantly with depth within the soil profiles, and two types of profile can be defined: type I in which a∗ decreases with increasing depth within the profile, and type II in which a∗ increases with increasing depth within the profile. (2) Overall, a∗ , b∗ (yellowness) and the b∗/a∗ ratio do not show a significant relationship with latitude. The a∗ of some of the Fujian profiles and the Guangdong soil profiles are significantly negatively correlated with the MAP, the b∗/a∗ of some of the Fujian is highly positively correlated with MAP. A precipitation threshold may exist for both sets of profiles. There is a high correlation coefficient between b∗/a∗ and MAT, and a temperature threshold of ∼23 °C is evident. b∗/a∗ is correlated with MAP and MAT, and it can potentially be used as a climate proxy. [ABSTRACT FROM AUTHOR]

Accurate assessments of soil properties are required to improve fertilizer management practices for crop production. Conventional chemical analysis in the laboratory is costly and time-consuming. Soil color is related to different soil compositions, while soil magnetic susceptibility (MS) has been found to reflect the abundance of magnetic minerals relevant to soil properties. Improving proximal sensing techniques for the analysis of soil color and MS provides opportunities for affordable and rapid assessments of soil properties. The aim of this study was to evaluate the potential use of soil color parameters and MS values to predict soil properties using stepwise multiple linear regression (SMLR), random forest (RF), and nonlinear regression approaches in lowland and upland fields in the central highlands of Madagascar. The target properties included the contents of soil organic carbon (SOC), total nitrogen (TN), oxalate-extractable phosphorus and iron (Feox), and the soil texture. The model prediction accuracy was assessed using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). The use of soil color parameters yielded an acceptable prediction accuracy of the Feox content (loge Feox) for all rice fields (R2 = 0.54, RMSE = 0.55, RPIQ = 1.70) using the RF algorithm, while the SMLR approach gave the most accurate prediction for upland fields with acceptable reliabilities for SOC, Feox, and clay and sand content prediction, with R2 ranging from 0.43 to 0.67 and RPIQ from 1.63 to 1.77. In lowland fields, TN content was predicted with acceptable accuracy (R2 = 0.34, RMSE = 0.49, RPIQ = 1.71) using SMLR with the color parameter. The combination of the soil color parameters with the MS value as predictor variables increased SOC prediction for lowland fields using the RF approach (R2 = 0.57, RMSE = 6.37, RPIQ = 1.96). Use of the soil color and MS parameters was revealed to be a promising way to simplify the assessment of soil properties in upland and lowland ecosystems by using RF and SMLR approaches. A combined use of the soil color and MS parameters improved the prediction accuracy for the SOC content. [ABSTRACT FROM AUTHOR]

An alternative approach for soil characterization is proposed, using digital images and machine learning. The present work investigated the Red, Green, and Blue (RGB) pattern with Artificial Neural Network (ANN) and Random Forest (RF) in the classification of Oxisol and Inceptisol collected in South Brazil. Furthermore, we also built ANN, RF, Partial Least Squares (PLS), and Support Vector Machine (SVM) models from digital images to quantify Total Carbon (TC) content in soil samples. Preliminary evaluations considered the hypothesis that alternative methods of classification and quantification of soil properties might provide results comparable to traditional methods. The TC content of 91 soil samples, 40 from Oxisol and 51 from Inceptisol were determined by dry combustion. ANN and RF as machine learning algorithms were applied to classify the two kinds of soils. The classification quality was assessed by the percentage of correctly classified instances. The results suggest using digital images and machine learning algorithms as a new methodology to soil classification. The best machine learning method was RF which can be used to classify soil types by the RGB channel colors of the soil images. The use of digital images and RF is a clean (eco-friendly), rapid, and affordable strategy analysis for TC quantification in soils. Therefore, the proposed methods represent an advancement in soil classification, such as the determination of the TC content of soils. [ABSTRACT FROM AUTHOR]

In Forensic Geology, color examination of sediments and soils is one of the first analyses for exclusionary/comparative purposes. Forensic colorimetry deals with the examination of the color of physical evidence related to criminal investigations. Color of soils and sediments has always been considered an essential parameter and one of the most common and crucial visually physical property for characterizing, describing, and comparing these geological materials, since the first steps of forensic investigations. This parameter may be evaluated both qualitatively and quantitative. In forensic comparative analyses of sediments and soils, most of the colorimetric methods are nowadays computational. These computational instruments, provided of standards and standardized illumination conditions, may obtain very high-resolution and non-destructive quantitative data. When adapted to sedimentological applications for forensic purposes, photometric instrumentations are powerful tools due to their low purchase and maintenance costs. Moreover, some devises are also portable and easily used at the crime scene. Recent advances in the colorimetry research showed as also the simple use of digital devises, such as low-cost flatbed scanners, may be easily applied for the color examination of acquired color images of sediments and soils, if calibrated with standards, as reference charts or database. The results of a simulated forensic casework devoted to the comparative analysis of the color of soils, acquired by means low-cost flatbed scanner, confirmed previous results on this useful application. [ABSTRACT FROM AUTHOR]

Soil color is commonly used as an indicator to classify soil and identify its properties. However, color-based soil assessments are susceptible to variations in light conditions and the subjectivity of visual evaluations. This study proposes a novel method of calibrating digital images of soil, regardless of lighting conditions, to ensure accurate identification. Two different color space models, RGB and CIELAB, were assessed in terms of their potential utility in calibrating changes to soil color in digital images. The latter system was determined to be suitable, as a result of its ability to accurately reflect illuminance and color temperature. Linear regression equations relating soil color and light conditions were developed based on digital images of four different types of soil samples, each photographed under 15 different light conditions. The proposed method can be applied to calibrate variations in the soil color obtained by digital images, thus allowing for more standardized, objective, and accurate classification and evaluation of soil based on its color. [ABSTRACT FROM AUTHOR]

This study examines the soil environment of eight charcoal kiln platforms and the neighboring soil in Czech and Bohemian low-altitude forest stands. Both mixed and undisturbed soil samples were used to assess the hydrophysical soil properties, nutrient content, cation exchange capacity, enzyme activity, and soil active carbon content, while soil color, stoniness, root density, and horizon thickness were estimated in the field. Charcoal-rich horizons had high total organic carbon concentrations and total nitrogen content (about 150% and 40% higher than in the organomineral horizons of the control plot, respectively), with total carbon stocks being higher than those in neighboring forest soils. Fine root density was highest in the charcoal-rich horizons, encouraged by high soil porosity, aeration, and favorable chemical properties. Enzyme group activity differed between individual soil horizons and kiln and control plots, with depolymerization enzyme activity highest in charcoal-rich horizons and humification enzyme activity highest in mineral horizons. Phosphatase, chitinase, and phenoloxidase activity were highest in charcoal-rich horizons, while β-glucosidase activity remained similar across horizons. After long-term abandonment, kiln sites shift from inhospitable sites to localized hotspots for plant and microbial growth, having more favorable physical, enzymatic, and chemical soil properties than the surrounding areas. This study confirmed that kiln production platforms act as microhabitat hotspots, also providing information on a wide spectrum of soil properties linked with soil microorganisms and root growth. [ABSTRACT FROM AUTHOR]

Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO. [ABSTRACT FROM AUTHOR]

This review aim was to discuss several notions regarding Hydrangea sp. like the level of the hortensia blossom color change as dependent on soil acidity. Hydrangea is an ornamental plant known for its many variants in flower color. The plant has also gained frost tolerance, achieved through the cold acclimatization process, which involves physiological and biochemical changes. Furthermore, hydrangeas flower contains anthocyanin pigments that changes its color depending on the pH of the soil. It has natural red, blue, and purple pigments and can be found in many forms within a plant. This pigment is activated by the pH of the soil and produces a distinct hue based on it. We shall acquire a blue tint in an acidic soil and a pink color in a basic soil. However, the pH is not the only one involved in the color changes; the Al that the plant acquires also plays a role. Although all of this Al accumulation is useful for changing the hue, it can reach very big proportions, therefore it must be managed, which is where detoxification comes in. [ABSTRACT FROM AUTHOR]

The current study focuses on the investigation of soil organic carbon in Technosols in Kula Municipality. It has several aims. There is a need of a provision of more data, regarding carbon sequestration rates in topsoils in plains that are formed in subhumid climatic conditions. Another aim is to check the comparability of the in-profile cultural layer with other ones that are built in different climatic conditions. Objects of the research are contemporary since buried soil horizons play a major part on the provision of essential ecosystem services. The characteristics of soil organic matter are determined by a chemical analysis of six soil samples in the laboratories of the Institute of Soil Science, Agrotechnologies and Plant Protection (ISSAPP) “N. Pushkarov”. The total carbon content is determined by the test of Turin and soil color is determined by Munsell Soil Color Charts (1975). Soil organic carbon values in topsoil vary from 670,000 tons/ha to 1,240,000 tons/ha. Organic carbon in the studied sites represents less than 1% of the soil sample. The study may be regarded as the first step in the assessment of Bulgarian Technosols and their role in the global carbon cycle. [ABSTRACT FROM AUTHOR]

The article offers information on the pottery from the Hellenistic-Byzantine Period rural settlement at el-Khirba, Shoham in Israel. The pottery from Stratum III include storage jars, jugs and lamps. The pottery from Stratum IIA dates from the mid-second to the early first century Before Common Era and include cooking pots, storage jars and jugs. It points out that the pottery forms found are typical of the Judean region and reflect the rise and decline of the activity at the site.

Despite occupying a large area of the globe and being the next agricultural frontier, sandy soils are seldom explored in scientific studies. Considering the high capacity of remote sensing in soil characterization, this work aimed to: (i) characterize sandy soils' profiles from proximal sensing; (ii) assess the ability of visible, near, and short-wave infrared (Vis-NIR-SWIR) as well as mid-infrared (MIR) spectroscopy to distinguish soil classes of highly sandy content; (iii) quantify physical and chemical attributes of sandy soil profiles from Vis-NIR-SWIR and MIR spectroscopy as well as X-ray fluorescence (pXRF). Samples were described and collected from 29 sandy soil profiles. The 127 samples went under Vis-NIR-SWIR and MIR spectroscopy, X-ray fluorescence, and chemical and physical analyses. The spectra were analyzed based on "Morphological Interpretation of Reflectance Spectrum" (MIRS), Principal Components Analysis (PCA), and cluster methodology to characterize soils. The integration of different information obtained by remote sensors, such as Vis-NIR-SWIR, MIR, and Portable X-ray Fluorescence (pXRF), allows for pedologically complex characterizations and conclusions in a short period and with low investment in analysis and reagents. The application of MIRS concepts in the VNS spectra of sandy soils showed high potential for distinguishing pedological classes of sandy soils. The MIR spectra did not show distinct patterns in the general shapes of the curves and reflectance intensities between sandy soil classes. However, even so, this region showed potential for identifying mineralogical constitution, texture, and OM contents, assuming high importance for the complementation of soil pedometric characterizations using VNS spectroscopy. The VNS and MIR data, combined or isolated, showed excellent predictive performance for the estimation of sandy soil attributes (R2 > 0.8). Sandy soil color indices, which are very important for soil classification, can be predicted with excellent accuracy (R2 from 0.74 to 0.99) using VNS spectroscopy or the combination of VNS + MIR. [ABSTRACT FROM AUTHOR]