Soil Texture Components Research Articles

SOIL CONSERVATION STRATEGIES FOR THE REDUCTION OF BIODIVERSITY IN MOUNTAIN SOILS: EXAMPLE OF ULUDAĞ NATIONAL PARK

Uludag National Park in Turkey is famous for its rich biodiversity. The park serves as an essential habitat for a wide variety of flora and fauna and contributes significantly to the conservation of various species. This study examines biodiversity conservation strategies in Uludağ National Park. Soil samples were taken from 17 locations near the summit where endangered endemic plants grow. Analyses of the soil samples reveal the relationships between elevation and soil texture components. According to the soil analysis results, there was a weak positive correlation between elevation and silt content (r = 0.414) and a weak negative correlation between elevation and sand content (r = -0.375). These findings indicate that silt content tends to increase and sand content tends to decrease with increasing elevation. The geomorphological features and soil structure of Uludağ National Park were also an essential part of the research. The region's metamorphic mica schists, granites and marbles affect the soil's physical properties. For example, soils are generally sandy and permeable in areas where granite parent material is present. In contrast, areas where mica-schist parent material is distributed are less resistant and more susceptible to erosion. As a result, effective soil conservation strategies must be implemented to protect biodiversity in Uludağ National Park. These strategies are essential to reduce soil erosion, increase organic matter accumulation and minimise the impacts of climate change. Furthermore, community participation and adaptive management strategies must be adopted for sustainable development and resource management. This study provides essential information for biodiversity conservation in the Uludağ region and contributes to developing conservation strategies.

Improvement of data imbalance for digital soil class mapping in Eastern China

A fine-resolution digital soil class map is needed. However, the problem of imbalanced data leads to an inaccurate spatial distribution of the digital soil class map, and the spatial resolution of digital soil class maps at a large scale is low in existing studies. Based on these points, an algorithm of over-sampling and under-sampling was introduced to solve the problem of imbalanced data, and to improve the performance of soil classification model. 316 topsoil samples with eight main soil classes at the great group level were collected in Eastern China. Eight out of twelve prediction variables were determined after the importance evaluation by “Mean Decrease Accuracy” in the random forest (RF) model, including digital elevation model (DEM), enhanced vegetation index (EVI), land surface wetness index (LSWI), land surface temperature (LST), normalized differenced vegetation index (NDVI), and soil texture components. RF model was also applied to complete digital soil class mapping, and the results of treated (over-sampling and under-sampling by randomly increasing or decreasing the number of samples) and untreated data were compared and discussed. Research results indicated that modeling by imbalanced data resulted in uncertain soil classes mapping, with minority classes were lost and with lower accuracies than those of balanced data (overall accuracy = 83.83 %, kappa coefficient = 0.79). After over-sampling and under-sampling treatments, these problems were well solved with an overall accuracy of 96.72 % and a kappa coefficient of 0.93. The accuracy of soil class prediction for minority classes were improved by 12.5 %–54.5 %. Compared to the existing conventional soil map, the new map with a fine resolution of 30 × 30 m is time-effective and more detailed. Validation (point-validation and map-to-map comparison) of the predicted map showed that the output is reliable and can provide a reference for other soil and environmental studies without major difficulties.

An Independent Validation of SoilGrids Accuracy for Soil Texture Components in Croatia

While SoilGrids is an important source of soil property data for a wide range of environmental studies worldwide, there is currently an extreme lack of studies evaluating its accuracy against independent ground truth soil sampling data. This study aimed to provide a comprehensive insight into the accuracy of SoilGrids layers for three physical soil properties representing soil texture components (clay, silt, and sand soil contents) using ground truth data in the heterogeneous landscape of Croatia. These ground truth data consisted of 686 soil samples collected within the national project at a 0–30 cm soil depth, representing the most recent official national data available. The main specificity of this study was that SoilGrids was created based on zero soil samples in the study area, according to the ISRIC WoSIS Soil Profile Database, which is very sparse for the wider surroundings of the study area. The accuracy assessment metrics indicated an overall low accuracy of the SoilGrids data compared with the ground truth data in Croatia, with the average coefficient of determination (R2) ranging from 0.039 for silt and sand to 0.267 for clay, while the normalized root-mean-square error (NRMSE) ranged from 0.362 to 2.553. Despite the great value of SoilGrids in a vast range of environmental studies, this study proved that the accuracy of its products is highly dependent on the presence of ground truth data in the study area.

Integrating no-tillage with agroforestry augments soil quality indicators in Kenya’s dry-land agroecosystems

Conservation agriculture with trees (CAWT) is one of the best-bet strategies for enhanced soil quality under extensive and intensive smallholder farming. CAWT is an agroforestry system that integrates legume trees and shrubs into cropping fields under minimum soil disturbance and tillage. This study identified principal soil quality indicators (SQI) under CAWT system. The study further assessed the effects of CAWT components; i.e. tillage (convention or no-tillage), leguminous trees/shrubs (Calliandra calothyrsus, Gliricidia sepium and Cajanus cajan), and their inter-row spacing (1.5 m, 3.0 m or 4.5 m) on the SQI in the dry-land agroecosystems of eastern Kenya. We finally reported on the suitability of the SQI under CAWT intervention towards maize production. The experimental trials were both researcher (Mother-trials; MTs) and farmer (Baby-trials; BTs) hosted and managed. Principal Component Analyses (PCAs) identified soil fertility and textural components as the main factors explaining soil quality under the CAWT system. In particular, the exchangeable bases (ExBas) such as ExCa, ExK, and ExMg), Cation-Exchange-Capacity (CEC), total soil nitrogen (TSN), soil organic Carbon (SOC), pH, available Phosphorus(P), electrical conductivity (EC), clay and bulk density (BD) were identified as the principal soil quality indicators under the CAWT system. Tree species and varied inter-row spacing, significantly affected available P, BD, pH, ExBas, CEC, SOC, and TSN. The tillage systems significantly (P < 0.05) influenced soil pH, ExBas, CEC, SOC and TSN. A high concentration of TSN was recorded in no-tillage (NT) blocks integrated with C.calothyrsus (41.9 and 41.6 Mg N ha−1) and G. sepium (35.7 and 32.3 Mg N ha−1) both spaced at 1.5 m at the MTs and BTs, respectively. Combining NT with C.calothyrsus spaced at 1.5 m or Pigeon pea at 3.0 m significantly increases available P (from 22.9 to 28.8 mg kg−1 and 23.4–26.0 mg kg−1) at the MTs, respectively. Significant rise in ExK (1.91–2.25 cmolc kg−1), ExCa (6.86–8.17 cmolc kg−1), and ExMg (2.35–2.78 cmolc kg−1) were observed in NT block’s sub-plots with G. sepium spaced at 3.0 m at the MTs. Conclusively, a shift towards CAWT showed evidence of improving soil quality, nutrient availability and increasing soil nutrient thresholds that can potentially support maize production. By establishing the minimum datasets for soil quality determination through this study, key stakeholders in agroforestry and conservation agriculture (CA) have an efficient cost-effective and rapid tool for soil quality assessment, especially in dry-land agro-ecosystems.

Prediction of soil textural constituents using reflected spectra in eastern Mazandaran Province

Textural components of soil play an essential role in erodibility and should be considered in many projects of conservation and environmental modeling processes. Traditional methods of determining soil texture are usually laborious, expensive and time consuming along with destructive effects on the environment. Meanwhile, spectroscopic technology using the spectral features and signatures from the whole reflected spectra of soil surface promises a competent method to study soil constituents. To investigate this issue, 113 points were selected and sampled randomly from 0-15 cm of soil surface in eastern parts of Mazandaran Province, Iran. Samples were haphazardly divided into 91 for model building and 22 for final verification and accuracy assessment processes. Applying the enhanced PLS-algorithm plus the FLOOCV approach along with spectral transformations and pre-processing, the modeling of each textural components were accomplished. Spectrally, sand and clay fractions were modeled with high accuracy as: R2c= 0.89, RMSEc= 7.42, SEc= 7.46 for sand and R2c= 0.82, RMSEc= 6.88, SEc= 6.92 for the clay content. Whereas, the silt predictive model was slightly weaker than the other constituents. The most effective spectral ranges involved in the modeling process, were also detected and recognized based on beta & spectral weight analyses and Marten’s uncertainty test. Additionally, the most influential spectroscopic ranges included were the visible, NIR and SWIR regions with the specified wavelengths. In general, the efficacy of spectroscopic technology in soil texture studies has been proven by this research. Using the computed spectral models, we are able to study the soil textural components at large scales faster, safer, timelier and also cheaper. That is absolutely true and applicable using the regionalized remotely sensed data but requires further investigation in different geographical regions.

Agroecological Determinants of Potato Spatiotemporal Yield Variation at the Landscape Level in the Central and Northern Ukraine

Global food security largely depends on the crop yield increase, so the study of the yield-limiting factors of potato (the second bread) is a pressing issue today. This study determines the contribution of the agroecological factors, namely, bioclimatic variables, soil indicators, and factors of landscape diversity, to the variation in potato yields. Conducted in Polissya and Forest-steppe zones of Ukraine during 1991–2017, this study has not only addressed the relationship between ecological determinants and potato yields, but also considered crop yields as a dynamic system. The dynamics of potato yields from the mid-1990s to the present is described by a log-logistic model. There are statistically significant regression dependencies between potato yield parameters and agroecological factors. Potato yield is dependent on the diversity of landscape cover. The relationship between yield parameters and landscape-ecological diversity is non-linear, which determines the presence of optimal landscape structure for the highest potato yields. Among climatic factors, the continental climate is of the greatest importance for potato yield. The high sensitivity of potato yield parameters to soil indices was found, and mostly the soil texture components (silt content), which largely determines the potato yield spatial variation.

Agroecological Determinants of Potato Spatiotemporal Yield Variation at the Landscape Level in the Central and Northern Ukraine

Global food security largely depends on the crop yield increase, so the study of the yield-limiting factors of potato (the second bread) is a pressing issue today. This study determines the contribution of the agroecological factors, namely, bioclimatic variables, soil indicators, and factors of landscape diversity, to the variation in potato yields. Conducted in Polissya and Forest-steppe zones of Ukraine during 1991–2017, this study has not only addressed the relationship between ecological determinants and potato yields, but also considered crop yields as a dynamic system. The dynamics of potato yields from the mid-1990s to the present is described by a log-logistic model. There are statistically significant regression dependencies between potato yield parameters and agroecological factors. Potato yield is dependent on the diversity of landscape cover. The relationship between yield parameters and landscape-ecological diversity is non-linear, which determines the presence of optimal landscape structure for the highest potato yields. Among climatic factors, the continental climate is of the greatest importance for potato yield. The high sensitivity of potato yield parameters to soil indices was found, and mostly the soil texture components (silt content), which largely determines the potato yield spatial variation.

Soil texture fractions modeling and mapping using LS-SVR algorithm

Soil texture is variable through space and controls most of the soil’s Physico-chemical, biological and hydrological characteristics and governs agricultural production and yield. Therefore, determining its variability and generating accurate soil texture maps have a key role in soil management and sustainable agriculture. The purpose of this study is to introduce a numerical algorithm named Least Square Support Vector Machine for Regression (LS-SVR) as a predictive model in Digital Soil Mapping (DSM) of soil texture fractions and evaluating its performances based on modeling evaluation criteria. In this study, the soil texture data of 49 soil profiles in Tabriz plain, Iran, was used. The important covariates were selected using Genetic Algorithm (GA). The model evaluation results based on ME, MAE, RMSE, and R2 indicate the high performance of LS-SVR in predicting soil texture components. The prediction RMSE for sand, silt, and clay was 6.82, 5.08 and 6.06, respectively. Silt prediction had the highest ME and the lowest MAE and RSME values. The algorithm simulated the complex spatial patterns of soil texture fractions and provided high accuracy predictions and maps. Therefore, the LS-SVR algorithm has the capability to be used as predictive models in soil texture digital mapping. This study highlighted the potential of the LS-SVR algorithm in high precision soil mapping. The generated maps can be used as basic information for environmental management and modeling.

Soil physiochemical properties and carbon sequestration of Urban landscapes in Lubbock, TX, USA

Soil organic matter (SOM) accumulation and carbon (C) sequestration are ecosystem services (ESs) provided by urban landscapes that are dominated by continuous grass cover, such as residential lawns. Organic matter and C sequestration are expected to increase over time, but few studies have determined potential soil organic carbon (SOC) accumulation from residential lawns in semiarid climates. The objectives were to evaluate physiochemical attributes of urban soils established under turfgrass landscapes of different ages and determine soil factors that differentiate urban soils in semiarid climate of Lubbock, TX. Soil samples (0−10 cm) were obtained from 10 residential lawns of homes built: pre-1970 (oldest), 1971−1990 (middle), 1991−2010 (newer), and after 2011 (newest). Soil texture, bulk density, extractable nutrients [phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and iron (Fe)], pH, SOM, SOC, and total nitrogen (TN) were determined. Bulk density and pH were highest in newest homes and lowest in oldest homes. Increasing years of lawn establishment increased SOM, SOC, and TN content. Linear regression determined 0.036 % annual increase in SOM for residential lawns, but SOC accumulated at 0.021 kg C m−2 yr-1 for 53.6 yr. In principal component (PC) analysis, SOM, SOC, and TN contributed most to PC1 (36.2 %) reflecting components separating residential lawns by years of establishment, and primarily soil texture components for PC2 (22.5 %). The results of this study show that home age is a strong determinant of soil carbon content in semiarid urban turfgrass systems, with the greatest accumulation threshold in oldest homes over 50 years of age.

Methods of Spatial Interpolation in The Representation of Soil Properties/Nineveh Governorate Soil Case Study

Spatial interpolation is a process used to guess cell values at points or locations where real models are not available. The principle of interpolation is based on Spatial autocorrelation and Spatial Dependence, which measures the degree of relationship or dependence between points or objects near and far and by using statistical techniques to address the effects of data values and the relationship between the various interrelated. The study relied on more than one method of spatial interpolation (Kriging - Inverse Distance Weighted-Radial Basis Functions-Global Polynomial-Local Polynomial). To represent data for one of the most important elements of the natural environment and the soil and within the spatial space of the governorate of Nineveh, In the present study, some of the physical properties of the soil are expressed (soil texture, pH, bulk density, and chemical components such as (organic matter, total Bases, cation exchange capacity, exchangeable sodium percentage, calcium carbonate, available water capacity, and electrical conductivity based on (92) points distributed according to the results of the spatial interpolation test based on the mean bias error values and root mean square error, which was used as a criterion for the differentiation between the methods of completion used in the study. The study also showed that the Kriging method is suitable for the parts of spatial completion of soil properties. It was possible to reach (27) maps to represent some of the physical and chemical properties of the soil of the study area.

Effects of leonardite on the coastal saline soil improvement

ABSTRACT This study investigated the influence of leonardite on coastal saline soil YX1 (very slightly saline), YX2 (slightly saline) and YX3(strongly saline) with 0/10/30/50 g/kg addition dosage during the 81-day incubation. Changes of pH, electrical conductivity, soil texture, micromorphology, functional groups and mineral constituents of soil were discussed. Results showed that the application of leonardite enhanced the urea hydrolysis, increased the activities of urease and phosphatase in the coastal saline soil. The urease and phosphatase activity reached the maximum on the 8th incubation day. Leonardite raised C and N content in the soil colloid as well as the C/N value. However, leonardite had limited effects on the soil texture and mineral components during the incubation period. The application of leonardite would promote the activity of soil micro-organisms and relative enzyme to improve the soil ecosystem. It could benefit the amelioration of coastal saline soil, and provide the theoretical direction for agricultural production.

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct Fits

A high-resolution soil water retention data set (81 repacked soil samples with 7729 observations) measured by the HYPROP system was used to develop and evaluate the performance of regression parametric pedotransfer functions (PTFs). A total of sixteen soil hydraulic models were evaluated including five unimodal water retention expressions of Brooks and Corey (BC model), Fredlund and Xing (FX model), Kosugi (K model), van Genuchten with four free parameters (VG model) and van Genuchten with five free parameters (VGm model). In addition, eleven bimodal, Peters–Durner–Iden (PDI) and bimodal-PDI variants of the original expressions were studied. Six modeling scenarios (S1 to S6) were examined with different combinations of the following input predictors: soil texture (percentages of sand, silt and clay), soil bulk density, organic matter content, percent of stable aggregates and saturated water content (θs). Although a majority of the model parameters showed low correlations with basic soil properties, most of the parametric PTFs provided reasonable water content estimations. The VGm parametric PTF with an RMSE of 0.034 cm3 cm−3 was the best PTF when all input predictors were considered. When averaged across modeling scenarios, the PDI variant of the K model with an RMSE of 0.045 cm3 cm−3 showed the highest performance. The best performance of all models occurred at S6 when θs was considered as an additional input predictor. The second-best performance for 11 out of the 16 models belonged to S1 with soil textural components as the only inputs. Our results do not recommend the development of parametric PTFs using bimodal variants because of their poor performance, which is attributed to their high number of free parameters.

Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area

This study evaluated the performance of deep learning (DL), artificial neural network (ANN) and k-nearest neighbour (kNN) models to estimate field capacity (FC) and permanent wilting point (PWP) using four combinations of soil data. The DL, ANN and kNN models are compared with the previous published pedotransfer functions (PTF). The data consist of 256 calcareous soil samples collected from Konya-Çumra plain, Turkey. The results demonstrated that the DL_a with inputs of soil texture components, bulk density, organic matter and lime contents, particle density and aggregate stability showed the best performances with coefficient of determination (R2) of 0.829, correlation coefficient (r) of 0.911, mean absolute error (MAE) of 0.027 and relative root mean square error (RRMSE) 9.397 % in FC estimation for calcareous soil samples. For the PWP estimation of calcareous soil samples, the kNN_b with soil texture components, bulk density, organic matter and lime content and particle density indicated the best performance with the value of R2 to 0.800, of r to 0.894, of MAE to 0.021 and RRMSE to 12.043 %. Lastly, the results showed that the DL, ANN and the kNN models perform better than the previously applied PTF for calcareous soils. Therefore, the DL model could be recommended for the estimation of FC when full soil data are available and the kNN model could be recommended for estimation of PWP with all combinations of soil data.

Impact assessment of coal mining induced subsidence on native soil of South Eastern Coal Fields: India

Understanding the consequences of mining is most important in order to prevent negative outcomes for the environment as natural systems are used by humans for agriculture/silviculture. The present manuscript deals with the impact of subsidence due to underground coal mining on native soil. Depth-wise changes in soil texture and nutrient components in four layers; 0–15 cm, 16–30 cm, 31–45 cm and 46–60 cm were quantified along the subsidence prone land of the study area in top (within the extension zone; Zone I), middle (within inner-edge zone; Zone II) and bottom (around centre of the subsidence trough; Zone III) of a three and half year mining subsided land and compared it with soil texture and nutrient component status of an adjacent undamaged zone (UZ). An alteration in the Physicochemical characteristics of the soil after subsidence was observed. It had a positive impact on most of the soil characteristic parameters at Zone III. Available nitrogen, phosphorous and potassium (AN, AP and AK) were increased by 13.00%, 44.47% and 26.7%, respectively in 0–15 cm layer; 16.42%, 45.12% and 28.08%, respectively in 16–30 cm layer; 15.74%, 47.45% and 22.97%, respectively in 31–45 cm layer and 14.86%, 38.94% and 18.53%, respectively in 46–60 cm layer. A significant increase in silt + clay content, organic carbon and electrical conductivity were also reported.

Evaluating fractal dimension of the soil particle size distributions and soil water retention curve obtained from soil texture components

ABSTRACT It is much easier to measure solid phase data or particle size distribution (PSD) than pore size distribution, therefore it is useful to determine relationship between fractal dimension of the soil particle size distributions (D PSD) and soil water retention curve (). The present research aims to firstly measure fractal dimension of the soil PSD using three radii 1 (clay), 25 (silt) and 1000 (sand) µm (DPSD3 ) and subsequently compare it with fractal dimension obtained from DPSD, and secondly develop a relationship between and . The results showed that the maximum and minimum values for were 2.93 and 2.65 and for were 2.91 and 2.63 and there was no significant difference between and . A linear relationship between and was developed for some of soils UNSODA databases. The relationship developed here was compared with three present methods to calculate from soil texture including (1) non-linear relationship with clay, (2) logarithmic relationship with clay, and (3) percent of sand, silt and clay for some of the northern soil of Iran. The results indicated that the proposed method in this study, especially in fine-grained soils, can efficiently predict with appropriate precision.

Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran

Soil texture is a physical property of soil and knowledge on its spatial distribution is essential for many agricultural and environmental activities especially in alluvial plains. This study aimed to predict the spatial distribution of soil fractions (i.e. percentages of sand, silt, and clay) in Sistan floodplain on a regional scale (area: 1500 km2). Random forest (RF), regression kriging-neural network residual kriging (RKNNRK), neural network residual kriging (NNRK), regression kriging (RK), and cokriging (COK) methods were used to map soil texture components over the region. Soil fractions were measured for 160 soil samples taken from the surface layer (0–30 cm) of various soil series in agriculture land of Sistan floodplain. The additive log-ratio (alr) transformation was applied to transform texture components prior to prediction. Remote sensing data including Landsat 8′ Bands (1–8), Band 4 to Band 8 ratio, Band 4 to Band 3 ratio, NDVI, GSI, Brightness Index, and Clay Index were used as auxiliary variables for interpolation of soil fractions. 80% of actual data was used for prediction and 20% of data was used for validation. The performance of methods used was evaluated using RMSE, ME and MAE criteria. The results showed that the RKNNRK model has the highest accuracy for prediction of sand (RMSE = 15.04%) and clay (RMSE = 8.77%) contents while the most accurate model for predicting silt content is NNRK (RMSE = 12.68%). RK and COK performed worse than NNRK and RKNNRK. A relatively high value of RMSE obtained for sand (17.89%), silt (14.15%), and clay (8.89%) contents by the RF model. This could be due to the low relief of study area, regional scale effects, low density of sampling points and high heterogeneity of soil texture components in floodplains. Our findings revealed that RKNNRK and NNRK models, when combined with remote sensing data, produce more accurate results and therefore can be used for appropriate mapping of soil fractions in floodplains and on a regional scale.

Effect of Macroporosity on Pedotransfer Function Estimates at the Field Scale

Core Ideas Estimation of Ks using established PTFs for one specific field was evaluated. Selected PTFs exhibited unsatisfactory prediction of Ks at the field scale. Taking macropore volume effect into account improved the prediction of Ks with PTFs. Saturated hydraulic conductivity (Ks) is one of the crucial hydraulic properties for assessing water and solute transport in soils. However, direct measurement of Ks is time consuming and arduous. Alternatively, pedotransfer functions (PTFs) have been developed to estimate Ks indirectly through more easily measurable soil properties that are part of regional, national, or international databases. These PTFs are usually based on datasets collected from large regions. However, their validity for a specific site remains unclear. The objectives of this study were to evaluate the performance of established PTFs in estimating Ks in a specific field and improve PTFs to arrive at a locally adapted estimation result for Ks. Forty‐one soil samples were collected from 10 locations at five depths at a farmland in western Kentucky for hydraulic conductivity and physical property measurements. The performance of seven PTFs in estimating Ks was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). At this scale, all the selected PTFs exhibited unsatisfactory prediction of Ks (high RMSE, low NSE and R2). In the field studied, approximately 60% of variance in Ks could be explained by soil texture and macropore components based on factor analysis. Clay content and macroporosity were identified as the most representative variables for each component. The performance of a PTF in estimating Ks for the field site investigated was significantly improved by including macroporosity (pores with diameter &gt;75 μm) as a predictor. The results confirmed that soil structure was crucial in characterizing soil hydraulic conductivity.

Assessment of soil texture from spectral reflectance data of bulk soil samples and their dry-sieved aggregate size fractions

Diffuse reflectance spectroscopy (DRS) continues to emerge as a rapid approach for estimating several soil properties including soil texture. To improve the accuracy of DRS approach for soil texture assessment, a data fusion approach was tested in which the reflectance spectra of bulk soil and its aggregate size fractions obtained by dry sieving approach were combined with three chemometric models such as partial-least-squares regression (PLSR), a locally-weighted PLSR (PLSRLW) and PLSR with feature selection (PLSRFS). This newly developed approach was tested using three different soil (Vertisols, Alfisols and Mixed) spectral libraries containing a total of 1013 soil samples each with 9 different aggregate size fractions. Because soil aggregation is influenced by the nature of clay minerals in addition to organic carbon, iron and aluminum oxides, XRD analyses were carried out in selected aggregate fractions. Results showed that there was reasonable improvement in the estimation accuracy of sand, silt and clay contents in KVS and KAS when an aggregate size fraction spectrum was added to bulk soil spectrum. For example, consideration of PLSRFS or PLSRLW models along with best-performing aggregate spectra could reduce RMSE values in the validation datasets in the order of 18% for clay to 31% for silt in KVS samples, 32% for silt to 49% for clay in KAS samples, and 6% for sand to 15% for silt in WBODS samples compared to when PLSR model was used alone in the F0 spectra. Correlation analysis showed that textural fractions were more strongly correlated to spectra of macroaggregates (aggregate size >0.20 mm) than those of microaggregates (aggregate size <0.13 mm). Correlation analysis between soil separates and the amount of soil mass within each aggregate size fraction showed that the microaggregates contained more information for soil textural components than macroaggregates. The proposed approach of using bulk soil spectra and aggregate fraction spectra may be a step forward in texture analysis through DRS approach. Although extra time is required for collecting soil aggregate fraction spectra, the method has advantage of allowing the measurement of geometric mean diameter using traditional methods.

Modeling sprinkler irrigation infiltration based on a fuzzy-logic approach

In this study, the irrigation water infiltration rate (IR) is defined by input variables in linguistic terms using a fuzzy-logic approach. A fuzzy-logic model was developed using data collected from published data. The model was trained with three fuzzy membership functions: triangular (‘trimf’), trapezoid (trapmf), and pi (‘pimf’). The fuzzy system considered the number of irrigation events, applied water depth, polyacrylamide application rate, water application time, water electrical conductivity, soil surface slope, and soil texture components as input variables. The inputs were classified in terms of low, medium, and high levels. The output variable (i.e., IR) was rated in terms of five levels: very low, low, medium, high, and very high. Using statistical analysis, the values of IR resulting from the developed fuzzy-logic model were compared with the observations from the experiments. The results confirm that the agreement between the observations and predictive results was acceptable, except for fuzzy 'trimf'. The coefficient of determination provided the greatest value when using the 'trapmf' and 'pimf', with the value estimated for the 'pimf' slightly higher than that of 'trapmf'. Based on the results that were obtained, irrigation managers can use the fuzzy-logic approach to modify their field practices during the growing season to improve on-farm water management.

Retrieval of Both Soil Moisture and Texture Using TerraSAR-X Images

The aim of this paper is to propose a methodology combing multi-temporal X-band SAR images (TerraSAR-X) with continuous ground thetaprobe measurements, for the retrieval of surface soil moisture and texture at a high spatial resolution. Our analysis is based on seven radar images acquired at a 36° incidence angle in the HH polarization, over a semi-arid site in Tunisia (North Africa). The soil moisture estimations are based on an empirical change detection approach using TerraSAR-X data and ground auxiliary thetaprobe network measurements. Two assumptions were tested: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced. By considering the estimated temporal dynamics of soil moisture, a methodology is proposed for the retrieval of clay and sand content (expressed as percentages) in soil. Two empirical relationships were established between the mean moisture values retrieved from the seven acquired radar images and the two soil texture components over 36 test fields. Validation of the proposed approach was carried out over a second set of 34 fields, showing that highly accurate clay estimations can be achieved. Maps of soil moisture, clay and sand percentages at the studied site are derived.