Pedotransfers Functions

Soil texture was examined in four crop fields with areas of 10 to 45 ha located in northern and central Poland. In each field, from 21 to 60 soil samples were collected using stratified sampling. The content (%) of soil particles, i.e., sand, silt and clay, was then evaluated using laboratory methods. The apparent electrical conductivity (ECa) was measured and used as ancillary data for the interpolation of soil texture. The obtained data were used to compare selected spatial interpolation methods according to the accuracy of prediction. The examined methods were evaluated based on the results of cross-validation tests. Two methods of validation were used: leave-oneout cross-validation and validation based on a test set of points, with approximately 30% randomly selected. The smallest root mean square error (RMSE) for the prediction of sand, silt and clay was observed for ordinary cokriging in which ECa was used as a covariate. The other three methods, i.e., inverse distance weighting, radial basis functioning and ordinary kriging, had very similar RMSE values, which were approximately 10% higher compared to ordinary cokriging.

Measurement of unsaturated hydraulic properties of soil is a time-consuming endeavor which limits the scale of studies. A stratified sampling scheme could enable more efficient use of samples but some information about the strata is needed a priori. The aim of this study is to define zones of soil hydraulic properties using remotely sensed and soil physical inputs. Samples were collected from 50 locations at 4 depth intervals in a 20.8 ha field located along the margin of the Venice Lagoon, Italy, and characterized by paleo-channel structures and highly heterogeneous soils. Water retention curves (WRC) and unsaturated hydraulic conductivity curves (UHC) were determined via inversion of measurements taken in the lab using the Wind method. Factorial kriging analysis (FKA) was applied separately to hydraulic parameters and soil physical properties and the 4 depths treated as independent samples. The mapped principal components (PCs) were used in a fuzzy-c means algorithm to define zones of like properties. To examine the physical significance of these zones, curve parameters and hydraulic curves were investigated. Zonation was able to distinguish between s (saturated water content), n (shape parameter) and (inverse of air entry), while r (residual water content) and K s (saturated conductivity) were not statistically different between the groups. For curve comparisons, WRCs were found to be significantly different between zones at all tensions while effective saturation curves (Se) differ for the majority of tensions (except at 28 cm), and UHCs did not differ. The spatial relevance of the zones was examined by overlaying hydraulic zones with zones defined using the FKA and fuzzy-c means approach from soil physical properties such as texture and bulk density. The hydraulic zones overlaid the physical property zones with an areal accuracy ranging from 46.66% to 92.41%. The similarity between these zone sets suggests that there is a potential to predict hydraulic zones from zones defined from soil physical properties. This work illustrates the potential to incorporate geospatial statistics in a stratified sampling scheme for hydraulic properties.

Zimmermann y Basile, Estimación de parámetros hidráulicos en suelos limosos mediante diferentes funciones de pedotransferencia Tecnología y Ciencias del Agua, vol. II, núm. 1, enero-marzo de 2011 Zimmermann y Basile, Estimación de parámetros hidráulicos en suelos limosos mediante diferentes funciones de pedotransferencia Tecnología y Ciencias del Agua, vol. II, núm. 1, enero-marzo de 2011 Zimmermann y Basile, Estimación de parámetros hidráulicos en suelos limosos mediante diferentes funciones de pedotransferencia

En este trabajo se han utilizado diferentes procedimientos para estimar propiedades hidráulicas de suelos limosos, representativos de un sector de la llanura argentina, partiendo de información granulométrica, y de parámetros fisico-químicos disponibles en las cartas de sue- los de ...

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data off...

The retention curves were measured in the soil from the Liz experimental catchment (Šumava). The sand table and pressure extractor methods were used to obtain a 13-point retention curve for undisturbed soil samples taken from 6 depths. The data points of the individual retention curves were fitted in with the analytical expression of and the reference retention curves were calculated for each depth by scaling. For the same soil, the retention curves were estimated by the artificial neural network method by and the use of the empirical Pedotransfer function. The numerical experiment, which represented the infiltration and redistribution processes, was conducted using of all three sets of retention curves. simulated water storages and pressure fields obtained using two sets of estimated parameters produced similar results, however they did not approximate well the modelling results obtained with the use of the measured reference parameter set. Of the two sets of pedotransfer functions (PTFs), which have been tested in this study, the empirical PTF of showed a slightly better agreement with the measured retention curves. The results give a guideline for the application of the retention curves estimation by the pedotransfer function for the soil from the Liz catchment.

Water retention in soil is used in many agronomic and environmental applications, but its direct measurement is time-consuming and expensive. Therefore, pedotransfer functions (PTFs) are alternatives to obtain this information faster and more economically. The objectives of this study were to generate and validate PTFs to estimate the water content at potentials of -33 kPa (field capacity) and -1500 kPa (permanent wilting point) for different soil classes from the central-south portion of Rio Grande do Sul State. The physical and chemical analyses database from soil surveys of the Celulose Riograndense Corp were used. The database is composed of particle size distribution (coarse and fine sand, silt and clay), soil organic matter, and water content data at the above mentioned potentials, besides other information concerning the behavior of the soil classes at field conditions. Firstly, the data were stratified by soil classes and depths, and then 70% of the data were separated for P...

Spatially explicit soil information is crucial for comprehending and managing many of Earth´s processes related to carbon, water, and other biogeochemical cycles. We introduced a gridded database of soil physical properties and hydraulic parameters at 100 meters spatial resolution. It covers the continental area of Chile and binational basins shared with Argentina for six standardized depths following the specifications of the GlobalSoilMap project. We generated soil maps based on digital soil mapping techniques based on more than 4000 observations, including unpublished data from remote areas. These maps were used as input for the pedotransfer function Rosetta V3 to obtain predictions of soil hydraulic properties, such as field capacity, permanent wilting point, total available water capacity, and other parameters of the water retention curve. The trained models outperformed several other DSM studies applied at the national and regional scale for soil physical properties (nRMSE ran...

Global warming is one of the most important problems of the present day. The increase of carbon dioxide (CO 2) and other greenhouse gases causes a great problem in the atmosphere. However, CO 2 gas constituted the biggest threat at present, though the other gases increase, too. One of the primary precautions to take for this is the context of soil earth atmospheric CO 2 through plants. Beside the context of soil carbon, some other factors are also affecting this context. Some of them are texture, bulk density (BD), cation exchange capacity (CEC), exchangeable cations (EC), soil reaction (pH) and electrical conductivity (Ec). In Harran plain, an organic carbon amount ranged from 0.28 to 4.95 kg C m-2. The statistic analyses data in the factors are: BD, organic carbon (OC), texture (clay, silt and sand%), CEC, EC, pH and Ec. The strongest correlation was found as BD and OC (R 2 = 99.96), but when compared with the other factors, it was detected as R 2 = 99.4. The reason for its height when we consider all the parameters is the involvement of organic carbon in the calculation. When we deal with them individually, the correlation falls below 50%. In this study, a point of high correlation is made between OC and BD. The study aims to determine, in the soils, BD, which is costly, hard and time consuming, and OC which is known by means of this correlation. In the comparison analysis of the entire data and the determination of the equations, student t test was used and the data were investigated at p<0.01 significance levels. However, ANOVA techniques were used regularly in this comparison.

La medición del agua retenida en suelos a diferentes potenciales hídricos es difícil y costosa en tiempo y dinero. Muchos modelos han sido desarrollados para predecir estas propiedades a partir de datos rutinarios y fáciles de medir. Dada la escasez de información edáfica de la Cuenca Alta del río Guárico, se evaluaron diferentes modelos publicados para predecir la retención de agua a –33 y –1500 kPa en suelos de esta región. La bondad de ajuste de cada modelo fue determinada mediante el error relativo promedio (ERP) y la desviación estándar del error relativo (DER) de los valores predichos. Los valores de estos índices deben aproximarse a cero para indicar exactitud y precisión de las predicciones, respectivamente. Los resultados indican que ninguna de las funciones empíricas probadas genera predicciones satisfactorias. Se requiere mayor información edáfica para desarrollar funciones de predicción locales, mejor adaptadas a las condiciones de estos suelos.

Palabras claves: regresión lineal múltiple, potencial de agua, capacidad de campo, punto de marchitez permanente, Aragua, Carabobo, Guárico, Venezuela.

West Tennessee’s supplemental irrigation management at a field level is profoundly affected by the spatial heterogeneity of soil moisture and the temporal variability of weather. The introduction of precision farming techniques has enabled farmers to collect site-specific data that provide valuable quantitative information for effective irrigation management. Consequently, a two-year on-farm irrigation experiment in a 73 ha cotton field in west Tennessee was conducted and a variety of farming data were collected to understand the relationship between crop yields, the spatial heterogeneity of soil water content, and supplemental irrigation management. The soil water content showed higher correlations with soil textural information including sand (r = −0.9), silt (r = 0.85), and clay (r = 0.83) than with soil bulk density (r = −0.27). Spatial statistical analysis of the collected soil samples (i.e., 400 samples: 100 locations at four depths from 0–1 m) showed that soil texture and soi...

The physical examination of a functioning cacao farm revealed varying pod production rates in its area. Agricultural soil nutrients assessment is usually through soil geochemical/chemical analyses which are laborious and expensive, necessitating faster/cheaper alternatives. This investigation assessed the physical properties that can substitute for geochemical analysis of soil nutrient. The study was executed at 0.3 m depth. The Volumetric Water Content-VWC and Apparent Electrical Conductivity-EC a of the soils were determined using VG-meter-200 moisture-meter and resistivity earth-meter. 912 ECa/VWC points were measured. Soil textural classes (51-sample) were established using Bouyoucos method. Falling head permeability test was conducted on nine cored soil samples for water infiltration assessment. The soils ECa (10-344 µS/cm) and VWC distributions (2-69%) showed similar variation, increase in VWC corresponds to rise in ECa value; soil moisture aids the mobility of ions in solution and a rise in ECa connotes presence of more dissolved ions. The soils were classified as sandy loam, loamy sand and sandy clayey loam. Soils' permeability ranged from 4.11x10-5-3.97x10-3 cm/sec; infiltration rate varied inversely with the ECa accounting for the moisture variation. Low permeable soil has high nutrient retention and water-holding capacities. Soil physical properties were effective in evaluating the nutrient inconsistency.

Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is too coarse to fully understand the spatial dynamics of crop status. The objective of this study was to downscale Landsat 7 (L7) reflectance from the native resolution of 30 9 30 m to that typical of yield maps (ca. 5 9 5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat (Triticum aestivum L.) in the 2002-2003 growing season. Geospatial yield measurements were available (1 per ca. 20 m 2). Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data were acquired (resolution \5 9 5 m) to characterize soil spatial variability. Geographicallyweighted regressions were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30 9 30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 \ R 2 \ 0.94). Maps for regression parameters and intercept were obtained at 30 9 30 m and used to estimate the L7 reflectance at 5 9 5 m resolution. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5 9 5 m estimated reflectance showed stronger correlations (average increase in explained variance = 3.2 %) with yield than at the 30 9 30 m resolution. Land resource managers, producers, agriculture consultants, extension specialists and Natural Resource Conservation Service field staff would be the beneficiaries of downscaled L7 reflectance data.

The data of 45 soil profiles from a 16 × 16 km grid across Slovenia was analysed to develop a local pedotransfer function (PTF) for bulk density (ρb) estimation. In total, 106 soil horizons were considered. Concentration of organic carbon (OC) was found to be well correlated (r =-0.861, p < 0.001) with ρb. Two separate line segments were fitted to the data, which was partitioned into two intervals, based on OC content (below 36.0 g/kg and above 36.0 g/kg). Nearly 80 % of the variability in ρb is explained with segmented regression. The local PTF was compared with published PTFs and four validations indices (MPE, SDPE, RMSPE and R 2) confirmed the highest prediction quality of the local PTF. The differences of carbon stock (C pool) estimation, based on usage of different PTFs could be higher than 160 t OC per hectare. Prediction of carbon stocks could be substantially improved by calibration of the models coefficients with data stratified according to each unique soil type. K e y w o rd s : pedotransfer function PTF, organic carbon OC, segmented regression, forest soil, carbon stock C pool

Nowadays, halting greenhouse gasses (GHG) emission is the world&#39;s major concern to mitigate global climate change. In oil palm cultivated tropical peatland, GHG emission is primarily constituted of CO2 flux emitted from aerobic heterotrophic respiration (Rh), the natural degradation process of organic material in an oxidative environment. By coupling descriptive and predictive statistical approaches, this study attempt to gain an in-depth understanding of the effects of zeolite rates and incubation time on CO2 emission that came from aerobic Rh in peat, as well as their decomposition process. This study found that zeolite amelioration up to 30% of the peat at field capacity and starting from the first month of observation (month-1) significantly restricted peat Rh, denoted by a reduced amount of observed CO2 flux (0.021 and 0.019-0.012 mg m-2 sec-1, respectively). Both factors and several soil variables exhibited some non-linear relationships with Rh at different magnitudes and ...

Soil water retention curve (SWRC) is one of the most important soil hydraulic properties, whose estimation is still under consideration. In this study, we used 315 soil samples from the UNSODA database to develop three models of point pedotransfer functions (PTFs) and to verify them. We also used an independent database, GRIZZLY, with 59 samples, to verify the developed point PTFs and to compare them with the Rosetta model. Multiple linear regression and stepwise methods were used to derive pedotransfer functions. In the first model, soil texture data ie sand, silt, and clay content, geometric mean particle-size diameter and geometric standard deviation as well as bulk density were used to develop point PTFs at 10 matric potentials. In the second model, water content at field capacity, and in the third model water content at field capacity and permanent wilting point were also used for developing PTFs at 9 and 8 matric potentials, respectively. To evaluate the accuracy and reliability of the point PTFs, we used crossvalidation eg repeated random splitting of the data set into subsets for development and validation. The calculated RMSE values showed that all three developed point PTFs estimated soil water retention curve better than the Rosetta model. K e y w o r d s: field capacity, point pedotransfer functions, permanent wilting point, soil water retention curve

Characterization of soil water retention, e.g., water content at field capacity (FC) and permanent wilting point (PWP) over a landscape plays a key role in efficient utilization of available scarce water resources in dry land agriculture; however, direct measurement thereof for multiple locations in the field is not always feasible. Therefore, pedotransfer functions (PTFs) were developed to estimate soil water retention at FC and PWP for dryland soils of India. A soil database available for Arid Western India (N =370) was used to develop PTFs. The developed PTFs were tested in two independent datasets from arid regions of India (N =36) and an arid region of USA (N =1789). While testing these PTFs using independent data from India, root mean square error (RMSE) was found to be 2.65 and 1.08 for FC and PWP, respectively, whereas for most of the tested 'established' PTFs, the RMSE was >3.41 and >1.15, respectively. Performance of the developed PTFs from the independent dataset from USA was comparable with estimates derived from 'established' PTFs. For wide applicability of the developed PTFs, a user-friendly soil moisture calculator was developed. The PTFs developed in this study may be quite useful to farmers for scheduling irrigation water as per soil type.

Soil bulk density (BD), although a key input parameter in many soil functions is often overlooked in soil studies, including surveys. Direct measurements over a large area are slow and expensive, especially in resource-poor mountain ecosystems. For soils of eastern Himalayan ecosystems, cost-effective indirect methods such as pedotransfer functions (PTFs) are lacking. We developed seven landuse-specific PTFs for BD estimation using a soil particle size distribution (sand, and silt) and soil organic carbon database of 1206 sampling sites. Land uses were 3 uncultivated (dense forests-DF, open forests-OF, and grasslands-GL), 3 cultivated (shifting cultivation-SC, upland agriculture-UA, lowland paddy-LP), and a perennial plantation (PL). PTFs were developed using five machine learning algorithms (MLAs) and a multi-linear regression (MLR). The best PTFs were chosen based on cross-validation performance with the values of the greatest coefficient of determination (R 2) and the smallest root mean square error (RMSE). In addition, the relative performance of thirteen (13) documented PTFs was evaluated on soils in the region. Among the MLAs, the artificial neural network (ANN) showed the best BD prediction for the whole dataset (R 2 : 0.58, RMSE: 0.09 Mg m − 3) and four (e.g. GL, UA, SC, and LP: R 2 of 0.65 to 0.69, RMSE of 0.06 to 0. 09 Mg m − 3) of the seven land use classes. For the remaining three land uses (e.g., DF, OF and PL), based on the greatest R 2 values (0.55 to 0.75), PTFs based on random forests (RF) made the best predictions. However, considering the smallest RMSE values (0.06-0.08 Mg m − 3), ANN-PTFs were another option for these three land uses. Of the land uses, the best prediction for BD was obtained for PL (R 2 : 0.75), while the DF with variable soil properties had the lowest predictive performance (R 2 : 0.55). The performance of MLAbased PTFs such as the support vector machine (SVM), ridge regression (RR), and extreme gradient boosting (XGB) and MLR-PTFs were lower than the ANN-PTFs and RF-PTFs. The precision of BD prediction for 13 published PTFs was inconsistent and inferior (R 2 : 0.13 to 0.39) to the ANN-PTFs and RF-PTFs (R 2 : 0.55 to 0.75) developed in this study. Since PTFs are very region-specific, we suggest ANN-PTFs to estimate the BD of grasslands and cultivated soils while the RF-PTFs (based on the greatest R 2) for dense and open forests and plantation soils in the eastern Himalayas and similar mountain ecosystems.

This study was carried out in three mountainous different soil textures and two depths. Field measurements of soil texture and bulk density, and laboratory measurements of soil water retention at field capacity (–33 kPa) and permanent wilting point (–1500 kPa), were taken to implement Rosetta. Calculated values of van Genuchten parameters were then compared to measured values. Results showed that Rosetta could be used to obtain values of van Genuchten parameters for a field with different soil textures. The determination coefficient (R2) of It showsthat 50% of determination coefficients(R2) gave more than 0.99 and 30% gave more than 0.98 and the others gave between (0.91-0.97) at applying the (PTFs) at two depths for the studied soils. The Root Mean Square Difference (RMSD) indicated that the lowest values of RMSE were exhibit by all soils (Zawita, Swaratoka and Koradare) and two depths D1 and D2 except D1 of Swaratoka Clay loam soil and at different textures.NSE elevation indicator...

Recent unprecedented demands on freshwater for irrigation have led to over-allocations and restrictions. Variable rate irrigation (VRI) aims to optimise scheduling according to soil differences using irrigation prescription maps coupled with software-driven variable rate irrigators and individual sprinkler control for site specific management.

Land surface and Earth System models require reliable soil maps to represent the influence of spatial variability of soil properties on ecosystem fluxes and storages. However, mapping soils using conventional in situ survey protocols is time-consuming and costly. We addressed the outdated spatial information on soil physico-chemical properties for a tropical region with a ~700-km longitudinal gradient of contrasting topography, climate, and vegetation (~98,000 km2; NE Brazil), by developing a novel hybrid machine learning framework and applying it to this region. This framework reduces prediction redundancies due to high multicollinearity by implementing a recursive feature selector algorithm for input selection; its core is composed of the Soil-Landscape Estimation and Evaluation Program (SLEEP) and a calibrated Gradient Boosting Model (GBM) capable of modeling the spatial distribution of soil properties at multiple and dynamic soil depths. The use of SLEEP and GBM allowed us to ex...

Resumen La capacidad de retención de humedad del suelo es necesaria para interpretar las relaciones suelo-agua; pero su determinación es lenta y costosa. Una alternativa más económica es estimarla por medio de ecuaciones de regresión lineal, a partir de otras características del suelo. Una muestra constituida por 489 horizontes de suelos aluviales de la Depresión del Lago de Valencia fue sometida a análisis de correlación y regresión, dando como resultado las siguientes ecuaciones para estimar la humedad retenida en el suelo a-33 kPa: w = 57,550 + 1,405 (% C.O.)-0,559 (% arena), y a-1500 kPa: w = 3,515 + 1,431 (%C.O.) +0,499 (%arcilla). Estas ecuaciones fueron comparadas con otros pares de ecuaciones reportadas en la literatura, aplicándolas a una muestra de 45 horizontes seleccionados al azar. Para cada ecuación se determinó el error cuadrático medio (ECM) de estimación como el promedio del cuadrado de las diferencias entre el valor medido y el valor estimado. El ECM de las ecuaciones desarrolladas en esta investigación resultó inferior al de las otras ecuaciones, demostrando que cada modelo de regresión es específico para un conjunto de suelos determinado y no debe ser extrapolado a otros suelos sin una validación previa. Palabras claves: regresión lineal múltiple, retención de humedad, textura, materia orgánica, lago de Valencia, suelos aluviales, Aragua, Carabobo.

Variasi kandungan karbon dan nitrogen di permukaan tanah dimodelkan dengan menggunakan fungsi pedotransfer yang memanfaatkan data dari Digital Elevation Model-DEM, Normalized Difference Vegetation Index-NDVI dan bioiklim. Data pengamatan tanah Puerto Rico, USA, digunakan untuk pengembangan model prediksi properti tanah, yang kemudian diaplikasikan di wilayah provinsi Jawa Barat, Indonesia. Fungsi pedotransfer yang disusun dengan menggunakan 22 parameter input derivasi dari tiga faktor pembentukan tanah (topografi, vegetasi, dan iklim) menghasilkan model dengan koefisien determinasi (R2) 71% dan 66%, masing-masing untuk kandungan karbon dan nitrogen di per-mukaan tanah. Hasil yang sebanding juga diperoleh dengan mereduksi jumlah input parameter berdasarkan parameter yang signifikan (RMSE 3,12% dan 0,05% untuk masing-masing kandungan karbon dan nitrogen tanah). Hasil ini menunjukkan bahwa fungsi pedotransfer dapat digunakan sebagai alat bantu pengambilan keputusan untuk memetakan variasi properti tanah dalam mendukung penyusunan kebijakan di bidang peningkatan sumber daya lahan untuk mengidentifikasi masalah daya tahan pangan.

The stock assessment of organic carbon and total nitrogen in the soil in addition to their relationships with site characteristics is of major importance whether at local, regional or global scale. The improvement of pedotransfer functions for these stocks evaluation in soils is a key for sustainability of agro-systems, especially in erodible systems of Mediterranean semi-arid areas. This work aimed to study relationships between total nitrogen stocks and other physico-chemical properties of clayey and sandy soils of Tunisian database and to do this, we used pedotransfer functions and structural equations modeling. For modeling total nitrogen stocks, two Tunisian soil databases composed from 450 horizons of clayey soils and 602 horizons of sandy soils were used. The optimal models of nitrogen stocks were given by two significant pedotransfer functions: (i) that of clayey soils with a standard error of prediction of 18.51 and associated p-value of 0.000 and (ii) that of sandy soils with a standard error of prediction of 5.76 and associated p-value of 0.016. Then, we perform a path analysis using structural equations modeling and Bayesian analysis to investigate simultaneously the interactions between the different components of the soil properties and their relationships with total nitrogen stocks. Results show that, in both soil types, the stock of total nitrogen is always controlled in the same way; it is significantly linked to chemical properties and bulk density more than by physical properties. The root mean square errors of the approximations were 0.080 and 0.043 for the clayey and sandy models, respectively.

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Pedotransfer functions (PTFs) describing soil water retention curves (WRCs) have been widely used in crop, soil, and land surface models. A limitation of the available PTFs is that they fail to account for shape changes in WRCs due to bulk density variations caused by soil tillage, compaction, and other processes. This study develops new PTFs that include bulk density effects on the WRC shape. A new framework is introduced to build the bulk density-associated PTFs based on a widely-used WRC dataset. The new PTFs were validated by comparing the performance with two common PTFs from the literature using two independent datasets. The results show that the newly developed PTFs provide reliable WRC estimates for the validation datasets, with mean RMSE values of 0.055 and 0.059 m 3 m-3 , respectively. The accuracy of the new PTFs is comparable or in some cases better than the common PTFs. While the literature PTFs investigated do not always properly describe bulk density effects on WRC changes, the new PTFs effectively account for such effects on the WRC shape, thus have the potential to be integrated into crop and soil management models to represent bulk density impacts on WRCs due to anthropogenic (e.g., plowing and compaction) and natural (e.g., wetting/drying) processes.

Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best perfo...

Las especies de la familia Tetranychidae exhiben una amplia plasticidad fenotípica como respuesta a las variaciones ambientales, lo cual podría conducir a identificaciones erróneas dada la similitud morfológica entre algunas especies. En el presente trabajo se evaluaron las variaciones quetotáxicas de poblaciones de Tetranychus urticae Koch y Eotetranychus lewisi (McGregor) recolectadas en diferentes plantas hospedantes y localidades en seis cantones de la Provincia de Tungurahua, Ecuador. Se tomaron muestras de hojas de plantas de fresa, mora y zanahoria blanca que mostraban síntomas de alimentación por tetraníquidos en los cantones Ambato, Cevallos, Baños, Mocha, Píllaro y Tisaleo. En general, las sedas idiosomales presentaron una amplia variación debida a la planta hospedante y a la localidad de procedencia. El Análisis de Componentes Principales (ACP) logró explicar el 82% de la variación observada entre variables medidas sobre las 18 muestras, las cuales lograron separar a las ...

Although every effort has been made to ensure the accuracy of the material contained in this publication, complete accuracy cannot be guaranteed. Neither the Environmental Protection Agency nor the author(s) accept any responsibility whatsoever for loss or damage occasioned or claimed to have been occasioned, in part or in full, as a consequence of any person acting, or refraining from acting, as a result of a matter contained in this publication. All or part of this publication may be reproduced without further permission, provided the source is acknowledged. SOILS The Soils Section of the Environmental RTDI Programme addresses the need for research in Ireland to inform policymakers and other stakeholders on a range of questions in this area. The reports in this series are intended as contributions to the necessary debate on soils and the environment.

Laccases are phenoloxidases, that can oxide phenolics. Catechol (1,2-dihydroxybenze) is one of the phenolics that is broadly utilized in various industries. Nanomaterials and conductive polymers can be utilized as a support medium for the laccase immobilization and phenolic biosensors. This paper describes a new enzymatic biosensor being developed to determine phenols such as catechol and showed the laccase catalytic reaction in the presence of phenolic substrates. It is based on the glassy carbon electrode fabricated with electrodepositing of poly (3,4-ethylenedioxythiophene) (PEDOT), carboxyl group functionalized multi-walled carbon nanotube (MWCNTCOOH), and gold nanoparticles in combination, thereafter immobilizing of the laccase on the electropolymerized material on the electrode surface using the covalent binding of laccase to MWCNTCOOH. The simply fabricated laccase biosensor response was characterized via voltammetry techniques and Fourier transform infrared (FTIR) spectroscopy. Using differential pulse voltammetry (DPV), the biosensor results indicated that the detection limit, sensitivity, and linear range are 0.35 M, 3.52 A mM-1, 1 – 4 M respectively, as well as the correlation coefficient of 0.95 under optimal conditions. By investigation of scan rate effect on cyclic voltammograms of laccase biosensor, transfer coefficient (α) and standard heterogeneous rate constant (ks) were estimated to be 0.68 and 0.083 s-1. The proposed biosensor incorporates the pleasant electrocatalytic properties of nanomaterials with the PEDOT properties to decrease the oxidation potential and improve the electron transfer rate and demonstrate a low detection limit and high sensitivity.

Water retention in soil is used in many agronomic and environmental applications, but its direct measurement is time-consuming and expensive. Therefore, pedotransfer functions (PTFs) are alternatives to obtain this information faster and more economically. The objectives of this study were to generate and validate PTFs to estimate the water content at potentials of -33 kPa (field capacity) and -1500 kPa (permanent wilting point) for different soil classes from the central-south portion of Rio Grande do Sul State. The physical and chemical analyses database from soil surveys of the Celulose Riograndense Corp were used. The database is composed of particle size distribution (coarse and fine sand, silt and clay), soil organic matter, and water content data at the above mentioned potentials, besides other information concerning the behavior of the soil classes at field conditions. Firstly, the data were stratified by soil classes and depths, and then 70% of the data were separated for P...

Resumen La capacidad de retención de humedad del suelo es necesaria para interpretar las relaciones suelo-agua; pero su determinación es lenta y costosa. Una alternativa más económica es estimarla por medio de ecuaciones de regresión lineal, a partir de otras características del suelo. Una muestra constituida por 489 horizontes de suelos aluviales de la Depresión del Lago de Valencia fue sometida a análisis de correlación y regresión, dando como resultado las siguientes ecuaciones para estimar la humedad retenida en el suelo a-33 kPa: w = 57,550 + 1,405 (% C.O.)-0,559 (% arena), y a-1500 kPa: w = 3,515 + 1,431 (%C.O.) +0,499 (%arcilla). Estas ecuaciones fueron comparadas con otros pares de ecuaciones reportadas en la literatura, aplicándolas a una muestra de 45 horizontes seleccionados al azar. Para cada ecuación se determinó el error cuadrático medio (ECM) de estimación como el promedio del cuadrado de las diferencias entre el valor medido y el valor estimado. El ECM de las ecuaciones desarrolladas en esta investigación resultó inferior al de las otras ecuaciones, demostrando que cada modelo de regresión es específico para un conjunto de suelos determinado y no debe ser extrapolado a otros suelos sin una validación previa. Palabras claves: regresión lineal múltiple, retención de humedad, textura, materia orgánica, lago de Valencia, suelos aluviales, Aragua, Carabobo.

HYPROP (Hydraulic Property Analyzer) system works based on the simplified evaporation method, which determines soil hydraulic properties in the laboratory. This system simultaneously monitors the changes in weight of soil samples as well as the soil matric potential at two depths throughout a drying process governed by evaporation from the soil surface. In this study, we examined the performance of the pseudo continuous pedotranfer function (PC-PTF) to predict the soil water retention curve (SWRC) using high resolution data measured by HYPROP system. The dataset consisted of 7963 measured water retention data points obtained from 81 Turkish soil samples from which 60%, 20% and 20% were randomly selected for training, crossvalidation and test subsets, respectively. The best PC-PTF developed in this study with a mean absolute error of 0.023 m 3 m-3 (a root mean square error of 0.033 m 3 m-3) and a correlation coefficient of 0.96 showed promising performance considering the typical performance range (RMSE = 0.034-0.085 m 3 m-3) for parametric PTFs predicting SWRC. The best PC-PTF used soil textural information, soil bulk density, the percentage of stable aggregates, soil organic matter content and the initial water content as the input attributes. PTFs developed in this study Journal of Hydrology: Measurement and Estimation of the Soil Water Retention Curve Using … 2 also ranked high among previously developed PC-PTFs (RMSE ranging from 0.027 to 0.056 m 3 m-3) using sparse datasets collected via the traditional equilibrium approach (i.e. sandbox apparatus /pressure plates extractor). We, therefore, recommend further application of the PC-PTF approach for development of PTFs using high resolution data obtained by HYPROP system.

Soil water is one of the most important information as input to land surface, ecological and hydrological models. Reliable soil water information with current coverage in the area is needed for proper cultivation management. A number of 460 soil profiles data were evaluated for the performance of estimates inverse distance weighting to map some of the soil quality properties. Maps with the investigated coverage were produced with the soil information available about soil texture and organic matter. It was observed from the results that increasing the number of soil profiles to cover the area in detail helped to overcome its unknown reliability. The results also demonstrated that hydraulic properties of field soils can improve predictions for such models using remote sensing data. A reliable soil water map can serve multiple purposes, including scientific research and application of models on different geographical scales. It is also essential for the development and spatial implemen...

Soil bulk density (ρ b) is important because of its direct effect on soil properties (e.g., porosity, soil moisture availability) and crop yield. Additionally, ρ b measurements are needed to express soil organic carbon (SOC) and other nutrient stocks on an area basis (kg ha −1). However, ρ b measurements are commonly missing from databases for reasons that include omission due to sampling constraints and laboratory mishandling. The objective of this study was to investigate the performance of novel pedotransfer functions (PTFs) in predicting ρ b as a function of textural class and basic pedon description information extracted from the horizon of interest (the horizon for which ρ b is being predicted), and ρ b , textural class, and basic pedon description information extracted from horizons above or below and directly adjacent or not adjacent to the horizon of interest. A total of 2,680 pedons (20,045 horizons) were gathered from the USDA-NRCS National Soil Survey Center characterization database. Twelve ρ b PTFs were developed by combining PTF types, database configurations, and horizon limiting depths. Different PTF types were created considering the direction of prediction in the soil profile: upward and downward prediction models. Multiple database configurations were used to mimic different scenarios of horizons missing ρ b values: random missing (e.g., ρ b sample lost in transit) and patterned or systematic missing (e.g., no ρ b samples collected for horizons N 30 cm depth). For each database configuration scenario, upward and downward models were developed separately. Three limiting depths (20, 30, and 50 cm) were tested to identify any threshold depth between upward and downward models. For both PTF types, validation results indicated that models derived from the database configuration mimicking random horizons missing ρ b performed better than those derived from the configuration mimicking clear patterns of missing ρ b measurements. All 12 PTFs performed well (RMSPE: 0.10-0.15 g cm −3). The threshold depth of 50 cm most successfully split the database between upward and downward models. For all PTFs, the ρ b of other horizons in the soil profile was the most important variable in predicting ρ b. The proposed PTFs provide reasonably accurate ρ b predictions, and have the potential to help researchers and other users to fill gaps in their database without complicated data acquisition.

In this study, the performance of 63 existing pedotransfer functions (PTFs) is evaluated to estimate oven-dry soil bulk density (BD) by using a dataset of 3316 soil cores taken in the farmlands of Campania (southern Italy). As expected, the lack of direct calibration yields prediction accuracy from unsatisfactory to rather weak. Therefore, we advance the working hypothesis that the use of hierarchical soil mapping information can make the application of existing PTFs more reliable. We show that grouping data according to land-systems classes or soil groups considerably improves the prediction ability quantified through the root mean squared error (RMSE) and the coefficient of determination (R 2). An independent data set of 105 soil cores taken from two hillslopes in the Upper Alento River Catchment in southern Campania was used to verify our assumption. The validation step shows that the knowledge of a soil-landscape map is an efficient tool for improving the prediction of BD. This approach will be employed in a subsequent study to develop site-specific PTFs for the study region.

The Hungarian Detailed Soil Hydrophysical Database, called MARTHA ver2.0 has been developed to collect information on measured soil hydraulic and physical characteristics in Hungary. Recently this is the largest detailed national hydrophysical database, containing controlled information from a total of 15,005 soil horizons. Two commonly used pedotransfer functions were tested to evaluate the accuracy of the predictions on the MARTHA data set, representative for Hungarian soils. In general, the application of both examined pedotransfer functions (Rajkai, 1988; Wösten et al., 1999) was not very successful, because these PTFs are representative for other soil groups. The classification tree method was used to evaluate the effect of soil structure on the goodness of estimations. It was found that using the soil structure data the inaccuracies of soil water retention predictions are more explainable and the structure may serve as a grouping variable for the development of class PTFs.

Precision Agriculture (PA) is a whole-farm management approach using information technology, satellite positioning (GNSS) data, remote sensing and proximal data gathering. These technologies have the goal of optimizing returns on inputs whilst potentially reducing environmental impacts. This study was conducted out to determine the acidity, salinity, field capacity, permanent wilting point and water holding capacity in precision agriculture by analyzing soil samples taken from the field in 32 points. Maps were drawn by obtaining data from the field. The purpose of this research is to use the geographic information system for comparing the obtained data from soil more quickly and easily than before and also the water amount in order to make precise decisions for agriculture progress and applying the appropriate inputs which is related to water. The present results also indicated that water holding capacity maps. These maps are usage for the irrigation management and the information from different points of the field. These data obtained the field has an important role in the management of precision agriculture.

A soil moisture content map is important for providing information about the distribution of moisture in a given area. Moisture content directly influences agricultural yield thus it is crucial to have accurate and reliable information about moisture distribution and content in the field. Since soil is a porous medium modified generalized Archie’s equation provides the basic formula to calculate moisture content data based on measured ECa. In this study we aimed to find a more accurate and cost effective method for measuring moisture content than manual field sampling. Locations of 25 sampling points were chosen from our research field as a reference. We assumed that soil moisture content could be calculated by measuring apparent electrical conductivity (ECa) using the Veris-3100 on-the-go soil mapping tool. Statistical analysis was carried out on the 10.791 ECa raw data in order to filter the outliers. The applied statistical method was ±1.5 interquartile (IRQ) distance approach. T...

Water retention in soil is used in many agronomic and environmental applications, but its direct measurement is time-consuming and expensive. Therefore, pedotransfer functions (PTFs) are alternatives to obtain this information faster and more economically. The objectives of this study were to generate and validate PTFs to estimate the water content at potentials of -33 kPa (field capacity) and -1500 kPa (permanent wilting point) for different soil classes from the central-south portion of Rio Grande do Sul State. The physical and chemical analyses database from soil surveys of the Celulose Riograndense Corp were used. The database is composed of particle size distribution (coarse and fine sand, silt and clay), soil organic matter, and water content data at the above mentioned potentials, besides other information concerning the behavior of the soil classes at field conditions. Firstly, the data were stratified by soil classes and depths, and then 70% of the data were separated for P...

Nowadays, halting greenhouse gasses (GHG) emission is the world&#39;s major concern to mitigate global climate change. In oil palm cultivated tropical peatland, GHG emission is primarily constituted of CO2 flux emitted from aerobic heterotrophic respiration (Rh), the natural degradation process of organic material in an oxidative environment. By coupling descriptive and predictive statistical approaches, this study attempt to gain an in-depth understanding of the effects of zeolite rates and incubation time on CO2 emission that came from aerobic Rh in peat, as well as their decomposition process. This study found that zeolite amelioration up to 30% of the peat at field capacity and starting from the first month of observation (month-1) significantly restricted peat Rh, denoted by a reduced amount of observed CO2 flux (0.021 and 0.019-0.012 mg m-2 sec-1, respectively). Both factors and several soil variables exhibited some non-linear relationships with Rh at different magnitudes and ...

Water retention in soil is used in many agronomic and environmental applications, but its direct measurement is time-consuming and expensive. Therefore, pedotransfer functions (PTFs) are alternatives to obtain this information faster and more economically. The objectives of this study were to generate and validate PTFs to estimate the water content at potentials of -33 kPa (field capacity) and -1500 kPa (permanent wilting point) for different soil classes from the central-south portion of Rio Grande do Sul State. The physical and chemical analyses database from soil surveys of the Celulose Riograndense Corp were used. The database is composed of particle size distribution (coarse and fine sand, silt and clay), soil organic matter, and water content data at the above mentioned potentials, besides other information concerning the behavior of the soil classes at field conditions. Firstly, the data were stratified by soil classes and depths, and then 70% of the data were separated for P...

Fine-resolution soil maps constitute important data for many different environmental studies. Digital soil mapping techniques represent a cost-effective method to obtain detailed information about soil types and soil properties over large areas. The main objective of the study was to extend predictions from 1:25,000 legacy soil surveys (including WRB soil groups, soil depth and soil texture classes) to the larger area of Cyprus. A multiple-trees classification technique, namely Random Forest (RF), was applied. Specific objectives were: (i) to analyze the role and importance of a large data set of environmental predictors, (ii) to investigate the effect of the number of training points, forest size (ntree), the numbers of predictors sampled per node (mtry) and tree size (nodesize) in RF; (iii) to compare RF-derived maps with maps derived with a multinomial logistic regression model, in terms of validation error (test set and independent profiles) and map uncertainty, using the confusion index and a newly developed reliability index. The optimized RF model was run using half of the input points available (over a million) and with ntree equal to 350. The mtry parameter was set to 5 (close to half the number of the environmental variables used) for both soil series and soil properties. The nodesize calibration showed no relevant performance increase and was kept at its default value (1). In terms of environmental variables, the model used 10 predictors, covering all the soil formation factors considered in the scorpan formula, to derive the three maps. Soil properties, derived from geochemistry data, showed a high importance in deriving soil groups, depths and texture. Random Forest constructed a better predictive model than multinomial logistic regression, showing comparable predictive uncertainty but much lower validation error. The RF-derived maps show very low out of bag (OOB) errors (around 10% for both soil groups and soil properties) but relatively high validation error from independent profiles (45% for soil depth, 51% for soil texture). The resulting reliability index was low in the main mountainous area of Cyprus, where predictions were extrapolations as indicated by the multivariate environmental similarity surface, but medium to high in the main agricultural areas of the country.

RESUMEN La estabilidad de agregados, por ser una característica dinámica, es considerada un indicador sensible de tendencias a la recuperación o degradación de los suelos. En la Argentina se han aplicado diferentes métodos de laboratorio para conocer su variación respecto al uso. El método de Le Bissonnais basado en el método de Henin, fue desarrollado por INRA de Francia. Se obtiene el diámetro medio ponderado de agregados estables con tres pretratamientos: humectación rápida (DMPe); disgregación mecánica luego de re-humectación con etanol (DMPd) y humectación lenta por capilaridad (DMPc) y, con el promedio se determina un valor que sintetiza la información (DMPm). A nivel mundial fue aplicado en suelos de diferentes condiciones de clima y manejo; sin embargo, es necesario conocer su comportamiento en suelos de la Argentina. Los objetivos fueron: i) evaluar el método Le Bissonnais para utilizarlo como indicador de degradación en lotes con diferente grado de intervención en tres órdenes de suelo (Molisol, Vertisol y Alfisol), ii) relacionar el DMP con el contenido de carbono orgánico (C), iii) correlacionar los métodos de Le Bissonnais y Hénin y iv) cuantificar el tiempo operativo requerido en el procedimiento de determinación de la estabilidad de agregados. El método resultó ser sensible para discriminar condiciones de uso y manejo en los tres suelos analizados, reflejando tendencias al deterioro y a la recuperación. Se encontró relación positiva y significativa entre contenido de carbono orgánico y estabilidad de agregados (R 2 =0,30). Hubo correlación significativa entre ambos métodos (r=0,51), que varió según el suelo, existiendo alto grado de correlación en Molisol, medio en Alfisol y sin asociación en Vertisol. Existieron asociaciones entre los coeficientes de variación de ambos métodos, siendo mayores con Hénin. Los resultados contribuirán a profundizar en el análisis de mecanismos de agregación y desagregación, propios de cada suelo y sistemas productivos que, mediante la determinación de valores umbrales permitirá conocer tendencias del recurso y así generar alertas tempranas de procesos de degradación.

In this study, the performance of 63 existing pedotransfer functions (PTFs) is evaluated to estimate oven-dry soil bulk density (BD) by using a dataset of 3316 soil cores taken in the farmlands of Campania (southern Italy). As expected, the lack of direct calibration yields prediction accuracy from unsatisfactory to rather weak. Therefore, we advance the working hypothesis that the use of hierarchical soil mapping information can make the application of existing PTFs more reliable. We show that grouping data according to land-systems classes or soil groups considerably improves the prediction ability quantified through the root mean squared error (RMSE) and the coefficient of determination (R 2). An independent data set of 105 soil cores taken from two hillslopes in the Upper Alento River Catchment in southern Campania was used to verify our assumption. The validation step shows that the knowledge of a soil-landscape map is an efficient tool for improving the prediction of BD. This approach will be employed in a subsequent study to develop site-specific PTFs for the study region.

Nowadays, halting greenhouse gasses (GHG) emission is the world's major concern to mitigate global climate change. In oil palm cultivated tropical peatland, GHG emission is primarily constituted of CO2 flux emitted from aerobic heterotrophic respiration (Rh), the natural degradation process of organic material in an oxidative environment. By coupling descriptive and predictive statistical approaches, this study attempt to gain an in-depth understanding of the effects of zeolite rates and incubation time on CO2 emission that came from aerobic Rh in peat, as well as their decomposition process. This study found that zeolite amelioration up to 30% of the peat at field capacity and starting from the first month of observation (month-1) significantly restricted peat Rh, denoted by a reduced amount of observed CO2 flux (0.021 and 0.019-0.012 mg m-2 sec-1, respectively). Both factors and several soil variables exhibited some non-linear relationships with Rh at different magnitudes and importance, showing the limitation of the traditional linear-based approach to interpreting their complex interrelationships, as well as predicting CO2 flux. This study highlights the vital role of a polynomial (GAM) and artificial intelligence (Cubist and GBM) -based pedotransfer models in improving our understanding regarding the dynamic of the peat decomposition process as affected by zeolite amendment.