Comparison of estimated soil bulk density using proximal soil sensing and pedotransfer functions

Development of land-use-specific pedotransfer functions for predicting bulk density of acidic topsoil in eastern Himalayas (India)

National variability in soil organic carbon stock predictions: Impact of bulk density pedotransfer functions

The advent of lab diffuse reflectance spectroscopy (LDRS) that exploits the fundamental vibration, overtones and combination of functional groups of soil components makes the soil study easier. The present research intends to predict sand content utilizing the proximal soil sensing (PSS) tech. Thus, in accord with the supplementary data layers and stratified randomized sampling (SRS) method, eventually, 128 samples were gathered from 20 cm of soil surface of Mazandaran Province, Iran. First of all, the sample set was subdivided into two subsets: calibration (96) and validation (32). Using the multivariate regression analysis-partial least squares regression (PLSR) algorithm with leave-one-out cross-validation (LOOCV) technique and some pre-processing algorithms, such as spectral averaging, smoothing and 1st derivative (1st-D), the definitive calibration model with two & four latent vectors (LVs/LFs) and correlation coefficient (RP), determination coefficient (R2 P), root mean square error (RMSEP), ratio of performance to deviation (RPDP) and ratio of performance to interquartile distance (RPIQP) respectively: 0.83&0.82, 0.68&0.67,8.68&8.83%,1.78&1.75,2.45&2.41, were validated and spotted as the most appropriate predictive model for the sand content prediction in the study region. Last, the potentiality of the visible-near infrared diffuse reflectance spectroscopy (VNIR-DRS) for sand content estimation in Mazandaran soils was proved. Also, it is feasible to upscale the sand prediction process utilizing the principal resulted model and the key spectral domains via airborne/satellite hyperspectral data, which emphatically shows the LDRS importance as a commencement point for characterizing the informative optical wavelengths. Likewise, that will be the infrastructure for spaceborne data modeling and upscaling process.

Prediction of soil bulk density in agricultural soils using mid-infrared spectroscopy

Evaluation of pedotransfer functions for predicting particle density of soils with low organic matter contents

Detailed soil maps are an essential tool for water resource, land management and agricultural planning. However, due to different soil processes and variation in geology, soil textural properties vary in space at different scales challenging the development of accurate soil maps. Despite the advances in sampling and efforts to produce accurate maps, uncertainties remain at many scales. Therefore, by implementing error evaluation methods such as comparing predictions and observations, it is possible to understand the performance of digital soil maps. The aim of this work was to obtain the uncertainties of soil categories from a Danish national soil map (AC map) based on measured contents of soil texture and soil organic matter. The AC maps describes the horizon characteristics (soil texture, soil organic matter, soil horizon depths and bulk density) at three depths corresponding to the A (0-30 cm), B (30-70 cm), and C (70-120 cm) horizon. In addition, it combines information on soil classification (soil texture), geology at a depth of ca. 1.5 meters as well as the national geological region. The map has a spatial resolution of 250 meters in the A and B horizons and 500 meters in the C horizon. The analysis is based on 38,000 textural points for the A horizon, 7,000 points for the B horizon, and almost 1,700 points for the C horizon. Considering mean and medians of the content of clay and organic matter together with the observed data, the AC map was validated. The uncertainties of the AC map, statistical correlation coefficient (R2), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) were used. The results showed that the AC map has an acceptable performance when predicting the clay content in the A horizon (R2 = 0.97, RMSE = 1.15%, and NSE = 0.91), B horizon (R2 = 0.95, RMSE = 1.85%, and NSE= 0.8), and C horizon (R2 = 0.74, RMSE = 5.32%, and NSE = 0.41, respectively. The performance of the prediction of organic matter content in the A horizon (R2 = 0.84, RMSE = 0.39%, and NSE = 0.65) and B horizon (R2 = 0.66, RMSE = 0.44%, and NSE = 0.32) horizons was acceptable as well. Also, the result of validation showed that the highest residual errors of AC maps for clay content and organic matter content has been related loamy (>15% clay) and peat soils. In conclusion, the AC maps, with its optimal accuracy especially in the A and B horizons, can be a suitable tool for use at variable scales in the analysis of crop growth and nitrate leaching modelling studies.Keywords: Soil texture, digital soil maps, soil variability, soil organic matter, clay, soil properties

Tropical regions such as Java, Indonesia, still lack publication of soil water retention (SWR) information, particularly at upper Citarum watershed. The SWR is one of the critical elements in water storage and movement in the soil and very important to solve ecological and environmental problems. However, getting the access requires a lot of laboratory meas-urement that is time-consuming and expensive. Therefore, utilizing pedotransfer functions (PTFs) to estimate the water in the soil is needed. This study aims to define soil properties related to the SWR and to evaluate the performance of existing PTFs in predicting SWR. The study was carried out at agroforestry land system soil at upper Citarum watershed, Indonesia. Ten point and two continuous existing PTFs developed for tropical regions were applied in this study. Pearson's correlation (r), mean error (ME), root mean square error (RMSE), and modelling efficiency (EF) were used for evaluation. Cation exchange capacity (CEC), organic carbon (OC), bulk density (BD), and clay were considered as potential soil properties for soil water retention prediction. The performance of PTFs by MINASNY, HARTEMINK [2011] at matric potential of –10 kPaand BOTULA [2013] at matric potential of –33 kPa and –1500 kPa were recommended for point PTFs, while PTFs by HODNETT, TOMASELLA [2002] was for continuous PTFs in predicting SWR. The accuracy of the point PTFs is almost better than the continuous PTFs in predicting SWR in agroforestry land system soil at upper Citarum watershed, Indonesia.

Water retention and saturated hydraulic conductivity are soil properties that are key determinants in crop growth and hydrological modelling. They are commonly estimated from basic soil characteristics such as bulk density, organic carbon content and texture by means of pedotransfer functions (PTFs). In order to assess and compare the inherent performance and the functional applicability in the Zambezi River Basin (ZRB) of the widely used Saxton & Rawls PTFs and a set of newly developed PTFs, we compiled measurements of water retention at pF0.0, 1.0, 2.0, 2.8, 3.4 and 4.2 and of saturated hydraulic conductivity (Ksat) on 631 soil samples throughout the ZRB. A total of 329 of the samples were related to 55 soil profiles available in the Africa Soil Profile database, whereas our own field campaign carried out in a 2,426‐km 2 subbasin of the ZRB provided the remaining 302 samples related to 119 soil profiles. Apart from evaluating the Saxton & Rawls PTFs, we developed multiple linear regression (MLR) PTFs, and PTFs derived by three machine learning (ML) models: artificial neural network (ANN), random forest (RF) and support vector machine (SVM). All PTFs were first evaluated based on a comparison of the estimated and measured property values by means of R 2 , mean absolute error (MAE) and root mean squared error (RMSE). For the ensemble of MLR‐PTF and ML‐PTFs, the R 2 of the six water content variables and the Ksat ranged from 0.55 to 0.85, whereas for the Saxton & Rawls PTFs the range was between 0.10 and 0.50. Secondly, all PTFs were subjected to a functional evaluation using the Food and Agriculture Organization (FAO) AquaCrop crop growth model. Dry season irrigation requirements for maize as computed by AquaCrop with measured versus estimated soil hydraulic properties revealed that ANN‐PTFs provide AquaCrop outputs that come closest to AquaCrop outputs generated with measured soil hydraulic properties. This study shows the importance of performing functional evaluation of pedotransfer functions before their widespread application. Highlights Developed machine learning and multiple linear regression pedotransfer functions (PTFs). The Saxton & Rawls PTFs are not recommended for use in the Zambezi River Basin. PTFs were functionally evaluated through use of estimated soil hydraulic properties in AquaCrop. More accurate PTFs have better functional performance, although differences are small.

Most pedotransfer functions (PTF) developed over the past three decades to generate water retention characteristics use soil texture, bulk density and organic carbon content as predictors. Despite the high number of PTFs published, most being class- or continuous-PTFs, the accuracy of prediction remains limited. In this study, we compared the performance of different class- and continuous-PTFs developed with a regional database. Results showed that the use of in situ volumetric water content at field capacity as a predictor led to much better estimation of water retention properties compared with using predictors derived from the texture, or the organic carbon content and bulk density. This was true regardless of the complexity of the PTFs developed. Results also showed that the best prediction quality was achieved by using the in situ volumetric water content at field capacity after stratification by texture. Comparison of in situ volumetric water content at field capacity, with the water retained at different matric potentials as measured in the laboratory, showed field capacity to approximate 100 hPa, whatever the soil texture. Finally, the lack accuracy of PTFs that do not use the in situ volumetric water content at field capacity as predictor did not appear due to the test soils being unrepresentative of the soils used to develop the PTFs, but were instead related to poor correlations between the predictors used and the water retention properties. Key words: Pedotransfer functions, root mean square error, mean error of prediction, standard deviation of prediction, texture, bulk density, organic carbon content

با برآورد ظرفیت تبادل کاتیونی با استفاده از ویژگی‏های پایه ای و زودیافت خاک می‏توان در وقت و هزینه صرفه‏جویی کرد. هدف این بررسی، مقایسه دو روش رگرسیون درختی و رگرسیون خطی چندگانه در برآورد ظرفیت تبادل کاتیونی (CEC) با استفاده از ویژگی‏هایخاک است. برای این منظور از داده های 106 نمونه خاک UNSODA استفاده شد. جهت برآورد CEC با استفاده از روش‏های رگرسیون درختی و رگرسیون خطی از ویژگی های اجزای بافت خاک،pH، ماده آلی و چگالی ظاهری استفاده شد. کارآیی روش رگرسیون درختی در برابر روش رگرسیون خطی چندگانه در برآورد CEC مقایسه شدند. نتایج نشان داد که در برآورد CEC با استفاده از روش های رگرسیونی تنها ضرایب ماده آلی (183/3) و درصد رس (274/0) که بیشترین همبستگی را با CEC دارند معنی دار شده و مدل رگرسیونی بر اساس این دو پارامتر توسعه یافت. همچنین از میان پارمترهای ورودی در روش رگرسیون درختی تنها پارامترهای ماده آلی و درصد رس در درخت رگرسیون ظاهر شد. روش رگرسیون درختی در دو مجموعه داده صحت سنجی و اعتبار سنجی بر اساس آماره های ارزیابی R2، RMSE، ME و GMER کارآیی بالاتری نسبت به روش های رگرسیونی خطی در برآورد CEC داشت. از میان روش های رگرسیونی خطی، مدل پیشنهادی کارآیی بالاتری نسبت به مدل های بل و ونکولن و بروسما و همکاران داشت.

Rapid determination of fat content: Advanced spectroscopic methods across diverse fish species

Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale?

An empirical model for estimating soil penetrometer resistance from relative bulk density, matric potential, and depth

A pedotransfer function for estimating bulk density of forest soil in Japan affected by volcanic ash

The soil moisture retention curve (MRC) is time consuming and expensive to measure directly. Several attempts have been made to establish a relation between readily available soil properties, like particle‐size distribution, organic matter content, and bulk density, and the soil moisture retention curve. Those relationships are referred to as pedotransfer functions (PTFs). The objective of this study was to evaluate some PTFs with respect to their accuracy in predicting the soil moisture retention curve. Five widely used and four more recently developed PTFs were selected for evaluation. Seven of the selected PTFs predict moisture retention function parameters, whereas the other two predict the moisture content at certain matric potentials. In order to quantify the prediction accuracy, the mean of the absolute value of mean differences (MAMD), the mean and the standard deviation of the root of mean squared differences (MRMSD and SDRMSD, respectively), and the mean of the Pearson correlation coefficient (Mr) were used. The evaluated PTFs were finally ranked based on these validation indices. The PTFs showed good to poor prediction accuracy with MAMD values ranging from 0.0312 to 0.0603 m 3 m −3 and with MRMSDs between 0.0412 and 0.0774 m 3 m −3 The SDRMSDs and Mrs ranged from 0.0212 to 0.0349 m 3 m −3 , and from 0.9468 to 0.9980, respectively. The validation indices computed by the PTF of Vereecken and coworkers gave the best results. Moreover, it predicts moisture retention function parameters, and therefore, this PTF is recommended most to predict the moisture retention curve from readily available soil properties.