Nitrogen pools in páramo soils under green onion cropping: vertical distribution, prediction, and management implications

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.

Monitoring soil volume wetness in heterogeneous soils by electrical resistivity. A field-based pedotransfer function

Saturated hydraulic conductivity (K sat) is an important soil parameter that governs water movement through horizons, pedons, and soil landscapes. K sat is infamous for its spatial and temporal variability, which contributes to the difficulty and considerable expense in measuring or otherwise quantifying it. Consequently, predictive methods such as pedotransfer functions (PTFs) that use physical soil properties, such as texture and bulk density, have been developed to derive K sat values. Soil texture and structure are key factors influencing K sat because of their direct relationship to pore size distribution. Quantitatively defining the combined effects of texture and structure on pore size distribution in a PTF is a difficult task. The objectives of this research were to: (i) estimate K sat based on pore characteristics derived from soil thin sections via image analysis; and (ii) compare the resultant values with field-measured K sat and with K sat estimated by a PTF using soil texture and bulk density parameters. We digitally scanned 39 thin sections from 11 pedons of soils derived from loess over till and/or over weathered sandstone. Soil voids were classified based on their size and shape. K sat was measured in the field using a Compact Constant-head Permeameter (Amoozemeter) and estimated using a Rosetta PTF. Simple and multiple linear regression (MLR) analyses were used to relate pore indexes and soil physical properties with measured and estimated K sat. The mean measured K sat was 0.74 cm h−1, whereas the PTF-estimated K sat from Rosetta and MLR were 0.36 cm h−1 and 0.49 cm h−1, respectively. The addition of pore characteristics into the model improved K sat predictions compared to predictions using Rosetta alone. The estimated K sat based on the model with added pore characteristics was better correlated with field-measured K sat (r = 0.82) than that based on Rosetta (r = 0.62). The addition of pore characteristics can improve K sat predictions. However, thin section void analysis from additional parent materials is needed.

Sample dimensions effect on prediction of soil water retention curve and saturated hydraulic conductivity

Solute-transport parameters are needed to assess the pollution risks of soil and groundwater resources. A reliable estimate of these parameters from easily measurable soil properties is therefore important. So, the correlations of the transport parameters for one metalloid compound (NaAsO2), six heavy metal compounds (Cd(NO3)2, Pb(NO3)2, Ni(NO3)2, ZnCl2, CuSO4 and Co(NO3)2), two pesticides (cartap and carbendazim) and one inert salt (CaCl2) with some basic properties of eight agricultural soils of Bangladesh were investigated. The purpose of this study was to generate information for development of non-parametric pedo-transfer functions for reactive solute transport through soils. The transport experiments with the solutes were done in repacked soil columns under unsaturated steady-state water flow conditions. The major solute-transport parameters – velocity of transport (V), dispersion coefficient (D), dispersivity (), retardation factor (R) and Peclet number (P) – were determined by analysing solute breakthrough curves (BTCs). The basic soil properties pertinent to solute transport: clay content, median grain diameter (D50), pore-size distribution index (n), bulk density (), organic carbon content (C) and pH were determined. The associations of the solute-transport parameters with these soil properties were investigated and evaluated. Both the solute dispersivity and retardation factor increased significantly (p<0.05) ( linearly and R following power law) with the increase in soil clay content. Dispersivity significantly decreased with the increase in median grain diameter following power law. The V, D,  and P values were weakly and negatively correlated with the soil bulk density. Retardation factor, R, was moderately and positively correlated with the ratio of clay content to organic carbon content. Dispersivity decreased but P increased, both significantly, with increasing pore-size distribution index, n. V, D and P were positively correlated with soil pH, while R and  were negatively correlated with it. The correlation of the solute-transport parameters with soil properties being significant (p < 0.05), in most cases, provides strong possibility of predicting solute-transport parameters from the basic soil properties through the development of pedo-transfer functions.

Soil hydraulic properties are needed in the modeling of water flow and solute movement in the vadose zone. Pedotransfer functions (PTFs) have received the attention of many researchers for indirect determination of hydraulic properties from basic soil properties as an alternative to direct measurement. The objective of this study was to compare the performance of cascade forward network (CFN), multiple-linear regression (MLR), and seemingly unrelated regression (SUR) methods using prediction capabilities of point and parametric PTFs developed by these methods. The point PTFs estimated field capacity (FC), permanent wilting point (PWP), available water capacity (AWC), and saturated hydraulic conductivity (Ks) and the parametric PTFs estimated the van Genuchten retention parameters. A total of 180 soil samples was extracted from the UNSODA database and divided into two groups as 135 for the development and 45 for the validation of the PTFs. The model performances were evaluated with three statistical tools: the maximum error (ME), the model efficiency (EF), and the D index (D) using the observed and predicted values of a given parameter. Despite the fact that the differences among the three methods in prediction accuracies of the point and parametric PTFs were not statistically significant (p > 0.05) except θr and α (p < 0.05) based on the ANOVA test, overall MLR and SUR were somewhat better than CFN in prediction of the point PTFs, whereas CFN performed better than the other two methods in prediction of the parametric PTFs. The F.F values of FC and θr for CFN, MLR, and SUR methods were 0.705. 0.805, 0.795 and 0.356, −0.290, −0.290, respectively, which refer to the best and worst predictions. Properties (Ks, θr, α) having some difficulty in prediction were better predicted by CFN and SUR methods, where these methods predict all hydraulic properties from basic soil properties simultaneously rather than individually as in MLR. This suggests that multivariate analysis using such functional relationships between hydraulic properties and basic soil properties can be utilized in developing more accurate point and parametric PTFs with less time and effort.

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.

Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (θs) has been generated in several methodologies for its estimation, such as Pedotransfer Functions (PTFs) and Artificial Neuronal Networks (ANNs). In this work, eight different PTFs were developed for the (θs) estimation, which relate to the proportion of sand and clay, bulk density (BD) as well as the saturated hydraulic conductivity (Ks). In addition, ANNs were developed with different combinations of input and hidden layers for the estimation of θs. The results showed R2 values from 0.9046≤R2≤0.9877 for the eight different PTFs, while with the ANNs, values of R2&gt;0.9891 were obtained. Finally, the root-mean-square error (RMSE) was obtained for each ANN configuration, with results ranging from 0.0245≤RMSE≤0.0262. It was found that with particular soil characteristic parameters (% Clay, % Silt, % Sand, BD and Ks), accurate estimate of θs is obtained. With the development of these models (PTFs and ANNs), high R2 values were obtained for 10 of the 12 textural classes.

Measurement of soil hydraulic properties is an arduous task, especially to meet the large scale data requirements. This has prompted numerous studies on surrogate estimation of hydraulic properties. Objective of this study was to build regional hydraulic database and develop pedotransfer functions (PTF) for estimation of water retention characteristics (WRC) of seasonally impounded shrink-swell soils. Soil samples from 41 soil profiles were analyzed in the laboratory for soil-water retention at nine pre-determined suction pressure points and basic soil properties. These data were used for calibrating neural PTFs to predict Van Genuchten (VG) function parameters for prediction of WRC from basic soil properties data. Routine method of deriving and relating retention function parameters to basic soil properties and Neuro-m method resulted in unacceptable PTFs (RMSE > 0.05 m3 m-3) . In place of parameters derived from SWRC, we used ‘Rosetta’ estimates of VG parameters to relate basic soil properties. Resultant hierarchical parametric PTFs with prediction accuracy of RMSE

Determination of the threshold velocity (TV) is a crucial step for wind erosion evaluation. Due to the difficulties of direct field measurements, pedotransfer functions (PTFs) and easily measurable soil properties could be used to save time and cost in predicting TV. Therefore, the present study was conducted to predict the TV using PTFs and to assess its influential parameters for calcareous soils of Fars Province, southern Iran. To this end, the TV was measured by a portable wind tunnel at 72 locations in different land uses and soil types across the study site. Various physicochemical and mechanical soil properties were used to develop six PTFs using multiple linear regression. Results showed that the TV varied from 3.0 m s−1in poor rangelands to 12.83 m s−1in saline lands. Soil surface shear strength (SS) with a correlation coefficient of 0.85 was the most influential parameter affecting the TV, followed by aggregate mean weight diameter (MWD). Results of the predictive models revealed that PTF 5, which was developed using SS and penetration resistance (PR;R2 = 0.86, RMSE = 0.85 m s−1), and PTF 6, which was developed using MWD and PR (R2 = 0.81, RMSE = 1.07 m s−1), had the highest performance for predicting the TV. PTF 5 was selected as the final model for predicting the TV since it only needed easily measurable soil properties without soil sample collection. We concluded that the use of PTFs could be an applicable alternative way to predict the TV, particularly at large scales.

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

A better understanding of the nitrogen (N) cycle in agricultural soils is crucial for developing sustainable and environmentally friendly N fertilizer management and to propose effective nitrous oxide (N2O) mitigation strategies. This laboratory study quantified gross nitrogen transformation rates in uncultivated and cultivated black soils in Northeast China. It also elucidated the contribution made by nitrification and denitrification to the emissions of N2O. In the laboratory, soil samples adjusted to 60 % water holding capacity (WHC) were spiked with 15NH4NO3 and NH415NO3 and incubated at 25 °C for 7 days. The size and 15N enrichment of the mineral N pools and the N2O emission rates were determined between 0 and 7 days. The results showed that the average N2O emission rate was 21.6 ng N2O-N kg−1 h−1 in cultivated soil, significantly higher than in the uncultivated soil (11.6 ng N2O-N kg−1 h−1). Denitrification was found to be responsible for 32.1 % of the N2O emission in uncultivated soil, and the ratio increased significantly to 43.2 % in cultivated soil, due to the decrease in soil pH. Most of the increase in net N2O-N emissions observed in the cultivated soil was resulting from the increased production of N2O through denitrification. Gross nitrification rate was significantly higher in the cultivated soil than in the uncultivated soil, and the ratio of gross nitrification rate/ammonium immobilization rate was 6.87 in cultivated soil, much larger than the uncultivated soil, indicating that nitrification was the dominant NH4+ consuming process in cultivated soil, and this will lead to the increased production of nitrate, whereas the increased contribution of denitrification to N2O emission promoted the larger emission of N2O. This double impact explains why the risk of N loss to the environment is increased by long-term cultivation and fertilization of native prairie sites, and controlling nitrification maybe effective to abate the negative environmental effects.

A Pedotransfer Function to Map Soil Bulk Density from Limited Data

Land surface models need information on soil hydraulic properties (SHP) that are often estimated using pedotransfer functions (PTFs). Due to a lack of calibration data, PTFs must be applied that were trained for regions and land use types outside the region of interest. In this study, we quantify the transferability of PTFs to new regions as function of mathematical complexity and number of covariates. For that purpose, we trained new PTFs for forest soils based on a dataset of 25 soil profiles from climatically moderate regions of Switzerland. In a second step, we tested the new and some existing PTFs in a drier and hotter Swiss region (Valais). Tests of transferability showed that increasing the mathematical complexity (from a linear model to a highly non-linear random forest model) was always beneficial for the predictive power in new regions. Increasing the number of covariates revealed a trade-off between improving the accuracy of the predicted soil water retention curve and reducing accuracy of the soil hydraulic conductivity. Interestingly, the use of environmental predictors (climate data) hampers transferability the most due to large climatic contrasts between the calibration and validation regions. These results suggest that transferability works better for PTFs using fewer predictors. We recommend the use of non-linear PTFs based on soil texture, soil density, and organic carbon content for optimal prediction accuracy in regions without training data. This work highlights that the models with the highest number of predictors are not optimal for achieving transferability and that reducing the number of predictors can be beneficial.

Amounts of fungal biomass in adjacent cultivated and uncultivated soils in central Iowa were estimated and compared by quantifying soil ergosterol concentrations and lengths of fungal hyphae present. Both indices of fungal biomass, with one exception, indicated that there was at least twice as much fungal biomass in uncultivated soil as in cultivated soil. Levels of microbial biomass carbon in uncultivated soils were also determined to be at least twice that in cultivated soils. Data collected in this study indicate that fungi may be more significantly affected by agricultural soil management practices than other components of the soil microbial community. For two of the soils examined, calculated estimates denote that fungal biomass carbon represented approximately 20% of the total microbial biomass carbon in cultivated soil and about 33% of the microbial biomass carbon in uncultivated soil. Results of this study indicate that conventional agricultural practices result in a significant reduction of fungal biomass production in soil. Implications of differences in fungal biomass between the soils are discussed.