Developing Pedotransfer Functions for Predicting FC and PWP

Using pedotransfer functions (PTF) is a useful way for field capacity (FC) and permanent wilting point (PWP) prediction. The aim of this study was to model PTF to estimate FC and PWP using regression tree (RT) and stepwise multiple linear regressions (SMLR). For this purpose, 165 and 45 soil samples from UNSODA and HYPRES datasets were used for development and validation of new PTFs, respectively. %Clay, geometric mean diameter (dg), and bulk density (BD) were selected as predictor variables due to the highest correlation and lowest multicollinearity. The results showed that clay percentage with W* = 0.89 and dg with W* = ?0.57 were the most effective variables to predict PWP and FC, respectively. The RT method had a better performance (R² = 0.80, ME = ?0.002 cm³cm^?3, RMSE = 0.05 cm³cm^?3 for FC and R² = 0.85, ME = 0.003 cm³cm^?3, RMSE = 0.03 cm³ cm^?3 for PWP) than SMLR in estimation of FC and PWP.     

Comparison of Different Methods of Estimating Saffron Yield Based on Soil Properties in Golestan Province

ABSTRACT

Evaluation of the relationship between soil properties and saffron yield estimation may contribute to agricultural planning in finding suitable lands for the growth of this valuable product. This study aimed to investigate the performance of artificial neural network (ANN), multiple linear regression (MLR), and adaptive neuro-fuzzy inference system (ANFIS) in terms of saffron yield estimation in some lands of Golestan province, Iran. To this end, 100 areas under saffron cultivation were selected. For rapid and low-cost saffron yield estimation, six different models were designed based on soil properties as inputs using MLR, ANN, and ANFIS methods. According to the results, ANN showed the highest accuracy (R² = 0.58–0.89) in estimating saffron yield as compared to MLR (R² = 0.41–0.47) and ANFIS (R² = 0.41–0.69) models. A comparison of the results obtained from the six models defined in these three methods indicated that Model 4 (R² _Reg = 0.45, R² _ANFIS = 0.57, R² _ANN = 0.87), with the inputs, organic phosphorus, potassium, and calcium carbonate, was the best model in terms of accuracy and speed in estimating saffron yield phosphorus. The RI indexes for ANN in the model were 50% and 34% relative to MLR and ANFIS, respectively, demonstrating the higher accuracy of ANN in saffron yield estimation. The study results can be used to identify lands suitable for saffron cultivation in the study area using organic phosphorus and organic matter levels in the soil.     

Estimation of bulk density of waterlogged soils from basic properties

Pedotransfer functions (PTFs) to predict bulk density (BD) from basic soil data are presented. Available data pertaining to seasonally impounded shrink–swell soils of Jabalpur district in the Madhya Pradesh state of India were used for the study. The data included horizon-wise information of 41 soil profiles in the study area covering nearly 5 million ha. Six independent variables, namely textural data (sand, silt and clay), field capacity (FC), permanent wilting point (PWP) and organic carbon content (OC) were used as input in hierarchical steps to establish dependencies, with bulk density as the dependent variable, using statistical regression and artificial neural networks. The PTFs derived using neural networks [average root mean square error (RMSE) 0.05] were relatively better than statistical regression PTFs (average RMSE > 0.1). The best-performing PTFs required input data on sand, silt content, FC and PWP, with lowest prediction errors (RMSE 0.01, maximum absolute error (MAE) 0.01) and highest values of index of agreement (d, 0.95) and R ² (0.65). Use of measures of structure, as well as information on pore structure, was found to be essential to derive acceptable PTFs. Inclusion of OC as an input variable showed relatively better fitting to the training data set, implying an underlying relationship between OC and BD, but the neural networks could not mimic the relationship when tested against subset.     

Simple methods for estimating field capacity using Mamdani inference system and regression tree

Using easily measurable soil properties could save time and cost for field capacity (FC) prediction. The objective of this study was to compare Mamdani fuzzy inference system (MFIS) and regression tree (RT) for FC predicting using such properties. One hundred and sixty-five soil samples from Unsaturated Soil hydraulic database data-set and 45 from Hydraulic Properties of European Soils data-set were used for the development and validation of MFIS and RT, respectively. Fuzzy rules and tree diagram based on the relationships between these predictor variables and the response variable FC were defined and 48 rules were written. Results showed a positive linear relevancy in terms of standardized independent variable weight, W*, between clay content and FC and negative linear relevancy between geometric mean particular size diameter (d_g) and FC. Among predictor variables, d_g (W* = 0.81) and bulk density (BD) (W* = 0.49) had the highest and lowest influence on FC, respectively. A tree diagram is presented for the prediction of FC using clay content, dg, and BD. Overall, based on statistical parameters, RT method (R² = 0.78, geometric mean error (GME) = 1.02, mean error (ME) = 0.01 cm³ cm^?3, and root mean square error (RMSE) = 0.04 cm³ cm^?3) showed a higher performance than MFIS method (R² = 0.72, GME = 1.16, ME = 0.08 cm³ cm^?3, and RMSE = 0.06 cm³ cm^?3) to predict FC.     

VNIR Soil Spectroscopy for Field Soil Analysis

The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (?50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R² = 0.39; RPD = 1.28; and air-dry spectra: R² = 0.80; RPD = 2.25). These point values were combined with the electromagnetic survey data to produce a soil organic carbon map, using a random forest data mining approach (validation model: R² = 0.52; RMSE = 3.21 Mg C/ha to 30 cm soil depth; prediction model: R² = 0.92; RMSE = 1.53 Mg C/ha to 30 cm soil depth). This spatial modeling method, using high-resolution sensor data, enables prediction of soil carbon stocks, and their spatial variability, at a resolution previously impractical using a solely laboratory-based approach.     

Estimating Mo,Cu, Ni,Cd Contents in the Crop Leaves Growing on Small Land Plots Using Satellite Data

ABSTRACT

The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During the acquisition of a SPOT 7 satellite image (28 July 2017) in situ sampling (38 samples) was done from the leaves of potatoes and beans growing close to the mining town of Kajaran (Armenia). To estimate HMs contents, multivariate regression (multiple linear regression (MLR), partial least squares regression (PLSR)), and artificial neural network (ANN) were used. As input data for the models raw, atmospherically corrected (Dark Object Subtraction (DOS)) and hyperspherical direction cosines (HSDC) normalized values of SPOT 7 spectral data in combination with one or combined log10, multiplicative scatter correction (MSC), standard normal variate transform (SNV) preprocessing methods were utilized. The best results were obtained for Cu using MLR (R² _cal. = 0.79, R² _CV = 0.70, RMSE_cal. = 11.27, RMSE_CV = 13.47) and ANN (R² _Train ≈ 0.80, R² _Test ≈ 0.72, RMSE_Train ≈ 11, RMSE_Test ≈ 13) models in case of bean leaves. The results are quite optimistic, however, further research with the use of high spatial/spectral resolution satellite images is needed to improve the accuracy of models.     

Assessment of the Crop Water Stress Index and Color Quality of Bur Clover (Medicago polymorpha L.) Under Different Irrigation Regimes

ABSTRACT

Relationship between canopy temperature and soil moisture is important for using the potential of canopy temperature as an indicator of crop water stress. A two-year field experiment was carried out during June to September 2016 and 2017 at the Research Station of College of Agriculture, Darab, Shiraz University, Iran, to determine crop water stress index (CWSI) for bur clover. Irrigation regimes including well-watered [Irrigation according to 100% field capacity (FC)], mild water stress (75% FC), severe water stress (50% FC), and most severe water stress (25% FC) were arranged in a randomized complete block design with four replications. In 2016, CWSI values showed an increasing trend from June (0.066 in well-watered) to August (0.821 in most severe water stress) as a result of higher vapor pressure deficit (VPD) and depression in canopy-air temperature differences (Tc-Ta). A similar trend was observed in the second year. In both years, by increase in mean temperature from June to August, Tc-Ta differential increased and the highest monthly average value of CWSI for all treatments was obtained in August. By enhancing water stress, the color grading score decreased sharply (from 6 to 3) and stayed constant (2) for August and September. Also, a negative relationship was observed between CWSI and dry matter production (R² = 0.88**) and color quality (R² = 0.94**). It was concluded that mild water stress (75% FC) with mean seasonal CWSI being ranged about 0.198 to 0.294, without any loss in visual color quality might be the best irrigation regime for bur clover production.     

Selection of a Suitable Soft Computing Model for Estimation of Soil Cation Exchange Capacity

ABSTRACT

Measuring of soil cation exchange capacity (CEC) is difficult and time-consuming. Therefore, it is essential to develop an indirect approach such as pedotransfer functions (PTFs) to predict this property from more readily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, adaptive neurofuzzy inference system, and an artificial neural network (ANN) model to develop PTFs for predicting soil CEC. One hundred and seventy-one soil samples were used into two subsets for training and testing of the models. The model's prediction capability was evaluated by statistical indicators that include RMSE, R², MBE, and RI. Results showed that the ANN model had the most reliable prediction when compared with other models. This study provides a strong basis for predicting soil CEC and identifying the most determinant properties influencing soil CEC in the north regions of Iran. Analytical framework results could be applied to other parts of the world with similar challenges.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Network; CEC: Cation Exchange Capacity; CV: Coefficient of Variation; FFBP: Feed-Forward Back-Propagation; FIS: Fuzzy Inference System; MBE: Mean Bias Error; MF: Membership Function; MLR: Multiple Linear Regressions; MNLR: Multiple Non-Linear Regressions; MLP: Multi-layer Perceptron; OC: Organic Carbon; PTFs: Pedotransfer Functions; R²: Determination Coefficient; RI: Relative Improvement; RMSE: Root Mean Square Error; SD: Standard Deviation     

Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area

Leaf Area (LA) is a key index of plant productivity and growth. A multiple linear regression technique is commonly applied to estimate LA as a non-destructive and quick method, but this technique is limited under the realistic situation. Thus, it is indispensable to elaborate new models for estimation. In this research, the performance of the Adaptive Neural-Based Fuzzy Inference System (ANFIS) in predicting the LA of 61 plant species (C) was investigated. Four parameters including leaf length (L), leaf width (W), C, and specific coefficient (K) for each plant were selected as input data to the ANFIS model and the LA as the output. Seven different ANFIS models including different combinations of input data were constructed to reveal the sensitivity analysis of the models. The normalized root mean square error (NRMSE), mean residual error (MRE), and linear regression were applied between observed LA and estimated LA by the models. The results indicated that ANFIS4-K_2min which employed all input data was the most accurate (NRMSE = 0.046 and R² = 0.997) and ANFIS1 which employed only the K input was the worst (NRMSE = 0.452 and R² = 0.778). In ranking, ANFIS4-K_2ave, ANFIS4-K_1min, ANFIS4-K_1ave, ANFIS3, and ANFIS2 ranked second, third, fourth, fifth, and sixth, respectively. The sensitivity analysis indicated that the predicted LA is more sensitive to the K, followed by L, W, and C. The results displayed that estimations are slightly overestimated. This study demonstrated that the ANFIS model could be accurate and faster alternative to the available laborious and time-consuming methods for LA prediction.     

Effect of biochar application rate on soil physical and hydraulic properties of a sandy loam

Biochar is used as a soil amendment for improving soil quality and enhancing carbon sequestration. In this study, a loamy sand soil was amended at different rates (0%, 25%, 50%, 75%, and 100% v/v) of biochar, and its physical and hydraulic properties were analyzed, including particle density, bulk density, porosity, infiltration, saturated hydraulic conductivity, and volumetric water content. The wilting rate of tomato (Solanum lycopersicum) grown in soil amended with various levels of biochar was evaluated on a scale of 0–10. Statistical analyses were conducted using linear regression. The results showed that bulk density decreased linearly (R² = 0.997) from 1.325 to 0.363 g cm^?3 while the particle density decreased (R² = 0.915) from 2.65 to 1.60 g cm^?3 with increased biochar amendment, with porosity increasing (R² = 0.994) from 0.500 to 0.773 cm³ cm^?3. The mean volumetric water content ranged from 3.90 to 14.00 cm³ cm^?3, while the wilting rate of tomato ranged from 4.67 to 9.50, respectively, for the non-amended soil and 100% biochar-amended soil. These results strongly suggest positive improvement of soil physical and hydraulic properties following addition of biochar amendment.     

Evaluation of the spatial pattern of surface soil water content of a karst hillslope in Southwest China using a state-space approach

This study aims to evaluate the effects of soil physicochemical properties and environmental factors on the spatial patterns of surface soil water content (SWC) based on the state-space approach and linear regression analysis. For this purpose, based on a grid sampling scheme (10 m × 10 m) applied to a 90 m × 120 m plot located on a karst hillslope of Southwest China, the SWC at 0–16 cm depth was measured 3 times across 130 sampling points, and soil texture, bulk density (BD), saturated hydraulic conductivity (K_s), organic carbon (SOC), and rock fragment content as well as site elevation (SE) were also measured at these locations. Results showed that the distribution pattern of SWC could be more successfully predicted by the first-order state-space models (R² = 67.5–99.9% and RMSE = 0.01–0.14) than the classic linear regression models (R² = 10.8–79.3% and RMSE = 0.11–0.24). The input combination containing silt content (Silt), K_s, and SOC produced the best state-space model, explaining 99.9% of the variation in SWC. And Silt was identified as the first-order controlling factor that explained 98.7% of the variation. In contrast, the best linear regression model using all of the variables only explained 79.3% of variation.     

Development of the Nondestructive Leaf Area Estimation Model for Valeriana (Valeriana jatamansi Jones)

Leaf area (LA) is an important parameter related to plant growth and physiology. An allometric model was developed to estimate the LA of endangered medicinal plant Valeriana jatamansi using linear measurements such as leaf length (L) and width (W). LA and other leaf dimensions were measured using a laser leaf area meter. Leaves from seven accessions of valeriana were collected from the experimental site during 2015. Different regression models were developed between LA and other leaf components, viz. L, W, etc. The linear model having LW as an independent variable (y = 0.487 + 0.644 LW) provided the best estimation [coefficients of determination (R²) = 0.974, root mean square error (RMSE) = 2.222, coefficient of variation (CV) = 4.529]. Validation of the selected model showed a higher correlation between the actual leaf area (ALA) and the predicted leaf area (PLA) [R² = 0.956, RMSE = 2.310, CV = 5.319, predicted residual error sum of squares (PRESS) = 1067.352].     

Comparative growth and physiological responses of two wheat (Triticum aestivum L.) cultivars differing in salt tolerance to salinity and cyclic drought stress

Two cultivars of wheat (Triticum aestivum L.) with differential salinity tolerance were compared by evaluating the growth attributes, pigment composition and accumulation of Na⁺, K⁺, Zn²⁺, Fe ²⁺, Mn ²⁺ and proline. Wheat cultivars Al-Moiaya (AM) (salt tolerant) and Habbe-Druma (HD) (salt sensitive) were subjected to four levels of salinity (1.21 dS m^?1, 4.4 dS m^?1, 8.8 dS m^?1 and 13.2 dS m^?1) in factorial combinations with three drought stress (FC 30%, FC 60% and FC 90%) treatments in a randomized complete block design. Plant dry weight, leaf area ratio (LAR), soluble protein and total chlorophyll (Chl) content were higher in AM than HD. Salt-tolerant AM maintains a higher K⁺/ Na⁺ ratio and thereby is able to grow better than the salt-sensitive HD under both the stresses. The lower foliar Na⁺ in AM resulted in retention of higher Chl content, reflected in the strong positive correlations between plant ion status and Chl contents (Na⁺-Chl r² = 0.83; Chl- Fe²⁺ r² = 0.76; Zn²⁺ r² = 0.93 and Mn²⁺ r² = 0.88). In conclusion, our results suggested that the K⁺/Na⁺ ratio, exclusion of Na⁺ and ion homeostasis play much more important roles in the tolerance to salinity and drought stress than the compatible osmolyte, proline.     

Remobilization efficiency and photosynthetic characteristics of five barley (Hordeum vulgare L.) cultivars under terminal drought stress

Drought is an important limiting factor which can cause major loss in barley productivity. A field experiment was conducted to investigate the effects of irrigation regimes on assimilate remobilization and photosynthetic characteristics of five barley cultivars in 2012 and 2013. There were four levels of irrigation including well-watered [soil moisture content in root depth kept at 100% field capacity (FC)], mild drought (75% FC), severe drought (50% FC), and very severe drought (25% FC). Results showed that Karoon and Valfajr cultivars had the maximum net photosynthetic rate (P_n) ranged from 16.3 to 19.3 µmol CO₂ m^?2 s^?1 under very severe drought. Stomatal conductance (gs) was affected by drought so that Karoon and Valfajr had the lowest gs under severe and very severe drought. By improving the drought, remobilization efficiency in Karoon and Valfajr increased from 18.3% in well-watered to 54.1% under severe drought. In both years under severe and very severe drought, maximum 1000-grain weight and grain yield was obtained in Valfajr and Karoon. Overall, in arid areas, applying suitable irrigation regimes such as mild or severe drought can control soil drying, so that suitable cultivars such as Karoon and Valfajr can rehydrate overnight, and yield might not be inhibited severely.     

A simple digital imaging method for estimating black-soil organic matter under visible spectrum

Soil organic matter (SOM) plays a key role in soil, and is used to determine soil quality. Conventional soil property analysis is relatively slow, expensive and laborious. Although using a spectrometer can quickly assess a large amount of organic matter content, it is an expensive, complex and undefined process. This article presents a potential simple method for estimating black-SOM that uses a digital camera that is cheaper and easier to operate than a spectrometer. RGB (red, green and blue) image-intensity values of the soil from a digital camera were measured, to research the relationship between black-SOM and RGB. The results show the red image-intensity values provide the greatest correlation with SOM, with a correlation coefficient (r) reaching 0.73. A comparison with spectrometer results for SOM predictions shows that the best prediction result for the digital camera (R² = 0.72, root-mean-square error [RMSE] = 0.40) is slightly better than the spectrometer (R² = 0.65, RMSE = 0.45) at certain band points. Thus, a low-cost digital camera that is easy to operate can be used as an alternative tool for the rapid and accurate estimation of black-SOM content.     

Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau,China

Journal of Soils and Sediments - Field capacity (FC) and permanent wilting point (PWP) are important physical properties for evaluating the available soil water storage, as well as being used as...     

Statistical and geostatistical techniques for geospatial modeling of soil cation exchange capacity

Soil cation exchange capacity (CEC) is a main criterion of soil quality and pollutant sequestration capacity. This research was carried out to evaluate cokriging prediction map of soil CEC spatial variability with the principal components derived from soil physical and chemical properties. Two hundred and forty-seven soil samples were collected that 75% of them were used for training soil CEC and 25% for testing of prediction. The first principal component (PC1) was highly correlated with soil CEC (r = 0.81, P < 0.01), whiles there was no significant correlation between CEC and PC2 (r = -0.19). Then, the PC1 was used as an auxiliary variable for the prediction of soil CEC in cokriging method. The determination coefficient (R²) of cross-validation for the test dataset was 0.47 for kriging and 0.71 for cokriging. Therefore, according to the results, principal components that have the highest positive and significant correlation with the dependent variable have the most potential for cokriging prediction.     

Modeling Soil Organic Carbon with DNDC and RothC Models in Different Wheat-Based Cropping Systems in North-Western India

ABSTRACT

The performance of DNDC (DeNitrification-DeComposition) and RothC (Rothamsted Carbon model) in simulating soil organic carbon (SOC) storage in soils under rice (Oryza sativa L.) – wheat (Triticum aestivum L.), maize (Zea mays L.) – wheat and cotton (Gossypium hirsutum L.) – wheat cropping systems was evaluated on field and regional scale. Field experiments consisted of N, NP, NK, PK, NPK, FYM, N + FYM, NPK + FYM, and control (UF) treatments. DNDC and RothC over-estimated SOC storage by 0.35–1.16 Mg C ha^?1 (6–21%) in surface layer with manure application, compared with inorganic fertilizer treatments by 1.01–1.16 Mg C ha^?1 (14–18%). Although RothC only slightly over-estimated SOC stocks, DNDC provided a better match for measured versus simulated SOC stocks (R ² = 0.783*, DNDC; 0.669*, RothC, p < .05). Model validation on independent datasets from long-term studies on rice–wheat (R ² = 0.935**, DNDC; R ² = 0.920**, RothC, p < .01) and maize–wheat (R ² = 0.895** for DNDC and R ² = 0.967** for RothC, p < .01) systems showed excellent agreement between measured and simulated SOC stocks. On a regional scale, change in SOC storage under Scenario 1 (NPK) was significant up to 8 years of simulation, with no change thereafter. In Scenario 2 (NPK + FYM), DNDC simulated SOC storage after 10 years was 2.0, 0.4, and 1.4 Mg C ha^?1 in three systems, respectively. Amount of C sequestered in silt + clay fraction varied between 0.31 and 0.97 kg C 10 years^?1 (Mg silt + clay)^?1 under Scenario 1, and between 0.78 and 2.67 kg C 10 years^?1 (Mg silt + clay)^?1 under Scenario 2.     

Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

The present study aims to evaluate the potential of near-infrared reflectance (NIR) spectroscopy to determine the carbon and nitrogen content in soils and also to assess the effectiveness of NIR spectroscopy to predict carbon and nitrogen content in freshly collected soil samples. Soil samples (n = 179) were collected from different locations in India. Soil carbon and nitrogen contents were successfully predicted (R² = 0.90 for carbon and R² = 0.85 for nitrogen) by NIR spectroscopy. The root mean square error (RMSE) and ratio performance deviation (RPD) for the validation of predicted equations for carbon and nitrogen were 0.83 and 2.83 and 0.01 and 6.98, respectively. The efficacy of NIR spectroscopy on the prediction of carbon and nitrogen content in Indian soils is highly reliable. Water content in soil samples could affect the NIR absorbance spectra and in turn affect the quantification of carbon and nitrogen.     

Digital soil mapping of available water content using proximal and remotely sensed data

Two‐thirds of all irrigated agriculture in Australia is undertaken within the Murray–Darling Basin. However, climate change predictions for this region suggest rainfall will decrease. To maintain profitability, more will need to be done by irrigators with less water. In this regard, irrigators need to be aware of the spatial distribution of the available water content (AWC) in the root‐zone (i.e. 0.0–0.90 m). To reduce the cost, digital soil mapping (DSM) techniques are being used to map soil properties related to AWC (e.g. soil texture). The purpose of this study was to create a DSM of the AWC at the district scale. This is achieved by determining AWC by the difference between laboratory measured permanent wilting point (PWP) and field capacity (FC) and using pressure plate apparatus. The PWP and FC data are coupled to remote (i.e. gamma‐ray spectrometry) and proximal (i.e. EM38 and EM34) sensed data and two trend surface parameters. Using a hierarchical spatial regression (HSR), we predict PWP and FC across the areas of Warren and Trangie in the lower Macquarie valley, Australia. The reliability of the DSM of PWP and FC were compared using prediction precision (RMSE – root mean square error) and bias (ME – mean error). The best results were achieved using EM38‐v, EM34‐20, eU and eTh. The DSM map of AWC is consistent with known Pedoderms and provides a basis for agricultural water management.