Soil science
P. Khosravani; M. Baghernejad; A.A. Moosavi; S.R. Fallah Shamsi
Abstract
IntroductionUnderstanding the particle size distribution (PSD) is of great importance for plant growth and soil management. In recent years, the science of soil has witnessed a significant increase in digital soil mapping (DSM) activities. In this regard, machine learning models (ML) have emerged as ...
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IntroductionUnderstanding the particle size distribution (PSD) is of great importance for plant growth and soil management. In recent years, the science of soil has witnessed a significant increase in digital soil mapping (DSM) activities. In this regard, machine learning models (ML) have emerged as an alternative and tool for DSM, which are mainly used for data mining and pattern recognition purposes, and are now widely used for regression and classification tasks in all fields of science. Hence, this study was undertaken to spatially model sand, silt, and clay particles utilizing machine learning models such as Random Forest (RF), Support Vector Regression (SVR), and the Co-Kriging geostatistical model. Additionally, auxiliary variables with high spatial resolution were incorporated into the analysis. This investigation was conducted in a section of the Marvdasht plain, located in Fars province. Materials and MethodsThe present study was conducted in a part of Marvdasht plain located between 35.82´41°52' to 1.07´57°52' east longitude and 35.02´48°29' to 14.72´2°30' north latitude, and 40 km north of Shiraz with an area of about 50,000 hectares. After determining the study area boundaries, the positions of 200 sampling points were determined using the R software and the conditioned Latin hypercube sampling method. In other words, for soil feature modeling, 200 samples were taken from two depths of zero to 30 and 30 to 60 centimeters in the study area. Then, the samples were transferred to the laboratory, dried, and passed through a 2 mm sieve. Finally, the soil texture components were measured by the hydrometer method. The environmental variables used in this study are a wide range of representatives of soil-forming factors that were prepared as much as possible from sources with minimum cost and high accessibility. In total, 75 environmental variables were prepared, and the raster format related to all environmental variables, including 39 elevation and altitude variables and 36 remote sensing measurement variables, was extracted. Finally, the factor-tuning inflation variance and Boruta algorithm were used to select the optimal variables. ResultsThe minimum amount of clay was measured at 10.21% and 10.45%, respectively, and the maximum amount was 32.65% and 36.35% at the surface and subsurface depths. The average amount of clay in all samples was 37.91% and 35.61%. The average amount of sand was measured at 25.65% and 26.02% at the surface and subsurface depths, respectively. The maximum amount of sand was observed in the northern and higher parts of the study area and was equal to 54.68% and the minimum amount was predicted in the low-lying areas of the study area. Low-lying areas and sedimentary plains in the central part of the study area contained high amounts of silt. Four depth variables valley depths (VD), texture (TE), topographic wetness index (TWI), and clay index (CI) related to geomorphometric parameters and the normalized difference vegetation index (NDVI) variable related to remote sensing indices were selected as optimal variables. The RF model with R2 of 54.0% and 36.0% for predicting sand, 48.0% and 64.0% for predicting silt, and 52.0% and 49.0% for predicting clay at both surface and subsurface depths performed better than the SVR and Co-Kriging models. The most effective variable in predicting the spatial distribution of soil particles was VD with relative importance of 60% and 65% for predicting sand at the surface and subsurface depths, 70% for predicting silt at the surface depth, and 70% and 65% for predicting clay at both surface and subsurface depths, respectively. Only TE and TWI variables were more important than VD for predicting silt at subsurface depth. These results show that topographic variables are effective in the spatial variation of soil particles. Unlike clay, the highest amount of sand in both depths was observed in the northern part and the highest part of the study area, and the lowest amount was predicted in the low-lying areas of the study area. ConclusionIn general, with the aim of this research, maps of the spatial distribution of soil texture components were prepared at both surface and subsurface depths using machine learning and geostatistical approaches along with environmental covariates in a part of Marvdasht plain. Among the selected environmental covariates, topographic attributes, especially the valley depth (VD), had the highest effect in justifying the spatial prediction of soil texture components. Also, the results of comparing the performance of machine learning models supported the higher efficiency of the RF model than other models. Therefore, the approach used in this study to prepare a map of soil texture components can be useful as a guide for mapping useful soil features in areas with similar climatic and topographic conditions.
abolfazl azadi; M. Baghernejad; N. A. Karimian; S. A. Abtahi
Abstract
Introduction: Phosphorus (P) is the second limiting nutrient in soils for crop production after nitrogen. Phosphorus is an essential nutrient in crop production. Determination of forms of soil phosphorus is important in the evaluation of soil phosphorus status. Various sequential P fractionation procedures ...
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Introduction: Phosphorus (P) is the second limiting nutrient in soils for crop production after nitrogen. Phosphorus is an essential nutrient in crop production. Determination of forms of soil phosphorus is important in the evaluation of soil phosphorus status. Various sequential P fractionation procedures have been used to identify the forms of P and to determine the distribution of P fractions in soils (Chang and Jackson, 1957, Williams et al., 1967; Hedley et al., 1982), but are not particularly sensitive to the various P compounds that may exist in calcareous soils. A Sequential fractionation scheme has been suggested for calcareous soils by which three types of Ca-phosphates i.e. dicalcium phosphate, octacalcium phosphate, and apatite could be identified (Jiang and Gu, 1989). These types of Ca-phosphates were described as Ca2-P (NaHCO3-extractable P), Ca8-P (NH4AC-extractable P) and Ca10-P (apatite type), respectively. In this study, the amount and distribution of soil inorganic phosphorus fractions were examined in 49 soil samples of Fars province according to the method described by Jiang and Gu (1989).
Materials and Methods: Based on the previous soil survey maps of Fars province and According to Soil Moisture and Temperature Regime Map of Iran (Banaei, 1998), three regions (abadeh, eghlid and noorabad) with different Soil Moisture and Temperature Regimes were selected. The soils were comprised Aridic, xeric, and ustic moisture regimes along with mesic, and hyperthemic temperature regimes. 49 representative samples were selected. The soil samples were air-dried and were passed through a 2-mm sieve before analysis. Particle size distribution was determined by hydrometer method (Gee and Bauder 1996). Also, Cation exchange capacity (CEC; Sumner and Miller 1996), calcium carbonate equivalent (Loeppert and Suarez 1996), organic matter content (Nelson and Sommers 1996), and pH by saturated paste method (Thomas 1996) were determined . Inorganic phosphorus sequential fractionation scheme was preformed according to the method described by Jiang and Gu (1989). Olsen-P fraction that was extracted by NaHCO3 (Olsen and Sommers 1982) was regarded as P-availability index. Also, Total-P by perchloric acid (HClO4) digestion (Sparks; 1996) and organic P were determined.. All of the extraction procedures were performed in duplicate and the amounts of P were colorimetrically measured in the supernatants by the ascorbic acid method of Murphy and Riley (1962).The relationships between forms of P and some of the soil properties were established using correlation method.
Results and Discussion: The chemical data of the soils showed that soils were calcareous with CCE range between 9.94 to 74.27 % ( average 51.10%) and pH range between 7.02 to 8.36 (average 7.85). Also, the amounts of CEC were between 5.35 to 29.39 cmol (+) kg-1(average 16.68 cmol (+) kg-1). The results showed a wide range in content of Phosphorus fractions. The amount of total Phosphate ranged from 301.87 to 1458.68 mg kg-1 with an average of 626.63 mg kg-1 . Calcium Phosphate ranged from 147.83 to 666.90 mg kg-1 with an average of 324.79 mg kg-1, that comprised 85 and 52 percent of inorganic and total Phosphorus, respectively. The amount of Fe-P ranged from 0.38 to 59.18 mg kg-1 with an average of 7.56 mg kg-1 that comprised 13.64 and 8.34 percent of inorganic and total Phosphorus, respectively. Also, the amount of Al-P ranged from 20.49 to 123.09 mg kg-1 with an average of 52.28 mg kg-1that comprised 1.97 and 1.21 percent of inorganic and total Phosphorus, respectively. The results of correlation study showed that available Phosphorus was significantly correlated with Ca2-P, Ca8-P, Al-P, Ca10-P, and Pt (total phosphorus). So, in calcareous soils, awareness of soil properties and phosphorus fractions and their relationships are important for evaluation of phosphorous status in soil and understanding of soil chemistry that influence soil fertility.
Conclusion: The relative abundance of inorganic P forms were in order of Ca10 – P > Ca8- P > Al –P> Ca2-P> Fe-P. Among the inorganic P fractions, Ca-P had the highest value and varied from 147.83 to 666.90 mg kg-1, which accounted for 53 percent of the sum of P fractions, occurred in H2SO4 extractable P fraction, which is attributed to primary Ca–P minerals, indicating their weak weathering nature. Also, correlation study showed that available Phosphorus was significantly correlated with Ca2-P, Ca8-P, Al-P, Ca10-P, and Pt. This result indicate that these fractions probably can be used by plant.
