Soil science
F. Jannati; F. Sarmadian
Abstract
IntroductionResearch and development in high-potential agricultural areas are of great importance for ensuring the food needs of the population and livestock. Neglecting these regions can lead to increased food prices and food shortages, which can have a negative impact on the economy and public health. ...
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IntroductionResearch and development in high-potential agricultural areas are of great importance for ensuring the food needs of the population and livestock. Neglecting these regions can lead to increased food prices and food shortages, which can have a negative impact on the economy and public health. Land suitability maps provide essential information for agricultural planning and are vital for reducing land degradation and evaluating sustainable land use. The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. These methods have been widely employed as primary tools for mapping and evaluating land suitability in various regions worldwide. Materials and MethodsIn this study, a total of 288 soil profiles were utilized to compute the land suitability index for wheat, barley, and alfalfa crops. Various environmental variables were included, such as topographic factors derived from the digital elevation model and spectral indices obtained from Landsat 8 satellite imagery. Eight key factors, namely slope percentage, climate, texture, gypsum content, equivalent calcium carbonate, electrical conductivity (EC), and sodium absorption ratio (SAR), were identified as influential in the assessment of land suitability. To quantify the degrees of land suitability for the target crops, a parametric approach based on the square root method was employed. Moreover, the random forest machine learning model was utilized for spatial modeling, zoning mapping, and determining the significance of environmental variables in the land suitability evaluation process. By incorporating these comprehensive methodologies, a more detailed and accurate understanding of the land suitability for wheat, barley, and alfalfa cultivation can be achieved, facilitating informed decision-making in agricultural planning and land management strategies. Results and DiscussionThe spatial prediction results demonstrated the effectiveness of the random forest model in classifying land suitability for wheat, barley, and alfalfa. The model achieved high accuracy, with Kappa coefficients of 81%, 84%, and 85% for wheat, barley, and alfalfa, respectively. The overall accuracies were also impressive, reaching 86% for wheat, 88% for barley, and 89% for alfalfa. Analyzing the land suitability assessment results, it was found that barley had the highest land suitability class, covering a significant portion of 40% in class S1. Alfalfa followed closely with 35.5% of the total area, and wheat occupied 32% in the same class. Delving into the predictive environmental variables for barley, Diffuse, SHt, and MrVBF emerged as the most influential factors. These variables played a crucial role in assessing the suitability of land for barley cultivation. Similarly, for wheat, the variables Diffuse, MrVBF, and TWI were identified as significant indicators, contributing to the accurate prediction of wheat performance. Regarding alfalfa, the variables MrVBF, Diffuse, and Valley_depth stood out as the most important variables, providing valuable insights into land suitability for alfalfa cultivation. In general, the limiting factors for irrigated cultivation of these crops were primarily associated with soil properties. In the northern regions, soil texture was identified as a significant limiting factor, impacting the suitability of the land for crop cultivation. On the other hand, in the southern regions, soil characteristics such as the percentage of lime, gypsum, salinity, and alkalinity were recognized as the most influential limiting factors, affecting the suitability of the land for successful crop production. These findings provide valuable information for land planners, farmers, and decision-makers in determining suitable areas for wheat, barley, and alfalfa cultivation. By considering the identified influential factors and addressing the limiting soil properties, agricultural practices can be optimized to maximize crop productivity and ensure sustainable land use. ConclusionThe research aimed to evaluate land suitability for wheat, barley, and alfalfa crops under irrigation. Data selection focused on the most limiting factors for these crops. The model achieved acceptable predictions for wheat, barley, and alfalfa, with Kappa coefficients of 0.81, 0.85, and 0.84, and overall accuracies of 0.86, 0.89, and 0.88, respectively. Barley had the highest percentage of suitable land (40%), followed by alfalfa (39.5%) and wheat (32%). Soil constraints varied across the study area, including texture, stoniness, lime, gypsum, salinity, and alkalinity. The analysis identified 31 soil types, and the random forest model yielded a digital soil map with a Kappa coefficient of 0.76 and overall accuracy of 0.81. The findings support effective land management and agricultural planning.
Soil science
Gordafarin Rezaie; F. Sarmadian; Ali Mohammadi Torkashvand; J. Seyedmohammadi; Maryam Marashi Aliabadi
Abstract
IntroductionKnowledge of the spatial distribution of soil salinity and soil organic carbon (SOC) leads to obtaining valuable information that is effective in decision-making for agricultural activities. More than a third of the world's land is affected by salt, which threatens the growth and production ...
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IntroductionKnowledge of the spatial distribution of soil salinity and soil organic carbon (SOC) leads to obtaining valuable information that is effective in decision-making for agricultural activities. More than a third of the world's land is affected by salt, which threatens the growth and production of crops, and prevents the development of sustainable agriculture. The high electrical conductivity (EC) content in soils poses significant challenges in arid and semi-arid regions, greatly impacting agricultural production. Saline and sodic soils often exhibit high levels of sodium which is a key characteristic. The presence of sodium ions leads to the destabilization of soil aggregates and the dispersion of soil particles resulting in the closure of soil pores. Consequently, unfavorable changes occur in the soil physical, chemical, and biological properties increasing its susceptibility to water and wind erosion. Additionally, high sodium levels can lead to the decomposition of soil organic carbon (SOC). SOC is crucial for water retention, cation exchange, and nutrient availability, making its reduction in agricultural soils a significant threat to sustainable soil management. Therefore, the investigation of soils in terms of EC and SOC contents and their spatial distribution is of great importance to support decision-makers in agricultural development planning to reduce challenges related to food security in arid and semi-arid regions.Materials and MethodsThis study was conducted with the aim of investigating the EC and SOC in topsoil (0-30 cm) and subsoil (30-60 cm) layers using four machine learning (ML) algorithms namely, random forest (RF), decision tree (DTr), support vector regression (SVR) and artificial neural network (ANN) performed in Qazvin Plain. The study area includes a part of agricultural lands and natural areas of Alborz and Qazvin provinces, between the Nazarabad and Abyek cities in Iran. This region with an area of 60,000 hectares is located at latitude 35° 54´ to 36° 54´ to the north and 50° 15´ to 50° 39´ to the east. This research was carried out in four stages including (i) soil sampling and measuring the physical and chemical properties of the soil and preparation of environmental covariates from a digital elevation model (DEM) with spatial resolution 12.5 m and Landsat 8 satellite imagery with spatial resolution 30 m by SAGA GIS and ENVI software, (ii) spatial modeling of soil EC and SOC in the topsoil and subsoil layers by the RF, SVR, ANN, and DTr ML algorithms, (iii) evaluating the efficiency of the ML algorithms and determining the relative importance of environmental covariates, and (iv) preparation of spatial prediction maps of EC and SOC in the topsoil (0-30 cm) and subsoil (30-60 cm) layers in the study area.Results and Discussion The result of the spatial prediction maps of EC showed that the studied area has non-saline to very saline soils up to a depth of 60 cm. It is also possible that the EC equivalent shows a decreasing trend in soil salinity with a depth from 6.05 to 5.55 ds/m from the topsoil to the subsoil layer. The highest amount of SOC was observed in the surface layer equal to 3.3%. Globally SOC content decreased from the surface (average of 0.84%) to depth (average of 0.4%). The high spatial variability of SOC showed that the soils of the study area are affected by management activity. Environmental covariates were extracted as a proxy of topography and remote sensing indices including elevation, diffuse Insolation (Diffuse), Multi-Resolution Index of Valley Bottom Flatness (MrVBF), Normalized Differences Vegetation Index (NDVI), SAGA wetness index (SWI) and wind Effect (WE) were used as representatives of soil formation factors. The topography parameters, including the elevation, diffuse insolation, and Multi-Resolution Index of Valley Bottom Flatness, were most closely related to EC and SOC variations in each topsoil and subsoil layer. Elevation can be justified around 50% and 35% of EC and 28.56% and 29.47% of SOC variations in the topsoil and subsoil layers, respectively, followed by the diffuse variable can succeed to justified 19.7% and 25.1% of EC and 27.28% and 27.67% of SOC spatial variations in the topsoil and subsoil layers, respectively.The results confirmed that the RF was recognized as outperforming the ML model for predicting EC in the topsoil (R2 =0.74, RMSE =0.36, and nRMSE= 0.07), as well as predicting SOC in topsoil and subsoil layers (R2= 90 and R2=0.80), followed by the DTr for predicting EC (R2 0.77, RMSE/0.9, and nRMSE 0.17) in the subsoil layer in comparison other models. Conclusion The RF (Random Forest) and DTr (Decision Tree) models incorporating topographic parameters demonstrated satisfactory accuracy in predicting the variation of topsoil and subsoil electrical conductivity (EC) and soil organic carbon (SOC) in the study area. Topography plays a crucial role in soil formation, and elevation-based topographic attributes are commonly used as key predictors in digital soil mapping projects. The variability in topography influences water flow and sedimentation processes which, in turn, affects soil development and the spatial distribution of soil properties. The resulting soil maps can be valuable tools for decision-making programs related to soil management in the region.
Soil science
F. Sarmadian; S. Teimuri Bardiani; Sh. Rahmani Siyalarz; N. Sayadi
Abstract
Introduction Farmers and agricultural products face many risks, including adverse weather conditions, pests, diseases, and changes in product prices, laws, and regulations. The first step in managing and minimizing many of these risks is often choosing the right crops for the area under cultivation; ...
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Introduction Farmers and agricultural products face many risks, including adverse weather conditions, pests, diseases, and changes in product prices, laws, and regulations. The first step in managing and minimizing many of these risks is often choosing the right crops for the area under cultivation; Therefore, knowing whether these lands are suitable for a particular crop can determine the success or failure of agricultural strategies. Because farmers are exposed to climate change and the economy, where agricultural frameworks are changing at an unprecedented rate, it is vital for them to be able to adapt to new trends. Increasing the availability of land suitability information for agricultural products will be a valuable aid for farmers and managers in this field to develop new agricultural strategies. At the same time, the growth of computational capabilities and increased access to geographic data has made land suitability assessment faster and easier.Materials and Methods The study area is located in Abik city, a city located in Qazvin province of Iran, between 50 degrees and 40 minutes to 50 degrees and 41 minutes east longitude and 35 degrees and 52 minutes to 36 degrees and 21 minutes north latitude. The average annual soil temperature at depth of less than 50 cm is 15.8 °C and has thermal heating regime. Furthermore, according to the average rainfall of the region, 222.7 mm, the humidity regime of the region is of Eridic type. Moisture and heat regimes were obtained by Newhall software. According to regional conditions and the size of the area, 60 profiles were drilled for network description and sampling. Field studies including determination, drilling, description of profiles, slope percentage, etc. were determined at the site. Information on soil physical and chemical properties were tested. Parametric, American (USDA) and LSP methods were used to evaluate the land. Necessary climatic characteristics for annual plants include the climatic variables that are necessary to determine the growing season, planting date and type of cultivar. The information of Buin Zahra synoptic station has been used. In this study, CROPWAT software was used to calculate the potential evapotranspiration. Land information such as slope, drainage Condition and flood absorption, as mentioned in the profile description card, was used to assess land suitability. Growth period was also obtained for the region using the area agronomical calendar. To calculate potential of production, the model AEZ which is provided by FAO, is used in this research.Results and Discussion The decrease in the suitability of the studied lands for the wheat crop is due to the salinity and sodium content of the lands and the presence of surface gravel and shallow soil depth. According to the provided tables and maps, 18% of the study area is unacceptable, 12.5% is average, 12.5% is good, 25% is very good, or very good and 31.25% of the total study area are in the excellent fitness class. The above values have been obtained by considering the rangeland and saline sections as well as the type of product in preparing the fit map. The accuracy of the preferred rational scoring method in land suitability is higher than the parametric method because in this method the land suitability maps of the area are obtained by logical collectors and the output map is the result of all parameters and constraints that the area may have. To have the desired. In the parametric method, this problem is summarized in soil properties and climatic conditions. Due to the lack of direct measurement of product performance, more accurate comparisons were not possible.Conclusion Most of the restrictions were in shallow hilly areas with shallow soils and pebbles, and salinity, alkalinity and gypsum did not impose any restrictions in these areas. Traffic in these areas was difficult and they were mostly in the S3 class by the parametric method and the poor and unacceptable class in the LSP. In land evaluation using LSP method, understanding the relationships of criteria with each other and the amount of impact that each has on the potential of land for different uses is essential. The LSP method is sensitive yet flexible, and may not work well if the data accuracy and number of parameters are low. The application of GIS-based LSP method showed a suitable tool to create accurate, flexible and rationally justifiable criteria in assessing the capability and suitability of land in agriculture. In such studies, by using the Bayer LSP method, prerequisites such as precisely defining the goals of users, managers and agricultural expertise should be considered. This method is a multi-criteria evaluation method that has been improved for measurement among decision makers, land management and other specialties.
Soil science
S.R. Mousavi; F. Sarmadian; M. Omid; P. Bogaert
Abstract
Introduction: Calcium Carbonate Equivalent (CCE) is one of the key soils properties in arid and semi-arid regions. The study of spatial variability of surface and subsurface layers is important in the sustainable land management of arable soils. This study aimed to model the spatial distribution of CCE ...
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Introduction: Calcium Carbonate Equivalent (CCE) is one of the key soils properties in arid and semi-arid regions. The study of spatial variability of surface and subsurface layers is important in the sustainable land management of arable soils. This study aimed to model the spatial distribution of CCE percentage by using three machine learning algorithms including Random Forest (RF), Decision Tree regression (DTr) and k-Nearest Neighbor (k-NN) at five standard depths of 0-5, 5-15, 15-30, 30-60, and 60-100 cm.Material and Methods: The study area with 60,000 ha includes the major part of the lands of Qazvin plain located on the border of Qazvin and Alborz provinces. Field and laboratory surveys included 278 representative profiles were excavated, described by the horizon, and determined physicochemical properties. The studied soils have a very high diversity in soil moisture (Aridic, Xeric, and Aquic) and temperature regimes (Thermic). These variations have led to the formation of eight great groups of soils in the region based in the USDA soil classification system with the three classes of Haploxerepts, Calcixerepts, and Haplocalcids were the dominant soil classes in the study area. A total of 22 environmental covariates, including 12 variables extracted from the primary and secondary derivation of digital elevation model (DEM), six remote sensing (RS) indicators, two climatic parameters, and two soil covariates were prepared, and then the most appropriate environmental covariates were selected using principal component analysis (PCA) and expert knowledge. The CCE percentage data were randomly divided into two parts, 80% for training and 20% for testing, which was then modeled by three machine learning algorithms RF, DTr, and k-NN, and were evaluated by some statistical indices as coefficient determination (R2), root mean square error (RMSE) and Bias.Results and Discussion: The results of harmonizing the CCE values at the genetic horizons with the standard depths showed the high efficiency of the spline depth function in providing an acceptable estimate with minimum error and maximum agreement between observed and predicted values. The PCA method showed that the first to fifth components with the explanation of more than 80% of cumulative variance were Multi-Resolution Index of Valley Bottom Flatness (MrVBF), Mean Annual Temperature (MAT), Greenness index (Greenness), Probability of Calcic horizon (Cal.hr), and Wind Effect environmental covariates which had the highest eigenvalues. Besides, Clay was selected on expert knowledge-based. The relative importance (RI) of the environmental covariates showed the spatial distribution of CCE were affected by Clay with an explanation of more than 57%, 41.8% and 45% of its variance at three surface depths of 0-5, 5-15, and 15-30 cm, while the Cal.hr covariate had the highest impact in the spatial prediction of CCE compared to other predictors as auxiliary variables with 67.8% and 52.8% justification, respectively, at two depths of 30-60 and 60-100 cm. Hence, using the calcic horizon probability Map (Cal.hr) as a derivative soil factor made it possible to produce more appropriate final maps, while preventing the reduction of the accuracy of the modeling results in the subsoils. The auxiliary variable of remote sensing, i.e., Greenness, could not show a significant impact on the expression of the variation of CCE percentage at all studied depths. Unlike remote sensing indices, the topographic attribute of the MrVBF, at two standard depths of 0-5 and 5-15 cm, the MAT at a depth of 15-30 cm, and the Wind Effect at the standard depths 30-60 and 60-100 cm, after the soil covariates, were the most effective in justifying the spatial variations of CCE%. RF algorithm with a range of R2 values of 0.83 - 0.76 and RMSE of 2.14% - 2.21% resulted in the highest accuracy and minimum error. Even though the DTr method presented R2 values (0.52-0.39) weaker than the RF in the validation dataset, in general, the results of its spatial predictions were similar to the RF model from the surface to the subsurface and more stable than the k-NN. Against RF and DTr, k-NN couldn’t display acceptable performance in the prediction of CCE% at all standardized depths.Conclusion: In general, it is necessary to understand the spatial distribution of CCE due to its effect on soil moisture accessibility and plant nutrient uptake. Therefore, in the present study, we tried to introduce the RF machine learning algorithm as a superior model with environmental variables that were selected by PCA and the expert knowledge variable selection method. The maps prepared by this approach have an acceptable level of reliability for agricultural and environmental management by managers, soil experts, and farmers.
Sona Azarneshan; farhad khormali; fereydoon sarmadian; farshad kiani; kamran Eftekhari
Abstract
Introduction: Assessing the soil quality of agricultural land is essential for the economic success and sustainability of the environment in developing countries. Recently, there are many types of methods for assessing soil quality, each of them uses different criteria. Considering that Qazvin plain ...
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Introduction: Assessing the soil quality of agricultural land is essential for the economic success and sustainability of the environment in developing countries. Recently, there are many types of methods for assessing soil quality, each of them uses different criteria. Considering that Qazvin plain is one of the most important regions of agricultural products in Iran as well as Middle East, so the assessment of the soil status using quantitative models of soil quality can be used as an indicator of the status of soils in relation to sustainable agriculture, optimal utilization of resources Natural and better land management. Among the quantitative models of soil quality index, cumulative model integrated quality index (IQI) and Nomero (NQI) index can be mentioned. Therefore, this study intends to evaluate the best quantitative and quality index model by examining and comparing two methods of selecting the appropriate criteria, Total data set (TDS) and Minimum (MDS) and the second order soil quality index, integrated quality index(IQI) and Nomero (NQI) index in Qazvin plain lands.
Material and Methods: The study area with 25220 hectares is located in east of Qazvin Province. The average annual precipitation is 275 mm and the soil moisture and temperature regimes are Thermic, Dry xeric and Weak Aridic, respectively. A total of 76 samples from the depth of 0-20 cm of the soil surface were studied and based on uniformity, soil type and land use. In this study, four types of criteria that affect the quality of soil in terms of their performance, including: upper limit, lower limit, optimal limit and descriptive function were selected. To qualify (normalize), the upper limit, lower limit and peak limit were selected. In the following, the Total Data Set (TDS) and the Minimum Data Set (MDS) set of data were used. In the TDS method, all of the measured characteristics (a total of 19 physicals, chemical and biological properties of the soil) were considered. Then, the degree of soil quality indices was determined based on the combination of TDS and MDS criteria and the final NQI and IQI quality indices.
Result and Dissection: Comparison of soil types in the region showed that the Aridisols had good, moderate and poor quality (19.35% of soil with good quality, 67.76% with moderate quality and 12.94% with poor quality), Entisols have good and medium quality (53.21% of the soil with good quality and 46.79% with moderate quality) and Inceptisols have very good, good, moderate and poor quality (96.9% Soils with very good quality, 66.73% with good quality, 15.85% with moderate quality and 13.44% with poor quality).
According to the TDS standard and the NQI model, the soils with qualities I, II and III were 30.67%, 66.86%, 47.2% of the total soils of the area (lands with poor quality soil quality were not observed in TDSNQI method). Therefore, according to this method, Aridisols has a very good, good and medium quality (13.26% of the soil with a very good quality rating, 73.88% with a good quality and 12.84% with a moderate quality grade), Entisols with The good quality (100% of the soil with good quality degree) and Inceptisols have a very good and good quality (28.11% of the soil with a very good quality grade, 71.88% with a good quality grade). The results of quantitative soil quality by using the MDS standard method and IQI model were showed, soils with very good, good, moderate and poor degree are 2.45, 16.45, 48.93 and 46.3 percent of total land area respectively.
The results of the combination of the MDS and the NQI model also showed that the soils with a very good, good and average grade are 30.67%, 66.86% and 47.2% of the total land, respectively. Also, the results of the combination of the MDS and NQI model showed that the soils with very good, good and average quality are 30.67%, 66.86% and 47.2% of the total land area respectively. The results of the evaluation based on 4 indicators showed that good quality (II) was prevalent in the studied soils and accounted for about 47% of the total area studied in Qazvin plain lands. The map of distribution of soil quality degrees, the distribution of soil degrees is relatively similar to all of four combination methods, the choice of criteria and model. By examining the linear relationship between the indices obtained from TDS and MDS criteria and the IQI and NQI indexes, it is observed that the correlation coefficient is more and more reliable than the NQI model when used in the IQI model (R2 = 0.77). So the highest correlation coefficient we observed two methods for selecting the TDS and MDS criteria when using the IQI model. In general, the results of this study indicate a better performance of the MDS criteria than TDS.
Conclusions: Therefore, the main results of this study suggest using the IQI model with the MDS selection method as the starting point in the global standard path for future studies. Special attention should be paid to the criteria chosen by the MDS. In addition, conducting a series of research into the future in order to modify the MDSIQI model can make it more relevant to international standards.
R. Taghizadeh Mehrjerdi; A. Amirian Chekan; F. Sarmadian
Abstract
There is an increasing demand for reliable large-scale soil datato meet the requirements of models for planning of land-usesystems, characterization of soil pollution, and prediction ofland degradation. Cation exchangecapacity (CEC) is among the most important soil propertiesthat are required in soil ...
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There is an increasing demand for reliable large-scale soil datato meet the requirements of models for planning of land-usesystems, characterization of soil pollution, and prediction ofland degradation. Cation exchangecapacity (CEC) is among the most important soil propertiesthat are required in soil databases. This paper applied a novel method for whole-soil profile predictions of CEC (to 1 m) across Dorudlocated in LorestanProvince. At present research, we combined equal-area spline depth functions with digital soil mapping techniques to predict the vertical and lateral variations of CEC across the study area where limited soil information exists (103 soil profiles). To model the relationship between CEC and environmental factors (i.e. Representative soil forming factors), derived from a digital elevation model and Landsat imagery, a regression tree was applied. Results indicated that some auxiliary data had more influence on the prediction model (i.e. B3 and modified catchment area). Our results also confirmed the regression tree model predicted target variable at the five specific depths with coefficient of determination of 0.84, 0.84, 0.84, 0.66, 0.27 and root mean square of 1.75, 1.84, 1.84, 2.11, and 2.16, respectively. Results showed a reasonable R2 in first four depths ranged from 0.66 to 0.84; while, it decreases to 0.27 in the last depth. Our results also confirmed that the regression tree as a predictive model, digital soil mappingtechniqueand equal area splinesare powerful tools to predict lateral and vertical variation of CEC.
