Soil science
M. Nael; S.S. Salehi; J. Hamzei; M. Zandi Baghche-Maryam
Abstract
IntroductionConservation agriculture (CA), as a sustainable cultivation system, aims at efficient use of natural resources with least environmental impacts, while achieving food security through increasing yield and crop diversification. CA consists of three main principles: 1- reduction or elimination ...
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IntroductionConservation agriculture (CA), as a sustainable cultivation system, aims at efficient use of natural resources with least environmental impacts, while achieving food security through increasing yield and crop diversification. CA consists of three main principles: 1- reduction or elimination of mechanical soil disturbance; 2- maintaining a permanent cover of crop residues on soil; and 3- diversification of crops. However, the total area under CA in Iran is less than 5% of arable lands. In Hamedan province, CA is mostly implemented in rainfed farming. Therefore, there is a necessity to expand CA in irrigated areas. Nonetheless, a lack of sufficient technical and local knowledge about CA acts as a barrier for its expansion in irrigated lands. Despite the large body of research conducted on CA, there is no detailed information about the combined effects of cover crops and conservation tillage systems on soil functioning and corn productivity in semi-arid regions of Hamedan province. Therefore, our aim was to study three-year effects of conservation tillage practices (no tillage and minimum tillage) and cover crops (hairy vetch and grass pea) on selected soil quality indicators and yield components of corn in a clay loam soil of a semi-arid region in Hamedan. Materials and Methods Combined effects of various tillage practices and cover crops on selected soil quality indicators and corn productivity were examined in a three-year experiment conducted in the research field of Bu-Ali Sina University. A factorial experiment in the basis of randomized complete block design with 3 replications and 2 factors were carried out, in which three levels of tillage practices (no tillage (NT), minimum tillage (MT), and conventional tillage (CT)), and three levels of cover crops (hairy vetch (V), grass pea (L), and no cover crop) were the treatments. Surface soil samples (0-15 cm) were collected two weeks after corn harvesting in the third year of experiment. Total organic carbon (TOC), organic carbon stock (CS), active carbon (AC), carbon management index (CMI), basal respiration (BR), alkaline phosphatase activity (APA), bulk density (BD), mean weight diameter of water-stable aggregates (MWD), and available phosphorous (P) and potassium (K) were determined. Corn yield components (including number of kernel rows per corn, number of grains per corn row, ear cob weight, hundred weight of grains, ear weight, grain weights per ear, biological yield and grain yield) were measured.Results and DiscussionThe highest TOC (0.96%), CS (18.7 ton/ha), AC (398 mg/kg), CMI (74.8), BR (0.118 mgCO2/g.d) and MWD (1.82 mm) were observed in MT treatment. However, no significant difference was detected between MT and CT in terms of AC, CS and CMI. Moreover, the lowest TOC (0.74%) was measured in NT, which showed no significant difference with CT treatment (0.83%). Reduced destruction of soil structure coupled with the increased MWD, and increased inputs of crop residues through MT, resulted to the protection of organic matter against microbial decomposition. Soil structuring, represented by BD, was improved under conservation tillage treatments (NT and MT).Among cover crops, hairy vetch treatment demonstrated the highest TOC (1.0%), CS (19.5 ton/ha), AC (427 mg/kg), CMI (80.3) and MWD (1.73 mm). However, these indicators, except CMI, were not significantly different between the two cover crops. On the contrary, these indicators were lowest in the control (no cover crop). Moreover, AC and CMI were not significantly different between grass pea and the control. Carbon stock was increased by 54 and 40% in hairy vetch and grass pea treatments, respectively, relative to the control. In general, cover crop cultivation combined with conservation tillage practices introduced additional biomass to the soil which in turn improved soil organic matter over time and enhanced soil quality.The lowest amounts of biological yield (1663 g/m2), grain yield (507 g/m2), hundred weight of grains 11.0 g), ear weight (91.4 g), grain weights per ear (62.9 g), and number of kernel rows per corn (13) were measured in CT system. In contrast, the highest grain yield (637 g/m2), hundred grain weight (13.6 g), ear weight (108.4 g), and grain weights per ear (81.9 g) were measured in NT treatment. However, the biological yield showed no significant difference between NT and CT. Soil quality improvement in conservation tillage treatments explains the enhancement of certain yield components. Biological yield and number of grains per row demonstrated significant difference between cover crop treatments; the maximum of biological yield (2103 g/m2) and of number of grains per row (44) was measured in hairy vetch treatment. Moreover, the lowest of biologigal yield (1589 g/m2) was observed in the control (no cover crop) treatment. Conclusions All soil quality indicators, except available P, were improved under MT as compared with CT. Our three-year study revealed that among conservation tillage treatments, MT improved majority of soil quality indicators compared to NT. Therefore, minimum tillage practice seems to be more sustainable in this study area. Conservation tillage treatments (MT and NT) also enhanced corn grain yield, grain weights per ear and number of grain rows per ear compared to to the CT. Both cover crops improved most soil quality indicators. Moreover, both cover crops induced significant effect on biological yield, although hairy vetch was more effective than grass pea. As a whole, the integration of minimum tillage with hairy vetch cover crop is considered as a sustainable cropping system for the improvement of soil quality and corn yield in this area.
Soil science
Niloofar Koosha; Kyumars Mohammadi Samani; Vahid Hosseini
Abstract
IntroductionA large part of forest and woodland ecosystems in Iran have been located in arid and semi-arid areas which low level of soil organic carbon (SOC) is considered as one of the main problems. Millions of trees together that make forest ecosystems, play a major role in carbon sequestration and ...
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IntroductionA large part of forest and woodland ecosystems in Iran have been located in arid and semi-arid areas which low level of soil organic carbon (SOC) is considered as one of the main problems. Millions of trees together that make forest ecosystems, play a major role in carbon sequestration and can sequester it in the form of biomass, above ground in plants and also underground in plants root or in the soil. Forest ecosystems play a significant role in absorbing and reducing greenhouse gases and therefore, can play a crucial role in decreasing global warming. Soil is one of the great sources of carbon storage, which plays a significant role in the atmospheric carbon deposition and dioxide gas. The carbon stored in the soil changes under some important driving factors such as: land use change, animal grazing, pollarding, exploitation (included forest harvesting), topography and forest trees, and types. One of the main sources of income for forest stakeholders in Zagros area is the Zagros oak forest. These people livelihoods are heavily dependent on natural resources, especially forest, known as a kind of traditional land use system called “Galazani”. Each family, in this system, has its own common ownership and manages their proprietorships called “Gallajar” which is a part of the woodlands and use some kind of traditional silvopastoral techniques to use these areas. Dominant livestock in the most part of theses area are goats and sheep. In the growing season, they usually feed on ground vegetation and in the winter time, they use dried oak leaves (leaf hay) that is stored before on some special trees call “Daar-Galla”. In the northern part of Zagros oak forest (Kurdistan province), there are some very special stands that are found around every village called sacred groves and are totally intact because of some spiritual values and taboos. There are no exploitation and grazing and even land use changing in these areas, and they show the real undisturbed forest lands in Zagros. The aim of this research was to study and compare soil carbon stock and some essential soil properties in sacred groves and pollarded forests (Gallajar) of northern Zagros forests in order to obtain more precise data in soil after high exploitation and pollarding.Materials and MethodsThe average annual rainfall in 25 recent years in the study is 690 mm and the average annual temperature is 14.2 degrees Celsius. The dominant trees species in the region are Lebanon oak, Aleppo oak and Persian oak. To conduct this investigation, three study areas included both sacred groves and Gallajars, in three main slope aspects including north, east and south facing aspects, were chosen. Then six plots (10 a) were randomly selected in each area and tree canopy (%) and litter percentage were determined in the field. Soil samples took in two depths (0-15 and 15-30 cm) in the center of each plots and then bulk density (BD) and some chemical soil properties included soil organic carbon, soil carbon stock, total nitrogen (N), phosphorus (P), potassium (K), electrical conductivity (EC) and pH were measured in the soil laboratory. A factorial randomized complete block design was used to analysis soil data.Results and DiscussionThe results showed that there were significant differences between soil depths for studied soil properties except BD, N and K and also there were significant differences in various slope aspects in studied parameters. However, no such a trend was observed in soil N and EC. The results also revealed that pollarding had significant effects on all studied soil properties. In addition, all studied soil properties including SOC stock, N, P, K and EC in sacred groves was higher than Gallajars while pH and BD were increased in pollarded areas. The amount of SOC stock, N, P and EC were greater at depth 0-15 compared to depth of 0-15 cm while, pH showed lower amount in the surface soil layer and K and BD had no significant differences in the two studied soil layers. SOC stock in northern, eastern and southern slope aspect were 72.6, 48.2 and 45 tons/ha, respectively. Pollarding and livestock grazing in Gallajars caused a significant decrease in tree canopy and, as a result, the litters on the grounds also reduced. Therefore, it seems that the reduction of trees and canopy cover affected soil properties significantly and reduced SOC stock meaningfully in the long term. Other essential chemical soil properties were also lower in Galajars compared to sacred groves.ConclusionFinally, we can claim that, some factors including pollarding and grazing can significantly reduce SOC stock and other studied soil properties in this research. On the one hand, people are using these forest areas as grazing pastures and also for pollarding trees to fed their livestock and the government could not have convinced them not to pollard the trees and, on the other hand, the results in this study showed that these pollarding operations are affecting forest stands and forest soil chemical properties and SOC stock significantly and reduce their quality considerably. It can be suggested that some new management treatments should be done in these forest areas through the training of local people, preparing sufficient fodder resources and providing enough facilities by the government to reduce pollarding by stakeholders. As a result, the natural process of production and decomposition of organic matter may be controlled in a better way, so that, the soil quality and carbon storage in these forests to be improved in the long term.
Soil science
Hamid Reza Matinfar; M. Jalali; Z. Dibaei
Abstract
Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques ...
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Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques to investigate the amount of carbon to soil using remote sensing data has been widely considered. Accordingly, the aim of this study was to investigate the feasibility of estimating soil organic matter using satellite imagery and to assess the ability of spectral and terrestrial data to model the amount of soil organic matter.Materials and Methods: The study area is located in Lorestan province, and Sarab Changai area. This area has hot and dry summers and cold and wet winters and the wet season starts in November and ends in May. A total of 156 samples of surface soil (0-30 cm) were collected using random sampling pattern. Data were categorized into two categories: 80% (117 points) for training and 20% (29 points) for validation. Three machine learning algorithms including Random Forest (RF), Cubist, and Partial least squares regression (PLSR) were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI measurement images, and in order to reduce the volume of data, the principle component analysis method (PCA) was used to select the features that have the greatest impact on quality.Results and Discussion: The results of descriptive statistics showed that soil organic carbon from 0.02 to 2.34% with an average of 0.56 and a coefficient of variation of 69.64% according to the Wilding standard was located in a high variability class (0.35). According to the average amount of soil organic carbon, it can be said that the amount of soil organic carbon in the region is low. At the same time, the high value of organic carbon change coefficient confirms its high spatial variability in the study area. These drastic changes can be attributed to land use change, land management, and other environmental elements in the study area. In other words, the low level of soil organic carbon can be attributed to the collection of plant debris and their non-return to the soil. Another factor in reducing the amount of organic carbon is land use change, which mainly has a negative impact on soil quality and yield. In general, land use, tillage operations, intensity and frequency of cultivation, plowing, fertilizing, type of crop, are effective in reducing and increasing the amount of soil organic carbon. Based on the analysis of effective auxiliary variables in predicting soil organic carbon, based on the principle component analysis for remote sensing data, it led to the selection of 4 auxiliary variables TSAVI, RVI, Band10, and Band11 as the most effective environmental factors. Comparison of different estimation approaches showed that the random forest model with the values of coefficient of determination (R2), root mean square error (RMSE) and mean square error (MSE) of 0.74, 0.17, and 0.02, respectively, was the best performance ratio another study used to estimate the organic carbon content of surface soil in the study area.Conclusion: In this study, considering the importance of soil organic carbon, the efficiency of three different digital mapping models to prepare soil organic carbon map in Khorramabad plain soils was evaluated. The results showed that auxiliary variables such as TSAVI, RVI, Band 10, and Band11 are the most important variables in estimating soil organic carbon in this area. The wide range of soil organic carbon changes can be affected by land use and farmers' managerial behaviors. Also, the results indicated that different models had different accuracy in estimating soil organic carbon and the random forest model was superior to the other models. On the other hand, it can be said that the use of remote sensing and satellite imagery can overcome the limitations of traditional methods and be used as a suitable alternative to study carbon to soil changes with the possibility of displaying results at different time and space scales. Due to the determination of soil organic carbon content and their spatial distribution throughout the region, the present results can be a scientific basis as well as a suitable database and data for the implementation of any field operations, management of agricultural inputs, and any study in sustainable agriculture with soil properties in this area. In general, the results of this study indicated the ability of remote sensing techniques and random forest learning model in simultaneous estimation of soil organic carbon location. Therefore, this method can be used as an alternative to conventional laboratory methods in determining some soil characteristics, including organic carbon.
Parisa Lahooti; Seyed Mostafa Emadi; mohammad ali bahmanyar; Mehdi Ghajar Sepanlou
Abstract
Introduction: Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct ...
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Introduction: Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct changes with atmosphere through the photosynthesis and the SOC decomposition. The depletion of C storage not only exacerbates the risk of soil erosion but also reduces agricultural production. An accurate knowledge of regional SOC contents and stocks and their spatial distribution are essential to optimize the soil management and land-use policy for SOC sequestration. Today, digital soil mapping methods such as geostatistics and artificial neural network (ANN) have focused more on SOC contents and stocks mapping. Geostatistics is a robust tool widely applied to model and quantify soil variation and analyze the spatial variability of SOC in large scale. The ANN as a nonlinear technique has been received much less attention for modeling SOC contents and stocks. Therefore, in this study, we aimed to develop and compare the performance of ordinary Kriging, co-kriging, inverse distance weighting (IDW) and artificial neural network models in predicting and mapping the SOC contents and stocks in East and Southeast of the Kohgiluyeh and Boyer-Ahmad province, southern Iran.
Materials and Methods: The composite soil samples were collected randomly from the 0-15 cm soil depths at 204 sampling sites at different land uses in east and southeast of the Kohgiluyeh and Boyer-Ahmad province. The collected soil samples were air-dried, ground, and sieved to pass through a 2 mm mesh. Soil properties such as organic carbon contents and stocks, pH, electrical conductivity (EC), bulk density (BD) and soil texture were determined according to the standard analysis protocols. The normality tests were done according to the Kolmogrov–Smirnov method, and the variability of SOC contents and stocks were analyzed by the classical statistics (mean, maximum, minimum, standard deviation, skewness, and coefficient of variations). The digital elevation model (DEM), slope gradient, precipitation and temperature and Normalized Difference Vegetation Index (NDVI) were used as co-variables (auxiliary data). The NDVI was obtained by the remotely sensed data of LANDSAT 8. The geostatistical parameters were calculated for each soil property as a result of corresponding semivariogram analysis. The spatial prediction maps of soil properties were generated by ordinary kriging (OK), cokriging (Co-K) inverse distance weighting (IDW) with powers of 1, 2, 3, 4 and 5 as well as the Artificial Neural Network (Multilayer Perception model, MLP) methods. The mentioned interpolation methods were used to prepare the SOC spatial distribution maps by using the 80 % of data as the training datasets. The prediction results were then evaluated by the validation data set (20 % of all data). The differences between the observation and prediction values were evaluated by Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (R2) and Concordance Correlation Coefficient (CCC). The spatial distribution maps of the SOC contents and stocks in the study area were finally developed by ArcGIS 10 software.
Results and Discussion: The SOC content for all samples largely varied from 0.20 to 3.96 % .The high coefficient of variation of 53.38 % demonstrates the strong spatial variation of SOC content in the study area. The SOC stocks had also a relatively high variability compared with other soil properties. Such strong variation could be attributed to the diverse soil types, land covers and other environmental conditions across the study area. The average SOC content for forest land use was significantly higher than the other land uses. The intensive tillage in cropland soils appears to have induced the acceleration of organic carbon oxidations leading to the lowest SOC contents and stocks. By increasing the mean precipitation within our study area (in eastern and northeastern regions), the SOC contents and stocks increased significantly. The inverse trend was, however, observed for temperature implying the fact that the higher the temperature, the lower the SOC. Gaussian model was found to be the best model for parameters such as SOC contents and stocks due to the lowest RSS and R2.Overall, the results denoted the higher ability of ANN compared to geostatistical techniques (cokriging, kriging and IDW methods) in estimating both soil organic carbon contents and stocks. According to the results, ANN (MLP) method with one hidden layers with 50 neurons performed better in estimating soil organic carbon contents and stocks atunsampled points, whereas the largest errors were obtained for IDW method.
Conclusions: The good performance of ANN method can be attributed to the division of the study area and the capability of ANN to capture the nonlinear relationships between SOC and environmental factors i.e. slope, DEM, precipitation, temperature and NDVI. The results suggest that the proposed structural method for ANN can play a vital role in improving the prediction accuracy of SOC spatial variability in large scale.
shahrokh fatehi; jahangard mohammadi; Mohammad Hassan Salehi; aziz momeni; Norair Toomanian; Azam Jafari
Abstract
Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not ...
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Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not only an understanding of how processes operates at different scales and how they can be linked across scales but also gathering more information at finer spatial resolution. This paper presents results of different downscaling techniques taking soil organic matter data as one of the main and basic environmental piece of information in Mereksubcatchment (covered about 24000 ha) located in Kermanshah province. Techniques include direct model and point sampling under generalized linear model, regression tree and artificial neural networks. Model performances with respect to different indices were compared.
Materials and Methods: legacy soil data is used in this research, 320 observation points were randomly selected. Soil samples were collected from 0-30 cm of the soil surface layer in 2008 year. After preliminary data processing and point pattern analysis, spatial structure information of organic carbon determined using variography. Then, the support point data were converted to block support of 50 m by using block ordinary kriging. Covariates obtained from three resources including digital elevation model, TM Landsat imagery and legacy polygon maps. 23 relief parameters were derived from digital elevation model with 10m × 10m grid-cell resolution. Environmental information obtained from Landsat imagery included, clay index, normalized difference vegetation index, grain size index. The image data were re-sampled from its original spatial resolution of 30*30m to resolution of 10m*10m. Geomorphology, lithology and land use maps were also included in modelling process as categorical auxiliary variables. All auxiliary variables aggregated to 50*50 grid resolutions using mean filtering. In this study Direct and point sampling downscaling techniques were used under different statistical and data mining algorithms, including generalized linear models, regression trees and artificial neural networks. The direct approach was implemented here using generalized linear models, regression trees and artificial neural networks in following three steps, (i) creating the spatial resolution of 50m*50m averaged over 10m*10m grid resolution environmental variables within each coarse grid resolution, (ii) establishing relationships between these coarse grid resolutions of 50m*50m environmental variables and soil organic carbon using GLMs, regression tree and neural networks and (iii) using parameter values gained in step 2 in combination with the original 10m*10mgrid resolution environmental variables to produce predictions of soil organic carbon with10m*10m grid resolution. In point sampling approach, within each coarse resolution (50m*50m), a fixed number of fine grid resolution (10m*10m) were randomly selected to calibrate models at high resolution. In this study, 5 fine grid resolutions (20% fine grid cell within each coarse grid cell) randomlywere sampled at. Then, each selected point overlied on an underlying fine-resolution grid and recorded its environmental variables and averaged fine grid resolution (10m*10m) within their corresponding coarse grid resolution (50m*50m). To calibrate model parameters, these averaged environmental variables were used. The calibrated parameters applied to fine-resolution environmental data in order to predict soil organic carbon at spatial resolution of 10m*10m. The prediction accuracy of the resulting soil organic carbon maps was evaluated using a K-fold validation approach. For this purpose, the entire dataset was divided into calibration (n = 240) and validation (n = 80) datasets four times at random. Prediction of soil organic carbon using calibration datasets and their validation was conducted for each split, and the average validation indices are reported here. The obtained values of the observed and predicted SOC were interpreted by calculating Adjusted R2 and the root mean square error (RMSE).
Results and Discussion: Point pattern analysis showed the sampling design is, generally, representative relative to geographical space .A semi-variogram was used to drive the spatial structure information of soil organic carbon. We used an exponential model to map soil organic carbon using block kriging. Grid resolution block kriging map was 50m*50m. Since the distribution of organic carbon variable and covariates were normal or close to normal for run generalized linear models selected Gaussian families and identity link function. The validation results of this model in point sampling was slightly (Adjusted R2=0.57 and RMSE=0.22) better than the direct method (Adjusted R2 =0.47 and RMSE=0.26).The results of modelling using regression tree in point sampling approach (Adjusted R2 =0.57and RMSE=0.22) is very close to the direct method (Adjusted R2 =0.57 and RMSE=0.23).In implementation of neural networks, the combination of the number of neurons and learning rate for direct downscaling method were obtained 10 and 0.10, respectively and for point sampling downscaling method were, 20 and 0.1 The results of validation obtained from the implementation of this model in point sampling approach (Adjusted R2 =0.45 and RMSE=0.27) is very close to the direct method (Adjusted R2 =0.47 and RMSE=0.28).Validation results indicated that in both downscaling approaches, regression tree (Adjusted R2=0.57, root mean square root (RMSE) =0.22-0.23) has higher accuracy and efficiency better than generalized linear models (Adjusted R2=0.49-0.57, RMSE=0.22-0.26) and neural network (Adjusted R2=0.45-0.47, RMSE=0.27-0.28).
Conclusion: In general, the results showed that the efficiency and accuracy of the sampling point approach is slightly better than the direct approach. Validation results indicated that in both downscaling approaches, regression tree has higher accuracy and performed better than neural network and generalized linear models. However, it is required to perform more research on the different ways of downscaling digital soil maps in the future.
