Asieh Naroie; Javad Zamani; Shapour Kohestani; Farideh Abbaszadeh Afshar
Abstract
Introduction: The application of biochar in soil as a method for disposal of organic wastes from environment has been considered by environmental scientists in recent years, due to the unique properties of these components. Biochar is a carbon-rich compound that is produced by burning different types ...
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Introduction: The application of biochar in soil as a method for disposal of organic wastes from environment has been considered by environmental scientists in recent years, due to the unique properties of these components. Biochar is a carbon-rich compound that is produced by burning different types of organic wastes under anaerobic or limited supply of oxygen, which called pyrolysis. Biochar due to its physicochemical properties such as porous structure, expanded specific surface area, high organic carbon content, active functional groups, and also high cation-exchange capacity could able to stabilize organic and mineral compounds. Many studies showed that the biochars enhance soil fertility and improve plant growth but if we want to recommend or apply a specific biochar as an amendment of soil, it's necessary to know about the effects of this biochar on the soil properties and growth of plant. So, the aim of this study was to find out the effect of two biochar (biochar of Date Palm's Leaves (DPL biochar) and biochar of Pistchio Harvesting wastes (PW biochar)) on the growth and heavy metals concentrations of Maize (Zea mays L.) under two different soil textures (Sandy and Sandy Loam).Materials and Methods: This study was conducted in a greenhouse condition on the growth of maize in two types of soil (Sandy and Sandy Loam) with application of 5 levels (0, 1, 2, 3 and 5% w/w) of two different types of biocahr (DPL biochar and PW biochar). Maize were cultivated in treatments for 38 days and at harvesting the shoot and root dry weight and shoot height were measured. Also, the concentration of heavy metals (including Fe, Zn, Cu, Mn, Ni, Pb, and Cd) in plant shoots were evaluated.Results and Discussion: The result showed that the growth of maize severely decreased due to the application of the biochar and the negative effect of PW biochar was more than DPL biochar. Meanwhile, the negative effect of PW biochar on plant growth in sandy soil was more than other one (i.e. Sandy Loam soil), which medium (2 and 3% w/w) and high (5% w/w) levels of this biochar caused the plant to stop growing. Also application of 5% of DPL biochar in Sandy Loam soil caused in a decrease of about 19, 69 and 72% in plant height, shoot dry weight and root dry weight of maize in compared with control (without biochar application in this soil), respectively and these ratios were about 15, 44 and 31% with application of 3% DPL biochar; while with application of 3% of PW biochar in sandy loam soil has decreased plant height, shoot dry weight and root dry weight of maize about 17, 53 and 37%, in compared to control respectively. These results approved the greater negative effect of PW biochar on plant growth. Assessment of soil salinity as the application of different levels of biochars showed that these materials increased salinity and thus had a negative effect on plant growth. In overall, the results of this study showed that the use of different biochars have different effects on plant growth, since most of biochars have high salinity, coarse-textured soils could more affected by salinity, because of the lower water holding capacity of this soils. Since, biochar is a stable substance, the results of the concentration of elements in the shoot of plants showed that the concentration of most elements not significantly affected by the application of biochar, however the increase in Fe concentration in sandy soil due to application of PW biochar, also Mn uptake in the effect of applying 1% of DPL biochar was observed. On the other hand, the results of this part of the research showed that DPL biochar at higher levels has even reduced the concentration of Mn in the plant. The results of this section also showed that the application of biochar in sandy loam soil, although it was significant on the concentration of heavy metals Pb and Cd in the plant and had slightly increased them, but their concentration was less than critical levels (dangerous) for human health.Conclusion: The effect of biochar on improving plant growth can be greatly influenced by the combined effect of biochar properties and soil conditions. The results showed that despite the many benefits of the soil application of biochar in the different scientific literatures, it is necessary to study the effect of biochar on soil properties and plant growth before applying any type of biochar in the soil.
Soil Genesis and Classification
Farideh Abbaszadeh Afshar
Abstract
Introduction Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. DSM is the computer-assisted production of digital maps of soil ...
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Introduction Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. DSM is the computer-assisted production of digital maps of soil type and soil properties. It typically implies use of mathematical and statistical models that combine information from soil observations with information contained in correlated variables and remote sensing images. Machine learning is a general term for a broad set of models used to discover patterns in data and to make predictions. Although machine learning is most often applied to large databases, it is an attractive tool for learning about and making spatial predictions of soil classes because knowledge about relationships between soil classes and environmental covariates is often poorly understood. Our objective was to compare multiple machine learning models (multinomial regression logistic, boosted regression trees and decision tree) for predicting soil great groups at Bam distinct in Kerman province. Materials and Methods The study area, Bam district was located between 58°4΄17˝ to 58°28΄8˝ E longitudes and 28°52΄51˝ to 29°9΄29˝ N latitudes (Fig. 1), at Kerman province, (Southeastern Iran). The area is surrounded by mountains (dominantly limestone and volcanic) from northwest toward southeast with major landforms included young alluvial fans and pediment, clay flat and hills. The mean annual precipitation, temperature and potential evapotranspiration are respectively 64 mm, 23.8◦C and 3000 mm with Aridic and Hyper thermic soil moisture and temperate regimes Stratified sampling scheme were defined in 100000 hectares, and 126 soil profiles were excavated and described by Key of soil taxonomy. Our objective was to perform and compare multiple machine learning models for predicting soil taxonomic classes (great group level). The models were used in this study including, multinomial logistic regression (MLR), boosted regression trees (BRT) and decision tree (DT). We used 80/20 training/testing split (80% of the pedon observations were used for model training and 20% for model testing). Kappa index (KI), overall accuracy (OC), Brier scores (BS), User accuracy (UA) and producer accuracy (PA) were used to compare model accuracy. Results and Discussion The profile description revealed the presence of two soil orders: Entisols and Aridisols that, subdivided in six suborders and eight great groups: Haplosalids, Haplocambids, Haplocalcids, Haplogypsids, Calcigypsids, Calciargids, Petrocalcids and Torriorthents. This testifies to the wide pedodiversity of the study area, considering that is characterized by the presence of eight soils great groups. Results showed that the geomorphology map contributed importantly to the prediction accuracy. This can be explained by the fact that the geomorphological surfaces have formed recently, or during a geological period with soil formation under conditions close to those of current processes in the arid regions. Terrain attributes and finally remote sensing indices after geomorphic surface were imported as predictors in the prediction. The best prediction result was obtained when characteristics derived from terrain, remote sensing and geomorphological processes were used together and when differentiation of geomorphological processes and overall heterogeneity identification and stratification of the study area was made. In areas where the distribution of predictors was more homogenous, the models can better understand and connect predictors and response. The spatial distribution of soils in the study area followed the distribution pattern of most geomorphological and terrain attributes. The results of model comparing indicated that decision tree was consistently the most accurate. The results of prediction accuracy of soil groups showed that the highest accuracy related Haplosalids, Calcigypsids and Petrocalcids soil great groups. The lowest of predictive quality was observed for Haplocalcids in three approaches. As a reliable and flexible approach, decision tree could be used successfully to prepare continuous digital soil maps. Conclusion The application of decision trees for prediction of soil types could be a promising alternative. In digital soil mapping, the best prediction result was obtained when parameters derived from terrain, remote sensing and geomorphological processes were used together and when differentiation of geomorphological processes and overall heterogeneity identification and stratification of the study area was made. In areas where the distribution of predictors was more homogenous, the models can better understand and connect predictors and response. Altogether, an extended digital terrain analysis approach and clear description of geomorphological, geological and pedological processes could be a promising key technology in future soil mapping.
