Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.Comment: 30pages, 9 figure

Evaluation of antioxidant properties of button mushroom in different harvest and morphological stages

This study was conducted to test the impact of flush number, mushroom size and cap openness on phenolic and flavonoid contents and antioxidant properties of button mushroom (Agaricus bisporus). Results showed that all tested facrors had a significant effect on dry matter and antioxidant properties of mushroom. The first flush had the highest dry matter in comparison with second and third flushs. Antioxiant activity and flavonoid content of mushrooms in second flush was significantly more than others but for phenol content, the first flush was the best. Surprisingly, the lowest antioxidant activity, phenol, and flavonoid contents were obseved in third flush. The highest antioxidant activity, phenol, and flavonoid content were recorded in large size, medium size, and small size of mushrooms, respectively. Cap of the mushroom showed significantly more antioxidant properties and flavanoid content, however, the phenol in stipe part was more than the cap part. Closed-cap mushrooms had significantly more dry matter and total phenol content, while no significant difference was seen in antioxidant activity and flavonoid compounds. In summary, mushrooms produced in third flush have lower dietary quality than first and second flushes, cap part of button mushroom was better than stipe and total antioxidant capacity was not affected by cap opening

The effects of acidic functional groups and particle size of biochar on Cd adsorption from aqueous solutions

The removal of Cd from the wastewater is necessary because of its harmful health effects. The practice of using biochar as a low-cost adsorbent for heavy metals removal from water bodies is common. However, the effects of total acidic functional groups and particle size class on heavy metals removal by biochar are not studied well. Therefore, this study was undertaken with the objective of determining the effects of total acidic functional groups and particle size class on Cd adsorption from aqueous solution by an empty fruit bunch biochar (EFBB) and a rice husk biochar (RHB). The results showed that there was no significant difference in the carbon content between the EFBB and RHB. However, higher quantity of total acidic functional groups was found in the EFBB compared to the RHB. The total acidic functional groups of EFBB were higher than of the RHB for the same particle size class. In contrast, the surface area of RHB was higher than the EFBB for the same size class. The Langmuir’s maximum adsorption capacity (Qmax) of EFBB was higher than RHB when compared at each particle size class. Significant correlations were observed between Qmax and the total acidic functional groups of both biochars. There were significant correlations between Qmax and the cation exchange capacity (CEC) as well. However, the correlations were non-significant between Qmax and particle size, surface area and pore volume of both biochars. It can be concluded that only the total acidic functional groups and the CEC were influential in determining the adsorption capacities of both EFBB and RHB for Cd adsorption

Effect of Different Culture Media on Broccoli (Brassica oleracea var. italica) Yield Components and Mineral Elements Concentration in Soilless Culture

Introduction: Broccoli is one of the valuable vegetables among brassicas which has received great attention throughout the world and is cultivated both in soil and soilless culture. Currently, we face restriction in high quality of the soils and water resources as two essential inputs in agriculture. Like other parts of the world, Iran is losing hundred hectares of its arable and fertile land annually due to salinity, alkalinity and waterlogging. One of the important strategies to overcome these adverse conditions is soilless culture systems. Among the different methods of soilless culture, substrate culture is more common and cheaper than others. Different kinds of organic and inorganic substances are used in soilless culture system, but the optimum mixture of growing medium is still a challenging issue. Physical and chemical characteristics of growing media can potentially affect the yield and product quality in direct and indirect ways. A good medium for soilless culture should have easy drainage, appropriate aeration, high water holding capacity and low price, as well as no weed seeds and pathogens. Therefore, this research was aimed to evaluate different prevalent growing media in broccoli soilless culture system. Materials and Methods: This experiment was conducted as an outdoor soilless culture system in outdoor hydroponic site in Sari Agricultural Sciences and Natural Recourses University (SANRU). To begin with, broccoli seeds were sown in transplanting tray, and after five weeks, the developed transplants were cultivated in growing bags in a soilless system. In this work, different mixtures of culture media were evaluated for yield component and mineral elements of broccoli. Ten kinds of different media comprising of cocopeat, perlite, sand, sawdust, sand+sawdust, sand+vermicompost, cocopeat+perlite, cocopeat+LECA, cocopeat+ pumice, and cocopeat+perlite+ vermicompost were compared in completely randomized design with tree replications. At the end of the growing season, vegetative growth and yield components of broccoli were measured. The macro nutrients including nitrogen (N), phosphorus (P), potassium (k), magnesium (Mg), calcium (Ca) and sulfur (S) were then analyzed in the harvested broccoli. Four important micro elements such as Iron (Fe), cooper (Cu), boron (B) and zinc (Zn) were measured as well. A statistical analysis was performed using analysis of variance in Statistical Analysis System (SAS) software (version 9.1) and means were compared using Duncan’s multiple range test at 0.05 and 0.01 probability levels. Results and Discussion: According to the results, culture medium showed no significant effect on plant height, dry matter and the number of auxiliary heads, while it significantly affected diameter and weight of main head (p≤0.01). The highest head diameter was seen in sand+vermicompost mixture which had no significant difference from cocopeat, cocopeat+LECA, and prlite+cocopeat+vemicompost. The mixture of sand+vermicompost resulted in the heaviest broccoli heads that were significantly greater than all other growing media used in the experiment. Since vermicompost contains some mineral elements like calcium, magnesium and phosphorus and some growth stimulators as well, mixing this substrate with sand can create an appropriate and ideal culture for root growth and development. Pure perlite and sawdust media contributed to the lowest yield with no significant differences from each other. The macro and micro nutrients of broccoli head were not significantly affected by growing medium, except for nitrogen and zinc. The highest concentration of nitrogen in broccoli head was recorded for pure perlite and sawdust which was significantly more than other media. The highest zinc concentration in broccoli head was observed in Sawdust medium (p≤0.05). A significant negative correlation was observed between plant height and three main macro nutrients (N, P and K). The negative correlation between some macro nutrients and plant growth can be related to the excessive amount of these elements in nutrient solution. Positive and significant correlation was also seen among plant height, head diameter and head weight. In other word, the tallest plants could produce bigger and heavier head. Conclusion: Based on the obtained results, it can be concluded that a mixture of organic and inorganic substances can be better than a single substance medium. On the other hand, our results showed that role of medium substances and composition is not as important as nutrient solution, so an appropriate nutrient solution with a proper rate can potentially provide all plant's needs regardless of media composition

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

publishedVersio

Assessment of Pb (II) Removal from Aqueous Solutions by Ascorbic Acid-stabilized Zero-valent Iron Nanoparticles Using Response Surface Methodology (RSM)

The growing pollution of water resources and the limited availability of water supplies have led to a growing interest by researchers to develop novel methods of water remediation and reuse. One such method is the use of ascorbic acid-stabilized zero-valent iron nanoparticles (AAS-ZVIN) for the removal of lead (Pb) from aqueous solutions. Using zero-valent iron nanoparticles stabilized with acid ascorbic under aerobic conditions, the present study was conducted to assess the efficiency of Pb removal from aqueous solutions and its optimization by the response surface methodology (RSM). For this purpose, use was made of the central composite design and the response surface methodology with the four input variables of ASS- ZVIN dose (0.5, 1, and 2 g L-1), pH (2, 5, and 7), contact time (5, 20, and 60 min), and initial Pb concentration (5, 10, and 20 mg L-1) to determine the optimal conditions for the process. Numerical optimization revealed that the optimum conditions for Pb removal (97.93%) included an ASS-ZVIN dose of 2 g L-1, an initial Pb (II) concentration of 25 mg L-1, a contact time of 60 min, and an initial solution pH of 7. The results also imply that not only does ASS-ZVIN offer a good potential for the remediation of water bodies contaminated with Pb, given its high reactivity for Pb removal, but that  the RSM optimization process can be successfully employed for the optimization of the process in question

Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation

Citrus spp. are one of the most important commercial crops with global marketing potential in the world, as in Iran. A soil management zone (MZ) as an appropriate approach is necessary to achieve sustainable production, along with improving soil management and increasing economic benefits in the commercial citrus plantations of northern Iran. As the first report, the biological and terrain attributes along with the physicochemical properties (57 soil samples, 0–30 cm) were used for MZ delineation using the integration of principal component analysis (PCA) and the fuzzy c-means clustering methods. An economic analysis based on the MZ results was also performed to determine the changes in each MZ using a relative cost (RC) value. The high correlation between soil properties and terrain attributes and the considerable spatial variation of these factors in the study area call for site-specific nutrient management. The optimal number of MZs was six and there was a significant heterogeneity variation among different MZs. The ranking of the MZs were MZ5 > MZ2 > MZ6 > MZ1 > MZ3 > MZ4 based on higher soil quality and lower costs per tree. The MZ4, MZ3, MZ1, MZ6, and MZ2 required 34.4, 30.6, 29.4, 9.77, and 9.44% more costs than MZ5 (as reference MZ) for achieving similar productivity, respectively. Therefore, this simple and cost-effective approach could be an initial step to utilize fertilizers site-specifically for data-scarce areas and reduce the soil property variability within the delineated MZs, which is fundamental for precision agriculture management

The interpolation methods and neural network to estimate the spatial variability of soil organic matter affected by land use type

The use of geostatistical methods and artificial neural network (ANN), which can predict the spatial variability of SOM is of outmost significance. The present research investigated the effects of different land use types at different altitudes on the spatial variability of SOM using the above-mentioned methods. A total of 249 combined samples from the depth of 0–15 cm on the basis of land use type and topography were collected from different parts of the research area (9545 km2). Using cross validation, the methods were compared and the best fitted one was selected on the basis of mean error (ME) and root mean square error (RMSE). The best-fitted semivariogram models for SOM (R2= 0.894) and SOC (R2= 0.761) were spherical. The cross-validation method indicated ANN as the most accurate method for the prediction of SOM and SOC with the ME and RMSE of 0.37 and 0.36 for SOM, and 0.37 and 0.35 for SOC, respectively

Functionalization of Graphene Oxide Nanosheets Can Reduce Their Cytotoxicity to Dental Pulp Stem Cells

Background. The dental pulp is a heterogeneous soft tissue that supplies nutrients and acts as a biosensor to identify pathogenic stimuli. Regeneration of the dental pulp is one of the desirable topics for researchers. Graphene oxide nanosheets (nGOs) help overexpression of the genes related to odontogenic differentiation of stem cells from dental pulps and increases attachment and proliferation of dental pulp stem cells. Despite its benefits, nGO may be considered as a threat to the environment and human health. Therefore, the purpose of this study was to evaluate the biocompatibility potential of graphene oxide (nGO), chitosan functionalized graphene oxide (nGO-CS), and carboxylated graphene (nGO-COOH) when exposed to human dental pulp stem cells (hDPSCs). Material and Methods. Some different aspects of biocompatibility of nGO, nGO-CS, and nGO-COOH were synthesized, and several intracellular effects induced by different concentrations of graphene-based nanosheets, including cell viability, intracellular oxidative damages, and various factors such as LDH, GSH, SOD, MDA, and MMP, were studied on hDPSCs. Results. According to results, IC50 was determined as 232.01, 467.81, and ≥1000 μg/mL for nGO, nGO-CS, and nGO-COOH, respectively. These results demonstrated the lower toxicity and higher cytocompatibility of nGO-CS and nGO-COOH compared to nGO. nGO-COOH not only has any adverse effect on the cell membrane and mitochondrial activity but also shows slight antioxidant activity at some concentrations. Conclusion. The findings help design safe and cytocompatible nGO derivatives for biomedical applications in dental fields

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

publishedVersio