Soil Cation Exchange Capacity
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2021 ◽  
Vol 12 ◽  
Author(s):  
Peijia Zhang ◽  
Hongjia Zhang ◽  
Guoqing Wu ◽  
Xiaoyuan Chen ◽  
Nazim Gruda ◽  
...  

Fulvic acids are organic compounds widely distributed in soils, and the application of fulvic acids is thought to increase crop yield and quality. However, the effects vary among various sources and doses of fulvic acids and environmental and growth conditions of crops. Here, we investigated the effects of bioresource-derived (corn straw) fulvic acids on plant production and quality of tomato plants and soil chemical properties in soil cultures while experiments on seed germination and hydroponics were conducted to explore the underlying mechanism. Base dressing with 2.7 g kg–1 increased the yield of tomato by 35.0% at most as increased fruit number. Fulvic acids increased the concentrations of minerals, such as Ca, Fe, and Zn and the concentrations of citric, malic, and some amino acids in berries of tomato but did not affect the concentrations of soluble sugars and aromatic substances in tomato fruits. Similarly, fulvic acids at 80–160 mg L–1 increased germination rate, growth vigor, and radicle elongation of tomato seeds while it increased plant biomass, concentrations of nutrients, and root length of tomato plants in hydroponics to the greatest extent in general. The increases in yield and quality can be attributed to the improvement in root growth and, thus, increased nutrient uptake. In addition, the base application of fulvic acids improved soil cation exchange capacity and soil organic matter to an extent. In conclusion, base dressing and the addition into solution of fulvic acids at moderate doses facilitate root growth and nutrient uptake and, thus, vegetable production and quality; therefore, fulvic acids can be an effective component for designing new biofertilizers for sustainable agricultural production.


Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 985
Author(s):  
Frédéric Feder

Sugarcane cultivation is suitable for the exploitation of organic waste products. However, minimum complementary mineral input is necessary for optimal fertilisation. Control mineral fertilisation treatments with mulch (MCM) or without mulch (MC) were compared with two organic waste treatments, a pig slurry with mulch (PSM) and without mulch (PS), and a sugarcane vinasse with mulch (SVM) and without mulch (SV) on a Nitisol in French Reunion Island. The sugarcane yields obtained with the different treatments differed each year. However, no trend was observed and no significant and recurrent effect of the presence of mulch or of the different treatments was identified over the course of the 4 year experiment. Soil pHw and pH KCl measured in the different treatments increased from year 3 in with the treatments including organic waste products (PS, PSM, SV and SVM) but remained constant with the treatments including only mineral fertilisation (MC and MCM). With the exception of PS and PSM, which were significantly higher in year 4, soil organic carbon content was not modified by the treatments. Soil cation exchange capacity increased only slightly with the PS and PSM treatments from year 3 on. The differences in yields and soil properties can be explained by the nature of the organic waste products, the accumulation of nutrients after several applications, and the specific characteristics of the sugarcane crop. The improvement in soil properties from the third year on was not reflected in the yield of sugarcane because it was too weak, and the crop explores a much larger volume of soil.


Plants ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1637
Author(s):  
Mahmoud A. Abdelfattah ◽  
Mostafa M. Rady ◽  
Hussein E. E. Belal ◽  
Eman E. Belal ◽  
Rahmah Al-Qthanin ◽  
...  

During the 2019 and 2020 seasons, nutrient-deficient virgin sandy soil was examined along with the investigation of the response of Phaseolus vulgaris plants to soil application with biocompost in integration with chemical fertilizers applied to soil and plants. Four treatments (100% of the recommended NPK fertilizer dose (control), 75% NPK applied to soil + 25% foliar spray, 75% NPK applied to soil + 25% foliar spray + leguminous compost (CL), and 75% NPK applied to soil + 25% foliar spray + CL containing Bacillus subtilis (biocompost; CLB)) were applied in a randomized complete block design. The 75% NPK applied to soil + 25% foliar spray + CLB was the best treatment, which exceeded other treatments in improving soil fertility and plant performance. It noticeably improved soil physicochemical properties, including available nutrients, activities of various soil enzymes (cellulase, invertase, urease, and catalase), soil cation exchange capacity, organic carbon content, and pH, as well as plant growth and productivity, and plant physiobiochemistry, including nutrients and water contents, and various antioxidant activities. The results of the 2020 season significantly outperformed those of the 2019 season, indicating the positive effects of biofertilizers as a strategy to combine soil supplementation with NPK fertilizers and allocate a portion of NPK fertilizers for foliar spraying of plants in nutrient-deficient sandy soils.


2021 ◽  
Author(s):  
Samad Emamgholizadeh ◽  
Babak Mohammadi

AbstractSoil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R2) of 0.229 Cmol + kg−1 (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R2) of 0.335 Cmol + kg−1 (0.843) and 0.279 Cmol + kg−1 (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.


2021 ◽  
Vol 12 ◽  
Author(s):  
André Froes de Borja Reis ◽  
Luiz Moro Rosso ◽  
Larry C. Purcell ◽  
Seth Naeve ◽  
Shaun N. Casteel ◽  
...  

Biological nitrogen (N)-fixation is the most important source of N for soybean [Glycine max (L.) Merr.], with considerable implications for sustainable intensification. Therefore, this study aimed to investigate the relevance of environmental factors driving N-fixation and to develop predictive models defining the role of N-fixation for improved productivity and increased seed protein concentration. Using the elastic net regularization of multiple linear regression, we analyzed 40 environmental factors related to weather, soil, and crop management. We selected the most important factors associated with the relative abundance of ureides (RAU) as an indicator of the fraction of N derived from N-fixation. The most relevant RAU predictors were N fertilization, atmospheric vapor pressure deficit (VPD) and precipitation during early reproductive growth (R1–R4 stages), sowing date, drought stress during seed filling (R5–R6), soil cation exchange capacity (CEC), and soil sulfate concentration before sowing. Soybean N-fixation ranged from 60 to 98% across locations and years (n = 95). The predictive model for RAU showed relative mean square error (RRMSE) of 4.5% and an R2 value of 0.69, estimated via cross-validation. In addition, we built similar predictive models of yield and seed protein to assess the association of RAU and these plant traits. The variable RAU was selected as a covariable for the models predicting yield and seed protein, but with a small magnitude relative to the sowing date for yield or soil sulfate for protein. The early-reproductive period VPD affected all independent variables, namely RAU, yield, and seed protein. The elastic net algorithm successfully depicted some otherwise challenging empirical relationships to assess with bivariate associations in observational data. This approach provides inference about environmental variables while predicting N-fixation. The outcomes of this study will provide a foundation for improving the understanding of N-fixation within the context of sustainable intensification of soybean production.


Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 558
Author(s):  
Modian Xie ◽  
Hongyi Li ◽  
Youwei Zhu ◽  
Jie Xue ◽  
Qihao You ◽  
...  

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.


Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 609
Author(s):  
Fen Huang ◽  
Xiaomei Wei ◽  
Tongbin Zhu ◽  
Zhuanxi Luo ◽  
Jianhua Cao

The bioavailable contents of heavy metals in karstic soils are a subject of increasing concern since the uptake of heavy metals by plants can pose a severe threat to food safety and public health. However, the bioavailable contents of heavy metals and their effective factors are poorly understood in karst regions. Calcareous soil and red soil developed from carbonate and clastic rocks, respectively, were chosen from a typical karst region (Guilin) of southwestern China, and the total (CT) and available (CA) contents of 11 heavy metals, as well as their influencing factors in soil profiles, were investigated. The results showed that calcareous soil has greater soil organic carbon, total nitrogen, available nitrogen, available potassium, and calcium (Ca) contents than red soil, but lower available phosphorus and C:N. Acid-soluble Ca (Aca) was the dominant fraction in both types of soil. Heavy metals were highly accumulated in calcareous soil, mainly controlled by secondary enrichment in the processing of carbonate rock weathering. For the majority of metals, calcareous soil had higher CT and lower CA than red soil. According to a redundancy analysis (RDA) and Pearson correlation coefficient, the high pH and Ca content in calcareous soils were primary factors influencing both the CT and CA of the metals, especially residual Ca to CT and Aca to CA. Additionally, higher soil cation exchange capacity and clay minerals also probably improved the immobility of heavy metals.


2021 ◽  
Author(s):  
Mahmood Shahabi ◽  
Mohammad Ali Ghorbani ◽  
Sujay Raghavendra Naganna ◽  
Sungwon Kim ◽  
Sinan Jasim Hadi ◽  
...  

Abstract The potential of the soil to hold plant nutrients is governed by cation exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In the present study, a multiple model integration scheme driven by hybrid GANN (MM-GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporating in the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate, calcium equivalent (CCE), and soil organic matter (OM) were used as model inputs to predict soil CEC. By the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, Silt, and CCE) as inputs provided superior soil CEC estimates with an NSE = 0.87. The proposed MM-GANN model is a reliable intelligence based approach for the assessment of soil quality parameters intended for sustainability and management prospects.


Author(s):  
Stanley Atonya ◽  
Luke OLANG ◽  
Lewis Morara

A comprehensive undertanding of land-use/cover(LUC) change processes, their trends and future trajectories is essential for the development of sustainable land-use management plans. While contemporay tools can today be employed to monitor historical land-cover changes, prediction of future change trajectories in most rural agro-ecological landscapes remains a challenge. This study evaluated potential LUC changes in the transboundary Sio-Malaba-Malakisi River Basin of Kenya and Uganda for the period 2017-2047. The land use change drivers were obtained through a rigorous fieldwork procedure and the Logistic Regression Model (LGM) to establish key factors for the simulation. The CLUE-S model was subsequently adapted to explore future LUC change trajectories under different scenarios. The model was validated using historical land cover maps for the period of 2008 and 2017, producing overall accuracy result of 85.7% and a Kappa coefficient of 0.78. The spatial distribution of vegetation cover types could be explained partially by proximate factors like soil cation exchange capacity, soil organic carbon and soil pH. On the other hand, built-up areas were mainly influenced by population density. Under the afforestation scenario, areas under forest cover expanded further occupying 54.7% of the basin. Conversely, under the intense agriculture scenario, cropland and pasture cover types occupied 78% of the basin. However, in a scenario where natural forest and wetlands were protected, cropland and pasture only expanded by 74%. The study successfully outlined proximate land cover change drivers, including potential future changes and could be used to support the development of sustainable long-term transboundary land-use plans and policy.


Soil Systems ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
J. Bernhard Wehr ◽  
Gunnar Kirchhof

International collaboration on agronomy projects often requires the shipment of soil samples between countries to conduct analyses. However, quarantine regulations in numerous countries restrict the importing of soil samples unless they are sterilized, or analysis is carried out only in quarantine facilities, which greatly increases cost. Yet, sterilization is only an option if it does not change the soil properties. There is conflicting information about the effect of irradiation on soil chemical properties. To assess the effect of gamma irradiation on some soil chemical properties, one hundred randomly selected air-dried (40 °C) soil samples were split into two samples. One sample was left untreated and the other sample was irradiated with 50 kGy as prescribed by Australian biosecurity regulations. Commonly measured agronomic soil chemical properties were then measured and results from the non-irradiated samples were compared to the irradiated samples. The results show no effect of irradiation on soil cation exchange capacity, exchangeable cations, total carbon and nitrogen content, and DTPA-extractable Zn. Small (<5%) but statistically significant effects of irradiation were observed for pH (1:5 water), electric conductivity (EC1:5), DTPA-extractable Cu, Fe and Mn, and Colwell P. The irradiation effects on Fe were greater in the topsoil than subsoil. Considering that irradiation-induced changes to soil chemical properties were below 5%, gamma irradiation can be considered a suitable method to sterilize air-dried soil to meet import requirements, without affecting the interpretation of soil fertility reports.


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