Optimization of Biofertilizer Formulation for Phosphorus Solubilizing by Pseudomonas fluorescens Ur21 via Response Surface Methodology

This study aimed to analyze and quantify the effect of different ratios of vermicompost, phosphate rock, and sulfur on P solubilization and release by Pseudomonas fluorescens Ur21, and to identify optimal levels of those variables for an efficient biofertilizer. Twenty experiments were defined by surface response methodology based on a central composite design (CCD), and the effects of various quantities of vermicompost, phosphate rock, and sulfur (encoded by -1, 0, or +1) on P solubilization was explored. The results show that the CCD model had high efficiency for predicting P solubilization (R-2 = 0.9035). The strongest effects of the included variables on the observed P solubilization were linear effects of sulfur and organic matter (vermicompost), a quadratic effect of phosphate rock, and an interactive effect of organic matter x phosphate rock. Statistical analysis of the coefficients in the CCD model revealed that vermicompost, vermicompost x phosphate rock, and phosphate rock x phosphate rock treatments increased P solubilization. The optimal predicted composition for maximal P solubilization by P. fluorescens Ur21 (at 1684.39 mg.kg(-1), with more than 90% of the added phosphate dissolved) was 58.8% vermicompost, 35.3% phosphate rock, and 5.8% sulfur. ANOVA analysis confirmed the model's accuracy and validity in terms of F value (10.41), p value (<0.001), and non-significant lack of fit

Refining and unifying the upper limits of the least limiting water range using soil and plant properties

The least limiting water range (LLWR) was introduced as an integrated soil water content indicator, measuring the impact of mechanical impedance, oxygen and water availability on water uptake and crop growth. However, a rigorous definition of the upper limit of the LLWR using plant physiological and soil physical concepts was not given. We introduce in this study an upper limit of the LLWR, based on soil physical and plant physiological properties. We further evaluate the sensitivity of this boundary to different soil and crop variables, and compare the sensitivity of the upper limit of the LLWR to previous definitions of soil water content at field capacity. The current study confirms that the upper limit of the LLWR can be predicted from knowledge of the soil moisture characteristic curve, plant root depth and oxygen consumption rate. The sensitivity analysis shows further that the upper limit of the LLWR approaches the volumetric soil water content at saturation when the oxygen consumption rate by plants becomes less than 2 A mu mol m(-3) s(-1). When plants are susceptible to aeration (e.g. potato and avocado), there is a big difference between the upper limit of the LLWR and the soil water content at field capacity, in particular for sandy soils. Results also show that the soil water content at aeration porosity corresponding to 10% cannot be considered as an appropriate upper limit of LLWR because it does not appropriately reflect the crop water requirements. Similar poor results are obtained when considering the soil water content at matric potential -0.033 MPa or when defining the soil water content at field capacity based on drainage flux rate. It is observed that the upper limit of the LLWR is higher than either soil water content at -0.033 MPa matric potential or soil water content at field capacity as based on drainage flux rate, especially in sandy soils

Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

Complete descriptions of the particle-size distribution (PSD) curve should provide more information about various soil properties as opposed to only the textural composition (sand, silt and clay (SSC) fractions). We evaluated the performance of 19 models describing PSD data of soils using a range of efficiency criteria. While different criteria produced different rankings of the models, six of the 19 models consistently performed the best. Mean errors of the six models were found to depend on the particle diameter, with larger error percentages occurring in the smaller size range. Neither SSC nor the geometric mean diameter and its standard deviation correlated significantly with the saturated hydraulic conductivity (Kfs); however, the parameters of several PSD models showed significant correlation with Kfs. Porosity, mean weight diameter of the aggregates, and bulk density also showed significant correlations with PSD model parameters. Results of this study are promising for developing more accurate pedotransfer functions

Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

Complete descriptions of the particle-size distribution (PSD) curve should provide more information about various soil properties as opposed to only the textural composition (sand, silt and clay (SSC) fractions). We evaluated the performance of 19 models describing PSD data of soils using a range of efficiency criteria. While different criteria produced different rankings of the models, six of the 19 models consistently performed the best. Mean errors of the six models were found to depend on the particle diameter, with larger error percentages occurring in the smaller size range. Neither SSC nor the geometric mean diameter and its standard deviation correlated significantly with the saturated hydraulic conductivity (Kfs); however, the parameters of several PSD models showed significant correlation with Kfs. Porosity, mean weight diameter of the aggregates, and bulk density also showed significant correlations with PSD model parameters. Results of this study are promising for developing more accurate pedotransfer functions

Application of response surface methodology for optimization of zinc elimination from a polluted soil using tartaric acid

Heavy metal wastes generated from mining activities are a major concern in developing countries such as Iran. Increasing concentrations of these metals in the soil make up a severe health hazard due to their non-degradability and toxicity. In this study, batch washing experiments were conducted in order to investigate the removal efficiency of zinc by biodegradable chelates, tartaric acid. For this purpose, soil samples were collected from the zinc contaminated soil in the region of the Angouran, Zanjan, Iran. Hence, optimization of batch washing conditions followed using a three-level central composite design approach based on the response surface methodology. The results demonstrated that the effects of pH, tartaric acid concentration, and interaction between selective factors on the zinc removal efficiency were all positive and significant (P < 0.05). An optimum zinc removal efficiency of 89.35 ±2.12% was achieved at tartaric acid concentration of 200 mM l −1 , pH of 4.46, and incubation time of 120 min as the optimal conditions. Accordingly, response surface methodology is appropriately capable to determine and optimize chemical soil washing process to remediate heavy metal polluted soil

Implications of Sediment Properties on Phosphorus Availability to the Selenastrum Capricornutum in Urmia Lake Rivers

Increasing anthropogenic loading of phosphorus (P) threatens aquatic ecosystems. The bioavailability of P in sediments for algal growth depends on several physiochemical properties. This study is aimed at selecting the best chemical extraction method to characterize P-availability for the alga Selenastrum capricornutum. Principal component analysis of the data identified two components that cover 79.3% of the total variation, and these components are dominated by particle size distribution, active calcium carbonate equivalence, and electical conductivity (EC). Many of the considered extractions are positively correlated with each other, with the exception being Bray-II. The sediments of some rivers have an Olsen-extractable P higher than 20 mg kg−1, that is considered a threshold value above which the aquatic environment may become negatively affected. The average rank order of P extraction by single extractants is: Colwell > Mehlich III > 0.1 m NaOH > Olsen > Morgan > Soltanpour and Schwab (AB-DTPA) > Bray II. The Colwell-extractable P concentrations of sediments varies from 1.44 to 88.0 mg kg−1. The Cowell extractant significantly correlates with algal growth (r2 = 0.92, P < 0.001) and gives a rough estimate of the amount of bioavailable P in sediments

Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

Complete descriptions of the particle-size distribution (PSD) curve should provide more information about various soil properties as opposed to only the textural composition (sand, silt and clay (SSC) fractions). We evaluated the performance of 19 models describing PSD data of soils using a range of efficiency criteria. While different criteria produced different rankings of the models, six of the 19 models consistently performed the best. Mean errors of the six models were found to depend on the particle diameter, with larger error percentages occurring in the smaller size range. Neither SSC nor the geometric mean diameter and its standard deviation correlated significantly with the saturated hydraulic conductivity (Kfs); however, the parameters of several PSD models showed significant correlation with Kfs. Porosity, mean weight diameter of the aggregates, and bulk density also showed significant correlations with PSD model parameters. Results of this study are promising for developing more accurate pedotransfer functions

Combining chemical and organic treatments enhances remediation performance and soil health in saline-sodic soils

Abstract We investigated the individual and synergistic impact of gypsum, elemental sulfur, vermicompost, biochar, and microbial inoculation on soil health improvement in degrading calcareous saline-sodic soils. We developed Linear and nonlinear soil health quantification frameworks to assess the efficacy of remedial practices. The combined inoculated chemical and organic treatments; gypsum + vermicompost and elemental sulfur + vermicompost with 134% (0.29 versus 0.68) and 116% (0.29 versus 0.62) increases in nonlinear index, significantly increased the efficacy of amendments compared with control. An increase in the overall soil health index ranged between 12 to 134%. Microbial inoculation further enhanced the impact of treatments on soil health. Soil health properties included in the indexes explained 29 to 87% of the variance in wheat growth. The findings bring insight into the cost-effective and environmentally sustainable practices to recover degraded saline-sodic soils. Furthermore, the introduced soil health indexes offer a quantitative evaluation of soil remediation strategies

Optimization of Biofertilizer Formulation for Phosphorus Solubilizing by Pseudomonas fluorescens Ur21 via Response Surface Methodology

This study aimed to analyze and quantify the effect of different ratios of vermicompost, phosphate rock, and sulfur on P solubilization and release by Pseudomonas fluorescens Ur21, and to identify optimal levels of those variables for an efficient biofertilizer. Twenty experiments were defined by surface response methodology based on a central composite design (CCD), and the effects of various quantities of vermicompost, phosphate rock, and sulfur (encoded by −1, 0, or +1) on P solubilization was explored. The results show that the CCD model had high efficiency for predicting P solubilization (R2 = 0.9035). The strongest effects of the included variables on the observed P solubilization were linear effects of sulfur and organic matter (vermicompost), a quadratic effect of phosphate rock, and an interactive effect of organic matter × phosphate rock. Statistical analysis of the coefficients in the CCD model revealed that vermicompost, vermicompost × phosphate rock, and phosphate rock × phosphate rock treatments increased P solubilization. The optimal predicted composition for maximal P solubilization by P. fluorescens Ur21 (at 1684.39 mg·kg−1, with more than 90% of the added phosphate dissolved) was 58.8% vermicompost, 35.3% phosphate rock, and 5.8% sulfur. ANOVA analysis confirmed the model’s accuracy and validity in terms of F value (10.41), p value (<0.001), and non-significant lack of fit