Methods of Ni Determination in Soils and Plants

Nikoli, Thomai

doi:10.1201/9781315154664-9

Cited by 2 publications

(2 citation statements)

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“…The bioavailability of Ni in all prediction models is statistically significantly influenced by pH, as confirmed by the results of other studies [24,25,45]. Clay also statistically significantly affects the bioavailability of Ni, as in many previous studies [25,45,46,48,49]. In some prediction models of Zhang et al [43], in cases where soils were either acid or alkaline, the clay had a statistically significant effect on Ni bioavailability.…”

Section: Nickelsupporting

confidence: 84%

Prediction Models for Bioavailability of Mn, Cu, Zn, Ni and Pb in Soils of Republic of Serbia

Dinić¹,

Maksimović²,

Stanojković-Sebić³

et al. 2019

Agronomy

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The bioavailability of trace elements (TEs) is one of the major factors for successful plant production and environmental protection. The aim of this study was to determine the extent to which TEs are bioavailable and which of the basic soil parameters affect bioavailability. The survey included agricultural soil samples taken from 240 locations on the territory of the Republic of Serbia, where the soil analytics were carried out. On the basis of the analyzed data the prediction models were derived based on the Freundlich model, showing the dependence between trace elements (TEs) extracted using the DTPA buffer solution in relation to the trace elements extracted using an aqua regia, the organic matter content (SOM), the clay fractions content, and soil pH. On one part of the samples, the prediction models were separated on the basis of a suspension for determining the pH in H2O and 1M KCl. The model was applied for the following TEs: Mn, Ni, Pb, Zn, and Cu. The content of the pseudo total forms of TEs statistically significantly influenced the bioavailability of TEs in all prediction models for all studied elements. The pH value statistically significantly affected the bioavailability of Ni, Mn, Pb, and Cu also in all prediction models. The impact of SOM and clay varied depending on the model and TEs. Multiple linear regression showed that the prediction models for TE Cu (R2 = 0.763–0.848) were the most reliable and that the bioavailability of Cu was significantly influenced by all the studied soil parameters except clay. Reliable results were also shown by the prediction models for TE Pb, but the values of the determination coefficient and investigated parameters that influenced the bioavailability varied depending on the model. The derived models for TE Mn, Zn, and Ni were less reliable (R2 is approximately 50% or less), and the effect of the tested parameters on bioavailability varied depending on the model and TEs.

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Section: Nickelsupporting

confidence: 84%

Prediction Models for Bioavailability of Mn, Cu, Zn, Ni and Pb in Soils of Republic of Serbia

Dinić¹,

Maksimović²,

Stanojković-Sebić³

et al. 2019

Agronomy

View full text Add to dashboard Cite

show abstract

“…Consistent with the results of this study, the results of other studies showed that clay was a statistically significant predictive parameter, or was correlated with bioavailable TEs (Mn: [56,89]; Ni: [56,96,100,101]; Pb: [55,90]; Zn: [48,55,89,90]; Cu: [55][56][57]84,89]) but, on the contrary, it had no effect on prediction or was not correlated (Mn: [55,56,84,90]; Ni: [48,54,56,84,90]; Pb: [48,56,57,84]; Zn: [56,57,84]; Cu: [48,56,90]), which depended on TEs and set research hypotheses.…”

Section: Soil Type Estimated Regression Model For Tesupporting

confidence: 92%

Influence of Soil Type on the Reliability of the Prediction Model for Bioavailability of Mn, Zn, Pb, Ni and Cu in the Soils of the Republic of Serbia

Maksimović¹,

Pivić²,

Stanojković-Sebić³

et al. 2021

Agronomy

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The principles of sustainable agriculture in the 21st century are based on the preservation of basic natural resources and environmental protection, which is achieved through a multidisciplinary approach in obtaining solutions and applying information technologies. Prediction models of bioavailability of trace elements (TEs) represent the basis for the development of machine learning and artificial intelligence in digital agriculture. Since the bioavailability of TEs is influenced by the physicochemical properties of the soil, which are characteristic of the soil type, in order to obtain more reliable prediction models in this study, the testing set from the previous study was grouped based on the soil type. The aim of this study was to examine the possibility of improvement in the prediction of bioavailability of TEs by using a different strategy of model development. After the training set was grouped based on the criteria for the new model development, the developed basic models were compared to the basic models from the previous study. The second step was to develop models based on the soil type (for the eight most common soil types in the Republic of Serbia—RS) and to compare their reliability to the basic models. From the total number of developed models by soil type (80), 75% were accepted as statistically reliable for predicting the bioavailability of TEs by soil type and 70% of prediction models had a higher determination coefficient (R2), compared to the basic models. For the Fluvisol soil type, all prediction models were accepted, while the least reliable prediction was for the Planosol type. As in the previous study of bioavailability prediction for TEs, the prediction models for Cu stood out, with more than half of the models with R2 greater than 0.90. Results of this study indicated that the formation of a testing set by soil type derives models whose predictions are more reliable than the basic ones. To improve the performance of prediction models, it is necessary to include additional physicochemical parameters and to conduct an adequate analysis of extensive testing sets with more comprehensive statistical techniques.

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Methods of Ni Determination in Soils and Plants

Cited by 2 publications

References 1 publication

Prediction Models for Bioavailability of Mn, Cu, Zn, Ni and Pb in Soils of Republic of Serbia

Prediction Models for Bioavailability of Mn, Cu, Zn, Ni and Pb in Soils of Republic of Serbia

Influence of Soil Type on the Reliability of the Prediction Model for Bioavailability of Mn, Zn, Pb, Ni and Cu in the Soils of the Republic of Serbia

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