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Record W7128422641 · doi:10.7868/s3034496425120108

MODERN METHODS FOR DETERMINING THE CONTENT OF MOBILE PHOSPHORUS IN SOILS

2025· article· en· W7128422641 on OpenAlexaboutno aff
A. N. Naliukhin

Bibliographic record

VenueАгрохимия / Agricultural Chemistry · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterExtraction (chemistry)PhosphorusSoil testCarbonateSoil pH

Abstract

fetched live from OpenAlex

There are more than 20 methods for determining available phosphorus in soil in the world. One of the most difficult methodological tasks is to select a solution for soil extracts that would extract only plant-available phosphates from the soil and would not transfer low- and inaccessible phosphorus compounds into the solution. All chemical methods used usually include soil extraction with solutions of different pH, a certain soil : solution ratio, and different interaction times. In Europe alone, there are more than a dozen different methods used as standard methods for determining available phosphorus in soils. In Germany and Austria, the lactate method (CAL) is widely used for routine soil analysis, while in the USA, Canada, and the Czech Republic, the Mehlich method is used for acidic and neutral soils. It uses a multi-element extractant (a mixture of 0.2 M CHCOOH, 0.25 M NHNO, 0.015 M NHF, 0.013 M HNO, 0.001 M EDTA), which allows for the simultaneous extraction of P, K, Ca, Mg, Na, Cu, Zn, Mn and Fe. In European countries, the Olsen method is generally accepted for carbonate soils. It is based on the extraction of available soil phosphates with a 0.5 M NaHCO solution with a pH of 8.5. In Russia, the following standardized methods are used to determine available phosphorus: for sod-podzolic and gray forest soils – the method of Kirsanov (0.2 M HCl, with a soil: solution ratio of 1 : 5), for non-carbonate chernozems – the method of Chirikov (0.5 M CHCOOH, 1 : 25), for carbonate soils – the method of Machigin (1% (NH)CO, 1 : 20). In some countries, for example in Brazil, anion exchange membranes have begun to be used. In general, the availability of phosphorus for plants is mainly determined by three indicators: 1 – the concentration of PO in the soil solution (the “intensity” factor), determined in low-salt extracts (the method of Karpinsky–Zamyatina, Skofield), 2 – the amount of PO in the solid phase of the soil, which can be easily accessed by plants (the “capacity” factor), methods of Kirsanov, Chirikov, Machigin, Olsen, Mehlich, and others, 3 – the ability of the soil to maintain the concentration of PO in the soil solution at a sufficiently high level for a long time (buffer capacity of phosphorus, PBC). The most promising methods are those based on the use of anion exchange membranes, in which, due to the sorption of phosphorus from the solution by anionites, further desorption and the entry of PO from the soil occurs, which allows the most complete imitation of the absorption of phosphorus by the root systems of plants and the most complete convergence of the results of the methods with the efficiency of phosphorus fertilizers and the yield of agricultural crops. This will allow the rational use of phosphorus fertilizers due to the limited reserves of phosphate raw materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.301
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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