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Record W4220995157 · doi:10.3389/fpls.2022.804058

Understanding the Adaptive Mechanisms of Plants to Enhance Phosphorus Use Efficiency on Podzolic Soils in Boreal Agroecosystems

2022· review· en· W4220995157 on OpenAlexaff
Muhammad Nadeem, Jiaxu Wu, Hamideh Ghaffari, Amana Jemal Kedir, Shamila Saleem, Alain Mollier, Jaswinder Singh, Mumtaz Cheema

Bibliographic record

VenueFrontiers in Plant Science · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsMcGill UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsAgronomySoil waterNutrientAgroecosystemPhosphorusEnvironmental scienceVertisolPodzolBiologyChemistryEcologyAgricultureSoil science

Abstract

fetched live from OpenAlex

Being a macronutrient, phosphorus (P) is the backbone to complete the growth cycle of plants. However, because of low mobility and high fixation, P becomes the least available nutrient in podzolic soils; hence, enhancing phosphorus use efficiency (PUE) can play an important role in different cropping systems/crop production practices to meet ever-increasing demands in food, fiber, and fuel. Additionally, the rapidly decreasing mineral phosphate rocks/stocks forced to explore alternative resources and methods to enhance PUE either through improved seed P reserves and their remobilization, P acquisition efficiency (PAE), or plant's internal P utilization efficiency (IPUE) or both for sustainable P management strategies. The objective of this review article is to explore and document important domains to enhance PUE in crop plants grown on Podzol in a boreal agroecosystem. We have discussed P availabilities in podzolic soils, root architecture and morphology, root exudates, phosphate transporters and their role in P uptake, different contributors to enhance PAE and IPUE, and strategies to improve plant PUE in crops grown on podzolic soils deficient in P and acidic in nature.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.270
Teacher spread0.180 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2022
Admission routes1
Has abstractyes

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