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Record W3093931532 · doi:10.4148/2378-5977.7980

Cation Exchange Resins as Indicator of In-Season Potassium Supply for Soybean in Kansas

2020· article· en· W3093931532 on OpenAlexaboutno aff
D. A. Charbonnier, Marta Jordana Arruda Coelho, Dorivar A. Ruiz Diaz

Bibliographic record

VenueKansas Agricultural Experiment Station Research Reports · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsFertilizerPotassiumCation-exchange capacityNutrientAdsorptionSoil waterWater contentSoil testMoistureAgronomyEnvironmental scienceChemistryGrowing seasonIon exchangeSoil nutrientsAnimal scienceSoil scienceIonBiologyGeology

Abstract

fetched live from OpenAlex

The use of ion-exchange resins to measure soil nutrient availability has potential applications for fertilizer recommendations. The objective of this study was to evaluate the relationship between potassium (K) adsorption by cation exchange resins (CER) and K uptake by soybean in field conditions. The study was conducted at two locations in Kansas during 2019. Two treatments were selected to evaluate the CER. Treatments included a check (0 lb K2O/a) and a high K rate with 150 lb K2O/a applied pre-plant and incorporated. The Plant Root Simulator (PRS, Western Ag Innovations, Saskatchewan, Canada) was used as an indicator of in-season K supply to soybean. In addition, whole plant samples were collected at V4, R2, R4, and R6 stages to measure plant K uptake. Soil moisture content was calculated based on soil samples collected at the beginning and end of each burial period. The CER was able to adsorb more K (measured as cumulative adsorption) when K fertilizer (150 lb K2O/a) was applied. Data showed a positive relation between CER values and soil moisture content. Preliminary results from this study suggest that CER can be used as an indicator of K supply, particularly in soils with low soil test K levels.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.330
Teacher spread0.273 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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