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Record W2750890345 · doi:10.4148/2378-5977.7438

Soybean: Genetic Gain × Fertilizer Nitrogen Interaction

2017· article· en· W2750890345 on OpenAlexaboutno aff
Osler Ortez, Fernando Salvagiotti, Eric Adee, Juan Martín Enrico, Ignacio A. Ciampitti

Bibliographic record

VenueKansas Agricultural Experiment Station Research Reports · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
FundersFluid Fertilizer Foundation
KeywordsFertilizerNitrogen fixationGrowing seasonYield (engineering)NitrogenAgronomyGenotypeInoculationBiologyHorticultureChemistryGene

Abstract

fetched live from OpenAlex

The United States (US) and Argentina (ARG) account for more than 50% of the global soybean production. Soybean yields are determined by the genotype, environment, and management practices (G × E × M) interaction. Overall, 50-60% of soybean nitrogen (N) demand is usually met by the biological nitrogen fixation (BNF) process. An unanswered scientific question concerns the ability of BNF process to satisfy soybean N demand at varying yield levels. The overall objective of this project was to study the contribution of N via utilization of different N strategies, evaluating soybean genotypes released in different eras. Four field experiments were conducted during the 2016 season: Ottawa (east central Kansas, US), Ashland Bottoms (central Kansas, US), Rossville (central Kansas, US), and Oliveros (Santa Fe province, Argentina). A wide variety of historical and modern soybean genotypes were used (from the 1980s, 1990s, 2000s and 2010s release decades) in the US and ARG, all tested under three N management strategies (S1: non-N applied but inoculated, S2: all N provided by fertilizer, and S3: late-N applied) and all seeds inoculated. At Ottawa, the study was planted in an area without previous soybean history with yields ranging from 21 to 30 bu/a. Modern genotype (2010) increased yields by 15% relative to the other varieties. As related to the N management approach, higher yields occurred when the N nutrition was based on S2 (overall 10% increase). At Ashland Bottoms, yields ranged from 47 to 65 bu/a, and the 1990s variety out-yielded the rest of the varieties by 13%. There was not statistical significance for N management at this location. At Rossville, yields ranged from 37 to 85 bu/a, with higher yields observed for the modern genotype (released after 2010). Regarding N strategies, S2 increased yields by 18% compared to S1. At ARG, yield ranged from 40 to 74 bu/a, with modern soybean varieties (released after 2010) yielding 34% greater than the rest of the varieties. Nitrogen application S2 increased yields by 5% when compared to the S1 strategy. Relative to yield potential, yield levels in Argentina were similar to those in central Kansas (Ashland Bottoms and Rossville).

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 categoriesScience and technology studies
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 score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.363
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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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