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Record W2886329158 · doi:10.4148/2378-5977.7607

Effect of Management Practices on Double-Crop Soybean Yields

2018· article· en· W2886329158 on OpenAlexaboutno aff
Dâmaris Sulzbach Santos Hansel, Jennifer Kimball, D. E. Shoup, Ignacio A. Ciampitti

Bibliographic record

VenueKansas Agricultural Experiment Station Research Reports · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsSowingAgronomyFertilizerGrowing seasonFungicideCropYield (engineering)Biology

Abstract

fetched live from OpenAlex

Double-crop soybean has great potential to increase profits and the use of agricultural land. However, there is a gap between double-crop versus full-season soybean yields. To address this yield difference, a study evaluating different management practices on double-crop soybean was conducted. A four-site-year experiment was conducted at Ottawa, KS, during the 2016 and 2017 growing season. In both years, the soybean variety planted was Asgrow 4232 (MG 4.2). The soybean was planted right after two different wheat harvest timings (Study 1, early-wheat harvest 18–20%; and Study 2, conventional-harvest 13–14%). Seven treatments were evaluated in each of the soybean planting dates: 1) common practice; 2) no seed treatment (without seed fungicide+ insecticide treatment); 3) non-stay green (without foliar fungicide + insecticide appli­cation); 4) high seeding rate (180,000 seeds/a); 5) wide rows (30-inch row-spacing); 6) nitrogen (N) fixation (without late-fertilizer N application); and 7) kitchen sink (includes all management practices). In the 2017 season, a treatment was added with the purpose of isolating the fertilizer effect, 8) no fertilization (F). Aboveground biomass and yield were recorded. For the 2016 season, there was a different response for early and late planting in relation to yield responses. For the early planting, there were no differences in yield. However, for the late planting, high plant population, wide-rows and kitchen sink showed greater yields. For the early planting, the differences in biomass were not related to differences in yield. For the late planting, greater biomass corresponded to superior yields, except for the kitchen sink treatment that presented low biomass and greater yields, potentially via increasing biomass partitioning to the seed. For the 2017 season, biomass and yield followed the same pattern, yields increased in parallel to biomass. For the early planting, greater yields were observed for the high plant population, no nitrogen applied in reproductive R3, and kitchen sink. There were no significant differences in yield among treatments for the late planting date in 2016. However, in both years’ yields were lower for late planting dates when compared with the early planting.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.072
GPT teacher head0.380
Teacher spread0.308 · 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
Published2018
Admission routes1
Has abstractyes

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