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Record W2334711429 · doi:10.5539/sar.v5n2p65

The Influence of Bahiagrass, Tillage, and cover crops on Organic Vegetable Production and Soil Quality in the Southern Coastal Plain

2016· article· en· W2334711429 on OpenAlexvenueno aff
Christine Bliss, P. C. Andersen, Brent V. Brodbeck, David L. Wright, Steve Olson, James J. Marois

Bibliographic record

VenueSustainable Agriculture Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCover cropAgronomyEnvironmental scienceCrop rotationTillageSoil qualityPaspalum notatumCrop yieldNo-till farmingOrganic farmingWeedCropSoil fertilitySoil waterAgricultureBiologySoil science

Abstract

fetched live from OpenAlex

<p>Conventional farming utilizing bahiagrass (Papsalum notatum Flugge) in rotation with crops has been shown to increase yield, improve soil quality, and decrease weed and disease pressure. Organic production systems in the Southern Coastal Plain are challenged with limited soil fertility and a wide array of insect, disease, and weed pests. The purpose of this study was to investigate the influence of sequential years in bahiagrass and tillage (conventional and conservation) on organic vegetable yield and soil indices. After 0-4 years in bahiagrass, a crop rotation of rye and oats (winter cover crop), bush beans (spring vegetable crop), soybean (summer cover crop), and broccoli (fall vegetable crop) was implemented. Vegetable crop yields, plant biomass, plant C and N, and soil C, N, and P were measured for the four crops in the rotation over a three year period. Two years or more of bahiagrass prior to initiating the vegetable crop rotation showed positive effects on vegetable crop yields and soil quality parameters. Tillage treatments did not have a consistent effect on measured parameters. Soil C was not impacted by years in bahiagrass but was influenced by years of crop production. Potential soil N and P mineralization indicated an increase of soil organic fractions with years in bahiagrass. Available N increased after cover crops, and available P decreased with increasing years in bahiagrass.</p>

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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 designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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