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Record W2518907930 · doi:10.5539/sar.v5n4p46

Profitability of Organic Vegetable Production via Sod Based Rotation and Conventional Versus Strip Tillage in the Southern Coastal Plain

2016· article· en· W2518907930 on OpenAlexvenueno aff
Mona Ahmadiani, Chun Li, Yaqin Liu, Esendugue Greg Fonsah, Christine Bliss, Brent V. Brodbeck, Peter Birkelund Andersen

Bibliographic record

VenueSustainable Agriculture Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTillageCrop rotationConventional tillageEnvironmental scienceMathematicsProfitability indexAgronomyCropAgricultural scienceEconomicsBiology

Abstract

fetched live from OpenAlex

<p class="sar-body"><span lang="EN-US">There are little economic data concerning the profitability of organic vegetable crops in the Southern Coastal Plain, especially in reference to sod-based rotation and tillage alternatives. A three-year experiment was conducted at the North Florida Research and Education Center-Quincy involving a crop rotation sequence of oats and rye (winter), bush beans (spring), soybean (summer) and broccoli (fall). Bush beans and broccoli were the cash crops. This paper presents analyses of the riskiness of organic production utilizing years in bahiagrass prior to initiating the crop rotation sequence and conventional tillage (CT) versus strip tillage (ST). Methods of “Risk-rated enterprise budget” and “Analyses of Variance-Covariance Matrix (ANOVA)” were utilized for determining relative profitability, and coefficient of variation was applied for measuring riskiness of each treatment. Three years of bahiagrass prior to initiating the crop rotation sequence, in combination with conventional tillage, had the highest profitability and ranked as the least risky scenario. The second most profitable treatment was conventional tillage with four years of bahiagrass. Focusing on strip tillage, four years of bahiagrass with strip-tillage ranked third in term of profitability.</span></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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.284

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 studies0.0000.000
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 head0.033
GPT teacher head0.280
Teacher spread0.247 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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