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Record W3118735368 · doi:10.5539/sar.v10n1p61

Technological Progress in US Agriculture: Implications for Biofuel Production

2021· article· en· W3118735368 on OpenAlexvenueno aff
Farzad Taheripour, D. Scott, Cristopher A. Hurt, Wallace E. Tyner

Bibliographic record

VenueSustainable Agriculture Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersFederal Aviation Administration
KeywordsLivestockAgricultureProductivityAgricultural economicsProduction (economics)Natural resource economicsAgricultural productivityBiofuelLand useTechnological changeFood processingBusinessEconomicsGeographyEconomic growthBiotechnologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

This paper evaluates in a holistic way major trends in US production of food, feed, and biofuel commodities over the period 1961-2014. It is motivated by literature that examines parts of the changes but does not integrate them. We develop a comprehensive data set and then conduct analysis of the major trends that emerge. We identify eight major trends and then combine them to four major themes. The first theme is the huge gain in agricultural productivity over this period. An important component of this theme is that the productivity gain was sufficient to achieve substantial total production growth as agricultural land declined over the period. Second, there has been a major transformation of the livestock sector as less efficient and more expensive beef has been replaced by more efficient and less expensive poultry. As this change has happened, the livestock sector has become more land efficient, less land used in livestock. The third major change is that US calorie production is now substantially more than the needs for food and feed. Finally, the first three major themes have enabled the fourth, which is growth of US renewable fuel production, while agricultural land declined over time.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.321
Teacher spread0.298 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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