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Record W2806990741 · doi:10.1093/ajae/aay023

A Century of U.S. Farm Productivity Growth: A Surge Then a Slowdown

2018· article· en· W2806990741 on OpenAlex
Matthew A. Andersen, Julian M. Alston, Philip G. Pardey, Aaron Smith

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmerican Journal of Agricultural Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
FundersUniversity of California, DavisCooperative State Research, Education, and Extension ServiceGiannini Foundation of Agricultural EconomicsAgricultural Research ServiceUniversity of WyomingU.S. Department of Agriculture
KeywordsProductivitySlowdownAgricultural economicsEconomicsQuarter (Canadian coin)Total factor productivityAgricultural productivityAgriculturePopulation growthMultifactor productivityPopulationGeographyDemographyMacroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract U.S. farm productivity growth has direct consequences for sustainably feeding the world's still rapidly growing population, as well as U.S. competitiveness in international markets. Using a newly expanded compilation of multifactor productivity (MFP) estimates and associated partial‐factor productivity (PFP) measures, we examine changes in the pattern of U.S. agricultural productivity growth over the past century and more. Considering the evidence as a whole, we detect sizable and significant slowdowns in the rate of productivity growth in recent decades. U.S. multifactor productivity grew at an annual average rate of just 1.16% per year during 1990–2007 compared with 1.42% per year for the period 1910–2007. U.S. yields of major crops grew at an annual average rate of 1.17% per year for 1990–2009 compared with 1.81% per year for 1936–1990. More subtly, but with potentially profound implications, the relatively high rates of MFP growth during the third quarter of the century are an historical aberration relative to the long‐run trend.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.179
Teacher spread0.170 · 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