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Record W2094614681 · doi:10.1016/s2095-6339(15)30013-7

The development and adoption of conservation tillage systems on the Canadian Prairies

2014· article· en· W2094614681 on OpenAlexaffabout
Lana Awada, C. W. Lindwall, B.H. Sonntag

Bibliographic record

VenueInternational Soil and Water Conservation Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTillageAgricultureConservation agricultureSoil conservationGreenhouse gasSustainabilityAgricultural machineryBusinessConventional tillageLand degradationAgricultural economicsAgroforestryNo-till farmingNatural resource economicsEnvironmental scienceGeographyAgronomyEconomicsSoil fertilitySoil waterSoil science

Abstract

fetched live from OpenAlex

One of the major agricultural innovations on the Canadian Prairies over the last 40 years has been the introduction of conservation tillage (CT). Conservation tillage-a system that includes minimum and zero tillage (ZT) -was introduced as an alternative to traditional (conventional) tillage (TT) to control soil degradation and to promote agricultural sustainability. The development and adoption of CT systems involved pioneer farmers, engineers, scientists, and farmer associations. By the end of the 1970s, CT started to take shape on the Prairies, but for a number of economic, technical, political and social reasons, the adoption of CT did not occur on any major scale before the 1990s. Today, more than 75% of the Prairie's cropland is under some form of CT with more than 50% under ZT. In this paper, the factors behind the development and adoption of conservation tillage technology on the Prairies in the period between 1930 and 2011 are reviewed. Then, some of the benefits of the adoption of CT on the Prairies are highlighted. The data show that CT and ZT became profitable for the majority of farmers during and after the 1990s, and that the increased use of CT contributed to the dramatic decrease in the area under summerfallow and to the increase in the area sown to canola and pulse crops. These changes contributed to the reduction of all forms of land degradation and to decreases in agricultural greenhouse gas (GHG) emissions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.061
GPT teacher head0.271
Teacher spread0.210 · 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

Citations88
Published2014
Admission routes2
Has abstractyes

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