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Record W2621529238 · doi:10.3220/rep_20_2_2014

Soil quality changes in field trials comparing organic reduced tillage to plough systems across Europe

2014· article· en· W2621529238 on OpenAlexfundno aff
Andreas Fließbach, Verena Hammerl, Daniele Antichi, Bàrberi, Paolo Bàrberi, Alfred Berner, Cornelia Bufe, Philippe Delfosse, Andreas Gattinger, Meike Grosse, Thorsten Haase, J Hess, Christophe Hissler, Philipp Koal, Andreas Kranzler, Maike Krauss, Paul Mäder, Josephine Peigné, Karin Pritsch, Endla Reintam, Andreas Surböck, Jean-François Vian, Freitas Vian, Michael Schloter

Bibliographic record

VenueCINECA IRIS Institutial research information system (University of Pisa) · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersBiovision Foundation for Ecological DevelopmentStiftung MercatorCoordination of European Transnational Research in Organic Food and Farming SystemsLiechtenstein Development ServiceBundesamt für LandwirtschaftStiftung Mercator SchweizDeutsche ForschungsgemeinschaftBundesministerium für Ernährung und LandwirtschaftEuropean CommissionBundesanstalt für Landwirtschaft und ErnährungInternational Development Research Centre
KeywordsTillageSoil qualityEnvironmental scienceSoil fertilityNo-till farmingSoil managementPloughStrip-tillSoil organic matterSoil carbonAgronomyMulch-tillMinimum tillageAgricultureSoil biodiversityAgroforestryAgricultural engineeringSoil waterSoil scienceEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

Conservation agriculture and organic farming are currently considered as environmentally friendly options for producing food. This study explores the motivations and problems of organic European farmers that apply at least two conservation techniques: (i) no-tillage, (ii) reduced tillage and/or (iii) green manure. We carried out a survey with 159 farmers located in 10 European countries. Data were analysed with a principal component analysis followed by clustering to identify groups of farmers with similar motivations and problems. The most important motivations are related to soil preservation and problems are mainly linked to agronomic conditions and crop management. There are three groups of farmers that share the same type of attitude: “atypical farmers”, “soil conservationists” and “agro-technically challenged farmers”. Further research may address in priority agronomic problems, such as weed infestation, caused by adoption of conservation agriculture in organic agriculture.

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)0.007
metaresearch head score (Gemma)0.002
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 score0.511
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.154
GPT teacher head0.342
Teacher spread0.188 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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