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Record W4388145190 · doi:10.13031/ja.15518

Effectiveness of Residue and Tillage Management on Runoff Pollutant Reduction from Agricultural Areas

2023· article· en· W4388145190 on OpenAlex

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

VenueJournal of the ASABE · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersOak Ridge Institute for Science and EducationNational Institute of Food and AgricultureOffice of Research and DevelopmentU.S. Department of EnergyU.S. Environmental Protection AgencyU.S. Department of Agriculture
KeywordsTillageSurface runoffEnvironmental scienceConventional tillageCrop residueAgronomyNutrientResidue (chemistry)Mulch-tillNo-till farmingNutrient managementCover cropConservation agricultureAgricultureAgroforestrySoil waterSoil scienceBiologyEcology

Abstract

fetched live from OpenAlex

Highlights No-till and no-till residue systems were effective in reducing runoff particulate and total nutrients but increased dissolved nutrients. Maintaining >30% residue cover reduced most runoff constituents, irrespective of no-till or tillage. No-till-residue prevented runoff nutrient losses and benefitted farm revenue by avoiding tillage. Abstract. Reduced tillage management conservation practices (No-till and Reduced-till) are widely adopted in agriculture; however, understanding their overall effectiveness for water quality protection is challenging. A meta-analysis was conducted to understand and quantify the effectiveness of residue and tillage management on runoff, sediment, and nutrient losses from agricultural fields. Annual runoff and the associated sediment, and nutrient (nitrogen and phosphorus) loads were compiled from 60 peer reviewed research articles published across the United States and Canada. A total of 1575 site-years of data were categorized into tillage (<30% surface cover), no-tillage (<30% surface cover), tillage with residue (>30% surface cover), no-tillage with residue (>30% surface cover), and pasture management. No-tillage, no-tillage-residue, and tillage-residue managements were evaluated for their effectiveness in reducing runoff, nutrients, and sediment loads compared to tillage. Synthesized and surveyed corn yield data were used to evaluate the economic cost effectiveness of no-tillage-residue management with respect to tillage. Across the site years (1968-2019) studied, median runoff depth for no-tillage and no-tillage-residue were 84% and 70% greater than tillage and tillage-residue management, respectively. No-tillage-residue management had up to 86% less sediment losses than tillage systems, on average, for both >30% and <30% surface cover. No-tillage-residue management was most effective, with a positive performance effectiveness of 65% to 90% in controlling sediments, particulate, and total nutrient losses in runoff compared to tillage. Cost effectiveness analysis revealed the benefits of no-tillage-residue management in reducing nutrient loads and increasing net-farm revenue by avoiding tillage operational costs. Except for dissolved phosphorus, no-tillage-residue management cost effectiveness for sediments and nutrient loads ranged from negative $6 to negative $102 per every Mg or kg of load reduction, indicating it had both economic and environmental benefits compared to tillage management. Overall, these results indicate that over the long-term, no-tillage and tillage, combined with greater than 30% residue cover, can effectively reduce sediment and nutrient losses. This work highlights the importance of crop residues on the soil surface to reduce runoff losses, even in no-tillage systems. Keywords: Conservation tillage, No-tillage, Residue cover, Tillage, Water quality.

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)0.000
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 score0.208
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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