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Record W1557209984 · doi:10.1007/3-7643-7380-6_12

Reducing agroecosystem vulnerability to weed invasion

2005· book-chapter· en· W1557209984 on OpenAlexaff
K. Neil Harker, George W. Clayton, John T. O’Donovan

Bibliographic record

VenueBirkhäuser-Verlag eBooks · 2005
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsWeedAgroforestryAgroecosystemWeed controlAgricultureHabitatCrop diversityIntercroppingEcosystem servicesEcologyBiologyAgronomyEcosystem

Abstract

fetched live from OpenAlex

There are many agronomic variables and management strategies other than herbicides that can be manipulated to discourage weed invasion. Combining several management strategies rather than relying on one will increase the likelihood of successful weed management. Encouraging optimal crop canopy health can guide decision-making and render agricultural land less susceptible to weed invasion. Then, when necessary, herbicides can be judiciously used to supplement cultural weed management techniques. In this review we have attempted to address two of the three major habitat characteristics that influence weed invasions - disturbances and, to a lesser extent, high resource ability. The remaining habitat characteristic, low species diversity, is difficult to address in modern agriculture, but can be an avenue of defence against invading species [89]. However, even intercropping, which is an effective ecological weed management technique [90], does not approach species diversity levels in natural ecosystems. A compromise to high species diversity in space is to maximize species diversity in time; this is best accomplished by ensuring that a given field is subjected to diverse rotational crops. Diverse crop rotations are probably the most effective management tool in maintaining crop health and limiting weed invasion opportunities. In the future, very clean (near weed-free) fields may not be considered acceptable [91]. We might do well to alter our view of what is desirable: from an “ultra-clean” crop with no weeds visible to a more species-rich field with sub-threshold communities of weeds. This approach could be termed “ecological weed management” [92]. Pest management in disciplines other than weed science may benefit from a few weeds [93, 94]. For example, root maggot (Delia spp) egg deposition and larval damage were reduced in plots where weeds were left in canola longer than the period recommended for optimal yields [95]. Combining and applying the techniques discussed above, reducing herbicide use, and tolerating low infestations of weeds may be the most sustainable form of weed management over the long-term. Ignoring ecological weed management techniques and maintaining current herbicide application practices will ensure a higher frequency of weed invasions of the resistant type [96, 97].

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.227
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueBirkhäuser-Verlag eBooksSame topicWeed Control and Herbicide ApplicationsFrench-language works237,207