Evidence map›Paper›PMID 29304005›Full record

ArticleInsects2018

Investigating the (Mis)Match between Natural Pest Control Knowledge and the Intensity of Pesticide Use.

David Mall, Ashley E Larsen, Emily A Martin

Abstract read
In one paragraph

Article in Insects, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

David MallDepartment of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. david.mall@posteo.de.ORCID 0000-0002-3749-0635
Ashley E LarsenBren School of Environmental Science & Management, University of California, Santa Barbara, CA 93106-5131, USA. larsen@bren.ucsb.edu.
Emily A MartinDepartment of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany. emily.martin@uni-wuerzburg.de.ORCID 0000-0001-5785-9105

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transforming modern agriculture towards both higher yields and greater sustainability is critical for preserving biodiversity in an increasingly populous and variable world. However, the intensity of agricultural practices varies strongly between crop systems. Given limited research capacity, it is crucial to focus efforts to increase sustainability in the crop systems that need it most. In this study, we investigate the match (or mismatch) between the intensity of pesticide use and the availability of knowledge on the ecosystem service of natural pest control across various crop systems. Using a systematic literature search on pest control and publicly available pesticide data, we find that pest control literature is not more abundant in crops where insecticide input per hectare is highest. Instead, pest control literature is most abundant, with the highest number of studies published, in crops with comparatively low insecticide input per hectare but with high world harvested area. These results suggest that a major increase of interest in agroecological research towards crops with high insecticide input, particularly cotton and horticultural crops such as citrus and high value-added vegetables, would help meet knowledge needs for a timely ecointensification of agriculture.

Indexed as

agricultural intensityagroecologybiological pest controlcropecological intensificationinsecticidesstudy system

Identifiers

PMID29304005
PMCPMC5872267

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.