Evidence map›Paper›PMID 39738301›Full record

ArticleScientific reports2024

Doppler weather radars as a game changer in desert locust swarm tracking.

N A Anjita, J Indu, P Thiruvengadam, Vishal Dixit, Arpita Rastogi, Bagavath Singh Arul Malar Kannan

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

N A AnjitaDepartment of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India.
J InduDepartment of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India. indusj@civil.iitb.ac.in.
P ThiruvengadamSchool of Meteorology, University of Oklahoma, Norman, OK, USA.
Vishal DixitInterdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai, India.
Arpita RastogiIndia Meteorological Department, New Delhi, 110003, India.
Bagavath Singh Arul Malar KannanIndia Meteorological Department, New Delhi, 110003, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Desert locusts, notorious for their ruinous impact on agriculture, threaten over 20% of Earth's landmass, prompting billions in losses and global food scarcity concerns. With billions of these locusts invading agrarian lands, this is no longer a thing of the past. Recent invasions, such as those in India, where losses reached US$ 3 billion in 2019-20 alone, underscore the urgency of action. By tapping into the existing Doppler Weather Radar (DWR) infrastructure, originally deployed for meteorological applications, the present study demonstrates a systematic approach to distinctly identify and track concentrations of desert locust swarms in near real-time using single and dual-polarization radars. Findings reveal the potential to establish early warning systems with lead times of 5-7 h and spatial coverage of approximately 100 km, empowering timely mitigation efforts. In stark contrast to satellite imagery, often constrained by resolution limitations that impede swarm detection, DWRs offer unparalleled spatial and temporal resolution, rendering them indispensable tools in this endeavour. Harnessing radar capabilities within the current infrastructure, unleashes unparalleled ecological monitoring potential and fundamentally transform practices in managing migratory pest management practices. Embracing these technological advancements becomes imperative to safeguard agricultural landscapes, uphold global food security, and effectively mitigate the ecological threats posed by migratory pests.

Indexed as

GrasshoppersRadarWeatherAnimal MigrationAnimalsIndiaAeroecologyAgricultural PestDesert LocustDWRRadar Entomology

Identifiers

PMID39738301
PMCPMC11685443

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LicenceCC BY-NC-ND
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Registered trials

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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.