Evidence map›Paper›PMID 28867871›Full record

ArticleAgricultural systems2017

Mapping regional risks from climate change for rainfed rice cultivation in India.

Kuntal Singh, Colin J McClean, Patrick Büker, Sue E Hartley, Jane K Hill

Open access · hybridAbstract read
In one paragraph

Article in Agricultural systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
8.1field-weighted citation impact, top 3% of its field
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

9 citing papers in PubMed, 82 citations in OpenAlex.

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  7. Undernutrition is feeding the tuberculosis pandemic: A perspective.Journal of clinical tuberculosis and other mycobacterial diseases · 2022
    Review
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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

5 authors at 2 institutions in 1 country.

Kuntal SinghDepartment of Biology, University of York, York YO10 5DD, UK.
Colin J McCleanEnvironment Department, University of York, York YO10 5NG, UK.
Patrick BükerStockholm Environment Institute, Environment Department, University of York, York YO10 5NG, UK.
Sue E HartleyDepartment of Biology, University of York, York YO10 5DD, UK.
Jane K HillDepartment of Biology, University of York, York YO10 5DD, UK.
University of York · GBStockholm Environment Institute · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration (

Indexed as

biomod2Boosted regression treesClimate envelope modelIndiaRainfed rice

Identifiers

PMID28867871
PMCPMC5555444
OpenAlexW2624308950

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.