Evidence map›Paper›PMID 38665186›Full record

ArticleCommunications earth & environment2023

Greening of human-dominated ecosystems in India.

Taejin Park, Murali K Gumma, Weile Wang, Pranay Panjala, Sunil K Dubey, Ramakrishna R Nemani

Abstract read
In one paragraph

Article in Communications earth & environment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Overlooked and overexploited: Extensive conversion of grasslands and wetlands driven by global food, feed, and bioenergy demand.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
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.

Taejin ParkNASA Ames Research Center, Moffett Field, California USA.ORCID 0000-0003-0698-6942
Murali K GummaInternational Crop Research Institute for Semi-Arid Tropics, Patancheru, Telangana India.ORCID 0000-0002-3760-3935
Weile WangNASA Ames Research Center, Moffett Field, California USA.
Pranay PanjalaInternational Crop Research Institute for Semi-Arid Tropics, Patancheru, Telangana India.ORCID 0000-0002-2111-6550
Sunil K DubeyMahabalonis Crop Forecasting Center, Pusa, Delhi, India.ORCID 0000-0003-2642-7961
Ramakrishna R NemaniNASA Ames Research Center, Moffett Field, California USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO

Indexed as

AgroecologyEnvironmental impact

Identifiers

PMID38665186
PMCPMC11041707

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.