Evidence map›Paper›PMID 38498537›Full record

ArticlePlants (Basel, Switzerland)2024

The Estimation of Grassland Aboveground Biomass and Analysis of Its Response to Climatic Factors Using a Random Forest Algorithm in Xinjiang, China.

Ping Dong, Changqing Jing, Gongxin Wang, Yuqing Shao, Yingzhi Gao

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 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
–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

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

5 authors.

Ping DongCollege of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China.ORCID 0009-0004-0913-0087
Changqing JingCollege of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China.
Gongxin WangCollege of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China.ORCID 0000-0002-1834-6160
Yuqing ShaoCollege of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China.
Yingzhi GaoCollege of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China.

Funding

National Natural Science Foundation of China 42161024The Autonomous Region Finance Forest and Grass Science and Technology Project funded XJLYKJ-2023-18
6 · The paper itself

Abstract

Aboveground biomass (AGB) is a key indicator of the physiological status and productivity of grasslands, and its accurate estimation is essential for understanding regional carbon cycles. In this study, we developed a suitable AGB model for grasslands in Xinjiang based on the random forest algorithm, using AGB observation data, remote sensing vegetation indices, and meteorological data. We estimated the grassland AGB from 2000 to 2022, analyzed its spatiotemporal changes, and explored its response to climatic factors. The results showed that (1) the model was reliable (R

Indexed as

aboveground biomassclimatic factorsgrassland typerandom forest

Identifiers

PMID38498537
PMCPMC10892206

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.