Evidence map›Paper›PMID 41190205›Full record

ArticleFrontiers in plant science2025

The impact of seasonal temperature and water transport on the growth of sunshine rose grapevines and precision irrigation strategies.

Ranran Wang, Zhen Han, Yingxiu Li, Suya Shang, Bo Li, Xin Lu

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. 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

6 authors.

Ranran Wang *College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China.
Zhen Han *Shandong Academy of Grape, Shandong Academy of Agricultural Science, Jinan, China.
Yingxiu LiCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China.
Suya ShangShandong Academy of Grape, Shandong Academy of Agricultural Science, Jinan, China.
Bo Li *Shandong Academy of Grape, Shandong Academy of Agricultural Science, Jinan, China.
Xin Lu *Faculty of Science and Engineering, Iwate University, Morioka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the seasonal variations in grapevine growth and sap flow, with a particular focus on how environmental factors influence key growth indicators. Grapevines are highly sensitive to seasonal changes, and understanding these variations is essential for optimizing vineyard management practices. Given the increasing importance of precision agriculture, high-precision sensors were employed to monitor sap flow, leaf temperatures, and ambient temperature over the course of a year. By collecting data on these physiological indicators, we aim to identify patterns that can improve our understanding of grapevine responses to environmental changes. Our findings reveal significant seasonal fluctuations in grapevine growth, with the most growth occurring during the warmer months (spring and summer) and slower growth in winter. The comparison of predictive models, including Prophet, LightGBM, and XGBoost, demonstrated that machine learning models were more accurate in predicting grapevine growth compared to traditional methods. These results offer important insights into the relationship between grapevine physiology and environmental conditions, providing a foundation for improving vineyard management practices. The grape variety utilized in this study is Sunshine Rose (

Indexed as

data-driven analysisplant growthprecision irrigationtemperaturetranspirationwater transport

Identifiers

PMID41190205
PMCPMC12580307

What OpenQuestion holds

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LicenceCC BY
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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.