MaxEnt with remote sensing for tea plantation suitability under climate change.

Shijie Wu, Pengfei Tian, Xiaochen Zhu, Hengxin Dong, Shiwen Liu

Abstract read

In one paragraph

Article in iScience, 2026. 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.

Shijie WuJiangsu Provincial University Key Laboratory of Agricultural and Ecological Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China.

Pengfei TianNanjing Meteorological Bureau, Nanjing 210019, China.

Xiaochen ZhuJiangsu Provincial University Key Laboratory of Agricultural and Ecological Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China.

Hengxin DongShandong Meteorological Bureau, Jinan 250031, China.

Shiwen LiuJiangsu Provincial University Key Laboratory of Agricultural and Ecological Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Premium tea cultivation is highly vulnerable to climate change, yet its future suitability remains insufficiently understood. In this study, we integrated spatially de-biased tea occurrence records derived from Gaofen-6 imagery and a U-Net deep learning framework with the MaxEnt model to project tea suitability in Nanping, Southeastern China, under multiple CMIP6 climate scenarios from 2021 to 2080. Random forest was used to cross-check model robustness. The results show that precipitation seasonality and precipitation of the wettest quarter are the main climatic drivers of tea suitability, and future warming is likely to shift highly suitable areas southward while increasing spatial fragmentation under high-emission scenarios. These findings provide a transferable framework for evaluating climate-sensitive high-value crops and support more adaptive agricultural planning under global environmental change.

Indexed as

agricultural scienceecologyremote sensing

Identifiers

PMID42245925

DOI10.1016/j.isci.2026.115887

PMCPMC13231076

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.