POPLAR VARIETIES RECOMMENDED FOR CULTIVATION IN UZBEKISTAN

Based on observations and comparisons of promising poplar varieties for improving forestry in Uzbekistan with other varieties, it was possible to identify poplar varieties and species suitable for the agroclimatic regions of the republic, which significantly assisted in selecting poplar species suitable for cultivation on non-saline and partially saline soils.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-11
DOI
https://doi.org/10.5281/zenodo.19519283
Primary Topic
Engineering and Agricultural Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

POPLAR VARIETIES RECOMMENDED FOR CULTIVATION IN UZBEKISTAN

Gulxayo Farxodova, Najmiddin Ochildiyev
Zenodo (CERN European Organization for Nuclear Research)
Engineering and Agricultural Innovations
article

POPLAR VARIETIES RECOMMENDED FOR CULTIVATION IN UZBEKISTAN

Gulxayo Farxodova, Najmiddin Ochildiyev
article en

Abstract

Based on observations and comparisons of promising poplar varieties for improving forestry in Uzbekistan with other varieties, it was possible to identify poplar varieties and species suitable for the agroclimatic regions of the republic, which significantly assisted in selecting poplar species suitable for cultivation on non-saline and partially saline soils.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 4%
Engineering and Agricultural Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.