About
Advance Your Forestry Research with IgMin Research
Forests play a crucial role in sustaining ecosystems, biodiversity, and climate resilience. At IgMin Research, we welcome global researchers and professionals to contribute to our forestry journal through impactful forestry journal submissions. Whether you are exploring sustainable forest management, biodiversity preservation, or climate-forest interactions, our open-access platform ensures wide visibility and fast-tracked peer review.
Our journal provides a trusted space for forestry research publishing, helping authors reach international audiences and make meaningful contributions to environmental science.
Here’s how to get started:
- Quick Submission: Submit your article easily through our one-step online form.
- Manuscript Guidelines: Review essential formatting and structural instructions before submission.
- Submit Forestry Article: Share your work with the global forestry research community.
Join us in advancing forestry knowledge and promoting sustainable practices for future generations.
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
Which articles are now trending?
Research Articles
- Clustering of Three-dimensional (3-D) Objects by Means of Phase- only Digital Holographic Information using Machine Learning
- Risks and Effects of Medicinal Plants as an Adjuvant Treatment in Mental Disorders during Pregnancy
- Investigation and Energy Modeling of New Generation Environmentally Friendly Energy Source Thorium Fueled Molten Salt Reactors
- Ammonia: A Trend of Dry Deposition in Vietnam
- Efficacy of Alternative Insecticides against Dusky Cotton Bug (Oxycarenus laetus) to Improve Yield Losses in Cotton Crops through Residue-based Bioassay
- Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
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