Canadian Journal of Forestry Research (CJFR)

Published September 20, 2026

| Version v1

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THE POTENTIAL OF AGROFORESTRY IN THE REHABILITATION OF DEGRADED LANDS IN CANADA

Authors/Creators

Dar, Munir (Editor)

Description

Dar, M. (2026). “The Potential of Agroforestry in the Rehabilitation of Degraded Lands in Canada.” Canadian Journal of Forestry Research (CJFR). https://doi.org/10.5281/zenodo.22864456, https://orcid.org/0009-0007-1445-4176

The Potential of Agroforestry in the Rehabilitation of Degraded Lands in Canada is an advanced scholarly work authored by Munir Dar, M.Sc. (Hons.) Forestry, a forestry specialist and the Publisher & Chief Editor of the Canadian Journal of Forestry Research (CJFR), Toronto, Canada. The study examines how agroforestry, an integrated land‑use system combining trees with crops, livestock, and ecological restoration, can serve as a powerful nature‑based solution for restoring degraded landscapes across Canada’s diverse ecozones.

Drawing on the author’s extensive academic and professional background, supported by multiple international scholarly identifiers including ORCID, SSRN, Google Scholar, ResearchGate, Web of Science, and Academia.edu, the research provides a rigorous, mixed‑methods analysis involving meta‑analysis, field case studies, and geospatial modelling. The study evaluates the biophysical, ecological, and socio‑economic benefits of agroforestry, demonstrating its effectiveness in improving soil organic carbon (SOC), reducing erosion, enhancing water retention, increasing biodiversity, and strengthening climate resilience.

Dar's (2026) work highlights agroforestry’s alignment with Canada’s climate commitments, Indigenous stewardship principles, and sustainable forestry policies. It emphasizes the urgent need for national implementation strategies and policy integration to address land degradation driven by intensive agriculture, deforestation, mining, wildfires, and climate‑induced disturbances. This research contributes significantly to the fields of ecological restoration, sustainable forestry, and climate resilience, offering evidence‑based recommendations for policymakers, land managers, and environmental researchers.

Journal article Open

Published September 12, 2026 | Version v1

Dataset Open

ADVANCING FORESTRY SCHOLARSHIP THROUGH OPEN ACCESS RESEARCH, SCIENTIFIC STEWARDSHIP AT ZENODO

Authors/Creators

Dar, Munir (Editor)1

Dar, Munir (2026). Advancing Forestry Scholarship Through Open‑Access Research and Scientific Stewardship: The Canadian Journal of Forestry Research (CJFR) Community. Zenodo. DOI: https://www.doi.org/10.5281/zenodo.22715805    

Annotation: In this work, Dar, Munir (2026) provides a comprehensive synthesis of the Canadian Journal of Forestry Research (CJFR) Community, a curated, DOI‑assigned scholarly repository dedicated to high‑quality forestry research and global scientific dissemination. The foundational description emphasizes that “CJFR operates as an exclusive monograph and research paper publisher, committed to rigorous peer review and academic excellence without functioning as a traditional periodical journal.” Dar, Munir (2026) examines CJFR’s mission, scholarly mandate, metadata and curation standards, DOI governance model, editorial protocols, and its broader role in strengthening global forestry research, environmental stewardship, and interdisciplinary scientific collaboration. The article highlights CJFR’s peer‑reviewed outputs, including monographs, research papers, technical reports, datasets, and field‑based observations and underscores its commitment to accessible, credible, and innovative forestry scholarship.

Keywords: CJFR; forestry research; forest ecology; environmental science; monograph publishing; DOI governance; open access; peer-reviewed; climate change; biodiversity; conservation; forest management; environmental policy; scientific repository; Zenodo community; metadata governance; Munir Dar.

Published November 11, 2025 | Version v1

Journal article

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THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SUSTAINABLE FOREST MANAGEMENT: A CASE STUDY OF GOVERNMENT AND EDUCATIONAL SECTORS IN PAKISTAN

1.  Dr.Muhammad Irshad Arshad, M. Sc Forestry, Ph. D. Wildlife Management.

Director, Punjab Wildlife Department, Faisalabad, Punjab, Pakistan

 2.  Munir Dar, M.Sc. (Forestry), Forestry Specialist

Publisher & Chief Editor, CJFR, Canada

 DOI: 10.5281/zenodo.17576085

ORCID ID: https://orcid.org/0009-0007-1445-4176

Google Scholar: https://scholar.google.ca/citations?user=7qq7WEkAAAAJ&hl=en

ResearchGate ID: https://www.researchgate.net/profile/Munir-Dar-3?ev=hdr_xprf 

Clarivate Web of Science Researcher ID: OHV-2983-2025

Academia.edu Scholar: https://yorku.academia.edu/munirdar

Keywords: Artificial Intelligence, Sustainable Forest Management, Pakistan, Remote Sensing, National AI Policy, Deforestation, Climate Change, Digital Governance.

Abstract: The escalating threats of deforestation, climate change, and illegal activities necessitate technological innovation in Pakistan's forestry sector. This paper examines the nascent integration of Artificial Intelligence (AI) tools and Machine Learning (ML) techniques within governmental conservation initiatives and academic curricula across the country, driven by the National AI Policy 2025 (Ministry of Information Technology and Telecommunication (MoITT), 2025). Drawing on qualitative reviews of policy documents, pilot projects (e.g., the Smart Forest initiative), and educational frameworks, we analyze current capacity, systemic barriers (including data fragmentation and high infrastructure costs), and prospects for AI adoption. Findings indicate that government-led adoption, primarily for real-time monitoring and disaster detection, is underway. However, this progress is hampered by significant deficiencies in geospatial data infrastructure, a crucial human capital gap in specialized AI-forestry skills, and a lack of sustainable domestic financing. The paper concludes by proposing a multi-pronged roadmap focusing on data standardization, interdisciplinary education, and innovative finance models to ensure AI becomes a scalable, foundational tool for Pakistan's ecological security and sustainable forest management (SFM).