Authors :
Jean Louis Cyubahiro Cyuma; Mayowa, B. George; Joy Onma Enyejo; Ibrahim Kachalla
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/mr48jhse
Scribd :
https://tinyurl.com/bdfbrs64
DOI :
https://doi.org/10.38124/ijisrt/25mar1335
Google Scholar
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 15 to 20 days to display the article.
Abstract :
Agroforestry systems have gained increasing recognition as sustainable land management solutions that integrate
trees, crops, and livestock to enhance biodiversity, improve soil health, and mitigate climate change. However, the increasing
frequency and intensity of wildfires present a significant threat to agroforestry landscapes, necessitating the development of
fire-adaptive strategies. This review explores the integration of smart agroforestry systems that leverage fire-resistant plant
species and controlled burning techniques to enhance ecosystem resilience and long-term sustainability. Fire-resistant plant
species serve as natural firebreaks, reducing fire propagation risks and maintaining soil stability. Controlled burning, a
traditional land management practice, is revisited through advanced monitoring technologies, including remote sensing,
IoT-enabled sensors, and predictive modeling, to optimize burn schedules and minimize environmental impact. The study
further examines the selection criteria for fire-resistant species, highlighting their physiological adaptations, moisture
retention capabilities, and regenerative properties that contribute to reduced wildfire susceptibility. Additionally, it assesses
the role of controlled burning in nutrient cycling, pest control, and carbon sequestration while mitigating the risks associated
with uncontrolled wildfires. The paper also investigates how machine learning and AI-driven decision support systems can
enhance fire prediction, landscape monitoring, and real-time adjustments in agroforestry operations. By integrating
agroecological principles, precision agriculture techniques, and climate-adaptive land management, smart agroforestry
systems offer a viable pathway for improving soil fertility, optimizing carbon storage, and sustaining rural livelihoods. Case
studies of successful implementations across fire-prone regions provide empirical insights into best practices, policy
recommendations, and the socio-economic implications of adopting fire-adaptive agroforestry strategies. The review
concludes by emphasizing the necessity of interdisciplinary collaborations among agronomists, ecologists, data scientists,
and policymakers to advance research and implementation frameworks for climate-smart agroforestry systems.
Keywords :
Smart Agroforestry; Fire-Resistant Plants; Controlled Burning; Sustainable Land Management; Wildfire Mitigation; Climate-Resilient Agriculture.
References :
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Agroforestry systems have gained increasing recognition as sustainable land management solutions that integrate
trees, crops, and livestock to enhance biodiversity, improve soil health, and mitigate climate change. However, the increasing
frequency and intensity of wildfires present a significant threat to agroforestry landscapes, necessitating the development of
fire-adaptive strategies. This review explores the integration of smart agroforestry systems that leverage fire-resistant plant
species and controlled burning techniques to enhance ecosystem resilience and long-term sustainability. Fire-resistant plant
species serve as natural firebreaks, reducing fire propagation risks and maintaining soil stability. Controlled burning, a
traditional land management practice, is revisited through advanced monitoring technologies, including remote sensing,
IoT-enabled sensors, and predictive modeling, to optimize burn schedules and minimize environmental impact. The study
further examines the selection criteria for fire-resistant species, highlighting their physiological adaptations, moisture
retention capabilities, and regenerative properties that contribute to reduced wildfire susceptibility. Additionally, it assesses
the role of controlled burning in nutrient cycling, pest control, and carbon sequestration while mitigating the risks associated
with uncontrolled wildfires. The paper also investigates how machine learning and AI-driven decision support systems can
enhance fire prediction, landscape monitoring, and real-time adjustments in agroforestry operations. By integrating
agroecological principles, precision agriculture techniques, and climate-adaptive land management, smart agroforestry
systems offer a viable pathway for improving soil fertility, optimizing carbon storage, and sustaining rural livelihoods. Case
studies of successful implementations across fire-prone regions provide empirical insights into best practices, policy
recommendations, and the socio-economic implications of adopting fire-adaptive agroforestry strategies. The review
concludes by emphasizing the necessity of interdisciplinary collaborations among agronomists, ecologists, data scientists,
and policymakers to advance research and implementation frameworks for climate-smart agroforestry systems.
Keywords :
Smart Agroforestry; Fire-Resistant Plants; Controlled Burning; Sustainable Land Management; Wildfire Mitigation; Climate-Resilient Agriculture.