https://hdpublication.com/index.php/jss <p><strong>Journal Siplieria Sciences ISSN 2709-2380 </strong>is an international journal open access and peer reviewed journal includes all the areas of research activities ini Life Sciences such as Agriculture, Fisheries, Marine Science, E<span class="tlid-translation translation" lang="en"><span class="" title="">nviroment, <span class="ILfuVd"><span class="hgKElc">Environmental Biology, Chemistry, Biology, Biochemistry, Zoology</span></span></span></span></p> <p>The articles published in Journal Siplieria Sciences have been double blind-reviewed by peer reviewers. The decision on whether the scientific article is accepted or not in this journal will be the Editorial Board’s right based on peer reviewer's recommendation. </p> Hoedspruit Development Publication en-US Journal Siplieria Sciences 2709-2380 https://hdpublication.com/index.php/jss/article/view/329 <p>Climate-smart agriculture (CSA) provides a vital framework for enhancing food security, resilience, and mitigation in Sub-Saharan Africa (SSA), where agriculture is highly vulnerable to climate variability and shocks. By integrating practices that sustainably increase productivity and reduce greenhouse gas emissions, CSA addresses the region’s dual challenges of climate change and food insecurity. Computational algorithms offer critical tools for supporting CSA through simulation, predictive analytics, optimization, and decision-making. However, their systematic application to SSA’s agricultural systems remains limited. This study investigates how algorithm development and optimization can strengthen CSA adoption in SSA. The objectives are to examine the current status of CSA practices and adoption drivers, explore algorithmic applications in modelling and resource management, identify integration barriers, and propose scalable pathways for sustainable deployment. A systematic review was conducted across six databases, focusing on literature published between 2010 and 2023. Screening yielded 32 eligible studies, which were synthesized through narrative and thematic analysis. Results highlight the use of algorithms such as particle swarm optimization, neural networks, and evolutionary computation in domains including yield prediction, drought risk assessment, irrigation scheduling, and crop disease detection. Key barriers include budgetary constraints, weak supply chains, policy gaps, and low farmer awareness, while opportunities lie in digital connectivity, climate information services, and institutional support. The findings suggest that integrating computational algorithms into CSA frameworks can enhance adaptive capacity, optimize resource use, and accelerate resilient food system transformations in SSA.</p> Mulala Jimaima Timothy Mwewa Fredrick Kayusi James Shabiti Mukombwe Yusuf Umer Petros Chavula Copyright (c) 2026 Journal Siplieria Sciences https://creativecommons.org/licenses/by-sa/4.0/ 2026-04-23 2026-04-23 7 2 1 16 10.48173/jss.v7i2.329