Sustainable Solutions to Land Degradation and Rainfall Variability in Sub-Saharan Africa - Integrating Traditional Water Management, Agricultural Intensification, and Machine Learning Approaches - Doctor of Philosophy Thesis

Aug 5, 2026

This thesis examines sustainable agricultural practices designed to address land degradation, climate variability, and food insecurity in sub-Saharan Africa, with a focus on rainfed agricultural systems. Erratic rainfall, soil degradation, and limited access to advanced technologies are linked to impacts on crop productivity. The research combines traditional knowledge with machine learning and remote sensing to support adaptive strategies. It evaluates traditional water harvesting approaches, including stone bunds and in-situ conservation, and integrates remote sensing-driven modeling to improve soil moisture predictions and guide water management. The work provides actionable insights for policymakers, practitioners, and researchers seeking resilient systems for food security.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages126

Reading Time5 h 18 min

AccessFree to download

Foreword

Abstract

Declaration

Acknowledgments

Keywords

Table of Contents

Università degli Studi di Sassari. Department of Agricultural Scineces Doctoral Research Course Under the Curriculum Desertification and Land Degradation Sustainable Solutions to Land Degradation and Rainfall Variability in Sub-Saharan Africa: Integrating Traditional Water Management, Agricultural Intensification, and Machine Learning Approaches A Thesis Submitted for the Degree of Doctor of Philosophy Supervisor: Professor. Giovanna Seddaiu Co-Supervisor: Professor.Ing. Alberto Carletti Ph.D. Candidate: Meron Lakew Tefera CICLO XVIII (2021-2024) Foreword This thesis consists of five manuscripts: 4( four) have been published, and one is currently under review and the fifth was presented as a poster at the AGU Conference 2024. The research was conducted as part of my PhD studies in the Department of Agricultural Sciences, specializing in Desertification and Land Degradation. This work was fully supported by the EWA-BELT Project, funded by the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 862848) . My studies were carried out at the University of Sassari, Italy, from October 2021 to October 2024, under the guidance of Professor Giovanna Seddaiu and Professor Ing. Alberto Carletti. Abstract This thesis examines sustainable agricultural practices designed to tackle the challenges of land degradation, climate variability, and food insecurity in sub-Saharan Africa, with a specific emphasis on rainfed agricultural systems. As the region grapples with the effects of erratic rainfall, soil degradation, and limited access to advanced technologies, this research explores solutions that blend traditional knowledge with machine learning and remote sensing. It investigates how climate variability impacts agricultural productivity and underscores the need for adaptive strategies. The thesis highlights traditional water harvesting techniques, such as stone bunds and in-situ water conservation methods, to improve soil moisture retention, reduce runoff, and boost crop yields. Furthermore, it integrates remote sensing and machine learning to enhance soil moisture predictions and inform more effective water management strategies. By combining age-old practices with innovative technologies, this research offers valuable contributions toward building resilient agricultural systems that improve productivity and sustainability in the face of climate change, providing practical insights for policymakers, practitioners, and researchers working towards lasting solutions for food security in sub-Saharan Africa. Keywords: Land Degradation, Rainfall Variability, In-situ water harvesting (IS_WH) , Machine Learning, Soil Moisture and Sub-Saharan Africa. Declaration I, the undersigned Meron Lakew Tefera, declare that this dissertation is the original report on my doctoral research at the University of Sassari, it has been written by myself and has not been submitted or presented, in whole or in part, for the award of any other academic degree or diploma elsewhere. Meron Lakew Tefera (February, 2024) Acknowledgments I would like to extend my heartfelt gratitude to everyone who has played a crucial role in my academic journey, leading to this significant achievement. First and foremost, I am deeply thankful to my supervisor, Professor Giovanna Seddaiu, for her unwavering belief in me and her continuous support throughout this process. Her insightful guidance, trust, and encouragement have been instrumental in shaping my work. I am especially grateful for the independence she allowed me in conducting this research, always motivating me to grow both as a researcher and as an individual. Her constructive advice, patience, and understanding have profoundly influenced my academic development, and for that, I will always be grateful. I would also like to express my sincere appreciation to my co-advisor, Professor Alberto Carletti, whose vast knowledge and constant encouragement greatly enr

FAQs

What problem does the thesis focus on in sub-Saharan Africa?

The thesis focuses on land degradation, climate variability, and food insecurity, especially in rainfed agricultural systems.

How does the research combine traditional practices with modern methods?

It integrates traditional water harvesting techniques (such as stone bunds and in-situ water conservation) with remote sensing and machine learning to support adaptive strategies.

What is the role of machine learning and remote sensing in the study?

Remote sensing and machine learning are used to enhance soil moisture predictions and to inform more effective water management strategies.

Aug 5, 2026

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