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Accueil du site → Doctorat → États-Unis → 2020 → A systems approach to analyze household vulnerability to food insecurity in rural southern mali using a spatially-explicit integrated social and biophysical model

Michigan State University (2020)

A systems approach to analyze household vulnerability to food insecurity in rural southern mali using a spatially-explicit integrated social and biophysical model

Paudel, Rajiv.

Titre : A systems approach to analyze household vulnerability to food insecurity in rural southern mali using a spatially-explicit integrated social and biophysical model

Auteur : Paudel, Rajiv.

Université de soutenance  : Michigan State University.

Grade : Doctor of Philosophy (PhD) 2020

Résumé partiel
Mali is expected to be profoundly impacted by climate change. Its mid-century temperature could increase by 2°C, which could have detrimental impacts on crop production. Besides, northern Mali is currently highly arid is not suitable for agriculture. The South has the responsibility to feed the national population. However, the region is experiencing rapid population growth. Mali is likely to struggle to feed its growing population under climate change. Food production and food security in the South have a wider influence on the national food supply. Food security lies at the interface of biophysical, climatic, and socio-economic systems and demands a systemic approach for evaluation. Using a biophysical crop model with an agent-based model of household food systems, we capture the dynamics within and across multiple associated systems. We focus in the South and use multidisciplinary tools to explore the trajectories of household food security under climate change and population growth in the region.The dissertation is organized into three research papers. Paper 1, entitled "A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-based Model Development.", focuses on exploring household food systems and identifying actors of household food security and their behaviors. Chiefly, we aim to extract the information needed to develop an ABM of household food systems. We apply largely automatic efficient approaches for information extraction from contextually rich qualitative field narratives. Using a combination of semantics and syntactic Natural Language Processing, we identify actors (agents) of household food security, their properties, and actions and interactions responsible for household food supply. The data extraction is primarily unsupervised and, apart from being efficient, it controls manual manipulation and bias introduction in the model development. We use the extracted information for developing a contextual model of household food security.

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Page publiée le 26 mai 2021