Modeling land use change from smallholder agricultural intensification

Agricultural expansion in the rural tropics brings much needed economic and social development in developing countries. On the other hand, agricultural development can result in the clearing of biologically-diverse and carbon-rich forests. To achieve both development and conservation objectives, many government policies and initiatives support agricultural intensification, especially in smallholdings, as a way to increase crop production without expanding farmlands. However, little is understood regarding how different smallholders might respond to such investments for yield intensification. It is also unclear what factors might influence a smallholder’s land-use decision making process. In this proposed research, I will use a bottom-up approach to evaluate whether investments in yield intensification for smallholder farmers would really translate to sustainable land use in Indonesia. I will do so by combining socioeconomic and GIS data in an agent-based model (Land-Use Dynamic Simulator multi-agent simulation model). The outputs of my research will provide decision makers with new and contextualized information to assist them in designing agricultural policies to suit varying socioeconomic, geographic and environmental contexts.

I study human dimensions of natural resource management and resource use by under-represented populations—often in developing nations—to enhance our understanding of conflicts involving land use, natural resources, and conservation from an interdisciplinary, systematic lens. My research spans subjects such as common pool resource management and policy, decentralization, and land use/land cover change drivers and trends relating to population rise and environmental change.

The particular focus of the research is the development of a spatial and Agent-Based Model to capture the motivations underlying the decision making of the various actors towards the investments in the quality of land and institutions, or other aspects of land use change. Inductive models (GIS and statistical based) can extrapolate existing land use patterns in time but cannot include actors decisions, learning and responses to new phenomena, e.g. new crops or soil conservation techniques. Therefore, more deductive (‘theory-driven’) approaches need to be used to complement the inductive (‘data-driven’) methods for a full grip on transition processes. Agent-Based Modeling is suitable for this work, in view of the number and types of actors (farmer, sedentary and transhumant herders, gender, ethnicity, wealth, local and supra-local) involved in land use and management. NetLogo framework could be use to facilitate modeling because it portray some desirable characteristics (agent based and spatially explicit). The model develop should provide social and anthropological insights in how farmers work and learn.

My research interests primarily lie at the interface of ecology and epidemiology, and include host-pathogen systems that are of public health or conservation concern. I use ecologic, epidemiologic and model-based investigations to understand how pathogens spread through, persist in, and impact host populations. Animal disease systems that I am currently working on include canine rabies, leptospirosis, chronic wasting disease, bighorn sheep pneumonia, raccoon roundworm (Baylisascaris procyonis), chytridiomycosis, and Lyme disease.

My research focuses on the interdisciplinary nexus of artificial intelligence (AI), digital economy, technological innovation, and industrial economics, with an emphasis on understanding how digital technologies reshape economic structures, policy frameworks, and societal norms. Key areas of interest include:

1. Artificial Intelligence & Digital Transformation
   Ethical and Governance Challenges of AI: Investigating algorithmic bias, data privacy, and accountability in AI systems; proposing frameworks for ethical AI development and deployment.
   AI Adoption and Economic Impact: Analyzing how AI-driven automation and innovation influence productivity, labor markets, and industrial competitiveness.
2. Digital Economy & Platform Markets
   Crowdfunding, Sharing Economy, and Digital Platforms: Examining user behavior, market dynamics, and performance drivers in emerging digital ecosystems (e.g., crowdfunding campaigns, app markets).
   Digital Innovation and Entrepreneurship: Studying the role of technological innovation in firm growth, particularly in internet-based industries.
3. Technological Innovation & Industrial Policy
   Innovation-Driven Industrial Competitiveness: Developing quantitative models (e.g., DEA, CGE) to assess the efficiency and competitiveness of emerging industries under technological disruption.
   Policy Evaluation and Simulation: Using computational modeling to analyze the economic and industrial impacts of trade policies, environmental regulations, and technological standards.
4. Resource Economics & Sustainable Development
   Water Resource Management and Policy: Evaluating the economic and environmental trade-offs of water conservation policies through general equilibrium modeling.
   Global Trade and Food Security: Assessing the impacts of international trade regulations (e.g., food safety standards) on domestic industries and global supply chains.
5. Cross-Disciplinary Methodological Innovation
   Integrating econometrics, data science, and behavioral economics to enhance the rigor and relevance of industrial and policy research.
   Leveraging big data analytics, machine learning, and agent-based modeling to uncover complex relationships in digital markets and technological ecosystems.

Prof. Vincenot’s main research interests lie in the theoretical development of Hybrid Mechanistic Simulation approaches based on Individual/Agent-Based Modeling and System Dynamics, and in their applications to a broad range of systems, with particular focus on Ecology.