Tag Archives: livelihoods

Cross-site Comparison of Land-Use Decision-Making

The cumulative effects of local land-use and livelihood changes are a global force of environmental and socio-economic change. Land-use changes result from decisions of individual farmers, pastoralists,  and housing consumers and developers (to name a few). Their decisions are influenced by not only local environmental, social, and economic conditions, but also by far-reaching forces such as economic globalization. The choice of a farmer in Brazil to grow soybeans, for example, can be influenced by the consumption of people in China.

Not all land-uses are created equal. Some have minimum impact on the environment, and some offer sustainable livelihoods for local farmers – finding land uses that accomplish both is difficult. Crafting policies to achieve this two-part goal must contend with both local and global considerations.

Location of one of the study sites near Taoyuan, Hunan Province, China.

A study site near Taoyuan, Hunan Province, China.

On January 29th, my colleagues and I published a paper in PLoS ONE, titled “Cross-site comparison of land-use decision-making and its consequences across land systems with a generalized agent-based model” that describes the development and application of an agent-based virtual laboratory for comparing  land-use and livelihood decision-making processes of rural farmers across geographically distant locations and qualitatively different land-use systems. We use this modeling system across multiple study sites to understand the underlying motivations and rationale of land-use and livelihood decisions of our ‘farmer agents’ and the landscape and livelihood changes that result under various environmental, demographic, and economic scenarios.

Since the traditional mode of scientific experimentation is not feasible with real land-use systems – we are talking about people’s land and livelihoods here – we use simulation-based cross-site comparisons to teach us about what drives the choice of particular land uses and livelihood strategies under different conditions. We use the set of study sites as local examples to synthesis more broadly applicable knowledge of which factors are most important in what contexts.

To explore this question, our investigation had to happen at the decision-making level – a task to which agent-based models are well suited. We also needed a modeling framework that was sufficiently general that it could be applied across multiple locations, yet realistic enough that it could be grounded in real-world data. These needs gave rise to an innovative agent-based virtual laboratory approach that provides a powerful tool for model-based experimentation and synthesis.

Such a model synthesis system can generate the kind of high-level knowledge needed to inform regional policies designed to foster sustainable local land uses and livelihood strategies. Cross-site comparisons use each study site as an example of alternative conditions and/or potential future states, which can aid scenario analysis and the exploration of potential adaptive responses to changing conditions. Furthermore, insights gained from the application of the modeling system to one site can improve our understanding of other similar sites, and foster future research and policy efforts that are sensitive to both the global influence on and local realities of land-use and livelihood change.

Click here to see the web story about this article on SESYNC’s blog.

Exploring Land-Livelihood Transitions

Figure5_rev (2)Rural livelihoods are changing rapidly with economic globalization and global environmental change, which have direct impacts to environmental and socio-economic suitability. All too often the most vulnerable communities – those with the least resources – face the greatest transitions triggered by changing local and global conditions. Those communities also have livelihoods tied to the land, which may lead to environmental degradation and/or fail to support livelihoods in the future. We must advance our understanding of the causes and consequences of land-livelihood transitions in order to avoid maladapted responses that can lead to a loss of land-livelihood sustainability.

My colleagues and I recently published an article in PLoS ONE that explores these issues with an innovative, generalized agent-based model. Because human decision-making drives land-livelihood transitions, a process-level explanation of adaptive responses is needed to explore the conditions under which land-livelihood transitions emerge. In the short-term, this approach advances the use of agent-based virtual laboratories in sustainability research. In coming generations of this modeling approach, we hope to use model insights to devise effective policy interventions aimed at the decision-making level for supporting sustainability .

Complexity in land-livelihood systems

China_FarmerRural livelihoods are inextricably linked to sustainable land-use, and vice versa.

This message seems to be popping-up continuously and forcefully in much of the research articles I’ve been reading lately. And I agree – certainly land-use lies at the heart of the sustainability question, since it is a means of food and income production as well as a main source of impacts to ecosystems. Something I read far less often (still looking if you have suggestions!) is a holistic framework for understanding the complex causes and consequences of land-use and livelihood changes.

The factors driving rural household land-use and livelihood decisions are incredibly complex –  originating and acting both locally and globally, and often creating both rapid and slow changes in incentives and constraints. For example, see this post about both fast and gradual changes occurring in Chinese food systems. Researchers, practitioners, and policy-makers alike are thus left with huge gaps in understanding of how land-use and livelihood changes come about, and you can forget about accurately predicting such changes and how they might influence environmental and/or livelihood sustainability.

Thinking about this challenge led me back to some of my earlier work in complex system science. In particular, I revisited one of my earlier papers about ‘induced coupling‘ – an idea that faster and slower processes sometimes become ‘coupled’ and lead to dramatic systemic changes. So I tried my hand at throwing together a simple version of what this might look like for a coupled land-livelihood system.HCSM_LLS

The red, downward arrows represent ‘entrainment’, or ‘slaving’, of the dynamics of lower-level variables by higher-level variables. The green, upward arrows represent processes of ‘self-organization’, or ‘revolt’, in which the dynamics of lower-level variables influence those of higher-level variables. Dashed arrows represent processes that link variables operating at the same time scales. If you would like to know more about this type of framework, referred to as hierarchical complex systems modeling, I will direct you to work by my friends and colleagues Brad Werner and Dylan McNamara (2007).

Now, the recognition that processes, or ‘drivers’, across multiple scales influence land-use and livelihood decisions is nothing new. However, rarely are temporal scales used as the organizing framework. This viewpoint has the potential to explain why certain drivers have different influences in different contexts due to the relative frequencies of interacting processes.

OK, great … so what? Beyond the potential to advance our fundamental understanding of the causes and consequences of livelihood and land-use changes, such a perspective could help craft policy interventions that address not only short-term needs of rural land-users, but also the effects of long-term challenges to sustainability and well-being.

As always, please feel free to yell at me on twitter @nickmags13 if you disagree, or if you prefer to disagree with me on a more regular basis don’t hesitate to follow this blog or subscribe to the RSS feed or email list. 😉

Video

Food Systems in China

Food_and_China

In honor of Chinese New Year this weekend, this post features an excellent video from The Perennial Plate that highlights two of my favorite things about China: the countryside and food. When you watch the video, you will see linkages between the Chinese passion for food, a rapidly changing agricultural economy, and underlying cultural stigmas associated with agricultural livelihoods. Interactions between these various elements are having profound impacts on rural livelihoods and land-use in the rapidly changing Chinese economy and culture.

A characteristic pattern of the new China, which struck me during my travels in the countryside and is apparent in this video as well, is the demographic disparity as one travels outside the cities. Older generations remain on the farm, work the land, and care for the young children, while many young adults travel to nearby cities in pursuit of higher wages. Such demographic patterns are reinforced by a long-held stigma against agricultural livelihoods and their association with a peasant’s social status.

Given my interest in sustainable agricultural practices and livelihoods, this video resonated with me personally, as well as reminding me of parallels with the many urban agriculture movements that have become so prevalent around the U.S. This story demonstrates the kind of new cultural and social linkages between urban and rural livelihoods that can be created in China as an adaptation (and perhaps reaction) to an increasingly urban and market-driven economy and society.

Tying into the themes of this blog – agent-based modeling and land-use change – the story told in this video is a reminder of the importance of the cultural and social contexts in which land-use and livelihood decisions are made. In particular, this is a vivid example of how cultural and socio-economic forces can create emergent urban-rural teleconnections that lead to new land-uses and livelihood strategies.

New Paper: Pattern-Oriented Modeling in Multi-Scale ABMs of Land Change

TGIS_screen_captureA particular challenge of investigating the causes of land-use change is the multi-scale nature of factors that influence land-use decisions. In an increasingly globalized world, land-use choices and livelihood strategies are linked to local AND regional to global forces. But attempts to incorporate such multi-scale causation in land change models often run into significant knowledge and data gaps – especially when trying to link incomplete and/or low quality global data to individual agents’ decision-making processes.

Figure4_mainOne way forward, which my co-author Dr. Erle Ellis and I present in this new open-access article in Transactions in GIS, is to use pattern-oriented modeling (Grimm et al., 2005) within an agent-based virtual laboratory to experimentally bound the possible values of uncertain parameters. By targeting characteristic patterns tied to important individual- and landscape-level processes – the selection of which are informed by theory, data, or both – ABMs can be designed and tested to be more realistic despite data limitations. We propose that this experimental method can help overcome significant data gaps, and help land change scientists begin to quantify some global trends in local land change processes.

Comments welcome!

Abstract

Local land-use and -cover changes (LUCCs) are the result of both the decisions and actions of individual land-users, and the larger global and regional economic, political, cultural, and environmental contexts in which land-use systems are embedded. However, the dearth of detailed empirical data and knowledge of the influences of global/regional forces on local land-use decisions is a substantial challenge to formulating multi-scale agent-based models (ABMs) of land change. Pattern-oriented modeling (POM) is a means to cope with such process and parameter uncertainty, and to design process-based land change models despite a lack of detailed process knowledge or empirical data. POM was applied to a simplified agent-based model of LUCC to design and test model relationships linking global market influence to agents’ land-use decisions within an example test site. Results demonstrated that evaluating alternative model parameterizations based on their ability to simultaneously reproduce target patterns led to more realistic land-use outcomes. This framework is promising as an agent-based virtual laboratory to test hypotheses of how and under what conditions driving forces of land change differ from a generalized model representation depending on the particular land-use system and location.

About the Agent-Based Virutal Labs (ABVLs) blog

Welcome to the Agent-Based Virtual Labs blog!Landscape_fig

This blog will cover issues relating broadly to the social, economic, and cultural interactions that are  changing the planet’s surface and climate. In particular, these issues will be explored with posts relating to agent-based modeling (ABM), and how ABMs can be used as virtual laboratories to ask questions about peoples’ motivations for observed behaviors that would be impossible to ask any other way. Along the way, topics informing the creation, use, and testing of ABMs will be included, as well as my areas of application of ABVLs such as land-use change, livelihoods in developing countries, and sustainability.

This blog will also be a hub of information for those interested in ABMs or my subject areas of interest. Following the navigation menu will lead you to collections of links for learning and teaching resources, other ABVLs-relevant blogs, and my research topics. Also, at regular intervals, posts will appear that contain an annotated list of links dedicated to topics ‘trending’ in the ABVLs world.

I hope you enjoy and please drop me a line!