Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making

Does non-farm employment increase rural households' consumption in western Ethiopia? Empirical evidence from the horo guduru wollega zone

In rural areas where farming is the primary source of income for farmers, engaging in non-farm activities can provide an additional source of income and improve household consumption. However, several social and economic factors present challenges to the involvement of rural households in non-farm activities. This study aimed to examine the impact of non-farm participation on household consumption in rural areas.A multistage sampling method was used to select the sample households from the study area. Heckman's two-step procedure was utilized to analyze survey data obtained from 383 rural household heads. The results indicated that participating in non-farm employment had a positive and significant effect on household consumption levels. The result indicated that households that engage in non-farm activities had higher levels of consumption compared to those who do not participate in such activities. The study also identified several factors that influence household consumption in rural areas. These factors include education, access to credit, distance to market, TLU (Tropical Livestock Unit), membership in "iqub" (a social financial system), health status, non-farm training, and gender. It was found that gender disparities exist, with female-headed households experiencing lower consumption levels than male-headed households. Based on the findings, the research recommended addressing gender disparities and improving rural infrastructure, particularly in relation to enhancing health services, electricity supply, road transport, and education. Efforts should also be made to overcome the challenges related to access to training and credit. The study emphasized the significance of recognizing the impact of supporting existing social financial systems, such as iqub, in improving household consumption in rural areas.

Novel Lines of Research on the Environmental and Human Health Impacts of Nut Consumption

Nuts have formed part of human diets throughout the ages. In recent decades, research has shown they are key foods in dietary patterns associated with lower chronic disease risk. The current state of climate change, however, has introduced an imperative to review the impact of dietary patterns on the environment with a shift to plant-based diets. Nuts emerge as a significant source of protein in plant-based diets and are a minimally processed and sustainable food. Research in this area is evolving to drive better production methods in varying climate conditions. Nevertheless, nut consumption remains an important contributor to human health. The mechanisms of action can be explained in terms of the nutrients they deliver. Studies of nut consumption have linked components such as monounsaturated fatty acids, plant omega-3 fatty acids, antioxidants, and plant sterols to improved lipoprotein profiles, lower blood pressure, and reduced cardiovascular disease risk. Preliminary research also indicates possible beneficial effects of nut consumption on reproductive health. In any case, the ultimate effects of foods on health are the results of multiple interactive factors, so where nuts fit within dietary patterns is a significant consideration for research translation. This has implications for research methodologies, including categorization within food groups and inclusion in Healthy Dietary Indices. The aim of this narrative review is to outline new focal points for investigation that examine the environmental and some novel human health impacts of nut consumption and discuss future directions for research.

Simulating Agroforestry Adoption in Rural Indonesia: The Potential of Trees on Farms for Livelihoods and Environment

In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry.

Climate change, hunger and rural health through the lens of farming styles: An agent-based model to assess the potential role of peasant farming

Undernutrition is a major contributor to the global-burden of disease, and global-level health impact models suggest that climate change-mediated reductions in food quantity and quality will negatively affect it. These models, however, capture just some of the processes that will shape future nutrition. We adopt an alternative standpoint, developing an agent-based model in which producer-consumer smallholders practice different ‘styles of farming’ in the global food system. The model represents a hypothetical rural community in which ‘orphan’ (subsistence) farmers may develop by adopting an ‘entrepreneurial’ style (highly market-dependent) or by maintaining a ‘peasant’ style (agroecology). We take a first look at the question: how might patterns of farming styles—under various style preference, climate, policy, and price transmission scenarios—impact on hunger and health-supporting conditions (incomes, work, inequality, ‘real land productivity’) in rural areas? imulations without climate change or agricultural policy found that style preference patterns influence production, food price, and incomes, and there were trade-offs between them. For instance, entrepreneurial-oriented futures had the highest production and lowest prices but were simultaneously those in which farms tended towards crisis. Simulations with climate change and agricultural policy found that peasant-orientated agroecology futures had the highest production, prices equal to or lower than those under entrepreneurial-oriented futures, and better supported rural health. There were, however, contradictory effects on nutrition, with benefits and harms for different groups. Collectively the findings suggest that when attempting to understand how climate change may impact on future nutrition and health, patterns of farming styles—along with the fates of the households that practice them—matter. These issues, including the potential role of peasant farming, have been neglected in previous global-level climate-nutrition modelling but go to the heart of current debates on the future of farming: thus, they should be given more prominence in future work.

Socio-ecological trajectories in a rural Austrian region from 1961 to 2011: comparing the theories of Malthus and Boserup via systemic-dynamic modelling

This paper investigates to what extent the theories of Thomas Robert Malthus and Ester Boserup are still useful to analyse population and land-use trajectories in an industrial society at a regional scale. Following a model-based approach toward long-term socio-ecological research, we built two system dynamic models, each representing one theory, and calculated socio-ecological trajectories from 1961 to 2011 for a study region located within the Eisenwurzen region in Austria. Comparing the model trajectories with empirical data reveals opposing results for the fit of the dynamics of 'population and technology' compared to 'land use and technology'. Technology strongly influenced population development, whereas its impact on land-use intensity faded over time. Although these theories are usually seen as opposing, both models identify population development as a main driver for land-use changes, mainly population decreases that contributed to farmland abandonment. We find out-migration to be essential when applying the investigated theories to contemporary societies.

Emergent conservation outcomes of shared risk perception in human-wildlife systems

Human perception of risks related to economic damages caused by nearby wildlife can be transmitted through social networks. Understanding how sharing risk information within a human community alters the spatial dynamics of human-wildlife interactions has important implications for the design and implementation of effective conservation actions. We developed an agent-based model that simulates farmer livelihood decisions and activities in an agricultural landscape shared with a population of a generic wildlife species (wildlife-human interactions in shared landscapes [WHISL]). In the model, based on risk perception and economic information, farmers decide how much labor to allocate to farming and whether and where to exclude wildlife from their farms (e.g., through fencing, trenches, or vegetation thinning). In scenarios where the risk perception of farmers was strongly influenced by other farmers, exclusion of wildlife was widespread, resulting in decreased quality of wildlife habitat and frequency of wildlife damages across the landscape. When economic losses from encounters with wildlife were high, perception of risk increased and led to highly synchronous behaviors by farmers in space and time. Interactions between wildlife and farmers sometimes led to a spillover effect of wildlife damage displaced from socially and spatially connected communities to less connected neighboring farms. The WHISL model is a useful conservation-planning tool because it provides a test bed for theories and predictions about human-wildlife dynamics across a range of different agricultural landscapes.

Agent-Based Modeling in Public Health: Current Applications and Future Directions

Agent-based modeling is a computational approach in which agents with a specified set of characteristics interact with each other and with their environment according to predefined rules. We review key areas in public health where agent-based modeling has been adopted, including both communicable and noncommunicable disease, health behaviors, and social epidemiology. We also describe the main strengths and limitations of this approach for questions with public health relevance. Finally, we describe both methodologic and substantive future directions that we believe will enhance the value of agent-based modeling for public health. In particular, advances in model validation, comparisons with other causal modeling procedures, and the expansion of the models to consider comorbidity and joint influences more systematically will improve the utility of this approach to inform public health research, practice, and policy.

Evolving the Anthropocene: linking multi-level selection with long-term social-ecological change

To what degree is cultural multi-level selection responsible for the rise of environmentally transformative human behaviors? And vice versa? From the clearing of vegetation using fire to the emergence of agriculture and beyond, human societies have increasingly sustained themselves through practices that enhance environmental productivity through ecosystem engineering. At the same time, human societies have increased in scale and complexity from mobile bands of hunter-gatherers to telecoupled world systems. We propose that these long-term changes are coupled through positive feedbacks among social and environmental changes, coevolved primarily through selection acting at the group level and above, and that this can be tested by combining archeological evidence with mechanistic experiments using an agent-based virtual laboratory (ABVL) approach. A more robust understanding of whether and how cultural multi-level selection couples human social change with environmental transformation may help in addressing the long-term sustainability challenges of the Anthropocene.

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances - in terms of model complexity, model evaluation, and model structure - can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from 'yet another model' to doing better science with models.

Exploring agent-level calculations of risk and returns in relation to observed land-use changes in the US Great Plains, 1870-1940

Land-use change in the U.S. Great Plains since agricultural settlement in the second half of the nineteenth century has been well documented. While aggregate historical trends are easily tracked, the decision-making of individual farmers is difficult to reconstruct. We use an agent-based model to tell the history of the settlement of the West by simulating farm-level agricultural decision making based on historical data about prices, yields, farming costs, and environmental conditions. The empirical setting for the model is the period between 1875 and 1940 in two townships in Kansas, one in the shortgrass region and the other in the mixed grass region. Annual historical data on yields and prices determine profitability of various land uses and thereby inform decision-making, in conjunction with the farmer's previous experience and randomly assigned levels of risk aversion. Results illustrating the level of agreement between model output and unique and detailed household-level records of historical land use and farm size suggest that economic behavior and natural endowments account for land change processes to some degree, but are incomplete. Discrepancies are examined to identify missing processes through model experiments, in which we adjust input and output prices, crop yields, agent memory, and risk aversion. These analyses demonstrate how agent-based modeling can be a useful laboratory for thinking about social and economic behavior in the past.

Experiments in globalisation, food security and land use decision making

The globalisation of trade affects land use, food production and environments around the world. In principle, globalisation can maximise productivity and efficiency if competition prompts specialisation on the basis of productive capacity. In reality, however, such specialisation is often constrained by practical or political barriers, including those intended to ensure national or regional food security. These are likely to produce globally sub-optimal distributions of land uses. Both outcomes are subject to the responses of individual land managers to economic and environmental stimuli, and these responses are known to be variable and often (economically) irrational. We investigate the consequences of stylised food security policies and globalisation of agricultural markets on land use patterns under a variety of modelled forms of land manager behaviour, including variation in production levels, tenacity, land use intensity and multi-functionality. We find that a system entirely dedicated to regional food security is inferior to an entirely globalised system in terms of overall production levels, but that several forms of behaviour limit the difference between the two, and that variations in land use intensity and functionality can substantially increase the provision of food and other ecosystem services in both cases. We also find emergent behaviour that results in the abandonment of productive land, the slowing of rates of land use change and the fragmentation or, conversely, concentration of land uses following changes in demand levels.

Cross-site comparison of land-use decision-making and its consequences across land systems with a generalized agent-based model

Local changes in land use result from the decisions and actions of land-users within land systems, which are structured by local and global environmental, economic, political, and cultural contexts. Such cross-scale causation presents a major challenge for developing a general understanding of how local decision-making shapes land-use changes at the global scale. This paper implements a generalized agent-based model (ABM) as a virtual laboratory to explore how global and local processes influence the land-use and livelihood decisions of local land-users, operationalized as settlement-level agents, across the landscapes of six real-world test sites. Test sites were chosen in USA, Laos, and China to capture globally-significant variation in population density, market influence, and environmental conditions, with land systems ranging from swidden to commercial agriculture. Publicly available global data were integrated into the ABM to model cross-scale effects of economic globalization on local land-use decisions. A suite of statistics was developed to assess the accuracy of model-predicted land-use outcomes relative to observed and random (i.e. null model) landscapes. At four of six sites, where environmental and demographic forces were important constraints on land-use choices, modeled land-use outcomes were more similar to those observed across sites than the null model. At the two sites in which market forces significantly influenced land-use and livelihood decisions, the model was a poorer predictor of land-use outcomes than the null model. Model successes and failures in simulating real-world land-use patterns enabled the testing of hypotheses on land-use decision-making and yielded insights on the importance of missing mechanisms. The virtual laboratory approach provides a practical framework for systematic improvement of both theory and predictive skill in land change science based on a continual process of experimentation and model enhancement.