Land-use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called ‘frontiers’, defined here as places experiencing marked transformations owing to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular, into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves and actors’ anticipations as key mechanisms by which frontiers emerge. Processes of differential rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics but can be inspirational for other frontier processes including for extractive resources, such as minerals.

Loneliness and social isolation among farmers are growing public health concerns. The contributing factors are manifold, and some of them are linked to structural change in agriculture, for instance because of higher workloads, rural depopulation, or reduced opportunities for collaboration. Our work explores the interconnections between loneliness, social contacts, and structural factors in agriculture based on a survey of 110 farm managers in the mountain region of Entlebuch, Switzerland combined with agricultural census data. We use path analysis, in which loneliness is the main outcome, and social contacts are an explanatory and explained variable. We find that 3 respondents report that they feel lonely frequently or very frequently, and the rest sometimes (20 workloads report feeling lonely more frequently, and this relationship is direct, as well as indirect because of less frequent social contacts. However, physical isolation is not a significant predictor of loneliness. Moreover, short food supply chains correlate with less frequent loneliness feelings. Our study sheds light on the effects that structural change can have on the social fabric of rural areas.

A crop boom is a sudden, nonlinear and intense expansion of a new crop. Despite their large impacts, boom-bust dynamics are not well understood; booms are largely unpredictable and difficult to steer once they unfold. Based on the striking resemblances between land regime shifts and crop booms, we apply complex systems theory, highlighting the potential for regime shifts, to provide new insights about crop boom dynamics. We analyse qualitative and quantitative data of rubber and banana plantation expansion in two forest frontier regions of northern Laos. We show that preconditions , including previous booms, explain the occurrence (why) of booms, and triggers like policy and market changes explain their timing (when). Yet, the most important features of booms, their intensity and nonlinearity (how), strongly depended on internal self-reinforcing feedbacks . We identify built-in feedbacks (neighbourhood effects and imitation) and emergent feedbacks (land rush) and show that they were social in nature, multi-scale from plot to region and subject to thresholds. We suggest that these are regular features of booms and propose a definition and causal-mechanistic explanation of crop booms, examining the overlap between booms and regime shifts and the role of frontiers. We then identify opportunities for management interventions before, during and after booms.

Spain is the largest producer of avocado and mango fruits in Europe. The majority of production is concentrated in the Axarquía region in the south, where subtropical fruit plantations and associated water demands have steadily increased over the last two decades. Between 2019-2024, the region underwent an extreme water crisis. Reservoir reserves became nearly depleted and groundwater levels dropped to sea level in several locations, where seawater intrusion is likely, causing large socioeconomic impacts including short-term harvest losses and a long-term loss in economic centrality. We examine the causal pathway that led to this crisis using a mixed-methods approach, combining data from key informant interviews, an exhaustive review of legal documents, and quantitative analysis of time series and spatially explicit data. In particular, we analyze dam water use for irrigation and urban use, meteorological data, reservoir and groundwater levels, and irrigation land cover maps. Our findings show that an unusual meteorological drought was the immediate cause for the decline in reservoir and groundwater reserves (hydrological drought), but the underlying cause was a chronic and structural long-term imbalance between water demand and resources resulting from several structural governance shortcomings: large uncertainties in water resource availability and use hampering effective planning, lack of enforcement of individual water quotas, and the absence of regulatory mechanisms to flexibly impose resource use restrictions at both micro and macro levels based on the overall resources of the system. We propose concrete policy interventions aimed at sustainably enhancing the resilience of the system that can be useful to efficiently manage water shortages in other regions with similar problems.

The expansion of commercial agriculture is one of the primary drivers of livelihood and land-use changes in the world. Globalisation and other factors have intensified this expansion to the point where booms in single cash crops overtake entire regions before going bust, a pattern that is particularly pervasive in resource frontiers. Using case studies across the Mekong Region, a place which serves as a harbinger for crop booms globally, we propose a new analytical framework for understanding and governing crop booms. We combine multiple theoretical approaches to study crop booms and draw on insights from case study work conducted across temporal and spatial scales. The framework consists of three components: 1) the nested nature of crop boom-bust trajectories, 2) the cyclical spatial and temporal patterns of crop booms, and 3) the variegated pathways and impacts of agrarian change. The framework presents new insights into the processes of agricultural intensification in frontier spaces. As such, it facilitates a better understanding of the drivers, characteristics and impacts of crop booms for researchers and decision-makers alike with the intention of supporting efforts to develop more sustainable pathways in the region and beyond.

Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study calls for an extended perspective when managing CASs, emphasizing the need to focus not only on thresholds of external conditions but also the rate at which those conditions change, particularly in the context of the collapse of surrounding systems that contribute to the focal system's resilience. Our analytical method opens a path to designing management policies that mitigate RIT impacts and enhance resilience in ecological, social, and socioecological systems. These policies could include controlling environmental change rates, fostering system adaptability, implementing adaptive management strategies, and building capacity and knowledge exchange. Our study contributes to the understanding of RIT dynamics and informs effective management strategies for complex adaptive systems in the face of rapid environmental change.

The sustainability of renewable resource harvesting may be threatened by environmental and socioeconomic changes that induce tipping points. Here, we propose a synthetic harvesting model with a comprehensive set of socioecological factors that have not been explored together, including market price and stock value, effort and processing costs, labour and natural capital elasticities, societal risk aversion, maximum sustainable yield ( MSY ), and population growth shape. We solve for harvest rate and stock biomass solutions by applying a timescale-separation between fast ecological dynamics and slow institutional adaptation that responds myopically to short-term net profit. The result is a cusp bifurcation with two composite bifurcation parameters: 1. consumptive scarcity λ c or the ratio of market price-to-processing cost divided by MSY (leading to a pitchfork), and 2. non-consumptive scarcity λ n or the stock value minus a scaled effort cost (leading to saddle-nodes or folds). Together, consumptive and non-consumptive scarcities create a cusp catastrophe. We further identify four tipping phenomena: 1. process (harvest rate) noise-induced tipping; 2. exogenous ( λ c ) rate+process noise-induced tipping; 3. exogenous noise-induced reduction in tipping; and 4. exogenous cycle-induced reduction in tipping. Case 2 represents the first mechanistically motivated example of rate-associated tipping in socioecological systems, while cases 3 and 4 resemble noise-induced stability. We discuss the empirical relevance of catastrophe and tipping in natural resource management. Our work shows that human institutional behaviour coupled with changing socioecological conditions can cause counterintuitive sustainability and resilience outcomes.

CONTEXT: Farm numbers are steadily declining in Europe and globally while farms become larger and more intensive. Driven in part by worsening macroeconomic conditions, these structural changes and the associated rationalization of agricultural supply chains have affected social relations in rural areas. In turn, farmers' social contacts influence farming decisions. Social and structural changes are thus interconnected, and they affect the resilience of rural areas through their influence on environmental, social, and economic capital. OBJECTIVE: We examine the connection between farm structures and farmers' social contacts in the UNESCO Biosphere Reserve Entlebuch (UBE), a mountain region in central Switzerland with a strong presence of family farms, and explore the implications of social and structural change for rural resilience. METHODS: We conduct a survey of N = 102 farming households and combine it with farm-level agricultural census data and interviews with key stakeholders (N = 13) to analyze farmers' current social contacts and their changes since the year 2000. We use regression and cluster analyses to examine the relationship between (changes in) social contacts and farm-level characteristics. RESULTS AND CONCLUSIONS: Farmers in the UBE have a high, but decreasing frequency of contacts with family, friends, and colleagues and lower, but increasing frequency of commercial and administrative contacts. Workloads have increased by 6% in five years, driven by farm-level expansion of agricultural area (+5%)-including expanding ecological compensation areas-and intensification in managed areas (+3%), leading to parallel processes of intensification and extensification. Since most of these family farms do not hire workers, growing workloads directly impinge on farmers' free time, affecting informal contacts most. Farm managers in larger and more intensive farms have more frequent and more diverse, but also more rapidly declining, social contacts. Our results point to a net loss in social capital as social contacts become less frequent and shift from local and informal to regional and national professional contacts. SIGNIFICANCE: A 17% decline in farm numbers in 15 years reflects the vulnerability of farms in this region. Growing financial strain, workloads, time pressure and the associated erosion of informal contacts contribute to this vulnerability. Policymakers from local to national should recognize the contribution of farmers' diverse social networks towards rural resilience and seek options to maintain and enhance such networks. Beyond direct interventions that foster social capital, policymakers should more rigorously consider the short-and long-term interconnections and tradeoffs between different forms of capital.

Crop booms in forest frontiers are a major contributor to land conversion and deforestation. In this study, we investigate the smallholder-driven northern Laos rubber boom in two case study areas (CSAs) with different speed and intensity of rubber expansion. We assess the relative importance of market, contextual, and behavioral factors in fostering or hindering the conversion of forest to rubber plantations. We develop a Bayesian network (BN) model of land-use change based on household surveys, expert interviews, market price data, and land use maps covering the period 2000–2017. We use regression analysis to inform the structure of the BN, compare model results, and analyze time-varying effects. The BN and regression models incorporate perceived price signals as a combination of market price and local price knowledge, and local self-reinforcing imitation dynamics as a combination of aggregate rubber conversion and imitation behavior elicited in the survey. Results show that deforestation was lower in strictly protected areas but not in forests with lesser protection status. Imitation had a large effect on rubber uptake in both areas. In the CSA that experienced the most intensive spread of rubber, price signals transmitted through social networks had a significant impact, especially throughout the stage of rapid expansion. Rubber expansion continued in both areas during periods of descending prices mainly because of increased cash availability and access to inputs. Our research sheds light on the underlying dynamics of crop booms and contributes to the understanding of agricultural expansion processes.

Agricultural transitions from subsistence to export-oriented production make households more reliant on volatile agricultural commodity markets and can increase households' exposure to crop price and yield shocks. At the same time, subsistence farming is also highly vulnerable to crop failures. In this work, we define household livelihood vulnerability as the probability of falling under an income threshold. We propose the use of a Bayesian network (BN) to calculate the income distribution based on household and community-level variables. BNs reflect relationships of dependence between variables and represent all variables as probability distributions, which allows for the explicit propagation of variability and uncertainty between variables. We focus on two agricultural frontier case study areas (CSAs) in northern Lao PDR that are at different stages in the transition from subsistence to export-oriented agriculture. Because agricultural production is the main livelihood activity in both CSAs, we develop a BN that calculates the probability distribution of net household agricultural production income. BN structure and parameterization are based on data collected in 110 household surveys across both CSAs, as well as interviews with villagers, government officials, and private sector actors. We analyze the effect of crop price and yield variability, land-use portfolio, and land holdings, on the probability of having a negative net agricultural income, which reflects a household's ability to meet its food consumption needs through cash crop sales. Results show that agricultural income is highly sensitive to rubber plantation area, rubber yield, and rubber price given the very large income potential of the crop. Households with larger agricultural areas have a lower probability of falling under an agricultural income threshold regardless of their diversification choices. Households that own more high-value cash crops are more buffered against rice yield shocks despite having higher agricultural income variability. However, low-income households are better off if they maintain a minimum level of rice sufficiency in combination with high-value cash crop production. Diversifying upland cash crops by increasing the share of cardamom (a low-value but low-volatility crop) at the expense of rubber (a highly lucrative crop with high price volatility) does not have a sizable beneficial impact, because returns from cardamom are significantly lower than for rubber. We show that BNs can be useful tools for the design and evaluation of rural development policies.

Crop booms in forest frontiers are a major contributor to deforestation and global change. Because of their nonlinearity, intensity, and unpredictability, booms are specific instances of land change, namely land system regime shifts, which require an analysis going beyond that of their drivers or individual actors' decisions. So far, the combined effect of behavioral dynamics at the household, village, and higher levels, which are often mutually-reinforcing, have not been considered in the empirical analysis of crop booms. In this paper, we aim to further the understanding and the theory behind the dynamics of crop booms and land regime shifts. We focus on the smallholder-driven northern Laos rubber boom and analyze two case study areas with different intensity of rubber expansion. We use a combination of household surveys and interviews with villagers, government officials and private sector actors to analyze the preconditions, triggers and reinforcing effects at household and higher levels that help explain the timing and extent of the boom. In particular, we focus on the role of information transmission and imitation in household decisions to adopt and expand rubber. Our findings show that the rapid expansion of rubber in northern Laos was in part the result of household decisions spurred by economic and policy triggers that changed the real and perceived benefits of growing rubber. In addition, there were higher-level and mutually-reinforcing dynamics, such as the conversion of village communal forests, a rush for land, and individual behavior contingent on others', including imitation. The transmission of information through social networks played a key role in rubber adoption decisions, but the diffusion of new norms and values was also important and may have accelerated adoption decisions. Rubber adoption and expansion decisions thus had normative and informational, as well as knowledge-based and imitation components.

The European Renewable Energy Directive (EU RED) requires biofuels to reduce greenhouse gas emissions (GHG) by 35% compared to fossil fuels in order to count towards mandatory biofuel quota or to be eligible for financial support schemes. This reduction target will rise to 50% in 2017. For biofuel producers this implies that they want or need to calculate their emissions. The purpose of this paper is to compare two calculation tools for economic operators that are on their way to the market: the "BioGrace tool" and the "Roundtable on Sustainable Biofuels (RSB) GHG tool" for GHG calculations under the Renewable Energy Directive (both of which are freely available). Greenhouse gas emissions from four production pathways were calculated: ethanol from wheat, ethanol from sugarcane, biodiesel from rapeseed and biodiesel from palm oil. In addition, three land use change (LUC) scenarios were calculated: for expansion of the biofuel cultivation area to grassland and to forest (10-30% canopy cover) and for improvement of agricultural practices. Both tools follow the methodology of the European Renewable Energy Directive and exactly the same input data along the production chain was used. Despite this, the results were significantly different. GHG emissions of the pathway ethanol from wheat were 21% lower when calculated with the BioGrace tool than with the RSB GHG tool. Differences were most pronounced in the cultivation phase with 20% deviation between the tools for biodiesel from palm oil and 35% deviation for ethanol from wheat and sugarcane. In practice this means that an economic operator can enhance the GHG performance of his biofuel by 20-35% by using a different calculation tool without improving the production process. We identified the use of different standard values in the two tools, in particular for the production of N-fertilisers, for chemicals and electricity and one methodological choice regarding the calculation of field N2O emissions as source of these differences. This methodological point is not specified in the Renewable Energy Directive, giving economic operators and tool developers free choice. GHG emissions from land use changes varied by -14% to 49% due to differences in carbon stock data, methodological differences in allocation and a lack of precise land use type definitions. We conclude from the results that there is a need for a deep harmonisation in the calculation process that goes beyond the methodological framework set up in current legislation. These findings are relevant because they show a policy gap, a regulatory gap that needs to be addressed by policy makers in order to guarantee a level playing field on the market and to create an incentive to improve the GHG performance of biofuel production. (C) 2012 Elsevier Ltd. All rights reserved.

The accuracy of wildfire air pollutant emission estimates was assessed by comparing observations of carbon monoxide (CO) and particulate matter (PM) concentrations in wildfire plumes to predictions of CO and PM concentrations, based on emission estimates and air quality models. The comparisons were done for observations made in southeast Texas in August and September of 2000. The fire emissions were estimated from acreage burned, fuel loading information, and fuel emission factor models. A total of 389km2 (96,100acres) burned in wildfires in the domain encompassing the Houston/Galveston-Beaumont/Port Arthur (HGBPA) area during August and September 2000. On the days of highest wildfire activity, the fires resulted in an estimated 3700tons of CO emissions, 250tons of volatile organic carbon (VOC) emissions, 340tons of PM2.5, and 50tons of NOx emissions; estimated CO and VOC emissions from the fires exceeded light duty gasoline vehicle emissions in the Houston area on those days. When the appropriate aircraft data were available, aloft measurements of CO in the fire plumes were compared to concentrations of CO predicted using the emission estimates. Concentrations estimated based on emission predictions and air quality models were within a factor of 2 of the observed values. The estimated emissions from fires were used, together with a gridded photochemical model, to characterize the extent of dispersion of the fire emissions and the photochemistry associated with the fire emissions. Although the dispersion and photochemical impacts varied from fire to fire, for wildfires less than 10,000acres, the greatest enhancements of CO and ozone concentrations due to the fire emissions were generally confined to regions within 10–100km of the fire. Within 10km of these fires, CO concentrations can exceed 2ppm and ozone concentrations can be enhanced by 60ppb. The extent of photo-oxidant formation in the plumes was limited by NOx availability and isoprene emissions from forested areas downwind of the fires provided most of the hydrocarbon reactivity in the plumes.

A modified version of the Global Biosphere Emissions and Interactions System (GloBEIS) was used to predict biogenic sesquiterpene emissions in the Houston-Galveston Area (HGA) in southeast Texas. The estimates were based on a land cover database containing more than 600 land cover categories at a resolution of approximately one kilometer and emission factors taken from literature. Average sesquiterpene emission fluxes were estimated to be between 0.07 and 0.65 kg carbon/km(2)-h. Fluxes at the lower end of the estimated range are consistent with observed concentrations of geologically modern carbon in Houston aerosol.

Accurate estimates of biogenic volatile organic compound emissions are critical for air quality planning in areas such as Eastern Texas where biogenic emissions comprise a significant fraction of the total volatile organic compound inventory. Uncertainties in biogenic volatile organic chemical emission estimates associated with different land use databases, surface temperature databases, and temperature interpolation methods were quantified and compared. The sensitivity of isoprene emissions to land use classification was investigated by comparing predictions based on land use data recently compiled for Eastern Texas to those based on the Biogenic Emissions Landcover Database version 3.1 (BELD3). Previous studies have only made these comparisons with the previous BELD version 2 database. Isoprene emission increased throughout much of Eastern Texas because areas classified as agricultural or savannah in BELD3 were more accurately classified as Post Oak, Live Oak, mesquite, and juniper in the new database. These results indicate the need for land use studies in areas poorly characterized in the BELD3. The sensitivity of isoprene emission estimates to uncertainties in surface temperatures were investigated by comparing predictions based on two different temperature databases and three different interpolation techniques. Spatial interpolations of surface temperatures collected at available Automated Surface Observing System (ASOS) stations in Houston, Austin, and Dallas were similar to the spatial interpolations of surface temperatures obtained from the ETA Data Assimilation System (EDAS). As a result, substantial variations in isoprene emissions were not observed over the majority of the modeling domain; however, differences of 4F over localized regions produced a 35% difference in isoprene emissions. Comparisons between the isoprene emissions of the three interpolation methods sometimes revealed large variations, with maximum temperature differences of 4F resulting in 60% differences in isoprene emissions in areas with the highest isoprene emissions. It was noted that the ASOS stations were clustered in urban areas and not in areas with the highest biogenic emissions. More ambient temperature monitors need to be located in rural locations to provide robust estimates of biogenic emissions and facilitate validation of interpolated temperature fields.