Food insecurity in the United States (U.S.) has surged since the COVID-19 pandemic at the same time as the fewest number of farms remain since the introduction modern agriculture. However, small-to mid-scale farms (<$350,000 in annual sales) are being lost at higher rates than those with more than $1 million in annual sales, which have experience growing commodity farm sales for the last two decades. Understanding these trends requires understanding where in the food supply chain value is captured and how continued structural transformations of agricultural value chains impact the economic viability of farms of various sizes. The farm share of the food dollar (FSFD) is a metric that provides insight into the distribution of food expenditures among agricultural value chain actors. However, its application has thus far been limited to state- or national-level comparisons, masking subnational, spatial variations in value capture that influence the viability and resilience of local and regional food systems. This article presents a county-level estimate of the farm share of the food dollar for fresh fruits, vegetables, and nuts (FVN) in 2021 for the contiguous U.S. estimated from multiple publicly available data sources. Relative to the United States Department of Agriculture national-level average estimate of $0.388 FSFD for FVNs, county-level estimated values varied widely and ranged between the maximum of $0.088 to values approaching zero (<$0.001). A cluster analysis identified seven clusters with inverse relationships between FSFD values and other structural and economic variables, such as farm size and proportion of cropland in commodity crops. In general, a negative spatial relationship between the presence of commodity row crops and FSFD for FVN was observed. Moreover, lower FSFD values corresponding with counties with larger average farm sizes and declining numbers of farms, and higher FSFD values were associated with higher local farm sales (e.g., direct to consumer, food hub, or wholesale market channels). Although this county-level estimate of FSFD was limited to FVN producers, it provides unique insights into structural challenges faced by smaller-scale producers in the U.S., such as the (lack of) market channel diversity and uneven distribution of (political, economic, and social) power in food supply chains. These challenges have far-reaching implications for producer livelihoods with knock-on impacts on diet-related health of rural communities.

Climate-smart agriculture (CSA) was introduced in 2010 by the United Nations Food and Agriculture Organization to help maintain the economic, environmental, and social sustainability of farming operations under changing climatic conditions. Since its introduction, CSA has received global recognition, with many nations incorporating its principles into their national policies. Extensive literature evaluates the benefits and effectiveness of agricultural practices in achieving CSA goals and documents the various factors that influence their adoption on farms. However, CSA has also been repeatedly contested, as scholars and experts worldwide have raised concerns about a lack of conceptual clarity, the prioritization of technological solutions, and the neglect of social, financial, and policy measures. Given CSA's rapid development over the past decade, an umbrella review of existing systematic reviews and meta-analyses was conducted to provide a comprehensive overview of how CSA has been conceptualized and studied in the scientific literature and to identify remaining gaps to inform future research efforts. The findings reveal inconsistencies in its interpretation; confusion about what constitutes a climate-smart practice or technology; and a tendency to reinforce a sustainable intensification-oriented framing of CSA, as the current scientific discourse pays little attention to farmers, their livelihoods and needs, and other actors and aspects of agriculture. Research needs to go beyond assessing individual agricultural practices or technologies and their effectiveness in meeting CSA goals. Instead, greater emphasis should be placed on participatory approaches that engage diverse stakeholders to co-produce solutions aligned with farmers' needs and prevailing challenges, while simultaneously reducing environmental impacts.

Costa Rica has recently transitioned from a minor pass-through location to a major transshipment hub connecting South American cocaine sources to transatlantic markets. This study investigated how much direct and indirect land-use change (LUC) was caused by narco-trafficking throughout the biodiverse Osa Peninsula and surrounding Area of Osa Conservation (ACOSA) from 1986 to 2019. Within 3 years of increased narco-trafficking activity, rates of forest and oil palm change were on average 8.76% and 29.5% slower than in control locations, respectively. This translated into 178.5 more hectares of forest cover and 2,013 less hectares of oil palm during the study period. Moreover, this effect persisted for forested areas for the duration of the study period with an overall 5.06% slower rate of change. Narco-trafficking intertwines with legal economies in Costa Rica in complex ways and manifests as spatially concentrated ‘narco-degradation’ rather than large-scale ‘narco-deforestation’ observed in other conflict regions.

IntroductionExisting agricultural studies in Alabama have explored state-level factors that impact commercial agricultural systems but often overlook specific structural and socio-economic constraints facing small-to-medium scale farms. In Alabama’s Black Belt region, small-to-medium scale farms operated by African American (Black) farmers face a myriad of challenges including a lack of knowledge on innovative farming practices, socio-economic factors, and limited access to resources.MethodsThis study aims to address this gap by exploring farm management practices in the Alabama Black Belt, to identify common barriers to adopting innovative strategies through a social ecological framework. We utilize semi-structured interviews (n = 25) combined with remotely sensed biophysical data to inform our multivariate Bayesian probability model.ResultsOur modeling suggests that farm management choices are likely driven by hierarchies of structural constraints rather than singular preferences, with distinct spatial clustering indicating the influence of unobserved local contexts. Specifically, the analysis points to access to extensive information networks - rather than simple awareness—as a potential primary driver for irrigation adoption. Furthermore, our findings suggest that leasehold tenure, and market specialization pressures may inhibit crop diversification, while labor availability emerges as a critical constraint for livestock integration.ConclusionThese results challenge generalized classifications of small-to-medium scale farms, highlighting a need for site-specific policies that address tenure security, information access, and labor deficits to improve food security for small-to-medium scale farms across Alabama Black Belt and the Southeastern US.

Farmers have time and again adopted new methods or technologies. However, recent increases in global temperatures and occurrences of extreme weather events, call for an urgency to address and reduce the risks associated with climate change. Irrigation is a key adaptation that reduces crop heat stress and enhances agricultural production. Alabama is considered water-rich but lately has experienced increased rainfall variability and temperature extremes. Various state-wide initiatives to increase irrigation have been implemented, but adoption remains limited. Existing studies have explored factors influencing irrigation uptake, but none have engaged in a state-level assessment of its adoption potential. In this study, we provide spatially explicit estimates of the potential to implement irrigation practices across the state. Moreover, we derive an irrigation adoption index map for Alabama to identify areas where implementation is more or less likely based on a multi-criteria analysis. The results highlight a large potential for expansion in areas that have high shares of existing irrigation. Such an analysis can enable targeted mobilization of resources towards areas where uptake is currently low but feasible through increased adaptation efforts. Additionally, these estimates can be further used to evaluate future water demands or conduct other regional analyses.

Climate policy faces increasingly complex challenges that span multiple human decision scales in nature-society systems. Contemporary climate policy models, while valuable and increasingly versatile in handling spatial and temporal scales, struggle to capture interacting multiscale decisions on the socioeconomic side. This perspective draws attention to the power of coupling among different modeling families, taking integrated assessment models (IAM), computable general equilibrium models (CGE), and agent-based models (ABM) as examples. Recent computational advances, maturity of models, availability of data, and interdisciplinary expertise make model coupling an increasingly feasible, effective, and useful tool for climate policy analysis. We examine the unique contributions of each modeling approach, highlight synergies from uniting their strengths, and discuss alternatives to and conditions for coupling. In addressing methodological challenges, we present examples of effective coupling of IAM-ABM-CGE, emphasizing the importance of maintaining model integrity while enhancing policy relevance. By bridging human decision scales and leveraging complementary strengths, coupled models can provide nuanced insights into climate-economy interactions, ultimately supporting effective and equitable-not just efficient and optimal-climate policies.

Transnational cocaine trafficking, or ‘narco-trafficking’, networks often move large shipments of harmful and illegal drugs by sea through the use of unregulated boats in remote maritime spaces. This study presents a framework for identifying narco-trafficking drop-off zones by detecting and analyzing Unreported and Unregulated Boats (UUBs) potentially linked to narco-trafficking in Costa Rica’s Guanacaste and Puntarenas regions, utilizing a combination of high-resolution satellite imagery, machine learning, and spatial analysis. By training a YoloV5 convolutional neural network model, we detected boat wakes in PlanetScope satellite images, which were then cross-referenced with legal vessel traffic data (Automatic Identification System and Vessel Monitoring System) to isolate UUBs. A spatial autocorrelation analysis revealed a positive association between UUB locations and narco-trafficking activity indicators, such as drug-related media reports, court arrest records, and cocaine seizure data. High-high clusters of UUBs and trafficking indicators suggested that particular coastal districts may serve as primary landing zones for illicit shipments, a finding consistent with secondary data on cocaine trafficking in the region. By integrating geospatial intelligence with contextual data sources, this study advances methodology for identifying narco-trafficking drop-off zones and contributes a spatially explicit perspective to the broader understanding of cocaine and other illicit supply chains. Despite the limitations of cloud cover and restricted nighttime visibility, this framework offers a proof-of-concept approach for identifying UUB concentrations and cocaine drop-off shipment zones. Future work should consider expanding temporal coverage and multiple imagery to further enhance the identification of narco-trafficking zones.

Agent-based modeling proliferates across applications and scientific disciplines. The downsides of this success are the plurality of code implementations and redundant solutions to recurring modeling tasks. It is especially critical for simulations concerned with modeling human behavior and social institutions. Reusable building blocks (RBBs) are seen as a solution due to their potential to foster standardization grounded in best practices, integration of domain knowledge (including qualitative social sciences) in code, and efficient model design. RBBs are compact code components representing mechanisms or processes useful across models and applications. RBBs have been extensively discussed in the agent-based community, with little progress in implementation. Here, we present an open-access online community platform-AGENTBLOCKS-designed to facilitate the sharing, comparison, review, reuse, and improvement of RBBs. As an international community effort, AGENTBLOCKS leverages lessons from past RBBs discussions and principles from other modeling communities that successfully apply modular, reusable code practices. The paper introduces the interface and structure of this repository, presents templates for RBBs documentation, provides tips to support aspiring users, and first examples. We highlight the need for alternative RBB implementations that share the same generic description. We also acknowledge that RBBs might represent different levels of interactions, starting from decisions concerning a single agent to interactions between multiple agents or agents and their environment. While initially designed to assist agent-based community, the platform can be utilized by other modelers (e.g. system dynamics, integrated assessment, equilibrium) who seek to improve the representation of human behavior, micro-level processes, heterogeneity, interactions, learning, and other complex dynamics. Naturally, the platform is only one element in the chain towards a successful adoption of best software development practices like RBBs. Future work should focus on populating the repository, refining review processes, and systematizing the variety of RBBs' implementations including engagement with domain experts. Following this initial phase, we hope to further support technical improvements of the platform and widen its impact in and beyond the agent-based community.

Transitions toward sustainable food systems are urgently needed to support planetary health. While farmer and consumer behavior are key drivers of sustainability, both are constrained by entrenched power structures that currently reinforce industrialized agriculture and supply chains. These structures are rarely represented in models of agri-food systems, limiting assessments of “deep” leverage points for transformation. Here, we utilize two variables—capital and values—to develop a stylized agent-based model of power dynamics between farmers, consumers, markets, and the state. Simulations show that a widespread shift toward sustainability-aligned values among both farmers and consumers is necessary for a system-wide transition. Moreover, interventions to limit power concentration can enable tipping points, with transitions occurring when only 20% of farmers’ and consumers’ values shift. Our model advances understanding of how power structures interact with individuals’ behavior to produce lock-ins as well as how mobilizing sustainability-aligned values could enable more desirable futures.

Achieving large-scale, transformative climate change adaptations in agriculture while mitigating further climate impacts and supporting sustainable and equitable rural livelihoods is a grand challenge for society. Transformation of the agri-food system is necessary and inevitable, but the extent to which transformation can be intentionally guided toward desirable states remains unclear. We argue that, instead of targeting leverage points (LPs) in isolation, coordinated interventions at multiple LPs and their interactions are necessary to create a broader system transformation toward more adaptive futures. Using the southeastern United States of America as a case study, we conceptualize a way of doing transformation research in agri-food systems that integrates multiple theoretical and practical perspectives of how transformative pathways can be constructed from ‘chains’ of interacting LPs. We outline several principles for transformative research, the core of which are participatory, transdisciplinary, and convergence research methods needed for articulating a shared vision. These principles embrace an action-oriented approach to research in which the act of assembling diverse networks of researchers, stakeholders, and community partners itself can activate community- and regional-level LPs to scale up changes. Finally, we present tangible examples of specific LPs and their interactions targeted by agri-food system interventions currently underway or planned. This work offers an ‘anticipatory’ vision for agri-food system transformation research that recognizes the need to normatively create an enabling environment to build momentum toward shared visions of secure, equitable, and sustainable regional agri-food systems.

Many contemporary social and environmental problems are increasingly 'wicked.' Convergence research offers an effective approach to tackle wicked problems by integrating diverse epistemologies, methodologies, and expertise. Yet, there exists little discussion of how to develop and employ a convergence research approach. This article describes our collaborative research efforts to achieve convergence research and team science. For over a decade, we have sought to understand how drug trafficking activities, and the counternarcotics efforts designed to thwart them, catalyze catastrophic changes in landscapes and communities. We first discuss how understanding our wicked problem called for epistemological convergence of diverse data through a team science approach. We then unpack the potential insights and challenges of methodological convergence by drawing upon examples from our land cover and land use change analysis. Third, we argue that the nature of complex, pressing problems requires convergence research to be politically engaged and accountable to the multiple communities affected. This article aims to provide research teams insight into how to pursue epistemological and methodological convergence while attending to the inherent politics of producing knowledge about wicked problems.

Water systems in the US are experiencing increasing challenges because of poor governance, unsustainable fiscal policies, an aging workforce, new environmental regulations, and concerns over environmental justice. These challenges will only increase if the specific constraints and barriers to system viability are not first identified and then translated into new policies and best management practices to ensure system sustainability, reliability, resilience, and equity of services. This paper proposes a methodology to accomplish this objective that integrates agent-based models, water distribution models, and sustainability performance models within a larger system dynamics framework.

International conservation efforts and prohibitionary drug intersect in unexpected ways throughout the Mesoamerican Biological Corridor (MBC). US-led counterdrug interdiction of transnational cocaine trafficking, or 'narco-trafficking', is increasingly pushing narco-traffickers and their associated environmental destruction into protected areas (PAs) to establish new smuggling routes. These locations are also where the greatest densities of jaguars (Panthera onca), an iconic and declining species, are found in Central America. Intersecting two geospatial datasets estimating 1) jaguar densities and 2) changes in landscape suitability for drug trafficking following counterdrug interdiction pressure, we estimated that roughly 69 % of the estimated population of jaguars in the MBC were found in areas of increased suitability for narco-traffickers. Moreover, jaguar populations within PAs were 2.5 and 34 times more likely to be in increased narco-trafficking suitability areas than those in jaguar corridors or other area without conservation designations, respectively. These findings illustrate the full costs of continuing current counterdrug interdiction policies alongside conventional conservation strategies and suggest that community-based conservation governance may more effectively discourage narcotrafficking activities and enhance conservation outcomes.

Complex social challenges such as narco trafficking can have unexpected consequences for biodiversity conservation. Here we show how international counter-drug strategies may increase the risk of narco trafficking, which is associated with deforestation, in two-thirds of the important landscapes for forest birds in Central America. Soberingly, over half of Nearctic-Neotropical migratory species had more than one quarter, and 20% of species had over half, of their global population in areas threatened by narco trafficking, suggesting the need for more holistic strategies to better protect native biodiversity. Narco trafficking and subsequent counter-drug interdiction strategies can lead to loss of biodiverse forests, which are important habitats for resident and migratory bird species. This study evaluates how such activities can threaten the bird habitat in Central American forests.

We assess how much of Central America is likely to be agriculturally suitable for cultivating coca ( Erythroxylum spp), the main ingredient in cocaine. Since 2017, organized criminal groups (not smallholders) have been establishing coca plantations in Central America for cocaine production. This has broken South America’s long monopoly on coca leaf production for the global cocaine trade and raised concerns about future expansion in the isthmus. Yet it is not clear how much of Central America has suitable biophysical characteristics for a crop domesticated in, and long associated with the Andean region. We combine geo-located data from coca cultivation locations in Colombia with reported coca sites in Central America to model the soil, climate, and topography of Central American landscapes that might be suitable for coca production under standard management practices. We find that 47% of northern Central America (Honduras, Guatemala, and Belize) has biophysical characteristics that appear highly suitable for coca-growing, while most of southern Central America does not. Biophysical factors, then, are unlikely to constrain coca’s spread in northern Central America. Whether or not the crop is more widely planted will depend on complex and multi-scalar social, economic, and political factors. Among them is whether Central American countries and their allies will continue to prioritize militarized approaches to the drug trade through coca eradication and drug interdiction, which are likely to induce further expansion, not contain it. Novel approaches to the drug trade will be required to avert this outcome.

Invariable warming trends of global climate and increase in uncertainties in seasonal precipitation are major threats to crop production and subsequently, to food security. Simulation is needed to understand the suitability of potential adaptation strategies to mitigate the impacts of uncertain climate change scenarios on agricultural production. This study investigates the influence of climate change on maize yield in the Mobile River Basin (MRB) in the southeastern United States using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. We use four climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socio-economic Pathways (SSPs) of SSP245 and SSP585 to represent future changes in solar radiation, precipitation and temperature. In this study, we simulate crop yields using climate data from the past (1985-2010), the experimental period (2011-2017), and future projections (2026-2050, 2050-2075, and 2076-2100). The simulated crop yields are compared to historical yields to evaluate the adaptation measures selected to mitigate the impact of future climate scenarios, assuming no effective adaptation measures or changes in farming practices. The findings indicated that by end of the 21st century, maize yield will fall by 8.2 % (-842 kg.ha(-1)) and 16.4% (-1684 kg.ha(-1)) under the SSP245 and SSP585 scenarios, respectively. Future climate change will have a significant impact on maize production in MRB, and will require optimal adaptation measures to manage agricultural production loss. We evaluate several adaptation strategies including optimization of planting date, fertilizer application date, implementing supplemental irrigation and modification of fertilizer doses. The study concludes that significant improvement in corn yield under the changed climatic patterns assumed as per the SSPs considered, is possible by planting one week ahead, fertilizing two weeks ahead, and using suitable supplementary irrigation during the cropping season. The findings of this study can be utilized in adapting to climate change and advancing sustainable agricultural development in the MRB.

This study aims to explore global food security, focusing on major cereal crops across different Agroecological Zones (AEZs). By projecting cereal production under different Shared Socioeconomic Pathways, insights into the challenges for achieving global food security by 2050 are drawn. The study identifies 'critical' risks in countries like Chad, Sudan, Algeria, Somalia, and Namibia in Africa, parts of Central Asia and the Middle East (Saudi Arabia), western USA, and Australia, due to high water stress combined with severe production deficits. However, implementing strategic interventions, like increasing harvested area, can significantly reduce these risks, potentially leading to surplus production in some regions. The regions still under cereal production deficit with such mitigation strategies are categorized in terms of risk to food security, considering water stress and import dependency. Iran, Venezuela, Sub-Saharan Africa, Saudi Arabia, parts of Southeastern Asia are projected to face persistent cereal production deficits and high import dependency by the mid-20th century. The study underlines the necessity for water-saving technologies and effective governance to balance crop production and water use, particularly in regions experiencing water scarcity.

Despite more than 40 years of counterdrug interdiction efforts in the Western Hemisphere, cocaine trafficking, or ‘‘narco-trafficking’’, networks continue to evolve and increase their global reach. Counterdrug interdiction continues to fall short of performance targets, due to the adaptability of narco-trafficking networks and spatially complex constraints on interdiction operations (e.g., resources, jurisdictional). Due to these dynamics, current modeling approaches offer limited strategic insights into time-varying, spatially optimal allocation of counterdrug interdiction assets. This study presents coupled agent-based and spatial optimization models to investigate the co-evolution of counterdrug interdiction deployment and narco-trafficking networks’ adaptive responses. Increased spatially optimized interdiction assets were found to increase seizure volumes. However, the value per seized shipment concurrently decreased and the number of active nodes increased or was unchanged. Narco-trafficking networks adaptively responded to increased interdiction pressure by spatially diversifying routes and dispersing shipment volumes. Thus, increased interdiction pressure had the unintended effect of expanding the spatial footprint of narco-trafficking networks. This coupled modeling approach enabled the study of narco-trafficking network evolution while being subjected to varying interdiction pressure as a spatially complex adaptive system. Capturing such co-evolution dynamics is essential for simulating traffickers’ realistic adaptive responses to a wide range of interdiction scenarios.

The US corn area footprint has changed significantly since the 20th century, declining in the southeastern states while exhibiting an increase or stable variations in the Midwest. As harvested acreage directly impacts the total corn production, understanding the influencing factors is crucial. This study assesses the role of potential drivers on the contrasting trajectories of harvested corn acreage between midwestern and southeastern US. Profit-acreage analysis reveals that antecedent profits/losses have a statistically significant influence on corn acreage changes, with southeastern US, which experienced more loss-making years, also experiencing more frequent reductions in corn acreage. The high number of loss-making years in the Southeast is primarily attributed to the region’s low corn yield, influenced by climate and other agro-environmental factors. Using a panel regression model, we find that the loss-making years in the Southeast could have reduced to fewer than 26 out of the considered 45 years, or almost similar to the average in the Midwest, by just increasing the irrigated corn area to 50%, a realistic irrigated corn area fraction already achieved in several Georgia counties. This underscores the potential for early policy interventions like irrigation facilitation to sustain and expand cropped acreage. However, we also find that this would only be economically feasible with incentives for both the installation and sustained operation of irrigation infrastructure.

Despite the global reach and economic scale of cocaine trafficking, our best geographic understanding of the global trade remains coarse. A more spatially disaggregated understanding of how the cocaine supply chain embeds across multiple locations is necessary for informing security policies and anticipating the spread and intensity of social and environmental harms associated with the cocaine trade. In this research, modeling methods used for legal supply chains are adapted to spatially disaggregate illicit supply chain flows. Profit and supply maximization model versions were compared to elucidate key decision parameters cocaine traffickers might be facing. Cocaine flows to EU+3 (Norway, Turkey, and United Kingdom) markets were estimated based on the smuggling capacity of major Central American ports and bilateral trade volumes of selected commodities most often seized with cocaine shipments. The resulting estimates of cocaine volumes diverted to EU+3 countries from Central America ranged between 938 and 1526 metric tons (MT). Generally, easier concealment and storage in Central America led to less volume supplied to the United States (US) and increased shipments to EU+3 markets. Importantly, the value of this modeling approach is not in the quantitative estimates produced, but in the methodological approach that provides the ability to rigorously ground any quantitative estimates of clandestine phenomenon in the best available data.