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Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices. 

Recent large‐scale land transactions, often framed as 'land grabbing,' are historically unprecedented. Millions of hectares of land have changed hands for agriculture‐driven development over the past decade, and their implementation generates substantial risk of land degradation. This paper investigates land transaction patterns and evaluate their potential socio‐environmental impacts in Cambodia, Ethiopia, Liberia, and Peru. We undertake a novel spatially explicit approach to quantify land transactions and conduct scenario‐based analyses to explore their implementation consequences on people, land, and carbon emission. Our results demonstrate that existing global datasets on land transactions substantially underestimate their incidence but can either exaggerate or underreport transacted areas. Although confirming that land transactions are more likely to occur in sparsely populated, poorer, and more forested areas, our scenario‐based analyses reveal that if fully implemented for agricultural development, land transactions in the four countries will affect more than one million people, yield over 2 Gt of carbon emissions, and disrupt vast swathes of forests. Our findings refute the 'empty land' discourse in government policy and highlight the consequences of land degradation that can occur at an unexpected scale in the 'global land rush.' Future policymaking needs to anticipate the risk of land degradation in terms of deforestation and carbon emission while pursuing agriculture‐driven development through land transactions.