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Calls for a “sustainable bioeconomy” have recently begun to proliferate in academic and political circles as an answer to global challenges such as the rapidly increasing world population and changing environmental conditions due to climate change. To build economies that rely on various sources of biomass rather than on fossil-based resources, bioeconomy policies highlight a need to explore ways in which productivity in agriculture can be increased. While the focus on productivity growth has largely been inspired by technology optimism and modernisation thinking, the local knowledge of farmers and other rural stakeholders – as a means of contributing to socially and ecologically more sustainable bioeconomy pathways – has frequently been sidelined. This perspective article proposes and discusses a participatory approach to strategy formulation and evaluation in agricultural transformation and bioeconomy development that is based on the methodological toolbox MARISCO (Adaptive MAnagement of vulnerability and RISk at COnservation sites). By offering a means of developing a comprehensive knowledge map, bringing together both insights and perspectives from diverse local stakeholders, this approach enables a systemic evaluation of the social and ecological effects of intervention strategies on the ground. The article outlines the benefits – and challenges – of such a systemic and participatory approach and briefly sketches how the results can be utilised in more sustainable governance processes in the development of bioeconomy strategies. We conclude that our adapted MARISCO methodology offers a viable tool to make more visible the perspectives and knowledge of biomass producers for policymakers and to contribute to a much-needed discursive shift in bioeconomy debates towards more holistic and inclusive perspectives.
Tajikistan's agricultural sector, primarily dominated by cotton cultivation, has experienced significant changes since the Soviet era. Although farmers introduced food crops into agricultural production to ensure food security after the collapse of the Soviet Union in 1991, the government still mandates that cotton cultivation occupy a considerable portion of Tajikistan's limited irrigated land. However, following the recent Covid-19 pandemic crisis, farmers have encountered the need to reassess their agricultural practices, given constrained governmental assistance and a range of socioeconomic considerations. This research adopts resilience as a conceptual framework to examine the coping strategies of smallholder farmers in the Khatlon region of Tajikistan, with a specific emphasis on the period spanning from 2019 to 2022. At the outset, our research employed the snowballing technique to increase respondent participation, later transitioning to a representative sample size that facilitated the collection of qualitative data from around 100 semi-structured interviews, 10 focus groups, and personal visits to agricultural fields over the course of four years. The paper not only demonstrates the overall creativity of Tajikistani farmers in growing and selecting non-cotton crops in this most recent period of crisis, but also points to wealthier farmers’ generally greater willingness to adopt technological innovations and gain new knowledge to apply to them. Although these strategies have been taken in order to address farmers’ immediate needs in troubled times against the backdrop of the existing state cotton policy, their sustainability remains uncertain. While the qualitative focus of this study bears certain inherent limitations, the data collected nevertheless show that this period of crisis has been generative for many farmers as they seek out new methods of subsistence.
Agricultural systems world-wide are facing an increasing level of uncertainty and risk in light of unprecedented climate and environmental change. The ecological challenges for agrarian sectors are further complicated by social factors such as growing world population and an expanding demand for biomass-based products. Formalized knowledge structures can benefit from approaches that consolidate informal local knowledge networks in understanding social and ecological change related causal chains (Tengö et al., 2013). This is especially relevant for the research-policy interface concerning sustainable agrarian system management.
As part of the junior research group TRANSECT, which investigates the social-ecological effects and interdependencies of agrarian transformations in Central and South Asia, a framework for knowledge mapping on sustainable land use systems is under development. We aim to bring together knowledge from local land users and other relevant stakeholders to develop future policy scenarios related to bio-based economy in the agrarian sector and to critically assess their social and ecological consequences on the ground. Our proposed framework is inspired by previous experiences with the MARISCO method (Ibisch and Hobson, 2014; www.marisco.training), a methodology for the adaptive management of risk and vulnerability, developed at the Center for Econics and Ecosystem Management in the Eberswalde University for Sustainable Development. This approach has been applied in various projects of natural resource management as a tool that allows the collection of complex and diffused knowledge from different sources, and to structure, evaluate, and prepare for the development of integrated solutions. The visual presentation of existing knowledge in the form of ‘knowledge maps’ that raise awareness of ignorance, and the systemic identification of knowledge gaps and risks are central features of MARISCO method (Ibisch and Hobson, 2014).
While the original methodology provides an established and proven toolbox for evaluating the ecological dimension of complex socio-environmental challenges, our inspired methodology elaborates on the social dimension of environmental and societal threats as well as policy interventions, for which we find it crucial to put a stronger emphasis on equity. In contrast to the more ecosystem-focused MARISCO method, a central feature of our approach is a socially differentiated analysis that distinguishes between different social groups or strata when evaluating social (and ecosystem) services and the (social) impact of stresses or threats.
We will introduce and elaborate on our proposed participatory and socially differentiated approach for knowledge mapping on sustainable land use systems with illustrative examples from experiences in Central Asia. As our current experience shows, a key challenge in designing and implementing such approaches is to take social-ecological complexity seriously while keeping the method manageable for regular workshop settings without overburdening participants.
This guide provides a structured methodology for developing and critically assessing strategies for sustainable food and biomass production through a participatory and systemic approach. It responds to the limitations of mainstream policies and strategies in agriculture and bioeconomy, which often prioritize technological solutions and lack bottom-up engagement of local stakeholders in knowledge-building and decision-making.
The PARSCO methodology presented in this guidebook is an adaptation of the MARISCO toolbox (Adaptive MAnagement of vulnerability and RISk at COnservation sites) developed at the Centre for Econics and Ecosystem Management. It has been tailored to assess and improve food and biomass production systems based on local knowledge, diverse stakeholder perspectives, and a systemic situation analysis using knowledge mapping and multi-criteria analysis tools. PARSCO is a landscape-based method primarily designed for application in local or regional settings, involving a series of participatory workshops with local stakeholders. It was developed over a period of five years at Eberswalde University for Sustainable Development and tested in workshops with smallholder farmers and other local actors in rural Kazakhstan, Tajikistan, and Pakistan.
The guidebook serves as a step-by-step manual for practitioners, planners, and transdisciplinary researchers applying PARSCO in project planning, policy evaluation, and sustainable land management. It emphasizes the importance of systemic thinking—understanding the interconnectedness and complex dynamics of social-ecological systems—and participatory decision-making to ensure inclusive, effective, and context-specific solutions. As a standalone guidebook, it can also be used as a complementary resource to existing MARISCO guides by offering more detailed guidance on the facilitation of participatory processes.
Abstract
The paper explores the adoption of organic cotton production by smallholder farmers in Tajikistan’s northern Sughd and southwestern Khatlon regions through the lens of political ecology. The research focuses on the complex interactions among socio-economic, environmental and political variables that influence the decision-making processes of Tajikistani smallholders as they navigate the transition towards adopting organic farming practices. This transition, initiated and guided by the external actors, largely from high-income nations, emphasizes broader power dynamics and asymmetries inherent within the global agricultural system. In addition to illuminating the pivotal role of smallholder farmers in sustainable agriculture and rural development, we ask how power relations, institutional frameworks, and market dynamics impacts the adoption of organic cotton by smallholders in Tajikistan, and the implications this has for local communities. By exploring these dimensions, we emphasize the need for supportive policies and the development of inclusive market mechanisms. This study acknowledges the vulnerability of smallholders to the volatility of the global organic cotton markets, influenced by external shocks such as pandemics, wars, and natural hazards. Considering the absence of domestic markets for organic produce in Tajikistan, we stress the reliance of smallholders on turbulent external markets, highlighting the urgent need for resilient and diversified market linkages to safeguard their livelihoods.
Accurate crop monitoring in smallholder-dominated regions is challenging due to fragmented fields, high crop diversity, and often limited ground truth data. This study presents a systematic and transferable framework for crop-type classification in smallholder systems by jointly evaluating multi-sensor data fusion, temporal feature aggregation, feature selection, and model applicability. This study evaluates crop-type classification accuracy across smallholder agricultural landscapes in Central and South Asia (Kazakhstan, Tajikistan, Pakistan), leveraging Sentinel-1 radar and Sentinel-2 optical data separately and combined. Employing Random Forest models, we systematically compare temporal aggregation approaches (monthly, bi-monthly, quarterly) and evaluate the impact of feature selection on model performance. Across all study regions, combined Sentinel-1 and Sentinel-2 data achieved overall classification accuracies of approximately 80–96%, with substantial performance gains relative to single-sensor models, particularly in regions where individual sensors showed limited discrimination capability. Depending on region and sensor, accuracy improvements ranged from a few percentage points to more pronounced gains, reflecting strong benefits of data fusion in heterogeneous smallholder systems. Finer temporal aggregation schemes, including monthly aggregation, yielded additional accuracy gains of approximately 1–3 percentage points compared to coarser aggregations, while feature selection further improved model performance by roughly 2–5 percentage points. Sentinel-1 proved particularly effective for structurally distinct crops such as cotton, while Sentinel-2 substantially improved classification of more diverse crop classes. Application of the Area of Applicability concept enabled spatially explicit identification of well-supported and extrapolated predictions, providing a quantitative basis for uncertainty assessment and future sampling strategies. Together, these results demonstrate the value of an integrated and transferable methodological framework for robust crop-type classification in smallholder agricultural systems using freely available Sentinel data.
