Search

BackgroundRhizosphere microbial communities are key regulators of plant performance, yet few studies have assessed the impact of different management approaches on the rhizosphere microbiomes of major crops. Rhizosphere microbial communities are shaped by interactions between agricultural management and host selection processes, but studies often consider these factors individually rather than in combination. We tested the impacts of management (M) and rhizosphere effects (R) on microbial community structure and co-occurrence networks of maize roots collected from long-term conventionally and organically managed maize-tomato agroecosystems. We also explored the interaction between these factors (M × R) and how it impacts rhizosphere microbial diversity and composition, differential abundance, indicator taxa, co-occurrence network structure, and microbial nitrogen-cycling processes.ResultsHost selection processes moderate the influence of agricultural management on rhizosphere microbial communities, although bacteria and fungi respond differently to plant selection and agricultural management. We found that plants recruit management-system-specific taxa and shift N-cycling pathways in the rhizosphere, distinguishing this soil compartment from bulk soil. Rhizosphere microbiomes from conventional and organic systems were more similar in diversity and network structure than communities from their respective bulk soils, and community composition was affected by both M and R effects. In contrast, fungal community composition was affected only by management, and network structure only by plant selection. Quantification of six nitrogen-cycling genes (nifH, amoA [bacterial and archaeal], nirK, nrfA, and nosZ) revealed that only nosZ abundance was affected by management and was higher in the organic system.ConclusionsPlant selection interacts with conventional and organic management practices to shape rhizosphere microbial community composition, co-occurrence patterns, and at least one nitrogen-cycling process. Reframing research priorities to better understand adaptive plant-microbe feedbacks and include roots as a significant moderating influence of management outcomes could help guide plant-oriented strategies to improve productivity and agroecosystem sustainability.

Adaptive management is a structured approach for people who must act despite uncertainty and complexity about what they are managing and the impacts of their actions. It is learning‐by‐doing through deliberate cycles of experimentation, review, and synthesis. However, understanding the processes of learning and how they relate to achieving resource management goals is in its infancy. Reflexive learning—a process of identifying and critically examining assumptions, values, and actions that frame knowledge—is critical to the effectiveness of adaptive management. It involves adaptive feedbacks between stakeholders as they examine assumptions, values, and actions. Adaptive management has been applied to environmental flows because it offers a system for making decisions about tradeoffs. In the Murray Darling Basin (MDB), Australia, adaptive management is applied as a cycle of plan, do, monitor, and learn, facilitated by short‐ and long‐term learning among stakeholders. An alternative conceptualization of adaptive management as an integration of single‐, double‐, and triple‐loop learning across multiple levels of governance is presented. This is applied to environmental flows in the MDB to map adaptive feedbacks of reflexive learning. At the lowest level of governance (Water Resource Planning Area), goals are assessed as Thresholds of Potential Concern related to flow‐ecology responses, which are reviewed every 3–6 years. At the second level of governance (Basin‐States), Water Management Targets are the key goals; reviewed and reframed every 6–10 years. The highest level of governance (the MDB) is concerned with policy targets, with review and reframing over 8–15 years. Feedbacks that generate reflexive learning are complex and require commitment to move through the modes of single‐, double‐, and triple‐loop learning. Effective adaptive management of environmental water requires practitioners to situate themselves within a matrix of information flow across modes of learning, levels of governance, and components of a social‐ecological system, where reflexive learning drives the achievement of management goals. [ABSTRACT FROM AUTHOR]

Adaptive resource management is a learning-by-doing approach to natural resource management. Its effective practice involves the activation, completion, and regeneration of the "adaptive management cycle" while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single-, double-, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance. Obtaining an appropriate balance between these three modes of learning has been difficult to achieve in practice and building capacity in this area can be achieved through an emphasis on reflexive learning, by employing adaptive feedback systems. A heuristic reflexive learning framework for adaptive resource management is presented in this manuscript. It is built on the conceptual pillars of the following: stakeholder driven adaptive feedback systems; strategic adaptive management (SAM); and hierarchy theory. The SAM Reflexive Learning Framework (SRLF) emphasizes the types, roles, and transfer of information within a reflexive learning context. Its adaptive feedback systems enhance the facilitation of single-, double-, and triple-loop learning. Focus on the reflexive learning process is further fostered by streamlining objectives within and across all governance levels; incorporating multiple interlinked adaptive management cycles; having learning as an ongoing, nested process; recognizing when and where to employ the three-modes of learning; distinguishing initiating conditions for this learning; and contemplating practitioner mandates for this learning across governance levels. The SRLF is a key enabler for implementing the "adaptive management cycle," and thereby translating the theory of adaptive resource management into practice. It promotes the heuristics of adaptive management within a cohesive framework and its deployment guides adaptive resource management within and beyond typical single-loop learning, across all governance levels. [ABSTRACT FROM AUTHOR]