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Soil degradation is a worsening global phenomenon driven by socio‐economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil–crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil–plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil–crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil.

Brazil typifies the land use changes happening in South America, where natural vegetation is continuously converted into agriculturally used lands, such as cattle pastures and croplands. Such changes in land use are always associated with changes in the soil nutrient cycles and result in altered greenhouse gas fluxes from the soil to the atmosphere. In this study, we analyzed literature values to extract patterns of direct nitrous oxide (N 2 O) emissions from soils of different ecosystems in Brazil. Fluxes from natural ecosystems exhibited a wide range: whereas median annual flux rates were highest in Amazonian and Atlantic rainforests (2.42 and 0.88 kg N ha −1 ), emissions from cerrado soils were close to zero. The decrease in emissions from pastures with increasing time after conversion was associated with pasture degradation. We found comparatively low N 2 O-N fluxes from croplands (−0.07 to 4.26 kg N ha −1 yr −1 , median 0.80 kg N ha −1 yr −1 ) and a low response to N fertilization. Contrary to the assumptions, soil parameters, such as pH, C org , and clay content emerged as poor predictors for N 2 O fluxes. This could be a result of the formation of micro-aggregates, which strongly affect the hydraulic properties of the soil, and consequently define nitrification and denitrification potentials. Since data from croplands mainly derived from areas that had been under natural cerrado vegetation before, it could explain the low emissions under agriculture. Measurements must be more frequent and regionally spread in order to enable sound national estimates. OPEN ACCESS RECEIVED

Abstract. Models of soil organic carbon (SOC) storage and turnover can be useful tools to analyse the effects of soil and crop management practices and climate change on soil organic carbon stocks. The aggregated structure of soil is known to protect SOC from decomposition and, thus, influence the potential for long-term sequestration. In turn, the turnover and storage of SOC affects soil aggregation, physical and hydraulic properties and the productive capacity of soil. These two-way interactions have not yet been explicitly considered in modelling approaches. In this study, we present and describe a new model of the dynamic feedbacks between soil organic matter (SOM) storage and soil physical properties (porosity, pore size distribution, bulk density and layer thickness). A sensitivity analysis was first performed to understand the behaviour of the model. The identifiability of model parameters was then investigated by calibrating the model against a synthetic data set. This analysis revealed that it would not be possible to unequivocally estimate all of the model parameters from the kind of data usually available in field trials. Based on this information, the model was tested against measurements of bulk density, SOC concentration and limited data on soil water retention and soil surface elevation made during 63 years in a field trial located near Uppsala (Sweden) in three treatments with different organic matter (OM) inputs (bare fallow, animal and green manure). The model was able to accurately reproduce the changes in SOC, soil bulk density and surface elevation observed in the field as well as soil water retention curves measured at the end of the experimental period in 2019 in two of the treatments. Treatment-specific variations in SOC dynamics caused by differences in OM input quality could be simulated very well by modifying the value for the OM retention coefficient ε (0.37 for animal manure and 0.14 for green manure). The model approach presented here may prove useful for management purposes, for example, in an analysis of carbon sequestration or soil degradation under land use and climate change.

Although inter-and transdisciplinary research has found its way to the forefront of calls, funding and publications, interdisciplinary projects often start from scratch constructing their research environment. In this article we will point to the enormous potential, the learnings, as well as some of the difficulties and pitfalls frequently encountered in large interdisciplinary project consortia. With this in mind, we aim to transparently document and reflect upon our research process, reminding the readers that the authors are not academic specialists in the field of inter-and transdisciplinarity nor in the sociology of knowledge. To explain our motivation, we want to share valuable experiences and point to some learnings, especially regarding the interdependencies between inter-and transdisciplinarity. After a brief historical retrospective of the expectations towards science, the article describes the trajectory of knowledge production and integration of a rather large research consortium attempting to overcome typical communicative and conceptual hurdles while negotiating the strict preconceptions of the respective disciplines. During the process of knowledge integration, scientific recognition and time budgets remain the crucial challenges. Besides joint field research, the construction of four storylines and the continuous integration of data into the various and increasingly interlinked models that ultimately culminate in our future scenarios led to constant communication and disputes among the subprojects involved. During the course of the project, it became obvious that a new generation of young scientists is developing: scientists working in interdisciplinary and transdisciplinary thought communities with a grasp of both fundamental science and transdisciplinary practice, combined with the soft skills necessary to reconcile both worlds.Zusammenfassung: Obwohl inter-und transdisziplinäre Forschung in aller Munde ist, beginnen Forschungskonsortien in der Regel ganz von vorne, wen sie ihre Forschungslandschaft aufbauen. In diesem Artikel werden wir auf die großen Potentiale und Lernprozesse sowie auf die Schwierigkeiten großer interdisziplinärer Forschungskonsortien hinweisen. Es ist unser Anliegen, unseren Forschungsprozess transparent zu dokumentieren und zu reflektieren. Wir weisen darauf hin, dass die wissenschaftlichen Schwerpunkte der AutorInnen nicht bei der Erforschung von Inter-und Transdisziplinarität liegen und wir auch keine Wissenssoziologen sind. Die Motivation dieser interdisziplinären Forschergruppe ist es, wertvolle Erfahrungen zu teilen und einige Lernerfahrungen besonders hervorzuheben. Nach einem kurzen Rückblick auf sich wandelnde Wissenschaftsbegriffe, beschreiben wir den Verlauf gemeinsamer Wissensproduktion und Integration im Carbiocial-Konsortium.; die Überwindung kommunikativer und konzeptioneller Hürden gehörten ebenso dazu, wie die Verhandlungsprozesse disziplinärer Grenzziehungen bzw. ihrer Lockerung. Hinsichtlich des Prozesses der Wissensintegration, bleiben die knappen Zeitbu...

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Abstract. Models of soil organic carbon (SOC) storage and turnover can be useful tools to analyze the effects of soil and crop management practices and climate change on soil organic carbon stocks. The aggregated structure of soil is known to protect SOC from decomposition, and thus influence the potential for long-term sequestration. In turn, the turnover and storage of SOC affects soil aggregation, physical and hydraulic properties and the productive capacity of soil. These interactions have not yet been explicitly considered in modelling approaches. In this study, we present and describe a new model of the dynamic feedbacks between SOM storage and soil physical properties (porosity, pore size distribution, bulk density and layer thickness). A sensitivity analysis was first performed to understand the behaviour of the model. The identifiability of model parameters was then investigated by calibrating the model against a synthetic data set. This analysis revealed that it would not be possible to unequivocally estimate all of the model parameters from the kind of data usually available in field trials. Based on this information, the model was tested against measurements of bulk density and SOC concentration, as well as limited data on soil water retention and soil surface elevation, made during 63 years in a field trial located near Uppsala (Sweden) in three treatments with different OM inputs (bare fallow, animal and green manure). The model was able to accurately reproduce the changes in SOC, soil bulk density, surface elevation and soil water retention curves observed in the field. Treatment-specific variations in SOC dynamics caused by differences in OM input quality could be simulated very well by modifying the value for the OM retention coefficient ε (0.37 for animal manure and 0.14 for green manure). The model approach presented here may prove useful for management purposes, for example, in an analysis of carbon sequestration or soil degradation under land use and climate change.