Johann Heinrich von Thünen-Institut

, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

A group-specific primer, F243 (positions 226 to 243, Escherichia coli numbering), was developed by comparison of sequences of genes encoding 16S rRNA (16S rDNA) for the detection of actinomycetes in the environment with PCR and temperature or denaturing gradient gel electrophoresis (TGGE or DGGE, respectively). The specificity of the forward primer in combination with different reverse ones was tested with genomic DNA from a variety of bacterial strains. Most actinomycetes investigated could be separated by TGGE and DGGE, with both techniques giving similar results. Two strategies were employed to study natural microbial communities. First, we used the selective amplification of actinomycete sequences (E. coli positions 226 to 528) for direct analysis of the products in denaturing gradients. Second, a nested PCR providing actinomycete-specific fragments (E. coli positions 226 to 1401) was used which served as template for a PCR when conserved primers were used. The products (E. coli positions 968 to 1401) of this indirect approach were then separated by use of gradient gels. Both approaches allowed detection of actinomycete communities in soil. The second strategy allowed the estimation of the relative abundance of actinomycetes within the bacterial community. Mixtures of PCR-derived 16S rDNA fragments were used as model communities consisting of five actinomycetes and five other bacterial species. Actinomycete products were obtained over a 100-fold dilution range of the actinomycete DNA in the model community by specific PCR; detection of the diluted actinomycete DNA was not possible when conserved primers were used. The methods tested for detection were applied to monitor actinomycete community changes in potato rhizosphere and to investigate actinomycete diversity in different soils.

[1] We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr−1), H (164 ± 15 J × 1018 yr−1), and GPP (119 ± 6 Pg C yr−1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr−1) was likely underestimated by 5–10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Land-use changes are the second largest source of human-induced greenhouse gas emission, mainly due to deforestation in the tropics and subtropics. CO2 emissions result from biomass and soil organic carbon (SOC) losses and may be offset with afforestation programs. However, the effect of land-use changes on SOC is poorly quantified due to insufficient data quality (only SOC concentrations and no SOC stocks, shallow sampling depth) and representativeness. In a global meta-analysis, 385 studies on land-use change in the tropics were explored to estimate the SOC stock changes for all major land-use change types. The highest SOC losses were caused by conversion of primary forest into cropland (−25%) and perennial crops (−30%) but forest conversion into grassland also reduced SOC stocks by 12%. Secondary forests stored less SOC than primary forests (−9%) underlining the importance of primary forests for C stores. SOC losses are partly reversible if agricultural land is afforested (+29%) or under cropland fallow (+32%) and with cropland conversion into grassland (+26%). Data on soil bulk density are critical in order to estimate SOC stock changes because (i) the bulk density changes with land-use and needs to be accounted for when calculating SOC stocks and (ii) soil sample mass has to be corrected for bulk density changes in order to compare land-use types on the same basis of soil mass. Without soil mass correction, land-use change effects would have been underestimated by 28%. Land-use change impact on SOC was not restricted to the surface soil, but relative changes were equally high in the subsoil, stressing the importance of sufficiently deep sampling.

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an 'extra' day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

Land-use change (LUC) is a major driving factor for the balance of soil organic carbon (SOC) stocks and the global carbon cycle. The temporal dynamic of SOC after LUC is especially important in temperate systems with a long reaction time. On the basis of 95 compiled studies covering 322 sites in the temperate zone, carbon response functions (CRFs) were derived to model the temporal dynamic of SOC after five different LUC types (mean soil depth of 30±6 cm). Grassland establishment caused a long lasting carbon sink with a relative stock change of 128±23% and afforestation on former cropland a sink of 116±54%, 100 years after LUC (mean±95% confidence interval). No new equilibrium was reached within 120 years. In contrast, there was no SOC sink following afforestation of grasslands and 75% of all observations showed SOC losses, even after 100 years. Only in the forest floor, there was carbon accumulation of 0.38±0.04 Mg ha−1 yr−1 in afforestations adding up to 38±4 Mg ha−1 labile carbon after 100 years. Carbon loss after deforestation (−32±20%) and grassland conversion to cropland (−36±5%), was rapid with a new SOC equilibrium being reached after 23 and 17 years, respectively. The change rate of SOC increased with temperature and precipitation but decreased with soil depth and clay content. Subsoil SOC changes followed the trend of the topsoil SOC changes but were smaller (25±5% of the total SOC changes) and with a high uncertainty due to a limited number of datasets. As a simple and robust model approach, the developed CRFs provide an easily applicable tool to estimate SOC stock changes after LUC to improve greenhouse gas reporting in the framework of UNFCCC.

The detailed crop specific descriptions of the phenological growth stages of grapevine are supplementary to the general BBCH-scale. It will be instrumental in standardising the national and international experimentation in viticulture. The phenological development of the grapevine is divided into growth phases (principal growth stages 0–9) and each growth phase is subdivided into growth steps (secondary growth stages 0–9). A two-digit code is attached to each growth stage. The description and coding of the phenological growth stages covers the period between dormancy and leaf fall.

Making agriculture sustainable is a global challenge. In the European Union (EU), the Common Agricultural Policy (CAP) is failing with respect to biodiversity, climate, soil, land degradation as well as socio-economic challenges.The European Commission's proposal for a CAP post-2020 provides a scope for enhanced sustainability. However, it also allows Member States to choose low-ambition implementation pathways. It therefore remains essential to address citizens' demands for sustainable agriculture and rectify systemic weaknesses in the CAP, using the full breadth of available scientific evidence and knowledge.Concerned about current attempts to dilute the environmental ambition of the future CAP, and the lack of concrete proposals for improving the CAP in the draft of the European Green Deal, we call on the European Parliament, Council and Commission to adopt 10 urgent action points for delivering sustainable food production, biodiversity conservation and climate mitigation.Knowledge is available to help moving towards evidence-based, sustainable European agriculture that can benefit people, nature and their joint futures.The statements made in this article have the broad support of the scientific community, as expressed by above 3,600 signatories to the preprint version of this manuscript. The list can be found here (https://doi.org/10.5281/zenodo.3685632).

Coastal global oceans are expected to undergo drastic changes driven by climate change and increasing anthropogenic pressures in coming decades. Predicting specific future conditions and assessing the best management strategies to maintain ecosystem integrity and sustainable resource use are difficult, because of multiple interacting pressures, uncertain projections, and a lack of test cases for management. We argue that the Baltic Sea can serve as a time machine to study consequences and mitigation of future coastal perturbations, due to its unique combination of an early history of multistressor disturbance and ecosystem deterioration and early implementation of cross-border environmental management to address these problems. The Baltic Sea also stands out in providing a strong scientific foundation and accessibility to long-term data series that provide a unique opportunity to assess the efficacy of management actions to address the breakdown of ecosystem functions. Trend reversals such as the return of top predators, recovering fish stocks, and reduced input of nutrient and harmful substances could be achieved only by implementing an international, cooperative governance structure transcending its complex multistate policy setting, with integrated management of watershed and sea. The Baltic Sea also demonstrates how rapidly progressing global pressures, particularly warming of Baltic waters and the surrounding catchment area, can offset the efficacy of current management approaches. This situation calls for management that is (i) conservative to provide a buffer against regionally unmanageable global perturbations, (ii) adaptive to react to new management challenges, and, ultimately, (iii) multisectorial and integrative to address conflicts associated with economic trade-offs.

Climatic warming may lead to increased or decreased future forest productivity. However, more frequent heat waves, droughts and storms and accompanying pathogen attacks are also expected for Europe and are considered to be increasingly important abiotic and biotic stress factors for forests. Adaptive forestry can help forest ecosystems to adapt to these new conditions in order to achieve management goals, maintain desired forest ecosystem services and reduce the risks of forest degradation. With a focus on central Europe, this paper presents the following management strategies: (1) conservation of forest structures, (2) active adaptation, and (3) passive adaptation. The feasibility and criteria for application of the different strategies are discussed. Forest adaptation may entail the establishment of ‘‘neonative’’ forests, including the use and intermixing of native and non-native tree species as well as non-local tree provenances that may adapt better to future climate conditions. An integrative adaptive management concept is proposed that combines (1) species suitability tests and modelling activities at the international scale, (2) priority mapping of adaptation strategies at the national to regional scale, and (3) implementation at the local scale. To achieve this, an international experimental trial system is required to test suitable adaptive measures throughout Europe and worldwide.

The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios.

Ocean biogeochemistry is a novel standard component of fifth phase of the Coupled Model Intercomparison Project (CMIP5) experiments which project future climate change caused by anthropogenic emissions of greenhouse gases. Of particular interest here is the evolution of the oceanic sink of carbon and the oceanic contribution to the climate‐carbon cycle feedback loop. The Hamburg ocean carbon cycle model (HAMOCC), a component of the Max Planck Institute for Meteorology Earth system model (MPI‐ESM), is employed to address these challenges. In this paper we describe the version of HAMOCC used in the CMIP5 experiments (HAMOCC 5.2) and its implementation in the MPI‐ESM to provide a documentation and basis for future CMIP5‐related studies. Modeled present day distributions of biogeochemical variables calculated in two different horizontal resolutions compare fairly well with observations. Statistical metrics indicate that the model performs better at the ocean surface and worse in the ocean interior. There is a tendency for improvements in the higher resolution model configuration in representing deeper ocean variables; however, there is little to no improvement at the ocean surface. An experiment with interactive carbon cycle driven by emissions of CO 2 produces a 25% higher variability in the oceanic carbon uptake over the historical period than the same model forced by prescribed atmospheric CO 2 concentrations. Furthermore, a climate warming of 3.5 K projected at atmospheric CO 2 concentration of four times the preindustrial value, reduced the atmosphere‐ocean CO 2 flux by 1 GtC yr −1 . Overall, the model shows consistent results in different configurations, being suitable for the type of simulations required within the CMIP5 experimental design.

Abstract The article discusses social innovation from a rural development perspective. The central questions addressed are: What are social innovations and why are they important for rural development? How can we gain more insights into the role and functioning of social innovations in rural development? Drawing on different approaches to conceptualise social innovations pursued in economy, management, sociology, psychology and regional economics, planning and development studies, the article outlines the central aspects on which the concept is built. Based on these insights a proposal for a concise basic definition of social innovations is given and a model of the social innovation process is introduced. Reasoning that a lack of social innovation is often one of the strongest restraints of the vitality and further development of rural communities in developed, democratic, capitalist, industrial countries, the second part orf the article highlights the need to put a stronger focus on social innovations in future rural development research. Building on these insights, the third part addresses open research questions and explains why an actor‐oriented network approach seems to be a promising potential methodological way to approach social innovations in rural development research.

Abstract Estimates of carbon leaching losses from different land use systems are few and their contribution to the net ecosystem carbon balance is uncertain. We investigated leaching of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and dissolved methane (CH 4 ), at forests, grasslands, and croplands across Europe. Biogenic contributions to DIC were estimated by means of its δ 13 C signature. Leaching of biogenic DIC was 8.3±4.9 g m −2 yr −1 for forests, 24.1±7.2 g m −2 yr −1 for grasslands, and 14.6±4.8 g m −2 yr −1 for croplands. DOC leaching equalled 3.5±1.3 g m −2 yr −1 for forests, 5.3±2.0 g m −2 yr −1 for grasslands, and 4.1±1.3 g m −2 yr −1 for croplands. The average flux of total biogenic carbon across land use systems was 19.4±4.0 g C m −2 yr −1 . Production of DOC in topsoils was positively related to their C/N ratio and DOC retention in subsoils was inversely related to the ratio of organic carbon to iron plus aluminium (hydr)oxides. Partial pressures of CO 2 in soil air and soil pH determined DIC concentrations and fluxes, but soil solutions were often supersaturated with DIC relative to soil air CO 2 . Leaching losses of biogenic carbon (DOC plus biogenic DIC) from grasslands equalled 5–98% (median: 22%) of net ecosystem exchange (NEE) plus carbon inputs with fertilization minus carbon removal with harvest. Carbon leaching increased the net losses from cropland soils by 24–105% (median: 25%). For the majority of forest sites, leaching hardly affected actual net ecosystem carbon balances because of the small solubility of CO 2 in acidic forest soil solutions and large NEE. Leaching of CH 4 proved to be insignificant compared with other fluxes of carbon. Overall, our results show that leaching losses are particularly important for the carbon balance of agricultural systems.

Trees with sufficient nutrition are known to allocate carbon preferentially to aboveground plant parts. Our global study of 49 forests revealed an even more fundamental carbon allocation response to nutrient availability: forests with high-nutrient availability use 58 ± 3% (mean ± SE; 17 forests) of their photosynthates for plant biomass production (BP), while forests with low-nutrient availability only convert 42 ± 2% (mean ± SE; 19 forests) of annual photosynthates to biomass. This nutrient effect largely overshadows previously observed differences in carbon allocation patterns among climate zones, forest types and age classes. If forests with low-nutrient availability use 16 ± 4% less of their photosynthates for plant growth, what are these used for? Current knowledge suggests that lower BP per unit photosynthesis in forests with low- versus forests with high-nutrient availability reflects not merely an increase in plant respiration, but likely results from reduced carbon allocation to unaccounted components of net primary production, particularly root symbionts.

Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and non-drought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.

Abstract Bioenergy from crops is expected to make a considerable contribution to climate change mitigation. However, bioenergy is not necessarily carbon neutral because emissions of CO 2 , N 2 O and CH 4 during crop production may reduce or completely counterbalance CO 2 savings of the substituted fossil fuels. These greenhouse gases ( GHG s) need to be included into the carbon footprint calculation of different bioenergy crops under a range of soil conditions and management practices. This review compiles existing knowledge on agronomic and environmental constraints and GHG balances of the major E uropean bioenergy crops, although it focuses on dedicated perennial crops such as M iscanthus and short rotation coppice species. Such second‐generation crops account for only 3% of the current E uropean bioenergy production, but field data suggest they emit 40% to >99% less N 2 O than conventional annual crops. This is a result of lower fertilizer requirements as well as a higher N‐use efficiency, due to effective N‐recycling. Perennial energy crops have the potential to sequester additional carbon in soil biomass if established on former cropland (0.44 Mg soil C ha −1 yr −1 for poplar and willow and 0.66 Mg soil C ha −1 yr −1 for M iscanthus ). However, there was no positive or even negative effects on the C balance if energy crops are established on former grassland. Increased bioenergy production may also result in direct and indirect land‐use changes with potential high C losses when native vegetation is converted to annual crops. Although dedicated perennial energy crops have a high potential to improve the GHG balance of bioenergy production, several agronomic and economic constraints still have to be overcome.

In political and lay discourses, social innovation has recently been seen as a promising solution to fill gaps caused by austerity politics, or as a means to meet the so‐called Grand Challenges of the twenty‐first century. This article focuses on factors supporting the success of social innovation; that is, factors that support the development of a social innovation that enhances the probability of a high rate of adoption. To achieve this, the questions addressed are: Which factors bring forward social innovation? Where in the innovation process do they take effect? To what extent can rural development policy purposefully exert influence on these factors? Drawing on findings in innovation and social innovation research as well as from the rural participative planning discourse, it is shown that three tiers of factors influence the success of social innovation. These are: (1) factors important for the success of the overall innovation process; (2) factors influencing the room to manoeuvre for the social innovation actor network; and (3) factors influencing the actual participation process. A closer look at each of these factors reveals that most of them are excluded from external steering, and only factors influencing the room to manoeuvre provide potential links for rural policy to support social innovation. This allows one to conclude that social innovation in rural development cannot be easily initiated or steered from the top down, raising the question of whether social innovation can be politically instrumentalised.

Abstract. Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of soil moisture-respiration relations based on 90 soils. With the use of linear models we show how the relationship between soil heterotrophic respiration and different measures of soil moisture is consistently affected by soil properties. The empirical models derived include main effects and moisture interaction effects of soil texture, organic carbon content and bulk density. When compared to other functions currently used in different soil biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamics.

Summary The R package TropFishR is a new analysis toolbox compiling single‐species stock assessment methods specifically designed for data‐limited fisheries analysis using length‐frequency data. It includes methods for (i) estimating biological stock characteristics such as growth and mortality parameters, (ii) exploring technical aspects of the fisheries (e.g. exploitation rate and selectivity characteristics), (iii) assessing size and composition of a fish stock by means of virtual population analysis (VPA), and (iv) assessing stock status with yield prediction and production models. This paper introduces the package and demonstrates the functionality of a selection of its core methods. TropFishR modernises traditional stock assessment methods by easing application and development and by combining it with advanced statistical approaches.