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Abstract Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are increasingly used to reconstruct mean annual air temperature (MAAT) during the early Paleogene. However, the application of this proxy in coal deposits is limited and brGDGTs have only been detected in immature coals (i.e. lignites). Using samples recovered from Schoningen, Germany (∼48°N palaeolatitude), we provide the first detailed study into the occurrence and distribution of brGDGTs through a sequence of early Eocene lignites and associated interbeds. BrGDGTs are abundant and present in every sample. In comparison to modern studies, changes in vegetation type do not appear to significantly impact brGDGT distributions; however, there are subtle differences between lignites – representing peat-forming environments – and siliciclastic nearshore marine interbed depositional environments. Using the most recent brGDGT temperature calibration (MATmr) developed for soils, we generate the first continental temperature record from central-western continental Europe through the early Eocene. Lignite-derived MAAT estimates range from 23 to 26 °C while those derived from the nearshore marine interbeds exceed 20 °C. These estimates are consistent with other mid-latitude environments and model simulations, indicating enhanced mid-latitude, early Eocene warmth. In the basal part of the section studied, warming is recorded in both the lignites (∼2 °C) and nearshore marine interbeds (∼2–3 °C). This culminates in a long-term temperature maximum, likely including the Early Eocene Climatic Optimum (EECO). Although this long-term warming trend is relatively well established in the marine realm, it has rarely been shown in terrestrial settings. Using a suite of model simulations we show that the magnitude of warming at Schoningen is broadly consistent with a doubling of CO2, in agreement with late Paleocene and early Eocene pCO2 estimates.

The hot summer of 2018 posed many challenges with regard to water shortages and yield losses, especially for agricultural production. These agricultural impacts might further pose consequent threats for the environment. In this paper, we deduce the impact of droughts on agricultural land management and on water quality owing to nitrate pollution. Using national statistics, we calculate a Germany-wide soil surface nitrogen budget for 2018 and deduce the additional N surplus owing to the dry weather conditions. Using a model farm approach, we compare fertilization practices and legal restrictions for arable and pig breeding farms. The results show that, nationwide, at least 464 kt of nitrogen were not transferred to plant biomass in 2018, which equals an additional average nitrogen surplus of 30 kg/ha. The surplus would even have amounted to 43 kg/ha, if farmers had continued their fertilization practice from preceding years, but German farmers applied 161 kt less nitrogen in 2018 than in the year before, presumably as a result of the new implications of the Nitrates Directive, and, especially on grassland, owing to the drought. As nitrogen surplus is regarded as an “agri-drinking water indicator” (ADWI), an increase of the surplus entails water pollution with nitrates. The examples of the model farms show that fertilization regimes with high shares of organic fertilizers produce higher nitrogen surpluses. Owing to the elevated concentrations on residual nitrogen in soils, the fertilization needs of crops in spring 2019 were less pronounced than in preceding years. Thus, the quantity of the continuously produced manure in livestock farms puts additional pressure on existing storage capacities. This may particularly be the case in the hot-spot regions of animal breeding in the north-west of Germany, where manure production, biogas plants, and manure imports are accumulating. The paper concludes that water shortages under climate change not only impact agricultural production and yields, but also place further challenges and threats to nutrient management and the environment. The paper discusses preventive and emergency management options for agriculture to support farmers in extremely dry and hot conditions.

Abstract. This study presents the development of tools for the sustainable thermal management of a shallow unconsolidated urban groundwater body in the city of Basel (Switzerland). The concept of the investigations is based on (1) a characterization of the present thermal state of the urban groundwater body, and (2) the evaluation of potential mitigation measures for the future thermal management of specific regions within the groundwater body. The investigations focus on thermal processes down-gradient of thermal groundwater use, effects of heated buildings in the subsurface as well as the thermal influence of river–groundwater interaction. Investigation methods include (1) short- and long-term data analysis, (2) high-resolution multilevel groundwater temperature monitoring, as well as (3) 3-D numerical groundwater flow and heat transport modeling and scenario development. The combination of these methods allows for the quantifying of the thermal influences on the investigated urban groundwater body, including the influences of thermal groundwater use and heated subsurface constructions. Subsequently, first implications for management strategies are discussed, including minimizing further groundwater temperature increase, targeting "potential natural" groundwater temperatures for specific aquifer regions and exploiting the thermal potential.

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

Plants under salt stress require additional energy supply to fuel salt tolerance mechanisms and growth. Bandehagh and Taylor (2020) establish that plants must strike a balance between energy supply and demand to maintain growth and development during salt stress. This review (1) summaries how salt stress affects different physiological and biochemical process altering the abundance of different metabolites that are feeding into regular and alternative respiratory pathways and shunts; (monomeric complex I, dimeric complex III and I + III2 supercomplex) found to be higher in halophyte mitochondria in comparison with glycophyte, implying efficient electron transfer from complex I to complex III in halophyte mitochondria. Further, the stability of ATP synthase (complex V) also found to be higher in halophyte suggesting halophyte mitochondria better equipped to supply additional ATP required to support salt stress response.Synthesis of organic compatible solutes is an important component for plant salt stress tolerance. In this regard, proline plays an important role in protecting plants from under salinity conditions and showed that salt tolerance is associated with changes in lipid metabolic processes. They also discovered the important role of phosphatidylserine (PS) in mediating enzyme activity, and exogenous application of PS alleviated the effects of NaCl tissue toxicity. The results showed that the superior K + retention ability in both mature and elongation zone of rice root is the key trait conferring its differential salinity stress tolerance. They suggested that besides the superior ability to activate root H + -ATPase pump operation, this key trait is also related to the reduced sensitivity of K + efflux channels to reactive oxygen species and the lower upregulation in OsGORK and higher upregulation of OsAKT1.A key trait long recognized to improve salinity tolerance in many plants is the maintenance of a low Na + /K + ratio. Transient expression experiment showed that JcHDZ07 is a nuclear-localized protein.In improving Na + exclusion ability to maintain root ion homeostasis to ensure a relatively 9 low shoot Na + concentration under saline conditions; 2) maintaining a high shoot sugar content under saline conditions which is enabled by protecting photosystems structures, enhancing photosynthetic performance and sucrose synthetase activity, and inhibiting sucrose degradation. Further, authors suggested that targeting the key genes related to the regulatory mechanisms could provide opportunities to breed more salt tolerant sweet sorghum.Overall, we hope this special issue of benefit to plant breeders and land managers by delivering novel information and insights on the salinity stress response, signalling and adaptive mechanisms operating in plants.

Natural ecosystems are threatened by climate change, fragmentation, and non-native species. Dispersal-limitation potentially compounds impacts of these factors on plant diversity, especially in isolated vegetation patches. Changes in climate can impact the phenology of native species in distinct ways from non-natives, potentially resulting in cascading impacts on native communities. Few empirical studies have examined the combined effects of climate change and dispersal limitation on community diversity or phenology. Using a five-year dispersal-restriction experiment in an invaded semi-arid annual plant system in Western Australia, we investigated the interactive effects of dispersal-restriction and inter-annual rainfall variation on community composition, species dominance and seed production timing. We found inter-annual rainfall variation to be the principal driver of community dynamics. Drought years had long-term, stable effects on community composition, with evidence of shifts from native toward non-native dominance. Surprisingly, community composition remained largely unchanged under dispersal restriction. A subtle dispersal rescue effect was evident for a dominant native annual forb and a dominant annual non-native grass but only in average rainfall years. The timing of seed production was primarily driven by annual rainfall with native and non-native grasses having opposite responses. There was no evidence that inter-annual variation in seeding timing affected community diversity over time. Our study demonstrates that dispersal is not a major factor in driving community diversity in this invaded, semi-arid system. Results do suggest, however, that increases in drought frequency likely benefit non-native species over natives in the long term. Climate Change Ecology, 2 ISSN:2666-9005