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Description: This meta-data on development and management of community managed small-scale irrigation schemes in Ethiopia is presented in a spreadsheet as a table after reviewing 83 published articles. The dataset contains research focus, data collection and analysis method, spatial scale, irrigation typology, irrigation water sources, source of energy for water conveyance from the source, main research results, scheme management systems, identified problems and provided solutions from each research article when available. The spatial scale was categorized as national, regional, district, basin, watershed or scheme, based on the spatial coverage of the research done. The irrigation typology was categorized as large-scale, medium-scale and small-scale based on the Ethiopian classification. Surface water sources such as river, lake, reservoir and flash flood and ground water sources of wells and springs or a combination of more than one source were mentioned as the water sources for irrigation schemes. The sources of energy for water abstraction were categorized as gravity, pump or a combination of both. Year of irrigation scheme commencement, available irrigation scheme infrastructure, number of beneficiaries (households), and stakeholders in scheme management were also extracted when available in the literature. Spatial coverage: Data was collected from community managed small-scale irrigation schemes located in different parts of Ethiopia. Location of some irrigation schemes (in UTM) is indicated in the dataset spreadsheet.

Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms. van der Meij et al_Biogeosciences2017_dataThe experimental set-up, treatments, data collection and data analyses are thoroughly described in the Biogeoscience manuscript ‘Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors’ doi:10.5194/bg-2016-452. Therefore we refer to the manuscript for detailed information an here we provide a brief summary to enable readers to follow what the data entail. The data were collected from a 2-year field experiment with plant rotations in a full factorial design. The plant treatments we focused on are legacy effects of the plant treatments (listed below) to the following oat crop. In this oat crop we quantified several plant traits both in situ and via remote sensing by use of UAV and hyperspectral and EGB sensors. The experiment was set-up in five randomized field blocks. We used part of the in situ collected data to parameterize the hyperspectral data based models and we validated these models with the other half of the field plots. Plant treatments Fa= fallow Lp= Lolium perenne Rs= Raphanus sativus Tr= Trifolium repens Vs= Vicia sativa Lp+Tr= 50:50 species mixture (relative to the monoculture seed densities) of the species Lp and Tr Rs+Vs= 50:50 species mixture (relative to the monoculture seed densities) of the species Rs and Vs

Study site This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 soil plots in the Hengill geothermal valley, Iceland, from 15th to 22nd August 2018. The plots, measuring approximately 1 m2, evenly span a temperature gradient of 10-35°C. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015 (see Robinson et al. 2021 for plot details and sampling regime). Soil properties Soil temperature was measured at 5 cm depth at each plot on 15th, 18th, and 22nd August, and a mean plot temperature calculated. Soil physiochemical properties were analysed from 3 soil cores of 3 cm in diameter, taken from the upper 10 cm soil stratum at each plot; one quarter of each subsample was pooled to obtain an estimate per plot. Aboveground plant matter, excluding roots, were removed from each core. Percentage soil moisture was calculated by measuring the weight of one pooled soil sample before and after drying for 24 h in a 70°C drying oven. Soil pH was obtained from 20 g of the dry soil by adding 100 ml distilled water, shaking for 5 min on 150 rpm, letting the sample stand for 2 h, and measuring soil pH from the water layer using an InoLab pH 720 (WTW) probe. Soil PO4, NH4, and NO3 concentrations were analysed from a second pooled soil; 60 g of fresh soil was extracted in 100 ml distilled water, filtered through a GF/C (1.2μm) glass microfiber filter (Whatman, GE Healthcare Europe GmbH), and analysed using a Lachat QuikChem 8000 analyser (Zallweger Analytics, Inc., Lachat Instruments Division, USA). Total mineral N was calculated as the sum of NH4 and NO3. Soil organic matter content (excluding dry root biomass) was calculated as the weight lost from an oven dried (105°C for 24 hours) soil sample after heating at 550 °C for 5 h. Decomposition rate of soil organic matter was measured using the Cotton-strip Assay method (Tiegs et al. 2013) by placing a 2.5 cm x 8 cm strip of Fredrix-brand unprimed 12-oz. heavyweight cotton fabric (Style #548) 5 cm belowground at 60 plots, concurrently with a Maxim Integrated DS1921G Thermocron iButton temperature logger, on 13th May 2015. The strips were collected on 3rd July, rinsed with stream water to remove residual soil, soaked in 96% ethanol for 30 seconds to kill bacteria and halt decomposition, and dried at 60 °C for 12 h. Using a universal testing machine (Instron 5866 with 500 kN tensile holding clamps), maximum tensile strench of each cotton strip was measured. % tensile loss (proxy for decomposition) was calculated as (C-T) / C x 100, where T is the maximum tensile strength for each strip collected from the field, and C is the mean tensile strength of seven control strips, which had not been placed in the ground. See Robinson et al. 2021 for detailed description of plots sampled in 2015. Microbial abundance Bacterial and fungal abundance was estimated from additional soil cores of 3 cm in diameter taken from the upper 4 cm soil stratum (including the litter layer) at each plot. DNA was extracted using the PowerSoil DNA Isolation Kit (Qiagen, Germany). DNA was quantified using the high-sensitivity Qubit assay (Thermo Fisher Scientific, Switzerland). Relative abundances of bacterial and fungal communities were determined by quantitative PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). PCR amplification of partial bacterial small-subunit ribosomal RNA genes (region V1–V3 of 16S; primers 27F and 512R) and fungal ribosomal internal transcribed spacers (region ITS2; primers IT3 and ITS4) was performed as described previously (Frey et al. 2020, Frey et al. 2021). For qPCR analyses, 2.5 ng DNA in a total volume of 6.6 µL and 8.4 µL GoTaq qPCRMaster Mix (Promega, Switzerland), containing 1.8 mM of each primer and 0.2 mg mL-1 of BSA, were used. The PCR conditions consisted of an initial denaturation at 95 ºC for 10 min, 40 cycles of denaturation at 95 ºC for 40 s, annealing at 58 ºC for 40 s and elongation at 72 ºC for 60 s followed by the final data acquisition step at 80 ºC for 60 s. The specificity of the amplification products was confirmed by melting-curve analysis. Three standard curves per target region (correlations ≥0.997) were obtained using tenfold serial dilutions (10-1 to 10-9 copies) of plasmids generated from cloned targets (Frey et al. 2020). Data were converted to represent the average copy number of targets per μg DNA and per g soil. Vegetation properties Vascular plant biomass was measured from a randomly placed 30 x 30 cm quadrat at each plot. To measure aboveground biomass (AGB) of plants, the aboveground layer of vegetation was cut and removed, dried at 70 °C for 24 h and weighed to obtain biomass per unit area. AGB was estimated as the biomass of graminoids plus forbs; total biomass of mosses was also estimated. Graminoid leaf N concentration was analysed from dried and ground leaf material using a LECO CNS-2000 analyser (LECO Corporation, Saint Joseph, MI, USA). Belowground biomass (BGB) of vascular plants was estimated from a soil core of 3 cm in diameter taken from the 10 cm upper soil stratum (excluding aboveground plant material) at each quadrat. Roots were extracted from the soil cores by rinsing in water using a 250-μm sieve, dried at 70 °C for 24 hours and weighed to obtain biomass per unit area. Root to shoot ratio was calculated as dry weight of BGB per cm2 divided by dry weight of AGB per cm2, and the total vascular plant biomass as the sum of AGB and BGB. Invertebrate community Enchytraied and nematode biomass was estimated from 3 soil cores of 3 cm in diameter taken from the upper 4 cm soil stratum (including litter layer) at each plot. Enchytraieds were extracted using wet funnels (O'Connor 1962) from a pooled sample of one half of each of the three soil cores, counted live, and classified into size classes (length 0-2, 2.1-4, 4.1-6, 6.1-8, 8.1-10, 10.1-12 or >12 mm) and their biomass was calculated according to Abrahamsen (1973). Nematodes were also extracted using wet funnels (Sohlenius 1979) from a pooled sample of a quarter of each of the three soil cores, counted live and preserved in 70% ethanol. Fifty individuals from each sample were identified and classified by trophic group (bacterivore, fungivoe, herbivore, omnivore, predator; Yeates et al. 1993). Soil micro-arthropods were extracted using a modified high-gradient-extractor (MacFayden 1961) from soil cores of 5.4 cm in diameter, taken from the upper 4 cm soil straum (including litter layer) at each plot. Total micro-arthropod biomass was calculated as the sum of all individual species' biomasses, obtained using length-weight regressions (see Robinson et al. 2021), and abundance of individual trophic groups (microbivore/detritivore, herbivore, omnivore, predator) calculated. Epigeal invertebrates were sampled by deploying five pitfall traps in each plot. White plastic cups of 7 cm in diameter and 8.5 cm in depth were filled with 10 ml of ethylene glycol and 30 ml of stream water, and left for 48 h before collection. Samples from the five traps at each plot were combined into a 250-μm sieve and stored in 70% ethanol. Invertebrate activity density (abundance) was estimate as the total number of individuals in the five traps, and total biomass as the sum of all individual species' biomasses. Invertebrates were identified to species level where possible and split into trophic groups, exluding adult Diptera, Hymenoptera, and Lepidoptera. Further details of sampling and collection of epigeal invertebrates are detailed in Robinson et al. (2018). References: Abrahamsen G. (1973) Studies on body-volume, body-surface area, density, and live weight of enchytraeidae (Oligochaeta). Pedobiologia 13: 6–15. Frey B, Carnol M, Dharmarajah A, Brunner I, Schleppi P. (2020) Only minor changes in the soil microbiome of a sub-alpine forest after 20 years of moderately increased nitrogen loads. Frontiers in Forests and Global Change 3: 77. Frey B, Walthert L, Perez-Mon C, Stierli B, Köchli R, Dharmarajah A, Brunner I (2021) Deep soil layers of drough-exposed forests harbor poorly known bacterial and fungal communities. Frontiers in Microbiology 12: 1061. MacFayden A. (1961) Improved funnel-type extractors for soil arthropods. Journal of Animal Ecology 30: 171–184. O’Connor FB. (1962) The extraction of Enchytraeidae from soil. In: P. W. Murphy (Ed.) Progress in soil zoology. Butterworth, London, UK; 279–285. Robinson SI, McLaughlin ÓB, Marteinsdóttir B, O'Gorman EJ. (2018) Soil temperature effects on the structure and diversity of plant and invertebrate communities in a natural warming experiment. Journal of Animal Ecology 87: 634–46. Robinson SI, Mikola J, Ovaskainen O, O’Gorman EJ. (2021) Temperature effects on the temporal dynamics of a subarctic invertebrate community. Journal of Animal Ecology 90: 1217-1227. Sohlenius B. (1979) A carbon budget for nematodes, rotifers and tardigrades in a Swedish coniferous forest soil. Holarctic Ecology 2: 30–40. Tiegs SD, Clapcott JE, Griffiths NA, Boulton AJ. (2013) A standardized cotton-strip assay for measuring organic-matter decomposition in streams. Ecological Indicators 32: 131–139. Yeates GW, Bongers T, De Goede RGM, Freckman DW, Georgieva SS. (1993) Feeding habits in soil nematode families and genera—an outline for soil ecologists. Journal of Nematology 25: 315–331. This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 plots in the Hengill geothermal valley, Iceland, from 15th to 22nd August 2018. The plots span a temperature gradient of 10-35 °C over the sampling period, and this temperature gradient is consistent over time. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015. See README_Robinson_Hengill2018.txt 

We utilize a variety of climate datasets to examine impacts of two mechanisms on precipitation in the Greater Horn of Africa (GHA) during northern-hemisphere summer. First, surface-pressure gradients draw moist air toward the GHA from the tropical Atlantic Ocean and Congo Basin. Variability of the strength of these gradients strongly influences GHA precipitation totals and accounts for important phenomena such as the 1960s–1980s rainfall decline and devastating 1984 drought. Following the 1980s, precipitation variability became increasingly influenced by the southern tropical Indian Ocean (STIO) region. Within this region, increases in sea-surface temperature, evaporation, and precipitation are linked with increased exports of dry mid-tropospheric air from the STIO region toward the GHA. Convergence of dry air above the GHA reduces local convection and precipitation. It also produces a clockwise circulation response near the ground that reduces moisture transports from the Congo Basin. Because precipitation originating in the Congo Basin has a unique isotopic signature, records of moisture transports from the Congo Basin may be preserved in the isotopic composition of annual tree rings in the Ethiopian Highlands. A negative trend in tree-ring oxygen-18 during the past half century suggests a decline in the proportion of precipitation originating from the Congo Basin. This trend may not be part of a natural cycle that will soon rebound because climate models characterize Indian Ocean warming as a principal signature of greenhouse-gas induced climate change. We therefore expect surface warming in the STIO region to continue to negatively impact GHA precipitation during northern-hemisphere summer.

We have developed a method that enables better taking into account of need of space of actions of moving objects in a building when Building Information Modeling (BIM) is used. The 3D spatial objects for real space requirements of actions created with our method can be used with various design tools as such in dimensioning or to generate video game colliders defining the need of space. Together with the gamified model of the structure from BIM they enable dimensioning in designs and simulations. We can dimension spatial objects for example related to safety. There are a lot of needs and applications for our methods. It is possible to design buildings and other structures that fit their purpose well.

This chapter presents an overview of positive motivators for students, lecturers, and educational managers to prioritize Sustainable Development in education. Very often, we implicitly assume that students and colleagues should all be motivated by the great challenges that the world faces. And if they appear not to react sufficiently to these challenges, we sometimes tend to give these challenges an apocalyptic character. But is this the right motivator for students and colleagues to work on Sustainable Development? We all know that if you only use a stick and no carrot… So why don’t we use more carrots? The bureaucracy that comes with tools for checking/auditing/evaluating the (SD content of) programs/curricula is not particularly a strong motivator for university lecturers. And building courses that add another subject to the erudition of the graduate might not be the right motivators for students that want to make a difference. We are often still in the process of convincing university managers to add SD to the curriculum, convincing colleagues to address SD, and convincing students to pick SD electives and address SD in their projects. How to motivate them to do this when this gives them no direct personal reward and even might increase their workload? The paper will explore options to develop motivating educating by reviewing case studies on educational renewal in four universities. It concludes that there are various options for more motivating education. However, to fully utilize these options, more priority should be given to education. Peer Reviewed

The Pannonian basin is an integral part of the convergence zone between the Eurasian and Nubian plates characterized by active subductions of oceanic and continental plates, and formation of backarc basins. The first part of this paper presents an overview of the evolution of the Alpine-Mediterranean region in order to understand the large scale crustal and upper mantle processes in and around the Pannonian basin, resulting a collage of terranes of Alpine and Adriatic origin. It will be followed by a summary of the history of sedimentation, volcanism and tectonic activity. As an illustration, three regional cross sections have been prepared on the base of seismic and borehole data. Reviewing current tectonic ideas and models, we come up with a speculative tectonic scenario depicting Alcapa and Tisza-Dacia as orogenic wedges detached from their mantle lithosphere in the Alpine and Adriatic/Dinaric collision zone during the Late Oligocene to Early Miocene. They suffered a dramatic thermal impact leading to crustal melting during extrusion, when these crustal flakes could have been directly superimposed on the asthenosphere in the Carpathian embayment. Since then, the large part of the Pannonian has been cooling and a new mantle lithosphere growing. Geothermal data show that the Pannonian basin with cessation of volcanic activity in the Late Miocene is still very hot and Miocene to Quaternary clastic basin fill, together with karstified Mesozoic carbonates form good geothermal reservoirs of regional extent. In addition to these gravity-driven aquifer systems, a strongly overpressured reservoir can be found below a regional pressure seal in synrift strata and fractured basement rocks. Eventually, we show maps of geothermal installations in the Pannonian basin and suggest that at the present level of knowledge and geophysical surveying it is easy to find additional resources, however proper water management is a critical issue to avoid harmful drawdown of the groundwater table.