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Integrated multitrophic aquaculture (IMTA) is a versatile technology emerging as an ecological and sustainable solution for traditional monoculture aquacultures in terms of effluent treatment. Nevertheless, IMTA is still poorly applied in aquaculture industry due to, among other reasons, the lack of effective, low-investment and low-maintenance solutions. In this study, one has developed a practical and low maintenance IMTA-pilot system, settled in a semi-intensive coastal aquaculture. The optimisation and performance of the system was validated using Ulva spp., a macroalgae that naturally grows in the fishponds of the local aquaculture. Several cultivation experiments were performed at lab-scale and in the IMTA-pilot system, in static mode. The specific growth rate (SGR), yield, nutrient removal, N and C enrichment, protein and pigment content were monitored. Ulva spp. successfully thrived in effluent from the fish species sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) production tanks and significantly reduced inorganic nutrient load in the effluent, particularly, NH4+, PO43− and NO3−. The enrichment of nitrogen in Ulva spp.’s tissues indicated nitrogen assimilation by the algae, though, the cultivated Ulva spp. showed lower amounts of protein and pigments in comparison to the wild type. This study indicates that the designed IMTA-pilot system is an efficient solution for fish effluent treatment and Ulva spp., a suitable effluent remediator.

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

1. While climate change is altering ecosystems on a global scale, not all ecosystems are responding in the same way. The resilience of ecological communities may depend on whether food webs are producer- or detritus-based (i.e. “green” or “brown” food webs, respectively), or both (i.e. “multi-channel” food web). 2. Food web theory suggests that the presence of multiple energy pathways can enhance community stability and resilience and may modulate the responses of ecological communities to disturbances such as climate change. Despite important advances in food web theory, few studies have empirically investigated the resilience of ecological communities to climate change stressors in ecosystems with different primary energy channels. 3. We conducted a factorial experiment using outdoor stream mesocosms to investigate the independent and interactive effects of warming and drought on invertebrate communities in food webs with different energy channel configurations. Warming had little effect on invertebrates, but stream drying negatively impacted total invertebrate abundance, biomass, richness, and diversity. 4. Although resistance to drying did not differ among energy channel treatments, recovery and overall resilience were higher in green mesocosms than in mixed and brown mesocosms. Resilience to drying also varied widely among taxa, with larger predatory taxa exhibiting lower resilience. 5. Our results suggest that the effects of drought on stream communities may vary regionally and depend on whether food webs are fueled by autochthonous or allochthonous basal resources. Communities inhabiting streams with large amounts of organic matter and more complex substrates that provide refugia may be more resilient to the loss of surface water than communities inhabiting streams with simpler, more homogeneous substrates.

AbstractThe integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

Identifying climate analogs We followed the methods of Abatzoglou et al. (2020) and Parks et al. (2022) to characterize climate and identify backward and forward climate analogs. The specific climate variables we used were average minimum temperature of the coldest month (Tmin), average maximum temperature of the warmest month (Tmax), annual actual evapotranspiration (AET), and annual climate water deficit (CWD). AET and CWD concurrently account for evaporative demand and availability of water (N. L. Stephenson, 1990). These four variables provide complementary information pertinent to ecological systems and collectively capture the major climatic constraints on species distributions and ecological processes across a range of taxa (Dobrowski et al., 2021; Lutz et al., 2010; Parker & Abatzoglou, 2016; N. Stephenson, 1998; C. M. Williams et al., 2015). Monthly data acquired from TerraClimate (Abatzoglou et al., 2018) were used to produce these annual summaries from 1961-1990 (resolution = ~4km), which were then averaged over the same time period to represent reference period climate normals. The reference time period (1961–1990) is meant to represent climate conditions and climate niches prior to the bulk of recent warming. Future climate conditions were also computed from TerraClimate (available from www.climatologylab.org/terraclimate.html) and correspond to a 2°C increase above pre-industrial levels that are likely to manifest by mid-21st century without immediate and massive changes in global climate policies (Friedlingstein et al., 2014). As with the reference period climate, we summarized the four +2°C climate metrics annually and over a 30-year time period to represent future climate normals. All analyses in this study were conducted in the R statistical platform (R Core Team, 2020). We identified backwards and forwards analogs by estimating the climatic dissimilarity between each protected focal pixel (resolution = ~4km to match gridded climate data) and all protected pixels within a 500-km radius using a standardized Mahalanobis distance (Mahony et al., 2017). We chose the 500-km search radius as it encompasses an upper range of dispersal for some terrestrial animals and plants (Chen et al., 2011) when assuming 2°C warming by the mid-21st century; this search radius has also been used in previous studies (Bellard et al., 2014; Parks et al., 2022; J. W. Williams et al., 2007). The Mahalanobis distance metric synthesized the four climate variables (i.e. Tmin, Tmax, AET, and CWD; fig. 2a) by measuring distance in multivariate space away from a centroid using principal components analysis of standardized anomalies. Mahalanobis distance scales multivariate mean climate conditions between a pixel and those within the search radius by the focal pixel’s covariance and magnitude of interannual climate variability (ICV) across the four metrics. For backwards analogs, we characterized +2°C ICV and reference period climate normals to calculate climatic dissimilarity; for forward analogs, we used reference period ICV and +2°C climatic normals to calculate climatic dissimilarity. We standardized Mahalanobis distance to account for data dimensionality by calculating a multivariate z-score (σd) based on a Chi distribution (Mahony et al., 2017). σd represents the climate similarity between each focal pixel and its candidate backward and forward analogs (i.e. all other protected terrestrial pixels within 500 km), and we considered any protected pixels with σd ≤ 0.5 as climate analogs (fig. 2b) (following Parks et al., 2022). We were unable to calculate Mahalanobis distance when there was no ICV for any one of the four variables, and as a consequence, these areas are omitted from all analyses; this affects, for example, a relatively small tropical area in South America (CWD=0 each year) and areas perennially covered by snow (CWD=0 each year; e.g. most of Greenland). We focused our analyses on protected areas as defined by the World Database on Protected Areas (WDPA) (IUCN & UNEP-WCMC, 2019) and included protected areas classified as IUCN (International Union of Conservation for Nature) Management Categories I-VI, except those identified as ‘proposed’, ‘marine’, or otherwise aquatic (e.g. wetland, riverine, endorheic). A large number of protected areas, however, were not assigned an IUCN category in the WDPA (identified as ‘Not Reported’, ‘Not Assigned’, or ‘Not Applicable’) but are likely to have reasonably high levels of protection (e.g. Kruger National Park in South Africa). We included these additional protected areas if the level of human modification was similar or less than that observed within IUCN category I-VI protected areas. To do so, we measured mean land-use intensity within each IUCN category I-VI protected area using the Human Modification Gradient (HMG) raster dataset (Kennedy et al., 2019) and calculated the 80th percentile of the resulting distribution. Any unassigned protected areas with a mean HMG less than or equal to this identified threshold were included in our study (following Dobrowski et al., 2021). We then converted this vector-based polygon dataset to raster format (resolution = ~4km to match gridded climate data; n=1,063,748 pixels). It is well-recognized that the WDPA contains a large number of duplicate and overlapping polygons (Palfrey et al., 2022; Vimal et al., 2021). Although this does not affect summaries across the globe or for individual countries (described below), it provides a challenge when trying to summarize by individual protected areas (due to double-counting). Consequently, we ‘cleaned’ the WDPA prior to summarizing the climate connectivity metrics for individual protected areas by removing polygons that exhibited ≥ 90% overlap with another; this resulted in 29,752 individual protected areas (available in the Electronic Supplemental Material). Least-cost path modelling Following Dobrowski and Parks (2016) and Carroll et al. (2018), we used least-cost path modelling (Adriaensen et al. 2003) to build potential climate-induced movement routes between each protected focal pixel and its backward and forward analogs. The least-cost models were parameterized with resistance surfaces based on climate dissimilarity and the human modification gradient (HMG) (Kennedy et al., 2019). For backward analog modelling, we characterized climatic dissimilarity (i.e. climatic resistance) using two intermediate surfaces, the first being the Mahalanobis distance between each focal pixel (using +2°C ICV) and all other pixels using reference period climate normals (fig. 2c) and the second being the Mahalanobis distance (using +2°C ICV) and all other pixels using +2°C climate normals (fig. 2d). These two surfaces provide a proxy for climate similarity designed to capture transient changes between the reference period and +2°C climate; these were then averaged to characterize the overall climatic resistance across time and space (fig. 2d). For forward analog modelling, the process is similar except we used reference period ICV when characterizing climatic resistance (fig. 2a-2d). We then multiplied the climatic resistance (fig. 2d) by HMG (fig. 2e) to create the final resistance surface for least-cost path modeling (cf. Parks et al., 2020). Prior to this step, we rescaled HMG from its native range (0–1) to 1–25 to correspond with the range of Mahalanobis distance values and thereby grant comparable weights to climatic resistance and HMG resistance (~95% of all Mahalanobis distance values are below 25 within a 500km radius). Open water was given a resistance=25 so that paths would avoid water when possible. Least-cost path modelling was achieved using the gdistance package (van Etten, 2017); paths represent the least accumulated cost across the final resistance surface (fig. 2f) between each focal pixel and analog (fig. 2g). Because paths were rarely straight lines, some were longer than the 500km that we established as a search radius. We removed these longer paths to abide by the biologically informed upper dispersal constraint. Calculating climate connectivity metrics and climate connectivity failure We calculated the length (i.e. dispersal exposure), land-use modification (i.e. human exposure), and climatic resistance (i.e. climate exposure) for each path, remembering that each focal pixel may have many analogs and resultant paths. Human exposure represents cumulative HMG (fig. 2e) across all pixels in a path and climate exposure represents cumulative climate resistance (fig. 2d) along a path. Human exposure and climate exposure were calculated by multiplying the mean HMG (unscaled; fig. 2f) and mean climate resistance (fig. 2d) along each path by the length of each path, respectively. Each path’s climate connectivity metric (dispersal, human, and climate exposure) was converted to a percentile (range = 0–100) to facilitate easier interpretation and comparison among metrics; relative to other protected pixels, small percentiles represent low exposure and large percentiles represent elevated exposure. We summarized (i.e. averaged the percentiles) dispersal exposure, human exposure, and climate exposure across each protected focal pixel (again, remembering that each pixel may have multiple analogs and resultant paths). Our fourth climate connectivity metric, analog exposure, can’t be summarized on a per-path basis, because by definition, there is no least-cost path when there are no protected climate analogs. Instead, protected pixels either do or do not have protected climate analogs. Focal pixels were identified as exhibiting climate connectivity failure when they exceeded the 75th percentile for dispersal or climate exposure, exceeded the 90th percentile for human exposure, or had no protected climate analog. We assumed that focal pixels exceeding these percentiles are located in landscapes that hinder successful range shifts among protected areas (i.e. climate connectivity failure) for a non-negligible proportion of extant species, considering that the biodiversity at a given site comprises mammals, birds, insects, mollusks, amphibians, reptiles, fish, crustaceans, annelids, vascular plants (e.g. trees grasses, shrubs), and non-vascular plants (e.g. fungi, mosses, lichens). The numerous and diverse species at a given site have a wide range of dispersal abilities, sensitivities to human land uses, and climatic tolerances. We used a higher threshold (90th percentile) for describing climate connectivity failure due to human exposure because large, remote protected areas in the network skew human exposure towards lower values from a global perspective. These percentile thresholds are likely conservative when considering the large number and diversity of species at a given site. In terms of dispersal, for example, many species have maximum dispersal capabilities on the range of 1 km/year or less (Jenkins et al., 2007; McLachlan et al., 2005; Schwartz et al., 2001). This represents dispersal of 75 km under 2°C warming in the 75 years covering the midpoint of the reference period (1975) to mid-21st century. In our study, the 75th percentile path length, corresponding to dispersal exposure, is ~385 km, well above such dispersal limits, supporting our assertion that the 75th percentile is conservative for estimating climate connectivity failure. Furthermore, the mean HMG value for a 100km path at the 90th percentile threshold is 0.22, which is well above the 0.1 threshold that Brennen et al. (2022) used to identify areas moderately to highly impacted by human land-uses. Lastly, the mean climatic distance for a 100km path at the 75th percentile is well over two standard deviations different, on average, from the focal pixel and analog. We report the percent of protected pixels across the globe and within each country that exhibits climate connectivity failure. We also assessed the potential for each of the 29,752 individual protected areas (e.g. Yellowstone National Park, Serengeti National Park) to undergo climate connectivity failure using a slightly different method. To do so, we calculated the mean percentile among pixels within each protected area for each of dispersal exposure, human exposure, and climate exposure (each metric was averaged across a protected area; the metrics themselves were not averaged with each other). We then calculated the percent of each protected area that did not have a protected climate analog (analog exposure). Although a binary approach (has or does not have an analog) is appropriate when evaluating individual focal pixels, a percent-based valuation is most appropriate and informative when evaluating individual protected areas with up to thousands of pixels. Individual protected areas exhibited climate connectivity failure if the mean dispersal exposure or climate exposure exceeded the 75th percentile, mean human exposure exceeded the 90th percentile, or the analog exposure exceeded 75%. References Abatzoglou, J. T., Dobrowski, S. Z., & Parks, S. A. (2020). Multivariate climate departures have outpaced univariate changes across global lands. Scientific Reports, 10(1), Article 1. https://doi.org/10.1038/s41598-020-60270-5 Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), Article 1. https://doi.org/10.1038/sdata.2017.191 Bellard, C., Leclerc, C., Leroy, B., Bakkenes, M., Veloz, S., Thuiller, W., & Courchamp, F. (2014). Vulnerability of biodiversity hotspots to global change. 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Protected-area targets could be undermined by climate change-driven shifts in ecoregions and biomes. Communications Earth & Environment, 2(1), Article 1. https://doi.org/10.1038/s43247-021-00270-z Dobrowski, S. Z., & Parks, S. A. (2016). Climate change velocity underestimates climate change exposure in mountainous regions. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms12349 Friedlingstein, P., Andrew, R. M., Rogelj, J., Peters, G. P., Canadell, J. G., Knutti, R., Luderer, G., Raupach, M. R., Schaeffer, M., van Vuuren, D. P., & Le Quéré, C. (2014). Persistent growth of CO2 emissions and implications for reaching climate targets. Nature Geoscience, 7(10), Article 10. https://doi.org/10.1038/ngeo2248 IUCN & UNEP-WCMC. (2019). Protected Planet: World Database on Protected Areas (WDPA). Accessed September 2019. Available at www.protectedplanet.net. (Accessed September 2019) [Map]. www.protected.planet.net Jenkins, D. G., Brescacin, C. R., Duxbury, C. V., Elliott, J. 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A closer look at novel climates: New methods and insights at continental to landscape scales. Global Change Biology, 23(9), 3934–3955. https://doi.org/10.1111/gcb.13645 McLachlan, J. S., Clark, J. S., & Manos, P. S. (2005). Molecular indicators of tree migration capacity under rapid climate change. Ecology, 86(8), 2088–2098. https://doi.org/10.1890/04-1036 Palfrey, R., Oldekop, J. A., & Holmes, G. (2022). Privately protected areas increase global protected area coverage and connectivity. Nature Ecology & Evolution, 6(6), Article 6. https://doi.org/10.1038/s41559-022-01715-0 Parker, L. E., & Abatzoglou, J. T. (2016). Projected changes in cold hardiness zones and suitable overwinter ranges of perennial crops over the United States. Environmental Research Letters, 11(3), 034001. https://doi.org/10.1088/1748-9326/11/3/034001 Parks, S. A., Carroll, C., Dobrowski, S. Z., & Allred, B. W. (2020). Human land uses reduce climate connectivity across North America. 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(2007). Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences, 104(14), 5738–5742. https://doi.org/10.1073/pnas.0606292104 Species across the planet are shifting their ranges to track suitable climate conditions in response to climate change. Given that protected areas have higher quality habitat and often harbor higher levels of biodiversity compared to unprotected lands, it is often assumed that protected areas can serve as steppingstones for species undergoing climate-induced range shifts. However, there are several factors that may impede successful range shifts among protected areas, including the distance that must be travelled, unfavorable human land uses and climate conditions along potential movement routes, and lack of analogous climates. Through a species-agnostic lens, we evaluate these factors across the global terrestrial protected area network as measures of climate connectivity, which is defined as the ability of a landscape to facilitate or impede climate-induced movement. We found that over half of protected land areas and two-thirds of the number of protected units across the globe are at risk of climate connectivity failure, casting doubt on whether many species can successfully undergo climate-induced range shifts among protected areas. Consequently, protected areas are unlikely to serve as steppingstones for a large number of species under a warming climate. As species disappear from protected areas without commensurate immigration of species suited to the emerging climate (due to climate connectivity failure), many protected areas may be left with a depauperate suite of species under climate change. Our findings are highly relevant given recent pledges to conserve 30% of the planet by 2030 (30x30), underscore the need for innovative land management strategies that allow for species range shifts, and suggest that assisted colonization may be necessary to promote species that are adapted to the emerging climate. There are three files in this repository: 1) backward.analogs - master.table.xlsx – results for backward analogs: · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by country; percent protected lands in each country that exhibit climate connectivity failure is also indicated. · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by protected area. Values represent the mean pixel-based percentile. Also included is a binary (0, 1) indicator of whether the protected area exhibits climate connectivity failure. 2) forward.analogs - master.table.xlsx – results for forward analogs: · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by country; percent protected lands in each country that exhibit climate connectivity failure is also indicated. · Each climate connectivity metric (dispersal exposure, human exposure, climate exposure, and analog exposure) is summarized by protected area. Values represent the mean pixel-based percentile. Also included is a binary (0, 1) indicator of whether the protected area exhibits climate connectivity failure. 3) PA_shapefile - cleaned.zip: This is the ‘cleaned’ (see Methods) protected area shapefile we used as a way to summarize dispersal exposure, human exposure, climate exposure, and analog exposure for each protected area. Note that two of these files are Microsoft Excel; they should be accessible via LibreOffice and R and potentially other open-source alternatives.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three soil quality indicators (SQIs) (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under grassland cover, no-tillage, cover crops, residue return and organic amendments were compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and May 2018 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include the following key descriptor variables and covariates useful for development of scoring functions: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class, pH, and SOC, 3) depth and timing of soil sampling, 4) analytical methods for SQI quantification, 5) units used in published works (i.e. equivalent mass, concentration), 6) SQI abundances, and 7) statistical significance of difference comparisons. *Note: Blank values in tables are considered unreported data.

A promising route to transition wastewater treatment facilities (WWTFs) from energy-consuming to net energy-positive is to retrofit existing facilities with process modifications, residual biosolid upcycling, and effluent thermal energy recovery. This study assesses the economics and life cycle environmental impacts of three proposed retrofits of WWTFs that consider thermochemical conversion technologies, namely, hydrothermal liquefaction, slow pyrolysis, and fast pyrolysis, along with advanced bioreactors. The results are in turn compared to the reference design, showing the retrofitting design with hydrothermal liquefaction, and an up-flow anaerobic sludge blanket has the highest net present value (NPV) of $177.36MM over a 20-year plant lifetime despite 15% higher annual production costs than the reference design. According to the ReCiPe method, chlorination is identified as the major contributor for most impact categories in all cases. There are several uncertainties embedded in the techno-economic analysis and life cycle assessment, including the discount rate, capital investment, sewer rate, and prices of main products; among which, the price of biochar presents the widest variation from $50 to $1900/t. Sensitivity analyses reveal that the variation of discount rates causes the most significant changes in NPVs. The impact of the biochar price is more pronounced in the slow pyrolysis-based pathway compared to the fast pyrolysis since biochar is the main product of slow pyrolysis.

Common to the Committee on Statistics of Drilling District 12 area are the recent exploration activities associated with the Central North American rift system or Mid-Continent geophysical anomaly (MGA), a major feature that runs from the Lake Superior area south into Kansas. For the last several years, much preliminary geologic and geophysical work has been undertaken, which usually proceeds a major play. The primary purpose is to test the Cambrian and Precambrian sediments know to have oil seeps in Wisconsin and Michigan. In 1984, Texaco USA drilled the first deep test, which was in Kansas. Although the well was apparently a dry hole, Texaco's findings have not been released. Kansas had a very active year with 7,451 completions, 45 more than those reported in 1983. The success rate of all wells drilled for oil or gas (7,307) was 57.5%, down slightly from 59.3% in 1983. Drilling for oil continued to predominate with 3,783 oil wells and 419 gas wells completed. Total footage was 22,486,535, up 4% from 1983. The average depth of a test drilled for oil or gas was 3,026 ft. In Missouri, the number of wells drilled for oil or gas declined 17% from 1983 levels. Most drilling continued to be in the western part of the state. A deep test in Vernon County penetrated 2,080 ft of Precambrian rocks. In Nebraska, 12 new discoveries were made in the western part of the state. Seven found new oil reserves, and 5 were tight holes; all were classified as new-field wildcats. The average depth was 5,465 ft in the 7 discoveries where the operator reported the total depth. In Mills County, Iowa, 4 wildcats were drilled to the Cambrian with depths from 3,000 to 3,300 ft. All were located approximately 35 mi north of the Tarkio field in northwestern Missouri. It is estimated that 2,000,000 ac are leased in Iowa along the MGA. In Minnesota, 400,000 ac were leased during 1984. The leases were concentrated mainly along the MGA from Duluth to the Iowa border. About 1,000 mi of Vibroseis was run across this feature. In Wisconsin, regional geophysical surveys along the MGA have been run. Companies are now doing more detailed seismic work. Acreage leased from October 1983 to January 1985 was estimated at 214,000 ac. A dry hole was drilled 1,000 ft into quartzite in Barron County.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three ‘Tier 2’ indicators (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under cover crops, grassland cover, organic amendments and residue return compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and December 2017 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control (conventional cropping with that found under soil health promoting practice) and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include key descriptor variables and covariates useful for development of scoring functions which include: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class and pH, 3) depth of sampling, 4) analytical methods for quantification (i.e.: loss on ignition, combustion), 5) units used in published works (i.e.: equivalent mass, concentration), 6) SOC class (L,M,H), and 7) statistical significance of difference comparisons.