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Soil erosion within agricultural landscapes has significant environmental and economic impacts and is strongly driven by reduced residue cover in agricultural fields. Large-area soil erosion models such as the Daily Erosion Project are important tools for understanding the patterns of soil erosion, but they rely on the accurate estimation of crop residue cover over large regions to infer the tillage practices, an erosion model input. Remote sensing analyses are becoming accepted as a reliable way to estimate crop residue cover, but most use localized training datasets that may not scale well outside small study areas. An alternative source of training data may be commonly conducted tillage surveys that capture information via rapid “windshield” surveys. In this study, we utilized the Google Earth Engine to assess the utility of three crop residue survey types (windshield tillage surveys, windshield binned residue surveys, and photo analysis surveys) and one synthetic survey (retroactively binned photo analysis data) as sources of training data for crop residue cover regressions. We found that neither windshield-based survey method was able to produce reliable regressions but that they can produce reasonable distinctions between low-residue and high-residue fields. On the other hand, both photo analysis and retroactively binned photo analysis survey data were able to produce reliable regressions with r2 values of 0.57 and 0.56, respectively. Overall, this study demonstrates that photo analysis surveys are the most reliable dataset to use when creating crop residue cover models, but we also acknowledge that these surveys are expensive to conduct and suggest some ways these surveys could be made more efficient in the future.

A database of curated genomes is needed to better assess soil microbial communities and their processes associated with differing land management and environmental impacts. Interpreting soil metagenomic datasets with existing sequence databases is challenging because these datasets are biased towards medical and biotechnology research and can result in misleading annotations. We have curated a database of 928 genomes of soil-associated organisms (888 bacteria, 34 archaea, and 6 fungi). Using this database as a representation of the current state of knowledge of soil microbes that are well-characterized, we evaluated its composition and compared it to broader microbial databases, specifically NCBI’s RefSeq, as well as 3,035 publicly available soil amplicon datasets. These comparisons identified phyla and functions that are enriched in soils as well as those that may be underrepresented in RefSoil. For example, RefSoil was observed to have increased representation of Firmicutes despite its low abundance in soil environments and also lacked representation of Acidobacteria and Verrucomicrobia, which are abundant in soils. Our comparison of RefSoil to soil amplicon datasets allowed us to identify targets that if cultured or sequenced would significantly increase the biodiversity represented within RefSoil. To demonstrate the opportunities to access these underrepresented targets, we employed single cell genomics in a pilot experiment to recover 14 genomes from the \"most wanted\" list, which improved RefSoil's representation of EMP sequences by 7% by abundance. This effort demonstrates the value of RefSoil in the guidance of future research efforts and the capability of single cell genomics as a practical means to fill the existing genomic data gaps.

A strong need exists for tools to assess the efficacy of conservation practices across large regions supporting informed policy decisions that may lead to better soil and water conservation while optimizing agricultural production options. Perennial warm‐season grasses (WSGs) such as switchgrass (Panicum virgatum), can be grown on marginally productive and/or environmentally sensitive lands to meet growing bioenergy demands while reducing water runoff and soil erosion compared to current row crop systems. Quantifying the soil and water conservation effects of WSG when strategically placed on the landscape would help support decisions favoring both economic and environmental benefits. We used the Daily Erosion Project (DEP) to simulate the effects of WSGs on hillslope water runoff and soil loss for 2008–2016 across eight major land resource areas (MLRA) in the Midwest United States. Four different scenarios (baseline or existing conditions and switchgrass grown on slopes ≥3%, ≥6%, and ≥10%) were modeled. Across all hillslope groups replacing row crops with switchgrass reduced yearly water runoff and soil loss by 3.2%–12.1% and 43.7%–95.5% compared with the baseline levels, respectively. Water and soil conservation efficiency (water runoff reductions or soil loss reductions associated with 1% increase in switchgrass coverage) increased with slope as 10% > 6% > 3% for all MLRAs. Switchgrass replacement on slopes ≥10% reduced average soil loss estimates as much as 22.6 Mg ha−1 year−1 for the most erosive MLRA (baseline soil erosion rate of 28.6 Mg ha−1 year−1) and resulted in all MLRA erosion estimates ≤6.0 Mg ha−1 year−1. For soil loss, an apparent interaction existed between slope group and total annual precipitation; as annual precipitation increased, the difference in soil loss between slope groups increased. Soil loss was more sensitive to these factors than was water runoff. Policy supporting a renewable energy industry while strategically improving soil and water resources seems globally advantageous. Spatial and temporal impact of strategic perennial warm season grass production on soil and water conservation in lieu of row crop production reveals favorable outcomes across multiple physiographic regions. The Daily Erosion Project modeling system used for this project illustrates a novel tool that utilizes a suite of remote sensing inputs and electronic databases to test scenarios and/or track change in soil erosion rates spatially and temporally across large regions.

The major water quality impairment in the Midwest United States is sediment eroded from agricultural lands. Yet, few understand where or when erosion occurs, or the dynamics of soil erosion, the relative impact of precipitation, topography, land management and severe events over time and space. The objectives of this project are to: 1) develop methodology for estimating near real time spatial and temporal soil erosion and water runoff losses; and 2) explore issues in applying the method to a large area by setting up and running a prototype system for the state of Iowa. To accomplish this, soil erosion and water runoff loss are estimated daily at the township level (∼10 by ∼10 km area) (∼6.2 mi) 2 and a map is posted on the Internet daily showing precipitation with runoff and soil erosion estimates for every Iowa township. We use Water Erosion Prediction Project (WEPP), a daily simulation model, to compute soil erosion and surface runoff. WEPP uses accumulated precipitation by 15-minute periods obtained with NEXRAD radar. Other needed weather data are obtained from an Iowa weather network. The National Resources Inventory provides soil, topography, cropping and management information required for running WEPP. Daily and annual spatial precipitation, runoff and erosion estimates illustrate a high level of spatial variability related to topography, precipitation characteristics, soils and management practices.

The use of models to target conservation efforts, or “precision conservation,” has become a research interest due to its ability to evaluate different conservation practices in multiple areas and determine where conservation expenditures will provide the best return. To determine if automation of the data collection process is possible, the intersection of USDA common land unit maps, which define the boundaries of agricultural lands, with vegetative cover maps, represented by the USDA cropland data layer, was analyzed for accuracy on approximately 80 central Iowa fields over two years. The resulting dataset successfully determined mean field centroid within 3 pixels, area with an r 2 of 0.99, crop cover with 95% accuracy, and when compiled over a period of years, crop rotations. The value of the resulting map can also be extended with the use of other technologies to determine field average of properties such as residue cover, tillage, yield, and biomass production. The dataset can also serve as an input for numerous precision conservation models and enable the ability to create maps of other field level processes or properties, such as residue cover, tillage, or fertilization, by defining the boundaries over which management uniformly occurs.

The world's agricultural soil resources continue to degrade due to rates of soil erosion and organic matter oxidation exceeding those found in native systems. This degradation is generally the result of intensive soil tillage, resulting in decreased amounts of soil residue cover, high runoff rates, and decreased soil organic matter. Degradation pressure is likely to increase in the future due to harvesting of plant residues for bioenergy and increased peak intensity of rainstorms due to climate change, increasing soil detachment and transport. Developing effective solutions to these problems require the use of models to estimate the extent and severity of these impacts across the landscape. Once impacts are known, appropriate management practices can be selected to counteract these effects. However, implementation of these models will require data on land management practices at the field level, a resolution not currently possible with any routinely collected dataset. This requires the development of new methods to efficiently gather a large amount of quality data. To this end, methodologies were developed for automatically determining management unit boundaries, corresponding crop and residue cover, and estimating surface roughness. Testing was conducted on agricultural fields in Boone, Hamilton, and Story counties in central Iowa with management unit area estimated to within 10% of the mean and management unit centroids located within 30 m on average and 165 m at a maximum. Crop cover was correctly identified better than 95% of the time. Errors were within USGS standard where applicable. Methodologies for determining residue cover were also determined, with an RMSE of 0.08 before green vegetation emergence and 0.11 after emergence. These errors compare favorably with the +/- 0.10 error in determining residue cover using published measurement methods. Radar estimation of surface roughness was not successfully demonstrated, as no difference was observed in radar backscatter between different surface roughness values.

A database of curated genomes is needed to better assess soil microbial communities and their processes associated with differing land management and environmental impacts. Interpreting soil metagenomic datasets with existing sequence databases is challenging because these datasets are biased towards medical and biotechnology research and can result in misleading annotations. We have curated a database of 922 genomes of soil-associated organisms (888 bacteria and 34 archaea). Using this database, we evaluated phyla and functions that are enriched in soils as well as those that may be underrepresented in RefSoil. Our comparison of RefSoil to soil amplicon datasets allowed us to identify targets that if cultured or sequenced would significantly increase the biodiversity represented within RefSoil. To demonstrate the opportunities to access these underrepresented targets, we employed single cell genomics in a pilot experiment to sequence 14 genomes. This effort demonstrates the value of RefSoil in the guidance of future research efforts and the capability of single cell genomics as a practical means to fill the existing genomic data gaps.

The opsin family of G-protein-coupled receptors are used as light detectors in animals. Opsin 5 (also known as neuropsin or OPN5) is a highly conserved opsin that is sensitive to visible violet light 1 , 2 . In mice, OPN5 is a known photoreceptor in the retina 3 and skin 4 but is also expressed in the hypothalamic preoptic area (POA) 5 . Here we describe a light-sensing pathway in which POA neurons that express Opn5 regulate thermogenesis in brown adipose tissue (BAT). We show that Opn5 is expressed in glutamatergic warm-sensing POA neurons that receive synaptic input from several thermoregulatory nuclei. We further show that Opn5 POA neurons project to BAT and decrease its activity under chemogenetic stimulation. Opn5 -null mice show overactive BAT, increased body temperature, and exaggerated thermogenesis when cold-challenged. Moreover, violet photostimulation during cold exposure acutely suppresses BAT temperature in wild-type mice but not in Opn5 -null mice. Direct measurements of intracellular cAMP ex vivo show that Opn5 POA neurons increase cAMP when stimulated with violet light. This analysis thus identifies a violet light-sensitive deep brain photoreceptor that normally suppresses BAT thermogenesis. Mice possess neurons in the preoptic area of the hypothalamus that are sensitive to violet light; these deep brain neurons sense light via OPN5 and regulate adaptive thermogenesis in brown fat.

Control of the heart rate (rate control) is central to atrial fibrillation management, even for patients who ultimately require control of the rhythm. We review heart rate control in patients with atrial fibrillation, including the rationale for the intervention, patient selection, and the treatments available. The choice of rate control depends on the symptoms and clinical characteristics of the patient, but for all patients with atrial fibrillation, rate control is part of the management. Choice of drugs is patient-dependent. β blockers, alone or in combination with digoxin, or non-dihydropyridine calcium-channel blockers (not in heart failure) effectively lower the heart rate. Digoxin is least effective, but a reasonable choice for physically inactive patients aged 80 years or older, in whom other treatments are ineffective or are contraindicated, and as an additional drug to other rate-controlling drugs, especially in heart failure when instituted cautiously. Atrioventricular node ablation with pacemaker insertion for rate control should be used as an approach of last resort but is also an option early in the management of patients with atrial fibrillation treated with cardiac resynchronisation therapy. However, catheter ablation of atrial fibrillation should be considered before atrioventricular node ablation. Although rate control is a top priority and one of the first management issues for all patients with atrial fibrillation, many issues remain.

During mouse postnatal eye development, the embryonic hyaloid vascular network regresses from the vitreous as an adaption for high-acuity vision. This process occurs with precisely controlled timing. Here, we show that opsin 5 (OPN5; also known as neuropsin)-dependent retinal light responses regulate vascular development in the postnatal eye. In Opn5 -null mice, hyaloid vessels regress precociously. We demonstrate that 380-nm light stimulation via OPN5 and VGAT (the vesicular GABA/glycine transporter) in retinal ganglion cells enhances the activity of inner retinal DAT (also known as SLC6A3; a dopamine reuptake transporter) and thus suppresses vitreal dopamine. In turn, dopamine acts directly on hyaloid vascular endothelial cells to suppress the activity of vascular endothelial growth factor receptor 2 (VEGFR2) and promote hyaloid vessel regression. With OPN5 loss of function, the vitreous dopamine level is elevated and results in premature hyaloid regression. These investigations identify violet light as a developmental timing cue that, via an OPN5–dopamine pathway, regulates optic axis clearance in preparation for visual function. Nguyen et al. show that neuropsin (OPN5) suppresses hyaloid vessel regression in the developing mouse retina in response to light, by regulation of the dopamine reuptake transporter and DRD2-dependent suppression of VEGFR2 activity.