The Original USDA-ARS Experimental Watersheds in Texas and Ohio: Contributions from the Past and Visions for the Future

The USDA Soil Conservation Service (USDA‐SCS) realized the importance of understanding hydrologic processes on agricultural fields and watersheds in the mid‐1930s. Based on this realization, the research program of the Hydrologic Division of SCS established three experimental watersheds across the U.S. to analyze the impact of landuse practices on soil erosion, flood events, water resources, and the agricultural economy. Two of the original watersheds remain in operation today within the USDA Agricultural Research Service (USDA‐ARS): the Blacklands Experimental Watershed (now the Grassland, Soil and Water Research Laboratory) near Riesel, Texas, and the North Appalachian Experimental Watershed near Coshocton, Ohio. These original watersheds were designed for collection of hydrologic data on small watersheds and evaluation of hydrologic and soil loss response as influenced by various agricultural land management practices. A major contribution of these experimental watersheds is the quantification of soil loss reduction under conservation management, which has led to a drastic reduction in soil loss from cultivated agriculture in the 20th century. Riesel watershed studies produced the scientific basis for several watershed models that are now used worldwide to manage water quality and also facilitated fundamental analysis of the agronomic and environmental effects of tillage, fertilizer, and pesticide alternatives. Coshocton watershed studies led to the development of no‐till and pasture management practices to control runoff, erosion, and chemical loss and were instrumental in understanding water quality and hydrologic effects of soil macropores and mining and reclamation activities. The long‐term hydrologic records at each site have also improved understanding and management of water resources in their respective geographic regions. Because of their historical and future value, the USDA‐ARS has a unique responsibility to maintain these long‐term experimental watersheds, which are vital for addressing emerging research needs to meet future water availability, environmental quality, and food and fiber demands

The social networks of manureshed management

Manureshed management—the strategic use of manure nutrients that prioritizes recycling between livestock systems and cropping systems—provides a comprehensive framework for sustainable nutrient management that necessitates the collaboration of many actors. Understanding the social dimensions of collaboration is critical to implement the strategic and technological requirements of functional manuresheds. To improve this understanding, we identified aspirational networks of actors involved in manureshed management across local, regional, and national scales, principally in the United States, elucidating key relationships and highlighting the breadth of interactions essential to successful manureshed management. We concluded that, although the social networks vary with scale, the involvement of a common core set of actors and relationships appears to be universal to the successful integration of modern livestock and crop production systems necessary for functional manuresheds. Our analysis also reveals that, in addition to agricultural producers, local actors in extension and advisory services and private and public sectors ensure optimal outcomes at all scales. For manureshed management to successfully integrate crop and livestock production and sustainably manage manure nutrient resources at each scale, the full complement of actors identified in these social networks is critical to generate innovation and ensure collaboration continuity

Soil erosion assessment—Mind the gap

Accurate assessment of erosion rates remains an elusive problem because soil loss is strongly nonunique with respect to the main drivers. In addressing the mechanistic causes of erosion responses, we discriminate between macroscale effects of external factors—long studied and referred to as “geomorphic external variability”, and microscale effects, introduced as “geomorphic internal variability.” The latter source of erosion variations represents the knowledge gap, an overlooked but vital element of geomorphic response, significantly impacting the low predictability skill of deterministic models at field‐catchment scales. This is corroborated with experiments using a comprehensive physical model that dynamically updates the soil mass and particle composition. As complete knowledge of microscale conditions for arbitrary location and time is infeasible, we propose that new predictive frameworks of soil erosion should embed stochastic components in deterministic assessments of external and internal types of geomorphic variability.Key PointsSoil loss response to runoff is strongly controlled by “geomorphic internal variability”: microscale factors intrinsic to geomorphic systemPredictive skill of deterministic soil loss models at event scale is likely to remain poorErosion estimates must communicate uncertainty due to geomorphic external and internal types of variabilityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/136017/1/grl55374-sup-0001-Supplementary.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/136017/2/grl55374.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/136017/3/grl55374_am.pd

Land Application of Organic Fertilizers or Amendments

4 pp., 5 photosApplying organic materials to your land can add beneficial nutrients to the soil. But when too much is applied, or when it is applied incorrectly, organic material can cause environmental problems. This publication will help you select the proper application rate, calibrate equipment so that the correct rate is applied, and learn how location, water, soil and tillage can all affect the process

Uncertainty of CERES-maize calibration under different irrigation strategies using pest optimization algorithm

© 2019 by the authors. An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals

Neuromyelitis optica and pregnancy during therapeutic B cell depletion: infant exposure to anti-AQP4 antibody and prevention of rebound relapses with low-dose rituximab postpartum

Neuromyelitis optica (NMO) predominantly affects women, some in childbearing age, and requires early therapeutic intervention to prevent disabling relapses. We report an anti-AQP4 antibody-seropositive patient who became pregnant seven months after low-dose (100 mg) rituximab application. Pregnancy showed no complications, and low-dose rituximab restarted two days after delivery resulted in neurological stability for 24 months. Remarkably, her otherwise healthy newborn presented with anti-AQP4 antibody and reduced B lymphocyte counts in umbilical cord blood, which normalized three months later. Confirming and extending previous reports, our case suggests that low-dose rituximab might be compatible with pregnancy and prevent rebound NMO disease activity postpartum

Evaluation Of WEPP For Runoff And Sediment Yield Prediction On Natural Gas Well Sites

This article discusses the evaluation of Water Erosion Prediction Project (WEPP) for runoff and sediment yield prediction on natural gas well sites, instead of focusing only on the effects on fields, rangelands, and forests

Ensemble evaluation of hydrological model hypotheses

It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error