USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.While rice is produced in some parts of the world in an upland, rainfed culture, almost all US-produced rice is grown with flood irrigation. In the dry-seeding system commonly used in the midsouthern US, the crop is usually flooded at approximately the V-4 (early tillering) growth stage and a continuous flood is maintained until after heading. The total amount of water used in rice production is quite large, and soil, fertilizers, and pesticides can be carried in the runoff from agricultural fields. Flood depth affects most aspects of flooded rice production, and remote monitoring of the flood depth could be quite valuable to many producers. The objective of this research is to develop and test a system for monitoring water depths in rice fields and alerting the producer so that less labor and energy is required to efficiently manage flood-irrigated rice. A prototype monitoring station was designed to measure water depth in a flooded rice field and transmit the information over a wireless link. A similar sensor and circuit performed satisfactorily in a raingage in 2006. In 2007, prototype monitoring stations will be installed in production rice fields. Concurrently with sensor durability testing, tests will be conducted to determine the limits of the wireless communication system. With daily reports of the water status in each paddy, field visits can be reduced. Over-pumping should be minimized by allowing better scheduling of field visits to stop the pump, and future systems should work with automatic pump control systems to stop the pump before runoff occurs

Effects of combined conservation practices on soil and water quality in the Central Mississippi River Basin

Conventional cultivation of claypan soils leads to soil and water quality degradation because of high runoff and associated soil erosion. The Goodwater Creek Experimental Watershed, which is part of the USDA Agricultural Research Service Benchmark Conservation Effects Assessment Project, Watershed Assessment Studies, was established to address these issues. Plot studies have highlighted trade-offs between erosion control and herbicide or nutrient runoff. There is a need for long-term field-scale evaluation of combined practices that reduce sediment, nutrient, and herbicide losses by runoff. A 36 ha field located in Missouri was under a conventional corn (Zea mays L.)-soybean (Glycine max L.) system from 1993 to 2003 with fertilizer application and tillage prior to planting in the spring. A precision agriculture system defined by two main management zones was implemented from 2004 to 2014: Wheat (Triticum aestivum L.) and soybean in 60% of the field, and corn and soybean in the remaining 40%. The system included no-till, cover crops, atrazine split-applications based on weed pressure, variable rates of nitrogen (N), and variable rates of fall-applied phosphorus (P). The objective of this study was to compare runoff water quality from the two management systems, based on flow and load duration curves, cumulative distribution functions, and conclusions from replicated plot studies. The precision agriculture system did not affect annual runoff, but it did increase the frequency of low flows. Sediment losses were reduced by 87% as a result of no-till and cover crops. Atrazine and P losses were lower than expected, despite the lack of incorporation into the soil. Atrazine losses were possibly lower because of the wheat area acting as a buffer, greater atrazine adsorption and retention in the field, and faster decay. Dissolved P losses as a fraction of applied remained the same, likely because of greater adsorption and lower runoff risk when applying P. Finally, nitrate-N (NO3-N) losses decreased and resulted in an overall decrease of N losses despite a slight increase of ammonium-N (NH4-N) losses. Explanations includeincluded a greater soil water content, a different timing of N applications, and N uptake by cover crops. Building on these successes, an aspirational management system is proposed to further improve on the performance and practicality of the precision agriculture system

Cropping system and landscape characteristics influence long-term grain crop profitability

Converting from standard tillage or no-tillage cropping systems to more conservation-based cropping systems that include no-tillage, cover crops, and reduced agrichemical inputs must be profitable for large-scale adoption. Therefore, research was conducted at the central Mississippi River Basin site of the USDA Long-Term Agroecosystem Research Network from 1996 to 2009 to determine how cropping systems, landscape position, and depth to claypan affected net economic return. Treatments consisted of three cropping systems {mulch-till corn (Zea mays L.)–soybean [Glycine max (L.) Merr.], MTCS; no-till corn–soybean, NTCS; no-till corn–soybean–wheat (Triticum aestivum L.) (NTCSW)–cover crop} and three landscape positions (summit, backslope, and footslope). Within each cropping system, landscape position influenced the depth to claypan and net returns, which were greatest in the summit and footslope positions. Across landscape positions, net return for NTCS was US$252 and$119 ha−1 yr−1 greater than MTCS and NTCSW, respectively. Net return of corn in MTCS and NTCSW was negative, whereas corn in NTCS averaged $97 ha−1 yr−1. Only NTCS corn exhibited a positive linear response in net return to depth to claypan. Soybean was much more profitable than corn, and both NTCS and NTCSW soybean were less influenced by landscape position and had at least$252 ha−1 yr−1 greater return than did MTCS soybean across landscape position. Results suggest that converting from MTCS to NTCS would have large positive impacts on reducing within-field variability and increasing profitability in the region, and modifications to the NTCSW system are needed to improve profitability

Reduced Efficacy of Anti-A\u3cem\u3eβ\u3c/em\u3e Immunotherapy in a Mouse Model of Amyloid Deposition and Vascular Cognitive Impairment Comorbidity

Vascular cognitive impairment and dementia (VCID) is the second most common form of dementia behind Alzheimer\u27s disease (AD). It is estimated that 40% of AD patients also have some form of VCID. One promising therapeutic for AD is anti-Aβ immunotherapy, which uses antibodies against Aβ to clear it from the brain. While successful in clearing Aβ and improving cognition in mice, anti-Aβ immunotherapy failed to reach primary cognitive outcomes in several different clinical trials. We hypothesized that one potential reason the anti-Aβ immunotherapy clinical trials were unsuccessful was due to this high percentage of VCID comorbidity in the AD population. We used our unique model of VCID-amyloid comorbidity to test this hypothesis. We placed 9-month-old wild-type and APP/PS1 mice on either a control diet or a diet that induces hyperhomocysteinemia (HHcy). After being placed on the diet for 3 months, the mice then received intraperotineal injections of either IgG2a control or 3D6 for another 3 months. While we found that treatment of our comorbidity model with 3D6 resulted in decreased total Aβ levels, there was no cognitive benefit of the anti-Aβ immunotherapy in our AD/VCID mice. Further, microhemorrhages were increased by 3D6 in the APP/PS1/control but further increased in an additive fashion when 3D6 was administered to the APP/PS1/HHcy mice. This suggests that the use of anti-Aβ immunotherapy in patients with both AD and VCID would be ineffective on cognitive outcomes

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Translational models for vascular cognitive impairment: a review including larger species.

BACKGROUND: Disease models are useful for prospective studies of pathology, identification of molecular and cellular mechanisms, pre-clinical testing of interventions, and validation of clinical biomarkers. Here, we review animal models relevant to vascular cognitive impairment (VCI). A synopsis of each model was initially presented by expert practitioners. Synopses were refined by the authors, and subsequently by the scientific committee of a recent conference (International Conference on Vascular Dementia 2015). Only peer-reviewed sources were cited. METHODS: We included models that mimic VCI-related brain lesions (white matter hypoperfusion injury, focal ischaemia, cerebral amyloid angiopathy) or reproduce VCI risk factors (old age, hypertension, hyperhomocysteinemia, high-salt/high-fat diet) or reproduce genetic causes of VCI (CADASIL-causing Notch3 mutations). CONCLUSIONS: We concluded that (1) translational models may reflect a VCI-relevant pathological process, while not fully replicating a human disease spectrum; (2) rodent models of VCI are limited by paucity of white matter; and (3) further translational models, and improved cognitive testing instruments, are required

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells