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The Mi-gene is widely used in different tomato cultivars to resist several Meloidogyne spp. (root-kot nematode; RKN), including M. incognita, M. javanica, and M. arenaria. Tomato cultivars with the Mi-gene are widely used in fields. However, factors such as temperatures, high initial population densities, and gene dosage can interfere with the expression of this gene. In addition, the presence of virulent species of RKN can limit the usefulness of the gene. One of the virulent species is M. haplanaria, which was identified infecting RKN-resistant tomato in Florida in 2015. The objectives of this study were to determine the initial damage threshold of M. haplanaria on tomato under greenhouse conditions and to analyze the impact of temperature and genetic background on virulence in tomato cultivars. The results showed a preliminary damage threshold of three eggs and J2/cm3 of soil. In addition, it was observed that M. haplanaria has a shorter life cycle than the virulent M. enterolobii and can infect, reproduce, and damage homozygous or heterozygous RKN-resistant tomato plants. This research demonstrated that M. haplanaria should be considered highly virulent on RKN-resistant tomato and is an important threat to agriculture in Florida.

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.

Soil moisture estimates are crucial for hydrologic modeling and agricultural decision-support efforts. These measurements are also pivotal for long-term inquiries regarding the impacts of climate change and the resulting droughts over large spatial and temporal scales. However, it has only been the past decade during which ground-based soil moisture sensory resources have become sufficient to tackle these important challenges. Despite this progress, random and systematic errors remain in ground-based soil moisture observations. Such errors must be quantified (and/or adequately minimized) before such observations can be used with full confidence. In response, this paper calibrates and analyzes US Climate Reference Network (USCRN) profile estimates at each of three sensors collocated at each USCRN location. With each USCRN location consisting of three independent, Hydraprobe measurements, triple collocation analysis of these sensory triads reveals the random error associated with this particular sensing technology in each individual location. This allows quantification of the accuracy of these individual profiles, the random errors associated with these measurements in different geographic locations, and offers the potential for more adept quality control procedures in near real time. Averaged over USCRN gauge locations nationally, this random error is determined to be approximately 0.012 m 3 /m 3 .

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m 3 /m 3 at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m 3 /m 3 . As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is ~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of ~0.05 m3/m3. The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date.

A commercial formulation of furfural was recently launched in the United States as a turfgrass nematicide. Three field trials evaluated efficacy of this commercial formulation on dwarf bermudagrass putting greens infested primarily with Belonolaimus longicaudatus, Meloidogyne graminis, or both these nematodes, and in some cases with Mesocriconema ornatum or Helicotylenchus pseudorobustus. In all these trials, furfural improved turf health but did not reduce population densities of B. longicaudatus, M. graminis, or the other plant-parasitic nematodes present. In two additional field trials, efficacy of furfural at increasing depths in the soil profile (0 to 5 cm, 5 to 10 cm, and 10 to 15 cm) against B. longicaudatus on bermudagrass was evaluated. Reduction in population density of B. longicaudatus was observed in furfural-treated plots for depths below 5 cm on several dates during both trials. However, no differences in population densities of B. longicaudatus were observed between the furfural-treated plots and the untreated control for soil depth of 0 to 5 cm during either trial. These results indicate that furfural applications can improve health of nematode-infested turf and can reduce population density of plant-parasitic nematodes in turf systems. Although the degree to which turf improvement is directly caused by nematicidal effects is still unclear, furfural does appear to be a useful nematode management tool for turf.

Recently a furfural nematicide Multiguard Protect EC was launched for use on turfgrasses in the United States. A series of greenhouse experiments were conducted to determine the concentration and exposure time required for this formulation to irreversibly affect Belonolaimus longicaudatus, and to study factors that might affect the practicality of furfural use in turfgrass systems. One experiment exposed B. longicaudatus to increasing concentrations of furfural (0 to 990 ppm) in vitro for either 24 or 48 hr, followed by inoculation onto bermudagrass. A second experiment evaluated effects of exposure of B. longicaudatus to increasing concentrations of furfural in soil solution on bermudagrass with or without an organic thatch layer. A third experiment evaluated effects on B. longicaudatus of increasing concentrations of furfural applied as a spray treatment to creeping bentgrass. Results from the in vitro exposure experiment found decreasing numbers of B. longicaudatus with increasing furfural concentration beginning with the lowest concentration tested (270 ppm). Belonolaimus longicaudatus were virtually eliminated with furfural concentrations ≥ 720 ppm. Similarly, exposure to increasing concentration of furfural in soil solution resulted in increasing reduction in numbers of B. longicaudatus. Presence of thatch slightly reduced the population density of B. longicaudatus. Spray application of furfural only reduced numbers of B. longicaudatus at the two highest rates (3,600 and 4,950 ppm).

In this study, a 1-D Ensemble Kalman Filter (EnKF) has been used to update the soil moisture states of the Soil and Water Assessment Tool (SWAT) model predictions. Experiments were conducted for the Cobb Creek Watershed in southeastern Oklahoma for 2006–2008. Assimilation of in situ data proved limited success in the top layers only, due to the weak surface and root-zone coupling of SWAT. Negative impact on stream flow and especially storm-scale runoff result from degraded deep-layer soil moisture prediction. This indicates that although the model is calibrated for stream flow, the parameters are not tuned to represent the realistic physical coupling of the system. Therefore the improved surface soil moisture failed to produce better runoff and flow estimates. Synthetic twin study proved the potential of assimilating remotely sensed surface soil moisture in improving SWAT's hydrologic predictions (root-zone soil moisture, evapotranpiration, surface runoff and stream flow) with EnKF. However, perturbations used to generate ensemble trajectories could result in systematic biases in model states and degradation of certain variables that cannot be corrected with EnKF. The mechanisms causing such biases and the methods to correct them need to be further investigated.

Belonolaimus longicaudatus is a recognized pathogen of cotton (Gossypium hirsutum), but insufficient information is available on the population dynamics and economic thresholds of B. longicaudatus in cotton production. In this study, data collected from a field in Florida were used to develop models predicting population increases of B. longicaudatus on cotton and population declines under clean fallow. Population densities of B. longicaudatus increased on cotton, reaching a carrying capacity of 139 nematodes/130 cm(3) of soil, but decreased exponentially during periods of bare fallow. The model indicated that population densities should decrease each year of monocropped cotton, if an alternate host is not present between sequential cotton crops. Economic thresholds derived from published damage functions and current prices for cotton and nematicides varied from 2 to 5 B. longicaudatus/130 cm(3) of soil, depending on the nematicide used.

Belonolaimus longicaudatus has been reported as damaging both potato (Solanum tuberosum) and cotton (Gossypium hirsutum). These crops are not normally grown in cropping systems together in areas where the soil is infested with B. longicaudatus. During the 1990s cotton was grown in a potato production region that was a suitable habitat for B. longicaudatus. It was not known how integrating the production of these two crops by rotation or double-cropping would affect the population densities of B. longicaudatus, other plant-parasitic nematodes common in the region, or crop yields. A 3-year field study evaluated the viability of both crops in monocropping, rotation, and double-cropping systems. Viability was evaluated using effects on population densities of plant-parasitic nematodes and yields. Rotation of cotton with potato was found to decrease population densities of B. longicaudatus and Meloidogyne incognita in comparison with continuous potato. Population densities of B. longicaudatus following double-cropping were greater than following continuous cotton. Yields of both potato and cotton in rotation were equivalent to either crop in monocropping. Yields of both crops were lower following double-cropping when nematicides were not used.

Belonolaimus longicaudatus has long been recognized as a pathogen of potato (Solanum tuberosum). However, a damage function relating expected yield of potato to population densities of B. longicaudatus at planting has not been derived, and the economic threshold for nematicide application is unknown. The objectives of this study were to derive the damage function of B. longicaudatus on potato and to calculate the economic threshold population density. The damage function data for B. longicaudatus on potato were obtained from an ongoing field study to evaluate cropping systems and nematode management practices. Soil samples were collected from experimental field plots, and nematodes were extracted from a 130-cm(3) subsample with a centrifugal-flotation method. A damage function was derived by linear regression of potato yield on nematode population density at planting. Based on this derived damage function and published potato prices, the economic threshold for nematicide application was calculated at 2 to 3 B. longicaudatus/130 cm(3) of soil, which was near the detection threshold based on methodology used in this study.

In a 3-year field study, population densities of Belonolaimus longicaudatus and other plant-parasitic nematodes and crop yields were compared between potato (Solanum tuberosum) cropping systems where either sorghum-sudangrass (Sorghum bicolor × S. arundinaceum) or velvetbean (Mucuna pruriens) was grown as a summer cover crop. Population densities of B. longicaudatus, Paratrichodorus minor, Tylenchorhynchus sp., and Mesocriconema sp. increased on sorghum-sudangrass. Population densities of P. minor and Mesocriconema sp. increased on velvetbean. Sorghum-sudangrass increased population densities of B. longicaudatus and Mesocriconema sp. on a subsequent potato crop compared to velvetbean. Potato yields following velvetbean were not greater than following sorghum-sudangrass despite reductions in population densities of B. longicaudatus.

Seashore paspalum (Paspalum vaginatum) has great potential for use in salt-affected turfgrass sites. Use of this grass on golf courses, athletic fields, and lawns in subtropical coastal areas may aid in conservation of freshwater resources. Belonolaimus longicaudatus and Hoplolaimus galeatus are considered among the most damaging root pathogens of turfgrasses in Florida. Glasshouse experiments were performed in 2002 and 2003 to examine the effects of increasing levels of irrigation salinity on B. longicaudatus and H. galeatus. Irrigation treatments were formulated by concentrating deionized water to six salinity levels (0, 5, 10, 15, 20, and 25 dS/m). Final population densities of H. galeatus followed a negative linear regression (r² = 0.92 and 0.83; P

Belonolaimus longicaudatus and Hoplolaimus galeatus are considered among the most damaging pathogens of turfgrasses in Florida. However, the host status of seashore paspalum (Paspalum vaginatum) is unknown. Glasshouse experiments were performed in 2002 and 2003 to determine the tolerance of 'SeaIsle 1' seashore paspalum to a population of B. longicaudatus and a population of H. galeatus, and to compare to 'Tifdwarf' bermudagrass for differences. Both nematode species reproduced well on either grass, but only B. longicaudatus consistently reduced root growth as measured by root length. Belonolaimus longicaudatus reduced root growth (P ≤ 0.05) by 35% to 45% at 120 days after inoculation on both grasses. In 2003, higher inoculum levels of H. galeatus reduced root growth (P ≤ 0.05) by 19.4% in seashore paspalum and by 14% in bermudagrass after 60 and 120 days of exposure, respectively. Percentage reductions in root length caused by H. galeatus and B. longicaudatus indicated no differences between grass species, although Tifdwarf bermudagrass supported higher soil population densities of both nematodes than SeaIsle 1 seashore paspalum.

Plant-parasitic nematodes can be very damaging to turfgrasses. The projected cancellation of the registration for fenamiphos in the near future has generated a great deal of interest in identifying acceptable alternative nematode management tactics for use on turfgrasses. Two field experiments were conducted to evaluate the effectiveness of repeated applications of several commercially available nematicides and root biostimulants for reducing population densities of plant-parasitic nematodes and (or) promoting health of bermudagrass in nematode-infested soil. One experimental site was infested with Hoplolaimus galeatus and Trichodorus obtusus, the second with Belonolaimus longicaudatus. In both trials, none of the experimental treatments reduced population densities (P ≤ 0.1) of plant-parasitic nematodes, or consistently promoted turf visual performance or turf root production. Nematologists with responsibility to advise turf managers regarding nematode management should thoroughly investigate the validity of product claims before advising clientele in their use.

A number of recent studies have focused on enhancing hydrologic prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing exclusively on the constraint of antecedent soil moisture conditions.

The HYDROS mission objective is to collect global scale measurements of the Earth's soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. In order to examine how HYDROS soil moisture retrieval will be performed and how the retrieval accuracy will be impacted by vegetation water content and surface heterogeneity, an observing system simulation experiment (OSSE) was conducted using a modeled geophysical domain in the south-central United States centered on the Arkansas-Red River basin for a one-month period in 1994. Three separate radiometer retrieval algorithms were evaluated: (1) a single-channel algorithm (H polarization), (2) a two-channel iterative algorithm, and (3) a two-channel reflectivity ratio algorithm. Analysis indicates that the HYDROS accuracy goal of 4% volumetric soil moisture can be met anywhere in the test basin except woodland areas. Nonlinear scaling of higher resolution ancillary vegetation data can adversely affect algorithm retrieval accuracies, especially in heavy tree areas on the east side of the basin

This article describes a curriculum review project at the Texas College of Osteopathic Medicine. The authors used the Core Curriculum in Osteopathic Education as a standard of comparison of the comprehensiveness of the college's instruction in osteopathic philosophy, principles, and practices and to clarify any differences between the Core Curriculum document and the college curriculum. The process involved a course-by-course search by student physicians and was reviewed by course faculty. Results revealed that the college's coverage was very similar to that proffered by the Core Curriculum. However, there were areas of instructional redundancy, questions of sequencing, and a few topics not addressed within the college curriculum. Thereafter, the faculty consolidated several courses, which produced a net savings of weekly contact hours and allowed further curricular change. Particular attention was given to the manner in which the other courses were scheduled, and, in turn, the consistency and stability of the entire first year was improved significantly.