International Students’ Awareness of U.S. Tax Filings

International students studying in the United States (U.S.) must follow many rules and regulations implemented by the federal government, including tax filing requirements. Generally, international students must file Form 8843 with the U.S. Internal Revenue Service (IRS) indicating that they are nonresidents exempt from taxation on worldwide income during their first five years in the U.S. In addition, they must file Form 1040-NR if they earn any taxable income in the U.S. This exploratory study examines the level of tax filing knowledge international students possess, as well as the characteristics of both international students and their educational institutions to ascertain if they may have a bearing on the students’ knowledge of the U.S. tax filings that should be filed. Data suggest that governmental and educational institutions may wish to bolster the dissemination of educational tax materials; otherwise, perhaps a significant number of international students may not be compliant with current IRS regulations

WIRELESS SENSOR NETWORK EFFECTIVELY CONTROLS CENTER PIVOT IRRIGATION OF SORGHUM

Automatic irrigation scheduling has been demonstrated using wired sensors and sensor network systems with subsurface drip and moving irrigation systems. However, there are limited studies that report on crop yield and water use efficiency resulting from the use of wireless networks to automatically schedule and control irrigations. In this 2011 study, a multinode wireless sensor network (WSN) system was mounted onto a six-span center pivot equipped with a commercial variable rate irrigation (VRI) system. Data from the WSN was used to calculate an integrated crop water stress index (iCWSI) threshold for automatic irrigation scheduling of grain sorghum. Crop response to the automatic method was compared with manual irrigation scheduling using weekly direct soil water measurements. The WSN system was operational throughout 98% of the growing season, and the delivery rates for data packets from the different nodes ranged between 90% and 98%. Dry grain yields and WUE in the automatic and manual treatment plots were not significantly different from each other at any of the irrigation levels. Crop water use and WUE were highest in the I80% irrigation treatment level. Average seasonal integrated crop water stress indices were negatively correlated to irrigation treatment amounts in both the manual and automatic plots and correlated well to crop water use. These results demonstrate that it is feasible to use WSN systems for irrigation management on a field-scale level

A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum

Variations of the crop water stress index (CWSI) have been used to characterize plant water stress and schedule irrigations. Usually, this thermal-based stress index has been calculated from measurements taken once daily or over a short period of time, near solar noon or after and in cloud free conditions. A method of integrating the CWSI over a day was developed to avoid the noise that may occur if weather prevents a clear CWSI signal near solar noon. This CWSI and time threshold (CWSI-TT) was the accumulated time that the CWSI was greater than a threshold value (0.45); and it was compared with a time threshold (CWSI-TT) based on a well-watered crop. We investigated the effectiveness of the CWSI-TT to automatically control irrigation of short and long season grain sorghum hybrids (Sorghum bicolor (L.) Moench, NC+ 5C35 and Pioneer 84G62); and to examine crop response to deficit irrigation treatments (i.e. 80%, 55%, 30% and 0% of full replenishment of soil water depletion to 1.5-m depth). Results from automated irrigation scheduling were compared to those from manual irrigation based on weekly neutron probe readings. In 2009, results from the Automatic irrigation were mixed; biomass yields in the 55% and 0% treatments, dry grain yields in the 80% and 0% treatments, and WUE in the 80%, 55%, and 0% treatments were not significantly different from those in the corresponding Manual treatments. However, dry grain yields in the 55% and 30% treatments were significantly less than those in the Manual control plots. These differences were due mainly to soil water variability in the beginning of the growing season. This conclusion is reinforced by the fact that IWUE for dry grain yield was not significantly different for 30% and 55% treatments, and was significantly greater for Automatic control at 80%. In 2010, there were no significant differences in biomass, dry grain yield, WUE, or IWUE for irrigation control methods when compared across the same amount treatments. Similar results between irrigation methods for at least the highest irrigation rate (80% of soil water depletion) in 2009 and among all irrigation treatment amounts in 2010 indicate that the CWSI-TT method can be an effective trigger for automatically scheduling either full or deficit irrigations for grain sorghum in a semi-arid region

Evaluation of a wireless infrared thermometer with a narrow field of view

Many agricultural studies rely on infrared sensors for remote measurement of surface temperatures for crop status monitoring and estimating sensible and latent heat fluxes. Historically, applications for these non-contact thermometers employed the use of hand-held or stationary industrial infrared thermometers (IRTs) wired to data loggers. Wireless sensors in agricultural applications are a practical alternative, but the availability of low cost wireless IRTs is limited. In this study, we designed prototype narrow (10◦) field of view wireless infrared sensor modules and evaluated the performance of the IRT sensor by comparing temperature readings of an object (Tobj) against a blackbody calibrator in a controlled temperature room at ambient temperatures of 15 ◦C, 25 ◦C, 35 ◦C, and 45 ◦C. Additional comparative readings were taken over plant and soil samples alongside a hand-held IRT and over an isothermal target in the outdoors next to a wired IRT. The average root mean square error (RMSE) and mean absolute error (MAE) between the collected IRT object temperature readings and the blackbody target ranged between 0.10 and 0.79 ◦C. The wireless IRT readings also compared well with the hand-held IRT and wired industrial IRT. Additional tests performed to investigate the influence of direct radiation on IRT measurements indicated that housing the sensor in white polyvinyl chloride provided ample shielding for the self-compensating circuitry of the IR detector. The relatively low cost of the wireless IRT modules and repeatable measurements against a blackbody calibrator and commercial IR thermometers demonstrated that these wireless prototypes have the potential to provide accurate surface radiometric temperature readings in outdoor applications. Further studies are needed to thoroughly test radio frequency communication and power consumption characteristics in an outdoor setting

USING PLANT CANOPY TEMPERATURE TO IMPROVE IRRIGATED CROP MANAGEMENT

Remotely sensed plant canopy temperature has long been recognized as having potential as a tool for irrigation management. However, a number of barriers have prevented its routine use in practice, such as the spatial and temporal resolution of remote sensing platforms, limitations in computing capacity and algorithm accuracy, and the cost and ruggedness of sensors and related components that can transmit and receive data wirelessly. Recent advances in all of these areas have made remote sensing more feasible in providing real-time feedback of field conditions. This can potentially reduce management time, maintain crop yield and crop water productivity, and detect unusual conditions such as equipment malfunctions or biotic stress sooner. Center pivots equipped with wireless infrared thermometers (IRTs) have been found to be suitable as a remote sensing platform. Canopy temperature-based algorithms have successfully automated drip and center pivot irrigation schedules where crop yield, water use efficiency, seasonal water use, and irrigation amounts applied were comparable to irrigations scheduled manually with a field-calibrated neutron probe. Even without automation, these algorithms can provide timely and valuable information on plant and soil water status, which can improve the management of irrigated crops

Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska

Thermal sensing provides rapid and accurate estimation of crop water stress through canopy temperature data. Canopy temperature is highly dependent on the transpiration rate of the leaves. It is usually assumed that any reduction in crop evapotranspiration (ET) leads to crop yield loss. As a result, an increase in canopy temperature due to a decrease in crop ET would indicate crop yield loss. This research evaluated the hypothesis that crop water stress could be detected using canopy temperature measurements (increased leaf temperature) from infrared thermometers (IRTs) before incurring crop yield loss. This would be possible in a narrow range when the photosynthesis rate (and carbon assimilation) is limited by solar radiation (energy-limiting water stress) while the leaf has abundant carbon dioxide for photosynthesis. Once photosynthesis becomes limited by carbon dioxide (carbon-dioxide-limiting water stress), then yield reduction would occur. In this field experiment, measured response variables included the integrated crop water stress index (iCWSI), ET, and crop yield for maize and soybean during the 2020 and 2021 growing seasons. The irrigation was applied at four different refill levels: rainfed (0%), deficit (50%), full (100%), and over (150%). The irrigation depth was prescribed using four different irrigation methods. The field was irrigated with a center pivot irrigation system, which was also used as a platform to mount IRT sensors. The iCWSI thresholds required for irrigation management were determined using the iCWSI dataset collected in 2020. The low, medium, and high iCWSI thresholds were 120, 150, and 180, respectively for maize and 110, 130, and 150, respectively for soybean. These thresholds should be updated with iCWSI data from future studies in this region to increase the credibility of the thresholds for irrigation management. The mean iCWSI values for consecutive days after a wetting event substantially increased with time for each irrigation level and a larger range in iCWSI values was observed among the irrigation levels after three days from a wetting event. The seasonal iCWSI for different levels were found to be negatively correlated with seasonal evapotranspiration for both years. The correlations between seasonal ET and crop yield were significant with the rainfed and deficit levels for maize (p-value \u3c 0.001) and soybean (p-value = 0.04) in 2020. The iCWSI and yield data for the fully watered plots indicated that thermal stress was detected using the sensing system without incurring yield loss (i.e., energy-limiting water stress). The ET and yield data for 2021 indicated that reduction in seasonal crop ET did not result in yield loss which also supported the hypothesis. Future studies should investigate whether this phenomenon of detecting crop water stress in an early stage without yield loss is observed in other climates and locations

Radiometer Footprint Model to Estimate Sunlit and Shaded Components for Row Crops

Th is article describes a geometric model for computing the relative proportion of sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil appearing in a circular or elliptical radiometer footprint for row crops, where the crop rows were modeled as continuous ellipses. Th e model was validated using digital photographs of row crops, where each component was determined by supervised classification. Root mean squared errors (RMSE) between modeled and observed components were 35, 49, 29, and 44% of observed means for sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil, respectively. Mean bias errors (MBE) were, respectively, –5.6, 16.6, –4.0, and –0.5% of observed means. Th e continuous ellipse model was compared to the commonly used clumping index model, where the latter estimates total vegetation and total soil, but does not resolve these into their sunlit or shaded components and does not account for radiometer footprint shape dimensions. Th e continuous ellipse model resulted in RMSE for vegetation and soil of 22 and 19%, respectively, whereas the clumping index model resulted in respective RMSE of 37 and 31%. Th e continuous ellipse model had MBE of 3.3 and –2.6% for vegetation and soil, respectively, which was slightly greater than the respective MBE of –1.5 and 1.4% for clumping index model. Given the model sensitivity and uncertainty of leaf area index (LAI), the RMSE and MBE resulting from the continuous ellipse model would not be expected to be less than 20% of the observed means, and model performance was therefore deemed reasonable in this study

TWO-SOURCE ENERGY BALANCE MODEL TO CALCULATE E, T, AND ET: COMPARISON OF PRIESTLEY-TAYLOR AND PENMAN-MONTEITH FORMULATIONS AND TWO TIME SCALING METHODS

The two-source energy balance (TSEB) model calculates the energy balance of the soil-canopy-atmosphere continuum, where transpiration is initially determined by the Priestley-Taylor equation. The TSEB was revised recently using the Penman-Monteith equation to replace the Priestley-Taylor formulation, thus better accounting for the impact of large and varying vapor pressure deficits (VPD) typical of advective, semiarid climates. This study is a comparison of the Priestley- Taylor and Penman-Monteith versions of the TSEB (termed TSEB-PT and TSEB-PM, respectively). Evaporation (E), transpiration (T), and evapotranspiration (ET) calculated by the TSEB-PT and TSEB-PM versions were compared to measurements obtained with microlysimeters, sap flow gauges, and weighing lysimeters, respectively, for fully irrigated cotton (Gossypium hirsutum L.) at Bushland, Texas. Radiometric surface temperature (TR) was used to calculate E, T, and ET in both TSEB versions in 15 min intervals and summed to intervals coinciding with times of measurements. In addition, a one-time-of-day TR measurement was used (9:45, 11:15, 12:45, 14:15, or 15:45 CST), and E, T, and ET were calculated for the appropriate measurement interval (i.e., daytime, nighttime, and 24 h) using the time scaling methods based on reference ET (TSCET) and reference temperature (TSCTEMP). Measured average values of E, T, and ET during the study period were 0.94 mm (24 h), 6.9 mm (7:00 to 22:00 CST), and 7.2 mm (24 h), respectively. The TSEB-PT consistently overestimated E and underestimated T, with RMSE/MBE of up to 2.8/1.8 mm and 4.1/-3.9 mm, respectively. In comparison, the TSEB-PM greatly reduced discrepancies between calculations and measurements, with respective RMSE/MBE for E and T of only up to 1.5/0.79 mm and 1.3/±0.76 mm, respectively. For 24 h ET, the TSEB-PT resulted in maximum RMSE/MBE of 3.2/-1.9 mm, and the TSEB-PM had maximum RMSE/MBE of 1.7/0.95 mm. Daytime ET model agreement was very similar for both model versions (RMSE/MBE usually \u3c1.1/ET or TSCTEMP methods, and results did not greatly differ for TSCET or TSCTEMP. Both time scaling methods were not very sensitive to the TR measurement time used, although morning (9:45 CST) TR measurement times did not perform as well as the other times

Search for the Neutron Decay n→\rightarrow X+γ\gamma where X is a dark matter particle

In a recent paper submitted to Physical Review Letters, Fornal and Grinstein have suggested that the discrepancy between two different methods of neutron lifetime measurements, the beam and bottle methods can be explained by a previously unobserved dark matter decay mode, n$\rightarrow$ X+$\gamma$ where X is a dark matter particle. We have performed a search for this decay mode over the allowed range of energies of the monoenergetic gamma ray for X to be a dark matter particle. We exclude the possibility of a sufficiently strong branch to explain the lifetime discrepancy with greater than 4 sigma confidence.Comment: 6 pages 3 figure