UARK (University of Arkansas )
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Genome-Wide Association Study/ Genomic Prediction of Cowpea Seed Protein and Black Seed Coat Color in Cowpea
Get PDFCowpea (Vigna unguiculata [L.] Walp) is a crucial crop in many regions, serving as a vital protein source for humans and livestock. Addressing the need to enhance its nutritional quality and make it easier to cultivate cowpeas of specific colors, this study utilized genome-wide association studies (GWAS) to identify markers associated with protein content and seed coat color patterns in cowpea.
In the protein content study, a GWAS was conducted on 161 cowpea accessions using 110,155 high-quality single-nucleotide polymorphisms (SNPs). Seven significant SNP markers were identified, all located at a locus on chromosome 8 associated with the gene Vigun08g039200, enhancing our understanding of the genetic basis for protein content variation in cowpea. Additionally, genomic prediction models were employed, yielding accuracies ranging from 42.9% to 52.1%, offering potential for early prediction of individual performance in breeding programs aimed at improving seed protein content and nutritional quality.
The second study focused on seed coat color and patterns, important traits for consumer preferences in cowpea. A GWAS was conducted on 315 cowpea lines, revealing associations between specific SNP markers and seed coat color on chromosome 5. Genes such as Vigun05g039700, Vigun05g039800, and Vigun05g041100 were identified as potential candidates linked to black seed coat. These findings underscore the utility of associated SNP markers in selecting desired seed colors and patterns through genomic breeding approaches in cowpea breeding programs.
These findings provide valuable insights into the genetic architecture underlying protein content and seed coat color traits in cowpea, facilitating the selection of desirable traits in breeding programs through genomic approaches. Such advancements hold promise for enhancing both the nutritional quality and aesthetic appeal of cowpea, thereby contributing to food security and consumer preferences
Struvite Effects on Greenhouse Gas Emissions from Flood- and Furrow-irrigated Rice in the Greenhouse
Get PDFOver time, as the influences of climate-change and declining, mineable, global phosphorus (P) reserves begin to impact agricultural production systems, alternative fertilizer sources and management practices, such as struvite (MgNH4PO4 · 6H2O) and furrow-irrigation, will be necessary to accommodate nutrient and water demands. The precipitation and use of struvite as a fertilizer-P source could reduce environmental damages caused by nutrient-rich wastewater streams, supplement domestic P supply, and improve the sustainability of flood-, and furrow-irrigated rice production by reducing greenhouse gas emissions. The study of struvite from both chemical and electrochemical precipitation processes is well documented, but currently there is a gap in the literature regarding the use of electrochemically precipitated struvite (ECST)-P sources created from actual municipal wastewater in agricultural production systems and the resulting impacts on crop and greenhouse gas (GHG) production. Thus, the objectives of this study were to i) quantify GHG [i.e., methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2)] fluxes, season-long emissions, and global warming potential (GWP) from a chemically precipitated struvite (CPST) and a synthetic and a real-wastewater-derived ECST compared to monoammonium phosphate (MAP) and an unamended control (UC) from floodirrigated rice (Oryza sativa) grown in a P-deficient silt-loam soil in the greenhouse during the 2022 growing season and ii) to quantify CH4, N2O, CO2 fluxes, season-long emissions, global warming potential (GWP), emissions intensity, plant response, and end-of-season soil properties from a synthetic and a real-wastewater-derived ECST and a CPST compared to MAP and an UC from simulated furrow-irrigated rice cultivated in a P-deficient silt-loam soil in the greenhouse during the 2023 growing season. Gas collection occurred weekly over a 140-day period during the 2022 growing season and over a 162-day period during the 2023 growing season for the flood- and furrow-irrigated studies, respectively. For the 2022 study, season-long CH4 emissions differed among fertilizer-P treatments and were smallest from the UC (29.9 kg CH4 ha-1 ), which was similar to both ECST-P sources. Additionally, the CO2-excluded GWP from the 2022 study was greatest from MAP (2881 kg CO2-equivalents ha-1 ), which was similar to CPST, and smallest from the UC (978 kg CO2-equivalents ha-1 ), which did not differ from either ECST-P source. Season-long emissions for N2O differed among fertilizer-P treatments for the 2023 simulated furrow-irrigation study and were greatest from the UC (6.1 kg N2O ha-1 ), which differ from and was at least 2.8 times greater than all other fertilizer-P treatments. For the 2023 study, for the vast majority of plant response properties and all GHG properties, ECSTSyn and ECSTReal did not differ from each other and rarely differed from MAP. Results of this study emphasized the possible agronomic and environmental benefits of struvite, both as CPST and ECST, as an efficient fertilizer-P source to improve the sustainability of both flood- and furrow-irrigated rice production in Arkansas. Additionally, as the majority of properties were similar between the two ECST fertilizer-P sources, results of this study indicate the comparability of ECST fertilizers created from actual wastewater to previously studied ECST fertilizer-P sources
Applying UAS LiDAR for Developing Small Project Terrain Models
Get PDFUnmanned aerial systems (UAS) LiDAR was used to collect survey data for small-area projects, particularly bridge replacement projects. The project aimed to compare UAS LiDAR data with conventional surveying methods. For the project, five bridge sites were selected for UAS LiDAR and conventional survey data collection, which include Lincoln Bridge 1 (East), Lincoln Bridge 2 (West), Humnoke, Frenchman’s Bayou, and Mountain Home. Data for each site was collected during the 2021–2022 winter. A DJI M600 Pro and Phoenix LiDAR Systems AL3-16 LiDAR unit were used for the UAS LiDAR measurements. The conventional survey equipment used included a global navigation satellite system (GNSS) real time kinematics (RTK) system and a robotic total station. LiDAR missions were planned using a combination of Google Earth, Phoenix LiDAR Systems’ Flightplanner, and Litchi Mission Hub. Flights were conducted at an altitude of 45 meters above ground level (AGL), with 50 percent side lap, ensuring comprehensive coverage. Weather and environmental conditions were carefully considered for optimal data collection. The LiDAR data were processed using Phoenix LiDAR System’s LiDARMill online software service. The processing involved GNSS data correction, trajectory optimization, and LiDAR data alignment and classification. Additionally, LAS tools were employed to enhance ground classification, reduce noise, and generate accurate digital elevation models. The accuracy of UAS LiDAR was assessed by comparing the UARK and ARDOT LiDAR datasets and ground survey data collected using GNSS and total station checkpoints for hard and soft surfaces. Errors between the UAS LiDAR and checkpoints were compared using aerial photos, bar graphs, and box and whisker plots, and direct raster comparisons were made whenever multiple LiDAR datasets were available. Overall, errors of up to approximately 1.0 inch can be expected for hard surfaces, with potential for both over- and under-predictions. An overprediction error of 1.0 inch to 3.5 inches can be expected for grass surfaces, depending on grass height. Larger overprediction errors of 2.0 inches to 5.0 inches can be expected for tall grass areas, while errors ranging between 2.0 inches and 7.0 inches can be expected for tree areas. The cost savings analysis showed that UAS LiDAR demonstrated an average cost reduction of 10,539.18 per bridge project compared to conventional survey methods, resulting in a 25 percent reduction in the cost of surveys for small-area bridge projects. Overall, UAS LiDAR proves to be a valuable tool for obtaining elevation data, with good accuracy for hard surfaces and expected variations for soft surfaces. Despite the limitations, the cost savings analysis puts UAS LiDAR surveying a clear advantage over both helicopter LiDAR and conventional surveying methods for small-area bridge projects
How to Situate High School Student Part-Time Work Trends: An [Incomplete] Empirical Glance
Get PDFRecent federal warnings about increases in child labor law violations coincide with various state efforts to dilute child labor protections. This Article confines itself to the array of outcomes attributable to lawful part time work performed by non-trafficked, full-time, U.S. high school students. This Article sets out to develop two modest and separate—though related—claims. The first claim is that clear and reliable answers do not emerge for such basic policy questions as, for example, whether student part-time work during high school constitutes a penalty or, instead, confers rewards to students. This Article’s second claim is methodological. Specifically, much of the existing research on the implications of part-time work on full-time students lacks a sufficiently developed and secure empirical footing. Data limitations as well as research design threats imposed by selection effects persistently emerge as meaningful challenges for much of the research in this area. Part I quickly and descriptively summarizes key longitudinal full-time high school student part-time employment trends. Part II engages with existing research on the effects of part-time work on various high school student outcomes and, in so doing, illustrates how a lack of a scholarly consensus on the most salient student outcome complicates—and obscures—potential policy implications from this research literature. Part III reviews the leading data sets in this policy space and illustrates how they fall short of supplying an adequate empirical footing necessary for helpful, reliable analyses of how part-time work intersects with an array of student outcomes. The conclusion emphasizes that what we do not yet know about the consequences of part-time work for full-time high school students, at least empirically, risks overwhelming what we do know
Summaries of Arkansas Cotton Research 2023
Get PDFWith current production costs and cotton prices, either record or near-record yields are needed for profitable cotton production in Arkansas. As usual, price volatility in 2023 added another level of difficulty in the quest for being profitable. Cotton futures traded between 80 and 90 cents per pound in 2023, with brief times below 80 cents and above 90 cents. In April, cotton futures dropped below 80 cents per pound, the lowest level in almost four months. This was due to an increase in certificated stocks, declining demand, and a growing world carryover. The average cotton price received by U.S. growers in 2023 was 79.0 cents compared to 85.0 in 2022, 75.8 in 2021, and 59.2 in 2020. Unfortunately, production costs have steadily increased. Great uncertainties still exist for the upcoming season, most of which are outside of our control. These include, but are not limited to, weather extremes, inflation, supply chain disruptions, rising interest rates, and a strengthening U.S. dollar
A Century of Scholarship: University of Arkansas School of Law Faculty Scholarship 1924–2023
Get PDFSteven R. Probst’s bibliography A Century of Scholarship: University of Arkansas School of Law Faculty Scholarship 1924–2023 marks an important anniversary: the 2024 centennial of the U of A School of Law. To honor this occasion, Probst has assembled, through extensive archival research, the list of publications that U of A law faculty have authored over the course of the school’s hundred-year history. A Century of Scholarship is a testament to the invaluable scholarly contributions of these extraordinary thinkers who have shaped our legal landscape.https://scholarworks.uark.edu/uapressasedit/1003/thumbnail.jp
B.R. Wells Arkansas Rice Research Studies 2023
Get PDFArkansas is the leading rice producer in the United States. The state represents 49.0% of total U.S. rice production and 49.6% of the total acres planted to rice in 2023. Rice cultural practices vary across the state and across the U.S. However, these practices are also dynamic and continue to evolve in response to changing political, environmental, and economic times. This survey was initiated in 2002 to monitor and record changes in the way Arkansas rice producers approach their livelihood. The survey was conducted by polling county extension agents in each of the counties in Arkansas that produce rice. Questions included topics such as tillage practices, water sources and irrigation methods, seeding methods, and precision leveling. Information from the University of Arkansas System Division of Agriculture DD50 Rice Management Program was included to summarize the variety acreage distribution across Arkansas. Other data was obtained from the USDA National Agricultural Statistics Service
Investigation of K-2 Teachers\u27 Self-Efficacy in One Rural School District
Get PDFIn the past four years education has undergone many changes, whether it be shifting to distance learning in 2020 or changing student behaviors, and these changes have undeniable effects on teachers and their self-efficacy. The purpose of this study is to investigate teachers’ sense of self-efficacy. A 24-question Qualtrics survey was distributed through email for kindergarten through second grade teachers between two elementary schools in one rural Arkansas school district. These 24 questions were adapted from The Teachers’ Sense of Efficacy Scale. The survey collected 16 total responses. The survey results provided evidence that teacher efficacy in instructional strategies is the participants’ greatest strength, with the items that measure efficacy in instructional strategies evoking the highest mean scores. One area of varying self-efficacy lies in classroom management, which contained both the highest and lowest reported mean scores from the participating teachers. Collecting qualitative data helped explain this discrepancy, where a theme emerged about how multi-faceted classroom management is. Two types of classroom management emerged in these themes: classroom instructional management (which garnered a higher sense of teacher efficacy) and classroom behavior management (which garnered the lowest teacher efficacy responses). Qualitative data were collected during two separate focus group interviews, one for each elementary school, during which participating teachers were asked to elaborate on their survey responses and the general quantitative data collected. Through coding, patterns and themes were discovered related to teacher efficacy in student engagement, instructional strategies, and classroom management. Additionally, focus group interviews revealed themes that described the trends in survey responses. The results of this study are most beneficial for teachers in kindergarten through second grade, administrators, and those involved in teacher preparation programs
Primary Care Employees\u27 Knowledge and Self Efficacy about Human Trafficking
Get PDFHuman trafficking is a worldwide issue that is detrimental to individuals, families, and communities. However, it is a hidden issue, and without proper training, it goes unnoticed. Many primary care employees do not know much about trafficking or how to identify trafficked persons, but with proper training they could potentially help clients that are trafficked persons. The purpose of this quasi-experimental study was to determine if human trafficking training would increase primary care employees’ knowledge and self-efficacy about human trafficking. The training included topics such as general knowledge, risk factors, identifiers, and the role of healthcare employees. Participants in the intervention group (n=25) took a pretest and posttest survey and received a 40-minute human trafficking training. Participants in the comparison group (n=13) took one survey. Quantitative data analysis was used to analyze the survey responses and there was a statistically significant positive difference in the mean score of the intervention group’s knowledge and self-efficacy tests pre/posttest and posttest with the comparison group. Primary care employees knew more about trafficking and expressed a greater confidence in their ability to identify clients who may be trafficked persons. Ultimately, the human trafficking training did increase primary care employees’ knowledge and self-efficacy about trafficking, indicating a need for further training in the healthcare field