Modifying Practices to Serve Underrepresented Preprofessional Students with Help from Gifted Education

Gifted education and honors education often parallel one another. By using a theoretical construct from gifted education as guidance, honors colleges could adjust their programs to spark interest and expedite talent development of minorities in STEM and health preprofessional tracks. Small improvements include adjusting advising models, using phenomena-based teaching practices to frame science content in a more feminine context, and making room for indigenous epistemologies in coursework. Adjustments to honors programs may bridge the gap between honors and preprofessional tracks while helping to increase diversity in STEM professional fields

Agronomic responses of corn to stand reducation at vegetative growth stages

Yield loss charts for hail associated with stand reduction assume that remaining plants lose the ability to compensate for lost plants by mid-vegetative growth. Yield losses and stand losses after V8 – leaf collar system – and throughout the remaining vegetative stages are 1:1 according to the current standards. We conducted field experiments from 2006 to 2009 at twelve site-years in Illinois, Iowa, and Ohio to determine responses of corn to stand reduction at the fifth, eighth, eleventh, and fifteenth leaf collar stages (V5, V8, V11, and V15, respectively). We also wanted to know whether these responses varied between uniform and random patterns of stand reduction with differences in within-row interplant spacing. When compared to a control of 36,000 plants per acre, grain yield decreased linearly as stand reduction increased from 16.7 to 50% (Table 3), but was not affected by the pattern of stand reduction. This rate of yield loss was greatest when stand reduction occurred at V11 or V15, and least when it occurred at V5. With 50% stand loss, yield was 83 and 69% of the control when stand loss occurred at V5 and V15, respectively. With 16.7% stand loss at V5, V8, or V11, yield averaged 96% of the control. Per-plant grain yield increased when stand loss occurred earlier and was more severe. With 50% stand loss at V11 or V15, per-plant grain yield increased by 37 to 46% compared to the control. Corn retains the ability to compensate for lost plants through the late vegetative stages, indicating that current standards for assessing the effect of stand loss in corn should be reevaluated

Inter‐ and intraindividual variability of urinary dextromethorphan/dextrorphan (DM/DX) ratios in CYP2D6 extensive metabolizers (EMS) with one or two active alleles

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109829/1/cptclpt200481.pd

Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Activeoptical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1 increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea mays L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (~V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea maysL.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Active-optical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed

Incorporating lessons from high-input research into a low-margin year

Increased soybean commodity prices in recent years have generated interest in developing high-input systems to increase yield. However, little information exists about the effects of input-intensive, high-yield management on soybean yield and profitability, as well as interactions with basic agronomic practices