Unmanned aircraft systems (UAS) allow us to collect aerial data at high spatial and temporal resolution. Raw images are taken along a predetermined flight path and processed into a single raster file covering the entire study area. Radiometric calibration using empirical or manufacturer methods is required to convert raw digital numbers into reflectance and to ensure data accuracy. The performance of five radiometric calibration methods commonly used was investigated in this study. Multispectral imagery was collected using a Parrot Sequoia camera. No method maximized data accuracy in all bands. Data accuracy was higher when the empirical calibration was applied to the processed raster rather than the raw images. Data accuracy achieved with the manufacturer-recommended method was comparable to the one achieved with the best empirical method. Radiometric error in each band varied linearly with pixel radiometric values. Smallest radiometric errors were obtained in the red-edge and near-infrared (NIR) bands. Accuracy of the composite indices was higher for the pixels representing a dense vegetative cover in comparison to a lighter cover or bare soil. Results provided a better understanding of the advantages and limitations of existing radiometric calibration methods as well as the impact of the radiometric error on data quality. The authors recommend that researchers evaluate the performance of their radiometric calibration before analyzing UAS imagery and interpreting the results.

Porous Asphalt (PA) pavements are an increasingly adopted tool in the green stormwater infrastructure toolbox to manage stormwater in urbanized watersheds across the United States. This technology has seen particular interest in western Washington State, where permeable pavements are recognized as an approved best management practice per the National Pollutant Discharge Elimination System (NPDES) municipal stormwater permit. Stormwater effluent concentrations from six PA cells were compared with runoff concentrations from three standard impervious asphalt cells to quantify pollutant removal efficiencies by porous asphalt systems. Additionally, the effects of maintenance and pavement age on pollutant removal efficiencies were examined. Twelve natural and artificial storms were examined over a five-year period. Street dirt and pollutant spikes were added to the pavements prior to some storm events to simulate high loading conditions. Results from this work show that porous asphalt pavements are highly efficient at removing particulate pollutants, specifically coarse sediments (98.7%), total Pb ( 98.4%), total Zn (97.8%), and total suspended solids (93.4%). Dissolved metals and Polycyclic Aromatic Hydrocarbons (PAH) were not significantly removed. Removal efficiencies for total Pb, total Zn, motor oil, and diesel H. improved with the age of the system. Annual maintenance of the pavements with a regenerative air street sweeper did not yield significant pollutant removal efficiency differences between maintained and unmaintained PA cells.

Optimization of planter performance such as uniform seeding depth is required to maximize crop yield potential. Typically, seeding depth is manually adjusted prior to planting by selecting a row-unit depth and a row-unit downforce to ensure proper seed-soil contact. Once set, row-unit depth and downforce are usually not adjusted again for a field although soil conditions may vary. Optimization of planter performance requires automated adjustments of planter settings to varying soil conditions, but development of precision technologies with such capabilities requires a better understanding of soil-planter interactions. The objective of this study was to evaluate seeding depth response to varying soil conditions between and within fields and to discuss implications for development and implementation of active planting technologies. A 6-row John Deere MaxEmerge Plus planter equipped with heavy-duty downforce springs was used to plant corn ( L.) in central Alabama during the 2014 and 2015 growing seasons. Three depths (4.4, 7.0, and 9.5 cm) and three downforces (corresponding to an additional row-unit weight of 0.0, 1.1, and 1.8 kN) were selected to represent common practices. Depth and downforce were not readjusted between fields and growing seasons. Seeding depth was measured after emergence. Corn seeding depth significantly varied with heterogeneous soil conditions between and within fields and the planter failed to achieve uniform seeding depth across a field. Differences in corn seeding depth between fields and growing seasons were as high as 2.1 cm for a given depth and downforce combination. Corn seeding depth significantly co-varied with field elevation but not with volumetric soil water content. Seeding depth varied with elevation at a rate ranging from -0.1 cm/m to -0.6 cm/m. Seeding depth co-variation to field elevation account for some but not all site-specific seeding depth variability identified within each field trial. These findings provide a better understanding of site-specific seeding depth variability and issues to address for the development of site-specific planting technologies to control seeding depth accuracy and improve uniformity. Keywords: Depth control, Downforce, Planter, Precision agriculture, Seeding depth, Uniformity.

Core Ideas: Evaluation of the influence of planting date on corn aflatoxin. Evaluation on how in‐season weather conditions influence corn aflatoxin. Evaluation of the influence of planting density on corn aflatoxin. Identification of the growing season periods when the aflatoxin risk is highest. Evaluation of the relationship between yield and corn aflatoxin. Aflatoxins are a group of toxins produced by fungi found on corn (Zea mays L.). Aflatoxin contamination can make it unmarketable. Fortunately, management practices that reduce stress during critical growth stages lessen contamination. A study was conducted at Fairhope, AL (2010–2014), and Prattville, AL (2013–2014), to evaluate the effect of planting dates, plant densities, and in‐season weather conditions on preharvest aflatoxin contamination. The experiment had a split‐split‐plot design replicated six times, with inoculation method assigned to the main plots, planting date to the subplots, and planting density to the sub‐subplots. Results showed that delaying planting from mid‐March to mid‐April reduced aflatoxin levels and increasing the planting density from 44,480 to 74,130 plants ha⁻¹ did not impact toxin accumulation. Multiple linear regression indicated that minimum air temperature and rainfall models could explain from 50 to 76% of the observed aflatoxin variability. However, the effect of both variables on aflatoxin contamination levels changed during the period pre‐silking (14 d prior) to physiological maturity. Minimum temperature alone had the strongest positive influence on aflatoxin over the 2 wk after mid‐silk. A reduction in rainfall during 2 wk prior mid‐silk and from Day 43 after mid‐silk to physiological maturity resulted on high aflatoxin contamination levels. In conclusion, a better understanding of the influence of weather variables on corn contamination may lead to better crop management and development of more accurate prediction systems.