Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (ECa) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal ECa variability; (ii) identify ECa-derived soil management zones (SMZs); (iii) establish correlations among ECa survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which ECa surveys could be conducted to capture temporal changes in field variability. Monthly ECa surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m−1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. The largest measured ECa values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean ECa, standard deviation (SD), and coefficient of variation (CV) were unaffected (p > 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar ECa and ECa variability. Results from this study provided valuable information regarding the application of ECa surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems.

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.

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Aims: This study aimed to determine the extent of Phi6 (Φ6) transfer between skin and surfaces relevant to consumer-facing environments based on inoculum matrix, surface type and contact time.Methods and Results: Φ6 transfer rates were determined from skin-to-fomite and fomite-to-skin influenced by inoculum matrix (artificial saliva and tripartite), surface type (aluminium, plastic, stainless steel, touchscreen, vinyl and wood) and contact time (5 and 10 s). Significant differences in estimated means were observed based on surface type (both transfer directions), inoculum matrix (skin-to-fomite) and contact time (both transfer directions). During a sequential transfer experiment from fomite-to-skin, the maximum number of consecutive transfer events observed was

Abstract. 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.