Commercializing targeted sprayer systems allows producers to reduce herbicide inputs but risks the possibility of not treating emerging weeds. Currently, targeted applications with the John Deere system allow for five spray sensitivity settings, and no published literature discusses the impact of these settings on detecting and spraying weeds of varying species, sizes, and positions in crops. Research was conducted in AR, IL, IN, MS, and NC in corn, cotton, and soybean to determine how various factors might influence the ability of targeted applications to treat weeds. These data included 21 weed species aggregated to six classes with height, width, and densities, ranging from 25 to 0.25 cm, 25 to 0.25 cm, and 14.3 to 0.04 plants m-2, respectively. Crop and weed density did not influence the likelihood of treating the weeds. As expected, the sensitivity setting alters the ability to treat weeds. Targeted applications (across sensitivity settings, median weed height and width, and density of 2.4 plants m-2) resulted in a treatment success of 99.6% to 84.4%, 99.1% to 68.8%, 98.9% to 62.9%, 99.1% to 70.3%, 98.0% to 48.3%, and 98.5% to 55.8% for Convolvulaceae, decumbent broadleaf weeds, Malvaceae, Poaceae, Amaranthaceae, and yellow nutsedge, respectively. Reducing the sensitivity setting reduced the ability to treat weeds. Size of weeds aided targeted application success, with larger weeds being more readily treated through easier detection. Based on these findings, various conditions could impact the outcome of targeted multi-nozzle applications. Additionally, the analyses highlight some of the parameters to consider when using these technologies.

New machine-vision technologies like the John Deere See & Spray™ could provide the opportunity to reduce herbicide use by detecting weeds and target-spraying herbicides simultaneously. Experiments were conducted for 2 yr in Keiser, AR, and Greenville, MS, to compare residual herbicide timings and targeted spray applications versus traditional broadcast herbicide programs in glyphosate/glufosinate/dicamba-resistant soybean. Treatments utilized consistent herbicides and rates with a preemergence (PRE) application followed by an early postemergence (EPOST) dicamba application followed by a mid-postemergence (MPOST) glufosinate application. All treatments included a residual at PRE and excluded or included a residual EPOST and MPOST. Additionally, the herbicide application method was considered, with traditional broadcast applications, broadcasted residual + targeted applications of postemergence herbicides (dual tank), or targeted applications of all herbicides (single tank). Targeted applications provided comparable control to broadcast applications with a ≤1% decrease in efficacy and overall control ≥93% for Palmer amaranth, broadleaf signalgrass, morningglory species, and purslane species. Additionally, targeted sprays slightly reduced soybean injury by at most 5 percentage points across all evaluations, and these effects did not translate to a yield increase at harvest. The relationship between weed area and targeted sprayed area also indicates that nozzle angle can influence potential herbicide savings, with narrower nozzle angles spraying less area. On average, targeted sprays saved a range of 28.4% to 62.4% on postemergence herbicides. On the basis of these results, with specific machine settings, targeted application programs could reduce the amount of herbicide applied while providing weed control comparable to that of traditional broadcast applications.

The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.

This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.

MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.