Core Ideas Preemergence herbicides may increase grow‐in time for hybrid bermudagrass sprigs. Preemergence herbicide treatments did not reduce sod tensile strength. Preemergence herbicides are viable options for weed control during establishment. Preemergence herbicides often negatively affect establishment of sprigged hybrid bermudagrass (Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy). However, limited research has quantified the effects of preemergence herbicides on establishment and tensile strength of warm‐season turfgrass species commonly grown as sod in the southeastern United States. Field research was conducted at Mississippi State University in 2016 and 2017 to evaluate the effects of atrazine, atrazine + S‐metolachlor, dithiopyr, flumioxazin, indaziflam, liquid‐ and granular‐applied oxadiazon, S‐metolachlor, pendimethalin, prodiamine, and simazine on grow‐in time and tensile strength of newly sprigged ‘Latitude 36’ hybrid bermudagrass. Plots (4.65 m2) were sprigged at a rate of 44 m3 ha−1 (468 US bushels ac−1), and preemergence herbicide treatments were applied at the recommended labeled rates 1 d after planting. Dithiopyr, flumioxazin, S‐metolachlor, and indaziflam increased days to reach 50% hybrid bermudagrass cover in both 2016 and 2017. Normalized difference vegetative index and relative chlorophyll index were similarly affected. No herbicide treatments reduced sod tensile strength in either year; however, prodiamine, pendimethalin, dithiopyr, flumioxazin, and S‐metolachlor unexpectedly increased sod tensile strength in 2017.

Distributed ledger technology applied to Big Data in agriculture presents challenges and opportunities. Opportunities exist to solve decades‐old farm data management problems. Real‐world examples of applying distributed ledger technology to current farm data problems in cotton include (1) yield monitor data quality assurance, (2) sustainability metrics and resource tracking of cotton lint quality data from ginner back to subfield locations, and (3) increasing supply chain coordination by providing more information to warehouse managers. The culmination of the discussion across three aspects of cotton production data is of interest to farmers, researchers, policy makers, and consumers.

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Skinning of sweetpotato (Ipomoea batatas) storage roots is one of the greatest concerns of sweetpotato producers. Although skinning injury is very common, the severity of the injury can vary widely. At an undefined threshold, sweetpotatoes with skinning injury are no longer sold for fresh consumption. The objectives of this study were to examine how skinning injury influences consumers’ willingness-to-pay (WTP) for sweetpotatoes and to identify differences in valuations when the extent of skinning injury is labeled. Image analysis was used to quantify skinning injury and then an incentive-compatible, nonhypothetical laboratory experimental auction was conducted to collect data on consumers’ WTP for five categories of sweetpotatoes: 0% to <1% skinning injury, 1.0% to 3.0%, 3.1% to 5.0%, 5.1% to 7.5%, and 7.6% to 10.0%. On average, consumers were willing to pay the most for sweetpotatoes with 0% to <1% skinning injury (up to $1.51/lb to $1.67/lb) and the least for sweetpotatoes with 7.6% to 10% (up to $0.76/lb to $0.85/lb), yet mean WTP values were nonzero for all skinning levels. Moreover, when the extent of skinning was labeled (relative to when they bid blindly), consumers were willing to pay price premiums for sweetpotatoes with low skinning injury levels (0% to 5%) and discounted sweetpotatoes with the highest skinning injury level (7.6% to 10.0%), suggesting that skinning levels of 7.6% and above may not be acceptable by consumers.

Previous researchers demonstrated the ability to adapt an AgLeader® Cotton Monitor to a peanut combine. It was demonstrated that the field weight could be accurately predicted with average errors of less than 10% across all trials when at least five calibration loads are applied. This project focused on expanding previous work performed at the University of Georgia and other peanut optical yield monitor work by incorporating a protective deflector plate for the sensors, obtaining multiple field weights, and using the peanut sale sheets to correlate yield monitor yield to sale weight. This study was a two-university, two-state effort, including Oklahoma State University (Oklahoma), and Mississippi State University (Mississippi). Data collected during this study included multiple loads which included yield monitor weight, field weight, field moisture content, and all the information presented on the standard USDA peanut grade sheet, when available. The multi-state effort allowed for the incorporation of the two major peanut types and for the incorporation of different soil types. The goal of this study was to develop guidelines for using, calibrating, and adapting the AgLeader® Cotton Monitor for peanut harvest. Five calibration loads referenced to buy-point net weight were typically needed to bring error within acceptable limits. Results indicated that multiple local calibrations were needed to ensure high data validity and yield estimation across multiple harvest environments. The data showed that peanut type (virginia, runner and spanish) and variable soil conditions impacted yield estimation.