Economics and Effectiveness of Alternative Weed Scouting Methods in Peanut

Abstract

On-farm trials were conducted in 16 North Carolina peanut fields to obtain estimates of scouting times and quality of herbicide recommendations for different weed scouting methods. The fields were monitored for weed species and population density using four scouting methods: windshield (estimate made from the edge of the field), whole-field (estimate based on walk through the field), range (weed densities rated on 1–5 scale at six locations in the field), and counts (weeds estimated by counting at six locations in the field). The herbicide application decision support system (HADSS) was used to determine theoretical net return over herbicide investment and yield loss ($ and %) for each treatment in each field. Three scouts estimated average weed population densities using each scouting method. These values were entered into HADSS to obtain treatment recommendations. Independently collected count data from all three scouts were combined to determine the optimal treatment in each field and the relative ranking of each available treatment. When using the whole-field method, scouts observed a greater number of weed species than when using the other methods. The windshield, whole-field, and range scouting methods tended to overestimate density slightly at low densities and underestimate density substantially at high densities, compared to the count method. The windshield method required the least amount of time to complete (6 min per field), but also resulted in the greatest average loss. Even for this method, recommendations had theoretical net returns within 10% of the return for the optimal treatment 80% of the time. The count method appears to have less economic risk than the windshield, whole-field, and range scouting methods.

Competing with the competitors in an endless competition: a systematic review of nonchemical weed management research in peanut (Arachis hypogea) in the United States

“…Peanut–weed interference and weed competitive mechanism studies are important to understand weed dynamics and make appropriate weed management decisions (Jordan et al 2003; Robinson et al 2007). Competitive index parameters generated from such studies, along with other factors such as soil moisture status and cost of weed control, have been integrated into computer models and decision aids such as the Herbicide Application Decision Support System (HADSS™) (a trade name registered by North Carolina State University, USA) and computerized economic threshold decision (HERB™) (a trade name registered by North Carolina State University, USA) to accurately predict the level of yield loss at a given weed density and size in order to estimate economic thresholds and devise appropriate weed management strategies (Bennett et al 2003; Scott et al 2002; White and Coble 1997).…”

Intra-Canopy Sensing Using Multi-Rotor sUAS: A New Approach for Crop Stress Detection and Diagnosis

“…With a limited number of crop scouts or qualified experts available (e.g., qualified plant pathologists), many crops are susceptible to uncontrolled stress, resulting in yield loss (Miller et al, 2009). Currently, most crop stress identification is performed manually by visual observation, and with various laboratory techniques to confirm the observations, which is time-consuming and fails to provide timely diagnoses (Barbedo, 2016;DeDecker, 2015;Doll et al, 1994;Green et al, 1990;Mahlein, 2016;Robinson et al, 2007;Türkoğlu and Hanbay, 2019;Vittetoe et al, 2020). A new technology-based system is needed to supplement traditional scouting methods for improved crop monitoring and protection.…”

Crop physiological considerations for combining variable-density planting to optimize seed costs and weed suppression

“…During the growing season, producers might not always have time to scout for weed escapes. When weed scouting does occur, producers go quickly through the field and have limited vantage points, especially when scouting from a tractor, roadside, or field entrances, and may not truly capture the distribution of weed populations throughout the field (Robinson et al 2007). For research, intensive grid sampling (by dividing large fields into many 6 m by 6 m squares on a grid) is used to quantify weed density or cover (Colbach et al 2000;Goudy et al 2001), but this method also takes a considerable time to collect and analyze data, which is impractical for commercial purposes.…”