A Risk Tool and Production Log Created using Microsoft Excel to Manage Pests in Peanut (Arachis hypogaea). Issue 1 (18th March 2022)

• Record Type: Journal Article
• Authors: Jordan, David L; Buol, Greg S; Brandenburg, Rick L; Shew, Barbara B; Wilkerson, Gail G; Lassiter, Bridget R; Dunne, Jeff; Gorny, Adrienne; Washburn, Derek; Hoisington, David; Rhoads, James
• Editors: Samuel-Foo, Michelle
• Is Part Of: Journal of integrated pest management. Volume 13:Issue 1(2022)
• Publication Date: 2022-03-18
• Subjects: agronomy -- crop rotation -- cultivar resistance -- decision tool -- IPM-Agriculture -- pesticides; Pests -- Integrated control -- Periodicals; Entomology -- Periodicals; 632.9
• DOI: 10.1093/jipm/pmac006
• Languages: English
• ISSNs: 2155-7470

Abstract

Abstract: Peanut ( Arachis hypogaea L.) growers and their advisors need to address a wide range of biotic and abiotic stresses to maximize yield and financial return. Mitigating risk to yield and financial investment requires knowledge of interactions among pests and strategies to manage pests, including chemical inputs, crop rotation, cultivar selection, field pest history, planting pattern and population, planting date, and tillage systems. Using Microsoft Excel, a comprehensive peanut risk tool was developed to assist growers and advisors in identifying and selecting production strategies to minimize risk to yield based on empirical data and practical experience while providing cost estimates of production practices. Initially, the risk tool was developed for North Carolina (USA) peanut production. However, the current platform is designed to facilitate the development of similar tools for other USA peanut regions, peanut production systems in other countries, and with the capability to develop risk tools for other crops. This article discusses components of the risk management tool developed for North Carolina peanut production. Benefits of the risk tool to practitioners, extension services, genetics and breeding programs, and formal classroom instruction will be discussed. One goal of this paper is to provide an example of how the Microsoft Excel framework used for peanut in North Carolina can be used for peanut in other regions of the USA and other countries.