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New Decision-Making Control System for Caterpillars on Soybean Fields

Authors :
Poliana Silvestre Pereira
Mayara Cristina Lopes
Kayo Heberth de Brito Reis
Hugo Daniel Dias de Souza
Guilherme Pratissoli Pancieri
Marcelo Coutinho Picanço
Renato Almeida Sarmento
Source :
Agronomy, Vol 13, Iss 10, p 2581 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Decision-making systems are essential to integrated pest management (IPM) programs, particularly in the context of soybean (Glycine max), the world’s most cultivated legume. As agricultural practices change, including adopting new cultivars, planting seasons, and planting regions, the challenges in pest management, mainly caterpillars (Lepidoptera larvae), also change. To address this, this study aimed to devise an updated decision-making approach tailored to the current soybean field conditions. Over two years, caterpillar densities were evaluated in 38 commercial soybean fields. The beating tray sampling technique was superior in precision and efficiency compared to the direct counting and beating cloth techniques. This technique involved assessing 61 plants per field to determine caterpillar density. Economic thresholds were determined at 7.11 caterpillars per beating tray for vegetative stages and 3.60 for reproductive stages. The new proposed sampling system was validated and demonstrated more precise and representative caterpillar density determination than the standard beating cloth system. Both methods exhibited similar costs and execution times. Therefore, this refined decision-making system has the potential for incorporation into soybean IPM programs due to its accuracy, representativeness, feasibility, speed, and cost-effectiveness. This study underscores the viability of integrating the newly developed decision-making system to enhance soybean pest management strategies.

Details

Language :
English
ISSN :
20734395
Volume :
13
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Agronomy
Publication Type :
Academic Journal
Accession number :
edsdoj.3abadb3c89c5488d937b50eab16b1964
Document Type :
article
Full Text :
https://doi.org/10.3390/agronomy13102581