Pest Control Technologies Applied in Peanut Production Systems

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Pest and Disease Management".

Deadline for manuscript submissions: 25 November 2024 | Viewed by 1381

Special Issue Editors


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Guest Editor
Department of Crop and Soil Sciences, North Carolina State University, 101 Derieux Place, 4207 Williams Hall, Raleigh, NC 27695, USA
Interests: peanut-based cropping systems; integrated pest management; cropping systems; crop management; cultivars; pesticides; entomology; plant pathology; nematology; weed science; agronomy
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Guest Editor
Feed the Future Innovation Lab for Peanut, College of Agricultural and Environmental Sciences, University of Georgia, 217 Hoke Smith Building Athens, Athens, GA 30602, USA
Interests: molecular biology and genetics; QTL mapping; phenotyping; conventional and genomic-based breeding; crop management; post-harvest storage and processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Pests cause major yield and quality losses in peanut production and can increase risks to human health. A wide range of practices, including cropping sequence, irrigation, planting patterns, plant density, planting date, and tillage systems, are used to minimize the impact of pests. These paractices are often coupled with the deployment of cultivars that express resistance to pathogens, including viruses and pesticides, in order to protect yield and increase financial returns. However, pest complexes are changing and the research community needs to rapidly develop effective strategies to address these issues. In this Special Issue, research findings associated with new technologies will be provided. Discussed technologies will include those employed in the field as well as techniques such as the use of molecular markers, high-throughput phenotyping, and other approaches that decrease the time required for cultivar release. Research papers and review articles will be considered in this Special Issue.

Dr. David Jordan
Dr. Dave Hoisington
Guest Editors

Manuscript Submission Information

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Keywords

  • integrated pest management
  • cultivars
  • breeding and genetics
  • pesticides
  • cropping systems

Published Papers (2 papers)

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Research

16 pages, 3137 KiB  
Article
Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection
by Ivan Chapu, Abhilash Chandel, Emmanuel Kofi Sie, David Kalule Okello, Richard Oteng-Frimpong, Robert Cyrus Ongom Okello, David Hoisington and Maria Balota
Agronomy 2024, 14(5), 947; https://doi.org/10.3390/agronomy14050947 - 30 Apr 2024
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Abstract
Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers [...] Read more.
Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively. Full article
(This article belongs to the Special Issue Pest Control Technologies Applied in Peanut Production Systems)
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12 pages, 4120 KiB  
Article
Management Efficacy and Response to Post-Application Precipitation of Fungicides for Southern Stem Rot of Peanut and Evaluation of Co-Application with Micronized Sulfur
by Daniel J. Anco, Justin Hiers and Brendan Zurweller
Agronomy 2024, 14(5), 893; https://doi.org/10.3390/agronomy14050893 - 25 Apr 2024
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Abstract
Southern stem rot (SSR) is caused by Athelia rolfsii and is an economically important disease of peanut (Arachis hypogaea L.). Application of protectant fungicides is an effective management component for reducing levels of this soil-borne disease. The majority of peanut hectarage in [...] Read more.
Southern stem rot (SSR) is caused by Athelia rolfsii and is an economically important disease of peanut (Arachis hypogaea L.). Application of protectant fungicides is an effective management component for reducing levels of this soil-borne disease. The majority of peanut hectarage in South Carolina and Mississippi is rainfed. Timely precipitation has the potential to aid the movement of foliar-applied fungicides through the canopy and into contact with soil interfaces where SSR infections occur. Questions have arisen as to the quantitative relationship of post-application precipitation and fungicide-active ingredient efficacy in managing SSR and protecting associated pod yield potentials. To examine this, fungicide efficacy experiments were screened for inclusion in a meta-analysis, from which eleven experiments conducted from 2015 to 2023 were selected and paired with environmental data from nearby weather stations. Precipitation during the two days following fungicide application was associated with significant reduction in SSR incidence (logit rate of −0.0039/mm) and increased pod yield (log slope of 0.0028/mm). Active ingredient interactions with precipitation among pod yield but not SSR incidence data were present for benzovindiflupyr plus azoxystrobin, flutolanil, and tebuconazole. Fungicides with the greatest levels of control per application at maximum label rates were inpyrfluxam (18.8%), benzovindiflupyr plus azoxystrobin (15.4%), flutolanil (12.3%), and prothioconazole plus tebuconazole (10.5%). Micronized sulfur neither contributed to SSR control nor pod yield increase. Tebuconazole was associated with the greatest % SSR control per fungicide product cost (0.47%/$/ha/application) but was also the treatment with the least amount of control (3.5%) at its maximum label rate. Maximum label rates of benzovindiflupyr plus azoxystrobin (USD 637) and inpyrfluxam (USD 548) were estimated as conferring the greatest returns over the chlorothalonil-only control. Results serve as a helpful reference for farmers and practitioners in selecting fungicide management options and targeting application times, as feasible, to utilize natural precipitation to improve management outcomes. Full article
(This article belongs to the Special Issue Pest Control Technologies Applied in Peanut Production Systems)
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