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Agronomy
Special Issues
Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry
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Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry

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

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 18580

Special Issue Editors

Dr. Mario Contarini

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Guest Editor
Department of Agriculture and Forest Sciences, Università degli Studi della Tuscia, 01100 Viterbo, Italy
Interests: applied entomology; forest entomology; entomopathogens fungi; insect ecology; IPM; biological control
Prof. Dr. Angelo Mazzaglia

E-Mail Website
Guest Editor
Department of Agriculture and Forest Sciences, Università degli Studi della Tuscia, 01100 Viterbo, Italy
Interests: Plant pathology; Molecular biology
Dr. Roberto Mannu

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Guest Editor
Department of Agriculture, Università degli Studi di Sassari, 07100 Sassari, Italy
Interests: IPM; insect ecology; applied entomology
Dr. Luca Rossini

E-Mail Website
Guest Editor
Department of Agriculture and Forest Sciences, Università degli Studi della Tuscia, 01100 Viterbo, Italy
Interests: pest population modelling; ecological modelling; applied entomology; decision support systems; computational ecology; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring is a qualitative and quantitative process useful for defining the presence and population size of insect species and plant pathogens in a defined geographical area or environment. Different monitoring strategies have been developed over time and implemented in several fields of application, such as the detection of pests and pathogens harmful to agricultural production and forestry or in ecological studies, to define entomological and microbial biodiversity in habitats. However, these methodologies satisfy these needs only by offering an estimate of the population abundance of a species or its presence in a given area. This fact imposes the continuous and constant search for new methods of monitoring as well as a refinement of the techniques used to date and useful, among other things, in the early detection of alien species in new environments or in the definition of decision support systems (DSS) in crop protection.

In this framework, submissions related to the following topics are welcome:

  • New trap design;
  • Traps attractivity;
  • Monitoring reliability;
  • How monitoring supports the validation of descriptive and predictive models;
  • New monitoring techniques;
  • New technologies and monitoring;
  • Statistical/computational methods to analyze monitoring data;
  • Relation between different monitoring techniques;
  • Monitoring as support for DSS;
  • Economic aspects related to monitoring techniques;
  • Environmental consequences of monitoring techniques.

Dr. Mario Contarini
Prof. Dr. Angelo Mazzaglia
Dr. Roberto Mannu
Dr. Luca Rossini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart detection
  • precision agriculture and forestry
  • integrated pest management
  • ecological monitoring
  • trap design
  • attractive blends
  • spatial distribution

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Published Papers (7 papers)

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Research

19 pages, 1983 KiB  
Article
A Physiologically Based ODE Model for an Old Pest: Modeling Life Cycle and Population Dynamics of Bactrocera oleae (Rossi)
by Luca Rossini, Octavio Augusto Bruzzone, Mario Contarini, Livio Bufacchi and Stefano Speranza
Agronomy 2022, 12(10), 2298; https://doi.org/10.3390/agronomy12102298 - 24 Sep 2022
Cited by 10 | Viewed by 2423
Abstract
The olive fruit fly Bactrocera oleae is one of the key insect pests infesting olive orchards in Mediterranean areas. Its coevolution with the olive tree, Olea europaea, made this pest highly specialized for this crop, being responsible for several yield reductions in [...] Read more.
The olive fruit fly Bactrocera oleae is one of the key insect pests infesting olive orchards in Mediterranean areas. Its coevolution with the olive tree, Olea europaea, made this pest highly specialized for this crop, being responsible for several yield reductions in terms of olive fruits and olive oil organoleptic properties. Monitoring is, to date, the main tool to assess the entity of infestations, but the increasing availability of biological information is making possible a quantitative interpretation of B. oleae’s biological traits in mathematical language. In this study, we aim to synthesize this plethora of information by applying a general physiologically based model theory of recent introduction. As a result, we obtained a parameterized model capable of describing B. oleae populations and with a high potential for implementation in Decision Support System programs. Besides the parameterization, model validation has been carried out in a three-year survey conducted in two representative productive areas of Sabina (Lazio, Central Italy). The model showed overall reliability in describing the field data trend, and it is a good starting point to be further improved. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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16 pages, 4719 KiB  
Article
Expression Pattern, Molecular Docking and Dynamics Simulation Analysis of CSP4 from Sirex nitobei Provides Molecular Basis of CSP Bound to Scent Molecules
by Pingping Guo, Enhua Hao, Han Li, Xi Yang, Pengfei Lu and Haili Qiao
Agronomy 2022, 12(9), 1994; https://doi.org/10.3390/agronomy12091994 - 24 Aug 2022
Viewed by 1473
Abstract
Insects stimulate specific behaviors by correctly recognizing scent molecules in the external environment. Sirex nitobei, a wood-boring wasp species native to Asia with a distribution area that includes the Palaearctic and Oriental regions, is a significant pest of conifers. Focusing on the [...] Read more.
Insects stimulate specific behaviors by correctly recognizing scent molecules in the external environment. Sirex nitobei, a wood-boring wasp species native to Asia with a distribution area that includes the Palaearctic and Oriental regions, is a significant pest of conifers. Focusing on the molecular mechanism of protein-ligand binding, this study resolved the tissue expression profile of CSP4 from S. nitobei (SnitCSP4) and probed its binding properties with target ligands using molecular docking and dynamics simulations to verify the odor recognition function of this protein. The open reading frame (ORF) of SnitCSP4 was 396 bp, encoding 131 amino acids. Tissue expression analysis revealed that SnitCSP4 was significantly expressed in female antennae and docking showed that all ligands were bound in hydrophobic cavities and close to many hydrophobic amino acid residues. GLN68 and LEU49 were important amino acid residues for SnitCSP4 to bind various odors, and THR9 was the key ligand-binding site in identifying (-)-globulol in the SnitCSP4. Molecular dynamics verified the docking results, confirming that SnitCSP4 bound well to two sex pheromone molecules, three host plant volatiles, and three symbiotic fungal volatiles, with (Z)-7-heptacosene, (Z)-9-nonacosene, and (-)-globulol binding being the most highly stable. These results mean that SnitCSP4 is critical for insects recognizing scent molecules, providing a favorable molecular basis for regulating the behavioral interactions between S. nitobei and the environment, and offering the possibility of developing new strategies for more environmentally friendly and effective control. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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12 pages, 1495 KiB  
Article
Monitoring the Bud Mite Pest in a Hazelnut Orchard of Central Italy: Do Plant Height and Irrigation Influence the Infestation Level?
by Mario Contarini, Luca Rossini, Nicolò Di Sora, Enrico de Lillo and Stefano Speranza
Agronomy 2022, 12(8), 1982; https://doi.org/10.3390/agronomy12081982 - 22 Aug 2022
Cited by 10 | Viewed by 2259
Abstract
Mite pests are a serious threat for hazelnut cultivations, causing economic losses every year. At least two species of big bud mites, Phytoptus avellanae (Acari: Phytoptidae) and Cecidophyopsis vermiformis (Acari: Eriophyidae), are involved in severe hazelnut bud infestations, even though few studies report [...] Read more.
Mite pests are a serious threat for hazelnut cultivations, causing economic losses every year. At least two species of big bud mites, Phytoptus avellanae (Acari: Phytoptidae) and Cecidophyopsis vermiformis (Acari: Eriophyidae), are involved in severe hazelnut bud infestations, even though few studies report P. avellanae as the most present and harmful. Great steps forward have been made in monitoring and management strategies of these mite pests, but a plethora of questions remains unanswered about their ecology and behaviour and how agronomical practices impact populations. Given this precondition, we conducted a four-year monitoring in an experimental hazelnut orchard located in the Viterbo hazelnut district, Central Italy, to: (i) explore the potential effect that irrigation has on mite infestations, (ii) assess if mites locate in a particular band height of hazelnut plants; and (iii) assess the overall field infestation over the years. This study showed that not-irrigated plants and plants irrigated by underground pipe systems were similarly infested. Mites tend to locate in the middle band of the plant, namely from 1.5 to 3 m from the ground. The four-year survey showed an overall increasing infestation trend, with a peak in 2021 for irrigated plants and 2022 for not-irrigated. These results are a milestone for further exploration of the biology and ecology of this pest and to formulate ad hoc monitoring and control strategies as well. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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15 pages, 1171 KiB  
Article
Development of Enumerative and Binomial Sequential Sampling Plans for Monitoring Lymantria dispar (L.) (Lepidoptera Erebidae) in Mediterranean Oak Forests
by Roberto Mannu, Maurizio Olivieri, Arturo Cocco and Andrea Lentini
Agronomy 2022, 12(7), 1501; https://doi.org/10.3390/agronomy12071501 - 23 Jun 2022
Cited by 3 | Viewed by 1720
Abstract
Lymantria dispar is the main threat to Mediterranean forests. Sampling methods used for monitoring the pest population density are generally very time-consuming for practical purposes, such as the delimitation of infested areas for control programs. Enumerative and binomial sequential sampling plans were developed [...] Read more.
Lymantria dispar is the main threat to Mediterranean forests. Sampling methods used for monitoring the pest population density are generally very time-consuming for practical purposes, such as the delimitation of infested areas for control programs. Enumerative and binomial sequential sampling plans were developed using data collected in cork oak forests in Sardinia (Italy). The Taylor’s power law (TPL) was used to evaluate the degree of aggregation of L. dispar egg masses among trees and to develop enumerative sampling plans at precision levels of 0.10 and 0.25 using the Green’s method. Furthermore, binomial plans were computed by Wald’s sequential probability ratio test. Lymantria dispar egg masses on trees were significantly aggregated and the degree of aggregation was similar in all population development phases. Overall, only 31 cork oak trees are to be monitored at the economic damage threshold of 2.5 egg masses/tree with a precision level of 0.25. Binomial sequential sampling plans also required lower sampling sizes (26.9–31.4 trees) than conventional sampling plans. Enumerative and binomial sampling plans could represent suitable methods for sampling L. dispar egg masses in Mediterranean forests, with the practical advantage of lower cost and time consumption than standard sampling plans. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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15 pages, 3659 KiB  
Article
Monitoring Coffee Leaf Rust (Hemileia vastatrix) on Commercial Coffee Farms in Hawaii: Early Insights from the First Year of Disease Incursion
by Luis F. Aristizábal and Melissa A. Johnson
Agronomy 2022, 12(5), 1134; https://doi.org/10.3390/agronomy12051134 - 8 May 2022
Cited by 12 | Viewed by 4455
Abstract
Coffee leaf rust (CLR, Hemileia vastatrix) is considered the most damaging coffee disease worldwide, causing reduced yields and even plant death. CLR was detected in Hawaii for the first time in 2020, and quickly spread across the state. We initiated a CLR [...] Read more.
Coffee leaf rust (CLR, Hemileia vastatrix) is considered the most damaging coffee disease worldwide, causing reduced yields and even plant death. CLR was detected in Hawaii for the first time in 2020, and quickly spread across the state. We initiated a CLR monitoring program in Kona, West Hawaii Island, to track the spread of this new invasive disease across a broad elevational gradient. The goals of the program were to assist growers in the early detection of CLR, to characterize patterns of disease incidence across the region, and to collect information on farm agronomics, management practices, and costs to apply fungicides, all of which can be used to develop Integrated Pest Management (IPM) strategies for this pathogen. We monitored 30 coffee lots in Kona, located between 204 and 875 m elevation. Average CLR incidence remained below 4% early in the season and increased to 36% during harvest. We observed no significant difference in CLR incidence between low-, mid- and high-elevation farms. A significant reduction in the number of leaves per branch was observed at the end of the harvest season, and a significant negative correlation was found between the number of leaves per branch and maximum CLR severity. Mean disease incidence and mean severity were observed to have a significant positive correlation. Incidence increased above threshold levels (5%), despite most growers applying preventative fungicides 3–10 times throughout the season, suggesting that improved coverage and timing of applications is needed along with the addition of systemic fungicides. Our study provides the first insights into CLR disease patterns under the unique and variable conditions under which Hawaiian coffee is grown, and will aid in the development of IPM programs that can be used to sustain Hawaii’s coffee industry under this new threat. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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15 pages, 2269 KiB  
Communication
Monitoring and Inference of Behavioral Resistance in Beneficial Insects to Insecticides in Two Pest Control Systems: IPM and Organic
by José Alfonso Gómez-Guzmán, María Sainz-Pérez and Ramón González-Ruiz
Agronomy 2022, 12(2), 538; https://doi.org/10.3390/agronomy12020538 - 21 Feb 2022
Cited by 4 | Viewed by 2718
Abstract
Pyrethrins are the most widely used insecticide class in olive groves with organic management. Although there are data sets about insect pests of stored products and human parasites developing resistance to pyrethrins, there is no information on the long-term effect on olive agroecosystems. [...] Read more.
Pyrethrins are the most widely used insecticide class in olive groves with organic management. Although there are data sets about insect pests of stored products and human parasites developing resistance to pyrethrins, there is no information on the long-term effect on olive agroecosystems. A field method based on the experimental induction of sublethal effects by means of insecticide application, and the monitoring of the response of insects through post-treatment sampling, has recently been developed. This method has allowed for the detection of populations behaviorally resistant to organophosphates in integrated pest management (IPM) and conventional crops. With the application of a similar methodology, this study aimed to verify the possible reaction of natural enemies in organic crops, using pyrethrins as an inducing insecticide. The study was carried out in 2019 in two olive groves in southern Spain (Jaén, Andalusia), one of them being IPM and the other being an organic production system. The results did not allow for verification of the behavioral resistance in populations of natural enemies of both IPM and organic management against pyrethrins, while against dimethoate, behavioral resistance was verified in IPM management. The possible causes involved in obtaining these results are discussed. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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17 pages, 2687 KiB  
Article
Seasonal, Landscape, and Attractant Effects on Lesser Grain Borer, Rhyzopertha dominica (F.), Captures in Northeast Kansas
by Deanna S. Scheff, James F. Campbell and Franklin H. Arthur
Agronomy 2022, 12(1), 99; https://doi.org/10.3390/agronomy12010099 - 31 Dec 2021
Cited by 12 | Viewed by 2132
Abstract
The lesser grain borer, Rhyzopertha dominica (F.), is a highly diverse feeder and widely distributed throughout the United States in agricultural and non-agricultural landscapes. Six four-funnel Lindgren traps were deployed in feed mill, grain elevator, and native prairie landscapes, to determine the most [...] Read more.
The lesser grain borer, Rhyzopertha dominica (F.), is a highly diverse feeder and widely distributed throughout the United States in agricultural and non-agricultural landscapes. Six four-funnel Lindgren traps were deployed in feed mill, grain elevator, and native prairie landscapes, to determine the most attractive food and pheromone combination (attractant) and patterns in seasonal captures. Traps were baited with combinations of wheat (crimped, high moisture, pre-fed) with or without an R. dominica specific aggregation pheromone in 2017 and 2018. Traps were deployed for 48 h, collected, and the number of R. dominica counted. Rhyzopertha dominica was captured among all landscapes with all attractants. There was a significant correlation between temperature and R. dominica captures, with peak captures occurring during the warmest months. Significantly more R. dominica adults were captured in traps containing the pheromone. In 2017, pheromone traps captured 818% more R. dominica and 543% more than in 2018. The pheromone component in the trap was more attractive than any natural stored wheat condition and should be included in future studies. Understanding the seasonal patterns and changes in capture rates in agricultural and non-agricultural landscapes may be useful in determining times of increased immigration pressure into the newly harvested grain. Full article
(This article belongs to the Special Issue Traditional and Innovative Pest and Disease Monitoring Strategies in Agroforestry)
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