Recorded and potential alien invertebrate pests in Finnish agriculture and horticulture

It is assumed that climate change will promote pest invasions and their establishment in new regions. We have updated the list of current alien invertebrate pest species in Finland and produced a list of potential new alien pests using a self-organizing map (SOM) that ranks species in terms of their risk of entry into Finland. The 76 pest species recorded included 66 insects, 5 nematodes, 2 mites and 3 slugs. Nearly half of the alien species appeared to have invaded Finland during the last 48 years. The SOM analysis is considered a viable tool for identification of potentially high-risk invasive pests from among the multitude of potential alien invaders, and represents a useful complement to local expert knowledge-based risk assessment of potentially invasive pests. Along with the comparisons with databases of current and potential pest species, SOM analysis suggests that in the changing climate, the habitats at greatest risk from exotic pests in Finland are horticultural: orchards, ornamental hardy-nursery stocks, landscape and ornamental tree nurseries, and greenhouses

Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)

After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset with a spatial lens to provide insights and project the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 in selected locations were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics aimed to identify the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10 FAW moth per trap), moderate (11–30 FAW moth per trap), and high (>30 FAW moth per trap). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies (predators, parasitoids, and pathogens) into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic interactions between the host plants, pests, and beneficial organisms. Nevertheless, the tool developed in this study provides a framework for field monitoring of FAW in Africa that may be a basis for a future decision support system (DSS).Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)publishedVersio

Maize Genetic Resources

Maize is one of the most economically important food crops worldwide. It is used for livestock feeds and human nutrition. Recent strategies have been adopted for improving maize crops. This book brings together recent advances, breeding strategies, and applications in the biological control, breeding, and genetic improvement of maize genetic resources. It also provides new insights and sheds light on new perspectives and future research work that have been carried out for further improvement of maize crops. This book is a useful resource for students, researchers, and scientists

Impacts, Monitoring and Management of Forest Pests and Diseases

Forest pests have diverse negative impacts on forestry economy, ecosystem services, biodiversity, and sustainable ecosystem management. The first step towards effectively managing forest pests would be to monitor their occurrence and assess their impact on forest ecosystems. The monitoring results can provide basic information for effective management strategies. The data from monitoring programs can result in the development of new methods for monitoring, assessing impact, and developing management techniques. This special issue aims to share information to assist in the effective management of forest pests, by understanding the responses of forest pests to natural and anthropogenic changes, and discussing new studies on the monitoring, assessment, and management of forest pests. The fourteen papers included in this issue focus on monitoring, assessing, and managing forest pests, including one editorial providing an overall idea of the monitoring, assessment and management of forest pests, two articles reviewing long-term changes in forest pests and forests, four papers focusing on the monitoring of forest pests, three papers on the assessment of forest pests, and four papers on the management of forest pests. These papers provide a better understanding of the structures and processes in forest ecosystems and fundamental information for the effective management of forest pests

Review on Honey Bee Colony Collapse: Problems, Consequence and Solutions

This thesis manuscript is a review on how climate change, nutritional stress, and other factors contribute to colony collapse disorder (CCD). This is a phenomenon when majority of adult bees vacate their hive to die, leaving what little what little is left of the colony to fend for themselves. This phenomenon is considered a serious threat to global food biosecurity. The big question is on when and why honeybees abandon the colony and die. This phenomenon remains a mystery. Unable to study their carcasses for analysis to better serve the pollinator species, there has been a rise in research focused on investigating the key drivers to CCD. My research suggested that the problem of CCD is complex and tied up to the greater problem of climate change and human activities. The focus of this paper is to understand how climate change in a rapidly warming world has contributed to the demise of honeybees. I focused on isolating the major factors and reviewed the data on global warming and its specific impact on honeybee colonies, where many other drivers participate. Evaluation of the interactive and synergistic effects of key drivers revealed how they affected mutually beneficial relationships, and ultimately the ecology of the bees and their habitat. I also investigated and assessed the overall interactive effects of potential synergies between climatic and nutritional stresses and understand how they affect honeybee colonies feeding responses. Notions were evaluated based on colony growth, composition of brood, mortality, total pollen and syrup collection. My review highly suggest that sociality, honeybees’ interdependence on one another are crucial in buffering the negative effects of environmental perturbations and increasing their resilience

Mapping and assessing ecosystem services in an agricultural landscape following a tiered approach

Agricultural ecosystems are anthropogenically highly transformed ecosystems, mainly designed to maximise the delivery of provisioning ecosystem services (ES) such as food, material and fuel, often at the expense of other ES. Especially, conventional agriculture and agricultural landscape simplification have become major causes of climate change, ecosystem degradation and biodiversity loss. At the same time, the production of provisioning services depends on other, mainly regulating, ES. In the long-term, the viability of agricultural ecosystems and the delivery of provisioning ES rely on more sustainable farming practices and the conservation of ES and biodiversity. This calls for a shift in the agricultural production paradigm, towards more multifunctional and sustainable agricultural landscapes. Spatially explicit assessments of ES are key components in supporting the shift towards sustainable land use management: they inform on how and where land use decisions can affect ecosystems, on potential trade-offs between the delivery of different ES and help to design targeted ES conservation measures. Understanding the distribution patterns and the main drivers influencing the delivery of ES is needed to determine where land use management measures can be improved to maximise the delivery of (specific) ES. Specifically, spatial information on ES can assist economical decisions underlying agricultural practices: for instance, higher pollination and natural pest control ES potentials can increase crop yields and save resources. The central question of this thesis is to assess how different ES assessment methods influence the predictions of ES supply potential, aiming to find the adapted level of information needed for an ES assessment at the local scale, in an agricultural landscape. To address this research question, several ES mapping and assessment methods, using simple (tier 1) to more complex (tiers 2 and 3) approaches, were developed and applied to a case study area in northern Germany. Additionally, this work aims at informing land use planners and decision-makers on the capacity of the landscape to deliver multiple ES. First, the ES matrix approach (tier 1) was used to assess the importance of spatial resolution and of accounting for ecosystem condition (tier 2). The two following studies developed and implemented more complex methods (tier 3) based on species distribution models (SDMs). SDMs were used to model the relationships between ES providers (ESP) (here wild bees and natural enemies of pests) and a combination of abiotic and biotic factors at different scales. The results of this thesis show that designing multifunctional landscapes ideally requires a rather comprehensive assessment. For most regulation and cultural ES, simple proxies are not suitable for a local quantitative assessment of ES, as they cannot sufficiently cover the spatial heterogeneity of ES capacities and functions that arise from different ecosystem properties and conditions. This is particularly the case of ES delivered by living and mobile organisms such as pollination and natural pest control, whose potentials are determined by multi-scale variables and processes. A comprehensive assessment of every ES is, however, often not feasible. This thesis shows how the use of different modelling methods and the tiered approach can assist in the assessment of multiple ES. Proxy indicators and models should be used whenever empirical data and knowledge of ecological processes are limited. Indicators and models are, however, only simplified representations of complex processes. ES mapping and assessment outputs should therefore be interpreted considering the assumptions behind the models and knowing the associated uncertainties.Landwirtschaftliche Ökosysteme (ÖS) sind anthropogen stark veränderte ÖS, die hauptsächlich darauf ausgelegt sind, die Bereitstellung von Ökosystemleistungen (ÖSL) wie Nahrung, Material und Brennstoff zu maximieren - oft auf Kosten anderer ÖSL. Insbesondere die konventionelle Landwirtschaft und die Vereinfachung der Agrarlandschaft sind wesentlich mitverantwortlich für den Klimawandel, die Verschlechterung von ÖS und den Verlust der biologischen Vielfalt. Gleichzeitig hängt die Fähigkeit eines ÖS Nahrung und andere Rohstoffe zur Verfügung zu stellen von anderen, hauptsächlich regulierenden, ÖSL ab. Langfristig hängen die Lebensfähigkeit landwirtschaftlicher ÖS und die Bereitstellung von ÖSL von nachhaltigeren landwirtschaftlichen Praktiken und der Erhaltung von Ökosystemen in gutem Zustand und der Biodiversität ab. Räumlich explizite Bewertungen von ÖSL sind ein Schlüssel zur Unterstützung eines nachhaltigen Landnutzungsmanagements: Sie informieren, wie und wo Ökosysteme beeinflussen werden können, über potenzielle Kompromisse zwischen der Bereitstellung verschiedener ÖSL und helfen bei der Entwicklung gezielter Maßnahmen zur Erhaltung von ÖSL. Insbesondere räumliche Informationen zu ÖSL können wirtschaftliche Entscheidungen unterstützen: Höhere Bestäubungs- und natürliche Schädlingsbekämpfungspotentiale von ÖSL können beispielsweise die Ernteerträge steigern und Ressourcen sparen. Die zentrale Frage dieser Arbeit ist es zu bewerten wie verschiedene ÖSL-Bewertungsmethoden die Vorhersagen des ÖSL-Versorgungspotentials auf lokaler Ebene beeinflussen. Dafür wurden mehrere ÖSL-Kartierungs- und Bewertungsmethoden unter Verwendung einfacher (Stufe 1) bis hin zu komplexeren (Stufen 2 und 3) Ansätzen entwickelt und auf ein Fallstudiengebiet in Norddeutschland angewendet. Darüber hinaus sollen Landnutzungsplaner und Entscheidungsträger über die Fähigkeit der Landschaft informiert werden mehrere ÖS bereitzustellen. Zunächst wurde der ÖSL-Matrix-Ansatz (Stufe 1) verwendet, um die Bedeutung der räumlichen Auflösung und der Berücksichtigung des Ökosystemzustands (Stufe 2) zu bewerten. Die beiden nachfolgenden Studien entwickelten und implementierten komplexere Methoden (Stufe 3) auf der Grundlage von Artenverteilungsmodellen („species distribution models“ - SDMs). SDMs wurden verwendet, um die Beziehungen zwischen ÖSL-Anbietern (hier Wildbienen und natürlichen Feinden) und mit abiotischen und biotischen Faktoren auf verschiedenen Skalen zu modellieren. Die Ergebnisse dieser Arbeit zeigen, dass die Gestaltung multifunktionaler Landschaften eine umfassende Bewertung erfordert. Für die meisten regulatorischen und kulturellen ÖSLs sind einfache Proxys nicht für eine lokale quantitative Bewertung von ÖSL geeignet, da sie die räumliche Heterogenität von ÖSL-Kapazitäten und -Funktionen, die sich aus unterschiedlichen Ökosystemeigenschaften und -bedingungen ergeben, nicht ausreichend abdecken können. Dies gilt insbesondere für ÖSL, die von lebenden und mobilen Organismen wie Bestäubung und Schädlingsbekämpfung geliefert werden, deren Potenziale durch mehrskalige Variablen und Prozesse bestimmt werden. Eine umfassende Bewertung aller ÖSL ist jedoch oft nicht praktikabel. Diese Arbeit zeigt, wie die Verwendung verschiedener Modellierungsmethoden und der gestufte Ansatz bei der Bewertung mehrerer ÖSL helfen können. Proxy-Indikatoren und -Modelle sollten verwendet werden, wenn empirische Daten und Kenntnisse über ökologische Prozesse begrenzt sind

Relación entre factores bióticos y abióticos en los cultivos de guayaba con la infestación de picudo (conotrachelus psidii, coleóptera: curculionidae) en Puente Nacional (Santander, Colombia)

Los insectos que son “plagas” causan daño a los sistemas naturales y aquellos que son manejados por el hombre y durante mucho tiempo se ha demostrado que las plagas generan resistencia a los pesticidas y se ha generado un problema ambiental con el mal uso de dichos productos. Los cultivos de guayaba en el país actualmente son atacados por picudo lo que genera grandes pérdidas económicas. En la actualidad los programas agrícolas están dirigidos a controlar las plagas juntando conocimientos biológicos, culturales y químicos, pues estas prácticas son de bajo costo y no degradan el medio ambiente. El objetivo de la presente investigación fue conocer y analizar algunos de los factores bióticos y abióticos que afectan la infestación de picudo a cultivos de guayaba; adicional a esto se estudió el crecimiento en diámetro polar y ecuatorial de frutos sanos e infestados a través del tiempo de desarrollo. Se analizó el crecimiento de 508 frutos en cuatro meses y la edad más vulnerable a la infestación; adicional a esto registros de daño en flores y botones florales. Para establecer las relaciones entre factores bióticos y abióticos con la infestación se tuvieron en cuentas las siguientes variables: altitud de la finca, altura de las plantas, cobertura de las plantas, distancia de siembra, aplicación de insecticidas y aplicación de fertilizantes. Los botones presentaron daño ocasionado por consumo y un desarrollo de tipo exponencial en 30 días de crecimiento. El crecimiento de los frutos sanos e infectados tardo en promedio 120 días y el diámetro ecuatorial y polar presento un crecimiento ajustado a la curva de Gompertz, dividida en tres etapas que se caracterizaron por la velocidad de crecimiento. La región estudiada presenta un porcentaje de infestación del 60% donde los cultivos no tecnificados alrededor de los cultivos pueden estar explicando cerca del 61% de la infestación, factores abióticos como temperatura, humedad y precipitación o factores biológicos como otras plagas pueden estar determinando el restante porcentaje de infestación. Las fincas estudiadas presentaron perdidas de aproximadamente de 3 toneladas de fruta por la infestación de picudo. Finalmente se concluye que las hembras de picudo solo ovipositan en los frutos y con preferencia en las primeras edades del desarrollo, dentro de los factores que se estudiaron y que explican la infestación son los cultivos de guayaba no tecnificados alrededor de los cultivos tecnificados.Abstract. The insect "pests "cause damage to natural systems and culture has long been demonstrated that pests develop resistance to pesticides and has generated an environmental problem. Guava crops in the country are attacked by the weevil causing economic losses. At present farm programs are aimed at controlling pests with biological, cultural and chemical knowledge , as these practices are inexpensive and do not degrade the environment. The objective of this research was to analyze some of the biotic and abiotic factors affecting infestation of crops guava weevil, en Puente Nacional (Santander), in addition to this growth was studied in polar and equatorial diameter of healthy fruit infested through the development time. Additional records of this damage flowers and flower buds, the growth of 480 fruits in four months and the most vulnerable to infestation age were analyzed. To establish the relationships between biotic and abiotic factors with infestation took into account the following variables: altitude of the farm, plant height, coverage, planting distance, application of insecticides and fertilizer application. The buttons had damage caused by consumption and exponential growth in 30 days. The growth of healthy and infected fruits slow on average 120 days and the polar and equatorial diameter presented a growth adjusted Gompertz curve, divided into three stages which are characterized by the growth rate. The study region has a percentage of 60% infestation where wild guava trees around crops can be explaining about 61% of the infestation, abiotic factors such as temperature, humidity and precipitation o biotic factors as others pests can be given the remainder of infestation. Farms studied had lost about 3 tons of fruit for weevil infestation. The conclude that female weevils oviposit only in fruit and preferably in the early ages, one of the factors which may explain the infestation is wild guava trees around crops.Maestrí

Harnessing data science to improve integrated management of invasive pest species across Africa: an application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae)

Open Access Journal; Published online: 11 Feb 2022After five years of its first report on the African continent, Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) is considered a major threat to maize, sorghum, and millet production in sub-Saharan Africa. Despite the rigorous work already conducted to reduce FAW prevalence, the dynamics and invasion mechanisms of FAW in Africa are still poorly understood. This study applied interdisciplinary tools, analytics, and algorithms on a FAW dataset with a spatial lens to provide insights and project the intensity of FAW infestation across Africa. The data collected between January 2018 and December 2020 in selected locations were matched with the monthly average data of the climatic and environmental variables. The multilevel analytics aimed to identify the key factors that influence the dynamics of spatial and temporal pest density and occurrence at a 2 km x 2 km grid resolution. The seasonal variations of the identified factors and dynamics were used to calibrate rule-based analytics employed to simulate the monthly densities and occurrence of the FAW for the years 2018, 2019, and 2020. Three FAW density level classes were inferred, i.e., low (0–10 FAW moth per trap), moderate (11–30 FAW moth per trap), and high (>30 FAW moth per trap). Results show that monthly density projections were sensitive to the type of FAW host vegetation and the seasonal variability of climatic factors. Moreover, the diversity in the climate patterns and cropping systems across the African sub-regions are considered the main drivers of FAW abundance and variation. An optimum overall accuracy of 53% was obtained across the three years and at a continental scale, however, a gradual increase in prediction accuracy was observed among the years, with 2020 predictions providing accuracies greater than 70%. Apart from the low amount of data in 2018 and 2019, the average level of accuracy obtained could also be explained by the non-inclusion of data related to certain key factors such as the influence of natural enemies (predators, parasitoids, and pathogens) into the analysis. Further detailed data on the occurrence and efficiency of FAW natural enemies in the region may help to complete the tri-trophic interactions between the host plants, pests, and beneficial organisms. Nevertheless, the tool developed in this study provides a framework for field monitoring of FAW in Africa that may be a basis for a future decision support system (DSS)

Assessing the role of environmental factors on Baltic cod recruitment, a complex adaptive system emergent property

For decades, fish recruitment has been a subject of intensive research with stock–recruitment models commonly used for recruitment prediction often only explaining a small fraction of the inter-annual recruitment variation. The use of environmental information to improve our ability to predict recruitment, could contribute considerably to fisheries management. However, the problem remains difficult because the mechanisms behind such complex relationships are often poorly understood; this in turn, makes it difficult to determine the forecast estimation robustness, leading to the failure of some relationships when new data become available. The utility of machine learning algorithms such as artificial neural networks (ANNs) for solving complex problems has been demonstrated in aquatic studies and has led many researchers to advocate ANNs as an attractive, non-linear alternative to traditional statistical methods. The goal of this study is to design a Baltic cod recruitment model (FishANN) that can account for complex ecosystem interactions. To this end, we (1) build a quantitative model representation of the conceptual understanding of the complex ecosystem interactions driving Baltic cod recruitment dynamics, and (2) apply the model to strengthen the current capability to project future changes in Baltic cod recruitment. FishANN is demonstrated to bring multiple stressors together into one model framework and estimate the relative importance of these stressors while interpreting the complex nonlinear interactions between them. Additional requirements to further improve the current study in the future are also proposed

Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers

Due to the ANNs architecture, the ISA methods that can manipulate synaptic weights selectedare Connection Weights (CW) and Garson’s Algorithm (GA). The ANNs-based classifiers thatcan provide such manipulation are Multi-Layer Perceptron (MLP) and Evolving Fuzzy NeuralNetworks (EFuNNs). The goals for this work are firstly to identify which of the twoclassifiers works best with the filtered/ranked data, secondly is to test the FR method by usinga selected dataset taken from the UCI Machine Learning Repository and in an onlineenvironment and lastly to attest the FR results by using another selected dataset taken fromthe same source and in the same environment. There are three groups of experimentsconducted to accomplish these goals. The results are promising when FR is applied, someefficiency and accuracy are noticeable compared to the original data.Keywords: artificial neural networks, input significance analysis; feature selection; featureranking; connection weights; Garson’s algorithm; multi-layer perceptron; evolving fuzzyneural networks