Assessment of microalgal biomass as a potential feedstock for sustainable, eco-friendly biostimulants and biopesticides in plant production

O uso excessivo e contínuo de agroquímicos, fertilizantes sintéticos e pesticidas, levou à poluição antropogénica de nutrientes, tendo causado um grande número de degradações ambientais. Além disso, os agroquímicos são poluentes ambientais que podem causar graves problemas de saúde humana. A expansão global de "zonas mortas" nos oceanos, nas quais os baixos níveis de oxigénio ameaçam a vida marinha, é apenas um dos muitos sinais de alerta de que medidas contrárias são necessárias com urgência. Os bioestimulantes e biopesticidas à base de microalgas representam uma alternativa promissora para alcançar uma maior sustentabilidade na agricultura moderna. A biomassa de microalgas contém numerosos aminoácidos e fitohormonas que promovem o crescimento das plantas, podendo aumentar a produtividade das culturas, estimulando o crescimento da raiz e da canópia. Além disso, sabe-se que as microalgas inibem o crescimento de vários agentes fitopatogénicos, devido às suas propriedades antimicrobianas, podendo ser uma alternativa sustentável aos pesticidas sintéticos no setor agrícola. Neste estudo, focámo-nos na aplicação de extratos aquosos de microalgas como fungicidas contra Sclerotium rolfsii, Rhizoctonia solani, Botrytis cinerea e Alternaria alternata. Esses fungos são agentes causais de doenças frequentes na agricultura, ameaçando a segurança alimentar global. As informações disponíveis, relacionadas com a utilização de microalgas na proteção de plantas e bioestimulação, são ainda escassas, embora as microalgas possam desempenhar um papel importante no desenvolvimento da agricultura sustentável. Secundariamente, sabendo-se que o uso agrícola de compostos de resíduos orgânicos apresenta vários benefícios relacionados com a fertilidade do solo e a resistência das plantas a algumas doenças, avaliou-se o efeito daquelas microalgas na compostagem de uma mistura de resíduos agrícolas comuns na região, devido à possibilidade de as microalgas poderem apresentar alguma influência na atividade microbiana responsável pela compostagem. O principal objetivo do presente estudo foi determinar as propriedades bioestimulantes e biofungicidas de microalgas e a sua capacidade de melhorar o processo de compostagem de resíduos orgânicos para um objetivo final de tornar a agricultura mais sustentável através do uso destes microrganismos fotossintéticos, nomeadamente Scenedesmus sp., Chlorella vulgaris, Nannochloropsis sp., Arthrospira (Spirulina) sp. e Phaeodactylum tricornutum. Para atingir o objetivo supracitado, os objetivos específicos desta dissertação são: (1) avaliar o controle de doenças de plantas com extratos aquosos de microalgas in vitro, e (2) avaliar e caracterizar processos de compostagem enriquecidos com microalgas. O Capítulo II descreve a aplicação promissora de extratos aquosos de Nannochloropsis sp., Phaeodactylum tricornutum, Scenedesmus obliquus e Spirulina sp. in vitro para o desenvolvimento de antifúngicos de origem algal. A supressão do crescimento por estes extratos foi observada nos fungos fitopatogénicos Sclerotium rolfsii, Rhizoctonia solani e Botrytis cinerea. De facto, as espécies de microalgas são uma fonte promissora de agentes antifúngicos não prejudiciais ao meio ambiente que podem reduzir o uso de fungicidas sintéticos e limitar o impacto ecológico do setor agrícola. Uma vez que a maioria dos estudos se foca nas propriedades antifúngicas de cianobactérias procarióticas, o presente estudo visou preencher a lacuna de conhecimento sobre o uso de microalgas eucarióticas como agentes antifúngicos. Para evitar métodos complexos de extração e etapas de purificação, que aumentam os custos e restringem as aplicações em larga escala de fungicidas à base de algas, foi usada uma extração simples à base de água. Assim, foram investigadas as propriedades de extratos aquosos de microalgas eucarióticas (Nannochloropsis sp., Phaeodactylum tricornutum, Scenedesmus obliquus e Chlorella vulgaris) e procarióticas (Spirulina sp.) in vitro quanto à sua atividade inibidora em relação aos fungos fitopatogénicos Sclerotium rolfsii e Alternaria alternata. A análise estatística revelou que Scenedesmus obliquus apresentou a maior atividade antifúngica de todas as estirpes de microalgas contra Sclerotium rolfsii, com inibições de crescimento de até 32,01 ± 4,82%. Nannochloropsis sp. mitigou Sclerotium rolfsii em até 13,96 ± 5,26%, enquanto Phaeodactylum tricornutum suprimiu o crescimento de Sclerotium rolfsii e Rhizoctonia solani em até 18,35 ± 3,45% (p <0,05). Além disso, Phaeodactylum tricornutum e Scenedesmus obliquus inibiram o crescimento de Botrytis cinerea em até 11,47 ± 2,06% (p <0,05). Assim, esses resultados sugerem que microalgas com atividade fungicida podem contribuir para uma agricultura mais sustentável ao inibir o crescimento de fitopatógenos fúngicos. No Capítulo III, encontra-se descrita a utilização de microalgas no processo de compostagem. Mais especificamente, este estudo investigou a suplementação de uma mistura de resíduos orgânicos com biomassa de microalgas secas de Nannochloropsis sp., Phaeodactylum tricornutum, Scenedesmus obliquus e Chlorella vulgaris. Até onde sabemos, este é o primeiro relatório que analisou o enriquecimento de materiais de compostagem frescos com pó de microalga seca. Uma vez que as microalgas produzem vários aminoácidos e fitohormonas que promovem o crescimento das plantas, seria de esperar que elas poderiam melhorar ainda mais as características dos compostos estimulantes das plantas, como a liberação de nutrientes que promovem o crescimento. As taxas de decomposição dependem das atividades metabólicas das populações microbianas que dependem, por sua vez, da disponibilidade de vários micro- e macronutrientes. Portanto, a co-compostagem de biomassa de microalgas rica em nutrientes poderá moldar comunidades microbianas e melhorar a qualidade do composto final com base na riqueza em nutrientes, como fósforo, azoto e potássio. Devido ao seu potencial para transformar e reciclar resíduos de diferentes origens em matéria orgânica, a compostagem terá um papel fundamental no caminho para uma sociedade mais sustentável. Em termos gerais, não foram observadas grandes variações nos parâmetros de pH, condutividade elétrica, matéria orgânica, matéria mineral, temperatura, volume e fitotoxicidade entre todas as pilhas de compostagem modificadas com microalgas, quando comparadas com o composto controlo (fase final). Portanto, o composto fortificado com microalgas poderá ser considerado uma alternativa sustentável promissora para aumentar ainda mais a produtividade das culturas no setor agrícola global, mas que requer ainda verificação experimental em ensaios de campo ou estufa.Continuous overuse of synthetic fertilizers and pesticides (agrochemicals) has led to excessive anthropogenic nutrient pollution and caused a vast number of environmental degradations. The global expansion of "dead zones" in the world's oceans, where oxygen-depleted water bodies threaten marine life, is just one of many warning signs indicating that counteractive measures are urgently needed. Moreover, long-term exposure to agrochemicals can cause major human health issues. Microalgae-based biostimulants and biopesticides represent a promising alternative to reduce those negative effects and achieve a higher sustainable value in modern agriculture. Microalgal biomass contains numerous plant growth-promoting amino acids and phytohormones that increase crop productivity by stimulating root and shoot growth. Compost can be seen as effective carrier for these bioactive compounds and may be applied as enriching soil amendment. Moreover, microalgae were found to inhibit the growth of several pathogens due to their antimicrobial properties. Hence, they can be seen as sustainable alternative for synthetic fertilizers and pesticides in the agricultural and horticultural sector. In this study we focused on the application of aqueous microalgal extracts as fungicides against the phytopathogenic fungi Sclerotium rolfsii, Rhizoctonia solani, Botrytis cinerea and Alternaria alternata. Those fungi are dominant causal agents for common diseases in agriculture and considered as major threat for global food security. Even though microalgae could play a major role in sustainable agriculture development, available literature related to microalgal crop protection and biostimulation is still scarce. Chapter II describes the promising antifungal application of aqueous extracts from Nannochloropsis sp., Phaeodactylum tricornutum, Scenedesmus obliquus and Spirulina sp. in vitro. Growth suppression was observed against the phytopathogenic fungi Sclerotium rolfsii, Rhizoctonia solani and Botrytis cinerea. In Chapter III, no major parameter variations in pH, electrical conductivity, organic matter, mineral matter, temperature, volume and phytotoxicity were observed among all microalgae-amended composting piles, when compared with the control compost (final phase). Future studies will evaluate the biostimulant properties of these composts in vivo

A Framework for Unbiased Model Selection Based on Boosting

Variable selection and model choice are of major concern in many statistical applications, especially in high-dimensional regression models. Boosting is a convenient statistical method that combines model fitting with intrinsic model selection. We investigate the impact of base-learner specification on the performance of boosting as a model selection procedure. We show that variable selection may be biased if the covariates are of different nature. Important examples are models combining continuous and categorical covariates, especially if the number of categories is large. In this case, least squares base-learners offer increased flexibility for the categorical covariate and lead to a preference even if the categorical covariate is non-informative. Similar difficulties arise when comparing linear and nonlinear base-learners for a continuous covariate. The additional flexibility in the nonlinear base-learner again yields a preference of the more complex modeling alternative. We investigate these problems from a theoretical perspective and suggest a framework for unbiased model selection based on a general class of penalized least squares base-learners. Making all base-learners comparable in terms of their degrees of freedom strongly reduces the selection bias observed in naive boosting specifications. The importance of unbiased model selection is demonstrated in simulations and an application to forest health models

Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost

We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial

GAMLSS for high-dimensional data – a flexible approach based on boosting

Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The proposed algorithm was applied to data of the Munich Rental Guide, which is used by landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net-rent predictions that resulted from the high-dimensional GAMLSS were found to be highly competitive while covariate-specific prediction intervals showed a major improvement over classical GAMs

Energy cooperatives in Switzerland: a study of energy cooperatives and their interrelations with local governments in the Swiss federalist system

This PhD thesis presents the first comprehensive analysis of energy cooperatives in Switzerland—the production of renewable energy organized on a cooperative basis. Drawing on approaches of interactive and multi-level governance, the conditions are examined under which energy cooperatives operate and contribute to the Swiss energy transition. The results of a survey and of four case studies portray these energy cooperatives not as isolated actors but as deeply embedded in local governance structures. While their development is found to be contingent upon support by municipalities, energy cooperatives also appear as suitable partners for municipalities to implement local energy policy. Building on these insights, the thesis elaborates an argument on the beneficial nature of federalist structures for collaboration between energy cooperatives and municipalities and thus for a citizen-oriented energy transition.Die vorliegende Dissertation unternimmt die erste umfassende Analyse von Energiegenossenschaften in der Schweiz – der genossenschaftlich organisierten Produktion erneuerbarer Energie. Aufbauend auf Ansätzen der interaktiven und Mehrebenen-Governance werden die Bedingungen untersucht, unter denen Energiegenossenschaften agieren und zur Schweizer Energiewende beitragen. Die Ergebnisse einer Umfrage und von vier Fallstudien porträtieren diese Energiegenossenschaften nicht als isolierte, sondern als stark in lokale Governance-Strukturen eingebettet Akteure. Während aber ihre Entwicklung stark von kommunaler Unterstützung abhängt, erweisen sich Energiegenossenschaften gleichzeitig als geeignete Partner für Gemeinden bei der Umsetzung lokaler Energiepolitik. Aufbauend auf diesen Erkenntnissen wird in dieser Arbeit ein Argument für den Vorteil föderalistischer Strukturen für die Zusammenarbeit zwischen Energiegenossenschaften und Gemeinden und damit für eine bürgernahe Energiewende entwickelt

An update on statistical boosting in biomedicine

Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine-learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=gamboostLSS

Identifying Value-adding Users in Enterprise Social Networks

Enterprise Social Networks (ESN) have been gaining increasing attention both in academia and practice. In previous works, different user types were identified in ESN. However, there is no clear definition of value-adding users, their characteristics and how this type of user can be identified. Based on a literature review, we show that value-adding users are defined in different ways in respect to different objectives, for example spreading knowledge, vivacity of the network or real-time feedback. Each of the value-adding users shows different characteristics that are allocated to the following dimensions: network structure, message, behavior, and social network affinity. Based on the objectives and characteristics, we conduct a single case study, analyze a dataset of a cooperating company, conduct several interviews, and thereby identify value-adding users with respect to objectives. So, we can show that our approach is applicable, useful and that it is a valuable means to take decisions