Modeling microbial regulation of pesticide turnover in soils

Pesticides are widely used for pest control in agriculture. Besides their intended use, their long-term fate in real systems is not well understood. They may persist in soils, thereby altering ecosystem functioning and ultimately affecting human health. Pesticide fate is assessed through dissipation experiments in the laboratory or the field. While field experiments provide a close representation of real systems, they are often costly and can be influenced by many unknown or uncontrollable variables. Laboratory experiments, on the other hand, are cheaper and have good control over the governing variables, but due to simplification, extrapolation of the results to real systems can be limited. Mechanistic models are a powerful tool to connect lab and field data and help us to improve our process understanding. Therefore, I used mechanistic, process-based models to assess key microbial regulations of pesticide degradation. I tested my model hypotheses with two pesticide classes: i) chlorophenoxy herbicides (MCPA (2-methyl-4-chlorophenoxyacetic acid) and 2,4-D (2,4-Dichlorophenoxyacetic acid)), and ii) triazines (atrazine (AT)), in an ideal scenario, where bacterial degraders and pesticides are co-localized. This thesis explores some potential controls of pesticide degradation in soils: i) regulated gene expression, ii) mass-transfer process across the bacterial cell membranes, iii) bioenergetic constraints, and iv) environmental factors (soil temperature and moisture). The models presented in this thesis show that including microbial regulations improves predictions of pesticide degradation, compared to conventional models based on Monod kinetics. The gene-centric models achieved a better representation of microbial dynamics and enable us to explore the relationship between functional genes and process rates, and the models that used transition state theory to account for bioenergetic constraints improved the description of degradation at low concentrations. However, the lack of informative data for the validation of model processes hampered model development. Therefore, in the fourth part of this thesis, I used atrazine with its rather complex degradation pathway to apply a prospective optimal design method to find the optimal experimental designs to enable us identifying the degradation pathway present in a given environment. The optimal designs found suggest to prioritize determining metabolites and biomass of specific degraders, which are not typically measured in environmental fate studies. These data will lead to more robust model formulations for risk assessment and decision-making. With this thesis, I revealed important regulations of pesticide degradation in soils that help to improve process understanding and model predictions. I provided simple model formulations, for example the Hill function for gene expression and transition state theory for bioenergetic growth constraints, which can easily be integrated into biogeochemical models. My thesis covers initial but essential steps towards a predictive pesticide degradation model usable for risk assessment and decision-making. I also discuss implication for further research, in particular how mechanistic process-based modeling could be combined with new technologies like omics and machine learning.Pestizide sind weit verbreitet in der landwirtschaftlichen Schädlingsbekämpfung. Anders als ihre Wirkungsweise, ist ihr Langzeitverbleib in der Umwelt nicht gut verstanden. Sie gelangen in den Boden und können sich dort anreichen und die Bodenfunktionen beeinträchtigen und letzendlich auch die menschliche Gesundheit gefährden. Die Ausbreitung von Pestiziden wird anhand von Abbauversuchen in Labor- und Feldexperimenten ermittelt. Feldexperimente bieten ein relativ genaues Abbild natürlicher Systeme, sind jedoch meist teuer und können durch unbekannte oder nicht kontrollierbare Faktoren stark beeinflusst werden. Laborexperimente sind in dieser Hinsicht kostengünstiger und bieten eine gute Kontrolle der einwirkenden Faktoren. Allerdings lassen sich die Ergebnisse nur begrenzt auf natürliche Systeme übertragen. Mechanistische Modelle sind ein mächtiges Werkzeug, um Labor- und Felddaten zusammenzuführen und helfen uns dabei, die mikrobiellen Regulationsmechanismen des Pestizidabbaus im Boden besser zu verstehen. Aus diesem Grund habe ich mechanistische, prozess basierte Modelle eingesetzt. Ich habe meine Modellhypothesen bei zwei Pestizidgruppen getestet: i) Chlorphenoxyherbiziden (MCPA (2-Methyl-4-chlorphenoxyessigsäure) und 2,4-D (2,4-Dichlorphenoxyessigsäure)) und ii) Triazinen (Atrazin (AT)), in einem Idealszenario, wo bakterielle Abbauer und Pestizid kolokalisiert auftreten. Meine Doktorarbeit konzentriert sich auf einige der potenziellen Kontrollmechanismen des Pestizidabbaus im Boden: i) regulierte Genexpression, ii) Massetransferprozesse durch die Zellmembran, iii) bioenergetische Limitierungen und iv) Umweltfaktoren (Bodentemperatur und Bodenfeuchte). Die in dieser Doktorarbeit vorgestellten Modelle zeigen, dass die Berücksichtigung mikrobieller Regulationen Vorhersagen des Pestizidabbaus verbessert, gegenüber herkömmlichen, auf Monod-Kinetik-basierenden Modellen. Die gen-basierten Modelle erreichten eine bessere Repräsentation der mikrobiellen Dynamik und geben uns die Möglichkeit, den Zusammenhang zwischen funktionellen Genen und Prozessraten herzustellen, wohingegen Modelle, die die Abbaugeschwindigkeit auf Grundlage der Theorie des Übergangszustandes limitieren, eine genauere Konzentrationen liefern. Der Mangel an Messdaten zur Validierung behinderte allerdings die Modellentwicklung. Daher benutzte ich ich im vierten Teil dieser Arbeit, am Beispiel von Atrazin, mit seinem eher komplexen Abbauweg, eine Methode des prospective optimal design, um das bestmögliche Experimentaldesign zu finden, mit dem wir den in einer bestimmten Umgebung vorherrschenden Abbauweg identifizieren können. Die gefundenen optimalen Designs weisen auf die Erfordenis hin, die Messung von Hauptmetaboliten und Biomasse von spezifischen Abbauern zu priorisieren, welche in Abbauversuchen typischerweise nicht gemessen werden. Die Informationen aus diesen Daten werden zu besseren Modellformulierungen führen, die sich für Risikoabschätzung und Entscheidungsfindung nutzen lassen. Mit dieser Doktorarbeit konnte ich für den Pestizidabbau im Boden wichtige Regulationsmechanismen aufdecken, und so, unser Verständnis und Vorhersagen solcher Prozesse verbessern. Ich stelle einfache Modellformulierungen bereit, beispielsweise die Hill-Funktion für Genexpression und eine Implementierung der Theorie des Übergangszustands, welche sich einfach in biogeochemische Modelle integrieren lassen. Meine Arbeit liefert grundlegende und entscheidende Schritte zur Entwicklung eines Vorhersagemodells für den Pestizidabbau und dessen Einsatz in Risikoabschätzung und Entscheidungsfindung. Darüber hinaus gebe ich einen Ausblick auf weiterführende Forschungsansätze, insbesondere wie sich mechanistische, prozess-basierte Modellansätze mit neuen Technologien wie omics und Machine Learning verbinden lassen könnten

"Validity of a scale of Latin American perception of fear and concern transmitted by the media during the pandemic (MED-LAT-COVID-19)"

"Introduction: The pandemic has caused fear, especially due to the daily disseminated news; however, there is not an instrument to measure this fear in multiple realities. Objective: To validate a scale for Latin American perception of fear and concern transmitted by the media during the pandemic. Methodology: This is an instrumental study. The survey was based on an instrument which was pre-validated in Peru and submitted to 15 experts in almost 10 countries. Subsequently, thousands of people were surveyed in 13 Latin American countries, whose answers were used for descriptive statistics for validation. Results: Confirmatory Factor Analysis (CFA) generated two re-specifications, where four items were eliminated from the original scale. With these changes, the global goodness of fit (absolute and incremental) were satisfactory (CFI ¼ 0.978; TLI ¼ 0.964; GFI ¼ 0.976; AGFI ¼ 0.949; RMSEA ¼ 0.075 and RMR ¼ 0.029). The first factor measures the media exaggeration (three questions); the second, the fear transmitted by the media (three questions); and the third, the fear transmitted by others different from the media (two questions). The Cronbach’s alpha coefficient was higher than 0.70 for the scale and its factors. Conclusion: The MED-LAT-COVID-19 scale reported a good adjustment. It has eight items in three factors, which could be measured in an isolated way, or along with other tests that assess mental health in the current pandemic context.

Guía de buenas prácticas para establecimientos lecheros : material de referencia de la Red de BPA

Con el objetivo de concentrar varios esfuerzos aislados que se han llevado a cabo en el país, INTA, la Fac. Cs. Agropecuarias de la UNC y APROCAL han tenido la iniciativa de convocar a especialistas y representantes del sector lechero nacional para trabajar en el desarrollo de una guía de buenas prácticas en el tambo consensuada entre los diferentes representantes del sector lechero. Esta guía procura ser una propuesta de fácil interpretación para ser consultada permanentemente por parte de quienes trabajan y conducen los establecimientos lecheros para apoyarse en aspectos que hacen al aseguramiento de la calidad en el tambo. A través de esta se pretende brindar recomendaciones de Buenas Prácticas para maximizar la producción y la calidad de leche en sistemas productivos sustentables.EEA PergaminoFIL: Negri Rodriguez, Livia María. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Tecnología de Alimentos; Argentina. Asociación PRO Calidad de la Leche y sus Derivados (PROCAL); ArgentinaFil: Aimar, María Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Asociación PRO Calidad de la Leche y sus Derivados (PROCAL); ArgentinaFil: Costamagna, Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Callieri, Carlos. DeLaval Productor de Maquinaria Lechera y Agrícola; Argentina. Asociación PRO Calidad de la Leche y sus Derivados (PROCAL); ArgentinaFil: Herrero, María Alejandra. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias; ArgentinaFil: Charlón, Verónica. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Leiva, Antonio. Sancor; ArgentinaFil: Tentor, Gonzalo. Buenas Prácticas Agropecuarias (BPA); ArgentinaFil: Raciti, Julio. Manfrey Informatica; ArgentinaFil: Rampone, Alberto. Universidad Nacional de Villa María; ArgentinaFil: Chavez, Javier. Lactodiagnóstico Sur; ArgentinaFil: Gaggiotti, Mónica del Carmen. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Boffa, Susana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Agencia de Extensión Rural La Paz. Oficina Técnica Hernandarias; ArgentinaFil: Mancuso, Walter. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Praraná; ArgentinaFil: Pautasso, Néstor. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Praraná; ArgentinaFil: Walter, Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Moltoni, Luciana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Ingeniería Rural; ArgentinaFil: Serrano, Pedro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Agencia de Extensión Rural Coronel Brandsen; ArgentinaFil: Abdala, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Gonzalez Pereyra, Ana Valeria. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias; Argentina. Lactodiagnóstico Sur; ArgentinaFil: Sardi, Graciela. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias; ArgentinaFil: Gigli, Isabel. Universidad Nacional de La Pampa; ArgentinaFil: Rodríguez, Julián. La Lacteo; ArgentinaFil: García, Karina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Brunas, Lucas. García Hermanos; ArgentinaFil: Bontá, Marcos. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias; Argentina. Lactodiagnóstico Sur; Argentin

Modeling bioavailability limitations of atrazine degradation in soils

Pesticide persistence in soils is a widespread environmental concern in agro-ecosystems. One particularly persistent pesticide is atrazine, which continues to be found in soils and groundwater in the EU despite having been banned since 2004. A range of physical and biological barriers, such as sorption and mass-transfer into bacterial cells, might limit atrazine degradation in soils. These effects have been observed in experiments and models working with simplified systems. We build on that work by developing a biogeochemical model of the degradation process. We extended existing engineered system models by including refined representations of mass-transfer processes across the cell membrane as well as thermodynamic growth constraints. We estimated model parameters by calibration with data on atrazine degradation, metabolite (hydroxyatrazine) formation, biomass, and isotope fractionation from a set of controlled retentostat/chemostat experiments. We then produced site-specific model predictions for arable topsoil and compared them with field observations of residual atrazine concentrations. We found that the model overestimated long-term atrazine biodegradation in soils, indicating that this process is likely not limited by bioavailability or energetic constraints of microbial growth. However, sorption-limited bioavailability, could explain the long-term fate and persistence of the main degradation metabolite hydroxyatrazine. Future studies should seek alternative controls that drive the observed atrazine persistence in soil. This work helps to bridge the gap between engineered and natural systems, allowing us to use laboratory setups to gain insight into real environmental systems

Impact of Drought on Ecohydrology of Southern California Grassland and Shrubland

Through their rooting profiles and water demands, plants affect the distribution of water in the soil profile. Simultaneously, soil water content controls plant development and interactions within and between plant communities. These plant-soil water feedbacks might vary across plant communities with different rooting depths and species composition. In semiarid environments, understanding these differences will be essential to predict how ecosystems will respond to drought, which may become more frequent and severe with climate change. In this study, we tested how plant-soil water feedbacks responded to drought in two contrasting ecosystem types—grassland and shrubland—in the coastal foothills of southern California. During years 5–8 of an ongoing precipitation manipulation experiment, we measured changes in plant communities and soil moisture up to 2 m depth. We observed different water use patterns in grassland and shrubland communities with distinct plant functional types and water use strategies. Drought treatment did not affect perennial, deep-rooted shrubs because they could access deep soil water pools. However, mid-rooted shrubs were sensitive to drought and experienced decreased productivity and die-off. As a result, water content actually increased with drought at soil depths from 50–150 cm. In grassland, biomass production by annual species, including annual grasses and forbs, declined with drought, resulting in lower water uptake from the surface soil layer. An opportunistic “live fast, die young“ life strategy allowed these species to recover quickly once water availability increased. Our results show how drought interacts with plant community composition to affect the soil water balance of semiarid ecosystems, information that could be integrated into global scale models

Impact of Drought on Ecohydrology of Southern California Grassland and Shrubland

Through their rooting profiles and water demands, plants affect the distribution of water in the soil profile. Simultaneously, soil water content controls plant development and interactions within and between plant communities. These plant-soil water feedbacks might vary across plant communities with different rooting depths and species composition. In semiarid environments, understanding these differences will be essential to predict how ecosystems will respond to drought, which may become more frequent and severe with climate change. In this study, we tested how plant-soil water feedbacks responded to drought in two contrasting ecosystem types—grassland and shrubland—in the coastal foothills of southern California. During years 5–8 of an ongoing precipitation manipulation experiment, we measured changes in plant communities and soil moisture up to 2 m depth. We observed different water use patterns in grassland and shrubland communities with distinct plant functional types and water use strategies. Drought treatment did not affect perennial, deep-rooted shrubs because they could access deep soil water pools. However, mid-rooted shrubs were sensitive to drought and experienced decreased productivity and die-off. As a result, water content actually increased with drought at soil depths from 50–150 cm. In grassland, biomass production by annual species, including annual grasses and forbs, declined with drought, resulting in lower water uptake from the surface soil layer. An opportunistic “live fast, die young“ life strategy allowed these species to recover quickly once water availability increased. Our results show how drought interacts with plant community composition to affect the soil water balance of semiarid ecosystems, information that could be integrated into global scale models