Objective: To analyze the change in health-related quality-of-life (HRQoL) in patients with Parkinson’s disease (PD) and to identify different classes of HRQoL decline. Methods: A longitudinal cohort study was performed to assess clinical parameters (unified PD rating scale, Beck Depression Inventory) and HRQoL data (EuroQol, Parkinson’s Disease Questionnaire [PDQ]-39) at baseline, 3, 6, 12, 24, and 36 months. A total of 145 patients with PD were consecutively recruited in the county of Northern Hessia, Germany, between January and June 2000. A latent growth mixture model was applied to analyse the heterogeneity in HRQoL trajectories. Results: We successfully applied latent mixture growth modeling in order to identify different classes of HRQoL trajectories in PD. Three growth models were developed and each resulted in a four-class model of distinct patterns using the generic EuroQol instruments’ outcomes (EuroQol-5 Dimensions and visual analogue scale) and the disease-specific PDQ- 39. The four classes were defined by individual trajectory characteristics. Classes one and two represented trajectories with moderate declines over 36 months, but with different initial intercepts. Class three consisted mainly of patients who passed away during the observation period and therefore had a large HRQoL decline. Class four was characterized by a low level of HRQoL at baseline and a significant subsequent decline. Conclusions: The findings provide a more elaborate understanding of the variability in HRQoL reduction in PD over time. The classification of different HRQoL subgroups may help to explain the response of PD patients to the natural history of the disease. Future research will enable the identification of HRQoL responder subgroups on different treatment regimens.

There is a growing societal concern about excessive water and fertilizer use in agricultural systems. High water productivity while maintaining high crop yields can be achieved with appropriate irrigation scheduling. Moreover, freshwater pollution through nitrogen (N) leaching due to the widespread use of N fertilizers demands for an efficient N fertilization management. However, sustainable crop management requires good knowledge of soil water and N dynamics as well as of crop water and N demand. Crop growth models, which describe physical and physiological processes of crop growth as well as water and matter transport, are considered as powerful tools to assist in the optimization of irrigation and fertilization management. It is of a general nature that the reliability of simulation based predictions depends on the quality and quantity of the data used for model calibration and validation which can be obtained e.g. in field experiments. A lack of data or low data quality for model calibration and validation may cause low performance and large uncertainties in simulation results. The large number of model parameters to be calibrated requires appropriate calibration methods and a sequential calibration strategy. Moreover, a simulation based planning of the field design saves costs and expenditure while supporting maximal outcomes of experiments. An adjustment of crop growth modeling and experiments is required for model improvement and development to reliably predict crop growth and to generalize predicted results. In this research study, a new approach for simulation based optimal experimental design was developed aiming to integrate simulation models, experiments, and optimization methods in one framework for optimal and sustainable irrigation and N fertilization management. The approach is composed of three steps: 1. The preprocessing consists of the calibration and validation of the crop growth model based on existing experimental data, the generation of long time-series of climate data, and the determination of the optimal irrigation control. 2. The implementation comprises the determination and experimental application of the simulation based optimized deficit irrigation and N fertilization schedules and an appropriate experimental data collection. 3. The postprocessing includes the evaluation of the experimental results namely observed yield, water productivity (WP), nitrogen use efficiency (NUE), and economic aspects, as well as a model evaluation. Five main tools were applied within the new approach: an algorithm for inverse model parametrization, a crop growth model for simulating crop growth, water balance and N balance, an optimization algorithm for deficit irrigation and N fertilization scheduling, and a stochastic weather generator. Furthermore, a water flow model was used to determine the optimal irrigation control functions and for simulation based estimation of the optimal field design. The approach was implemented within three case studies presented in this work. The new approach combines crop growth modeling and experiments with stochastic optimization. It contributes to a successful application of crop growth modeling based on an appropriate experimental data collection. The presented model calibration and validation procedure using the collected data facilitates reliable predictions. The stochastic optimization framework for deficit irrigation and N fertilization scheduling proved to be a powerful tool to enhance yield, WP, NUE and profit. / In der heutigen Gesellschaft gibt es zunehmend Bedenken gegenüber übermäßigem Wasser- und Düngereinsatz in der Landwirtschaft. Eine hohe Wasserproduktivität kann jedoch durch geeignete Bewässerungspläne mit hohen landwirtschaftlichen Erträgen in Einklang gebracht werden. Die mit der weitverbreiteten Stickstoffdüngung einhergehende Gewässerbelastung aufgrund von Stickstoffauswaschung erfordert zudem ein effizientes Stickstoffmanagement. Eine entsprechende ressourceneffiziente Landbewirtschaftung bedarf präzise Kenntnisse der Bodenwasser- und Stickstoffdynamiken sowie des Pflanzenwasser- und Stickstoffbedarfs. Als leistungsfähige Werkzeuge zur Unterstützung bei der Optimierung von Bewässerungs-und Düngungsplänen werden Pflanzenwachstumsmodelle eingesetzt, welche die physischen und physiologischen Prozesse des Pflanzenwachstums sowie die physikalischen Prozesse des Wasser- und Stofftransports abbilden. Hierbei hängt die Zuverlässigkeit dieser simulationsbasierten Vorhersagen von der Qualität und Quantität der bei der Modellkalibrierung und -validierung verwendeten Daten ab, welche beispielsweise in Feldversuchen erfasst werden. Fehlende Daten oder Daten mangelhafter Qualität bei der Modellkalibrierung und -validierung führen zu unzuverlässigen Simulationsergebnissen und großen Unsicherheiten bei der Vorhersage. Die große Anzahl an zu kalibrierenden Parametern erfordert zudem geeignete Kalibrierungsmethoden sowie eine sequenzielle Kalibrierungsstrategie. Darüber hinaus kann eine simulationsbasierte Planung des Versuchsdesigns Kosten und Aufwand reduzieren und zu weiteren experimentellen Erkenntnissen führen. Die Abstimmung von Pflanzenwachstumsmodellen und Versuchen ist zudem für die Modellentwicklung und -verbesserung sowie für eine Verallgemeinerung von Simulationsergebnissen unabdingbar. Im Rahmen dieser Arbeit wurde ein neuer Ansatz für ein simulationsbasiertes optimales Versuchsdesign entwickelt. Ziel war es, Simulationsmodelle, Versuche und Optimierungsmethoden in einem Ansatz für optimales und nachhaltiges Bewässerungs- und Düngungsmanagement zu integrieren. Der Ansatz besteht aus drei Schritten: 1. Die Vorbereitungsphase beinhaltet die auf existierenden Versuchsdaten basierende Kalibrierung und Validierung des Pflanzenwachstumsmodells, die Generierung von Klimazeitreihen und die Bestimmung der optimalen Bewässerungssteuerung. 2. Die Durchführungsphase setzt sich aus der Erstellung und experimentellen Anwendung der simulationsbasierten optimierten Defizitbewässerungs- und Stickstoffdüngungspläne und der Erfassung der relevanten Versuchsdaten zusammen. 3. Die Auswertungsphase schließt eine Evaluierung der Versuchsergebnisse anhand ermittelter Erträge, Wasserproduktivitäten (WP), Stickstoffnutzungseffizienzen (NUE) und ökonomischer Aspekte, sowie eine Modellevaluierung ein. In dem neuen Ansatz kamen im Wesentlichen folgende fünf Werkzeuge zur Anwendung: Ein Algorithmus zur inversen Modellparametrisierung, ein Pflanzenwachstumsmodell, welches das Pflanzenwachstum sowie die Wasser- und Stickstoffbilanzen abbildet, ein evolutionärer Optimierungsalgorithmus für die Generierung von defizitären Bewässerungs- und Stickstoffplänen und ein stochastischer Wettergenerator. Zudem diente ein Bodenwasserströmungsmodell der Ermittlung der optimalen Bewässerungssteuerung und der simulationsbasierten Optimierung des Versuchsdesigns. Der in dieser Arbeit vorgestellte Ansatz wurde in drei Fallbeispielen angewandt. Der neue Ansatz kombiniert Pflanzenwachstumsmodellierung und Experimente mit stochastischer Optimierung. Er leistet einen Beitrag zu einer erfolgreichen Pflanzenwachstumsmodellierung basierend auf der Erfassung relevanter Versuchsdaten. Die vorgestellte Modellkalibrierung und -validierung unter Verwendung der erfassten Versuchsdaten trug wesentlich zu zuverlässigen Simulationsergebnissen bei. Darüber hinaus stellt die hier vorgestellte stochastische Optimierung von defizitären Bewässerungs- und Stickstoffplänen ein leistungsfähiges Werkzeug dar, um Erträge, WP, NUE und den Profit zu erhöhen.

A model was created to predict the thermo-mechanical fatigue crack growth rates under typical engine spectrum loading conditions. This model serves as both a crack growth analysis tool to determine residual lifetime of ageing turbine components and as a design tool to assess the effects of temperature and loading variables on crack propagation. The material used in the development of this model was a polycrystalline superalloy, Inconel 100 (IN-100). The first step in creating a reliable model was to define the first order effects that influence TMF crack growth in a typical engine spectrum. Load interaction effects were determined to be major contributors to lifetime estimates by influencing crack growth rates based upon previous load histories. A yield zone model was modified to include temperature dependent properties that controlled the effects of crack growth retardation and acceleration based upon overloads and underloads, respectively. Multiple overload effects were included in the model to create enhanced retardation compared to single overload tests. Temperature interaction effects were also considered very important due to the wide temperature ranges of turbine engine components. Oxidation and changing temperature effects were accounted for by accelerating crack growth in regions that had been affected by higher temperatures. Constant amplitude crack growth rates were used as a baseline, upon which load and temperature interaction effects were applied. Experimental data of isolated first order effects was used to calibrate and verify the model. Experimental data provided the means to verify that the model was a good fit to experimental results. The load interaction effects were described by a yield zone model, which included temperature dependent properties. These properties were determined experimentally and were essential in the model's development to include load and temperature contributions. Other interesting factors became apparent through testing. It was seen that specific combinations of strain rate and temperature would lead to serrated yielding, discovered to be the Portevin-Le Chatelier effect. This effect manifested itself as enhanced hardening, leading to unstable strain bursts in specimens that cyclically yielded while changing temperature.

Current literature lacks longitudinal understandings of the association between childhood sexual/physical violence, alcohol use, depressive symptoms, and indiscriminant sexual behaviors among young women, as well as the racial/ethnic differences in these associations. Therefore, using the 1994-2008 National Longitudinal Study of Adolescent Health, this study examined a) heterogeneous growth trajectories of problem alcohol use during the transition from adolescents to young adulthood and the impact of childhood sexual/physical violence on drinking trajectories, b) the long-term impact of childhood sexual/physical violence on alcohol use and depressive symptoms, and c) the structural associations between childhood sexual/physical violence and indiscriminant sexual behaviors by examining alcohol use and depressive symptoms as mediators between White and African-American women. First, with 1,702 women, LCGM was used to identify trajectories of problem alcohol use using the first three waves. Four trajectories of problem alcohol use emerged: stable abstainers; decliners (moderate-low); incliners (low-moderate); and rapid incliners (low-high). From the bivariate level analyses, in reference to stable abstainers, White women who experienced childhood sexual/physical violence were more likely to be rapid incliners (low-high). Second, with 1,756 women, autoregressive cross-lagged path models were performed to test longitudinal associations between childhood sexual/physical violence, problem alcohol use, and depressive symptoms of White and African-American women. Both groups demonstrated significant association between childhood sexual/physical violence and subsequent development of depressive symptoms, while only White women demonstrated significant association with subsequent problem alcohol use. Third, with 1,388 women, SEM and multigroup SEM were used to test pathways between childhood sexual/physical violence and indiscriminant sexual behaviors for White and African-American women. SEM indicates that problem alcohol use and depressive symptoms mediated the proposed relationship. Multigroup SEM indicates that, for White women, both problem alcohol use and depressive symptoms mediated the association between childhood sexual/physical violence and indiscriminant sexual behaviors, while only depressive symptoms mediated the proposed association for African-American women. These findings highlight the importance of designing and providing effective prevention and treatment programs for women who experienced childhood sexual/physical violence to interrupt subsequent problem alcohol use, depressive symptoms, and indiscriminant sexual behaviors. / text

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Submitted by Claudia Rocha (claudia.rocha@udesc.br) on 2017-12-06T15:15:51Z No. of bitstreams: 1 PGEF16MA055.pdf: 2636965 bytes, checksum: 1227c615c043f057d8dbc346cd119e1f (MD5) / Made available in DSpace on 2017-12-06T15:15:51Z (GMT). No. of bitstreams: 1 PGEF16MA055.pdf: 2636965 bytes, checksum: 1227c615c043f057d8dbc346cd119e1f (MD5) Previous issue date: 2016-02-19 / FAPESC / The objective of this study was used the geostatistics and dendrochronology together with morphometric variables and dendrometric for, based on the diameter increment, evaluate the growth in Araucaria angustifolia in southern Brazil, adjust growth models and generate mapping the distribution the increase for landscape management aimed at sustainable interventions in its ecosystem. Data were collected in native forest fragments in four areas distributed in three municipalities in the mountainous plateau of Santa Catarina: São Joaquim, Urupema and Panel. Were sampled 256 trees, which gathered up the dendrometric and morphometric variables, as well as its position in space. The morphometric analysis index indicated that the species is different degrees of competition. Covariance analysis showed no difference in the shape-size ratio in sample areas. The crown insertion height correlated positively and better fit with the overall height, and negatively correlated with the proportion of canopy. The negative correlation with pc% indicates that a higher percentage of canopy corresponding to minor hic. The relationship between the proportion of crown according to the canopy trees with length indicates that greater length cup mantle and have a higher proportion of canopy thus improved growth capacity. For the crown diameter according to the diameter at breast height it was a positive correlation, that is, with the increase in size increases with the diameter of the crown. Interdimensional relationships analyzed by covariance showed differences between growing trees in the forest and free growth. growing trees without competition have greater and greater pc% cc than trees growing in competition, as well as have higher DC than on growth in the forest. It was possible to determine the potential crown diameter, growth space, the number of trees per hectare and basal area per hectare, serving as a resource for future interventions of forest management. For the analysis of increment in diameter and the age, we used the trunk partial analysis. In São Joaquim 1 the average for the mean annual increment in diameter was 0, 45 cm.ano-1 in São Joaquim 2, was 0,69 cm.ano-1, Urupema, 0,82 cm.ano- 1 and Painel, 0,94 cm.ano-1. Covariance analysis showed no differences in the average annual increase in the study sites. The incremental adjustment in the diameter at breast height and age showed that the trees showed a gradual decrease in the increase with increase in diameter and advancing age. Pearson correlation analysis for the annual periodic increment in basal area with morphometric variables and dendrometric showed that the variables with the highest correlation were proportion of cup and diameter, with a positive correlation value of 0,40 and 0,30. The generalized linear model Gamma - identity presented the best statistical criteria in setting annual periodic increment in basal area by diameter, percentage of canopy and height. The use of cup dimensions variables can be inserted in the modeling of the annual periodic increment in basal area of Araucaria angustifolia. In geostatistical analysis initially evaluated the data from classical statistics, then proceeded to the adjustment of the semivariogram. Later, we used the ordinary kriging for the interpolation of data. The standard deviation values show that there is greater variability in the panel data. There is a positive skew for all data, making it necessary to be transformed in some cases. The exponential model demonstrated better adjustment to the areas of São Joaquin and Painel already in Urupema the best model resulted in the spherical model. With the data interpolation maps was possible to visualize the spatial distribution of mean annual increment in diameter covered the four sites, identifying the areas with the highest and lowest diameter increment. The results generated in this study can understand the structure and growth of distribution Araucaria in each study site, to facilitate the management of the landscape and the species in southern Brazil. Current legislation restricts the sustainable use of the species, its natural regeneration and the increase in the rates, so reforms are needed in the legislation to ensure the perpetuity of the type Araucaria Forest / O objetivo do presente trabalho foi utilizar a geoestatística e a dendrocronologia em conjunto com as variáveis morfométricas e dendrométricas para, com base no incremento diamétrico, avaliar o crescimento no tempo de Araucaria angustifolia no sul do Brasil, ajustar modelos de crescimento e gerar mapas da distribuição do incremento para manejo da paisagem visando intervenções sustentadas em seu ecossistema. Os dados foram coletados em fragmentos de floresta nativa, em quatro áreas distribuídas em três municípios do planalto serrano de Santa Catarina: São Joaquim, Urupema e Painel. Foram amostradas 256 árvores, das quais coletou-se as variáveis dendrométricas e morfométricas, como também seu posicionamento no espaço. A análise dos índices morfométricos indicou que a espécie encontra-se em diferentes graus de competição. A análise de covariância demonstrou que há diferença na relação forma-dimensão nas áreas de amostragem. A altura de inserção de copa apresentou correlação positiva e melhor ajuste com a altura total, e correlação negativa com a proporção de copa. A correlação negativa com pc% indica que um maior percentual de copa corresponde a menor hic. A relação entre a proporção de copa em função do comprimento de copa indica que árvores com maior comprimento de copa apresentam maior manto e proporção de copa, consequentemente, melhor capacidade de crescimento. Para o diâmetro de copa em função do diâmetro à altura do peito a correlação foi positiva, ou seja, com o aumento em dimensão aumenta proporcionalmente o diâmetro de copa. As relações interdimensionais, analisadas pela covariância, demonstraram diferenças entre árvores de crescimento na floresta e de crescimento livre. Árvores crescendo sem competição apresentam maior pc% e maior cc do que árvores crescendo em competição, assim como, apresentam maior dc do que sob crescimento no interior da floresta. Foi possível determinar o diâmetro de copa potencial, o espaço de crescimento, o número de árvores por hectare e área basal por hectare, servindo como subsídio para futuras intervenções de manejo florestal. Para a análise do incremento em diâmetro e da idade, utilizou-se da análise parcial de tronco. Em São Joaquim 1 a média para o incremento médio anual em diâmetro foi de 0,45 cm.ano-1, em São Joaquim 2, foi de 0,69 cm.ano-1, em Urupema, 0,82 cm.ano-1 e em Painel, 0,94 cm.ano-1. A análise de covariância mostrou existir diferenças no incremento médio anual nos sítios de estudo. O ajuste do incremento em função do diâmetro à altura do peito e da idade mostrou que as árvores apresentaram diminuição gradativa do incremento com aumento do diâmetro e avanço da idade. A análise de correlação de Pearson para o incremento periódico anual em área basal com as variáveis morfométricas e dendrométricas, demonstrou que as variáveis com maior correlação foram proporção de copa e diâmetro, com correlação positiva de valor 0,40 e 0,30. O modelo linear generalizado Gamma - identidade apresentou os melhores critérios estatísticos no ajuste do incremento periódico anual em área basal em função do diâmetro, percentual de copa e altura. O uso de variáveis de dimensões da copa, podem ser inseridas na modelagem do incremento periódico anual em área basal de Araucaria angustifolia. Na análise geoestatística inicialmente avaliou-se os dados a partir da estatística clássica, em seguida procedeu-se o ajuste dos semivariogramas. Posteriormente, utilizou-se da Krigagem ordinária para a interpolação dos dados. Os valores do desvio padrão mostram que há maior variabilidade nos dados de Painel. Existe uma assimetria positiva para todos os dados, tornando necessária a transformação dos mesmos em alguns casos. O modelo exponencial demostrou melhor ajuste para as áreas de São Joaquim e Painel, já em Urupema o melhor modelo resultou no modelo esférico. Com os mapas de interpolação dos dados foi possível visualizar a distribuição espacial do incremento médio anual em diâmetro nos quatro sítios abordados, identificando as áreas com maior e menor incremento em diâmetro. Os resultados gerados neste trabalho possibilitam perceber a estrutura e a distribuição de crescimento da araucária em cada sítio de estudo, contribuindo para o manejo da paisagem e da espécie no sul do Brasil. A legislação atual restringe o uso sustentável da espécie, sua regeneração natural e o aumento nas taxas de incremento, assim, são necessárias reformas na legislação vigente para garantir a perpetuidade da tipologia Floresta com Araucária

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Die exponierten Sandstein-Felsriffe im Nationalpark Sächsische Schweiz stellen einen Waldgrenzstandort dar. Erkenntnisse über das Baumwachstum unter diesen nährstoffarmen und trockenen Bedingungen können dazu beitragen, die Reaktionen normaler Bestände auf mögliche Klimaveränderungen besser vorherzusagen. Für die vorliegende Arbeit erfolgte die Anlage von insgesamt zwölf Versuchsflächen in den zwei Teilgebieten des Nationalparks Sächsische Schweiz, auf denen das Wachstum der Kiefern und die zuwachsbeeinflussenden Faktoren untersucht wurden. Die Auswertung der entnommenen Bohrspäne zeigt, dass Kiefern auch bei äußerst geringen Zuwachsraten jahrelang überleben können. Es wurden vielfach schmale Jahrringe aus lediglich zwei bis drei Zellreihen sowie Einzelbäume mit bis zu 29 vollständig ausgefallenen Jahrringen gefunden. Die Analyse der Zusammenhänge von Radialzuwachs, Höhenzuwachs, Bestandesdaten, Konkurrenzindizes, Standort- und Witterungsparametern nimmt einen großen Teil der Arbeit ein und mündet in der Darstellung entsprechender Wuchsmodelle.

There is a growing societal concern about excessive water and fertilizer use in agricultural systems. High water productivity while maintaining high crop yields can be achieved with appropriate irrigation scheduling. Moreover, freshwater pollution through nitrogen (N) leaching due to the widespread use of N fertilizers demands for an efficient N fertilization management. However, sustainable crop management requires good knowledge of soil water and N dynamics as well as of crop water and N demand. Crop growth models, which describe physical and physiological processes of crop growth as well as water and matter transport, are considered as powerful tools to assist in the optimization of irrigation and fertilization management. It is of a general nature that the reliability of simulation based predictions depends on the quality and quantity of the data used for model calibration and validation which can be obtained e.g. in field experiments. A lack of data or low data quality for model calibration and validation may cause low performance and large uncertainties in simulation results. The large number of model parameters to be calibrated requires appropriate calibration methods and a sequential calibration strategy. Moreover, a simulation based planning of the field design saves costs and expenditure while supporting maximal outcomes of experiments. An adjustment of crop growth modeling and experiments is required for model improvement and development to reliably predict crop growth and to generalize predicted results. In this research study, a new approach for simulation based optimal experimental design was developed aiming to integrate simulation models, experiments, and optimization methods in one framework for optimal and sustainable irrigation and N fertilization management. The approach is composed of three steps: 1. The preprocessing consists of the calibration and validation of the crop growth model based on existing experimental data, the generation of long time-series of climate data, and the determination of the optimal irrigation control. 2. The implementation comprises the determination and experimental application of the simulation based optimized deficit irrigation and N fertilization schedules and an appropriate experimental data collection. 3. The postprocessing includes the evaluation of the experimental results namely observed yield, water productivity (WP), nitrogen use efficiency (NUE), and economic aspects, as well as a model evaluation. Five main tools were applied within the new approach: an algorithm for inverse model parametrization, a crop growth model for simulating crop growth, water balance and N balance, an optimization algorithm for deficit irrigation and N fertilization scheduling, and a stochastic weather generator. Furthermore, a water flow model was used to determine the optimal irrigation control functions and for simulation based estimation of the optimal field design. The approach was implemented within three case studies presented in this work. The new approach combines crop growth modeling and experiments with stochastic optimization. It contributes to a successful application of crop growth modeling based on an appropriate experimental data collection. The presented model calibration and validation procedure using the collected data facilitates reliable predictions. The stochastic optimization framework for deficit irrigation and N fertilization scheduling proved to be a powerful tool to enhance yield, WP, NUE and profit.:Contents Nomenclature ..............................................................................................................................xii 1 Introduction..................................................................................................................................1 I Fundamentals and literature review ........................................................................................5 2 Water productivity in crop production ....................................................................................7 2.1 Water productivity .................................................................................................................7 2.2 Increase of crop yield ..........................................................................................................9 2.3 Irrigation ...............................................................................................................................10 2.3.1 Irrigation methods ...........................................................................................................10 2.3.2 Irrigation scheduling and irrigation control ................................................................11 2.3.3 The influence of the field design on profitability .......................................................12 2.4 The concept of controlled deficit irrigation ...................................................................14 3 Nitrogen use efficiency in crop production .........................................................................17 3.1 Nitrogen use efficiency ....................................................................................................18 3.2 N fertilization management .............................................................................................18 3.3 Combination of controlled deficit irrigation and deficit N fertilization ......................19 4 Crop growth modeling ............................................................................................................21 4.1 Physiological crop growth models ..................................................................................21 4.1.1 Model description of SVAT model Daisy ....................................................................22 4.1.2 Model description of crop growth model Pilote .........................................................24 4.2 Optimal experimental design for model parametrization ...........................................25 4.2.1 Experimental design ......................................................................................................25 4.2.2 Model parameter estimation ........................................................................................26 4.2.3 Model parameter estimation based on greenhouse data .......................................27 5 Irrigation and N fertilization scheduling ..............................................................................29 5.1 Irrigation scheduling .........................................................................................................29 5.2 N fertilization scheduling .................................................................................................30 5.3 Combination of irrigation and N fertilization scheduling ............................................30 II New approach for simulation based optimal experimental design ................................33 6 Preprocessing steps ...............................................................................................................37 6.1 Model parametrization and assessment .......................................................................37 6.1.1 Calibration of the soil parameters ...............................................................................38 6.1.2 Calibration of the plant parameters ............................................................................39 6.1.3 Model assessment .........................................................................................................41 6.1.4 Preliminary simulations for an optimal experimental layout ..................................43 6.2 Generation of long time-series of climate data ............................................................44 6.3 Determination of the optimal irrigation control functions ...........................................44 7 Stochastic optimization framework ......................................................................................47 7.1 Stochastic optimization framework .................................................................................47 7.1.1 Optimization algorithm ...................................................................................................47 7.1.2 Generation of the crop water (nitrogen) production functions ................................48 7.1.3 Application of the stochastic optimization framework ..............................................48 7.1.4 Crop growth model requirements ................................................................................49 8 Data collection during the experimentation .......................................................................51 9 Postprocessing steps .............................................................................................................55 9.1 Evaluation of the experimental results ...........................................................................55 9.1.1 Yield and total dry matter ..............................................................................................55 9.1.2 Water productivity and nitrogen use efficiency .........................................................55 9.1.3 Economic aspects ..........................................................................................................55 9.1.4 Evaluation of the model results ....................................................................................56 III Application of the new approach to three case studies ...................................................57 10 Evaluation of model transferability ....................................................................................59 10.1 Objectives and summary ................................................................................................59 10.2 Experimental site and experimental setup .................................................................61 10.3 Data collection during the experimentation ................................................................63 10.4 Calibration and validation of the model parameters .................................................63 10.4.1 Model setup and soil parametrization ......................................................................64 10.4.2 Plant parameter calibration and validation .............................................................67 10.5 Application of the stochastic optimization framework ...............................................75 10.5.1 Generation of the climate data ...................................................................................75 10.5.2 Estimation of the yield potential of wheat ................................................................75 10.5.3 Estimation of the water productivity potential of barley .........................................77 10.6 Discussion and conclusions ..........................................................................................81 11 Real-time irrigation scheduling ..........................................................................................83 11.1 Objectives and summary ................................................................................................83 11.2 Experimental site and field design ...............................................................................85 11.3 Data collection during the experiment ........................................................................86 11.4 Calibration and setup of the crop growth model Pilote .............................................87 11.5 Derivation of optimal irrigation control functions for different drip line spacings 88 11.5.1 Initial Hydrus 2D/3D simulations ...............................................................................88 11.5.2 Determination of the irrigation control functions .....................................................89 11.5.3 Verifying measurements ..............................................................................................92 11.6 Real-time deficit irrigation scheduling .........................................................................93 11.7 Evaluation of the experimental results .........................................................................96 11.7.1 Crop yields .....................................................................................................................96 11.7.2 Water productivity .........................................................................................................97 11.7.3 Prognostic simulations ................................................................................................98 11.7.4 Economic aspects ........................................................................................................99 11.8 Discussion and conclusions ........................................................................................100 12 Multicriterial optimization...................................................................................................103 12.1 Objectives and summary .............................................................................................103 12.2 Experimental site and experimental setup ...............................................................105 12.3 Data collection during the experiment ......................................................................105 12.4 Experimental layout ......................................................................................................106 12.5 Calibration and validation of the model parameters ..............................................107 12.5.1 Calibration of the soil parameters ...........................................................................107 12.5.2 Calibration and validation of the plant parameters .............................................107 12.5.3 Setup of SVAT model Daisy .....................................................................................108 12.6 Generation of the climate data ....................................................................................109 12.7 Optimized irrigation and N fertilization scheduling .................................................109 12.8 Evaluation of the experimental results .......................................................................111 12.8.1 Observed plant variables and weather data .........................................................111 12.8.2 Water productivities and nitrogen use efficiencies ...............................................111 12.8.3 Chlorophyll Meter values ..........................................................................................112 12.8.4 Recalculation of soil parameters .............................................................................113 12.9 Postprocessing simulations of yield and water and N dynamics..........................114 12.9.1 Yield predictions using Daisy 1D ............................................................................114 12.9.2 Yield predictions using Daisy 2D ............................................................................119 12.10 Discussion and conclusions .....................................................................................121 IV General discussion, conclusions and outlook ...............................................................123 13 General discussion ............................................................................................................125 14 General conclusions and outlook ....................................................................................133 Appendix ....................................................................................................................................134 A Tables and Figures ...............................................................................................................137 B Model setup and weather files ...........................................................................................145 List of Tables .............................................................................................................................153 List of Figures ............................................................................................................................153 References ................................................................................................................................159 / In der heutigen Gesellschaft gibt es zunehmend Bedenken gegenüber übermäßigem Wasser- und Düngereinsatz in der Landwirtschaft. Eine hohe Wasserproduktivität kann jedoch durch geeignete Bewässerungspläne mit hohen landwirtschaftlichen Erträgen in Einklang gebracht werden. Die mit der weitverbreiteten Stickstoffdüngung einhergehende Gewässerbelastung aufgrund von Stickstoffauswaschung erfordert zudem ein effizientes Stickstoffmanagement. Eine entsprechende ressourceneffiziente Landbewirtschaftung bedarf präzise Kenntnisse der Bodenwasser- und Stickstoffdynamiken sowie des Pflanzenwasser- und Stickstoffbedarfs. Als leistungsfähige Werkzeuge zur Unterstützung bei der Optimierung von Bewässerungs-und Düngungsplänen werden Pflanzenwachstumsmodelle eingesetzt, welche die physischen und physiologischen Prozesse des Pflanzenwachstums sowie die physikalischen Prozesse des Wasser- und Stofftransports abbilden. Hierbei hängt die Zuverlässigkeit dieser simulationsbasierten Vorhersagen von der Qualität und Quantität der bei der Modellkalibrierung und -validierung verwendeten Daten ab, welche beispielsweise in Feldversuchen erfasst werden. Fehlende Daten oder Daten mangelhafter Qualität bei der Modellkalibrierung und -validierung führen zu unzuverlässigen Simulationsergebnissen und großen Unsicherheiten bei der Vorhersage. Die große Anzahl an zu kalibrierenden Parametern erfordert zudem geeignete Kalibrierungsmethoden sowie eine sequenzielle Kalibrierungsstrategie. Darüber hinaus kann eine simulationsbasierte Planung des Versuchsdesigns Kosten und Aufwand reduzieren und zu weiteren experimentellen Erkenntnissen führen. Die Abstimmung von Pflanzenwachstumsmodellen und Versuchen ist zudem für die Modellentwicklung und -verbesserung sowie für eine Verallgemeinerung von Simulationsergebnissen unabdingbar. Im Rahmen dieser Arbeit wurde ein neuer Ansatz für ein simulationsbasiertes optimales Versuchsdesign entwickelt. Ziel war es, Simulationsmodelle, Versuche und Optimierungsmethoden in einem Ansatz für optimales und nachhaltiges Bewässerungs- und Düngungsmanagement zu integrieren. Der Ansatz besteht aus drei Schritten: 1. Die Vorbereitungsphase beinhaltet die auf existierenden Versuchsdaten basierende Kalibrierung und Validierung des Pflanzenwachstumsmodells, die Generierung von Klimazeitreihen und die Bestimmung der optimalen Bewässerungssteuerung. 2. Die Durchführungsphase setzt sich aus der Erstellung und experimentellen Anwendung der simulationsbasierten optimierten Defizitbewässerungs- und Stickstoffdüngungspläne und der Erfassung der relevanten Versuchsdaten zusammen. 3. Die Auswertungsphase schließt eine Evaluierung der Versuchsergebnisse anhand ermittelter Erträge, Wasserproduktivitäten (WP), Stickstoffnutzungseffizienzen (NUE) und ökonomischer Aspekte, sowie eine Modellevaluierung ein. In dem neuen Ansatz kamen im Wesentlichen folgende fünf Werkzeuge zur Anwendung: Ein Algorithmus zur inversen Modellparametrisierung, ein Pflanzenwachstumsmodell, welches das Pflanzenwachstum sowie die Wasser- und Stickstoffbilanzen abbildet, ein evolutionärer Optimierungsalgorithmus für die Generierung von defizitären Bewässerungs- und Stickstoffplänen und ein stochastischer Wettergenerator. Zudem diente ein Bodenwasserströmungsmodell der Ermittlung der optimalen Bewässerungssteuerung und der simulationsbasierten Optimierung des Versuchsdesigns. Der in dieser Arbeit vorgestellte Ansatz wurde in drei Fallbeispielen angewandt. Der neue Ansatz kombiniert Pflanzenwachstumsmodellierung und Experimente mit stochastischer Optimierung. Er leistet einen Beitrag zu einer erfolgreichen Pflanzenwachstumsmodellierung basierend auf der Erfassung relevanter Versuchsdaten. Die vorgestellte Modellkalibrierung und -validierung unter Verwendung der erfassten Versuchsdaten trug wesentlich zu zuverlässigen Simulationsergebnissen bei. Darüber hinaus stellt die hier vorgestellte stochastische Optimierung von defizitären Bewässerungs- und Stickstoffplänen ein leistungsfähiges Werkzeug dar, um Erträge, WP, NUE und den Profit zu erhöhen.:Contents Nomenclature ..............................................................................................................................xii 1 Introduction..................................................................................................................................1 I Fundamentals and literature review ........................................................................................5 2 Water productivity in crop production ....................................................................................7 2.1 Water productivity .................................................................................................................7 2.2 Increase of crop yield ..........................................................................................................9 2.3 Irrigation ...............................................................................................................................10 2.3.1 Irrigation methods ...........................................................................................................10 2.3.2 Irrigation scheduling and irrigation control ................................................................11 2.3.3 The influence of the field design on profitability .......................................................12 2.4 The concept of controlled deficit irrigation ...................................................................14 3 Nitrogen use efficiency in crop production .........................................................................17 3.1 Nitrogen use efficiency ....................................................................................................18 3.2 N fertilization management .............................................................................................18 3.3 Combination of controlled deficit irrigation and deficit N fertilization ......................19 4 Crop growth modeling ............................................................................................................21 4.1 Physiological crop growth models ..................................................................................21 4.1.1 Model description of SVAT model Daisy ....................................................................22 4.1.2 Model description of crop growth model Pilote .........................................................24 4.2 Optimal experimental design for model parametrization ...........................................25 4.2.1 Experimental design ......................................................................................................25 4.2.2 Model parameter estimation ........................................................................................26 4.2.3 Model parameter estimation based on greenhouse data .......................................27 5 Irrigation and N fertilization scheduling ..............................................................................29 5.1 Irrigation scheduling .........................................................................................................29 5.2 N fertilization scheduling .................................................................................................30 5.3 Combination of irrigation and N fertilization scheduling ............................................30 II New approach for simulation based optimal experimental design ................................33 6 Preprocessing steps ...............................................................................................................37 6.1 Model parametrization and assessment .......................................................................37 6.1.1 Calibration of the soil parameters ...............................................................................38 6.1.2 Calibration of the plant parameters ............................................................................39 6.1.3 Model assessment .........................................................................................................41 6.1.4 Preliminary simulations for an optimal experimental layout ..................................43 6.2 Generation of long time-series of climate data ............................................................44 6.3 Determination of the optimal irrigation control functions ...........................................44 7 Stochastic optimization framework ......................................................................................47 7.1 Stochastic optimization framework .................................................................................47 7.1.1 Optimization algorithm ...................................................................................................47 7.1.2 Generation of the crop water (nitrogen) production functions ................................48 7.1.3 Application of the stochastic optimization framework ..............................................48 7.1.4 Crop growth model requirements ................................................................................49 8 Data collection during the experimentation .......................................................................51 9 Postprocessing steps .............................................................................................................55 9.1 Evaluation of the experimental results ...........................................................................55 9.1.1 Yield and total dry matter ..............................................................................................55 9.1.2 Water productivity and nitrogen use efficiency .........................................................55 9.1.3 Economic aspects ..........................................................................................................55 9.1.4 Evaluation of the model results ....................................................................................56 III Application of the new approach to three case studies ...................................................57 10 Evaluation of model transferability ....................................................................................59 10.1 Objectives and summary ................................................................................................59 10.2 Experimental site and experimental setup .................................................................61 10.3 Data collection during the experimentation ................................................................63 10.4 Calibration and validation of the model parameters .................................................63 10.4.1 Model setup and soil parametrization ......................................................................64 10.4.2 Plant parameter calibration and validation .............................................................67 10.5 Application of the stochastic optimization framework ...............................................75 10.5.1 Generation of the climate data ...................................................................................75 10.5.2 Estimation of the yield potential of wheat ................................................................75 10.5.3 Estimation of the water productivity potential of barley .........................................77 10.6 Discussion and conclusions ..........................................................................................81 11 Real-time irrigation scheduling ..........................................................................................83 11.1 Objectives and summary ................................................................................................83 11.2 Experimental site and field design ...............................................................................85 11.3 Data collection during the experiment ........................................................................86 11.4 Calibration and setup of the crop growth model Pilote .............................................87 11.5 Derivation of optimal irrigation control functions for different drip line spacings 88 11.5.1 Initial Hydrus 2D/3D simulations ...............................................................................88 11.5.2 Determination of the irrigation control functions .....................................................89 11.5.3 Verifying measurements ..............................................................................................92 11.6 Real-time deficit irrigation scheduling .........................................................................93 11.7 Evaluation of the experimental results .........................................................................96 11.7.1 Crop yields .....................................................................................................................96 11.7.2 Water productivity .........................................................................................................97 11.7.3 Prognostic simulations ................................................................................................98 11.7.4 Economic aspects ........................................................................................................99 11.8 Discussion and conclusions ........................................................................................100 12 Multicriterial optimization...................................................................................................103 12.1 Objectives and summary .............................................................................................103 12.2 Experimental site and experimental setup ...............................................................105 12.3 Data collection during the experiment ......................................................................105 12.4 Experimental layout ......................................................................................................106 12.5 Calibration and validation of the model parameters ..............................................107 12.5.1 Calibration of the soil parameters ...........................................................................107 12.5.2 Calibration and validation of the plant parameters .............................................107 12.5.3 Setup of SVAT model Daisy .....................................................................................108 12.6 Generation of the climate data ....................................................................................109 12.7 Optimized irrigation and N fertilization scheduling .................................................109 12.8 Evaluation of the experimental results .......................................................................111 12.8.1 Observed plant variables and weather data .........................................................111 12.8.2 Water productivities and nitrogen use efficiencies ...............................................111 12.8.3 Chlorophyll Meter values ..........................................................................................112 12.8.4 Recalculation of soil parameters .............................................................................113 12.9 Postprocessing simulations of yield and water and N dynamics..........................114 12.9.1 Yield predictions using Daisy 1D ............................................................................114 12.9.2 Yield predictions using Daisy 2D ............................................................................119 12.10 Discussion and conclusions .....................................................................................121 IV General discussion, conclusions and outlook ...............................................................123 13 General discussion ............................................................................................................125 14 General conclusions and outlook ....................................................................................133 Appendix ....................................................................................................................................134 A Tables and Figures ...............................................................................................................137 B Model setup and weather files ...........................................................................................145 List of Tables .............................................................................................................................153 List of Figures ............................................................................................................................153 References ................................................................................................................................159

Objective: To analyze the change in health-related quality-of-life (HRQoL) in patients with Parkinson’s disease (PD) and to identify different classes of HRQoL decline. Methods: A longitudinal cohort study was performed to assess clinical parameters (unified PD rating scale, Beck Depression Inventory) and HRQoL data (EuroQol, Parkinson’s Disease Questionnaire [PDQ]-39) at baseline, 3, 6, 12, 24, and 36 months. A total of 145 patients with PD were consecutively recruited in the county of Northern Hessia, Germany, between January and June 2000. A latent growth mixture model was applied to analyse the heterogeneity in HRQoL trajectories. Results: We successfully applied latent mixture growth modeling in order to identify different classes of HRQoL trajectories in PD. Three growth models were developed and each resulted in a four-class model of distinct patterns using the generic EuroQol instruments’ outcomes (EuroQol-5 Dimensions and visual analogue scale) and the disease-specific PDQ- 39. The four classes were defined by individual trajectory characteristics. Classes one and two represented trajectories with moderate declines over 36 months, but with different initial intercepts. Class three consisted mainly of patients who passed away during the observation period and therefore had a large HRQoL decline. Class four was characterized by a low level of HRQoL at baseline and a significant subsequent decline. Conclusions: The findings provide a more elaborate understanding of the variability in HRQoL reduction in PD over time. The classification of different HRQoL subgroups may help to explain the response of PD patients to the natural history of the disease. Future research will enable the identification of HRQoL responder subgroups on different treatment regimens.